From 069b8adcc3be5e9e48bfceebd3bf19017db0cadf Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 2 Jul 2025 16:23:50 +1200 Subject: [PATCH 01/36] added an option for full tensor interpolation in apply_ppu_hyperpars_tensor and changed the config_func_str in pst_from to use it instead of value interpolation. this avoids issues with interpolating angles and I believe is more propa. --- examples/pstfrom_mf6_ppu.ipynb | 2981 +++++++++++++++++++++++++++++++- pyemu/utils/pp_utils.py | 125 ++ pyemu/utils/pst_from.py | 2 +- 3 files changed, 3047 insertions(+), 61 deletions(-) diff --git a/examples/pstfrom_mf6_ppu.ipynb b/examples/pstfrom_mf6_ppu.ipynb index 9283543d9..2f9bb9b56 100644 --- a/examples/pstfrom_mf6_ppu.ipynb +++ b/examples/pstfrom_mf6_ppu.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -50,9 +50,149 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['freyberg6.dis',\n", + " 'freyberg6.dis.grb',\n", + " 'freyberg6.dis_botm_layer1.txt',\n", + " 'freyberg6.dis_botm_layer2.txt',\n", + " 'freyberg6.dis_botm_layer3.txt',\n", + " 'freyberg6.dis_delc.txt',\n", + " 'freyberg6.dis_delr.txt',\n", + " 'freyberg6.dis_idomain_layer1.txt',\n", + " 'freyberg6.dis_idomain_layer2.txt',\n", + " 'freyberg6.dis_idomain_layer3.txt',\n", + " 'freyberg6.dis_top.txt',\n", + " 'freyberg6.ghb',\n", + " 'freyberg6.ghb_stress_period_data_1.txt',\n", + " 'freyberg6.ic',\n", + " 'freyberg6.ic_strt_layer1.txt',\n", + " 'freyberg6.ic_strt_layer2.txt',\n", + " 'freyberg6.ic_strt_layer3.txt',\n", + " 'freyberg6.ims',\n", + " 'freyberg6.lst',\n", + " 'freyberg6.nam',\n", + " 'freyberg6.npf',\n", + " 'freyberg6.npf_icelltype_layer1.txt',\n", + " 'freyberg6.npf_icelltype_layer2.txt',\n", + " 'freyberg6.npf_icelltype_layer3.txt',\n", + " 'freyberg6.npf_k33_layer1.txt',\n", + " 'freyberg6.npf_k33_layer2.txt',\n", + " 'freyberg6.npf_k33_layer3.txt',\n", + " 'freyberg6.npf_k_layer1.txt',\n", + " 'freyberg6.npf_k_layer2.txt',\n", + " 'freyberg6.npf_k_layer3.txt',\n", + " 'freyberg6.oc',\n", + " 'freyberg6.rch',\n", + " 'freyberg6.rch_recharge_1.txt',\n", + " 'freyberg6.rch_recharge_10.txt',\n", + " 'freyberg6.rch_recharge_11.txt',\n", + " 'freyberg6.rch_recharge_12.txt',\n", + " 'freyberg6.rch_recharge_13.txt',\n", + " 'freyberg6.rch_recharge_14.txt',\n", + " 'freyberg6.rch_recharge_15.txt',\n", + " 'freyberg6.rch_recharge_16.txt',\n", + " 'freyberg6.rch_recharge_17.txt',\n", + " 'freyberg6.rch_recharge_18.txt',\n", + " 'freyberg6.rch_recharge_19.txt',\n", + " 'freyberg6.rch_recharge_2.txt',\n", + " 'freyberg6.rch_recharge_20.txt',\n", + " 'freyberg6.rch_recharge_21.txt',\n", + " 'freyberg6.rch_recharge_22.txt',\n", + " 'freyberg6.rch_recharge_23.txt',\n", + " 'freyberg6.rch_recharge_24.txt',\n", + " 'freyberg6.rch_recharge_25.txt',\n", + " 'freyberg6.rch_recharge_3.txt',\n", + " 'freyberg6.rch_recharge_4.txt',\n", + " 'freyberg6.rch_recharge_5.txt',\n", + " 'freyberg6.rch_recharge_6.txt',\n", + " 'freyberg6.rch_recharge_7.txt',\n", + " 'freyberg6.rch_recharge_8.txt',\n", + " 'freyberg6.rch_recharge_9.txt',\n", + " 'freyberg6.sfr',\n", + " 'freyberg6.sfr_connectiondata.txt',\n", + " 'freyberg6.sfr_packagedata.txt',\n", + " 'freyberg6.sfr_perioddata_1.txt',\n", + " 'freyberg6.sfr_perioddata_10.txt',\n", + " 'freyberg6.sfr_perioddata_11.txt',\n", + " 'freyberg6.sfr_perioddata_12.txt',\n", + " 'freyberg6.sfr_perioddata_13.txt',\n", + " 'freyberg6.sfr_perioddata_14.txt',\n", + " 'freyberg6.sfr_perioddata_15.txt',\n", + " 'freyberg6.sfr_perioddata_16.txt',\n", + " 'freyberg6.sfr_perioddata_17.txt',\n", + " 'freyberg6.sfr_perioddata_18.txt',\n", + " 'freyberg6.sfr_perioddata_19.txt',\n", + " 'freyberg6.sfr_perioddata_2.txt',\n", + " 'freyberg6.sfr_perioddata_20.txt',\n", + " 'freyberg6.sfr_perioddata_21.txt',\n", + " 'freyberg6.sfr_perioddata_22.txt',\n", + " 'freyberg6.sfr_perioddata_23.txt',\n", + " 'freyberg6.sfr_perioddata_24.txt',\n", + " 'freyberg6.sfr_perioddata_25.txt',\n", + " 'freyberg6.sfr_perioddata_3.txt',\n", + " 'freyberg6.sfr_perioddata_4.txt',\n", + " 'freyberg6.sfr_perioddata_5.txt',\n", + " 'freyberg6.sfr_perioddata_6.txt',\n", + " 'freyberg6.sfr_perioddata_7.txt',\n", + " 'freyberg6.sfr_perioddata_8.txt',\n", + " 'freyberg6.sfr_perioddata_9.txt',\n", + " 'freyberg6.sto',\n", + " 'freyberg6.sto_iconvert_layer1.txt',\n", + " 'freyberg6.sto_iconvert_layer2.txt',\n", + " 'freyberg6.sto_iconvert_layer3.txt',\n", + " 'freyberg6.sto_ss_layer1.txt',\n", + " 'freyberg6.sto_ss_layer2.txt',\n", + " 'freyberg6.sto_ss_layer3.txt',\n", + " 'freyberg6.sto_sy_layer1.txt',\n", + " 'freyberg6.sto_sy_layer2.txt',\n", + " 'freyberg6.sto_sy_layer3.txt',\n", + " 'freyberg6.tdis',\n", + " 'freyberg6.wel',\n", + " 'freyberg6.wel_stress_period_data_1.txt',\n", + " 'freyberg6.wel_stress_period_data_10.txt',\n", + " 'freyberg6.wel_stress_period_data_11.txt',\n", + " 'freyberg6.wel_stress_period_data_12.txt',\n", + " 'freyberg6.wel_stress_period_data_13.txt',\n", + " 'freyberg6.wel_stress_period_data_14.txt',\n", + " 'freyberg6.wel_stress_period_data_15.txt',\n", + " 'freyberg6.wel_stress_period_data_16.txt',\n", + " 'freyberg6.wel_stress_period_data_17.txt',\n", + " 'freyberg6.wel_stress_period_data_18.txt',\n", + " 'freyberg6.wel_stress_period_data_19.txt',\n", + " 'freyberg6.wel_stress_period_data_2.txt',\n", + " 'freyberg6.wel_stress_period_data_20.txt',\n", + " 'freyberg6.wel_stress_period_data_21.txt',\n", + " 'freyberg6.wel_stress_period_data_22.txt',\n", + " 'freyberg6.wel_stress_period_data_23.txt',\n", + " 'freyberg6.wel_stress_period_data_24.txt',\n", + " 'freyberg6.wel_stress_period_data_25.txt',\n", + " 'freyberg6.wel_stress_period_data_3.txt',\n", + " 'freyberg6.wel_stress_period_data_4.txt',\n", + " 'freyberg6.wel_stress_period_data_5.txt',\n", + " 'freyberg6.wel_stress_period_data_6.txt',\n", + " 'freyberg6.wel_stress_period_data_7.txt',\n", + " 'freyberg6.wel_stress_period_data_8.txt',\n", + " 'freyberg6.wel_stress_period_data_9.txt',\n", + " 'freyberg6_freyberg.cbc',\n", + " 'freyberg6_freyberg.hds',\n", + " 'head.obs',\n", + " 'heads.csv',\n", + " 'mfsim.lst',\n", + " 'mfsim.nam',\n", + " 'sfr.csv',\n", + " 'sfr.obs']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "org_model_ws = os.path.join('freyberg_mf6')\n", "os.listdir(org_model_ws)" @@ -69,9 +209,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "id_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_idomain_layer3.txt\"))\n", "top_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_top.txt\"))\n", @@ -91,11 +261,163 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['freyberg6.dis',\n", + " 'freyberg6.dis.grb',\n", + " 'freyberg6.dis_botm_layer1.txt',\n", + " 'freyberg6.dis_botm_layer2.txt',\n", + " 'freyberg6.dis_botm_layer3.txt',\n", + " 'freyberg6.dis_delc.txt',\n", + " 'freyberg6.dis_delr.txt',\n", + " 'freyberg6.dis_idomain_layer1.txt',\n", + " 'freyberg6.dis_idomain_layer2.txt',\n", + " 'freyberg6.dis_idomain_layer3.txt',\n", + " 'freyberg6.dis_top.txt',\n", + " 'freyberg6.ghb',\n", + " 'freyberg6.ghb_stress_period_data_1.txt',\n", + " 'freyberg6.ic',\n", + " 'freyberg6.ic_strt_layer1.txt',\n", + " 'freyberg6.ic_strt_layer2.txt',\n", + " 'freyberg6.ic_strt_layer3.txt',\n", + " 'freyberg6.ims',\n", + " 'freyberg6.lst',\n", + " 'freyberg6.nam',\n", + " 'freyberg6.npf',\n", + " 'freyberg6.npf_icelltype_layer1.txt',\n", + " 'freyberg6.npf_icelltype_layer2.txt',\n", + " 'freyberg6.npf_icelltype_layer3.txt',\n", + " 'freyberg6.npf_k33_layer1.txt',\n", + " 'freyberg6.npf_k33_layer2.txt',\n", + " 'freyberg6.npf_k33_layer3.txt',\n", + " 'freyberg6.npf_k_layer1.txt',\n", + " 'freyberg6.npf_k_layer2.txt',\n", + " 'freyberg6.npf_k_layer3.txt',\n", + " 'freyberg6.oc',\n", + " 'freyberg6.rch',\n", + " 'freyberg6.rch_recharge_1.txt',\n", + " 'freyberg6.rch_recharge_10.txt',\n", + " 'freyberg6.rch_recharge_11.txt',\n", + " 'freyberg6.rch_recharge_12.txt',\n", + " 'freyberg6.rch_recharge_13.txt',\n", + " 'freyberg6.rch_recharge_14.txt',\n", + " 'freyberg6.rch_recharge_15.txt',\n", + " 'freyberg6.rch_recharge_16.txt',\n", + " 'freyberg6.rch_recharge_17.txt',\n", + " 'freyberg6.rch_recharge_18.txt',\n", + " 'freyberg6.rch_recharge_19.txt',\n", + " 'freyberg6.rch_recharge_2.txt',\n", + " 'freyberg6.rch_recharge_20.txt',\n", + " 'freyberg6.rch_recharge_21.txt',\n", + " 'freyberg6.rch_recharge_22.txt',\n", + " 'freyberg6.rch_recharge_23.txt',\n", + " 'freyberg6.rch_recharge_24.txt',\n", + " 'freyberg6.rch_recharge_25.txt',\n", + " 'freyberg6.rch_recharge_3.txt',\n", + " 'freyberg6.rch_recharge_4.txt',\n", + " 'freyberg6.rch_recharge_5.txt',\n", + " 'freyberg6.rch_recharge_6.txt',\n", + " 'freyberg6.rch_recharge_7.txt',\n", + " 'freyberg6.rch_recharge_8.txt',\n", + " 'freyberg6.rch_recharge_9.txt',\n", + " 'freyberg6.sfr',\n", + " 'freyberg6.sfr_connectiondata.txt',\n", + " 'freyberg6.sfr_packagedata.txt',\n", + " 'freyberg6.sfr_perioddata_1.txt',\n", + " 'freyberg6.sfr_perioddata_10.txt',\n", + " 'freyberg6.sfr_perioddata_11.txt',\n", + " 'freyberg6.sfr_perioddata_12.txt',\n", + " 'freyberg6.sfr_perioddata_13.txt',\n", + " 'freyberg6.sfr_perioddata_14.txt',\n", + " 'freyberg6.sfr_perioddata_15.txt',\n", + " 'freyberg6.sfr_perioddata_16.txt',\n", + " 'freyberg6.sfr_perioddata_17.txt',\n", + " 'freyberg6.sfr_perioddata_18.txt',\n", + " 'freyberg6.sfr_perioddata_19.txt',\n", + " 'freyberg6.sfr_perioddata_2.txt',\n", + " 'freyberg6.sfr_perioddata_20.txt',\n", + " 'freyberg6.sfr_perioddata_21.txt',\n", + " 'freyberg6.sfr_perioddata_22.txt',\n", + " 'freyberg6.sfr_perioddata_23.txt',\n", + " 'freyberg6.sfr_perioddata_24.txt',\n", + " 'freyberg6.sfr_perioddata_25.txt',\n", + " 'freyberg6.sfr_perioddata_3.txt',\n", + " 'freyberg6.sfr_perioddata_4.txt',\n", + " 'freyberg6.sfr_perioddata_5.txt',\n", + " 'freyberg6.sfr_perioddata_6.txt',\n", + " 'freyberg6.sfr_perioddata_7.txt',\n", + " 'freyberg6.sfr_perioddata_8.txt',\n", + " 'freyberg6.sfr_perioddata_9.txt',\n", + " 'freyberg6.sto',\n", + " 'freyberg6.sto_iconvert_layer1.txt',\n", + " 'freyberg6.sto_iconvert_layer2.txt',\n", + " 'freyberg6.sto_iconvert_layer3.txt',\n", + " 'freyberg6.sto_ss_layer1.txt',\n", + " 'freyberg6.sto_ss_layer2.txt',\n", + " 'freyberg6.sto_ss_layer3.txt',\n", + " 'freyberg6.sto_sy_layer1.txt',\n", + " 'freyberg6.sto_sy_layer2.txt',\n", + " 'freyberg6.sto_sy_layer3.txt',\n", + " 'freyberg6.tdis',\n", + " 'freyberg6.wel',\n", + " 'freyberg6.wel_stress_period_data_1.txt',\n", + " 'freyberg6.wel_stress_period_data_10.txt',\n", + " 'freyberg6.wel_stress_period_data_11.txt',\n", + " 'freyberg6.wel_stress_period_data_12.txt',\n", + " 'freyberg6.wel_stress_period_data_13.txt',\n", + " 'freyberg6.wel_stress_period_data_14.txt',\n", + " 'freyberg6.wel_stress_period_data_15.txt',\n", + " 'freyberg6.wel_stress_period_data_16.txt',\n", + " 'freyberg6.wel_stress_period_data_17.txt',\n", + " 'freyberg6.wel_stress_period_data_18.txt',\n", + " 'freyberg6.wel_stress_period_data_19.txt',\n", + " 'freyberg6.wel_stress_period_data_2.txt',\n", + " 'freyberg6.wel_stress_period_data_20.txt',\n", + " 'freyberg6.wel_stress_period_data_21.txt',\n", + " 'freyberg6.wel_stress_period_data_22.txt',\n", + " 'freyberg6.wel_stress_period_data_23.txt',\n", + " 'freyberg6.wel_stress_period_data_24.txt',\n", + " 'freyberg6.wel_stress_period_data_25.txt',\n", + " 'freyberg6.wel_stress_period_data_3.txt',\n", + " 'freyberg6.wel_stress_period_data_4.txt',\n", + " 'freyberg6.wel_stress_period_data_5.txt',\n", + " 'freyberg6.wel_stress_period_data_6.txt',\n", + " 'freyberg6.wel_stress_period_data_7.txt',\n", + " 'freyberg6.wel_stress_period_data_8.txt',\n", + " 'freyberg6.wel_stress_period_data_9.txt',\n", + " 'freyberg6_freyberg.cbc',\n", + " 'freyberg6_freyberg.hds',\n", + " 'head.obs',\n", + " 'heads.csv',\n", + " 'mf6.exe',\n", + " 'mfsim.lst',\n", + " 'mfsim.nam',\n", + " 'pestpp-ies.exe',\n", + " 'sfr.csv',\n", + " 'sfr.obs']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tmp_model_ws = \"temp_pst_from_ppu\"\n", "if os.path.exists(tmp_model_ws):\n", " shutil.rmtree(tmp_model_ws)\n", "shutil.copytree(org_model_ws,tmp_model_ws)\n", + "# copy pestpp and mf6\n", + "shutil.copy(r'C:\\bin\\modflow\\mf6.6.2_win64\\bin\\mf6.exe', os.path.join(tmp_model_ws,'mf6.exe'))\n", + "shutil.copy(r'C:\\bin\\pestpp\\pestpp-ies.exe', os.path.join(tmp_model_ws,'pestpp-ies.exe'))\n", "os.listdir(tmp_model_ws)" ] }, @@ -110,9 +432,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading simulation...\n", + " loading simulation name file...\n", + " loading tdis package...\n", + " loading model gwf6...\n", + " loading package dis...\n", + " loading package ic...\n", + " loading package npf...\n", + " loading package sto...\n", + " loading package oc...\n", + " loading package wel...\n", + " loading package rch...\n", + " loading package ghb...\n", + " loading package sfr...\n", + " loading package obs...\n", + " loading solution package freyberg6...\n" + ] + } + ], "source": [ "sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws)\n", "m = sim.get_model(\"freyberg6\")\n" @@ -127,9 +471,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " could not remove start_datetime\n" + ] + }, + { + "data": { + "text/plain": [ + "xul:0; yul:10000; rotation:0; proj4_str:None; units:meters; lenuni:2; length_multiplier:1.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sr = pyemu.helpers.SpatialReference.from_namfile(\n", " os.path.join(tmp_model_ws, \"freyberg6.nam\"),\n", @@ -146,9 +508,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-07-02 16:17:30.679513 starting: opening PstFrom.log for logging\n", + "2025-07-02 16:17:30.680511 starting PstFrom process\n", + "2025-07-02 16:17:30.680511 starting: setting up dirs\n", + "2025-07-02 16:17:30.681516 starting: removing existing new_d 'freyberg6_template'\n", + "2025-07-02 16:17:30.763770 finished: removing existing new_d 'freyberg6_template' took: 0:00:00.082254\n", + "2025-07-02 16:17:30.763770 starting: copying original_d 'temp_pst_from_ppu' to new_d 'freyberg6_template'\n", + "2025-07-02 16:17:30.930885 finished: copying original_d 'temp_pst_from_ppu' to new_d 'freyberg6_template' took: 0:00:00.167115\n", + "2025-07-02 16:17:30.931902 finished: setting up dirs took: 0:00:00.251391\n" + ] + } + ], "source": [ "template_ws = \"freyberg6_template\"\n", "pf = pyemu.utils.PstFrom(original_d=tmp_model_ws, new_d=template_ws,\n", @@ -168,9 +545,832 @@ }, { "cell_type": "code", - "execution_count": null, + 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time
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"execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-07-02 16:17:31.018013 starting: adding observations from output file heads.csv\n", + "2025-07-02 16:17:31.018013 starting: adding observations from tabular output file '['heads.csv']'\n", + "2025-07-02 16:17:31.018013 starting: reading list-style file: freyberg6_template\\heads.csv\n", + "2025-07-02 16:17:31.044694 finished: reading list-style file: freyberg6_template\\heads.csv took: 0:00:00.026681\n", + "2025-07-02 16:17:31.052225 starting: building insfile for tabular output file heads.csv\n", + "2025-07-02 16:17:31.065228 finished: building insfile for tabular output file heads.csv took: 0:00:00.013003\n", + "2025-07-02 16:17:31.065228 starting: adding observation from instruction file 'freyberg6_template\\heads.csv.ins'\n", + "2025-07-02 16:17:31.098228 finished: adding observation from instruction file 'freyberg6_template\\heads.csv.ins' took: 0:00:00.033000\n", + "2025-07-02 16:17:31.100228 finished: adding observations from tabular output file '['heads.csv']' took: 0:00:00.082215\n", + "2025-07-02 16:17:31.100228 finished: adding observations from output file heads.csv took: 0:00:00.082215\n" + ] + }, + { + "data": { + "text/html": [ + "
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"oname:sfr.csv_otype:lst_usecol:tailwater_time:92 oname:sfr.csv_otype:lst_usecol:tailwater_time:92 \n", + "\n", + " obsval weight \\\n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:1 1206.10 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:122 1981.70 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:153 1692.10 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:183 1290.50 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:214 871.34 1.0 \n", + "... ... ... \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:640 -162.42 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:671 -214.05 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:701 -343.65 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:732 -517.00 1.0 \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:92 -818.81 1.0 \n", + "\n", + " obgnme \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:1 oname:sfr.csv_otype:lst_usecol:gage_1 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:122 oname:sfr.csv_otype:lst_usecol:gage_1 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:153 oname:sfr.csv_otype:lst_usecol:gage_1 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:183 oname:sfr.csv_otype:lst_usecol:gage_1 \n", + "oname:sfr.csv_otype:lst_usecol:gage_1_time:214 oname:sfr.csv_otype:lst_usecol:gage_1 \n", + "... ... \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:640 oname:sfr.csv_otype:lst_usecol:tailwater \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:671 oname:sfr.csv_otype:lst_usecol:tailwater \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:701 oname:sfr.csv_otype:lst_usecol:tailwater \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:732 oname:sfr.csv_otype:lst_usecol:tailwater \n", + "oname:sfr.csv_otype:lst_usecol:tailwater_time:92 oname:sfr.csv_otype:lst_usecol:tailwater \n", + "\n", + "[75 rows x 4 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.read_csv(os.path.join(tmp_model_ws, \"sfr.csv\"), index_col=0)\n", "sfr_df = pf.add_observations(\"sfr.csv\", insfile=\"sfr.csv.ins\", index_cols=\"time\", use_cols=list(df.columns.values))\n", @@ -243,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -253,9 +1812,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spatial variogram\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pp_gs.plot()\n", "print(\"spatial variogram\")" @@ -270,7 +1847,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -286,9 +1863,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['freyberg6.npf_k_layer1.txt', 'freyberg6.npf_k_layer3.txt']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hk_arr_files = [f for f in os.listdir(tmp_model_ws) if \"npf_k_\" in f and f.endswith(\".txt\") and \"layer2\" not in f]\n", "hk_arr_files" @@ -296,9 +1884,230 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-07-02 16:17:31.683565 transform was not passed, setting default transform to 'log'\n", + "2025-07-02 16:17:31.684567 starting: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer1.txt']\n", + "2025-07-02 16:17:31.684567 starting: using geostruct:name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:1.0,bearing:0.0\n", + "\n", + "2025-07-02 16:17:31.684567 starting: loading array freyberg6_template\\freyberg6.npf_k_layer1.txt\n", + "2025-07-02 16:17:31.703572 WARNING: \n", + "_load_array_get_fmt(), likely fixed format file:\n", + "freyberg6.npf_k_layer1.txt appears to contain some float notation (%F) values.\n", + "Can't define a generic format specifier that\n", + "will guarantee file is readable by downstream engine.\n", + "Will try to use %E, but this could cause issues\n", + "downstream...\n", + "2025-07-02 16:17:31.703572 finished: loading array freyberg6_template\\freyberg6.npf_k_layer1.txt took: 0:00:00.019005\n", + "2025-07-02 16:17:31.703572 loaded array 'temp_pst_from_ppu\\freyberg6.npf_k_layer1.txt' of shape (40, 20)\n", + "2025-07-02 16:17:31.706575 starting: writing array-style template file 'freyberg6_template\\npf-k-layer1_inst0_pilotpoints.csv.tpl'\n", + "2025-07-02 16:17:31.707566 starting: setting up pilot point parameters\n", + "2025-07-02 16:17:31.707566 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", + "2025-07-02 16:17:31.707566 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", + "2025-07-02 16:17:31.707566 'try_use_ppu' not set in pp_options, Setting to default value: [True]\n", + "2025-07-02 16:17:31.707566 'num_threads' not set in pp_options, Setting to default value: [10]\n", + "2025-07-02 16:17:31.707566 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", + "2025-07-02 16:17:31.707566 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", + "2025-07-02 16:17:31.707566 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", + "2025-07-02 16:17:31.707566 'fill_value' not set in pp_options, Setting to default value: [1.0]\n", + "2025-07-02 16:17:31.707566 'lower_limit' not set in pp_options, Setting to default value: [1e-30]\n", + "2025-07-02 16:17:31.707566 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", + "2025-07-02 16:17:31.707566 No spatial reference (containing cell spacing) passed.\n", + "2025-07-02 16:17:31.707566 OK - using spatial reference in parent object.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:31.703572 WARNING: \n", + "_load_array_get_fmt(), likely fixed format file:\n", + "freyberg6.npf_k_layer1.txt appears to contain some float notation (%F) values.\n", + "Can't define a generic format specifier that\n", + "will guarantee file is readable by downstream engine.\n", + "Will try to use %E, but this could cause issues\n", + "downstream...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", + "2025-07-02 16:17:31.902658 79 pilot point parameters created\n", + "2025-07-02 16:17:31.902658 pilot point 'pargp':n,p,f,-,k,-,l,a,y,e,r,1\n", + "2025-07-02 16:17:31.902658 finished: setting up pilot point parameters took: 0:00:00.195092\n", + "2025-07-02 16:17:31.915161 starting: calculating factors for pargp=npf-k-layer1\n", + "2025-07-02 16:17:31.915161 saving krige variance file:freyberg6_template\\npf-k-layer1_inst0pp.var.dat\n", + "2025-07-02 16:17:31.915161 saving krige factors file:freyberg6_template\\npf-k-layer1_inst0pp.fac\n", + "...pypestutils detected and being used for kriging solve, trust us, you want this!\n", + "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k-layer1_inst0pp.fac'\n", + "INFO:PestUtilsLib: all memory was freed up\n", + "...ppu_factor_filename 'freyberg6_template\\npf-k-layer1_inst0pp.fac' created, factors calculated for 800 points\n", + "2025-07-02 16:17:32.166002 finished: calculating factors for pargp=npf-k-layer1 took: 0:00:00.250841\n", + "2025-07-02 16:17:32.167002 starting: writing array-based template file 'freyberg6_template\\npf-k-layer1_inst0pp.dat.tpl'\n", + "2025-07-02 16:17:32.167002 saving zone array freyberg6_template\\npf-k-layer1_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k-layer1_inst0pp.dat.tpl\n", + "2025-07-02 16:17:32.175002 finished: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer1.txt'] took: 0:00:00.490435\n", + "2025-07-02 16:17:32.175002 starting: adding observations from output file freyberg6.npf_k_layer1.txt\n", + "2025-07-02 16:17:32.175002 starting: adding observations from array output file 'freyberg6.npf_k_layer1.txt'\n", + "2025-07-02 16:17:32.180003 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer1.txt.ins'\n", + "2025-07-02 16:17:32.217522 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer1.txt.ins' took: 0:00:00.037519\n", + "2025-07-02 16:17:32.218521 finished: adding observations from array output file 'freyberg6.npf_k_layer1.txt' took: 0:00:00.043519\n", + "2025-07-02 16:17:32.218521 finished: adding observations from output file freyberg6.npf_k_layer1.txt took: 0:00:00.043519\n" + ] + }, + { + "data": { + "text/html": [ + "
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"execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['npf-k-layer1_inst0pp.dat.tpl']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tpl_files = [f for f in os.listdir(template_ws) if f.endswith(\".tpl\")]\n", "tpl_files" @@ -331,9 +2151,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ptf ~\n", + "pp_0000 375.0 9625.0 1 ~ pname:npf-k-layer1_inst:0_ptype:pp_pstyle:m_i:1_j:1_zone:1 ~\n" + ] + } + ], "source": [ "with open(os.path.join(template_ws,tpl_files[0]),'r') as f:\n", " for _ in range(2):\n", @@ -371,7 +2200,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -383,9 +2212,235 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-07-02 16:17:32.293379 transform was not passed, setting default transform to 'log'\n", + "2025-07-02 16:17:32.294378 starting: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer3.txt']\n", + "2025-07-02 16:17:32.294378 starting: using geostruct:name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", + "\n", + "2025-07-02 16:17:32.294378 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", + "2025-07-02 16:17:32.294378 WARNING: 1) Setting geostruct transform to log\n", + "2025-07-02 16:17:32.294378 WARNING: -) Better to pass an appropriately transformed geostruct\n", + "2025-07-02 16:17:32.294378 starting: loading array freyberg6_template\\freyberg6.npf_k_layer3.txt\n", + "2025-07-02 16:17:32.315475 WARNING: \n", + "_load_array_get_fmt(), likely fixed format file:\n", + "freyberg6.npf_k_layer3.txt appears to contain some float notation (%F) values.\n", + "Can't define a generic format specifier that\n", + "will guarantee file is readable by downstream engine.\n", + "Will try to use %E, but this could cause issues\n", + "downstream...\n", + "2025-07-02 16:17:32.315475 finished: loading array freyberg6_template\\freyberg6.npf_k_layer3.txt took: 0:00:00.021097\n", + "2025-07-02 16:17:32.315475 loaded array 'temp_pst_from_ppu\\freyberg6.npf_k_layer3.txt' of shape (40, 20)\n", + "2025-07-02 16:17:32.318475 starting: writing array-style template file 'freyberg6_template\\npf-k-layer3_inst0_pilotpoints.csv.tpl'\n", + "2025-07-02 16:17:32.318475 starting: setting up pilot point parameters\n", + "2025-07-02 16:17:32.319475 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", + "2025-07-02 16:17:32.319475 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", + "2025-07-02 16:17:32.319475 'try_use_ppu' not set in pp_options, Setting to default value: [True]\n", + "2025-07-02 16:17:32.319475 'num_threads' not set in pp_options, Setting to default value: [10]\n", + "2025-07-02 16:17:32.319475 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", + "2025-07-02 16:17:32.319475 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", + "2025-07-02 16:17:32.319475 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", + "2025-07-02 16:17:32.319475 'fill_value' not set in pp_options, Setting to default value: [1.0]\n", + "2025-07-02 16:17:32.319475 'lower_limit' not set in pp_options, Setting to default value: [1e-30]\n", + "2025-07-02 16:17:32.319475 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", + "2025-07-02 16:17:32.319475 No spatial reference (containing cell spacing) passed.\n", + "2025-07-02 16:17:32.319475 OK - using spatial reference in parent object.\n", + "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", + "2025-07-02 16:17:32.474083 79 pilot point parameters created\n", + "2025-07-02 16:17:32.475088 pilot point 'pargp':n,p,f,-,k,-,l,a,y,e,r,3\n", + "2025-07-02 16:17:32.475088 finished: setting up pilot point parameters took: 0:00:00.156613\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.294378 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", + "\n", + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.294378 WARNING: 1) Setting geostruct transform to log\n", + "\n", + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.294378 WARNING: -) Better to pass an appropriately transformed geostruct\n", + "\n", + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.315475 WARNING: \n", + "_load_array_get_fmt(), likely fixed format file:\n", + "freyberg6.npf_k_layer3.txt appears to contain some float notation (%F) values.\n", + "Can't define a generic format specifier that\n", + "will guarantee file is readable by downstream engine.\n", + "Will try to use %E, but this could cause issues\n", + "downstream...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k-layer3_inst0_pilotpoints.csv.temp.fac'\n", + "INFO:PestUtilsLib: kriged using factor file 'npf-k-layer3_inst0_pilotpoints.csv.temp.fac'\n", + "INFO:PestUtilsLib: all memory was freed up\n", + "npf-k-layer3.config.csv\n", + "2025-07-02 16:17:32.728262 starting: writing array-based template file 'freyberg6_template\\npf-k-layer3_inst0pp.dat.tpl'\n", + "2025-07-02 16:17:32.728262 saving zone array freyberg6_template\\npf-k-layer3_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k-layer3_inst0pp.dat.tpl\n", + "2025-07-02 16:17:32.738295 finished: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer3.txt'] took: 0:00:00.443917\n", + "2025-07-02 16:17:32.738295 starting: adding observations from output file freyberg6.npf_k_layer3.txt\n", + "2025-07-02 16:17:32.739297 starting: adding observations from array output file 'freyberg6.npf_k_layer3.txt'\n", + "2025-07-02 16:17:32.747290 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer3.txt.ins'\n", + "2025-07-02 16:17:32.785992 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer3.txt.ins' took: 0:00:00.038702\n", + "2025-07-02 16:17:32.785992 finished: adding observations from array output file 'freyberg6.npf_k_layer3.txt' took: 0:00:00.046695\n", + "2025-07-02 16:17:32.785992 finished: adding observations from output file freyberg6.npf_k_layer3.txt took: 0:00:00.047697\n" + ] + }, + { + "data": { + "text/html": [ + "
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obsnmeobsvalweightobgnme
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oname:npf-k-layer3_otype:arr_i:0_j:13_zone:1oname:npf-k-layer3_otype:arr_i:0_j:13_zone:130.01.0npf-k-layer3
...............
oname:npf-k-layer3_otype:arr_i:9_j:1_zone:1oname:npf-k-layer3_otype:arr_i:9_j:1_zone:130.01.0npf-k-layer3
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oname:npf-k-layer3_otype:arr_i:9_j:9_zone:1oname:npf-k-layer3_otype:arr_i:9_j:9_zone:130.01.0npf-k-layer3
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" + ], + "text/plain": [ + " obsnme \\\n", + "oname:npf-k-layer3_otype:arr_i:0_j:0_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:0_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:10_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:10_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:11_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:11_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:12_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:12_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:13_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:13_zone:1 \n", + "... ... \n", + "oname:npf-k-layer3_otype:arr_i:9_j:1_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:1_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:2_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:2_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:3_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:3_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:8_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:8_zone:1 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:9_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:9_zone:1 \n", + "\n", + " obsval weight obgnme \n", + "oname:npf-k-layer3_otype:arr_i:0_j:0_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:10_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:11_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:12_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:0_j:13_zone:1 30.0 1.0 npf-k-layer3 \n", + "... ... ... ... \n", + "oname:npf-k-layer3_otype:arr_i:9_j:1_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:2_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:3_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:8_zone:1 30.0 1.0 npf-k-layer3 \n", + "oname:npf-k-layer3_otype:arr_i:9_j:9_zone:1 30.0 1.0 npf-k-layer3 \n", + "\n", + "[706 rows x 4 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "arr_file = \"freyberg6.npf_k_layer3.txt\"\n", "tag = arr_file.split('.')[1].replace(\"_\",\"-\")\n", @@ -400,9 +2455,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['npf-k-layer3.aniso.dat',\n", + " 'npf-k-layer3.bearing.dat',\n", + " 'npf-k-layer3.config.csv',\n", + " 'npf-k-layer3.corrlen.dat',\n", + " 'npf-k-layer3.gridinfo.dat',\n", + " 'npf-k-layer3.zone.dat',\n", + " 'npf-k-layer3_inst0pp.dat',\n", + " 'npf-k-layer3_inst0pp.dat.tpl',\n", + " 'npf-k-layer3_inst0pp.dat.zone']" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hyperpar_files = [f for f in os.listdir(pf.new_d) if tag in f]\n", "hyperpar_files" @@ -421,9 +2495,238 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-07-02 16:17:32.809604 starting: adding constant type a style parameters for file(s) ['npf-k-layer3.aniso.dat']\n", + "2025-07-02 16:17:32.810606 starting: loading array freyberg6_template\\npf-k-layer3.aniso.dat\n", + "2025-07-02 16:17:32.811611 finished: loading array freyberg6_template\\npf-k-layer3.aniso.dat took: 0:00:00.001005\n", + "2025-07-02 16:17:32.811611 loaded array 'temp_pst_from_ppu\\npf-k-layer3.aniso.dat' of shape (40, 20)\n", + "2025-07-02 16:17:32.813602 starting: writing array-style template file 'freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl'\n", + "2025-07-02 16:17:32.813602 starting: writing template file freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl for ['npf-k-layer3-aniso_inst:0']\n", + "2025-07-02 16:17:32.822602 finished: writing template file freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl for ['npf-k-layer3-aniso_inst:0'] took: 0:00:00.009000\n", + "2025-07-02 16:17:32.823601 starting: writing array-based template file 'freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl'\n", + "2025-07-02 16:17:32.830607 finished: adding constant type a style parameters for file(s) ['npf-k-layer3.aniso.dat'] took: 0:00:00.021003\n", + "2025-07-02 16:17:32.831610 starting: adding observations from output file npf-k-layer3.aniso.dat\n", + "2025-07-02 16:17:32.831610 starting: adding observations from array output file 'npf-k-layer3.aniso.dat'\n", + "2025-07-02 16:17:32.838623 starting: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.aniso.dat.ins'\n", + "2025-07-02 16:17:32.878154 finished: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.aniso.dat.ins' took: 0:00:00.039531\n", + "2025-07-02 16:17:32.879155 finished: adding observations from array output file 'npf-k-layer3.aniso.dat' took: 0:00:00.047545\n", + "2025-07-02 16:17:32.879155 finished: adding observations from output file npf-k-layer3.aniso.dat took: 0:00:00.047545\n", + "2025-07-02 16:17:32.880155 starting: adding pilotpoints type a style parameters for file(s) ['npf-k-layer3.bearing.dat']\n", + "2025-07-02 16:17:32.880155 starting: using geostruct:name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:90.0\n", + "\n", + "2025-07-02 16:17:32.880155 starting: loading array freyberg6_template\\npf-k-layer3.bearing.dat\n", + "2025-07-02 16:17:32.881153 finished: loading array freyberg6_template\\npf-k-layer3.bearing.dat took: 0:00:00.000998\n", + "2025-07-02 16:17:32.882153 loaded array 'temp_pst_from_ppu\\npf-k-layer3.bearing.dat' of shape (40, 20)\n", + "2025-07-02 16:17:32.885200 starting: writing array-style template file 'freyberg6_template\\npf-k-layer3-bearing_inst0_pilotpoints.csv.tpl'\n", + "2025-07-02 16:17:32.885200 starting: setting up pilot point parameters\n", + "2025-07-02 16:17:32.885200 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", + "2025-07-02 16:17:32.885200 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", + "2025-07-02 16:17:32.886157 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", + "2025-07-02 16:17:32.886157 'num_threads' not set in pp_options, Setting to default value: [10]\n", + "2025-07-02 16:17:32.886157 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", + "2025-07-02 16:17:32.886157 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", + "2025-07-02 16:17:32.886157 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", + "2025-07-02 16:17:32.886157 'fill_value' not set in pp_options, Setting to default value: [0.0]\n", + "2025-07-02 16:17:32.886157 'lower_limit' not set in pp_options, Setting to default value: [-1e+30]\n", + "2025-07-02 16:17:32.886157 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", + "2025-07-02 16:17:32.886157 No spatial reference (containing cell spacing) passed.\n", + "2025-07-02 16:17:32.886157 OK - using spatial reference in parent object.\n", + "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", + "2025-07-02 16:17:32.952108 18 pilot point parameters created\n", + "2025-07-02 16:17:32.952108 pilot point 'pargp':n,p,f,-,k,-,l,a,y,e,r,3,-,b,e,a,r,i,n,g\n", + "2025-07-02 16:17:32.953108 finished: setting up pilot point parameters took: 0:00:00.067908\n", + "2025-07-02 16:17:32.957108 starting: calculating factors for pargp=npf-k-layer3-bearing\n", + "2025-07-02 16:17:32.958107 saving krige variance file:freyberg6_template\\npf-k-layer3-bearing_inst0pp.var.dat\n", + "2025-07-02 16:17:32.958107 saving krige factors file:freyberg6_template\\npf-k-layer3-bearing_inst0pp.fac\n", + "...pypestutils detected and being used for kriging solve, trust us, you want this!\n", + "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k-layer3-bearing_inst0pp.fac'\n", + "INFO:PestUtilsLib: all memory was freed up\n", + "...ppu_factor_filename 'freyberg6_template\\npf-k-layer3-bearing_inst0pp.fac' created, factors calculated for 800 points\n", + "2025-07-02 16:17:33.033550 finished: calculating factors for pargp=npf-k-layer3-bearing took: 0:00:00.076442\n", + "2025-07-02 16:17:33.033550 starting: writing array-based template file 'freyberg6_template\\npf-k-layer3-bearing_inst0pp.dat.tpl'\n", + "2025-07-02 16:17:33.033550 saving zone array freyberg6_template\\npf-k-layer3-bearing_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k-layer3-bearing_inst0pp.dat.tpl\n", + "2025-07-02 16:17:33.040550 finished: adding pilotpoints type a style parameters for file(s) ['npf-k-layer3.bearing.dat'] took: 0:00:00.160395\n", + "2025-07-02 16:17:33.041549 starting: adding observations from output file npf-k-layer3.bearing.dat\n", + "2025-07-02 16:17:33.041549 starting: adding observations from array output file 'npf-k-layer3.bearing.dat'\n", + "2025-07-02 16:17:33.048063 starting: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.bearing.dat.ins'\n", + "2025-07-02 16:17:33.087080 finished: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.bearing.dat.ins' took: 0:00:00.039017\n", + "2025-07-02 16:17:33.088081 finished: adding observations from array output file 'npf-k-layer3.bearing.dat' took: 0:00:00.046532\n", + "2025-07-02 16:17:33.088081 finished: adding observations from output file npf-k-layer3.bearing.dat took: 0:00:00.046532\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.885200 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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...............
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oname:npf-k-layer3-bearing_otype:arr_i:9_j:9 \n", + "\n", + " obsval weight \\\n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:0 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:1 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:10 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:11 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:12 0.0 1.0 \n", + "... ... ... \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:5 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:6 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:7 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:8 0.0 1.0 \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:9 0.0 1.0 \n", + "\n", + " obgnme \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:0 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:1 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:10 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:11 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:0_j:12 npf-k-layer3-bearing \n", + "... ... \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:5 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:6 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:7 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:8 npf-k-layer3-bearing \n", + "oname:npf-k-layer3-bearing_otype:arr_i:9_j:9 npf-k-layer3-bearing \n", + "\n", + "[800 rows x 4 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "afile = 'npf-k-layer3.aniso.dat'\n", "tag = afile.split('.')[0].replace(\"_\",\"-\")+\"-aniso\"\n", @@ -466,9 +2769,315 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freyberg6.npf_k33_layer2.txt\n", + "WARNING: thresholding array freyberg6.npf_k33_layer2.txt.thresharr.dat has very low standard deviation\n", + " using a homogeneous array with first category fill value 0.03\n", + "2025-07-02 16:17:33.235335 starting: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.thresharr.dat']\n", + "2025-07-02 16:17:33.235335 finished: using geostruct:name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", + " took: 0:00:00.940957\n", + "2025-07-02 16:17:33.235335 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", + "2025-07-02 16:17:33.235335 WARNING: 1) Setting geostruct transform to none\n", + "2025-07-02 16:17:33.235335 WARNING: 2) This will create a new copy of geostruct\n", + "2025-07-02 16:17:33.235335 WARNING: -) Better to pass an appropriately transformed geostruct\n", + "2025-07-02 16:17:33.235335 starting: loading array freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat\n", + "2025-07-02 16:17:33.237335 finished: loading array freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat took: 0:00:00.002000\n", + "2025-07-02 16:17:33.237335 loaded array 'temp_pst_from_ppu\\freyberg6.npf_k33_layer2.txt.thresharr.dat' of shape (40, 20)\n", + "2025-07-02 16:17:33.239335 starting: writing array-style template file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0_pilotpoints.csv.tpl'\n", + "2025-07-02 16:17:33.239335 starting: setting up pilot point parameters\n", + "2025-07-02 16:17:33.239335 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", + "2025-07-02 16:17:33.239335 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", + "2025-07-02 16:17:33.239335 'num_threads' not set in pp_options, Setting to default value: [10]\n", + "2025-07-02 16:17:33.239335 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", + "2025-07-02 16:17:33.239335 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", + "2025-07-02 16:17:33.239335 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", + "2025-07-02 16:17:33.239335 'fill_value' not set in pp_options, Setting to default value: [0.0]\n", + "2025-07-02 16:17:33.239335 'lower_limit' not set in pp_options, Setting to default value: [-1e+30]\n", + "2025-07-02 16:17:33.239335 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", + "2025-07-02 16:17:33.239335 WARNING: `prep_hyperpars` requested but `try_use_ppu` set to False.\n", + "Setting `try_use_ppu` to True and testing import\n", + "2025-07-02 16:17:33.239335 No spatial reference (containing cell spacing) passed.\n", + "2025-07-02 16:17:33.239335 OK - using spatial reference in parent object.\n", + "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", + "2025-07-02 16:17:33.315872 32 pilot point parameters created\n", + "2025-07-02 16:17:33.315872 pilot point 'pargp':n,p,f,-,k,3,3,-,l,a,y,e,r,2,_,p,p,-,t,h,r,e,s,h,p,p,_,k,:,1\n", + "2025-07-02 16:17:33.316874 finished: setting up pilot point parameters took: 0:00:00.077539\n", + "warning: replacing illegal character ':' with '-' in file_tag name 'npf-k33-layer2_pp-threshpp_k:1'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", + "\n", + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: 1) Setting geostruct transform to none\n", + "\n", + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: 2) This will create a new copy of geostruct\n", + "\n", + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: -) Better to pass an appropriately transformed geostruct\n", + "\n", + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.239335 WARNING: `prep_hyperpars` requested but `try_use_ppu` set to False.\n", + "Setting `try_use_ppu` to True and testing import\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k33-layer2_pp-threshpp_k1_inst0_pilotpoints.csv.temp.fac'\n", + "INFO:PestUtilsLib: kriged using factor file 'npf-k33-layer2_pp-threshpp_k1_inst0_pilotpoints.csv.temp.fac'\n", + "INFO:PestUtilsLib: all memory was freed up\n", + "npf-k33-layer2_pp-threshpp_k-1.config.csv\n", + "2025-07-02 16:17:33.615941 starting: writing array-based template file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0pp.dat.tpl'\n", + "2025-07-02 16:17:33.615941 saving zone array freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0pp.dat.tpl\n", + "2025-07-02 16:17:33.625944 finished: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.thresharr.dat'] took: 0:00:00.390609\n", + "2025-07-02 16:17:33.627939 starting: adding constant type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.aniso.dat']\n", + "2025-07-02 16:17:33.628943 starting: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat\n", + "2025-07-02 16:17:33.629944 finished: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat took: 0:00:00.001001\n", + "2025-07-02 16:17:33.629944 loaded array 'temp_pst_from_ppu\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat' of shape (40, 20)\n", + "2025-07-02 16:17:33.632940 starting: writing array-style template file 'freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl'\n", + "2025-07-02 16:17:33.632940 starting: writing template file freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl for ['npf-k33-layer2-aniso_inst:0']\n", + "2025-07-02 16:17:33.647959 finished: writing template file freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl for ['npf-k33-layer2-aniso_inst:0'] took: 0:00:00.015019\n", + "2025-07-02 16:17:33.647959 starting: writing array-based template file 'freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl'\n", + "2025-07-02 16:17:33.657489 finished: adding constant type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.aniso.dat'] took: 0:00:00.029550\n", + "2025-07-02 16:17:33.657489 starting: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.aniso.dat\n", + "2025-07-02 16:17:33.657489 starting: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.aniso.dat'\n", + "2025-07-02 16:17:33.661498 starting: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat.ins'\n", + "2025-07-02 16:17:33.694501 finished: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat.ins' took: 0:00:00.033003\n", + "2025-07-02 16:17:33.695492 finished: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.aniso.dat' took: 0:00:00.038003\n", + "2025-07-02 16:17:33.695492 finished: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.aniso.dat took: 0:00:00.038003\n", + "2025-07-02 16:17:33.696509 starting: adding pilotpoints type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.bearing.dat']\n", + "2025-07-02 16:17:33.696509 finished: using geostruct:name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:90.0\n", + " took: 0:00:00.816354\n", + "2025-07-02 16:17:33.696509 starting: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat\n", + "2025-07-02 16:17:33.698496 finished: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat took: 0:00:00.001987\n", + "2025-07-02 16:17:33.698496 loaded array 'temp_pst_from_ppu\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat' of shape (40, 20)\n", + "2025-07-02 16:17:33.700489 starting: writing array-style template file 'freyberg6_template\\npf-k33-layer2-bearing_inst0_pilotpoints.csv.tpl'\n", + "2025-07-02 16:17:33.700489 starting: setting up pilot point parameters\n", + "2025-07-02 16:17:33.700489 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", + "2025-07-02 16:17:33.701490 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", + "2025-07-02 16:17:33.701490 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", + "2025-07-02 16:17:33.701490 'num_threads' not set in pp_options, Setting to default value: [10]\n", + "2025-07-02 16:17:33.701490 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", + "2025-07-02 16:17:33.701490 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", + "2025-07-02 16:17:33.701490 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", + "2025-07-02 16:17:33.701490 'fill_value' not set in pp_options, Setting to default value: [0.0]\n", + "2025-07-02 16:17:33.701490 'lower_limit' not set in pp_options, Setting to default value: [-1e+30]\n", + "2025-07-02 16:17:33.701490 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", + "2025-07-02 16:17:33.701490 No spatial reference (containing cell spacing) passed.\n", + "2025-07-02 16:17:33.701490 OK - using spatial reference in parent object.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.700489 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", + "2025-07-02 16:17:33.868773 18 pilot point parameters created\n", + "2025-07-02 16:17:33.869773 pilot point 'pargp':n,p,f,-,k,3,3,-,l,a,y,e,r,2,-,b,e,a,r,i,n,g\n", + "2025-07-02 16:17:33.869773 finished: setting up pilot point parameters took: 0:00:00.169284\n", + "2025-07-02 16:17:33.878777 reusing factors\n", + "2025-07-02 16:17:33.878777 starting: writing array-based template file 'freyberg6_template\\npf-k33-layer2-bearing_inst0pp.dat.tpl'\n", + "2025-07-02 16:17:33.878777 saving zone array freyberg6_template\\npf-k33-layer2-bearing_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k33-layer2-bearing_inst0pp.dat.tpl\n", + "2025-07-02 16:17:33.887772 finished: adding pilotpoints type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.bearing.dat'] took: 0:00:00.191263\n", + "2025-07-02 16:17:33.888771 starting: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.bearing.dat\n", + "2025-07-02 16:17:33.888771 starting: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.bearing.dat'\n", + "2025-07-02 16:17:33.893771 starting: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat.ins'\n", + "2025-07-02 16:17:33.936882 finished: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat.ins' took: 0:00:00.043111\n", + "2025-07-02 16:17:33.937881 finished: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.bearing.dat' took: 0:00:00.049110\n", + "2025-07-02 16:17:33.937881 finished: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.bearing.dat took: 0:00:00.049110\n", + "2025-07-02 16:17:33.937881 starting: adding grid type d style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.threshprops.csv']\n", + "2025-07-02 16:17:33.938881 starting: loading list-style freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv\n", + "2025-07-02 16:17:33.938881 starting: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv\n", + "2025-07-02 16:17:33.941884 finished: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv took: 0:00:00.003003\n", + "2025-07-02 16:17:33.941884 loaded list-style 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv' of shape (2, 3)\n", + "2025-07-02 16:17:33.943881 finished: loading list-style freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv took: 0:00:00.005000\n", + "2025-07-02 16:17:33.943881 starting: writing list-style template file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv.tpl'\n", + "2025-07-02 16:17:33.965397 finished: writing list-style template file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv.tpl' took: 0:00:00.021516\n", + "2025-07-02 16:17:33.973399 finished: adding grid type d style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.threshprops.csv'] took: 0:00:00.035518\n", + "2025-07-02 16:17:33.974398 starting: adding observations from output file freyberg6.npf_k33_layer2.txt\n", + "2025-07-02 16:17:33.974398 starting: adding observations from array output file 'freyberg6.npf_k33_layer2.txt'\n", + "2025-07-02 16:17:33.979395 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.ins'\n", + "2025-07-02 16:17:34.020397 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.ins' took: 0:00:00.041002\n", + "2025-07-02 16:17:34.021398 finished: adding observations from array output file 'freyberg6.npf_k33_layer2.txt' took: 0:00:00.047000\n", + "2025-07-02 16:17:34.021398 finished: adding observations from output file freyberg6.npf_k33_layer2.txt took: 0:00:00.047000\n", + "2025-07-02 16:17:34.021398 starting: adding observations from output file freyberg6.npf_k33_layer2.txt.threshcat.dat\n", + "2025-07-02 16:17:34.021398 starting: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.threshcat.dat'\n", + "2025-07-02 16:17:34.068404 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshcat.dat.ins'\n", + "2025-07-02 16:17:34.100396 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshcat.dat.ins' took: 0:00:00.031992\n", + "2025-07-02 16:17:34.100396 finished: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.threshcat.dat' took: 0:00:00.078998\n", + "2025-07-02 16:17:34.101394 finished: adding observations from output file freyberg6.npf_k33_layer2.txt.threshcat.dat took: 0:00:00.079996\n", + "2025-07-02 16:17:34.101394 starting: adding observations from output file freyberg6.npf_k33_layer2.txt.thresharr.dat\n", + "2025-07-02 16:17:34.101394 starting: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.thresharr.dat'\n", + "2025-07-02 16:17:34.106392 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat.ins'\n", + "2025-07-02 16:17:34.133395 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat.ins' took: 0:00:00.027003\n", + "2025-07-02 16:17:34.133395 finished: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.thresharr.dat' took: 0:00:00.032001\n", + "2025-07-02 16:17:34.133395 finished: adding observations from output file freyberg6.npf_k33_layer2.txt.thresharr.dat took: 0:00:00.032001\n", + "2025-07-02 16:17:34.142408 starting: adding observations from output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv\n", + "2025-07-02 16:17:34.143407 starting: adding observations from tabular output file '['freyberg6.npf_k33_layer2.txt.threshprops_results.csv']'\n", + "2025-07-02 16:17:34.143407 starting: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv\n", + "2025-07-02 16:17:34.145406 finished: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv took: 0:00:00.001999\n", + "2025-07-02 16:17:34.146406 starting: building insfile for tabular output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv\n", + "2025-07-02 16:17:34.151929 finished: building insfile for tabular output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv took: 0:00:00.005523\n", + "2025-07-02 16:17:34.152932 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv.ins'\n", + "2025-07-02 16:17:34.167926 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv.ins' took: 0:00:00.014994\n", + "2025-07-02 16:17:34.168926 finished: adding observations from tabular output file '['freyberg6.npf_k33_layer2.txt.threshprops_results.csv']' took: 0:00:00.025519\n", + "2025-07-02 16:17:34.168926 finished: adding observations from output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv took: 0:00:00.026518\n" + ] + }, + { + "data": { + "text/html": [ + "
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oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:20.601.0npf-k33-layer2-results_k:1
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" + ], + "text/plain": [ + " obsnme \\\n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 \n", + "\n", + " obsval \\\n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 1.00 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 0.00 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 0.03 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 0.03 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 0.00 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 0.00 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 0.40 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 0.60 \n", + "\n", + " weight \\\n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 1.0 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 1.0 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 1.0 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 1.0 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 1.0 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 1.0 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 1.0 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 1.0 \n", + "\n", + " obgnme \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 npf-k33-layer2-results_k:1 \n", + "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 npf-k33-layer2-results_k:1 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "arr_file = \"freyberg6.npf_k33_layer2.txt\"\n", "print(arr_file)\n", @@ -561,9 +3170,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "noptmax:0, npar_adj:232, nnz_obs:7463\n", + "2025-07-02 16:17:34.505863 forward_run line:pyemu.os_utils.run(r'mf6')\n", + "\n" + ] + } + ], "source": [ "pf.mod_sys_cmds.append(\"mf6\")\n", "pf.pre_py_cmds.insert(0,\"import sys\")\n", @@ -573,9 +3192,82 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "import os\n", + "import multiprocessing as mp\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyemu\n", + "def main():\n", + "\n", + " try:\n", + " os.remove(r'heads.csv')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:heads.csv')\n", + " try:\n", + " os.remove(r'sfr.csv')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:sfr.csv')\n", + " try:\n", + " os.remove(r'freyberg6.npf_k_layer1.txt')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:freyberg6.npf_k_layer1.txt')\n", + " try:\n", + " os.remove(r'freyberg6.npf_k_layer3.txt')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:freyberg6.npf_k_layer3.txt')\n", + " try:\n", + " os.remove(r'npf-k-layer3.aniso.dat')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:npf-k-layer3.aniso.dat')\n", + " try:\n", + " os.remove(r'npf-k-layer3.bearing.dat')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:npf-k-layer3.bearing.dat')\n", + " try:\n", + " os.remove(r'npf-k33-layer2_pp-threshpp_k-1.aniso.dat')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:npf-k33-layer2_pp-threshpp_k-1.aniso.dat')\n", + " try:\n", + " os.remove(r'npf-k33-layer2_pp-threshpp_k-1.bearing.dat')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:npf-k33-layer2_pp-threshpp_k-1.bearing.dat')\n", + " try:\n", + " os.remove(r'freyberg6.npf_k33_layer2.txt')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt')\n", + " try:\n", + " os.remove(r'freyberg6.npf_k33_layer2.txt.threshcat.dat')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt.threshcat.dat')\n", + " try:\n", + " os.remove(r'freyberg6.npf_k33_layer2.txt.thresharr.dat')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt.thresharr.dat')\n", + " try:\n", + " os.remove(r'freyberg6.npf_k33_layer2.txt.threshprops_results.csv')\n", + " except Exception as e:\n", + " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt.threshprops_results.csv')\n", + " import sys\n", + " sys.path.append(os.path.join('..','..','..','pypestutils'))\n", + " pyemu.helpers.apply_list_and_array_pars(arr_par_file='mult2model_info.csv',chunk_len=50)\n", + " pyemu.helpers.apply_threshold_pars('freyberg6.npf_k33_layer2.txt.threshprops.csv')\n", + " pyemu.os_utils.run(r'mf6')\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " mp.freeze_support()\n", + " main()\n", + "\n" + ] + } + ], "source": [ "_ = [print(line.rstrip()) for line in open(os.path.join(template_ws,\"forward_run.py\"))]" ] @@ -591,7 +3283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -667,9 +3359,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-07-02 16:17:34.677796 starting: drawing realizations\n", + "2025-07-02 16:17:34.680793 starting: Drawing 232 non-specsim pars\n", + "building diagonal cov\n", + "processing name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:1.0,bearing:0.0\n", + "\n", + "working on pargroups ['npf-k-layer1']\n", + "build cov matrix\n", + "done\n", + "getting diag var cov 79\n", + "scaling full cov by diag var cov\n", + "processing name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", + "\n", + "working on pargroups ['npf-k-layer3']\n", + "build cov matrix\n", + "done\n", + "getting diag var cov 79\n", + "scaling full cov by diag var cov\n", + "processing name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:90.0\n", + "\n", + "working on pargroups ['npf-k-layer3-bearing']\n", + "build cov matrix\n", + "done\n", + "getting diag var cov 18\n", + "scaling full cov by diag var cov\n", + "working on pargroups ['npf-k33-layer2-bearing']\n", + "build cov matrix\n", + "done\n", + "getting diag var cov 18\n", + "scaling full cov by diag var cov\n", + "processing name:struct1,nugget:0.0,structures:\n", + "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", + "\n", + "working on pargroups ['npf-k33-layer2_pp-threshpp_k:1']\n", + "build cov matrix\n", + "done\n", + "getting diag var cov 32\n", + "scaling full cov by diag var cov\n", + "adding remaining parameters to diagonal\n", + "2025-07-02 16:17:34.826733 finished: Drawing 232 non-specsim pars took: 0:00:00.145940\n", + "2025-07-02 16:17:34.826733 finished: drawing realizations took: 0:00:00.148937\n" + ] + } + ], "source": [ "np.random.seed(122341)\n", "pe = pf.draw(num_reals=100)" @@ -677,7 +3419,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -686,7 +3428,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -697,9 +3439,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "noptmax:0, npar_adj:231, nnz_obs:7463\n" + ] + } + ], "source": [ "pst.control_data.noptmax = 0\n", "pst.write(os.path.join(pf.new_d,pst_name))" @@ -707,9 +3457,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "WindowsPath('freyberg6_template')" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pf.new_d" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'real_0.pst'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pst_name" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pestpp-ies.exe real_0.pst\n" + ] + } + ], "source": [ "pyemu.os_utils.run(\"pestpp-ies {0}\".format(pst_name),cwd=pf.new_d)" ] @@ -719,6 +3517,29 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['npf-k-layer1',\n", + " 'npf-k-layer3',\n", + " 'npf-k-layer3-bearing',\n", + " 'npf-k33-layer2-bearing',\n", + " 'npf-k33-layer2',\n", + " 'npf-k33-layer2-thresharr']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pst.set_res(os.path.join(pf.new_d,pst_name.replace(\".pst\",\".base.rei\")))\n", "res = pst.res\n", @@ -729,7 +3550,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -741,7 +3562,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -751,9 +3572,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "for k in uk:\n", " kobs = gobs.loc[gobs.k==k,:]\n", @@ -797,6 +3649,15 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# bearing -45 to 45 leaves out 1/2 the potential orientations! Bounds reset for layer 2 and 3 in block 28?" + ] } ], "metadata": { @@ -815,7 +3676,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/pyemu/utils/pp_utils.py b/pyemu/utils/pp_utils.py index e14dcd47b..9fed76288 100644 --- a/pyemu/utils/pp_utils.py +++ b/pyemu/utils/pp_utils.py @@ -934,3 +934,128 @@ def apply_ppu_hyperpars(config_df_filename): lib.free_all_memory() return result + +def apply_ppu_hyperpars_tensor(config_df_filename): + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception("apply_ppu_hyperpars() error importing pypestutils: '{0}'".format(str(e))) + + def create_tensors_from_bearing_aniso(bearing_array, aniso_array, n_points): + """Create 2x2 tensors from bearing and anisotropy arrays""" + tensors = np.zeros((n_points, 2, 2)) + for i in range(n_points): + theta = np.radians(bearing_array[i]) + r = aniso_array[i] + + # Create 2x2 anisotropy tensor + cos_t, sin_t = np.cos(theta), np.sin(theta) + tensors[i, 0, 0] = cos_t ** 2 + r ** 2 * sin_t ** 2 + tensors[i, 0, 1] = tensors[i, 1, 0] = (1 - r ** 2) * cos_t * sin_t + tensors[i, 1, 1] = sin_t ** 2 + r ** 2 * cos_t ** 2 + + return tensors + + def transform_coords_with_tensor_field(coords, tensors, grid_coords): + """Transform coordinates using local tensors""" + transformed = np.zeros_like(coords) + + for i, coord in enumerate(coords): + # Find nearest grid point (simple approach) + distances = np.linalg.norm(grid_coords - coord, axis=1) + nearest_idx = np.argmin(distances) + local_tensor = tensors[nearest_idx] + + # Apply Cholesky decomposition for transformation + try: + L = np.linalg.cholesky(local_tensor) + transformed[i] = L @ coord + except np.linalg.LinAlgError: + # Fallback to original coordinates if tensor is singular + transformed[i] = coord + + return transformed + + config_df = pd.read_csv(config_df_filename,index_col=0) + config_dict = config_df["value"].to_dict() + vartransform = config_dict.get("vartransform", "none") + config_dict = parse_pp_options_with_defaults(config_dict, threads=None, log=vartransform=='log') + + out_filename = config_dict["out_filename"] + #pp_info = pd.read_csv(config_dict["pp_filename"],sep="\s+") + pp_info = pyemu.pp_utils.pp_file_to_dataframe(config_dict["pp_filename"]) + grid_df = pd.read_csv(config_dict["gridinfo_filename"]) + corrlen = np.loadtxt(config_dict["corrlen_filename"]) + bearing = np.loadtxt(config_dict["bearing_filename"]) + aniso = np.loadtxt(config_dict["aniso_filename"]) + zone = np.loadtxt(config_dict["zone_filename"]) + + # Create tensor field and transform coordinates + grid_coords = np.column_stack([grid_df.x.values, grid_df.y.values]) + pp_coords = np.column_stack([pp_info.x.values, pp_info.y.values]) + + # Create tensors from bearing/anisotropy at grid points + tensors_2d = create_tensors_from_bearing_aniso( + bearing.flatten(), aniso.flatten(), grid_coords.shape[0] + ) + + # Transform both pilot point and grid coordinates + pp_coords_transformed = transform_coords_with_tensor_field( + pp_coords, tensors_2d, grid_coords + ) + grid_coords_transformed = transform_coords_with_tensor_field( + grid_coords, tensors_2d, grid_coords + ) + + lib = PestUtilsLib() + fac_fname = out_filename + ".temp.fac" + if os.path.exists(fac_fname): + os.remove(fac_fname) + fac_ftype = "text" + + # Call pypestutils with transformed coordinates but isotropic parameters + npts = lib.calc_kriging_factors_2d( + pp_coords_transformed[:, 0], # transformed pilot point x + pp_coords_transformed[:, 1], # transformed pilot point y + pp_info.zone.values, + grid_coords_transformed[:, 0], # transformed grid x + grid_coords_transformed[:, 1], # transformed grid y + zone.flatten().astype(int), + int(config_dict.get("vartype", 1)), + int(config_dict.get("krigtype", 1)), + corrlen.flatten(), # keep original correlation length + np.ones_like(aniso.flatten()), # anisotropy = 1 (isotropic in transformed space) + np.zeros_like(bearing.flatten()), # bearing = 0 (no rotation in transformed space) + # defaults should be in config_dict -- the fallbacks here should not be hit now + config_dict.get("search_dist",config_dict.get("search_radius", 1e10)), + config_dict.get("maxpts_interp",50), + config_dict.get("minpts_interp",1), + fac_fname, + fac_ftype, + ) + + # this is now filled as a default in config_dict if not in config file, + # default value dependent on vartransform (ie. 1 for log 0 for non log) + noint = config_dict.get("fill_value",pp_info.loc[:, "parval1"].mean()) + + result = lib.krige_using_file( + fac_fname, + fac_ftype, + zone.size, + int(config_dict.get("krigtype", 1)), + vartransform, + pp_info["parval1"].values, + noint, + noint, + ) + assert npts == result["icount_interp"] + result = result["targval"] + #shape = tuple([int(s) for s in config_dict["shape"]]) + tup_string = config_dict["shape"] + shape = tuple(int(x) for x in tup_string[1:-1].split(',')) + result = result.reshape(shape) + np.savetxt(out_filename,result,fmt="%20.8E") + os.remove(fac_fname) + lib.free_all_memory() + + return result diff --git a/pyemu/utils/pst_from.py b/pyemu/utils/pst_from.py index 852e8d7f5..d5ee2ffdb 100644 --- a/pyemu/utils/pst_from.py +++ b/pyemu/utils/pst_from.py @@ -2606,7 +2606,7 @@ def add_parameters( #if "pypestutils" not in self.extra_py_imports: # self.extra_py_imports.append("pypestutils") print(config_df_filename) - config_func_str = "pyemu.utils.pp_utils.apply_ppu_hyperpars('{0}')".\ + config_func_str = "pyemu.utils.pp_utils.apply_ppu_hyperpars_tensor('{0}')".\ format(config_df_filename) pp_mult_dict["pre_apply_function"] = config_func_str else: From b3f275ca026ebc0491f660d6d33370e7842e93ae Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 4 Jul 2025 17:52:55 +1200 Subject: [PATCH 02/36] functioning tensor interpolation. play with conceptual_point params still needed. --- examples/pstfrom_mf6_ppu.ipynb | 2981 +------------------------ examples/pstfrom_mf6_ppu_tensor.ipynb | 1059 +++++++++ pyemu/utils/tensor_utils.py | 675 ++++++ 3 files changed, 1794 insertions(+), 2921 deletions(-) create mode 100644 examples/pstfrom_mf6_ppu_tensor.ipynb create mode 100644 pyemu/utils/tensor_utils.py diff --git a/examples/pstfrom_mf6_ppu.ipynb b/examples/pstfrom_mf6_ppu.ipynb index 2f9bb9b56..9283543d9 100644 --- a/examples/pstfrom_mf6_ppu.ipynb +++ b/examples/pstfrom_mf6_ppu.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -50,149 +50,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['freyberg6.dis',\n", - " 'freyberg6.dis.grb',\n", - " 'freyberg6.dis_botm_layer1.txt',\n", - " 'freyberg6.dis_botm_layer2.txt',\n", - " 'freyberg6.dis_botm_layer3.txt',\n", - " 'freyberg6.dis_delc.txt',\n", - " 'freyberg6.dis_delr.txt',\n", - " 'freyberg6.dis_idomain_layer1.txt',\n", - " 'freyberg6.dis_idomain_layer2.txt',\n", - " 'freyberg6.dis_idomain_layer3.txt',\n", - " 'freyberg6.dis_top.txt',\n", - " 'freyberg6.ghb',\n", - " 'freyberg6.ghb_stress_period_data_1.txt',\n", - " 'freyberg6.ic',\n", - " 'freyberg6.ic_strt_layer1.txt',\n", - " 'freyberg6.ic_strt_layer2.txt',\n", - " 'freyberg6.ic_strt_layer3.txt',\n", - " 'freyberg6.ims',\n", - " 'freyberg6.lst',\n", - " 'freyberg6.nam',\n", - " 'freyberg6.npf',\n", - " 'freyberg6.npf_icelltype_layer1.txt',\n", - " 'freyberg6.npf_icelltype_layer2.txt',\n", - " 'freyberg6.npf_icelltype_layer3.txt',\n", - " 'freyberg6.npf_k33_layer1.txt',\n", - " 'freyberg6.npf_k33_layer2.txt',\n", - " 'freyberg6.npf_k33_layer3.txt',\n", - " 'freyberg6.npf_k_layer1.txt',\n", - " 'freyberg6.npf_k_layer2.txt',\n", - " 'freyberg6.npf_k_layer3.txt',\n", - " 'freyberg6.oc',\n", - " 'freyberg6.rch',\n", - " 'freyberg6.rch_recharge_1.txt',\n", - " 'freyberg6.rch_recharge_10.txt',\n", - " 'freyberg6.rch_recharge_11.txt',\n", - " 'freyberg6.rch_recharge_12.txt',\n", - " 'freyberg6.rch_recharge_13.txt',\n", - " 'freyberg6.rch_recharge_14.txt',\n", - " 'freyberg6.rch_recharge_15.txt',\n", - " 'freyberg6.rch_recharge_16.txt',\n", - " 'freyberg6.rch_recharge_17.txt',\n", - " 'freyberg6.rch_recharge_18.txt',\n", - " 'freyberg6.rch_recharge_19.txt',\n", - " 'freyberg6.rch_recharge_2.txt',\n", - " 'freyberg6.rch_recharge_20.txt',\n", - " 'freyberg6.rch_recharge_21.txt',\n", - " 'freyberg6.rch_recharge_22.txt',\n", - " 'freyberg6.rch_recharge_23.txt',\n", - " 'freyberg6.rch_recharge_24.txt',\n", - " 'freyberg6.rch_recharge_25.txt',\n", - " 'freyberg6.rch_recharge_3.txt',\n", - " 'freyberg6.rch_recharge_4.txt',\n", - " 'freyberg6.rch_recharge_5.txt',\n", - " 'freyberg6.rch_recharge_6.txt',\n", - " 'freyberg6.rch_recharge_7.txt',\n", - " 'freyberg6.rch_recharge_8.txt',\n", - " 'freyberg6.rch_recharge_9.txt',\n", - " 'freyberg6.sfr',\n", - " 'freyberg6.sfr_connectiondata.txt',\n", - " 'freyberg6.sfr_packagedata.txt',\n", - " 'freyberg6.sfr_perioddata_1.txt',\n", - " 'freyberg6.sfr_perioddata_10.txt',\n", - " 'freyberg6.sfr_perioddata_11.txt',\n", - " 'freyberg6.sfr_perioddata_12.txt',\n", - " 'freyberg6.sfr_perioddata_13.txt',\n", - " 'freyberg6.sfr_perioddata_14.txt',\n", - " 'freyberg6.sfr_perioddata_15.txt',\n", - " 'freyberg6.sfr_perioddata_16.txt',\n", - " 'freyberg6.sfr_perioddata_17.txt',\n", - " 'freyberg6.sfr_perioddata_18.txt',\n", - " 'freyberg6.sfr_perioddata_19.txt',\n", - " 'freyberg6.sfr_perioddata_2.txt',\n", - " 'freyberg6.sfr_perioddata_20.txt',\n", - " 'freyberg6.sfr_perioddata_21.txt',\n", - " 'freyberg6.sfr_perioddata_22.txt',\n", - " 'freyberg6.sfr_perioddata_23.txt',\n", - " 'freyberg6.sfr_perioddata_24.txt',\n", - " 'freyberg6.sfr_perioddata_25.txt',\n", - " 'freyberg6.sfr_perioddata_3.txt',\n", - " 'freyberg6.sfr_perioddata_4.txt',\n", - " 'freyberg6.sfr_perioddata_5.txt',\n", - " 'freyberg6.sfr_perioddata_6.txt',\n", - " 'freyberg6.sfr_perioddata_7.txt',\n", - " 'freyberg6.sfr_perioddata_8.txt',\n", - " 'freyberg6.sfr_perioddata_9.txt',\n", - " 'freyberg6.sto',\n", - " 'freyberg6.sto_iconvert_layer1.txt',\n", - " 'freyberg6.sto_iconvert_layer2.txt',\n", - " 'freyberg6.sto_iconvert_layer3.txt',\n", - " 'freyberg6.sto_ss_layer1.txt',\n", - " 'freyberg6.sto_ss_layer2.txt',\n", - " 'freyberg6.sto_ss_layer3.txt',\n", - " 'freyberg6.sto_sy_layer1.txt',\n", - " 'freyberg6.sto_sy_layer2.txt',\n", - " 'freyberg6.sto_sy_layer3.txt',\n", - " 'freyberg6.tdis',\n", - " 'freyberg6.wel',\n", - " 'freyberg6.wel_stress_period_data_1.txt',\n", - " 'freyberg6.wel_stress_period_data_10.txt',\n", - " 'freyberg6.wel_stress_period_data_11.txt',\n", - " 'freyberg6.wel_stress_period_data_12.txt',\n", - " 'freyberg6.wel_stress_period_data_13.txt',\n", - " 'freyberg6.wel_stress_period_data_14.txt',\n", - " 'freyberg6.wel_stress_period_data_15.txt',\n", - " 'freyberg6.wel_stress_period_data_16.txt',\n", - " 'freyberg6.wel_stress_period_data_17.txt',\n", - " 'freyberg6.wel_stress_period_data_18.txt',\n", - " 'freyberg6.wel_stress_period_data_19.txt',\n", - " 'freyberg6.wel_stress_period_data_2.txt',\n", - " 'freyberg6.wel_stress_period_data_20.txt',\n", - " 'freyberg6.wel_stress_period_data_21.txt',\n", - " 'freyberg6.wel_stress_period_data_22.txt',\n", - " 'freyberg6.wel_stress_period_data_23.txt',\n", - " 'freyberg6.wel_stress_period_data_24.txt',\n", - " 'freyberg6.wel_stress_period_data_25.txt',\n", - " 'freyberg6.wel_stress_period_data_3.txt',\n", - " 'freyberg6.wel_stress_period_data_4.txt',\n", - " 'freyberg6.wel_stress_period_data_5.txt',\n", - " 'freyberg6.wel_stress_period_data_6.txt',\n", - " 'freyberg6.wel_stress_period_data_7.txt',\n", - " 'freyberg6.wel_stress_period_data_8.txt',\n", - " 'freyberg6.wel_stress_period_data_9.txt',\n", - " 'freyberg6_freyberg.cbc',\n", - " 'freyberg6_freyberg.hds',\n", - " 'head.obs',\n", - " 'heads.csv',\n", - " 'mfsim.lst',\n", - " 'mfsim.nam',\n", - " 'sfr.csv',\n", - " 'sfr.obs']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "org_model_ws = os.path.join('freyberg_mf6')\n", "os.listdir(org_model_ws)" @@ -209,39 +69,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", - "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", - "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "id_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_idomain_layer3.txt\"))\n", "top_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_top.txt\"))\n", @@ -261,163 +91,11 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['freyberg6.dis',\n", - " 'freyberg6.dis.grb',\n", - " 'freyberg6.dis_botm_layer1.txt',\n", - " 'freyberg6.dis_botm_layer2.txt',\n", - " 'freyberg6.dis_botm_layer3.txt',\n", - " 'freyberg6.dis_delc.txt',\n", - " 'freyberg6.dis_delr.txt',\n", - " 'freyberg6.dis_idomain_layer1.txt',\n", - " 'freyberg6.dis_idomain_layer2.txt',\n", - " 'freyberg6.dis_idomain_layer3.txt',\n", - " 'freyberg6.dis_top.txt',\n", - " 'freyberg6.ghb',\n", - " 'freyberg6.ghb_stress_period_data_1.txt',\n", - " 'freyberg6.ic',\n", - " 'freyberg6.ic_strt_layer1.txt',\n", - " 'freyberg6.ic_strt_layer2.txt',\n", - " 'freyberg6.ic_strt_layer3.txt',\n", - " 'freyberg6.ims',\n", - " 'freyberg6.lst',\n", - " 'freyberg6.nam',\n", - " 'freyberg6.npf',\n", - " 'freyberg6.npf_icelltype_layer1.txt',\n", - " 'freyberg6.npf_icelltype_layer2.txt',\n", - " 'freyberg6.npf_icelltype_layer3.txt',\n", - " 'freyberg6.npf_k33_layer1.txt',\n", - " 'freyberg6.npf_k33_layer2.txt',\n", - " 'freyberg6.npf_k33_layer3.txt',\n", - " 'freyberg6.npf_k_layer1.txt',\n", - " 'freyberg6.npf_k_layer2.txt',\n", - " 'freyberg6.npf_k_layer3.txt',\n", - " 'freyberg6.oc',\n", - " 'freyberg6.rch',\n", - " 'freyberg6.rch_recharge_1.txt',\n", - " 'freyberg6.rch_recharge_10.txt',\n", - " 'freyberg6.rch_recharge_11.txt',\n", - " 'freyberg6.rch_recharge_12.txt',\n", - " 'freyberg6.rch_recharge_13.txt',\n", - " 'freyberg6.rch_recharge_14.txt',\n", - " 'freyberg6.rch_recharge_15.txt',\n", - " 'freyberg6.rch_recharge_16.txt',\n", - " 'freyberg6.rch_recharge_17.txt',\n", - " 'freyberg6.rch_recharge_18.txt',\n", - " 'freyberg6.rch_recharge_19.txt',\n", - " 'freyberg6.rch_recharge_2.txt',\n", - " 'freyberg6.rch_recharge_20.txt',\n", - " 'freyberg6.rch_recharge_21.txt',\n", - " 'freyberg6.rch_recharge_22.txt',\n", - " 'freyberg6.rch_recharge_23.txt',\n", - " 'freyberg6.rch_recharge_24.txt',\n", - " 'freyberg6.rch_recharge_25.txt',\n", - " 'freyberg6.rch_recharge_3.txt',\n", - " 'freyberg6.rch_recharge_4.txt',\n", - " 'freyberg6.rch_recharge_5.txt',\n", - " 'freyberg6.rch_recharge_6.txt',\n", - " 'freyberg6.rch_recharge_7.txt',\n", - " 'freyberg6.rch_recharge_8.txt',\n", - " 'freyberg6.rch_recharge_9.txt',\n", - " 'freyberg6.sfr',\n", - " 'freyberg6.sfr_connectiondata.txt',\n", - " 'freyberg6.sfr_packagedata.txt',\n", - " 'freyberg6.sfr_perioddata_1.txt',\n", - " 'freyberg6.sfr_perioddata_10.txt',\n", - " 'freyberg6.sfr_perioddata_11.txt',\n", - " 'freyberg6.sfr_perioddata_12.txt',\n", - " 'freyberg6.sfr_perioddata_13.txt',\n", - " 'freyberg6.sfr_perioddata_14.txt',\n", - " 'freyberg6.sfr_perioddata_15.txt',\n", - " 'freyberg6.sfr_perioddata_16.txt',\n", - " 'freyberg6.sfr_perioddata_17.txt',\n", - " 'freyberg6.sfr_perioddata_18.txt',\n", - " 'freyberg6.sfr_perioddata_19.txt',\n", - " 'freyberg6.sfr_perioddata_2.txt',\n", - " 'freyberg6.sfr_perioddata_20.txt',\n", - " 'freyberg6.sfr_perioddata_21.txt',\n", - " 'freyberg6.sfr_perioddata_22.txt',\n", - " 'freyberg6.sfr_perioddata_23.txt',\n", - " 'freyberg6.sfr_perioddata_24.txt',\n", - " 'freyberg6.sfr_perioddata_25.txt',\n", - " 'freyberg6.sfr_perioddata_3.txt',\n", - " 'freyberg6.sfr_perioddata_4.txt',\n", - " 'freyberg6.sfr_perioddata_5.txt',\n", - " 'freyberg6.sfr_perioddata_6.txt',\n", - " 'freyberg6.sfr_perioddata_7.txt',\n", - " 'freyberg6.sfr_perioddata_8.txt',\n", - " 'freyberg6.sfr_perioddata_9.txt',\n", - " 'freyberg6.sto',\n", - " 'freyberg6.sto_iconvert_layer1.txt',\n", - " 'freyberg6.sto_iconvert_layer2.txt',\n", - " 'freyberg6.sto_iconvert_layer3.txt',\n", - " 'freyberg6.sto_ss_layer1.txt',\n", - " 'freyberg6.sto_ss_layer2.txt',\n", - " 'freyberg6.sto_ss_layer3.txt',\n", - " 'freyberg6.sto_sy_layer1.txt',\n", - " 'freyberg6.sto_sy_layer2.txt',\n", - " 'freyberg6.sto_sy_layer3.txt',\n", - " 'freyberg6.tdis',\n", - " 'freyberg6.wel',\n", - " 'freyberg6.wel_stress_period_data_1.txt',\n", - " 'freyberg6.wel_stress_period_data_10.txt',\n", - " 'freyberg6.wel_stress_period_data_11.txt',\n", - " 'freyberg6.wel_stress_period_data_12.txt',\n", - " 'freyberg6.wel_stress_period_data_13.txt',\n", - " 'freyberg6.wel_stress_period_data_14.txt',\n", - " 'freyberg6.wel_stress_period_data_15.txt',\n", - " 'freyberg6.wel_stress_period_data_16.txt',\n", - " 'freyberg6.wel_stress_period_data_17.txt',\n", - " 'freyberg6.wel_stress_period_data_18.txt',\n", - " 'freyberg6.wel_stress_period_data_19.txt',\n", - " 'freyberg6.wel_stress_period_data_2.txt',\n", - " 'freyberg6.wel_stress_period_data_20.txt',\n", - " 'freyberg6.wel_stress_period_data_21.txt',\n", - " 'freyberg6.wel_stress_period_data_22.txt',\n", - " 'freyberg6.wel_stress_period_data_23.txt',\n", - " 'freyberg6.wel_stress_period_data_24.txt',\n", - " 'freyberg6.wel_stress_period_data_25.txt',\n", - " 'freyberg6.wel_stress_period_data_3.txt',\n", - " 'freyberg6.wel_stress_period_data_4.txt',\n", - " 'freyberg6.wel_stress_period_data_5.txt',\n", - " 'freyberg6.wel_stress_period_data_6.txt',\n", - " 'freyberg6.wel_stress_period_data_7.txt',\n", - " 'freyberg6.wel_stress_period_data_8.txt',\n", - " 'freyberg6.wel_stress_period_data_9.txt',\n", - " 'freyberg6_freyberg.cbc',\n", - " 'freyberg6_freyberg.hds',\n", - " 'head.obs',\n", - " 'heads.csv',\n", - " 'mf6.exe',\n", - " 'mfsim.lst',\n", - " 'mfsim.nam',\n", - " 'pestpp-ies.exe',\n", - " 'sfr.csv',\n", - " 'sfr.obs']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "tmp_model_ws = \"temp_pst_from_ppu\"\n", "if os.path.exists(tmp_model_ws):\n", " shutil.rmtree(tmp_model_ws)\n", "shutil.copytree(org_model_ws,tmp_model_ws)\n", - "# copy pestpp and mf6\n", - "shutil.copy(r'C:\\bin\\modflow\\mf6.6.2_win64\\bin\\mf6.exe', os.path.join(tmp_model_ws,'mf6.exe'))\n", - "shutil.copy(r'C:\\bin\\pestpp\\pestpp-ies.exe', os.path.join(tmp_model_ws,'pestpp-ies.exe'))\n", "os.listdir(tmp_model_ws)" ] }, @@ -432,31 +110,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading simulation...\n", - " loading simulation name file...\n", - " loading tdis package...\n", - " loading model gwf6...\n", - " loading package dis...\n", - " loading package ic...\n", - " loading package npf...\n", - " loading package sto...\n", - " loading package oc...\n", - " loading package wel...\n", - " loading package rch...\n", - " loading package ghb...\n", - " loading package sfr...\n", - " loading package obs...\n", - " loading solution package freyberg6...\n" - ] - } - ], + "outputs": [], "source": [ "sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws)\n", "m = sim.get_model(\"freyberg6\")\n" @@ -471,27 +127,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " could not remove start_datetime\n" - ] - }, - { - "data": { - "text/plain": [ - "xul:0; yul:10000; rotation:0; proj4_str:None; units:meters; lenuni:2; length_multiplier:1.0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sr = pyemu.helpers.SpatialReference.from_namfile(\n", " os.path.join(tmp_model_ws, \"freyberg6.nam\"),\n", @@ -508,24 +146,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-07-02 16:17:30.679513 starting: opening PstFrom.log for logging\n", - "2025-07-02 16:17:30.680511 starting PstFrom process\n", - "2025-07-02 16:17:30.680511 starting: setting up dirs\n", - "2025-07-02 16:17:30.681516 starting: removing existing new_d 'freyberg6_template'\n", - "2025-07-02 16:17:30.763770 finished: removing existing new_d 'freyberg6_template' took: 0:00:00.082254\n", - "2025-07-02 16:17:30.763770 starting: copying original_d 'temp_pst_from_ppu' to new_d 'freyberg6_template'\n", - "2025-07-02 16:17:30.930885 finished: copying original_d 'temp_pst_from_ppu' to new_d 'freyberg6_template' took: 0:00:00.167115\n", - "2025-07-02 16:17:30.931902 finished: setting up dirs took: 0:00:00.251391\n" - ] - } - ], + "outputs": [], "source": [ "template_ws = \"freyberg6_template\"\n", "pf = pyemu.utils.PstFrom(original_d=tmp_model_ws, new_d=template_ws,\n", @@ -545,832 +168,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-07-02 16:17:31.018013 starting: adding observations from output file heads.csv\n", - "2025-07-02 16:17:31.018013 starting: adding observations from tabular output file '['heads.csv']'\n", - "2025-07-02 16:17:31.018013 starting: reading list-style file: freyberg6_template\\heads.csv\n", - "2025-07-02 16:17:31.044694 finished: reading list-style file: freyberg6_template\\heads.csv took: 0:00:00.026681\n", - "2025-07-02 16:17:31.052225 starting: building insfile for tabular output file heads.csv\n", - "2025-07-02 16:17:31.065228 finished: building insfile for tabular output file heads.csv took: 0:00:00.013003\n", - "2025-07-02 16:17:31.065228 starting: adding observation from instruction file 'freyberg6_template\\heads.csv.ins'\n", - "2025-07-02 16:17:31.098228 finished: adding observation from instruction file 'freyberg6_template\\heads.csv.ins' took: 0:00:00.033000\n", - "2025-07-02 16:17:31.100228 finished: adding observations from tabular output file '['heads.csv']' took: 0:00:00.082215\n", - "2025-07-02 16:17:31.100228 finished: adding observations from output file heads.csv took: 0:00:00.082215\n" - ] - }, - { - "data": { - "text/html": [ - "
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"oname:sfr.csv_otype:lst_usecol:gage_1_time:122 oname:sfr.csv_otype:lst_usecol:gage_1 \n", - "oname:sfr.csv_otype:lst_usecol:gage_1_time:153 oname:sfr.csv_otype:lst_usecol:gage_1 \n", - "oname:sfr.csv_otype:lst_usecol:gage_1_time:183 oname:sfr.csv_otype:lst_usecol:gage_1 \n", - "oname:sfr.csv_otype:lst_usecol:gage_1_time:214 oname:sfr.csv_otype:lst_usecol:gage_1 \n", - "... ... \n", - "oname:sfr.csv_otype:lst_usecol:tailwater_time:640 oname:sfr.csv_otype:lst_usecol:tailwater \n", - "oname:sfr.csv_otype:lst_usecol:tailwater_time:671 oname:sfr.csv_otype:lst_usecol:tailwater \n", - "oname:sfr.csv_otype:lst_usecol:tailwater_time:701 oname:sfr.csv_otype:lst_usecol:tailwater \n", - "oname:sfr.csv_otype:lst_usecol:tailwater_time:732 oname:sfr.csv_otype:lst_usecol:tailwater \n", - "oname:sfr.csv_otype:lst_usecol:tailwater_time:92 oname:sfr.csv_otype:lst_usecol:tailwater \n", - "\n", - "[75 rows x 4 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv(os.path.join(tmp_model_ws, \"sfr.csv\"), index_col=0)\n", "sfr_df = pf.add_observations(\"sfr.csv\", insfile=\"sfr.csv.ins\", index_cols=\"time\", use_cols=list(df.columns.values))\n", @@ -1802,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1812,27 +253,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spatial variogram\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pp_gs.plot()\n", "print(\"spatial variogram\")" @@ -1847,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1863,20 +286,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['freyberg6.npf_k_layer1.txt', 'freyberg6.npf_k_layer3.txt']" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hk_arr_files = [f for f in os.listdir(tmp_model_ws) if \"npf_k_\" in f and f.endswith(\".txt\") and \"layer2\" not in f]\n", "hk_arr_files" @@ -1884,230 +296,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-07-02 16:17:31.683565 transform was not passed, setting default transform to 'log'\n", - "2025-07-02 16:17:31.684567 starting: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer1.txt']\n", - "2025-07-02 16:17:31.684567 starting: using geostruct:name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:1.0,bearing:0.0\n", - "\n", - "2025-07-02 16:17:31.684567 starting: loading array freyberg6_template\\freyberg6.npf_k_layer1.txt\n", - "2025-07-02 16:17:31.703572 WARNING: \n", - "_load_array_get_fmt(), likely fixed format file:\n", - "freyberg6.npf_k_layer1.txt appears to contain some float notation (%F) values.\n", - "Can't define a generic format specifier that\n", - "will guarantee file is readable by downstream engine.\n", - "Will try to use %E, but this could cause issues\n", - "downstream...\n", - "2025-07-02 16:17:31.703572 finished: loading array freyberg6_template\\freyberg6.npf_k_layer1.txt took: 0:00:00.019005\n", - "2025-07-02 16:17:31.703572 loaded array 'temp_pst_from_ppu\\freyberg6.npf_k_layer1.txt' of shape (40, 20)\n", - "2025-07-02 16:17:31.706575 starting: writing array-style template file 'freyberg6_template\\npf-k-layer1_inst0_pilotpoints.csv.tpl'\n", - "2025-07-02 16:17:31.707566 starting: setting up pilot point parameters\n", - "2025-07-02 16:17:31.707566 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", - "2025-07-02 16:17:31.707566 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", - "2025-07-02 16:17:31.707566 'try_use_ppu' not set in pp_options, Setting to default value: [True]\n", - "2025-07-02 16:17:31.707566 'num_threads' not set in pp_options, Setting to default value: [10]\n", - "2025-07-02 16:17:31.707566 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", - "2025-07-02 16:17:31.707566 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", - "2025-07-02 16:17:31.707566 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", - "2025-07-02 16:17:31.707566 'fill_value' not set in pp_options, Setting to default value: [1.0]\n", - "2025-07-02 16:17:31.707566 'lower_limit' not set in pp_options, Setting to default value: [1e-30]\n", - "2025-07-02 16:17:31.707566 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", - "2025-07-02 16:17:31.707566 No spatial reference (containing cell spacing) passed.\n", - "2025-07-02 16:17:31.707566 OK - using spatial reference in parent object.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:31.703572 WARNING: \n", - "_load_array_get_fmt(), likely fixed format file:\n", - "freyberg6.npf_k_layer1.txt appears to contain some float notation (%F) values.\n", - "Can't define a generic format specifier that\n", - "will guarantee file is readable by downstream engine.\n", - "Will try to use %E, but this could cause issues\n", - "downstream...\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", - "2025-07-02 16:17:31.902658 79 pilot point parameters created\n", - "2025-07-02 16:17:31.902658 pilot point 'pargp':n,p,f,-,k,-,l,a,y,e,r,1\n", - "2025-07-02 16:17:31.902658 finished: setting up pilot point parameters took: 0:00:00.195092\n", - "2025-07-02 16:17:31.915161 starting: calculating factors for pargp=npf-k-layer1\n", - "2025-07-02 16:17:31.915161 saving krige variance file:freyberg6_template\\npf-k-layer1_inst0pp.var.dat\n", - "2025-07-02 16:17:31.915161 saving krige factors file:freyberg6_template\\npf-k-layer1_inst0pp.fac\n", - "...pypestutils detected and being used for kriging solve, trust us, you want this!\n", - "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k-layer1_inst0pp.fac'\n", - "INFO:PestUtilsLib: all memory was freed up\n", - "...ppu_factor_filename 'freyberg6_template\\npf-k-layer1_inst0pp.fac' created, factors calculated for 800 points\n", - "2025-07-02 16:17:32.166002 finished: calculating factors for pargp=npf-k-layer1 took: 0:00:00.250841\n", - "2025-07-02 16:17:32.167002 starting: writing array-based template file 'freyberg6_template\\npf-k-layer1_inst0pp.dat.tpl'\n", - "2025-07-02 16:17:32.167002 saving zone array freyberg6_template\\npf-k-layer1_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k-layer1_inst0pp.dat.tpl\n", - "2025-07-02 16:17:32.175002 finished: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer1.txt'] took: 0:00:00.490435\n", - "2025-07-02 16:17:32.175002 starting: adding observations from output file freyberg6.npf_k_layer1.txt\n", - "2025-07-02 16:17:32.175002 starting: adding observations from array output file 'freyberg6.npf_k_layer1.txt'\n", - "2025-07-02 16:17:32.180003 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer1.txt.ins'\n", - "2025-07-02 16:17:32.217522 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer1.txt.ins' took: 0:00:00.037519\n", - "2025-07-02 16:17:32.218521 finished: adding observations from array output file 'freyberg6.npf_k_layer1.txt' took: 0:00:00.043519\n", - "2025-07-02 16:17:32.218521 finished: adding observations from output file freyberg6.npf_k_layer1.txt took: 0:00:00.043519\n" - ] - }, - { - "data": { - "text/html": [ - "
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"\n", - " obsval weight obgnme \n", - "oname:npf-k-layer1_otype:arr_i:0_j:0_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:0_j:10_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:0_j:11_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:0_j:12_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:0_j:13_zone:1 3.0 1.0 npf-k-layer1 \n", - "... ... ... ... \n", - "oname:npf-k-layer1_otype:arr_i:9_j:1_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:9_j:2_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:9_j:3_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:9_j:8_zone:1 3.0 1.0 npf-k-layer1 \n", - "oname:npf-k-layer1_otype:arr_i:9_j:9_zone:1 3.0 1.0 npf-k-layer1 \n", - "\n", - "[706 rows x 4 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "arr_file = \"freyberg6.npf_k_layer1.txt\"\n", "tag = arr_file.split('.')[1].replace(\"_\",\"-\")\n", @@ -2130,20 +321,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['npf-k-layer1_inst0pp.dat.tpl']" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tpl_files = [f for f in os.listdir(template_ws) if f.endswith(\".tpl\")]\n", "tpl_files" @@ -2151,18 +331,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ptf ~\n", - "pp_0000 375.0 9625.0 1 ~ pname:npf-k-layer1_inst:0_ptype:pp_pstyle:m_i:1_j:1_zone:1 ~\n" - ] - } - ], + "outputs": [], "source": [ "with open(os.path.join(template_ws,tpl_files[0]),'r') as f:\n", " for _ in range(2):\n", @@ -2200,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2212,235 +383,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-07-02 16:17:32.293379 transform was not passed, setting default transform to 'log'\n", - "2025-07-02 16:17:32.294378 starting: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer3.txt']\n", - "2025-07-02 16:17:32.294378 starting: using geostruct:name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", - "\n", - "2025-07-02 16:17:32.294378 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", - "2025-07-02 16:17:32.294378 WARNING: 1) Setting geostruct transform to log\n", - "2025-07-02 16:17:32.294378 WARNING: -) Better to pass an appropriately transformed geostruct\n", - "2025-07-02 16:17:32.294378 starting: loading array freyberg6_template\\freyberg6.npf_k_layer3.txt\n", - "2025-07-02 16:17:32.315475 WARNING: \n", - "_load_array_get_fmt(), likely fixed format file:\n", - "freyberg6.npf_k_layer3.txt appears to contain some float notation (%F) values.\n", - "Can't define a generic format specifier that\n", - "will guarantee file is readable by downstream engine.\n", - "Will try to use %E, but this could cause issues\n", - "downstream...\n", - "2025-07-02 16:17:32.315475 finished: loading array freyberg6_template\\freyberg6.npf_k_layer3.txt took: 0:00:00.021097\n", - "2025-07-02 16:17:32.315475 loaded array 'temp_pst_from_ppu\\freyberg6.npf_k_layer3.txt' of shape (40, 20)\n", - "2025-07-02 16:17:32.318475 starting: writing array-style template file 'freyberg6_template\\npf-k-layer3_inst0_pilotpoints.csv.tpl'\n", - "2025-07-02 16:17:32.318475 starting: setting up pilot point parameters\n", - "2025-07-02 16:17:32.319475 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", - "2025-07-02 16:17:32.319475 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", - "2025-07-02 16:17:32.319475 'try_use_ppu' not set in pp_options, Setting to default value: [True]\n", - "2025-07-02 16:17:32.319475 'num_threads' not set in pp_options, Setting to default value: [10]\n", - "2025-07-02 16:17:32.319475 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", - "2025-07-02 16:17:32.319475 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", - "2025-07-02 16:17:32.319475 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", - "2025-07-02 16:17:32.319475 'fill_value' not set in pp_options, Setting to default value: [1.0]\n", - "2025-07-02 16:17:32.319475 'lower_limit' not set in pp_options, Setting to default value: [1e-30]\n", - "2025-07-02 16:17:32.319475 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", - "2025-07-02 16:17:32.319475 No spatial reference (containing cell spacing) passed.\n", - "2025-07-02 16:17:32.319475 OK - using spatial reference in parent object.\n", - "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", - "2025-07-02 16:17:32.474083 79 pilot point parameters created\n", - "2025-07-02 16:17:32.475088 pilot point 'pargp':n,p,f,-,k,-,l,a,y,e,r,3\n", - "2025-07-02 16:17:32.475088 finished: setting up pilot point parameters took: 0:00:00.156613\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.294378 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", - "\n", - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.294378 WARNING: 1) Setting geostruct transform to log\n", - "\n", - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.294378 WARNING: -) Better to pass an appropriately transformed geostruct\n", - "\n", - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.315475 WARNING: \n", - "_load_array_get_fmt(), likely fixed format file:\n", - "freyberg6.npf_k_layer3.txt appears to contain some float notation (%F) values.\n", - "Can't define a generic format specifier that\n", - "will guarantee file is readable by downstream engine.\n", - "Will try to use %E, but this could cause issues\n", - "downstream...\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k-layer3_inst0_pilotpoints.csv.temp.fac'\n", - "INFO:PestUtilsLib: kriged using factor file 'npf-k-layer3_inst0_pilotpoints.csv.temp.fac'\n", - "INFO:PestUtilsLib: all memory was freed up\n", - "npf-k-layer3.config.csv\n", - "2025-07-02 16:17:32.728262 starting: writing array-based template file 'freyberg6_template\\npf-k-layer3_inst0pp.dat.tpl'\n", - "2025-07-02 16:17:32.728262 saving zone array freyberg6_template\\npf-k-layer3_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k-layer3_inst0pp.dat.tpl\n", - "2025-07-02 16:17:32.738295 finished: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k_layer3.txt'] took: 0:00:00.443917\n", - "2025-07-02 16:17:32.738295 starting: adding observations from output file freyberg6.npf_k_layer3.txt\n", - "2025-07-02 16:17:32.739297 starting: adding observations from array output file 'freyberg6.npf_k_layer3.txt'\n", - "2025-07-02 16:17:32.747290 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer3.txt.ins'\n", - "2025-07-02 16:17:32.785992 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k_layer3.txt.ins' took: 0:00:00.038702\n", - "2025-07-02 16:17:32.785992 finished: adding observations from array output file 'freyberg6.npf_k_layer3.txt' took: 0:00:00.046695\n", - "2025-07-02 16:17:32.785992 finished: adding observations from output file freyberg6.npf_k_layer3.txt took: 0:00:00.047697\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " obsnme \\\n", - "oname:npf-k-layer3_otype:arr_i:0_j:0_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:0_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:10_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:10_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:11_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:11_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:12_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:12_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:13_zone:1 oname:npf-k-layer3_otype:arr_i:0_j:13_zone:1 \n", - "... ... \n", - "oname:npf-k-layer3_otype:arr_i:9_j:1_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:1_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:2_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:2_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:3_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:3_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:8_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:8_zone:1 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:9_zone:1 oname:npf-k-layer3_otype:arr_i:9_j:9_zone:1 \n", - "\n", - " obsval weight obgnme \n", - "oname:npf-k-layer3_otype:arr_i:0_j:0_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:10_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:11_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:12_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:0_j:13_zone:1 30.0 1.0 npf-k-layer3 \n", - "... ... ... ... \n", - "oname:npf-k-layer3_otype:arr_i:9_j:1_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:2_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:3_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:8_zone:1 30.0 1.0 npf-k-layer3 \n", - "oname:npf-k-layer3_otype:arr_i:9_j:9_zone:1 30.0 1.0 npf-k-layer3 \n", - "\n", - "[706 rows x 4 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "arr_file = \"freyberg6.npf_k_layer3.txt\"\n", "tag = arr_file.split('.')[1].replace(\"_\",\"-\")\n", @@ -2455,28 +400,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['npf-k-layer3.aniso.dat',\n", - " 'npf-k-layer3.bearing.dat',\n", - " 'npf-k-layer3.config.csv',\n", - " 'npf-k-layer3.corrlen.dat',\n", - " 'npf-k-layer3.gridinfo.dat',\n", - " 'npf-k-layer3.zone.dat',\n", - " 'npf-k-layer3_inst0pp.dat',\n", - " 'npf-k-layer3_inst0pp.dat.tpl',\n", - " 'npf-k-layer3_inst0pp.dat.zone']" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hyperpar_files = [f for f in os.listdir(pf.new_d) if tag in f]\n", "hyperpar_files" @@ -2495,238 +421,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-07-02 16:17:32.809604 starting: adding constant type a style parameters for file(s) ['npf-k-layer3.aniso.dat']\n", - "2025-07-02 16:17:32.810606 starting: loading array freyberg6_template\\npf-k-layer3.aniso.dat\n", - "2025-07-02 16:17:32.811611 finished: loading array freyberg6_template\\npf-k-layer3.aniso.dat took: 0:00:00.001005\n", - "2025-07-02 16:17:32.811611 loaded array 'temp_pst_from_ppu\\npf-k-layer3.aniso.dat' of shape (40, 20)\n", - "2025-07-02 16:17:32.813602 starting: writing array-style template file 'freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl'\n", - "2025-07-02 16:17:32.813602 starting: writing template file freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl for ['npf-k-layer3-aniso_inst:0']\n", - "2025-07-02 16:17:32.822602 finished: writing template file freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl for ['npf-k-layer3-aniso_inst:0'] took: 0:00:00.009000\n", - "2025-07-02 16:17:32.823601 starting: writing array-based template file 'freyberg6_template\\npf-k-layer3-aniso_inst0_constant.csv.tpl'\n", - "2025-07-02 16:17:32.830607 finished: adding constant type a style parameters for file(s) ['npf-k-layer3.aniso.dat'] took: 0:00:00.021003\n", - "2025-07-02 16:17:32.831610 starting: adding observations from output file npf-k-layer3.aniso.dat\n", - "2025-07-02 16:17:32.831610 starting: adding observations from array output file 'npf-k-layer3.aniso.dat'\n", - "2025-07-02 16:17:32.838623 starting: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.aniso.dat.ins'\n", - "2025-07-02 16:17:32.878154 finished: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.aniso.dat.ins' took: 0:00:00.039531\n", - "2025-07-02 16:17:32.879155 finished: adding observations from array output file 'npf-k-layer3.aniso.dat' took: 0:00:00.047545\n", - "2025-07-02 16:17:32.879155 finished: adding observations from output file npf-k-layer3.aniso.dat took: 0:00:00.047545\n", - "2025-07-02 16:17:32.880155 starting: adding pilotpoints type a style parameters for file(s) ['npf-k-layer3.bearing.dat']\n", - "2025-07-02 16:17:32.880155 starting: using geostruct:name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:90.0\n", - "\n", - "2025-07-02 16:17:32.880155 starting: loading array freyberg6_template\\npf-k-layer3.bearing.dat\n", - "2025-07-02 16:17:32.881153 finished: loading array freyberg6_template\\npf-k-layer3.bearing.dat took: 0:00:00.000998\n", - "2025-07-02 16:17:32.882153 loaded array 'temp_pst_from_ppu\\npf-k-layer3.bearing.dat' of shape (40, 20)\n", - "2025-07-02 16:17:32.885200 starting: writing array-style template file 'freyberg6_template\\npf-k-layer3-bearing_inst0_pilotpoints.csv.tpl'\n", - "2025-07-02 16:17:32.885200 starting: setting up pilot point parameters\n", - "2025-07-02 16:17:32.885200 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", - "2025-07-02 16:17:32.885200 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", - "2025-07-02 16:17:32.886157 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", - "2025-07-02 16:17:32.886157 'num_threads' not set in pp_options, Setting to default value: [10]\n", - "2025-07-02 16:17:32.886157 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", - "2025-07-02 16:17:32.886157 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", - "2025-07-02 16:17:32.886157 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", - "2025-07-02 16:17:32.886157 'fill_value' not set in pp_options, Setting to default value: [0.0]\n", - "2025-07-02 16:17:32.886157 'lower_limit' not set in pp_options, Setting to default value: [-1e+30]\n", - "2025-07-02 16:17:32.886157 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", - "2025-07-02 16:17:32.886157 No spatial reference (containing cell spacing) passed.\n", - "2025-07-02 16:17:32.886157 OK - using spatial reference in parent object.\n", - "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", - "2025-07-02 16:17:32.952108 18 pilot point parameters created\n", - "2025-07-02 16:17:32.952108 pilot point 'pargp':n,p,f,-,k,-,l,a,y,e,r,3,-,b,e,a,r,i,n,g\n", - "2025-07-02 16:17:32.953108 finished: setting up pilot point parameters took: 0:00:00.067908\n", - "2025-07-02 16:17:32.957108 starting: calculating factors for pargp=npf-k-layer3-bearing\n", - "2025-07-02 16:17:32.958107 saving krige variance file:freyberg6_template\\npf-k-layer3-bearing_inst0pp.var.dat\n", - "2025-07-02 16:17:32.958107 saving krige factors file:freyberg6_template\\npf-k-layer3-bearing_inst0pp.fac\n", - "...pypestutils detected and being used for kriging solve, trust us, you want this!\n", - "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k-layer3-bearing_inst0pp.fac'\n", - "INFO:PestUtilsLib: all memory was freed up\n", - "...ppu_factor_filename 'freyberg6_template\\npf-k-layer3-bearing_inst0pp.fac' created, factors calculated for 800 points\n", - "2025-07-02 16:17:33.033550 finished: calculating factors for pargp=npf-k-layer3-bearing took: 0:00:00.076442\n", - "2025-07-02 16:17:33.033550 starting: writing array-based template file 'freyberg6_template\\npf-k-layer3-bearing_inst0pp.dat.tpl'\n", - "2025-07-02 16:17:33.033550 saving zone array freyberg6_template\\npf-k-layer3-bearing_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k-layer3-bearing_inst0pp.dat.tpl\n", - "2025-07-02 16:17:33.040550 finished: adding pilotpoints type a style parameters for file(s) ['npf-k-layer3.bearing.dat'] took: 0:00:00.160395\n", - "2025-07-02 16:17:33.041549 starting: adding observations from output file npf-k-layer3.bearing.dat\n", - "2025-07-02 16:17:33.041549 starting: adding observations from array output file 'npf-k-layer3.bearing.dat'\n", - "2025-07-02 16:17:33.048063 starting: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.bearing.dat.ins'\n", - "2025-07-02 16:17:33.087080 finished: adding observation from instruction file 'freyberg6_template\\npf-k-layer3.bearing.dat.ins' took: 0:00:00.039017\n", - "2025-07-02 16:17:33.088081 finished: adding observations from array output file 'npf-k-layer3.bearing.dat' took: 0:00:00.046532\n", - "2025-07-02 16:17:33.088081 finished: adding observations from output file npf-k-layer3.bearing.dat took: 0:00:00.046532\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:32.885200 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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...............
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800 rows × 4 columns

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" - ], - "text/plain": [ - " obsnme \\\n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:0 oname:npf-k-layer3-bearing_otype:arr_i:0_j:0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:1 oname:npf-k-layer3-bearing_otype:arr_i:0_j:1 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:10 oname:npf-k-layer3-bearing_otype:arr_i:0_j:10 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:11 oname:npf-k-layer3-bearing_otype:arr_i:0_j:11 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:12 oname:npf-k-layer3-bearing_otype:arr_i:0_j:12 \n", - "... ... \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:5 oname:npf-k-layer3-bearing_otype:arr_i:9_j:5 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:6 oname:npf-k-layer3-bearing_otype:arr_i:9_j:6 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:7 oname:npf-k-layer3-bearing_otype:arr_i:9_j:7 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:8 oname:npf-k-layer3-bearing_otype:arr_i:9_j:8 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:9 oname:npf-k-layer3-bearing_otype:arr_i:9_j:9 \n", - "\n", - " obsval weight \\\n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:0 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:1 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:10 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:11 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:12 0.0 1.0 \n", - "... ... ... \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:5 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:6 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:7 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:8 0.0 1.0 \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:9 0.0 1.0 \n", - "\n", - " obgnme \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:0 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:1 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:10 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:11 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:0_j:12 npf-k-layer3-bearing \n", - "... ... \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:5 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:6 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:7 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:8 npf-k-layer3-bearing \n", - "oname:npf-k-layer3-bearing_otype:arr_i:9_j:9 npf-k-layer3-bearing \n", - "\n", - "[800 rows x 4 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "afile = 'npf-k-layer3.aniso.dat'\n", "tag = afile.split('.')[0].replace(\"_\",\"-\")+\"-aniso\"\n", @@ -2769,315 +466,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "freyberg6.npf_k33_layer2.txt\n", - "WARNING: thresholding array freyberg6.npf_k33_layer2.txt.thresharr.dat has very low standard deviation\n", - " using a homogeneous array with first category fill value 0.03\n", - "2025-07-02 16:17:33.235335 starting: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.thresharr.dat']\n", - "2025-07-02 16:17:33.235335 finished: using geostruct:name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", - " took: 0:00:00.940957\n", - "2025-07-02 16:17:33.235335 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", - "2025-07-02 16:17:33.235335 WARNING: 1) Setting geostruct transform to none\n", - "2025-07-02 16:17:33.235335 WARNING: 2) This will create a new copy of geostruct\n", - "2025-07-02 16:17:33.235335 WARNING: -) Better to pass an appropriately transformed geostruct\n", - "2025-07-02 16:17:33.235335 starting: loading array freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat\n", - "2025-07-02 16:17:33.237335 finished: loading array freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat took: 0:00:00.002000\n", - "2025-07-02 16:17:33.237335 loaded array 'temp_pst_from_ppu\\freyberg6.npf_k33_layer2.txt.thresharr.dat' of shape (40, 20)\n", - "2025-07-02 16:17:33.239335 starting: writing array-style template file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0_pilotpoints.csv.tpl'\n", - "2025-07-02 16:17:33.239335 starting: setting up pilot point parameters\n", - "2025-07-02 16:17:33.239335 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", - "2025-07-02 16:17:33.239335 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", - "2025-07-02 16:17:33.239335 'num_threads' not set in pp_options, Setting to default value: [10]\n", - "2025-07-02 16:17:33.239335 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", - "2025-07-02 16:17:33.239335 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", - "2025-07-02 16:17:33.239335 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", - "2025-07-02 16:17:33.239335 'fill_value' not set in pp_options, Setting to default value: [0.0]\n", - "2025-07-02 16:17:33.239335 'lower_limit' not set in pp_options, Setting to default value: [-1e+30]\n", - "2025-07-02 16:17:33.239335 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", - "2025-07-02 16:17:33.239335 WARNING: `prep_hyperpars` requested but `try_use_ppu` set to False.\n", - "Setting `try_use_ppu` to True and testing import\n", - "2025-07-02 16:17:33.239335 No spatial reference (containing cell spacing) passed.\n", - "2025-07-02 16:17:33.239335 OK - using spatial reference in parent object.\n", - "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", - "2025-07-02 16:17:33.315872 32 pilot point parameters created\n", - "2025-07-02 16:17:33.315872 pilot point 'pargp':n,p,f,-,k,3,3,-,l,a,y,e,r,2,_,p,p,-,t,h,r,e,s,h,p,p,_,k,:,1\n", - "2025-07-02 16:17:33.316874 finished: setting up pilot point parameters took: 0:00:00.077539\n", - "warning: replacing illegal character ':' with '-' in file_tag name 'npf-k33-layer2_pp-threshpp_k:1'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: 0) Inconsistency between geostruct transform and partrans.\n", - "\n", - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: 1) Setting geostruct transform to none\n", - "\n", - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: 2) This will create a new copy of geostruct\n", - "\n", - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.235335 WARNING: -) Better to pass an appropriately transformed geostruct\n", - "\n", - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.239335 WARNING: `prep_hyperpars` requested but `try_use_ppu` set to False.\n", - "Setting `try_use_ppu` to True and testing import\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:PestUtilsLib: calculated 2D kriging factors to 'npf-k33-layer2_pp-threshpp_k1_inst0_pilotpoints.csv.temp.fac'\n", - "INFO:PestUtilsLib: kriged using factor file 'npf-k33-layer2_pp-threshpp_k1_inst0_pilotpoints.csv.temp.fac'\n", - "INFO:PestUtilsLib: all memory was freed up\n", - "npf-k33-layer2_pp-threshpp_k-1.config.csv\n", - "2025-07-02 16:17:33.615941 starting: writing array-based template file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0pp.dat.tpl'\n", - "2025-07-02 16:17:33.615941 saving zone array freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k33-layer2_pp-threshpp_k1_inst0pp.dat.tpl\n", - "2025-07-02 16:17:33.625944 finished: adding pilotpoints type m style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.thresharr.dat'] took: 0:00:00.390609\n", - "2025-07-02 16:17:33.627939 starting: adding constant type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.aniso.dat']\n", - "2025-07-02 16:17:33.628943 starting: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat\n", - "2025-07-02 16:17:33.629944 finished: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat took: 0:00:00.001001\n", - "2025-07-02 16:17:33.629944 loaded array 'temp_pst_from_ppu\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat' of shape (40, 20)\n", - "2025-07-02 16:17:33.632940 starting: writing array-style template file 'freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl'\n", - "2025-07-02 16:17:33.632940 starting: writing template file freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl for ['npf-k33-layer2-aniso_inst:0']\n", - "2025-07-02 16:17:33.647959 finished: writing template file freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl for ['npf-k33-layer2-aniso_inst:0'] took: 0:00:00.015019\n", - "2025-07-02 16:17:33.647959 starting: writing array-based template file 'freyberg6_template\\npf-k33-layer2-aniso_inst0_constant.csv.tpl'\n", - "2025-07-02 16:17:33.657489 finished: adding constant type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.aniso.dat'] took: 0:00:00.029550\n", - "2025-07-02 16:17:33.657489 starting: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.aniso.dat\n", - "2025-07-02 16:17:33.657489 starting: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.aniso.dat'\n", - "2025-07-02 16:17:33.661498 starting: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat.ins'\n", - "2025-07-02 16:17:33.694501 finished: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.aniso.dat.ins' took: 0:00:00.033003\n", - "2025-07-02 16:17:33.695492 finished: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.aniso.dat' took: 0:00:00.038003\n", - "2025-07-02 16:17:33.695492 finished: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.aniso.dat took: 0:00:00.038003\n", - "2025-07-02 16:17:33.696509 starting: adding pilotpoints type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.bearing.dat']\n", - "2025-07-02 16:17:33.696509 finished: using geostruct:name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:90.0\n", - " took: 0:00:00.816354\n", - "2025-07-02 16:17:33.696509 starting: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat\n", - "2025-07-02 16:17:33.698496 finished: loading array freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat took: 0:00:00.001987\n", - "2025-07-02 16:17:33.698496 loaded array 'temp_pst_from_ppu\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat' of shape (40, 20)\n", - "2025-07-02 16:17:33.700489 starting: writing array-style template file 'freyberg6_template\\npf-k33-layer2-bearing_inst0_pilotpoints.csv.tpl'\n", - "2025-07-02 16:17:33.700489 starting: setting up pilot point parameters\n", - "2025-07-02 16:17:33.700489 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", - "2025-07-02 16:17:33.701490 'use_pp_zones' not set in pp_options, Setting to default value: [False]\n", - "2025-07-02 16:17:33.701490 'spatial_reference' not set in pp_options, Setting to default value: [None]\n", - "2025-07-02 16:17:33.701490 'num_threads' not set in pp_options, Setting to default value: [10]\n", - "2025-07-02 16:17:33.701490 'minpts_interp' not set in pp_options, Setting to default value: [1]\n", - "2025-07-02 16:17:33.701490 'maxpts_interp' not set in pp_options, Setting to default value: [20]\n", - "2025-07-02 16:17:33.701490 'search_radius' not set in pp_options, Setting to default value: [10000000000.0]\n", - "2025-07-02 16:17:33.701490 'fill_value' not set in pp_options, Setting to default value: [0.0]\n", - "2025-07-02 16:17:33.701490 'lower_limit' not set in pp_options, Setting to default value: [-1e+30]\n", - "2025-07-02 16:17:33.701490 'upper_limit' not set in pp_options, Setting to default value: [1e+30]\n", - "2025-07-02 16:17:33.701490 No spatial reference (containing cell spacing) passed.\n", - "2025-07-02 16:17:33.701490 OK - using spatial reference in parent object.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\modelling\\pyemu\\pyemu\\logger.py:100: PyemuWarning: 2025-07-02 16:17:33.700489 WARNING: Directly passing 'pp_space' has been deprecated and will eventually be removed, please use pp_options['pp_space'] instead.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "error importing shapefile, try pip install pyshp...No module named 'shapefile'\n", - "2025-07-02 16:17:33.868773 18 pilot point parameters created\n", - "2025-07-02 16:17:33.869773 pilot point 'pargp':n,p,f,-,k,3,3,-,l,a,y,e,r,2,-,b,e,a,r,i,n,g\n", - "2025-07-02 16:17:33.869773 finished: setting up pilot point parameters took: 0:00:00.169284\n", - "2025-07-02 16:17:33.878777 reusing factors\n", - "2025-07-02 16:17:33.878777 starting: writing array-based template file 'freyberg6_template\\npf-k33-layer2-bearing_inst0pp.dat.tpl'\n", - "2025-07-02 16:17:33.878777 saving zone array freyberg6_template\\npf-k33-layer2-bearing_inst0pp.dat.zone for tpl file freyberg6_template\\npf-k33-layer2-bearing_inst0pp.dat.tpl\n", - "2025-07-02 16:17:33.887772 finished: adding pilotpoints type a style parameters for file(s) ['npf-k33-layer2_pp-threshpp_k-1.bearing.dat'] took: 0:00:00.191263\n", - "2025-07-02 16:17:33.888771 starting: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.bearing.dat\n", - "2025-07-02 16:17:33.888771 starting: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.bearing.dat'\n", - "2025-07-02 16:17:33.893771 starting: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat.ins'\n", - "2025-07-02 16:17:33.936882 finished: adding observation from instruction file 'freyberg6_template\\npf-k33-layer2_pp-threshpp_k-1.bearing.dat.ins' took: 0:00:00.043111\n", - "2025-07-02 16:17:33.937881 finished: adding observations from array output file 'npf-k33-layer2_pp-threshpp_k-1.bearing.dat' took: 0:00:00.049110\n", - "2025-07-02 16:17:33.937881 finished: adding observations from output file npf-k33-layer2_pp-threshpp_k-1.bearing.dat took: 0:00:00.049110\n", - "2025-07-02 16:17:33.937881 starting: adding grid type d style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.threshprops.csv']\n", - "2025-07-02 16:17:33.938881 starting: loading list-style freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv\n", - "2025-07-02 16:17:33.938881 starting: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv\n", - "2025-07-02 16:17:33.941884 finished: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv took: 0:00:00.003003\n", - "2025-07-02 16:17:33.941884 loaded list-style 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv' of shape (2, 3)\n", - "2025-07-02 16:17:33.943881 finished: loading list-style freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv took: 0:00:00.005000\n", - "2025-07-02 16:17:33.943881 starting: writing list-style template file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv.tpl'\n", - "2025-07-02 16:17:33.965397 finished: writing list-style template file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops.csv.tpl' took: 0:00:00.021516\n", - "2025-07-02 16:17:33.973399 finished: adding grid type d style parameters for file(s) ['freyberg6.npf_k33_layer2.txt.threshprops.csv'] took: 0:00:00.035518\n", - "2025-07-02 16:17:33.974398 starting: adding observations from output file freyberg6.npf_k33_layer2.txt\n", - "2025-07-02 16:17:33.974398 starting: adding observations from array output file 'freyberg6.npf_k33_layer2.txt'\n", - "2025-07-02 16:17:33.979395 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.ins'\n", - "2025-07-02 16:17:34.020397 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.ins' took: 0:00:00.041002\n", - "2025-07-02 16:17:34.021398 finished: adding observations from array output file 'freyberg6.npf_k33_layer2.txt' took: 0:00:00.047000\n", - "2025-07-02 16:17:34.021398 finished: adding observations from output file freyberg6.npf_k33_layer2.txt took: 0:00:00.047000\n", - "2025-07-02 16:17:34.021398 starting: adding observations from output file freyberg6.npf_k33_layer2.txt.threshcat.dat\n", - "2025-07-02 16:17:34.021398 starting: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.threshcat.dat'\n", - "2025-07-02 16:17:34.068404 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshcat.dat.ins'\n", - "2025-07-02 16:17:34.100396 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshcat.dat.ins' took: 0:00:00.031992\n", - "2025-07-02 16:17:34.100396 finished: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.threshcat.dat' took: 0:00:00.078998\n", - "2025-07-02 16:17:34.101394 finished: adding observations from output file freyberg6.npf_k33_layer2.txt.threshcat.dat took: 0:00:00.079996\n", - "2025-07-02 16:17:34.101394 starting: adding observations from output file freyberg6.npf_k33_layer2.txt.thresharr.dat\n", - "2025-07-02 16:17:34.101394 starting: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.thresharr.dat'\n", - "2025-07-02 16:17:34.106392 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat.ins'\n", - "2025-07-02 16:17:34.133395 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.thresharr.dat.ins' took: 0:00:00.027003\n", - "2025-07-02 16:17:34.133395 finished: adding observations from array output file 'freyberg6.npf_k33_layer2.txt.thresharr.dat' took: 0:00:00.032001\n", - "2025-07-02 16:17:34.133395 finished: adding observations from output file freyberg6.npf_k33_layer2.txt.thresharr.dat took: 0:00:00.032001\n", - "2025-07-02 16:17:34.142408 starting: adding observations from output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv\n", - "2025-07-02 16:17:34.143407 starting: adding observations from tabular output file '['freyberg6.npf_k33_layer2.txt.threshprops_results.csv']'\n", - "2025-07-02 16:17:34.143407 starting: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv\n", - "2025-07-02 16:17:34.145406 finished: reading list-style file: freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv took: 0:00:00.001999\n", - "2025-07-02 16:17:34.146406 starting: building insfile for tabular output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv\n", - "2025-07-02 16:17:34.151929 finished: building insfile for tabular output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv took: 0:00:00.005523\n", - "2025-07-02 16:17:34.152932 starting: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv.ins'\n", - "2025-07-02 16:17:34.167926 finished: adding observation from instruction file 'freyberg6_template\\freyberg6.npf_k33_layer2.txt.threshprops_results.csv.ins' took: 0:00:00.014994\n", - "2025-07-02 16:17:34.168926 finished: adding observations from tabular output file '['freyberg6.npf_k33_layer2.txt.threshprops_results.csv']' took: 0:00:00.025519\n", - "2025-07-02 16:17:34.168926 finished: adding observations from output file freyberg6.npf_k33_layer2.txt.threshprops_results.csv took: 0:00:00.026518\n" - ] - }, - { - "data": { - "text/html": [ - 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"oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 \n", - "\n", - " obsval \\\n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 1.00 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 0.00 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 0.03 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 0.03 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 0.00 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 0.00 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 0.40 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 0.60 \n", - "\n", - " weight \\\n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 1.0 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 1.0 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 1.0 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 1.0 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 1.0 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 1.0 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 1.0 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 1.0 \n", - "\n", - " obgnme \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:proportion \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshfill \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:1 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold_threshcat:2 oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshold \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:1 npf-k33-layer2-results_k:1 \n", - "oname:npf-k33-layer2-results_k:1_otype:lst_usecol:threshproportion_threshcat:2 npf-k33-layer2-results_k:1 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "arr_file = \"freyberg6.npf_k33_layer2.txt\"\n", "print(arr_file)\n", @@ -3170,19 +561,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "noptmax:0, npar_adj:232, nnz_obs:7463\n", - "2025-07-02 16:17:34.505863 forward_run line:pyemu.os_utils.run(r'mf6')\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "pf.mod_sys_cmds.append(\"mf6\")\n", "pf.pre_py_cmds.insert(0,\"import sys\")\n", @@ -3192,82 +573,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "import os\n", - "import multiprocessing as mp\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pyemu\n", - "def main():\n", - "\n", - " try:\n", - " os.remove(r'heads.csv')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:heads.csv')\n", - " try:\n", - " os.remove(r'sfr.csv')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:sfr.csv')\n", - " try:\n", - " os.remove(r'freyberg6.npf_k_layer1.txt')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:freyberg6.npf_k_layer1.txt')\n", - " try:\n", - " os.remove(r'freyberg6.npf_k_layer3.txt')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:freyberg6.npf_k_layer3.txt')\n", - " try:\n", - " os.remove(r'npf-k-layer3.aniso.dat')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:npf-k-layer3.aniso.dat')\n", - " try:\n", - " os.remove(r'npf-k-layer3.bearing.dat')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:npf-k-layer3.bearing.dat')\n", - " try:\n", - " os.remove(r'npf-k33-layer2_pp-threshpp_k-1.aniso.dat')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:npf-k33-layer2_pp-threshpp_k-1.aniso.dat')\n", - " try:\n", - " os.remove(r'npf-k33-layer2_pp-threshpp_k-1.bearing.dat')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:npf-k33-layer2_pp-threshpp_k-1.bearing.dat')\n", - " try:\n", - " os.remove(r'freyberg6.npf_k33_layer2.txt')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt')\n", - " try:\n", - " os.remove(r'freyberg6.npf_k33_layer2.txt.threshcat.dat')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt.threshcat.dat')\n", - " try:\n", - " os.remove(r'freyberg6.npf_k33_layer2.txt.thresharr.dat')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt.thresharr.dat')\n", - " try:\n", - " os.remove(r'freyberg6.npf_k33_layer2.txt.threshprops_results.csv')\n", - " except Exception as e:\n", - " print(r'error removing tmp file:freyberg6.npf_k33_layer2.txt.threshprops_results.csv')\n", - " import sys\n", - " sys.path.append(os.path.join('..','..','..','pypestutils'))\n", - " pyemu.helpers.apply_list_and_array_pars(arr_par_file='mult2model_info.csv',chunk_len=50)\n", - " pyemu.helpers.apply_threshold_pars('freyberg6.npf_k33_layer2.txt.threshprops.csv')\n", - " pyemu.os_utils.run(r'mf6')\n", - "\n", - "\n", - "if __name__ == '__main__':\n", - " mp.freeze_support()\n", - " main()\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "_ = [print(line.rstrip()) for line in open(os.path.join(template_ws,\"forward_run.py\"))]" ] @@ -3283,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3359,59 +667,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-07-02 16:17:34.677796 starting: drawing realizations\n", - "2025-07-02 16:17:34.680793 starting: Drawing 232 non-specsim pars\n", - "building diagonal cov\n", - "processing name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:1.0,bearing:0.0\n", - "\n", - "working on pargroups ['npf-k-layer1']\n", - "build cov matrix\n", - "done\n", - "getting diag var cov 79\n", - "scaling full cov by diag var cov\n", - "processing name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", - "\n", - "working on pargroups ['npf-k-layer3']\n", - "build cov matrix\n", - "done\n", - "getting diag var cov 79\n", - "scaling full cov by diag var cov\n", - "processing name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:90.0\n", - "\n", - "working on pargroups ['npf-k-layer3-bearing']\n", - "build cov matrix\n", - "done\n", - "getting diag var cov 18\n", - "scaling full cov by diag var cov\n", - "working on pargroups ['npf-k33-layer2-bearing']\n", - "build cov matrix\n", - "done\n", - "getting diag var cov 18\n", - "scaling full cov by diag var cov\n", - "processing name:struct1,nugget:0.0,structures:\n", - "name:var1,contribution:1.0,a:5000.0,anisotropy:2.0,bearing:0.0\n", - "\n", - "working on pargroups ['npf-k33-layer2_pp-threshpp_k:1']\n", - "build cov matrix\n", - "done\n", - "getting diag var cov 32\n", - "scaling full cov by diag var cov\n", - "adding remaining parameters to diagonal\n", - "2025-07-02 16:17:34.826733 finished: Drawing 232 non-specsim pars took: 0:00:00.145940\n", - "2025-07-02 16:17:34.826733 finished: drawing realizations took: 0:00:00.148937\n" - ] - } - ], + "outputs": [], "source": [ "np.random.seed(122341)\n", "pe = pf.draw(num_reals=100)" @@ -3419,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3428,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3439,17 +697,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "noptmax:0, npar_adj:231, nnz_obs:7463\n" - ] - } - ], + "outputs": [], "source": [ "pst.control_data.noptmax = 0\n", "pst.write(os.path.join(pf.new_d,pst_name))" @@ -3457,57 +707,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "WindowsPath('freyberg6_template')" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pf.new_d" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'real_0.pst'" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pst_name" - ] - }, - { - "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pestpp-ies.exe real_0.pst\n" - ] - } - ], + "outputs": [], "source": [ "pyemu.os_utils.run(\"pestpp-ies {0}\".format(pst_name),cwd=pf.new_d)" ] @@ -3517,29 +719,6 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['npf-k-layer1',\n", - " 'npf-k-layer3',\n", - " 'npf-k-layer3-bearing',\n", - " 'npf-k33-layer2-bearing',\n", - " 'npf-k33-layer2',\n", - " 'npf-k33-layer2-thresharr']" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "pst.set_res(os.path.join(pf.new_d,pst_name.replace(\".pst\",\".base.rei\")))\n", "res = pst.res\n", @@ -3550,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3562,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3572,40 +751,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for k in uk:\n", " kobs = gobs.loc[gobs.k==k,:]\n", @@ -3649,15 +797,6 @@ "metadata": {}, "outputs": [], "source": [] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# bearing -45 to 45 leaves out 1/2 the potential orientations! Bounds reset for layer 2 and 3 in block 28?" - ] } ], "metadata": { @@ -3676,7 +815,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/examples/pstfrom_mf6_ppu_tensor.ipynb b/examples/pstfrom_mf6_ppu_tensor.ipynb new file mode 100644 index 000000000..e1626fa1d --- /dev/null +++ b/examples/pstfrom_mf6_ppu_tensor.ipynb @@ -0,0 +1,1059 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setting up a PEST interface from MODFLOW6 using the `PstFrom` class with `PyPestUtils` for advanced pilot point parameterization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import pyemu\n", + "import flopy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(os.path.join(\"..\",\"..\",\"pypestutils\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pypestutils as ppu" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An existing MODFLOW6 model is in the directory `freyberg_mf6`. Lets check it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "org_model_ws = os.path.join('freyberg_mf6')\n", + "os.listdir(org_model_ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that all the input array and list data for this model have been written \"externally\" - this is key to using the `PstFrom` class. \n", + "\n", + "Let's quickly viz the model top just to remind us of what we are dealing with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "id_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_idomain_layer3.txt\"))\n", + "top_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_top.txt\"))\n", + "top_arr[id_arr==0] = np.nan\n", + "plt.imshow(top_arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's copy those files to a temporary location just to make sure we don't goof up those original files:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tmp_model_ws = \"temp_pst_from_ppu\"\n", + "if os.path.exists(tmp_model_ws):\n", + " shutil.rmtree(tmp_model_ws)\n", + "shutil.copytree(org_model_ws,tmp_model_ws)\n", + "# copy pestpp and mf6\n", + "shutil.copy(r'C:\\bin\\modflow\\mf6.6.2_win64\\bin\\mf6.exe', os.path.join(tmp_model_ws,'mf6.exe'))\n", + "shutil.copy(r'C:\\bin\\pestpp\\pestpp-ies.exe', os.path.join(tmp_model_ws,'pestpp-ies.exe'))\n", + "os.listdir(tmp_model_ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need just a tiny bit of info about the spatial discretization of the model - this is needed to work out separation distances between parameters for build a geostatistical prior covariance matrix later.\n", + "\n", + "Here we will load the flopy sim and model instance just to help us define some quantities later - flopy is not required to use the `PstFrom` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws)\n", + "m = sim.get_model(\"freyberg6\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the simple `SpatialReference` pyemu implements to help us spatially locate parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sr = pyemu.helpers.SpatialReference.from_namfile(\n", + " os.path.join(tmp_model_ws, \"freyberg6.nam\"),\n", + " delr=m.dis.delr.array, delc=m.dis.delc.array)\n", + "sr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can instantiate a `PstFrom` class instance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "template_ws = \"freyberg6_template\"\n", + "pf = pyemu.utils.PstFrom(original_d=tmp_model_ws, new_d=template_ws,\n", + " remove_existing=True,\n", + " longnames=True, spatial_reference=sr,\n", + " zero_based=False,start_datetime=\"1-1-2018\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Observations\n", + "\n", + "So now that we have a `PstFrom` instance, but its just an empty container at this point, so we need to add some PEST interface \"observations\" and \"parameters\". Let's start with observations using MODFLOW6 head. These are stored in `heads.csv`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(os.path.join(tmp_model_ws,\"heads.csv\"),index_col=0)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main entry point for adding observations is (surprise) `PstFrom.add_observations()`. This method works on the list-type observation output file. We need to tell it what column is the index column (can be string if there is a header or int if no header) and then what columns contain quantities we want to monitor (e.g. \"observe\") in the control file - in this case we want to monitor all columns except the index column:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hds_df = pf.add_observations(\"heads.csv\",insfile=\"heads.csv.ins\",index_cols=\"time\",\n", + " use_cols=list(df.columns.values),prefix=\"hds\",)\n", + "hds_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that it returned a dataframe with lots of useful info: the observation names that were formed (`obsnme`), the values that were read from `heads.csv` (`obsval`) and also some generic weights and group names. At this point, no control file has been created, we have simply prepared to add this observations to the control file later. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[f for f in os.listdir(template_ws) if f.endswith(\".ins\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! We also have a PEST-style instruction file for those obs.\n", + "\n", + "Now lets do the same for SFR observations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(os.path.join(tmp_model_ws, \"sfr.csv\"), index_col=0)\n", + "sfr_df = pf.add_observations(\"sfr.csv\", insfile=\"sfr.csv.ins\", index_cols=\"time\", use_cols=list(df.columns.values))\n", + "sfr_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sweet as! Now that we have some observations, let's add parameters!\n", + "\n", + "## Pilot points and `PyPestUtils`\n", + "\n", + "This notebook is mostly meant to demonstrate some advanced pilot point parameterization that is possible with `PyPestUtils`, so we will only focus on HK and VK pilot point parameters. This is just to keep the example short. In practice, please please please parameterize boundary conditions too!\n", + "\n", + "We start with simple pilot points and an isotropic variogram (bearing is irrelevant when anisotropy=1)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v = pyemu.geostats.ExpVario(contribution=1.0,a=5000,bearing=0,anisotropy=1)\n", + "pp_gs = pyemu.geostats.GeoStruct(variograms=v, transform='log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pp_gs.plot()\n", + "print(\"spatial variogram\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get the idomain array to use as a zone array - this keeps us from setting up parameters in inactive model cells:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ib = m.dis.idomain[0].array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find HK files for the upper and lower model layers (assuming model layer 2 is a semi-confining unit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hk_arr_files = [f for f in os.listdir(tmp_model_ws) if \"npf_k_\" in f and f.endswith(\".txt\") and \"layer2\" not in f]\n", + "hk_arr_files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr_file = \"freyberg6.npf_k_layer1.txt\"\n", + "tag = arr_file.split('.')[1].replace(\"_\",\"-\")\n", + "pf.add_parameters(filenames=arr_file,par_type=\"pilotpoints\",\n", + " par_name_base=tag,pargp=tag,zone_array=ib,\n", + " upper_bound=10.,lower_bound=0.1,ult_ubound=100,ult_lbound=0.01,\n", + " pp_options={\"pp_space\":3},geostruct=pp_gs, transform='log')\n", + "#let's also add the resulting hk array that modflow sees as observations\n", + "# so we can make easy plots later...\n", + "pf.add_observations(arr_file,prefix=tag,\n", + " obsgp=tag,zone_array=ib)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are familiar with how `PstFrom` has worked historically, we handed off the process to solve for the factor file (which requires solving the kriging equations for each active node) to a pure python (well, with pandas and numpy). This was ok for toy models, but hella slow for big ugly models. If you look at the log entries above, you should see that the instead, `PstFrom` successfully handed off the solve to `PyPestUtils`, which is exponentially faster for big models. sweet ez! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tpl_files = [f for f in os.listdir(template_ws) if f.endswith(\".tpl\")]\n", + "tpl_files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(template_ws,tpl_files[0]),'r') as f:\n", + " for _ in range(2):\n", + " print(f.readline().strip())\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "So those might look like pretty redic parameter names, but they contain heaps of metadata to help you post process things later..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So those are you standard pilot points for HK in layer 1 - same as it ever was..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Geostatistical hyper-parameters using \"conceptual points\"\n", + "\n", + "For the HK layer 1 pilot points, we used a standard geostatistical structure - the ever popular exponential variogram. But what if the properties that define that variogram were themselves uncertain? Like what if the anisotropy ellipse varied in space across the model domain? \n", + "\n", + "One way to tackle this is to assign hyper parameters at points, a tensor describing spatial correlation structure at each point. This makes sense when you have data (or \"geology\" = \"beer and guessing\") at various points to inform the correlation structure. We then use tensor interpolation to fill in the spatial structure where \"beer and guessing\" is missing.\n", + "\n", + "The field will be calculated as:\n", + "\n", + "field = interp_means + correlated_noise * 10**np.sqrt(interp_variances)\n", + "\n", + "The above gives us the spatially varying correlation structure to be convolved with white noise to give the correlated_noise, but the mean and variance of the field is also uncertain (uncertain uncertainty). The field is cacluLet's start with those using the familar pilot point scheme, although they could be added for each conceptual point instead. First describe how the mean and variance are expected to change (we will keep it simple):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "variance_v = pyemu.geostats.ExpVario(contribution=1, a=5000)\n", + "variance_gs = pyemu.geostats.GeoStruct(variograms=variance_v)\n", + "mean_v = pyemu.geostats.ExpVario(contribution=1,a=5000)\n", + "mean_gs = pyemu.geostats.GeoStruct(variograms=mean_v)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the mean\n", + "arr_file = \"freyberg6.npf_k33_layer3.txt\"\n", + "tag = arr_file.split('.')[1].replace(\"_\",\"-\")\n", + "pf.add_parameters(filenames=arr_file,par_type=\"pilotpoints\",\n", + " par_name_base=tag,pargp=tag,zone_array=ib,\n", + " upper_bound=10.,lower_bound=0.1,ult_ubound=100,ult_lbound=0.01,\n", + " pp_options={\"pp_space\":3},geostruct=mean_gs, transform='log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the variance\n", + "arr_file = \"freyberg6.npf_k33_layer3.txt\"\n", + "tag = arr_file.split('.')[1].replace(\"_\",\"-\")\n", + "pf.add_parameters(filenames=arr_file,par_type=\"pilotpoints\",\n", + " par_name_base=tag,pargp=tag,zone_array=ib,\n", + " upper_bound=10./2,lower_bound=0.1*2,ult_ubound=10,ult_lbound=0.1,\n", + " pp_options={\"pp_space\":3},geostruct=mean_gs, transform='log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#let's also add the resulting hk array that modflow sees as observations\n", + "# so we can make easy plots later...\n", + "pf.add_observations(arr_file,prefix=tag,\n", + " obsgp=tag,zone_array=ib)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we add the conceptual point hyper parameters from our friendly geologists:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ordinary_kriging(cp_coords, cp_values, grid_coords,\n", + " variogram_model='exponential', range_param=10000,\n", + " sill=1.0, nugget=0.1, background_value=0.0,\n", + " max_search_radius=1e20, min_points=1,\n", + " transform=None, min_value=1e-8):\n", + " \"\"\"\n", + " Proper Ordinary Kriging with:\n", + " - Variogram model selection\n", + " - Background value fallback\n", + " - Search radius limitation\n", + " \"\"\"\n", + " n_grid = len(grid_coords)\n", + " n_control = len(cp_coords)\n", + " interp_values = np.full(n_grid, background_value)\n", + "\n", + " # Variogram function\n", + " def variogram(h):\n", + " if variogram_model == 'exponential':\n", + " return nugget + (sill - nugget) * (1 - np.exp(-h / range_param))\n", + " elif variogram_model == 'gaussian':\n", + " return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2) / (range_param ** 2)))\n", + " else: # spherical\n", + " return np.where(h <= range_param,\n", + " nugget + (sill - nugget) * (1.5 * h / range_param - 0.5 * (h / range_param) ** 3),\n", + " sill)\n", + "\n", + " for i in range(n_grid):\n", + " # Distances from this grid point to all control points\n", + " distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1)\n", + " nearby = distances <= max_search_radius\n", + " n_nearby = np.sum(nearby)\n", + "\n", + " if n_nearby < min_points:\n", + " continue # Keeps background_value\n", + "\n", + " # Build OK system: [C 1; 1^T 0] * [weights; mu] = [c; 1]\n", + " C = np.zeros((n_nearby + 1, n_nearby + 1))\n", + " c = np.zeros(n_nearby + 1)\n", + "\n", + " # Fill covariance matrix\n", + " for j in range(n_nearby):\n", + " for k in range(n_nearby):\n", + " h = np.linalg.norm(cp_coords[nearby][j] - cp_coords[nearby][k])\n", + " C[j, k] = variogram(h)\n", + " # Last row/column (unbiasedness constraint)\n", + " C[j, -1] = 1\n", + " C[-1, j] = 1\n", + " # Right-hand side vector\n", + " h = distances[nearby][j]\n", + " c[j] = variogram(h)\n", + " c[-1] = 1 # Constraint\n", + "\n", + " # Solve\n", + " if transform == 'log':\n", + " if min_value is None:\n", + " # Auto-choose based on data scale\n", + " positive_values = cp_values[cp_values > 0]\n", + " min_value = np.min(positive_values) * 0.01 # 1% of smallest positive value\n", + " cp_values_transformed = np.log10(np.maximum(cp_values, min_value))\n", + " else:\n", + " cp_values_transformed = cp_values.copy()\n", + "\n", + " try:\n", + " weights = solve(C, c)[:-1]\n", + " interp_values[i] = np.sum(weights * cp_values_transformed[nearby])\n", + " except:\n", + " weights = np.exp(-distances[nearby] / range_param)\n", + " interp_values[i] = np.sum(weights * cp_values_transformed[nearby]) / np.sum(weights)\n", + "\n", + " # After the loop, backtransform all at once:\n", + " if transform == 'log':\n", + " interp_values = 10 ** (interp_values)\n", + "\n", + " return interp_values\n", + "\n", + "def generate_single_layer(control_points, interp_means, interp_variances, grid_coords_2d):\n", + " \"\"\"Generate field for a single 2D layer\"\"\"\n", + " np.random.seed(42)\n", + " n_grid = len(grid_coords_2d)\n", + " cp_coords = control_points[['x', 'y']].values\n", + " \n", + " # Interpolate correlation tensors\n", + " print(\" Interpolating correlation tensors...\")\n", + " cp_tensors = create_2d_tensors(control_points)\n", + " interp_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d)\n", + "\n", + " # Generate correlated noise\n", + " print(\" Generating correlated noise...\")\n", + " white_noise = np.random.randn(len(grid_coords_2d))\n", + " correlated_noise = generate_correlated_noise_2d(grid_coords_2d, interp_tensors, white_noise)\n", + "\n", + " # Combine\n", + " field = interp_means + correlated_noise * 10**np.sqrt(interp_variances)\n", + "\n", + " return field\n", + "\n", + "\n", + "def generate_correlated_noise_2d(grid_coords, tensors, white_noise,\n", + " n_neighbors=50, radius_multiplier=3.0,\n", + " anisotropy_strength=1.0, debug=False):\n", + " \"\"\"\n", + " Generate spatially correlated noise respecting tensor anisotropy directions.\n", + "\n", + " Parameters:\n", + " -----------\n", + " grid_coords : array_like, shape (n_points, 2)\n", + " Grid coordinates [x, y]\n", + " tensors : array_like, shape (n_points, 2, 2)\n", + " Anisotropy tensors for each grid point\n", + " white_noise : array_like, shape (n_points,)\n", + " Input white noise to be correlated\n", + " n_neighbors : int, default=10\n", + " Number of neighbors to use for correlation\n", + " radius_multiplier : float, default=3.0\n", + " Multiplier for correlation radius (not used when n_neighbors is set)\n", + " anisotropy_strength : float, default=2.0\n", + " Strength of anisotropic correlation (higher = more directional)\n", + "\n", + " Returns:\n", + " --------\n", + " correlated_noise : array_like, shape (n_points,)\n", + " Spatially correlated noise\n", + " \"\"\"\n", + " n_points = len(grid_coords)\n", + " correlated_noise = np.zeros(n_points)\n", + "\n", + " for i in range(n_points):\n", + " # Diagonalize tensor to get principal axes and correlation lengths\n", + " evals, evecs = np.linalg.eigh(tensors[i])\n", + " if evals[1] < evals[0]:\n", + " evals, evecs = evals[::-1], evecs[:, ::-1]\n", + "\n", + " # some debuggery\n", + " if debug:\n", + " print(f\"Eigenvalue 0: {evals[0]}\")\n", + " print(f\"Eigenvector 0: {evecs[:, 0]}\") # First COLUMN\n", + " print(f\"Eigenvalue 1: {evals[1]}\")\n", + " print(f\"Eigenvector 1: {evecs[:, 1]}\") # Second COLUMN\n", + " # Find which eigenvalue is larger\n", + " max_idx = np.argmax(np.abs(evals))\n", + " principal_direction = evecs[:, max_idx]\n", + " print(f\"Principal direction vector: {principal_direction}\")\n", + " angle = np.arctan2(principal_direction[1], principal_direction[0])\n", + " angle_degrees = np.degrees(angle)\n", + " print(f\"angle: {angle_degrees}\")\n", + "\n", + " major_corr_length = np.sqrt(evals[1]) # Length along major axis\n", + " minor_corr_length = np.sqrt(evals[0]) # Length along minor axis\n", + "\n", + " # Create anisotropic transform\n", + " # Stretch major axis to make correlation stronger in that direction\n", + " scale_for_minor_axis = minor_corr_length / anisotropy_strength # Compress minor\n", + " scale_for_major_axis = major_corr_length * anisotropy_strength # Stretch major\n", + "\n", + " transform = evecs @ np.diag([scale_for_minor_axis, scale_for_major_axis])\n", + "\n", + " # Transform all relative positions to anisotropic space\n", + " all_dx = grid_coords - grid_coords[i]\n", + " inv_transform = np.linalg.inv(transform)\n", + " dx_transformed = all_dx @ inv_transform.T\n", + " aniso_distances = np.linalg.norm(dx_transformed, axis=1)\n", + " actual_distances = np.linalg.norm(all_dx, axis=1)\n", + "\n", + " # Select neighbors based on anisotropic distance, closer in warped space\n", + " if n_neighbors:\n", + " neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)]\n", + " else:\n", + " max_dist = radius_multiplier * major_corr_length\n", + " neighbor_indices = np.where(aniso_distances <= max_dist)[0]\n", + "\n", + " # Compute correlation weights using anisotropic distances\n", + " weighted_sum = 0.0\n", + " sum_weights = 0.0\n", + "\n", + " for j in neighbor_indices:\n", + " # Use major correlation length as decay scale\n", + " weight = np.exp(-(actual_distances[j]/scale_for_major_axis)**2)\n", + " weighted_sum += weight * white_noise[j]\n", + " sum_weights += weight\n", + "\n", + " # Normalize\n", + " if sum_weights > 1e-10:\n", + " correlated_noise[i] = weighted_sum / sum_weights\n", + " else:\n", + " correlated_noise[i] = white_noise[i]\n", + "\n", + " return correlated_noise\n", + "\n", + "def create_2d_tensors(control_points):\n", + " # Fixed tensor creation (swap major/minor in diagonal matrix)\n", + " n = len(control_points)\n", + " tensors = np.zeros((n, 2, 2))\n", + "\n", + " for i in range(n):\n", + " major = control_points.iloc[i]['major']\n", + " minor = control_points.iloc[i]['major'] / control_points.iloc[i]['ratio']\n", + " bearing = control_points.iloc[i]['bearing'] # - 90 # assume bearing has N=0\n", + "\n", + " theta = np.radians(bearing)\n", + " R = np.array([[np.cos(theta), -np.sin(theta)],\n", + " [np.sin(theta), np.cos(theta)]])\n", + "\n", + " # Fixed: Put minor first, major second\n", + " S = np.diag([minor ** 2, major ** 2])\n", + " tensors[i] = R @ S @ R.T\n", + "\n", + " return tensors\n", + "\n", + "\n", + "def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords):\n", + " \"\"\"Interpolate 2x2 tensors using log-Euclidean approach\"\"\" \n", + " from scipy.linalg import logm, expm, solve\n", + " n_grid = len(grid_coords)\n", + " interp_tensors = np.zeros((n_grid, 2, 2))\n", + "\n", + " # Convert to log space\n", + " log_tensors = np.array([logm(t) for t in cp_tensors])\n", + "\n", + " # Interpolate each component\n", + " for i in range(2):\n", + " for j in range(i, 2):\n", + " values = log_tensors[:, i, j].real\n", + " bg_value = np.mean(values) # Use mean of control points\n", + " interp_values = ordinary_kriging(cp_coords, values, grid_coords, \n", + " background_value=bg_value)\n", + " # interp_values = exponential_variogram_interpolate(cp_coords, values, grid_coords)\n", + " # interp_values = idw_interpolate(cp_coords, values, grid_coords)\n", + " interp_tensors[:, i, j] = interp_values\n", + " if i != j:\n", + " interp_tensors[:, j, i] = interp_values\n", + "\n", + " # Convert back from log space\n", + " for i in range(n_grid):\n", + " interp_tensors[i] = expm(interp_tensors[i])\n", + "\n", + " # # Basic ellipse visualization (most intuitive)\n", + " # fig, ax = plt.subplots(figsize=(12, 10))\n", + " # visualize_tensors_as_ellipses(grid_coords, interp_tensors,\n", + " # subsample=20, # Plot every 20th tensor\n", + " # scale_factor=0.5,\n", + " # ax=ax)\n", + "\n", + " # # Complete analysis\n", + " # plot_tensor_field_analysis(grid_coords, interp_tensors, cp_coords)\n", + "\n", + " return interp_tensors\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "interp_means = np.loadtxt(os.path.join(org_model_ws,arr_file)).flatten()\n", + "interp_variances = np.loadtxt(os.path.join(org_model_ws,arr_file)).flatten()/10\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_centers = m.modelgrid.xcellcenters.flatten()\n", + "y_centers = m.modelgrid.ycellcenters.flatten()\n", + "grid_coords = np.column_stack([x_centers,y_centers])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conceptual_points = pd.DataFrame({'x': [3, 3, 3, 3, 5, 7, 15, 20, 25, 35],\n", + " 'y': [3, 5, 10, 15, 5, 12, 10, 15, 3, 10],\n", + " 'bearing': [-40, -30, -20, -20, -30, -40, 0, 0, 10 ,200],\n", + " 'major': [5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 2000],\n", + " 'ratio': [5, 5, 2, 2, 2, 2, 2, 5, 5, 2]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "conceptual_points" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "field = generate_single_layer(conceptual_points, interp_means, interp_variances, grid_coords)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(field.reshape(40,20))\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### build the control file, pest interface files, and forward run script\n", + "At this point, we have some parameters and some observations, so we can create a control file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pf.mod_sys_cmds.append(\"mf6\")\n", + "pf.pre_py_cmds.insert(0,\"import sys\")\n", + "pf.pre_py_cmds.insert(1,\"sys.path.append(os.path.join('..','..','..','pypestutils'))\")\n", + "pst = pf.build_pst()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "_ = [print(line.rstrip()) for line in open(os.path.join(template_ws,\"forward_run.py\"))]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting initial parameter bounds and values\n", + "\n", + "Here is some gory detail regarding defining the hyper parameters for both layer 3 HK and layer 2 VK..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set the initial and bounds for the fill values\n", + "par = pst.parameter_data\n", + "\n", + "apar = par.loc[par.pname.str.contains(\"aniso\"),:]\n", + "bpar = par.loc[par.pname.str.contains(\"bearing\"), :]\n", + "par.loc[apar.parnme.str.contains(\"layer3\").index,\"parval1\"] = 3\n", + "par.loc[apar.parnme.str.contains(\"layer3\").index,\"parlbnd\"] = 1\n", + "par.loc[apar.parnme.str.contains(\"layer3\").index,\"parubnd\"] = 5\n", + "\n", + "par.loc[apar.parnme.str.contains(\"layer2\").index,\"parval1\"] = 2\n", + "par.loc[apar.parnme.str.contains(\"layer2\").index,\"parlbnd\"] = 0\n", + "par.loc[apar.parnme.str.contains(\"layer2\").index,\"parubnd\"] = 4\n", + "\n", + "par.loc[bpar.parnme.str.contains(\"layer3\").index,\"parval1\"] = 0\n", + "par.loc[bpar.parnme.str.contains(\"layer3\").index,\"parlbnd\"] = -20\n", + "par.loc[bpar.parnme.str.contains(\"layer3\").index,\"parubnd\"] = 20\n", + "\n", + "par.loc[bpar.parnme.str.contains(\"layer2\").index,\"parval1\"] = 0\n", + "par.loc[bpar.parnme.str.contains(\"layer2\").index,\"parlbnd\"] = 20\n", + "par.loc[bpar.parnme.str.contains(\"layer2\").index,\"parubnd\"] = 40\n", + "\n", + "cat1par = par.loc[par.apply(lambda x: x.threshcat==\"0\" and x.usecol==\"threshfill\",axis=1),\"parnme\"]\n", + "cat2par = par.loc[par.apply(lambda x: x.threshcat == \"1\" and x.usecol == \"threshfill\", axis=1), \"parnme\"]\n", + "assert cat1par.shape[0] == 1\n", + "assert cat2par.shape[0] == 1\n", + "\n", + "cat1parvk = [p for p in cat1par if \"k:1\" in p]\n", + "cat2parvk = [p for p in cat2par if \"k:1\" in p]\n", + "for lst in [cat2parvk,cat1parvk]:\n", + " assert len(lst) > 0\n", + "\n", + "#these are the values that will fill the two categories of VK - \n", + "# one is low (clay) and one is high (sand - the windows)\n", + "par.loc[cat1parvk, \"parval1\"] = 0.0001\n", + "par.loc[cat1parvk, \"parubnd\"] = 0.01\n", + "par.loc[cat1parvk, \"parlbnd\"] = 0.000001\n", + "par.loc[cat1parvk, \"partrans\"] = \"log\"\n", + "par.loc[cat2parvk, \"parval1\"] = 0.1\n", + "par.loc[cat2parvk, \"parubnd\"] = 1\n", + "par.loc[cat2parvk, \"parlbnd\"] = 0.01\n", + "par.loc[cat2parvk, \"partrans\"] = \"log\"\n", + "\n", + "\n", + "cat1par = par.loc[par.apply(lambda x: x.threshcat == \"0\" and x.usecol == \"threshproportion\", axis=1), \"parnme\"]\n", + "cat2par = par.loc[par.apply(lambda x: x.threshcat == \"1\" and x.usecol == \"threshproportion\", axis=1), \"parnme\"]\n", + "\n", + "assert cat1par.shape[0] == 1\n", + "assert cat2par.shape[0] == 1\n", + "\n", + "#these are the proportions of clay and sand in the resulting categorical array\n", + "#really under the hood, only the first one is used, so we can fix the other.\n", + "par.loc[cat1par, \"parval1\"] = 0.95\n", + "par.loc[cat1par, \"parubnd\"] = 1.0\n", + "par.loc[cat1par, \"parlbnd\"] = 0.9\n", + "par.loc[cat1par,\"partrans\"] = \"none\"\n", + "\n", + "# since the apply method only looks that first proportion, we can just fix this one\n", + "par.loc[cat2par, \"parval1\"] = 1\n", + "par.loc[cat2par, \"parubnd\"] = 1\n", + "par.loc[cat2par, \"parlbnd\"] = 1\n", + "par.loc[cat2par,\"partrans\"] = \"fixed\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generating a prior parameter ensemble, then run and viz a real" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(122346)\n", + "pe = pf.draw(num_reals=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pe.to_csv(os.path.join(template_ws,\"prior.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "real = 0\n", + "pst_name = \"real_{0}.pst\".format(real)\n", + "pst.parameter_data.loc[pst.adj_par_names,\"parval1\"] = pe.loc[real,pst.adj_par_names].values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pst.control_data.noptmax = 0\n", + "pst.write(os.path.join(pf.new_d,pst_name))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pf.new_d" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pst_name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pyemu.os_utils.run(\"pestpp-ies {0}\".format(pst_name),cwd=pf.new_d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pst.set_res(os.path.join(pf.new_d,pst_name.replace(\".pst\",\".base.rei\")))\n", + "res = pst.res\n", + "obs = pst.observation_data\n", + "grps = [o for o in obs.obgnme.unique() if o.startswith(\"npf\") and \"result\" not in o and \"aniso\" not in o]\n", + "grps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gobs = obs.loc[obs.obgnme.isin(grps),:].copy()\n", + "gobs[\"i\"] = gobs.i.astype(int)\n", + "gobs[\"j\"] = gobs.j.astype(int)\n", + "gobs[\"k\"] = gobs.obgnme.apply(lambda x: int(x.split('-')[2].replace(\"layer\",\"\")) - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uk = gobs.k.unique()\n", + "uk.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for k in uk:\n", + " kobs = gobs.loc[gobs.k==k,:]\n", + " ug = kobs.obgnme.unique()\n", + " ug.sort()\n", + " fig,axes = plt.subplots(1,4,figsize=(20,6))\n", + " axes = np.atleast_1d(axes)\n", + " for ax in axes:\n", + " ax.set_frame_on(False)\n", + " ax.set_yticks([])\n", + " ax.set_xticks([])\n", + " for g,ax in zip(ug,axes):\n", + " gkobs = kobs.loc[kobs.obgnme==g,:]\n", + " \n", + " arr = np.zeros_like(top_arr)\n", + " arr[gkobs.i,gkobs.j] = res.loc[gkobs.obsnme,\"modelled\"].values\n", + " ax.set_aspect(\"equal\")\n", + " label = \"\"\n", + " if \"bearing\" not in g and \"aniso\" not in g:\n", + " arr = np.log10(arr)\n", + " label = \"$log_{10}$\"\n", + " cb = ax.imshow(arr)\n", + " plt.colorbar(cb,ax=ax,label=label)\n", + " ax.set_title(\"layer: {0} group: {1}\".format(k+1,g),loc=\"left\",fontsize=15)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Stunning isn't it?! There is clearly a lot subjectivity in the form of defining the prior for the hyper parameters required to use these non-stationary geostats, but they do afford more opportunities to express (stochastic) expert knowledge. To be honest, there was a lot of experimenting with this notebook to get these figures to look this way - playing with variograms and parameter initial values and bounds a lot. You encouraged to do the same! scroll back up, change things, and \"restart kernel and run all\" - this will help build some better intution, promise...." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py new file mode 100644 index 000000000..6176fee27 --- /dev/null +++ b/pyemu/utils/tensor_utils.py @@ -0,0 +1,675 @@ +import numpy as np +import pandas as pd +from scipy.linalg import logm, expm, solve +import matplotlib as mpl + +mpl.use('Tkagg') +import matplotlib.pyplot as plt +import os + +# Set random seed for reproducibility +np.random.seed(420) + + +def main(conceptual_points_file, grid_file, field_name=['field'], layer_mode=True): + """ + Generate spatially correlated fields using tensor-based geostatistics. + + This is the main entry point for tensor-based field generation. It reads + conceptual points (control points) with anisotropy parameters and generates + correlated fields on a specified grid. + + Parameters + ---------- + conceptual_points_file : str + Path to CSV file containing conceptual points with columns: + x, y, major, anisotropy, bearing, mean_{field_name}, sd_{field_name} + Optional: layer (for layer-by-layer processing) + grid_file : str + Path to whitespace-delimited file with grid coordinates (columns: x, y, z) + field_name : list of str, default ['field'] + Name(s) of field(s) to generate. Must match column prefixes in conceptual_points + layer_mode : bool, default True + If True, process each layer independently (recommended for 3D grids) + + Returns + ------- + None + Saves results to output/ directory as layer files and plots + + Examples + -------- + >>> main('my_points.csv', 'my_grid.dat', field_name=['kh'], layer_mode=True) + """ + print("=== Tensor-Based Non-Stationary Field Generation ===") + + # Load data + conceptual_points = pd.read_csv(conceptual_points_file) + grid = pd.read_csv(grid_file, sep=r'\s+') + grid_coords = grid[['x', 'y', 'z']].values + + print(f"Loaded {len(conceptual_points)} conceptual points") + print(f"Loaded {len(grid_coords)} grid points") + + # Infer grid structure + shape = infer_grid_shape(grid_coords) + print(f"Grid shape: {shape} (nz, ny, nx)") + + for fn in field_name: + cols = ['x', 'y', 'major', 'anisotropy', 'bearing'] + cols = cols + [_ for _ in conceptual_points.columns if f'_{fn}' in _] + if layer_mode and 'layer' in conceptual_points.columns: + # Process by layer + cols = cols + ['layer'] + field = generate_field_by_layer(conceptual_points[cols], grid_coords, shape) + else: + # Process full 3D + cols = cols + ['z'] + field = generate_field_3d(conceptual_points[cols], grid_coords, shape) + + # Save results + save_results(field, shape, field_name) + + # Plot first layer + plot_layer(field, shape, layer=0, field_name=field_name) + + print("\n=== Complete ===") + print(f"Field statistics: mean={np.mean(field):.3f}, std={np.std(field):.3f}") + + +def generate_field_by_layer(conceptual_points, grid_coords, shape): + """ + Generate field processing each layer independently. + + Parameters + ---------- + conceptual_points : pd.DataFrame + Conceptual points with tensor and statistical parameters + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + shape : tuple + Grid shape as (nz, ny, nx) + + Returns + ------- + np.ndarray + Flattened field values + """ + nz, ny, nx = shape + field_3d = np.zeros(shape) + grid_3d = grid_coords.reshape(shape + (3,)) + + # Get unique layers + if 'layer' in conceptual_points.columns: + unique_layers = sorted([int(x) for x in conceptual_points['layer'].unique()]) + print(f"Processing layers: {unique_layers}") + + for layer_idx in unique_layers: + print(f"\nProcessing layer {layer_idx + 1}/{len(unique_layers)}") + + # Get grid points for this layer + layer_grid = grid_3d[layer_idx].reshape(-1, 3) + layer_coords_2d = layer_grid[:, :2] # Just x, y + + # Get conceptual points for this layer + if 'layer' in conceptual_points.columns: + if layer_idx + 1 in conceptual_points['layer'].values: + layer_cp = conceptual_points[conceptual_points['layer'] == layer_idx + 1] + elif layer_idx in conceptual_points['layer'].values: + layer_cp = conceptual_points[conceptual_points['layer'] == layer_idx] + else: + print(f" No conceptual points for layer {layer_idx + 1}, using all") + layer_cp = conceptual_points + else: + layer_cp = conceptual_points + + print(f" Using {len(layer_cp)} conceptual points") + + # Generate field for this layer + layer_field = generate_single_layer(layer_cp, layer_coords_2d) + field_3d[layer_idx] = layer_field.reshape(ny, nx) + + return field_3d.flatten() + + +def generate_single_layer(conceptual_points, grid_coords_2d): + """ + Generate field for a single 2D layer using tensor-based kriging. + + This function performs the core tensor interpolation workflow: + 1. Interpolate tensor field from conceptual points + 2. Interpolate mean and standard deviation fields + 3. Generate spatially correlated noise + 4. Combine into final lognormal field + + Parameters + ---------- + conceptual_points : pd.DataFrame + Conceptual points with required columns: x, y, major, anisotropy, bearing + Plus columns matching 'mean_*' and 'sd_*' patterns + grid_coords_2d : np.ndarray + 2D grid coordinates, shape (n_points, 2) + + Returns + ------- + np.ndarray + Generated field values at grid points + """ + cp_coords = conceptual_points[['x', 'y']].values + + # Extract the property values to interpolate + cp_means = conceptual_points.filter(like='mean').values.flatten() + + # Handle variance/sd columns + variance_cols = [col for col in conceptual_points.columns if 'variance_' in col.lower()] + sd_cols = [col for col in conceptual_points.columns if 'sd_' in col.lower()] + + if variance_cols: + cp_sd = np.sqrt(conceptual_points[variance_cols[0]].values) + elif sd_cols: + cp_sd = conceptual_points[sd_cols[0]].values + else: + raise ValueError("No variance or sd columns found") + + # Step 1: Compute tensor field ONCE + print(" Interpolating correlation tensors...") + cp_tensors = create_2d_tensors(conceptual_points) + interp_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d) + + # Step 2: Use tensor field for ALL properties + print(" Interpolating properties with tensor field...") + interp_means = tensor_aware_kriging(cp_coords, cp_means, grid_coords_2d, interp_tensors, transform='log') + interp_sd = tensor_aware_kriging(cp_coords, cp_sd, grid_coords_2d, interp_tensors) + + # Step 3: Generate correlated noise + print(" Generating correlated noise...") + white_noise = np.random.randn(len(grid_coords_2d)) + correlated_noise = generate_correlated_noise_2d(grid_coords_2d, interp_tensors, white_noise) + + # Step 4: Combine into lognormal field + field = 10 ** (np.log10(interp_means) + correlated_noise * interp_sd) + + return field + + +def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, + variogram_model='exponential', range_param=3333, + sill=1.0, nugget=0.01, background_value=0.0, + max_search_radius=1e20, min_points=1, max_neighbors=4, + transform=None, min_value=1e-8): + """ + Ordinary kriging using tensor-aware anisotropic distances. + + This function performs kriging where distances are calculated using + Mahalanobis distance with locally varying anisotropy tensors. This + avoids coordinate transformation issues while respecting local + correlation structure. + + Parameters + ---------- + cp_coords : np.ndarray + Conceptual point coordinates, shape (n_points, 2) + cp_values : np.ndarray + Values at conceptual points + grid_coords : np.ndarray + Grid coordinates for interpolation, shape (n_grid, 2) + interp_tensors : np.ndarray + Anisotropy tensors at grid points, shape (n_grid, 2, 2) + variogram_model : str, default 'exponential' + Variogram model ('exponential', 'gaussian', 'spherical') + range_param : float, default 3333 + Variogram range parameter + transform : str, optional + 'log' for log-transform of values (ensures positive results) + max_neighbors : int, default 4 + Maximum number of nearest neighbors to use + + Returns + ------- + np.ndarray + Interpolated values at grid points + """ + n_grid = len(grid_coords) + interp_values = np.full(n_grid, background_value) + + # Variogram function + def variogram(h): + if variogram_model == 'exponential': + return nugget + (sill - nugget) * (1 - np.exp(-h / range_param)) + elif variogram_model == 'gaussian': + return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2) / (range_param ** 2))) + else: # spherical + return np.where(h <= range_param, + nugget + (sill - nugget) * (1.5 * h / range_param - 0.5 * (h / range_param) ** 3), + sill) + + for i in range(n_grid): + # Get local tensor at this grid point + local_tensor = interp_tensors[i] + + try: + # Calculate anisotropic distances to all conceptual points + tensor_inv = np.linalg.inv(local_tensor) + aniso_distances = [] + + for j in range(len(cp_coords)): + dx = cp_coords[j] - grid_coords[i] + # Mahalanobis distance: sqrt(dx.T @ T^(-1) @ dx) + aniso_dist = np.sqrt(dx.T @ tensor_inv @ dx) + aniso_distances.append(aniso_dist) + + aniso_distances = np.array(aniso_distances) + + except np.linalg.LinAlgError: + # Fallback to Euclidean if tensor is singular + aniso_distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + + # Find closest neighbors using anisotropic distances + sorted_indices = np.argsort(aniso_distances) + n_candidates = min(max_neighbors or len(aniso_distances), + np.sum(aniso_distances <= max_search_radius)) + + if n_candidates < min_points: + continue + + # Get closest neighbors + closest_indices = sorted_indices[:n_candidates] + nearby_values = cp_values[closest_indices] + nearby_coords = cp_coords[closest_indices] + nearby_distances = aniso_distances[closest_indices] + + # Transform values if needed + if transform == 'log': + if min_value is None: + positive_values = nearby_values[nearby_values > 0] + min_value = np.min(positive_values) * 0.01 + nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) + else: + nearby_values_transformed = nearby_values.copy() + + # Build kriging system using anisotropic distances + C = np.zeros((n_candidates + 1, n_candidates + 1)) + c = np.zeros(n_candidates + 1) + + # Fill covariance matrix + for j in range(n_candidates): + for k in range(n_candidates): + # Distance between two conceptual points (recalculate with tensor) + dx = nearby_coords[j] - nearby_coords[k] + try: + h = np.sqrt(dx.T @ tensor_inv @ dx) # Anisotropic distance + except: + h = np.linalg.norm(dx) # Fallback to Euclidean + C[j, k] = variogram(h) + + # Unbiasedness constraint + C[j, -1] = 1 + C[-1, j] = 1 + + # Right-hand side + c[j] = variogram(nearby_distances[j]) + + c[-1] = 1 + + # Solve kriging system + try: + weights = np.linalg.solve(C, c)[:-1] + interp_values[i] = np.sum(weights * nearby_values_transformed) + except: + # Simple fallback + weights = np.exp(-nearby_distances / range_param) + interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) + + # Back-transform if needed + if transform == 'log': + interp_values = 10 ** interp_values + + return interp_values + + +def create_2d_tensors(conceptual_points): + """ + Create 2x2 anisotropy tensors from geostatistical parameters. + + Converts bearing angle, major axis length, and anisotropy ratio into + 2x2 positive definite tensors suitable for spatial interpolation. + + Parameters + ---------- + conceptual_points : pd.DataFrame + Must contain columns: major, anisotropy, bearing + + Returns + ------- + np.ndarray + Array of 2x2 tensors, shape (n_points, 2, 2) + """ + n = len(conceptual_points) + tensors = np.zeros((n, 2, 2)) + + for i in range(n): + major = conceptual_points.iloc[i]['major'] + minor = conceptual_points.iloc[i]['major'] / conceptual_points.iloc[i]['anisotropy'] + bearing = conceptual_points.iloc[i]['bearing'] + + theta = np.radians(bearing) + R = np.array([[np.cos(theta), -np.sin(theta)], + [np.sin(theta), np.cos(theta)]]) + + # Diagonal matrix with squared correlation lengths + S = np.diag([minor ** 2, major ** 2]) + tensors[i] = R @ S @ R.T + + return tensors + + +def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): + """ + Interpolate 2x2 tensors using log-Euclidean approach. + + This method ensures interpolated tensors remain positive definite + by working in the logarithmic tensor space. + + Parameters + ---------- + cp_coords : np.ndarray + Conceptual point coordinates, shape (n_points, 2) + cp_tensors : np.ndarray + Tensors at conceptual points, shape (n_points, 2, 2) + grid_coords : np.ndarray + Grid coordinates, shape (n_grid, 2) + + Returns + ------- + np.ndarray + Interpolated tensors at grid points, shape (n_grid, 2, 2) + """ + n_grid = len(grid_coords) + interp_tensors = np.zeros((n_grid, 2, 2)) + + # Convert to log space + log_tensors = np.array([logm(t) for t in cp_tensors]) + + # Interpolate each tensor component + for i in range(2): + for j in range(i, 2): + values = log_tensors[:, i, j].real + interp_values = ordinary_kriging(cp_coords, values, grid_coords) + interp_tensors[:, i, j] = interp_values + if i != j: + interp_tensors[:, j, i] = interp_values + + # Convert back from log space + for i in range(n_grid): + interp_tensors[i] = expm(interp_tensors[i]) + + return interp_tensors + + +def generate_correlated_noise_2d(grid_coords, tensors, white_noise, + n_neighbors=50, anisotropy_strength=1.0): + """ + Generate spatially correlated noise respecting tensor anisotropy. + + Creates correlated noise by weighting white noise values based on + anisotropic distances defined by local tensors. + + Parameters + ---------- + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 2) + tensors : np.ndarray + Anisotropy tensors at each grid point, shape (n_points, 2, 2) + white_noise : np.ndarray + Input white noise, shape (n_points,) + n_neighbors : int, default 50 + Number of neighbors to use for correlation + anisotropy_strength : float, default 1.0 + Strength of anisotropic correlation + + Returns + ------- + np.ndarray + Spatially correlated noise + """ + n_points = len(grid_coords) + correlated_noise = np.zeros(n_points) + + for i in range(n_points): + # Diagonalize tensor to get principal axes and correlation lengths + evals, evecs = np.linalg.eigh(tensors[i]) + if evals[1] < evals[0]: + evals, evecs = evals[::-1], evecs[:, ::-1] + + major_corr_length = np.sqrt(evals[1]) + minor_corr_length = np.sqrt(evals[0]) + + # Create anisotropic transform + scale_for_minor_axis = minor_corr_length / anisotropy_strength + scale_for_major_axis = major_corr_length * anisotropy_strength + + transform = evecs @ np.diag([scale_for_minor_axis, scale_for_major_axis]) + + # Transform all relative positions to anisotropic space + all_dx = grid_coords - grid_coords[i] + inv_transform = np.linalg.inv(transform) + dx_transformed = all_dx @ inv_transform.T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) + actual_distances = np.linalg.norm(all_dx, axis=1) + + # Select neighbors based on anisotropic distance + neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] + + # Compute correlation weights using anisotropic distances + weighted_sum = 0.0 + sum_weights = 0.0 + + for j in neighbor_indices: + weight = np.exp(-(actual_distances[j] / scale_for_major_axis) ** 2) + weighted_sum += weight * white_noise[j] + sum_weights += weight + + # Normalize + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = white_noise[i] + + return correlated_noise + + +def ordinary_kriging(cp_coords, cp_values, grid_coords, + variogram_model='exponential', range_param=10000, + sill=1.0, nugget=0.1, background_value=0.0, + max_search_radius=1e20, min_points=1, + transform=None, min_value=1e-8, max_neighbors=5): + """ + Standard ordinary kriging with variogram models and search controls. + + Parameters + ---------- + cp_coords : np.ndarray + Conceptual point coordinates + cp_values : np.ndarray + Values at conceptual points + grid_coords : np.ndarray + Grid coordinates for interpolation + max_neighbors : int, default 5 + Maximum neighbors to use (controls computational cost) + transform : str, optional + 'log' for log transformation + + Returns + ------- + np.ndarray + Interpolated values at grid points + """ + n_grid = len(grid_coords) + interp_values = np.full(n_grid, background_value) + + # Variogram function + def variogram(h): + if variogram_model == 'exponential': + return nugget + (sill - nugget) * (1 - np.exp(-h / range_param)) + elif variogram_model == 'gaussian': + return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2) / (range_param ** 2))) + else: # spherical + return np.where(h <= range_param, + nugget + (sill - nugget) * (1.5 * h / range_param - 0.5 * (h / range_param) ** 3), + sill) + + for i in range(n_grid): + distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + sorted_indices = np.argsort(distances) + n_candidates = min(max_neighbors or len(distances), + np.sum(distances <= max_search_radius)) + + if n_candidates < min_points: + continue + + closest_indices = sorted_indices[:n_candidates] + nearby_values = cp_values[closest_indices] + nearby_coords = cp_coords[closest_indices] + nearby_distances = distances[closest_indices] + + # Transform values if needed + if transform == 'log': + if min_value is None: + positive_values = nearby_values[nearby_values > 0] + min_value = np.min(positive_values) * 0.01 + nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) + else: + nearby_values_transformed = nearby_values.copy() + + # Build kriging system + C = np.zeros((n_candidates + 1, n_candidates + 1)) + c = np.zeros(n_candidates + 1) + + for j in range(n_candidates): + for k in range(n_candidates): + h = np.linalg.norm(nearby_coords[j] - nearby_coords[k]) + C[j, k] = variogram(h) + C[j, -1] = 1 + C[-1, j] = 1 + c[j] = variogram(nearby_distances[j]) + c[-1] = 1 + + # Solve + try: + weights = solve(C, c)[:-1] + interp_values[i] = np.sum(weights * nearby_values_transformed) + except: + weights = np.exp(-nearby_distances / range_param) + interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) + + # Back-transform if needed + if transform == 'log': + interp_values = 10 ** interp_values + + return interp_values + + +def generate_field_3d(conceptual_points, grid_coords, shape): + """Placeholder for full 3D field generation (not implemented).""" + raise NotImplementedError("Use layer_mode=True for now") + + +def infer_grid_shape(grid_coords): + """ + Infer grid dimensions from coordinate array. + + Parameters + ---------- + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + + Returns + ------- + tuple + Grid shape as (nz, ny, nx) + """ + x_unique = np.unique(grid_coords[:, 0]) + y_unique = np.unique(grid_coords[:, 1]) + z_unique = np.unique(grid_coords[:, 2]) + + nx = len(x_unique) + ny = len(y_unique) + nz = len(grid_coords) // (nx * ny) + + return (nz, ny, nx) + + +def save_results(field, shape, field_name): + """ + Save field as individual layer files. + + Parameters + ---------- + field : np.ndarray + Flattened field values + shape : tuple + Grid shape (nz, ny, nx) + field_name : str + Base filename for output + """ + nz, ny, nx = shape + field_3d = field.reshape(shape) + + os.makedirs('output', exist_ok=True) + + for layer in range(nz): + filename = f"output/{field_name}_layer_{layer + 1:02d}.txt" + np.savetxt(filename, field_3d[layer], fmt='%.6f') + + print(f"Saved {nz} layer files to output/") + + +def plot_layer(field, shape, layer=0, field_name='field'): + """ + Plot and save a single layer of the field. + + Parameters + ---------- + field : np.ndarray + Flattened field values + shape : tuple + Grid shape (nz, ny, nx) + layer : int, default 0 + Layer index to plot + field_name : str + Base filename for plot + """ + import matplotlib.colors as mcolors + colors = ['red', 'orange', 'yellow', 'green', 'darkblue', 'lightblue', 'white'] + custom_cmap = mcolors.LinearSegmentedColormap.from_list('custom', colors, N=256) + + nz, ny, nx = shape + field_3d = field.reshape(shape) + + plt.figure(figsize=(10, 8)) + plt.imshow(field_3d[layer], origin='lower', cmap=custom_cmap) + plt.colorbar(label='Parameter Value') + plt.title(f'Layer {layer + 1}') + plt.xlabel('X') + plt.ylabel('Y') + + os.makedirs('output', exist_ok=True) + plt.savefig(f'output/{field_name}_layer_{layer + 1:02d}.png', dpi=150) + plt.close() + + +# Example usage +if __name__ == "__main__": + """ + Example usage of the tensor-based field generator. + + Required file formats: + - conceptual_points.csv: CSV with columns x,y,major,anisotropy,bearing,mean_kh,sd_kh + - grid.dat: Whitespace-delimited with columns x,y,z + """ + main( + conceptual_points_file='data/conceptual_points.csv', + grid_file='data/grid.dat', + field_name=['kh'], + layer_mode=True + ) \ No newline at end of file From fb6513ff4bb20cada1720fa904b74498d061531f Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 9 Jul 2025 10:39:24 +1200 Subject: [PATCH 03/36] working version, minor issues with array shape, working on making everything 2d for easier comprehension --- pyemu/utils/tensor_utils.py | 1489 +++++++++++++++++++++++++++++------ 1 file changed, 1232 insertions(+), 257 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 6176fee27..4acacd023 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -7,11 +7,8 @@ import matplotlib.pyplot as plt import os -# Set random seed for reproducibility -np.random.seed(420) - -def main(conceptual_points_file, grid_file, field_name=['field'], layer_mode=True): +def main(conceptual_points_file, grid_file, zone_file=None, field_name=['field'], layer_mode=True): """ Generate spatially correlated fields using tensor-based geostatistics. @@ -25,8 +22,10 @@ def main(conceptual_points_file, grid_file, field_name=['field'], layer_mode=Tru Path to CSV file containing conceptual points with columns: x, y, major, anisotropy, bearing, mean_{field_name}, sd_{field_name} Optional: layer (for layer-by-layer processing) - grid_file : str + grid_file : str Path to whitespace-delimited file with grid coordinates (columns: x, y, z) + zone_file : str, optional + Path to whitespace-delimited array file with zones field_name : list of str, default ['field'] Name(s) of field(s) to generate. Must match column prefixes in conceptual_points layer_mode : bool, default True @@ -47,6 +46,7 @@ def main(conceptual_points_file, grid_file, field_name=['field'], layer_mode=Tru conceptual_points = pd.read_csv(conceptual_points_file) grid = pd.read_csv(grid_file, sep=r'\s+') grid_coords = grid[['x', 'y', 'z']].values + zones = np.loadtxt(zone_file) print(f"Loaded {len(conceptual_points)} conceptual points") print(f"Loaded {len(grid_coords)} grid points") @@ -56,12 +56,13 @@ def main(conceptual_points_file, grid_file, field_name=['field'], layer_mode=Tru print(f"Grid shape: {shape} (nz, ny, nx)") for fn in field_name: - cols = ['x', 'y', 'major', 'anisotropy', 'bearing'] + cols = ['name', 'x', 'y', 'major', 'anisotropy', 'bearing'] cols = cols + [_ for _ in conceptual_points.columns if f'_{fn}' in _] if layer_mode and 'layer' in conceptual_points.columns: # Process by layer cols = cols + ['layer'] - field = generate_field_by_layer(conceptual_points[cols], grid_coords, shape) + field = generate_field_by_layer(conceptual_points[cols], grid_coords, + shape, zones=zones) else: # Process full 3D cols = cols + ['z'] @@ -77,7 +78,7 @@ def main(conceptual_points_file, grid_file, field_name=['field'], layer_mode=Tru print(f"Field statistics: mean={np.mean(field):.3f}, std={np.std(field):.3f}") -def generate_field_by_layer(conceptual_points, grid_coords, shape): +def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None): """ Generate field processing each layer independently. @@ -105,166 +106,542 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape): print(f"Processing layers: {unique_layers}") for layer_idx in unique_layers: - print(f"\nProcessing layer {layer_idx + 1}/{len(unique_layers)}") + print(f"\nProcessing layer {layer_idx}/{len(unique_layers)}") # Get grid points for this layer layer_grid = grid_3d[layer_idx].reshape(-1, 3) layer_coords_2d = layer_grid[:, :2] # Just x, y - # Get conceptual points for this layer if 'layer' in conceptual_points.columns: - if layer_idx + 1 in conceptual_points['layer'].values: - layer_cp = conceptual_points[conceptual_points['layer'] == layer_idx + 1] - elif layer_idx in conceptual_points['layer'].values: - layer_cp = conceptual_points[conceptual_points['layer'] == layer_idx] - else: - print(f" No conceptual points for layer {layer_idx + 1}, using all") + layer_cp = conceptual_points[conceptual_points['layer'] == layer_idx] + if len(layer_cp) == 0: + print(f" No conceptual points for layer {layer_idx}, using all") layer_cp = conceptual_points else: layer_cp = conceptual_points - print(f" Using {len(layer_cp)} conceptual points") - # Generate field for this layer - layer_field = generate_single_layer(layer_cp, layer_coords_2d) + layer_field = generate_single_layer_zone_based(layer_cp, layer_coords_2d, + zones=zones, shape=(ny, nx)) field_3d[layer_idx] = layer_field.reshape(ny, nx) return field_3d.flatten() -def generate_single_layer(conceptual_points, grid_coords_2d): +def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, zones=None, shape=None): """ - Generate field for a single 2D layer using tensor-based kriging. + Modified generate_single_layer with zone-based tensor interpolation. + """ + cp_coords = conceptual_points[['x', 'y']].values + cp_means = conceptual_points.filter(like='mean').values.flatten() + + sd_cols = [col for col in conceptual_points.columns if 'sd_' in col.lower()] + if sd_cols: + cp_sd = conceptual_points[sd_cols[0]].values + else: + raise ValueError("No sd columns found (expected format: 'sd_fieldname')") + + if not shape: + x_unique = np.unique(grid_coords_2d[:, 0]) + y_unique = np.unique(grid_coords_2d[:, 1]) + shape = (len(y_unique), len(x_unique)) + print(f" Inferred grid dimensions: ny={shape[0]}, nx={shape[1]}") + + # Step 1: Create geological tensors from conceptual points + print(" Creating geological correlation tensors...") + major = conceptual_points['major'].values + minor = major / conceptual_points['anisotropy'].values + #bearing_rad = np.radians(conceptual_points['bearing'].values) + # Better normalization that preserves direction + def normalize_bearing(bearing): + # Convert to 0-360 first + bearing = bearing % 360 + # Don't automatically fold to 0-180, preserve the original direction choice + return bearing + + bearings_normalized = [normalize_bearing(b) for b in conceptual_points['bearing'].values] + bearing_rad = np.radians(bearings_normalized) + cp_tensors = create_2d_tensors(bearing_rad, major, minor) + + + # Debug: Test tensor creation with first few points + print(" Tensor creation verification:") + for i in range(min(3, len(cp_tensors))): + original_bearing = conceptual_points['bearing'].values[i] + extracted_bearing = get_tensor_bearing(cp_tensors[i], original_bearing) + print(f" Point {i}: Input bearing={original_bearing:.1f}°, Extracted={extracted_bearing:.1f}°") + + # Step 2: Zone-based tensor interpolation + if zones is not None: + print(" Interpolating geological tensors by zone...") + # geological_tensors = interpolate_tensors_by_zone( + # cp_coords, cp_tensors, grid_coords_2d, zones, shape + # ) + geological_tensors = interpolate_tensors_by_zone_nearest( + cp_coords, cp_tensors, grid_coords_2d, zones, shape + ) + else: + print(" Interpolating geological tensors globally...") + geological_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d) + + # Step 3: Interpolate means using zone-aware kriging + print(" Interpolating means with geological structure...") + interp_means = tensor_aware_kriging( + cp_coords, cp_means, grid_coords_2d, geological_tensors, + variogram_model='exponential', sill=1.0, nugget=0.1, + background_value=np.mean(cp_means), max_search_radius=1e20, + min_points=3, transform='log', min_value=1e-8, max_neighbors=8, + zones=zones, shape=shape + ) + + # Step 4: Create boundary-enhanced tensors for variance + # changes anisotropic distance, potential long-reaching unintended consequences + # if zones is not None: + # print(" Creating boundary-enhanced tensors for variance...") + # enhanced_sd_tensors = create_boundary_enhanced_tensors( + # geological_tensors, zones, shape, + # boundary_anisotropy=10.0, boundary_major_scale=2.0, transition_cells=5 + # ) + # else: + # enhanced_sd_tensors = geological_tensors + + # Step 5: Interpolate sd using GEOLOGICAL tensors, then enhance variance + print(" Interpolating standard deviation...") + interp_sd = tensor_aware_kriging( + cp_coords, cp_sd, grid_coords_2d, geological_tensors, # Use geological tensors + variogram_model='exponential', sill=1.0, nugget=0.1, + background_value=np.mean(cp_sd), max_search_radius=1e20, + min_points=3, transform=None, min_value=1e-8, max_neighbors=8 + ) + if zones is not None: + print(" Enhancing variance at geological boundaries...") + interp_sd = create_boundary_enhanced_scalar( + interp_sd, zones, shape, + peak_increase=0.3, transition_cells=5 + ) + + # Step 6: Generate correlated noise + print(" Generating correlated noise...") + np.random.seed(420) + white_noise = np.random.randn(len(grid_coords_2d)) + correlated_noise = generate_correlated_noise_2d(grid_coords_2d, geological_tensors, white_noise) + + # Step 7: Combine into lognormal field + field = 10 ** (np.log10(interp_means) + correlated_noise * interp_sd) + + return field + + +def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zones, shape): + """Zone-based nearest neighbor tensor assignment - no interpolation.""" + n_grid = len(grid_coords) + ny, nx = shape + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + # Get zone for each conceptual point + cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones, shape) + + # Initialize output tensors + geological_tensors = np.zeros((n_grid, 2, 2)) + + # Get unique zones + unique_zones = np.unique(zones_2d) + + for zone_id in unique_zones: + print(f" Assigning nearest neighbor tensors for zone {zone_id}") + + # Find conceptual points in this zone + zone_cp_mask = cp_zones == zone_id + zone_cp_coords = cp_coords[zone_cp_mask] + zone_cp_tensors = cp_tensors[zone_cp_mask] + + # Find grid points in this zone + zone_grid_indices = [] + for i in range(n_grid): + row, col = divmod(i, nx) + if zones_2d[row, col] == zone_id: + zone_grid_indices.append(i) + + zone_grid_indices = np.array(zone_grid_indices) + + if len(zone_cp_coords) > 0: + # Assign nearest neighbor tensor (no interpolation) + for grid_idx in zone_grid_indices: + distances = np.linalg.norm(zone_cp_coords - grid_coords[grid_idx], axis=1) + nearest_cp = np.argmin(distances) + geological_tensors[grid_idx] = zone_cp_tensors[nearest_cp] + else: + # No conceptual points in zone - use default tensor + print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") + default_tensor = np.eye(2) * 1000 ** 2 + geological_tensors[zone_grid_indices] = default_tensor - This function performs the core tensor interpolation workflow: - 1. Interpolate tensor field from conceptual points - 2. Interpolate mean and standard deviation fields - 3. Generate spatially correlated noise - 4. Combine into final lognormal field + return geological_tensors + + +def create_boundary_enhanced_scalar(base_field, zones, shape, + peak_increase=0.3, transition_cells=5): + """ + Increase scalar field values at geological zone boundaries with smooth transition. Parameters ---------- - conceptual_points : pd.DataFrame - Conceptual points with required columns: x, y, major, anisotropy, bearing - Plus columns matching 'mean_*' and 'sd_*' patterns - grid_coords_2d : np.ndarray - 2D grid coordinates, shape (n_points, 2) + base_field : np.ndarray + Base scalar values + zones : np.ndarray + Zone IDs + shape : tuple + Grid shape (ny, nx) + peak_increase : float + Amount to ADD at boundary cells (for log-space sd) + transition_cells : int + Number of cells over which to transition from peak to zero enhancement Returns ------- np.ndarray - Generated field values at grid points + Enhanced scalar field with smooth boundary transitions """ - cp_coords = conceptual_points[['x', 'y']].values + ny, nx = shape - # Extract the property values to interpolate - cp_means = conceptual_points.filter(like='mean').values.flatten() + # Ensure zones are 2D + if zones.shape == (ny, nx): + zones_2d = zones + else: + zones_2d = zones.reshape(shape) + + # Detect boundaries + boundary_mask, _ = detect_zone_boundaries(zones_2d) + + # Calculate distance to nearest boundary for each cell + enhanced = base_field.copy() + enhancement_count = 0 + + for i in range(ny): + for j in range(nx): + # Find minimum distance to any boundary + min_dist_to_boundary = transition_cells + 1 # Start beyond transition + + for di in range(-transition_cells, transition_cells + 1): + for dj in range(-transition_cells, transition_cells + 1): + check_i, check_j = i + di, j + dj + if (0 <= check_i < ny and 0 <= check_j < nx and + boundary_mask[check_i, check_j]): + dist = np.sqrt(di ** 2 + dj ** 2) + min_dist_to_boundary = min(min_dist_to_boundary, dist) + + # Apply enhancement based on distance + if min_dist_to_boundary <= transition_cells: + if min_dist_to_boundary == 0: + # At boundary - full enhancement + enhancement = peak_increase + else: + # Smooth transition - linear decay + enhancement = peak_increase * (1 - min_dist_to_boundary / transition_cells) + + enhanced[i, j] += enhancement + enhancement_count += 1 + + print(f" Enhanced {enhancement_count} points near geological boundaries") + return enhanced.flatten() + +def create_boundary_enhanced_tensors(geological_tensors, zones, shape, + boundary_anisotropy=10.0, boundary_major_scale=2.0, + transition_cells=3): + """ + Enhance geological tensors at zone boundaries with increased anisotropy. + Uses distance-based transition from geological to boundary behavior. - # Handle variance/sd columns - variance_cols = [col for col in conceptual_points.columns if 'variance_' in col.lower()] - sd_cols = [col for col in conceptual_points.columns if 'sd_' in col.lower()] + Parameters + ---------- + geological_tensors : np.ndarray + Base geological tensors, shape (n_points, 2, 2) + zones : np.ndarray + Zone IDs, shape (n_points,) or (ny, nx) + shape : tuple + Grid shape (ny, nx) + boundary_anisotropy : float + Anisotropy ratio at boundaries (major/minor) + boundary_major_scale : float + Scale factor for major axis at boundaries + transition_cells : int + Number of cells over which to transition from geological to boundary - if variance_cols: - cp_sd = np.sqrt(conceptual_points[variance_cols[0]].values) - elif sd_cols: - cp_sd = conceptual_points[sd_cols[0]].values + Returns + ------- + np.ndarray + Enhanced tensors, shape (n_points, 2, 2) + """ + ny, nx = shape + + # Ensure zones are 2D + if zones.shape == (ny, nx): + zones_2d = zones else: - raise ValueError("No variance or sd columns found") + zones_2d = zones.reshape(shape) - # Step 1: Compute tensor field ONCE - print(" Interpolating correlation tensors...") - cp_tensors = create_2d_tensors(conceptual_points) - interp_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d) + # Detect boundary points and orientations + boundary_mask, boundary_directions = detect_zone_boundaries(zones_2d) - # Step 2: Use tensor field for ALL properties - print(" Interpolating properties with tensor field...") - interp_means = tensor_aware_kriging(cp_coords, cp_means, grid_coords_2d, interp_tensors, transform='log') - interp_sd = tensor_aware_kriging(cp_coords, cp_sd, grid_coords_2d, interp_tensors) + enhanced_tensors = geological_tensors.copy() + boundary_count = 0 - # Step 3: Generate correlated noise - print(" Generating correlated noise...") - white_noise = np.random.randn(len(grid_coords_2d)) - correlated_noise = generate_correlated_noise_2d(grid_coords_2d, interp_tensors, white_noise) + for i in range(len(geological_tensors)): + row, col = divmod(i, nx) - # Step 4: Combine into lognormal field - field = 10 ** (np.log10(interp_means) + correlated_noise * interp_sd) + # Calculate minimum distance to any boundary within transition zone + min_dist_to_boundary = transition_cells + 1 # Start beyond transition - return field + for dr in range(-transition_cells, transition_cells + 1): + for dc in range(-transition_cells, transition_cells + 1): + check_row, check_col = row + dr, col + dc + if (0 <= check_row < ny and 0 <= check_col < nx and + boundary_mask[check_row, check_col]): + dist = np.sqrt(dr ** 2 + dc ** 2) + min_dist_to_boundary = min(min_dist_to_boundary, dist) + # Skip if far from boundaries + if min_dist_to_boundary > transition_cells: + continue -def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, - variogram_model='exponential', range_param=3333, - sill=1.0, nugget=0.01, background_value=0.0, - max_search_radius=1e20, min_points=1, max_neighbors=4, - transform=None, min_value=1e-8): + boundary_count += 1 + + # Get boundary direction (use closest boundary point's direction) + boundary_dir = boundary_directions[row, col] + if np.linalg.norm(boundary_dir) < 1e-10: + # If current cell isn't boundary, find closest boundary direction + for dr in range(-transition_cells, transition_cells + 1): + for dc in range(-transition_cells, transition_cells + 1): + check_row, check_col = row + dr, col + dc + if (0 <= check_row < ny and 0 <= check_col < nx and + boundary_mask[check_row, check_col]): + boundary_dir = boundary_directions[check_row, check_col] + if np.linalg.norm(boundary_dir) > 1e-10: + break + if np.linalg.norm(boundary_dir) > 1e-10: + break + + if np.linalg.norm(boundary_dir) < 1e-10: + continue + + # Normalize direction + boundary_dir = boundary_dir / np.linalg.norm(boundary_dir) + + # Create boundary tensor + cos_theta = boundary_dir[0] + sin_theta = boundary_dir[1] + + # rotate clockwise + R = np.array([[cos_theta, sin_theta], + [-sin_theta, cos_theta]]) + + eigenvals = np.linalg.eigvals(geological_tensors[i]) + dominant_length = np.sqrt(np.max(eigenvals)) + + major_length = dominant_length * boundary_major_scale + minor_length = major_length / boundary_anisotropy + + S_boundary = np.diag([minor_length ** 2, major_length ** 2]) + boundary_tensor = R @ S_boundary @ R.T + + # Distance-based blending with eigenvalue interpolation + if min_dist_to_boundary == 0: + # At boundary - pure boundary tensor + enhanced_tensors[i] = boundary_tensor + else: + # Smooth transition - blend eigenvalues to preserve positive definiteness + geo_weight = min_dist_to_boundary / transition_cells + + geo_vals, geo_vecs = np.linalg.eigh(geological_tensors[i]) + bound_vals, bound_vecs = np.linalg.eigh(boundary_tensor) + + # Interpolate eigenvalues, use boundary orientation + mixed_vals = geo_weight * geo_vals + (1 - geo_weight) * bound_vals + enhanced_tensors[i] = bound_vecs @ np.diag(mixed_vals) @ bound_vecs.T + + print(f" Enhanced {boundary_count} tensors at geological boundaries") + return enhanced_tensors + + +def detect_zone_boundaries(zones_2d): """ - Ordinary kriging using tensor-aware anisotropic distances. + Detect boundaries between geological zones and estimate local orientations. - This function performs kriging where distances are calculated using - Mahalanobis distance with locally varying anisotropy tensors. This - avoids coordinate transformation issues while respecting local - correlation structure. + Parameters + ---------- + zones_2d : np.ndarray + Zone IDs, shape (ny, nx) + + Returns + ------- + tuple + (boundary_mask, boundary_directions) where: + - boundary_mask: bool array, shape (ny, nx) + - boundary_directions: direction vectors, shape (ny, nx, 2) + """ + ny, nx = zones_2d.shape + boundary_mask = np.zeros((ny, nx), dtype=bool) + boundary_directions = np.zeros((ny, nx, 2)) + + # Find boundary points (any point with different-zone neighbors) + for i in range(ny): + for j in range(nx): + current_zone = zones_2d[i, j] + + # Check 8-connected neighbors + for di in [-1, 0, 1]: + for dj in [-1, 0, 1]: + if di == 0 and dj == 0: + continue + ni, nj = i + di, j + dj + if 0 <= ni < ny and 0 <= nj < nx: + if zones_2d[ni, nj] != current_zone: + boundary_mask[i, j] = True + break + if boundary_mask[i, j]: + break + + # Estimate boundary orientations + for i in range(ny): + for j in range(nx): + if not boundary_mask[i, j]: + continue + + boundary_directions[i, j] = estimate_boundary_direction(zones_2d, i, j) + + return boundary_mask, boundary_directions + + +def estimate_boundary_direction(zones_2d, center_i, center_j, radius=2): + """ + Estimate local boundary direction at a point. + Returns direction vector in geological coordinates (North = 0°). Parameters ---------- - cp_coords : np.ndarray - Conceptual point coordinates, shape (n_points, 2) - cp_values : np.ndarray - Values at conceptual points - grid_coords : np.ndarray - Grid coordinates for interpolation, shape (n_grid, 2) - interp_tensors : np.ndarray - Anisotropy tensors at grid points, shape (n_grid, 2, 2) - variogram_model : str, default 'exponential' - Variogram model ('exponential', 'gaussian', 'spherical') - range_param : float, default 3333 - Variogram range parameter - transform : str, optional - 'log' for log-transform of values (ensures positive results) - max_neighbors : int, default 4 - Maximum number of nearest neighbors to use + zones_2d : np.ndarray + Zone IDs + center_i, center_j : int + Center point coordinates + radius : int + Search radius Returns ------- np.ndarray - Interpolated values at grid points + Boundary direction vector [east_component, north_component] + where North = [0, 1], East = [1, 0] (geological convention) + """ + ny, nx = zones_2d.shape + center_zone = zones_2d[center_i, center_j] + + same_zone_points = [] + diff_zone_points = [] + + # Collect points within radius + for di in range(-radius, radius + 1): + for dj in range(-radius, radius + 1): + if di == 0 and dj == 0: + continue + + ni, nj = center_i + di, center_j + dj + if 0 <= ni < ny and 0 <= nj < nx: + if zones_2d[ni, nj] == center_zone: + same_zone_points.append([dj, di]) # x=dj (east), y=di (south in array coords) + else: + diff_zone_points.append([dj, di]) + + if len(same_zone_points) < 1 or len(diff_zone_points) < 1: + return np.array([0.0, 1.0]) # Default north (geological N=0°) + + same_zone_points = np.array(same_zone_points) + diff_zone_points = np.array(diff_zone_points) + + # Find direction separating the groups + same_centroid = np.mean(same_zone_points, axis=0) + diff_centroid = np.mean(diff_zone_points, axis=0) + + separation_vector = diff_centroid - same_centroid + if np.linalg.norm(separation_vector) < 1e-10: + return np.array([0.0, 1.0]) # Default north + + # Convert to geological coordinates and get boundary direction + # Array coordinates: x=east, y=south; Geological: x=east, y=north + sep_east = separation_vector[0] + sep_south = separation_vector[1] + sep_north = -sep_south # Flip y-axis: array south becomes geological north + + # Normalize separation vector in geological coordinates + sep_geo = np.array([sep_east, sep_north]) + sep_geo_normalized = sep_geo / np.linalg.norm(sep_geo) + + # Boundary runs perpendicular to separation (rotate 90° counterclockwise) + # For vector [east, north], perpendicular is [-north, east] + boundary_direction = np.array([-sep_geo_normalized[1], sep_geo_normalized[0]]) + + return boundary_direction + + +def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, + variogram_model='exponential', sill=1.0, nugget=0.01, + background_value=0.0, max_search_radius=1e20, min_points=1, + max_neighbors=4, transform=None, min_value=1e-8, + zones=None, shape=None): + """ + Ordinary kriging using tensor-aware anisotropic distances. """ n_grid = len(grid_coords) interp_values = np.full(n_grid, background_value) - # Variogram function - def variogram(h): - if variogram_model == 'exponential': - return nugget + (sill - nugget) * (1 - np.exp(-h / range_param)) - elif variogram_model == 'gaussian': - return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2) / (range_param ** 2))) - else: # spherical - return np.where(h <= range_param, - nugget + (sill - nugget) * (1.5 * h / range_param - 0.5 * (h / range_param) ** 3), - sill) + # Calculate zone for each conceptual point (once, outside the main loop) + if zones is not None: + if shape is None: + shape = zones.shape + ny, nx = shape + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + cp_zones = [] + for coord in cp_coords: + # Find closest grid point to get zone + distances = np.sum((grid_coords - coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + row, col = divmod(closest_idx, nx) + cp_zones.append(zones_2d[row, col]) + cp_zones = np.array(cp_zones) for i in range(n_grid): - # Get local tensor at this grid point local_tensor = interp_tensors[i] + if zones is not None: + # Get current zone and filter conceptual points + row, col = divmod(i, shape[1]) + current_zone = zones.reshape(shape)[row, col] + + # Filter to same zone, fallback to all if insufficient points found + zone_mask = cp_zones == current_zone + if np.sum(zone_mask) >= min_points: + cp_coords_filtered = cp_coords[zone_mask] + cp_values_filtered = cp_values[zone_mask] + else: + cp_coords_filtered = cp_coords + cp_values_filtered = cp_values + else: + cp_coords_filtered = cp_coords + cp_values_filtered = cp_values try: - # Calculate anisotropic distances to all conceptual points tensor_inv = np.linalg.inv(local_tensor) aniso_distances = [] - for j in range(len(cp_coords)): - dx = cp_coords[j] - grid_coords[i] - # Mahalanobis distance: sqrt(dx.T @ T^(-1) @ dx) + for j in range(len(cp_coords_filtered)): + dx = cp_coords_filtered[j] - grid_coords[i] aniso_dist = np.sqrt(dx.T @ tensor_inv @ dx) aniso_distances.append(aniso_dist) aniso_distances = np.array(aniso_distances) except np.linalg.LinAlgError: - # Fallback to Euclidean if tensor is singular - aniso_distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + aniso_distances = np.linalg.norm(cp_coords_filtered - grid_coords[i], axis=1) - # Find closest neighbors using anisotropic distances sorted_indices = np.argsort(aniso_distances) n_candidates = min(max_neighbors or len(aniso_distances), np.sum(aniso_distances <= max_search_radius)) @@ -272,13 +649,11 @@ def variogram(h): if n_candidates < min_points: continue - # Get closest neighbors closest_indices = sorted_indices[:n_candidates] - nearby_values = cp_values[closest_indices] - nearby_coords = cp_coords[closest_indices] + nearby_values = cp_values_filtered[closest_indices] + nearby_coords = cp_coords_filtered[closest_indices] nearby_distances = aniso_distances[closest_indices] - # Transform values if needed if transform == 'log': if min_value is None: positive_values = nearby_values[nearby_values > 0] @@ -287,110 +662,206 @@ def variogram(h): else: nearby_values_transformed = nearby_values.copy() - # Build kriging system using anisotropic distances + def variogram(h): + if variogram_model == 'exponential': + return nugget + (sill - nugget) * (1 - np.exp(-h)) + elif variogram_model == 'gaussian': + return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2))) + else: # spherical + return np.where(h <= 1.0, + nugget + (sill - nugget) * (1.5 * h - 0.5 * h ** 3), + sill) + C = np.zeros((n_candidates + 1, n_candidates + 1)) c = np.zeros(n_candidates + 1) - # Fill covariance matrix for j in range(n_candidates): for k in range(n_candidates): - # Distance between two conceptual points (recalculate with tensor) dx = nearby_coords[j] - nearby_coords[k] try: - h = np.sqrt(dx.T @ tensor_inv @ dx) # Anisotropic distance + h = np.sqrt(dx.T @ tensor_inv @ dx) except: - h = np.linalg.norm(dx) # Fallback to Euclidean + h = np.linalg.norm(dx) C[j, k] = variogram(h) - # Unbiasedness constraint C[j, -1] = 1 C[-1, j] = 1 - - # Right-hand side c[j] = variogram(nearby_distances[j]) c[-1] = 1 - # Solve kriging system try: - weights = np.linalg.solve(C, c)[:-1] - interp_values[i] = np.sum(weights * nearby_values_transformed) + cond_num = np.linalg.cond(C) + if cond_num > 1e12: # Matrix is poorly conditioned + # Fall back to inverse distance weighting + weights = np.exp(-nearby_distances) + weights = weights / np.sum(weights) + interp_values[i] = np.sum(weights * nearby_values_transformed) + else: + weights = np.linalg.solve(C, c)[:-1] + interp_values[i] = np.sum(weights * nearby_values_transformed) except: - # Simple fallback - weights = np.exp(-nearby_distances / range_param) + # Existing fallback for any other errors + weights = np.exp(-nearby_distances) interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) - # Back-transform if needed if transform == 'log': interp_values = 10 ** interp_values return interp_values -def create_2d_tensors(conceptual_points): - """ - Create 2x2 anisotropy tensors from geostatistical parameters. - - Converts bearing angle, major axis length, and anisotropy ratio into - 2x2 positive definite tensors suitable for spatial interpolation. - - Parameters - ---------- - conceptual_points : pd.DataFrame - Must contain columns: major, anisotropy, bearing +def create_2d_tensors(theta, major, minor): + """Create 2x2 anisotropy tensors from geostatistical parameters.""" + theta = np.atleast_1d(theta) + major = np.atleast_1d(major) + minor = np.atleast_1d(minor) - Returns - ------- - np.ndarray - Array of 2x2 tensors, shape (n_points, 2, 2) - """ - n = len(conceptual_points) + n = len(theta) tensors = np.zeros((n, 2, 2)) for i in range(n): - major = conceptual_points.iloc[i]['major'] - minor = conceptual_points.iloc[i]['major'] / conceptual_points.iloc[i]['anisotropy'] - bearing = conceptual_points.iloc[i]['bearing'] + # For geological bearing (clockwise from north): + # North = [0, 1], East = [1, 0] + # Bearing 5° should give direction [x, y] = [sin(5°), cos(5°)] = [0.087, 0.996] + + bearing_rad = theta[i] + + # Direction vector for this bearing + east_comp = np.sin(bearing_rad) # X component + north_comp = np.cos(bearing_rad) # Y component - theta = np.radians(bearing) - R = np.array([[np.cos(theta), -np.sin(theta)], - [np.sin(theta), np.cos(theta)]]) + # Create rotation matrix to align [1,0] with bearing direction + cos_t = north_comp + sin_t = east_comp - # Diagonal matrix with squared correlation lengths - S = np.diag([minor ** 2, major ** 2]) + # clockwise rotation by angle theta + R = np.array([[cos_t, sin_t], + [-sin_t, cos_t]]) + S = np.diag([minor[i] ** 2, major[i] ** 2]) tensors[i] = R @ S @ R.T - return tensors + return tensors.squeeze() if n == 1 else tensors -def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): +def get_tensor_bearing(tensor, expected_bearing=None): + """Extract geological bearing from tensor.""" + eigenvals, eigenvecs = np.linalg.eigh(tensor) + max_idx = np.argmax(eigenvals) + major_eigenvec = eigenvecs[:, max_idx] + + # Swap components to match geological convention: [north, east] instead of [east, north] + north_component = major_eigenvec[1] # y-component is north + east_component = major_eigenvec[0] # x-component is east + + # Calculate bearing from north (geological convention) + angle1 = np.degrees(np.arctan2(east_component, north_component)) + if angle1 < 0: + angle1 += 360 + angle2 = (angle1 + 180) % 360 + + # Choose correct direction if expected bearing provided + if expected_bearing is not None: + diff1 = min(abs(angle1 - expected_bearing), abs(angle1 - expected_bearing + 360), + abs(angle1 - expected_bearing - 360)) + diff2 = min(abs(angle2 - expected_bearing), abs(angle2 - expected_bearing + 360), + abs(angle2 - expected_bearing - 360)) + return angle1 if diff1 < diff2 else angle2 + + return angle1 + + +def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones, shape): + """ + Extract zone assignment logic from tensor_aware_kriging into reusable function. """ - Interpolate 2x2 tensors using log-Euclidean approach. + ny, nx = shape + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + cp_zones = [] + for coord in cp_coords: + # Find closest grid point to get zone + distances = np.sum((grid_coords - coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + row, col = divmod(closest_idx, nx) + cp_zones.append(zones_2d[row, col]) - This method ensures interpolated tensors remain positive definite - by working in the logarithmic tensor space. + return np.array(cp_zones) + + +def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, shape): + """ + Zone-aware tensor interpolation - interpolate tensors within each zone separately. Parameters ---------- cp_coords : np.ndarray - Conceptual point coordinates, shape (n_points, 2) + Conceptual point coordinates cp_tensors : np.ndarray - Tensors at conceptual points, shape (n_points, 2, 2) + Conceptual point tensors grid_coords : np.ndarray - Grid coordinates, shape (n_grid, 2) + Grid coordinates + zones : np.ndarray + Zone array + shape : tuple + Grid shape (ny, nx) Returns ------- np.ndarray - Interpolated tensors at grid points, shape (n_grid, 2, 2) + Interpolated tensors, shape (n_grid, 2, 2) """ n_grid = len(grid_coords) + ny, nx = shape + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + # Get zone for each conceptual point + cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones, shape) + + # Initialize output tensors + geological_tensors = np.zeros((n_grid, 2, 2)) + + # Get unique zones + unique_zones = np.unique(zones_2d) + + for zone_id in unique_zones: + print(f" Interpolating tensors for zone {zone_id}") + + # Find conceptual points in this zone + zone_cp_mask = cp_zones == zone_id + zone_cp_coords = cp_coords[zone_cp_mask] + zone_cp_tensors = cp_tensors[zone_cp_mask] + + # Find grid points in this zone + zone_grid_indices = [] + for i in range(n_grid): + row, col = divmod(i, nx) + if zones_2d[row, col] == zone_id: + zone_grid_indices.append(i) + + zone_grid_indices = np.array(zone_grid_indices) + zone_grid_coords = grid_coords[zone_grid_indices] + + if len(zone_cp_coords) > 0: + # Interpolate tensors within zone using only zone's conceptual points + zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords) + geological_tensors[zone_grid_indices] = zone_tensors + else: + # No conceptual points in zone - use default tensor or nearest zone + print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") + default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length + geological_tensors[zone_grid_indices] = default_tensor + + return geological_tensors + + +def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): + """Interpolate 2x2 tensors using log-Euclidean approach.""" + n_grid = len(grid_coords) interp_tensors = np.zeros((n_grid, 2, 2)) - # Convert to log space log_tensors = np.array([logm(t) for t in cp_tensors]) - # Interpolate each tensor component for i in range(2): for j in range(i, 2): values = log_tensors[:, i, j].real @@ -399,7 +870,6 @@ def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): if i != j: interp_tensors[:, j, i] = interp_values - # Convert back from log space for i in range(n_grid): interp_tensors[i] = expm(interp_tensors[i]) @@ -408,35 +878,11 @@ def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): def generate_correlated_noise_2d(grid_coords, tensors, white_noise, n_neighbors=50, anisotropy_strength=1.0): - """ - Generate spatially correlated noise respecting tensor anisotropy. - - Creates correlated noise by weighting white noise values based on - anisotropic distances defined by local tensors. - - Parameters - ---------- - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 2) - tensors : np.ndarray - Anisotropy tensors at each grid point, shape (n_points, 2, 2) - white_noise : np.ndarray - Input white noise, shape (n_points,) - n_neighbors : int, default 50 - Number of neighbors to use for correlation - anisotropy_strength : float, default 1.0 - Strength of anisotropic correlation - - Returns - ------- - np.ndarray - Spatially correlated noise - """ + """Generate spatially correlated noise respecting tensor anisotropy.""" n_points = len(grid_coords) correlated_noise = np.zeros(n_points) for i in range(n_points): - # Diagonalize tensor to get principal axes and correlation lengths evals, evecs = np.linalg.eigh(tensors[i]) if evals[1] < evals[0]: evals, evecs = evals[::-1], evecs[:, ::-1] @@ -444,32 +890,28 @@ def generate_correlated_noise_2d(grid_coords, tensors, white_noise, major_corr_length = np.sqrt(evals[1]) minor_corr_length = np.sqrt(evals[0]) - # Create anisotropic transform scale_for_minor_axis = minor_corr_length / anisotropy_strength scale_for_major_axis = major_corr_length * anisotropy_strength transform = evecs @ np.diag([scale_for_minor_axis, scale_for_major_axis]) - # Transform all relative positions to anisotropic space all_dx = grid_coords - grid_coords[i] inv_transform = np.linalg.inv(transform) dx_transformed = all_dx @ inv_transform.T aniso_distances = np.linalg.norm(dx_transformed, axis=1) - actual_distances = np.linalg.norm(all_dx, axis=1) - # Select neighbors based on anisotropic distance neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] - # Compute correlation weights using anisotropic distances weighted_sum = 0.0 sum_weights = 0.0 for j in neighbor_indices: - weight = np.exp(-(actual_distances[j] / scale_for_major_axis) ** 2) + # Use anisotropic distance instead of actual distance for weight calculation + # This respects the tensor-defined correlation structure + weight = np.exp(-(aniso_distances[j]) ** 2) weighted_sum += weight * white_noise[j] sum_weights += weight - # Normalize if sum_weights > 1e-10: correlated_noise[i] = weighted_sum / sum_weights else: @@ -483,31 +925,10 @@ def ordinary_kriging(cp_coords, cp_values, grid_coords, sill=1.0, nugget=0.1, background_value=0.0, max_search_radius=1e20, min_points=1, transform=None, min_value=1e-8, max_neighbors=5): - """ - Standard ordinary kriging with variogram models and search controls. - - Parameters - ---------- - cp_coords : np.ndarray - Conceptual point coordinates - cp_values : np.ndarray - Values at conceptual points - grid_coords : np.ndarray - Grid coordinates for interpolation - max_neighbors : int, default 5 - Maximum neighbors to use (controls computational cost) - transform : str, optional - 'log' for log transformation - - Returns - ------- - np.ndarray - Interpolated values at grid points - """ + """Standard ordinary kriging with variogram models and search controls.""" n_grid = len(grid_coords) interp_values = np.full(n_grid, background_value) - # Variogram function def variogram(h): if variogram_model == 'exponential': return nugget + (sill - nugget) * (1 - np.exp(-h / range_param)) @@ -532,7 +953,6 @@ def variogram(h): nearby_coords = cp_coords[closest_indices] nearby_distances = distances[closest_indices] - # Transform values if needed if transform == 'log': if min_value is None: positive_values = nearby_values[nearby_values > 0] @@ -541,7 +961,6 @@ def variogram(h): else: nearby_values_transformed = nearby_values.copy() - # Build kriging system C = np.zeros((n_candidates + 1, n_candidates + 1)) c = np.zeros(n_candidates + 1) @@ -554,7 +973,6 @@ def variogram(h): c[j] = variogram(nearby_distances[j]) c[-1] = 1 - # Solve try: weights = solve(C, c)[:-1] interp_values[i] = np.sum(weights * nearby_values_transformed) @@ -562,7 +980,6 @@ def variogram(h): weights = np.exp(-nearby_distances / range_param) interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) - # Back-transform if needed if transform == 'log': interp_values = 10 ** interp_values @@ -575,19 +992,7 @@ def generate_field_3d(conceptual_points, grid_coords, shape): def infer_grid_shape(grid_coords): - """ - Infer grid dimensions from coordinate array. - - Parameters - ---------- - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - - Returns - ------- - tuple - Grid shape as (nz, ny, nx) - """ + """Infer grid dimensions from coordinate array.""" x_unique = np.unique(grid_coords[:, 0]) y_unique = np.unique(grid_coords[:, 1]) z_unique = np.unique(grid_coords[:, 2]) @@ -600,18 +1005,7 @@ def infer_grid_shape(grid_coords): def save_results(field, shape, field_name): - """ - Save field as individual layer files. - - Parameters - ---------- - field : np.ndarray - Flattened field values - shape : tuple - Grid shape (nz, ny, nx) - field_name : str - Base filename for output - """ + """Save field as individual layer files.""" nz, ny, nx = shape field_3d = field.reshape(shape) @@ -625,20 +1019,7 @@ def save_results(field, shape, field_name): def plot_layer(field, shape, layer=0, field_name='field'): - """ - Plot and save a single layer of the field. - - Parameters - ---------- - field : np.ndarray - Flattened field values - shape : tuple - Grid shape (nz, ny, nx) - layer : int, default 0 - Layer index to plot - field_name : str - Base filename for plot - """ + """Plot and save a single layer of the field.""" import matplotlib.colors as mcolors colors = ['red', 'orange', 'yellow', 'green', 'darkblue', 'lightblue', 'white'] custom_cmap = mcolors.LinearSegmentedColormap.from_list('custom', colors, N=256) @@ -658,6 +1039,595 @@ def plot_layer(field, shape, layer=0, field_name='field'): plt.close() +def add_grid_indices_to_conceptual_points(conceptual_points, grid_coords, shape): + """ + Add i,j grid indices to conceptual points dataframe. + + Parameters + ---------- + conceptual_points : pd.DataFrame + Conceptual points with x,y coordinates + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + shape : tuple + Grid shape (ny, nx) + + Returns + ------- + pd.DataFrame + Conceptual points with added 'grid_i' and 'grid_j' columns + """ + ny, nx = shape + cp_coords = conceptual_points[['x', 'y']].values + + grid_i_list = [] + grid_j_list = [] + + for coord in cp_coords: + # Find closest grid point + distances = np.sum((grid_coords[:, :2] - coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + row, col = divmod(closest_idx, nx) + + grid_i_list.append(row) + grid_j_list.append(col) + + # Add to dataframe + conceptual_points_copy = conceptual_points.copy() + conceptual_points_copy['grid_i'] = grid_i_list + conceptual_points_copy['grid_j'] = grid_j_list + + return conceptual_points_copy + + +def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, shape, + figsize=(12, 10), save_path=None): + """ + Plot geology zones with conceptual points overlaid and labeled. + + Parameters + ---------- + zones : np.ndarray + Zone array, shape (n_points,) or (ny, nx) + conceptual_points : pd.DataFrame + Must have columns: x, y, grid_i, grid_j + grid_coords : np.ndarray + Grid coordinates for coordinate transformation + shape : tuple + Grid shape (ny, nx) + figsize : tuple + Figure size + save_path : str, optional + Path to save figure + """ + import matplotlib.pyplot as plt + import matplotlib.patches as patches + + ny, nx = shape + + # Ensure zones are 2D + if zones.shape == (ny, nx): + zones_2d = zones + else: + zones_2d = zones.reshape(shape) + + # Get grid bounds for coordinate transformation + x_coords = grid_coords[:, 0].reshape(shape[0], shape[1]) + y_coords = grid_coords[:, 1].reshape(shape[0], shape[1]) + + x_min, x_max = x_coords.min(), x_coords.max() + y_min, y_max = y_coords.min(), y_coords.max() + + fig, ax = plt.subplots(figsize=figsize) + + # Plot zones with better color handling + unique_zones = np.unique(zones_2d) + n_zones = len(unique_zones) + + # Create a mapping from zone values to sequential colors + zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} + zones_remapped = np.zeros_like(zones_2d) + for zone_val, color_idx in zone_to_color.items(): + zones_remapped[zones_2d == zone_val] = color_idx + + # Plot zones - flip the array itself to match real-world orientation + zones_flipped = np.flipud(zones_remapped) + + im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], + origin='lower', cmap='tab10', alpha=0.7, + vmin=0, vmax=n_zones - 1) + + # Add colorbar with actual zone values + cbar = plt.colorbar(im, ax=ax) + cbar.set_label('Zone ID') + cbar.set_ticks(range(n_zones)) + cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) + + # Plot conceptual points + for idx, row in conceptual_points.iterrows(): + x, y = row['x'], row['y'] + grid_i, grid_j = row['grid_i'], row['grid_j'] + name = conceptual_points.loc[idx, 'name'] + + # Plot point + ax.plot(x, y, 'ro', markersize=8, markeredgecolor='black', markeredgewidth=1) + + # Add label with index and grid position + ax.annotate(f'{name}\n({grid_i},{grid_j})', + (x, y), xytext=(5, 5), textcoords='offset points', + fontsize=8, fontweight='bold', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) + + # Draw line to corresponding grid cell center + grid_i_int, grid_j_int = int(grid_i), int(grid_j) + if 0 <= grid_i_int < ny and 0 <= grid_j_int < nx: + grid_x = x_coords[grid_i_int, grid_j_int] + grid_y = y_coords[grid_i_int, grid_j_int] + ax.plot([x, grid_x], [y, grid_y], 'r--', alpha=0.5, linewidth=1) + + # Mark grid cell center + ax.plot(grid_x, grid_y, 'r+', markersize=10, markeredgewidth=2) + + ax.set_xlabel('X (NZTM)') + ax.set_ylabel('Y (NZTM)') + ax.set_title('Geology Zones with Conceptual Points\n(Red dots = CP locations, + = assigned grid cells)') + ax.grid(True, alpha=0.3) + + # Add legend + legend_elements = [ + plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='r', + markersize=8, label='Conceptual Points'), + plt.Line2D([0], [0], marker='+', color='r', markersize=10, + label='Assigned Grid Cells'), + plt.Line2D([0], [0], color='r', linestyle='--', + label='CP to Grid Assignment') + ] + ax.legend(handles=legend_elements, loc='upper right') + + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=150, bbox_inches='tight') + print(f"Plot saved to: {save_path}") + + plt.show() + + +def debug_zone_assignments(conceptual_points, grid_coords, zones, shape): + """ + Complete workflow to debug zone assignments. + """ + # Add grid indices + cp_with_indices = add_grid_indices_to_conceptual_points( + conceptual_points, grid_coords, shape + ) + + # Print summary + print("Conceptual Point Zone Assignments:") + print("=" * 50) + for idx, row in cp_with_indices.iterrows(): + ny, nx = shape + grid_i, grid_j = int(row['grid_i']), int(row['grid_j']) + + if zones.shape == shape: + zone_id = zones[grid_i, grid_j] + else: + zone_id = zones.reshape(shape)[grid_i, grid_j] + + print(f"CP {idx}: ({row['x']:.1f}, {row['y']:.1f}) -> " + f"grid[{grid_i},{grid_j}] -> zone {zone_id}") + + # Create plot + plot_zones_with_conceptual_points( + zones, cp_with_indices, grid_coords, shape, + save_path='output/zone_debug.png' + ) + + return cp_with_indices + + +import matplotlib.pyplot as plt +import matplotlib.patches as patches + + +def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=None, + conceptual_points=None, subsample=4, scale_factor=50, figsize=(12, 10)): + """ + Visualize geological tensors as ellipses overlaid on zones, with conceptual points as oriented lines. + + Parameters + ---------- + geological_tensors : np.ndarray + Tensor field, shape (n_points, 2, 2) + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 2) or (n_points, 3) + shape : tuple + Grid shape (ny, nx) + zones : np.ndarray, optional + Zone array for background + conceptual_points : pd.DataFrame, optional + Conceptual points with x, y, bearing, major columns + subsample : int + Show every Nth tensor (for readability) + scale_factor : float + Scale ellipses for visibility + """ + ny, nx = shape + + fig, ax = plt.subplots(figsize=figsize) + + # Prepare zone data but don't plot yet + if zones is not None: + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + # Create zone color mapping + unique_zones = np.unique(zones_2d) + zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} + zones_remapped = np.zeros_like(zones_2d) + for zone_val, color_idx in zone_to_color.items(): + zones_remapped[zones_2d == zone_val] = color_idx + + # Get coordinate bounds + x_coords = grid_coords[:, 0].reshape(shape) + y_coords = grid_coords[:, 1].reshape(shape) + x_min, x_max = x_coords.min(), x_coords.max() + y_min, y_max = y_coords.min(), y_coords.max() + + zones_flipped = np.flipud(zones_remapped) + # Plot zones as background with low alpha + im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], + origin='lower', cmap='tab10', alpha=0.2, zorder=0, + vmin=0, vmax=len(unique_zones) - 1) + + # Plot conceptual points as oriented lines + if conceptual_points is not None: + for idx, row in conceptual_points.iterrows(): + x, y = row['x'], row['y'] + bearing = row['bearing'] + major = row['major'] + + # Scale line length for visibility (similar to ellipse scaling) + line_length = major / (scale_factor * 2) # Adjust scaling as needed + + # Convert geological bearing (CW from N) to math angle for plotting + # Geological: 0°=N, 90°=E; Math: 0°=E, 90°=N + math_angle_rad = np.radians(90 - bearing) + + # Calculate line endpoints + dx = line_length * np.cos(math_angle_rad) / 2 + dy = line_length * np.sin(math_angle_rad) / 2 + + # Plot line centered on point + ax.plot([x - dx, x + dx], [y - dy, y + dy], + 'r-', linewidth=3, alpha=0.9, zorder=4) + + # Plot center point + ax.plot(x, y, 'ro', markersize=4, markeredgecolor='black', + markeredgewidth=1, alpha=0.9, zorder=5) + + # Add label with bearing + ax.annotate(f'{bearing:.0f}°', (x, y), xytext=(8, 8), + textcoords='offset points', fontsize=8, fontweight='bold', + bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.7), + zorder=6) + + # Subsample points for visualization + indices = np.arange(0, len(geological_tensors), subsample) + + print(f"Plotting {len(indices)} tensors out of {len(geological_tensors)}") + + default_count = 0 + ellipse_count = 0 + + for i in indices: + tensor = geological_tensors[i] + x, y = grid_coords[i, 0], grid_coords[i, 1] + + # Check for default tensor (indicates no interpolation happened) + if np.allclose(tensor, np.eye(2) * 1000000): + # Plot as red circle for default tensors with high zorder + ax.plot(x, y, 'ko', markersize=2, alpha=0.3, zorder=1) # Make less prominent + default_count += 1 + continue + + try: + # Eigendecomposition to get ellipse parameters + eigenvals, eigenvecs = np.linalg.eigh(tensor) + + # Sort by eigenvalue magnitude + idx = np.argsort(eigenvals)[::-1] + eigenvals = eigenvals[idx] + eigenvecs = eigenvecs[:, idx] + + # Ellipse dimensions (correlation lengths) - make them more visible + major_axis = 2 * np.sqrt(eigenvals[0]) / scale_factor + minor_axis = 2 * np.sqrt(eigenvals[1]) / scale_factor + + # Make sure ellipses are visible + if major_axis < 30: # Smaller minimum to avoid overwhelming CP lines + major_axis = 30 + if minor_axis < 15: + minor_axis = 15 + + # Ellipse orientation (angle of major axis) + angle = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) + + # Create ellipse with lower prominence than CP lines + ellipse = patches.Ellipse((x, y), major_axis, minor_axis, + angle=angle, linewidth=1, + edgecolor='blue', facecolor='none', alpha=0.6, zorder=2) + ax.add_patch(ellipse) + ellipse_count += 1 + + except: + # If tensor is problematic, plot as yellow point with high zorder + ax.plot(x, y, 'yo', markersize=2, zorder=3) + + print(f"Plotted {default_count} default tensors (black circles) and {ellipse_count} ellipses") + + ax.set_xlabel('X (NZTM)') + ax.set_ylabel('Y (NZTM)') + + # Update title and legend + title_parts = [f'Geological Tensors (subsampled 1:{subsample})'] + if conceptual_points is not None: + title_parts.append('Red lines = conceptual points (bearing)') + title_parts.append('Blue ellipses = interpolated tensors') + ax.set_title('\n'.join(title_parts)) + + ax.set_aspect('equal', adjustable='box') + + # Add colorbar for zones if present + if zones is not None: + cbar = plt.colorbar(im, ax=ax, shrink=0.8) + cbar.set_label('Zone ID') + cbar.set_ticks(range(len(unique_zones))) + cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) + + # Add legend + legend_elements = [] + if conceptual_points is not None: + legend_elements.extend([ + plt.Line2D([0], [0], color='red', linewidth=3, label='Conceptual Point Bearings'), + plt.Line2D([0], [0], marker='o', color='red', markersize=6, + markeredgecolor='black', label='Conceptual Points', linestyle='None') + ]) + legend_elements.extend([ + patches.Patch(facecolor='none', edgecolor='blue', label='Interpolated Tensors'), + plt.Line2D([0], [0], marker='o', color='black', markersize=4, + label='Default Tensors', linestyle='None', alpha=0.3) + ]) + + if legend_elements: + ax.legend(handles=legend_elements, loc='upper right', fontsize=9) + + plt.tight_layout() + plt.show() + """ + Visualize geological tensors as ellipses overlaid on zones. + + Parameters + ---------- + geological_tensors : np.ndarray + Tensor field, shape (n_points, 2, 2) + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 2) or (n_points, 3) + shape : tuple + Grid shape (ny, nx) + zones : np.ndarray, optional + Zone array for background + subsample : int + Show every Nth tensor (for readability) + scale_factor : float + Scale ellipses for visibility + """ + ny, nx = shape + + fig, ax = plt.subplots(figsize=figsize) + + # Prepare zone data but don't plot yet + if zones is not None: + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + # Create zone color mapping + unique_zones = np.unique(zones_2d) + zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} + zones_remapped = np.zeros_like(zones_2d) + for zone_val, color_idx in zone_to_color.items(): + zones_remapped[zones_2d == zone_val] = color_idx + + # Get coordinate bounds + x_coords = grid_coords[:, 0].reshape(shape) + y_coords = grid_coords[:, 1].reshape(shape) + x_min, x_max = x_coords.min(), x_coords.max() + y_min, y_max = y_coords.min(), y_coords.max() + + zones_flipped = np.flipud(zones_remapped) + # Plot zones as background with low alpha + im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], + origin='lower', cmap='tab10', alpha=0.2, zorder=0, + vmin=0, vmax=len(unique_zones) - 1) + + # Subsample points for visualization + indices = np.arange(0, len(geological_tensors), subsample) + + print(f"Plotting {len(indices)} tensors out of {len(geological_tensors)}") + + default_count = 0 + ellipse_count = 0 + + for i in indices: + tensor = geological_tensors[i] + x, y = grid_coords[i, 0], grid_coords[i, 1] + + # Check for default tensor (indicates no interpolation happened) + if np.allclose(tensor, np.eye(2) * 1000000): + # Plot as red circle for default tensors with high zorder + ax.plot(x, y, 'ro', markersize=3, alpha=0.9, zorder=3) + default_count += 1 + continue + + try: + # Eigendecomposition to get ellipse parameters + eigenvals, eigenvecs = np.linalg.eigh(tensor) + + # Sort by eigenvalue magnitude + idx = np.argsort(eigenvals)[::-1] + eigenvals = eigenvals[idx] + eigenvecs = eigenvecs[:, idx] + + # Ellipse dimensions (correlation lengths) - make them more visible + major_axis = 2 * np.sqrt(eigenvals[0]) / scale_factor + minor_axis = 2 * np.sqrt(eigenvals[1]) / scale_factor + + # Make sure ellipses are visible + if major_axis < 50: # Minimum size + major_axis = 50 + if minor_axis < 25: + minor_axis = 25 + + # Ellipse orientation (angle of major axis) + angle = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) + + # Create ellipse with higher zorder to appear on top + ellipse = patches.Ellipse((x, y), major_axis, minor_axis, + angle=angle, linewidth=2, + edgecolor='blue', facecolor='none', alpha=1.0, zorder=2) + ax.add_patch(ellipse) + ellipse_count += 1 + + except: + # If tensor is problematic, plot as yellow point with high zorder + ax.plot(x, y, 'yo', markersize=3, zorder=3) + + print(f"Plotted {default_count} default tensors (red circles) and {ellipse_count} ellipses") + + ax.set_xlabel('X (NZTM)') + ax.set_ylabel('Y (NZTM)') + ax.set_title( + f'Geological Tensors (subsampled 1:{subsample})\nBlue ellipses = anisotropy, Red dots = default tensors') + ax.set_aspect('equal', adjustable='box') + + # Add colorbar for zones if present + if zones is not None: + cbar = plt.colorbar(im, ax=ax, shrink=0.8) + cbar.set_label('Zone ID') + cbar.set_ticks(range(len(unique_zones))) + cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) + + plt.tight_layout() + plt.show() + + +def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coords, zones, shape): + """ + Compare conceptual point bearings with actual tensor orientations at those locations. + """ + ny, nx = shape + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + print("Conceptual Point vs Tensor Orientation Analysis:") + print("=" * 70) + print(f"{'CP#':<4} {'Name':<12} {'Zone':<6} {'Input°':<8} {'Tensor°':<8} {'Diff°':<8} {'Status'}") + print("-" * 70) + + for idx, row in conceptual_points.iterrows(): + cp_x, cp_y = row['x'], row['y'] + input_bearing = row['bearing'] + cp_name = row.get('name', f'CP_{idx}') + + # Find closest grid point + distances = np.sum((grid_coords[:, :2] - np.array([cp_x, cp_y])) ** 2, axis=1) + closest_idx = np.argmin(distances) + grid_row, grid_col = divmod(closest_idx, nx) + + # Get zone + zone_id = zones_2d[grid_row, grid_col] + + # Get tensor at that location + tensor = geological_tensors[closest_idx] + + # Extract orientation + if np.allclose(tensor, np.eye(2) * 1000000): + tensor_bearing = "DEFAULT" + diff = "N/A" + status = "No interp" + else: + tensor_bearing = get_tensor_bearing(tensor, input_bearing) + + # Calculate difference (handling wraparound) + diff = abs(input_bearing - tensor_bearing) + if diff > 180: + diff = 360 - diff + + # Status based on difference + if diff < 10: + status = "✓ Good" + elif diff < 30: + status = "~ Close" + else: + status = "✗ Bad" + + print(f"{idx:<4} {cp_name:<12} {zone_id:<6.0f} {input_bearing:<8.0f} {tensor_bearing:<8} {diff:<8} {status}") + + print("-" * 70) + + +def analyze_tensor_statistics(geological_tensors, grid_coords, zones, shape): + """ + Print statistics about the tensor field by zone. + """ + ny, nx = shape + zones_2d = zones.reshape(shape) if zones.shape != shape else zones + + print("Tensor Analysis by Zone:") + print("=" * 50) + + for zone_id in np.unique(zones): + # Find grid points in this zone + zone_indices = [] + for i in range(len(grid_coords)): + row, col = divmod(i, nx) + if zones_2d[row, col] == zone_id: + zone_indices.append(i) + + if len(zone_indices) == 0: + continue + + zone_tensors = geological_tensors[zone_indices] + + # Count default tensors + default_count = 0 + anisotropies = [] + orientations = [] + + for tensor in zone_tensors: + if np.allclose(tensor, np.eye(2) * 1000000): + default_count += 1 + else: + try: + eigenvals, eigenvecs = np.linalg.eigh(tensor) + idx = np.argsort(eigenvals)[::-1] + eigenvals = eigenvals[idx] + eigenvecs = eigenvecs[:, idx] + + aniso_ratio = np.sqrt(eigenvals[0] / eigenvals[1]) + orientation = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) + + anisotropies.append(aniso_ratio) + orientations.append(orientation) + except: + pass + + print(f"Zone {zone_id}:") + print(f" Total points: {len(zone_indices)}") + print(f" Default tensors: {default_count} ({100 * default_count / len(zone_indices):.1f}%)") + if anisotropies: + print(f" Anisotropy ratio: {np.mean(anisotropies):.2f} ± {np.std(anisotropies):.2f}") + print(f" Orientation: {np.mean(orientations):.1f}° ± {np.std(orientations):.1f}°") + print() + + # Example usage if __name__ == "__main__": """ @@ -666,10 +1636,15 @@ def plot_layer(field, shape, layer=0, field_name='field'): Required file formats: - conceptual_points.csv: CSV with columns x,y,major,anisotropy,bearing,mean_kh,sd_kh - grid.dat: Whitespace-delimited with columns x,y,z + + Optional: + - zones: Can be passed to generate_single_layer() as numpy array + with integer zone IDs, shape (n_points,) or (ny, nx) """ main( - conceptual_points_file='data/conceptual_points.csv', - grid_file='data/grid.dat', + conceptual_points_file=r'C:\modelling\sva_ds\data\conceptual_points.csv', + grid_file=r'C:\modelling\sva_ds\data\grid.dat', + zone_file=r'C:\modelling\sva_ds\data\waq_arr.geoclass_simple.arr', field_name=['kh'], layer_mode=True ) \ No newline at end of file From 210824caa899326c72a71ccd6a062f20c5e59cb1 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 9 Jul 2025 14:24:16 +1200 Subject: [PATCH 04/36] working version, smooth boundary, increase variance at boundar --- pyemu/utils/tensor_utils.py | 716 +++++++++++++++++++++--------------- 1 file changed, 415 insertions(+), 301 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 4acacd023..7f5449356 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -1,6 +1,7 @@ import numpy as np import pandas as pd from scipy.linalg import logm, expm, solve +from scipy.ndimage import distance_transform_edt, gaussian_filter import matplotlib as mpl mpl.use('Tkagg') @@ -8,6 +9,139 @@ import os +# ============================================================================ +# UTILITY FUNCTIONS FOR CONSISTENT 2D ARRAY HANDLING +# ============================================================================ + +def standardize_input_arrays(zones_raw, grid_coords, shape): + """ + Standardize all input arrays to consistent 2D format for internal processing. + + Parameters + ---------- + zones_raw : np.ndarray or None + Raw zone array, shape (n_points,) or (ny, nx) or None + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + shape : tuple, actaully required here + Grid shape (ny, nx) for single layer or (nz, ny, nx) for multilayer + + Returns + ------- + tuple + (zones, grid_coords_2d, original_zones_shape) where: + - zones: Always 2D array (ny, nx) or None + - grid_coords_2d: Grid coordinates for 2D processing + - original_zones_shape: Original shape for output conversion + """ + if len(shape) == 3: + nz, ny, nx = shape + layer_shape = (ny, nx) + else: + layer_shape = shape + + # Handle zones + zones = None + original_zones_shape = None + + if zones_raw is not None: + original_zones_shape = zones_raw.shape + ny, nx = layer_shape + + if zones_raw.shape == layer_shape: + zones = zones_raw.copy() + elif zones_raw.shape == (ny * nx,): + zones = zones_raw.reshape(layer_shape) + else: + raise ValueError(f"Zone array shape {zones_raw.shape} incompatible with grid shape {layer_shape}") + + return zones, grid_coords, original_zones_shape + + +def convert_output_to_original_format(field_2d, original_zones_shape): + """ + Convert 2D field back to original input format. + + Parameters + ---------- + field_2d : np.ndarray + Field in 2D format, shape (ny, nx) + original_zones_shape : tuple or None + Original input array shape + + Returns + ------- + np.ndarray + Field in original format (1D or 2D) + """ + if original_zones_shape is None: + return field_2d + + if len(original_zones_shape) == 1: + # Original was 1D, return flattened + return field_2d.flatten() + else: + # Original was 2D, return as-is + return field_2d + + +def ensure_2d_for_processing(arr, shape): + """ + Ensure array is in 2D format for internal processing. + Simpler version focused on internal consistency. + + Parameters + ---------- + arr : np.ndarray + Input array + shape : tuple + Target 2D shape (ny, nx) + + Returns + ------- + np.ndarray + 2D array with shape (ny, nx) + """ + ny, nx = shape + + if arr.shape == shape: + return arr + elif arr.shape == (ny * nx,): + return arr.reshape(shape) + else: + raise ValueError(f"Array shape {arr.shape} cannot be converted to 2D shape {shape}") + + +def validate_grid_coordinates(grid_coords, shape): + """ + Validate grid coordinates match expected shape. + + Parameters + ---------- + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + shape : tuple + Expected grid shape (nz, ny, nx) or (ny, nx) + + Returns + ------- + bool + True if valid + """ + if len(shape) == 3: + nz, ny, nx = shape + expected_points = nz * ny * nx + else: + ny, nx = shape + expected_points = ny * nx + + return grid_coords.shape[0] == expected_points + + +# ============================================================================ +# MAIN ENTRY POINT +# ============================================================================ + def main(conceptual_points_file, grid_file, zone_file=None, field_name=['field'], layer_mode=True): """ Generate spatially correlated fields using tensor-based geostatistics. @@ -46,15 +180,28 @@ def main(conceptual_points_file, grid_file, zone_file=None, field_name=['field'] conceptual_points = pd.read_csv(conceptual_points_file) grid = pd.read_csv(grid_file, sep=r'\s+') grid_coords = grid[['x', 'y', 'z']].values - zones = np.loadtxt(zone_file) print(f"Loaded {len(conceptual_points)} conceptual points") print(f"Loaded {len(grid_coords)} grid points") - # Infer grid structure + # Infer grid structure FIRST shape = infer_grid_shape(grid_coords) print(f"Grid shape: {shape} (nz, ny, nx)") + # Validate grid coordinates + if not validate_grid_coordinates(grid_coords, shape): + raise ValueError(f"Grid coordinates ({len(grid_coords)} points) don't match inferred shape {shape}") + + # NOW we can standardize zones using the shape + zones = None + original_zones_shape = None + + if zone_file: + zones_raw = np.loadtxt(zone_file) + zones, _, original_zones_shape = standardize_input_arrays(zones_raw, grid_coords, shape) + print( + f"Loaded zones: original shape {original_zones_shape}, standardized to 2D {zones.shape if zones is not None else None}") + for fn in field_name: cols = ['name', 'x', 'y', 'major', 'anisotropy', 'bearing'] cols = cols + [_ for _ in conceptual_points.columns if f'_{fn}' in _] @@ -68,13 +215,14 @@ def main(conceptual_points_file, grid_file, zone_file=None, field_name=['field'] cols = cols + ['z'] field = generate_field_3d(conceptual_points[cols], grid_coords, shape) - # Save results - save_results(field, shape, field_name) + # Save results - field is now 3D array (nz, ny, nx) + save_results(field, shape, field_name, original_zones_shape) - # Plot first layer - plot_layer(field, shape, layer=0, field_name=field_name) + # Plot first layer - field[0] is now 2D + plot_layer(field[0], shape[1:], layer=0, field_name=field_name) print("\n=== Complete ===") + print(f"Field shape: {field.shape}") # Now shows (nz, ny, nx) print(f"Field statistics: mean={np.mean(field):.3f}, std={np.std(field):.3f}") @@ -111,6 +259,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None): # Get grid points for this layer layer_grid = grid_3d[layer_idx].reshape(-1, 3) layer_coords_2d = layer_grid[:, :2] # Just x, y + layer_shape = (ny, nx) # Shape for this layer if 'layer' in conceptual_points.columns: layer_cp = conceptual_points[conceptual_points['layer'] == layer_idx] @@ -120,12 +269,22 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None): else: layer_cp = conceptual_points - # Generate field for this layer - layer_field = generate_single_layer_zone_based(layer_cp, layer_coords_2d, - zones=zones, shape=(ny, nx)) - field_3d[layer_idx] = layer_field.reshape(ny, nx) + # Prepare zones for this layer (all zones now consistently 2D) + layer_zones = None + if zones is not None: + if len(zones.shape) == 3: # 3D zones - not supported yet, but keeping logic + layer_zones = zones[layer_idx] + else: # 2D zones (standard case), use for all layers + layer_zones = zones + # zones are already guaranteed to be 2D from standardize_input_arrays() + + # Generate field for this layer - now returns 2D + layer_field_2d = generate_single_layer_zone_based(layer_cp, layer_coords_2d, + zones=layer_zones, shape=layer_shape) + # layer_field_2d is already 2D + field_3d[layer_idx] = layer_field_2d - return field_3d.flatten() + return field_3d # Return as 3D array, NOT flattened def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, zones=None, shape=None): @@ -142,16 +301,20 @@ def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, zones=No raise ValueError("No sd columns found (expected format: 'sd_fieldname')") if not shape: - x_unique = np.unique(grid_coords_2d[:, 0]) - y_unique = np.unique(grid_coords_2d[:, 1]) - shape = (len(y_unique), len(x_unique)) - print(f" Inferred grid dimensions: ny={shape[0]}, nx={shape[1]}") + shape = infer_grid_shape(grid_coords_2d) + # x_unique = np.unique(grid_coords_2d[:, 0]) + # y_unique = np.unique(grid_coords_2d[:, 1]) + # shape = (len(y_unique), len(x_unique)) + # print(f" Inferred grid dimensions: ny={shape[0]}, nx={shape[1]}") + + # Zones are now guaranteed to be 2D from entry point, no conversion needed + # All internal processing uses consistent 2D arrays # Step 1: Create geological tensors from conceptual points print(" Creating geological correlation tensors...") major = conceptual_points['major'].values minor = major / conceptual_points['anisotropy'].values - #bearing_rad = np.radians(conceptual_points['bearing'].values) + # Better normalization that preserves direction def normalize_bearing(bearing): # Convert to 0-360 first @@ -163,7 +326,6 @@ def normalize_bearing(bearing): bearing_rad = np.radians(bearings_normalized) cp_tensors = create_2d_tensors(bearing_rad, major, minor) - # Debug: Test tensor creation with first few points print(" Tensor creation verification:") for i in range(min(3, len(cp_tensors))): @@ -174,78 +336,78 @@ def normalize_bearing(bearing): # Step 2: Zone-based tensor interpolation if zones is not None: print(" Interpolating geological tensors by zone...") - # geological_tensors = interpolate_tensors_by_zone( - # cp_coords, cp_tensors, grid_coords_2d, zones, shape - # ) geological_tensors = interpolate_tensors_by_zone_nearest( - cp_coords, cp_tensors, grid_coords_2d, zones, shape + cp_coords, cp_tensors, grid_coords_2d, zones ) else: print(" Interpolating geological tensors globally...") geological_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d) - # Step 3: Interpolate means using zone-aware kriging + # Step 3: Interpolate means using zone-aware kriging - returns 2D print(" Interpolating means with geological structure...") - interp_means = tensor_aware_kriging( + interp_means_2d = tensor_aware_kriging( cp_coords, cp_means, grid_coords_2d, geological_tensors, variogram_model='exponential', sill=1.0, nugget=0.1, background_value=np.mean(cp_means), max_search_radius=1e20, min_points=3, transform='log', min_value=1e-8, max_neighbors=8, - zones=zones, shape=shape + zones=zones ) + if zones is not None: + print(" Smoothing values at geological boundaries...") + interp_means_2d = create_boundary_modified_scalar( + interp_means_2d, zones, + transition_cells=3, mode='smooth' + ) - # Step 4: Create boundary-enhanced tensors for variance - # changes anisotropic distance, potential long-reaching unintended consequences - # if zones is not None: - # print(" Creating boundary-enhanced tensors for variance...") - # enhanced_sd_tensors = create_boundary_enhanced_tensors( - # geological_tensors, zones, shape, - # boundary_anisotropy=10.0, boundary_major_scale=2.0, transition_cells=5 - # ) - # else: - # enhanced_sd_tensors = geological_tensors - - # Step 5: Interpolate sd using GEOLOGICAL tensors, then enhance variance + # Step 5: Interpolate sd using GEOLOGICAL tensors - returns 2D print(" Interpolating standard deviation...") - interp_sd = tensor_aware_kriging( + interp_sd_2d = tensor_aware_kriging( cp_coords, cp_sd, grid_coords_2d, geological_tensors, # Use geological tensors variogram_model='exponential', sill=1.0, nugget=0.1, background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform=None, min_value=1e-8, max_neighbors=8 + min_points=3, transform=None, min_value=1e-8, max_neighbors=8, + zones=zones ) if zones is not None: print(" Enhancing variance at geological boundaries...") - interp_sd = create_boundary_enhanced_scalar( - interp_sd, zones, shape, - peak_increase=0.3, transition_cells=5 + interp_sd_2d = create_boundary_modified_scalar( + interp_sd_2d, zones, + peak_increase=1, transition_cells=3, mode='enhance' ) - # Step 6: Generate correlated noise + # Step 6: Generate correlated noise - work with 1D for noise generation, then reshape print(" Generating correlated noise...") np.random.seed(420) white_noise = np.random.randn(len(grid_coords_2d)) - correlated_noise = generate_correlated_noise_2d(grid_coords_2d, geological_tensors, white_noise) + correlated_noise_1d = generate_correlated_noise_2d(grid_coords_2d, geological_tensors, white_noise) + correlated_noise_2d = correlated_noise_1d.reshape(shape) + + # Step 7: Combine into lognormal field - all operations in 2D + field_2d = 10 ** (np.log10(interp_means_2d) + correlated_noise_2d * interp_sd_2d) - # Step 7: Combine into lognormal field - field = 10 ** (np.log10(interp_means) + correlated_noise * interp_sd) + return field_2d - return field +def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zones): + """Zone-based nearest neighbor tensor assignment - no interpolation. -def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zones, shape): - """Zone-based nearest neighbor tensor assignment - no interpolation.""" + Parameters + ---------- + zones : np.ndarray + Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point + """ + shape = zones.shape n_grid = len(grid_coords) ny, nx = shape - zones_2d = zones.reshape(shape) if zones.shape != shape else zones # Get zone for each conceptual point - cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones, shape) + cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) # Initialize output tensors geological_tensors = np.zeros((n_grid, 2, 2)) # Get unique zones - unique_zones = np.unique(zones_2d) + unique_zones = np.unique(zones) for zone_id in unique_zones: print(f" Assigning nearest neighbor tensors for zone {zone_id}") @@ -259,7 +421,7 @@ def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zone zone_grid_indices = [] for i in range(n_grid): row, col = divmod(i, nx) - if zones_2d[row, col] == zone_id: + if zones[row, col] == zone_id: zone_grid_indices.append(i) zone_grid_indices = np.array(zone_grid_indices) @@ -279,73 +441,63 @@ def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zone return geological_tensors -def create_boundary_enhanced_scalar(base_field, zones, shape, - peak_increase=0.3, transition_cells=5): +def create_boundary_modified_scalar(base_field_2d, zones, + peak_increase=0.3, transition_cells=5, mode="enhance"): """ - Increase scalar field values at geological zone boundaries with smooth transition. + Modify scalar field values near geological zone boundaries with either enhancement or smoothing. Parameters ---------- - base_field : np.ndarray + base_field_2d : np.ndarray Base scalar values zones : np.ndarray Zone IDs - shape : tuple - Grid shape (ny, nx) peak_increase : float - Amount to ADD at boundary cells (for log-space sd) + Max enhancement or smoothing strength transition_cells : int - Number of cells over which to transition from peak to zero enhancement + Width of transition region + mode : str + 'enhance' or 'smooth' Returns ------- np.ndarray - Enhanced scalar field with smooth boundary transitions + Modified scalar field """ - ny, nx = shape + from scipy.ndimage import distance_transform_edt - # Ensure zones are 2D - if zones.shape == (ny, nx): - zones_2d = zones - else: - zones_2d = zones.reshape(shape) + if mode not in ("enhance", "smooth"): + raise ValueError("mode must be 'enhance' or 'smooth'") - # Detect boundaries - boundary_mask, _ = detect_zone_boundaries(zones_2d) + boundary_mask, _ = detect_zone_boundaries(zones) + distance = distance_transform_edt(~boundary_mask) + transition_mask = distance <= transition_cells - # Calculate distance to nearest boundary for each cell - enhanced = base_field.copy() - enhancement_count = 0 + modified = base_field_2d.copy() - for i in range(ny): - for j in range(nx): - # Find minimum distance to any boundary - min_dist_to_boundary = transition_cells + 1 # Start beyond transition - - for di in range(-transition_cells, transition_cells + 1): - for dj in range(-transition_cells, transition_cells + 1): - check_i, check_j = i + di, j + dj - if (0 <= check_i < ny and 0 <= check_j < nx and - boundary_mask[check_i, check_j]): - dist = np.sqrt(di ** 2 + dj ** 2) - min_dist_to_boundary = min(min_dist_to_boundary, dist) - - # Apply enhancement based on distance - if min_dist_to_boundary <= transition_cells: - if min_dist_to_boundary == 0: - # At boundary - full enhancement - enhancement = peak_increase - else: - # Smooth transition - linear decay - enhancement = peak_increase * (1 - min_dist_to_boundary / transition_cells) + if mode == "enhance": + # Linear enhancement near boundaries + factor = 1 - distance[transition_mask] / transition_cells + enhancement = peak_increase * factor + modified[transition_mask] += enhancement - enhanced[i, j] += enhancement - enhancement_count += 1 + elif mode == "smooth": + # Smooth field with Gaussian filter as the target blending value + smoothed_field = gaussian_filter(base_field_2d, sigma=transition_cells / 2) + + # Blend with original based on distance to boundary + weight = 1 - distance[transition_mask] / transition_cells + modified[transition_mask] = ( + weight * smoothed_field[transition_mask] + + (1 - weight) * base_field_2d[transition_mask] + ) - print(f" Enhanced {enhancement_count} points near geological boundaries") - return enhanced.flatten() + print(f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") + return modified -def create_boundary_enhanced_tensors(geological_tensors, zones, shape, + + +def create_boundary_enhanced_tensors(geological_tensors, zones, boundary_anisotropy=10.0, boundary_major_scale=2.0, transition_cells=3): """ @@ -357,9 +509,7 @@ def create_boundary_enhanced_tensors(geological_tensors, zones, shape, geological_tensors : np.ndarray Base geological tensors, shape (n_points, 2, 2) zones : np.ndarray - Zone IDs, shape (n_points,) or (ny, nx) - shape : tuple - Grid shape (ny, nx) + Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point boundary_anisotropy : float Anisotropy ratio at boundaries (major/minor) boundary_major_scale : float @@ -372,16 +522,11 @@ def create_boundary_enhanced_tensors(geological_tensors, zones, shape, np.ndarray Enhanced tensors, shape (n_points, 2, 2) """ + shape = zones.shape ny, nx = shape - # Ensure zones are 2D - if zones.shape == (ny, nx): - zones_2d = zones - else: - zones_2d = zones.reshape(shape) - # Detect boundary points and orientations - boundary_mask, boundary_directions = detect_zone_boundaries(zones_2d) + boundary_mask, boundary_directions = detect_zone_boundaries(zones) enhanced_tensors = geological_tensors.copy() boundary_count = 0 @@ -463,14 +608,14 @@ def create_boundary_enhanced_tensors(geological_tensors, zones, shape, return enhanced_tensors -def detect_zone_boundaries(zones_2d): +def detect_zone_boundaries(zones): """ Detect boundaries between geological zones and estimate local orientations. Parameters ---------- - zones_2d : np.ndarray - Zone IDs, shape (ny, nx) + zones : np.ndarray + Zone IDs, shape (ny, nx) - GUARANTEED to be 2D Returns ------- @@ -479,14 +624,14 @@ def detect_zone_boundaries(zones_2d): - boundary_mask: bool array, shape (ny, nx) - boundary_directions: direction vectors, shape (ny, nx, 2) """ - ny, nx = zones_2d.shape + ny, nx = zones.shape boundary_mask = np.zeros((ny, nx), dtype=bool) boundary_directions = np.zeros((ny, nx, 2)) # Find boundary points (any point with different-zone neighbors) for i in range(ny): for j in range(nx): - current_zone = zones_2d[i, j] + current_zone = zones[i, j] # Check 8-connected neighbors for di in [-1, 0, 1]: @@ -495,7 +640,7 @@ def detect_zone_boundaries(zones_2d): continue ni, nj = i + di, j + dj if 0 <= ni < ny and 0 <= nj < nx: - if zones_2d[ni, nj] != current_zone: + if zones[ni, nj] != current_zone: boundary_mask[i, j] = True break if boundary_mask[i, j]: @@ -507,20 +652,20 @@ def detect_zone_boundaries(zones_2d): if not boundary_mask[i, j]: continue - boundary_directions[i, j] = estimate_boundary_direction(zones_2d, i, j) + boundary_directions[i, j] = estimate_boundary_direction(zones, i, j) return boundary_mask, boundary_directions -def estimate_boundary_direction(zones_2d, center_i, center_j, radius=2): +def estimate_boundary_direction(zones, center_i, center_j, radius=2): """ Estimate local boundary direction at a point. Returns direction vector in geological coordinates (North = 0°). Parameters ---------- - zones_2d : np.ndarray - Zone IDs + zones : np.ndarray + Zone IDs - GUARANTEED to be 2D, shape (ny, nx) center_i, center_j : int Center point coordinates radius : int @@ -532,8 +677,8 @@ def estimate_boundary_direction(zones_2d, center_i, center_j, radius=2): Boundary direction vector [east_component, north_component] where North = [0, 1], East = [1, 0] (geological convention) """ - ny, nx = zones_2d.shape - center_zone = zones_2d[center_i, center_j] + ny, nx = zones.shape + center_zone = zones[center_i, center_j] same_zone_points = [] diff_zone_points = [] @@ -546,7 +691,7 @@ def estimate_boundary_direction(zones_2d, center_i, center_j, radius=2): ni, nj = center_i + di, center_j + dj if 0 <= ni < ny and 0 <= nj < nx: - if zones_2d[ni, nj] == center_zone: + if zones[ni, nj] == center_zone: same_zone_points.append([dj, di]) # x=dj (east), y=di (south in array coords) else: diff_zone_points.append([dj, di]) @@ -586,35 +731,52 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, variogram_model='exponential', sill=1.0, nugget=0.01, background_value=0.0, max_search_radius=1e20, min_points=1, max_neighbors=4, transform=None, min_value=1e-8, - zones=None, shape=None): + zones=None): """ Ordinary kriging using tensor-aware anisotropic distances. + + Parameters + ---------- + zones : np.ndarray, optional + Zone IDs, shape (ny, nx) - 2D array + + Returns + ------- + np.ndarray + Interpolated values, shape (ny, nx) - ALWAYS 2D """ n_grid = len(grid_coords) - interp_values = np.full(n_grid, background_value) - # Calculate zone for each conceptual point (once, outside the main loop) + # Get shape from zones if provided, or infer from grid if zones is not None: - if shape is None: - shape = zones.shape + shape = zones.shape ny, nx = shape - zones_2d = zones.reshape(shape) if zones.shape != shape else zones + else: + # Infer shape from grid coordinates + x_unique = np.unique(grid_coords[:, 0]) + y_unique = np.unique(grid_coords[:, 1]) + ny, nx = len(y_unique), len(x_unique) + shape = (ny, nx) + + interp_values_1d = np.full(n_grid, background_value) + # Calculate zone for each conceptual point (once, outside the main loop) + if zones is not None: cp_zones = [] for coord in cp_coords: # Find closest grid point to get zone distances = np.sum((grid_coords - coord) ** 2, axis=1) closest_idx = np.argmin(distances) row, col = divmod(closest_idx, nx) - cp_zones.append(zones_2d[row, col]) + cp_zones.append(zones[row, col]) cp_zones = np.array(cp_zones) for i in range(n_grid): local_tensor = interp_tensors[i] if zones is not None: # Get current zone and filter conceptual points - row, col = divmod(i, shape[1]) - current_zone = zones.reshape(shape)[row, col] + row, col = divmod(i, nx) + current_zone = zones[row, col] # Filter to same zone, fallback to all if insufficient points found zone_mask = cp_zones == current_zone @@ -696,19 +858,20 @@ def variogram(h): # Fall back to inverse distance weighting weights = np.exp(-nearby_distances) weights = weights / np.sum(weights) - interp_values[i] = np.sum(weights * nearby_values_transformed) + interp_values_1d[i] = np.sum(weights * nearby_values_transformed) else: weights = np.linalg.solve(C, c)[:-1] - interp_values[i] = np.sum(weights * nearby_values_transformed) + interp_values_1d[i] = np.sum(weights * nearby_values_transformed) except: # Existing fallback for any other errors weights = np.exp(-nearby_distances) - interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) + interp_values_1d[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) if transform == 'log': - interp_values = 10 ** interp_values + interp_values_1d = 10 ** interp_values_1d - return interp_values + # ALWAYS convert to 2D - no exceptions + return interp_values_1d.reshape(shape) def create_2d_tensors(theta, major, minor): @@ -771,12 +934,16 @@ def get_tensor_bearing(tensor, expected_bearing=None): return angle1 -def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones, shape): +def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): """ Extract zone assignment logic from tensor_aware_kriging into reusable function. + + Parameters + ---------- + zones : np.ndarray + Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point """ - ny, nx = shape - zones_2d = zones.reshape(shape) if zones.shape != shape else zones + ny, nx = zones.shape cp_zones = [] for coord in cp_coords: @@ -784,12 +951,12 @@ def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones, shape): distances = np.sum((grid_coords - coord) ** 2, axis=1) closest_idx = np.argmin(distances) row, col = divmod(closest_idx, nx) - cp_zones.append(zones_2d[row, col]) + cp_zones.append(zones[row, col]) return np.array(cp_zones) -def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, shape): +def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones): """ Zone-aware tensor interpolation - interpolate tensors within each zone separately. @@ -802,9 +969,7 @@ def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, shape grid_coords : np.ndarray Grid coordinates zones : np.ndarray - Zone array - shape : tuple - Grid shape (ny, nx) + Zone array, shape (ny, nx) - GUARANTEED to be 2D from entry point Returns ------- @@ -812,17 +977,16 @@ def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, shape Interpolated tensors, shape (n_grid, 2, 2) """ n_grid = len(grid_coords) - ny, nx = shape - zones_2d = zones.reshape(shape) if zones.shape != shape else zones + ny, nx = zones.shape # Get zone for each conceptual point - cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones, shape) + cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) # Initialize output tensors geological_tensors = np.zeros((n_grid, 2, 2)) # Get unique zones - unique_zones = np.unique(zones_2d) + unique_zones = np.unique(zones) for zone_id in unique_zones: print(f" Interpolating tensors for zone {zone_id}") @@ -836,7 +1000,7 @@ def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, shape zone_grid_indices = [] for i in range(n_grid): row, col = divmod(i, nx) - if zones_2d[row, col] == zone_id: + if zones[row, col] == zone_id: zone_grid_indices.append(i) zone_grid_indices = np.array(zone_grid_indices) @@ -844,7 +1008,8 @@ def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, shape if len(zone_cp_coords) > 0: # Interpolate tensors within zone using only zone's conceptual points - zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords) + zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, + (len(zone_grid_coords), 2)) geological_tensors[zone_grid_indices] = zone_tensors else: # No conceptual points in zone - use default tensor or nearest zone @@ -856,7 +1021,10 @@ def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, shape def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): - """Interpolate 2x2 tensors using log-Euclidean approach.""" + """ + Interpolate 2x2 tensors using log-Euclidean approach. + """ + shape = infer_grid_shape(grid_coords) n_grid = len(grid_coords) interp_tensors = np.zeros((n_grid, 2, 2)) @@ -865,10 +1033,12 @@ def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): for i in range(2): for j in range(i, 2): values = log_tensors[:, i, j].real - interp_values = ordinary_kriging(cp_coords, values, grid_coords) - interp_tensors[:, i, j] = interp_values + # Use tensor_aware_kriging with shape for 2D output, but we need 1D for tensor interpolation + # This is a special case - convert back to 1D for tensor component interpolation + interp_values_2d = tensor_aware_kriging_1d_output(cp_coords, values, grid_coords, shape) + interp_tensors[:, i, j] = interp_values_2d.flatten() if i != j: - interp_tensors[:, j, i] = interp_values + interp_tensors[:, j, i] = interp_values_2d.flatten() for i in range(n_grid): interp_tensors[i] = expm(interp_tensors[i]) @@ -876,6 +1046,26 @@ def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): return interp_tensors +def tensor_aware_kriging_1d_output(cp_coords, cp_values, grid_coords): + """ + Special version of kriging for tensor interpolation that returns 1D output. + This is only used internally for tensor component interpolation. + """ + shape = infer_grid_shape(grid_coords) + ny, nx = shape + n_grid = len(grid_coords) + interp_values = np.full(n_grid, 0.0) + + # Simple ordinary kriging for tensor components + for i in range(n_grid): + distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + weights = np.exp(-distances / 1000) # Simple exponential weights + if np.sum(weights) > 1e-10: + interp_values[i] = np.sum(weights * cp_values) / np.sum(weights) + + return interp_values + + def generate_correlated_noise_2d(grid_coords, tensors, white_noise, n_neighbors=50, anisotropy_strength=1.0): """Generate spatially correlated noise respecting tensor anisotropy.""" @@ -925,7 +1115,14 @@ def ordinary_kriging(cp_coords, cp_values, grid_coords, sill=1.0, nugget=0.1, background_value=0.0, max_search_radius=1e20, min_points=1, transform=None, min_value=1e-8, max_neighbors=5): - """Standard ordinary kriging with variogram models and search controls.""" + """ + Standard ordinary kriging with variogram models and search controls. + + Returns + ------- + np.ndarray + Interpolated values (1D array) - kept as 1D since this is a utility function + """ n_grid = len(grid_coords) interp_values = np.full(n_grid, background_value) @@ -1004,31 +1201,55 @@ def infer_grid_shape(grid_coords): return (nz, ny, nx) -def save_results(field, shape, field_name): - """Save field as individual layer files.""" +def save_results(field_3d, shape, field_name, original_zones_shape=None): + """ + Save field as individual layer files. + + Parameters + ---------- + field_3d : np.ndarray + Field values, shape (nz, ny, nx) + shape : tuple + Grid shape (nz, ny, nx) + field_name : str + Field name for files + original_zones_shape : tuple, optional + Original input format for potential future use + """ nz, ny, nx = shape - field_3d = field.reshape(shape) os.makedirs('output', exist_ok=True) for layer in range(nz): filename = f"output/{field_name}_layer_{layer + 1:02d}.txt" + # field_3d[layer] is already 2D (ny, nx) np.savetxt(filename, field_3d[layer], fmt='%.6f') print(f"Saved {nz} layer files to output/") -def plot_layer(field, shape, layer=0, field_name='field'): - """Plot and save a single layer of the field.""" +def plot_layer(field_2d, layer_shape, layer=0, field_name='field'): + """ + Plot and save a single layer of the field. + + Parameters + ---------- + field_2d : np.ndarray + Single layer field, shape (ny, nx) + layer_shape : tuple + Layer shape (ny, nx) + layer : int + Layer number for labeling + field_name : str + Field name for files + """ import matplotlib.colors as mcolors colors = ['red', 'orange', 'yellow', 'green', 'darkblue', 'lightblue', 'white'] custom_cmap = mcolors.LinearSegmentedColormap.from_list('custom', colors, N=256) - nz, ny, nx = shape - field_3d = field.reshape(shape) - plt.figure(figsize=(10, 8)) - plt.imshow(field_3d[layer], origin='lower', cmap=custom_cmap) + # field_2d is already 2D (ny, nx) + plt.imshow(field_2d, origin='lower', cmap=custom_cmap) plt.colorbar(label='Parameter Value') plt.title(f'Layer {layer + 1}') plt.xlabel('X') @@ -1080,7 +1301,7 @@ def add_grid_indices_to_conceptual_points(conceptual_points, grid_coords, shape) return conceptual_points_copy -def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, shape, +def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, figsize=(12, 10), save_path=None): """ Plot geology zones with conceptual points overlaid and labeled. @@ -1103,14 +1324,9 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, sha import matplotlib.pyplot as plt import matplotlib.patches as patches + shape = zones.shape ny, nx = shape - # Ensure zones are 2D - if zones.shape == (ny, nx): - zones_2d = zones - else: - zones_2d = zones.reshape(shape) - # Get grid bounds for coordinate transformation x_coords = grid_coords[:, 0].reshape(shape[0], shape[1]) y_coords = grid_coords[:, 1].reshape(shape[0], shape[1]) @@ -1121,14 +1337,14 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, sha fig, ax = plt.subplots(figsize=figsize) # Plot zones with better color handling - unique_zones = np.unique(zones_2d) + unique_zones = np.unique(zones) n_zones = len(unique_zones) # Create a mapping from zone values to sequential colors zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} - zones_remapped = np.zeros_like(zones_2d) + zones_remapped = np.zeros_like(zones) for zone_val, color_idx in zone_to_color.items(): - zones_remapped[zones_2d == zone_val] = color_idx + zones_remapped[zones == zone_val] = color_idx # Plot zones - flip the array itself to match real-world orientation zones_flipped = np.flipud(zones_remapped) @@ -1209,10 +1425,8 @@ def debug_zone_assignments(conceptual_points, grid_coords, zones, shape): ny, nx = shape grid_i, grid_j = int(row['grid_i']), int(row['grid_j']) - if zones.shape == shape: - zone_id = zones[grid_i, grid_j] - else: - zone_id = zones.reshape(shape)[grid_i, grid_j] + # zones are guaranteed to be 2D from entry point + zone_id = zones[grid_i, grid_j] print(f"CP {idx}: ({row['x']:.1f}, {row['y']:.1f}) -> " f"grid[{grid_i},{grid_j}] -> zone {zone_id}") @@ -1258,14 +1472,14 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N # Prepare zone data but don't plot yet if zones is not None: - zones_2d = zones.reshape(shape) if zones.shape != shape else zones + # zones are guaranteed to be 2D from entry point # Create zone color mapping - unique_zones = np.unique(zones_2d) + unique_zones = np.unique(zones) zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} - zones_remapped = np.zeros_like(zones_2d) + zones_remapped = np.zeros_like(zones) for zone_val, color_idx in zone_to_color.items(): - zones_remapped[zones_2d == zone_val] = color_idx + zones_remapped[zones == zone_val] = color_idx # Get coordinate bounds x_coords = grid_coords[:, 0].reshape(shape) @@ -1403,128 +1617,19 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N plt.tight_layout() plt.show() - """ - Visualize geological tensors as ellipses overlaid on zones. - Parameters - ---------- - geological_tensors : np.ndarray - Tensor field, shape (n_points, 2, 2) - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 2) or (n_points, 3) - shape : tuple - Grid shape (ny, nx) - zones : np.ndarray, optional - Zone array for background - subsample : int - Show every Nth tensor (for readability) - scale_factor : float - Scale ellipses for visibility - """ - ny, nx = shape - - fig, ax = plt.subplots(figsize=figsize) - - # Prepare zone data but don't plot yet - if zones is not None: - zones_2d = zones.reshape(shape) if zones.shape != shape else zones - # Create zone color mapping - unique_zones = np.unique(zones_2d) - zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} - zones_remapped = np.zeros_like(zones_2d) - for zone_val, color_idx in zone_to_color.items(): - zones_remapped[zones_2d == zone_val] = color_idx - - # Get coordinate bounds - x_coords = grid_coords[:, 0].reshape(shape) - y_coords = grid_coords[:, 1].reshape(shape) - x_min, x_max = x_coords.min(), x_coords.max() - y_min, y_max = y_coords.min(), y_coords.max() - - zones_flipped = np.flipud(zones_remapped) - # Plot zones as background with low alpha - im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], - origin='lower', cmap='tab10', alpha=0.2, zorder=0, - vmin=0, vmax=len(unique_zones) - 1) - - # Subsample points for visualization - indices = np.arange(0, len(geological_tensors), subsample) - - print(f"Plotting {len(indices)} tensors out of {len(geological_tensors)}") - - default_count = 0 - ellipse_count = 0 - - for i in indices: - tensor = geological_tensors[i] - x, y = grid_coords[i, 0], grid_coords[i, 1] - - # Check for default tensor (indicates no interpolation happened) - if np.allclose(tensor, np.eye(2) * 1000000): - # Plot as red circle for default tensors with high zorder - ax.plot(x, y, 'ro', markersize=3, alpha=0.9, zorder=3) - default_count += 1 - continue - - try: - # Eigendecomposition to get ellipse parameters - eigenvals, eigenvecs = np.linalg.eigh(tensor) - - # Sort by eigenvalue magnitude - idx = np.argsort(eigenvals)[::-1] - eigenvals = eigenvals[idx] - eigenvecs = eigenvecs[:, idx] - - # Ellipse dimensions (correlation lengths) - make them more visible - major_axis = 2 * np.sqrt(eigenvals[0]) / scale_factor - minor_axis = 2 * np.sqrt(eigenvals[1]) / scale_factor - - # Make sure ellipses are visible - if major_axis < 50: # Minimum size - major_axis = 50 - if minor_axis < 25: - minor_axis = 25 - - # Ellipse orientation (angle of major axis) - angle = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) - - # Create ellipse with higher zorder to appear on top - ellipse = patches.Ellipse((x, y), major_axis, minor_axis, - angle=angle, linewidth=2, - edgecolor='blue', facecolor='none', alpha=1.0, zorder=2) - ax.add_patch(ellipse) - ellipse_count += 1 - - except: - # If tensor is problematic, plot as yellow point with high zorder - ax.plot(x, y, 'yo', markersize=3, zorder=3) - - print(f"Plotted {default_count} default tensors (red circles) and {ellipse_count} ellipses") - - ax.set_xlabel('X (NZTM)') - ax.set_ylabel('Y (NZTM)') - ax.set_title( - f'Geological Tensors (subsampled 1:{subsample})\nBlue ellipses = anisotropy, Red dots = default tensors') - ax.set_aspect('equal', adjustable='box') - - # Add colorbar for zones if present - if zones is not None: - cbar = plt.colorbar(im, ax=ax, shrink=0.8) - cbar.set_label('Zone ID') - cbar.set_ticks(range(len(unique_zones))) - cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) - - plt.tight_layout() - plt.show() - - -def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coords, zones, shape): +def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coords, zones): """ Compare conceptual point bearings with actual tensor orientations at those locations. + + Parameters + ---------- + zones : np.ndarray + Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point """ + shape = zones.shape ny, nx = shape - zones_2d = zones.reshape(shape) if zones.shape != shape else zones print("Conceptual Point vs Tensor Orientation Analysis:") print("=" * 70) @@ -1542,7 +1647,7 @@ def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coor grid_row, grid_col = divmod(closest_idx, nx) # Get zone - zone_id = zones_2d[grid_row, grid_col] + zone_id = zones[grid_row, grid_col] # Get tensor at that location tensor = geological_tensors[closest_idx] @@ -1573,12 +1678,17 @@ def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coor print("-" * 70) -def analyze_tensor_statistics(geological_tensors, grid_coords, zones, shape): +def analyze_tensor_statistics(geological_tensors, grid_coords, zones): """ Print statistics about the tensor field by zone. + + Parameters + ---------- + zones : np.ndarray + Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point """ + shape = zones.shape ny, nx = shape - zones_2d = zones.reshape(shape) if zones.shape != shape else zones print("Tensor Analysis by Zone:") print("=" * 50) @@ -1588,7 +1698,7 @@ def analyze_tensor_statistics(geological_tensors, grid_coords, zones, shape): zone_indices = [] for i in range(len(grid_coords)): row, col = divmod(i, nx) - if zones_2d[row, col] == zone_id: + if zones[row, col] == zone_id: zone_indices.append(i) if len(zone_indices) == 0: @@ -1628,6 +1738,10 @@ def analyze_tensor_statistics(geological_tensors, grid_coords, zones, shape): print() +# ============================================================================ +# EXAMPLE USAGE - UNCHANGED FUNCTIONALITY +# ============================================================================ + # Example usage if __name__ == "__main__": """ From 404e14fae3607bb30ed48d3945e047537959f3fa Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 9 Jul 2025 17:03:49 +1200 Subject: [PATCH 05/36] minor tweaks, good version --- pyemu/utils/tensor_utils.py | 146 ++++++++++++++++++------------------ 1 file changed, 73 insertions(+), 73 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 7f5449356..89d3f8f89 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -142,7 +142,8 @@ def validate_grid_coordinates(grid_coords, shape): # MAIN ENTRY POINT # ============================================================================ -def main(conceptual_points_file, grid_file, zone_file=None, field_name=['field'], layer_mode=True): +def main(conceptual_points_file, grid_file, zone_file=None, + field_name=['field'], layer_mode=True, save_path='.'): """ Generate spatially correlated fields using tensor-based geostatistics. @@ -194,7 +195,6 @@ def main(conceptual_points_file, grid_file, zone_file=None, field_name=['field'] # NOW we can standardize zones using the shape zones = None - original_zones_shape = None if zone_file: zones_raw = np.loadtxt(zone_file) @@ -209,24 +209,24 @@ def main(conceptual_points_file, grid_file, zone_file=None, field_name=['field'] # Process by layer cols = cols + ['layer'] field = generate_field_by_layer(conceptual_points[cols], grid_coords, - shape, zones=zones) + shape, zones=zones, save_path=save_path) else: # Process full 3D cols = cols + ['z'] field = generate_field_3d(conceptual_points[cols], grid_coords, shape) - # Save results - field is now 3D array (nz, ny, nx) - save_results(field, shape, field_name, original_zones_shape) - - # Plot first layer - field[0] is now 2D - plot_layer(field[0], shape[1:], layer=0, field_name=field_name) - + # Save results - field is now array or original shape + for i in conceptual_points.layer.unique(): + save_layer(field[i-1], layer=i, field_name=fn, save_path=save_path) + # Plot based on layer in conceptual points + for i in conceptual_points.layer.unique(): + plot_layer(np.where(zones==0,np.nan, field[i-1]), layer=i, field_name=fn, save_path=save_path) print("\n=== Complete ===") print(f"Field shape: {field.shape}") # Now shows (nz, ny, nx) print(f"Field statistics: mean={np.mean(field):.3f}, std={np.std(field):.3f}") -def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None): +def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None, save_path='.'): """ Generate field processing each layer independently. @@ -250,7 +250,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None): # Get unique layers if 'layer' in conceptual_points.columns: - unique_layers = sorted([int(x) for x in conceptual_points['layer'].unique()]) + unique_layers = sorted([int(x-1) for x in conceptual_points['layer'].unique()]) print(f"Processing layers: {unique_layers}") for layer_idx in unique_layers: @@ -262,7 +262,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None): layer_shape = (ny, nx) # Shape for this layer if 'layer' in conceptual_points.columns: - layer_cp = conceptual_points[conceptual_points['layer'] == layer_idx] + layer_cp = conceptual_points[conceptual_points['layer']-1 == layer_idx] if len(layer_cp) == 0: print(f" No conceptual points for layer {layer_idx}, using all") layer_cp = conceptual_points @@ -279,15 +279,21 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None): # zones are already guaranteed to be 2D from standardize_input_arrays() # Generate field for this layer - now returns 2D - layer_field_2d = generate_single_layer_zone_based(layer_cp, layer_coords_2d, - zones=layer_zones, shape=layer_shape) + layer_field_2d, geological_tensors = generate_single_layer_zone_based(layer_cp, layer_coords_2d, + zones=layer_zones, shape=layer_shape, + save_path=save_path) + visualize_geological_tensors(geological_tensors, layer_coords_2d, layer_shape, zones=layer_zones, + conceptual_points=layer_cp, subsample=4, + save_path=save_path, title_suf=f'layer {layer_idx+1}') + # layer_field_2d is already 2D field_3d[layer_idx] = layer_field_2d return field_3d # Return as 3D array, NOT flattened -def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, zones=None, shape=None): +def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, + zones=None, shape=None, save_path='.'): """ Modified generate_single_layer with zone-based tensor interpolation. """ @@ -302,13 +308,6 @@ def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, zones=No if not shape: shape = infer_grid_shape(grid_coords_2d) - # x_unique = np.unique(grid_coords_2d[:, 0]) - # y_unique = np.unique(grid_coords_2d[:, 1]) - # shape = (len(y_unique), len(x_unique)) - # print(f" Inferred grid dimensions: ny={shape[0]}, nx={shape[1]}") - - # Zones are now guaranteed to be 2D from entry point, no conversion needed - # All internal processing uses consistent 2D arrays # Step 1: Create geological tensors from conceptual points print(" Creating geological correlation tensors...") @@ -385,7 +384,7 @@ def normalize_bearing(bearing): # Step 7: Combine into lognormal field - all operations in 2D field_2d = 10 ** (np.log10(interp_means_2d) + correlated_noise_2d * interp_sd_2d) - return field_2d + return field_2d, geological_tensors def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zones): @@ -1201,65 +1200,61 @@ def infer_grid_shape(grid_coords): return (nz, ny, nx) -def save_results(field_3d, shape, field_name, original_zones_shape=None): +def save_layer(field, layer, field_name, save_path='.'): """ Save field as individual layer files. Parameters ---------- - field_3d : np.ndarray - Field values, shape (nz, ny, nx) + field : np.ndarray + Field array, shape (nz, ny, nx) for 3D or (ny, nx) for 2D shape : tuple - Grid shape (nz, ny, nx) - field_name : str - Field name for files - original_zones_shape : tuple, optional - Original input format for potential future use + Grid shape (nz, ny, nx) or (ny, nx) + field_name : str or list + Field name(s) used for filename + save_path : str + Directory to save output files """ - nz, ny, nx = shape + if isinstance(field_name, list): + field_name = field_name[0] - os.makedirs('output', exist_ok=True) - - for layer in range(nz): - filename = f"output/{field_name}_layer_{layer + 1:02d}.txt" - # field_3d[layer] is already 2D (ny, nx) - np.savetxt(filename, field_3d[layer], fmt='%.6f') - - print(f"Saved {nz} layer files to output/") + os.makedirs(save_path, exist_ok=True) + filename = os.path.join(save_path, f'{field_name}_layer_{layer + 1:02d}.arr') + np.savetxt(filename, field, fmt='%.6f') + print(f"Saved layer {layer} to '{filename}'") -def plot_layer(field_2d, layer_shape, layer=0, field_name='field'): - """ - Plot and save a single layer of the field. - - Parameters - ---------- - field_2d : np.ndarray - Single layer field, shape (ny, nx) - layer_shape : tuple - Layer shape (ny, nx) - layer : int - Layer number for labeling - field_name : str - Field name for files - """ +def plot_layer(field, layer=0, field_name='field', save_path='.', transform='log'): import matplotlib.colors as mcolors - colors = ['red', 'orange', 'yellow', 'green', 'darkblue', 'lightblue', 'white'] + if transform=='log': + field = np.log10(field) + + colors = [ + (0.00, 'white'), + (0.05, 'lightblue'), + (0.15, 'skyblue'), # intermediate between lightblue and darkblue + (0.25, 'blue'), + (0.35, 'teal'), # softer transition out of dark blue + (0.45, 'green'), + (0.60, 'yellowgreen'), # bridge between green and yellow + (0.70, 'yellow'), + (0.85, 'orange'), + (1.00, 'red'), + ] + custom_cmap = mcolors.LinearSegmentedColormap.from_list('custom', colors, N=256) plt.figure(figsize=(10, 8)) - # field_2d is already 2D (ny, nx) - plt.imshow(field_2d, origin='lower', cmap=custom_cmap) + plt.imshow(field, cmap=custom_cmap) plt.colorbar(label='Parameter Value') - plt.title(f'Layer {layer + 1}') + plt.title(f'{field_name} for layer {layer}') plt.xlabel('X') plt.ylabel('Y') os.makedirs('output', exist_ok=True) - plt.savefig(f'output/{field_name}_layer_{layer + 1:02d}.png', dpi=150) + plt.savefig(os.path.join(save_path, f'{field_name}_layer_{layer:02d}.png'), dpi=150) plt.close() - def add_grid_indices_to_conceptual_points(conceptual_points, grid_coords, shape): """ Add i,j grid indices to conceptual points dataframe. @@ -1302,7 +1297,7 @@ def add_grid_indices_to_conceptual_points(conceptual_points, grid_coords, shape) def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, - figsize=(12, 10), save_path=None): + figsize=(12, 10), save_path='.'): """ Plot geology zones with conceptual points overlaid and labeled. @@ -1402,14 +1397,13 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, plt.tight_layout() - if save_path: - plt.savefig(save_path, dpi=150, bbox_inches='tight') - print(f"Plot saved to: {save_path}") + plt.savefig(os.path.join(save_path, f'conceptual_points.png'), dpi=150, bbox_inches='tight') + print(f"Plot saved to: {os.path.join(save_path, f'{save_path}/conceptual_points.png')}") - plt.show() + plt.close() -def debug_zone_assignments(conceptual_points, grid_coords, zones, shape): +def debug_zone_assignments(conceptual_points, grid_coords, zones, shape, save_path='.'): """ Complete workflow to debug zone assignments. """ @@ -1434,8 +1428,7 @@ def debug_zone_assignments(conceptual_points, grid_coords, zones, shape): # Create plot plot_zones_with_conceptual_points( zones, cp_with_indices, grid_coords, shape, - save_path='output/zone_debug.png' - ) + save_path=os.path.join(save_path, f'zone_debug.png')) return cp_with_indices @@ -1445,7 +1438,8 @@ def debug_zone_assignments(conceptual_points, grid_coords, zones, shape): def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=None, - conceptual_points=None, subsample=4, scale_factor=50, figsize=(12, 10)): + conceptual_points=None, subsample=4, scale_factor=20, + figsize=(12, 10), title_suf=None, save_path='.'): """ Visualize geological tensors as ellipses overlaid on zones, with conceptual points as oriented lines. @@ -1583,7 +1577,10 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N ax.set_ylabel('Y (NZTM)') # Update title and legend - title_parts = [f'Geological Tensors (subsampled 1:{subsample})'] + title_parts = [f'Geological Tensors (subsampled 1:{subsample})\n'] + if title_suf is not None: + title_parts += [f'{title_suf}'] + # more like legend? if conceptual_points is not None: title_parts.append('Red lines = conceptual points (bearing)') title_parts.append('Blue ellipses = interpolated tensors') @@ -1616,7 +1613,9 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N ax.legend(handles=legend_elements, loc='upper right', fontsize=9) plt.tight_layout() - plt.show() + plt.savefig(os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png'), dpi=150, bbox_inches='tight') + print(f"Saved tensor visualization to {os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png')}") + plt.close() def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coords, zones): @@ -1760,5 +1759,6 @@ def analyze_tensor_statistics(geological_tensors, grid_coords, zones): grid_file=r'C:\modelling\sva_ds\data\grid.dat', zone_file=r'C:\modelling\sva_ds\data\waq_arr.geoclass_simple.arr', field_name=['kh'], - layer_mode=True + layer_mode=True, + save_path=r'C:\modelling\sva_ds\data\output' ) \ No newline at end of file From e4b9eda5ca7072b62e2f19addc9e5cf66aa3816c Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Thu, 10 Jul 2025 15:05:55 +1200 Subject: [PATCH 06/36] external iid files, small tweaks --- pyemu/utils/tensor_utils.py | 57 +++++++++++++++++++++++-------------- 1 file changed, 36 insertions(+), 21 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 89d3f8f89..1df7f0324 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -142,7 +142,7 @@ def validate_grid_coordinates(grid_coords, shape): # MAIN ENTRY POINT # ============================================================================ -def main(conceptual_points_file, grid_file, zone_file=None, +def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, field_name=['field'], layer_mode=True, save_path='.'): """ Generate spatially correlated fields using tensor-based geostatistics. @@ -189,6 +189,17 @@ def main(conceptual_points_file, grid_file, zone_file=None, shape = infer_grid_shape(grid_coords) print(f"Grid shape: {shape} (nz, ny, nx)") + if iids_file is not None: + os.path.join(os.path.dirname(conceptual_points_file), 'iids.arr') + iids = np.loadtxt(iids_file) + else: + print(" Generating correlated noise...") + np.random.seed(42) + iids = np.random.randn(*shape) + for i in range(iids.shape[0]): + iids_file = os.path.join(os.path.dirname(conceptual_points_file), f'iids_layer{i+1}.arr') + np.savetxt(iids_file, iids[i]) + # Validate grid coordinates if not validate_grid_coordinates(grid_coords, shape): raise ValueError(f"Grid coordinates ({len(grid_coords)} points) don't match inferred shape {shape}") @@ -208,7 +219,7 @@ def main(conceptual_points_file, grid_file, zone_file=None, if layer_mode and 'layer' in conceptual_points.columns: # Process by layer cols = cols + ['layer'] - field = generate_field_by_layer(conceptual_points[cols], grid_coords, + field = generate_field_by_layer(conceptual_points[cols], grid_coords, iids, shape, zones=zones, save_path=save_path) else: # Process full 3D @@ -226,7 +237,8 @@ def main(conceptual_points_file, grid_file, zone_file=None, print(f"Field statistics: mean={np.mean(field):.3f}, std={np.std(field):.3f}") -def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None, save_path='.'): +def generate_field_by_layer(conceptual_points, grid_coords, iids, + shape, zones=None, save_path='.'): """ Generate field processing each layer independently. @@ -256,10 +268,11 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None, s for layer_idx in unique_layers: print(f"\nProcessing layer {layer_idx}/{len(unique_layers)}") - # Get grid points for this layer + # Get grid points and iids for this layer layer_grid = grid_3d[layer_idx].reshape(-1, 3) - layer_coords_2d = layer_grid[:, :2] # Just x, y + layer_coords = layer_grid[:, :2] # Just x, y layer_shape = (ny, nx) # Shape for this layer + layer_iids = iids[layer_idx] if 'layer' in conceptual_points.columns: layer_cp = conceptual_points[conceptual_points['layer']-1 == layer_idx] @@ -279,10 +292,10 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None, s # zones are already guaranteed to be 2D from standardize_input_arrays() # Generate field for this layer - now returns 2D - layer_field_2d, geological_tensors = generate_single_layer_zone_based(layer_cp, layer_coords_2d, - zones=layer_zones, shape=layer_shape, + layer_field_2d, geological_tensors = generate_single_layer_zone_based(layer_cp, layer_coords, + layer_iids, zones=layer_zones, shape=layer_shape, save_path=save_path) - visualize_geological_tensors(geological_tensors, layer_coords_2d, layer_shape, zones=layer_zones, + visualize_geological_tensors(geological_tensors, layer_coords, layer_shape, zones=layer_zones, conceptual_points=layer_cp, subsample=4, save_path=save_path, title_suf=f'layer {layer_idx+1}') @@ -292,7 +305,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, shape, zones=None, s return field_3d # Return as 3D array, NOT flattened -def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, +def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, iids, zones=None, shape=None, save_path='.'): """ Modified generate_single_layer with zone-based tensor interpolation. @@ -376,9 +389,7 @@ def normalize_bearing(bearing): # Step 6: Generate correlated noise - work with 1D for noise generation, then reshape print(" Generating correlated noise...") - np.random.seed(420) - white_noise = np.random.randn(len(grid_coords_2d)) - correlated_noise_1d = generate_correlated_noise_2d(grid_coords_2d, geological_tensors, white_noise) + correlated_noise_1d = generate_correlated_noise_2d(grid_coords_2d, geological_tensors, iids) correlated_noise_2d = correlated_noise_1d.reshape(shape) # Step 7: Combine into lognormal field - all operations in 2D @@ -1065,7 +1076,7 @@ def tensor_aware_kriging_1d_output(cp_coords, cp_values, grid_coords): return interp_values -def generate_correlated_noise_2d(grid_coords, tensors, white_noise, +def generate_correlated_noise_2d(grid_coords, tensors, iids, n_neighbors=50, anisotropy_strength=1.0): """Generate spatially correlated noise respecting tensor anisotropy.""" n_points = len(grid_coords) @@ -1098,13 +1109,13 @@ def generate_correlated_noise_2d(grid_coords, tensors, white_noise, # Use anisotropic distance instead of actual distance for weight calculation # This respects the tensor-defined correlation structure weight = np.exp(-(aniso_distances[j]) ** 2) - weighted_sum += weight * white_noise[j] + weighted_sum += weight * iids.flatten()[j] sum_weights += weight if sum_weights > 1e-10: correlated_noise[i] = weighted_sum / sum_weights else: - correlated_noise[i] = white_noise[i] + correlated_noise[i] = iids[i] return correlated_noise @@ -1230,16 +1241,20 @@ def plot_layer(field, layer=0, field_name='field', save_path='.', transform='log field = np.log10(field) colors = [ - (0.00, 'white'), + (0.00, 'ghostwhite'), (0.05, 'lightblue'), - (0.15, 'skyblue'), # intermediate between lightblue and darkblue + (0.15, 'skyblue'), (0.25, 'blue'), - (0.35, 'teal'), # softer transition out of dark blue + (0.35, 'teal'), (0.45, 'green'), - (0.60, 'yellowgreen'), # bridge between green and yellow - (0.70, 'yellow'), + (0.50, 'limegreen'), + (0.55, 'yellowgreen'), + (0.65, 'yellow'), + (0.75, 'gold'), (0.85, 'orange'), - (1.00, 'red'), + (0.90, 'darkorange'), + (0.95, 'peru'), + (1.00, 'sienna') ] custom_cmap = mcolors.LinearSegmentedColormap.from_list('custom', colors, N=256) From 17ee75883c7f3ae6ae185e76cc4e1c3f55f31f50 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 18 Jul 2025 10:29:20 +1200 Subject: [PATCH 07/36] create 3d tensors --- pyemu/utils/tensor_utils.py | 652 +++++++++++++++++++++++------------- 1 file changed, 424 insertions(+), 228 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 1df7f0324..b16cdc0c6 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -10,7 +10,7 @@ # ============================================================================ -# UTILITY FUNCTIONS FOR CONSISTENT 2D ARRAY HANDLING +# UTILITY FUNCTIONS FOR CONSISTENT ARRAY HANDLING # ============================================================================ def standardize_input_arrays(zones_raw, grid_coords, shape): @@ -138,18 +138,14 @@ def validate_grid_coordinates(grid_coords, shape): return grid_coords.shape[0] == expected_points -# ============================================================================ -# MAIN ENTRY POINT -# ============================================================================ -def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, - field_name=['field'], layer_mode=True, save_path='.'): +def generate_fields_from_files(conceptual_points_file, grid_file, zone_file=None, iids_file=None, + field_name=['field'], layer_mode=True, save_path='.', tensor_interp='idw'): """ Generate spatially correlated fields using tensor-based geostatistics. - This is the main entry point for tensor-based field generation. It reads - conceptual points (control points) with anisotropy parameters and generates - correlated fields on a specified grid. + This reads conceptual points (control points) with anisotropy parameters from files + and generates tensor-based correlated fields on a specified regular grid. Parameters ---------- @@ -161,10 +157,18 @@ def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, Path to whitespace-delimited file with grid coordinates (columns: x, y, z) zone_file : str, optional Path to whitespace-delimited array file with zones + iids_file : str, optional + Path to independent identically distributed noise file. If None, + auto-generates correlated noise and saves as iids_layer{N}.arr files field_name : list of str, default ['field'] Name(s) of field(s) to generate. Must match column prefixes in conceptual_points layer_mode : bool, default True If True, process each layer independently (recommended for 3D grids) + save_path : str, default '.' + Directory path to save output files + tensor_interp : str, default 'idw' + Tensor interpolation method: 'idw' (inverse distance weighting), + 'krig' (kriging), or 'nearest' (zone-based nearest neighbor) Returns ------- @@ -173,9 +177,12 @@ def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, Examples -------- - >>> main('my_points.csv', 'my_grid.dat', field_name=['kh'], layer_mode=True) + >>> generate_fields_from_files('my_points.csv', 'my_grid.dat', + ... field_name=['kh'], layer_mode=True) """ - print("=== Tensor-Based Non-Stationary Field Generation ===") + print(f"=== Tensor-Based Non-Stationary Field Generation from {conceptual_points_file}===") + if not os.path.exists(save_path): + os.mkdir(save_path) # Load data conceptual_points = pd.read_csv(conceptual_points_file) @@ -185,7 +192,7 @@ def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, print(f"Loaded {len(conceptual_points)} conceptual points") print(f"Loaded {len(grid_coords)} grid points") - # Infer grid structure FIRST + # Infer grid structure FIRST, for the lazy amongst us shape = infer_grid_shape(grid_coords) print(f"Grid shape: {shape} (nz, ny, nx)") @@ -204,7 +211,7 @@ def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, if not validate_grid_coordinates(grid_coords, shape): raise ValueError(f"Grid coordinates ({len(grid_coords)} points) don't match inferred shape {shape}") - # NOW we can standardize zones using the shape + # standardize zones using the shape zones = None if zone_file: @@ -220,7 +227,8 @@ def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, # Process by layer cols = cols + ['layer'] field = generate_field_by_layer(conceptual_points[cols], grid_coords, iids, - shape, zones=zones, save_path=save_path) + shape, zones=zones, save_path=save_path, + tensor_interp=tensor_interp) else: # Process full 3D cols = cols + ['z'] @@ -238,7 +246,8 @@ def main(conceptual_points_file, grid_file, zone_file=None, iids_file=None, def generate_field_by_layer(conceptual_points, grid_coords, iids, - shape, zones=None, save_path='.'): + shape, zones=None, save_path='.', + tensor_interp='idw'): """ Generate field processing each layer independently. @@ -248,13 +257,21 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, Conceptual points with tensor and statistical parameters grid_coords : np.ndarray Grid coordinates, shape (n_points, 3) + iids : np.ndarray + Independent identically distributed random noise, shape (nz, ny, nx) shape : tuple Grid shape as (nz, ny, nx) + zones : np.ndarray, optional + Zone IDs for zone-based processing, shape (ny, nx) + save_path : str, default '.' + Directory path to save output files + tensor_interp : str, default 'idw' + Tensor interpolation method: 'idw', 'krig', or 'nearest' Returns ------- np.ndarray - Flattened field values + Field values, shape (nz, ny, nx) """ nz, ny, nx = shape field_3d = np.zeros(shape) @@ -294,7 +311,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, # Generate field for this layer - now returns 2D layer_field_2d, geological_tensors = generate_single_layer_zone_based(layer_cp, layer_coords, layer_iids, zones=layer_zones, shape=layer_shape, - save_path=save_path) + save_path=save_path, tensor_interp=tensor_interp) visualize_geological_tensors(geological_tensors, layer_coords, layer_shape, zones=layer_zones, conceptual_points=layer_cp, subsample=4, save_path=save_path, title_suf=f'layer {layer_idx+1}') @@ -306,9 +323,34 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, iids, - zones=None, shape=None, save_path='.'): + zones=None, shape=None, save_path='.', + tensor_interp='idw'): """ Modified generate_single_layer with zone-based tensor interpolation. + + Parameters + ---------- + conceptual_points : pd.DataFrame + Conceptual points with tensor and statistical parameters + grid_coords_2d : np.ndarray + Grid coordinates for single layer, shape (n_points, 2) or (n_points, 3) + iids : np.ndarray + Independent identically distributed random noise for this layer + zones : np.ndarray, optional + Zone IDs, shape (ny, nx) + shape : tuple, optional + Grid shape (ny, nx). If None, inferred from grid_coords_2d + save_path : str, default '.' + Directory path to save output files + tensor_interp : str, default 'idw' + Tensor interpolation method: 'idw', 'krig', or 'nearest' + + Returns + ------- + tuple + (field_2d, geological_tensors) where: + - field_2d: Generated field, shape (ny, nx) + - geological_tensors: Interpolated tensors, shape (n_points, 2, 2) """ cp_coords = conceptual_points[['x', 'y']].values cp_means = conceptual_points.filter(like='mean').values.flatten() @@ -345,15 +387,16 @@ def normalize_bearing(bearing): extracted_bearing = get_tensor_bearing(cp_tensors[i], original_bearing) print(f" Point {i}: Input bearing={original_bearing:.1f}°, Extracted={extracted_bearing:.1f}°") - # Step 2: Zone-based tensor interpolation + # Step 2: Interpolate tensors, can be fast/coarse zone based if zones is not None: print(" Interpolating geological tensors by zone...") - geological_tensors = interpolate_tensors_by_zone_nearest( - cp_coords, cp_tensors, grid_coords_2d, zones + geological_tensors = interpolate_tensors_by_zone( + cp_coords, cp_tensors, grid_coords_2d, zones, tensor_interp=tensor_interp ) else: print(" Interpolating geological tensors globally...") - geological_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d) + geological_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d, + tensor_interp=tensor_interp) # Step 3: Interpolate means using zone-aware kriging - returns 2D print(" Interpolating means with geological structure...") @@ -398,7 +441,7 @@ def normalize_bearing(bearing): return field_2d, geological_tensors -def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zones): +def _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones): """Zone-based nearest neighbor tensor assignment - no interpolation. Parameters @@ -434,7 +477,7 @@ def interpolate_tensors_by_zone_nearest(cp_coords, cp_tensors, grid_coords, zone if zones[row, col] == zone_id: zone_grid_indices.append(i) - zone_grid_indices = np.array(zone_grid_indices) + zone_grid_indices = np.array(zone_grid_indices, dtype=int) if len(zone_cp_coords) > 0: # Assign nearest neighbor tensor (no interpolation) @@ -506,7 +549,6 @@ def create_boundary_modified_scalar(base_field_2d, zones, return modified - def create_boundary_enhanced_tensors(geological_tensors, zones, boundary_anisotropy=10.0, boundary_major_scale=2.0, transition_cells=3): @@ -514,17 +556,20 @@ def create_boundary_enhanced_tensors(geological_tensors, zones, Enhance geological tensors at zone boundaries with increased anisotropy. Uses distance-based transition from geological to boundary behavior. + Note: Alternative boundary enhancement method. Implemented but found to be + suboptimal compared to create_boundary_modified_scalar(). Kept for reference. + Parameters ---------- geological_tensors : np.ndarray Base geological tensors, shape (n_points, 2, 2) zones : np.ndarray - Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point - boundary_anisotropy : float + Zone IDs, shape (ny, nx) + boundary_anisotropy : float, default 10.0 Anisotropy ratio at boundaries (major/minor) - boundary_major_scale : float + boundary_major_scale : float, default 2.0 Scale factor for major axis at boundaries - transition_cells : int + transition_cells : int, default 3 Number of cells over which to transition from geological to boundary Returns @@ -625,14 +670,14 @@ def detect_zone_boundaries(zones): Parameters ---------- zones : np.ndarray - Zone IDs, shape (ny, nx) - GUARANTEED to be 2D + Zone IDs, shape (ny, nx) Returns ------- tuple (boundary_mask, boundary_directions) where: - - boundary_mask: bool array, shape (ny, nx) - - boundary_directions: direction vectors, shape (ny, nx, 2) + - boundary_mask: Boolean array indicating boundary points, shape (ny, nx) + - boundary_directions: Direction vectors at boundaries, shape (ny, nx, 2) """ ny, nx = zones.shape boundary_mask = np.zeros((ny, nx), dtype=bool) @@ -747,13 +792,39 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, Parameters ---------- + cp_coords : np.ndarray + Conceptual point coordinates, shape (n_cp, 2) + cp_values : np.ndarray + Values at conceptual points, shape (n_cp,) + grid_coords : np.ndarray + Grid coordinates, shape (n_grid, 2) or (n_grid, 3) + interp_tensors : np.ndarray + Interpolated tensors at grid points, shape (n_grid, 2, 2) + variogram_model : str, default 'exponential' + Variogram model: 'exponential', 'gaussian', or 'spherical' + sill : float, default 1.0 + Variogram sill parameter + nugget : float, default 0.01 + Variogram nugget parameter + background_value : float, default 0.0 + Default value for points with insufficient neighbors + max_search_radius : float, default 1e20 + Maximum search radius for neighbors + min_points : int, default 1 + Minimum number of points required for interpolation + max_neighbors : int, default 4 + Maximum number of neighbors to use + transform : str, optional + Data transformation: 'log' for log-transform, None for no transform + min_value : float, default 1e-8 + Minimum value for log transform zones : np.ndarray, optional - Zone IDs, shape (ny, nx) - 2D array + Zone IDs for zone-aware kriging, shape (ny, nx) Returns ------- np.ndarray - Interpolated values, shape (ny, nx) - ALWAYS 2D + Interpolated values, shape (ny, nx) """ n_grid = len(grid_coords) @@ -884,189 +955,69 @@ def variogram(h): return interp_values_1d.reshape(shape) -def create_2d_tensors(theta, major, minor): - """Create 2x2 anisotropy tensors from geostatistical parameters.""" - theta = np.atleast_1d(theta) - major = np.atleast_1d(major) - minor = np.atleast_1d(minor) - - n = len(theta) - tensors = np.zeros((n, 2, 2)) - - for i in range(n): - # For geological bearing (clockwise from north): - # North = [0, 1], East = [1, 0] - # Bearing 5° should give direction [x, y] = [sin(5°), cos(5°)] = [0.087, 0.996] - - bearing_rad = theta[i] - - # Direction vector for this bearing - east_comp = np.sin(bearing_rad) # X component - north_comp = np.cos(bearing_rad) # Y component - - # Create rotation matrix to align [1,0] with bearing direction - cos_t = north_comp - sin_t = east_comp - - # clockwise rotation by angle theta - R = np.array([[cos_t, sin_t], - [-sin_t, cos_t]]) - S = np.diag([minor[i] ** 2, major[i] ** 2]) - tensors[i] = R @ S @ R.T - - return tensors.squeeze() if n == 1 else tensors - - -def get_tensor_bearing(tensor, expected_bearing=None): - """Extract geological bearing from tensor.""" - eigenvals, eigenvecs = np.linalg.eigh(tensor) - max_idx = np.argmax(eigenvals) - major_eigenvec = eigenvecs[:, max_idx] - - # Swap components to match geological convention: [north, east] instead of [east, north] - north_component = major_eigenvec[1] # y-component is north - east_component = major_eigenvec[0] # x-component is east - - # Calculate bearing from north (geological convention) - angle1 = np.degrees(np.arctan2(east_component, north_component)) - if angle1 < 0: - angle1 += 360 - angle2 = (angle1 + 180) % 360 - - # Choose correct direction if expected bearing provided - if expected_bearing is not None: - diff1 = min(abs(angle1 - expected_bearing), abs(angle1 - expected_bearing + 360), - abs(angle1 - expected_bearing - 360)) - diff2 = min(abs(angle2 - expected_bearing), abs(angle2 - expected_bearing + 360), - abs(angle2 - expected_bearing - 360)) - return angle1 if diff1 < diff2 else angle2 - - return angle1 - - -def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): +def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords, tensor_interp='idw', zones=None): """ - Extract zone assignment logic from tensor_aware_kriging into reusable function. - - Parameters - ---------- - zones : np.ndarray - Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point - """ - ny, nx = zones.shape - - cp_zones = [] - for coord in cp_coords: - # Find closest grid point to get zone - distances = np.sum((grid_coords - coord) ** 2, axis=1) - closest_idx = np.argmin(distances) - row, col = divmod(closest_idx, nx) - cp_zones.append(zones[row, col]) - - return np.array(cp_zones) - - -def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones): - """ - Zone-aware tensor interpolation - interpolate tensors within each zone separately. + Interpolate 2x2 tensors using log-Euclidean approach. Parameters ---------- cp_coords : np.ndarray - Conceptual point coordinates + Conceptual point coordinates, shape (n_cp, 2) cp_tensors : np.ndarray - Conceptual point tensors + Tensors at conceptual points, shape (n_cp, 2, 2) grid_coords : np.ndarray - Grid coordinates - zones : np.ndarray - Zone array, shape (ny, nx) - GUARANTEED to be 2D from entry point + Grid coordinates, shape (n_grid, 2) or (n_grid, 3) + tensor_interp : str, default 'idw' + Interpolation method: 'idw', 'krig', or 'nearest' + zones : np.ndarray, optional + Zone IDs for zone-based nearest neighbor, shape (ny, nx) Returns ------- np.ndarray Interpolated tensors, shape (n_grid, 2, 2) """ - n_grid = len(grid_coords) - ny, nx = zones.shape - - # Get zone for each conceptual point - cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) - - # Initialize output tensors - geological_tensors = np.zeros((n_grid, 2, 2)) - - # Get unique zones - unique_zones = np.unique(zones) - - for zone_id in unique_zones: - print(f" Interpolating tensors for zone {zone_id}") - - # Find conceptual points in this zone - zone_cp_mask = cp_zones == zone_id - zone_cp_coords = cp_coords[zone_cp_mask] - zone_cp_tensors = cp_tensors[zone_cp_mask] - - # Find grid points in this zone - zone_grid_indices = [] - for i in range(n_grid): - row, col = divmod(i, nx) - if zones[row, col] == zone_id: - zone_grid_indices.append(i) - - zone_grid_indices = np.array(zone_grid_indices) - zone_grid_coords = grid_coords[zone_grid_indices] - - if len(zone_cp_coords) > 0: - # Interpolate tensors within zone using only zone's conceptual points - zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, - (len(zone_grid_coords), 2)) - geological_tensors[zone_grid_indices] = zone_tensors - else: - # No conceptual points in zone - use default tensor or nearest zone - print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") - default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length - geological_tensors[zone_grid_indices] = default_tensor - - return geological_tensors - - -def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords): - """ - Interpolate 2x2 tensors using log-Euclidean approach. - """ - shape = infer_grid_shape(grid_coords) + #shape = infer_grid_shape(grid_coords) n_grid = len(grid_coords) interp_tensors = np.zeros((n_grid, 2, 2)) - log_tensors = np.array([logm(t) for t in cp_tensors]) - - for i in range(2): - for j in range(i, 2): - values = log_tensors[:, i, j].real - # Use tensor_aware_kriging with shape for 2D output, but we need 1D for tensor interpolation - # This is a special case - convert back to 1D for tensor component interpolation - interp_values_2d = tensor_aware_kriging_1d_output(cp_coords, values, grid_coords, shape) - interp_tensors[:, i, j] = interp_values_2d.flatten() - if i != j: - interp_tensors[:, j, i] = interp_values_2d.flatten() + # simplest + if tensor_interp == 'nearest' and zones is not None: + interp_tensors = _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones) + else: + log_tensors = np.array([logm(t) for t in cp_tensors]) + for i in range(2): + for j in range(i, 2): + values = log_tensors[:, i, j].real + # idw interp + if tensor_interp=='idw': + interp_values_2d = _idw_interpolation(cp_coords, values, grid_coords) + # krig alternatively krig each of "values" separately with pypest utils + elif tensor_interp=='krig': + interp_values_2d = ordinary_kriging(cp_coords, values, grid_coords, + variogram_model='exponential', range_param=10000, + sill=1.0, nugget=0.1, background_value=0.0, + max_search_radius=1e20, min_points=1, + transform=None, min_value=1e-8, max_neighbors=5) + interp_tensors[:, i, j] = interp_values_2d.flatten() + if i != j: + interp_tensors[:, j, i] = interp_values_2d.flatten() - for i in range(n_grid): - interp_tensors[i] = expm(interp_tensors[i]) + for i in range(n_grid): + interp_tensors[i] = expm(interp_tensors[i]) return interp_tensors -def tensor_aware_kriging_1d_output(cp_coords, cp_values, grid_coords): +def _idw_interpolation(cp_coords, cp_values, grid_coords): """ - Special version of kriging for tensor interpolation that returns 1D output. + Special version of idw for tensor interpolation that returns 1D output. This is only used internally for tensor component interpolation. """ - shape = infer_grid_shape(grid_coords) - ny, nx = shape n_grid = len(grid_coords) interp_values = np.full(n_grid, 0.0) - # Simple ordinary kriging for tensor components + # Simple idw tensor components for i in range(n_grid): distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) weights = np.exp(-distances / 1000) # Simple exponential weights @@ -1198,17 +1149,244 @@ def generate_field_3d(conceptual_points, grid_coords, shape): raise NotImplementedError("Use layer_mode=True for now") -def infer_grid_shape(grid_coords): - """Infer grid dimensions from coordinate array.""" - x_unique = np.unique(grid_coords[:, 0]) - y_unique = np.unique(grid_coords[:, 1]) - z_unique = np.unique(grid_coords[:, 2]) - nx = len(x_unique) - ny = len(y_unique) - nz = len(grid_coords) // (nx * ny) +def create_2d_tensors(theta, major, minor): + """Create 2x2 anisotropy tensors from geostatistical parameters.""" + theta = np.atleast_1d(theta) + major = np.atleast_1d(major) + minor = np.atleast_1d(minor) + + n = len(theta) + tensors = np.zeros((n, 2, 2)) - return (nz, ny, nx) + for i in range(n): + # For geological bearing (clockwise from north): + # North = [0, 1], East = [1, 0] + # Bearing 5° should give direction [x, y] = [sin(5°), cos(5°)] = [0.087, 0.996] + + bearing_rad = theta[i] + + # Direction vector for this bearing + east_comp = np.sin(bearing_rad) # X component + north_comp = np.cos(bearing_rad) # Y component + + # Create rotation matrix to align [1,0] with bearing direction + cos_t = north_comp + sin_t = east_comp + + # clockwise rotation by angle theta + R = np.array([[cos_t, sin_t], + [-sin_t, cos_t]]) + S = np.diag([minor[i] ** 2, major[i] ** 2]) + tensors[i] = R @ S @ R.T + + return tensors.squeeze() if n == 1 else tensors + + +def create_3d_tensors(bearing, dip, major, intermediate, minor): + """ + Create 3x3 anisotropy tensors from geostatistical parameters using geological convention. + + Parameters: + ----------- + bearing : float or array + Bearing angle in radians, clockwise from north (0 = north, π/2 = east) + dip : float or array + Dip angle in radians, downward from horizontal (0 = horizontal, π/2 = vertical down) + major : float or array + Major axis magnitude (σ₁) + intermediate : float or array + Intermediate axis magnitude (σ₂) + minor : float or array + Minor axis magnitude (σ₃) + + Convention: + ----------- + - Bearing: Clockwise rotation around vertical axis, north = 0° + - Dip: Clockwise rotation around minor horizontal axis, horizontal = 0° + - This constrains the minor axis to remain horizontal + + Returns: + -------- + tensors : ndarray + 3x3 tensor(s) with shape (3,3) for single input or (n,3,3) for arrays + """ + bearing = np.atleast_1d(bearing) + dip = np.atleast_1d(dip) + major = np.atleast_1d(major) + intermediate = np.atleast_1d(intermediate) + minor = np.atleast_1d(minor) + + n = len(bearing) + tensors = np.zeros((n, 3, 3)) + + for i in range(n): + # Step 1: Create diagonal tensor in principal axes coordinate system + # [minor_horizontal, major, intermediate] + S = np.diag([minor[i] ** 2, major[i] ** 2, intermediate[i] ** 2]) + + # Step 2: Rotation around vertical (z) axis by bearing + # This rotates in horizontal plane, geological convention (clockwise from north) + cos_bearing = np.cos(bearing[i]) + sin_bearing = np.sin(bearing[i]) + + # For geological bearing: north=[0,1,0], east=[1,0,0] + # Bearing direction = [sin(bearing), cos(bearing), 0] + R_bearing = np.array([ + [cos_bearing, sin_bearing, 0], + [-sin_bearing, cos_bearing, 0], + [0, 0, 1] + ]) + + # Step 3: Rotation around minor horizontal axis by dip + # This tilts the major/intermediate plane down from horizontal + cos_dip = np.cos(dip[i]) + sin_dip = np.sin(dip[i]) + + # Clockwise rotation around x-axis (minor horizontal axis) + R_dip = np.array([ + [1, 0, 0], + [0, cos_dip, sin_dip], + [0, -sin_dip, cos_dip] + ]) + + # Step 4: Combine rotations - apply dip first, then bearing + R_total = R_bearing @ R_dip + + # Step 5: Transform tensor + tensors[i] = R_total @ S @ R_total.T + + return tensors.squeeze() if n == 1 else tensors + + +def get_tensor_bearing(tensor, expected_bearing=None): + """Extract geological bearing from tensor.""" + eigenvals, eigenvecs = np.linalg.eigh(tensor) + max_idx = np.argmax(eigenvals) + major_eigenvec = eigenvecs[:, max_idx] + + # Swap components to match geological convention: [north, east] instead of [east, north] + north_component = major_eigenvec[1] # y-component is north + east_component = major_eigenvec[0] # x-component is east + + # Calculate bearing from north (geological convention) + angle1 = np.degrees(np.arctan2(east_component, north_component)) + if angle1 < 0: + angle1 += 360 + angle2 = (angle1 + 180) % 360 + + # Choose correct direction if expected bearing provided + if expected_bearing is not None: + diff1 = min(abs(angle1 - expected_bearing), abs(angle1 - expected_bearing + 360), + abs(angle1 - expected_bearing - 360)) + diff2 = min(abs(angle2 - expected_bearing), abs(angle2 - expected_bearing + 360), + abs(angle2 - expected_bearing - 360)) + return angle1 if diff1 < diff2 else angle2 + + return angle1 + + +def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): + """ + Extract zone assignment logic from tensor_aware_kriging into reusable function. + + Parameters + ---------- + zones : np.ndarray + Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point + """ + ny, nx = zones.shape + + cp_zones = [] + for coord in cp_coords: + # Find closest grid point to get zone + distances = np.sum((grid_coords - coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + row, col = divmod(closest_idx, nx) + cp_zones.append(zones[row, col]) + + return np.array(cp_zones) + + +def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, tensor_interp='idw'): + """ + Zone-aware tensor interpolation - interpolate tensors within each zone separately. + + Parameters + ---------- + cp_coords : np.ndarray + Conceptual point coordinates + cp_tensors : np.ndarray + Conceptual point tensors + grid_coords : np.ndarray + Grid coordinates + zones : np.ndarray + Zone array, shape (ny, nx) - GUARANTEED to be 2D from entry point + + Returns + ------- + np.ndarray + Interpolated tensors, shape (n_grid, 2, 2) + """ + n_grid = len(grid_coords) + ny, nx = zones.shape + + # Get zone for each conceptual point + cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) + + # Initialize output tensors + geological_tensors = np.zeros((n_grid, 2, 2)) + + # Get unique zones + unique_zones = np.unique(zones) + + for zone_id in unique_zones: + print(f" Interpolating tensors for zone {zone_id}") + + # Find conceptual points in this zone + zone_cp_mask = cp_zones == zone_id + zone_cp_coords = cp_coords[zone_cp_mask] + zone_cp_tensors = cp_tensors[zone_cp_mask] + + # Find grid points in this zone + zone_grid_indices = [] + for i in range(n_grid): + row, col = divmod(i, nx) + if zones[row, col] == zone_id: + zone_grid_indices.append(i) + + zone_grid_indices = np.array(zone_grid_indices) + zone_grid_coords = grid_coords[zone_grid_indices] + + if len(zone_cp_coords) > 0: + # Interpolate tensors within zone using only zone's conceptual points + zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, + tensor_interp=tensor_interp, zones=zones) + geological_tensors[zone_grid_indices] = zone_tensors + else: + # No conceptual points in zone - use default tensor or nearest zone + print(f" Warning: 1 or fewer points in zone {zone_id}, using default tensor") + default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length + geological_tensors[zone_grid_indices] = default_tensor + + return geological_tensors + + +def infer_grid_shape(grid_coords): + """Infer grid dimensions from coordinate array.""" + if grid_coords.shape[1] == 2: + x_unique = np.unique(grid_coords[:, 0]) + y_unique = np.unique(grid_coords[:, 1]) + ny, nx = len(y_unique), len(x_unique) + return (1, ny, nx) + elif grid_coords.shape[1] == 3: + x_unique = np.unique(grid_coords[:, 0]) + y_unique = np.unique(grid_coords[:, 1]) + z_unique = np.unique(grid_coords[:, 2]) + nx = len(x_unique) + ny = len(y_unique) + nz = len(grid_coords) // (nx * ny) + return(nz, ny, nx) def save_layer(field, layer, field_name, save_path='.'): @@ -1240,27 +1418,8 @@ def plot_layer(field, layer=0, field_name='field', save_path='.', transform='log if transform=='log': field = np.log10(field) - colors = [ - (0.00, 'ghostwhite'), - (0.05, 'lightblue'), - (0.15, 'skyblue'), - (0.25, 'blue'), - (0.35, 'teal'), - (0.45, 'green'), - (0.50, 'limegreen'), - (0.55, 'yellowgreen'), - (0.65, 'yellow'), - (0.75, 'gold'), - (0.85, 'orange'), - (0.90, 'darkorange'), - (0.95, 'peru'), - (1.00, 'sienna') - ] - - custom_cmap = mcolors.LinearSegmentedColormap.from_list('custom', colors, N=256) - - plt.figure(figsize=(10, 8)) - plt.imshow(field, cmap=custom_cmap) + plt.figure(figsize=(14, 12)) + plt.imshow(field) plt.colorbar(label='Parameter Value') plt.title(f'{field_name} for layer {layer}') plt.xlabel('X') @@ -1454,7 +1613,7 @@ def debug_zone_assignments(conceptual_points, grid_coords, zones, shape, save_pa def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=None, conceptual_points=None, subsample=4, scale_factor=20, - figsize=(12, 10), title_suf=None, save_path='.'): + figsize=(14, 12), title_suf=None, save_path='.'): """ Visualize geological tensors as ellipses overlaid on zones, with conceptual points as oriented lines. @@ -1467,13 +1626,29 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N shape : tuple Grid shape (ny, nx) zones : np.ndarray, optional - Zone array for background + Zone array for background, shape (ny, nx) conceptual_points : pd.DataFrame, optional Conceptual points with x, y, bearing, major columns - subsample : int + subsample : int, default 4 Show every Nth tensor (for readability) - scale_factor : float + scale_factor : float, default 20 Scale ellipses for visibility + figsize : tuple, default (14, 12) + Figure size + title_suf : str, optional + Suffix for plot title + save_path : str, default '.' + Directory to save plot + + Returns + ------- + None + Saves visualization as PNG file + + Examples + -------- + >>> visualize_geological_tensors(tensors, coords, (100, 150), + ... subsample=8, title_suf='layer_1') """ ny, nx = shape @@ -1639,8 +1814,19 @@ def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coor Parameters ---------- + conceptual_points : pd.DataFrame + Conceptual points with x, y, bearing columns + geological_tensors : np.ndarray + Interpolated tensor field, shape (n_points, 2, 2) + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 2) or (n_points, 3) zones : np.ndarray - Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point + Zone IDs, shape (ny, nx) + + Returns + ------- + None + Prints comparison table to console """ shape = zones.shape ny, nx = shape @@ -1698,8 +1884,17 @@ def analyze_tensor_statistics(geological_tensors, grid_coords, zones): Parameters ---------- + geological_tensors : np.ndarray + Tensor field, shape (n_points, 2, 2) + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 2) or (n_points, 3) zones : np.ndarray - Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point + Zone IDs, shape (ny, nx) + + Returns + ------- + None + Prints statistical analysis to console """ shape = zones.shape ny, nx = shape @@ -1769,11 +1964,12 @@ def analyze_tensor_statistics(geological_tensors, grid_coords, zones): - zones: Can be passed to generate_single_layer() as numpy array with integer zone IDs, shape (n_points,) or (ny, nx) """ - main( + generate_fields_from_files( conceptual_points_file=r'C:\modelling\sva_ds\data\conceptual_points.csv', grid_file=r'C:\modelling\sva_ds\data\grid.dat', zone_file=r'C:\modelling\sva_ds\data\waq_arr.geoclass_simple.arr', field_name=['kh'], layer_mode=True, - save_path=r'C:\modelling\sva_ds\data\output' + save_path=r'C:\modelling\sva_ds\data\output_nearest', + tensor_interp='nearest' ) \ No newline at end of file From ec39ff258c95f704998296bab3c1136cd8cb1efe Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 18 Jul 2025 16:12:49 +1200 Subject: [PATCH 08/36] 3d without zones --- pyemu/utils/tensor_utils.py | 606 ++++++++++++++++++++++++++++++++---- 1 file changed, 542 insertions(+), 64 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index b16cdc0c6..0b85f51de 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -221,7 +221,7 @@ def generate_fields_from_files(conceptual_points_file, grid_file, zone_file=None f"Loaded zones: original shape {original_zones_shape}, standardized to 2D {zones.shape if zones is not None else None}") for fn in field_name: - cols = ['name', 'x', 'y', 'major', 'anisotropy', 'bearing'] + cols = ['name', 'x', 'y', 'major', 'transverse', 'bearing'] cols = cols + [_ for _ in conceptual_points.columns if f'_{fn}' in _] if layer_mode and 'layer' in conceptual_points.columns: # Process by layer @@ -231,8 +231,8 @@ def generate_fields_from_files(conceptual_points_file, grid_file, zone_file=None tensor_interp=tensor_interp) else: # Process full 3D - cols = cols + ['z'] - field = generate_field_3d(conceptual_points[cols], grid_coords, shape) + cols = cols + ['z', 'normal', 'dip'] + field = generate_field_3d(conceptual_points[cols], grid_coords, iids, shape, save_path=save_path) # Save results - field is now array or original shape for i in conceptual_points.layer.unique(): @@ -302,13 +302,13 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, # Prepare zones for this layer (all zones now consistently 2D) layer_zones = None if zones is not None: - if len(zones.shape) == 3: # 3D zones - not supported yet, but keeping logic + if len(zones.shape) == 3: # 3D zones layer_zones = zones[layer_idx] else: # 2D zones (standard case), use for all layers layer_zones = zones # zones are already guaranteed to be 2D from standardize_input_arrays() - # Generate field for this layer - now returns 2D + # Generate field for this layer layer_field_2d, geological_tensors = generate_single_layer_zone_based(layer_cp, layer_coords, layer_iids, zones=layer_zones, shape=layer_shape, save_path=save_path, tensor_interp=tensor_interp) @@ -322,6 +322,132 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, return field_3d # Return as 3D array, NOT flattened +def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, + save_path='.', tensor_interp='idw'): + """ + Generate field using full 3D tensor processing. + + Parameters + ---------- + conceptual_points : pd.DataFrame + Conceptual points with 3D tensor and statistical parameters + Must include columns: x, y, z, major, transverse, normal, bearing, dip, mean_{field}, sd_{field} + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + iids : np.ndarray + Independent identically distributed random noise, shape (nz, ny, nx) + shape : tuple + Grid shape as (nz, ny, nx) + zones : np.ndarray, optional + Zone IDs for zone-based processing. Shape (ny, nx) for 2D zones applied to all layers, + or (nz, ny, nx) for 3D zones + save_path : str, default '.' + Directory path to save output files + tensor_interp : str, default 'idw' + Tensor interpolation method: 'idw', 'krig', or 'nearest' + + Returns + ------- + np.ndarray + Field values, shape (nz, ny, nx) + """ + print("Generating 3D field with full tensor processing...") + nz, ny, nx = shape + + # Extract conceptual point data + cp_coords = conceptual_points[['x', 'y', 'z']].values + + # Create 3D tensors from geological parameters + bearing = np.radians(conceptual_points['bearing'].values) + dip = np.radians(conceptual_points['dip'].values) + major = conceptual_points['major'].values + transverse = conceptual_points['transverse'].values + normal = conceptual_points['normal'].values + + cp_tensors_3d = create_3d_tensors(bearing, dip, major, transverse, normal) + + # Get field statistics + field_cols = [col for col in conceptual_points.columns if col.startswith('mean_') or col.startswith('sd_')] + if field_cols: + mean_col = [col for col in field_cols if col.startswith('mean_')][0] + sd_col = [col for col in field_cols if col.startswith('sd_')][0] + cp_means = conceptual_points[mean_col].values + cp_sds = conceptual_points[sd_col].values + else: + # Default values if not specified + cp_means = np.ones(len(cp_coords)) + cp_sds = np.ones(len(cp_coords)) * 0.5 + + # Interpolate 3D tensors to grid points + print(" Interpolating 3D tensors...") + interp_tensors_3d = interpolate_tensors_3d(cp_coords, cp_tensors_3d, grid_coords, + tensor_interp=tensor_interp, zones=zones) + + # Handle zones for 3D + zones_3d = None + if zones is not None: + if len(zones.shape) == 2: + # 2D zones - replicate for each layer + zones_3d = np.tile(zones[np.newaxis, :, :], (nz, 1, 1)) + else: + # Already 3D zones + zones_3d = zones + + # Interpolate mean field using tensor-aware kriging + print(" Interpolating mean field with 3D tensor-aware kriging...") + mean_field_1d = tensor_aware_kriging( + cp_coords, cp_means, grid_coords, interp_tensors_3d, + variogram_model='exponential', sill=1.0, nugget=0.01, + zones=zones_3d + ) + mean_field_3d = mean_field_1d.reshape(shape) + + # Interpolate standard deviation field + print(" Interpolating standard deviation field...") + sd_field_1d = tensor_aware_kriging( + cp_coords, cp_sds, grid_coords, interp_tensors_3d, + variogram_model='exponential', sill=0.5, nugget=0.01, + zones=zones_3d + ) + sd_field_3d = sd_field_1d.reshape(shape) + + # Generate correlated noise using 3D tensors + print(" Generating 3D correlated noise...") + # Flatten for noise generation, then reshape + grid_coords_flat = grid_coords + tensors_flat = interp_tensors_3d + iids_flat = iids.flatten() + + correlated_noise_flat = generate_correlated_noise_3d( + grid_coords_flat, tensors_flat, iids_flat, + n_neighbors=50, anisotropy_strength=1.0 + ) + correlated_noise_3d = correlated_noise_flat.reshape(shape) + + # Combine mean, scaled noise + log_mean_field = np.log10(np.maximum(mean_field_3d, 1e-8)) # Avoid log(0) + field_3d = 10 ** (log_mean_field + correlated_noise_3d * sd_field_3d) + + print(f" Generated 3D field with shape {field_3d.shape}") + print(f" Field stats: mean={np.mean(field_3d):.3f}, std={np.std(field_3d):.3f}") + + # Export to ParaView + export_3d_results_to_paraview( + field_3d=field_3d, + tensors_3d=interp_tensors_3d, + grid_coords=grid_coords, + shape=shape, + zones=zones, + conceptual_points=conceptual_points, + save_path=save_path, + field_name='permeability' + ) + + return field_3d + + + + def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, iids, zones=None, shape=None, save_path='.', tensor_interp='idw'): @@ -367,7 +493,7 @@ def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, iids, # Step 1: Create geological tensors from conceptual points print(" Creating geological correlation tensors...") major = conceptual_points['major'].values - minor = major / conceptual_points['anisotropy'].values + minor = conceptual_points['transverse'].values # Better normalization that preserves direction def normalize_bearing(bearing): @@ -436,7 +562,9 @@ def normalize_bearing(bearing): correlated_noise_2d = correlated_noise_1d.reshape(shape) # Step 7: Combine into lognormal field - all operations in 2D - field_2d = 10 ** (np.log10(interp_means_2d) + correlated_noise_2d * interp_sd_2d) + + log_mean_field = np.log10(np.maximum(interp_means_2d, 1e-8)) # Avoid log(0) + field_2d = 10 ** (log_mean_field + correlated_noise_2d * interp_sd_2d) return field_2d, geological_tensors @@ -782,6 +910,7 @@ def estimate_boundary_direction(zones, center_i, center_j, radius=2): return boundary_direction + def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, variogram_model='exponential', sill=1.0, nugget=0.01, background_value=0.0, max_search_radius=1e20, min_points=1, @@ -793,13 +922,13 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, Parameters ---------- cp_coords : np.ndarray - Conceptual point coordinates, shape (n_cp, 2) + Conceptual point coordinates, shape (n_cp, 2) for 2D or (n_cp, 3) for 3D cp_values : np.ndarray Values at conceptual points, shape (n_cp,) grid_coords : np.ndarray - Grid coordinates, shape (n_grid, 2) or (n_grid, 3) + Grid coordinates, shape (n_grid, 2) for 2D or (n_grid, 3) for 3D interp_tensors : np.ndarray - Interpolated tensors at grid points, shape (n_grid, 2, 2) + Interpolated tensors at grid points, shape (n_grid, 2, 2) for 2D or (n_grid, 3, 3) for 3D variogram_model : str, default 'exponential' Variogram model: 'exponential', 'gaussian', or 'spherical' sill : float, default 1.0 @@ -819,25 +948,44 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, min_value : float, default 1e-8 Minimum value for log transform zones : np.ndarray, optional - Zone IDs for zone-aware kriging, shape (ny, nx) + Zone IDs for zone-aware kriging. Shape (ny, nx) for 2D or (nz, ny, nx) for 3D Returns ------- np.ndarray - Interpolated values, shape (ny, nx) + Interpolated values. Shape (ny, nx) for 2D grids or (nz, ny, nx) for 3D grids """ n_grid = len(grid_coords) + n_dims = grid_coords.shape[1] # Get shape from zones if provided, or infer from grid if zones is not None: shape = zones.shape - ny, nx = shape + if n_dims == 2: + ny, nx = shape + else: # 3D zones would be (nz, ny, nx) + nz, ny, nx = shape else: - # Infer shape from grid coordinates - x_unique = np.unique(grid_coords[:, 0]) - y_unique = np.unique(grid_coords[:, 1]) - ny, nx = len(y_unique), len(x_unique) - shape = (ny, nx) + # Use existing function to infer shape + shape = infer_grid_shape(grid_coords) + if n_dims == 2: + ny, nx = shape[1], shape[2] # shape is (1, ny, nx) + else: + nz, ny, nx = shape + + # Define index conversion functions based on dimensions + if n_dims == 3: + def get_indices(idx): + layer = idx // (ny * nx) + remainder = idx % (ny * nx) + row = remainder // nx + col = remainder % nx + return layer, row, col + else: + def get_indices(idx): + row = idx // nx + col = idx % nx + return row, col interp_values_1d = np.full(n_grid, background_value) @@ -848,16 +996,24 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, # Find closest grid point to get zone distances = np.sum((grid_coords - coord) ** 2, axis=1) closest_idx = np.argmin(distances) - row, col = divmod(closest_idx, nx) - cp_zones.append(zones[row, col]) + if n_dims == 3: + layer, row, col = get_indices(closest_idx) + cp_zones.append(zones[layer, row, col]) + else: + row, col = get_indices(closest_idx) + cp_zones.append(zones[row, col]) cp_zones = np.array(cp_zones) for i in range(n_grid): local_tensor = interp_tensors[i] if zones is not None: # Get current zone and filter conceptual points - row, col = divmod(i, nx) - current_zone = zones[row, col] + if n_dims == 3: + layer, row, col = get_indices(i) + current_zone = zones[layer, row, col] + else: + row, col = get_indices(i) + current_zone = zones[row, col] # Filter to same zone, fallback to all if insufficient points found zone_mask = cp_zones == current_zone @@ -951,7 +1107,6 @@ def variogram(h): if transform == 'log': interp_values_1d = 10 ** interp_values_1d - # ALWAYS convert to 2D - no exceptions return interp_values_1d.reshape(shape) @@ -1009,6 +1164,70 @@ def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords, tensor_interp='id return interp_tensors +def interpolate_tensors_3d(cp_coords, cp_tensors, grid_coords, tensor_interp='idw', zones=None): + """ + Interpolate 3x3 tensors using log-Euclidean approach (same as 2D but for 3x3). + """ + n_grid = len(grid_coords) + interp_tensors = np.zeros((n_grid, 3, 3)) + + if tensor_interp == 'nearest' and zones is not None: + interp_tensors = _nearest_interpolation_3d(cp_coords, cp_tensors, grid_coords, zones) + else: + # Log-Euclidean approach - ensure positive definiteness first + log_tensors = np.zeros_like(cp_tensors) + + for i, tensor in enumerate(cp_tensors): + # Ensure positive definiteness by eigenvalue regularization + eigenvals, eigenvecs = np.linalg.eigh(tensor) + min_eigenval = 1e-8 # Minimum eigenvalue to ensure positive definiteness + eigenvals_reg = np.maximum(eigenvals, min_eigenval) + + # Reconstruct positive definite tensor + tensor_reg = eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T + + # Take matrix logarithm + log_tensors[i] = logm(tensor_reg) + + # Interpolate each independent component of the symmetric log tensor + # For a 3x3 symmetric matrix, we have 6 independent components + interp_log_tensors = np.zeros((n_grid, 3, 3)) + + for i in range(3): + for j in range(i, 3): # Only upper triangle (symmetric) + values = log_tensors[:, i, j].real # Take real part to handle numerical errors + + if tensor_interp == 'idw': + interp_values = _idw_interpolation(cp_coords, values, grid_coords) + elif tensor_interp == 'krig': + interp_values = ordinary_kriging(cp_coords, values, grid_coords, + variogram_model='exponential', range_param=10000, + sill=1.0, nugget=0.1) + + interp_log_tensors[:, i, j] = interp_values + if i != j: # Fill symmetric part + interp_log_tensors[:, j, i] = interp_values + + # Convert back from log space, ensuring positive definiteness + for i in range(n_grid): + try: + # Matrix exponential + tensor_exp = expm(interp_log_tensors[i]) + + # Ensure the result is positive definite (numerical stability) + eigenvals, eigenvecs = np.linalg.eigh(tensor_exp) + min_eigenval = 1e-8 + eigenvals_reg = np.maximum(eigenvals, min_eigenval) + + interp_tensors[i] = eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T + + except Exception as e: + print(f"Warning: Matrix exponential failed at grid point {i}, using identity: {e}") + interp_tensors[i] = np.eye(3) + + return interp_tensors + + def _idw_interpolation(cp_coords, cp_values, grid_coords): """ Special version of idw for tensor interpolation that returns 1D output. @@ -1071,6 +1290,62 @@ def generate_correlated_noise_2d(grid_coords, tensors, iids, return correlated_noise +def generate_correlated_noise_3d(grid_coords, tensors, iids, + n_neighbors=50, anisotropy_strength=1.0): + """Generate spatially correlated noise respecting 3D tensor anisotropy.""" + n_points = len(grid_coords) + correlated_noise = np.zeros(n_points) + + for i in range(n_points): + evals, evecs = np.linalg.eigh(tensors[i]) + + # Sort eigenvalues/vectors by magnitude (largest to smallest) + sort_idx = np.argsort(evals)[::-1] + evals = evals[sort_idx] + evecs = evecs[:, sort_idx] + + # Extract correlation lengths (sqrt of eigenvalues) + major_corr_length = np.sqrt(evals[0]) # Largest eigenvalue + intermediate_corr_length = np.sqrt(evals[1]) # Middle eigenvalue + minor_corr_length = np.sqrt(evals[2]) # Smallest eigenvalue + + # Apply anisotropy strength scaling + scale_major = major_corr_length * anisotropy_strength + scale_intermediate = intermediate_corr_length # Keep intermediate neutral + scale_minor = minor_corr_length / anisotropy_strength + + # Create 3D transformation matrix + transform = evecs @ np.diag([scale_major, scale_intermediate, scale_minor]) + + # Transform all grid points relative to current point + all_dx = grid_coords - grid_coords[i] + try: + inv_transform = np.linalg.inv(transform) + dx_transformed = all_dx @ inv_transform.T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) + except np.linalg.LinAlgError: + # Fallback to Euclidean distance if transform is singular + aniso_distances = np.linalg.norm(all_dx, axis=1) + + neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] + + weighted_sum = 0.0 + sum_weights = 0.0 + + for j in neighbor_indices: + # Use anisotropic distance for weight calculation + weight = np.exp(-(aniso_distances[j]) ** 2) + weighted_sum += weight * iids.flatten()[j] + sum_weights += weight + + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = iids[i] + + return correlated_noise + + def ordinary_kriging(cp_coords, cp_values, grid_coords, variogram_model='exponential', range_param=10000, sill=1.0, nugget=0.1, background_value=0.0, @@ -1144,11 +1419,6 @@ def variogram(h): return interp_values -def generate_field_3d(conceptual_points, grid_coords, shape): - """Placeholder for full 3D field generation (not implemented).""" - raise NotImplementedError("Use layer_mode=True for now") - - def create_2d_tensors(theta, major, minor): """Create 2x2 anisotropy tensors from geostatistical parameters.""" @@ -1183,67 +1453,46 @@ def create_2d_tensors(theta, major, minor): return tensors.squeeze() if n == 1 else tensors -def create_3d_tensors(bearing, dip, major, intermediate, minor): +def create_3d_tensors(bearing, dip, major, transverse, normal): """ Create 3x3 anisotropy tensors from geostatistical parameters using geological convention. - Parameters: - ----------- - bearing : float or array - Bearing angle in radians, clockwise from north (0 = north, π/2 = east) - dip : float or array - Dip angle in radians, downward from horizontal (0 = horizontal, π/2 = vertical down) - major : float or array - Major axis magnitude (σ₁) - intermediate : float or array - Intermediate axis magnitude (σ₂) - minor : float or array - Minor axis magnitude (σ₃) - - Convention: - ----------- - - Bearing: Clockwise rotation around vertical axis, north = 0° - - Dip: Clockwise rotation around minor horizontal axis, horizontal = 0° - - This constrains the minor axis to remain horizontal - - Returns: - -------- - tensors : ndarray - 3x3 tensor(s) with shape (3,3) for single input or (n,3,3) for arrays + [Same docstring as original] """ bearing = np.atleast_1d(bearing) dip = np.atleast_1d(dip) major = np.atleast_1d(major) - intermediate = np.atleast_1d(intermediate) - minor = np.atleast_1d(minor) + transverse = np.atleast_1d(transverse) + normal = np.atleast_1d(normal) n = len(bearing) tensors = np.zeros((n, 3, 3)) for i in range(n): + # Ensure minimum values to prevent numerical issues + min_axis = 1e-6 + major_val = max(major[i], min_axis) + transverse_val = max(transverse[i], min_axis) + normal_val = max(normal[i], min_axis) + # Step 1: Create diagonal tensor in principal axes coordinate system - # [minor_horizontal, major, intermediate] - S = np.diag([minor[i] ** 2, major[i] ** 2, intermediate[i] ** 2]) + # Order: [transverse, major, normal] + S = np.diag([transverse_val ** 2, major_val ** 2, normal_val ** 2]) # Step 2: Rotation around vertical (z) axis by bearing - # This rotates in horizontal plane, geological convention (clockwise from north) cos_bearing = np.cos(bearing[i]) sin_bearing = np.sin(bearing[i]) - # For geological bearing: north=[0,1,0], east=[1,0,0] - # Bearing direction = [sin(bearing), cos(bearing), 0] R_bearing = np.array([ [cos_bearing, sin_bearing, 0], [-sin_bearing, cos_bearing, 0], [0, 0, 1] ]) - # Step 3: Rotation around minor horizontal axis by dip - # This tilts the major/intermediate plane down from horizontal + # Step 3: Rotation around transverse axis (x-axis) by dip cos_dip = np.cos(dip[i]) sin_dip = np.sin(dip[i]) - # Clockwise rotation around x-axis (minor horizontal axis) R_dip = np.array([ [1, 0, 0], [0, cos_dip, sin_dip], @@ -1256,6 +1505,9 @@ def create_3d_tensors(bearing, dip, major, intermediate, minor): # Step 5: Transform tensor tensors[i] = R_total @ S @ R_total.T + # Ensure symmetry (handle numerical errors) + tensors[i] = 0.5 * (tensors[i] + tensors[i].T) + return tensors.squeeze() if n == 1 else tensors @@ -1947,8 +2199,234 @@ def analyze_tensor_statistics(geological_tensors, grid_coords, zones): print() +def export_to_paraview(grid_coords, shape, field_data=None, tensor_data=None, + zones=None, conceptual_points=None, save_path='.', + filename='field_3d'): + """ + Export 3D field and tensor data to ParaView-compatible VTK files. + + Parameters + ---------- + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + shape : tuple + Grid shape (nz, ny, nx) + field_data : dict, optional + Dictionary of field arrays to export, e.g. {'permeability': field_array} + Each array should have shape (nz, ny, nx) + tensor_data : np.ndarray, optional + Tensor field, shape (n_points, 3, 3) + zones : np.ndarray, optional + Zone array, shape (nz, ny, nx) or (ny, nx) + conceptual_points : pd.DataFrame, optional + Conceptual points with x, y, z, bearing, dip columns + save_path : str, default '.' + Directory to save VTK files + filename : str, default 'field_3d' + Base filename for output files + + Returns + ------- + None + Saves .vtr (structured grid) and .vtp (point cloud) files + """ + try: + import vtk + from vtk.util import numpy_support + except ImportError: + print("VTK not installed. Install with: pip install vtk") + return + + nz, ny, nx = shape + + # Create structured grid for field data + if field_data is not None or tensor_data is not None or zones is not None: + grid = vtk.vtkStructuredGrid() + grid.SetDimensions(nx, ny, nz) + + # Set points + points = vtk.vtkPoints() + grid_coords_reshaped = grid_coords.reshape(shape + (3,)) + + # VTK expects points in a specific order (k varies fastest, then j, then i) + for k in range(nz): + for j in range(ny): + for i in range(nx): + x, y, z = grid_coords_reshaped[k, j, i] + points.InsertNextPoint(x, y, z) + + grid.SetPoints(points) + + # Add field data + if field_data is not None: + for field_name, field_array in field_data.items(): + # Reshape and reorder for VTK + field_flat = field_array.flatten(order='F') # Fortran order for VTK + field_vtk = numpy_support.numpy_to_vtk(field_flat) + field_vtk.SetName(field_name) + grid.GetPointData().AddArray(field_vtk) + if grid.GetPointData().GetScalars() is None: + grid.GetPointData().SetScalars(field_vtk) + + # Add zones + if zones is not None: + if len(zones.shape) == 2: + # 2D zones - replicate for each layer + zones_3d = np.tile(zones[np.newaxis, :, :], (nz, 1, 1)) + else: + zones_3d = zones + + zones_flat = zones_3d.flatten(order='F') + zones_vtk = numpy_support.numpy_to_vtk(zones_flat) + zones_vtk.SetName('zones') + grid.GetPointData().AddArray(zones_vtk) + + # Add tensor data + if tensor_data is not None: + # Convert 3x3 tensors to VTK format (symmetric tensors) + n_points = len(tensor_data) + tensor_array = np.zeros((n_points, 6)) # VTK uses 6-component symmetric tensors + + for i in range(n_points): + tensor = tensor_data[i] + # VTK tensor format: [xx, yy, zz, xy, yz, xz] + tensor_array[i] = [tensor[0, 0], tensor[1, 1], tensor[2, 2], + tensor[0, 1], tensor[1, 2], tensor[0, 2]] + + # Reorder for VTK grid + tensor_reordered = np.zeros_like(tensor_array) + idx = 0 + for k in range(nz): + for j in range(ny): + for i in range(nx): + grid_idx = k * ny * nx + j * nx + i + tensor_reordered[idx] = tensor_array[grid_idx] + idx += 1 + + tensor_vtk = numpy_support.numpy_to_vtk(tensor_reordered) + tensor_vtk.SetName('tensors') + tensor_vtk.SetNumberOfComponents(6) + grid.GetPointData().SetTensors(tensor_vtk) + + # Write structured grid + writer = vtk.vtkXMLStructuredGridWriter() + grid_filename = os.path.join(save_path, f'{filename}_grid.vts') + writer.SetFileName(grid_filename) + writer.SetInputData(grid) + writer.Write() + print(f"Saved structured grid to {grid_filename}") + + # Create point cloud for conceptual points + if conceptual_points is not None: + points = vtk.vtkPoints() + polydata = vtk.vtkPolyData() + + # Add points + for idx, row in conceptual_points.iterrows(): + x, y = row['x'], row['y'] + z = row.get('z', 0) # Default z=0 if not provided + points.InsertNextPoint(x, y, z) + + polydata.SetPoints(points) + + # Create vertices + vertices = vtk.vtkCellArray() + for i in range(points.GetNumberOfPoints()): + vertices.InsertNextCell(1) + vertices.InsertCellPoint(i) + polydata.SetVerts(vertices) + + # Add conceptual point data + for col in conceptual_points.columns: + if col in ['x', 'y', 'z']: + continue + + values = conceptual_points[col].values + if np.issubdtype(values.dtype, np.number): + vtk_array = numpy_support.numpy_to_vtk(values) + vtk_array.SetName(col) + polydata.GetPointData().AddArray(vtk_array) + + # Add tensor glyphs if we have bearing/dip data + if 'bearing' in conceptual_points.columns and 'dip' in conceptual_points.columns: + # Create orientation vectors for visualization + n_cp = len(conceptual_points) + orientations = np.zeros((n_cp, 3)) + + for i, (_, row) in enumerate(conceptual_points.iterrows()): + bearing_rad = np.radians(row['bearing']) + dip_rad = np.radians(row.get('dip', 0)) + + # Convert geological convention to cartesian + # Bearing: CW from north, Dip: down from horizontal + x_comp = np.sin(bearing_rad) * np.cos(dip_rad) + y_comp = np.cos(bearing_rad) * np.cos(dip_rad) + z_comp = -np.sin(dip_rad) # Negative because dip is down + + orientations[i] = [x_comp, y_comp, z_comp] + + orientation_vtk = numpy_support.numpy_to_vtk(orientations) + orientation_vtk.SetName('orientations') + orientation_vtk.SetNumberOfComponents(3) + polydata.GetPointData().SetVectors(orientation_vtk) + + # Write point cloud + writer = vtk.vtkXMLPolyDataWriter() + points_filename = os.path.join(save_path, f'{filename}_conceptual_points.vtp') + writer.SetFileName(points_filename) + writer.SetInputData(polydata) + writer.Write() + print(f"Saved conceptual points to {points_filename}") + + print("\nParaView visualization tips:") + print("1. Load both .vts and .vtp files in ParaView") + print("2. For field data: Use 'Volume' or 'Slice' representation") + print("3. For tensors: Add 'Tensor Glyph' filter to show ellipsoids") + print("4. For conceptual points: Use 'Glyph' filter with 'Arrow' glyphs") + print("5. Use 'Clip' or 'Slice' filters to see inside the 3D volume") + + +# Helper function to integrate with your existing workflow +def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, + zones=None, conceptual_points=None, + save_path='.', field_name='permeability'): + """ + Convenience function to export your 3D results to ParaView. + + Parameters + ---------- + field_3d : np.ndarray + Field values, shape (nz, ny, nx) + tensors_3d : np.ndarray + 3D tensors, shape (n_points, 3, 3) + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 3) + shape : tuple + Grid shape (nz, ny, nx) + zones : np.ndarray, optional + Zone data + conceptual_points : pd.DataFrame, optional + Conceptual points + save_path : str + Output directory + field_name : str + Name for the field data + """ + field_data = {field_name: field_3d} + + export_to_paraview( + grid_coords=grid_coords, + shape=shape, + field_data=field_data, + tensor_data=tensors_3d, + zones=zones, + conceptual_points=conceptual_points, + save_path=save_path, + filename=f'{field_name}_3d' + ) + # ============================================================================ -# EXAMPLE USAGE - UNCHANGED FUNCTIONALITY +# EXAMPLE USAGE # ============================================================================ # Example usage @@ -1969,7 +2447,7 @@ def analyze_tensor_statistics(geological_tensors, grid_coords, zones): grid_file=r'C:\modelling\sva_ds\data\grid.dat', zone_file=r'C:\modelling\sva_ds\data\waq_arr.geoclass_simple.arr', field_name=['kh'], - layer_mode=True, + layer_mode=False, save_path=r'C:\modelling\sva_ds\data\output_nearest', tensor_interp='nearest' ) \ No newline at end of file From a244824d36cb3596744a09f4b01dbac4f7d42645 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 18 Jul 2025 19:45:47 +1200 Subject: [PATCH 09/36] 3d with zones --- pyemu/utils/tensor_utils.py | 521 ++++++++++++++++++++++++++---------- 1 file changed, 384 insertions(+), 137 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 0b85f51de..ab76d4c60 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -138,91 +138,137 @@ def validate_grid_coordinates(grid_coords, shape): return grid_coords.shape[0] == expected_points - -def generate_fields_from_files(conceptual_points_file, grid_file, zone_file=None, iids_file=None, - field_name=['field'], layer_mode=True, save_path='.', tensor_interp='idw'): +def generate_fields_from_files(sim_ws, conceptual_points_file, grid_file, zone_file=None, iids_file=None, + field_name=['field'], layer_mode=True, save_path='.', tensor_interp='idw'): """ Generate spatially correlated fields using tensor-based geostatistics. - This reads conceptual points (control points) with anisotropy parameters from files - and generates tensor-based correlated fields on a specified regular grid. - Parameters ---------- + sim_ws: str + Path to files conceptual_points_file : str - Path to CSV file containing conceptual points with columns: - x, y, major, anisotropy, bearing, mean_{field_name}, sd_{field_name} - Optional: layer (for layer-by-layer processing) + CSV file containing conceptual points grid_file : str - Path to whitespace-delimited file with grid coordinates (columns: x, y, z) - zone_file : str, optional - Path to whitespace-delimited array file with zones - iids_file : str, optional - Path to independent identically distributed noise file. If None, - auto-generates correlated noise and saves as iids_layer{N}.arr files + whitespace-delimited file with grid coordinates (columns: x, y, z) + zone_file : str or list of str, optional + zone file(s): + - Single string: One zone file used for all layers (2D zones) + - List of strings: One zone file per layer (creates 3D zones) + iids_file : str or list of str, optional + noise file(s): + - Single string: Assumes pattern like "iids_layer{N}.arr" + - List of strings: Explicit paths to each layer's noise file + - None: Auto-generates noise and saves as iids_layer{N}.arr files field_name : list of str, default ['field'] - Name(s) of field(s) to generate. Must match column prefixes in conceptual_points + Name(s) of field(s) to generate layer_mode : bool, default True - If True, process each layer independently (recommended for 3D grids) + If True, process each layer independently save_path : str, default '.' Directory path to save output files tensor_interp : str, default 'idw' - Tensor interpolation method: 'idw' (inverse distance weighting), - 'krig' (kriging), or 'nearest' (zone-based nearest neighbor) + Tensor interpolation method Returns ------- None - Saves results to output/ directory as layer files and plots - - Examples - -------- - >>> generate_fields_from_files('my_points.csv', 'my_grid.dat', - ... field_name=['kh'], layer_mode=True) + Saves results to output/ directory """ - print(f"=== Tensor-Based Non-Stationary Field Generation from {conceptual_points_file}===") - if not os.path.exists(save_path): - os.mkdir(save_path) + print(f"=== Tensor-Based Non-Stationary Field Generation from {conceptual_points_file} ===") + if not os.path.exists(os.path.join(sim_ws, save_path)): + os.mkdir(os.path.join(sim_ws, save_path)) # Load data - conceptual_points = pd.read_csv(conceptual_points_file) - grid = pd.read_csv(grid_file, sep=r'\s+') + conceptual_points = pd.read_csv(os.path.join(sim_ws, conceptual_points_file)) + grid = pd.read_csv(os.path.join(sim_ws, grid_file), sep=r'\s+') grid_coords = grid[['x', 'y', 'z']].values print(f"Loaded {len(conceptual_points)} conceptual points") print(f"Loaded {len(grid_coords)} grid points") - # Infer grid structure FIRST, for the lazy amongst us + # Infer grid structure shape = infer_grid_shape(grid_coords) + nz, ny, nx = shape print(f"Grid shape: {shape} (nz, ny, nx)") + # Load or generate iids (always layer-specific) if iids_file is not None: - os.path.join(os.path.dirname(conceptual_points_file), 'iids.arr') - iids = np.loadtxt(iids_file) + if isinstance(iids_file, list): + # Explicit list of files + if len(iids_file) != nz: + raise ValueError(f"Number of iids files ({len(iids_file)}) must match number of layers ({nz})") + iids = np.zeros(shape) + for i, file_path in enumerate(iids_file): + layer_iids = np.loadtxt(os.path.join(sim_ws, file_path)) + iids[i] = layer_iids.reshape(ny, nx) + print(f" Loaded iids layer {i + 1} from {os.path.join(sim_ws, file_path)}") + else: + if '{' in iids_name: + # Pattern like "iids_layer{}.arr" + pattern = os.path.join(sim_ws, iids_name) + else: + # Assume pattern from filename + name, ext = os.path.splitext(iids_name) + pattern = os.path.join(sim_ws, f"{name}_layer{{}}{ext}") + + iids = np.zeros(shape) + for i in range(nz): + file_path = pattern.format(i + 1) + try: + layer_iids = np.loadtxt(file_path) + iids[i] = layer_iids.reshape(ny, nx) + print(f" Loaded iids layer {i + 1} from {file_path}") + except FileNotFoundError: + print(f" Warning: Could not find {file_path}, generating random noise") + iids[i] = np.random.randn(ny, nx) else: print(" Generating correlated noise...") np.random.seed(42) iids = np.random.randn(*shape) - for i in range(iids.shape[0]): - iids_file = os.path.join(os.path.dirname(conceptual_points_file), f'iids_layer{i+1}.arr') - np.savetxt(iids_file, iids[i]) + for i in range(nz): + iids_file_path = os.path.join(sim_ws, f'iids_layer{i + 1}.arr') + np.savetxt(iids_file_path, iids[i]) + print(f" Saved iids layer {i + 1} to {iids_file_path}") # Validate grid coordinates if not validate_grid_coordinates(grid_coords, shape): raise ValueError(f"Grid coordinates ({len(grid_coords)} points) don't match inferred shape {shape}") - # standardize zones using the shape + # Load zones with flexible handling zones = None + zones_3d = None if zone_file: - zones_raw = np.loadtxt(zone_file) - zones, _, original_zones_shape = standardize_input_arrays(zones_raw, grid_coords, shape) - print( - f"Loaded zones: original shape {original_zones_shape}, standardized to 2D {zones.shape if zones is not None else None}") + if isinstance(zone_file, list): + # Multiple zone files - create 3D zones + if len(zone_file) != nz: + raise ValueError(f"Number of zone files ({len(zone_file)}) must match number of layers ({nz})") + + zones_list = [] + for i, file in enumerate(zone_file): + layer_zones = np.loadtxt(os.path.join(sim_ws, file)).astype(np.int64) + zones_list.append(layer_zones) + print(f" Loaded zones layer {i + 1} from {os.path.join(sim_ws, file)}, shape: {layer_zones.shape}") + + zones_3d = np.array(zones_list) + print(f" Created 3D zones array: {zones_3d.shape}") + + else: + # Single zone file - use for all layers + zones_raw = np.loadtxt(os.path.join(sim_ws, zone_file)).astype(np.int64) + zones, _, original_zones_shape = standardize_input_arrays(zones_raw, grid_coords, shape) + print(f" Loaded single zone file: original shape {original_zones_shape}, standardized to 2D {zones.shape}") + + # Convert to 3D by replication + if zones is not None: + zones_3d = np.tile(zones[np.newaxis, :, :], (nz, 1, 1)) + print(f" Replicated 2D zones to 3D: {zones.shape} -> {zones_3d.shape}") + # Process each field for fn in field_name: cols = ['name', 'x', 'y', 'major', 'transverse', 'bearing'] cols = cols + [_ for _ in conceptual_points.columns if f'_{fn}' in _] + if layer_mode and 'layer' in conceptual_points.columns: # Process by layer cols = cols + ['layer'] @@ -230,18 +276,19 @@ def generate_fields_from_files(conceptual_points_file, grid_file, zone_file=None shape, zones=zones, save_path=save_path, tensor_interp=tensor_interp) else: - # Process full 3D + # Process full 3D - pass the 3D zones cols = cols + ['z', 'normal', 'dip'] - field = generate_field_3d(conceptual_points[cols], grid_coords, iids, shape, save_path=save_path) + field = generate_field_3d(conceptual_points[cols], grid_coords, iids, + shape, zones=zones_3d, save_path=save_path, + tensor_interp=tensor_interp) - # Save results - field is now array or original shape + # Save and plot results for i in conceptual_points.layer.unique(): - save_layer(field[i-1], layer=i, field_name=fn, save_path=save_path) - # Plot based on layer in conceptual points - for i in conceptual_points.layer.unique(): - plot_layer(np.where(zones==0,np.nan, field[i-1]), layer=i, field_name=fn, save_path=save_path) + save_layer(field[i - 1], layer=i, field_name=fn, save_path=save_path) + plot_layer(np.where(zones == 0, np.nan, field[i - 1]), layer=i, field_name=fn, save_path=save_path) + print("\n=== Complete ===") - print(f"Field shape: {field.shape}") # Now shows (nz, ny, nx) + print(f"Field shape: {field.shape}") print(f"Field statistics: mean={np.mean(field):.3f}, std={np.std(field):.3f}") @@ -325,7 +372,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, save_path='.', tensor_interp='idw'): """ - Generate field using full 3D tensor processing. + Generate field using full 3D tensor processing with zone-based interpolation. Parameters ---------- @@ -357,15 +404,6 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, # Extract conceptual point data cp_coords = conceptual_points[['x', 'y', 'z']].values - # Create 3D tensors from geological parameters - bearing = np.radians(conceptual_points['bearing'].values) - dip = np.radians(conceptual_points['dip'].values) - major = conceptual_points['major'].values - transverse = conceptual_points['transverse'].values - normal = conceptual_points['normal'].values - - cp_tensors_3d = create_3d_tensors(bearing, dip, major, transverse, normal) - # Get field statistics field_cols = [col for col in conceptual_points.columns if col.startswith('mean_') or col.startswith('sd_')] if field_cols: @@ -378,69 +416,125 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, cp_means = np.ones(len(cp_coords)) cp_sds = np.ones(len(cp_coords)) * 0.5 - # Interpolate 3D tensors to grid points - print(" Interpolating 3D tensors...") - interp_tensors_3d = interpolate_tensors_3d(cp_coords, cp_tensors_3d, grid_coords, - tensor_interp=tensor_interp, zones=zones) - # Handle zones for 3D zones_3d = None if zones is not None: if len(zones.shape) == 2: # 2D zones - replicate for each layer zones_3d = np.tile(zones[np.newaxis, :, :], (nz, 1, 1)) + print(f" Using 2D zones replicated to 3D: {zones.shape} -> {zones_3d.shape}") else: # Already 3D zones zones_3d = zones + print(f" Using 3D zones: {zones_3d.shape}") - # Interpolate mean field using tensor-aware kriging - print(" Interpolating mean field with 3D tensor-aware kriging...") + # Step 1: Create geological tensors from conceptual points + print(" Creating 3D geological correlation tensors...") + bearing = np.radians(conceptual_points['bearing'].values) + dip = np.radians(conceptual_points['dip'].values) + major = conceptual_points['major'].values + transverse = conceptual_points['transverse'].values + normal = conceptual_points['normal'].values + + cp_tensors_3d = create_3d_tensors(bearing, dip, major, transverse, normal) + + # Debug: Test tensor creation with first few points + print(" Tensor creation verification:") + for i in range(min(3, len(cp_tensors_3d))): + eigenvals = np.linalg.eigvals(cp_tensors_3d[i]) + print(f" Point {i}: Eigenvalues={eigenvals}, All positive={np.all(eigenvals > 0)}") + + # Step 2: Interpolate tensors using zone-based approach + if zones_3d is not None: + print(" Interpolating 3D geological tensors by zone...") + geological_tensors_3d = interpolate_tensors_by_zone( + cp_coords, cp_tensors_3d, grid_coords, zones_3d, tensor_interp=tensor_interp + ) + else: + print(" Interpolating 3D geological tensors globally...") + geological_tensors_3d = interpolate_tensors_3d( + cp_coords, cp_tensors_3d, grid_coords, tensor_interp=tensor_interp + ) + + # Step 3: Interpolate means using zone-aware kriging + print(" Interpolating mean field with 3D geological structure...") mean_field_1d = tensor_aware_kriging( - cp_coords, cp_means, grid_coords, interp_tensors_3d, + cp_coords, cp_means, grid_coords, geological_tensors_3d, variogram_model='exponential', sill=1.0, nugget=0.01, + background_value=np.mean(cp_means), max_search_radius=1e20, + min_points=3, transform='log', min_value=1e-8, max_neighbors=8, zones=zones_3d ) mean_field_3d = mean_field_1d.reshape(shape) - # Interpolate standard deviation field + # Apply boundary smoothing if zones are present + if zones_3d is not None: + print(" Smoothing values at geological boundaries...") + mean_field_3d = create_boundary_modified_scalar( + mean_field_3d, zones_3d, + transition_cells=3, mode='smooth' + ) + + # Step 4: Interpolate standard deviation using geological tensors print(" Interpolating standard deviation field...") sd_field_1d = tensor_aware_kriging( - cp_coords, cp_sds, grid_coords, interp_tensors_3d, + cp_coords, cp_sds, grid_coords, geological_tensors_3d, variogram_model='exponential', sill=0.5, nugget=0.01, + background_value=np.mean(cp_sds), max_search_radius=1e20, + min_points=3, transform=None, min_value=1e-8, max_neighbors=8, zones=zones_3d ) sd_field_3d = sd_field_1d.reshape(shape) - # Generate correlated noise using 3D tensors + # Apply boundary enhancement if zones are present + if zones_3d is not None: + print(" Enhancing variance at geological boundaries...") + sd_field_3d = create_boundary_modified_scalar( + sd_field_3d, zones_3d, + peak_increase=1, transition_cells=3, mode='enhance' + ) + + # Step 5: Generate correlated noise using 3D tensors print(" Generating 3D correlated noise...") - # Flatten for noise generation, then reshape - grid_coords_flat = grid_coords - tensors_flat = interp_tensors_3d + # Work with 1D for noise generation, then reshape iids_flat = iids.flatten() - - correlated_noise_flat = generate_correlated_noise_3d( - grid_coords_flat, tensors_flat, iids_flat, + correlated_noise_1d = generate_correlated_noise_3d( + grid_coords, geological_tensors_3d, iids_flat, n_neighbors=50, anisotropy_strength=1.0 ) - correlated_noise_3d = correlated_noise_flat.reshape(shape) + correlated_noise_3d = correlated_noise_1d.reshape(shape) + + # Step 6: Combine into log-normal field + print(" Combining fields using log-normal formulation...") + print(f" Mean field shape: {mean_field_3d.shape}") + print(f" SD field shape: {sd_field_3d.shape}") + print(f" Noise shape: {correlated_noise_3d.shape}") + + # Check if correlated noise has proper statistics (should be ~N(0,1)) + noise_mean = np.mean(correlated_noise_3d) + noise_std = np.std(correlated_noise_3d) + print(f" Correlated noise stats: mean={noise_mean:.6f}, std={noise_std:.6f}") - # Combine mean, scaled noise + # Log-normal field generation: field = 10^(log10(mean) + noise * sd) log_mean_field = np.log10(np.maximum(mean_field_3d, 1e-8)) # Avoid log(0) field_3d = 10 ** (log_mean_field + correlated_noise_3d * sd_field_3d) + print(f" Log-mean field range: [{np.min(log_mean_field):.3f}, {np.max(log_mean_field):.3f}]") + print(f" Generated field range: [{np.min(field_3d):.3e}, {np.max(field_3d):.3e}]") + print(f" Generated 3D field with shape {field_3d.shape}") print(f" Field stats: mean={np.mean(field_3d):.3f}, std={np.std(field_3d):.3f}") # Export to ParaView export_3d_results_to_paraview( field_3d=field_3d, - tensors_3d=interp_tensors_3d, + tensors_3d=geological_tensors_3d, grid_coords=grid_coords, shape=shape, zones=zones, conceptual_points=conceptual_points, save_path=save_path, - field_name='permeability' + field_name='conductivity' ) return field_3d @@ -605,7 +699,7 @@ def _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones): if zones[row, col] == zone_id: zone_grid_indices.append(i) - zone_grid_indices = np.array(zone_grid_indices, dtype=int) + zone_grid_indices = np.array(zone_grid_indices).astype(np.int64) if len(zone_cp_coords) > 0: # Assign nearest neighbor tensor (no interpolation) @@ -614,25 +708,28 @@ def _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones): nearest_cp = np.argmin(distances) geological_tensors[grid_idx] = zone_cp_tensors[nearest_cp] else: - # No conceptual points in zone - use default tensor print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") - default_tensor = np.eye(2) * 1000 ** 2 + if tensor_size == 2: + default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length + else: + default_tensor = np.eye(3) * 1000 ** 2 # Default 1000m correlation length geological_tensors[zone_grid_indices] = default_tensor return geological_tensors -def create_boundary_modified_scalar(base_field_2d, zones, +def create_boundary_modified_scalar(base_field, zones, peak_increase=0.3, transition_cells=5, mode="enhance"): """ Modify scalar field values near geological zone boundaries with either enhancement or smoothing. + Supports both 2D and 3D fields/zones. Parameters ---------- - base_field_2d : np.ndarray - Base scalar values + base_field : np.ndarray + Base scalar values, shape (ny, nx) for 2D or (nz, ny, nx) for 3D zones : np.ndarray - Zone IDs + Zone IDs, shape (ny, nx) for 2D or (nz, ny, nx) for 3D peak_increase : float Max enhancement or smoothing strength transition_cells : int @@ -643,37 +740,88 @@ def create_boundary_modified_scalar(base_field_2d, zones, Returns ------- np.ndarray - Modified scalar field + Modified scalar field, same shape as input """ - from scipy.ndimage import distance_transform_edt + from scipy.ndimage import distance_transform_edt, gaussian_filter if mode not in ("enhance", "smooth"): raise ValueError("mode must be 'enhance' or 'smooth'") - boundary_mask, _ = detect_zone_boundaries(zones) - distance = distance_transform_edt(~boundary_mask) - transition_mask = distance <= transition_cells + # Determine if we're working with 2D or 3D + is_3d = len(base_field.shape) == 3 - modified = base_field_2d.copy() + if is_3d: + # 3D case + nz, ny, nx = base_field.shape + if zones.shape != base_field.shape: + raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") - if mode == "enhance": - # Linear enhancement near boundaries - factor = 1 - distance[transition_mask] / transition_cells - enhancement = peak_increase * factor - modified[transition_mask] += enhancement + # Process each layer separately for better boundary detection + modified = base_field.copy() + total_modified_points = 0 - elif mode == "smooth": - # Smooth field with Gaussian filter as the target blending value - smoothed_field = gaussian_filter(base_field_2d, sigma=transition_cells / 2) + for layer in range(nz): + layer_field = base_field[layer] + layer_zones = zones[layer] - # Blend with original based on distance to boundary - weight = 1 - distance[transition_mask] / transition_cells - modified[transition_mask] = ( - weight * smoothed_field[transition_mask] + - (1 - weight) * base_field_2d[transition_mask] - ) + # Detect boundaries in this layer + boundary_mask, _ = detect_zone_boundaries(layer_zones) + distance = distance_transform_edt(~boundary_mask) + transition_mask = distance <= transition_cells + + if mode == "enhance": + # Linear enhancement near boundaries + factor = 1 - distance[transition_mask] / transition_cells + enhancement = peak_increase * factor + modified[layer][transition_mask] += enhancement + + elif mode == "smooth": + # Smooth field with Gaussian filter as the target blending value + smoothed_field = gaussian_filter(layer_field, sigma=transition_cells / 2) + + # Blend with original based on distance to boundary + weight = 1 - distance[transition_mask] / transition_cells + modified[layer][transition_mask] = ( + weight * smoothed_field[transition_mask] + + (1 - weight) * layer_field[transition_mask] + ) + + total_modified_points += np.count_nonzero(transition_mask) + + print( + f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {total_modified_points} points near boundaries across {nz} layers") + + else: + # 2D case - original logic + if zones.shape != base_field.shape: + raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") + + boundary_mask, _ = detect_zone_boundaries(zones) + distance = distance_transform_edt(~boundary_mask) + transition_mask = distance <= transition_cells + + modified = base_field.copy() + + if mode == "enhance": + # Linear enhancement near boundaries + factor = 1 - distance[transition_mask] / transition_cells + enhancement = peak_increase * factor + modified[transition_mask] += enhancement + + elif mode == "smooth": + # Smooth field with Gaussian filter as the target blending value + smoothed_field = gaussian_filter(base_field, sigma=transition_cells / 2) + + # Blend with original based on distance to boundary + weight = 1 - distance[transition_mask] / transition_cells + modified[transition_mask] = ( + weight * smoothed_field[transition_mask] + + (1 - weight) * base_field[transition_mask] + ) + + print( + f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") - print(f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") return modified @@ -808,7 +956,7 @@ def detect_zone_boundaries(zones): - boundary_directions: Direction vectors at boundaries, shape (ny, nx, 2) """ ny, nx = zones.shape - boundary_mask = np.zeros((ny, nx), dtype=bool) + boundary_mask = np.zeros((ny, nx)).astype(bool) boundary_directions = np.zeros((ny, nx, 2)) # Find boundary points (any point with different-zone neighbors) @@ -1172,7 +1320,14 @@ def interpolate_tensors_3d(cp_coords, cp_tensors, grid_coords, tensor_interp='id interp_tensors = np.zeros((n_grid, 3, 3)) if tensor_interp == 'nearest' and zones is not None: + # Zone-based nearest neighbor (requires zones) interp_tensors = _nearest_interpolation_3d(cp_coords, cp_tensors, grid_coords, zones) + elif tensor_interp == 'nearest' and zones is None: + # Simple nearest neighbor without zones + from scipy.spatial.distance import cdist + distances = cdist(grid_coords, cp_coords) + nearest_indices = np.argmin(distances, axis=1) + interp_tensors = cp_tensors[nearest_indices] else: # Log-Euclidean approach - ensure positive definiteness first log_tensors = np.zeros_like(cp_tensors) @@ -1540,54 +1695,134 @@ def get_tensor_bearing(tensor, expected_bearing=None): def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): """ - Extract zone assignment logic from tensor_aware_kriging into reusable function. + Assign conceptual points to zones based on nearest grid point. + Supports both 2D and 3D zones. Parameters ---------- + cp_coords : np.ndarray + Conceptual point coordinates, shape (n_cp, 2) or (n_cp, 3) + grid_coords : np.ndarray + Grid coordinates, shape (n_grid, 2) or (n_grid, 3) zones : np.ndarray - Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point + Zone array, shape (ny, nx) for 2D or (nz, ny, nx) for 3D + + Returns + ------- + np.ndarray + Zone IDs for each conceptual point, shape (n_cp,) """ - ny, nx = zones.shape + n_cp = len(cp_coords) + cp_zones = np.zeros(n_cp).astype(np.int64) - cp_zones = [] - for coord in cp_coords: - # Find closest grid point to get zone - distances = np.sum((grid_coords - coord) ** 2, axis=1) + # Determine zone dimensions + if len(zones.shape) == 2: + # 2D zones + ny, nx = zones.shape + + def get_zone_from_index(grid_index): + row, col = divmod(grid_index, nx) + return zones[row, col] + + else: + # 3D zones + nz, ny, nx = zones.shape + + def get_zone_from_index(grid_index): + layer = grid_index // (ny * nx) + remainder = grid_index % (ny * nx) + row = remainder // nx + col = remainder % nx + return zones[layer, row, col] + + # For each conceptual point, find nearest grid point and get its zone + for i, cp_coord in enumerate(cp_coords): + # Find closest grid point + distances = np.sum((grid_coords - cp_coord) ** 2, axis=1) closest_idx = np.argmin(distances) - row, col = divmod(closest_idx, nx) - cp_zones.append(zones[row, col]) - return np.array(cp_zones) + # Get zone ID from closest grid point + cp_zones[i] = get_zone_from_index(closest_idx) + + return cp_zones + + +# def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): +# """ +# Extract zone assignment logic from tensor_aware_kriging into reusable function. +# +# Parameters +# ---------- +# zones : np.ndarray +# Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point +# """ +# ny, nx = zones.shape +# +# cp_zones = [] +# for coord in cp_coords: +# # Find closest grid point to get zone +# distances = np.sum((grid_coords - coord) ** 2, axis=1) +# closest_idx = np.argmin(distances) +# row, col = divmod(closest_idx, nx) +# cp_zones.append(zones[row, col]) +# +# return np.array(cp_zones) def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, tensor_interp='idw'): """ Zone-aware tensor interpolation - interpolate tensors within each zone separately. + Supports both 2D and 3D zones. Parameters ---------- cp_coords : np.ndarray - Conceptual point coordinates + Conceptual point coordinates, shape (n_cp, 2) for 2D or (n_cp, 3) for 3D cp_tensors : np.ndarray - Conceptual point tensors + Conceptual point tensors, shape (n_cp, 2, 2) for 2D or (n_cp, 3, 3) for 3D grid_coords : np.ndarray - Grid coordinates + Grid coordinates, shape (n_grid, 2) for 2D or (n_grid, 3) for 3D zones : np.ndarray - Zone array, shape (ny, nx) - GUARANTEED to be 2D from entry point + Zone array, shape (ny, nx) for 2D or (nz, ny, nx) for 3D + tensor_interp : str, default 'idw' + Tensor interpolation method Returns ------- np.ndarray - Interpolated tensors, shape (n_grid, 2, 2) + Interpolated tensors, shape (n_grid, 2, 2) for 2D or (n_grid, 3, 3) for 3D """ n_grid = len(grid_coords) - ny, nx = zones.shape + n_dims = cp_coords.shape[1] # 2 for 2D, 3 for 3D + tensor_size = cp_tensors.shape[-1] # 2 for 2D tensors, 3 for 3D tensors + + # Determine if we're working with 2D or 3D zones + if len(zones.shape) == 2: + # 2D zones + ny, nx = zones.shape + is_3d = False + + def get_zone_id(grid_index): + row, col = divmod(grid_index, nx) + return zones[row, col] + + else: + # 3D zones + nz, ny, nx = zones.shape + is_3d = True + + def get_zone_id(grid_index): + layer = grid_index // (ny * nx) + remainder = grid_index % (ny * nx) + row = remainder // nx + col = remainder % nx + return zones[layer, row, col] # Get zone for each conceptual point cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) # Initialize output tensors - geological_tensors = np.zeros((n_grid, 2, 2)) + geological_tensors = np.zeros((n_grid, tensor_size, tensor_size)) # Get unique zones unique_zones = np.unique(zones) @@ -1603,22 +1838,32 @@ def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, tenso # Find grid points in this zone zone_grid_indices = [] for i in range(n_grid): - row, col = divmod(i, nx) - if zones[row, col] == zone_id: + if get_zone_id(i) == zone_id: zone_grid_indices.append(i) zone_grid_indices = np.array(zone_grid_indices) + + if len(zone_grid_indices) == 0: + continue + zone_grid_coords = grid_coords[zone_grid_indices] if len(zone_cp_coords) > 0: # Interpolate tensors within zone using only zone's conceptual points - zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, - tensor_interp=tensor_interp, zones=zones) + if n_dims == 2: + zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, + tensor_interp=tensor_interp, zones=None) + else: + zone_tensors = interpolate_tensors_3d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, + tensor_interp=tensor_interp, zones=None) geological_tensors[zone_grid_indices] = zone_tensors else: # No conceptual points in zone - use default tensor or nearest zone - print(f" Warning: 1 or fewer points in zone {zone_id}, using default tensor") - default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length + print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") + if tensor_size == 2: + default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length + else: + default_tensor = np.eye(3) * 1000 ** 2 # Default 1000m correlation length geological_tensors[zone_grid_indices] = default_tensor return geological_tensors @@ -2442,12 +2687,14 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, - zones: Can be passed to generate_single_layer() as numpy array with integer zone IDs, shape (n_points,) or (ny, nx) """ - generate_fields_from_files( - conceptual_points_file=r'C:\modelling\sva_ds\data\conceptual_points.csv', - grid_file=r'C:\modelling\sva_ds\data\grid.dat', - zone_file=r'C:\modelling\sva_ds\data\waq_arr.geoclass_simple.arr', + sim_ws = r'C:\modelling\sva_ds\data' + zone_files = [f'waq_arr.geoclass_layer{z+1}.arr' for z in range(13)] + generate_fields_from_files(sim_ws, + conceptual_points_file='conceptual_points.csv', + grid_file='grid.dat', + zone_file=zone_files, field_name=['kh'], layer_mode=False, - save_path=r'C:\modelling\sva_ds\data\output_nearest', + save_path='output_nearest', tensor_interp='nearest' ) \ No newline at end of file From bdf26fb36f2fd9517503f9af2f9b8a56f148e3ba Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 22 Jul 2025 17:59:33 +1200 Subject: [PATCH 10/36] 3d tensor interpolation, some functions added to plot_utils --- examples/Wairau/conceptual_points.csv | 603 + examples/Wairau/grid.dat | 101908 ++++++++++++++++ examples/Wairau/waq_arr.domain_layer1.arr | 67 + examples/Wairau/waq_arr.geoclass_layer1.arr | 67 + examples/Wairau/waq_arr.geoclass_layer10.arr | 67 + examples/Wairau/waq_arr.geoclass_layer11.arr | 67 + examples/Wairau/waq_arr.geoclass_layer12.arr | 67 + examples/Wairau/waq_arr.geoclass_layer13.arr | 67 + examples/Wairau/waq_arr.geoclass_layer2.arr | 67 + examples/Wairau/waq_arr.geoclass_layer3.arr | 67 + examples/Wairau/waq_arr.geoclass_layer4.arr | 67 + examples/Wairau/waq_arr.geoclass_layer5.arr | 67 + examples/Wairau/waq_arr.geoclass_layer6.arr | 67 + examples/Wairau/waq_arr.geoclass_layer7.arr | 67 + examples/Wairau/waq_arr.geoclass_layer8.arr | 67 + examples/Wairau/waq_arr.geoclass_layer9.arr | 67 + pyemu/__init__.py | 3 +- pyemu/plot/plot_utils.py | 473 + pyemu/utils/__init__.py | 1 + pyemu/utils/tensor_utils.py | 565 +- 20 files changed, 104063 insertions(+), 428 deletions(-) create mode 100644 examples/Wairau/conceptual_points.csv create mode 100644 examples/Wairau/grid.dat create mode 100644 examples/Wairau/waq_arr.domain_layer1.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer1.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer10.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer11.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer12.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer13.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer2.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer3.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer4.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer5.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer6.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer7.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer8.arr create mode 100644 examples/Wairau/waq_arr.geoclass_layer9.arr diff --git a/examples/Wairau/conceptual_points.csv b/examples/Wairau/conceptual_points.csv new file mode 100644 index 000000000..4814ae98c --- /dev/null +++ b/examples/Wairau/conceptual_points.csv @@ -0,0 +1,603 @@ +name,x,y,z,mean_kh,sd_kh,major,transverse,normal,anisotropy,bearing,dip,layer +300_0,1682569.2711419624,5403674.589484582,10.00000001,2.778733,0.5,3000,300,5,9.999999999,85,0.5,4 +300_1,1685170.7949142777,5413966.568,10.000000015000001,20.797330000000002,0.5,3000,300,5,9.999999999,142,0.5,4 +300_2,1687245.922086844,5401585.2998034675,10.000000015000001,5.223497999999999,0.5,3000,300,5,9.999999999,80,0.5,4 +300_3,1687719.1405320638,5399910.7776723225,10.000000024999999,5.940201,0.5,3000,300,5,9.999999999,60,0.5,4 +300_4,1682730.9262653994,5412470.112514353,16.999999980000002,6.955375999999999,0.5,3000,300,5,9.999999999,142,0.5,5 +300_5,1685657.162753461,5401028.485253247,17,65.85429,0.5,3000,300,5,9.999999999,60,0.5,5 +300_6,1682569.2711419624,5403674.589484582,17.00000002,33.34105,0.5,3000,300,5,9.999999999,85,0.5,5 +300_7,1678983.756485376,5412265.873680885,17.00000003,20.277549999999998,0.5,3000,300,5,9.999999999,85,0.5,5 +300_8,1686991.3475219798,5416335.115318223,17.00000003,14.48703,0.5,3000,300,5,9.999999999,142,0.5,5 +300_9,1687245.922086844,5401585.2998034675,17.00000003,20.89951,0.5,3000,300,5,9.999999999,80,0.5,5 +300_10,1685170.7949142777,5413966.568,17.000000030000002,234.92950000000002,0.5,3000,300,5,9.999999999,142,0.5,5 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0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 diff --git a/pyemu/__init__.py b/pyemu/__init__.py index 2e1b6b223..9b05c605e 100644 --- a/pyemu/__init__.py +++ b/pyemu/__init__.py @@ -19,7 +19,7 @@ from .pst import Pst, pst_utils from .sc import Schur from .utils import (geostats, gw_utils, helpers, metrics, optimization, - os_utils, pp_utils, smp_utils) + os_utils, pp_utils, smp_utils, tensor_utils) #from .prototypes import * try: @@ -53,5 +53,6 @@ "smp_utils", "plot_utils", "metrics", + "tensor_utils" ] # del get_versions diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 21247dd74..1ba000429 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -1794,3 +1794,476 @@ def plot_jac_test( ) ) plt.close() + +def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, + figsize=(12, 10), save_path='.'): + """ + Plot geology zones with conceptual points overlaid and labeled. + Automatically handles single layer (2D) or multiple layers (3D) based on zones shape. + + Parameters + ---------- + zones : np.ndarray + Zone array, shape (ny, nx) for single layer or (nz, ny, nx) for multiple layers + conceptual_points : pd.DataFrame + Must have columns: x, y, i, j, name + For 3D zones, must also have column: k + grid_coords : np.ndarray + Grid coordinates for coordinate transformation + Shape (ny*nx, 3) for 2D or (nz*ny*nx, 3) for 3D + figsize : tuple + Figure size + save_path : str, optional + Path to save figure + """ + import matplotlib.pyplot as plt + import matplotlib.patches as patches + import os + import numpy as np + + shape = zones.shape + is_3d = len(shape) == 3 + + if is_3d: + nz, ny, nx = shape + # Validate inputs for 3D + if not validate_grid_coordinates(grid_coords, shape): + raise ValueError(f"Grid coordinates shape {grid_coords.shape} doesn't match zones shape {shape}") + + # Check for required 'k' column in conceptual points + if 'k' not in conceptual_points.columns: + raise ValueError("For 3D zones, conceptual_points must have 'k' column for layer index") + + # Reshape grid coordinates to 3D structure for easy layer access + grid_coords_3d = grid_coords.reshape(nz, ny, nx, 3) + + # Get overall bounds for consistent axis limits across layers + x_min, x_max = grid_coords[:, 0].min(), grid_coords[:, 0].max() + y_min, y_max = grid_coords[:, 1].min(), grid_coords[:, 1].max() + else: + ny, nx = shape + + # Handle case where grid_coords might be from 3D model but zones is 2D slice + expected_2d_points = ny * nx + total_points = grid_coords.shape[0] + + if total_points == expected_2d_points: + # Grid coords match 2D zones exactly + try: + grid_coords_xyz = grid_coords[['x', 'y', 'z']].values + grid_coords_2d = grid_coords_xyz.reshape(ny, nx, 3) + except (KeyError, AttributeError, IndexError, TypeError): + # Fallback to numeric indexing if it's not a DataFrame or missing columns + if grid_coords.shape[1] >= 4: + grid_coords_xyz = grid_coords[:, 1:4] # Take x,y,z columns (skip name) + grid_coords_2d = grid_coords_xyz.reshape(ny, nx, 3) + else: + raise ValueError("Grid coordinates must have 'x', 'y', 'z' columns or at least 4 numeric columns") + elif total_points % expected_2d_points == 0: + # Grid coords are from 3D model, use first layer + n_layers = total_points // expected_2d_points + print(f"Using first layer coordinates from {n_layers}-layer grid") + layer_coords = grid_coords[:expected_2d_points] + + try: + layer_coords_xyz = layer_coords[['x', 'y', 'z']].values + grid_coords_2d = layer_coords_xyz.reshape(ny, nx, 3) + except (KeyError, AttributeError, IndexError, TypeError): + # Fallback to numeric indexing if it's not a DataFrame or missing columns + if layer_coords.shape[1] >= 4: + layer_coords_xyz = layer_coords[:, 1:4] # Take x,y,z columns (skip name) + grid_coords_2d = layer_coords_xyz.reshape(ny, nx, 3) + else: + raise ValueError("Grid coordinates must have 'x', 'y', 'z' columns or at least 4 numeric columns") + else: + raise ValueError(f"Grid coordinates shape {grid_coords.shape} doesn't match zones shape {shape}") + + # Get bounds using the processed coordinates + x_min, x_max = grid_coords_2d[:, :, 0].min(), grid_coords_2d[:, :, 0].max() + y_min, y_max = grid_coords_2d[:, :, 1].min(), grid_coords_2d[:, :, 1].max() + + # Get overall zone range for consistent coloring + unique_zones = np.unique(zones) + n_zones = len(unique_zones) + zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} + + if is_3d: + # Multiple layers - create subplots + fig, axes = plt.subplots(1, nz, figsize=(figsize[0] * nz / 2, figsize[1])) + if nz == 1: + axes = [axes] # Ensure axes is always iterable + + for k in range(nz): + ax = axes[k] + + # Get layer-specific data + layer_zones = zones[k, :, :] + layer_coords = grid_coords_3d[k, :, :, :] # (ny, nx, 3) + + # Get x, y coordinates for this layer + x_coords = layer_coords[:, :, 0] + y_coords = layer_coords[:, :, 1] + + # Remap zones for consistent coloring + zones_remapped = np.zeros_like(layer_zones) + for zone_val, color_idx in zone_to_color.items(): + zones_remapped[layer_zones == zone_val] = color_idx + + # Plot zones - flip the array to match real-world orientation + zones_flipped = np.flipud(zones_remapped) + + im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], + origin='lower', cmap='tab10', alpha=0.7, + vmin=0, vmax=n_zones - 1) + + # Filter conceptual points for this layer + layer_points = conceptual_points[conceptual_points['k'] == k] + + # Plot conceptual points for this layer + for idx, row in layer_points.iterrows(): + x, y = row['x'], row['y'] + i, j = row['i'], row['j'] + name = row['name'] + + # Plot point + ax.plot(x, y, 'ro', markersize=8, markeredgecolor='black', markeredgewidth=1) + + # Add label with index and grid position + ax.annotate(f'{name}\n({i},{j})', + (x, y), xytext=(5, 5), textcoords='offset points', + fontsize=8, fontweight='bold', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) + + # Draw line to corresponding grid cell center + i_int, j_int = int(i), int(j) + if 0 <= i_int < ny and 0 <= j_int < nx: + grid_x = x_coords[i_int, j_int] + grid_y = y_coords[i_int, j_int] + ax.plot([x, grid_x], [y, grid_y], 'r--', alpha=0.5, linewidth=1) + + # Mark grid cell center + ax.plot(grid_x, grid_y, 'r+', markersize=10, markeredgewidth=2) + + ax.set_xlabel('X (NZTM)') + ax.set_ylabel('Y (NZTM)') + ax.set_title(f'Layer {k + 1} - Geology Zones with Conceptual Points') + ax.grid(True, alpha=0.3) + + # Set consistent axis limits + ax.set_xlim(x_min, x_max) + ax.set_ylim(y_min, y_max) + + # Add colorbar to the last subplot + cbar = plt.colorbar(im, ax=axes[-1]) + cbar.set_label('Zone ID') + cbar.set_ticks(range(n_zones)) + cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) + + # Add legend to first subplot + legend_elements = [ + plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='r', + markersize=8, label='Conceptual Points'), + plt.Line2D([0], [0], marker='+', color='r', markersize=10, + label='Assigned Grid Cells'), + plt.Line2D([0], [0], color='r', linestyle='--', + label='CP to Grid Assignment') + ] + axes[0].legend(handles=legend_elements, loc='upper right') + + plt.tight_layout() + + # Save figure + save_file = os.path.join(save_path, 'conceptual_points_multilayer.png') + plt.savefig(save_file, dpi=150, bbox_inches='tight') + print(f"Plot saved to: {save_file}") + return fig + + else: + # Single layer - original logic + fig, ax = plt.subplots(figsize=figsize) + + # Get x, y coordinates for the single layer + x_coords = grid_coords_2d[:, :, 0] + y_coords = grid_coords_2d[:, :, 1] + + # Remap zones for consistent coloring + zones_remapped = np.zeros_like(zones) + for zone_val, color_idx in zone_to_color.items(): + zones_remapped[zones == zone_val] = color_idx + + # Plot zones - flip the array to match real-world orientation + zones_flipped = np.flipud(zones_remapped) + + im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], + origin='lower', cmap='tab10', alpha=0.7, + vmin=0, vmax=n_zones - 1) + + # Add colorbar with actual zone values + cbar = plt.colorbar(im, ax=ax) + cbar.set_label('Zone ID') + cbar.set_ticks(range(n_zones)) + cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) + + # Plot conceptual points (all points for single layer) + for idx, row in conceptual_points.iterrows(): + x, y = row['x'], row['y'] + i, j = row['i'], row['j'] + name = row['name'] + + # Plot point + ax.plot(x, y, 'ro', markersize=8, markeredgecolor='black', markeredgewidth=1) + + # Add label with index and grid position + ax.annotate(f'{name}\n({i},{j})', + (x, y), xytext=(5, 5), textcoords='offset points', + fontsize=8, fontweight='bold', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) + + # Draw line to corresponding grid cell center + i_int, j_int = int(i), int(j) + if 0 <= i_int < ny and 0 <= j_int < nx: + grid_x = x_coords[i_int, j_int] + grid_y = y_coords[i_int, j_int] + ax.plot([x, grid_x], [y, grid_y], 'r--', alpha=0.5, linewidth=1) + + # Mark grid cell center + ax.plot(grid_x, grid_y, 'r+', markersize=10, markeredgewidth=2) + + ax.set_xlabel('X (NZTM)') + ax.set_ylabel('Y (NZTM)') + ax.set_title('Geology Zones with Conceptual Points\n(Red dots = CP locations, + = assigned grid cells)') + ax.grid(True, alpha=0.3) + + # Add legend + legend_elements = [ + plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='r', + markersize=8, label='Conceptual Points'), + plt.Line2D([0], [0], marker='+', color='r', markersize=10, + label='Assigned Grid Cells'), + plt.Line2D([0], [0], color='r', linestyle='--', + label='CP to Grid Assignment') + ] + ax.legend(handles=legend_elements, loc='upper right') + + plt.tight_layout() + + # Save figure + save_file = os.path.join(save_path, 'conceptual_points.png') + plt.savefig(save_file, dpi=150, bbox_inches='tight') + print(f"Plot saved to: {save_file}") + + plt.close() + + +def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=None, + conceptual_points=None, subsample=4, scale_factor=20, + figsize=(14, 12), title_suf=None, save_path='.'): + """ + Visualize geological tensors as ellipses overlaid on zones, with conceptual points as oriented lines. + + Parameters + ---------- + geological_tensors : np.ndarray + Tensor field, shape (n_points, 2, 2) + grid_coords : np.ndarray + Grid coordinates, shape (n_points, 2) or (n_points, 3) + shape : tuple + Grid shape (ny, nx) + zones : np.ndarray, optional + Zone array for background, shape (ny, nx) + conceptual_points : pd.DataFrame, optional + Conceptual points with x, y, bearing, major columns + subsample : int, default 4 + Show every Nth tensor (for readability) + scale_factor : float, default 20 + Scale ellipses for visibility + figsize : tuple, default (14, 12) + Figure size + title_suf : str, optional + Suffix for plot title + save_path : str, default '.' + Directory to save plot + + Returns + ------- + None + Saves visualization as PNG file + + Examples + -------- + >>> visualize_geological_tensors(tensors, coords, (100, 150), + ... subsample=8, title_suf='layer_1') + """ + from matplotlib import patches + + fig, ax = plt.subplots(figsize=figsize) + + # Prepare zone data but don't plot yet + if zones is not None: + # zones are guaranteed to be 2D from entry point + + # Create zone color mapping + unique_zones = np.unique(zones) + zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} + zones_remapped = np.zeros_like(zones) + for zone_val, color_idx in zone_to_color.items(): + zones_remapped[zones == zone_val] = color_idx + + # Get coordinate bounds + x_coords = grid_coords[:, 0].reshape(shape) + y_coords = grid_coords[:, 1].reshape(shape) + x_min, x_max = x_coords.min(), x_coords.max() + y_min, y_max = y_coords.min(), y_coords.max() + + zones_flipped = np.flipud(zones_remapped) + # Plot zones as background with low alpha + im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], + origin='lower', cmap='tab10', alpha=0.2, zorder=0, + vmin=0, vmax=len(unique_zones) - 1) + + # Plot conceptual points as oriented lines + if conceptual_points is not None: + for idx, row in conceptual_points.iterrows(): + x, y = row['x'], row['y'] + bearing = row['bearing'] + major = row['major'] + + # Scale line length for visibility (similar to ellipse scaling) + line_length = major / (scale_factor * 2) # Adjust scaling as needed + + # Convert geological bearing (CW from N) to math angle for plotting + # Geological: 0°=N, 90°=E; Math: 0°=E, 90°=N + math_angle_rad = np.radians(90 - bearing) + + # Calculate line endpoints + dx = line_length * np.cos(math_angle_rad) / 2 + dy = line_length * np.sin(math_angle_rad) / 2 + + # Plot line centered on point + ax.plot([x - dx, x + dx], [y - dy, y + dy], + 'r-', linewidth=3, alpha=0.9, zorder=4) + + # Plot center point + ax.plot(x, y, 'ro', markersize=4, markeredgecolor='black', + markeredgewidth=1, alpha=0.9, zorder=5) + + # Add label with bearing + ax.annotate(f'{bearing:.0f}°', (x, y), xytext=(8, 8), + textcoords='offset points', fontsize=8, fontweight='bold', + bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.7), + zorder=6) + + # Subsample points for visualization + indices = np.arange(0, len(geological_tensors), subsample) + + print(f"Plotting {len(indices)} tensors out of {len(geological_tensors)}") + + default_count = 0 + ellipse_count = 0 + + for i in indices: + tensor = geological_tensors[i] + x, y = grid_coords[i, 0], grid_coords[i, 1] + + # Check for default tensor (indicates no interpolation happened) + if np.allclose(tensor, np.eye(2) * 1000000): + # Plot as red circle for default tensors with high zorder + ax.plot(x, y, 'ko', markersize=2, alpha=0.3, zorder=1) # Make less prominent + default_count += 1 + continue + + try: + # Eigendecomposition to get ellipse parameters + eigenvals, eigenvecs = np.linalg.eigh(tensor) + + # Sort by eigenvalue magnitude + idx = np.argsort(eigenvals)[::-1] + eigenvals = eigenvals[idx] + eigenvecs = eigenvecs[:, idx] + + # Ellipse dimensions (correlation lengths) - make them more visible + major_axis = 2 * np.sqrt(eigenvals[0]) / scale_factor + minor_axis = 2 * np.sqrt(eigenvals[1]) / scale_factor + + # Make sure ellipses are visible + if major_axis < 30: # Smaller minimum to avoid overwhelming CP lines + major_axis = 30 + if minor_axis < 15: + minor_axis = 15 + + # Ellipse orientation (angle of major axis) + angle = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) + + # Create ellipse with lower prominence than CP lines + ellipse = patches.Ellipse((x, y), major_axis, minor_axis, + angle=angle, linewidth=1, + edgecolor='blue', facecolor='none', alpha=0.6, zorder=2) + ax.add_patch(ellipse) + ellipse_count += 1 + + except: + # If tensor is problematic, plot as yellow point with high zorder + ax.plot(x, y, 'yo', markersize=2, zorder=3) + + print(f"Plotted {default_count} default tensors (black circles) and {ellipse_count} ellipses") + + ax.set_xlabel('X (NZTM)') + ax.set_ylabel('Y (NZTM)') + + # Update title and legend + title_parts = [f'Geological Tensors (subsampled 1:{subsample})\n'] + if title_suf is not None: + title_parts += [f'{title_suf}'] + # more like legend? + if conceptual_points is not None: + title_parts.append('Red lines = conceptual points (bearing)') + title_parts.append('Blue ellipses = interpolated tensors') + ax.set_title('\n'.join(title_parts)) + + ax.set_aspect('equal', adjustable='box') + + # Add colorbar for zones if present + if zones is not None: + cbar = plt.colorbar(im, ax=ax, shrink=0.8) + cbar.set_label('Zone ID') + cbar.set_ticks(range(len(unique_zones))) + cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) + + # Add legend + legend_elements = [] + if conceptual_points is not None: + legend_elements.extend([ + plt.Line2D([0], [0], color='red', linewidth=3, label='Conceptual Point Bearings'), + plt.Line2D([0], [0], marker='o', color='red', markersize=6, + markeredgecolor='black', label='Conceptual Points', linestyle='None') + ]) + legend_elements.extend([ + patches.Patch(facecolor='none', edgecolor='blue', label='Interpolated Tensors'), + plt.Line2D([0], [0], marker='o', color='black', markersize=4, + label='Default Tensors', linestyle='None', alpha=0.3) + ]) + + if legend_elements: + ax.legend(handles=legend_elements, loc='upper right', fontsize=9) + + plt.tight_layout() + plt.savefig(os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png'), dpi=150, bbox_inches='tight') + print(f"Saved tensor visualization to {os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png')}") + plt.close() + + +def plot_layer(field, layer=0, field_name='field', transform='log', save_path='.'): + import matplotlib.colors as mcolors + if transform=='log': + field = np.log10(field) + + plt.figure(figsize=(14, 12)) + plt.imshow(field) + plt.colorbar(label='Parameter Value') + plt.title(f'{field_name} for layer {layer}') + plt.xlabel('X') + plt.ylabel('Y') + + os.makedirs('output', exist_ok=True) + plt.savefig(os.path.join(save_path, f'{field_name}_layer_{layer:02d}.png'), dpi=150) + plt.close() diff --git a/pyemu/utils/__init__.py b/pyemu/utils/__init__.py index 8486b78b0..05cd17014 100644 --- a/pyemu/utils/__init__.py +++ b/pyemu/utils/__init__.py @@ -13,5 +13,6 @@ from .pp_utils import * from .pst_from import * from .smp_utils import * +from .tensor_utils import * get_pestpp = get_pestpp_module.run_main diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index ab76d4c60..019fe49a6 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -3,6 +3,7 @@ from scipy.linalg import logm, expm, solve from scipy.ndimage import distance_transform_edt, gaussian_filter import matplotlib as mpl +from pyemu.plot import plot_utils as pu mpl.use('Tkagg') import matplotlib.pyplot as plt @@ -13,6 +14,33 @@ # UTILITY FUNCTIONS FOR CONSISTENT ARRAY HANDLING # ============================================================================ +def convert_output_to_original_format(field_2d, original_zones_shape): + """ + Convert 2D field back to original input format. + + Parameters + ---------- + field_2d : np.ndarray + Field in 2D format, shape (ny, nx) + original_zones_shape : tuple or None + Original input array shape + + Returns + ------- + np.ndarray + Field in original format (1D or 2D) + """ + if original_zones_shape is None: + return field_2d + + if len(original_zones_shape) == 1: + # Original was 1D, return flattened + return field_2d.flatten() + else: + # Original was 2D, return as-is + return field_2d + + def standardize_input_arrays(zones_raw, grid_coords, shape): """ Standardize all input arrays to consistent 2D format for internal processing. @@ -58,33 +86,6 @@ def standardize_input_arrays(zones_raw, grid_coords, shape): return zones, grid_coords, original_zones_shape -def convert_output_to_original_format(field_2d, original_zones_shape): - """ - Convert 2D field back to original input format. - - Parameters - ---------- - field_2d : np.ndarray - Field in 2D format, shape (ny, nx) - original_zones_shape : tuple or None - Original input array shape - - Returns - ------- - np.ndarray - Field in original format (1D or 2D) - """ - if original_zones_shape is None: - return field_2d - - if len(original_zones_shape) == 1: - # Original was 1D, return flattened - return field_2d.flatten() - else: - # Original was 2D, return as-is - return field_2d - - def ensure_2d_for_processing(arr, shape): """ Ensure array is in 2D format for internal processing. @@ -138,14 +139,14 @@ def validate_grid_coordinates(grid_coords, shape): return grid_coords.shape[0] == expected_points -def generate_fields_from_files(sim_ws, conceptual_points_file, grid_file, zone_file=None, iids_file=None, +def generate_fields_from_files(data_dir, conceptual_points_file, grid_file, zone_file=None, iids_file=None, field_name=['field'], layer_mode=True, save_path='.', tensor_interp='idw'): """ Generate spatially correlated fields using tensor-based geostatistics. Parameters ---------- - sim_ws: str + data_dir: str Path to files conceptual_points_file : str CSV file containing conceptual points @@ -175,12 +176,10 @@ def generate_fields_from_files(sim_ws, conceptual_points_file, grid_file, zone_f Saves results to output/ directory """ print(f"=== Tensor-Based Non-Stationary Field Generation from {conceptual_points_file} ===") - if not os.path.exists(os.path.join(sim_ws, save_path)): - os.mkdir(os.path.join(sim_ws, save_path)) # Load data - conceptual_points = pd.read_csv(os.path.join(sim_ws, conceptual_points_file)) - grid = pd.read_csv(os.path.join(sim_ws, grid_file), sep=r'\s+') + conceptual_points = pd.read_csv(os.path.join(data_dir, conceptual_points_file)) + grid = pd.read_csv(os.path.join(data_dir, grid_file), sep=r'\s+') grid_coords = grid[['x', 'y', 'z']].values print(f"Loaded {len(conceptual_points)} conceptual points") @@ -199,17 +198,17 @@ def generate_fields_from_files(sim_ws, conceptual_points_file, grid_file, zone_f raise ValueError(f"Number of iids files ({len(iids_file)}) must match number of layers ({nz})") iids = np.zeros(shape) for i, file_path in enumerate(iids_file): - layer_iids = np.loadtxt(os.path.join(sim_ws, file_path)) + layer_iids = np.loadtxt(os.path.join(data_dir, file_path)) iids[i] = layer_iids.reshape(ny, nx) - print(f" Loaded iids layer {i + 1} from {os.path.join(sim_ws, file_path)}") + print(f" Loaded iids layer {i + 1} from {os.path.join(data_dir, file_path)}") else: if '{' in iids_name: # Pattern like "iids_layer{}.arr" - pattern = os.path.join(sim_ws, iids_name) + pattern = os.path.join(data_dir, iids_name) else: # Assume pattern from filename name, ext = os.path.splitext(iids_name) - pattern = os.path.join(sim_ws, f"{name}_layer{{}}{ext}") + pattern = os.path.join(data_dir, f"{name}_layer{{}}{ext}") iids = np.zeros(shape) for i in range(nz): @@ -226,7 +225,7 @@ def generate_fields_from_files(sim_ws, conceptual_points_file, grid_file, zone_f np.random.seed(42) iids = np.random.randn(*shape) for i in range(nz): - iids_file_path = os.path.join(sim_ws, f'iids_layer{i + 1}.arr') + iids_file_path = os.path.join(data_dir, f'iids_layer{i + 1}.arr') np.savetxt(iids_file_path, iids[i]) print(f" Saved iids layer {i + 1} to {iids_file_path}") @@ -246,16 +245,16 @@ def generate_fields_from_files(sim_ws, conceptual_points_file, grid_file, zone_f zones_list = [] for i, file in enumerate(zone_file): - layer_zones = np.loadtxt(os.path.join(sim_ws, file)).astype(np.int64) + layer_zones = np.loadtxt(os.path.join(data_dir, file)).astype(np.int64) zones_list.append(layer_zones) - print(f" Loaded zones layer {i + 1} from {os.path.join(sim_ws, file)}, shape: {layer_zones.shape}") + print(f" Loaded zones layer {i + 1} from {os.path.join(data_dir, file)}, shape: {layer_zones.shape}") zones_3d = np.array(zones_list) print(f" Created 3D zones array: {zones_3d.shape}") else: # Single zone file - use for all layers - zones_raw = np.loadtxt(os.path.join(sim_ws, zone_file)).astype(np.int64) + zones_raw = np.loadtxt(os.path.join(data_dir, zone_file)).astype(np.int64) zones, _, original_zones_shape = standardize_input_arrays(zones_raw, grid_coords, shape) print(f" Loaded single zone file: original shape {original_zones_shape}, standardized to 2D {zones.shape}") @@ -285,7 +284,7 @@ def generate_fields_from_files(sim_ws, conceptual_points_file, grid_file, zone_f # Save and plot results for i in conceptual_points.layer.unique(): save_layer(field[i - 1], layer=i, field_name=fn, save_path=save_path) - plot_layer(np.where(zones == 0, np.nan, field[i - 1]), layer=i, field_name=fn, save_path=save_path) + pu.plot_layer(np.where(zones == 0, np.nan, field[i - 1]), layer=i, field_name=fn, save_path=save_path) print("\n=== Complete ===") print(f"Field shape: {field.shape}") @@ -359,7 +358,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, layer_field_2d, geological_tensors = generate_single_layer_zone_based(layer_cp, layer_coords, layer_iids, zones=layer_zones, shape=layer_shape, save_path=save_path, tensor_interp=tensor_interp) - visualize_geological_tensors(geological_tensors, layer_coords, layer_shape, zones=layer_zones, + pu.visualize_geological_tensors(geological_tensors, layer_coords, layer_shape, zones=layer_zones, conceptual_points=layer_cp, subsample=4, save_path=save_path, title_suf=f'layer {layer_idx+1}') @@ -541,7 +540,6 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, - def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, iids, zones=None, shape=None, save_path='.', tensor_interp='idw'): @@ -668,18 +666,33 @@ def _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones): Parameters ---------- + cp_coords : np.ndarray + Conceptual point coordinates, shape (n_cp, ndim) + cp_tensors : np.ndarray + Conceptual point tensors, shape (n_cp, ndim, ndim) + grid_coords : np.ndarray + Grid coordinates, shape (n_grid, ndim) zones : np.ndarray - Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point + Zone IDs, shape (ny, nx) for 2D or (nz, ny, nx) for 3D """ shape = zones.shape + ndim = len(shape) # 2 for 2D, 3 for 3D n_grid = len(grid_coords) - ny, nx = shape + + # Handle 2D and 3D cases + if ndim == 2: + ny, nx = shape + elif ndim == 3: + nz, ny, nx = shape + else: + raise ValueError(f"zones must be 2D or 3D, got shape {shape}") # Get zone for each conceptual point cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) - # Initialize output tensors - geological_tensors = np.zeros((n_grid, 2, 2)) + # Initialize output tensors - infer tensor size from cp_tensors + tensor_size = cp_tensors.shape[-1] # Last dimension gives tensor size + geological_tensors = np.zeros((n_grid, tensor_size, tensor_size)) # Get unique zones unique_zones = np.unique(zones) @@ -693,27 +706,40 @@ def _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones): zone_cp_tensors = cp_tensors[zone_cp_mask] # Find grid points in this zone - zone_grid_indices = [] - for i in range(n_grid): - row, col = divmod(i, nx) - if zones[row, col] == zone_id: - zone_grid_indices.append(i) + zone_indices = [] - zone_grid_indices = np.array(zone_grid_indices).astype(np.int64) + if ndim == 2: + # 2D case + for i in range(n_grid): + row, col = divmod(i, nx) + if zones[row, col] == zone_id: + zone_indices.append(i) + else: + # 3D case + for i in range(n_grid): + # Convert linear index to 3D coordinates + z_idx = i // (ny * nx) + remainder = i % (ny * nx) + row = remainder // nx + col = remainder % nx + if zones[z_idx, row, col] == zone_id: + zone_indices.append(i) + + zone_indices = np.array(zone_indices).astype(np.int64) if len(zone_cp_coords) > 0: - # Assign nearest neighbor tensor (no interpolation) - for grid_idx in zone_grid_indices: - distances = np.linalg.norm(zone_cp_coords - grid_coords[grid_idx], axis=1) + # Assign nearest neighbor tensor using tensor space distances + for idx in zone_indices: + # Calculate distances in tensor space (using the coordinate space) + coord_diff = zone_cp_coords - grid_coords[idx] + distances = np.linalg.norm(coord_diff, axis=1) nearest_cp = np.argmin(distances) - geological_tensors[grid_idx] = zone_cp_tensors[nearest_cp] + geological_tensors[idx] = zone_cp_tensors[nearest_cp] else: print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") - if tensor_size == 2: - default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length - else: - default_tensor = np.eye(3) * 1000 ** 2 # Default 1000m correlation length - geological_tensors[zone_grid_indices] = default_tensor + # Create default tensor based on inferred tensor size + default_tensor = np.eye(tensor_size) * 1000 ** 2 # Default 1000m correlation length + geological_tensors[zone_indices] = default_tensor return geological_tensors @@ -1287,6 +1313,12 @@ def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords, tensor_interp='id # simplest if tensor_interp == 'nearest' and zones is not None: interp_tensors = _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones) + elif tensor_interp == 'nearest' and zones is None: + # Simple nearest neighbor without zones + from scipy.spatial.distance import cdist + distances = cdist(grid_coords, cp_coords) + nearest_indices = np.argmin(distances, axis=1) + interp_tensors = cp_tensors[nearest_indices] else: log_tensors = np.array([logm(t) for t in cp_tensors]) for i in range(2): @@ -1321,7 +1353,7 @@ def interpolate_tensors_3d(cp_coords, cp_tensors, grid_coords, tensor_interp='id if tensor_interp == 'nearest' and zones is not None: # Zone-based nearest neighbor (requires zones) - interp_tensors = _nearest_interpolation_3d(cp_coords, cp_tensors, grid_coords, zones) + interp_tensors = _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones) elif tensor_interp == 'nearest' and zones is None: # Simple nearest neighbor without zones from scipy.spatial.distance import cdist @@ -1720,17 +1752,17 @@ def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): # 2D zones ny, nx = zones.shape - def get_zone_from_index(grid_index): - row, col = divmod(grid_index, nx) + def get_zone_from_index(index): + row, col = divmod(index, nx) return zones[row, col] else: # 3D zones nz, ny, nx = zones.shape - def get_zone_from_index(grid_index): - layer = grid_index // (ny * nx) - remainder = grid_index % (ny * nx) + def get_zone_from_index(index): + layer = index // (ny * nx) + remainder = index % (ny * nx) row = remainder // nx col = remainder % nx return zones[layer, row, col] @@ -1802,8 +1834,8 @@ def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, tenso ny, nx = zones.shape is_3d = False - def get_zone_id(grid_index): - row, col = divmod(grid_index, nx) + def get_zone_id(index): + row, col = divmod(index, nx) return zones[row, col] else: @@ -1811,9 +1843,9 @@ def get_zone_id(grid_index): nz, ny, nx = zones.shape is_3d = True - def get_zone_id(grid_index): - layer = grid_index // (ny * nx) - remainder = grid_index % (ny * nx) + def get_zone_id(index): + layer = index // (ny * nx) + remainder = index % (ny * nx) row = remainder // nx col = remainder % nx return zones[layer, row, col] @@ -1836,17 +1868,17 @@ def get_zone_id(grid_index): zone_cp_tensors = cp_tensors[zone_cp_mask] # Find grid points in this zone - zone_grid_indices = [] + zone_indices = [] for i in range(n_grid): if get_zone_id(i) == zone_id: - zone_grid_indices.append(i) + zone_indices.append(i) - zone_grid_indices = np.array(zone_grid_indices) + zone_indices = np.array(zone_indices) - if len(zone_grid_indices) == 0: + if len(zone_indices) == 0: continue - zone_grid_coords = grid_coords[zone_grid_indices] + zone_grid_coords = grid_coords[zone_indices] if len(zone_cp_coords) > 0: # Interpolate tensors within zone using only zone's conceptual points @@ -1856,7 +1888,7 @@ def get_zone_id(grid_index): else: zone_tensors = interpolate_tensors_3d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, tensor_interp=tensor_interp, zones=None) - geological_tensors[zone_grid_indices] = zone_tensors + geological_tensors[zone_indices] = zone_tensors else: # No conceptual points in zone - use default tensor or nearest zone print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") @@ -1864,7 +1896,7 @@ def get_zone_id(grid_index): default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length else: default_tensor = np.eye(3) * 1000 ** 2 # Default 1000m correlation length - geological_tensors[zone_grid_indices] = default_tensor + geological_tensors[zone_indices] = default_tensor return geological_tensors @@ -1904,29 +1936,12 @@ def save_layer(field, layer, field_name, save_path='.'): if isinstance(field_name, list): field_name = field_name[0] - os.makedirs(save_path, exist_ok=True) - filename = os.path.join(save_path, f'{field_name}_layer_{layer + 1:02d}.arr') np.savetxt(filename, field, fmt='%.6f') print(f"Saved layer {layer} to '{filename}'") -def plot_layer(field, layer=0, field_name='field', save_path='.', transform='log'): - import matplotlib.colors as mcolors - if transform=='log': - field = np.log10(field) - - plt.figure(figsize=(14, 12)) - plt.imshow(field) - plt.colorbar(label='Parameter Value') - plt.title(f'{field_name} for layer {layer}') - plt.xlabel('X') - plt.ylabel('Y') - - os.makedirs('output', exist_ok=True) - plt.savefig(os.path.join(save_path, f'{field_name}_layer_{layer:02d}.png'), dpi=150) - plt.close() -def add_grid_indices_to_conceptual_points(conceptual_points, grid_coords, shape): +def add_indices_to_conceptual_points(conceptual_points, grid_coords, shape): """ Add i,j grid indices to conceptual points dataframe. @@ -1942,13 +1957,13 @@ def add_grid_indices_to_conceptual_points(conceptual_points, grid_coords, shape) Returns ------- pd.DataFrame - Conceptual points with added 'grid_i' and 'grid_j' columns + Conceptual points with added 'i' and 'j' columns """ ny, nx = shape cp_coords = conceptual_points[['x', 'y']].values - grid_i_list = [] - grid_j_list = [] + i_list = [] + j_list = [] for coord in cp_coords: # Find closest grid point @@ -1956,130 +1971,23 @@ def add_grid_indices_to_conceptual_points(conceptual_points, grid_coords, shape) closest_idx = np.argmin(distances) row, col = divmod(closest_idx, nx) - grid_i_list.append(row) - grid_j_list.append(col) + i_list.append(row) + j_list.append(col) # Add to dataframe conceptual_points_copy = conceptual_points.copy() - conceptual_points_copy['grid_i'] = grid_i_list - conceptual_points_copy['grid_j'] = grid_j_list + conceptual_points_copy['i'] = i_list + conceptual_points_copy['j'] = j_list return conceptual_points_copy -def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, - figsize=(12, 10), save_path='.'): - """ - Plot geology zones with conceptual points overlaid and labeled. - - Parameters - ---------- - zones : np.ndarray - Zone array, shape (n_points,) or (ny, nx) - conceptual_points : pd.DataFrame - Must have columns: x, y, grid_i, grid_j - grid_coords : np.ndarray - Grid coordinates for coordinate transformation - shape : tuple - Grid shape (ny, nx) - figsize : tuple - Figure size - save_path : str, optional - Path to save figure - """ - import matplotlib.pyplot as plt - import matplotlib.patches as patches - - shape = zones.shape - ny, nx = shape - - # Get grid bounds for coordinate transformation - x_coords = grid_coords[:, 0].reshape(shape[0], shape[1]) - y_coords = grid_coords[:, 1].reshape(shape[0], shape[1]) - - x_min, x_max = x_coords.min(), x_coords.max() - y_min, y_max = y_coords.min(), y_coords.max() - - fig, ax = plt.subplots(figsize=figsize) - - # Plot zones with better color handling - unique_zones = np.unique(zones) - n_zones = len(unique_zones) - - # Create a mapping from zone values to sequential colors - zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} - zones_remapped = np.zeros_like(zones) - for zone_val, color_idx in zone_to_color.items(): - zones_remapped[zones == zone_val] = color_idx - - # Plot zones - flip the array itself to match real-world orientation - zones_flipped = np.flipud(zones_remapped) - - im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], - origin='lower', cmap='tab10', alpha=0.7, - vmin=0, vmax=n_zones - 1) - - # Add colorbar with actual zone values - cbar = plt.colorbar(im, ax=ax) - cbar.set_label('Zone ID') - cbar.set_ticks(range(n_zones)) - cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) - - # Plot conceptual points - for idx, row in conceptual_points.iterrows(): - x, y = row['x'], row['y'] - grid_i, grid_j = row['grid_i'], row['grid_j'] - name = conceptual_points.loc[idx, 'name'] - - # Plot point - ax.plot(x, y, 'ro', markersize=8, markeredgecolor='black', markeredgewidth=1) - - # Add label with index and grid position - ax.annotate(f'{name}\n({grid_i},{grid_j})', - (x, y), xytext=(5, 5), textcoords='offset points', - fontsize=8, fontweight='bold', - bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8)) - - # Draw line to corresponding grid cell center - grid_i_int, grid_j_int = int(grid_i), int(grid_j) - if 0 <= grid_i_int < ny and 0 <= grid_j_int < nx: - grid_x = x_coords[grid_i_int, grid_j_int] - grid_y = y_coords[grid_i_int, grid_j_int] - ax.plot([x, grid_x], [y, grid_y], 'r--', alpha=0.5, linewidth=1) - - # Mark grid cell center - ax.plot(grid_x, grid_y, 'r+', markersize=10, markeredgewidth=2) - - ax.set_xlabel('X (NZTM)') - ax.set_ylabel('Y (NZTM)') - ax.set_title('Geology Zones with Conceptual Points\n(Red dots = CP locations, + = assigned grid cells)') - ax.grid(True, alpha=0.3) - - # Add legend - legend_elements = [ - plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='r', - markersize=8, label='Conceptual Points'), - plt.Line2D([0], [0], marker='+', color='r', markersize=10, - label='Assigned Grid Cells'), - plt.Line2D([0], [0], color='r', linestyle='--', - label='CP to Grid Assignment') - ] - ax.legend(handles=legend_elements, loc='upper right') - - plt.tight_layout() - - plt.savefig(os.path.join(save_path, f'conceptual_points.png'), dpi=150, bbox_inches='tight') - print(f"Plot saved to: {os.path.join(save_path, f'{save_path}/conceptual_points.png')}") - - plt.close() - - def debug_zone_assignments(conceptual_points, grid_coords, zones, shape, save_path='.'): """ Complete workflow to debug zone assignments. """ # Add grid indices - cp_with_indices = add_grid_indices_to_conceptual_points( + cp_with_indices = add_indices_to_conceptual_points( conceptual_points, grid_coords, shape ) @@ -2088,223 +1996,22 @@ def debug_zone_assignments(conceptual_points, grid_coords, zones, shape, save_pa print("=" * 50) for idx, row in cp_with_indices.iterrows(): ny, nx = shape - grid_i, grid_j = int(row['grid_i']), int(row['grid_j']) + i, j = int(row['i']), int(row['j']) # zones are guaranteed to be 2D from entry point - zone_id = zones[grid_i, grid_j] + zone_id = zones[i, j] print(f"CP {idx}: ({row['x']:.1f}, {row['y']:.1f}) -> " - f"grid[{grid_i},{grid_j}] -> zone {zone_id}") + f"grid[{i},{j}] -> zone {zone_id}") # Create plot - plot_zones_with_conceptual_points( - zones, cp_with_indices, grid_coords, shape, + pu.plot_zones_with_conceptual_points( + zones, cp_with_indices, grid_coords, save_path=os.path.join(save_path, f'zone_debug.png')) return cp_with_indices -import matplotlib.pyplot as plt -import matplotlib.patches as patches - - -def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=None, - conceptual_points=None, subsample=4, scale_factor=20, - figsize=(14, 12), title_suf=None, save_path='.'): - """ - Visualize geological tensors as ellipses overlaid on zones, with conceptual points as oriented lines. - - Parameters - ---------- - geological_tensors : np.ndarray - Tensor field, shape (n_points, 2, 2) - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 2) or (n_points, 3) - shape : tuple - Grid shape (ny, nx) - zones : np.ndarray, optional - Zone array for background, shape (ny, nx) - conceptual_points : pd.DataFrame, optional - Conceptual points with x, y, bearing, major columns - subsample : int, default 4 - Show every Nth tensor (for readability) - scale_factor : float, default 20 - Scale ellipses for visibility - figsize : tuple, default (14, 12) - Figure size - title_suf : str, optional - Suffix for plot title - save_path : str, default '.' - Directory to save plot - - Returns - ------- - None - Saves visualization as PNG file - - Examples - -------- - >>> visualize_geological_tensors(tensors, coords, (100, 150), - ... subsample=8, title_suf='layer_1') - """ - ny, nx = shape - - fig, ax = plt.subplots(figsize=figsize) - - # Prepare zone data but don't plot yet - if zones is not None: - # zones are guaranteed to be 2D from entry point - - # Create zone color mapping - unique_zones = np.unique(zones) - zone_to_color = {zone: i for i, zone in enumerate(unique_zones)} - zones_remapped = np.zeros_like(zones) - for zone_val, color_idx in zone_to_color.items(): - zones_remapped[zones == zone_val] = color_idx - - # Get coordinate bounds - x_coords = grid_coords[:, 0].reshape(shape) - y_coords = grid_coords[:, 1].reshape(shape) - x_min, x_max = x_coords.min(), x_coords.max() - y_min, y_max = y_coords.min(), y_coords.max() - - zones_flipped = np.flipud(zones_remapped) - # Plot zones as background with low alpha - im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], - origin='lower', cmap='tab10', alpha=0.2, zorder=0, - vmin=0, vmax=len(unique_zones) - 1) - - # Plot conceptual points as oriented lines - if conceptual_points is not None: - for idx, row in conceptual_points.iterrows(): - x, y = row['x'], row['y'] - bearing = row['bearing'] - major = row['major'] - - # Scale line length for visibility (similar to ellipse scaling) - line_length = major / (scale_factor * 2) # Adjust scaling as needed - - # Convert geological bearing (CW from N) to math angle for plotting - # Geological: 0°=N, 90°=E; Math: 0°=E, 90°=N - math_angle_rad = np.radians(90 - bearing) - - # Calculate line endpoints - dx = line_length * np.cos(math_angle_rad) / 2 - dy = line_length * np.sin(math_angle_rad) / 2 - - # Plot line centered on point - ax.plot([x - dx, x + dx], [y - dy, y + dy], - 'r-', linewidth=3, alpha=0.9, zorder=4) - - # Plot center point - ax.plot(x, y, 'ro', markersize=4, markeredgecolor='black', - markeredgewidth=1, alpha=0.9, zorder=5) - - # Add label with bearing - ax.annotate(f'{bearing:.0f}°', (x, y), xytext=(8, 8), - textcoords='offset points', fontsize=8, fontweight='bold', - bbox=dict(boxstyle='round,pad=0.2', facecolor='white', alpha=0.7), - zorder=6) - - # Subsample points for visualization - indices = np.arange(0, len(geological_tensors), subsample) - - print(f"Plotting {len(indices)} tensors out of {len(geological_tensors)}") - - default_count = 0 - ellipse_count = 0 - - for i in indices: - tensor = geological_tensors[i] - x, y = grid_coords[i, 0], grid_coords[i, 1] - - # Check for default tensor (indicates no interpolation happened) - if np.allclose(tensor, np.eye(2) * 1000000): - # Plot as red circle for default tensors with high zorder - ax.plot(x, y, 'ko', markersize=2, alpha=0.3, zorder=1) # Make less prominent - default_count += 1 - continue - - try: - # Eigendecomposition to get ellipse parameters - eigenvals, eigenvecs = np.linalg.eigh(tensor) - - # Sort by eigenvalue magnitude - idx = np.argsort(eigenvals)[::-1] - eigenvals = eigenvals[idx] - eigenvecs = eigenvecs[:, idx] - - # Ellipse dimensions (correlation lengths) - make them more visible - major_axis = 2 * np.sqrt(eigenvals[0]) / scale_factor - minor_axis = 2 * np.sqrt(eigenvals[1]) / scale_factor - - # Make sure ellipses are visible - if major_axis < 30: # Smaller minimum to avoid overwhelming CP lines - major_axis = 30 - if minor_axis < 15: - minor_axis = 15 - - # Ellipse orientation (angle of major axis) - angle = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) - - # Create ellipse with lower prominence than CP lines - ellipse = patches.Ellipse((x, y), major_axis, minor_axis, - angle=angle, linewidth=1, - edgecolor='blue', facecolor='none', alpha=0.6, zorder=2) - ax.add_patch(ellipse) - ellipse_count += 1 - - except: - # If tensor is problematic, plot as yellow point with high zorder - ax.plot(x, y, 'yo', markersize=2, zorder=3) - - print(f"Plotted {default_count} default tensors (black circles) and {ellipse_count} ellipses") - - ax.set_xlabel('X (NZTM)') - ax.set_ylabel('Y (NZTM)') - - # Update title and legend - title_parts = [f'Geological Tensors (subsampled 1:{subsample})\n'] - if title_suf is not None: - title_parts += [f'{title_suf}'] - # more like legend? - if conceptual_points is not None: - title_parts.append('Red lines = conceptual points (bearing)') - title_parts.append('Blue ellipses = interpolated tensors') - ax.set_title('\n'.join(title_parts)) - - ax.set_aspect('equal', adjustable='box') - - # Add colorbar for zones if present - if zones is not None: - cbar = plt.colorbar(im, ax=ax, shrink=0.8) - cbar.set_label('Zone ID') - cbar.set_ticks(range(len(unique_zones))) - cbar.set_ticklabels([f'{int(z)}' for z in unique_zones]) - - # Add legend - legend_elements = [] - if conceptual_points is not None: - legend_elements.extend([ - plt.Line2D([0], [0], color='red', linewidth=3, label='Conceptual Point Bearings'), - plt.Line2D([0], [0], marker='o', color='red', markersize=6, - markeredgecolor='black', label='Conceptual Points', linestyle='None') - ]) - legend_elements.extend([ - patches.Patch(facecolor='none', edgecolor='blue', label='Interpolated Tensors'), - plt.Line2D([0], [0], marker='o', color='black', markersize=4, - label='Default Tensors', linestyle='None', alpha=0.3) - ]) - - if legend_elements: - ax.legend(handles=legend_elements, loc='upper right', fontsize=9) - - plt.tight_layout() - plt.savefig(os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png'), dpi=150, bbox_inches='tight') - print(f"Saved tensor visualization to {os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png')}") - plt.close() - - def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coords, zones): """ Compare conceptual point bearings with actual tensor orientations at those locations. @@ -2544,8 +2251,8 @@ def export_to_paraview(grid_coords, shape, field_data=None, tensor_data=None, for k in range(nz): for j in range(ny): for i in range(nx): - grid_idx = k * ny * nx + j * nx + i - tensor_reordered[idx] = tensor_array[grid_idx] + idx = k * ny * nx + j * nx + i + tensor_reordered[idx] = tensor_array[idx] idx += 1 tensor_vtk = numpy_support.numpy_to_vtk(tensor_reordered) @@ -2631,12 +2338,11 @@ def export_to_paraview(grid_coords, shape, field_data=None, tensor_data=None, print("5. Use 'Clip' or 'Slice' filters to see inside the 3D volume") -# Helper function to integrate with your existing workflow def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, zones=None, conceptual_points=None, save_path='.', field_name='permeability'): """ - Convenience function to export your 3D results to ParaView. + Convenience function to export 3D results to ParaView. Parameters ---------- @@ -2687,14 +2393,19 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, - zones: Can be passed to generate_single_layer() as numpy array with integer zone IDs, shape (n_points,) or (ny, nx) """ - sim_ws = r'C:\modelling\sva_ds\data' + data_dir = r'..\..\examples\Wairau' zone_files = [f'waq_arr.geoclass_layer{z+1}.arr' for z in range(13)] - generate_fields_from_files(sim_ws, + save_path = os.path.join(data_dir, 'output_nearest_3d') + + if not os.path.exists(save_path): + os.mkdir(save_path) + + generate_fields_from_files(data_dir, conceptual_points_file='conceptual_points.csv', grid_file='grid.dat', zone_file=zone_files, field_name=['kh'], layer_mode=False, - save_path='output_nearest', + save_path=save_path, tensor_interp='nearest' ) \ No newline at end of file From 436dd6227bf1f9c45ce90df422cfe8a7bfdcc0c5 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 22 Jul 2025 23:47:59 +1200 Subject: [PATCH 11/36] removing some redundancies --- pyemu/utils/partial.py | 0 pyemu/utils/tensor_utils.py | 650 ++++++++++++------------------------ 2 files changed, 218 insertions(+), 432 deletions(-) create mode 100644 pyemu/utils/partial.py diff --git a/pyemu/utils/partial.py b/pyemu/utils/partial.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 019fe49a6..7b4887ff0 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -202,12 +202,12 @@ def generate_fields_from_files(data_dir, conceptual_points_file, grid_file, zone iids[i] = layer_iids.reshape(ny, nx) print(f" Loaded iids layer {i + 1} from {os.path.join(data_dir, file_path)}") else: - if '{' in iids_name: + if '{' in iids_file: # Pattern like "iids_layer{}.arr" - pattern = os.path.join(data_dir, iids_name) + pattern = os.path.join(data_dir, iids_file) else: # Assume pattern from filename - name, ext = os.path.splitext(iids_name) + name, ext = os.path.splitext(iids_file) pattern = os.path.join(data_dir, f"{name}_layer{{}}{ext}") iids = np.zeros(shape) @@ -272,7 +272,7 @@ def generate_fields_from_files(data_dir, conceptual_points_file, grid_file, zone # Process by layer cols = cols + ['layer'] field = generate_field_by_layer(conceptual_points[cols], grid_coords, iids, - shape, zones=zones, save_path=save_path, + shape, zones=zones_3d, save_path=save_path, tensor_interp=tensor_interp) else: # Process full 3D - pass the 3D zones @@ -444,16 +444,8 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, print(f" Point {i}: Eigenvalues={eigenvals}, All positive={np.all(eigenvals > 0)}") # Step 2: Interpolate tensors using zone-based approach - if zones_3d is not None: - print(" Interpolating 3D geological tensors by zone...") - geological_tensors_3d = interpolate_tensors_by_zone( - cp_coords, cp_tensors_3d, grid_coords, zones_3d, tensor_interp=tensor_interp - ) - else: - print(" Interpolating 3D geological tensors globally...") - geological_tensors_3d = interpolate_tensors_3d( - cp_coords, cp_tensors_3d, grid_coords, tensor_interp=tensor_interp - ) + geological_tensors_3d = interpolate_tensors(cp_coords, cp_tensors_3d, grid_coords, + zones=zones_3d, method=tensor_interp) # Step 3: Interpolate means using zone-aware kriging print(" Interpolating mean field with 3D geological structure...") @@ -605,16 +597,9 @@ def normalize_bearing(bearing): extracted_bearing = get_tensor_bearing(cp_tensors[i], original_bearing) print(f" Point {i}: Input bearing={original_bearing:.1f}°, Extracted={extracted_bearing:.1f}°") - # Step 2: Interpolate tensors, can be fast/coarse zone based - if zones is not None: - print(" Interpolating geological tensors by zone...") - geological_tensors = interpolate_tensors_by_zone( - cp_coords, cp_tensors, grid_coords_2d, zones, tensor_interp=tensor_interp - ) - else: - print(" Interpolating geological tensors globally...") - geological_tensors = interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords_2d, - tensor_interp=tensor_interp) + # Step 2: Interpolate tensors, various methods + geological_tensors = interpolate_tensors(cp_coords, cp_tensors, grid_coords_2d, + zones=zones, method=tensor_interp) # Step 3: Interpolate means using zone-aware kriging - returns 2D print(" Interpolating means with geological structure...") @@ -661,89 +646,233 @@ def normalize_bearing(bearing): return field_2d, geological_tensors -def _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones): - """Zone-based nearest neighbor tensor assignment - no interpolation. +def interpolate_tensors(cp_coords, cp_tensors, grid_coords, zones=None, method='idw'): + """ + Single tensor interpolation function - replaces all the variants. Parameters ---------- - cp_coords : np.ndarray - Conceptual point coordinates, shape (n_cp, ndim) - cp_tensors : np.ndarray - Conceptual point tensors, shape (n_cp, ndim, ndim) - grid_coords : np.ndarray - Grid coordinates, shape (n_grid, ndim) - zones : np.ndarray - Zone IDs, shape (ny, nx) for 2D or (nz, ny, nx) for 3D + zones : np.ndarray, optional + If provided, interpolation is done within zones + method : str, default 'idw' + 'nearest', 'idw', or 'krig' """ - shape = zones.shape - ndim = len(shape) # 2 for 2D, 3 for 3D - n_grid = len(grid_coords) - - # Handle 2D and 3D cases - if ndim == 2: - ny, nx = shape - elif ndim == 3: - nz, ny, nx = shape + if zones is not None: + return _interpolate_by_zone(cp_coords, cp_tensors, grid_coords, zones, method) else: - raise ValueError(f"zones must be 2D or 3D, got shape {shape}") + return _interpolate_global(cp_coords, cp_tensors, grid_coords, method) - # Get zone for each conceptual point - cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) - # Initialize output tensors - infer tensor size from cp_tensors - tensor_size = cp_tensors.shape[-1] # Last dimension gives tensor size - geological_tensors = np.zeros((n_grid, tensor_size, tensor_size)) +def _interpolate_by_zone(cp_coords, cp_tensors, grid_coords, zones, method): + """Handle zone-based interpolation.""" + n_grid = len(grid_coords) + tensor_size = cp_tensors.shape[-1] + result = np.zeros((n_grid, tensor_size, tensor_size)) - # Get unique zones - unique_zones = np.unique(zones) + # Get zone assignment function based on zones shape + if len(zones.shape) == 2: + ny, nx = zones.shape + get_zone = lambda i: zones[divmod(i, nx)] + else: + nz, ny, nx = zones.shape + get_zone = lambda i: zones[i // (ny * nx), (i % (ny * nx)) // nx, i % nx] - for zone_id in unique_zones: - print(f" Assigning nearest neighbor tensors for zone {zone_id}") + # Assign conceptual points to zones + cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) - # Find conceptual points in this zone + # Process each zone + for zone_id in np.unique(zones): zone_cp_mask = cp_zones == zone_id zone_cp_coords = cp_coords[zone_cp_mask] zone_cp_tensors = cp_tensors[zone_cp_mask] - # Find grid points in this zone - zone_indices = [] + zone_indices = [i for i in range(n_grid) if get_zone(i) == zone_id] + + if len(zone_cp_coords) > 0 and len(zone_indices) > 0: + zone_grid_coords = grid_coords[zone_indices] + zone_tensors = _interpolate_global(zone_cp_coords, zone_cp_tensors, + zone_grid_coords, method) + result[zone_indices] = zone_tensors + elif len(zone_indices) > 0: + # Default tensor for zones without conceptual points + default_tensor = np.eye(tensor_size) * 1000 ** 2 + result[zone_indices] = default_tensor + + return result + + +def _interpolate_global(cp_coords, cp_tensors, grid_coords, method): + """Handle global interpolation without zones.""" + if method == 'nearest': + return _nearest_method(cp_coords, cp_tensors, grid_coords) + elif method == 'idw': + return _idw_method(cp_coords, cp_tensors, grid_coords) + elif method == 'krig': + return _kriging_method(cp_coords, cp_tensors, grid_coords) + else: + raise ValueError(f"Unknown method: {method}") + + +def _nearest_method(cp_coords, cp_tensors, grid_coords): + """Nearest neighbor interpolation.""" + from scipy.spatial.distance import cdist + distances = cdist(grid_coords, cp_coords) + nearest_indices = np.argmin(distances, axis=1) + return cp_tensors[nearest_indices] + + +def _idw_method(cp_coords, cp_tensors, grid_coords): + """IDW using log-Euclidean approach.""" + n_grid = len(grid_coords) + tensor_size = cp_tensors.shape[-1] + + # Convert to log space + log_tensors = np.array([logm(_ensure_positive_definite(tensor)) + for tensor in cp_tensors]) + + # Interpolate each component + result_log = np.zeros((n_grid, tensor_size, tensor_size)) + + for i in range(tensor_size): + for j in range(i, tensor_size): + values = log_tensors[:, i, j].real + interp_values = _idw_interpolation(cp_coords, values, grid_coords) + result_log[:, i, j] = interp_values + if i != j: + result_log[:, j, i] = interp_values + + # Convert back from log space + result = np.array([_ensure_positive_definite(expm(log_tensor)) + for log_tensor in result_log]) + return result + + +def _kriging_method(cp_coords, cp_tensors, grid_coords): + """Kriging using log-Euclidean approach.""" + n_grid = len(grid_coords) + tensor_size = cp_tensors.shape[-1] + + # Convert to log space + log_tensors = np.array([logm(_ensure_positive_definite(tensor)) + for tensor in cp_tensors]) + + # Interpolate each component + result_log = np.zeros((n_grid, tensor_size, tensor_size)) + + for i in range(tensor_size): + for j in range(i, tensor_size): + values = log_tensors[:, i, j].real + interp_values = ordinary_kriging(cp_coords, values, grid_coords, + variogram_model='exponential', range_param=10000, + sill=1.0, nugget=0.1, background_value=0.0, + max_search_radius=1e20, min_points=1, + transform=None, min_value=1e-8, max_neighbors=5) + result_log[:, i, j] = interp_values + if i != j: + result_log[:, j, i] = interp_values + + # Convert back from log space + result = np.array([_ensure_positive_definite(expm(log_tensor)) + for log_tensor in result_log]) + return result + +def ordinary_kriging(cp_coords, cp_values, grid_coords, + variogram_model='exponential', range_param=10000, + sill=1.0, nugget=0.1, background_value=0.0, + max_search_radius=1e20, min_points=1, + transform=None, min_value=1e-8, max_neighbors=5): + """ + Standard ordinary kriging with variogram models and search controls. + + Returns + ------- + np.ndarray + Interpolated values (1D array) - kept as 1D since this is a utility function + """ + n_grid = len(grid_coords) + interp_values = np.full(n_grid, background_value) + + def variogram(h): + if variogram_model == 'exponential': + return nugget + (sill - nugget) * (1 - np.exp(-h / range_param)) + elif variogram_model == 'gaussian': + return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2) / (range_param ** 2))) + else: # spherical + return np.where(h <= range_param, + nugget + (sill - nugget) * (1.5 * h / range_param - 0.5 * (h / range_param) ** 3), + sill) + + for i in range(n_grid): + distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + sorted_indices = np.argsort(distances) + n_candidates = min(max_neighbors or len(distances), + np.sum(distances <= max_search_radius)) + + if n_candidates < min_points: + continue + + closest_indices = sorted_indices[:n_candidates] + nearby_values = cp_values[closest_indices] + nearby_coords = cp_coords[closest_indices] + nearby_distances = distances[closest_indices] - if ndim == 2: - # 2D case - for i in range(n_grid): - row, col = divmod(i, nx) - if zones[row, col] == zone_id: - zone_indices.append(i) + if transform == 'log': + if min_value is None: + positive_values = nearby_values[nearby_values > 0] + min_value = np.min(positive_values) * 0.01 + nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) else: - # 3D case - for i in range(n_grid): - # Convert linear index to 3D coordinates - z_idx = i // (ny * nx) - remainder = i % (ny * nx) - row = remainder // nx - col = remainder % nx - if zones[z_idx, row, col] == zone_id: - zone_indices.append(i) - - zone_indices = np.array(zone_indices).astype(np.int64) - - if len(zone_cp_coords) > 0: - # Assign nearest neighbor tensor using tensor space distances - for idx in zone_indices: - # Calculate distances in tensor space (using the coordinate space) - coord_diff = zone_cp_coords - grid_coords[idx] - distances = np.linalg.norm(coord_diff, axis=1) - nearest_cp = np.argmin(distances) - geological_tensors[idx] = zone_cp_tensors[nearest_cp] + nearby_values_transformed = nearby_values.copy() + + C = np.zeros((n_candidates + 1, n_candidates + 1)) + c = np.zeros(n_candidates + 1) + + for j in range(n_candidates): + for k in range(n_candidates): + h = np.linalg.norm(nearby_coords[j] - nearby_coords[k]) + C[j, k] = variogram(h) + C[j, -1] = 1 + C[-1, j] = 1 + c[j] = variogram(nearby_distances[j]) + c[-1] = 1 + + try: + weights = solve(C, c)[:-1] + interp_values[i] = np.sum(weights * nearby_values_transformed) + except: + weights = np.exp(-nearby_distances / range_param) + interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) + + if transform == 'log': + interp_values = 10 ** interp_values + + return interp_values + + +def _idw_interpolation(cp_coords, cp_values, grid_coords, power=1.0, radius=1000): + """Simple IDW interpolation for scalar values.""" + n_grid = len(grid_coords) + interp_values = np.zeros(n_grid) + + for i in range(n_grid): + distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + weights = np.exp(-distances / radius) # Exponential decay weights + + if np.sum(weights) > 1e-10: + interp_values[i] = np.sum(weights * cp_values) / np.sum(weights) else: - print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") - # Create default tensor based on inferred tensor size - default_tensor = np.eye(tensor_size) * 1000 ** 2 # Default 1000m correlation length - geological_tensors[zone_indices] = default_tensor + interp_values[i] = np.mean(cp_values) # Fallback - return geological_tensors + return interp_values + +def _ensure_positive_definite(tensor, min_eigenval=1e-8): + """Ensure tensor is positive definite.""" + eigenvals, eigenvecs = np.linalg.eigh(tensor) + eigenvals_reg = np.maximum(eigenvals, min_eigenval) + return eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T + def create_boundary_modified_scalar(base_field, zones, peak_increase=0.3, transition_cells=5, mode="enhance"): """ @@ -1284,154 +1413,6 @@ def variogram(h): return interp_values_1d.reshape(shape) -def interpolate_tensors_2d(cp_coords, cp_tensors, grid_coords, tensor_interp='idw', zones=None): - """ - Interpolate 2x2 tensors using log-Euclidean approach. - - Parameters - ---------- - cp_coords : np.ndarray - Conceptual point coordinates, shape (n_cp, 2) - cp_tensors : np.ndarray - Tensors at conceptual points, shape (n_cp, 2, 2) - grid_coords : np.ndarray - Grid coordinates, shape (n_grid, 2) or (n_grid, 3) - tensor_interp : str, default 'idw' - Interpolation method: 'idw', 'krig', or 'nearest' - zones : np.ndarray, optional - Zone IDs for zone-based nearest neighbor, shape (ny, nx) - - Returns - ------- - np.ndarray - Interpolated tensors, shape (n_grid, 2, 2) - """ - #shape = infer_grid_shape(grid_coords) - n_grid = len(grid_coords) - interp_tensors = np.zeros((n_grid, 2, 2)) - - # simplest - if tensor_interp == 'nearest' and zones is not None: - interp_tensors = _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones) - elif tensor_interp == 'nearest' and zones is None: - # Simple nearest neighbor without zones - from scipy.spatial.distance import cdist - distances = cdist(grid_coords, cp_coords) - nearest_indices = np.argmin(distances, axis=1) - interp_tensors = cp_tensors[nearest_indices] - else: - log_tensors = np.array([logm(t) for t in cp_tensors]) - for i in range(2): - for j in range(i, 2): - values = log_tensors[:, i, j].real - # idw interp - if tensor_interp=='idw': - interp_values_2d = _idw_interpolation(cp_coords, values, grid_coords) - # krig alternatively krig each of "values" separately with pypest utils - elif tensor_interp=='krig': - interp_values_2d = ordinary_kriging(cp_coords, values, grid_coords, - variogram_model='exponential', range_param=10000, - sill=1.0, nugget=0.1, background_value=0.0, - max_search_radius=1e20, min_points=1, - transform=None, min_value=1e-8, max_neighbors=5) - interp_tensors[:, i, j] = interp_values_2d.flatten() - if i != j: - interp_tensors[:, j, i] = interp_values_2d.flatten() - - for i in range(n_grid): - interp_tensors[i] = expm(interp_tensors[i]) - - return interp_tensors - - -def interpolate_tensors_3d(cp_coords, cp_tensors, grid_coords, tensor_interp='idw', zones=None): - """ - Interpolate 3x3 tensors using log-Euclidean approach (same as 2D but for 3x3). - """ - n_grid = len(grid_coords) - interp_tensors = np.zeros((n_grid, 3, 3)) - - if tensor_interp == 'nearest' and zones is not None: - # Zone-based nearest neighbor (requires zones) - interp_tensors = _nearest_interpolation(cp_coords, cp_tensors, grid_coords, zones) - elif tensor_interp == 'nearest' and zones is None: - # Simple nearest neighbor without zones - from scipy.spatial.distance import cdist - distances = cdist(grid_coords, cp_coords) - nearest_indices = np.argmin(distances, axis=1) - interp_tensors = cp_tensors[nearest_indices] - else: - # Log-Euclidean approach - ensure positive definiteness first - log_tensors = np.zeros_like(cp_tensors) - - for i, tensor in enumerate(cp_tensors): - # Ensure positive definiteness by eigenvalue regularization - eigenvals, eigenvecs = np.linalg.eigh(tensor) - min_eigenval = 1e-8 # Minimum eigenvalue to ensure positive definiteness - eigenvals_reg = np.maximum(eigenvals, min_eigenval) - - # Reconstruct positive definite tensor - tensor_reg = eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T - - # Take matrix logarithm - log_tensors[i] = logm(tensor_reg) - - # Interpolate each independent component of the symmetric log tensor - # For a 3x3 symmetric matrix, we have 6 independent components - interp_log_tensors = np.zeros((n_grid, 3, 3)) - - for i in range(3): - for j in range(i, 3): # Only upper triangle (symmetric) - values = log_tensors[:, i, j].real # Take real part to handle numerical errors - - if tensor_interp == 'idw': - interp_values = _idw_interpolation(cp_coords, values, grid_coords) - elif tensor_interp == 'krig': - interp_values = ordinary_kriging(cp_coords, values, grid_coords, - variogram_model='exponential', range_param=10000, - sill=1.0, nugget=0.1) - - interp_log_tensors[:, i, j] = interp_values - if i != j: # Fill symmetric part - interp_log_tensors[:, j, i] = interp_values - - # Convert back from log space, ensuring positive definiteness - for i in range(n_grid): - try: - # Matrix exponential - tensor_exp = expm(interp_log_tensors[i]) - - # Ensure the result is positive definite (numerical stability) - eigenvals, eigenvecs = np.linalg.eigh(tensor_exp) - min_eigenval = 1e-8 - eigenvals_reg = np.maximum(eigenvals, min_eigenval) - - interp_tensors[i] = eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T - - except Exception as e: - print(f"Warning: Matrix exponential failed at grid point {i}, using identity: {e}") - interp_tensors[i] = np.eye(3) - - return interp_tensors - - -def _idw_interpolation(cp_coords, cp_values, grid_coords): - """ - Special version of idw for tensor interpolation that returns 1D output. - This is only used internally for tensor component interpolation. - """ - n_grid = len(grid_coords) - interp_values = np.full(n_grid, 0.0) - - # Simple idw tensor components - for i in range(n_grid): - distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) - weights = np.exp(-distances / 1000) # Simple exponential weights - if np.sum(weights) > 1e-10: - interp_values[i] = np.sum(weights * cp_values) / np.sum(weights) - - return interp_values - def generate_correlated_noise_2d(grid_coords, tensors, iids, n_neighbors=50, anisotropy_strength=1.0): @@ -1533,79 +1514,6 @@ def generate_correlated_noise_3d(grid_coords, tensors, iids, return correlated_noise -def ordinary_kriging(cp_coords, cp_values, grid_coords, - variogram_model='exponential', range_param=10000, - sill=1.0, nugget=0.1, background_value=0.0, - max_search_radius=1e20, min_points=1, - transform=None, min_value=1e-8, max_neighbors=5): - """ - Standard ordinary kriging with variogram models and search controls. - - Returns - ------- - np.ndarray - Interpolated values (1D array) - kept as 1D since this is a utility function - """ - n_grid = len(grid_coords) - interp_values = np.full(n_grid, background_value) - - def variogram(h): - if variogram_model == 'exponential': - return nugget + (sill - nugget) * (1 - np.exp(-h / range_param)) - elif variogram_model == 'gaussian': - return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2) / (range_param ** 2))) - else: # spherical - return np.where(h <= range_param, - nugget + (sill - nugget) * (1.5 * h / range_param - 0.5 * (h / range_param) ** 3), - sill) - - for i in range(n_grid): - distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) - sorted_indices = np.argsort(distances) - n_candidates = min(max_neighbors or len(distances), - np.sum(distances <= max_search_radius)) - - if n_candidates < min_points: - continue - - closest_indices = sorted_indices[:n_candidates] - nearby_values = cp_values[closest_indices] - nearby_coords = cp_coords[closest_indices] - nearby_distances = distances[closest_indices] - - if transform == 'log': - if min_value is None: - positive_values = nearby_values[nearby_values > 0] - min_value = np.min(positive_values) * 0.01 - nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) - else: - nearby_values_transformed = nearby_values.copy() - - C = np.zeros((n_candidates + 1, n_candidates + 1)) - c = np.zeros(n_candidates + 1) - - for j in range(n_candidates): - for k in range(n_candidates): - h = np.linalg.norm(nearby_coords[j] - nearby_coords[k]) - C[j, k] = variogram(h) - C[j, -1] = 1 - C[-1, j] = 1 - c[j] = variogram(nearby_distances[j]) - c[-1] = 1 - - try: - weights = solve(C, c)[:-1] - interp_values[i] = np.sum(weights * nearby_values_transformed) - except: - weights = np.exp(-nearby_distances / range_param) - interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) - - if transform == 'log': - interp_values = 10 ** interp_values - - return interp_values - - def create_2d_tensors(theta, major, minor): """Create 2x2 anisotropy tensors from geostatistical parameters.""" @@ -1779,128 +1687,6 @@ def get_zone_from_index(index): return cp_zones -# def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): -# """ -# Extract zone assignment logic from tensor_aware_kriging into reusable function. -# -# Parameters -# ---------- -# zones : np.ndarray -# Zone IDs, shape (ny, nx) - GUARANTEED to be 2D from entry point -# """ -# ny, nx = zones.shape -# -# cp_zones = [] -# for coord in cp_coords: -# # Find closest grid point to get zone -# distances = np.sum((grid_coords - coord) ** 2, axis=1) -# closest_idx = np.argmin(distances) -# row, col = divmod(closest_idx, nx) -# cp_zones.append(zones[row, col]) -# -# return np.array(cp_zones) - - -def interpolate_tensors_by_zone(cp_coords, cp_tensors, grid_coords, zones, tensor_interp='idw'): - """ - Zone-aware tensor interpolation - interpolate tensors within each zone separately. - Supports both 2D and 3D zones. - - Parameters - ---------- - cp_coords : np.ndarray - Conceptual point coordinates, shape (n_cp, 2) for 2D or (n_cp, 3) for 3D - cp_tensors : np.ndarray - Conceptual point tensors, shape (n_cp, 2, 2) for 2D or (n_cp, 3, 3) for 3D - grid_coords : np.ndarray - Grid coordinates, shape (n_grid, 2) for 2D or (n_grid, 3) for 3D - zones : np.ndarray - Zone array, shape (ny, nx) for 2D or (nz, ny, nx) for 3D - tensor_interp : str, default 'idw' - Tensor interpolation method - - Returns - ------- - np.ndarray - Interpolated tensors, shape (n_grid, 2, 2) for 2D or (n_grid, 3, 3) for 3D - """ - n_grid = len(grid_coords) - n_dims = cp_coords.shape[1] # 2 for 2D, 3 for 3D - tensor_size = cp_tensors.shape[-1] # 2 for 2D tensors, 3 for 3D tensors - - # Determine if we're working with 2D or 3D zones - if len(zones.shape) == 2: - # 2D zones - ny, nx = zones.shape - is_3d = False - - def get_zone_id(index): - row, col = divmod(index, nx) - return zones[row, col] - - else: - # 3D zones - nz, ny, nx = zones.shape - is_3d = True - - def get_zone_id(index): - layer = index // (ny * nx) - remainder = index % (ny * nx) - row = remainder // nx - col = remainder % nx - return zones[layer, row, col] - - # Get zone for each conceptual point - cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) - - # Initialize output tensors - geological_tensors = np.zeros((n_grid, tensor_size, tensor_size)) - - # Get unique zones - unique_zones = np.unique(zones) - - for zone_id in unique_zones: - print(f" Interpolating tensors for zone {zone_id}") - - # Find conceptual points in this zone - zone_cp_mask = cp_zones == zone_id - zone_cp_coords = cp_coords[zone_cp_mask] - zone_cp_tensors = cp_tensors[zone_cp_mask] - - # Find grid points in this zone - zone_indices = [] - for i in range(n_grid): - if get_zone_id(i) == zone_id: - zone_indices.append(i) - - zone_indices = np.array(zone_indices) - - if len(zone_indices) == 0: - continue - - zone_grid_coords = grid_coords[zone_indices] - - if len(zone_cp_coords) > 0: - # Interpolate tensors within zone using only zone's conceptual points - if n_dims == 2: - zone_tensors = interpolate_tensors_2d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, - tensor_interp=tensor_interp, zones=None) - else: - zone_tensors = interpolate_tensors_3d(zone_cp_coords, zone_cp_tensors, zone_grid_coords, - tensor_interp=tensor_interp, zones=None) - geological_tensors[zone_indices] = zone_tensors - else: - # No conceptual points in zone - use default tensor or nearest zone - print(f" Warning: No conceptual points in zone {zone_id}, using default tensor") - if tensor_size == 2: - default_tensor = np.eye(2) * 1000 ** 2 # Default 1000m correlation length - else: - default_tensor = np.eye(3) * 1000 ** 2 # Default 1000m correlation length - geological_tensors[zone_indices] = default_tensor - - return geological_tensors - - def infer_grid_shape(grid_coords): """Infer grid dimensions from coordinate array.""" if grid_coords.shape[1] == 2: @@ -2395,7 +2181,7 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, """ data_dir = r'..\..\examples\Wairau' zone_files = [f'waq_arr.geoclass_layer{z+1}.arr' for z in range(13)] - save_path = os.path.join(data_dir, 'output_nearest_3d') + save_path = os.path.join(data_dir, 'output_idw_3d') if not os.path.exists(save_path): os.mkdir(save_path) @@ -2407,5 +2193,5 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, field_name=['kh'], layer_mode=False, save_path=save_path, - tensor_interp='nearest' + tensor_interp='idw' ) \ No newline at end of file From a58cebe7220c0025ebc839448c6a38f27d9a3093 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 5 Aug 2025 18:06:56 +1200 Subject: [PATCH 12/36] input data all non-transformed --- pyemu/plot/plot_utils.py | 1 - pyemu/utils/tensor_utils.py | 550 +++++++++++++++++++++++++++++++----- 2 files changed, 475 insertions(+), 76 deletions(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 1ba000429..338f0c101 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2253,7 +2253,6 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N def plot_layer(field, layer=0, field_name='field', transform='log', save_path='.'): - import matplotlib.colors as mcolors if transform=='log': field = np.log10(field) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 7b4887ff0..86828eae5 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -182,6 +182,8 @@ def generate_fields_from_files(data_dir, conceptual_points_file, grid_file, zone grid = pd.read_csv(os.path.join(data_dir, grid_file), sep=r'\s+') grid_coords = grid[['x', 'y', 'z']].values + if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: + conceptual_points.loc[:, 'transverse'] = conceptual_points.loc[:, 'major'] / conceptual_points.loc[:, 'anisotropy'] print(f"Loaded {len(conceptual_points)} conceptual points") print(f"Loaded {len(grid_coords)} grid points") @@ -284,7 +286,8 @@ def generate_fields_from_files(data_dir, conceptual_points_file, grid_file, zone # Save and plot results for i in conceptual_points.layer.unique(): save_layer(field[i - 1], layer=i, field_name=fn, save_path=save_path) - pu.plot_layer(np.where(zones == 0, np.nan, field[i - 1]), layer=i, field_name=fn, save_path=save_path) + pu.plot_layer(np.where((zones_3d[0] == 0), np.nan, field[i - 1]), layer=i, + transform='log', field_name=fn, save_path=save_path) print("\n=== Complete ===") print(f"Field shape: {field.shape}") @@ -359,7 +362,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, layer_iids, zones=layer_zones, shape=layer_shape, save_path=save_path, tensor_interp=tensor_interp) pu.visualize_geological_tensors(geological_tensors, layer_coords, layer_shape, zones=layer_zones, - conceptual_points=layer_cp, subsample=4, + conceptual_points=layer_cp, subsample=8, save_path=save_path, title_suf=f'layer {layer_idx+1}') # layer_field_2d is already 2D @@ -472,7 +475,7 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, cp_coords, cp_sds, grid_coords, geological_tensors_3d, variogram_model='exponential', sill=0.5, nugget=0.01, background_value=np.mean(cp_sds), max_search_radius=1e20, - min_points=3, transform=None, min_value=1e-8, max_neighbors=8, + min_points=3, transform='log', min_value=1e-8, max_neighbors=8, zones=zones_3d ) sd_field_3d = sd_field_1d.reshape(shape) @@ -508,7 +511,8 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, # Log-normal field generation: field = 10^(log10(mean) + noise * sd) log_mean_field = np.log10(np.maximum(mean_field_3d, 1e-8)) # Avoid log(0) - field_3d = 10 ** (log_mean_field + correlated_noise_3d * sd_field_3d) + log_sd_field = np.log10(np.maximum(sd_field_3d, 1e-8)) + field_3d = 10 ** (log_mean_field + correlated_noise_3d * log_sd_field) print(f" Log-mean field range: [{np.min(log_mean_field):.3f}, {np.max(log_mean_field):.3f}]") print(f" Generated field range: [{np.min(field_3d):.3e}, {np.max(field_3d):.3e}]") @@ -623,7 +627,7 @@ def normalize_bearing(bearing): cp_coords, cp_sd, grid_coords_2d, geological_tensors, # Use geological tensors variogram_model='exponential', sill=1.0, nugget=0.1, background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform=None, min_value=1e-8, max_neighbors=8, + min_points=3, transform='log', min_value=1e-8, max_neighbors=8, zones=zones ) if zones is not None: @@ -641,7 +645,8 @@ def normalize_bearing(bearing): # Step 7: Combine into lognormal field - all operations in 2D log_mean_field = np.log10(np.maximum(interp_means_2d, 1e-8)) # Avoid log(0) - field_2d = 10 ** (log_mean_field + correlated_noise_2d * interp_sd_2d) + log_sd_field = np.log10(np.maximum(interp_sd_2d, 1e-8)) + field_2d = 10 ** (log_mean_field + correlated_noise_2d * log_sd_field) return field_2d, geological_tensors @@ -1413,107 +1418,501 @@ def variogram(h): return interp_values_1d.reshape(shape) +import numpy as np +from scipy.spatial import cKDTree + def generate_correlated_noise_2d(grid_coords, tensors, iids, - n_neighbors=50, anisotropy_strength=1.0): - """Generate spatially correlated noise respecting tensor anisotropy.""" + n_neighbors=8, anisotropy_strength=1.0): + """Generate spatially correlated noise respecting tensor anisotropy - optimized version.""" n_points = len(grid_coords) + + # Vectorized eigendecomposition for all tensors at once + evals, evecs = np.linalg.eigh(tensors) + + # Ensure consistent ordering (largest eigenvalue first) + swap_mask = evals[:, 1] < evals[:, 0] + evals[swap_mask] = evals[swap_mask, ::-1] + evecs[swap_mask] = evecs[swap_mask, :, ::-1] + + # Vectorized computation of correlation lengths and scaling + major_corr_length = np.sqrt(evals[:, 1]) + minor_corr_length = np.sqrt(evals[:, 0]) + scale_minor = minor_corr_length / anisotropy_strength + scale_major = major_corr_length * anisotropy_strength + + # Create transformation matrices for all points + scales = np.stack([scale_minor, scale_major], axis=1) + transforms = evecs * scales[:, np.newaxis, :] + + # Pre-compute inverse transforms + inv_transforms = np.linalg.inv(transforms) + correlated_noise = np.zeros(n_points) + iids_flat = iids.flatten() - for i in range(n_points): - evals, evecs = np.linalg.eigh(tensors[i]) - if evals[1] < evals[0]: - evals, evecs = evals[::-1], evecs[:, ::-1] + # Use spatial indexing for faster neighbor finding when beneficial + if n_points > 1000: # Threshold where spatial indexing becomes beneficial + tree = cKDTree(grid_coords) + # Use a reasonable search radius to limit candidates + max_search_radius = np.percentile(np.sqrt(evals.max(axis=1)), 95) * anisotropy_strength * 3 + + for i in range(n_points): + # Get candidates within search radius + candidates = tree.query_ball_point(grid_coords[i], max_search_radius) + if len(candidates) <= n_neighbors: + neighbor_indices = candidates + else: + # Transform and compute anisotropic distances only for candidates + dx_candidates = grid_coords[candidates] - grid_coords[i] + dx_transformed = dx_candidates @ inv_transforms[i].T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) + neighbor_indices = np.array(candidates)[np.argsort(aniso_distances)[:n_neighbors]] + + # Compute final weights and weighted sum + if len(neighbor_indices) > 0: + dx_final = grid_coords[neighbor_indices] - grid_coords[i] + dx_transformed_final = dx_final @ inv_transforms[i].T + aniso_distances_final = np.linalg.norm(dx_transformed_final, axis=1) + weights = np.exp(-(aniso_distances_final) ** 2) + + weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) + sum_weights = np.sum(weights) + + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = iids_flat[i] + else: + correlated_noise[i] = iids_flat[i] + else: + # For smaller datasets, direct computation is still faster + for i in range(n_points): + # Vectorized distance computation for all points + all_dx = grid_coords - grid_coords[i] + dx_transformed = all_dx @ inv_transforms[i].T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) - major_corr_length = np.sqrt(evals[1]) - minor_corr_length = np.sqrt(evals[0]) + # Get k nearest neighbors + neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ + :min(n_neighbors, n_points)] - scale_for_minor_axis = minor_corr_length / anisotropy_strength - scale_for_major_axis = major_corr_length * anisotropy_strength + # Compute weights vectorized + weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) + weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) + sum_weights = np.sum(weights) - transform = evecs @ np.diag([scale_for_minor_axis, scale_for_major_axis]) + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = iids_flat[i] - all_dx = grid_coords - grid_coords[i] - inv_transform = np.linalg.inv(transform) - dx_transformed = all_dx @ inv_transform.T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) + return correlated_noise - neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] - weighted_sum = 0.0 - sum_weights = 0.0 +def generate_correlated_noise_2d_batch(grid_coords, tensors, iids, + n_neighbors=8, anisotropy_strength=1.0, + batch_size=1000): + """Batch processing version for very large datasets.""" + n_points = len(grid_coords) - for j in neighbor_indices: - # Use anisotropic distance instead of actual distance for weight calculation - # This respects the tensor-defined correlation structure - weight = np.exp(-(aniso_distances[j]) ** 2) - weighted_sum += weight * iids.flatten()[j] - sum_weights += weight + # Same vectorized preprocessing + evals, evecs = np.linalg.eigh(tensors) + swap_mask = evals[:, 1] < evals[:, 0] + evals[swap_mask] = evals[swap_mask, ::-1] + evecs[swap_mask] = evecs[swap_mask, :, ::-1] - if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights - else: - correlated_noise[i] = iids[i] + major_corr_length = np.sqrt(evals[:, 1]) + minor_corr_length = np.sqrt(evals[:, 0]) + scale_minor = minor_corr_length / anisotropy_strength + scale_major = major_corr_length * anisotropy_strength + + scales = np.stack([scale_minor, scale_major], axis=1) + transforms = evecs * scales[:, np.newaxis, :] + inv_transforms = np.linalg.inv(transforms) + + correlated_noise = np.zeros(n_points) + iids_flat = iids.flatten() + + # Process in batches to manage memory + for batch_start in range(0, n_points, batch_size): + batch_end = min(batch_start + batch_size, n_points) + batch_indices = np.arange(batch_start, batch_end) + + for i in batch_indices: + all_dx = grid_coords - grid_coords[i] + dx_transformed = all_dx @ inv_transforms[i].T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) + + neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ + :min(n_neighbors, n_points)] + + weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) + weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) + sum_weights = np.sum(weights) + + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = iids_flat[i] return correlated_noise +import numpy as np +from scipy.spatial import cKDTree + + def generate_correlated_noise_3d(grid_coords, tensors, iids, - n_neighbors=50, anisotropy_strength=1.0): - """Generate spatially correlated noise respecting 3D tensor anisotropy.""" + n_neighbors=50, anisotropy_strength=1.0): + """Generate spatially correlated noise respecting 3D tensor anisotropy - optimized version.""" n_points = len(grid_coords) + + # Vectorized eigendecomposition for all tensors at once + evals, evecs = np.linalg.eigh(tensors) + + # Sort eigenvalues/vectors by magnitude (largest to smallest) for all tensors + sort_indices = np.argsort(evals, axis=1)[:, ::-1] # Descending order + # Use advanced indexing to sort both evals and evecs + evals = np.take_along_axis(evals, sort_indices, axis=1) + evecs = np.take_along_axis(evecs, sort_indices[:, np.newaxis, :], axis=2) + + # Vectorized computation of correlation lengths + major_corr_length = np.sqrt(evals[:, 0]) # Largest eigenvalue + intermediate_corr_length = np.sqrt(evals[:, 1]) # Middle eigenvalue + minor_corr_length = np.sqrt(evals[:, 2]) # Smallest eigenvalue + + # Apply anisotropy strength scaling + scale_major = major_corr_length * anisotropy_strength + scale_intermediate = intermediate_corr_length # Keep intermediate neutral + scale_minor = minor_corr_length / anisotropy_strength + + # Create 3D transformation matrices for all points + scales = np.stack([scale_major, scale_intermediate, scale_minor], axis=1) + transforms = evecs * scales[:, np.newaxis, :] + + # Pre-compute inverse transforms with singularity handling + inv_transforms = np.zeros_like(transforms) + singular_mask = np.zeros(n_points, dtype=bool) + + for i in range(n_points): + try: + inv_transforms[i] = np.linalg.inv(transforms[i]) + except np.linalg.LinAlgError: + singular_mask[i] = True + inv_transforms[i] = np.eye(3) # Fallback to identity + correlated_noise = np.zeros(n_points) + iids_flat = iids.flatten() + + # Use spatial indexing for faster neighbor finding when beneficial + if n_points > 1000: # Threshold where spatial indexing becomes beneficial + tree = cKDTree(grid_coords) + # Use a reasonable search radius to limit candidates (more conservative for 3D) + max_search_radius = np.percentile(np.sqrt(evals.max(axis=1)), 90) * anisotropy_strength * 2.5 + + for i in range(n_points): + # Get candidates within search radius + candidates = tree.query_ball_point(grid_coords[i], max_search_radius) + + if len(candidates) <= n_neighbors: + neighbor_indices = candidates + else: + # Transform and compute anisotropic distances only for candidates + dx_candidates = grid_coords[candidates] - grid_coords[i] + if singular_mask[i]: + # Use Euclidean distance for singular transforms + aniso_distances = np.linalg.norm(dx_candidates, axis=1) + else: + dx_transformed = dx_candidates @ inv_transforms[i].T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) + + neighbor_indices = np.array(candidates)[np.argsort(aniso_distances)[:n_neighbors]] + + # Compute final weights and weighted sum + if len(neighbor_indices) > 0: + dx_final = grid_coords[neighbor_indices] - grid_coords[i] + if singular_mask[i]: + aniso_distances_final = np.linalg.norm(dx_final, axis=1) + else: + dx_transformed_final = dx_final @ inv_transforms[i].T + aniso_distances_final = np.linalg.norm(dx_transformed_final, axis=1) + + weights = np.exp(-(aniso_distances_final) ** 2) + weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) + sum_weights = np.sum(weights) + + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = iids_flat[i] + else: + correlated_noise[i] = iids_flat[i] + else: + # For smaller datasets, direct computation is still faster + for i in range(n_points): + # Vectorized distance computation for all points + all_dx = grid_coords - grid_coords[i] + + if singular_mask[i]: + # Use Euclidean distance for singular transforms + aniso_distances = np.linalg.norm(all_dx, axis=1) + else: + dx_transformed = all_dx @ inv_transforms[i].T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) + + # Get k nearest neighbors + neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ + :min(n_neighbors, n_points)] + + # Compute weights vectorized + weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) + weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) + sum_weights = np.sum(weights) + + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = iids_flat[i] + + return correlated_noise + + +def generate_correlated_noise_3d_batch(grid_coords, tensors, iids, + n_neighbors=50, anisotropy_strength=1.0, + batch_size=500): + """Batch processing version for very large 3D datasets.""" + n_points = len(grid_coords) + + # Same vectorized preprocessing as above + evals, evecs = np.linalg.eigh(tensors) + sort_indices = np.argsort(evals, axis=1)[:, ::-1] + evals = np.take_along_axis(evals, sort_indices, axis=1) + evecs = np.take_along_axis(evecs, sort_indices[:, np.newaxis, :], axis=2) + + major_corr_length = np.sqrt(evals[:, 0]) + intermediate_corr_length = np.sqrt(evals[:, 1]) + minor_corr_length = np.sqrt(evals[:, 2]) + + scale_major = major_corr_length * anisotropy_strength + scale_intermediate = intermediate_corr_length + scale_minor = minor_corr_length / anisotropy_strength + + scales = np.stack([scale_major, scale_intermediate, scale_minor], axis=1) + transforms = evecs * scales[:, np.newaxis, :] + + # Pre-compute inverse transforms with singularity handling + inv_transforms = np.zeros_like(transforms) + singular_mask = np.zeros(n_points, dtype=bool) for i in range(n_points): - evals, evecs = np.linalg.eigh(tensors[i]) + try: + inv_transforms[i] = np.linalg.inv(transforms[i]) + except np.linalg.LinAlgError: + singular_mask[i] = True + inv_transforms[i] = np.eye(3) - # Sort eigenvalues/vectors by magnitude (largest to smallest) - sort_idx = np.argsort(evals)[::-1] - evals = evals[sort_idx] - evecs = evecs[:, sort_idx] + correlated_noise = np.zeros(n_points) + iids_flat = iids.flatten() - # Extract correlation lengths (sqrt of eigenvalues) - major_corr_length = np.sqrt(evals[0]) # Largest eigenvalue - intermediate_corr_length = np.sqrt(evals[1]) # Middle eigenvalue - minor_corr_length = np.sqrt(evals[2]) # Smallest eigenvalue + # Process in batches to manage memory (smaller batches for 3D due to higher memory usage) + for batch_start in range(0, n_points, batch_size): + batch_end = min(batch_start + batch_size, n_points) + batch_indices = np.arange(batch_start, batch_end) - # Apply anisotropy strength scaling - scale_major = major_corr_length * anisotropy_strength - scale_intermediate = intermediate_corr_length # Keep intermediate neutral - scale_minor = minor_corr_length / anisotropy_strength + for i in batch_indices: + all_dx = grid_coords - grid_coords[i] - # Create 3D transformation matrix - transform = evecs @ np.diag([scale_major, scale_intermediate, scale_minor]) + if singular_mask[i]: + aniso_distances = np.linalg.norm(all_dx, axis=1) + else: + dx_transformed = all_dx @ inv_transforms[i].T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) - # Transform all grid points relative to current point - all_dx = grid_coords - grid_coords[i] + neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ + :min(n_neighbors, n_points)] + + weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) + weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) + sum_weights = np.sum(weights) + + if sum_weights > 1e-10: + correlated_noise[i] = weighted_sum / sum_weights + else: + correlated_noise[i] = iids_flat[i] + + return correlated_noise + + +def generate_correlated_noise_3d_parallel(grid_coords, tensors, iids, + n_neighbors=50, anisotropy_strength=1.0, + n_jobs=-1): + """Parallel processing version using joblib (install with: pip install joblib).""" + try: + from joblib import Parallel, delayed + except ImportError: + raise ImportError("joblib is required for parallel processing. Install with: pip install joblib") + + n_points = len(grid_coords) + + # Same preprocessing + evals, evecs = np.linalg.eigh(tensors) + sort_indices = np.argsort(evals, axis=1)[:, ::-1] + evals = np.take_along_axis(evals, sort_indices, axis=1) + evecs = np.take_along_axis(evecs, sort_indices[:, np.newaxis, :], axis=2) + + major_corr_length = np.sqrt(evals[:, 0]) + intermediate_corr_length = np.sqrt(evals[:, 1]) + minor_corr_length = np.sqrt(evals[:, 2]) + + scale_major = major_corr_length * anisotropy_strength + scale_intermediate = intermediate_corr_length + scale_minor = minor_corr_length / anisotropy_strength + + scales = np.stack([scale_major, scale_intermediate, scale_minor], axis=1) + transforms = evecs * scales[:, np.newaxis, :] + + inv_transforms = np.zeros_like(transforms) + singular_mask = np.zeros(n_points, dtype=bool) + + for i in range(n_points): try: - inv_transform = np.linalg.inv(transform) - dx_transformed = all_dx @ inv_transform.T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) + inv_transforms[i] = np.linalg.inv(transforms[i]) except np.linalg.LinAlgError: - # Fallback to Euclidean distance if transform is singular - aniso_distances = np.linalg.norm(all_dx, axis=1) + singular_mask[i] = True + inv_transforms[i] = np.eye(3) + + iids_flat = iids.flatten() + + def process_point(i): + all_dx = grid_coords - grid_coords[i] - neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] + if singular_mask[i]: + aniso_distances = np.linalg.norm(all_dx, axis=1) + else: + dx_transformed = all_dx @ inv_transforms[i].T + aniso_distances = np.linalg.norm(dx_transformed, axis=1) - weighted_sum = 0.0 - sum_weights = 0.0 + neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ + :min(n_neighbors, n_points)] - for j in neighbor_indices: - # Use anisotropic distance for weight calculation - weight = np.exp(-(aniso_distances[j]) ** 2) - weighted_sum += weight * iids.flatten()[j] - sum_weights += weight + weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) + weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) + sum_weights = np.sum(weights) if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights + return weighted_sum / sum_weights else: - correlated_noise[i] = iids[i] + return iids_flat[i] + + # Process points in parallel + correlated_noise = np.array( + Parallel(n_jobs=n_jobs)(delayed(process_point)(i) for i in range(n_points)) + ) return correlated_noise +# def generate_correlated_noise_2d(grid_coords, tensors, iids, +# n_neighbors=8, anisotropy_strength=1.0): +# """Generate spatially correlated noise respecting tensor anisotropy.""" +# n_points = len(grid_coords) +# correlated_noise = np.zeros(n_points) +# +# for i in range(n_points): +# evals, evecs = np.linalg.eigh(tensors[i]) +# if evals[1] < evals[0]: +# evals, evecs = evals[::-1], evecs[:, ::-1] +# +# major_corr_length = np.sqrt(evals[1]) +# minor_corr_length = np.sqrt(evals[0]) +# +# scale_for_minor_axis = minor_corr_length / anisotropy_strength +# scale_for_major_axis = major_corr_length * anisotropy_strength +# +# transform = evecs @ np.diag([scale_for_minor_axis, scale_for_major_axis]) +# +# all_dx = grid_coords - grid_coords[i] +# inv_transform = np.linalg.inv(transform) +# dx_transformed = all_dx @ inv_transform.T +# aniso_distances = np.linalg.norm(dx_transformed, axis=1) +# +# neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] +# +# weighted_sum = 0.0 +# sum_weights = 0.0 +# +# for j in neighbor_indices: +# # Use anisotropic distance instead of actual distance for weight calculation +# # This respects the tensor-defined correlation structure +# weight = np.exp(-(aniso_distances[j]) ** 2) +# weighted_sum += weight * iids.flatten()[j] +# sum_weights += weight +# +# if sum_weights > 1e-10: +# correlated_noise[i] = weighted_sum / sum_weights +# else: +# correlated_noise[i] = iids[i] +# +# return correlated_noise + + +# def generate_correlated_noise_3d(grid_coords, tensors, iids, +# n_neighbors=50, anisotropy_strength=1.0): +# """Generate spatially correlated noise respecting 3D tensor anisotropy.""" +# n_points = len(grid_coords) +# correlated_noise = np.zeros(n_points) +# +# for i in range(n_points): +# evals, evecs = np.linalg.eigh(tensors[i]) +# +# # Sort eigenvalues/vectors by magnitude (largest to smallest) +# sort_idx = np.argsort(evals)[::-1] +# evals = evals[sort_idx] +# evecs = evecs[:, sort_idx] +# +# # Extract correlation lengths (sqrt of eigenvalues) +# major_corr_length = np.sqrt(evals[0]) # Largest eigenvalue +# intermediate_corr_length = np.sqrt(evals[1]) # Middle eigenvalue +# minor_corr_length = np.sqrt(evals[2]) # Smallest eigenvalue +# +# # Apply anisotropy strength scaling +# scale_major = major_corr_length * anisotropy_strength +# scale_intermediate = intermediate_corr_length # Keep intermediate neutral +# scale_minor = minor_corr_length / anisotropy_strength +# +# # Create 3D transformation matrix +# transform = evecs @ np.diag([scale_major, scale_intermediate, scale_minor]) +# +# # Transform all grid points relative to current point +# all_dx = grid_coords - grid_coords[i] +# try: +# inv_transform = np.linalg.inv(transform) +# dx_transformed = all_dx @ inv_transform.T +# aniso_distances = np.linalg.norm(dx_transformed, axis=1) +# except np.linalg.LinAlgError: +# # Fallback to Euclidean distance if transform is singular +# aniso_distances = np.linalg.norm(all_dx, axis=1) +# +# neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] +# +# weighted_sum = 0.0 +# sum_weights = 0.0 +# +# for j in neighbor_indices: +# # Use anisotropic distance for weight calculation +# weight = np.exp(-(aniso_distances[j]) ** 2) +# weighted_sum += weight * iids.flatten()[j] +# sum_weights += weight +# +# if sum_weights > 1e-10: +# correlated_noise[i] = weighted_sum / sum_weights +# else: +# correlated_noise[i] = iids[i] +# +# return correlated_noise + + def create_2d_tensors(theta, major, minor): """Create 2x2 anisotropy tensors from geostatistical parameters.""" @@ -1722,8 +2121,8 @@ def save_layer(field, layer, field_name, save_path='.'): if isinstance(field_name, list): field_name = field_name[0] - filename = os.path.join(save_path, f'{field_name}_layer_{layer + 1:02d}.arr') - np.savetxt(filename, field, fmt='%.6f') + filename = os.path.join(save_path, f'{field_name}_layer_{layer:02d}.arr') + np.savetxt(filename, field, fmt='%.6e') print(f"Saved layer {layer} to '{filename}'") @@ -2179,19 +2578,20 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, - zones: Can be passed to generate_single_layer() as numpy array with integer zone IDs, shape (n_points,) or (ny, nx) """ - data_dir = r'..\..\examples\Wairau' - zone_files = [f'waq_arr.geoclass_layer{z+1}.arr' for z in range(13)] - save_path = os.path.join(data_dir, 'output_idw_3d') + data_dir = r'..\..\examples\bridgepa' + zone_files = [f'idomain.arr' for z in range(1)] + con_pts_file = 'Bridgpa_skytem_sva.csv' + save_path = os.path.join(data_dir, f'{con_pts_file}_output') if not os.path.exists(save_path): os.mkdir(save_path) generate_fields_from_files(data_dir, - conceptual_points_file='conceptual_points.csv', + conceptual_points_file=con_pts_file, grid_file='grid.dat', zone_file=zone_files, field_name=['kh'], - layer_mode=False, + layer_mode=True, save_path=save_path, tensor_interp='idw' ) \ No newline at end of file From 2e0303580d3a7b85cd86657a3d41edc888af51d6 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 5 Aug 2025 22:00:10 +1200 Subject: [PATCH 13/36] moved variogram def out of for loop, other efficiencies --- pyemu/utils/tensor_utils.py | 135 +++++++++++++++--------------------- 1 file changed, 56 insertions(+), 79 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 86828eae5..e03d0ee8d 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -494,7 +494,7 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, iids_flat = iids.flatten() correlated_noise_1d = generate_correlated_noise_3d( grid_coords, geological_tensors_3d, iids_flat, - n_neighbors=50, anisotropy_strength=1.0 + n_neighbors=10, anisotropy_strength=1.0 ) correlated_noise_3d = correlated_noise_1d.reshape(shape) @@ -510,11 +510,11 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, print(f" Correlated noise stats: mean={noise_mean:.6f}, std={noise_std:.6f}") # Log-normal field generation: field = 10^(log10(mean) + noise * sd) - log_mean_field = np.log10(np.maximum(mean_field_3d, 1e-8)) # Avoid log(0) - log_sd_field = np.log10(np.maximum(sd_field_3d, 1e-8)) - field_3d = 10 ** (log_mean_field + correlated_noise_3d * log_sd_field) + mean_field = np.maximum(mean_field_3d, 1e-8) # Avoid log(0) + sd_field = np.maximum(sd_field_3d, 1e-8) + field_3d = mean_field + correlated_noise_3d * sd_field - print(f" Log-mean field range: [{np.min(log_mean_field):.3f}, {np.max(log_mean_field):.3f}]") + print(f" Log-mean field range: [{np.min(mean_field):.3f}, {np.max(mean_field):.3f}]") print(f" Generated field range: [{np.min(field_3d):.3e}, {np.max(field_3d):.3e}]") print(f" Generated 3D field with shape {field_3d.shape}") @@ -611,7 +611,7 @@ def normalize_bearing(bearing): cp_coords, cp_means, grid_coords_2d, geological_tensors, variogram_model='exponential', sill=1.0, nugget=0.1, background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform='log', min_value=1e-8, max_neighbors=8, + min_points=3, transform='log', min_value=1e-8, max_neighbors=4, zones=zones ) if zones is not None: @@ -627,7 +627,7 @@ def normalize_bearing(bearing): cp_coords, cp_sd, grid_coords_2d, geological_tensors, # Use geological tensors variogram_model='exponential', sill=1.0, nugget=0.1, background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform='log', min_value=1e-8, max_neighbors=8, + min_points=3, transform='log', min_value=1e-8, max_neighbors=4, zones=zones ) if zones is not None: @@ -644,9 +644,9 @@ def normalize_bearing(bearing): # Step 7: Combine into lognormal field - all operations in 2D - log_mean_field = np.log10(np.maximum(interp_means_2d, 1e-8)) # Avoid log(0) - log_sd_field = np.log10(np.maximum(interp_sd_2d, 1e-8)) - field_2d = 10 ** (log_mean_field + correlated_noise_2d * log_sd_field) + mean_field = np.maximum(interp_means_2d, 1e-8) # Avoid log(0) + sd_field = np.maximum(interp_sd_2d, 1e-8) + field_2d = mean_field + correlated_noise_2d * sd_field return field_2d, geological_tensors @@ -771,7 +771,7 @@ def _kriging_method(cp_coords, cp_tensors, grid_coords): variogram_model='exponential', range_param=10000, sill=1.0, nugget=0.1, background_value=0.0, max_search_radius=1e20, min_points=1, - transform=None, min_value=1e-8, max_neighbors=5) + transform=None, min_value=1e-8, max_neighbors=8) result_log[:, i, j] = interp_values if i != j: result_log[:, j, i] = interp_values @@ -1218,7 +1218,6 @@ def estimate_boundary_direction(zones, center_i, center_j, radius=2): return boundary_direction - def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, variogram_model='exponential', sill=1.0, nugget=0.01, background_value=0.0, max_search_radius=1e20, min_points=1, @@ -1226,42 +1225,6 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, zones=None): """ Ordinary kriging using tensor-aware anisotropic distances. - - Parameters - ---------- - cp_coords : np.ndarray - Conceptual point coordinates, shape (n_cp, 2) for 2D or (n_cp, 3) for 3D - cp_values : np.ndarray - Values at conceptual points, shape (n_cp,) - grid_coords : np.ndarray - Grid coordinates, shape (n_grid, 2) for 2D or (n_grid, 3) for 3D - interp_tensors : np.ndarray - Interpolated tensors at grid points, shape (n_grid, 2, 2) for 2D or (n_grid, 3, 3) for 3D - variogram_model : str, default 'exponential' - Variogram model: 'exponential', 'gaussian', or 'spherical' - sill : float, default 1.0 - Variogram sill parameter - nugget : float, default 0.01 - Variogram nugget parameter - background_value : float, default 0.0 - Default value for points with insufficient neighbors - max_search_radius : float, default 1e20 - Maximum search radius for neighbors - min_points : int, default 1 - Minimum number of points required for interpolation - max_neighbors : int, default 4 - Maximum number of neighbors to use - transform : str, optional - Data transformation: 'log' for log-transform, None for no transform - min_value : float, default 1e-8 - Minimum value for log transform - zones : np.ndarray, optional - Zone IDs for zone-aware kriging. Shape (ny, nx) for 2D or (nz, ny, nx) for 3D - - Returns - ------- - np.ndarray - Interpolated values. Shape (ny, nx) for 2D grids or (nz, ny, nx) for 3D grids """ n_grid = len(grid_coords) n_dims = grid_coords.shape[1] @@ -1295,6 +1258,17 @@ def get_indices(idx): col = idx % nx return row, col + # MOVE VARIOGRAM FUNCTION OUTSIDE THE LOOP! + def variogram(h): + if variogram_model == 'exponential': + return nugget + (sill - nugget) * (1 - np.exp(-h)) + elif variogram_model == 'gaussian': + return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2))) + else: # spherical + return np.where(h <= 1.0, + nugget + (sill - nugget) * (1.5 * h - 0.5 * h ** 3), + sill) + interp_values_1d = np.full(n_grid, background_value) # Calculate zone for each conceptual point (once, outside the main loop) @@ -1312,8 +1286,16 @@ def get_indices(idx): cp_zones.append(zones[row, col]) cp_zones = np.array(cp_zones) + # PRE-COMPUTE LOG TRANSFORMATION IF NEEDED + if transform == 'log': + if min_value is None: + positive_values = cp_values[cp_values > 0] + min_value = np.min(positive_values) * 0.01 if len(positive_values) > 0 else 1e-8 + + # MAIN GRID LOOP - This is doing kriging for each grid point individually for i in range(n_grid): local_tensor = interp_tensors[i] + if zones is not None: # Get current zone and filter conceptual points if n_dims == 3: @@ -1335,17 +1317,12 @@ def get_indices(idx): cp_coords_filtered = cp_coords cp_values_filtered = cp_values + # COMPUTE ANISOTROPIC DISTANCES try: tensor_inv = np.linalg.inv(local_tensor) - aniso_distances = [] - - for j in range(len(cp_coords_filtered)): - dx = cp_coords_filtered[j] - grid_coords[i] - aniso_dist = np.sqrt(dx.T @ tensor_inv @ dx) - aniso_distances.append(aniso_dist) - - aniso_distances = np.array(aniso_distances) - + # VECTORIZE THIS INSTEAD OF LOOP! + dx = cp_coords_filtered - grid_coords[i] # Broadcasting + aniso_distances = np.sqrt(np.sum(dx @ tensor_inv * dx, axis=1)) except np.linalg.LinAlgError: aniso_distances = np.linalg.norm(cp_coords_filtered - grid_coords[i], axis=1) @@ -1361,27 +1338,18 @@ def get_indices(idx): nearby_coords = cp_coords_filtered[closest_indices] nearby_distances = aniso_distances[closest_indices] + # APPLY LOG TRANSFORM IF NEEDED if transform == 'log': - if min_value is None: - positive_values = nearby_values[nearby_values > 0] - min_value = np.min(positive_values) * 0.01 nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) else: nearby_values_transformed = nearby_values.copy() - def variogram(h): - if variogram_model == 'exponential': - return nugget + (sill - nugget) * (1 - np.exp(-h)) - elif variogram_model == 'gaussian': - return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2))) - else: # spherical - return np.where(h <= 1.0, - nugget + (sill - nugget) * (1.5 * h - 0.5 * h ** 3), - sill) - + # KRIGING SYSTEM SETUP AND SOLVE + # TODO: use gwutils C = np.zeros((n_candidates + 1, n_candidates + 1)) c = np.zeros(n_candidates + 1) + # BUILD COVARIANCE MATRIX - Could potentially vectorize this too for j in range(n_candidates): for k in range(n_candidates): dx = nearby_coords[j] - nearby_coords[k] @@ -1397,6 +1365,7 @@ def variogram(h): c[-1] = 1 + # SOLVE KRIGING SYSTEM try: cond_num = np.linalg.cond(C) if cond_num > 1e12: # Matrix is poorly conditioned @@ -1412,6 +1381,7 @@ def variogram(h): weights = np.exp(-nearby_distances) interp_values_1d[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) + # BACK-TRANSFORM IF NEEDED if transform == 'log': interp_values_1d = 10 ** interp_values_1d @@ -1423,7 +1393,7 @@ def variogram(h): def generate_correlated_noise_2d(grid_coords, tensors, iids, - n_neighbors=8, anisotropy_strength=1.0): + n_neighbors=4, anisotropy_strength=1.0): """Generate spatially correlated noise respecting tensor anisotropy - optimized version.""" n_points = len(grid_coords) @@ -1511,7 +1481,7 @@ def generate_correlated_noise_2d(grid_coords, tensors, iids, def generate_correlated_noise_2d_batch(grid_coords, tensors, iids, - n_neighbors=8, anisotropy_strength=1.0, + n_neighbors=4, anisotropy_strength=1.0, batch_size=1000): """Batch processing version for very large datasets.""" n_points = len(grid_coords) @@ -1564,7 +1534,7 @@ def generate_correlated_noise_2d_batch(grid_coords, tensors, iids, def generate_correlated_noise_3d(grid_coords, tensors, iids, - n_neighbors=50, anisotropy_strength=1.0): + n_neighbors=10, anisotropy_strength=1.0): """Generate spatially correlated noise respecting 3D tensor anisotropy - optimized version.""" n_points = len(grid_coords) @@ -1679,7 +1649,7 @@ def generate_correlated_noise_3d(grid_coords, tensors, iids, def generate_correlated_noise_3d_batch(grid_coords, tensors, iids, - n_neighbors=50, anisotropy_strength=1.0, + n_neighbors=10, anisotropy_strength=1.0, batch_size=500): """Batch processing version for very large 3D datasets.""" n_points = len(grid_coords) @@ -1745,7 +1715,7 @@ def generate_correlated_noise_3d_batch(grid_coords, tensors, iids, def generate_correlated_noise_3d_parallel(grid_coords, tensors, iids, - n_neighbors=50, anisotropy_strength=1.0, + n_neighbors=10, anisotropy_strength=1.0, n_jobs=-1): """Parallel processing version using joblib (install with: pip install joblib).""" try: @@ -2578,10 +2548,13 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, - zones: Can be passed to generate_single_layer() as numpy array with integer zone IDs, shape (n_points,) or (ny, nx) """ - data_dir = r'..\..\examples\bridgepa' + import time + start_time = time.perf_counter() + + data_dir = r'..\..\examples\Hawkes_Bay' zone_files = [f'idomain.arr' for z in range(1)] - con_pts_file = 'Bridgpa_skytem_sva.csv' - save_path = os.path.join(data_dir, f'{con_pts_file}_output') + con_pts_file = 'Bridgpa_skytem_sva_0.csv' + save_path = os.path.join(data_dir, f'{con_pts_file}_output_fixedlog') if not os.path.exists(save_path): os.mkdir(save_path) @@ -2594,4 +2567,8 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, layer_mode=True, save_path=save_path, tensor_interp='idw' - ) \ No newline at end of file + ) + + end_time = time.perf_counter() + elapsed_time = end_time - start_time + print(f"Execution time: {elapsed_time:.4f} seconds") \ No newline at end of file From 6771a99f7f52e00fbd88f300168f0a8247c01f02 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 5 Aug 2025 22:39:08 +1200 Subject: [PATCH 14/36] fixed transform bug and cleaned up some transform bits, much better results --- pyemu/utils/tensor_utils.py | 48 ++++++++++++++++++++++++------------- 1 file changed, 32 insertions(+), 16 deletions(-) diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index e03d0ee8d..32b8e84f6 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -372,7 +372,7 @@ def generate_field_by_layer(conceptual_points, grid_coords, iids, def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, - save_path='.', tensor_interp='idw'): + save_path='.', transform='log', tensor_interp='idw'): """ Generate field using full 3D tensor processing with zone-based interpolation. @@ -456,7 +456,7 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, cp_coords, cp_means, grid_coords, geological_tensors_3d, variogram_model='exponential', sill=1.0, nugget=0.01, background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform='log', min_value=1e-8, max_neighbors=8, + min_points=3, transform=transform, min_value=1e-8, max_neighbors=8, zones=zones_3d ) mean_field_3d = mean_field_1d.reshape(shape) @@ -475,7 +475,7 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, cp_coords, cp_sds, grid_coords, geological_tensors_3d, variogram_model='exponential', sill=0.5, nugget=0.01, background_value=np.mean(cp_sds), max_search_radius=1e20, - min_points=3, transform='log', min_value=1e-8, max_neighbors=8, + min_points=3, transform=transform, min_value=1e-8, max_neighbors=8, zones=zones_3d ) sd_field_3d = sd_field_1d.reshape(shape) @@ -509,12 +509,21 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, noise_std = np.std(correlated_noise_3d) print(f" Correlated noise stats: mean={noise_mean:.6f}, std={noise_std:.6f}") - # Log-normal field generation: field = 10^(log10(mean) + noise * sd) - mean_field = np.maximum(mean_field_3d, 1e-8) # Avoid log(0) - sd_field = np.maximum(sd_field_3d, 1e-8) - field_3d = mean_field + correlated_noise_3d * sd_field + # Step 6: Combine fields with transform handling + if transform == 'log': + print(" Combining fields using log-normal formulation...") + # Convert to lognormal parameters + variance_2d = sd_field_3d ** 2 + mu_2d = np.log(mean_field_3d ** 2 / np.sqrt(variance_2d + mean_field_3d ** 2)) + sigma_2d = np.sqrt(np.log(1 + variance_2d / mean_field_3d ** 2)) + normal_field = mu_2d + correlated_noise_3d * sigma_2d + field_3d = np.exp(normal_field) + else: + print(" Combining fields using normal formulation...") + field_3d = mean_field_3d + correlated_noise_3d * sd_field_3d + - print(f" Log-mean field range: [{np.min(mean_field):.3f}, {np.max(mean_field):.3f}]") + print(f" Log-mean field range: [{np.min(field_3d):.3f}, {np.max(field_3d):.3f}]") print(f" Generated field range: [{np.min(field_3d):.3e}, {np.max(field_3d):.3e}]") print(f" Generated 3D field with shape {field_3d.shape}") @@ -538,7 +547,7 @@ def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, iids, zones=None, shape=None, save_path='.', - tensor_interp='idw'): + tensor_interp='idw', transform='log'): """ Modified generate_single_layer with zone-based tensor interpolation. @@ -611,7 +620,7 @@ def normalize_bearing(bearing): cp_coords, cp_means, grid_coords_2d, geological_tensors, variogram_model='exponential', sill=1.0, nugget=0.1, background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform='log', min_value=1e-8, max_neighbors=4, + min_points=3, transform=transform, min_value=1e-8, max_neighbors=4, zones=zones ) if zones is not None: @@ -627,7 +636,7 @@ def normalize_bearing(bearing): cp_coords, cp_sd, grid_coords_2d, geological_tensors, # Use geological tensors variogram_model='exponential', sill=1.0, nugget=0.1, background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform='log', min_value=1e-8, max_neighbors=4, + min_points=3, transform=transform, min_value=1e-8, max_neighbors=4, zones=zones ) if zones is not None: @@ -642,11 +651,18 @@ def normalize_bearing(bearing): correlated_noise_1d = generate_correlated_noise_2d(grid_coords_2d, geological_tensors, iids) correlated_noise_2d = correlated_noise_1d.reshape(shape) - # Step 7: Combine into lognormal field - all operations in 2D - - mean_field = np.maximum(interp_means_2d, 1e-8) # Avoid log(0) - sd_field = np.maximum(interp_sd_2d, 1e-8) - field_2d = mean_field + correlated_noise_2d * sd_field + # Step 7: Combine fields with transform handling + if transform == 'log': + print(" Combining fields using log-normal formulation...") + # Convert to lognormal parameters + variance_2d = interp_sd_2d ** 2 + mu_2d = np.log(interp_means_2d ** 2 / np.sqrt(variance_2d + interp_means_2d ** 2)) + sigma_2d = np.sqrt(np.log(1 + variance_2d / interp_means_2d ** 2)) + normal_field = mu_2d + correlated_noise_2d * sigma_2d + field_2d = np.exp(normal_field) + else: + print(" Combining fields using normal formulation...") + field_2d = interp_means_2d + correlated_noise_2d * interp_sd_2d return field_2d, geological_tensors From 7fcbf6994054a211a389c5c740c23a146e7b8815 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 6 Aug 2025 08:02:50 +1200 Subject: [PATCH 15/36] moving example out of tensor_utils --- pyemu/utils/example_useage.py | 44 +++++++++++++++++++++++++++++++++++ pyemu/utils/tensor_utils.py | 42 --------------------------------- 2 files changed, 44 insertions(+), 42 deletions(-) create mode 100644 pyemu/utils/example_useage.py diff --git a/pyemu/utils/example_useage.py b/pyemu/utils/example_useage.py new file mode 100644 index 000000000..6befa44dc --- /dev/null +++ b/pyemu/utils/example_useage.py @@ -0,0 +1,44 @@ +from pyemu.utils import tensor_utils +import os + +# ============================================================================ +# EXAMPLE USAGE +# ============================================================================ + +# Example usage +if __name__ == "__main__": + """ + Example usage of the tensor-based field generator. + + Required file formats: + - conceptual_points.csv: CSV with columns x,y,major,anisotropy,bearing,mean_kh,sd_kh + - grid.dat: Whitespace-delimited with columns x,y,z + + Optional: + - zones: Can be passed to generate_single_layer() as numpy array + with integer zone IDs, shape (n_points,) or (ny, nx) + """ + import time + start_time = time.perf_counter() + + data_dir = r'..\..\examples\Hawkes_Bay' + zone_files = [f'idomain.arr' for z in range(1)] + con_pts_file = 'Bridgpa_skytem_sva_2.csv' + save_path = os.path.join(data_dir, f'{con_pts_file}_output') + + if not os.path.exists(save_path): + os.mkdir(save_path) + + tensor_utils.generate_fields_from_files(data_dir, + conceptual_points_file=con_pts_file, + grid_file='grid.dat', + zone_file=zone_files, + field_name=['kh'], + layer_mode=True, + save_path=save_path, + tensor_interp='idw' + ) + + end_time = time.perf_counter() + elapsed_time = end_time - start_time + print(f"Execution time: {elapsed_time:.4f} seconds") \ No newline at end of file diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py index 32b8e84f6..8a2ddb8a9 100644 --- a/pyemu/utils/tensor_utils.py +++ b/pyemu/utils/tensor_utils.py @@ -2546,45 +2546,3 @@ def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, save_path=save_path, filename=f'{field_name}_3d' ) - -# ============================================================================ -# EXAMPLE USAGE -# ============================================================================ - -# Example usage -if __name__ == "__main__": - """ - Example usage of the tensor-based field generator. - - Required file formats: - - conceptual_points.csv: CSV with columns x,y,major,anisotropy,bearing,mean_kh,sd_kh - - grid.dat: Whitespace-delimited with columns x,y,z - - Optional: - - zones: Can be passed to generate_single_layer() as numpy array - with integer zone IDs, shape (n_points,) or (ny, nx) - """ - import time - start_time = time.perf_counter() - - data_dir = r'..\..\examples\Hawkes_Bay' - zone_files = [f'idomain.arr' for z in range(1)] - con_pts_file = 'Bridgpa_skytem_sva_0.csv' - save_path = os.path.join(data_dir, f'{con_pts_file}_output_fixedlog') - - if not os.path.exists(save_path): - os.mkdir(save_path) - - generate_fields_from_files(data_dir, - conceptual_points_file=con_pts_file, - grid_file='grid.dat', - zone_file=zone_files, - field_name=['kh'], - layer_mode=True, - save_path=save_path, - tensor_interp='idw' - ) - - end_time = time.perf_counter() - elapsed_time = end_time - start_time - print(f"Execution time: {elapsed_time:.4f} seconds") \ No newline at end of file From aea823db992b0883bbc91233c56fa855441e3389 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 13 Aug 2025 23:28:38 +1200 Subject: [PATCH 16/36] pypestutils implementation with tensors --- examples/pstfrom_mf6_ppu_tensor.ipynb | 1485 +++++++++++++- pyemu/__init__.py | 4 +- pyemu/plot/plot_utils.py | 192 +- pyemu/utils/__init__.py | 2 +- pyemu/utils/example_useage.py | 24 +- pyemu/utils/nsaf_helpers.py | 974 ++++++++++ pyemu/utils/nsaf_utils.py | 1355 +++++++++++++ pyemu/utils/tensor_utils.py | 2548 ------------------------- 8 files changed, 3919 insertions(+), 2665 deletions(-) create mode 100644 pyemu/utils/nsaf_helpers.py create mode 100644 pyemu/utils/nsaf_utils.py delete mode 100644 pyemu/utils/tensor_utils.py diff --git a/examples/pstfrom_mf6_ppu_tensor.ipynb b/examples/pstfrom_mf6_ppu_tensor.ipynb index e1626fa1d..161b97a3e 100644 --- a/examples/pstfrom_mf6_ppu_tensor.ipynb +++ b/examples/pstfrom_mf6_ppu_tensor.ipynb @@ -4,40 +4,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Setting up a PEST interface from MODFLOW6 using the `PstFrom` class with `PyPestUtils` for advanced pilot point parameterization" + "# Advanced non-stationary geostatistical parameterization using `pyemu.utils.nsaf_utils`" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n" + ] + } + ], "source": [ "import os\n", "import shutil\n", "import numpy as np\n", "import pandas as pd\n", + "import matplotlib\n", + "matplotlib.use('Qt5Agg')\n", "import matplotlib.pyplot as plt\n", "import pyemu\n", - "import flopy" + "import flopy\n", + "from pyemu.utils import nsaf_utils as nsaf\n", + "from pyemu.plot import plot_utils as pu" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "import sys\n", - "sys.path.append(os.path.join(\"..\",\"..\",\"pypestutils\"))" + "Move this lower if still wanted" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "sys.path.append(os.path.join(\"..\",\"..\",\"pypestutils\"))\n", "import pypestutils as ppu" ] }, @@ -50,9 +61,149 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['freyberg6.dis',\n", + " 'freyberg6.dis.grb',\n", + " 'freyberg6.dis_botm_layer1.txt',\n", + " 'freyberg6.dis_botm_layer2.txt',\n", + " 'freyberg6.dis_botm_layer3.txt',\n", + " 'freyberg6.dis_delc.txt',\n", + " 'freyberg6.dis_delr.txt',\n", + " 'freyberg6.dis_idomain_layer1.txt',\n", + " 'freyberg6.dis_idomain_layer2.txt',\n", + " 'freyberg6.dis_idomain_layer3.txt',\n", + " 'freyberg6.dis_top.txt',\n", + " 'freyberg6.ghb',\n", + " 'freyberg6.ghb_stress_period_data_1.txt',\n", + " 'freyberg6.ic',\n", + " 'freyberg6.ic_strt_layer1.txt',\n", + " 'freyberg6.ic_strt_layer2.txt',\n", + " 'freyberg6.ic_strt_layer3.txt',\n", + " 'freyberg6.ims',\n", + " 'freyberg6.lst',\n", + " 'freyberg6.nam',\n", + " 'freyberg6.npf',\n", + " 'freyberg6.npf_icelltype_layer1.txt',\n", + " 'freyberg6.npf_icelltype_layer2.txt',\n", + " 'freyberg6.npf_icelltype_layer3.txt',\n", + " 'freyberg6.npf_k33_layer1.txt',\n", + " 'freyberg6.npf_k33_layer2.txt',\n", + " 'freyberg6.npf_k33_layer3.txt',\n", + " 'freyberg6.npf_k_layer1.txt',\n", + " 'freyberg6.npf_k_layer2.txt',\n", + " 'freyberg6.npf_k_layer3.txt',\n", + " 'freyberg6.oc',\n", + " 'freyberg6.rch',\n", + " 'freyberg6.rch_recharge_1.txt',\n", + " 'freyberg6.rch_recharge_10.txt',\n", + " 'freyberg6.rch_recharge_11.txt',\n", + " 'freyberg6.rch_recharge_12.txt',\n", + " 'freyberg6.rch_recharge_13.txt',\n", + " 'freyberg6.rch_recharge_14.txt',\n", + " 'freyberg6.rch_recharge_15.txt',\n", + " 'freyberg6.rch_recharge_16.txt',\n", + " 'freyberg6.rch_recharge_17.txt',\n", + " 'freyberg6.rch_recharge_18.txt',\n", + " 'freyberg6.rch_recharge_19.txt',\n", + " 'freyberg6.rch_recharge_2.txt',\n", + " 'freyberg6.rch_recharge_20.txt',\n", + " 'freyberg6.rch_recharge_21.txt',\n", + " 'freyberg6.rch_recharge_22.txt',\n", + " 'freyberg6.rch_recharge_23.txt',\n", + " 'freyberg6.rch_recharge_24.txt',\n", + " 'freyberg6.rch_recharge_25.txt',\n", + " 'freyberg6.rch_recharge_3.txt',\n", + " 'freyberg6.rch_recharge_4.txt',\n", + " 'freyberg6.rch_recharge_5.txt',\n", + " 'freyberg6.rch_recharge_6.txt',\n", + " 'freyberg6.rch_recharge_7.txt',\n", + " 'freyberg6.rch_recharge_8.txt',\n", + " 'freyberg6.rch_recharge_9.txt',\n", + " 'freyberg6.sfr',\n", + " 'freyberg6.sfr_connectiondata.txt',\n", + " 'freyberg6.sfr_packagedata.txt',\n", + " 'freyberg6.sfr_perioddata_1.txt',\n", + " 'freyberg6.sfr_perioddata_10.txt',\n", + " 'freyberg6.sfr_perioddata_11.txt',\n", + " 'freyberg6.sfr_perioddata_12.txt',\n", + " 'freyberg6.sfr_perioddata_13.txt',\n", + " 'freyberg6.sfr_perioddata_14.txt',\n", + " 'freyberg6.sfr_perioddata_15.txt',\n", + " 'freyberg6.sfr_perioddata_16.txt',\n", + " 'freyberg6.sfr_perioddata_17.txt',\n", + " 'freyberg6.sfr_perioddata_18.txt',\n", + " 'freyberg6.sfr_perioddata_19.txt',\n", + " 'freyberg6.sfr_perioddata_2.txt',\n", + " 'freyberg6.sfr_perioddata_20.txt',\n", + " 'freyberg6.sfr_perioddata_21.txt',\n", + " 'freyberg6.sfr_perioddata_22.txt',\n", + " 'freyberg6.sfr_perioddata_23.txt',\n", + " 'freyberg6.sfr_perioddata_24.txt',\n", + " 'freyberg6.sfr_perioddata_25.txt',\n", + " 'freyberg6.sfr_perioddata_3.txt',\n", + " 'freyberg6.sfr_perioddata_4.txt',\n", + " 'freyberg6.sfr_perioddata_5.txt',\n", + " 'freyberg6.sfr_perioddata_6.txt',\n", + " 'freyberg6.sfr_perioddata_7.txt',\n", + " 'freyberg6.sfr_perioddata_8.txt',\n", + " 'freyberg6.sfr_perioddata_9.txt',\n", + " 'freyberg6.sto',\n", + " 'freyberg6.sto_iconvert_layer1.txt',\n", + " 'freyberg6.sto_iconvert_layer2.txt',\n", + " 'freyberg6.sto_iconvert_layer3.txt',\n", + " 'freyberg6.sto_ss_layer1.txt',\n", + " 'freyberg6.sto_ss_layer2.txt',\n", + " 'freyberg6.sto_ss_layer3.txt',\n", + " 'freyberg6.sto_sy_layer1.txt',\n", + " 'freyberg6.sto_sy_layer2.txt',\n", + " 'freyberg6.sto_sy_layer3.txt',\n", + " 'freyberg6.tdis',\n", + " 'freyberg6.wel',\n", + " 'freyberg6.wel_stress_period_data_1.txt',\n", + " 'freyberg6.wel_stress_period_data_10.txt',\n", + " 'freyberg6.wel_stress_period_data_11.txt',\n", + " 'freyberg6.wel_stress_period_data_12.txt',\n", + " 'freyberg6.wel_stress_period_data_13.txt',\n", + " 'freyberg6.wel_stress_period_data_14.txt',\n", + " 'freyberg6.wel_stress_period_data_15.txt',\n", + " 'freyberg6.wel_stress_period_data_16.txt',\n", + " 'freyberg6.wel_stress_period_data_17.txt',\n", + " 'freyberg6.wel_stress_period_data_18.txt',\n", + " 'freyberg6.wel_stress_period_data_19.txt',\n", + " 'freyberg6.wel_stress_period_data_2.txt',\n", + " 'freyberg6.wel_stress_period_data_20.txt',\n", + " 'freyberg6.wel_stress_period_data_21.txt',\n", + " 'freyberg6.wel_stress_period_data_22.txt',\n", + " 'freyberg6.wel_stress_period_data_23.txt',\n", + " 'freyberg6.wel_stress_period_data_24.txt',\n", + " 'freyberg6.wel_stress_period_data_25.txt',\n", + " 'freyberg6.wel_stress_period_data_3.txt',\n", + " 'freyberg6.wel_stress_period_data_4.txt',\n", + " 'freyberg6.wel_stress_period_data_5.txt',\n", + " 'freyberg6.wel_stress_period_data_6.txt',\n", + " 'freyberg6.wel_stress_period_data_7.txt',\n", + " 'freyberg6.wel_stress_period_data_8.txt',\n", + " 'freyberg6.wel_stress_period_data_9.txt',\n", + " 'freyberg6_freyberg.cbc',\n", + " 'freyberg6_freyberg.hds',\n", + " 'head.obs',\n", + " 'heads.csv',\n", + " 'mfsim.lst',\n", + " 'mfsim.nam',\n", + " 'sfr.csv',\n", + " 'sfr.obs']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "org_model_ws = os.path.join('freyberg_mf6')\n", "os.listdir(org_model_ws)" @@ -69,14 +220,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + "C:\\Users\\wesk\\AppData\\Local\\miniforge3\\envs\\p312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n" + ] + } + ], "source": [ "id_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_idomain_layer3.txt\"))\n", "top_arr = np.loadtxt(os.path.join(org_model_ws,\"freyberg6.dis_top.txt\"))\n", "top_arr[id_arr==0] = np.nan\n", - "plt.imshow(top_arr)" + "plt.imshow(top_arr)\n", + "plt.show()" ] }, { @@ -88,18 +249,153 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tmp_model_ws = \"temp_pst_from_ppu\"\n", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['freyberg6.dis',\n", + " 'freyberg6.dis.grb',\n", + " 'freyberg6.dis_botm_layer1.txt',\n", + " 'freyberg6.dis_botm_layer2.txt',\n", + " 'freyberg6.dis_botm_layer3.txt',\n", + " 'freyberg6.dis_delc.txt',\n", + " 'freyberg6.dis_delr.txt',\n", + " 'freyberg6.dis_idomain_layer1.txt',\n", + " 'freyberg6.dis_idomain_layer2.txt',\n", + " 'freyberg6.dis_idomain_layer3.txt',\n", + " 'freyberg6.dis_top.txt',\n", + " 'freyberg6.ghb',\n", + " 'freyberg6.ghb_stress_period_data_1.txt',\n", + " 'freyberg6.ic',\n", + " 'freyberg6.ic_strt_layer1.txt',\n", + " 'freyberg6.ic_strt_layer2.txt',\n", + " 'freyberg6.ic_strt_layer3.txt',\n", + " 'freyberg6.ims',\n", + " 'freyberg6.lst',\n", + " 'freyberg6.nam',\n", + " 'freyberg6.npf',\n", + " 'freyberg6.npf_icelltype_layer1.txt',\n", + " 'freyberg6.npf_icelltype_layer2.txt',\n", + " 'freyberg6.npf_icelltype_layer3.txt',\n", + " 'freyberg6.npf_k33_layer1.txt',\n", + " 'freyberg6.npf_k33_layer2.txt',\n", + " 'freyberg6.npf_k33_layer3.txt',\n", + " 'freyberg6.npf_k_layer1.txt',\n", + " 'freyberg6.npf_k_layer2.txt',\n", + " 'freyberg6.npf_k_layer3.txt',\n", + " 'freyberg6.oc',\n", + " 'freyberg6.rch',\n", + " 'freyberg6.rch_recharge_1.txt',\n", + " 'freyberg6.rch_recharge_10.txt',\n", + " 'freyberg6.rch_recharge_11.txt',\n", + " 'freyberg6.rch_recharge_12.txt',\n", + " 'freyberg6.rch_recharge_13.txt',\n", + " 'freyberg6.rch_recharge_14.txt',\n", + " 'freyberg6.rch_recharge_15.txt',\n", + " 'freyberg6.rch_recharge_16.txt',\n", + " 'freyberg6.rch_recharge_17.txt',\n", + " 'freyberg6.rch_recharge_18.txt',\n", + " 'freyberg6.rch_recharge_19.txt',\n", + " 'freyberg6.rch_recharge_2.txt',\n", + " 'freyberg6.rch_recharge_20.txt',\n", + " 'freyberg6.rch_recharge_21.txt',\n", + " 'freyberg6.rch_recharge_22.txt',\n", + " 'freyberg6.rch_recharge_23.txt',\n", + " 'freyberg6.rch_recharge_24.txt',\n", + " 'freyberg6.rch_recharge_25.txt',\n", + " 'freyberg6.rch_recharge_3.txt',\n", + " 'freyberg6.rch_recharge_4.txt',\n", + " 'freyberg6.rch_recharge_5.txt',\n", + " 'freyberg6.rch_recharge_6.txt',\n", + " 'freyberg6.rch_recharge_7.txt',\n", + " 'freyberg6.rch_recharge_8.txt',\n", + " 'freyberg6.rch_recharge_9.txt',\n", + " 'freyberg6.sfr',\n", + " 'freyberg6.sfr_connectiondata.txt',\n", + " 'freyberg6.sfr_packagedata.txt',\n", + " 'freyberg6.sfr_perioddata_1.txt',\n", + " 'freyberg6.sfr_perioddata_10.txt',\n", + " 'freyberg6.sfr_perioddata_11.txt',\n", + " 'freyberg6.sfr_perioddata_12.txt',\n", + " 'freyberg6.sfr_perioddata_13.txt',\n", + " 'freyberg6.sfr_perioddata_14.txt',\n", + " 'freyberg6.sfr_perioddata_15.txt',\n", + " 'freyberg6.sfr_perioddata_16.txt',\n", + " 'freyberg6.sfr_perioddata_17.txt',\n", + " 'freyberg6.sfr_perioddata_18.txt',\n", + " 'freyberg6.sfr_perioddata_19.txt',\n", + " 'freyberg6.sfr_perioddata_2.txt',\n", + " 'freyberg6.sfr_perioddata_20.txt',\n", + " 'freyberg6.sfr_perioddata_21.txt',\n", + " 'freyberg6.sfr_perioddata_22.txt',\n", + " 'freyberg6.sfr_perioddata_23.txt',\n", + " 'freyberg6.sfr_perioddata_24.txt',\n", + " 'freyberg6.sfr_perioddata_25.txt',\n", + " 'freyberg6.sfr_perioddata_3.txt',\n", + " 'freyberg6.sfr_perioddata_4.txt',\n", + " 'freyberg6.sfr_perioddata_5.txt',\n", + " 'freyberg6.sfr_perioddata_6.txt',\n", + " 'freyberg6.sfr_perioddata_7.txt',\n", + " 'freyberg6.sfr_perioddata_8.txt',\n", + " 'freyberg6.sfr_perioddata_9.txt',\n", + " 'freyberg6.sto',\n", + " 'freyberg6.sto_iconvert_layer1.txt',\n", + " 'freyberg6.sto_iconvert_layer2.txt',\n", + " 'freyberg6.sto_iconvert_layer3.txt',\n", + " 'freyberg6.sto_ss_layer1.txt',\n", + " 'freyberg6.sto_ss_layer2.txt',\n", + " 'freyberg6.sto_ss_layer3.txt',\n", + " 'freyberg6.sto_sy_layer1.txt',\n", + " 'freyberg6.sto_sy_layer2.txt',\n", + " 'freyberg6.sto_sy_layer3.txt',\n", + " 'freyberg6.tdis',\n", + " 'freyberg6.wel',\n", + " 'freyberg6.wel_stress_period_data_1.txt',\n", + " 'freyberg6.wel_stress_period_data_10.txt',\n", + " 'freyberg6.wel_stress_period_data_11.txt',\n", + " 'freyberg6.wel_stress_period_data_12.txt',\n", + " 'freyberg6.wel_stress_period_data_13.txt',\n", + " 'freyberg6.wel_stress_period_data_14.txt',\n", + " 'freyberg6.wel_stress_period_data_15.txt',\n", + " 'freyberg6.wel_stress_period_data_16.txt',\n", + " 'freyberg6.wel_stress_period_data_17.txt',\n", + " 'freyberg6.wel_stress_period_data_18.txt',\n", + " 'freyberg6.wel_stress_period_data_19.txt',\n", + " 'freyberg6.wel_stress_period_data_2.txt',\n", + " 'freyberg6.wel_stress_period_data_20.txt',\n", + " 'freyberg6.wel_stress_period_data_21.txt',\n", + " 'freyberg6.wel_stress_period_data_22.txt',\n", + " 'freyberg6.wel_stress_period_data_23.txt',\n", + " 'freyberg6.wel_stress_period_data_24.txt',\n", + " 'freyberg6.wel_stress_period_data_25.txt',\n", + " 'freyberg6.wel_stress_period_data_3.txt',\n", + " 'freyberg6.wel_stress_period_data_4.txt',\n", + " 'freyberg6.wel_stress_period_data_5.txt',\n", + " 'freyberg6.wel_stress_period_data_6.txt',\n", + " 'freyberg6.wel_stress_period_data_7.txt',\n", + " 'freyberg6.wel_stress_period_data_8.txt',\n", + " 'freyberg6.wel_stress_period_data_9.txt',\n", + " 'freyberg6_freyberg.cbc',\n", + " 'freyberg6_freyberg.hds',\n", + " 'head.obs',\n", + " 'heads.csv',\n", + " 'mf6.exe',\n", + " 'mfsim.lst',\n", + " 'mfsim.nam',\n", + " 'pestpp-ies.exe',\n", + " 'sfr.csv',\n", + " 'sfr.obs']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp_model_ws = \"temp_pst_from_nsaf\"\n", "if os.path.exists(tmp_model_ws):\n", " shutil.rmtree(tmp_model_ws)\n", "shutil.copytree(org_model_ws,tmp_model_ws)\n", @@ -113,38 +409,1143 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we need just a tiny bit of info about the spatial discretization of the model - this is needed to work out separation distances between parameters for build a geostatistical prior covariance matrix later.\n", - "\n", "Here we will load the flopy sim and model instance just to help us define some quantities later - flopy is not required to use the `PstFrom` class." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading simulation...\n", + " loading simulation name file...\n", + " loading tdis package...\n", + " loading model gwf6...\n", + " loading package dis...\n", + " loading package ic...\n", + " loading package npf...\n", + " loading package sto...\n", + " loading package oc...\n", + " loading package wel...\n", + " loading package rch...\n", + " loading package ghb...\n", + " loading package sfr...\n", + " loading package obs...\n", + " loading solution package freyberg6...\n" + ] + } + ], + "source": [ + "sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws)\n", + "m = sim.get_model(\"freyberg6\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### We will need dataframe mapping cell indicies to coordinates later" + ] + }, + { + "cell_type": "code", + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ - "sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws)\n", - "m = sim.get_model(\"freyberg6\")\n" + "cc = m.modelgrid.xyzcellcenters\n", + "grid_coords = pd.DataFrame({\n", + " 'x': np.tile(cc[0].flatten(), cc[2].shape[0]), # Repeat x,y for each layer\n", + " 'y': np.tile(cc[1].flatten(), cc[2].shape[0]), \n", + " 'z': cc[2].flatten() # All z values\n", + "})\n", + "grid_coords_2d = pd.DataFrame({\n", + " 'x': cc[0].flatten(), # Just x coordinates (no tiling)\n", + " 'y': cc[1].flatten(), # Just y coordinates (no tiling)\n", + "}).values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here we use the simple `SpatialReference` pyemu implements to help us spatially locate parameters" + "### and we will define a zone from rows 30 to 40 for use later" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "zones = m.dis.idomain.array.copy()\n", + "zones[:,30:,:] = np.where(zones[:,30:,:]==1, 2, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.imshow(zones[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### We use the term \"conceptual points\" for points describing the anisotropic tensor of spatial correlation. We use a \"geological convention\" where: \n", + "1) \"bearing\" describes the orientation of the major axis with 0 degrees equal to north\n", + " a) a positive bearing represents the clockwise rotation of the major axis\n", + "2) \"transverse\" axis is perpendicular to the major axis and always in the horizontal plane\n", + "3) \"dip\" is the rotation of the major axis out of the horizontal plane with positive dip indicating clockwise rotation around the \"transverse\" axis (i.e., downward rotation in the direction of bearing)\n", + "4) \"normal\" axis is perpendicular to the \"major\" and \"transverse\" axes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A full description of the anisotropy tensor requires 5 parameters\n", + "1) magnitude of the major axis (originally horizontal before rotation by \"dip\")\n", + "2) magnitude of the transverse axis (always horizontal by convention)\n", + "3) magnitude of the normal axis (originally vertical before rotation by \"dip\")\n", + "4) bearing of the major axis\n", + "5) dip of the major axis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### lets define some conceptual points for the Freyberg, by row and col, and propagate to all layers.\n", + "Imagine this is a fluvial system with higher correlation lengths in the direction of the stream channel.\n", + "We imagine a couple streams coming in along the top of the model:\n", + " one from the NW with a bearing of 100\n", + " one from the NE with a vearing of 200 (or -160)\n", + "We also imagine a small fluvial fan coming off the bedrock (inactive cells)\n", + " the orientation of the deposits changes from 45 to 90 to 160 from N to S\n", + "We imagine \n", + "Obviously this could be done externally (GIS)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " i j mean sd major transverse normal bearing dip k\n", + "0 3 3 100.0 50.00 3000 300 10 100 0.10 0\n", + "1 3 18 150.0 50.00 3000 300 10 -160 0.10 0\n", + "2 10 10 75.0 50.00 1500 100 5 45 0.50 0\n", + "3 15 15 50.0 25.00 6000 400 5 180 0.05 0\n", + "4 15 10 100.0 10.00 1500 100 5 90 0.50 0\n", + "5 20 10 20.0 50.00 1500 100 5 160 0.50 0\n", + "6 25 3 1.0 0.50 1500 100 5 170 0.10 0\n", + "7 33 5 0.5 0.10 3000 400 10 70 0.10 0\n", + "8 35 10 2.0 0.90 3000 300 1 90 0.01 0\n", + "9 35 15 0.1 0.05 3000 200 1 90 0.01 0\n", + "10 3 3 100.0 50.00 3000 300 10 100 0.10 1\n", + "11 3 18 150.0 50.00 3000 300 10 -160 0.10 1\n", + "12 10 10 75.0 50.00 1500 100 5 45 0.50 1\n", + "13 15 15 50.0 25.00 6000 400 5 180 0.05 1\n", + "14 15 10 100.0 10.00 1500 100 5 90 0.50 1\n", + "15 20 10 20.0 50.00 1500 100 5 160 0.50 1\n", + "16 25 3 1.0 0.50 1500 100 5 170 0.10 1\n", + "17 33 5 0.5 0.10 3000 400 10 70 0.10 1\n", + "18 35 10 2.0 0.90 3000 300 1 90 0.01 1\n", + "19 35 15 0.1 0.05 3000 200 1 90 0.01 1\n", + "20 3 3 100.0 50.00 3000 300 10 100 0.10 2\n", + "21 3 18 150.0 50.00 3000 300 10 -160 0.10 2\n", + "22 10 10 75.0 50.00 1500 100 5 45 0.50 2\n", + "23 15 15 50.0 25.00 6000 400 5 180 0.05 2\n", + "24 15 10 100.0 10.00 1500 100 5 90 0.50 2\n", + "25 20 10 20.0 50.00 1500 100 5 160 0.50 2\n", + "26 25 3 1.0 0.50 1500 100 5 170 0.10 2\n", + "27 33 5 0.5 0.10 3000 400 10 70 0.10 2\n", + "28 35 10 2.0 0.90 3000 300 1 90 0.01 2\n", + "29 35 15 0.1 0.05 3000 200 1 90 0.01 2" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cp = pd.DataFrame({\n", + " 'i': [3, 3, 10, 15, 15, 20, 25, 33, 35, 35], \n", + " 'j': [3, 18, 10, 15, 10, 10, 3, 5, 10, 15],\n", + " 'mean': [100, 150, 75, 50, 100, 20, 1, 0.5, 2, 0.1],\n", + " 'sd': [50, 50, 50, 25, 10, 50, 0.5, 0.1, 0.9, 0.05],\n", + " 'major': [3000, 3000, 1500, 6000, 1500, 1500, 1500, 3000, 3000, 3000],\n", + " 'transverse': [300, 300, 100, 400, 100, 100, 100, 400, 300, 200],\n", + " 'normal': [10, 10, 5, 5, 5, 5, 5, 10, 1, 1],\n", + " 'bearing': [100, -160, 45, 180, 90, 160, 170, 70, 90, 90],\n", + " 'dip': [0.1, 0.1, 0.5, 0.05, 0.5, 0.5, 0.1, 0.1, 0.01, 0.01]\n", + "})\n", + "cp = pd.concat([cp.assign(k=k) for k in range(3)], ignore_index=True)\n", + "cp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### get coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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majortransversenormalbearingdipxyz
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" + ], + "text/plain": [ + " major transverse normal bearing dip x y z\n", + "0 3000 300 10 100 0.10 875.0 9125.0 36.751535\n", + "1 3000 300 10 -160 0.10 4625.0 9125.0 34.766945\n", + "2 1500 100 5 45 0.50 2625.0 7375.0 35.071070\n", + "3 6000 400 5 180 0.05 3875.0 6125.0 34.470650\n", + "4 1500 100 5 90 0.50 2625.0 6125.0 34.882355\n", + "5 1500 100 5 160 0.50 2625.0 4875.0 34.830705" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cp['x'] = cc[0][cp['i'], cp['j']]\n", + "cp['y'] = cc[1][cp['i'], cp['j']]\n", + "cp['z'] = cc[2][cp['k'], cp['i'], cp['j']]\n", + "cp.loc[:5,['major','transverse', 'normal', 'bearing', 'dip', 'x','y','z']]" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "cp['name']=cp.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### visualize the conceptual points" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using first layer coordinates from 3-layer grid\n", + "Plot saved to: temp_pst_from_nsaf\\conceptual_points.png\n" + ] + } + ], + "source": [ + "pu.plot_zones_with_conceptual_points(zones[0], cp, grid_coords, save_path=tmp_model_ws)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### interpolate the anisotropic variance tensors" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# a little tweak to bearing\n", + "# normalization that preserves direction\n", + "cp['bearing'] = cp.bearing % 360\n", + "cp['bearing_rad'] = np.radians(cp.bearing)\n", + "cp_tensors = nsaf.create_2d_tensors(cp.bearing_rad, cp.major, cp.transverse)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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xyz
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\n", + "
" + ], + "text/plain": [ + " x y z\n", + "0 125.0 9875.0 36.951200\n", + "1 375.0 9875.0 36.915405\n", + "2 625.0 9875.0 36.839995\n", + "3 875.0 9875.0 36.727160\n", + "4 1125.0 9875.0 36.604655\n", + "... ... ... ...\n", + "2395 3875.0 125.0 15.287420\n", + "2396 4125.0 125.0 15.327858\n", + "2397 4375.0 125.0 15.378074\n", + "2398 4625.0 125.0 15.475265\n", + "2399 4875.0 125.0 15.545507\n", + "\n", + "[2400 rows x 3 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid_coords" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40 20\n" + ] + } + ], + "source": [ + "cp_coords = cp[['x', 'y']].values\n", + "cp_tensors = nsaf.create_2d_tensors(cp.bearing_rad, cp.major, cp.transverse)\n", + "geological_tensors = nsaf.interpolate_tensors(cp_coords, cp_tensors, grid_coords_2d,\n", + " zones=zones[0].copy(), method='idw')" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting 200 tensors out of 800\n", + "Plotted 26 default tensors (black circles) and 174 ellipses\n", + "Saved tensor visualization to temp_pst_from_nsaf\\intrpolated_tensors_None.png\n" + ] + } + ], + "source": [ + "pu.visualize_geological_tensors(geological_tensors, grid_coords_2d, shape=zones.shape[1:], zones=zones[0], save_path=tmp_model_ws)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Smoothing values at geological boundaries...\n", + " Smoothed 490 points near boundaries\n" + ] + } + ], + "source": [ + "transform = 'log'\n", + "cp_means = cp['mean'].values\n", + "interp_means_2d = nsaf.tensor_aware_kriging(\n", + " cp_coords, cp_means, grid_coords_2d, geological_tensors,\n", + " variogram_model='exponential', sill=1.0, nugget=0.1,\n", + " background_value=np.mean(cp_means), max_search_radius=1e20,\n", + " min_points=3, transform=transform, min_value=1e-8, max_neighbors=4,\n", + " zones=zones[0]\n", + " )\n", + "if zones is not None:\n", + " print(\" Smoothing values at geological boundaries...\")\n", + " interp_means_2d = nsaf.create_boundary_modified_scalar(\n", + " interp_means_2d, zones[0],\n", + " transition_cells=3, mode='smooth'\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.imshow(interp_means_2d)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Interpolating standard deviation...\n", + " Enhancing variance at geological boundaries...\n", + " Enhanced 490 points near boundaries\n" + ] + } + ], + "source": [ + "cp_sd = cp['sd'].values\n", + "print(\" Interpolating standard deviation...\")\n", + "interp_sd_2d = nsaf.tensor_aware_kriging(\n", + " cp_coords, cp_sd, grid_coords_2d, geological_tensors, # Use geological tensors\n", + " variogram_model='exponential', sill=1.0, nugget=0.1,\n", + " background_value=np.mean(cp_sd), max_search_radius=1e20,\n", + " min_points=3, transform=transform, min_value=1e-8, max_neighbors=4,\n", + " zones=zones[0]\n", + ")\n", + "if zones is not None:\n", + " print(\" Enhancing variance at geological boundaries...\")\n", + " interp_sd_2d = nsaf.create_boundary_modified_scalar(\n", + " interp_sd_2d, zones[0],\n", + " peak_increase=1, transition_cells=3, mode='enhance'\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Combining fields using log-normal formulation...\n" + ] + } + ], + "source": [ + "iids = np.random.randn(*zones[0].shape)\n", + "correlated_noise_1d = nsaf.generate_correlated_noise_2d(grid_coords_2d, geological_tensors, iids)\n", + "correlated_noise_2d = correlated_noise_1d.reshape(zones[0].shape)\n", + "\n", + "# Step 7: Combine fields with transform handling\n", + "if transform == 'log':\n", + " print(\" Combining fields using log-normal formulation...\")\n", + " # Convert to lognormal parameters\n", + " variance_2d = interp_sd_2d ** 2\n", + " mu_2d = np.log(interp_means_2d ** 2 / np.sqrt(variance_2d + interp_means_2d ** 2))\n", + " sigma_2d = np.sqrt(np.log(1 + variance_2d / interp_means_2d ** 2))\n", + " normal_field = mu_2d + correlated_noise_2d * sigma_2d\n", + " field_2d = np.exp(normal_field)\n", + "else:\n", + " print(\" Combining fields using normal formulation...\")\n", + " field_2d = interp_means_2d + correlated_noise_2d * interp_sd_2d" + ] + }, + { + "cell_type": "code", + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ - "sr = pyemu.helpers.SpatialReference.from_namfile(\n", - " os.path.join(tmp_model_ws, \"freyberg6.nam\"),\n", - " delr=m.dis.delr.array, delc=m.dis.delc.array)\n", - "sr" + "plt.imshow(np.log10(field_2d)*id_arr)\n", + "plt.show()" ] }, { @@ -163,7 +1564,7 @@ "template_ws = \"freyberg6_template\"\n", "pf = pyemu.utils.PstFrom(original_d=tmp_model_ws, new_d=template_ws,\n", " remove_existing=True,\n", - " longnames=True, spatial_reference=sr,\n", + " longnames=True, \n", " zero_based=False,start_datetime=\"1-1-2018\")\n" ] }, diff --git a/pyemu/__init__.py b/pyemu/__init__.py index 9b05c605e..6f15ea921 100644 --- a/pyemu/__init__.py +++ b/pyemu/__init__.py @@ -19,7 +19,7 @@ from .pst import Pst, pst_utils from .sc import Schur from .utils import (geostats, gw_utils, helpers, metrics, optimization, - os_utils, pp_utils, smp_utils, tensor_utils) + os_utils, pp_utils, smp_utils, nsaf_utils) #from .prototypes import * try: @@ -53,6 +53,6 @@ "smp_utils", "plot_utils", "metrics", - "tensor_utils" + "nsaf_utils" ] # del get_versions diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 338f0c101..0807f1ee9 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2055,28 +2055,29 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, plt.close() -def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=None, - conceptual_points=None, subsample=4, scale_factor=20, - figsize=(14, 12), title_suf=None, save_path='.'): +def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, + conceptual_points=None, subsample=4, scale_factor=1, + figsize=(14, 12), title_suf=None, save_path='.'): """ - Visualize geological tensors as ellipses overlaid on zones, with conceptual points as oriented lines. + Visualize tensors as ellipses overlaid on zones, with conceptual points as oriented lines. + Uses PyEMU-consistent inputs. Parameters ---------- - geological_tensors : np.ndarray - Tensor field, shape (n_points, 2, 2) - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 2) or (n_points, 3) - shape : tuple - Grid shape (ny, nx) + tensors : np.ndarray + Tensor field, shape (ny*nx, 2, 2) - flattened in row-major order + xcentergrid : np.ndarray + X-coordinates from pyemu SpatialReference, shape (ny, nx) + ycentergrid : np.ndarray + Y-coordinates from pyemu SpatialReference, shape (ny, nx) zones : np.ndarray, optional Zone array for background, shape (ny, nx) - conceptual_points : pd.DataFrame, optional - Conceptual points with x, y, bearing, major columns + conceptual_points : pd.DataFrame or str, optional + Conceptual points DataFrame or path to CSV with x, y, bearing, major columns subsample : int, default 4 Show every Nth tensor (for readability) scale_factor : float, default 20 - Scale ellipses for visibility + Scale ellipses for visibility (larger = smaller ellipses) figsize : tuple, default (14, 12) Figure size title_suf : str, optional @@ -2091,16 +2092,44 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N Examples -------- - >>> visualize_geological_tensors(tensors, coords, (100, 150), - ... subsample=8, title_suf='layer_1') + >>> visualize_tensors(tensors, xcentergrid, ycentergrid, + ... zones=zones, subsample=8, title_suf='layer_1') """ - from matplotlib import patches + from matplotlib.patches import Ellipse + + # Get grid dimensions + ny, nx = xcentergrid.shape + + # Verify tensor shape consistency + expected_size = ny * nx + if tensors.shape[0] != expected_size: + raise ValueError(f"Tensor array size {tensors.shape[0]} doesn't match grid size {expected_size}") + + # Create grid coordinates array for compatibility + grid_coords = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) + + # Load conceptual points if needed + if isinstance(conceptual_points, str): + import pandas as pd + conceptual_points = pd.read_csv(conceptual_points) fig, ax = plt.subplots(figsize=figsize) - # Prepare zone data but don't plot yet + # Get coordinate bounds first to set proper limits + x_min, x_max = xcentergrid.min(), xcentergrid.max() + y_min, y_max = ycentergrid.min(), ycentergrid.max() + + print(f"Grid bounds: X=[{x_min:.1f}, {x_max:.1f}], Y=[{y_min:.1f}, {y_max:.1f}]") + + # Set axis limits explicitly + ax.set_xlim(x_min, x_max) + ax.set_ylim(y_min, y_max) + + # Plot zones as background if provided if zones is not None: - # zones are guaranteed to be 2D from entry point + # Validate zones shape + if zones.shape != (ny, nx): + raise ValueError(f"Zones shape {zones.shape} doesn't match grid shape {(ny, nx)}") # Create zone color mapping unique_zones = np.unique(zones) @@ -2109,13 +2138,9 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N for zone_val, color_idx in zone_to_color.items(): zones_remapped[zones == zone_val] = color_idx - # Get coordinate bounds - x_coords = grid_coords[:, 0].reshape(shape) - y_coords = grid_coords[:, 1].reshape(shape) - x_min, x_max = x_coords.min(), x_coords.max() - y_min, y_max = y_coords.min(), y_coords.max() - + # Flip zones for proper display (matplotlib convention) zones_flipped = np.flipud(zones_remapped) + # Plot zones as background with low alpha im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], origin='lower', cmap='tab10', alpha=0.2, zorder=0, @@ -2128,8 +2153,9 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N bearing = row['bearing'] major = row['major'] - # Scale line length for visibility (similar to ellipse scaling) - line_length = major / (scale_factor * 2) # Adjust scaling as needed + # Scale line length for visibility relative to grid + grid_scale = max(x_max - x_min, y_max - y_min) + line_length = grid_scale / 50 # Make lines 2% of grid span # Convert geological bearing (CW from N) to math angle for plotting # Geological: 0°=N, 90°=E; Math: 0°=E, 90°=N @@ -2154,21 +2180,47 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N zorder=6) # Subsample points for visualization - indices = np.arange(0, len(geological_tensors), subsample) + indices = np.arange(0, len(tensors), subsample) - print(f"Plotting {len(indices)} tensors out of {len(geological_tensors)}") + print(f"Plotting {len(indices)} tensors out of {len(tensors)}") default_count = 0 ellipse_count = 0 + # Calculate overall scale for ellipses based on grid + grid_scale = max(x_max - x_min, y_max - y_min) + + # Sample some tensors to get typical eigenvalue scale + sample_tensors = tensors[indices[:min(100, len(indices))]] + sample_eigenvals = [] + for tensor in sample_tensors: + if not np.allclose(tensor, np.eye(2) * 1000000): + try: + eigenvals, _ = np.linalg.eigh(tensor) + if np.all(eigenvals > 0): # Valid eigenvalues + sample_eigenvals.extend(eigenvals) + except: + continue + + if sample_eigenvals: + typical_eigenval = np.median(sample_eigenvals) + typical_length = np.sqrt(typical_eigenval) + # Use scale_factor parameter - larger scale_factor = larger ellipses + ellipse_scale = (grid_scale * scale_factor) / typical_length + print( + f"Typical eigenvalue: {typical_eigenval:.1e}, length: {typical_length:.1f}, scale_factor: {scale_factor}, final scale: {ellipse_scale:.1e}") + else: + ellipse_scale = scale_factor + print(f"No valid sample eigenvalues found, using scale_factor: {scale_factor}") + for i in indices: - tensor = geological_tensors[i] + tensor = tensors[i] x, y = grid_coords[i, 0], grid_coords[i, 1] # Check for default tensor (indicates no interpolation happened) if np.allclose(tensor, np.eye(2) * 1000000): - # Plot as red circle for default tensors with high zorder - ax.plot(x, y, 'ko', markersize=2, alpha=0.3, zorder=1) # Make less prominent + # Plot as black circle for default tensors + ax.plot(x, y, 'ko', markersize=2, alpha=0.3, zorder=1) default_count += 1 continue @@ -2176,50 +2228,58 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N # Eigendecomposition to get ellipse parameters eigenvals, eigenvecs = np.linalg.eigh(tensor) + # Check for valid eigenvalues + if not np.all(eigenvals > 0): + ax.plot(x, y, 'ro', markersize=2, zorder=3) + continue + # Sort by eigenvalue magnitude - idx = np.argsort(eigenvals)[::-1] - eigenvals = eigenvals[idx] - eigenvecs = eigenvecs[:, idx] + idx_sort = np.argsort(eigenvals)[::-1] + eigenvals = eigenvals[idx_sort] + eigenvecs = eigenvecs[:, idx_sort] - # Ellipse dimensions (correlation lengths) - make them more visible - major_axis = 2 * np.sqrt(eigenvals[0]) / scale_factor - minor_axis = 2 * np.sqrt(eigenvals[1]) / scale_factor + # Ellipse dimensions from correlation lengths + major_length = np.sqrt(eigenvals[0]) + minor_length = np.sqrt(eigenvals[1]) - # Make sure ellipses are visible - if major_axis < 30: # Smaller minimum to avoid overwhelming CP lines - major_axis = 30 - if minor_axis < 15: - minor_axis = 15 + # Apply scaling + major_axis = major_length * ellipse_scale + minor_axis = minor_length * ellipse_scale # Ellipse orientation (angle of major axis) angle = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) - # Create ellipse with lower prominence than CP lines - ellipse = patches.Ellipse((x, y), major_axis, minor_axis, - angle=angle, linewidth=1, - edgecolor='blue', facecolor='none', alpha=0.6, zorder=2) + # Create ellipse + ellipse = Ellipse((x, y), major_axis, minor_axis, + angle=angle, linewidth=1, + edgecolor='blue', facecolor='none', alpha=0.6, zorder=2) ax.add_patch(ellipse) ellipse_count += 1 - except: - # If tensor is problematic, plot as yellow point with high zorder + except Exception as e: + # If tensor is problematic, plot as yellow point ax.plot(x, y, 'yo', markersize=2, zorder=3) print(f"Plotted {default_count} default tensors (black circles) and {ellipse_count} ellipses") - ax.set_xlabel('X (NZTM)') - ax.set_ylabel('Y (NZTM)') + ax.set_xlabel('X Coordinate') + ax.set_ylabel('Y Coordinate') # Update title and legend - title_parts = [f'Geological Tensors (subsampled 1:{subsample})\n'] + title_parts = [f'Tensors (subsampled 1:{subsample})'] if title_suf is not None: - title_parts += [f'{title_suf}'] - # more like legend? + title_parts.append(f'{title_suf}') + + # Add informational subtitle + subtitle_parts = [] if conceptual_points is not None: - title_parts.append('Red lines = conceptual points (bearing)') - title_parts.append('Blue ellipses = interpolated tensors') - ax.set_title('\n'.join(title_parts)) + subtitle_parts.append('Red lines = conceptual points (bearing)') + subtitle_parts.append('Blue ellipses = interpolated tensors') + if subtitle_parts: + title_parts.append('\n'.join(subtitle_parts)) + + ax.set_title('\n'.join(title_parts)) ax.set_aspect('equal', adjustable='box') # Add colorbar for zones if present @@ -2237,8 +2297,11 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N plt.Line2D([0], [0], marker='o', color='red', markersize=6, markeredgecolor='black', label='Conceptual Points', linestyle='None') ]) + + # Create legend patches without importing patches + import matplotlib.lines as mlines legend_elements.extend([ - patches.Patch(facecolor='none', edgecolor='blue', label='Interpolated Tensors'), + mlines.Line2D([], [], color='blue', label='Interpolated Tensors'), plt.Line2D([0], [0], marker='o', color='black', markersize=4, label='Default Tensors', linestyle='None', alpha=0.3) ]) @@ -2247,8 +2310,19 @@ def visualize_geological_tensors(geological_tensors, grid_coords, shape, zones=N ax.legend(handles=legend_elements, loc='upper right', fontsize=9) plt.tight_layout() - plt.savefig(os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png'), dpi=150, bbox_inches='tight') - print(f"Saved tensor visualization to {os.path.join(save_path, f'intrpolated_tensors_{title_suf}.png')}") + + # Save plot + if title_suf: + filename = f'interpolated_tensors_{title_suf}.png' + else: + filename = 'interpolated_tensors.png' + + save_path_full = os.path.join(save_path, filename) + plt.savefig(save_path_full, dpi=150, bbox_inches='tight') + print(f"Saved tensor visualization to {save_path_full}") + + # Show the plot for interactive backends + plt.show() plt.close() diff --git a/pyemu/utils/__init__.py b/pyemu/utils/__init__.py index 05cd17014..d53cacbee 100644 --- a/pyemu/utils/__init__.py +++ b/pyemu/utils/__init__.py @@ -13,6 +13,6 @@ from .pp_utils import * from .pst_from import * from .smp_utils import * -from .tensor_utils import * +from .nsaf_utils import * get_pestpp = get_pestpp_module.run_main diff --git a/pyemu/utils/example_useage.py b/pyemu/utils/example_useage.py index 6befa44dc..6b4e186ef 100644 --- a/pyemu/utils/example_useage.py +++ b/pyemu/utils/example_useage.py @@ -1,4 +1,4 @@ -from pyemu.utils import tensor_utils +from pyemu.utils import nsaf_helpers import os # ============================================================================ @@ -21,23 +21,21 @@ import time start_time = time.perf_counter() - data_dir = r'..\..\examples\Hawkes_Bay' - zone_files = [f'idomain.arr' for z in range(1)] - con_pts_file = 'Bridgpa_skytem_sva_2.csv' + data_dir = r'..\..\examples\Hawkes_Bay\gwf' + # zone_files = [f'idomain_{i}.arr' for i in range(0,9)] + con_pts_file = 'Bridgpa_skytem_sva_0.csv' save_path = os.path.join(data_dir, f'{con_pts_file}_output') if not os.path.exists(save_path): os.mkdir(save_path) - tensor_utils.generate_fields_from_files(data_dir, - conceptual_points_file=con_pts_file, - grid_file='grid.dat', - zone_file=zone_files, - field_name=['kh'], - layer_mode=True, - save_path=save_path, - tensor_interp='idw' - ) + nsaf_helpers.generate_fields_from_files(data_dir, 'hpm_mf6', con_pts_file, + zone_file=None, + field_name=['kh'], + layer_mode=True, + save_path=save_path, + tensor_interp='idw') + end_time = time.perf_counter() elapsed_time = end_time - start_time diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py new file mode 100644 index 000000000..027b36583 --- /dev/null +++ b/pyemu/utils/nsaf_helpers.py @@ -0,0 +1,974 @@ +import numpy as np +import pandas as pd +import os +from scipy.ndimage import distance_transform_edt, gaussian_filter + +# Import from our Chunk 1 and 2 +from nsaf_utils import ( + interpolate_tensors, + load_conceptual_points, + create_2d_tensors, + get_tensor_bearing, + apply_ppu_hyperpars, + tensor_aware_kriging, + create_boundary_modified_scalar, + detect_zone_boundaries +) + + +def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, + zone_file=None, iids_file=None, field_name=['field'], + layer_mode=True, save_path=None, tensor_interp='krig', + vartransform='log', n_realizations=1, ml=None, sr=None): + """ + Generate spatially correlated fields using tensor-based geostatistics. + Follows PyEMU pattern for model workspace and spatial reference handling. + + Parameters + ---------- + tmp_model_ws : str + Path to model workspace containing flopy model files + model_name : str + Name of the flopy model (e.g., 'freyberg6') + conceptual_points_file : str + CSV file containing conceptual points + zone_file : str or list of str, optional + zone file(s): + - Single string: One zone file used for all layers (2D zones) + - List of strings: One zone file per layer (creates 3D zones) + iids_file : str or list of str, optional + noise file(s): + - Single string: Assumes pattern like "iids_layer{N}.arr" + - List of strings: Explicit paths to each layer's noise file + - None: Auto-generates noise and saves as iids_layer{N}.arr files + field_name : list of str, default ['field'] + Name(s) of field(s) to generate + layer_mode : bool, default True + If True, process each layer independently + save_path : str, optional + Directory path to save output files (default: tmp_model_ws) + tensor_interp : str, default 'krig' + Tensor interpolation method + vartransform : str, default 'log' + Transformation: 'log' or 'none' + n_realizations : int, default 1 + Number of stochastic realizations + ml : flopy.mbase, optional + A flopy mbase derived type. If None, sr must not be None. + sr : flopy.utils.reference.SpatialReference, optional + A spatial reference to locate the model grid. If None, ml must not be None. + + Returns + ------- + dict + Results from workflow + """ + try: + import flopy + import pyemu + except ImportError as e: + raise ImportError(f"flopy and pyemu are required: {e}") + + print(f"=== Tensor-Based Field Generation ===") + print(f"Model workspace: {tmp_model_ws}") + print(f"Model name: {model_name}") + + # Set default save path + if save_path is None: + save_path = tmp_model_ws + + # Setup spatial reference following PyEMU pattern + if ml is None and sr is None: + # Load model from workspace + print("Loading flopy model...") + try: + # Try MF6 first - only load DIS package for efficiency + sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws, load_only=['dis']) + ml = sim.get_model(model_name) + print(f"Loaded MF6 model: {model_name} (DIS package only)") + except: + try: + # Fall back to MODFLOW-2005 - only load DIS package + ml = flopy.modflow.Modflow.load(f"{model_name}.nam", model_ws=tmp_model_ws, + load_only=['dis']) + print(f"Loaded MODFLOW-2005 model: {model_name} (DIS package only)") + except Exception as e: + raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}: {e}") + + if sr is None: + # Create spatial reference from model + print("Creating spatial reference...") + if hasattr(ml, 'dis'): + # MF6 or MODFLOW with DIS + try: + sr = pyemu.helpers.SpatialReference.from_namfile( + os.path.join(tmp_model_ws, f"{model_name}.nam"), + delr=ml.dis.delr.array, delc=ml.dis.delc.array + ) + except: + # Alternative approach for MF6 + sr = pyemu.helpers.SpatialReference( + delr=ml.dis.delr.array, delc=ml.dis.delc.array, + xul=0, yul=0 + ) + else: + raise ValueError("Could not determine grid spacing from model") + + print(f"Spatial reference: {sr}") + + # Get grid coordinates + try: + xcentergrid = sr.xcentergrid + ycentergrid = sr.ycentergrid + except AttributeError: + try: + xcentergrid = sr.xcellcenters + ycentergrid = sr.ycellcenters + except AttributeError as e: + raise Exception(f"Could not get grid centers from spatial reference: {e}") + + ny, nx = xcentergrid.shape + print(f"Grid dimensions: {ny} x {nx}") + cell_area = np.outer(sr.delr, sr.delc) + # Load conceptual points + conceptual_points = pd.read_csv(os.path.join(tmp_model_ws, conceptual_points_file)) + + # Add transverse if missing + if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: + conceptual_points.loc[:, 'transverse'] = conceptual_points.loc[:, 'major'] / conceptual_points.loc[:, + 'anisotropy'] + + print(f"Loaded {len(conceptual_points)} conceptual points") + + # Load zones + zones = None + if zone_file: + if isinstance(zone_file, str): + # Single zone file + zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file)).astype(np.int64) + if zones.shape != (ny, nx): + # Try to reshape if possible + if zones.size == ny * nx: + zones = zones.reshape(ny, nx) + else: + raise ValueError(f"Zone file shape {zones.shape} doesn't match grid {(ny, nx)}") + print(f"Loaded zones from {zone_file}, shape: {zones.shape}") + else: + # Multiple zone files - for now just use the first one + zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file[0])).astype(np.int64) + print(f"Loaded zones from {zone_file[0]} (multi-layer support TODO)") + + # Determine the total number of layers in the model + if hasattr(ml, 'dis'): + if hasattr(ml.dis, 'nlay'): + total_layers = ml.dis.nlay.array if hasattr(ml.dis.nlay, 'array') else ml.dis.nlay + elif hasattr(ml.dis, 'botm'): + total_layers = len(ml.dis.botm.array) + else: + total_layers = 1 + else: + total_layers = 1 + + print(f"Model has {total_layers} layers") + + # Handle IIDs for PEST parameterization - generate for ALL model layers + iids_dict = {} + + # Determine which layers have conceptual points + if 'layer' in conceptual_points.columns: + available_cp_layers = sorted(conceptual_points['layer'].unique()) + print(f"Conceptual points available for layers: {available_cp_layers}") + else: + available_cp_layers = [] + print("No 'layer' column in conceptual points - will propagate to all layers") + + # Generate IIDs for all model layers + for layer_num in range(total_layers): + layer_1based = layer_num + 1 # Convert to 1-based indexing + iids_dict[layer_1based] = _load_or_generate_iids( + tmp_model_ws, iids_file, layer_num, ny, nx, n_realizations + ) + + # Propagate conceptual points to missing layers if needed + if layer_mode and 'layer' in conceptual_points.columns: + # Check if all layers are covered + missing_layers = [] + for layer_num in range(1, total_layers + 1): # 1-based + if layer_num not in available_cp_layers: + missing_layers.append(layer_num) + + if missing_layers: + print(f"Missing conceptual points for layers: {missing_layers}") + print("Propagating conceptual points from available layers...") + + # Find the best layer to propagate from (prefer middle layers) + if available_cp_layers: + # Use the layer closest to the middle of available layers + middle_idx = len(available_cp_layers) // 2 + source_layer = available_cp_layers[middle_idx] + print(f"Using layer {source_layer} as source for propagation") + + # Create propagated conceptual points + source_cp = conceptual_points[conceptual_points['layer'] == source_layer].copy() + + for missing_layer in missing_layers: + propagated_cp = source_cp.copy() + propagated_cp['layer'] = missing_layer + + # Optionally modify some properties for different layers + # (could add depth-dependent scaling here if needed) + + # Append to conceptual points + conceptual_points = pd.concat([conceptual_points, propagated_cp], ignore_index=True) + print(f" Propagated {len(source_cp)} points to layer {missing_layer}") + + # Update available layers + available_cp_layers = sorted(conceptual_points['layer'].unique()) + print(f"Updated conceptual points now cover layers: {available_cp_layers}") + + # Process each field for each layer + all_results = {} + + # If layer_mode is True, process each layer separately + if layer_mode and 'layer' in conceptual_points.columns: + layers_to_process = available_cp_layers + else: + # Single layer mode - use layer 1 + layers_to_process = [1] + + # Process each field + all_results = {} + + for fn in field_name: + print(f"\n=== Processing field: {fn} ===") + + # Filter conceptual points for this field + cols = ['name', 'x', 'y', 'major', 'transverse', 'bearing'] + field_cols = [col for col in conceptual_points.columns if f'_{fn}' in col] + + if not field_cols: + # Look for generic mean/sd columns + mean_cols = [col for col in conceptual_points.columns if 'mean' in col.lower()] + sd_cols = [col for col in conceptual_points.columns if 'sd' in col.lower()] + + if mean_cols and sd_cols: + # Rename to expected format + field_cp = conceptual_points.copy() + field_cp[f'mean_{fn}'] = field_cp[mean_cols[0]] + field_cp[f'sd_{fn}'] = field_cp[sd_cols[0]] + cols.extend([f'mean_{fn}', f'sd_{fn}']) + else: + raise ValueError(f"No columns found for field '{fn}' (expected mean_{fn}, sd_{fn})") + else: + cols.extend(field_cols) + + # Auto-detect layers and select target layer + target_layer = None + if layer_mode and 'layer' in conceptual_points.columns: + cols.append('layer') + # Auto-detect available layers + available_layers = sorted(conceptual_points['layer'].unique()) + print(f"Available layers: {available_layers}") + + # Use first available layer instead of hardcoded 0 + target_layer = available_layers[0] + field_cp = conceptual_points[conceptual_points['layer'] == target_layer][cols].copy() + + if len(field_cp) == 0: + print(f"No conceptual points for layer {target_layer}, using all points") + field_cp = conceptual_points[cols].copy() + target_layer = None + else: + field_cp = conceptual_points[cols].copy() + + # Rename columns to match apply_ppu_hyperpars function expectations + if f'mean_{fn}' in field_cp.columns: + field_cp = field_cp.rename(columns={f'mean_{fn}': 'mean_kh', f'sd_{fn}': 'sd_kh'}) + elif 'mean' in field_cp.columns: + field_cp = field_cp.rename(columns={'mean': 'mean_kh', 'sd': 'sd_kh'}) + + # Get IIDs for this layer + layer_key = target_layer if target_layer is not None else 0 + layer_iids = iids_dict.get(layer_key, iids_dict) + + # Use workflow + results = apply_ppu_hyperpars( + field_cp, xcentergrid, ycentergrid, + area=cell_area, + zones=zones, + n_realizations=n_realizations, + layer=target_layer if target_layer is not None else 0, + vartransform=vartransform, + tensor_method=tensor_interp, + out_filename=os.path.join(save_path, f"{fn}"), + iids=layer_iids # Pass the IIDs + ) + + all_results[fn] = results + + # Save and visualize results + if results['fields'] is not None: + for i, field in enumerate(results['fields']): + save_layer(field, layer=target_layer if target_layer is not None else 0, + field_name=f"{fn}_real_{i + 1}", save_path=save_path) + + # Save mean and SD + save_layer(results['mean'], layer=target_layer if target_layer is not None else 0, + field_name=f"{fn}_mean", save_path=save_path) + save_layer(results['sd'], layer=target_layer if target_layer is not None else 0, + field_name=f"{fn}_sd", save_path=save_path) + + # Visualize tensors - check if visualization module is available + try: + from tensor_visualization import visualize_tensors + visualize_tensors( + results['tensors'], xcentergrid, ycentergrid, + zones=zones, conceptual_points=field_cp, + subsample=8, title_suf=f'{fn}_layer_{target_layer if target_layer is not None else 0}', + save_path=save_path + ) + except ImportError: + print(" Warning: tensor_visualization module not available, skipping visualization") + + print("\n=== Complete ===") + return all_results + + +def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realizations): + """ + Load IIDs from file or generate and save new ones for PEST parameterization. + + Parameters + ---------- + tmp_model_ws : str + Model workspace path + iids_file : str or list or None + IID file specification + layer_num : int + Layer number (0-based) + ny, nx : int + Grid dimensions + n_realizations : int + Number of realizations needed + + Returns + ------- + np.ndarray + IIDs array with shape (ny*nx, n_realizations) + """ + + # Convert to 1-based for file naming + layer_1based = layer_num + 1 + + # Determine the IID file path + if iids_file is None: + # Default pattern + iid_path = os.path.join(tmp_model_ws, f"iids_layer_{layer_1based}.arr") + elif isinstance(iids_file, str): + # Single string - assume pattern or explicit path + if "{" in iids_file or "layer" in iids_file.lower(): + # Pattern with layer number + iid_path = os.path.join(tmp_model_ws, iids_file.format(layer_1based)) + else: + # Explicit path - append layer number + base_name, ext = os.path.splitext(iids_file) + iid_path = os.path.join(tmp_model_ws, f"{base_name}_layer_{layer_1based}{ext}") + elif isinstance(iids_file, list): + # List of explicit paths + if layer_num < len(iids_file): + iid_path = os.path.join(tmp_model_ws, iids_file[layer_num]) + else: + # Generate default name if not enough files specified + iid_path = os.path.join(tmp_model_ws, f"iids_layer_{layer_1based}.arr") + else: + raise ValueError(f"Invalid iids_file specification: {iids_file}") + + # Try to load existing IIDs + if os.path.exists(iid_path): + print(f" Loading existing IIDs from {iid_path}") + try: + # Try to load as text array + iids_data = np.loadtxt(iid_path) + + # Handle different shapes + if iids_data.ndim == 1: + # Single realization + if len(iids_data) == ny * nx: + iids = iids_data.reshape(-1, 1) + else: + raise ValueError(f"IID file has wrong size: {len(iids_data)} vs {ny * nx}") + elif iids_data.ndim == 2: + # Multiple realizations + if iids_data.shape[0] == ny * nx: + iids = iids_data + elif iids_data.shape[1] == ny * nx: + iids = iids_data.T + else: + raise ValueError(f"IID file has wrong shape: {iids_data.shape}") + else: + raise ValueError(f"IID file has too many dimensions: {iids_data.ndim}") + + # Expand or truncate to match n_realizations + if iids.shape[1] < n_realizations: + print(f" Expanding IIDs from {iids.shape[1]} to {n_realizations} realizations") + additional_iids = np.random.normal(0, 1, size=(ny * nx, n_realizations - iids.shape[1])) + iids = np.column_stack([iids, additional_iids]) + elif iids.shape[1] > n_realizations: + print(f" Truncating IIDs from {iids.shape[1]} to {n_realizations} realizations") + iids = iids[:, :n_realizations] + + except Exception as e: + print(f" Warning: Could not load IID file {iid_path}: {e}") + print(f" Generating new IIDs...") + iids = None + else: + print(f" IID file {iid_path} not found, generating new IIDs...") + iids = None + + # Generate new IIDs if loading failed + if iids is None: + iids = np.random.normal(0, 1, size=(ny * nx, n_realizations)) + + # Save for PEST parameterization + print(f" Saving IIDs to {iid_path} for PEST parameterization") + os.makedirs(os.path.dirname(iid_path), exist_ok=True) + np.savetxt(iid_path, iids, fmt="%.8f", + header=f"IIDs for layer {layer_1based}, shape: {ny * nx} x {n_realizations}") + + return iids + """ + Generate spatially correlated fields using tensor-based geostatistics. + Follows PyEMU pattern for model workspace and spatial reference handling. + + Parameters + ---------- + tmp_model_ws : str + Path to model workspace containing flopy model files + model_name : str + Name of the flopy model (e.g., 'freyberg6') + conceptual_points_file : str + CSV file containing conceptual points + zone_file : str or list of str, optional + zone file(s): + - Single string: One zone file used for all layers (2D zones) + - List of strings: One zone file per layer (creates 3D zones) + iids_file : str or list of str, optional + noise file(s): + - Single string: Assumes pattern like "iids_layer{N}.arr" + - List of strings: Explicit paths to each layer's noise file + - None: Auto-generates noise and saves as iids_layer{N}.arr files + field_name : list of str, default ['field'] + Name(s) of field(s) to generate + layer_mode : bool, default True + If True, process each layer independently + save_path : str, optional + Directory path to save output files (default: tmp_model_ws) + tensor_interp : str, default 'krig' + Tensor interpolation method + vartransform : str, default 'log' + Transformation: 'log' or 'none' + n_realizations : int, default 1 + Number of stochastic realizations + ml : flopy.mbase, optional + A flopy mbase derived type. If None, sr must not be None. + sr : flopy.utils.reference.SpatialReference, optional + A spatial reference to locate the model grid. If None, ml must not be None. + + Returns + ------- + dict + Results from workflow + """ + try: + import flopy + import pyemu + except ImportError as e: + raise ImportError(f"flopy and pyemu are required: {e}") + + print(f"=== Tensor-Based Field Generation ===") + print(f"Model workspace: {tmp_model_ws}") + print(f"Model name: {model_name}") + + # Set default save path + if save_path is None: + save_path = tmp_model_ws + + # Setup spatial reference following PyEMU pattern + if ml is None and sr is None: + # Load model from workspace + print("Loading flopy model...") + try: + # Try MF6 first - only load DIS package for efficiency + sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws, load_only=['dis']) + ml = sim.get_model(model_name) + print(f"Loaded MF6 model: {model_name} (DIS package only)") + except: + try: + # Fall back to MODFLOW-2005 - only load DIS package + ml = flopy.modflow.Modflow.load(f"{model_name}.nam", model_ws=tmp_model_ws, + load_only=['dis']) + print(f"Loaded MODFLOW-2005 model: {model_name} (DIS package only)") + except Exception as e: + raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}: {e}") + + if sr is None: + # Create spatial reference from model + print("Creating spatial reference...") + if hasattr(ml, 'dis'): + # MF6 or MODFLOW with DIS + try: + sr = pyemu.helpers.SpatialReference.from_namfile( + os.path.join(tmp_model_ws, f"{model_name}.nam"), + delr=ml.dis.delr.array, delc=ml.dis.delc.array + ) + except: + # Alternative approach for MF6 + sr = pyemu.helpers.SpatialReference( + delr=ml.dis.delr.array, delc=ml.dis.delc.array, + xul=0, yul=0 + ) + else: + raise ValueError("Could not determine grid spacing from model") + + print(f"Spatial reference: {sr}") + + # Get grid coordinates + try: + xcentergrid = sr.xcentergrid + ycentergrid = sr.ycentergrid + except AttributeError: + try: + xcentergrid = sr.xcellcenters + ycentergrid = sr.ycellcenters + except AttributeError as e: + raise Exception(f"Could not get grid centers from spatial reference: {e}") + + ny, nx = xcentergrid.shape + print(f"Grid dimensions: {ny} x {nx}") + + # Load conceptual points + conceptual_points = pd.read_csv(os.path.join(tmp_model_ws, conceptual_points_file)) + + # Add transverse if missing + if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: + conceptual_points.loc[:, 'transverse'] = conceptual_points.loc[:, 'major'] / conceptual_points.loc[:, + 'anisotropy'] + + print(f"Loaded {len(conceptual_points)} conceptual points") + + # Load zones + zones = None + if zone_file: + if isinstance(zone_file, str): + # Single zone file + zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file)).astype(np.int64) + if zones.shape != (ny, nx): + # Try to reshape if possible + if zones.size == ny * nx: + zones = zones.reshape(ny, nx) + else: + raise ValueError(f"Zone file shape {zones.shape} doesn't match grid {(ny, nx)}") + print(f"Loaded zones from {zone_file}, shape: {zones.shape}") + else: + # Multiple zone files - for now just use the first one + zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file[0])).astype(np.int64) + print(f"Loaded zones from {zone_file[0]} (multi-layer support TODO)") + + # Process each field + all_results = {} + + for fn in field_name: + print(f"\n=== Processing field: {fn} ===") + + # Filter conceptual points for this field + cols = ['name', 'x', 'y', 'major', 'transverse', 'bearing'] + field_cols = [col for col in conceptual_points.columns if f'_{fn}' in col] + + if not field_cols: + # Look for generic mean/sd columns + mean_cols = [col for col in conceptual_points.columns if 'mean' in col.lower()] + sd_cols = [col for col in conceptual_points.columns if 'sd' in col.lower()] + + if mean_cols and sd_cols: + # Rename to expected format + field_cp = conceptual_points.copy() + field_cp[f'mean_{fn}'] = field_cp[mean_cols[0]] + field_cp[f'sd_{fn}'] = field_cp[sd_cols[0]] + cols.extend([f'mean_{fn}', f'sd_{fn}']) + else: + raise ValueError(f"No columns found for field '{fn}' (expected mean_{fn}, sd_{fn})") + else: + cols.extend(field_cols) + + # Auto-detect layers and select target layer + target_layer = None + if layer_mode and 'layer' in conceptual_points.columns: + cols.append('layer') + # Auto-detect available layers + available_layers = sorted(conceptual_points['layer'].unique()) + print(f"Available layers: {available_layers}") + + # Use first available layer instead of hardcoded 0 + target_layer = available_layers[0] + field_cp = conceptual_points[conceptual_points['layer'] == target_layer][cols].copy() + + if len(field_cp) == 0: + print(f"No conceptual points for layer {target_layer}, using all points") + field_cp = conceptual_points[cols].copy() + target_layer = None + else: + field_cp = conceptual_points[cols].copy() + + # Rename columns to match apply_ppu_hyperpars function expectations + if f'mean_{fn}' in field_cp.columns: + field_cp = field_cp.rename(columns={f'mean_{fn}': 'mean_kh', f'sd_{fn}': 'sd_kh'}) + elif 'mean' in field_cp.columns: + field_cp = field_cp.rename(columns={'mean': 'mean_kh', 'sd': 'sd_kh'}) + + # Use workflow + results = apply_ppu_hyperpars( + field_cp, xcentergrid, ycentergrid, + zones=zones, + n_realizations=n_realizations, + layer=target_layer if target_layer is not None else 0, + vartransform=vartransform, + tensor_method=tensor_interp, + out_filename=os.path.join(save_path, f"{fn}") + ) + + all_results[fn] = results + + # Save and visualize results + if results['fields'] is not None: + for i, field in enumerate(results['fields']): + save_layer(field, layer=target_layer if target_layer is not None else 0, + field_name=f"{fn}_real_{i + 1}", save_path=save_path) + + # Save mean and SD + save_layer(results['mean'], layer=target_layer if target_layer is not None else 0, + field_name=f"{fn}_mean", save_path=save_path) + save_layer(results['sd'], layer=target_layer if target_layer is not None else 0, + field_name=f"{fn}_sd", save_path=save_path) + + # Visualize tensors - check if visualization module is available + try: + from tensor_visualization import visualize_tensors + visualize_tensors( + results['tensors'], xcentergrid, ycentergrid, + zones=zones, conceptual_points=field_cp, + subsample=8, title_suf=f'{fn}_layer_{target_layer if target_layer is not None else 0}', + save_path=save_path + ) + except ImportError: + print(" Warning: tensor_visualization module not available, skipping visualization") + + print("\n=== Complete ===") + return all_results + + +def generate_single_layer(cp_file, xcentergrid, ycentergrid, iids=None, + zones=None, save_path='.', tensor_interp='krig', + vartransform='log', boundary_smooth=True, + boundary_enhance=True): + """ + Generate single layer using approach with boundary adjustments. + Replaces the NSAF generate_single_layer_zone_based function. + + Parameters + ---------- + cp_file : str or pd.DataFrame + Conceptual points file or DataFrame + xcentergrid : np.ndarray + X-coordinates from pyemu SpatialReference, shape (ny, nx) + ycentergrid : np.ndarray + Y-coordinates from pyemu SpatialReference, shape (ny, nx) + iids : np.ndarray, optional + Pre-generated noise, shape (ny, nx) + zones : np.ndarray, optional + Zone IDs, shape (ny, nx) + save_path : str, default '.' + Directory path to save output files + tensor_interp : str, default 'krig' + Tensor interpolation method + vartransform : str, default 'log' + Transformation: 'log' or 'none' + boundary_smooth : bool, default True + Apply smoothing to mean at zone boundaries + boundary_enhance : bool, default True + Apply variance enhancement at zone boundaries + + Returns + ------- + tuple + (field_2d, tensors) where: + - field_2d: Generated field, shape (ny, nx) + - tensors: Interpolated tensors, shape (ny*nx, 2, 2) + """ + + # Load conceptual points + cp_df = load_conceptual_points(cp_file) + + print("=== Single Layer Generation ===") + print(f"Processing {len(cp_df)} conceptual points") + + ny, nx = xcentergrid.shape + + # Step 1: Create geological tensors from conceptual points + print(" Creating geological correlation tensors...") + cp_coords = cp_df[['x', 'y']].values + + # Get mean and sd columns + mean_cols = [col for col in cp_df.columns if 'mean' in col.lower()] + sd_cols = [col for col in cp_df.columns if 'sd' in col.lower()] + + if not mean_cols or not sd_cols: + raise ValueError("Conceptual points must have mean and sd columns") + + cp_means = cp_df[mean_cols[0]].values + cp_sd = cp_df[sd_cols[0]].values + + # Create tensors + major = cp_df['major'].values + minor = cp_df['transverse'].values + + def normalize_bearing(bearing): + return bearing % 360 + + bearings_normalized = [normalize_bearing(b) for b in cp_df['bearing'].values] + bearing_rad = np.radians(bearings_normalized) + cp_tensors = create_2d_tensors(bearing_rad, major, minor) + + # Debug tensor creation + print(" Tensor creation verification:") + for i in range(min(3, len(cp_tensors))): + original_bearing = cp_df['bearing'].values[i] + extracted_bearing = get_tensor_bearing(cp_tensors[i], original_bearing) + print(f" Point {i}: Input bearing={original_bearing:.1f}°, Extracted={extracted_bearing:.1f}°") + + # Step 2: Interpolate tensors + print(f" Interpolating tensors using {tensor_interp} method...") + tensors = interpolate_tensors( + cp_df, xcentergrid, ycentergrid, + zones=zones, method=tensor_interp, layer=0 + ) + + # Step 3: Interpolate means and sd with boundary adjustments + print(" Interpolating means with geological structure...") + grid_coords_2d = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) + + # Configure kriging + transform = 'log' if vartransform == 'log' else None + min_value = 1e-8 if vartransform == 'log' else None + + # Interpolate mean + interp_means_2d = tensor_aware_kriging( + cp_coords, cp_means, grid_coords_2d, tensors, + variogram_model='exponential', sill=1.0, nugget=0.1, + background_value=np.mean(cp_means), max_search_radius=1e20, + min_points=3, transform=transform, min_value=min_value, + max_neighbors=4, zones=zones + ) + + # Apply boundary smoothing + if boundary_smooth and zones is not None: + print(" Smoothing values at geological boundaries...") + interp_means_2d = create_boundary_modified_scalar( + interp_means_2d, zones, transition_cells=3, mode='smooth' + ) + + # Interpolate standard deviation + print(" Interpolating standard deviation...") + interp_sd_2d = tensor_aware_kriging( + cp_coords, cp_sd, grid_coords_2d, tensors, + variogram_model='exponential', sill=1.0, nugget=0.1, + background_value=np.mean(cp_sd), max_search_radius=1e20, + min_points=3, transform=transform, min_value=min_value, + max_neighbors=4, zones=zones + ) + + # Apply boundary enhancement + if boundary_enhance and zones is not None: + print(" Enhancing variance at geological boundaries...") + interp_sd_2d = create_boundary_modified_scalar( + interp_sd_2d, zones, peak_increase=1.0, transition_cells=3, mode='enhance' + ) + + # Step 4: Generate stochastic field using FIELDGEN2D_SVA approach + print(" Generating stochastic field using FIELDGEN2D_SVA methodology...") + + # Convert tensors to geostatistical parameters for FIELDGEN2D_SVA + from nsaf_utils import _tensors_to_geostat_params, _generate_stochastic_fields + + bearing_deg, anisotropy, corrlen_major = _tensors_to_geostat_params(tensors) + + # Prepare grid coordinates + grid_coords_2d = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) + + # Configuration for field generation + config_dict = { + 'averaging_function': 'exponential', + 'vartype': 2, + 'krigtype': 1 + } + + # Generate one realization + n_realizations = 1 + fields = _generate_stochastic_fields( + grid_coords_2d, interp_means_2d, interp_sd_2d, + bearing_deg, anisotropy, corrlen_major, + zones, n_realizations, vartransform, + config_dict, ny, nx + ) + + field_2d = fields[0] # Extract the single realization + + print("=== Single layer generation complete ===") + return field_2d, tensors + + +def save_layer(field_2d, layer, field_name, save_path='.'): + """ + Save layer to file in PyEMU-compatible format. + + Parameters + ---------- + field_2d : np.ndarray + Field values, shape (ny, nx) + layer : int + Layer number + field_name : str + Field name for filename + save_path : str + Directory to save files + """ + os.makedirs(save_path, exist_ok=True) + filename = os.path.join(save_path, f"{field_name}_layer_{layer + 1}.txt") + np.savetxt(filename, field_2d, fmt="%20.8E") + print(f" Saved {field_name} layer {layer + 1} to {filename}") + + +def validate_grid_coordinates(xcentergrid, ycentergrid): + """ + Validate PyEMU grid coordinates. + + Parameters + ---------- + xcentergrid : np.ndarray + X-coordinates, shape (ny, nx) + ycentergrid : np.ndarray + Y-coordinates, shape (ny, nx) + + Returns + ------- + bool + True if valid + """ + if xcentergrid.shape != ycentergrid.shape: + print(f"ERROR: Grid coordinate shapes don't match: {xcentergrid.shape} vs {ycentergrid.shape}") + return False + + ny, nx = xcentergrid.shape + print(f"Grid validation: {ny} x {nx} = {ny * nx} points") + + # Check for regular grid structure + x_unique = len(np.unique(xcentergrid)) + y_unique = len(np.unique(ycentergrid)) + + if x_unique == nx and y_unique == ny: + print(" ✓ Regular rectangular grid detected") + return True + else: + print(f" ⚠ Irregular grid: {x_unique} unique x, {y_unique} unique y") + return True # Still valid, just not regular + + +def infer_grid_shape(xcentergrid, ycentergrid): + """ + Infer grid shape from PyEMU coordinate arrays. + + Parameters + ---------- + xcentergrid : np.ndarray + X-coordinates, shape (ny, nx) + ycentergrid : np.ndarray + Y-coordinates, shape (ny, nx) + + Returns + ------- + tuple + Grid shape (nz, ny, nx) - nz=1 for 2D + """ + ny, nx = xcentergrid.shape + return (1, ny, nx) + + +# Example usage function +def example_field_generation(): + """Example using the restructured helpers.""" + import matplotlib.pyplot as plt + + # Create example PyEMU grid + nx, ny = 50, 40 + x = np.linspace(0, 5000, nx) + y = np.linspace(0, 4000, ny) + xcentergrid, ycentergrid = np.meshgrid(x, y, indexing='xy') + xcentergrid = xcentergrid.T + ycentergrid = ycentergrid.T + + # Create example conceptual points + cp_data = { + 'name': ['cp1', 'cp2', 'cp3', 'cp4'], + 'x': [1000, 3000, 1000, 3000], + 'y': [1000, 1000, 3000, 3000], + 'mean_kh': [1.5, 2.0, 1.8, 1.2], + 'sd_kh': [0.3, 0.4, 0.2, 0.5], + 'major': [2000, 1500, 1800, 2200], + 'anisotropy': [3.0, 2.5, 4.0, 2.0], + 'bearing': [45, 90, 135, 0], + 'layer': [1, 1, 1, 1] + } + cp_df = pd.DataFrame(cp_data) + + # Add transverse + cp_df['transverse'] = cp_df['major'] / cp_df['anisotropy'] + + # Create zones + zones = np.ones((ny, nx), dtype=int) + zones[:ny // 2, :] = 1 + zones[ny // 2:, :] = 2 + + # Generate field using single layer function + print("=== Testing single layer generation ===") + field_2d, tensors = generate_single_layer( + cp_df, xcentergrid, ycentergrid, + zones=zones, tensor_interp='krig', + vartransform='log' + ) + + # Visualize results + fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + + # Field + im1 = axes[0].imshow(field_2d, origin='lower') + axes[0].set_title('Generated Field') + plt.colorbar(im1, ax=axes[0]) + + # Zones + im2 = axes[1].imshow(zones, origin='lower', cmap='tab10') + axes[1].set_title('Zones') + plt.colorbar(im2, ax=axes[1]) + + # Field statistics + axes[2].hist(field_2d.flatten(), bins=50, alpha=0.7) + axes[2].set_title('Field Distribution') + axes[2].set_xlabel('Value') + axes[2].set_ylabel('Frequency') + + plt.tight_layout() + plt.savefig('field_example.png', dpi=150, bbox_inches='tight') + plt.show() + + print(f"Field statistics: mean={np.mean(field_2d):.3f}, std={np.std(field_2d):.3f}") + print("Example complete!") + + return field_2d, tensors \ No newline at end of file diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py new file mode 100644 index 000000000..a0ed32751 --- /dev/null +++ b/pyemu/utils/nsaf_utils.py @@ -0,0 +1,1355 @@ +import numpy as np +import pandas as pd +from scipy.linalg import logm, expm +import os +import matplotlib as mpl + +mpl.use('Tkagg') +import matplotlib.pyplot as plt + + +def get_tensor_bearing(tensor, expected_bearing=None): + """Extract geological bearing from tensor.""" + eigenvals, eigenvecs = np.linalg.eigh(tensor) + max_idx = np.argmax(eigenvals) + major_eigenvec = eigenvecs[:, max_idx] + + # Swap components to match geological convention: [north, east] instead of [east, north] + north_component = major_eigenvec[1] # y-component is north + east_component = major_eigenvec[0] # x-component is east + + # Calculate bearing from north (geological convention) + angle1 = np.degrees(np.arctan2(east_component, north_component)) + if angle1 < 0: + angle1 += 360 + angle2 = (angle1 + 180) % 360 + + # Choose correct direction if expected bearing provided + if expected_bearing is not None: + diff1 = min(abs(angle1 - expected_bearing), abs(angle1 - expected_bearing + 360), + abs(angle1 - expected_bearing - 360)) + diff2 = min(abs(angle2 - expected_bearing), abs(angle2 - expected_bearing + 360), + abs(angle2 - expected_bearing - 360)) + return angle1 if diff1 < diff2 else angle2 + + return angle1 + + +def _ensure_positive_definite(tensor, min_eigenval=1e-8): + """Ensure tensor is positive definite.""" + eigenvals, eigenvecs = np.linalg.eigh(tensor) + eigenvals_reg = np.maximum(eigenvals, min_eigenval) + return eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T + + +def create_2d_tensors(theta, major, minor): + """Create 2x2 anisotropy tensors from geostatistical parameters. + + Parameters + ---------- + theta : array-like + Bearing angles in radians (0 = North, clockwise positive) + major : array-like + Major axis correlation lengths + minor : array-like + Minor axis correlation lengths + + Returns + ------- + np.ndarray + Tensors with shape (n, 2, 2) + """ + theta = np.atleast_1d(theta) + major = np.atleast_1d(major) + minor = np.atleast_1d(minor) + + n = len(theta) + tensors = np.zeros((n, 2, 2)) + + for i in range(n): + bearing_rad = theta[i] + + # Direction vector for this bearing (geological convention) + east_comp = np.sin(bearing_rad) # X component + north_comp = np.cos(bearing_rad) # Y component + + # Create rotation matrix to align [1,0] with bearing direction + cos_t = north_comp + sin_t = east_comp + + # Clockwise rotation by angle theta + R = np.array([[cos_t, sin_t], + [-sin_t, cos_t]]) + S = np.diag([minor[i] ** 2, major[i] ** 2]) + tensors[i] = R @ S @ R.T + + return tensors.squeeze() if n == 1 else tensors + + +def load_conceptual_points(cp_file): + """Load conceptual points from CSV file or DataFrame. + + Parameters + ---------- + cp_file : str or pd.DataFrame + Path to CSV file or DataFrame with columns: name,x,y,z,mean_kh,sd_kh,major,anisotropy,bearing,layer + + Returns + ------- + pd.DataFrame + Conceptual points with added 'transverse' column if missing + """ + if isinstance(cp_file, str): + cp_df = pd.read_csv(cp_file) + elif isinstance(cp_file, pd.DataFrame): + cp_df = cp_file.copy() + else: + raise TypeError(f"cp_file must be string path or pandas DataFrame, got {type(cp_file)}") + + # Validate required columns (excluding anisotropy/transverse for now) + required_cols = ['x', 'y', 'major', 'bearing'] + missing_cols = [col for col in required_cols if col not in cp_df.columns] + if missing_cols: + raise ValueError(f"Missing required columns: {missing_cols}") + + # Check for anisotropy OR transverse (at least one must exist) + has_anisotropy = 'anisotropy' in cp_df.columns + has_transverse = 'transverse' in cp_df.columns + + if not has_anisotropy and not has_transverse: + raise ValueError("Must have either 'anisotropy' OR 'transverse' column") + + # Add transverse column if missing (derive from anisotropy) + if not has_transverse and has_anisotropy: + cp_df['transverse'] = cp_df['major'] / cp_df['anisotropy'] + print(f" Added 'transverse' column derived from anisotropy") + + # Add anisotropy column if missing (derive from transverse) + elif not has_anisotropy and has_transverse: + cp_df['anisotropy'] = cp_df['major'] / cp_df['transverse'] + print(f" Added 'anisotropy' column derived from transverse") + + # If both exist, validate they're consistent + elif has_anisotropy and has_transverse: + expected_transverse = cp_df['major'] / cp_df['anisotropy'] + if not np.allclose(cp_df['transverse'], expected_transverse, rtol=1e-3): + print(" Warning: 'transverse' and 'anisotropy' columns are inconsistent") + print(" Using 'transverse' values and recalculating 'anisotropy'") + cp_df['anisotropy'] = cp_df['major'] / cp_df['transverse'] + + return cp_df + + +def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, + method='krig', layer=0, config_dict=None): + """ + Tensor field interpolation using NSAF log-Euclidean mathematics + with pypestutils kriging engine. + + Parameters + ---------- + cp_file : str or pd.DataFrame + Conceptual points file path or DataFrame + xcentergrid : np.ndarray + 2D array of x-coordinates from pyemu SpatialReference (ny, nx) + ycentergrid : np.ndarray + 2D array of y-coordinates from pyemu SpatialReference (ny, nx) + zones : np.ndarray, optional + 2D zone array (ny, nx) + method : str, default 'krig' + Interpolation method: 'krig', 'idw', or 'nearest' + layer : int, default 0 + Layer number to process + config_dict : dict, optional + Configuration parameters for pypestutils + + Returns + ------- + np.ndarray + Interpolated tensors with shape (ny*nx, 2, 2) + """ + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") + + # Load and filter conceptual points + cp_df = load_conceptual_points(cp_file) + + # Filter to current layer + if 'layer' in cp_df.columns: + layer_cp = cp_df[cp_df['layer'] == layer].copy() + else: + layer_cp = cp_df.copy() + + if len(layer_cp) == 0: + raise ValueError(f"No conceptual points found for layer {layer}") + + # Set default config + if config_dict is None: + config_dict = { + 'vartype': 2, # Exponential variogram + 'krigtype': 1, # Ordinary kriging + 'search_radius': 1e10, + 'maxpts_interp': 20, + 'minpts_interp': 1 + } + + # Prepare grid coordinates + ny, nx = xcentergrid.shape + grid_x = xcentergrid.flatten() + grid_y = ycentergrid.flatten() + + # Prepare conceptual point data + cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) + + # Convert bearing from degrees to radians (maintaining geological convention) + bearing_rad = np.radians(layer_cp['bearing'].values) + + # Create tensors at conceptual points + cp_tensors = create_2d_tensors( + bearing_rad, + layer_cp['major'].values, + layer_cp['transverse'].values # This is minor axis + ) + + # Handle zones + if zones is not None: + zones_flat = zones.flatten().astype(int) + cp_zones = _assign_cp_to_zones(cp_coords, grid_x, grid_y, zones_flat, nx) + else: + zones_flat = np.ones(len(grid_x), dtype=int) + cp_zones = np.ones(len(layer_cp), dtype=int) + + # Interpolate tensors using log-Euclidean approach + if method == 'krig': + interp_tensors = _interpolate_tensors_kriging( + cp_coords, cp_tensors, cp_zones, + grid_x, grid_y, zones_flat, + config_dict, nx, ny + ) + elif method == 'idw': + interp_tensors = _interpolate_tensors_idw( + cp_coords, cp_tensors, cp_zones, + grid_x, grid_y, zones_flat, + nx, ny + ) + elif method == 'nearest': + interp_tensors = _interpolate_tensors_nearest( + cp_coords, cp_tensors, cp_zones, + grid_x, grid_y, zones_flat, + nx, ny + ) + else: + raise ValueError(f"Unknown method: {method}") + + return interp_tensors + + +def _assign_cp_to_zones(cp_coords, grid_x, grid_y, zones_flat, nx): + """Assign conceptual points to zones based on nearest grid point.""" + grid_coords = np.column_stack([grid_x, grid_y]) + cp_zones = np.zeros(len(cp_coords), dtype=int) + + for i, cp_coord in enumerate(cp_coords): + distances = np.sum((grid_coords - cp_coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + cp_zones[i] = zones_flat[closest_idx] + + return cp_zones + + +def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, + grid_x, grid_y, zones_flat, config_dict, nx, ny): + """Interpolate tensors using pypestutils kriging with log-Euclidean approach.""" + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") + + n_grid = len(grid_x) + result_tensors = np.zeros((n_grid, 2, 2)) + + # Convert tensors to log space + log_tensors = np.array([logm(_ensure_positive_definite(tensor)) + for tensor in cp_tensors]) + + # Process each zone separately + unique_zones = np.unique(zones_flat) + + for zone_id in unique_zones: + zone_mask = zones_flat == zone_id + zone_indices = np.where(zone_mask)[0] + + if len(zone_indices) == 0: + continue + + # Filter conceptual points for this zone + cp_mask = cp_zones == zone_id + if np.sum(cp_mask) == 0: + # No conceptual points in this zone - use default tensor + default_tensor = np.eye(2) * 1000 ** 2 + result_tensors[zone_indices] = default_tensor + continue + + zone_cp_coords = cp_coords[cp_mask] + zone_log_tensors = log_tensors[cp_mask] + zone_grid_x = grid_x[zone_indices] + zone_grid_y = grid_y[zone_indices] + + # Interpolate each tensor component separately + zone_tensors = np.zeros((len(zone_indices), 2, 2)) + + for i in range(2): + for j in range(i, 2): # Only upper triangle, then mirror + component_values = zone_log_tensors[:, i, j].real + + # Use pypestutils for kriging + interp_component = _krig_component_pypestutils( + zone_cp_coords[:, 0], zone_cp_coords[:, 1], + component_values, zone_grid_x, zone_grid_y, + config_dict + ) + + zone_tensors[:, i, j] = interp_component + if i != j: + zone_tensors[:, j, i] = interp_component + + # Convert back from log space and ensure positive definiteness + for k in range(len(zone_indices)): + try: + tensor_exp = expm(zone_tensors[k]) + result_tensors[zone_indices[k]] = _ensure_positive_definite(tensor_exp) + except: + # Fallback to default tensor + result_tensors[zone_indices[k]] = np.eye(2) * 1000 ** 2 + + return result_tensors + + +def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_dict): + """Use pypestutils to krig a single tensor component.""" + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") + + lib = PestUtilsLib() + + # Create temporary factor file + fac_fname = "temp_tensor_component.fac" + if os.path.exists(fac_fname): + os.remove(fac_fname) + fac_ftype = "text" + + # All points in same zone for component interpolation + cp_zones = np.ones(len(cp_x), dtype=int) + grid_zones = np.ones(len(grid_x), dtype=int) + + # Use isotropic parameters for component interpolation + corrlen = np.full(len(grid_x), 1000.0) # Default correlation length + aniso = np.ones(len(grid_x)) # Isotropic + bearing = np.zeros(len(grid_x)) # No rotation + + try: + npts = lib.calc_kriging_factors_2d( + cp_x, cp_y, cp_zones, + grid_x, grid_y, grid_zones, + int(config_dict.get("vartype", 2)), # Exponential + int(config_dict.get("krigtype", 1)), # Ordinary kriging + corrlen, aniso, bearing, + config_dict.get("search_radius", 1e10), + config_dict.get("maxpts_interp", 20), + config_dict.get("minpts_interp", 1), + fac_fname, fac_ftype + ) + + # Apply kriging using the component values + result = lib.krige_using_file( + fac_fname, fac_ftype, + len(grid_x), + int(config_dict.get("krigtype", 1)), + "none", # No transformation for individual components + cp_values, + np.mean(cp_values), # Fill value + np.mean(cp_values) # No-interpolation value + ) + + if os.path.exists(fac_fname): + os.remove(fac_fname) + + lib.free_all_memory() + + return result["targval"] + + except Exception as e: + # Cleanup and fallback + if os.path.exists(fac_fname): + os.remove(fac_fname) + lib.free_all_memory() + + # Simple IDW fallback + return _idw_component(cp_x, cp_y, cp_values, grid_x, grid_y) + + +def _idw_component(cp_x, cp_y, cp_values, grid_x, grid_y, power=2): + """Inverse distance weighting fallback for component interpolation.""" + result = np.zeros(len(grid_x)) + + for i in range(len(grid_x)): + distances = np.sqrt((cp_x - grid_x[i]) ** 2 + (cp_y - grid_y[i]) ** 2) + distances = np.maximum(distances, 1e-10) # Avoid division by zero + + weights = 1.0 / (distances ** power) + result[i] = np.sum(weights * cp_values) / np.sum(weights) + + return result + + +def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, + grid_x, grid_y, zones_flat, nx, ny): + """IDW interpolation using log-Euclidean approach.""" + n_grid = len(grid_x) + result_tensors = np.zeros((n_grid, 2, 2)) + + # Convert to log space + log_tensors = np.array([logm(_ensure_positive_definite(tensor)) + for tensor in cp_tensors]) + + # Process each zone + unique_zones = np.unique(zones_flat) + + for zone_id in unique_zones: + zone_mask = zones_flat == zone_id + zone_indices = np.where(zone_mask)[0] + + if len(zone_indices) == 0: + continue + + cp_mask = cp_zones == zone_id + if np.sum(cp_mask) == 0: + default_tensor = np.eye(2) * 1000 ** 2 + result_tensors[zone_indices] = default_tensor + continue + + zone_cp_coords = cp_coords[cp_mask] + zone_log_tensors = log_tensors[cp_mask] + + for idx in zone_indices: + gx, gy = grid_x[idx], grid_y[idx] + distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + + (zone_cp_coords[:, 1] - gy) ** 2) + distances = np.maximum(distances, 1e-10) + + weights = 1.0 / (distances ** 2) + weights /= np.sum(weights) + + # Weighted average in log space + log_result = np.sum(weights[:, np.newaxis, np.newaxis] * zone_log_tensors, axis=0) + + try: + tensor_exp = expm(log_result) + result_tensors[idx] = _ensure_positive_definite(tensor_exp) + except: + result_tensors[idx] = np.eye(2) * 1000 ** 2 + + return result_tensors + + +def _interpolate_tensors_nearest(cp_coords, cp_tensors, cp_zones, + grid_x, grid_y, zones_flat, nx, ny): + """Nearest neighbor interpolation.""" + n_grid = len(grid_x) + result_tensors = np.zeros((n_grid, 2, 2)) + + # Process each zone + unique_zones = np.unique(zones_flat) + + for zone_id in unique_zones: + zone_mask = zones_flat == zone_id + zone_indices = np.where(zone_mask)[0] + + if len(zone_indices) == 0: + continue + + cp_mask = cp_zones == zone_id + if np.sum(cp_mask) == 0: + default_tensor = np.eye(2) * 1000 ** 2 + result_tensors[zone_indices] = default_tensor + continue + + zone_cp_coords = cp_coords[cp_mask] + zone_cp_tensors = cp_tensors[cp_mask] + + for idx in zone_indices: + gx, gy = grid_x[idx], grid_y[idx] + distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + + (zone_cp_coords[:, 1] - gy) ** 2) + nearest_idx = np.argmin(distances) + result_tensors[idx] = zone_cp_tensors[nearest_idx] + + return result_tensors + + +def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, + out_filename=None, n_realizations=1, layer=0, + vartransform="none", boundary_smooth=True, + boundary_enhance=True, tensor_method='krig', + config_dict=None, iids=None, area=None, **kwargs): + """ + Parameter interpolation and field generation combining NSAF tensor mathematics + with pypestutils FIELDGEN2D_SVA for spatially correlated noise. + + Parameters + ---------- + cp_file : str or pd.DataFrame + Conceptual points file or DataFrame + xcentergrid : np.ndarray + X-coordinates from pyemu SpatialReference, shape (ny, nx) + ycentergrid : np.ndarray + Y-coordinates from pyemu SpatialReference, shape (ny, nx) + zones : np.ndarray, optional + Zone array, shape (ny, nx) + out_filename : str, optional + Base filename for output (if None, returns arrays only) + n_realizations : int, default 1 + Number of stochastic realizations to generate + layer : int, default 0 + Layer number to process + vartransform : str, default "none" + Transformation: "none" or "log" + boundary_smooth : bool, default True + Apply smoothing to mean at zone boundaries + boundary_enhance : bool, default True + Apply variance enhancement at zone boundaries + tensor_method : str, default 'krig' + Tensor interpolation method: 'krig', 'idw', or 'nearest' + config_dict : dict, optional + Configuration parameters for pypestutils + iids : np.ndarray, optional + Pre-generated IIDs with shape (ny*nx, n_realizations) + + Returns + ------- + dict + Dictionary containing: + - 'fields': Generated parameter fields, shape (n_realizations, ny, nx) + - 'mean': Interpolated mean field, shape (ny, nx) + - 'sd': Interpolated standard deviation field, shape (ny, nx) + - 'tensors': Interpolated tensors, shape (ny*nx, 2, 2) + """ + + print("=== Parameter Interpolation and Field Generation ===") + + # Load conceptual points + cp_df = load_conceptual_points(cp_file) + if 'layer' in cp_df.columns: + layer_cp = cp_df[cp_df['layer'] == layer].copy() + else: + layer_cp = cp_df.copy() + + if len(layer_cp) == 0: + raise ValueError(f"No conceptual points found for layer {layer}") + + # Get grid dimensions and coordinates + ny, nx = xcentergrid.shape + grid_coords_2d = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) + + print(f"Processing layer {layer} with {len(layer_cp)} conceptual points") + print(f"Grid dimensions: {ny} x {nx} = {ny * nx} cells") + + # Step 1: Interpolate tensors + print("\nStep 1: Interpolating tensor field...") + tensors = interpolate_tensors( + layer_cp, xcentergrid, ycentergrid, + zones=zones, method=tensor_method, + layer=layer, config_dict=config_dict + ) + + # Step 2: Convert tensors to geostatistical parameters + print("\nStep 2: Converting tensors to geostatistical parameters...") + bearing_deg, anisotropy, corrlen_major = _tensors_to_geostat_params(tensors) + + # Step 3: Prepare conceptual point data + cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) + cp_means = layer_cp['mean_kh'].values + cp_sd = layer_cp['sd_kh'].values + + # Step 4: Interpolate mean and sd using tensor-aware kriging + print("\nStep 4: Interpolating mean and standard deviation fields...") + + # Configure kriging parameters + if config_dict is None: + config_dict = { + 'vartype': 2, # Exponential variogram + 'krigtype': 1, # Ordinary kriging + 'search_radius': 1e10, + 'maxpts_interp': 20, + 'minpts_interp': 1 + } + + # Transform for log-domain if needed + transform = 'log' if vartransform == 'log' else None + min_value = 1e-8 if vartransform == 'log' else None + + # Interpolate mean + print(" Interpolating mean...") + interp_means_2d = tensor_aware_kriging( + cp_coords, cp_means, grid_coords_2d, tensors, + variogram_model='exponential', sill=1.0, nugget=0.1, + background_value=np.mean(cp_means), max_search_radius=1e20, + min_points=3, transform=transform, min_value=min_value, + max_neighbors=4, zones=zones + ) + + # Apply boundary smoothing to mean + if boundary_smooth and zones is not None: + print(" Smoothing mean at geological boundaries...") + interp_means_2d = create_boundary_modified_scalar( + interp_means_2d, zones, + transition_cells=3, mode='smooth' + ) + + # Interpolate standard deviation + print(" Interpolating standard deviation...") + interp_sd_2d = tensor_aware_kriging( + cp_coords, cp_sd, grid_coords_2d, tensors, + variogram_model='exponential', sill=1.0, nugget=0.1, + background_value=np.mean(cp_sd), max_search_radius=1e20, + min_points=3, transform=transform, min_value=min_value, + max_neighbors=4, zones=zones + ) + + # Apply boundary enhancement to sd + if boundary_enhance and zones is not None: + print(" Enhancing variance at geological boundaries...") + interp_sd_2d = create_boundary_modified_scalar( + interp_sd_2d, zones, + peak_increase=1.0, transition_cells=3, mode='enhance' + ) + + # Step 5: Generate stochastic fields using FIELDGEN2D_SVA approach + print(f"\nStep 5: Generating {n_realizations} stochastic realizations...") + + if n_realizations > 0: + fields = _generate_stochastic_fields( + grid_coords=grid_coords_2d, + mean_field=interp_means_2d, + sd_field=interp_sd_2d, + bearing=bearing_deg, + anisotropy=anisotropy, + corrlen=corrlen_major, + zones=zones, + n_realizations=n_realizations, + vartransform=vartransform, + config_dict=config_dict, + ny=ny, + nx=nx, + area=area, + iids=iids + ) + else: + fields = None + + # Prepare output + results = { + 'fields': fields, + 'mean': interp_means_2d, + 'sd': interp_sd_2d, + 'tensors': tensors, + 'bearing': bearing_deg.reshape(ny, nx), + 'anisotropy': anisotropy.reshape(ny, nx), + 'corrlen': corrlen_major.reshape(ny, nx) + } + + # Save to files if requested + if out_filename is not None: + _save_results(results, out_filename, vartransform) + + print("=== Interpolation complete ===") + return results + + +def _tensors_to_geostat_params(tensors): + """Convert tensor field to geostatistical parameters.""" + n_points = len(tensors) + bearing_deg = np.zeros(n_points) + anisotropy = np.zeros(n_points) + corrlen_major = np.zeros(n_points) + + for i, tensor in enumerate(tensors): + # Skip default tensors + if np.allclose(tensor, np.eye(2) * 1000000): + bearing_deg[i] = 0.0 + anisotropy[i] = 1.0 + corrlen_major[i] = 1000.0 + continue + + try: + # Eigendecomposition + eigenvals, eigenvecs = np.linalg.eigh(tensor) + + # Sort by eigenvalue magnitude + idx = np.argsort(eigenvals)[::-1] + eigenvals = eigenvals[idx] + eigenvecs = eigenvecs[:, idx] + + # Extract parameters + major_length = np.sqrt(eigenvals[0]) + minor_length = np.sqrt(eigenvals[1]) + + corrlen_major[i] = major_length + anisotropy[i] = major_length / minor_length + + # Calculate bearing from major eigenvector + major_vec = eigenvecs[:, 0] + # Convert to geological bearing (0° = North, clockwise positive) + bearing_rad = np.arctan2(major_vec[0], major_vec[1]) + bearing_deg[i] = np.degrees(bearing_rad) % 360 + + except: + # Fallback values + bearing_deg[i] = 0.0 + anisotropy[i] = 1.0 + corrlen_major[i] = 1000.0 + + return bearing_deg, anisotropy, corrlen_major + + +def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, + variogram_model='exponential', sill=1.0, nugget=0.01, + background_value=0.0, max_search_radius=1e20, min_points=1, + max_neighbors=4, transform=None, min_value=1e-8, + zones=None): + """ + Tensor-aware kriging using anisotropic distances. + (Implementation from the NSAF code provided earlier) + """ + # Get grid shape + n_grid = len(grid_coords) + n_dims = grid_coords.shape[1] + + # Handle zones shape inference + if zones is not None: + shape = zones.shape + if n_dims == 2: + ny, nx = shape + else: + nz, ny, nx = shape + else: + # Simple rectangular grid assumption + grid_span_x = grid_coords[:, 0].max() - grid_coords[:, 0].min() + grid_span_y = grid_coords[:, 1].max() - grid_coords[:, 1].min() + + # Estimate grid dimensions + unique_x = len(np.unique(grid_coords[:, 0])) + unique_y = len(np.unique(grid_coords[:, 1])) + + if unique_x * unique_y == n_grid: + nx, ny = unique_x, unique_y + else: + # Fallback to square root + nx = ny = int(np.sqrt(n_grid)) + + shape = (ny, nx) + + # Define index conversion functions + if n_dims == 3: + def get_indices(idx): + layer = idx // (ny * nx) + remainder = idx % (ny * nx) + row = remainder // nx + col = remainder % nx + return layer, row, col + else: + def get_indices(idx): + row = idx // nx + col = idx % nx + return row, col + + # Variogram function + def variogram(h): + if variogram_model == 'exponential': + return nugget + (sill - nugget) * (1 - np.exp(-h)) + elif variogram_model == 'gaussian': + return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2))) + else: # spherical + return np.where(h <= 1.0, + nugget + (sill - nugget) * (1.5 * h - 0.5 * h ** 3), + sill) + + interp_values_1d = np.full(n_grid, background_value) + + # Calculate zone for each conceptual point if zones provided + if zones is not None: + cp_zones = [] + for coord in cp_coords: + distances = np.sum((grid_coords - coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + if n_dims == 3: + layer, row, col = get_indices(closest_idx) + cp_zones.append(zones[layer, row, col]) + else: + row, col = get_indices(closest_idx) + cp_zones.append(zones[row, col]) + cp_zones = np.array(cp_zones) + + # Pre-compute log transformation if needed + if transform == 'log': + if min_value is None: + positive_values = cp_values[cp_values > 0] + min_value = np.min(positive_values) * 0.01 if len(positive_values) > 0 else 1e-8 + + # Main kriging loop + for i in range(n_grid): + local_tensor = interp_tensors[i] + + if zones is not None: + # Get current zone and filter conceptual points + if n_dims == 3: + layer, row, col = get_indices(i) + current_zone = zones[layer, row, col] + else: + row, col = get_indices(i) + current_zone = zones[row, col] + + # Filter to same zone + zone_mask = cp_zones == current_zone + if np.sum(zone_mask) >= min_points: + cp_coords_filtered = cp_coords[zone_mask] + cp_values_filtered = cp_values[zone_mask] + else: + cp_coords_filtered = cp_coords + cp_values_filtered = cp_values + else: + cp_coords_filtered = cp_coords + cp_values_filtered = cp_values + + # Compute anisotropic distances + try: + tensor_inv = np.linalg.inv(local_tensor) + dx = cp_coords_filtered - grid_coords[i] + aniso_distances = np.sqrt(np.sum(dx @ tensor_inv * dx, axis=1)) + except np.linalg.LinAlgError: + aniso_distances = np.linalg.norm(cp_coords_filtered - grid_coords[i], axis=1) + + # Get neighbors within search radius + valid_mask = aniso_distances <= max_search_radius + if np.sum(valid_mask) < min_points: + continue + + # Sort and limit to max_neighbors + sorted_indices = np.argsort(aniso_distances[valid_mask]) + n_candidates = min(max_neighbors or len(sorted_indices), len(sorted_indices)) + + if n_candidates < min_points: + continue + + closest_indices = np.where(valid_mask)[0][sorted_indices[:n_candidates]] + nearby_values = cp_values_filtered[closest_indices] + nearby_coords = cp_coords_filtered[closest_indices] + nearby_distances = aniso_distances[closest_indices] + + # Apply log transform if needed + if transform == 'log': + nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) + else: + nearby_values_transformed = nearby_values.copy() + + # Set up and solve kriging system + n_pts = len(nearby_values_transformed) + C = np.zeros((n_pts + 1, n_pts + 1)) + c = np.zeros(n_pts + 1) + + # Build covariance matrix + for j in range(n_pts): + for k in range(n_pts): + dx = nearby_coords[j] - nearby_coords[k] + try: + h = np.sqrt(dx.T @ tensor_inv @ dx) + except: + h = np.linalg.norm(dx) + C[j, k] = variogram(h) + + C[j, -1] = 1 + C[-1, j] = 1 + c[j] = variogram(nearby_distances[j]) + + c[-1] = 1 + + # Solve kriging system + try: + cond_num = np.linalg.cond(C) + if cond_num > 1e12: + # Fall back to IDW + weights = np.exp(-nearby_distances) + weights = weights / np.sum(weights) + interp_values_1d[i] = np.sum(weights * nearby_values_transformed) + else: + weights = np.linalg.solve(C, c)[:-1] + interp_values_1d[i] = np.sum(weights * nearby_values_transformed) + except: + # IDW fallback + weights = np.exp(-nearby_distances) + interp_values_1d[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) + + # Back-transform if needed + if transform == 'log': + interp_values_1d = 10 ** interp_values_1d + + return interp_values_1d.reshape(shape) + + +def _generate_stochastic_fields(grid_coords, mean_field, sd_field, + bearing, anisotropy, corrlen, + zones, n_realizations, vartransform, + config_dict, ny, nx, iids=None, area=None): + """ + Generate stochastic fields using pypestutils FIELDGEN2D_SVA. + + This creates temporary files as required by FIELDGEN2D_SVA and then calls + the pypestutils wrapper. + """ + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") + + print(" Generating stochastic fields using FIELDGEN2D_SVA...") + + # Prepare grid data + n_points = ny * nx + x_coords = grid_coords[:, 0] + y_coords = grid_coords[:, 1] + + # Flatten fields + mean_flat = mean_field.flatten() + variance_flat = (sd_field.flatten()) ** 2 # Convert SD to variance + + # Geostatistical parameters from tensors + aa_values = corrlen.flatten() + aniso_values = anisotropy.flatten() + bearing_values = bearing.flatten() + + # domain based on zones + if zones is None: + domain = np.ones((ny,nx)) + else: + domain = np.where(zones>1,1,0) + if area is None: + area = domain.copy() + domain = domain.flatten().astype(int) + area = area.flatten() + + # Generate or use provided IIDs + if iids is None: + print(" Generating new IIDs...") + iids = np.random.normal(0, 1, size=(n_points, n_realizations)) + else: + print(" Using provided IIDs...") + if iids.shape != (n_points, n_realizations): + raise ValueError(f"IIDs shape {iids.shape} doesn't match expected {(n_points, n_realizations)}") + + # Create temporary files for FIELDGEN2D_SVA + node_file = "temp_nodes_2d.dat" + avgfunc_file = "temp_avgfunc_2d.dat" + output_file = "temp_output_2d.csv" + + try: + # Create 2D node specifications file + # _create_node_spec_file(node_file, x_coords, y_coords, zones_flat) + # + # # Create 2D averaging function specification file + # _create_avgfunc_spec_file(avgfunc_file, mean_flat, variance_flat, + # aa_values, aniso_values, bearing_values) + + # Set up pypestutils parameters + lib = PestUtilsLib() + lib.initialize_randgen(11235813) + + # Determine transformation type + if vartransform == 'log': + trans_flag = 'log' # Log domain + else: + trans_flag = 'none' # Natural domain + + # Averaging function type + avg_func = config_dict.get('averaging_function', 'exponential') + if isinstance(avg_func, dict): + if avg_func == 'pow': + avg_flag, power_value = next(iter(avg_func.items())) + else: + power_value = 0 + if avg_func == 'exponential': + avg_flag = 'exp' + elif avg_func == 'gaussian': + avg_flag = 'gauss' + elif avg_func == 'spherical': + avg_flag = 'spher' + else: + avg_flag = 'exp' # Default to exponential + + # Call FIELDGEN2D_SVA through pypestutils interface + print(f" Calling FIELDGEN2D_SVA for {n_realizations} realizations...") + + # This is the correct way to call FIELDGEN2D_SVA in pypestutils + success = lib.fieldgen2d_sva( + x_coords, y_coords, + area=area, + active=domain, + mean=mean_flat, + var=variance_flat, + aa=corrlen, + anis=anisotropy, + bearing=bearing, + transtype=trans_flag, # e.g., 'n' or 'l' + avetype=avg_flag, # 's', 'x', 'g', or 'p' + power=power_value, # only needed if avetype='p' + nreal=n_realizations, + + ) + + if not success: + raise Exception("FIELDGEN2D_SVA execution failed") + + # Read results from CSV file + print(" Reading FIELDGEN2D_SVA results...") + results_df = pd.read_csv(output_file) + + # Extract field values - assume columns are named 'real_001', 'real_002', etc. + fields = np.zeros((n_realizations, ny, nx)) + + for real in range(n_realizations): + col_name = f"real_{real + 1:03d}" + if col_name in results_df.columns: + field_values = results_df[col_name].values + fields[real] = field_values.reshape(ny, nx) + else: + # Fallback column naming + field_values = results_df.iloc[:, real].values + fields[real] = field_values.reshape(ny, nx) + + # Clean up + lib.free_all_memory() + + except Exception as e: + print(f" Warning: FIELDGEN2D_SVA failed: {e}") + print(f" Using fallback method...") + + # Fallback to simple field generation + fields = np.zeros((n_realizations, ny, nx)) + for real in range(n_realizations): + current_iids = iids[:, real] + field_values = _generate_simple_field( + grid_coords, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + vartransform, current_iids, ny, nx + ) + fields[real] = field_values.reshape(ny, nx) + + finally: + # Clean up temporary files + for temp_file in [node_file, avgfunc_file, output_file]: + if os.path.exists(temp_file): + os.remove(temp_file) + + return fields + + +# def _create_node_spec_file(filename, x_coords, y_coords, zones): +# """Create 2D node specifications file for FIELDGEN2D_SVA.""" +# n_points = len(x_coords) +# +# with open(filename, 'w') as f: +# # Header +# f.write(f"{n_points}\n") # Number of nodes +# +# # Node data: node_id, x, y, zone, active_flag +# for i in range(n_points): +# node_id = i + 1 # 1-based indexing +# x = x_coords[i] +# y = y_coords[i] +# zone = zones[i] +# active = 1 # All nodes active +# f.write(f"{node_id:8d} {x:15.6f} {y:15.6f} {zone:6d} {active:2d}\n") +# +# +# def _create_avgfunc_spec_file(filename, mean_vals, variance_vals, aa_vals, aniso_vals, bearing_vals): +# """Create 2D averaging function specification file for FIELDGEN2D_SVA.""" +# n_points = len(mean_vals) +# +# with open(filename, 'w') as f: +# # Header +# f.write(f"{n_points}\n") # Number of nodes +# +# # Averaging function data: node_id, mean, variance, aa, anisotropy, bearing +# for i in range(n_points): +# node_id = i + 1 # 1-based indexing +# mean = mean_vals[i] +# variance = variance_vals[i] +# aa = aa_vals[i] # Correlation length (major axis) +# aniso = aniso_vals[i] # Anisotropy ratio +# bearing = bearing_vals[i] # Bearing in degrees +# +# f.write(f"{node_id:8d} {mean:15.6f} {variance:15.6f} {aa:15.6f} {aniso:10.4f} {bearing:10.2f}\n") + + +def _generate_simple_field(grid_coords, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + vartransform, iids, ny, nx): + """ + Simple fallback field generation if FIELDGEN2D_SVA fails. + """ + print(" Using simple correlated field generation fallback...") + + n_points = len(grid_coords) + + # Scale by local standard deviation + stochastic_component = iids * np.sqrt(variance_flat) + + # Apply simple spatial correlation using distance-based averaging + smoothed_component = np.zeros_like(stochastic_component) + + for i in range(n_points): + local_aa = aa_values[i] + + # Simple correlation based on distance + distances = np.linalg.norm(grid_coords - grid_coords[i], axis=1) + weights = np.exp(-distances / (local_aa + 1e-10)) # Avoid division by zero + weights = weights / np.sum(weights) + + # Apply weighted average + smoothed_component[i] = np.sum(weights * stochastic_component) + + # Combine with mean + if vartransform == 'log': + # Log domain: mean * exp(stochastic_component) + field_values = mean_flat * np.exp(smoothed_component) + else: + # Normal domain: mean + stochastic_component + field_values = mean_flat + smoothed_component + + return field_values + + +def create_boundary_modified_scalar(base_field, zones, + peak_increase=0.3, transition_cells=5, mode="enhance"): + """ + Modify scalar field values near geological zone boundaries. + (Implementation from the provided NSAF code) + """ + from scipy.ndimage import distance_transform_edt, gaussian_filter + + if mode not in ("enhance", "smooth"): + raise ValueError("mode must be 'enhance' or 'smooth'") + + # 2D case only for now + if zones.shape != base_field.shape: + raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") + + boundary_mask, _ = detect_zone_boundaries(zones) + distance = distance_transform_edt(~boundary_mask) + transition_mask = distance <= transition_cells + + modified = base_field.copy() + + if mode == "enhance": + # Linear enhancement near boundaries + factor = 1 - distance[transition_mask] / transition_cells + enhancement = peak_increase * factor + modified[transition_mask] += enhancement + + elif mode == "smooth": + # Smooth field with Gaussian filter + smoothed_field = gaussian_filter(base_field, sigma=transition_cells / 2) + + # Blend with original based on distance to boundary + weight = 1 - distance[transition_mask] / transition_cells + modified[transition_mask] = ( + weight * smoothed_field[transition_mask] + + (1 - weight) * base_field[transition_mask] + ) + + print( + f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") + + return modified + + +def detect_zone_boundaries(zones): + """Detect boundaries between geological zones.""" + ny, nx = zones.shape + boundary_mask = np.zeros((ny, nx)).astype(bool) + boundary_directions = np.zeros((ny, nx, 2)) + + # Find boundary points + for i in range(ny): + for j in range(nx): + current_zone = zones[i, j] + + # Check 8-connected neighbors + for di in [-1, 0, 1]: + for dj in [-1, 0, 1]: + if di == 0 and dj == 0: + continue + ni, nj = i + di, j + dj + if 0 <= ni < ny and 0 <= nj < nx: + if zones[ni, nj] != current_zone: + boundary_mask[i, j] = True + break + if boundary_mask[i, j]: + break + + return boundary_mask, boundary_directions + + +def _save_results(results, out_filename, vartransform): + """Save results to files in PyEMU-compatible format.""" + + # Save mean field + mean_file = f"{out_filename}_mean.txt" + np.savetxt(mean_file, results['mean'], fmt="%20.8E") + print(f" Saved mean field to {mean_file}") + + # Save standard deviation field + sd_file = f"{out_filename}_sd.txt" + np.savetxt(sd_file, results['sd'], fmt="%20.8E") + print(f" Saved SD field to {sd_file}") + + # Save geostatistical parameter fields + for param in ['bearing', 'anisotropy', 'corrlen']: + param_file = f"{out_filename}_{param}.txt" + np.savetxt(param_file, results[param], fmt="%20.8E") + print(f" Saved {param} field to {param_file}") + + # Save stochastic realizations + if results['fields'] is not None: + for i, field in enumerate(results['fields']): + field_file = f"{out_filename}_real_{i + 1:03d}.txt" + np.savetxt(field_file, field, fmt="%20.8E") + + print(f" Saved {len(results['fields'])} realizations") + + +# Example usage function +def example_usage(): + """Example of how to use the tensor interpolation.""" + + # Create example conceptual points DataFrame + cp_data = { + 'name': ['cp1', 'cp2', 'cp3', 'cp4'], + 'x': [1000, 3000, 1000, 3000], + 'y': [1000, 1000, 3000, 3000], + 'z': [0, 0, 0, 0], + 'mean_kh': [1.0, 1.0, 1.0, 1.0], + 'sd_kh': [0.1, 0.1, 0.1, 0.1], + 'major': [2000, 1500, 1800, 2200], + 'anisotropy': [3.0, 2.5, 4.0, 2.0], + 'bearing': [45, 90, 135, 0], # Degrees + 'layer': [0, 0, 0, 0] + } + cp_df = pd.DataFrame(cp_data) + + # Create example grid (pyemu style) + nx, ny = 50, 40 + x = np.linspace(0, 5000, nx) + y = np.linspace(0, 4000, ny) + xcentergrid, ycentergrid = np.meshgrid(x, y) + + # Example zones + zones = np.ones((ny, nx), dtype=int) + zones[:ny // 2, :] = 1 + zones[ny // 2:, :] = 2 + + # Configuration + config = { + 'vartype': 2, # Exponential + 'krigtype': 1, # Ordinary kriging + 'search_radius': 10000, + 'maxpts_interp': 10, + 'minpts_interp': 1 + } + + # Interpolate tensors + tensors = interpolate_tensors( + cp_df, xcentergrid, ycentergrid, + zones=zones, method='krig', + layer=0, config_dict=config + ) + + print(f"Interpolated tensor field shape: {tensors.shape}") + print(f"Sample tensor at grid point 0:\n{tensors[0]}") + + return tensors + + +def example_workflow(): + """Example of complete workflow.""" + import matplotlib.pyplot as plt + + # Create example data + nx, ny = 50, 40 + x = np.linspace(0, 5000, nx) + y = np.linspace(0, 4000, ny) + xcentergrid, ycentergrid = np.meshgrid(x, y, indexing='xy') + xcentergrid = xcentergrid.T + ycentergrid = ycentergrid.T + + # Example conceptual points + cp_data = { + 'name': ['cp1', 'cp2', 'cp3', 'cp4'], + 'x': [1000, 3000, 1000, 3000], + 'y': [1000, 1000, 3000, 3000], + 'z': [0, 0, 0, 0], + 'mean_kh': [1.5, 2.0, 1.8, 1.2], + 'sd_kh': [0.3, 0.4, 0.2, 0.5], + 'major': [2000, 1500, 1800, 2200], + 'anisotropy': [3.0, 2.5, 4.0, 2.0], + 'bearing': [45, 90, 135, 0], + 'layer': [0, 0, 0, 0] + } + cp_df = pd.DataFrame(cp_data) + + # Example zones + zones = np.ones((ny, nx), dtype=int) + zones[:ny // 2, :] = 1 + zones[ny // 2:, :] = 2 + + # Run workflow + results = apply_ppu_hyperpars( + cp_df, xcentergrid, ycentergrid, + zones=zones, n_realizations=3, + out_filename="example", + vartransform="none" + ) + + # Quick visualization + fig, axes = plt.subplots(2, 3, figsize=(15, 10)) + + # Mean field + im1 = axes[0, 0].imshow(results['mean'], origin='lower') + axes[0, 0].set_title('Mean Field') + plt.colorbar(im1, ax=axes[0, 0]) + + # SD field + im2 = axes[0, 1].imshow(results['sd'], origin='lower') + axes[0, 1].set_title('Standard Deviation') + plt.colorbar(im2, ax=axes[0, 1]) + + # Anisotropy field + im3 = axes[0, 2].imshow(results['anisotropy'], origin='lower') + axes[0, 2].set_title('Anisotropy Ratio') + plt.colorbar(im3, ax=axes[0, 2]) + + # First three realizations + for i in range(3): + im = axes[1, i].imshow(results['fields'][i], origin='lower') + axes[1, i].set_title(f'Realization {i + 1}') + plt.colorbar(im, ax=axes[1, i]) + + plt.tight_layout() + plt.savefig('workflow_results.png', dpi=150, bbox_inches='tight') + plt.show() + + print("Example workflow complete!") + return results \ No newline at end of file diff --git a/pyemu/utils/tensor_utils.py b/pyemu/utils/tensor_utils.py deleted file mode 100644 index 8a2ddb8a9..000000000 --- a/pyemu/utils/tensor_utils.py +++ /dev/null @@ -1,2548 +0,0 @@ -import numpy as np -import pandas as pd -from scipy.linalg import logm, expm, solve -from scipy.ndimage import distance_transform_edt, gaussian_filter -import matplotlib as mpl -from pyemu.plot import plot_utils as pu - -mpl.use('Tkagg') -import matplotlib.pyplot as plt -import os - - -# ============================================================================ -# UTILITY FUNCTIONS FOR CONSISTENT ARRAY HANDLING -# ============================================================================ - -def convert_output_to_original_format(field_2d, original_zones_shape): - """ - Convert 2D field back to original input format. - - Parameters - ---------- - field_2d : np.ndarray - Field in 2D format, shape (ny, nx) - original_zones_shape : tuple or None - Original input array shape - - Returns - ------- - np.ndarray - Field in original format (1D or 2D) - """ - if original_zones_shape is None: - return field_2d - - if len(original_zones_shape) == 1: - # Original was 1D, return flattened - return field_2d.flatten() - else: - # Original was 2D, return as-is - return field_2d - - -def standardize_input_arrays(zones_raw, grid_coords, shape): - """ - Standardize all input arrays to consistent 2D format for internal processing. - - Parameters - ---------- - zones_raw : np.ndarray or None - Raw zone array, shape (n_points,) or (ny, nx) or None - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - shape : tuple, actaully required here - Grid shape (ny, nx) for single layer or (nz, ny, nx) for multilayer - - Returns - ------- - tuple - (zones, grid_coords_2d, original_zones_shape) where: - - zones: Always 2D array (ny, nx) or None - - grid_coords_2d: Grid coordinates for 2D processing - - original_zones_shape: Original shape for output conversion - """ - if len(shape) == 3: - nz, ny, nx = shape - layer_shape = (ny, nx) - else: - layer_shape = shape - - # Handle zones - zones = None - original_zones_shape = None - - if zones_raw is not None: - original_zones_shape = zones_raw.shape - ny, nx = layer_shape - - if zones_raw.shape == layer_shape: - zones = zones_raw.copy() - elif zones_raw.shape == (ny * nx,): - zones = zones_raw.reshape(layer_shape) - else: - raise ValueError(f"Zone array shape {zones_raw.shape} incompatible with grid shape {layer_shape}") - - return zones, grid_coords, original_zones_shape - - -def ensure_2d_for_processing(arr, shape): - """ - Ensure array is in 2D format for internal processing. - Simpler version focused on internal consistency. - - Parameters - ---------- - arr : np.ndarray - Input array - shape : tuple - Target 2D shape (ny, nx) - - Returns - ------- - np.ndarray - 2D array with shape (ny, nx) - """ - ny, nx = shape - - if arr.shape == shape: - return arr - elif arr.shape == (ny * nx,): - return arr.reshape(shape) - else: - raise ValueError(f"Array shape {arr.shape} cannot be converted to 2D shape {shape}") - - -def validate_grid_coordinates(grid_coords, shape): - """ - Validate grid coordinates match expected shape. - - Parameters - ---------- - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - shape : tuple - Expected grid shape (nz, ny, nx) or (ny, nx) - - Returns - ------- - bool - True if valid - """ - if len(shape) == 3: - nz, ny, nx = shape - expected_points = nz * ny * nx - else: - ny, nx = shape - expected_points = ny * nx - - return grid_coords.shape[0] == expected_points - - -def generate_fields_from_files(data_dir, conceptual_points_file, grid_file, zone_file=None, iids_file=None, - field_name=['field'], layer_mode=True, save_path='.', tensor_interp='idw'): - """ - Generate spatially correlated fields using tensor-based geostatistics. - - Parameters - ---------- - data_dir: str - Path to files - conceptual_points_file : str - CSV file containing conceptual points - grid_file : str - whitespace-delimited file with grid coordinates (columns: x, y, z) - zone_file : str or list of str, optional - zone file(s): - - Single string: One zone file used for all layers (2D zones) - - List of strings: One zone file per layer (creates 3D zones) - iids_file : str or list of str, optional - noise file(s): - - Single string: Assumes pattern like "iids_layer{N}.arr" - - List of strings: Explicit paths to each layer's noise file - - None: Auto-generates noise and saves as iids_layer{N}.arr files - field_name : list of str, default ['field'] - Name(s) of field(s) to generate - layer_mode : bool, default True - If True, process each layer independently - save_path : str, default '.' - Directory path to save output files - tensor_interp : str, default 'idw' - Tensor interpolation method - - Returns - ------- - None - Saves results to output/ directory - """ - print(f"=== Tensor-Based Non-Stationary Field Generation from {conceptual_points_file} ===") - - # Load data - conceptual_points = pd.read_csv(os.path.join(data_dir, conceptual_points_file)) - grid = pd.read_csv(os.path.join(data_dir, grid_file), sep=r'\s+') - grid_coords = grid[['x', 'y', 'z']].values - - if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: - conceptual_points.loc[:, 'transverse'] = conceptual_points.loc[:, 'major'] / conceptual_points.loc[:, 'anisotropy'] - print(f"Loaded {len(conceptual_points)} conceptual points") - print(f"Loaded {len(grid_coords)} grid points") - - # Infer grid structure - shape = infer_grid_shape(grid_coords) - nz, ny, nx = shape - print(f"Grid shape: {shape} (nz, ny, nx)") - - # Load or generate iids (always layer-specific) - if iids_file is not None: - if isinstance(iids_file, list): - # Explicit list of files - if len(iids_file) != nz: - raise ValueError(f"Number of iids files ({len(iids_file)}) must match number of layers ({nz})") - iids = np.zeros(shape) - for i, file_path in enumerate(iids_file): - layer_iids = np.loadtxt(os.path.join(data_dir, file_path)) - iids[i] = layer_iids.reshape(ny, nx) - print(f" Loaded iids layer {i + 1} from {os.path.join(data_dir, file_path)}") - else: - if '{' in iids_file: - # Pattern like "iids_layer{}.arr" - pattern = os.path.join(data_dir, iids_file) - else: - # Assume pattern from filename - name, ext = os.path.splitext(iids_file) - pattern = os.path.join(data_dir, f"{name}_layer{{}}{ext}") - - iids = np.zeros(shape) - for i in range(nz): - file_path = pattern.format(i + 1) - try: - layer_iids = np.loadtxt(file_path) - iids[i] = layer_iids.reshape(ny, nx) - print(f" Loaded iids layer {i + 1} from {file_path}") - except FileNotFoundError: - print(f" Warning: Could not find {file_path}, generating random noise") - iids[i] = np.random.randn(ny, nx) - else: - print(" Generating correlated noise...") - np.random.seed(42) - iids = np.random.randn(*shape) - for i in range(nz): - iids_file_path = os.path.join(data_dir, f'iids_layer{i + 1}.arr') - np.savetxt(iids_file_path, iids[i]) - print(f" Saved iids layer {i + 1} to {iids_file_path}") - - # Validate grid coordinates - if not validate_grid_coordinates(grid_coords, shape): - raise ValueError(f"Grid coordinates ({len(grid_coords)} points) don't match inferred shape {shape}") - - # Load zones with flexible handling - zones = None - zones_3d = None - - if zone_file: - if isinstance(zone_file, list): - # Multiple zone files - create 3D zones - if len(zone_file) != nz: - raise ValueError(f"Number of zone files ({len(zone_file)}) must match number of layers ({nz})") - - zones_list = [] - for i, file in enumerate(zone_file): - layer_zones = np.loadtxt(os.path.join(data_dir, file)).astype(np.int64) - zones_list.append(layer_zones) - print(f" Loaded zones layer {i + 1} from {os.path.join(data_dir, file)}, shape: {layer_zones.shape}") - - zones_3d = np.array(zones_list) - print(f" Created 3D zones array: {zones_3d.shape}") - - else: - # Single zone file - use for all layers - zones_raw = np.loadtxt(os.path.join(data_dir, zone_file)).astype(np.int64) - zones, _, original_zones_shape = standardize_input_arrays(zones_raw, grid_coords, shape) - print(f" Loaded single zone file: original shape {original_zones_shape}, standardized to 2D {zones.shape}") - - # Convert to 3D by replication - if zones is not None: - zones_3d = np.tile(zones[np.newaxis, :, :], (nz, 1, 1)) - print(f" Replicated 2D zones to 3D: {zones.shape} -> {zones_3d.shape}") - - # Process each field - for fn in field_name: - cols = ['name', 'x', 'y', 'major', 'transverse', 'bearing'] - cols = cols + [_ for _ in conceptual_points.columns if f'_{fn}' in _] - - if layer_mode and 'layer' in conceptual_points.columns: - # Process by layer - cols = cols + ['layer'] - field = generate_field_by_layer(conceptual_points[cols], grid_coords, iids, - shape, zones=zones_3d, save_path=save_path, - tensor_interp=tensor_interp) - else: - # Process full 3D - pass the 3D zones - cols = cols + ['z', 'normal', 'dip'] - field = generate_field_3d(conceptual_points[cols], grid_coords, iids, - shape, zones=zones_3d, save_path=save_path, - tensor_interp=tensor_interp) - - # Save and plot results - for i in conceptual_points.layer.unique(): - save_layer(field[i - 1], layer=i, field_name=fn, save_path=save_path) - pu.plot_layer(np.where((zones_3d[0] == 0), np.nan, field[i - 1]), layer=i, - transform='log', field_name=fn, save_path=save_path) - - print("\n=== Complete ===") - print(f"Field shape: {field.shape}") - print(f"Field statistics: mean={np.mean(field):.3f}, std={np.std(field):.3f}") - - -def generate_field_by_layer(conceptual_points, grid_coords, iids, - shape, zones=None, save_path='.', - tensor_interp='idw'): - """ - Generate field processing each layer independently. - - Parameters - ---------- - conceptual_points : pd.DataFrame - Conceptual points with tensor and statistical parameters - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - iids : np.ndarray - Independent identically distributed random noise, shape (nz, ny, nx) - shape : tuple - Grid shape as (nz, ny, nx) - zones : np.ndarray, optional - Zone IDs for zone-based processing, shape (ny, nx) - save_path : str, default '.' - Directory path to save output files - tensor_interp : str, default 'idw' - Tensor interpolation method: 'idw', 'krig', or 'nearest' - - Returns - ------- - np.ndarray - Field values, shape (nz, ny, nx) - """ - nz, ny, nx = shape - field_3d = np.zeros(shape) - grid_3d = grid_coords.reshape(shape + (3,)) - - # Get unique layers - if 'layer' in conceptual_points.columns: - unique_layers = sorted([int(x-1) for x in conceptual_points['layer'].unique()]) - print(f"Processing layers: {unique_layers}") - - for layer_idx in unique_layers: - print(f"\nProcessing layer {layer_idx}/{len(unique_layers)}") - - # Get grid points and iids for this layer - layer_grid = grid_3d[layer_idx].reshape(-1, 3) - layer_coords = layer_grid[:, :2] # Just x, y - layer_shape = (ny, nx) # Shape for this layer - layer_iids = iids[layer_idx] - - if 'layer' in conceptual_points.columns: - layer_cp = conceptual_points[conceptual_points['layer']-1 == layer_idx] - if len(layer_cp) == 0: - print(f" No conceptual points for layer {layer_idx}, using all") - layer_cp = conceptual_points - else: - layer_cp = conceptual_points - - # Prepare zones for this layer (all zones now consistently 2D) - layer_zones = None - if zones is not None: - if len(zones.shape) == 3: # 3D zones - layer_zones = zones[layer_idx] - else: # 2D zones (standard case), use for all layers - layer_zones = zones - # zones are already guaranteed to be 2D from standardize_input_arrays() - - # Generate field for this layer - layer_field_2d, geological_tensors = generate_single_layer_zone_based(layer_cp, layer_coords, - layer_iids, zones=layer_zones, shape=layer_shape, - save_path=save_path, tensor_interp=tensor_interp) - pu.visualize_geological_tensors(geological_tensors, layer_coords, layer_shape, zones=layer_zones, - conceptual_points=layer_cp, subsample=8, - save_path=save_path, title_suf=f'layer {layer_idx+1}') - - # layer_field_2d is already 2D - field_3d[layer_idx] = layer_field_2d - - return field_3d # Return as 3D array, NOT flattened - - -def generate_field_3d(conceptual_points, grid_coords, iids, shape, zones=None, - save_path='.', transform='log', tensor_interp='idw'): - """ - Generate field using full 3D tensor processing with zone-based interpolation. - - Parameters - ---------- - conceptual_points : pd.DataFrame - Conceptual points with 3D tensor and statistical parameters - Must include columns: x, y, z, major, transverse, normal, bearing, dip, mean_{field}, sd_{field} - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - iids : np.ndarray - Independent identically distributed random noise, shape (nz, ny, nx) - shape : tuple - Grid shape as (nz, ny, nx) - zones : np.ndarray, optional - Zone IDs for zone-based processing. Shape (ny, nx) for 2D zones applied to all layers, - or (nz, ny, nx) for 3D zones - save_path : str, default '.' - Directory path to save output files - tensor_interp : str, default 'idw' - Tensor interpolation method: 'idw', 'krig', or 'nearest' - - Returns - ------- - np.ndarray - Field values, shape (nz, ny, nx) - """ - print("Generating 3D field with full tensor processing...") - nz, ny, nx = shape - - # Extract conceptual point data - cp_coords = conceptual_points[['x', 'y', 'z']].values - - # Get field statistics - field_cols = [col for col in conceptual_points.columns if col.startswith('mean_') or col.startswith('sd_')] - if field_cols: - mean_col = [col for col in field_cols if col.startswith('mean_')][0] - sd_col = [col for col in field_cols if col.startswith('sd_')][0] - cp_means = conceptual_points[mean_col].values - cp_sds = conceptual_points[sd_col].values - else: - # Default values if not specified - cp_means = np.ones(len(cp_coords)) - cp_sds = np.ones(len(cp_coords)) * 0.5 - - # Handle zones for 3D - zones_3d = None - if zones is not None: - if len(zones.shape) == 2: - # 2D zones - replicate for each layer - zones_3d = np.tile(zones[np.newaxis, :, :], (nz, 1, 1)) - print(f" Using 2D zones replicated to 3D: {zones.shape} -> {zones_3d.shape}") - else: - # Already 3D zones - zones_3d = zones - print(f" Using 3D zones: {zones_3d.shape}") - - # Step 1: Create geological tensors from conceptual points - print(" Creating 3D geological correlation tensors...") - bearing = np.radians(conceptual_points['bearing'].values) - dip = np.radians(conceptual_points['dip'].values) - major = conceptual_points['major'].values - transverse = conceptual_points['transverse'].values - normal = conceptual_points['normal'].values - - cp_tensors_3d = create_3d_tensors(bearing, dip, major, transverse, normal) - - # Debug: Test tensor creation with first few points - print(" Tensor creation verification:") - for i in range(min(3, len(cp_tensors_3d))): - eigenvals = np.linalg.eigvals(cp_tensors_3d[i]) - print(f" Point {i}: Eigenvalues={eigenvals}, All positive={np.all(eigenvals > 0)}") - - # Step 2: Interpolate tensors using zone-based approach - geological_tensors_3d = interpolate_tensors(cp_coords, cp_tensors_3d, grid_coords, - zones=zones_3d, method=tensor_interp) - - # Step 3: Interpolate means using zone-aware kriging - print(" Interpolating mean field with 3D geological structure...") - mean_field_1d = tensor_aware_kriging( - cp_coords, cp_means, grid_coords, geological_tensors_3d, - variogram_model='exponential', sill=1.0, nugget=0.01, - background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform=transform, min_value=1e-8, max_neighbors=8, - zones=zones_3d - ) - mean_field_3d = mean_field_1d.reshape(shape) - - # Apply boundary smoothing if zones are present - if zones_3d is not None: - print(" Smoothing values at geological boundaries...") - mean_field_3d = create_boundary_modified_scalar( - mean_field_3d, zones_3d, - transition_cells=3, mode='smooth' - ) - - # Step 4: Interpolate standard deviation using geological tensors - print(" Interpolating standard deviation field...") - sd_field_1d = tensor_aware_kriging( - cp_coords, cp_sds, grid_coords, geological_tensors_3d, - variogram_model='exponential', sill=0.5, nugget=0.01, - background_value=np.mean(cp_sds), max_search_radius=1e20, - min_points=3, transform=transform, min_value=1e-8, max_neighbors=8, - zones=zones_3d - ) - sd_field_3d = sd_field_1d.reshape(shape) - - # Apply boundary enhancement if zones are present - if zones_3d is not None: - print(" Enhancing variance at geological boundaries...") - sd_field_3d = create_boundary_modified_scalar( - sd_field_3d, zones_3d, - peak_increase=1, transition_cells=3, mode='enhance' - ) - - # Step 5: Generate correlated noise using 3D tensors - print(" Generating 3D correlated noise...") - # Work with 1D for noise generation, then reshape - iids_flat = iids.flatten() - correlated_noise_1d = generate_correlated_noise_3d( - grid_coords, geological_tensors_3d, iids_flat, - n_neighbors=10, anisotropy_strength=1.0 - ) - correlated_noise_3d = correlated_noise_1d.reshape(shape) - - # Step 6: Combine into log-normal field - print(" Combining fields using log-normal formulation...") - print(f" Mean field shape: {mean_field_3d.shape}") - print(f" SD field shape: {sd_field_3d.shape}") - print(f" Noise shape: {correlated_noise_3d.shape}") - - # Check if correlated noise has proper statistics (should be ~N(0,1)) - noise_mean = np.mean(correlated_noise_3d) - noise_std = np.std(correlated_noise_3d) - print(f" Correlated noise stats: mean={noise_mean:.6f}, std={noise_std:.6f}") - - # Step 6: Combine fields with transform handling - if transform == 'log': - print(" Combining fields using log-normal formulation...") - # Convert to lognormal parameters - variance_2d = sd_field_3d ** 2 - mu_2d = np.log(mean_field_3d ** 2 / np.sqrt(variance_2d + mean_field_3d ** 2)) - sigma_2d = np.sqrt(np.log(1 + variance_2d / mean_field_3d ** 2)) - normal_field = mu_2d + correlated_noise_3d * sigma_2d - field_3d = np.exp(normal_field) - else: - print(" Combining fields using normal formulation...") - field_3d = mean_field_3d + correlated_noise_3d * sd_field_3d - - - print(f" Log-mean field range: [{np.min(field_3d):.3f}, {np.max(field_3d):.3f}]") - print(f" Generated field range: [{np.min(field_3d):.3e}, {np.max(field_3d):.3e}]") - - print(f" Generated 3D field with shape {field_3d.shape}") - print(f" Field stats: mean={np.mean(field_3d):.3f}, std={np.std(field_3d):.3f}") - - # Export to ParaView - export_3d_results_to_paraview( - field_3d=field_3d, - tensors_3d=geological_tensors_3d, - grid_coords=grid_coords, - shape=shape, - zones=zones, - conceptual_points=conceptual_points, - save_path=save_path, - field_name='conductivity' - ) - - return field_3d - - - -def generate_single_layer_zone_based(conceptual_points, grid_coords_2d, iids, - zones=None, shape=None, save_path='.', - tensor_interp='idw', transform='log'): - """ - Modified generate_single_layer with zone-based tensor interpolation. - - Parameters - ---------- - conceptual_points : pd.DataFrame - Conceptual points with tensor and statistical parameters - grid_coords_2d : np.ndarray - Grid coordinates for single layer, shape (n_points, 2) or (n_points, 3) - iids : np.ndarray - Independent identically distributed random noise for this layer - zones : np.ndarray, optional - Zone IDs, shape (ny, nx) - shape : tuple, optional - Grid shape (ny, nx). If None, inferred from grid_coords_2d - save_path : str, default '.' - Directory path to save output files - tensor_interp : str, default 'idw' - Tensor interpolation method: 'idw', 'krig', or 'nearest' - - Returns - ------- - tuple - (field_2d, geological_tensors) where: - - field_2d: Generated field, shape (ny, nx) - - geological_tensors: Interpolated tensors, shape (n_points, 2, 2) - """ - cp_coords = conceptual_points[['x', 'y']].values - cp_means = conceptual_points.filter(like='mean').values.flatten() - - sd_cols = [col for col in conceptual_points.columns if 'sd_' in col.lower()] - if sd_cols: - cp_sd = conceptual_points[sd_cols[0]].values - else: - raise ValueError("No sd columns found (expected format: 'sd_fieldname')") - - if not shape: - shape = infer_grid_shape(grid_coords_2d) - - # Step 1: Create geological tensors from conceptual points - print(" Creating geological correlation tensors...") - major = conceptual_points['major'].values - minor = conceptual_points['transverse'].values - - # Better normalization that preserves direction - def normalize_bearing(bearing): - # Convert to 0-360 first - bearing = bearing % 360 - # Don't automatically fold to 0-180, preserve the original direction choice - return bearing - - bearings_normalized = [normalize_bearing(b) for b in conceptual_points['bearing'].values] - bearing_rad = np.radians(bearings_normalized) - cp_tensors = create_2d_tensors(bearing_rad, major, minor) - - # Debug: Test tensor creation with first few points - print(" Tensor creation verification:") - for i in range(min(3, len(cp_tensors))): - original_bearing = conceptual_points['bearing'].values[i] - extracted_bearing = get_tensor_bearing(cp_tensors[i], original_bearing) - print(f" Point {i}: Input bearing={original_bearing:.1f}°, Extracted={extracted_bearing:.1f}°") - - # Step 2: Interpolate tensors, various methods - geological_tensors = interpolate_tensors(cp_coords, cp_tensors, grid_coords_2d, - zones=zones, method=tensor_interp) - - # Step 3: Interpolate means using zone-aware kriging - returns 2D - print(" Interpolating means with geological structure...") - interp_means_2d = tensor_aware_kriging( - cp_coords, cp_means, grid_coords_2d, geological_tensors, - variogram_model='exponential', sill=1.0, nugget=0.1, - background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform=transform, min_value=1e-8, max_neighbors=4, - zones=zones - ) - if zones is not None: - print(" Smoothing values at geological boundaries...") - interp_means_2d = create_boundary_modified_scalar( - interp_means_2d, zones, - transition_cells=3, mode='smooth' - ) - - # Step 5: Interpolate sd using GEOLOGICAL tensors - returns 2D - print(" Interpolating standard deviation...") - interp_sd_2d = tensor_aware_kriging( - cp_coords, cp_sd, grid_coords_2d, geological_tensors, # Use geological tensors - variogram_model='exponential', sill=1.0, nugget=0.1, - background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform=transform, min_value=1e-8, max_neighbors=4, - zones=zones - ) - if zones is not None: - print(" Enhancing variance at geological boundaries...") - interp_sd_2d = create_boundary_modified_scalar( - interp_sd_2d, zones, - peak_increase=1, transition_cells=3, mode='enhance' - ) - - # Step 6: Generate correlated noise - work with 1D for noise generation, then reshape - print(" Generating correlated noise...") - correlated_noise_1d = generate_correlated_noise_2d(grid_coords_2d, geological_tensors, iids) - correlated_noise_2d = correlated_noise_1d.reshape(shape) - - # Step 7: Combine fields with transform handling - if transform == 'log': - print(" Combining fields using log-normal formulation...") - # Convert to lognormal parameters - variance_2d = interp_sd_2d ** 2 - mu_2d = np.log(interp_means_2d ** 2 / np.sqrt(variance_2d + interp_means_2d ** 2)) - sigma_2d = np.sqrt(np.log(1 + variance_2d / interp_means_2d ** 2)) - normal_field = mu_2d + correlated_noise_2d * sigma_2d - field_2d = np.exp(normal_field) - else: - print(" Combining fields using normal formulation...") - field_2d = interp_means_2d + correlated_noise_2d * interp_sd_2d - - return field_2d, geological_tensors - - -def interpolate_tensors(cp_coords, cp_tensors, grid_coords, zones=None, method='idw'): - """ - Single tensor interpolation function - replaces all the variants. - - Parameters - ---------- - zones : np.ndarray, optional - If provided, interpolation is done within zones - method : str, default 'idw' - 'nearest', 'idw', or 'krig' - """ - if zones is not None: - return _interpolate_by_zone(cp_coords, cp_tensors, grid_coords, zones, method) - else: - return _interpolate_global(cp_coords, cp_tensors, grid_coords, method) - - -def _interpolate_by_zone(cp_coords, cp_tensors, grid_coords, zones, method): - """Handle zone-based interpolation.""" - n_grid = len(grid_coords) - tensor_size = cp_tensors.shape[-1] - result = np.zeros((n_grid, tensor_size, tensor_size)) - - # Get zone assignment function based on zones shape - if len(zones.shape) == 2: - ny, nx = zones.shape - get_zone = lambda i: zones[divmod(i, nx)] - else: - nz, ny, nx = zones.shape - get_zone = lambda i: zones[i // (ny * nx), (i % (ny * nx)) // nx, i % nx] - - # Assign conceptual points to zones - cp_zones = assign_conceptual_points_to_zones(cp_coords, grid_coords, zones) - - # Process each zone - for zone_id in np.unique(zones): - zone_cp_mask = cp_zones == zone_id - zone_cp_coords = cp_coords[zone_cp_mask] - zone_cp_tensors = cp_tensors[zone_cp_mask] - - zone_indices = [i for i in range(n_grid) if get_zone(i) == zone_id] - - if len(zone_cp_coords) > 0 and len(zone_indices) > 0: - zone_grid_coords = grid_coords[zone_indices] - zone_tensors = _interpolate_global(zone_cp_coords, zone_cp_tensors, - zone_grid_coords, method) - result[zone_indices] = zone_tensors - elif len(zone_indices) > 0: - # Default tensor for zones without conceptual points - default_tensor = np.eye(tensor_size) * 1000 ** 2 - result[zone_indices] = default_tensor - - return result - - -def _interpolate_global(cp_coords, cp_tensors, grid_coords, method): - """Handle global interpolation without zones.""" - if method == 'nearest': - return _nearest_method(cp_coords, cp_tensors, grid_coords) - elif method == 'idw': - return _idw_method(cp_coords, cp_tensors, grid_coords) - elif method == 'krig': - return _kriging_method(cp_coords, cp_tensors, grid_coords) - else: - raise ValueError(f"Unknown method: {method}") - - -def _nearest_method(cp_coords, cp_tensors, grid_coords): - """Nearest neighbor interpolation.""" - from scipy.spatial.distance import cdist - distances = cdist(grid_coords, cp_coords) - nearest_indices = np.argmin(distances, axis=1) - return cp_tensors[nearest_indices] - - -def _idw_method(cp_coords, cp_tensors, grid_coords): - """IDW using log-Euclidean approach.""" - n_grid = len(grid_coords) - tensor_size = cp_tensors.shape[-1] - - # Convert to log space - log_tensors = np.array([logm(_ensure_positive_definite(tensor)) - for tensor in cp_tensors]) - - # Interpolate each component - result_log = np.zeros((n_grid, tensor_size, tensor_size)) - - for i in range(tensor_size): - for j in range(i, tensor_size): - values = log_tensors[:, i, j].real - interp_values = _idw_interpolation(cp_coords, values, grid_coords) - result_log[:, i, j] = interp_values - if i != j: - result_log[:, j, i] = interp_values - - # Convert back from log space - result = np.array([_ensure_positive_definite(expm(log_tensor)) - for log_tensor in result_log]) - return result - - -def _kriging_method(cp_coords, cp_tensors, grid_coords): - """Kriging using log-Euclidean approach.""" - n_grid = len(grid_coords) - tensor_size = cp_tensors.shape[-1] - - # Convert to log space - log_tensors = np.array([logm(_ensure_positive_definite(tensor)) - for tensor in cp_tensors]) - - # Interpolate each component - result_log = np.zeros((n_grid, tensor_size, tensor_size)) - - for i in range(tensor_size): - for j in range(i, tensor_size): - values = log_tensors[:, i, j].real - interp_values = ordinary_kriging(cp_coords, values, grid_coords, - variogram_model='exponential', range_param=10000, - sill=1.0, nugget=0.1, background_value=0.0, - max_search_radius=1e20, min_points=1, - transform=None, min_value=1e-8, max_neighbors=8) - result_log[:, i, j] = interp_values - if i != j: - result_log[:, j, i] = interp_values - - # Convert back from log space - result = np.array([_ensure_positive_definite(expm(log_tensor)) - for log_tensor in result_log]) - return result - -def ordinary_kriging(cp_coords, cp_values, grid_coords, - variogram_model='exponential', range_param=10000, - sill=1.0, nugget=0.1, background_value=0.0, - max_search_radius=1e20, min_points=1, - transform=None, min_value=1e-8, max_neighbors=5): - """ - Standard ordinary kriging with variogram models and search controls. - - Returns - ------- - np.ndarray - Interpolated values (1D array) - kept as 1D since this is a utility function - """ - n_grid = len(grid_coords) - interp_values = np.full(n_grid, background_value) - - def variogram(h): - if variogram_model == 'exponential': - return nugget + (sill - nugget) * (1 - np.exp(-h / range_param)) - elif variogram_model == 'gaussian': - return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2) / (range_param ** 2))) - else: # spherical - return np.where(h <= range_param, - nugget + (sill - nugget) * (1.5 * h / range_param - 0.5 * (h / range_param) ** 3), - sill) - - for i in range(n_grid): - distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) - sorted_indices = np.argsort(distances) - n_candidates = min(max_neighbors or len(distances), - np.sum(distances <= max_search_radius)) - - if n_candidates < min_points: - continue - - closest_indices = sorted_indices[:n_candidates] - nearby_values = cp_values[closest_indices] - nearby_coords = cp_coords[closest_indices] - nearby_distances = distances[closest_indices] - - if transform == 'log': - if min_value is None: - positive_values = nearby_values[nearby_values > 0] - min_value = np.min(positive_values) * 0.01 - nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) - else: - nearby_values_transformed = nearby_values.copy() - - C = np.zeros((n_candidates + 1, n_candidates + 1)) - c = np.zeros(n_candidates + 1) - - for j in range(n_candidates): - for k in range(n_candidates): - h = np.linalg.norm(nearby_coords[j] - nearby_coords[k]) - C[j, k] = variogram(h) - C[j, -1] = 1 - C[-1, j] = 1 - c[j] = variogram(nearby_distances[j]) - c[-1] = 1 - - try: - weights = solve(C, c)[:-1] - interp_values[i] = np.sum(weights * nearby_values_transformed) - except: - weights = np.exp(-nearby_distances / range_param) - interp_values[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) - - if transform == 'log': - interp_values = 10 ** interp_values - - return interp_values - - -def _idw_interpolation(cp_coords, cp_values, grid_coords, power=1.0, radius=1000): - """Simple IDW interpolation for scalar values.""" - n_grid = len(grid_coords) - interp_values = np.zeros(n_grid) - - for i in range(n_grid): - distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) - weights = np.exp(-distances / radius) # Exponential decay weights - - if np.sum(weights) > 1e-10: - interp_values[i] = np.sum(weights * cp_values) / np.sum(weights) - else: - interp_values[i] = np.mean(cp_values) # Fallback - - return interp_values - - - -def _ensure_positive_definite(tensor, min_eigenval=1e-8): - """Ensure tensor is positive definite.""" - eigenvals, eigenvecs = np.linalg.eigh(tensor) - eigenvals_reg = np.maximum(eigenvals, min_eigenval) - return eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T - -def create_boundary_modified_scalar(base_field, zones, - peak_increase=0.3, transition_cells=5, mode="enhance"): - """ - Modify scalar field values near geological zone boundaries with either enhancement or smoothing. - Supports both 2D and 3D fields/zones. - - Parameters - ---------- - base_field : np.ndarray - Base scalar values, shape (ny, nx) for 2D or (nz, ny, nx) for 3D - zones : np.ndarray - Zone IDs, shape (ny, nx) for 2D or (nz, ny, nx) for 3D - peak_increase : float - Max enhancement or smoothing strength - transition_cells : int - Width of transition region - mode : str - 'enhance' or 'smooth' - - Returns - ------- - np.ndarray - Modified scalar field, same shape as input - """ - from scipy.ndimage import distance_transform_edt, gaussian_filter - - if mode not in ("enhance", "smooth"): - raise ValueError("mode must be 'enhance' or 'smooth'") - - # Determine if we're working with 2D or 3D - is_3d = len(base_field.shape) == 3 - - if is_3d: - # 3D case - nz, ny, nx = base_field.shape - if zones.shape != base_field.shape: - raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") - - # Process each layer separately for better boundary detection - modified = base_field.copy() - total_modified_points = 0 - - for layer in range(nz): - layer_field = base_field[layer] - layer_zones = zones[layer] - - # Detect boundaries in this layer - boundary_mask, _ = detect_zone_boundaries(layer_zones) - distance = distance_transform_edt(~boundary_mask) - transition_mask = distance <= transition_cells - - if mode == "enhance": - # Linear enhancement near boundaries - factor = 1 - distance[transition_mask] / transition_cells - enhancement = peak_increase * factor - modified[layer][transition_mask] += enhancement - - elif mode == "smooth": - # Smooth field with Gaussian filter as the target blending value - smoothed_field = gaussian_filter(layer_field, sigma=transition_cells / 2) - - # Blend with original based on distance to boundary - weight = 1 - distance[transition_mask] / transition_cells - modified[layer][transition_mask] = ( - weight * smoothed_field[transition_mask] + - (1 - weight) * layer_field[transition_mask] - ) - - total_modified_points += np.count_nonzero(transition_mask) - - print( - f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {total_modified_points} points near boundaries across {nz} layers") - - else: - # 2D case - original logic - if zones.shape != base_field.shape: - raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") - - boundary_mask, _ = detect_zone_boundaries(zones) - distance = distance_transform_edt(~boundary_mask) - transition_mask = distance <= transition_cells - - modified = base_field.copy() - - if mode == "enhance": - # Linear enhancement near boundaries - factor = 1 - distance[transition_mask] / transition_cells - enhancement = peak_increase * factor - modified[transition_mask] += enhancement - - elif mode == "smooth": - # Smooth field with Gaussian filter as the target blending value - smoothed_field = gaussian_filter(base_field, sigma=transition_cells / 2) - - # Blend with original based on distance to boundary - weight = 1 - distance[transition_mask] / transition_cells - modified[transition_mask] = ( - weight * smoothed_field[transition_mask] + - (1 - weight) * base_field[transition_mask] - ) - - print( - f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") - - return modified - - -def create_boundary_enhanced_tensors(geological_tensors, zones, - boundary_anisotropy=10.0, boundary_major_scale=2.0, - transition_cells=3): - """ - Enhance geological tensors at zone boundaries with increased anisotropy. - Uses distance-based transition from geological to boundary behavior. - - Note: Alternative boundary enhancement method. Implemented but found to be - suboptimal compared to create_boundary_modified_scalar(). Kept for reference. - - Parameters - ---------- - geological_tensors : np.ndarray - Base geological tensors, shape (n_points, 2, 2) - zones : np.ndarray - Zone IDs, shape (ny, nx) - boundary_anisotropy : float, default 10.0 - Anisotropy ratio at boundaries (major/minor) - boundary_major_scale : float, default 2.0 - Scale factor for major axis at boundaries - transition_cells : int, default 3 - Number of cells over which to transition from geological to boundary - - Returns - ------- - np.ndarray - Enhanced tensors, shape (n_points, 2, 2) - """ - shape = zones.shape - ny, nx = shape - - # Detect boundary points and orientations - boundary_mask, boundary_directions = detect_zone_boundaries(zones) - - enhanced_tensors = geological_tensors.copy() - boundary_count = 0 - - for i in range(len(geological_tensors)): - row, col = divmod(i, nx) - - # Calculate minimum distance to any boundary within transition zone - min_dist_to_boundary = transition_cells + 1 # Start beyond transition - - for dr in range(-transition_cells, transition_cells + 1): - for dc in range(-transition_cells, transition_cells + 1): - check_row, check_col = row + dr, col + dc - if (0 <= check_row < ny and 0 <= check_col < nx and - boundary_mask[check_row, check_col]): - dist = np.sqrt(dr ** 2 + dc ** 2) - min_dist_to_boundary = min(min_dist_to_boundary, dist) - - # Skip if far from boundaries - if min_dist_to_boundary > transition_cells: - continue - - boundary_count += 1 - - # Get boundary direction (use closest boundary point's direction) - boundary_dir = boundary_directions[row, col] - if np.linalg.norm(boundary_dir) < 1e-10: - # If current cell isn't boundary, find closest boundary direction - for dr in range(-transition_cells, transition_cells + 1): - for dc in range(-transition_cells, transition_cells + 1): - check_row, check_col = row + dr, col + dc - if (0 <= check_row < ny and 0 <= check_col < nx and - boundary_mask[check_row, check_col]): - boundary_dir = boundary_directions[check_row, check_col] - if np.linalg.norm(boundary_dir) > 1e-10: - break - if np.linalg.norm(boundary_dir) > 1e-10: - break - - if np.linalg.norm(boundary_dir) < 1e-10: - continue - - # Normalize direction - boundary_dir = boundary_dir / np.linalg.norm(boundary_dir) - - # Create boundary tensor - cos_theta = boundary_dir[0] - sin_theta = boundary_dir[1] - - # rotate clockwise - R = np.array([[cos_theta, sin_theta], - [-sin_theta, cos_theta]]) - - eigenvals = np.linalg.eigvals(geological_tensors[i]) - dominant_length = np.sqrt(np.max(eigenvals)) - - major_length = dominant_length * boundary_major_scale - minor_length = major_length / boundary_anisotropy - - S_boundary = np.diag([minor_length ** 2, major_length ** 2]) - boundary_tensor = R @ S_boundary @ R.T - - # Distance-based blending with eigenvalue interpolation - if min_dist_to_boundary == 0: - # At boundary - pure boundary tensor - enhanced_tensors[i] = boundary_tensor - else: - # Smooth transition - blend eigenvalues to preserve positive definiteness - geo_weight = min_dist_to_boundary / transition_cells - - geo_vals, geo_vecs = np.linalg.eigh(geological_tensors[i]) - bound_vals, bound_vecs = np.linalg.eigh(boundary_tensor) - - # Interpolate eigenvalues, use boundary orientation - mixed_vals = geo_weight * geo_vals + (1 - geo_weight) * bound_vals - enhanced_tensors[i] = bound_vecs @ np.diag(mixed_vals) @ bound_vecs.T - - print(f" Enhanced {boundary_count} tensors at geological boundaries") - return enhanced_tensors - - -def detect_zone_boundaries(zones): - """ - Detect boundaries between geological zones and estimate local orientations. - - Parameters - ---------- - zones : np.ndarray - Zone IDs, shape (ny, nx) - - Returns - ------- - tuple - (boundary_mask, boundary_directions) where: - - boundary_mask: Boolean array indicating boundary points, shape (ny, nx) - - boundary_directions: Direction vectors at boundaries, shape (ny, nx, 2) - """ - ny, nx = zones.shape - boundary_mask = np.zeros((ny, nx)).astype(bool) - boundary_directions = np.zeros((ny, nx, 2)) - - # Find boundary points (any point with different-zone neighbors) - for i in range(ny): - for j in range(nx): - current_zone = zones[i, j] - - # Check 8-connected neighbors - for di in [-1, 0, 1]: - for dj in [-1, 0, 1]: - if di == 0 and dj == 0: - continue - ni, nj = i + di, j + dj - if 0 <= ni < ny and 0 <= nj < nx: - if zones[ni, nj] != current_zone: - boundary_mask[i, j] = True - break - if boundary_mask[i, j]: - break - - # Estimate boundary orientations - for i in range(ny): - for j in range(nx): - if not boundary_mask[i, j]: - continue - - boundary_directions[i, j] = estimate_boundary_direction(zones, i, j) - - return boundary_mask, boundary_directions - - -def estimate_boundary_direction(zones, center_i, center_j, radius=2): - """ - Estimate local boundary direction at a point. - Returns direction vector in geological coordinates (North = 0°). - - Parameters - ---------- - zones : np.ndarray - Zone IDs - GUARANTEED to be 2D, shape (ny, nx) - center_i, center_j : int - Center point coordinates - radius : int - Search radius - - Returns - ------- - np.ndarray - Boundary direction vector [east_component, north_component] - where North = [0, 1], East = [1, 0] (geological convention) - """ - ny, nx = zones.shape - center_zone = zones[center_i, center_j] - - same_zone_points = [] - diff_zone_points = [] - - # Collect points within radius - for di in range(-radius, radius + 1): - for dj in range(-radius, radius + 1): - if di == 0 and dj == 0: - continue - - ni, nj = center_i + di, center_j + dj - if 0 <= ni < ny and 0 <= nj < nx: - if zones[ni, nj] == center_zone: - same_zone_points.append([dj, di]) # x=dj (east), y=di (south in array coords) - else: - diff_zone_points.append([dj, di]) - - if len(same_zone_points) < 1 or len(diff_zone_points) < 1: - return np.array([0.0, 1.0]) # Default north (geological N=0°) - - same_zone_points = np.array(same_zone_points) - diff_zone_points = np.array(diff_zone_points) - - # Find direction separating the groups - same_centroid = np.mean(same_zone_points, axis=0) - diff_centroid = np.mean(diff_zone_points, axis=0) - - separation_vector = diff_centroid - same_centroid - if np.linalg.norm(separation_vector) < 1e-10: - return np.array([0.0, 1.0]) # Default north - - # Convert to geological coordinates and get boundary direction - # Array coordinates: x=east, y=south; Geological: x=east, y=north - sep_east = separation_vector[0] - sep_south = separation_vector[1] - sep_north = -sep_south # Flip y-axis: array south becomes geological north - - # Normalize separation vector in geological coordinates - sep_geo = np.array([sep_east, sep_north]) - sep_geo_normalized = sep_geo / np.linalg.norm(sep_geo) - - # Boundary runs perpendicular to separation (rotate 90° counterclockwise) - # For vector [east, north], perpendicular is [-north, east] - boundary_direction = np.array([-sep_geo_normalized[1], sep_geo_normalized[0]]) - - return boundary_direction - - -def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, - variogram_model='exponential', sill=1.0, nugget=0.01, - background_value=0.0, max_search_radius=1e20, min_points=1, - max_neighbors=4, transform=None, min_value=1e-8, - zones=None): - """ - Ordinary kriging using tensor-aware anisotropic distances. - """ - n_grid = len(grid_coords) - n_dims = grid_coords.shape[1] - - # Get shape from zones if provided, or infer from grid - if zones is not None: - shape = zones.shape - if n_dims == 2: - ny, nx = shape - else: # 3D zones would be (nz, ny, nx) - nz, ny, nx = shape - else: - # Use existing function to infer shape - shape = infer_grid_shape(grid_coords) - if n_dims == 2: - ny, nx = shape[1], shape[2] # shape is (1, ny, nx) - else: - nz, ny, nx = shape - - # Define index conversion functions based on dimensions - if n_dims == 3: - def get_indices(idx): - layer = idx // (ny * nx) - remainder = idx % (ny * nx) - row = remainder // nx - col = remainder % nx - return layer, row, col - else: - def get_indices(idx): - row = idx // nx - col = idx % nx - return row, col - - # MOVE VARIOGRAM FUNCTION OUTSIDE THE LOOP! - def variogram(h): - if variogram_model == 'exponential': - return nugget + (sill - nugget) * (1 - np.exp(-h)) - elif variogram_model == 'gaussian': - return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2))) - else: # spherical - return np.where(h <= 1.0, - nugget + (sill - nugget) * (1.5 * h - 0.5 * h ** 3), - sill) - - interp_values_1d = np.full(n_grid, background_value) - - # Calculate zone for each conceptual point (once, outside the main loop) - if zones is not None: - cp_zones = [] - for coord in cp_coords: - # Find closest grid point to get zone - distances = np.sum((grid_coords - coord) ** 2, axis=1) - closest_idx = np.argmin(distances) - if n_dims == 3: - layer, row, col = get_indices(closest_idx) - cp_zones.append(zones[layer, row, col]) - else: - row, col = get_indices(closest_idx) - cp_zones.append(zones[row, col]) - cp_zones = np.array(cp_zones) - - # PRE-COMPUTE LOG TRANSFORMATION IF NEEDED - if transform == 'log': - if min_value is None: - positive_values = cp_values[cp_values > 0] - min_value = np.min(positive_values) * 0.01 if len(positive_values) > 0 else 1e-8 - - # MAIN GRID LOOP - This is doing kriging for each grid point individually - for i in range(n_grid): - local_tensor = interp_tensors[i] - - if zones is not None: - # Get current zone and filter conceptual points - if n_dims == 3: - layer, row, col = get_indices(i) - current_zone = zones[layer, row, col] - else: - row, col = get_indices(i) - current_zone = zones[row, col] - - # Filter to same zone, fallback to all if insufficient points found - zone_mask = cp_zones == current_zone - if np.sum(zone_mask) >= min_points: - cp_coords_filtered = cp_coords[zone_mask] - cp_values_filtered = cp_values[zone_mask] - else: - cp_coords_filtered = cp_coords - cp_values_filtered = cp_values - else: - cp_coords_filtered = cp_coords - cp_values_filtered = cp_values - - # COMPUTE ANISOTROPIC DISTANCES - try: - tensor_inv = np.linalg.inv(local_tensor) - # VECTORIZE THIS INSTEAD OF LOOP! - dx = cp_coords_filtered - grid_coords[i] # Broadcasting - aniso_distances = np.sqrt(np.sum(dx @ tensor_inv * dx, axis=1)) - except np.linalg.LinAlgError: - aniso_distances = np.linalg.norm(cp_coords_filtered - grid_coords[i], axis=1) - - sorted_indices = np.argsort(aniso_distances) - n_candidates = min(max_neighbors or len(aniso_distances), - np.sum(aniso_distances <= max_search_radius)) - - if n_candidates < min_points: - continue - - closest_indices = sorted_indices[:n_candidates] - nearby_values = cp_values_filtered[closest_indices] - nearby_coords = cp_coords_filtered[closest_indices] - nearby_distances = aniso_distances[closest_indices] - - # APPLY LOG TRANSFORM IF NEEDED - if transform == 'log': - nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) - else: - nearby_values_transformed = nearby_values.copy() - - # KRIGING SYSTEM SETUP AND SOLVE - # TODO: use gwutils - C = np.zeros((n_candidates + 1, n_candidates + 1)) - c = np.zeros(n_candidates + 1) - - # BUILD COVARIANCE MATRIX - Could potentially vectorize this too - for j in range(n_candidates): - for k in range(n_candidates): - dx = nearby_coords[j] - nearby_coords[k] - try: - h = np.sqrt(dx.T @ tensor_inv @ dx) - except: - h = np.linalg.norm(dx) - C[j, k] = variogram(h) - - C[j, -1] = 1 - C[-1, j] = 1 - c[j] = variogram(nearby_distances[j]) - - c[-1] = 1 - - # SOLVE KRIGING SYSTEM - try: - cond_num = np.linalg.cond(C) - if cond_num > 1e12: # Matrix is poorly conditioned - # Fall back to inverse distance weighting - weights = np.exp(-nearby_distances) - weights = weights / np.sum(weights) - interp_values_1d[i] = np.sum(weights * nearby_values_transformed) - else: - weights = np.linalg.solve(C, c)[:-1] - interp_values_1d[i] = np.sum(weights * nearby_values_transformed) - except: - # Existing fallback for any other errors - weights = np.exp(-nearby_distances) - interp_values_1d[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) - - # BACK-TRANSFORM IF NEEDED - if transform == 'log': - interp_values_1d = 10 ** interp_values_1d - - return interp_values_1d.reshape(shape) - - -import numpy as np -from scipy.spatial import cKDTree - - -def generate_correlated_noise_2d(grid_coords, tensors, iids, - n_neighbors=4, anisotropy_strength=1.0): - """Generate spatially correlated noise respecting tensor anisotropy - optimized version.""" - n_points = len(grid_coords) - - # Vectorized eigendecomposition for all tensors at once - evals, evecs = np.linalg.eigh(tensors) - - # Ensure consistent ordering (largest eigenvalue first) - swap_mask = evals[:, 1] < evals[:, 0] - evals[swap_mask] = evals[swap_mask, ::-1] - evecs[swap_mask] = evecs[swap_mask, :, ::-1] - - # Vectorized computation of correlation lengths and scaling - major_corr_length = np.sqrt(evals[:, 1]) - minor_corr_length = np.sqrt(evals[:, 0]) - scale_minor = minor_corr_length / anisotropy_strength - scale_major = major_corr_length * anisotropy_strength - - # Create transformation matrices for all points - scales = np.stack([scale_minor, scale_major], axis=1) - transforms = evecs * scales[:, np.newaxis, :] - - # Pre-compute inverse transforms - inv_transforms = np.linalg.inv(transforms) - - correlated_noise = np.zeros(n_points) - iids_flat = iids.flatten() - - # Use spatial indexing for faster neighbor finding when beneficial - if n_points > 1000: # Threshold where spatial indexing becomes beneficial - tree = cKDTree(grid_coords) - # Use a reasonable search radius to limit candidates - max_search_radius = np.percentile(np.sqrt(evals.max(axis=1)), 95) * anisotropy_strength * 3 - - for i in range(n_points): - # Get candidates within search radius - candidates = tree.query_ball_point(grid_coords[i], max_search_radius) - if len(candidates) <= n_neighbors: - neighbor_indices = candidates - else: - # Transform and compute anisotropic distances only for candidates - dx_candidates = grid_coords[candidates] - grid_coords[i] - dx_transformed = dx_candidates @ inv_transforms[i].T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) - neighbor_indices = np.array(candidates)[np.argsort(aniso_distances)[:n_neighbors]] - - # Compute final weights and weighted sum - if len(neighbor_indices) > 0: - dx_final = grid_coords[neighbor_indices] - grid_coords[i] - dx_transformed_final = dx_final @ inv_transforms[i].T - aniso_distances_final = np.linalg.norm(dx_transformed_final, axis=1) - weights = np.exp(-(aniso_distances_final) ** 2) - - weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) - sum_weights = np.sum(weights) - - if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights - else: - correlated_noise[i] = iids_flat[i] - else: - correlated_noise[i] = iids_flat[i] - else: - # For smaller datasets, direct computation is still faster - for i in range(n_points): - # Vectorized distance computation for all points - all_dx = grid_coords - grid_coords[i] - dx_transformed = all_dx @ inv_transforms[i].T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) - - # Get k nearest neighbors - neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ - :min(n_neighbors, n_points)] - - # Compute weights vectorized - weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) - weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) - sum_weights = np.sum(weights) - - if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights - else: - correlated_noise[i] = iids_flat[i] - - return correlated_noise - - -def generate_correlated_noise_2d_batch(grid_coords, tensors, iids, - n_neighbors=4, anisotropy_strength=1.0, - batch_size=1000): - """Batch processing version for very large datasets.""" - n_points = len(grid_coords) - - # Same vectorized preprocessing - evals, evecs = np.linalg.eigh(tensors) - swap_mask = evals[:, 1] < evals[:, 0] - evals[swap_mask] = evals[swap_mask, ::-1] - evecs[swap_mask] = evecs[swap_mask, :, ::-1] - - major_corr_length = np.sqrt(evals[:, 1]) - minor_corr_length = np.sqrt(evals[:, 0]) - scale_minor = minor_corr_length / anisotropy_strength - scale_major = major_corr_length * anisotropy_strength - - scales = np.stack([scale_minor, scale_major], axis=1) - transforms = evecs * scales[:, np.newaxis, :] - inv_transforms = np.linalg.inv(transforms) - - correlated_noise = np.zeros(n_points) - iids_flat = iids.flatten() - - # Process in batches to manage memory - for batch_start in range(0, n_points, batch_size): - batch_end = min(batch_start + batch_size, n_points) - batch_indices = np.arange(batch_start, batch_end) - - for i in batch_indices: - all_dx = grid_coords - grid_coords[i] - dx_transformed = all_dx @ inv_transforms[i].T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) - - neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ - :min(n_neighbors, n_points)] - - weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) - weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) - sum_weights = np.sum(weights) - - if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights - else: - correlated_noise[i] = iids_flat[i] - - return correlated_noise - - -import numpy as np -from scipy.spatial import cKDTree - - -def generate_correlated_noise_3d(grid_coords, tensors, iids, - n_neighbors=10, anisotropy_strength=1.0): - """Generate spatially correlated noise respecting 3D tensor anisotropy - optimized version.""" - n_points = len(grid_coords) - - # Vectorized eigendecomposition for all tensors at once - evals, evecs = np.linalg.eigh(tensors) - - # Sort eigenvalues/vectors by magnitude (largest to smallest) for all tensors - sort_indices = np.argsort(evals, axis=1)[:, ::-1] # Descending order - # Use advanced indexing to sort both evals and evecs - evals = np.take_along_axis(evals, sort_indices, axis=1) - evecs = np.take_along_axis(evecs, sort_indices[:, np.newaxis, :], axis=2) - - # Vectorized computation of correlation lengths - major_corr_length = np.sqrt(evals[:, 0]) # Largest eigenvalue - intermediate_corr_length = np.sqrt(evals[:, 1]) # Middle eigenvalue - minor_corr_length = np.sqrt(evals[:, 2]) # Smallest eigenvalue - - # Apply anisotropy strength scaling - scale_major = major_corr_length * anisotropy_strength - scale_intermediate = intermediate_corr_length # Keep intermediate neutral - scale_minor = minor_corr_length / anisotropy_strength - - # Create 3D transformation matrices for all points - scales = np.stack([scale_major, scale_intermediate, scale_minor], axis=1) - transforms = evecs * scales[:, np.newaxis, :] - - # Pre-compute inverse transforms with singularity handling - inv_transforms = np.zeros_like(transforms) - singular_mask = np.zeros(n_points, dtype=bool) - - for i in range(n_points): - try: - inv_transforms[i] = np.linalg.inv(transforms[i]) - except np.linalg.LinAlgError: - singular_mask[i] = True - inv_transforms[i] = np.eye(3) # Fallback to identity - - correlated_noise = np.zeros(n_points) - iids_flat = iids.flatten() - - # Use spatial indexing for faster neighbor finding when beneficial - if n_points > 1000: # Threshold where spatial indexing becomes beneficial - tree = cKDTree(grid_coords) - # Use a reasonable search radius to limit candidates (more conservative for 3D) - max_search_radius = np.percentile(np.sqrt(evals.max(axis=1)), 90) * anisotropy_strength * 2.5 - - for i in range(n_points): - # Get candidates within search radius - candidates = tree.query_ball_point(grid_coords[i], max_search_radius) - - if len(candidates) <= n_neighbors: - neighbor_indices = candidates - else: - # Transform and compute anisotropic distances only for candidates - dx_candidates = grid_coords[candidates] - grid_coords[i] - if singular_mask[i]: - # Use Euclidean distance for singular transforms - aniso_distances = np.linalg.norm(dx_candidates, axis=1) - else: - dx_transformed = dx_candidates @ inv_transforms[i].T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) - - neighbor_indices = np.array(candidates)[np.argsort(aniso_distances)[:n_neighbors]] - - # Compute final weights and weighted sum - if len(neighbor_indices) > 0: - dx_final = grid_coords[neighbor_indices] - grid_coords[i] - if singular_mask[i]: - aniso_distances_final = np.linalg.norm(dx_final, axis=1) - else: - dx_transformed_final = dx_final @ inv_transforms[i].T - aniso_distances_final = np.linalg.norm(dx_transformed_final, axis=1) - - weights = np.exp(-(aniso_distances_final) ** 2) - weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) - sum_weights = np.sum(weights) - - if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights - else: - correlated_noise[i] = iids_flat[i] - else: - correlated_noise[i] = iids_flat[i] - else: - # For smaller datasets, direct computation is still faster - for i in range(n_points): - # Vectorized distance computation for all points - all_dx = grid_coords - grid_coords[i] - - if singular_mask[i]: - # Use Euclidean distance for singular transforms - aniso_distances = np.linalg.norm(all_dx, axis=1) - else: - dx_transformed = all_dx @ inv_transforms[i].T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) - - # Get k nearest neighbors - neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ - :min(n_neighbors, n_points)] - - # Compute weights vectorized - weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) - weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) - sum_weights = np.sum(weights) - - if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights - else: - correlated_noise[i] = iids_flat[i] - - return correlated_noise - - -def generate_correlated_noise_3d_batch(grid_coords, tensors, iids, - n_neighbors=10, anisotropy_strength=1.0, - batch_size=500): - """Batch processing version for very large 3D datasets.""" - n_points = len(grid_coords) - - # Same vectorized preprocessing as above - evals, evecs = np.linalg.eigh(tensors) - sort_indices = np.argsort(evals, axis=1)[:, ::-1] - evals = np.take_along_axis(evals, sort_indices, axis=1) - evecs = np.take_along_axis(evecs, sort_indices[:, np.newaxis, :], axis=2) - - major_corr_length = np.sqrt(evals[:, 0]) - intermediate_corr_length = np.sqrt(evals[:, 1]) - minor_corr_length = np.sqrt(evals[:, 2]) - - scale_major = major_corr_length * anisotropy_strength - scale_intermediate = intermediate_corr_length - scale_minor = minor_corr_length / anisotropy_strength - - scales = np.stack([scale_major, scale_intermediate, scale_minor], axis=1) - transforms = evecs * scales[:, np.newaxis, :] - - # Pre-compute inverse transforms with singularity handling - inv_transforms = np.zeros_like(transforms) - singular_mask = np.zeros(n_points, dtype=bool) - - for i in range(n_points): - try: - inv_transforms[i] = np.linalg.inv(transforms[i]) - except np.linalg.LinAlgError: - singular_mask[i] = True - inv_transforms[i] = np.eye(3) - - correlated_noise = np.zeros(n_points) - iids_flat = iids.flatten() - - # Process in batches to manage memory (smaller batches for 3D due to higher memory usage) - for batch_start in range(0, n_points, batch_size): - batch_end = min(batch_start + batch_size, n_points) - batch_indices = np.arange(batch_start, batch_end) - - for i in batch_indices: - all_dx = grid_coords - grid_coords[i] - - if singular_mask[i]: - aniso_distances = np.linalg.norm(all_dx, axis=1) - else: - dx_transformed = all_dx @ inv_transforms[i].T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) - - neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ - :min(n_neighbors, n_points)] - - weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) - weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) - sum_weights = np.sum(weights) - - if sum_weights > 1e-10: - correlated_noise[i] = weighted_sum / sum_weights - else: - correlated_noise[i] = iids_flat[i] - - return correlated_noise - - -def generate_correlated_noise_3d_parallel(grid_coords, tensors, iids, - n_neighbors=10, anisotropy_strength=1.0, - n_jobs=-1): - """Parallel processing version using joblib (install with: pip install joblib).""" - try: - from joblib import Parallel, delayed - except ImportError: - raise ImportError("joblib is required for parallel processing. Install with: pip install joblib") - - n_points = len(grid_coords) - - # Same preprocessing - evals, evecs = np.linalg.eigh(tensors) - sort_indices = np.argsort(evals, axis=1)[:, ::-1] - evals = np.take_along_axis(evals, sort_indices, axis=1) - evecs = np.take_along_axis(evecs, sort_indices[:, np.newaxis, :], axis=2) - - major_corr_length = np.sqrt(evals[:, 0]) - intermediate_corr_length = np.sqrt(evals[:, 1]) - minor_corr_length = np.sqrt(evals[:, 2]) - - scale_major = major_corr_length * anisotropy_strength - scale_intermediate = intermediate_corr_length - scale_minor = minor_corr_length / anisotropy_strength - - scales = np.stack([scale_major, scale_intermediate, scale_minor], axis=1) - transforms = evecs * scales[:, np.newaxis, :] - - inv_transforms = np.zeros_like(transforms) - singular_mask = np.zeros(n_points, dtype=bool) - - for i in range(n_points): - try: - inv_transforms[i] = np.linalg.inv(transforms[i]) - except np.linalg.LinAlgError: - singular_mask[i] = True - inv_transforms[i] = np.eye(3) - - iids_flat = iids.flatten() - - def process_point(i): - all_dx = grid_coords - grid_coords[i] - - if singular_mask[i]: - aniso_distances = np.linalg.norm(all_dx, axis=1) - else: - dx_transformed = all_dx @ inv_transforms[i].T - aniso_distances = np.linalg.norm(dx_transformed, axis=1) - - neighbor_indices = np.argpartition(aniso_distances, min(n_neighbors - 1, n_points - 1))[ - :min(n_neighbors, n_points)] - - weights = np.exp(-(aniso_distances[neighbor_indices]) ** 2) - weighted_sum = np.sum(weights * iids_flat[neighbor_indices]) - sum_weights = np.sum(weights) - - if sum_weights > 1e-10: - return weighted_sum / sum_weights - else: - return iids_flat[i] - - # Process points in parallel - correlated_noise = np.array( - Parallel(n_jobs=n_jobs)(delayed(process_point)(i) for i in range(n_points)) - ) - - return correlated_noise - - -# def generate_correlated_noise_2d(grid_coords, tensors, iids, -# n_neighbors=8, anisotropy_strength=1.0): -# """Generate spatially correlated noise respecting tensor anisotropy.""" -# n_points = len(grid_coords) -# correlated_noise = np.zeros(n_points) -# -# for i in range(n_points): -# evals, evecs = np.linalg.eigh(tensors[i]) -# if evals[1] < evals[0]: -# evals, evecs = evals[::-1], evecs[:, ::-1] -# -# major_corr_length = np.sqrt(evals[1]) -# minor_corr_length = np.sqrt(evals[0]) -# -# scale_for_minor_axis = minor_corr_length / anisotropy_strength -# scale_for_major_axis = major_corr_length * anisotropy_strength -# -# transform = evecs @ np.diag([scale_for_minor_axis, scale_for_major_axis]) -# -# all_dx = grid_coords - grid_coords[i] -# inv_transform = np.linalg.inv(transform) -# dx_transformed = all_dx @ inv_transform.T -# aniso_distances = np.linalg.norm(dx_transformed, axis=1) -# -# neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] -# -# weighted_sum = 0.0 -# sum_weights = 0.0 -# -# for j in neighbor_indices: -# # Use anisotropic distance instead of actual distance for weight calculation -# # This respects the tensor-defined correlation structure -# weight = np.exp(-(aniso_distances[j]) ** 2) -# weighted_sum += weight * iids.flatten()[j] -# sum_weights += weight -# -# if sum_weights > 1e-10: -# correlated_noise[i] = weighted_sum / sum_weights -# else: -# correlated_noise[i] = iids[i] -# -# return correlated_noise - - -# def generate_correlated_noise_3d(grid_coords, tensors, iids, -# n_neighbors=50, anisotropy_strength=1.0): -# """Generate spatially correlated noise respecting 3D tensor anisotropy.""" -# n_points = len(grid_coords) -# correlated_noise = np.zeros(n_points) -# -# for i in range(n_points): -# evals, evecs = np.linalg.eigh(tensors[i]) -# -# # Sort eigenvalues/vectors by magnitude (largest to smallest) -# sort_idx = np.argsort(evals)[::-1] -# evals = evals[sort_idx] -# evecs = evecs[:, sort_idx] -# -# # Extract correlation lengths (sqrt of eigenvalues) -# major_corr_length = np.sqrt(evals[0]) # Largest eigenvalue -# intermediate_corr_length = np.sqrt(evals[1]) # Middle eigenvalue -# minor_corr_length = np.sqrt(evals[2]) # Smallest eigenvalue -# -# # Apply anisotropy strength scaling -# scale_major = major_corr_length * anisotropy_strength -# scale_intermediate = intermediate_corr_length # Keep intermediate neutral -# scale_minor = minor_corr_length / anisotropy_strength -# -# # Create 3D transformation matrix -# transform = evecs @ np.diag([scale_major, scale_intermediate, scale_minor]) -# -# # Transform all grid points relative to current point -# all_dx = grid_coords - grid_coords[i] -# try: -# inv_transform = np.linalg.inv(transform) -# dx_transformed = all_dx @ inv_transform.T -# aniso_distances = np.linalg.norm(dx_transformed, axis=1) -# except np.linalg.LinAlgError: -# # Fallback to Euclidean distance if transform is singular -# aniso_distances = np.linalg.norm(all_dx, axis=1) -# -# neighbor_indices = np.argsort(aniso_distances)[:min(n_neighbors, n_points)] -# -# weighted_sum = 0.0 -# sum_weights = 0.0 -# -# for j in neighbor_indices: -# # Use anisotropic distance for weight calculation -# weight = np.exp(-(aniso_distances[j]) ** 2) -# weighted_sum += weight * iids.flatten()[j] -# sum_weights += weight -# -# if sum_weights > 1e-10: -# correlated_noise[i] = weighted_sum / sum_weights -# else: -# correlated_noise[i] = iids[i] -# -# return correlated_noise - - - -def create_2d_tensors(theta, major, minor): - """Create 2x2 anisotropy tensors from geostatistical parameters.""" - theta = np.atleast_1d(theta) - major = np.atleast_1d(major) - minor = np.atleast_1d(minor) - - n = len(theta) - tensors = np.zeros((n, 2, 2)) - - for i in range(n): - # For geological bearing (clockwise from north): - # North = [0, 1], East = [1, 0] - # Bearing 5° should give direction [x, y] = [sin(5°), cos(5°)] = [0.087, 0.996] - - bearing_rad = theta[i] - - # Direction vector for this bearing - east_comp = np.sin(bearing_rad) # X component - north_comp = np.cos(bearing_rad) # Y component - - # Create rotation matrix to align [1,0] with bearing direction - cos_t = north_comp - sin_t = east_comp - - # clockwise rotation by angle theta - R = np.array([[cos_t, sin_t], - [-sin_t, cos_t]]) - S = np.diag([minor[i] ** 2, major[i] ** 2]) - tensors[i] = R @ S @ R.T - - return tensors.squeeze() if n == 1 else tensors - - -def create_3d_tensors(bearing, dip, major, transverse, normal): - """ - Create 3x3 anisotropy tensors from geostatistical parameters using geological convention. - - [Same docstring as original] - """ - bearing = np.atleast_1d(bearing) - dip = np.atleast_1d(dip) - major = np.atleast_1d(major) - transverse = np.atleast_1d(transverse) - normal = np.atleast_1d(normal) - - n = len(bearing) - tensors = np.zeros((n, 3, 3)) - - for i in range(n): - # Ensure minimum values to prevent numerical issues - min_axis = 1e-6 - major_val = max(major[i], min_axis) - transverse_val = max(transverse[i], min_axis) - normal_val = max(normal[i], min_axis) - - # Step 1: Create diagonal tensor in principal axes coordinate system - # Order: [transverse, major, normal] - S = np.diag([transverse_val ** 2, major_val ** 2, normal_val ** 2]) - - # Step 2: Rotation around vertical (z) axis by bearing - cos_bearing = np.cos(bearing[i]) - sin_bearing = np.sin(bearing[i]) - - R_bearing = np.array([ - [cos_bearing, sin_bearing, 0], - [-sin_bearing, cos_bearing, 0], - [0, 0, 1] - ]) - - # Step 3: Rotation around transverse axis (x-axis) by dip - cos_dip = np.cos(dip[i]) - sin_dip = np.sin(dip[i]) - - R_dip = np.array([ - [1, 0, 0], - [0, cos_dip, sin_dip], - [0, -sin_dip, cos_dip] - ]) - - # Step 4: Combine rotations - apply dip first, then bearing - R_total = R_bearing @ R_dip - - # Step 5: Transform tensor - tensors[i] = R_total @ S @ R_total.T - - # Ensure symmetry (handle numerical errors) - tensors[i] = 0.5 * (tensors[i] + tensors[i].T) - - return tensors.squeeze() if n == 1 else tensors - - -def get_tensor_bearing(tensor, expected_bearing=None): - """Extract geological bearing from tensor.""" - eigenvals, eigenvecs = np.linalg.eigh(tensor) - max_idx = np.argmax(eigenvals) - major_eigenvec = eigenvecs[:, max_idx] - - # Swap components to match geological convention: [north, east] instead of [east, north] - north_component = major_eigenvec[1] # y-component is north - east_component = major_eigenvec[0] # x-component is east - - # Calculate bearing from north (geological convention) - angle1 = np.degrees(np.arctan2(east_component, north_component)) - if angle1 < 0: - angle1 += 360 - angle2 = (angle1 + 180) % 360 - - # Choose correct direction if expected bearing provided - if expected_bearing is not None: - diff1 = min(abs(angle1 - expected_bearing), abs(angle1 - expected_bearing + 360), - abs(angle1 - expected_bearing - 360)) - diff2 = min(abs(angle2 - expected_bearing), abs(angle2 - expected_bearing + 360), - abs(angle2 - expected_bearing - 360)) - return angle1 if diff1 < diff2 else angle2 - - return angle1 - - -def assign_conceptual_points_to_zones(cp_coords, grid_coords, zones): - """ - Assign conceptual points to zones based on nearest grid point. - Supports both 2D and 3D zones. - - Parameters - ---------- - cp_coords : np.ndarray - Conceptual point coordinates, shape (n_cp, 2) or (n_cp, 3) - grid_coords : np.ndarray - Grid coordinates, shape (n_grid, 2) or (n_grid, 3) - zones : np.ndarray - Zone array, shape (ny, nx) for 2D or (nz, ny, nx) for 3D - - Returns - ------- - np.ndarray - Zone IDs for each conceptual point, shape (n_cp,) - """ - n_cp = len(cp_coords) - cp_zones = np.zeros(n_cp).astype(np.int64) - - # Determine zone dimensions - if len(zones.shape) == 2: - # 2D zones - ny, nx = zones.shape - - def get_zone_from_index(index): - row, col = divmod(index, nx) - return zones[row, col] - - else: - # 3D zones - nz, ny, nx = zones.shape - - def get_zone_from_index(index): - layer = index // (ny * nx) - remainder = index % (ny * nx) - row = remainder // nx - col = remainder % nx - return zones[layer, row, col] - - # For each conceptual point, find nearest grid point and get its zone - for i, cp_coord in enumerate(cp_coords): - # Find closest grid point - distances = np.sum((grid_coords - cp_coord) ** 2, axis=1) - closest_idx = np.argmin(distances) - - # Get zone ID from closest grid point - cp_zones[i] = get_zone_from_index(closest_idx) - - return cp_zones - - -def infer_grid_shape(grid_coords): - """Infer grid dimensions from coordinate array.""" - if grid_coords.shape[1] == 2: - x_unique = np.unique(grid_coords[:, 0]) - y_unique = np.unique(grid_coords[:, 1]) - ny, nx = len(y_unique), len(x_unique) - return (1, ny, nx) - elif grid_coords.shape[1] == 3: - x_unique = np.unique(grid_coords[:, 0]) - y_unique = np.unique(grid_coords[:, 1]) - z_unique = np.unique(grid_coords[:, 2]) - nx = len(x_unique) - ny = len(y_unique) - nz = len(grid_coords) // (nx * ny) - return(nz, ny, nx) - - -def save_layer(field, layer, field_name, save_path='.'): - """ - Save field as individual layer files. - - Parameters - ---------- - field : np.ndarray - Field array, shape (nz, ny, nx) for 3D or (ny, nx) for 2D - shape : tuple - Grid shape (nz, ny, nx) or (ny, nx) - field_name : str or list - Field name(s) used for filename - save_path : str - Directory to save output files - """ - if isinstance(field_name, list): - field_name = field_name[0] - - filename = os.path.join(save_path, f'{field_name}_layer_{layer:02d}.arr') - np.savetxt(filename, field, fmt='%.6e') - print(f"Saved layer {layer} to '{filename}'") - - -def add_indices_to_conceptual_points(conceptual_points, grid_coords, shape): - """ - Add i,j grid indices to conceptual points dataframe. - - Parameters - ---------- - conceptual_points : pd.DataFrame - Conceptual points with x,y coordinates - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - shape : tuple - Grid shape (ny, nx) - - Returns - ------- - pd.DataFrame - Conceptual points with added 'i' and 'j' columns - """ - ny, nx = shape - cp_coords = conceptual_points[['x', 'y']].values - - i_list = [] - j_list = [] - - for coord in cp_coords: - # Find closest grid point - distances = np.sum((grid_coords[:, :2] - coord) ** 2, axis=1) - closest_idx = np.argmin(distances) - row, col = divmod(closest_idx, nx) - - i_list.append(row) - j_list.append(col) - - # Add to dataframe - conceptual_points_copy = conceptual_points.copy() - conceptual_points_copy['i'] = i_list - conceptual_points_copy['j'] = j_list - - return conceptual_points_copy - - -def debug_zone_assignments(conceptual_points, grid_coords, zones, shape, save_path='.'): - """ - Complete workflow to debug zone assignments. - """ - # Add grid indices - cp_with_indices = add_indices_to_conceptual_points( - conceptual_points, grid_coords, shape - ) - - # Print summary - print("Conceptual Point Zone Assignments:") - print("=" * 50) - for idx, row in cp_with_indices.iterrows(): - ny, nx = shape - i, j = int(row['i']), int(row['j']) - - # zones are guaranteed to be 2D from entry point - zone_id = zones[i, j] - - print(f"CP {idx}: ({row['x']:.1f}, {row['y']:.1f}) -> " - f"grid[{i},{j}] -> zone {zone_id}") - - # Create plot - pu.plot_zones_with_conceptual_points( - zones, cp_with_indices, grid_coords, - save_path=os.path.join(save_path, f'zone_debug.png')) - - return cp_with_indices - - -def analyze_cp_tensor_alignment(conceptual_points, geological_tensors, grid_coords, zones): - """ - Compare conceptual point bearings with actual tensor orientations at those locations. - - Parameters - ---------- - conceptual_points : pd.DataFrame - Conceptual points with x, y, bearing columns - geological_tensors : np.ndarray - Interpolated tensor field, shape (n_points, 2, 2) - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 2) or (n_points, 3) - zones : np.ndarray - Zone IDs, shape (ny, nx) - - Returns - ------- - None - Prints comparison table to console - """ - shape = zones.shape - ny, nx = shape - - print("Conceptual Point vs Tensor Orientation Analysis:") - print("=" * 70) - print(f"{'CP#':<4} {'Name':<12} {'Zone':<6} {'Input°':<8} {'Tensor°':<8} {'Diff°':<8} {'Status'}") - print("-" * 70) - - for idx, row in conceptual_points.iterrows(): - cp_x, cp_y = row['x'], row['y'] - input_bearing = row['bearing'] - cp_name = row.get('name', f'CP_{idx}') - - # Find closest grid point - distances = np.sum((grid_coords[:, :2] - np.array([cp_x, cp_y])) ** 2, axis=1) - closest_idx = np.argmin(distances) - grid_row, grid_col = divmod(closest_idx, nx) - - # Get zone - zone_id = zones[grid_row, grid_col] - - # Get tensor at that location - tensor = geological_tensors[closest_idx] - - # Extract orientation - if np.allclose(tensor, np.eye(2) * 1000000): - tensor_bearing = "DEFAULT" - diff = "N/A" - status = "No interp" - else: - tensor_bearing = get_tensor_bearing(tensor, input_bearing) - - # Calculate difference (handling wraparound) - diff = abs(input_bearing - tensor_bearing) - if diff > 180: - diff = 360 - diff - - # Status based on difference - if diff < 10: - status = "✓ Good" - elif diff < 30: - status = "~ Close" - else: - status = "✗ Bad" - - print(f"{idx:<4} {cp_name:<12} {zone_id:<6.0f} {input_bearing:<8.0f} {tensor_bearing:<8} {diff:<8} {status}") - - print("-" * 70) - - -def analyze_tensor_statistics(geological_tensors, grid_coords, zones): - """ - Print statistics about the tensor field by zone. - - Parameters - ---------- - geological_tensors : np.ndarray - Tensor field, shape (n_points, 2, 2) - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 2) or (n_points, 3) - zones : np.ndarray - Zone IDs, shape (ny, nx) - - Returns - ------- - None - Prints statistical analysis to console - """ - shape = zones.shape - ny, nx = shape - - print("Tensor Analysis by Zone:") - print("=" * 50) - - for zone_id in np.unique(zones): - # Find grid points in this zone - zone_indices = [] - for i in range(len(grid_coords)): - row, col = divmod(i, nx) - if zones[row, col] == zone_id: - zone_indices.append(i) - - if len(zone_indices) == 0: - continue - - zone_tensors = geological_tensors[zone_indices] - - # Count default tensors - default_count = 0 - anisotropies = [] - orientations = [] - - for tensor in zone_tensors: - if np.allclose(tensor, np.eye(2) * 1000000): - default_count += 1 - else: - try: - eigenvals, eigenvecs = np.linalg.eigh(tensor) - idx = np.argsort(eigenvals)[::-1] - eigenvals = eigenvals[idx] - eigenvecs = eigenvecs[:, idx] - - aniso_ratio = np.sqrt(eigenvals[0] / eigenvals[1]) - orientation = np.degrees(np.arctan2(eigenvecs[1, 0], eigenvecs[0, 0])) - - anisotropies.append(aniso_ratio) - orientations.append(orientation) - except: - pass - - print(f"Zone {zone_id}:") - print(f" Total points: {len(zone_indices)}") - print(f" Default tensors: {default_count} ({100 * default_count / len(zone_indices):.1f}%)") - if anisotropies: - print(f" Anisotropy ratio: {np.mean(anisotropies):.2f} ± {np.std(anisotropies):.2f}") - print(f" Orientation: {np.mean(orientations):.1f}° ± {np.std(orientations):.1f}°") - print() - - -def export_to_paraview(grid_coords, shape, field_data=None, tensor_data=None, - zones=None, conceptual_points=None, save_path='.', - filename='field_3d'): - """ - Export 3D field and tensor data to ParaView-compatible VTK files. - - Parameters - ---------- - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - shape : tuple - Grid shape (nz, ny, nx) - field_data : dict, optional - Dictionary of field arrays to export, e.g. {'permeability': field_array} - Each array should have shape (nz, ny, nx) - tensor_data : np.ndarray, optional - Tensor field, shape (n_points, 3, 3) - zones : np.ndarray, optional - Zone array, shape (nz, ny, nx) or (ny, nx) - conceptual_points : pd.DataFrame, optional - Conceptual points with x, y, z, bearing, dip columns - save_path : str, default '.' - Directory to save VTK files - filename : str, default 'field_3d' - Base filename for output files - - Returns - ------- - None - Saves .vtr (structured grid) and .vtp (point cloud) files - """ - try: - import vtk - from vtk.util import numpy_support - except ImportError: - print("VTK not installed. Install with: pip install vtk") - return - - nz, ny, nx = shape - - # Create structured grid for field data - if field_data is not None or tensor_data is not None or zones is not None: - grid = vtk.vtkStructuredGrid() - grid.SetDimensions(nx, ny, nz) - - # Set points - points = vtk.vtkPoints() - grid_coords_reshaped = grid_coords.reshape(shape + (3,)) - - # VTK expects points in a specific order (k varies fastest, then j, then i) - for k in range(nz): - for j in range(ny): - for i in range(nx): - x, y, z = grid_coords_reshaped[k, j, i] - points.InsertNextPoint(x, y, z) - - grid.SetPoints(points) - - # Add field data - if field_data is not None: - for field_name, field_array in field_data.items(): - # Reshape and reorder for VTK - field_flat = field_array.flatten(order='F') # Fortran order for VTK - field_vtk = numpy_support.numpy_to_vtk(field_flat) - field_vtk.SetName(field_name) - grid.GetPointData().AddArray(field_vtk) - if grid.GetPointData().GetScalars() is None: - grid.GetPointData().SetScalars(field_vtk) - - # Add zones - if zones is not None: - if len(zones.shape) == 2: - # 2D zones - replicate for each layer - zones_3d = np.tile(zones[np.newaxis, :, :], (nz, 1, 1)) - else: - zones_3d = zones - - zones_flat = zones_3d.flatten(order='F') - zones_vtk = numpy_support.numpy_to_vtk(zones_flat) - zones_vtk.SetName('zones') - grid.GetPointData().AddArray(zones_vtk) - - # Add tensor data - if tensor_data is not None: - # Convert 3x3 tensors to VTK format (symmetric tensors) - n_points = len(tensor_data) - tensor_array = np.zeros((n_points, 6)) # VTK uses 6-component symmetric tensors - - for i in range(n_points): - tensor = tensor_data[i] - # VTK tensor format: [xx, yy, zz, xy, yz, xz] - tensor_array[i] = [tensor[0, 0], tensor[1, 1], tensor[2, 2], - tensor[0, 1], tensor[1, 2], tensor[0, 2]] - - # Reorder for VTK grid - tensor_reordered = np.zeros_like(tensor_array) - idx = 0 - for k in range(nz): - for j in range(ny): - for i in range(nx): - idx = k * ny * nx + j * nx + i - tensor_reordered[idx] = tensor_array[idx] - idx += 1 - - tensor_vtk = numpy_support.numpy_to_vtk(tensor_reordered) - tensor_vtk.SetName('tensors') - tensor_vtk.SetNumberOfComponents(6) - grid.GetPointData().SetTensors(tensor_vtk) - - # Write structured grid - writer = vtk.vtkXMLStructuredGridWriter() - grid_filename = os.path.join(save_path, f'{filename}_grid.vts') - writer.SetFileName(grid_filename) - writer.SetInputData(grid) - writer.Write() - print(f"Saved structured grid to {grid_filename}") - - # Create point cloud for conceptual points - if conceptual_points is not None: - points = vtk.vtkPoints() - polydata = vtk.vtkPolyData() - - # Add points - for idx, row in conceptual_points.iterrows(): - x, y = row['x'], row['y'] - z = row.get('z', 0) # Default z=0 if not provided - points.InsertNextPoint(x, y, z) - - polydata.SetPoints(points) - - # Create vertices - vertices = vtk.vtkCellArray() - for i in range(points.GetNumberOfPoints()): - vertices.InsertNextCell(1) - vertices.InsertCellPoint(i) - polydata.SetVerts(vertices) - - # Add conceptual point data - for col in conceptual_points.columns: - if col in ['x', 'y', 'z']: - continue - - values = conceptual_points[col].values - if np.issubdtype(values.dtype, np.number): - vtk_array = numpy_support.numpy_to_vtk(values) - vtk_array.SetName(col) - polydata.GetPointData().AddArray(vtk_array) - - # Add tensor glyphs if we have bearing/dip data - if 'bearing' in conceptual_points.columns and 'dip' in conceptual_points.columns: - # Create orientation vectors for visualization - n_cp = len(conceptual_points) - orientations = np.zeros((n_cp, 3)) - - for i, (_, row) in enumerate(conceptual_points.iterrows()): - bearing_rad = np.radians(row['bearing']) - dip_rad = np.radians(row.get('dip', 0)) - - # Convert geological convention to cartesian - # Bearing: CW from north, Dip: down from horizontal - x_comp = np.sin(bearing_rad) * np.cos(dip_rad) - y_comp = np.cos(bearing_rad) * np.cos(dip_rad) - z_comp = -np.sin(dip_rad) # Negative because dip is down - - orientations[i] = [x_comp, y_comp, z_comp] - - orientation_vtk = numpy_support.numpy_to_vtk(orientations) - orientation_vtk.SetName('orientations') - orientation_vtk.SetNumberOfComponents(3) - polydata.GetPointData().SetVectors(orientation_vtk) - - # Write point cloud - writer = vtk.vtkXMLPolyDataWriter() - points_filename = os.path.join(save_path, f'{filename}_conceptual_points.vtp') - writer.SetFileName(points_filename) - writer.SetInputData(polydata) - writer.Write() - print(f"Saved conceptual points to {points_filename}") - - print("\nParaView visualization tips:") - print("1. Load both .vts and .vtp files in ParaView") - print("2. For field data: Use 'Volume' or 'Slice' representation") - print("3. For tensors: Add 'Tensor Glyph' filter to show ellipsoids") - print("4. For conceptual points: Use 'Glyph' filter with 'Arrow' glyphs") - print("5. Use 'Clip' or 'Slice' filters to see inside the 3D volume") - - -def export_3d_results_to_paraview(field_3d, tensors_3d, grid_coords, shape, - zones=None, conceptual_points=None, - save_path='.', field_name='permeability'): - """ - Convenience function to export 3D results to ParaView. - - Parameters - ---------- - field_3d : np.ndarray - Field values, shape (nz, ny, nx) - tensors_3d : np.ndarray - 3D tensors, shape (n_points, 3, 3) - grid_coords : np.ndarray - Grid coordinates, shape (n_points, 3) - shape : tuple - Grid shape (nz, ny, nx) - zones : np.ndarray, optional - Zone data - conceptual_points : pd.DataFrame, optional - Conceptual points - save_path : str - Output directory - field_name : str - Name for the field data - """ - field_data = {field_name: field_3d} - - export_to_paraview( - grid_coords=grid_coords, - shape=shape, - field_data=field_data, - tensor_data=tensors_3d, - zones=zones, - conceptual_points=conceptual_points, - save_path=save_path, - filename=f'{field_name}_3d' - ) From 0f8c1b43c05fc861501f1f2a836ce982d3c4e311 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Thu, 14 Aug 2025 20:09:52 +1200 Subject: [PATCH 17/36] pypestutils implementation todo:clean up file handling and figures --- pyemu/plot/plot_utils.py | 6 +- pyemu/utils/nsaf_helpers.py | 534 +++++++----------------------- pyemu/utils/nsaf_utils.py | 634 ++++++++++++++++++++---------------- 3 files changed, 490 insertions(+), 684 deletions(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 0807f1ee9..0b78f75a6 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2056,7 +2056,7 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, - conceptual_points=None, subsample=4, scale_factor=1, + conceptual_points=None, subsample=4, scale_factor=0.1, figsize=(14, 12), title_suf=None, save_path='.'): """ Visualize tensors as ellipses overlaid on zones, with conceptual points as oriented lines. @@ -2322,7 +2322,7 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, print(f"Saved tensor visualization to {save_path_full}") # Show the plot for interactive backends - plt.show() + #plt.show() plt.close() @@ -2331,7 +2331,7 @@ def plot_layer(field, layer=0, field_name='field', transform='log', save_path='. field = np.log10(field) plt.figure(figsize=(14, 12)) - plt.imshow(field) + plt.imshow(field[layer]) plt.colorbar(label='Parameter Value') plt.title(f'{field_name} for layer {layer}') plt.xlabel('X') diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index 027b36583..907c363ed 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -22,7 +22,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, vartransform='log', n_realizations=1, ml=None, sr=None): """ Generate spatially correlated fields using tensor-based geostatistics. - Follows PyEMU pattern for model workspace and spatial reference handling. + CLEAN version that works with flexible property columns. Parameters ---------- @@ -33,14 +33,9 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, conceptual_points_file : str CSV file containing conceptual points zone_file : str or list of str, optional - zone file(s): - - Single string: One zone file used for all layers (2D zones) - - List of strings: One zone file per layer (creates 3D zones) + zone file(s) iids_file : str or list of str, optional - noise file(s): - - Single string: Assumes pattern like "iids_layer{N}.arr" - - List of strings: Explicit paths to each layer's noise file - - None: Auto-generates noise and saves as iids_layer{N}.arr files + noise file(s) field_name : list of str, default ['field'] Name(s) of field(s) to generate layer_mode : bool, default True @@ -79,7 +74,6 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # Setup spatial reference following PyEMU pattern if ml is None and sr is None: - # Load model from workspace print("Loading flopy model...") try: # Try MF6 first - only load DIS package for efficiency @@ -95,70 +89,47 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, except Exception as e: raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}: {e}") - if sr is None: - # Create spatial reference from model - print("Creating spatial reference...") - if hasattr(ml, 'dis'): - # MF6 or MODFLOW with DIS - try: - sr = pyemu.helpers.SpatialReference.from_namfile( - os.path.join(tmp_model_ws, f"{model_name}.nam"), - delr=ml.dis.delr.array, delc=ml.dis.delc.array - ) - except: - # Alternative approach for MF6 - sr = pyemu.helpers.SpatialReference( - delr=ml.dis.delr.array, delc=ml.dis.delc.array, - xul=0, yul=0 - ) - else: - raise ValueError("Could not determine grid spacing from model") - - print(f"Spatial reference: {sr}") - # Get grid coordinates try: - xcentergrid = sr.xcentergrid - ycentergrid = sr.ycentergrid - except AttributeError: - try: - xcentergrid = sr.xcellcenters - ycentergrid = sr.ycellcenters - except AttributeError as e: - raise Exception(f"Could not get grid centers from spatial reference: {e}") + xcentergrid = ml.modelgrid.xcellcenters + ycentergrid = ml.modelgrid.ycellcenters + except AttributeError as e: + raise Exception(f"Could not get grid centers from spatial reference: {e}") ny, nx = xcentergrid.shape print(f"Grid dimensions: {ny} x {nx}") - cell_area = np.outer(sr.delr, sr.delc) + print( + f"Grid extent: X=[{xcentergrid.min():.1f}, {xcentergrid.max():.1f}], Y=[{ycentergrid.min():.1f}, {ycentergrid.max():.1f}]") + + # Get cell areas for area parameter + cell_area = np.outer(ml.dis.delc.array, ml.dis.delr.array) + # Load conceptual points conceptual_points = pd.read_csv(os.path.join(tmp_model_ws, conceptual_points_file)) + print(f"Loaded {len(conceptual_points)} conceptual points") + print( + f"Conceptual points extent: X=[{conceptual_points['x'].min():.1f}, {conceptual_points['x'].max():.1f}], Y=[{conceptual_points['y'].min():.1f}, {conceptual_points['y'].max():.1f}]") # Add transverse if missing if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: - conceptual_points.loc[:, 'transverse'] = conceptual_points.loc[:, 'major'] / conceptual_points.loc[:, - 'anisotropy'] - - print(f"Loaded {len(conceptual_points)} conceptual points") + conceptual_points['transverse'] = conceptual_points['major'] / conceptual_points['anisotropy'] # Load zones zones = None if zone_file: if isinstance(zone_file, str): - # Single zone file zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file)).astype(np.int64) if zones.shape != (ny, nx): - # Try to reshape if possible if zones.size == ny * nx: zones = zones.reshape(ny, nx) else: raise ValueError(f"Zone file shape {zones.shape} doesn't match grid {(ny, nx)}") print(f"Loaded zones from {zone_file}, shape: {zones.shape}") else: - # Multiple zone files - for now just use the first one zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file[0])).astype(np.int64) print(f"Loaded zones from {zone_file[0]} (multi-layer support TODO)") - # Determine the total number of layers in the model + # Determine layers if hasattr(ml, 'dis'): if hasattr(ml.dis, 'nlay'): total_layers = ml.dis.nlay.array if hasattr(ml.dis.nlay, 'array') else ml.dis.nlay @@ -171,164 +142,136 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, print(f"Model has {total_layers} layers") - # Handle IIDs for PEST parameterization - generate for ALL model layers - iids_dict = {} - - # Determine which layers have conceptual points + # Handle layers in conceptual points if 'layer' in conceptual_points.columns: available_cp_layers = sorted(conceptual_points['layer'].unique()) print(f"Conceptual points available for layers: {available_cp_layers}") else: - available_cp_layers = [] - print("No 'layer' column in conceptual points - will propagate to all layers") + available_cp_layers = [1] + print("No 'layer' column in conceptual points - using layer 1") # Generate IIDs for all model layers + iids_dict = {} for layer_num in range(total_layers): - layer_1based = layer_num + 1 # Convert to 1-based indexing + layer_1based = layer_num + 1 iids_dict[layer_1based] = _load_or_generate_iids( tmp_model_ws, iids_file, layer_num, ny, nx, n_realizations ) - # Propagate conceptual points to missing layers if needed - if layer_mode and 'layer' in conceptual_points.columns: - # Check if all layers are covered - missing_layers = [] - for layer_num in range(1, total_layers + 1): # 1-based - if layer_num not in available_cp_layers: - missing_layers.append(layer_num) - - if missing_layers: - print(f"Missing conceptual points for layers: {missing_layers}") - print("Propagating conceptual points from available layers...") - - # Find the best layer to propagate from (prefer middle layers) - if available_cp_layers: - # Use the layer closest to the middle of available layers - middle_idx = len(available_cp_layers) // 2 - source_layer = available_cp_layers[middle_idx] - print(f"Using layer {source_layer} as source for propagation") - - # Create propagated conceptual points - source_cp = conceptual_points[conceptual_points['layer'] == source_layer].copy() - - for missing_layer in missing_layers: - propagated_cp = source_cp.copy() - propagated_cp['layer'] = missing_layer - - # Optionally modify some properties for different layers - # (could add depth-dependent scaling here if needed) - - # Append to conceptual points - conceptual_points = pd.concat([conceptual_points, propagated_cp], ignore_index=True) - print(f" Propagated {len(source_cp)} points to layer {missing_layer}") - - # Update available layers - available_cp_layers = sorted(conceptual_points['layer'].unique()) - print(f"Updated conceptual points now cover layers: {available_cp_layers}") - - # Process each field for each layer - all_results = {} - - # If layer_mode is True, process each layer separately - if layer_mode and 'layer' in conceptual_points.columns: - layers_to_process = available_cp_layers - else: - # Single layer mode - use layer 1 - layers_to_process = [1] - # Process each field all_results = {} for fn in field_name: print(f"\n=== Processing field: {fn} ===") - # Filter conceptual points for this field - cols = ['name', 'x', 'y', 'major', 'transverse', 'bearing'] - field_cols = [col for col in conceptual_points.columns if f'_{fn}' in col] - - if not field_cols: - # Look for generic mean/sd columns - mean_cols = [col for col in conceptual_points.columns if 'mean' in col.lower()] - sd_cols = [col for col in conceptual_points.columns if 'sd' in col.lower()] - - if mean_cols and sd_cols: - # Rename to expected format - field_cp = conceptual_points.copy() - field_cp[f'mean_{fn}'] = field_cp[mean_cols[0]] - field_cp[f'sd_{fn}'] = field_cp[sd_cols[0]] - cols.extend([f'mean_{fn}', f'sd_{fn}']) - else: - raise ValueError(f"No columns found for field '{fn}' (expected mean_{fn}, sd_{fn})") + # SIMPLIFIED: Look for columns with field name pattern + mean_col = None + sd_col = None + + # First, try field-specific columns + if f'mean_{fn}' in conceptual_points.columns: + mean_col = f'mean_{fn}' + sd_col = f'sd_{fn}' + elif f'{fn}_mean' in conceptual_points.columns: + mean_col = f'{fn}_mean' + sd_col = f'{fn}_sd' else: - cols.extend(field_cols) + # Look for generic columns + mean_candidates = [col for col in conceptual_points.columns if 'mean' in col.lower()] + sd_candidates = [col for col in conceptual_points.columns if 'sd' in col.lower()] + + if mean_candidates and sd_candidates: + mean_col = mean_candidates[0] # Use first match + sd_col = sd_candidates[0] + print(f"Using generic columns: mean='{mean_col}', sd='{sd_col}'") + else: + raise ValueError(f"No mean/sd columns found for field '{fn}'") + + # Verify columns exist + if mean_col not in conceptual_points.columns: + raise ValueError(f"Mean column '{mean_col}' not found") + if sd_col not in conceptual_points.columns: + raise ValueError(f"SD column '{sd_col}' not found") - # Auto-detect layers and select target layer - target_layer = None + # Handle layer processing if layer_mode and 'layer' in conceptual_points.columns: - cols.append('layer') - # Auto-detect available layers - available_layers = sorted(conceptual_points['layer'].unique()) - print(f"Available layers: {available_layers}") - - # Use first available layer instead of hardcoded 0 - target_layer = available_layers[0] - field_cp = conceptual_points[conceptual_points['layer'] == target_layer][cols].copy() - - if len(field_cp) == 0: - print(f"No conceptual points for layer {target_layer}, using all points") - field_cp = conceptual_points[cols].copy() - target_layer = None - else: - field_cp = conceptual_points[cols].copy() - - # Rename columns to match apply_ppu_hyperpars function expectations - if f'mean_{fn}' in field_cp.columns: - field_cp = field_cp.rename(columns={f'mean_{fn}': 'mean_kh', f'sd_{fn}': 'sd_kh'}) - elif 'mean' in field_cp.columns: - field_cp = field_cp.rename(columns={'mean': 'mean_kh', 'sd': 'sd_kh'}) - - # Get IIDs for this layer - layer_key = target_layer if target_layer is not None else 0 - layer_iids = iids_dict.get(layer_key, iids_dict) - - # Use workflow - results = apply_ppu_hyperpars( - field_cp, xcentergrid, ycentergrid, - area=cell_area, - zones=zones, - n_realizations=n_realizations, - layer=target_layer if target_layer is not None else 0, - vartransform=vartransform, - tensor_method=tensor_interp, - out_filename=os.path.join(save_path, f"{fn}"), - iids=layer_iids # Pass the IIDs - ) + # Process each available layer + for target_layer in available_cp_layers: + print(f"\n--- Processing layer {target_layer} ---") + + # Filter conceptual points for this layer + layer_cp = conceptual_points[conceptual_points['layer'] == target_layer].copy() + + if len(layer_cp) == 0: + print(f"No conceptual points for layer {target_layer}, skipping") + continue + + # Check data ranges + print(f"Conceptual point data for layer {target_layer}:") + print(f" {mean_col}: [{layer_cp[mean_col].min():.2f}, {layer_cp[mean_col].max():.2f}]") + print(f" {sd_col}: [{layer_cp[sd_col].min():.2f}, {layer_cp[sd_col].max():.2f}]") + + # Get IIDs for this layer + layer_iids = iids_dict.get(target_layer, iids_dict[1]) + + # apply_ppu_hyperpars + results = apply_ppu_hyperpars( + layer_cp, xcentergrid, ycentergrid, + area=cell_area, + zones=zones, + n_realizations=n_realizations, + layer=target_layer - 1, # Convert to 0-based for internal use + vartransform=vartransform, + tensor_method=tensor_interp, + out_filename=os.path.join(save_path, f"{fn}_layer_{target_layer}"), + iids=layer_iids, + mean_col=mean_col, # Pass column names + sd_col=sd_col, + active=ml.dis.idomain.array[target_layer].flatten() + ) - all_results[fn] = results + # Store results with layer key + result_key = f"{fn}_layer_{target_layer}" + all_results[result_key] = results - # Save and visualize results - if results['fields'] is not None: - for i, field in enumerate(results['fields']): - save_layer(field, layer=target_layer if target_layer is not None else 0, - field_name=f"{fn}_real_{i + 1}", save_path=save_path) + # Save results + if results['fields'] is not None: + for i, field in enumerate(results['fields']): + save_layer(field, layer=target_layer - 1, + field_name=f"{fn}_real_{i + 1}", save_path=save_path) - # Save mean and SD - save_layer(results['mean'], layer=target_layer if target_layer is not None else 0, - field_name=f"{fn}_mean", save_path=save_path) - save_layer(results['sd'], layer=target_layer if target_layer is not None else 0, - field_name=f"{fn}_sd", save_path=save_path) + save_layer(results['mean'], layer=target_layer - 1, + field_name=f"{fn}_mean", save_path=save_path) + save_layer(results['sd'], layer=target_layer - 1, + field_name=f"{fn}_sd", save_path=save_path) - # Visualize tensors - check if visualization module is available - try: - from tensor_visualization import visualize_tensors - visualize_tensors( - results['tensors'], xcentergrid, ycentergrid, - zones=zones, conceptual_points=field_cp, - subsample=8, title_suf=f'{fn}_layer_{target_layer if target_layer is not None else 0}', - save_path=save_path + print(f"Results for layer {target_layer}:") + print(f" Mean field: [{results['mean'].min():.3f}, {results['mean'].max():.3f}]") + print(f" SD field: [{results['sd'].min():.3f}, {results['sd'].max():.3f}]") + + else: + # Single layer mode + target_layer = 1 + + # Get IIDs for this layer + layer_iids = iids_dict.get(1, None) + + # Use workflow + results = apply_ppu_hyperpars( + conceptual_points, xcentergrid, ycentergrid, + area=cell_area, + zones=zones, + n_realizations=n_realizations, + layer=0, + vartransform=vartransform, + tensor_method=tensor_interp, + out_filename=os.path.join(save_path, f"{fn}"), + iids=layer_iids, + mean_col=mean_col, + sd_col=sd_col ) - except ImportError: - print(" Warning: tensor_visualization module not available, skipping visualization") + + all_results[fn] = results print("\n=== Complete ===") return all_results @@ -337,6 +280,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realizations): """ Load IIDs from file or generate and save new ones for PEST parameterization. + FIXED version with proper array handling. Parameters ---------- @@ -436,238 +380,10 @@ def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realiza header=f"IIDs for layer {layer_1based}, shape: {ny * nx} x {n_realizations}") return iids - """ - Generate spatially correlated fields using tensor-based geostatistics. - Follows PyEMU pattern for model workspace and spatial reference handling. - - Parameters - ---------- - tmp_model_ws : str - Path to model workspace containing flopy model files - model_name : str - Name of the flopy model (e.g., 'freyberg6') - conceptual_points_file : str - CSV file containing conceptual points - zone_file : str or list of str, optional - zone file(s): - - Single string: One zone file used for all layers (2D zones) - - List of strings: One zone file per layer (creates 3D zones) - iids_file : str or list of str, optional - noise file(s): - - Single string: Assumes pattern like "iids_layer{N}.arr" - - List of strings: Explicit paths to each layer's noise file - - None: Auto-generates noise and saves as iids_layer{N}.arr files - field_name : list of str, default ['field'] - Name(s) of field(s) to generate - layer_mode : bool, default True - If True, process each layer independently - save_path : str, optional - Directory path to save output files (default: tmp_model_ws) - tensor_interp : str, default 'krig' - Tensor interpolation method - vartransform : str, default 'log' - Transformation: 'log' or 'none' - n_realizations : int, default 1 - Number of stochastic realizations - ml : flopy.mbase, optional - A flopy mbase derived type. If None, sr must not be None. - sr : flopy.utils.reference.SpatialReference, optional - A spatial reference to locate the model grid. If None, ml must not be None. - - Returns - ------- - dict - Results from workflow - """ - try: - import flopy - import pyemu - except ImportError as e: - raise ImportError(f"flopy and pyemu are required: {e}") - - print(f"=== Tensor-Based Field Generation ===") - print(f"Model workspace: {tmp_model_ws}") - print(f"Model name: {model_name}") - - # Set default save path - if save_path is None: - save_path = tmp_model_ws - - # Setup spatial reference following PyEMU pattern - if ml is None and sr is None: - # Load model from workspace - print("Loading flopy model...") - try: - # Try MF6 first - only load DIS package for efficiency - sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws, load_only=['dis']) - ml = sim.get_model(model_name) - print(f"Loaded MF6 model: {model_name} (DIS package only)") - except: - try: - # Fall back to MODFLOW-2005 - only load DIS package - ml = flopy.modflow.Modflow.load(f"{model_name}.nam", model_ws=tmp_model_ws, - load_only=['dis']) - print(f"Loaded MODFLOW-2005 model: {model_name} (DIS package only)") - except Exception as e: - raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}: {e}") - - if sr is None: - # Create spatial reference from model - print("Creating spatial reference...") - if hasattr(ml, 'dis'): - # MF6 or MODFLOW with DIS - try: - sr = pyemu.helpers.SpatialReference.from_namfile( - os.path.join(tmp_model_ws, f"{model_name}.nam"), - delr=ml.dis.delr.array, delc=ml.dis.delc.array - ) - except: - # Alternative approach for MF6 - sr = pyemu.helpers.SpatialReference( - delr=ml.dis.delr.array, delc=ml.dis.delc.array, - xul=0, yul=0 - ) - else: - raise ValueError("Could not determine grid spacing from model") - - print(f"Spatial reference: {sr}") - - # Get grid coordinates - try: - xcentergrid = sr.xcentergrid - ycentergrid = sr.ycentergrid - except AttributeError: - try: - xcentergrid = sr.xcellcenters - ycentergrid = sr.ycellcenters - except AttributeError as e: - raise Exception(f"Could not get grid centers from spatial reference: {e}") - - ny, nx = xcentergrid.shape - print(f"Grid dimensions: {ny} x {nx}") - - # Load conceptual points - conceptual_points = pd.read_csv(os.path.join(tmp_model_ws, conceptual_points_file)) - - # Add transverse if missing - if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: - conceptual_points.loc[:, 'transverse'] = conceptual_points.loc[:, 'major'] / conceptual_points.loc[:, - 'anisotropy'] - - print(f"Loaded {len(conceptual_points)} conceptual points") - - # Load zones - zones = None - if zone_file: - if isinstance(zone_file, str): - # Single zone file - zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file)).astype(np.int64) - if zones.shape != (ny, nx): - # Try to reshape if possible - if zones.size == ny * nx: - zones = zones.reshape(ny, nx) - else: - raise ValueError(f"Zone file shape {zones.shape} doesn't match grid {(ny, nx)}") - print(f"Loaded zones from {zone_file}, shape: {zones.shape}") - else: - # Multiple zone files - for now just use the first one - zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file[0])).astype(np.int64) - print(f"Loaded zones from {zone_file[0]} (multi-layer support TODO)") - - # Process each field - all_results = {} - - for fn in field_name: - print(f"\n=== Processing field: {fn} ===") - - # Filter conceptual points for this field - cols = ['name', 'x', 'y', 'major', 'transverse', 'bearing'] - field_cols = [col for col in conceptual_points.columns if f'_{fn}' in col] - - if not field_cols: - # Look for generic mean/sd columns - mean_cols = [col for col in conceptual_points.columns if 'mean' in col.lower()] - sd_cols = [col for col in conceptual_points.columns if 'sd' in col.lower()] - - if mean_cols and sd_cols: - # Rename to expected format - field_cp = conceptual_points.copy() - field_cp[f'mean_{fn}'] = field_cp[mean_cols[0]] - field_cp[f'sd_{fn}'] = field_cp[sd_cols[0]] - cols.extend([f'mean_{fn}', f'sd_{fn}']) - else: - raise ValueError(f"No columns found for field '{fn}' (expected mean_{fn}, sd_{fn})") - else: - cols.extend(field_cols) - - # Auto-detect layers and select target layer - target_layer = None - if layer_mode and 'layer' in conceptual_points.columns: - cols.append('layer') - # Auto-detect available layers - available_layers = sorted(conceptual_points['layer'].unique()) - print(f"Available layers: {available_layers}") - - # Use first available layer instead of hardcoded 0 - target_layer = available_layers[0] - field_cp = conceptual_points[conceptual_points['layer'] == target_layer][cols].copy() - - if len(field_cp) == 0: - print(f"No conceptual points for layer {target_layer}, using all points") - field_cp = conceptual_points[cols].copy() - target_layer = None - else: - field_cp = conceptual_points[cols].copy() - - # Rename columns to match apply_ppu_hyperpars function expectations - if f'mean_{fn}' in field_cp.columns: - field_cp = field_cp.rename(columns={f'mean_{fn}': 'mean_kh', f'sd_{fn}': 'sd_kh'}) - elif 'mean' in field_cp.columns: - field_cp = field_cp.rename(columns={'mean': 'mean_kh', 'sd': 'sd_kh'}) - - # Use workflow - results = apply_ppu_hyperpars( - field_cp, xcentergrid, ycentergrid, - zones=zones, - n_realizations=n_realizations, - layer=target_layer if target_layer is not None else 0, - vartransform=vartransform, - tensor_method=tensor_interp, - out_filename=os.path.join(save_path, f"{fn}") - ) - - all_results[fn] = results - - # Save and visualize results - if results['fields'] is not None: - for i, field in enumerate(results['fields']): - save_layer(field, layer=target_layer if target_layer is not None else 0, - field_name=f"{fn}_real_{i + 1}", save_path=save_path) - - # Save mean and SD - save_layer(results['mean'], layer=target_layer if target_layer is not None else 0, - field_name=f"{fn}_mean", save_path=save_path) - save_layer(results['sd'], layer=target_layer if target_layer is not None else 0, - field_name=f"{fn}_sd", save_path=save_path) - - # Visualize tensors - check if visualization module is available - try: - from tensor_visualization import visualize_tensors - visualize_tensors( - results['tensors'], xcentergrid, ycentergrid, - zones=zones, conceptual_points=field_cp, - subsample=8, title_suf=f'{fn}_layer_{target_layer if target_layer is not None else 0}', - save_path=save_path - ) - except ImportError: - print(" Warning: tensor_visualization module not available, skipping visualization") - - print("\n=== Complete ===") - return all_results def generate_single_layer(cp_file, xcentergrid, ycentergrid, iids=None, - zones=None, save_path='.', tensor_interp='krig', + zones=None, save_path='.', tensor_interp='idw', vartransform='log', boundary_smooth=True, boundary_enhance=True): """ diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index a0ed32751..f62dac412 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -141,7 +141,7 @@ def load_conceptual_points(cp_file): def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, - method='krig', layer=0, config_dict=None): + method='idw', layer=0, config_dict=None): """ Tensor field interpolation using NSAF log-Euclidean mathematics with pypestutils kriging engine. @@ -178,7 +178,7 @@ def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, # Filter to current layer if 'layer' in cp_df.columns: - layer_cp = cp_df[cp_df['layer'] == layer].copy() + layer_cp = cp_df[cp_df['layer'] - 1 == layer].copy() else: layer_cp = cp_df.copy() @@ -347,7 +347,10 @@ def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_di grid_zones = np.ones(len(grid_x), dtype=int) # Use isotropic parameters for component interpolation - corrlen = np.full(len(grid_x), 1000.0) # Default correlation length + x_extent = np.max(grid_x) - np.min(grid_x) + y_extent = np.max(grid_y) - np.min(grid_y) + domain_extent = max(x_extent, y_extent) + corrlen = np.full(len(grid_x), np.max(10000.0, domain_extent/3)) aniso = np.ones(len(grid_x)) # Isotropic bearing = np.zeros(len(grid_x)) # No rotation @@ -358,7 +361,7 @@ def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_di int(config_dict.get("vartype", 2)), # Exponential int(config_dict.get("krigtype", 1)), # Ordinary kriging corrlen, aniso, bearing, - config_dict.get("search_radius", 1e10), + config_dict.get("search_radius", 1e20), config_dict.get("maxpts_interp", 20), config_dict.get("minpts_interp", 1), fac_fname, fac_ftype @@ -495,11 +498,14 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, out_filename=None, n_realizations=1, layer=0, vartransform="none", boundary_smooth=True, boundary_enhance=True, tensor_method='krig', - config_dict=None, iids=None, area=None, **kwargs): + config_dict=None, iids=None, area=None, + mean_col='mean', sd_col='sd', **kwargs): """ Parameter interpolation and field generation combining NSAF tensor mathematics with pypestutils FIELDGEN2D_SVA for spatially correlated noise. + FLEXIBLE version that works with any property by specifying column names. + Parameters ---------- cp_file : str or pd.DataFrame @@ -528,6 +534,12 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, Configuration parameters for pypestutils iids : np.ndarray, optional Pre-generated IIDs with shape (ny*nx, n_realizations) + area : np.ndarray, optional + Cell areas with shape (ny, nx) + mean_col : str, default 'mean' + Name of the mean column in conceptual points + sd_col : str, default 'sd' + Name of the standard deviation column in conceptual points Returns ------- @@ -544,19 +556,39 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, # Load conceptual points cp_df = load_conceptual_points(cp_file) if 'layer' in cp_df.columns: - layer_cp = cp_df[cp_df['layer'] == layer].copy() + layer_cp = cp_df[cp_df['layer'] - 1 == layer].copy() else: layer_cp = cp_df.copy() if len(layer_cp) == 0: raise ValueError(f"No conceptual points found for layer {layer}") + # Validate required columns + required_base_cols = ['x', 'y', 'major', 'bearing'] + missing_cols = [col for col in required_base_cols if col not in layer_cp.columns] + if missing_cols: + raise ValueError(f"Missing required columns: {missing_cols}") + + # Check for transverse/anisotropy + if 'transverse' not in layer_cp.columns: + if 'anisotropy' in layer_cp.columns: + layer_cp['transverse'] = layer_cp['major'] / layer_cp['anisotropy'] + else: + raise ValueError("Must have either 'transverse' or 'anisotropy' column") + + # Validate mean and sd columns + if mean_col not in layer_cp.columns: + raise ValueError(f"Mean column '{mean_col}' not found in conceptual points") + if sd_col not in layer_cp.columns: + raise ValueError(f"Standard deviation column '{sd_col}' not found in conceptual points") + # Get grid dimensions and coordinates ny, nx = xcentergrid.shape grid_coords_2d = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) print(f"Processing layer {layer} with {len(layer_cp)} conceptual points") print(f"Grid dimensions: {ny} x {nx} = {ny * nx} cells") + print(f"Using columns: mean='{mean_col}', sd='{sd_col}'") # Step 1: Interpolate tensors print("\nStep 1: Interpolating tensor field...") @@ -572,8 +604,12 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, # Step 3: Prepare conceptual point data cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) - cp_means = layer_cp['mean_kh'].values - cp_sd = layer_cp['sd_kh'].values + cp_means = layer_cp[mean_col].values + cp_sd = layer_cp[sd_col].values.clip(1e-8,None) + + print(f"Conceptual point data ranges:") + print(f" {mean_col}: [{cp_means.min():.3f}, {cp_means.max():.3f}]") + print(f" {sd_col}: [{cp_sd.min():.3f}, {cp_sd.max():.3f}]") # Step 4: Interpolate mean and sd using tensor-aware kriging print("\nStep 4: Interpolating mean and standard deviation fields...") @@ -581,8 +617,8 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, # Configure kriging parameters if config_dict is None: config_dict = { - 'vartype': 2, # Exponential variogram - 'krigtype': 1, # Ordinary kriging + 'vartype': 'gaussian', + 'krigtype': 1, 'search_radius': 1e10, 'maxpts_interp': 20, 'minpts_interp': 1 @@ -628,6 +664,10 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, peak_increase=1.0, transition_cells=3, mode='enhance' ) + print(f"Interpolated field ranges:") + print(f" Mean: [{interp_means_2d.min():.3f}, {interp_means_2d.max():.3f}]") + print(f" SD: [{interp_sd_2d.min():.3f}, {interp_sd_2d.max():.3f}]") + # Step 5: Generate stochastic fields using FIELDGEN2D_SVA approach print(f"\nStep 5: Generating {n_realizations} stochastic realizations...") @@ -641,12 +681,13 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, corrlen=corrlen_major, zones=zones, n_realizations=n_realizations, - vartransform=vartransform, + vartransform='n', config_dict=config_dict, ny=ny, nx=nx, area=area, - iids=iids + iids=iids, + active=kwargs.get('active', None) ) else: fields = None @@ -722,13 +763,26 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, max_neighbors=4, transform=None, min_value=1e-8, zones=None): """ - Tensor-aware kriging using anisotropic distances. - (Implementation from the NSAF code provided earlier) + Tensor-aware kriging using pypestutils with anisotropic correlation structures. + Clean version without automatic transform detection. """ + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") + # Get grid shape n_grid = len(grid_coords) n_dims = grid_coords.shape[1] + print(f" Tensor-aware kriging: {len(cp_coords)} conceptual points -> {n_grid} grid points") + print( + f" Conceptual points range: X=[{cp_coords[:, 0].min():.1f}, {cp_coords[:, 0].max():.1f}], Y=[{cp_coords[:, 1].min():.1f}, {cp_coords[:, 1].max():.1f}]") + print( + f" Grid points range: X=[{grid_coords[:, 0].min():.1f}, {grid_coords[:, 0].max():.1f}], Y=[{grid_coords[:, 1].min():.1f}, {grid_coords[:, 1].max():.1f}]") + print(f" Value range: [{cp_values.min():.6f}, {cp_values.max():.6f}], background: {background_value:.6f}") + print(f" Transform requested: {transform}") + # Handle zones shape inference if zones is not None: shape = zones.shape @@ -736,179 +790,155 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, ny, nx = shape else: nz, ny, nx = shape + print(f" Using zones with shape: {shape}") else: - # Simple rectangular grid assumption - grid_span_x = grid_coords[:, 0].max() - grid_coords[:, 0].min() - grid_span_y = grid_coords[:, 1].max() - grid_coords[:, 1].min() - - # Estimate grid dimensions - unique_x = len(np.unique(grid_coords[:, 0])) - unique_y = len(np.unique(grid_coords[:, 1])) - - if unique_x * unique_y == n_grid: - nx, ny = unique_x, unique_y + # Infer grid dimensions + x_coords = grid_coords[:, 0] + y_coords = grid_coords[:, 1] + + unique_x = np.unique(x_coords) + unique_y = np.unique(y_coords) + nx = len(unique_x) + ny = len(unique_y) + + if nx * ny != n_grid: + side_length = int(np.sqrt(n_grid)) + nx = ny = side_length + print(f" Warning: Could not infer grid dimensions, using square grid {nx}x{ny}") else: - # Fallback to square root - nx = ny = int(np.sqrt(n_grid)) + print(f" Inferred grid dimensions: ny={ny}, nx={nx}") shape = (ny, nx) - # Define index conversion functions - if n_dims == 3: - def get_indices(idx): - layer = idx // (ny * nx) - remainder = idx % (ny * nx) - row = remainder // nx - col = remainder % nx - return layer, row, col + # Convert tensors to geostatistical parameters for pypestutils + print(" Converting tensors to geostatistical parameters...") + bearing_deg, anisotropy_ratios, corrlen_major = _tensors_to_geostat_params(interp_tensors) + + # Prepare grid coordinates + grid_x = grid_coords[:, 0] + grid_y = grid_coords[:, 1] + + # Handle zones + if zones is not None: + zones_flat = zones.flatten().astype(int) + cp_zones = _assign_cp_to_zones(cp_coords, grid_x, grid_y, zones_flat, nx) + else: + zones_flat = np.ones(len(grid_x), dtype=int) + cp_zones = np.ones(len(cp_coords), dtype=int) + + # Handle transformation - NO automatic detection, just pass through + cp_values_transformed = cp_values.copy() # Always use values as-is + + # Set pypestutils transform flag + if transform == 'log': + transform_flag = "log" + print(f" Using log transform in pypestutils") else: - def get_indices(idx): - row = idx // nx - col = idx % nx - return row, col + transform_flag = "none" + print(f" Using no transform in pypestutils") + + print(f" Input values to pypestutils: [{cp_values_transformed.min():.6f}, {cp_values_transformed.max():.6f}]") + + # Use pypestutils for kriging + print(" Calling pypestutils for tensor-aware kriging...") + + try: + lib = PestUtilsLib() - # Variogram function - def variogram(h): + # Create temporary factor file + fac_fname = "temp_tensor_aware_kriging.fac" + if os.path.exists(fac_fname): + os.remove(fac_fname) + fac_ftype = "text" + + # Set up variogram type if variogram_model == 'exponential': - return nugget + (sill - nugget) * (1 - np.exp(-h)) + vartype = 2 elif variogram_model == 'gaussian': - return nugget + (sill - nugget) * (1 - np.exp(-(h ** 2))) - else: # spherical - return np.where(h <= 1.0, - nugget + (sill - nugget) * (1.5 * h - 0.5 * h ** 3), - sill) + vartype = 3 + elif variogram_model == 'spherical': + vartype = 1 + else: + vartype = 2 # Default to exponential - interp_values_1d = np.full(n_grid, background_value) + print(f" Pypestutils parameters:") + print(f" Variogram: {variogram_model} (type {vartype})") + print(f" Transform: {transform_flag}") + print(f" Search radius: {max_search_radius}") + print(f" Max neighbors: {max_neighbors}") - # Calculate zone for each conceptual point if zones provided - if zones is not None: - cp_zones = [] - for coord in cp_coords: - distances = np.sum((grid_coords - coord) ** 2, axis=1) - closest_idx = np.argmin(distances) - if n_dims == 3: - layer, row, col = get_indices(closest_idx) - cp_zones.append(zones[layer, row, col]) - else: - row, col = get_indices(closest_idx) - cp_zones.append(zones[row, col]) - cp_zones = np.array(cp_zones) + # Calculate kriging factors using tensor-derived anisotropy + npts = lib.calc_kriging_factors_2d( + cp_coords[:, 0], cp_coords[:, 1], cp_zones, + grid_x, grid_y, zones_flat, + vartype, # Variogram type + 1, # Ordinary kriging + corrlen_major, # Correlation lengths from tensors + anisotropy_ratios, # Anisotropy ratios from tensors + bearing_deg, # Bearings from tensors + max_search_radius, + max_neighbors, + min_points, + fac_fname, fac_ftype + ) - # Pre-compute log transformation if needed - if transform == 'log': - if min_value is None: - positive_values = cp_values[cp_values > 0] - min_value = np.min(positive_values) * 0.01 if len(positive_values) > 0 else 1e-8 - - # Main kriging loop - for i in range(n_grid): - local_tensor = interp_tensors[i] - - if zones is not None: - # Get current zone and filter conceptual points - if n_dims == 3: - layer, row, col = get_indices(i) - current_zone = zones[layer, row, col] - else: - row, col = get_indices(i) - current_zone = zones[row, col] - - # Filter to same zone - zone_mask = cp_zones == current_zone - if np.sum(zone_mask) >= min_points: - cp_coords_filtered = cp_coords[zone_mask] - cp_values_filtered = cp_values[zone_mask] - else: - cp_coords_filtered = cp_coords - cp_values_filtered = cp_values - else: - cp_coords_filtered = cp_coords - cp_values_filtered = cp_values + print(f" Pypestutils kriging factors calculated for {npts} interpolation points") - # Compute anisotropic distances - try: - tensor_inv = np.linalg.inv(local_tensor) - dx = cp_coords_filtered - grid_coords[i] - aniso_distances = np.sqrt(np.sum(dx @ tensor_inv * dx, axis=1)) - except np.linalg.LinAlgError: - aniso_distances = np.linalg.norm(cp_coords_filtered - grid_coords[i], axis=1) - - # Get neighbors within search radius - valid_mask = aniso_distances <= max_search_radius - if np.sum(valid_mask) < min_points: - continue + # Apply kriging + result = lib.krige_using_file( + fac_fname, fac_ftype, + len(grid_x), + 1, # Ordinary kriging + transform_flag, # Pass through the transform flag as specified + cp_values_transformed, # Use values exactly as provided + background_value, # Use background exactly as provided + background_value # Use no-interpolation value exactly as provided + ) + + # Clean up + if os.path.exists(fac_fname): + os.remove(fac_fname) + lib.free_all_memory() - # Sort and limit to max_neighbors - sorted_indices = np.argsort(aniso_distances[valid_mask]) - n_candidates = min(max_neighbors or len(sorted_indices), len(sorted_indices)) + # Get results - NO back-transformation here, pypestutils handles it + interp_values_1d = result["targval"] - if n_candidates < min_points: - continue + print(f" Pypestutils kriging complete") + print(f" Result range: [{interp_values_1d.min():.6f}, {interp_values_1d.max():.6f}]") - closest_indices = np.where(valid_mask)[0][sorted_indices[:n_candidates]] - nearby_values = cp_values_filtered[closest_indices] - nearby_coords = cp_coords_filtered[closest_indices] - nearby_distances = aniso_distances[closest_indices] + return interp_values_1d.reshape(shape) - # Apply log transform if needed - if transform == 'log': - nearby_values_transformed = np.log10(np.maximum(nearby_values, min_value)) - else: - nearby_values_transformed = nearby_values.copy() - - # Set up and solve kriging system - n_pts = len(nearby_values_transformed) - C = np.zeros((n_pts + 1, n_pts + 1)) - c = np.zeros(n_pts + 1) - - # Build covariance matrix - for j in range(n_pts): - for k in range(n_pts): - dx = nearby_coords[j] - nearby_coords[k] - try: - h = np.sqrt(dx.T @ tensor_inv @ dx) - except: - h = np.linalg.norm(dx) - C[j, k] = variogram(h) - - C[j, -1] = 1 - C[-1, j] = 1 - c[j] = variogram(nearby_distances[j]) - - c[-1] = 1 - - # Solve kriging system - try: - cond_num = np.linalg.cond(C) - if cond_num > 1e12: - # Fall back to IDW - weights = np.exp(-nearby_distances) - weights = weights / np.sum(weights) - interp_values_1d[i] = np.sum(weights * nearby_values_transformed) - else: - weights = np.linalg.solve(C, c)[:-1] - interp_values_1d[i] = np.sum(weights * nearby_values_transformed) - except: - # IDW fallback - weights = np.exp(-nearby_distances) - interp_values_1d[i] = np.sum(weights * nearby_values_transformed) / np.sum(weights) + except Exception as e: + print(f" Error: Pypestutils kriging failed: {e}") + print(f" Falling back to simple IDW interpolation...") - # Back-transform if needed - if transform == 'log': - interp_values_1d = 10 ** interp_values_1d + # Fallback to simple IDW + interp_values_1d = np.full(n_grid, background_value) + + for i in range(n_grid): + distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + distances = np.maximum(distances, 1e-10) + + # Simple IDW weights + weights = 1.0 / (distances ** 2) + weights = weights / np.sum(weights) + + interp_values_1d[i] = np.sum(weights * cp_values_transformed) - return interp_values_1d.reshape(shape) + print(f" IDW fallback complete") + print(f" Result range: [{interp_values_1d.min():.6f}, {interp_values_1d.max():.6f}]") + + return interp_values_1d.reshape(shape) def _generate_stochastic_fields(grid_coords, mean_field, sd_field, bearing, anisotropy, corrlen, zones, n_realizations, vartransform, - config_dict, ny, nx, iids=None, area=None): + config_dict, ny, nx, iids=None, area=None, + active=None): """ Generate stochastic fields using pypestutils FIELDGEN2D_SVA. - - This creates temporary files as required by FIELDGEN2D_SVA and then calls - the pypestutils wrapper. + FINAL FIXED version with correct reshaping logic. """ try: from pypestutils.pestutilslib import PestUtilsLib @@ -922,24 +952,24 @@ def _generate_stochastic_fields(grid_coords, mean_field, sd_field, x_coords = grid_coords[:, 0] y_coords = grid_coords[:, 1] - # Flatten fields - mean_flat = mean_field.flatten() - variance_flat = (sd_field.flatten()) ** 2 # Convert SD to variance + # Flatten fields - ENSURE PROPER ORDERING + mean_flat = mean_field.flatten().clip(1e-8, None) + variance_native = sd_field.flatten() ** 2 + log_variance_flat = np.log(1 + variance_native / mean_flat ** 2) + trans_flag = 1 # Log domain # Geostatistical parameters from tensors aa_values = corrlen.flatten() aniso_values = anisotropy.flatten() bearing_values = bearing.flatten() - # domain based on zones - if zones is None: - domain = np.ones((ny,nx)) - else: - domain = np.where(zones>1,1,0) + # FIXED: domain based on zones + if active is None: + active = np.ones(n_points, dtype=int) if area is None: - area = domain.copy() - domain = domain.flatten().astype(int) - area = area.flatten() + area = np.ones(n_points) + else: + area = area.flatten() # Generate or use provided IIDs if iids is None: @@ -950,88 +980,73 @@ def _generate_stochastic_fields(grid_coords, mean_field, sd_field, if iids.shape != (n_points, n_realizations): raise ValueError(f"IIDs shape {iids.shape} doesn't match expected {(n_points, n_realizations)}") - # Create temporary files for FIELDGEN2D_SVA - node_file = "temp_nodes_2d.dat" - avgfunc_file = "temp_avgfunc_2d.dat" - output_file = "temp_output_2d.csv" - try: - # Create 2D node specifications file - # _create_node_spec_file(node_file, x_coords, y_coords, zones_flat) - # - # # Create 2D averaging function specification file - # _create_avgfunc_spec_file(avgfunc_file, mean_flat, variance_flat, - # aa_values, aniso_values, bearing_values) - # Set up pypestutils parameters lib = PestUtilsLib() lib.initialize_randgen(11235813) - # Determine transformation type - if vartransform == 'log': - trans_flag = 'log' # Log domain - else: - trans_flag = 'none' # Natural domain - # Averaging function type - avg_func = config_dict.get('averaging_function', 'exponential') + avg_func = config_dict.get('averaging_function', 'gaussian') if isinstance(avg_func, dict): - if avg_func == 'pow': + if 'pow' in avg_func: + avg_flag = 4 + power_value = avg_func['pow'] + else: avg_flag, power_value = next(iter(avg_func.items())) else: power_value = 0 - if avg_func == 'exponential': - avg_flag = 'exp' - elif avg_func == 'gaussian': - avg_flag = 'gauss' - elif avg_func == 'spherical': - avg_flag = 'spher' - else: - avg_flag = 'exp' # Default to exponential + if avg_func == 'exponential': + avg_flag = 2 + elif avg_func == 'gaussian': + avg_flag = 3 + elif avg_func == 'spherical': + avg_flag = 1 + else: + avg_flag = 2 # Default to exponential # Call FIELDGEN2D_SVA through pypestutils interface print(f" Calling FIELDGEN2D_SVA for {n_realizations} realizations...") - # This is the correct way to call FIELDGEN2D_SVA in pypestutils - success = lib.fieldgen2d_sva( - x_coords, y_coords, - area=area, - active=domain, - mean=mean_flat, - var=variance_flat, - aa=corrlen, - anis=anisotropy, - bearing=bearing, - transtype=trans_flag, # e.g., 'n' or 'l' - avetype=avg_flag, # 's', 'x', 'g', or 'p' - power=power_value, # only needed if avetype='p' - nreal=n_realizations, + fields = lib.fieldgen2d_sva( + x_coords, y_coords, + area=area, + active=active, + mean=mean_flat, + var=log_variance_flat, + aa=aa_values, + anis=aniso_values, + bearing=bearing_values, + transtype=trans_flag, + avetype=avg_flag, + power=power_value, + nreal=n_realizations, + ) - ) + # Handle the output format from FIELDGEN2D_SVA + if fields.shape == (n_points, n_realizations): + # Output is (n_points, n_realizations) - transpose to (n_realizations, n_points) + fields = fields.T # Now shape is (n_realizations, n_points) + # Reshape each realization to (ny, nx) + fields = fields.reshape((n_realizations, ny, nx)) - if not success: - raise Exception("FIELDGEN2D_SVA execution failed") + elif fields.shape == (n_realizations * n_points,): + # Output is flattened - reshape directly + fields = fields.reshape((n_realizations, ny, nx)) - # Read results from CSV file - print(" Reading FIELDGEN2D_SVA results...") - results_df = pd.read_csv(output_file) + elif fields.shape == (n_realizations, n_points): + # Output is already (n_realizations, n_points) - just reshape spatial dimensions + fields = fields.reshape((n_realizations, ny, nx)) - # Extract field values - assume columns are named 'real_001', 'real_002', etc. - fields = np.zeros((n_realizations, ny, nx)) + else: + raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") - for real in range(n_realizations): - col_name = f"real_{real + 1:03d}" - if col_name in results_df.columns: - field_values = results_df[col_name].values - fields[real] = field_values.reshape(ny, nx) - else: - # Fallback column naming - field_values = results_df.iloc[:, real].values - fields[real] = field_values.reshape(ny, nx) + print(f" Successfully generated {n_realizations} realizations with shape {fields.shape}") # Clean up lib.free_all_memory() + return fields + except Exception as e: print(f" Warning: FIELDGEN2D_SVA failed: {e}") print(f" Using fallback method...") @@ -1041,19 +1056,13 @@ def _generate_stochastic_fields(grid_coords, mean_field, sd_field, for real in range(n_realizations): current_iids = iids[:, real] field_values = _generate_simple_field( - grid_coords, mean_flat, variance_flat, + grid_coords, mean_flat, log_variance_flat, aa_values, aniso_values, bearing_values, vartransform, current_iids, ny, nx ) fields[real] = field_values.reshape(ny, nx) - finally: - # Clean up temporary files - for temp_file in [node_file, avgfunc_file, output_file]: - if os.path.exists(temp_file): - os.remove(temp_file) - - return fields + return fields # def _create_node_spec_file(filename, x_coords, y_coords, zones): @@ -1098,36 +1107,88 @@ def _generate_simple_field(grid_coords, mean_flat, variance_flat, aa_values, aniso_values, bearing_values, vartransform, iids, ny, nx): """ - Simple fallback field generation if FIELDGEN2D_SVA fails. + IMPROVED simple fallback field generation if FIELDGEN2D_SVA fails. + Fixed to eliminate checkering and provide proper spatial correlation. """ - print(" Using simple correlated field generation fallback...") + print(" Using improved simple correlated field generation fallback...") n_points = len(grid_coords) - # Scale by local standard deviation - stochastic_component = iids * np.sqrt(variance_flat) + # Convert bearing from degrees to radians for calculations + bearing_rad = np.radians(bearing_values) - # Apply simple spatial correlation using distance-based averaging - smoothed_component = np.zeros_like(stochastic_component) + # Create spatially correlated field using moving average approach + correlated_field = np.zeros(n_points) - for i in range(n_points): + # For computational efficiency, use a subset of points for correlation + # This prevents the O(n²) complexity from being too slow + max_correlation_points = min(2000, n_points) + + if n_points > max_correlation_points: + # Sample points for correlation calculation + correlation_indices = np.random.choice(n_points, max_correlation_points, replace=False) + correlation_indices = np.sort(correlation_indices) + else: + correlation_indices = np.arange(n_points) + + # Apply spatial correlation + for i in correlation_indices: + # Get local correlation parameters local_aa = aa_values[i] + local_aniso = aniso_values[i] + local_bearing = bearing_rad[i] + + # Calculate distances to all points + dx = grid_coords[:, 0] - grid_coords[i, 0] + dy = grid_coords[:, 1] - grid_coords[i, 1] + + # Apply anisotropic transformation + # Rotate coordinates to align with anisotropy ellipse + cos_bear = np.cos(local_bearing) + sin_bear = np.sin(local_bearing) + + # Rotate to principal axes (geological bearing: 0° = North, clockwise positive) + dx_rot = dx * sin_bear + dy * cos_bear # East component (major axis) + dy_rot = -dx * cos_bear + dy * sin_bear # North component (minor axis) + + # Scale by anisotropy (major axis has length aa, minor axis has length aa/aniso) + minor_aa = local_aa / local_aniso + dx_scaled = dx_rot / local_aa + dy_scaled = dy_rot / minor_aa + + # Calculate anisotropic distance + aniso_distances = np.sqrt(dx_scaled ** 2 + dy_scaled ** 2) + + # Exponential correlation function + correlation_weights = np.exp(-aniso_distances) - # Simple correlation based on distance - distances = np.linalg.norm(grid_coords - grid_coords[i], axis=1) - weights = np.exp(-distances / (local_aa + 1e-10)) # Avoid division by zero - weights = weights / np.sum(weights) + # Normalize weights + correlation_weights = correlation_weights / np.sum(correlation_weights) - # Apply weighted average - smoothed_component[i] = np.sum(weights * stochastic_component) + # Apply correlation to the random field + correlated_field[i] = np.sum(correlation_weights * iids) - # Combine with mean + # For points not in correlation_indices, use interpolation + if n_points > max_correlation_points: + # Simple interpolation for remaining points + remaining_indices = np.setdiff1d(np.arange(n_points), correlation_indices) + + for i in remaining_indices: + # Find nearest correlation points + distances_to_corr = np.sum((grid_coords[correlation_indices] - grid_coords[i]) ** 2, axis=1) + nearest_corr_idx = correlation_indices[np.argmin(distances_to_corr)] + correlated_field[i] = correlated_field[nearest_corr_idx] + + # Scale by local standard deviation + stochastic_component = correlated_field * np.sqrt(variance_flat) + + # Combine with mean field if vartransform == 'log': # Log domain: mean * exp(stochastic_component) - field_values = mean_flat * np.exp(smoothed_component) + field_values = mean_flat * np.exp(stochastic_component) else: # Normal domain: mean + stochastic_component - field_values = mean_flat + smoothed_component + field_values = mean_flat + stochastic_component return field_values @@ -1284,16 +1345,20 @@ def example_usage(): def example_workflow(): - """Example of complete workflow.""" + """Example of complete workflow with FIXED grid handling.""" import matplotlib.pyplot as plt - # Create example data - nx, ny = 50, 40 + # Create example data - FIXED grid creation + nx, ny = 50, 40 # nx = columns, ny = rows x = np.linspace(0, 5000, nx) y = np.linspace(0, 4000, ny) - xcentergrid, ycentergrid = np.meshgrid(x, y, indexing='xy') - xcentergrid = xcentergrid.T - ycentergrid = ycentergrid.T + + # Use consistent indexing - 'ij' indexing is clearer for grid applications + # This creates ycentergrid with shape (ny, nx) and xcentergrid with shape (ny, nx) + ycentergrid, xcentergrid = np.meshgrid(y, x, indexing='ij') + + # Now ycentergrid.shape = (40, 50) and xcentergrid.shape = (40, 50) + # Row 0 corresponds to y[0], Column 0 corresponds to x[0] # Example conceptual points cp_data = { @@ -1310,7 +1375,7 @@ def example_workflow(): } cp_df = pd.DataFrame(cp_data) - # Example zones + # Example zones - shape (ny, nx) = (40, 50) zones = np.ones((ny, nx), dtype=int) zones[:ny // 2, :] = 1 zones[ny // 2:, :] = 2 @@ -1318,38 +1383,63 @@ def example_workflow(): # Run workflow results = apply_ppu_hyperpars( cp_df, xcentergrid, ycentergrid, - zones=zones, n_realizations=3, + zones=zones, n_realizations=1, out_filename="example", vartransform="none" ) - # Quick visualization + # Verify shapes + print(f"Grid shapes: xcentergrid {xcentergrid.shape}, ycentergrid {ycentergrid.shape}") + print(f"Results shapes: mean {results['mean'].shape}, zones {zones.shape}") + print(f"Expected: ({ny}, {nx}) = (40, 50)") + + # Check for problematic values in realizations + if results['fields'] is not None: + for i, field in enumerate(results['fields']): + n_zeros = np.sum(field == 0) + n_total = field.size + print(f"Realization {i}: {n_zeros}/{n_total} zero values ({100 * n_zeros / n_total:.1f}%)") + print(f" Min: {field.min():.3f}, Max: {field.max():.3f}, Mean: {field.mean():.3f}") + + # Quick visualization with proper coordinate mapping fig, axes = plt.subplots(2, 3, figsize=(15, 10)) # Mean field - im1 = axes[0, 0].imshow(results['mean'], origin='lower') + im1 = axes[0, 0].imshow(results['mean'], origin='lower', extent=[0, 5000, 0, 4000]) axes[0, 0].set_title('Mean Field') + axes[0, 0].set_xlabel('X (m)') + axes[0, 0].set_ylabel('Y (m)') plt.colorbar(im1, ax=axes[0, 0]) # SD field - im2 = axes[0, 1].imshow(results['sd'], origin='lower') + im2 = axes[0, 1].imshow(results['sd'], origin='lower', extent=[0, 5000, 0, 4000]) axes[0, 1].set_title('Standard Deviation') + axes[0, 1].set_xlabel('X (m)') + axes[0, 1].set_ylabel('Y (m)') plt.colorbar(im2, ax=axes[0, 1]) # Anisotropy field - im3 = axes[0, 2].imshow(results['anisotropy'], origin='lower') + im3 = axes[0, 2].imshow(results['anisotropy'], origin='lower', extent=[0, 5000, 0, 4000]) axes[0, 2].set_title('Anisotropy Ratio') + axes[0, 2].set_xlabel('X (m)') + axes[0, 2].set_ylabel('Y (m)') plt.colorbar(im3, ax=axes[0, 2]) # First three realizations - for i in range(3): - im = axes[1, i].imshow(results['fields'][i], origin='lower') - axes[1, i].set_title(f'Realization {i + 1}') - plt.colorbar(im, ax=axes[1, i]) + if results['fields'] is not None: + for i in range(min(3, len(results['fields']))): + im = axes[1, i].imshow(results['fields'][i], origin='lower', extent=[0, 5000, 0, 4000]) + axes[1, i].set_title(f'Realization {i + 1}') + axes[1, i].set_xlabel('X (m)') + axes[1, i].set_ylabel('Y (m)') + plt.colorbar(im, ax=axes[1, i]) plt.tight_layout() plt.savefig('workflow_results.png', dpi=150, bbox_inches='tight') plt.show() print("Example workflow complete!") - return results \ No newline at end of file + return results + +if __name__ == "__main__": + example_workflow() \ No newline at end of file From 3c86600b88c4bac6562bf6ed68486db196fcb10f Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 15 Aug 2025 22:43:13 +1200 Subject: [PATCH 18/36] proper back transformation, circle example, todo fieldgen auto filenaming? --- pyemu/plot/plot_utils.py | 60 +++++ pyemu/utils/nsaf_helpers.py | 198 +++++++++----- .../{example_useage.py => nsaf_useage.py} | 18 +- pyemu/utils/nsaf_utils.py | 244 ++++++------------ 4 files changed, 277 insertions(+), 243 deletions(-) rename pyemu/utils/{example_useage.py => nsaf_useage.py} (68%) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 0b78f75a6..0b87ad8e5 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2340,3 +2340,63 @@ def plot_layer(field, layer=0, field_name='field', transform='log', save_path='. os.makedirs('output', exist_ok=True) plt.savefig(os.path.join(save_path, f'{field_name}_layer_{layer:02d}.png'), dpi=150) plt.close() + + +def visualize_nsaf(field_2d, cp_df, x, y): + # Visualize results + fig, axes = plt.subplots(2, 2, figsize=(12, 12)) + + # Field + im1 = axes[0, 0].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r') + axes[0, 0].set_title('Generated Tangential Field') + axes[0, 0].set_aspect('equal') + plt.colorbar(im1, ax=axes[0, 0]) + + # Field with conceptual points + im2 = axes[0, 1].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r', alpha=0.8) + + for idx, row in cp_df.iterrows(): + x_idx = np.argmin(np.abs(x - row['x'])) + y_idx = np.argmin(np.abs(y - row['y'])) + axes[0, 1].plot(x_idx, y_idx, 'ko', markersize=3) + + # Draw bearing direction + bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + dx = 4 * np.cos(bearing_rad) + dy = 4 * np.sin(bearing_rad) + axes[0, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, + fc='red', ec='red', alpha=0.8) + + axes[0, 1].set_title('Field with Tangential Vectors') + axes[0, 1].set_aspect('equal') + plt.colorbar(im2, ax=axes[0, 1]) + + # Field statistics + axes[1, 0].hist(field_2d.flatten(), bins=50, alpha=0.7, color='purple') + axes[1, 0].set_title('Field Distribution') + axes[1, 0].set_xlabel('Value') + axes[1, 0].set_ylabel('Frequency') + + # Bearing vectors only + for idx, row in cp_df.iterrows(): + x_idx = np.argmin(np.abs(x - row['x'])) + y_idx = np.argmin(np.abs(y - row['y'])) + + bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + dx = 3 * np.cos(bearing_rad) + dy = 3 * np.sin(bearing_rad) + axes[1, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, + fc='blue', ec='blue', alpha=0.6) + + axes[1, 1].set_title('Tangential Bearing Vectors') + axes[1, 1].set_xlim(0, field_2d.shape[1] - 1) + axes[1, 1].set_ylim(0, field_2d.shape[1] - 1) + axes[1, 1].set_aspect('equal') + + plt.tight_layout() + plt.savefig('tangential_field_example.png', dpi=150, bbox_inches='tight') + plt.show() + + print(f"Field statistics: mean={np.mean(field_2d):.3f}, std={np.std(field_2d):.3f}") + print(f"Number of conceptual points: {len(cp_df)}") + print("Tangential field generation complete!") \ No newline at end of file diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index 907c363ed..888542cf1 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -18,7 +18,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, zone_file=None, iids_file=None, field_name=['field'], - layer_mode=True, save_path=None, tensor_interp='krig', + layer_mode=True, save_dir=None, tensor_interp='krig', vartransform='log', n_realizations=1, ml=None, sr=None): """ Generate spatially correlated fields using tensor-based geostatistics. @@ -40,7 +40,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, Name(s) of field(s) to generate layer_mode : bool, default True If True, process each layer independently - save_path : str, optional + save_dir : str, optional Directory path to save output files (default: tmp_model_ws) tensor_interp : str, default 'krig' Tensor interpolation method @@ -68,10 +68,6 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, print(f"Model workspace: {tmp_model_ws}") print(f"Model name: {model_name}") - # Set default save path - if save_path is None: - save_path = tmp_model_ws - # Setup spatial reference following PyEMU pattern if ml is None and sr is None: print("Loading flopy model...") @@ -214,6 +210,12 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # Get IIDs for this layer layer_iids = iids_dict.get(target_layer, iids_dict[1]) + # Set outfile + if save_dir is None: + outfile = os.path.join(tmp_model_ws, f"{fn}_layer_{target_layer}.arr") + else: # TODO: ability to pass file basename + outfile = os.path.join(save_dir, f"{fn}_layer_{target_layer}.arr") + # apply_ppu_hyperpars results = apply_ppu_hyperpars( layer_cp, xcentergrid, ycentergrid, @@ -223,27 +225,27 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, layer=target_layer - 1, # Convert to 0-based for internal use vartransform=vartransform, tensor_method=tensor_interp, - out_filename=os.path.join(save_path, f"{fn}_layer_{target_layer}"), + out_filename=outfile, iids=layer_iids, mean_col=mean_col, # Pass column names sd_col=sd_col, - active=ml.dis.idomain.array[target_layer].flatten() + active=ml.dis.idomain.array[target_layer-1].flatten() ) - # Store results with layer key + # Store results with layer key, already done via pypest utils result_key = f"{fn}_layer_{target_layer}" all_results[result_key] = results - # Save results - if results['fields'] is not None: - for i, field in enumerate(results['fields']): - save_layer(field, layer=target_layer - 1, - field_name=f"{fn}_real_{i + 1}", save_path=save_path) - - save_layer(results['mean'], layer=target_layer - 1, - field_name=f"{fn}_mean", save_path=save_path) - save_layer(results['sd'], layer=target_layer - 1, - field_name=f"{fn}_sd", save_path=save_path) + # # Save results + # if results['fields'] is not None: + # for i, field in enumerate(results['fields']): + # save_layer(field, layer=target_layer - 1, + # field_name=f"{fn}_real_{i + 1}", save_path=save_path) + # + # save_layer(results['mean'], layer=target_layer - 1, + # field_name=f"{fn}_mean", save_path=save_path) + # save_layer(results['sd'], layer=target_layer - 1, + # field_name=f"{fn}_sd", save_path=save_path) print(f"Results for layer {target_layer}:") print(f" Mean field: [{results['mean'].min():.3f}, {results['mean'].max():.3f}]") @@ -262,10 +264,10 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, area=cell_area, zones=zones, n_realizations=n_realizations, - layer=0, + layer=target_layer-1, vartransform=vartransform, tensor_method=tensor_interp, - out_filename=os.path.join(save_path, f"{fn}"), + out_filename=outfile, iids=layer_iids, mean_col=mean_col, sd_col=sd_col @@ -383,7 +385,7 @@ def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realiza def generate_single_layer(cp_file, xcentergrid, ycentergrid, iids=None, - zones=None, save_path='.', tensor_interp='idw', + zones=None, tensor_interp='idw', vartransform='log', boundary_smooth=True, boundary_enhance=True): """ @@ -402,8 +404,6 @@ def generate_single_layer(cp_file, xcentergrid, ycentergrid, iids=None, Pre-generated noise, shape (ny, nx) zones : np.ndarray, optional Zone IDs, shape (ny, nx) - save_path : str, default '.' - Directory path to save output files tensor_interp : str, default 'krig' Tensor interpolation method vartransform : str, default 'log' @@ -618,73 +618,133 @@ def infer_grid_shape(xcentergrid, ycentergrid): return (1, ny, nx) -# Example usage function def example_field_generation(): - """Example using the restructured helpers.""" + """Example using concentric circles of tangentially-oriented anisotropic tensors.""" import matplotlib.pyplot as plt + import numpy as np + import pandas as pd - # Create example PyEMU grid - nx, ny = 50, 40 + # Create example PyEMU grid - square domain + nx, ny = 100, 100 x = np.linspace(0, 5000, nx) - y = np.linspace(0, 4000, ny) + y = np.linspace(0, 5000, ny) xcentergrid, ycentergrid = np.meshgrid(x, y, indexing='xy') xcentergrid = xcentergrid.T ycentergrid = ycentergrid.T - # Create example conceptual points - cp_data = { - 'name': ['cp1', 'cp2', 'cp3', 'cp4'], - 'x': [1000, 3000, 1000, 3000], - 'y': [1000, 1000, 3000, 3000], - 'mean_kh': [1.5, 2.0, 1.8, 1.2], - 'sd_kh': [0.3, 0.4, 0.2, 0.5], - 'major': [2000, 1500, 1800, 2200], - 'anisotropy': [3.0, 2.5, 4.0, 2.0], - 'bearing': [45, 90, 135, 0], - 'layer': [1, 1, 1, 1] - } - cp_df = pd.DataFrame(cp_data) - - # Add transverse + # Define circle center and radius + center_x = 2500 + center_y = 2500 + max_radius = 1500 + + # Create conceptual points on concentric circles + cp_list = [] + n_circles = 6 + + for circle_idx in range(n_circles): + radius_fraction = (circle_idx + 0.3) / n_circles + cp_radius = max_radius * radius_fraction + n_points = 12 + circle_idx * 3 + + for i in range(n_points): + angle = 2 * np.pi * i / n_points + + # Position on circle + cp_x = center_x + cp_radius * np.cos(angle) + cp_y = center_y + cp_radius * np.sin(angle) + + # Tangential bearing in geological convention (N=0°, clockwise positive) + # Convert angle to geological bearing, then add 90° for tangent + geo_radial = np.degrees(np.arctan2(cp_x - center_x, cp_y - center_y)) + bearing = (geo_radial + 90) % 360 + + # Each circle gets unique value + mean_kh = 10 * (circle_idx + 1) + major_length = 150 + 50 * radius_fraction + + cp_list.append({ + 'name': f'cp_circle{circle_idx}_point{i}', + 'x': cp_x, + 'y': cp_y, + 'mean_kh': mean_kh, + 'sd_kh': mean_kh * 0.1, + 'major': major_length, + 'anisotropy': 4.0, + 'bearing': bearing, + 'layer': 1 + }) + + cp_df = pd.DataFrame(cp_list) cp_df['transverse'] = cp_df['major'] / cp_df['anisotropy'] - # Create zones - zones = np.ones((ny, nx), dtype=int) - zones[:ny // 2, :] = 1 - zones[ny // 2:, :] = 2 - - # Generate field using single layer function - print("=== Testing single layer generation ===") + # Generate field + print("=== Testing tangential field generation ===") field_2d, tensors = generate_single_layer( cp_df, xcentergrid, ycentergrid, - zones=zones, tensor_interp='krig', - vartransform='log' + zones=None, + tensor_interp='krig', + vartransform='None' ) # Visualize results - fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + fig, axes = plt.subplots(2, 2, figsize=(12, 12)) # Field - im1 = axes[0].imshow(field_2d, origin='lower') - axes[0].set_title('Generated Field') - plt.colorbar(im1, ax=axes[0]) - - # Zones - im2 = axes[1].imshow(zones, origin='lower', cmap='tab10') - axes[1].set_title('Zones') - plt.colorbar(im2, ax=axes[1]) + im1 = axes[0, 0].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r') + axes[0, 0].set_title('Generated Tangential Field') + axes[0, 0].set_aspect('equal') + plt.colorbar(im1, ax=axes[0, 0]) + + # Field with conceptual points + im2 = axes[0, 1].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r', alpha=0.8) + + for idx, row in cp_df.iterrows(): + x_idx = np.argmin(np.abs(x - row['x'])) + y_idx = np.argmin(np.abs(y - row['y'])) + axes[0, 1].plot(x_idx, y_idx, 'ko', markersize=3) + + # Draw bearing direction + bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + dx = 4 * np.cos(bearing_rad) + dy = 4 * np.sin(bearing_rad) + axes[0, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, + fc='red', ec='red', alpha=0.8) + + axes[0, 1].set_title('Field with Tangential Vectors') + axes[0, 1].set_aspect('equal') + plt.colorbar(im2, ax=axes[0, 1]) # Field statistics - axes[2].hist(field_2d.flatten(), bins=50, alpha=0.7) - axes[2].set_title('Field Distribution') - axes[2].set_xlabel('Value') - axes[2].set_ylabel('Frequency') + axes[1, 0].hist(field_2d.flatten(), bins=50, alpha=0.7, color='purple') + axes[1, 0].set_title('Field Distribution') + axes[1, 0].set_xlabel('Value') + axes[1, 0].set_ylabel('Frequency') + + # Bearing vectors only + for idx, row in cp_df.iterrows(): + x_idx = np.argmin(np.abs(x - row['x'])) + y_idx = np.argmin(np.abs(y - row['y'])) + + bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + dx = 3 * np.cos(bearing_rad) + dy = 3 * np.sin(bearing_rad) + axes[1, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, + fc='blue', ec='blue', alpha=0.6) + + axes[1, 1].set_title('Tangential Bearing Vectors') + axes[1, 1].set_xlim(0, nx - 1) + axes[1, 1].set_ylim(0, ny - 1) + axes[1, 1].set_aspect('equal') plt.tight_layout() - plt.savefig('field_example.png', dpi=150, bbox_inches='tight') + plt.savefig('tangential_field_example.png', dpi=150, bbox_inches='tight') plt.show() print(f"Field statistics: mean={np.mean(field_2d):.3f}, std={np.std(field_2d):.3f}") - print("Example complete!") + print(f"Number of conceptual points: {len(cp_df)}") + print("Tangential field generation complete!") + + return field_2d, tensors, cp_df - return field_2d, tensors \ No newline at end of file +if __name__ == "__main__": + example_field_generation() \ No newline at end of file diff --git a/pyemu/utils/example_useage.py b/pyemu/utils/nsaf_useage.py similarity index 68% rename from pyemu/utils/example_useage.py rename to pyemu/utils/nsaf_useage.py index 6b4e186ef..e7d42cb23 100644 --- a/pyemu/utils/example_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -21,19 +21,19 @@ import time start_time = time.perf_counter() - data_dir = r'..\..\examples\Hawkes_Bay\gwf' - # zone_files = [f'idomain_{i}.arr' for i in range(0,9)] - con_pts_file = 'Bridgpa_skytem_sva_0.csv' - save_path = os.path.join(data_dir, f'{con_pts_file}_output') + data_dir = r'..\..\examples\Wairau\waq_arr' + zone_files = [f'waq_arr.geoclass_layer{i+1}.arr' for i in range(0,13)] + con_pts_file = 'conceptual_points.csv' + save_dir = os.path.join(data_dir, f'{con_pts_file}_output') - if not os.path.exists(save_path): - os.mkdir(save_path) + if not os.path.exists(save_dir): + os.mkdir(save_dir) - nsaf_helpers.generate_fields_from_files(data_dir, 'hpm_mf6', con_pts_file, - zone_file=None, + nsaf_helpers.generate_fields_from_files(data_dir, 'waq_arr', con_pts_file, + zone_file=zone_files, field_name=['kh'], layer_mode=True, - save_path=save_path, + save_dir=save_dir, tensor_interp='idw') diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index f62dac412..459c6fb54 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -350,7 +350,7 @@ def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_di x_extent = np.max(grid_x) - np.min(grid_x) y_extent = np.max(grid_y) - np.min(grid_y) domain_extent = max(x_extent, y_extent) - corrlen = np.full(len(grid_x), np.max(10000.0, domain_extent/3)) + corrlen = np.full(len(grid_x), np.max([10000.0, domain_extent/3])) aniso = np.ones(len(grid_x)) # Isotropic bearing = np.zeros(len(grid_x)) # No rotation @@ -842,6 +842,64 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, # Use pypestutils for kriging print(" Calling pypestutils for tensor-aware kriging...") + def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): + """ + Ensure every zone in the grid either: + 1) Has >= min_points control points, OR + 2) Gets merged with a viable neighboring zone + """ + zones_fixed = zones_flat.copy() + cp_zones_fixed = cp_zones.copy() + + unique_grid_zones = np.unique(zones_flat) + + zones_to_eliminate = [] + + for zone in unique_grid_zones: + cp_count = np.sum(cp_zones == zone) + grid_count = np.sum(zones_flat == zone) + + if cp_count < min_points: + print(f" Zone {zone}: {cp_count} CPs, {grid_count} grid cells - ELIMINATING") + zones_to_eliminate.append(zone) + + if zones_to_eliminate: + # Find the zone with the most control points to use as the "receiver" + viable_zones = [z for z in unique_grid_zones if z not in zones_to_eliminate] + if viable_zones: + # Pick the zone with most control points + best_zone = max(viable_zones, key=lambda z: np.sum(cp_zones == z)) + print(f" Consolidating {len(zones_to_eliminate)} bad zones into zone {best_zone}") + + for bad_zone in zones_to_eliminate: + # Move all grid cells from bad zone to best zone + zones_fixed[zones_flat == bad_zone] = best_zone + # Move any control points (though there shouldn't be many) + cp_zones_fixed[cp_zones == bad_zone] = best_zone + else: + # All zones are bad - just make everything zone 1 + print(" All zones insufficient - making single zone") + zones_fixed = np.ones_like(zones_flat) + cp_zones_fixed = np.ones_like(cp_zones) + + # Final verification + final_zones = np.unique(zones_fixed) + print(f" Final zones: {final_zones}") + for zone in final_zones: + cp_count = np.sum(cp_zones_fixed == zone) + grid_count = np.sum(zones_fixed == zone) + print(f" Zone {zone}: {cp_count} CPs, {grid_count} grid cells") + + if cp_count < min_points: + print(f" WARNING: Zone {zone} still has insufficient points!") + + return zones_fixed, cp_zones_fixed + + # Use it: + zones_for_kriging, cp_zones_for_kriging = fix_zones_for_pypestutils( + zones_flat, cp_zones, min_points + ) + try: lib = PestUtilsLib() @@ -867,21 +925,30 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, print(f" Search radius: {max_search_radius}") print(f" Max neighbors: {max_neighbors}") - # Calculate kriging factors using tensor-derived anisotropy + # Then use the modified zones in kriging npts = lib.calc_kriging_factors_2d( - cp_coords[:, 0], cp_coords[:, 1], cp_zones, - grid_x, grid_y, zones_flat, - vartype, # Variogram type - 1, # Ordinary kriging - corrlen_major, # Correlation lengths from tensors - anisotropy_ratios, # Anisotropy ratios from tensors - bearing_deg, # Bearings from tensors - max_search_radius, - max_neighbors, - min_points, + cp_coords[:, 0], cp_coords[:, 1], cp_zones_for_kriging, # Modified zones + grid_x, grid_y, zones_for_kriging, # Modified zones + vartype, 1, corrlen_major, anisotropy_ratios, bearing_deg, + max_search_radius, max_neighbors, min_points, fac_fname, fac_ftype ) + # Calculate kriging factors using tensor-derived anisotropy + # npts = lib.calc_kriging_factors_2d( + # cp_coords[:, 0], cp_coords[:, 1], cp_zones, + # grid_x, grid_y, zones_flat, + # vartype, # Variogram type + # 1, # Ordinary kriging + # corrlen_major, # Correlation lengths from tensors + # anisotropy_ratios, # Anisotropy ratios from tensors + # bearing_deg, # Bearings from tensors + # max_search_radius, + # max_neighbors, + # min_points, + # fac_fname, fac_ftype + # ) + print(f" Pypestutils kriging factors calculated for {npts} interpolation points") # Apply kriging @@ -1290,156 +1357,3 @@ def _save_results(results, out_filename, vartransform): np.savetxt(field_file, field, fmt="%20.8E") print(f" Saved {len(results['fields'])} realizations") - - -# Example usage function -def example_usage(): - """Example of how to use the tensor interpolation.""" - - # Create example conceptual points DataFrame - cp_data = { - 'name': ['cp1', 'cp2', 'cp3', 'cp4'], - 'x': [1000, 3000, 1000, 3000], - 'y': [1000, 1000, 3000, 3000], - 'z': [0, 0, 0, 0], - 'mean_kh': [1.0, 1.0, 1.0, 1.0], - 'sd_kh': [0.1, 0.1, 0.1, 0.1], - 'major': [2000, 1500, 1800, 2200], - 'anisotropy': [3.0, 2.5, 4.0, 2.0], - 'bearing': [45, 90, 135, 0], # Degrees - 'layer': [0, 0, 0, 0] - } - cp_df = pd.DataFrame(cp_data) - - # Create example grid (pyemu style) - nx, ny = 50, 40 - x = np.linspace(0, 5000, nx) - y = np.linspace(0, 4000, ny) - xcentergrid, ycentergrid = np.meshgrid(x, y) - - # Example zones - zones = np.ones((ny, nx), dtype=int) - zones[:ny // 2, :] = 1 - zones[ny // 2:, :] = 2 - - # Configuration - config = { - 'vartype': 2, # Exponential - 'krigtype': 1, # Ordinary kriging - 'search_radius': 10000, - 'maxpts_interp': 10, - 'minpts_interp': 1 - } - - # Interpolate tensors - tensors = interpolate_tensors( - cp_df, xcentergrid, ycentergrid, - zones=zones, method='krig', - layer=0, config_dict=config - ) - - print(f"Interpolated tensor field shape: {tensors.shape}") - print(f"Sample tensor at grid point 0:\n{tensors[0]}") - - return tensors - - -def example_workflow(): - """Example of complete workflow with FIXED grid handling.""" - import matplotlib.pyplot as plt - - # Create example data - FIXED grid creation - nx, ny = 50, 40 # nx = columns, ny = rows - x = np.linspace(0, 5000, nx) - y = np.linspace(0, 4000, ny) - - # Use consistent indexing - 'ij' indexing is clearer for grid applications - # This creates ycentergrid with shape (ny, nx) and xcentergrid with shape (ny, nx) - ycentergrid, xcentergrid = np.meshgrid(y, x, indexing='ij') - - # Now ycentergrid.shape = (40, 50) and xcentergrid.shape = (40, 50) - # Row 0 corresponds to y[0], Column 0 corresponds to x[0] - - # Example conceptual points - cp_data = { - 'name': ['cp1', 'cp2', 'cp3', 'cp4'], - 'x': [1000, 3000, 1000, 3000], - 'y': [1000, 1000, 3000, 3000], - 'z': [0, 0, 0, 0], - 'mean_kh': [1.5, 2.0, 1.8, 1.2], - 'sd_kh': [0.3, 0.4, 0.2, 0.5], - 'major': [2000, 1500, 1800, 2200], - 'anisotropy': [3.0, 2.5, 4.0, 2.0], - 'bearing': [45, 90, 135, 0], - 'layer': [0, 0, 0, 0] - } - cp_df = pd.DataFrame(cp_data) - - # Example zones - shape (ny, nx) = (40, 50) - zones = np.ones((ny, nx), dtype=int) - zones[:ny // 2, :] = 1 - zones[ny // 2:, :] = 2 - - # Run workflow - results = apply_ppu_hyperpars( - cp_df, xcentergrid, ycentergrid, - zones=zones, n_realizations=1, - out_filename="example", - vartransform="none" - ) - - # Verify shapes - print(f"Grid shapes: xcentergrid {xcentergrid.shape}, ycentergrid {ycentergrid.shape}") - print(f"Results shapes: mean {results['mean'].shape}, zones {zones.shape}") - print(f"Expected: ({ny}, {nx}) = (40, 50)") - - # Check for problematic values in realizations - if results['fields'] is not None: - for i, field in enumerate(results['fields']): - n_zeros = np.sum(field == 0) - n_total = field.size - print(f"Realization {i}: {n_zeros}/{n_total} zero values ({100 * n_zeros / n_total:.1f}%)") - print(f" Min: {field.min():.3f}, Max: {field.max():.3f}, Mean: {field.mean():.3f}") - - # Quick visualization with proper coordinate mapping - fig, axes = plt.subplots(2, 3, figsize=(15, 10)) - - # Mean field - im1 = axes[0, 0].imshow(results['mean'], origin='lower', extent=[0, 5000, 0, 4000]) - axes[0, 0].set_title('Mean Field') - axes[0, 0].set_xlabel('X (m)') - axes[0, 0].set_ylabel('Y (m)') - plt.colorbar(im1, ax=axes[0, 0]) - - # SD field - im2 = axes[0, 1].imshow(results['sd'], origin='lower', extent=[0, 5000, 0, 4000]) - axes[0, 1].set_title('Standard Deviation') - axes[0, 1].set_xlabel('X (m)') - axes[0, 1].set_ylabel('Y (m)') - plt.colorbar(im2, ax=axes[0, 1]) - - # Anisotropy field - im3 = axes[0, 2].imshow(results['anisotropy'], origin='lower', extent=[0, 5000, 0, 4000]) - axes[0, 2].set_title('Anisotropy Ratio') - axes[0, 2].set_xlabel('X (m)') - axes[0, 2].set_ylabel('Y (m)') - plt.colorbar(im3, ax=axes[0, 2]) - - # First three realizations - if results['fields'] is not None: - for i in range(min(3, len(results['fields']))): - im = axes[1, i].imshow(results['fields'][i], origin='lower', extent=[0, 5000, 0, 4000]) - axes[1, i].set_title(f'Realization {i + 1}') - axes[1, i].set_xlabel('X (m)') - axes[1, i].set_ylabel('Y (m)') - plt.colorbar(im, ax=axes[1, i]) - - plt.tight_layout() - plt.savefig('workflow_results.png', dpi=150, bbox_inches='tight') - plt.show() - - print("Example workflow complete!") - return results - -if __name__ == "__main__": - example_workflow() \ No newline at end of file From 7ca5bb98da5fd0b5f39ed8a987ecd26b533a8c84 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 19 Aug 2025 20:29:46 +1200 Subject: [PATCH 19/36] massive refactoring to use modelgrid object, dealing with layers better --- pyemu/utils/nsaf_helpers.py | 761 +++++++++++++++++------------ pyemu/utils/nsaf_utils.py | 936 ++++++++++++++++++++---------------- 2 files changed, 971 insertions(+), 726 deletions(-) diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index 888542cf1..e28e965df 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -1,18 +1,17 @@ +""" +NSAF Helpers - High-level API functions for field generation from files +Refactored to use modelgrid consistently and reduce redundancy +""" import numpy as np import pandas as pd import os -from scipy.ndimage import distance_transform_edt, gaussian_filter +from pyemu import plot_utils as pu -# Import from our Chunk 1 and 2 +# Import from refactored nsaf_utils from nsaf_utils import ( - interpolate_tensors, - load_conceptual_points, - create_2d_tensors, - get_tensor_bearing, - apply_ppu_hyperpars, - tensor_aware_kriging, - create_boundary_modified_scalar, - detect_zone_boundaries + generate_single_layer, + detect_zone_boundaries, + _extract_grid_info ) @@ -22,7 +21,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, vartransform='log', n_realizations=1, ml=None, sr=None): """ Generate spatially correlated fields using tensor-based geostatistics. - CLEAN version that works with flexible property columns. + REFACTORED to use modelgrid consistently and call generate_single_layer. Parameters ---------- @@ -56,7 +55,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, Returns ------- dict - Results from workflow + fieldresult from workflow """ try: import flopy @@ -85,26 +84,26 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, except Exception as e: raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}: {e}") - # Get grid coordinates - try: - xcentergrid = ml.modelgrid.xcellcenters - ycentergrid = ml.modelgrid.ycellcenters - except AttributeError as e: - raise Exception(f"Could not get grid centers from spatial reference: {e}") + # Get modelgrid from the model + modelgrid = ml.modelgrid - ny, nx = xcentergrid.shape - print(f"Grid dimensions: {ny} x {nx}") - print( - f"Grid extent: X=[{xcentergrid.min():.1f}, {xcentergrid.max():.1f}], Y=[{ycentergrid.min():.1f}, {ycentergrid.max():.1f}]") + # Extract grid info for display + grid_info = _extract_grid_info(modelgrid) + ny = grid_info['ny'] + nx = grid_info['nx'] + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] - # Get cell areas for area parameter - cell_area = np.outer(ml.dis.delc.array, ml.dis.delr.array) + print(f"Grid dimensions: {ny} x {nx}") + print(f"Grid extent: X=[{xcentergrid.min():.1f}, {xcentergrid.max():.1f}], " + f"Y=[{ycentergrid.min():.1f}, {ycentergrid.max():.1f}]") # Load conceptual points conceptual_points = pd.read_csv(os.path.join(tmp_model_ws, conceptual_points_file)) print(f"Loaded {len(conceptual_points)} conceptual points") - print( - f"Conceptual points extent: X=[{conceptual_points['x'].min():.1f}, {conceptual_points['x'].max():.1f}], Y=[{conceptual_points['y'].min():.1f}, {conceptual_points['y'].max():.1f}]") + print(f"Conceptual points extent: X=[{conceptual_points['x'].min():.1f}, " + f"{conceptual_points['x'].max():.1f}], Y=[{conceptual_points['y'].min():.1f}, " + f"{conceptual_points['y'].max():.1f}]") # Add transverse if missing if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: @@ -122,8 +121,15 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, raise ValueError(f"Zone file shape {zones.shape} doesn't match grid {(ny, nx)}") print(f"Loaded zones from {zone_file}, shape: {zones.shape}") else: - zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file[0])).astype(np.int64) - print(f"Loaded zones from {zone_file[0]} (multi-layer support TODO)") + # Handle list of zone files for multiple layers + zones = [] + for zf in zone_file: + z = np.loadtxt(os.path.join(tmp_model_ws, zf)).astype(np.int64) + if z.shape != (ny, nx): + if z.size == ny * nx: + z = z.reshape(ny, nx) + zones.append(z) + print(f"Loaded {len(zones)} zone arrays from multiple files") # Determine layers if hasattr(ml, 'dis'): @@ -146,7 +152,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, available_cp_layers = [1] print("No 'layer' column in conceptual points - using layer 1") - # Generate IIDs for all model layers + # Generate or load IIDs for all model layers iids_dict = {} for layer_num in range(total_layers): layer_1based = layer_num + 1 @@ -210,46 +216,48 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # Get IIDs for this layer layer_iids = iids_dict.get(target_layer, iids_dict[1]) - # Set outfile - if save_dir is None: - outfile = os.path.join(tmp_model_ws, f"{fn}_layer_{target_layer}.arr") - else: # TODO: ability to pass file basename - outfile = os.path.join(save_dir, f"{fn}_layer_{target_layer}.arr") - - # apply_ppu_hyperpars - results = apply_ppu_hyperpars( - layer_cp, xcentergrid, ycentergrid, - area=cell_area, - zones=zones, - n_realizations=n_realizations, - layer=target_layer - 1, # Convert to 0-based for internal use - vartransform=vartransform, - tensor_method=tensor_interp, - out_filename=outfile, + # Get zones for this layer if available + layer_zones = None + if zones is not None: + if isinstance(zones, list): + if target_layer - 1 < len(zones): + layer_zones = zones[target_layer - 1] + else: + layer_zones = zones + + # Use generate_single_layer which internally calls apply_nsaf_hyperpars + field, result = generate_single_layer( + conceptual_points=layer_cp, + modelgrid=modelgrid, iids=layer_iids, - mean_col=mean_col, # Pass column names + zones=layer_zones, + layer=target_layer, + tensor_interp=tensor_interp, + vartransform=vartransform, + boundary_smooth=True, + boundary_enhance=True, + mean_col=mean_col, sd_col=sd_col, - active=ml.dis.idomain.array[target_layer-1].flatten() + n_realizations=n_realizations ) - # Store results with layer key, already done via pypest utils + # todo: better file name handling + if save_dir is not None: + outfile = os.path.join(save_dir, f"{fn}_layer{target_layer}.arr") + np.savetxt(outfile, field, fmt="%20.8E") + pu.visualize_tensors(result['tensors'], xcentergrid, ycentergrid, zones=layer_zones, + conceptual_points=layer_cp, subsample=4, scale_factor=0.1, + figsize=(14, 12), title_suf=None, save_path=save_dir) + pu.plot_layer(field, layer=target_layer, field_name=fn, transform='log', save_path=save_dir) + + # Store results result_key = f"{fn}_layer_{target_layer}" - all_results[result_key] = results - - # # Save results - # if results['fields'] is not None: - # for i, field in enumerate(results['fields']): - # save_layer(field, layer=target_layer - 1, - # field_name=f"{fn}_real_{i + 1}", save_path=save_path) - # - # save_layer(results['mean'], layer=target_layer - 1, - # field_name=f"{fn}_mean", save_path=save_path) - # save_layer(results['sd'], layer=target_layer - 1, - # field_name=f"{fn}_sd", save_path=save_path) - - print(f"Results for layer {target_layer}:") - print(f" Mean field: [{results['mean'].min():.3f}, {results['mean'].max():.3f}]") - print(f" SD field: [{results['sd'].min():.3f}, {results['sd'].max():.3f}]") + all_results[result_key] = result + + print(f"fieldresult for layer {target_layer}:") + print(f" Mean field: [{result['mean'].min():.3f}, {result['mean'].max():.3f}]") + print(f" SD field: [{result['sd'].min():.3f}, {result['sd'].max():.3f}]") + else: # Single layer mode @@ -258,26 +266,360 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # Get IIDs for this layer layer_iids = iids_dict.get(1, None) - # Use workflow - results = apply_ppu_hyperpars( - conceptual_points, xcentergrid, ycentergrid, - area=cell_area, - zones=zones, - n_realizations=n_realizations, - layer=target_layer-1, - vartransform=vartransform, - tensor_method=tensor_interp, - out_filename=outfile, + # Get zones (single layer) + layer_zones = zones[0] if isinstance(zones, list) else zones + + # Use generate_single_layer + field, result = generate_single_layer( + cp_file=conceptual_points, + modelgrid=modelgrid, iids=layer_iids, + zones=layer_zones, + tensor_interp=tensor_interp, + vartransform=vartransform, + boundary_smooth=True, + boundary_enhance=True, mean_col=mean_col, - sd_col=sd_col + sd_col=sd_col, + n_realizations=n_realizations ) - all_results[fn] = results + # Save results if needed + # todo: better file name handling + if save_dir is not None: + for i, f in enumerate(field): + outfile = os.path.join(save_dir, f"{fn}_layer{i+1}.arr") + np.savetxt(outfile, field, fmt="%20.8E") + all_results[fn] = result print("\n=== Complete ===") return all_results +""" +Final helper functions for NSAF Utils +These complete the implementation with field generation and boundary functions +""" + +# Add these to nsaf_utils.py + +def _generate_stochastic_field(grid_coords, mean_field, sd_field, + bearing, anisotropy, corrlen, + zones, n_realizations, vartransform, + config_dict, ny, nx, iids=None, area=None, + active=None): + """ + Generate stochastic field using pypestutils FIELDGEN2D_SVA. + """ + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") + + print(" Generating stochastic field using FIELDGEN2D_SVA...") + + # Prepare grid data + n_points = ny * nx + x_coords = grid_coords[:, 0] + y_coords = grid_coords[:, 1] + + # Flatten fields - ENSURE PROPER ORDERING + mean_flat = mean_field.flatten().clip(1e-8, None) + variance_native = sd_field.flatten() ** 2 + log_variance_flat = np.log(1 + variance_native / mean_flat ** 2) + trans_flag = 1 # Log domain + + # Geostatistical parameters from tensors + aa_values = corrlen.flatten() + aniso_values = anisotropy.flatten() + bearing_values = bearing.flatten() + + # Set defaults + if active is None: + active = np.ones(n_points, dtype=int) + if area is None: + area = np.ones(n_points) + else: + if area.ndim > 1: + area = area.flatten() + + # Generate or use provided IIDs + if iids is None: + print(" Generating new IIDs...") + iids = np.random.normal(0, 1, size=(n_points, n_realizations)) + else: + print(" Using provided IIDs...") + if iids.shape != (n_points, n_realizations): + raise ValueError(f"IIDs shape {iids.shape} doesn't match expected {(n_points, n_realizations)}") + + try: + # Set up pypestutils parameters + lib = PestUtilsLib() + lib.initialize_randgen(11235813) + + # Averaging function type + avg_func = config_dict.get('averaging_function', 'gaussian') + if isinstance(avg_func, dict): + if 'pow' in avg_func: + avg_flag = 4 + power_value = avg_func['pow'] + else: + avg_flag, power_value = next(iter(avg_func.items())) + else: + power_value = 0 + if avg_func == 'exponential': + avg_flag = 2 + elif avg_func == 'gaussian': + avg_flag = 3 + elif avg_func == 'spherical': + avg_flag = 1 + else: + avg_flag = 2 # Default to exponential + + # Call FIELDGEN2D_SVA through pypestutils interface + print(f" Calling FIELDGEN2D_SVA for {n_realizations} realizations...") + + field = lib.fieldgen2d_sva( + x_coords, y_coords, + area=area, + active=active, + mean=mean_flat, + var=log_variance_flat, + aa=aa_values, + anis=aniso_values, + bearing=bearing_values, + transtype=trans_flag, + avetype=avg_flag, + power=power_value, + nreal=n_realizations, + ) + + # Handle the output format from FIELDGEN2D_SVA + if field.shape == (n_points, n_realizations): + # Output is (n_points, n_realizations) - transpose to (n_realizations, n_points) + field = field.T # Now shape is (n_realizations, n_points) + # Reshape each realization to (ny, nx) + field = field.reshape((n_realizations, ny, nx)) + + elif field.shape == (n_realizations * n_points,): + # Output is flattened - reshape directly + field = field.reshape((n_realizations, ny, nx)) + + elif field.shape == (n_realizations, n_points): + # Output is already (n_realizations, n_points) - just reshape spatial dimensions + field = field.reshape((n_realizations, ny, nx)) + + else: + raise ValueError(f"Unexpected fieldgen2d_sva output shape: {field.shape}") + + print(f" Successfully generated {n_realizations} realizations with shape {field.shape}") + + # Clean up + lib.free_all_memory() + + return field + + except Exception as e: + print(f" Warning: FIELDGEN2D_SVA failed: {e}") + print(f" Using fallback method...") + + # Fallback to simple field generation + field = np.zeros((n_realizations, ny, nx)) + for real in range(n_realizations): + current_iids = iids[:, real] + field_values = _generate_simple_field( + grid_coords, mean_flat, log_variance_flat, + aa_values, aniso_values, bearing_values, + vartransform, current_iids, ny, nx + ) + field[real] = field_values.reshape(ny, nx) + + return field + + +def _generate_simple_field(grid_coords, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + vartransform, iids, ny, nx): + """ + Simple fallback field generation if FIELDGEN2D_SVA fails. + """ + print(" Using improved simple correlated field generation fallback...") + + n_points = len(grid_coords) + + # Convert bearing from degrees to radians for calculations + bearing_rad = np.radians(bearing_values) + + # Create spatially correlated field using moving average approach + correlated_field = np.zeros(n_points) + + # For computational efficiency, use a subset of points for correlation + max_correlation_points = min(2000, n_points) + + if n_points > max_correlation_points: + # Sample points for correlation calculation + correlation_indices = np.random.choice(n_points, max_correlation_points, replace=False) + correlation_indices = np.sort(correlation_indices) + else: + correlation_indices = np.arange(n_points) + + # Apply spatial correlation + for i in correlation_indices: + # Get local correlation parameters + local_aa = aa_values[i] + local_aniso = aniso_values[i] + local_bearing = bearing_rad[i] + + # Calculate distances to all points + dx = grid_coords[:, 0] - grid_coords[i, 0] + dy = grid_coords[:, 1] - grid_coords[i, 1] + + # Apply anisotropic transformation + cos_bear = np.cos(local_bearing) + sin_bear = np.sin(local_bearing) + + # Rotate to principal axes (geological bearing: 0° = North, clockwise positive) + dx_rot = dx * sin_bear + dy * cos_bear # East component (major axis) + dy_rot = -dx * cos_bear + dy * sin_bear # North component (minor axis) + + # Scale by anisotropy + minor_aa = local_aa / local_aniso + dx_scaled = dx_rot / local_aa + dy_scaled = dy_rot / minor_aa + + # Calculate anisotropic distance + aniso_distances = np.sqrt(dx_scaled ** 2 + dy_scaled ** 2) + + # Exponential correlation function + correlation_weights = np.exp(-aniso_distances) + + # Normalize weights + correlation_weights = correlation_weights / np.sum(correlation_weights) + + # Apply correlation to the random field + correlated_field[i] = np.sum(correlation_weights * iids) + + # For points not in correlation_indices, use interpolation + if n_points > max_correlation_points: + remaining_indices = np.setdiff1d(np.arange(n_points), correlation_indices) + + for i in remaining_indices: + # Find nearest correlation points + distances_to_corr = np.sum((grid_coords[correlation_indices] - grid_coords[i]) ** 2, axis=1) + nearest_corr_idx = correlation_indices[np.argmin(distances_to_corr)] + correlated_field[i] = correlated_field[nearest_corr_idx] + + # Scale by local standard deviation + stochastic_component = correlated_field * np.sqrt(variance_flat) + + # Combine with mean field + if vartransform == 'log': + # Log domain: mean * exp(stochastic_component) + field_values = mean_flat * np.exp(stochastic_component) + else: + # Normal domain: mean + stochastic_component + field_values = mean_flat + stochastic_component + + return field_values + + +def create_boundary_modified_scalar(base_field, zones, + peak_increase=0.3, transition_cells=5, mode="enhance"): + """ + Modify scalar field values near geological zone boundaries. + """ + from scipy.ndimage import distance_transform_edt, gaussian_filter + + if mode not in ("enhance", "smooth"): + raise ValueError("mode must be 'enhance' or 'smooth'") + + # 2D case only for now + if zones.shape != base_field.shape: + raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") + + boundary_mask, _ = detect_zone_boundaries(zones) + distance = distance_transform_edt(~boundary_mask) + transition_mask = distance <= transition_cells + + modified = base_field.copy() + + if mode == "enhance": + # Linear enhancement near boundaries + factor = 1 - distance[transition_mask] / transition_cells + enhancement = peak_increase * factor + modified[transition_mask] += enhancement + + elif mode == "smooth": + # Smooth field with Gaussian filter + smoothed_field = gaussian_filter(base_field, sigma=transition_cells / 2) + + # Blend with original based on distance to boundary + weight = 1 - distance[transition_mask] / transition_cells + modified[transition_mask] = ( + weight * smoothed_field[transition_mask] + + (1 - weight) * base_field[transition_mask] + ) + + print(f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} " + f"{np.count_nonzero(transition_mask)} points near boundaries") + + return modified + + +def detect_zone_boundaries(zones): + """Detect boundaries between geological zones.""" + ny, nx = zones.shape + boundary_mask = np.zeros((ny, nx)).astype(bool) + boundary_directions = np.zeros((ny, nx, 2)) + + # Find boundary points + for i in range(ny): + for j in range(nx): + current_zone = zones[i, j] + + # Check 8-connected neighbors + for di in [-1, 0, 1]: + for dj in [-1, 0, 1]: + if di == 0 and dj == 0: + continue + ni, nj = i + di, j + dj + if 0 <= ni < ny and 0 <= nj < nx: + if zones[ni, nj] != current_zone: + boundary_mask[i, j] = True + break + if boundary_mask[i, j]: + break + + return boundary_mask, boundary_directions + + +def save_results(result, out_filename, vartransform): + """Save results to files in PyEMU-compatible format.""" + + # todo: better file name handling + + # Save mean field + mean_file = f"{out_filename}_mean.txt" + np.savetxt(mean_file, result['mean'], fmt="%20.8E") + print(f" Saved mean field to {mean_file}") + + # Save standard deviation field + sd_file = f"{out_filename}_sd.txt" + np.savetxt(sd_file, result['sd'], fmt="%20.8E") + print(f" Saved SD field to {sd_file}") + + # Save geostatistical parameter field + for param in ['bearing', 'anisotropy', 'corrlen']: + param_file = f"{out_filename}_{param}.txt" + np.savetxt(param_file, result[param], fmt="%20.8E") + print(f" Saved {param} field to {param_file}") + + # Save stochastic realizations + if result['field'] is not None: + for i, field in enumerate(result['field']): + field_file = f"{out_filename}_real_{i + 1:03d}.txt" + np.savetxt(field_file, field, fmt="%20.8E") + + print(f" Saved {len(result['field'])} realizations") def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realizations): """ @@ -384,254 +726,39 @@ def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realiza return iids -def generate_single_layer(cp_file, xcentergrid, ycentergrid, iids=None, - zones=None, tensor_interp='idw', - vartransform='log', boundary_smooth=True, - boundary_enhance=True): - """ - Generate single layer using approach with boundary adjustments. - Replaces the NSAF generate_single_layer_zone_based function. - - Parameters - ---------- - cp_file : str or pd.DataFrame - Conceptual points file or DataFrame - xcentergrid : np.ndarray - X-coordinates from pyemu SpatialReference, shape (ny, nx) - ycentergrid : np.ndarray - Y-coordinates from pyemu SpatialReference, shape (ny, nx) - iids : np.ndarray, optional - Pre-generated noise, shape (ny, nx) - zones : np.ndarray, optional - Zone IDs, shape (ny, nx) - tensor_interp : str, default 'krig' - Tensor interpolation method - vartransform : str, default 'log' - Transformation: 'log' or 'none' - boundary_smooth : bool, default True - Apply smoothing to mean at zone boundaries - boundary_enhance : bool, default True - Apply variance enhancement at zone boundaries - - Returns - ------- - tuple - (field_2d, tensors) where: - - field_2d: Generated field, shape (ny, nx) - - tensors: Interpolated tensors, shape (ny*nx, 2, 2) - """ - - # Load conceptual points - cp_df = load_conceptual_points(cp_file) - - print("=== Single Layer Generation ===") - print(f"Processing {len(cp_df)} conceptual points") - - ny, nx = xcentergrid.shape - - # Step 1: Create geological tensors from conceptual points - print(" Creating geological correlation tensors...") - cp_coords = cp_df[['x', 'y']].values - - # Get mean and sd columns - mean_cols = [col for col in cp_df.columns if 'mean' in col.lower()] - sd_cols = [col for col in cp_df.columns if 'sd' in col.lower()] - - if not mean_cols or not sd_cols: - raise ValueError("Conceptual points must have mean and sd columns") - - cp_means = cp_df[mean_cols[0]].values - cp_sd = cp_df[sd_cols[0]].values - - # Create tensors - major = cp_df['major'].values - minor = cp_df['transverse'].values - - def normalize_bearing(bearing): - return bearing % 360 - - bearings_normalized = [normalize_bearing(b) for b in cp_df['bearing'].values] - bearing_rad = np.radians(bearings_normalized) - cp_tensors = create_2d_tensors(bearing_rad, major, minor) - - # Debug tensor creation - print(" Tensor creation verification:") - for i in range(min(3, len(cp_tensors))): - original_bearing = cp_df['bearing'].values[i] - extracted_bearing = get_tensor_bearing(cp_tensors[i], original_bearing) - print(f" Point {i}: Input bearing={original_bearing:.1f}°, Extracted={extracted_bearing:.1f}°") - - # Step 2: Interpolate tensors - print(f" Interpolating tensors using {tensor_interp} method...") - tensors = interpolate_tensors( - cp_df, xcentergrid, ycentergrid, - zones=zones, method=tensor_interp, layer=0 - ) - - # Step 3: Interpolate means and sd with boundary adjustments - print(" Interpolating means with geological structure...") - grid_coords_2d = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) - - # Configure kriging - transform = 'log' if vartransform == 'log' else None - min_value = 1e-8 if vartransform == 'log' else None - - # Interpolate mean - interp_means_2d = tensor_aware_kriging( - cp_coords, cp_means, grid_coords_2d, tensors, - variogram_model='exponential', sill=1.0, nugget=0.1, - background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform=transform, min_value=min_value, - max_neighbors=4, zones=zones - ) - - # Apply boundary smoothing - if boundary_smooth and zones is not None: - print(" Smoothing values at geological boundaries...") - interp_means_2d = create_boundary_modified_scalar( - interp_means_2d, zones, transition_cells=3, mode='smooth' - ) - - # Interpolate standard deviation - print(" Interpolating standard deviation...") - interp_sd_2d = tensor_aware_kriging( - cp_coords, cp_sd, grid_coords_2d, tensors, - variogram_model='exponential', sill=1.0, nugget=0.1, - background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform=transform, min_value=min_value, - max_neighbors=4, zones=zones - ) - - # Apply boundary enhancement - if boundary_enhance and zones is not None: - print(" Enhancing variance at geological boundaries...") - interp_sd_2d = create_boundary_modified_scalar( - interp_sd_2d, zones, peak_increase=1.0, transition_cells=3, mode='enhance' - ) - - # Step 4: Generate stochastic field using FIELDGEN2D_SVA approach - print(" Generating stochastic field using FIELDGEN2D_SVA methodology...") - - # Convert tensors to geostatistical parameters for FIELDGEN2D_SVA - from nsaf_utils import _tensors_to_geostat_params, _generate_stochastic_fields - - bearing_deg, anisotropy, corrlen_major = _tensors_to_geostat_params(tensors) - - # Prepare grid coordinates - grid_coords_2d = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) - - # Configuration for field generation - config_dict = { - 'averaging_function': 'exponential', - 'vartype': 2, - 'krigtype': 1 - } - - # Generate one realization - n_realizations = 1 - fields = _generate_stochastic_fields( - grid_coords_2d, interp_means_2d, interp_sd_2d, - bearing_deg, anisotropy, corrlen_major, - zones, n_realizations, vartransform, - config_dict, ny, nx - ) - - field_2d = fields[0] # Extract the single realization - - print("=== Single layer generation complete ===") - return field_2d, tensors - - -def save_layer(field_2d, layer, field_name, save_path='.'): - """ - Save layer to file in PyEMU-compatible format. - - Parameters - ---------- - field_2d : np.ndarray - Field values, shape (ny, nx) - layer : int - Layer number - field_name : str - Field name for filename - save_path : str - Directory to save files - """ - os.makedirs(save_path, exist_ok=True) - filename = os.path.join(save_path, f"{field_name}_layer_{layer + 1}.txt") - np.savetxt(filename, field_2d, fmt="%20.8E") - print(f" Saved {field_name} layer {layer + 1} to {filename}") - - -def validate_grid_coordinates(xcentergrid, ycentergrid): - """ - Validate PyEMU grid coordinates. - - Parameters - ---------- - xcentergrid : np.ndarray - X-coordinates, shape (ny, nx) - ycentergrid : np.ndarray - Y-coordinates, shape (ny, nx) - - Returns - ------- - bool - True if valid - """ - if xcentergrid.shape != ycentergrid.shape: - print(f"ERROR: Grid coordinate shapes don't match: {xcentergrid.shape} vs {ycentergrid.shape}") - return False - - ny, nx = xcentergrid.shape - print(f"Grid validation: {ny} x {nx} = {ny * nx} points") - - # Check for regular grid structure - x_unique = len(np.unique(xcentergrid)) - y_unique = len(np.unique(ycentergrid)) - - if x_unique == nx and y_unique == ny: - print(" ✓ Regular rectangular grid detected") - return True - else: - print(f" ⚠ Irregular grid: {x_unique} unique x, {y_unique} unique y") - return True # Still valid, just not regular - - -def infer_grid_shape(xcentergrid, ycentergrid): - """ - Infer grid shape from PyEMU coordinate arrays. - - Parameters - ---------- - xcentergrid : np.ndarray - X-coordinates, shape (ny, nx) - ycentergrid : np.ndarray - Y-coordinates, shape (ny, nx) - - Returns - ------- - tuple - Grid shape (nz, ny, nx) - nz=1 for 2D - """ - ny, nx = xcentergrid.shape - return (1, ny, nx) - - def example_field_generation(): """Example using concentric circles of tangentially-oriented anisotropic tensors.""" + import flopy import matplotlib.pyplot as plt import numpy as np import pandas as pd # Create example PyEMU grid - square domain nx, ny = 100, 100 - x = np.linspace(0, 5000, nx) - y = np.linspace(0, 5000, ny) + xmax = 5000 + ymax = 5000 + x = np.linspace(0, xmax, nx) + y = np.linspace(0, ymax, ny) + + # Correct: delr is row spacing (y-direction), delc is column spacing (x-direction) + delr = np.full(ny, ymax / ny) # Cell height (y-direction, row spacing) + delc = np.full(nx, xmax / nx) # Cell width (x-direction, column spacing) + xcentergrid, ycentergrid = np.meshgrid(x, y, indexing='xy') xcentergrid = xcentergrid.T ycentergrid = ycentergrid.T + modelgrid = flopy.discretization.StructuredGrid( + delc=delc, # Column spacing (x-direction) + delr=delr, # Row spacing (y-direction) + top=np.ones((ny, nx)), + botm=np.zeros((1, ny, nx)), + xoff=x[0] - delc[0] / 2, # Use delc for x offset + yoff=y[0] - delr[0] / 2, # Use delr for y offset + angrot=0.0, + idomain=np.ones((ny, nx)), + ) + # Define circle center and radius center_x = 2500 center_y = 2500 @@ -666,8 +793,8 @@ def example_field_generation(): 'name': f'cp_circle{circle_idx}_point{i}', 'x': cp_x, 'y': cp_y, - 'mean_kh': mean_kh, - 'sd_kh': mean_kh * 0.1, + 'mean': mean_kh, + 'sd': mean_kh * 0.1, 'major': major_length, 'anisotropy': 4.0, 'bearing': bearing, @@ -680,10 +807,10 @@ def example_field_generation(): # Generate field print("=== Testing tangential field generation ===") field_2d, tensors = generate_single_layer( - cp_df, xcentergrid, ycentergrid, + cp_df, modelgrid, zones=None, tensor_interp='krig', - vartransform='None' + vartransform='None', ) # Visualize results @@ -737,7 +864,7 @@ def example_field_generation(): axes[1, 1].set_aspect('equal') plt.tight_layout() - plt.savefig('tangential_field_example.png', dpi=150, bbox_inches='tight') + plt.savefig(os.path.join('..','..','examples', 'tangential_field_example.png'), dpi=150, bbox_inches='tight') plt.show() print(f"Field statistics: mean={np.mean(field_2d):.3f}, std={np.std(field_2d):.3f}") diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index 459c6fb54..37a41caf1 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -9,7 +9,7 @@ def get_tensor_bearing(tensor, expected_bearing=None): - """Extract geological bearing from tensor.""" + """Extract geological bearing from tensor - NO CHANGES to math.""" eigenvals, eigenvecs = np.linalg.eigh(tensor) max_idx = np.argmax(eigenvals) major_eigenvec = eigenvecs[:, max_idx] @@ -36,14 +36,14 @@ def get_tensor_bearing(tensor, expected_bearing=None): def _ensure_positive_definite(tensor, min_eigenval=1e-8): - """Ensure tensor is positive definite.""" + """Ensure tensor is positive definite - NO CHANGES.""" eigenvals, eigenvecs = np.linalg.eigh(tensor) eigenvals_reg = np.maximum(eigenvals, min_eigenval) return eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T def create_2d_tensors(theta, major, minor): - """Create 2x2 anisotropy tensors from geostatistical parameters. + """Create 2x2 anisotropy tensors from geostatistical parameters - NO CHANGES to math. Parameters ---------- @@ -87,7 +87,7 @@ def create_2d_tensors(theta, major, minor): def load_conceptual_points(cp_file): - """Load conceptual points from CSV file or DataFrame. + """Load conceptual points from CSV file or DataFrame - SIMPLIFIED. Parameters ---------- @@ -106,7 +106,7 @@ def load_conceptual_points(cp_file): else: raise TypeError(f"cp_file must be string path or pandas DataFrame, got {type(cp_file)}") - # Validate required columns (excluding anisotropy/transverse for now) + # Validate required columns (excluding anisotropy/transverse for now) required_cols = ['x', 'y', 'major', 'bearing'] missing_cols = [col for col in required_cols if col not in cp_df.columns] if missing_cols: @@ -140,23 +140,108 @@ def load_conceptual_points(cp_file): return cp_df -def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, - method='idw', layer=0, config_dict=None): +def _extract_grid_info(modelgrid): + """Extract grid information from flopy modelgrid object. + + Parameters + ---------- + modelgrid : flopy.discretization.grid + Flopy modelgrid object + + Returns + ------- + dict + Dictionary with grid information including: + - xcentergrid: X coordinates of cell centers + - ycentergrid: Y coordinates of cell centers + - nx, ny: Grid dimensions + - area: Cell areas + - grid_type: Type of grid + """ + grid_info = {} + + # Get grid type + grid_info['grid_type'] = modelgrid.grid_type + + # Get cell centers + if hasattr(modelgrid, 'xcellcenters'): + grid_info['xcentergrid'] = modelgrid.xcellcenters + grid_info['ycentergrid'] = modelgrid.ycellcenters + else: + raise AttributeError("modelgrid missing xcellcenters/ycellcenters") + + # Get dimensions + if modelgrid.grid_type == 'structured': + grid_info['ny'] = modelgrid.nrow + grid_info['nx'] = modelgrid.ncol + # Calculate areas from delr, delc + if hasattr(modelgrid, 'delr') and hasattr(modelgrid, 'delc'): + delr = modelgrid.delr + delc = modelgrid.delc + grid_info['area'] = np.outer(delc, delr) + else: + # Uniform area assumption + grid_info['area'] = np.ones((grid_info['ny'], grid_info['nx'])) + elif modelgrid.grid_type == 'vertex': + # Vertex grid - reshape if needed + xc = grid_info['xcentergrid'] + yc = grid_info['ycentergrid'] + if len(xc.shape) == 1: + grid_info['xcentergrid'] = xc.reshape(-1, 1) + grid_info['ycentergrid'] = yc.reshape(-1, 1) + grid_info['nx'] = 1 + grid_info['ny'] = xc.shape[0] + else: + grid_info['ny'], grid_info['nx'] = xc.shape + # Calculate areas for vertex grid + grid_info['area'] = _calculate_vertex_areas(modelgrid) + else: + # Fallback for other grid types + xc = grid_info['xcentergrid'] + grid_info['ny'], grid_info['nx'] = xc.shape + grid_info['area'] = np.ones((grid_info['ny'], grid_info['nx'])) + + return grid_info + + +def _calculate_vertex_areas(modelgrid): + """Calculate cell areas for vertex grid using shoelace formula.""" + ncpl = modelgrid.ncpl if hasattr(modelgrid, 'ncpl') else len(modelgrid.xcellcenters) + areas = np.zeros(ncpl) + + for i in range(ncpl): + try: + verts = modelgrid.get_cell_vertices(i) + # Shoelace formula + x = [v[0] for v in verts] + y = [v[1] for v in verts] + area = 0.5 * abs(sum(x[j] * y[j + 1] - x[j + 1] * y[j] for j in range(-1, len(x) - 1))) + areas[i] = area + except: + areas[i] = 1.0 # Default area + + # Reshape if needed + if hasattr(modelgrid, 'ncpl'): + return areas.reshape(-1, 1) + else: + return areas.reshape(modelgrid.nrow, modelgrid.ncol) + + +def interpolate_tensors(cp_file, modelgrid, zones=None, method='idw', + layer=0, config_dict=None): """ - Tensor field interpolation using NSAF log-Euclidean mathematics - with pypestutils kriging engine. + Tensor field interpolation using NSAF log-Euclidean mathematics. + REFACTORED to use modelgrid consistently. Parameters ---------- cp_file : str or pd.DataFrame Conceptual points file path or DataFrame - xcentergrid : np.ndarray - 2D array of x-coordinates from pyemu SpatialReference (ny, nx) - ycentergrid : np.ndarray - 2D array of y-coordinates from pyemu SpatialReference (ny, nx) + modelgrid : flopy.discretization.grid + Flopy modelgrid object zones : np.ndarray, optional 2D zone array (ny, nx) - method : str, default 'krig' + method : str, default 'idw' Interpolation method: 'krig', 'idw', or 'nearest' layer : int, default 0 Layer number to process @@ -173,12 +258,19 @@ def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, except Exception as e: raise Exception(f"Error importing pypestutils: {e}") + # Extract grid info + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + ny = grid_info['ny'] + nx = grid_info['nx'] + # Load and filter conceptual points cp_df = load_conceptual_points(cp_file) # Filter to current layer if 'layer' in cp_df.columns: - layer_cp = cp_df[cp_df['layer'] - 1 == layer].copy() + layer_cp = cp_df[cp_df['layer'] == layer].copy() else: layer_cp = cp_df.copy() @@ -196,9 +288,8 @@ def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, } # Prepare grid coordinates - ny, nx = xcentergrid.shape - grid_x = xcentergrid.flatten() - grid_y = ycentergrid.flatten() + xcentergrid = xcentergrid.flatten() + ycentergrid = ycentergrid.flatten() # Prepare conceptual point data cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) @@ -216,28 +307,28 @@ def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, # Handle zones if zones is not None: zones_flat = zones.flatten().astype(int) - cp_zones = _assign_cp_to_zones(cp_coords, grid_x, grid_y, zones_flat, nx) + cp_zones = _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx) else: - zones_flat = np.ones(len(grid_x), dtype=int) + zones_flat = np.ones(len(xcentergrid), dtype=int) cp_zones = np.ones(len(layer_cp), dtype=int) # Interpolate tensors using log-Euclidean approach if method == 'krig': interp_tensors = _interpolate_tensors_kriging( cp_coords, cp_tensors, cp_zones, - grid_x, grid_y, zones_flat, + xcentergrid, ycentergrid, zones_flat, config_dict, nx, ny ) elif method == 'idw': interp_tensors = _interpolate_tensors_idw( cp_coords, cp_tensors, cp_zones, - grid_x, grid_y, zones_flat, + xcentergrid, ycentergrid, zones_flat, nx, ny ) elif method == 'nearest': interp_tensors = _interpolate_tensors_nearest( cp_coords, cp_tensors, cp_zones, - grid_x, grid_y, zones_flat, + xcentergrid, ycentergrid, zones_flat, nx, ny ) else: @@ -245,29 +336,15 @@ def interpolate_tensors(cp_file, xcentergrid, ycentergrid, zones=None, return interp_tensors - -def _assign_cp_to_zones(cp_coords, grid_x, grid_y, zones_flat, nx): - """Assign conceptual points to zones based on nearest grid point.""" - grid_coords = np.column_stack([grid_x, grid_y]) - cp_zones = np.zeros(len(cp_coords), dtype=int) - - for i, cp_coord in enumerate(cp_coords): - distances = np.sum((grid_coords - cp_coord) ** 2, axis=1) - closest_idx = np.argmin(distances) - cp_zones[i] = zones_flat[closest_idx] - - return cp_zones - - def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, - grid_x, grid_y, zones_flat, config_dict, nx, ny): + xcentergrid, ycentergrid, zones_flat, config_dict, nx, ny): """Interpolate tensors using pypestutils kriging with log-Euclidean approach.""" try: from pypestutils.pestutilslib import PestUtilsLib except Exception as e: raise Exception(f"Error importing pypestutils: {e}") - n_grid = len(grid_x) + n_grid = len(xcentergrid) result_tensors = np.zeros((n_grid, 2, 2)) # Convert tensors to log space @@ -294,8 +371,8 @@ def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, zone_cp_coords = cp_coords[cp_mask] zone_log_tensors = log_tensors[cp_mask] - zone_grid_x = grid_x[zone_indices] - zone_grid_y = grid_y[zone_indices] + zone_xcentergrid = xcentergrid[zone_indices] + zone_ycentergrid = ycentergrid[zone_indices] # Interpolate each tensor component separately zone_tensors = np.zeros((len(zone_indices), 2, 2)) @@ -307,7 +384,7 @@ def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, # Use pypestutils for kriging interp_component = _krig_component_pypestutils( zone_cp_coords[:, 0], zone_cp_coords[:, 1], - component_values, zone_grid_x, zone_grid_y, + component_values, zone_xcentergrid, zone_ycentergrid, config_dict ) @@ -327,7 +404,7 @@ def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, return result_tensors -def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_dict): +def _krig_component_pypestutils(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, config_dict): """Use pypestutils to krig a single tensor component.""" try: from pypestutils.pestutilslib import PestUtilsLib @@ -344,20 +421,20 @@ def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_di # All points in same zone for component interpolation cp_zones = np.ones(len(cp_x), dtype=int) - grid_zones = np.ones(len(grid_x), dtype=int) + grid_zones = np.ones(len(xcentergrid), dtype=int) # Use isotropic parameters for component interpolation - x_extent = np.max(grid_x) - np.min(grid_x) - y_extent = np.max(grid_y) - np.min(grid_y) + x_extent = np.max(xcentergrid) - np.min(xcentergrid) + y_extent = np.max(ycentergrid) - np.min(ycentergrid) domain_extent = max(x_extent, y_extent) - corrlen = np.full(len(grid_x), np.max([10000.0, domain_extent/3])) - aniso = np.ones(len(grid_x)) # Isotropic - bearing = np.zeros(len(grid_x)) # No rotation + corrlen = np.full(len(xcentergrid), np.max([10000.0, domain_extent/3])) + aniso = np.ones(len(xcentergrid)) # Isotropic + bearing = np.zeros(len(xcentergrid)) # No rotation try: npts = lib.calc_kriging_factors_2d( cp_x, cp_y, cp_zones, - grid_x, grid_y, grid_zones, + xcentergrid, ycentergrid, grid_zones, int(config_dict.get("vartype", 2)), # Exponential int(config_dict.get("krigtype", 1)), # Ordinary kriging corrlen, aniso, bearing, @@ -370,7 +447,7 @@ def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_di # Apply kriging using the component values result = lib.krige_using_file( fac_fname, fac_ftype, - len(grid_x), + len(xcentergrid), int(config_dict.get("krigtype", 1)), "none", # No transformation for individual components cp_values, @@ -392,15 +469,15 @@ def _krig_component_pypestutils(cp_x, cp_y, cp_values, grid_x, grid_y, config_di lib.free_all_memory() # Simple IDW fallback - return _idw_component(cp_x, cp_y, cp_values, grid_x, grid_y) + return _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid) -def _idw_component(cp_x, cp_y, cp_values, grid_x, grid_y, power=2): +def _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, power=2): """Inverse distance weighting fallback for component interpolation.""" - result = np.zeros(len(grid_x)) + result = np.zeros(len(xcentergrid)) - for i in range(len(grid_x)): - distances = np.sqrt((cp_x - grid_x[i]) ** 2 + (cp_y - grid_y[i]) ** 2) + for i in range(len(xcentergrid)): + distances = np.sqrt((cp_x - xcentergrid[i]) ** 2 + (cp_y - ycentergrid[i]) ** 2) distances = np.maximum(distances, 1e-10) # Avoid division by zero weights = 1.0 / (distances ** power) @@ -410,9 +487,9 @@ def _idw_component(cp_x, cp_y, cp_values, grid_x, grid_y, power=2): def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, - grid_x, grid_y, zones_flat, nx, ny): + xcentergrid, ycentergrid, zones_flat, nx, ny): """IDW interpolation using log-Euclidean approach.""" - n_grid = len(grid_x) + n_grid = len(xcentergrid) result_tensors = np.zeros((n_grid, 2, 2)) # Convert to log space @@ -439,7 +516,7 @@ def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, zone_log_tensors = log_tensors[cp_mask] for idx in zone_indices: - gx, gy = grid_x[idx], grid_y[idx] + gx, gy = xcentergrid[idx], ycentergrid[idx] distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + (zone_cp_coords[:, 1] - gy) ** 2) distances = np.maximum(distances, 1e-10) @@ -460,9 +537,9 @@ def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, def _interpolate_tensors_nearest(cp_coords, cp_tensors, cp_zones, - grid_x, grid_y, zones_flat, nx, ny): + xcentergrid, ycentergrid, zones_flat, nx, ny): """Nearest neighbor interpolation.""" - n_grid = len(grid_x) + n_grid = len(xcentergrid) result_tensors = np.zeros((n_grid, 2, 2)) # Process each zone @@ -485,7 +562,7 @@ def _interpolate_tensors_nearest(cp_coords, cp_tensors, cp_zones, zone_cp_tensors = cp_tensors[cp_mask] for idx in zone_indices: - gx, gy = grid_x[idx], grid_y[idx] + gx, gy = xcentergrid[idx], ycentergrid[idx] distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + (zone_cp_coords[:, 1] - gy) ** 2) nearest_idx = np.argmin(distances) @@ -494,26 +571,41 @@ def _interpolate_tensors_nearest(cp_coords, cp_tensors, cp_zones, return result_tensors -def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, - out_filename=None, n_realizations=1, layer=0, - vartransform="none", boundary_smooth=True, - boundary_enhance=True, tensor_method='krig', - config_dict=None, iids=None, area=None, - mean_col='mean', sd_col='sd', **kwargs): +def _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx): + """Assign conceptual points to zones based on nearest grid point.""" + grid_coords = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) + cp_zones = np.zeros(len(cp_coords), dtype=int) + + for i, cp_coord in enumerate(cp_coords): + distances = np.sum((grid_coords - cp_coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + cp_zones[i] = zones_flat[closest_idx] + + return cp_zones + + +""" +NSAF Utils - Main API functions refactored to use modelgrid consistently +""" + + +def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename=None, + n_realizations=1, layer=0, vartransform="none", + boundary_smooth=True, boundary_enhance=True, + tensor_method='krig', config_dict=None, iids=None, + mean_col='mean', sd_col='sd', **kwargs): """ Parameter interpolation and field generation combining NSAF tensor mathematics with pypestutils FIELDGEN2D_SVA for spatially correlated noise. - FLEXIBLE version that works with any property by specifying column names. + REFACTORED to use modelgrid consistently throughout. Parameters ---------- - cp_file : str or pd.DataFrame - Conceptual points file or DataFrame - xcentergrid : np.ndarray - X-coordinates from pyemu SpatialReference, shape (ny, nx) - ycentergrid : np.ndarray - Y-coordinates from pyemu SpatialReference, shape (ny, nx) + conceptual_points : pd.DataFrame + Conceptual points DataFrame + modelgrid : flopy.discretization.grid + Flopy modelgrid object containing grid information zones : np.ndarray, optional Zone array, shape (ny, nx) out_filename : str, optional @@ -534,8 +626,6 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, Configuration parameters for pypestutils iids : np.ndarray, optional Pre-generated IIDs with shape (ny*nx, n_realizations) - area : np.ndarray, optional - Cell areas with shape (ny, nx) mean_col : str, default 'mean' Name of the mean column in conceptual points sd_col : str, default 'sd' @@ -553,12 +643,16 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, print("=== Parameter Interpolation and Field Generation ===") + # Extract grid information from modelgrid + grid_info = _extract_grid_info(modelgrid) + ny = grid_info['ny'] + nx = grid_info['nx'] + # Load conceptual points - cp_df = load_conceptual_points(cp_file) - if 'layer' in cp_df.columns: - layer_cp = cp_df[cp_df['layer'] - 1 == layer].copy() + if 'layer' in conceptual_points.columns: + layer_cp = conceptual_points[conceptual_points['layer'] == layer].copy() else: - layer_cp = cp_df.copy() + layer_cp = conceptual_points.copy() if len(layer_cp) == 0: raise ValueError(f"No conceptual points found for layer {layer}") @@ -582,10 +676,6 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, if sd_col not in layer_cp.columns: raise ValueError(f"Standard deviation column '{sd_col}' not found in conceptual points") - # Get grid dimensions and coordinates - ny, nx = xcentergrid.shape - grid_coords_2d = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) - print(f"Processing layer {layer} with {len(layer_cp)} conceptual points") print(f"Grid dimensions: {ny} x {nx} = {ny * nx} cells") print(f"Using columns: mean='{mean_col}', sd='{sd_col}'") @@ -593,9 +683,9 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, # Step 1: Interpolate tensors print("\nStep 1: Interpolating tensor field...") tensors = interpolate_tensors( - layer_cp, xcentergrid, ycentergrid, - zones=zones, method=tensor_method, - layer=layer, config_dict=config_dict + layer_cp, modelgrid, zones=zones, + method=tensor_method, layer=layer, + config_dict=config_dict ) # Step 2: Convert tensors to geostatistical parameters @@ -605,7 +695,7 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, # Step 3: Prepare conceptual point data cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) cp_means = layer_cp[mean_col].values - cp_sd = layer_cp[sd_col].values.clip(1e-8,None) + cp_sd = layer_cp[sd_col].values.clip(1e-8, None) print(f"Conceptual point data ranges:") print(f" {mean_col}: [{cp_means.min():.3f}, {cp_means.max():.3f}]") @@ -626,15 +716,17 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, # Transform for log-domain if needed transform = 'log' if vartransform == 'log' else None - min_value = 1e-8 if vartransform == 'log' else None + min_mean = 1e-8 if vartransform == 'log' else None + max_mean = 1e8 if vartransform == 'log' else None + bounds = (min_mean, max_mean) # Interpolate mean print(" Interpolating mean...") interp_means_2d = tensor_aware_kriging( - cp_coords, cp_means, grid_coords_2d, tensors, - variogram_model='exponential', sill=1.0, nugget=0.1, + cp_coords, cp_means, modelgrid, tensors, + variogram_model='exponential', background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform=transform, min_value=min_value, + min_points=3, transform=transform, bounds=bounds, max_neighbors=4, zones=zones ) @@ -646,14 +738,18 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, transition_cells=3, mode='smooth' ) + min_sd = 1e-8 if vartransform == 'log' else None + max_sd = 1e8 if vartransform == 'log' else None + bounds = (min_sd, max_sd) + # Interpolate standard deviation print(" Interpolating standard deviation...") interp_sd_2d = tensor_aware_kriging( - cp_coords, cp_sd, grid_coords_2d, tensors, - variogram_model='exponential', sill=1.0, nugget=0.1, + cp_coords, cp_sd, modelgrid, tensors, + variogram_model='exponential', background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform=transform, min_value=min_value, - max_neighbors=4, zones=zones + min_points=3, transform=transform, bounds=bounds, + max_neighbors=4, zones=zones, ) # Apply boundary enhancement to sd @@ -672,22 +768,22 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, print(f"\nStep 5: Generating {n_realizations} stochastic realizations...") if n_realizations > 0: + # Get active cells if available + active = kwargs.get('active', None) + if active is None and hasattr(modelgrid, 'idomain'): + active = modelgrid.idomain[layer-1].flatten() if modelgrid.idomain.ndim == 3 else (modelgrid.idomain.flatten()) + fields = _generate_stochastic_fields( - grid_coords=grid_coords_2d, + modelgrid=modelgrid, mean_field=interp_means_2d, sd_field=interp_sd_2d, bearing=bearing_deg, anisotropy=anisotropy, corrlen=corrlen_major, - zones=zones, n_realizations=n_realizations, vartransform='n', config_dict=config_dict, - ny=ny, - nx=nx, - area=area, - iids=iids, - active=kwargs.get('active', None) + active=active ) else: fields = None @@ -705,11 +801,243 @@ def apply_ppu_hyperpars(cp_file, xcentergrid, ycentergrid, zones=None, # Save to files if requested if out_filename is not None: - _save_results(results, out_filename, vartransform) + save_results(results, out_filename, vartransform) print("=== Interpolation complete ===") return results +def _generate_stochastic_fields(modelgrid, mean_field, sd_field, + bearing, anisotropy, corrlen, + n_realizations, vartransform, + config_dict, iids=None, area=None, + active=None): + """ + Generate stochastic fields using pypestutils FIELDGEN2D_SVA. + FINAL FIXED version with correct reshaping logic. + """ + try: + from pypestutils.pestutilslib import PestUtilsLib + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") + + print(" Generating stochastic fields using FIELDGEN2D_SVA...") + + # Prepare grid data + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + ny = grid_info['ny'] + nx = grid_info['nx'] + area = grid_info['area'] + + n_points = ny * nx + + # Flatten fields - ENSURE PROPER ORDERING + mean_flat = mean_field.flatten().clip(1e-8, None) + variance_native = sd_field.flatten() ** 2 + log_variance_flat = np.log(1 + variance_native / mean_flat ** 2) + trans_flag = 1 # Log domain + + # Geostatistical parameters from tensors + aa_values = corrlen.flatten() + aniso_values = anisotropy.flatten() + bearing_values = bearing.flatten() + + # FIXED: domain based on zones + if active is None: + active = np.ones(n_points) + active = active.astype(int) + if area is None: + area = np.ones(n_points) + else: + area = area.flatten() + + # Generate or use provided IIDs + if iids is None: + print(" Generating new IIDs...") + iids = np.random.normal(0, 1, size=(n_points, n_realizations)) + else: + print(" Using provided IIDs...") + if iids.shape != (n_points, n_realizations): + raise ValueError(f"IIDs shape {iids.shape} doesn't match expected {(n_points, n_realizations)}") + + try: + # Set up pypestutils parameters + lib = PestUtilsLib() + lib.initialize_randgen(11235813) + + # Averaging function type + avg_func = config_dict.get('averaging_function', 'gaussian') + if isinstance(avg_func, dict): + if 'pow' in avg_func: + avg_flag = 4 + power_value = avg_func['pow'] + else: + avg_flag, power_value = next(iter(avg_func.items())) + else: + power_value = 0 + if avg_func == 'exponential': + avg_flag = 2 + elif avg_func == 'gaussian': + avg_flag = 3 + elif avg_func == 'spherical': + avg_flag = 1 + else: + avg_flag = 2 # Default to exponential + + # Call FIELDGEN2D_SVA through pypestutils interface, does not take iid file + # Need FIELDGEN2D_SVA1 to pass iid files, but can't access in current ppu + print(f" Calling FIELDGEN2D_SVA for {n_realizations} realizations...") + + fields = lib.fieldgen2d_sva( + xcentergrid.flatten(), ycentergrid.flatten(), + area=area, + active=active, + mean=mean_flat, + var=log_variance_flat, + aa=aa_values, + anis=aniso_values, + bearing=bearing_values, + transtype=trans_flag, + avetype=avg_flag, + power=power_value, + nreal=n_realizations, + ) + + # Handle the output format from FIELDGEN2D_SVA + if fields.shape == (n_points, n_realizations): + # Output is (n_points, n_realizations) - transpose to (n_realizations, n_points) + fields = fields.T # Now shape is (n_realizations, n_points) + # Reshape each realization to (ny, nx) + fields = fields.reshape((n_realizations, ny, nx)) + + elif fields.shape == (n_realizations * n_points,): + # Output is flattened - reshape directly + fields = fields.reshape((n_realizations, ny, nx)) + + elif fields.shape == (n_realizations, n_points): + # Output is already (n_realizations, n_points) - just reshape spatial dimensions + fields = fields.reshape((n_realizations, ny, nx)) + + else: + raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") + + print(f" Successfully generated {n_realizations} realizations with shape {fields.shape}") + + # Clean up + lib.free_all_memory() + + return fields + + except Exception as e: + print(f" Warning: FIELDGEN2D_SVA failed: {e}") + print(f" Using fallback method...") + + # Fallback to simple field generation + fields = np.zeros((n_realizations, ny, nx)) + for real in range(n_realizations): + current_iids = iids[:, real] + field_values = _generate_simple_field( + modelgrid, mean_flat, log_variance_flat, + aa_values, aniso_values, bearing_values, + vartransform, current_iids + ) + fields[real] = field_values.reshape(ny, nx) + + return fields + +def _generate_simple_field(modelgrid, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + vartransform, iids): + """ + IMPROVED simple fallback field generation if FIELDGEN2D_SVA fails. + Fixed to eliminate checkering and provide proper spatial correlation. + """ + print(" Using improved simple correlated field generation fallback...") + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + grid_coords = np.stack([xcentergrid, ycentergrid]) + n_points = len(xcentergrid) + + # Convert bearing from degrees to radians for calculations + bearing_rad = np.radians(bearing_values) + + # Create spatially correlated field using moving average approach + correlated_field = np.zeros(n_points) + + # For computational efficiency, use a subset of points for correlation + # This prevents the O(n²) complexity from being too slow + max_correlation_points = min(2000, n_points) + + if n_points > max_correlation_points: + # Sample points for correlation calculation + correlation_indices = np.random.choice(n_points, max_correlation_points, replace=False) + correlation_indices = np.sort(correlation_indices) + else: + correlation_indices = np.arange(n_points) + + # Apply spatial correlation + for i in correlation_indices: + # Get local correlation parameters + local_aa = aa_values[i] + local_aniso = aniso_values[i] + local_bearing = bearing_rad[i] + + # Calculate distances to all points + dx = xcentergrid[:] - xcentergrid[i] + dy = ycentergrid[:] - ycentergrid[i] + + # Apply anisotropic transformation + # Rotate coordinates to align with anisotropy ellipse + cos_bear = np.cos(local_bearing) + sin_bear = np.sin(local_bearing) + + # Rotate to principal axes (geological bearing: 0° = North, clockwise positive) + dx_rot = dx * sin_bear + dy * cos_bear # East component (major axis) + dy_rot = -dx * cos_bear + dy * sin_bear # North component (minor axis) + + # Scale by anisotropy (major axis has length aa, minor axis has length aa/aniso) + minor_aa = local_aa / local_aniso + dx_scaled = dx_rot / local_aa + dy_scaled = dy_rot / minor_aa + + # Calculate anisotropic distance + aniso_distances = np.sqrt(dx_scaled ** 2 + dy_scaled ** 2) + + # Exponential correlation function + correlation_weights = np.exp(-aniso_distances) + + # Normalize weights + correlation_weights = correlation_weights / np.sum(correlation_weights) + + # Apply correlation to the random field + correlated_field[i] = np.sum(correlation_weights.flatten() * iids) + + # For points not in correlation_indices, use interpolation + if n_points > max_correlation_points: + # Simple interpolation for remaining points + remaining_indices = np.setdiff1d(np.arange(n_points), correlation_indices) + + for i in remaining_indices: + # Find nearest correlation points + distances_to_corr = np.sum((grid_coords[correlation_indices] - grid_coords[i]) ** 2, axis=1) + nearest_corr_idx = correlation_indices[np.argmin(distances_to_corr)] + correlated_field[i] = correlated_field[nearest_corr_idx] + + # Scale by local standard deviation + stochastic_component = correlated_field * np.sqrt(variance_flat) + + # Combine with mean field + if vartransform == 'log': + # Log domain: mean * exp(stochastic_component) + field_values = mean_flat * np.exp(stochastic_component) + else: + # Normal domain: mean + stochastic_component + field_values = mean_flat + stochastic_component + + return field_values + def _tensors_to_geostat_params(tensors): """Convert tensor field to geostatistical parameters.""" @@ -756,12 +1084,82 @@ def _tensors_to_geostat_params(tensors): return bearing_deg, anisotropy, corrlen_major +def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, + tensor_interp='idw', vartransform='log', layer=0, + boundary_smooth=True, boundary_enhance=True, + mean_col='mean', sd_col='sd', n_realizations=1): + """ + Generate single layer using NSAF approach with boundary adjustments. + REFACTORED to use modelgrid and call apply_nsaf_hyperpars internally. + + Parameters + ---------- + conceptual_points : pd.DataFrame + Conceptual points DataFrame + modelgrid : flopy.discretization.grid + Flopy modelgrid object + iids : np.ndarray, optional + Pre-generated noise, shape (ny*nx, n_realizations) + zones : np.ndarray, optional + Zone IDs, shape (ny, nx) + tensor_interp : str, default 'idw' + Tensor interpolation method + vartransform : str, default 'log' + Transformation: 'log' or 'none' + boundary_smooth : bool, default True + Apply smoothing to mean at zone boundaries + boundary_enhance : bool, default True + Apply variance enhancement at zone boundaries + mean_col : str, default 'mean' + Name of mean column in conceptual points + sd_col : str, default 'sd' + Name of sd column in conceptual points + n_realizations : int, default 1 + Number of realizations to generate + + Returns + ------- + tuple + (fields, results_dict) where: + - fields: Generated field(s), shape (n_realizations, ny, nx) or (ny, nx) if n_realizations=1 + - results_dict: Full results dictionary from apply_nsaf_hyperpars + """ + + # Call the main function with single layer settings + results = apply_nsaf_hyperpars( + conceptual_points=conceptual_points, + modelgrid=modelgrid, + zones=zones, + out_filename=None, + n_realizations=n_realizations, + layer=layer, + vartransform=vartransform, + boundary_smooth=boundary_smooth, + boundary_enhance=boundary_enhance, + tensor_method=tensor_interp, + config_dict=None, + iids=iids, + mean_col=mean_col, + sd_col=sd_col + ) -def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, - variogram_model='exponential', sill=1.0, nugget=0.01, + # Extract fields + if results['fields'] is not None: + if n_realizations == 1: + fields = results['fields'][0] # Return 2D array for single realization + else: + fields = results['fields'] # Return 3D array for multiple realizations + else: + # If no stochastic fields, return mean field + fields = results['mean'] + + return fields, results + +def tensor_aware_kriging(cp_coords, cp_values, modelgrid, interp_tensors, + variogram_model='exponential', background_value=0.0, max_search_radius=1e20, min_points=1, - max_neighbors=4, transform=None, min_value=1e-8, - zones=None): + max_neighbors=4, transform=None, bounds=(1e-8, 1e8), + zones=None, n_dims=2): """ Tensor-aware kriging using pypestutils with anisotropic correlation structures. Clean version without automatic transform detection. @@ -772,14 +1170,16 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, raise Exception(f"Error importing pypestutils: {e}") # Get grid shape - n_grid = len(grid_coords) - n_dims = grid_coords.shape[1] + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + n_grid = len(xcentergrid) * len(ycentergrid) - print(f" Tensor-aware kriging: {len(cp_coords)} conceptual points -> {n_grid} grid points") + print(f" Tensor-aware kriging: {len(cp_coords)} conceptual points -> {len(xcentergrid)} grid points") print( f" Conceptual points range: X=[{cp_coords[:, 0].min():.1f}, {cp_coords[:, 0].max():.1f}], Y=[{cp_coords[:, 1].min():.1f}, {cp_coords[:, 1].max():.1f}]") print( - f" Grid points range: X=[{grid_coords[:, 0].min():.1f}, {grid_coords[:, 0].max():.1f}], Y=[{grid_coords[:, 1].min():.1f}, {grid_coords[:, 1].max():.1f}]") + f" Grid points range: X=[{xcentergrid[0].min():.1f}, {xcentergrid[0].max():.1f}], Y=[{ycentergrid[1].min():.1f}, {ycentergrid[1].max():.1f}]") print(f" Value range: [{cp_values.min():.6f}, {cp_values.max():.6f}], background: {background_value:.6f}") print(f" Transform requested: {transform}") @@ -792,12 +1192,8 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, nz, ny, nx = shape print(f" Using zones with shape: {shape}") else: - # Infer grid dimensions - x_coords = grid_coords[:, 0] - y_coords = grid_coords[:, 1] - - unique_x = np.unique(x_coords) - unique_y = np.unique(y_coords) + unique_x = np.unique(xcentergrid) + unique_y = np.unique(ycentergrid) nx = len(unique_x) ny = len(unique_y) @@ -814,16 +1210,12 @@ def tensor_aware_kriging(cp_coords, cp_values, grid_coords, interp_tensors, print(" Converting tensors to geostatistical parameters...") bearing_deg, anisotropy_ratios, corrlen_major = _tensors_to_geostat_params(interp_tensors) - # Prepare grid coordinates - grid_x = grid_coords[:, 0] - grid_y = grid_coords[:, 1] - # Handle zones if zones is not None: zones_flat = zones.flatten().astype(int) - cp_zones = _assign_cp_to_zones(cp_coords, grid_x, grid_y, zones_flat, nx) + cp_zones = _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx) else: - zones_flat = np.ones(len(grid_x), dtype=int) + zones_flat = np.ones(len(xcentergrid)*len(xcentergrid), dtype=int) cp_zones = np.ones(len(cp_coords), dtype=int) # Handle transformation - NO automatic detection, just pass through @@ -928,33 +1320,18 @@ def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): # Then use the modified zones in kriging npts = lib.calc_kriging_factors_2d( cp_coords[:, 0], cp_coords[:, 1], cp_zones_for_kriging, # Modified zones - grid_x, grid_y, zones_for_kriging, # Modified zones + xcentergrid.flatten(), ycentergrid.flatten(), zones_for_kriging, # Modified zones vartype, 1, corrlen_major, anisotropy_ratios, bearing_deg, max_search_radius, max_neighbors, min_points, fac_fname, fac_ftype ) - # Calculate kriging factors using tensor-derived anisotropy - # npts = lib.calc_kriging_factors_2d( - # cp_coords[:, 0], cp_coords[:, 1], cp_zones, - # grid_x, grid_y, zones_flat, - # vartype, # Variogram type - # 1, # Ordinary kriging - # corrlen_major, # Correlation lengths from tensors - # anisotropy_ratios, # Anisotropy ratios from tensors - # bearing_deg, # Bearings from tensors - # max_search_radius, - # max_neighbors, - # min_points, - # fac_fname, fac_ftype - # ) - print(f" Pypestutils kriging factors calculated for {npts} interpolation points") # Apply kriging result = lib.krige_using_file( fac_fname, fac_ftype, - len(grid_x), + len(xcentergrid.flatten()), 1, # Ordinary kriging transform_flag, # Pass through the transform flag as specified cp_values_transformed, # Use values exactly as provided @@ -973,6 +1350,9 @@ def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): print(f" Pypestutils kriging complete") print(f" Result range: [{interp_values_1d.min():.6f}, {interp_values_1d.max():.6f}]") + if bounds is not None: + interp_values_1d = np.clip(interp_values_1d, min=bounds[0], max=bounds[1]) + return interp_values_1d.reshape(shape) except Exception as e: @@ -982,8 +1362,8 @@ def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): # Fallback to simple IDW interp_values_1d = np.full(n_grid, background_value) - for i in range(n_grid): - distances = np.linalg.norm(cp_coords - grid_coords[i], axis=1) + for i in range(len(ycentergrid.flatten())): + distances = np.linalg.norm(cp_coords - np.stack([xcentergrid.flatten(), ycentergrid.flatten()]).T[i], axis=1) distances = np.maximum(distances, 1e-10) # Simple IDW weights @@ -997,269 +1377,6 @@ def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): return interp_values_1d.reshape(shape) - -def _generate_stochastic_fields(grid_coords, mean_field, sd_field, - bearing, anisotropy, corrlen, - zones, n_realizations, vartransform, - config_dict, ny, nx, iids=None, area=None, - active=None): - """ - Generate stochastic fields using pypestutils FIELDGEN2D_SVA. - FINAL FIXED version with correct reshaping logic. - """ - try: - from pypestutils.pestutilslib import PestUtilsLib - except Exception as e: - raise Exception(f"Error importing pypestutils: {e}") - - print(" Generating stochastic fields using FIELDGEN2D_SVA...") - - # Prepare grid data - n_points = ny * nx - x_coords = grid_coords[:, 0] - y_coords = grid_coords[:, 1] - - # Flatten fields - ENSURE PROPER ORDERING - mean_flat = mean_field.flatten().clip(1e-8, None) - variance_native = sd_field.flatten() ** 2 - log_variance_flat = np.log(1 + variance_native / mean_flat ** 2) - trans_flag = 1 # Log domain - - # Geostatistical parameters from tensors - aa_values = corrlen.flatten() - aniso_values = anisotropy.flatten() - bearing_values = bearing.flatten() - - # FIXED: domain based on zones - if active is None: - active = np.ones(n_points, dtype=int) - if area is None: - area = np.ones(n_points) - else: - area = area.flatten() - - # Generate or use provided IIDs - if iids is None: - print(" Generating new IIDs...") - iids = np.random.normal(0, 1, size=(n_points, n_realizations)) - else: - print(" Using provided IIDs...") - if iids.shape != (n_points, n_realizations): - raise ValueError(f"IIDs shape {iids.shape} doesn't match expected {(n_points, n_realizations)}") - - try: - # Set up pypestutils parameters - lib = PestUtilsLib() - lib.initialize_randgen(11235813) - - # Averaging function type - avg_func = config_dict.get('averaging_function', 'gaussian') - if isinstance(avg_func, dict): - if 'pow' in avg_func: - avg_flag = 4 - power_value = avg_func['pow'] - else: - avg_flag, power_value = next(iter(avg_func.items())) - else: - power_value = 0 - if avg_func == 'exponential': - avg_flag = 2 - elif avg_func == 'gaussian': - avg_flag = 3 - elif avg_func == 'spherical': - avg_flag = 1 - else: - avg_flag = 2 # Default to exponential - - # Call FIELDGEN2D_SVA through pypestutils interface - print(f" Calling FIELDGEN2D_SVA for {n_realizations} realizations...") - - fields = lib.fieldgen2d_sva( - x_coords, y_coords, - area=area, - active=active, - mean=mean_flat, - var=log_variance_flat, - aa=aa_values, - anis=aniso_values, - bearing=bearing_values, - transtype=trans_flag, - avetype=avg_flag, - power=power_value, - nreal=n_realizations, - ) - - # Handle the output format from FIELDGEN2D_SVA - if fields.shape == (n_points, n_realizations): - # Output is (n_points, n_realizations) - transpose to (n_realizations, n_points) - fields = fields.T # Now shape is (n_realizations, n_points) - # Reshape each realization to (ny, nx) - fields = fields.reshape((n_realizations, ny, nx)) - - elif fields.shape == (n_realizations * n_points,): - # Output is flattened - reshape directly - fields = fields.reshape((n_realizations, ny, nx)) - - elif fields.shape == (n_realizations, n_points): - # Output is already (n_realizations, n_points) - just reshape spatial dimensions - fields = fields.reshape((n_realizations, ny, nx)) - - else: - raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") - - print(f" Successfully generated {n_realizations} realizations with shape {fields.shape}") - - # Clean up - lib.free_all_memory() - - return fields - - except Exception as e: - print(f" Warning: FIELDGEN2D_SVA failed: {e}") - print(f" Using fallback method...") - - # Fallback to simple field generation - fields = np.zeros((n_realizations, ny, nx)) - for real in range(n_realizations): - current_iids = iids[:, real] - field_values = _generate_simple_field( - grid_coords, mean_flat, log_variance_flat, - aa_values, aniso_values, bearing_values, - vartransform, current_iids, ny, nx - ) - fields[real] = field_values.reshape(ny, nx) - - return fields - - -# def _create_node_spec_file(filename, x_coords, y_coords, zones): -# """Create 2D node specifications file for FIELDGEN2D_SVA.""" -# n_points = len(x_coords) -# -# with open(filename, 'w') as f: -# # Header -# f.write(f"{n_points}\n") # Number of nodes -# -# # Node data: node_id, x, y, zone, active_flag -# for i in range(n_points): -# node_id = i + 1 # 1-based indexing -# x = x_coords[i] -# y = y_coords[i] -# zone = zones[i] -# active = 1 # All nodes active -# f.write(f"{node_id:8d} {x:15.6f} {y:15.6f} {zone:6d} {active:2d}\n") -# -# -# def _create_avgfunc_spec_file(filename, mean_vals, variance_vals, aa_vals, aniso_vals, bearing_vals): -# """Create 2D averaging function specification file for FIELDGEN2D_SVA.""" -# n_points = len(mean_vals) -# -# with open(filename, 'w') as f: -# # Header -# f.write(f"{n_points}\n") # Number of nodes -# -# # Averaging function data: node_id, mean, variance, aa, anisotropy, bearing -# for i in range(n_points): -# node_id = i + 1 # 1-based indexing -# mean = mean_vals[i] -# variance = variance_vals[i] -# aa = aa_vals[i] # Correlation length (major axis) -# aniso = aniso_vals[i] # Anisotropy ratio -# bearing = bearing_vals[i] # Bearing in degrees -# -# f.write(f"{node_id:8d} {mean:15.6f} {variance:15.6f} {aa:15.6f} {aniso:10.4f} {bearing:10.2f}\n") - - -def _generate_simple_field(grid_coords, mean_flat, variance_flat, - aa_values, aniso_values, bearing_values, - vartransform, iids, ny, nx): - """ - IMPROVED simple fallback field generation if FIELDGEN2D_SVA fails. - Fixed to eliminate checkering and provide proper spatial correlation. - """ - print(" Using improved simple correlated field generation fallback...") - - n_points = len(grid_coords) - - # Convert bearing from degrees to radians for calculations - bearing_rad = np.radians(bearing_values) - - # Create spatially correlated field using moving average approach - correlated_field = np.zeros(n_points) - - # For computational efficiency, use a subset of points for correlation - # This prevents the O(n²) complexity from being too slow - max_correlation_points = min(2000, n_points) - - if n_points > max_correlation_points: - # Sample points for correlation calculation - correlation_indices = np.random.choice(n_points, max_correlation_points, replace=False) - correlation_indices = np.sort(correlation_indices) - else: - correlation_indices = np.arange(n_points) - - # Apply spatial correlation - for i in correlation_indices: - # Get local correlation parameters - local_aa = aa_values[i] - local_aniso = aniso_values[i] - local_bearing = bearing_rad[i] - - # Calculate distances to all points - dx = grid_coords[:, 0] - grid_coords[i, 0] - dy = grid_coords[:, 1] - grid_coords[i, 1] - - # Apply anisotropic transformation - # Rotate coordinates to align with anisotropy ellipse - cos_bear = np.cos(local_bearing) - sin_bear = np.sin(local_bearing) - - # Rotate to principal axes (geological bearing: 0° = North, clockwise positive) - dx_rot = dx * sin_bear + dy * cos_bear # East component (major axis) - dy_rot = -dx * cos_bear + dy * sin_bear # North component (minor axis) - - # Scale by anisotropy (major axis has length aa, minor axis has length aa/aniso) - minor_aa = local_aa / local_aniso - dx_scaled = dx_rot / local_aa - dy_scaled = dy_rot / minor_aa - - # Calculate anisotropic distance - aniso_distances = np.sqrt(dx_scaled ** 2 + dy_scaled ** 2) - - # Exponential correlation function - correlation_weights = np.exp(-aniso_distances) - - # Normalize weights - correlation_weights = correlation_weights / np.sum(correlation_weights) - - # Apply correlation to the random field - correlated_field[i] = np.sum(correlation_weights * iids) - - # For points not in correlation_indices, use interpolation - if n_points > max_correlation_points: - # Simple interpolation for remaining points - remaining_indices = np.setdiff1d(np.arange(n_points), correlation_indices) - - for i in remaining_indices: - # Find nearest correlation points - distances_to_corr = np.sum((grid_coords[correlation_indices] - grid_coords[i]) ** 2, axis=1) - nearest_corr_idx = correlation_indices[np.argmin(distances_to_corr)] - correlated_field[i] = correlated_field[nearest_corr_idx] - - # Scale by local standard deviation - stochastic_component = correlated_field * np.sqrt(variance_flat) - - # Combine with mean field - if vartransform == 'log': - # Log domain: mean * exp(stochastic_component) - field_values = mean_flat * np.exp(stochastic_component) - else: - # Normal domain: mean + stochastic_component - field_values = mean_flat + stochastic_component - - return field_values - - def create_boundary_modified_scalar(base_field, zones, peak_increase=0.3, transition_cells=5, mode="enhance"): """ @@ -1331,7 +1448,8 @@ def detect_zone_boundaries(zones): return boundary_mask, boundary_directions -def _save_results(results, out_filename, vartransform): + +def save_results(results, out_filename): """Save results to files in PyEMU-compatible format.""" # Save mean field @@ -1356,4 +1474,4 @@ def _save_results(results, out_filename, vartransform): field_file = f"{out_filename}_real_{i + 1:03d}.txt" np.savetxt(field_file, field, fmt="%20.8E") - print(f" Saved {len(results['fields'])} realizations") + print(f" Saved {len(results['fields'])} realizations") \ No newline at end of file From 663204a2a44fef88001a37b3f6544c93fa0a3b0e Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 19 Aug 2025 20:30:17 +1200 Subject: [PATCH 20/36] plot 1 layer at a time --- pyemu/plot/plot_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 0b87ad8e5..5069b50a2 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2331,7 +2331,7 @@ def plot_layer(field, layer=0, field_name='field', transform='log', save_path='. field = np.log10(field) plt.figure(figsize=(14, 12)) - plt.imshow(field[layer]) + plt.imshow(field) plt.colorbar(label='Parameter Value') plt.title(f'{field_name} for layer {layer}') plt.xlabel('X') From a8e8ab32e22c8242fb7409c38f06f27594531234 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Thu, 21 Aug 2025 11:45:31 +1200 Subject: [PATCH 21/36] useage tweaks --- pyemu/plot/plot_utils.py | 274 ++++++++++++++++++++++++------------ pyemu/utils/nsaf_helpers.py | 252 +++++++++++++++------------------ pyemu/utils/nsaf_useage.py | 8 +- pyemu/utils/nsaf_utils.py | 104 +++++++------- pyemu/utils/partial.py | 0 5 files changed, 351 insertions(+), 287 deletions(-) delete mode 100644 pyemu/utils/partial.py diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 5069b50a2..c91ae1ee2 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -1913,7 +1913,7 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, zones_flipped = np.flipud(zones_remapped) im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], - origin='lower', cmap='tab10', alpha=0.7, + origin='upper', cmap='tab10', alpha=0.7, vmin=0, vmax=n_zones - 1) # Filter conceptual points for this layer @@ -1995,7 +1995,7 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, zones_flipped = np.flipud(zones_remapped) im = ax.imshow(zones_flipped, extent=[x_min, x_max, y_min, y_max], - origin='lower', cmap='tab10', alpha=0.7, + origin='upper', cmap='tab10', alpha=0.7, vmin=0, vmax=n_zones - 1) # Add colorbar with actual zone values @@ -2056,7 +2056,7 @@ def plot_zones_with_conceptual_points(zones, conceptual_points, grid_coords, def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, - conceptual_points=None, subsample=4, scale_factor=0.1, + conceptual_points=None, subsample=4, max_ellipse_size=0.1, figsize=(14, 12), title_suf=None, save_path='.'): """ Visualize tensors as ellipses overlaid on zones, with conceptual points as oriented lines. @@ -2076,8 +2076,8 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, Conceptual points DataFrame or path to CSV with x, y, bearing, major columns subsample : int, default 4 Show every Nth tensor (for readability) - scale_factor : float, default 20 - Scale ellipses for visibility (larger = smaller ellipses) + max_ellipse_size : float, default 0.1 + Maximum ellipse size as fraction of domain extent (0-1) figsize : tuple, default (14, 12) Figure size title_suf : str, optional @@ -2093,9 +2093,11 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, Examples -------- >>> visualize_tensors(tensors, xcentergrid, ycentergrid, - ... zones=zones, subsample=8, title_suf='layer_1') + ... zones=zones, subsample=8, max_ellipse_size=0.05) """ from matplotlib.patches import Ellipse + from matplotlib.patches import Rectangle + import matplotlib.patches as patches # Get grid dimensions ny, nx = xcentergrid.shape @@ -2115,11 +2117,17 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, fig, ax = plt.subplots(figsize=figsize) - # Get coordinate bounds first to set proper limits + # Get coordinate bounds and calculate domain extents x_min, x_max = xcentergrid.min(), xcentergrid.max() y_min, y_max = ycentergrid.min(), ycentergrid.max() + # Calculate domain extents + ew_extent = x_max - x_min # East-West extent + ns_extent = y_max - y_min # North-South extent + max_domain_length = max(ew_extent, ns_extent) + print(f"Grid bounds: X=[{x_min:.1f}, {x_max:.1f}], Y=[{y_min:.1f}, {y_max:.1f}]") + print(f"Domain extents: E-W={ew_extent:.1f}, N-S={ns_extent:.1f}, Max={max_domain_length:.1f}") # Set axis limits explicitly ax.set_xlim(x_min, x_max) @@ -2154,8 +2162,7 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, major = row['major'] # Scale line length for visibility relative to grid - grid_scale = max(x_max - x_min, y_max - y_min) - line_length = grid_scale / 50 # Make lines 2% of grid span + line_length = max_domain_length / 50 # Make lines 2% of max domain extent # Convert geological bearing (CW from N) to math angle for plotting # Geological: 0°=N, 90°=E; Math: 0°=E, 90°=N @@ -2187,31 +2194,58 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, default_count = 0 ellipse_count = 0 - # Calculate overall scale for ellipses based on grid - grid_scale = max(x_max - x_min, y_max - y_min) + # Sample ALL valid tensors to get statistics for scaling + all_eigenvals = [] + valid_tensor_count = 0 - # Sample some tensors to get typical eigenvalue scale - sample_tensors = tensors[indices[:min(100, len(indices))]] - sample_eigenvals = [] - for tensor in sample_tensors: + for tensor in tensors: if not np.allclose(tensor, np.eye(2) * 1000000): try: eigenvals, _ = np.linalg.eigh(tensor) if np.all(eigenvals > 0): # Valid eigenvalues - sample_eigenvals.extend(eigenvals) + all_eigenvals.extend(eigenvals) + valid_tensor_count += 1 except: continue - if sample_eigenvals: - typical_eigenval = np.median(sample_eigenvals) - typical_length = np.sqrt(typical_eigenval) - # Use scale_factor parameter - larger scale_factor = larger ellipses - ellipse_scale = (grid_scale * scale_factor) / typical_length - print( - f"Typical eigenvalue: {typical_eigenval:.1e}, length: {typical_length:.1f}, scale_factor: {scale_factor}, final scale: {ellipse_scale:.1e}") + if all_eigenvals: + all_eigenvals = np.array(all_eigenvals) + max_eigenval = np.max(all_eigenvals) + percentile_95 = np.percentile(all_eigenvals, 95) + median_eigenval = np.median(all_eigenvals) + + max_correlation_length = np.sqrt(max_eigenval) + p95_correlation_length = np.sqrt(percentile_95) + median_correlation_length = np.sqrt(median_eigenval) + + print(f"Eigenvalue statistics from {valid_tensor_count} valid tensors:") + print(f" Max eigenvalue: {max_eigenval:.1e} -> correlation length: {max_correlation_length:.1f}") + print(f" 95th percentile: {percentile_95:.1e} -> correlation length: {p95_correlation_length:.1f}") + print(f" Median eigenvalue: {median_eigenval:.1e} -> correlation length: {median_correlation_length:.1f}") + + # Option 1: Scale to max (might be outlier) + target_max_size = max_ellipse_size * max_domain_length + ellipse_scale_max = target_max_size / max_correlation_length + + # Option 2: Scale to 95th percentile (more robust) + ellipse_scale_p95 = target_max_size / p95_correlation_length + + print(f"Target max ellipse size: {target_max_size:.1f}") + print(f"Scale factor using max: {ellipse_scale_max:.1e}") + print(f"Scale factor using 95th percentile: {ellipse_scale_p95:.1e}") + print(f"Max correlation length / domain length ratio: {max_correlation_length / max_domain_length:.3f}") + + # Use the true maximum for scaling (not 95th percentile) + ellipse_scale = target_max_size / max_correlation_length + scaling_reference = max_correlation_length + + print(f"Using true maximum for scaling: {ellipse_scale:.1e}") + print(f"This ensures largest ellipse will be exactly {target_max_size:.1f} units") + else: - ellipse_scale = scale_factor - print(f"No valid sample eigenvalues found, using scale_factor: {scale_factor}") + ellipse_scale = max_ellipse_size * max_domain_length + scaling_reference = 1.0 # fallback + print(f"No valid eigenvalues found, using fallback scale: {ellipse_scale}") for i in indices: tensor = tensors[i] @@ -2266,7 +2300,7 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, ax.set_ylabel('Y Coordinate') # Update title and legend - title_parts = [f'Tensors (subsampled 1:{subsample})'] + title_parts = [f'Tensors (subsampled 1:{subsample}, max size = {max_ellipse_size:.1%} of domain)'] if title_suf is not None: title_parts.append(f'{title_suf}') @@ -2313,90 +2347,150 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, # Save plot if title_suf: - filename = f'interpolated_tensors_{title_suf}.png' - else: - filename = 'interpolated_tensors.png' + save_path = save_path.replace('.png', f'_{title_suf}.png') - save_path_full = os.path.join(save_path, filename) - plt.savefig(save_path_full, dpi=150, bbox_inches='tight') - print(f"Saved tensor visualization to {save_path_full}") + plt.savefig(save_path, dpi=150, bbox_inches='tight') + print(f"Saved tensor visualization to {save_path}") # Show the plot for interactive backends - #plt.show() + # plt.show() plt.close() -def plot_layer(field, layer=0, field_name='field', transform='log', save_path='.'): - if transform=='log': +def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, + transform=None, title_suf=None, save_path='nsaf_visualization.png'): + # Apply transform if specified + if transform == 'log': + field = np.where(results['fields'][0]==0,np.nan,results['fields'][0]) field = np.log10(field) + sd = np.where(results['sd'] == 0, np.nan, results['sd']) + sd = np.log10(sd) + mean = np.where(results['mean'] == 0, np.nan, results['mean']) + mean = np.log10(mean) + field_label = 'log10(Field)' + else: + field = results['field'][0] + sd = results['sd'] + mean = results['mean'] + field_label = 'Field' - plt.figure(figsize=(14, 12)) - plt.imshow(field) - plt.colorbar(label='Parameter Value') - plt.title(f'{field_name} for layer {layer}') - plt.xlabel('X') - plt.ylabel('Y') + # Visualize results + fig, axes = plt.subplots(2, 2, figsize=(16, 12)) - os.makedirs('output', exist_ok=True) - plt.savefig(os.path.join(save_path, f'{field_name}_layer_{layer:02d}.png'), dpi=150) - plt.close() + # Field with conceptual points using real coordinates + im1 = axes[0, 0].imshow(field, origin='upper', cmap='RdYlBu_r', + extent=[xcentergrid.min(), xcentergrid.max(), + ycentergrid.min(), ycentergrid.max()]) + for idx, row in cp_df.iterrows(): + x, y = row['x'], row['y'] + # Plot point + axes[0, 0].plot(x, y, 'ko', markersize=5, markeredgecolor='white', markeredgewidth=1) -def visualize_nsaf(field_2d, cp_df, x, y): - # Visualize results - fig, axes = plt.subplots(2, 2, figsize=(12, 12)) + # Draw bearing direction - scale arrow based on domain size + domain_scale = max(xcentergrid.max() - xcentergrid.min(), + ycentergrid.max() - ycentergrid.min()) + arrow_length = domain_scale * 0.02 # 2% of domain size - # Field - im1 = axes[0, 0].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r') - axes[0, 0].set_title('Generated Tangential Field') - axes[0, 0].set_aspect('equal') - plt.colorbar(im1, ax=axes[0, 0]) + bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + dx = arrow_length * np.cos(bearing_rad) + dy = arrow_length * np.sin(bearing_rad) + + axes[0, 0].arrow(x, y, dx, dy, + head_width=domain_scale * 0.01, head_length=domain_scale * 0.01, + fc='red', ec='red', alpha=0.8, linewidth=2) - # Field with conceptual points - im2 = axes[0, 1].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r', alpha=0.8) + axes[0, 0].set_title('Field with Conceptual Point Vectors') + axes[0, 0].set_xlabel('X Coordinate') + axes[0, 0].set_ylabel('Y Coordinate') + axes[0, 0].set_aspect('equal') + plt.colorbar(im1, ax=axes[0, 0], label=field_label) + + # mean field + im2 = axes[0, 1].imshow(results['mean'], origin='upper', cmap='RdYlBu_r', + extent=[xcentergrid.min(), xcentergrid.max(), + ycentergrid.min(), ycentergrid.max()]) + axes[0, 1].set_title(f'Mean field for {field_label} {title_suf or ""}') + axes[0, 1].set_xlabel('X Coordinate') + axes[0, 1].set_ylabel('Y Coordinate') + plt.colorbar(im2, ax=axes[0, 1], label=field_label) + + # sd field + im3 = axes[1, 0].imshow(results['sd'], origin='upper', cmap='RdYlBu_r', alpha=0.8, + extent=[xcentergrid.min(), xcentergrid.max(), + ycentergrid.min(), ycentergrid.max()]) + axes[1, 0].set_title(f'Standard Deviation field for {field_label} {title_suf or ""}') + axes[1, 0].set_xlabel('X Coordinate') + axes[1, 0].set_ylabel('Y Coordinate') + plt.colorbar(im3, ax=axes[1, 0], label=field_label) + + + # Field statistics histogram + axes[1, 1].hist(field.flatten(), bins=50, alpha=0.7, color='purple', edgecolor='black') + axes[1, 1].set_title(f'{field_label} Distribution') + axes[1, 1].set_xlabel(f'{field_label} Value') + axes[1, 1].set_ylabel('Frequency') + axes[1, 1].grid(True, alpha=0.3) + + # Add statistics text + mean_val = np.mean(field) + std_val = np.std(field) + axes[1, 0].axvline(mean_val, color='red', linestyle='--', alpha=0.7, label=f'Mean: {mean_val:.3f}') + axes[1, 0].axvline(mean_val + std_val, color='orange', linestyle='--', alpha=0.7, + label=f'+1σ: {mean_val + std_val:.3f}') + axes[1, 0].axvline(mean_val - std_val, color='orange', linestyle='--', alpha=0.7, + label=f'-1σ: {mean_val - std_val:.3f}') + axes[1, 0].legend() + + # Conceptual points layout - show bearing vectors in domain coordinates + axes[1, 1].set_xlim(xcentergrid.min(), xcentergrid.max()) + axes[1, 1].set_ylim(ycentergrid.min(), ycentergrid.max()) for idx, row in cp_df.iterrows(): - x_idx = np.argmin(np.abs(x - row['x'])) - y_idx = np.argmin(np.abs(y - row['y'])) - axes[0, 1].plot(x_idx, y_idx, 'ko', markersize=3) + x, y = row['x'], row['y'] + + # Plot point + axes[1, 1].plot(x, y, 'bo', markersize=6, markeredgecolor='black', markeredgewidth=1, alpha=0.8) # Draw bearing direction - bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting - dx = 4 * np.cos(bearing_rad) - dy = 4 * np.sin(bearing_rad) - axes[0, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, - fc='red', ec='red', alpha=0.8) - - axes[0, 1].set_title('Field with Tangential Vectors') - axes[0, 1].set_aspect('equal') - plt.colorbar(im2, ax=axes[0, 1]) - - # Field statistics - axes[1, 0].hist(field_2d.flatten(), bins=50, alpha=0.7, color='purple') - axes[1, 0].set_title('Field Distribution') - axes[1, 0].set_xlabel('Value') - axes[1, 0].set_ylabel('Frequency') - - # Bearing vectors only - for idx, row in cp_df.iterrows(): - x_idx = np.argmin(np.abs(x - row['x'])) - y_idx = np.argmin(np.abs(y - row['y'])) + domain_scale = max(xcentergrid.max() - xcentergrid.min(), + ycentergrid.max() - ycentergrid.min()) + arrow_length = domain_scale * 0.05 # 5% of domain size for visibility bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting - dx = 3 * np.cos(bearing_rad) - dy = 3 * np.sin(bearing_rad) - axes[1, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, - fc='blue', ec='blue', alpha=0.6) - - axes[1, 1].set_title('Tangential Bearing Vectors') - axes[1, 1].set_xlim(0, field_2d.shape[1] - 1) - axes[1, 1].set_ylim(0, field_2d.shape[1] - 1) + dx = arrow_length * np.cos(bearing_rad) + dy = arrow_length * np.sin(bearing_rad) + + axes[1, 1].arrow(x, y, dx, dy, + head_width=domain_scale * 0.015, head_length=domain_scale * 0.015, + fc='blue', ec='blue', alpha=0.7, linewidth=2) + + # Add bearing label + axes[1, 1].annotate(f'{row["bearing"]:.0f}°', + (x, y), xytext=(5, 5), textcoords='offset points', + fontsize=8, alpha=0.8) + + # Fix the title string formatting + stats_title = (f'Conceptual Points Layout\n' + f'Stats: mean={mean_val:.3f}, std={std_val:.3f}\n' + f'Number of conceptual points: {len(cp_df)}') + + axes[1, 1].set_title(stats_title) + axes[1, 1].set_xlabel('X Coordinate') + axes[1, 1].set_ylabel('Y Coordinate') axes[1, 1].set_aspect('equal') + axes[1, 1].grid(True, alpha=0.3) plt.tight_layout() - plt.savefig('tangential_field_example.png', dpi=150, bbox_inches='tight') - plt.show() - print(f"Field statistics: mean={np.mean(field_2d):.3f}, std={np.std(field_2d):.3f}") - print(f"Number of conceptual points: {len(cp_df)}") - print("Tangential field generation complete!") \ No newline at end of file + # Save plot + if title_suf: + base_name = save_path.replace('.png', '') + save_path = f"{base_name}_{title_suf}.png" + + plt.savefig(save_path, dpi=150, bbox_inches='tight') + print(f"Saved NSAF visualization to {save_path}") + + # Show the plot for interactive backends + # plt.show() + plt.close() \ No newline at end of file diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index e28e965df..7f48e7b46 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -5,7 +5,6 @@ import numpy as np import pandas as pd import os -from pyemu import plot_utils as pu # Import from refactored nsaf_utils from nsaf_utils import ( @@ -18,7 +17,8 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, zone_file=None, iids_file=None, field_name=['field'], layer_mode=True, save_dir=None, tensor_interp='krig', - vartransform='log', n_realizations=1, ml=None, sr=None): + transform='log', n_realizations=1, ml=None, sr=None, + boundary_enhance=False, boundary_smooth=False): """ Generate spatially correlated fields using tensor-based geostatistics. REFACTORED to use modelgrid consistently and call generate_single_layer. @@ -43,7 +43,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, Directory path to save output files (default: tmp_model_ws) tensor_interp : str, default 'krig' Tensor interpolation method - vartransform : str, default 'log' + transform : str, default 'log' Transformation: 'log' or 'none' n_realizations : int, default 1 Number of stochastic realizations @@ -110,7 +110,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, conceptual_points['transverse'] = conceptual_points['major'] / conceptual_points['anisotropy'] # Load zones - zones = None + zones = modelgrid.idomain if zone_file: if isinstance(zone_file, str): zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file)).astype(np.int64) @@ -219,79 +219,59 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # Get zones for this layer if available layer_zones = None if zones is not None: - if isinstance(zones, list): + if isinstance(zones, list) or (isinstance(zones, np.ndarray) & (len(zones.shape)==3)): if target_layer - 1 < len(zones): layer_zones = zones[target_layer - 1] else: layer_zones = zones # Use generate_single_layer which internally calls apply_nsaf_hyperpars - field, result = generate_single_layer( + field, results = generate_single_layer( conceptual_points=layer_cp, modelgrid=modelgrid, iids=layer_iids, zones=layer_zones, layer=target_layer, tensor_interp=tensor_interp, - vartransform=vartransform, - boundary_smooth=True, - boundary_enhance=True, + transform=transform, + boundary_smooth=boundary_smooth, + boundary_enhance=boundary_enhance, mean_col=mean_col, sd_col=sd_col, n_realizations=n_realizations ) - # todo: better file name handling - if save_dir is not None: - outfile = os.path.join(save_dir, f"{fn}_layer{target_layer}.arr") - np.savetxt(outfile, field, fmt="%20.8E") - pu.visualize_tensors(result['tensors'], xcentergrid, ycentergrid, zones=layer_zones, - conceptual_points=layer_cp, subsample=4, scale_factor=0.1, - figsize=(14, 12), title_suf=None, save_path=save_dir) - pu.plot_layer(field, layer=target_layer, field_name=fn, transform='log', save_path=save_dir) - # Store results result_key = f"{fn}_layer_{target_layer}" - all_results[result_key] = result + all_results[result_key] = results print(f"fieldresult for layer {target_layer}:") - print(f" Mean field: [{result['mean'].min():.3f}, {result['mean'].max():.3f}]") - print(f" SD field: [{result['sd'].min():.3f}, {result['sd'].max():.3f}]") + print(f" Mean field: [{results['mean'].min():.3f}, {results['mean'].max():.3f}]") + print(f" SD field: [{results['sd'].min():.3f}, {results['sd'].max():.3f}]") + # todo: better file name handling + if save_dir is not None: + from pyemu import plot_utils as pu + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + fname = f"layer{target_layer}.arr" + np.savetxt(os.path.join(save_dir, fname), field, fmt="%20.8E") + fig_path = os.path.join(save_dir, 'figure') + if not os.path.exists(fig_path): + os.mkdir(fig_path) + pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones[target_layer-1], + conceptual_points=layer_cp, subsample=4, max_ellipse_size=0.1, + figsize=(14, 12), title_suf=mean_col, + save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) + + pu.visualize_nsaf(field, layer_cp, xcentergrid, ycentergrid, + transform='log', title_suf=mean_col, + save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) else: - # Single layer mode - target_layer = 1 - - # Get IIDs for this layer - layer_iids = iids_dict.get(1, None) - - # Get zones (single layer) - layer_zones = zones[0] if isinstance(zones, list) else zones - - # Use generate_single_layer - field, result = generate_single_layer( - cp_file=conceptual_points, - modelgrid=modelgrid, - iids=layer_iids, - zones=layer_zones, - tensor_interp=tensor_interp, - vartransform=vartransform, - boundary_smooth=True, - boundary_enhance=True, - mean_col=mean_col, - sd_col=sd_col, - n_realizations=n_realizations - ) - - # Save results if needed - # todo: better file name handling - if save_dir is not None: - for i, f in enumerate(field): - outfile = os.path.join(save_dir, f"{fn}_layer{i+1}.arr") - np.savetxt(outfile, field, fmt="%20.8E") - - all_results[fn] = result + # todo 3d + pass print("\n=== Complete ===") return all_results @@ -304,7 +284,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, def _generate_stochastic_field(grid_coords, mean_field, sd_field, bearing, anisotropy, corrlen, - zones, n_realizations, vartransform, + zones, n_realizations, transform, config_dict, ny, nx, iids=None, area=None, active=None): """ @@ -429,7 +409,7 @@ def _generate_stochastic_field(grid_coords, mean_field, sd_field, field_values = _generate_simple_field( grid_coords, mean_flat, log_variance_flat, aa_values, aniso_values, bearing_values, - vartransform, current_iids, ny, nx + transform, current_iids, ny, nx ) field[real] = field_values.reshape(ny, nx) @@ -438,7 +418,7 @@ def _generate_stochastic_field(grid_coords, mean_field, sd_field, def _generate_simple_field(grid_coords, mean_flat, variance_flat, aa_values, aniso_values, bearing_values, - vartransform, iids, ny, nx): + transform, iids, ny, nx): """ Simple fallback field generation if FIELDGEN2D_SVA fails. """ @@ -512,7 +492,7 @@ def _generate_simple_field(grid_coords, mean_flat, variance_flat, stochastic_component = correlated_field * np.sqrt(variance_flat) # Combine with mean field - if vartransform == 'log': + if transform == 'log': # Log domain: mean * exp(stochastic_component) field_values = mean_flat * np.exp(stochastic_component) else: @@ -592,7 +572,7 @@ def detect_zone_boundaries(zones): return boundary_mask, boundary_directions -def save_results(result, out_filename, vartransform): +def save_results(result, out_filename, transform): """Save results to files in PyEMU-compatible format.""" # todo: better file name handling @@ -737,41 +717,53 @@ def example_field_generation(): nx, ny = 100, 100 xmax = 5000 ymax = 5000 - x = np.linspace(0, xmax, nx) - y = np.linspace(0, ymax, ny) # Correct: delr is row spacing (y-direction), delc is column spacing (x-direction) delr = np.full(ny, ymax / ny) # Cell height (y-direction, row spacing) delc = np.full(nx, xmax / nx) # Cell width (x-direction, column spacing) - xcentergrid, ycentergrid = np.meshgrid(x, y, indexing='xy') - xcentergrid = xcentergrid.T - ycentergrid = ycentergrid.T - modelgrid = flopy.discretization.StructuredGrid( - delc=delc, # Column spacing (x-direction) - delr=delr, # Row spacing (y-direction) + delc=delc, delr=delr, top=np.ones((ny, nx)), botm=np.zeros((1, ny, nx)), - xoff=x[0] - delc[0] / 2, # Use delc for x offset - yoff=y[0] - delr[0] / 2, # Use delr for y offset + xoff=0, + yoff=0, angrot=0.0, idomain=np.ones((ny, nx)), ) - # Define circle center and radius + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + center_x = 2500 center_y = 2500 - max_radius = 1500 + zones = np.ones((ny, nx)) + zones[(ycentergrid < center_y) & (xcentergrid > center_x)] = 2 # SE + zones[(ycentergrid < center_y) & (xcentergrid < center_x)] = 3 # SW + zones[(ycentergrid > center_y) & (xcentergrid < center_x)] = 4 # NE + # Define circle center and radius + + max_radius = 2500*np.sqrt(2) # Create conceptual points on concentric circles cp_list = [] - n_circles = 6 + n_circles = 4 + + # Define zone-specific mean values + zone_mean_multipliers = { + 1: 1.0, # NW quadrant - baseline + 2: 2.0, # SE quadrant - 2x higher + 3: 3.0, # SW quadrant - 2x lower + 4: 4.0 # NE quadrant - 4x higher + } for circle_idx in range(n_circles): radius_fraction = (circle_idx + 0.3) / n_circles cp_radius = max_radius * radius_fraction - n_points = 12 + circle_idx * 3 + n_points = 6 + circle_idx * 4 + base_mean_kh = 2 ** (circle_idx - n_circles) # Base value from circle + major_length = 500 + 50 * radius_fraction for i in range(n_points): angle = 2 * np.pi * i / n_points @@ -780,25 +772,34 @@ def example_field_generation(): cp_x = center_x + cp_radius * np.cos(angle) cp_y = center_y + cp_radius * np.sin(angle) - # Tangential bearing in geological convention (N=0°, clockwise positive) - # Convert angle to geological bearing, then add 90° for tangent + # Determine which zone this point is in + if cp_y > center_y and cp_x > center_x: + point_zone = 1 # NW + elif cp_y < center_y and cp_x > center_x: + point_zone = 2 # SE + elif cp_y < center_y and cp_x < center_x: + point_zone = 3 # SW + else: # cp_y > center_y and cp_x < center_x + point_zone = 4 # NE + + # Apply zone-specific multiplier + zone_multiplier = zone_mean_multipliers[point_zone] + mean_kh = base_mean_kh * zone_multiplier + + # Tangential bearing calculation geo_radial = np.degrees(np.arctan2(cp_x - center_x, cp_y - center_y)) bearing = (geo_radial + 90) % 360 - # Each circle gets unique value - mean_kh = 10 * (circle_idx + 1) - major_length = 150 + 50 * radius_fraction - cp_list.append({ - 'name': f'cp_circle{circle_idx}_point{i}', + 'name': f'cp_circle{circle_idx}_point{i}_zone{point_zone}', 'x': cp_x, 'y': cp_y, 'mean': mean_kh, - 'sd': mean_kh * 0.1, + 'sd': mean_kh * 0.2, 'major': major_length, 'anisotropy': 4.0, 'bearing': bearing, - 'layer': 1 + 'zone': point_zone # Optional: track which zone }) cp_df = pd.DataFrame(cp_list) @@ -806,72 +807,43 @@ def example_field_generation(): # Generate field print("=== Testing tangential field generation ===") - field_2d, tensors = generate_single_layer( + results = generate_single_layer( cp_df, modelgrid, - zones=None, + zones=zones, tensor_interp='krig', - vartransform='None', + #transform='log', + boundary_smooth={'transition_cells': 5}, + boundary_enhance={'transition_cells': 5, + 'peak_increase': 2} ) - # Visualize results - fig, axes = plt.subplots(2, 2, figsize=(12, 12)) - - # Field - im1 = axes[0, 0].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r') - axes[0, 0].set_title('Generated Tangential Field') - axes[0, 0].set_aspect('equal') - plt.colorbar(im1, ax=axes[0, 0]) - - # Field with conceptual points - im2 = axes[0, 1].imshow(np.log10(field_2d), origin='lower', cmap='RdYlBu_r', alpha=0.8) - - for idx, row in cp_df.iterrows(): - x_idx = np.argmin(np.abs(x - row['x'])) - y_idx = np.argmin(np.abs(y - row['y'])) - axes[0, 1].plot(x_idx, y_idx, 'ko', markersize=3) - - # Draw bearing direction - bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting - dx = 4 * np.cos(bearing_rad) - dy = 4 * np.sin(bearing_rad) - axes[0, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, - fc='red', ec='red', alpha=0.8) - - axes[0, 1].set_title('Field with Tangential Vectors') - axes[0, 1].set_aspect('equal') - plt.colorbar(im2, ax=axes[0, 1]) - - # Field statistics - axes[1, 0].hist(field_2d.flatten(), bins=50, alpha=0.7, color='purple') - axes[1, 0].set_title('Field Distribution') - axes[1, 0].set_xlabel('Value') - axes[1, 0].set_ylabel('Frequency') - - # Bearing vectors only - for idx, row in cp_df.iterrows(): - x_idx = np.argmin(np.abs(x - row['x'])) - y_idx = np.argmin(np.abs(y - row['y'])) - - bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting - dx = 3 * np.cos(bearing_rad) - dy = 3 * np.sin(bearing_rad) - axes[1, 1].arrow(x_idx, y_idx, dx, dy, head_width=1, head_length=1, - fc='blue', ec='blue', alpha=0.6) - - axes[1, 1].set_title('Tangential Bearing Vectors') - axes[1, 1].set_xlim(0, nx - 1) - axes[1, 1].set_ylim(0, ny - 1) - axes[1, 1].set_aspect('equal') - - plt.tight_layout() - plt.savefig(os.path.join('..','..','examples', 'tangential_field_example.png'), dpi=150, bbox_inches='tight') - plt.show() - - print(f"Field statistics: mean={np.mean(field_2d):.3f}, std={np.std(field_2d):.3f}") + print(f"Field statistics: mean={np.mean(results['fields'][0]):.3f}, std={np.std(results['fields'][0]):.3f}") print(f"Number of conceptual points: {len(cp_df)}") print("Tangential field generation complete!") - return field_2d, tensors, cp_df + save_dir = os.path.join('..','..','examples','tangential_nsaf') + if not os.path.exists(save_dir): + os.mkdir(save_dir) + if save_dir is not None: + from pyemu import plot_utils as pu + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + fname = f"tangential_example.arr" + np.savetxt(os.path.join(save_dir, fname), results['fields'][0], fmt="%20.8E") + fig_path = os.path.join(save_dir, 'figure') + if not os.path.exists(fig_path): + os.mkdir(fig_path) + pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones, + conceptual_points=cp_df, subsample=4, max_ellipse_size=0.1, + figsize=(14, 12), title_suf='tangential', + save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) + + pu.visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, + transform='log', title_suf='tangential', + save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) + + return results, cp_df if __name__ == "__main__": example_field_generation() \ No newline at end of file diff --git a/pyemu/utils/nsaf_useage.py b/pyemu/utils/nsaf_useage.py index e7d42cb23..8a04d59d5 100644 --- a/pyemu/utils/nsaf_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -24,7 +24,7 @@ data_dir = r'..\..\examples\Wairau\waq_arr' zone_files = [f'waq_arr.geoclass_layer{i+1}.arr' for i in range(0,13)] con_pts_file = 'conceptual_points.csv' - save_dir = os.path.join(data_dir, f'{con_pts_file}_output') + save_dir = os.path.join(data_dir) if not os.path.exists(save_dir): os.mkdir(save_dir) @@ -34,7 +34,11 @@ field_name=['kh'], layer_mode=True, save_dir=save_dir, - tensor_interp='idw') + tensor_interp='idw', + boundary_smooth={'transition_cells': 5}, + boundary_enhance={'transition_cells': 5, + 'peak_increase': 0.1} + ) end_time = time.perf_counter() diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index 37a41caf1..e837e360d 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -344,7 +344,7 @@ def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, except Exception as e: raise Exception(f"Error importing pypestutils: {e}") - n_grid = len(xcentergrid) + n_grid = np.prod(xcentergrid.shape) result_tensors = np.zeros((n_grid, 2, 2)) # Convert tensors to log space @@ -489,7 +489,7 @@ def _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, power=2): def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, xcentergrid, ycentergrid, zones_flat, nx, ny): """IDW interpolation using log-Euclidean approach.""" - n_grid = len(xcentergrid) + n_grid = np.prod(xcentergrid.shape) result_tensors = np.zeros((n_grid, 2, 2)) # Convert to log space @@ -539,7 +539,7 @@ def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, def _interpolate_tensors_nearest(cp_coords, cp_tensors, cp_zones, xcentergrid, ycentergrid, zones_flat, nx, ny): """Nearest neighbor interpolation.""" - n_grid = len(xcentergrid) + n_grid = np.prod(xcentergrid.shape) result_tensors = np.zeros((n_grid, 2, 2)) # Process each zone @@ -590,8 +590,8 @@ def _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx): def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename=None, - n_realizations=1, layer=0, vartransform="none", - boundary_smooth=True, boundary_enhance=True, + n_realizations=1, layer=0, transform="none", + boundary_smooth=False, boundary_enhance=False, tensor_method='krig', config_dict=None, iids=None, mean_col='mean', sd_col='sd', **kwargs): """ @@ -614,9 +614,9 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= Number of stochastic realizations to generate layer : int, default 0 Layer number to process - vartransform : str, default "none" + transform : str, default "none" Transformation: "none" or "log" - boundary_smooth : bool, default True + boundary_smooth : bool, or dict default False Apply smoothing to mean at zone boundaries boundary_enhance : bool, default True Apply variance enhancement at zone boundaries @@ -715,9 +715,9 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= } # Transform for log-domain if needed - transform = 'log' if vartransform == 'log' else None - min_mean = 1e-8 if vartransform == 'log' else None - max_mean = 1e8 if vartransform == 'log' else None + transform = 'log' if transform == 'log' else None + min_mean = 1e-8 if transform == 'log' else None + max_mean = 1e8 if transform == 'log' else None bounds = (min_mean, max_mean) # Interpolate mean @@ -732,14 +732,20 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= # Apply boundary smoothing to mean if boundary_smooth and zones is not None: + if isinstance(boundary_smooth, dict): + smooth_params = {**boundary_smooth, **boundary_smooth} + else: # Must be True (boolean) + smooth_params = boundary_smooth + print(" Smoothing mean at geological boundaries...") interp_means_2d = create_boundary_modified_scalar( - interp_means_2d, zones, - transition_cells=3, mode='smooth' + interp_means_2d, zones, transform=transform, + transition_cells=smooth_params['transition_cells'], + mode='smooth' ) - min_sd = 1e-8 if vartransform == 'log' else None - max_sd = 1e8 if vartransform == 'log' else None + min_sd = 1e-8 if transform == 'log' else None + max_sd = 1e8 if transform == 'log' else None bounds = (min_sd, max_sd) # Interpolate standard deviation @@ -754,10 +760,16 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= # Apply boundary enhancement to sd if boundary_enhance and zones is not None: + if isinstance(boundary_smooth, dict): + smooth_params = {**boundary_enhance, **boundary_smooth} + else: # Must be True (boolean) + smooth_params = boundary_enhance print(" Enhancing variance at geological boundaries...") interp_sd_2d = create_boundary_modified_scalar( - interp_sd_2d, zones, - peak_increase=1.0, transition_cells=3, mode='enhance' + interp_sd_2d, zones, transform=transform, + peak_increase=smooth_params['peak_increase'], + transition_cells=smooth_params['transition_cells'], + mode='enhance' ) print(f"Interpolated field ranges:") @@ -781,7 +793,7 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= anisotropy=anisotropy, corrlen=corrlen_major, n_realizations=n_realizations, - vartransform='n', + transform='n', config_dict=config_dict, active=active ) @@ -801,16 +813,15 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= # Save to files if requested if out_filename is not None: - save_results(results, out_filename, vartransform) + save_results(results, out_filename, transform) print("=== Interpolation complete ===") return results def _generate_stochastic_fields(modelgrid, mean_field, sd_field, bearing, anisotropy, corrlen, - n_realizations, vartransform, - config_dict, iids=None, area=None, - active=None): + transform, config_dict, iids=None, area=None, + n_realizations=1, active=None): """ Generate stochastic fields using pypestutils FIELDGEN2D_SVA. FINAL FIXED version with correct reshaping logic. @@ -904,7 +915,7 @@ def _generate_stochastic_fields(modelgrid, mean_field, sd_field, nreal=n_realizations, ) - # Handle the output format from FIELDGEN2D_SVA + # FIELDGEN2D_SVA returns n_realizations, we use 1 if fields.shape == (n_points, n_realizations): # Output is (n_points, n_realizations) - transpose to (n_realizations, n_points) fields = fields.T # Now shape is (n_realizations, n_points) @@ -940,7 +951,7 @@ def _generate_stochastic_fields(modelgrid, mean_field, sd_field, field_values = _generate_simple_field( modelgrid, mean_flat, log_variance_flat, aa_values, aniso_values, bearing_values, - vartransform, current_iids + transform, current_iids ) fields[real] = field_values.reshape(ny, nx) @@ -948,7 +959,7 @@ def _generate_stochastic_fields(modelgrid, mean_field, sd_field, def _generate_simple_field(modelgrid, mean_flat, variance_flat, aa_values, aniso_values, bearing_values, - vartransform, iids): + transform, iids): """ IMPROVED simple fallback field generation if FIELDGEN2D_SVA fails. Fixed to eliminate checkering and provide proper spatial correlation. @@ -1029,7 +1040,7 @@ def _generate_simple_field(modelgrid, mean_flat, variance_flat, stochastic_component = correlated_field * np.sqrt(variance_flat) # Combine with mean field - if vartransform == 'log': + if transform == 'log': # Log domain: mean * exp(stochastic_component) field_values = mean_flat * np.exp(stochastic_component) else: @@ -1085,9 +1096,9 @@ def _tensors_to_geostat_params(tensors): return bearing_deg, anisotropy, corrlen_major def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, - tensor_interp='idw', vartransform='log', layer=0, - boundary_smooth=True, boundary_enhance=True, - mean_col='mean', sd_col='sd', n_realizations=1): + tensor_interp='idw', transform='log', layer=1, + boundary_smooth=False, boundary_enhance=False, + mean_col='mean', sd_col='sd', n_realizations=1, save_dir='.'): """ Generate single layer using NSAF approach with boundary adjustments. REFACTORED to use modelgrid and call apply_nsaf_hyperpars internally. @@ -1104,7 +1115,7 @@ def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, Zone IDs, shape (ny, nx) tensor_interp : str, default 'idw' Tensor interpolation method - vartransform : str, default 'log' + transform : str, default 'log' Transformation: 'log' or 'none' boundary_smooth : bool, default True Apply smoothing to mean at zone boundaries @@ -1133,7 +1144,7 @@ def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, out_filename=None, n_realizations=n_realizations, layer=layer, - vartransform=vartransform, + transform=transform, boundary_smooth=boundary_smooth, boundary_enhance=boundary_enhance, tensor_method=tensor_interp, @@ -1143,17 +1154,7 @@ def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, sd_col=sd_col ) - # Extract fields - if results['fields'] is not None: - if n_realizations == 1: - fields = results['fields'][0] # Return 2D array for single realization - else: - fields = results['fields'] # Return 3D array for multiple realizations - else: - # If no stochastic fields, return mean field - fields = results['mean'] - - return fields, results + return results def tensor_aware_kriging(cp_coords, cp_values, modelgrid, interp_tensors, variogram_model='exponential', @@ -1173,7 +1174,7 @@ def tensor_aware_kriging(cp_coords, cp_values, modelgrid, interp_tensors, grid_info = _extract_grid_info(modelgrid) xcentergrid = grid_info['xcentergrid'] ycentergrid = grid_info['ycentergrid'] - n_grid = len(xcentergrid) * len(ycentergrid) + n_grid = np.prod(xcentergrid.shape) print(f" Tensor-aware kriging: {len(cp_coords)} conceptual points -> {len(xcentergrid)} grid points") print( @@ -1215,7 +1216,7 @@ def tensor_aware_kriging(cp_coords, cp_values, modelgrid, interp_tensors, zones_flat = zones.flatten().astype(int) cp_zones = _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx) else: - zones_flat = np.ones(len(xcentergrid)*len(xcentergrid), dtype=int) + zones_flat = np.ones(n_grid, dtype=int) cp_zones = np.ones(len(cp_coords), dtype=int) # Handle transformation - NO automatic detection, just pass through @@ -1378,17 +1379,16 @@ def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): return interp_values_1d.reshape(shape) def create_boundary_modified_scalar(base_field, zones, - peak_increase=0.3, transition_cells=5, mode="enhance"): + peak_increase=0.3, transition_cells=5, mode="enhance", + transform=None): """ Modify scalar field values near geological zone boundaries. - (Implementation from the provided NSAF code) """ from scipy.ndimage import distance_transform_edt, gaussian_filter if mode not in ("enhance", "smooth"): raise ValueError("mode must be 'enhance' or 'smooth'") - # 2D case only for now if zones.shape != base_field.shape: raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") @@ -1399,25 +1399,19 @@ def create_boundary_modified_scalar(base_field, zones, modified = base_field.copy() if mode == "enhance": - # Linear enhancement near boundaries factor = 1 - distance[transition_mask] / transition_cells - enhancement = peak_increase * factor - modified[transition_mask] += enhancement + modified[transition_mask] *= (1 + peak_increase * factor) elif mode == "smooth": # Smooth field with Gaussian filter - smoothed_field = gaussian_filter(base_field, sigma=transition_cells / 2) - - # Blend with original based on distance to boundary + smoothed_field = gaussian_filter(base_field, sigma=transition_cells) weight = 1 - distance[transition_mask] / transition_cells modified[transition_mask] = ( weight * smoothed_field[transition_mask] + (1 - weight) * base_field[transition_mask] ) - print( - f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") - + print(f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") return modified diff --git a/pyemu/utils/partial.py b/pyemu/utils/partial.py deleted file mode 100644 index e69de29bb..000000000 From df44e57e03127f5216bae14a49617431de0c7ec4 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Sat, 30 Aug 2025 14:38:16 +1200 Subject: [PATCH 22/36] model files for HBHP, dis only --- examples/HBHP/grid.dat | 151303 ++++++++++++++++++++++++++ examples/HBHP/hpm_mf6.dis | 42 + examples/HBHP/hpm_mf6.dis_delc.txt | 1 + examples/HBHP/hpm_mf6.dis_delr.txt | 1 + examples/HBHP/hpm_mf6.ims | 28 + examples/HBHP/hpm_mf6.nam | 21 + examples/HBHP/hpm_mf6.tdis | 14 + examples/HBHP/mfsim.nam | 19 + 8 files changed, 151429 insertions(+) create mode 100644 examples/HBHP/grid.dat create mode 100644 examples/HBHP/hpm_mf6.dis create mode 100644 examples/HBHP/hpm_mf6.dis_delc.txt create mode 100644 examples/HBHP/hpm_mf6.dis_delr.txt create mode 100644 examples/HBHP/hpm_mf6.ims create mode 100644 examples/HBHP/hpm_mf6.nam create mode 100644 examples/HBHP/hpm_mf6.tdis create mode 100644 examples/HBHP/mfsim.nam diff --git a/examples/HBHP/grid.dat b/examples/HBHP/grid.dat new file mode 100644 index 000000000..baf0f103b --- /dev/null +++ b/examples/HBHP/grid.dat @@ -0,0 +1,151303 @@ +,x,y,z +0,1891510.0,5624550.0,0.0 +1,1891610.0,5624550.0,0.0 +2,1891710.0,5624550.0,0.0 +3,1891810.0,5624550.0,0.0 +4,1891910.0,5624550.0,0.0 +5,1892010.0,5624550.0,0.0 +6,1892110.0,5624550.0,0.0 +7,1892210.0,5624550.0,0.0 +8,1892310.0,5624550.0,0.0 +9,1892410.0,5624550.0,0.0 +10,1892510.0,5624550.0,0.0 +11,1892610.0,5624550.0,0.0 +12,1892710.0,5624550.0,0.0 +13,1892810.0,5624550.0,0.0 +14,1892910.0,5624550.0,0.0 +15,1893010.0,5624550.0,0.0 +16,1893110.0,5624550.0,0.0 +17,1893210.0,5624550.0,0.0 +18,1893310.0,5624550.0,0.0 +19,1893410.0,5624550.0,0.0 +20,1893510.0,5624550.0,0.0 +21,1893610.0,5624550.0,0.0 +22,1893710.0,5624550.0,0.0 +23,1893810.0,5624550.0,0.0 +24,1893910.0,5624550.0,0.0 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'hpm_mf6.dis_delr.txt' FACTOR 1.0 + delc + OPEN/CLOSE 'hpm_mf6.dis_delc.txt' FACTOR 1.0 + top + OPEN/CLOSE '..\shared_props\top.txt' FACTOR 1.0 + botm LAYERED + OPEN/CLOSE '..\shared_props\botm_0.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_1.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_2.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_3.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_4.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_5.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_6.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_7.txt' FACTOR 1.0 + OPEN/CLOSE '..\shared_props\botm_8.txt' FACTOR 1.0 + idomain LAYERED + OPEN/CLOSE '..\shared_props\idomain_0.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_1.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_2.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_3.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_4.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_5.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_6.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_7.txt' FACTOR 1 + OPEN/CLOSE '..\shared_props\idomain_8.txt' FACTOR 1 +END griddata + diff --git a/examples/HBHP/hpm_mf6.dis_delc.txt b/examples/HBHP/hpm_mf6.dis_delc.txt new file mode 100644 index 000000000..f225b1226 --- /dev/null +++ b/examples/HBHP/hpm_mf6.dis_delc.txt @@ -0,0 +1 @@ + 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 100.00000000 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100.00000000 diff --git a/examples/HBHP/hpm_mf6.ims b/examples/HBHP/hpm_mf6.ims new file mode 100644 index 000000000..ff7378fb6 --- /dev/null +++ b/examples/HBHP/hpm_mf6.ims @@ -0,0 +1,28 @@ +# File generated by Flopy version 3.4.2 on 04/27/2024 at 08:12:37. +BEGIN options + PRINT_OPTION all + COMPLEXITY complex +END options + +BEGIN nonlinear + OUTER_DVCLOSE 0.00100000 + OUTER_MAXIMUM 500 + UNDER_RELAXATION dbd + UNDER_RELAXATION_GAMMA 0.20000000 + UNDER_RELAXATION_THETA 0.70000000 + UNDER_RELAXATION_KAPPA 0.10000000 + UNDER_RELAXATION_MOMENTUM 0.00100000 + BACKTRACKING_NUMBER 20 + BACKTRACKING_TOLERANCE 1.10000000 + BACKTRACKING_REDUCTION_FACTOR 0.20000000 + BACKTRACKING_RESIDUAL_LIMIT 50.00000000 +END nonlinear + +BEGIN linear + INNER_MAXIMUM 100 + INNER_DVCLOSE 1.00000000E-04 + inner_rclose 0.01000000 + PRECONDITIONER_LEVELS 8 + PRECONDITIONER_DROP_TOLERANCE 1.00000000E-04 +END linear + diff --git a/examples/HBHP/hpm_mf6.nam b/examples/HBHP/hpm_mf6.nam new file mode 100644 index 000000000..a825e07c1 --- /dev/null +++ b/examples/HBHP/hpm_mf6.nam @@ -0,0 +1,21 @@ +# File generated by Flopy version 3.4.2 on 04/27/2024 at 08:12:37. +BEGIN options + SAVE_FLOWS + NEWTON UNDER_RELAXATION +END options + +BEGIN packages + DIS6 hpm_mf6.dis dis + IC6 hpm_mf6.ic ic + SFR6 hpm_mf6.sfr sfr_0 + WEL6 hpm_mf6.wel wel_0 + NPF6 hpm_mf6.npf npf + OC6 hpm_mf6.oc oc + RCH6 hpm_mf6.rcha rcha_0 + GHB6 hpm_mf6_coast.ghb ghb_0 + GHB6 hpm_mf6_bound.ghb ghb_1 + DRN6 hpm_mf6.drn drn_0 + MVR6 hpm_mf6.mvr mvr + OBS6 hpm_mf6.obs obs_4 +END packages + diff --git a/examples/HBHP/hpm_mf6.tdis b/examples/HBHP/hpm_mf6.tdis new file mode 100644 index 000000000..075ccc25b --- /dev/null +++ b/examples/HBHP/hpm_mf6.tdis @@ -0,0 +1,14 @@ +# File generated by Flopy version 3.4.2 on 04/27/2024 at 08:12:37. +BEGIN options + TIME_UNITS days + START_DATE_TIME 01-01-1940 +END options + +BEGIN dimensions + NPER 1 +END dimensions + +BEGIN perioddata + 1.00000000 1 1.00000000 +END perioddata + diff --git a/examples/HBHP/mfsim.nam b/examples/HBHP/mfsim.nam new file mode 100644 index 000000000..763a5e460 --- /dev/null +++ b/examples/HBHP/mfsim.nam @@ -0,0 +1,19 @@ +# File generated by Flopy version 3.4.2 on 04/27/2024 at 08:12:37. +BEGIN options +END options + +BEGIN timing + TDIS6 hpm_mf6.tdis +END timing + +BEGIN models + gwf6 hpm_mf6.nam hpm_mf6 +END models + +BEGIN exchanges +END exchanges + +BEGIN solutiongroup 1 + ims6 hpm_mf6.ims hpm_mf6 +END solutiongroup 1 + From 3d9e8345cba53fb25ba39f8baf87f36b6f9a34fb Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Sat, 30 Aug 2025 14:42:16 +1200 Subject: [PATCH 23/36] model_name as parameter --- pyemu/utils/nsaf_useage.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/pyemu/utils/nsaf_useage.py b/pyemu/utils/nsaf_useage.py index 8a04d59d5..16c245a63 100644 --- a/pyemu/utils/nsaf_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -21,15 +21,17 @@ import time start_time = time.perf_counter() - data_dir = r'..\..\examples\Wairau\waq_arr' - zone_files = [f'waq_arr.geoclass_layer{i+1}.arr' for i in range(0,13)] + model_name = 'hpm_mf6' + data_dir = r'..\..\examples\HBHP' + # need to ensure proper name structure for zone files here + zone_files = None #[f'{model_name}.geoclass_layer{i+1}.arr' for i in range(0,13)] con_pts_file = 'conceptual_points.csv' save_dir = os.path.join(data_dir) if not os.path.exists(save_dir): os.mkdir(save_dir) - nsaf_helpers.generate_fields_from_files(data_dir, 'waq_arr', con_pts_file, + nsaf_helpers.generate_fields_from_files(data_dir, model_name, con_pts_file, zone_file=zone_files, field_name=['kh'], layer_mode=True, From f06bcbdee2121558538878dc0800be13bb177830 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Sat, 30 Aug 2025 14:53:28 +1200 Subject: [PATCH 24/36] additional modflow files required for dis --- examples/HBHP/{ => hpm_mf6}/grid.dat | 0 examples/HBHP/{ => hpm_mf6}/hpm_mf6.dis | 0 examples/HBHP/{ => hpm_mf6}/hpm_mf6.dis_delc.txt | 0 examples/HBHP/{ => hpm_mf6}/hpm_mf6.dis_delr.txt | 0 examples/HBHP/{ => hpm_mf6}/hpm_mf6.ims | 0 examples/HBHP/{ => hpm_mf6}/hpm_mf6.nam | 0 examples/HBHP/{ => hpm_mf6}/hpm_mf6.tdis | 0 examples/HBHP/{ => hpm_mf6}/mfsim.nam | 0 pyemu/utils/nsaf_useage.py | 2 +- 9 files changed, 1 insertion(+), 1 deletion(-) rename examples/HBHP/{ => hpm_mf6}/grid.dat (100%) rename examples/HBHP/{ => hpm_mf6}/hpm_mf6.dis (100%) rename examples/HBHP/{ => hpm_mf6}/hpm_mf6.dis_delc.txt (100%) rename examples/HBHP/{ => hpm_mf6}/hpm_mf6.dis_delr.txt (100%) rename examples/HBHP/{ => hpm_mf6}/hpm_mf6.ims (100%) rename examples/HBHP/{ => hpm_mf6}/hpm_mf6.nam (100%) rename examples/HBHP/{ => hpm_mf6}/hpm_mf6.tdis (100%) rename examples/HBHP/{ => hpm_mf6}/mfsim.nam (100%) diff --git a/examples/HBHP/grid.dat b/examples/HBHP/hpm_mf6/grid.dat similarity index 100% rename from examples/HBHP/grid.dat rename to examples/HBHP/hpm_mf6/grid.dat diff --git a/examples/HBHP/hpm_mf6.dis b/examples/HBHP/hpm_mf6/hpm_mf6.dis similarity index 100% rename from examples/HBHP/hpm_mf6.dis rename to examples/HBHP/hpm_mf6/hpm_mf6.dis diff --git a/examples/HBHP/hpm_mf6.dis_delc.txt b/examples/HBHP/hpm_mf6/hpm_mf6.dis_delc.txt similarity index 100% rename from examples/HBHP/hpm_mf6.dis_delc.txt rename to examples/HBHP/hpm_mf6/hpm_mf6.dis_delc.txt diff --git a/examples/HBHP/hpm_mf6.dis_delr.txt b/examples/HBHP/hpm_mf6/hpm_mf6.dis_delr.txt similarity index 100% rename from examples/HBHP/hpm_mf6.dis_delr.txt rename to examples/HBHP/hpm_mf6/hpm_mf6.dis_delr.txt diff --git a/examples/HBHP/hpm_mf6.ims b/examples/HBHP/hpm_mf6/hpm_mf6.ims similarity index 100% rename from examples/HBHP/hpm_mf6.ims rename to examples/HBHP/hpm_mf6/hpm_mf6.ims diff --git a/examples/HBHP/hpm_mf6.nam b/examples/HBHP/hpm_mf6/hpm_mf6.nam similarity index 100% rename from examples/HBHP/hpm_mf6.nam rename to examples/HBHP/hpm_mf6/hpm_mf6.nam diff --git a/examples/HBHP/hpm_mf6.tdis b/examples/HBHP/hpm_mf6/hpm_mf6.tdis similarity index 100% rename from examples/HBHP/hpm_mf6.tdis rename to examples/HBHP/hpm_mf6/hpm_mf6.tdis diff --git a/examples/HBHP/mfsim.nam b/examples/HBHP/hpm_mf6/mfsim.nam similarity index 100% rename from examples/HBHP/mfsim.nam rename to examples/HBHP/hpm_mf6/mfsim.nam diff --git a/pyemu/utils/nsaf_useage.py b/pyemu/utils/nsaf_useage.py index 16c245a63..90bf36413 100644 --- a/pyemu/utils/nsaf_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -22,7 +22,7 @@ start_time = time.perf_counter() model_name = 'hpm_mf6' - data_dir = r'..\..\examples\HBHP' + data_dir = r'..\..\examples\HBHP\hpm_mf6' # need to ensure proper name structure for zone files here zone_files = None #[f'{model_name}.geoclass_layer{i+1}.arr' for i in range(0,13)] con_pts_file = 'conceptual_points.csv' From 1283c519407d15d0df0aa92b108d4f63cf1a9550 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Sat, 30 Aug 2025 16:36:18 +1200 Subject: [PATCH 25/36] forgot shared_props folder from hbhp model structure --- examples/HBHP/shared_props/botm_0.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_1.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_2.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_3.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_4.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_5.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_6.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_7.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/botm_8.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_0.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_1.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_2.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_3.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_4.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_5.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_6.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_7.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/idomain_8.txt | 302 +++++++++++++++++++++++ examples/HBHP/shared_props/top.txt | 302 +++++++++++++++++++++++ 19 files changed, 5738 insertions(+) create mode 100644 examples/HBHP/shared_props/botm_0.txt create mode 100644 examples/HBHP/shared_props/botm_1.txt create mode 100644 examples/HBHP/shared_props/botm_2.txt create mode 100644 examples/HBHP/shared_props/botm_3.txt create mode 100644 examples/HBHP/shared_props/botm_4.txt create mode 100644 examples/HBHP/shared_props/botm_5.txt create mode 100644 examples/HBHP/shared_props/botm_6.txt create mode 100644 examples/HBHP/shared_props/botm_7.txt create mode 100644 examples/HBHP/shared_props/botm_8.txt create mode 100644 examples/HBHP/shared_props/idomain_0.txt create mode 100644 examples/HBHP/shared_props/idomain_1.txt create mode 100644 examples/HBHP/shared_props/idomain_2.txt create mode 100644 examples/HBHP/shared_props/idomain_3.txt create mode 100644 examples/HBHP/shared_props/idomain_4.txt create mode 100644 examples/HBHP/shared_props/idomain_5.txt create mode 100644 examples/HBHP/shared_props/idomain_6.txt create mode 100644 examples/HBHP/shared_props/idomain_7.txt create mode 100644 examples/HBHP/shared_props/idomain_8.txt create mode 100644 examples/HBHP/shared_props/top.txt diff --git a/examples/HBHP/shared_props/botm_0.txt b/examples/HBHP/shared_props/botm_0.txt new file mode 100644 index 000000000..3816b41e7 --- /dev/null +++ b/examples/HBHP/shared_props/botm_0.txt @@ -0,0 +1,302 @@ + -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 -2.9603158 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14.167688 14.057981 13.938897 13.745004 13.659862 13.368757 13.014429 12.348449 11.744731 11.193149 11.017005 10.866395 10.538962 10.150026 9.8495666 9.7646043 9.7751492 10.041184 10.720522 11.63369 12.090058 12.516924 12.93924 13.293458 13.820212 14.367391 15.102024 15.093424 15.217922 15.009681 14.71275 14.101781 13.664666 12.918714 12.254186 11.803152 11.460337 11.172895 10.892262 10.37588 10.382249 10.673543 10.985141 10.82096 11.281527 10.970146 10.678716 10.375658 10.007043 9.6547896 9.0558791 8.5121154 8.2831046 8.0509236 7.8615848 7.6614092 7.6316788 7.5489776 7.4096009 7.309382 7.2188636 7.3012769 7.5615972 7.8906164 7.9958679 7.9414645 7.6563002 7.2352978 6.5528056 6.0146151 5.5453377 5.5261039 5.9120324 6.1566433 6.1629799 6.4055935 6.7316569 6.5476065 6.7961292 7.3824376 6.5349757 3.3467473 6.4284388 6.2894972 7.4942969 7.7045037 7.3277913 6.6914844 6.1672858 6.1513354 6.2593203 6.4274746 6.7226463 7.0764164 7.6201946 8.2847844 8.7250053 8.4841481 8.1808754 8.639997 8.3684049 8.7025233 9.5360518 8.3150648 6.1487251 6.6717328 7.3427476 8.3223741 7.3218737 6.9269085 6.7076197 6.4765039 6.2909828 6.117949 6.0240734 5.9708597 5.8348205 5.77463 5.7365848 5.8657557 5.5661974 5.3772274 5.2093468 5.1554367 5.2763294 5.9630898 7.3427727 8.5498182 10.368594 12.092626 14.284956 16.117459 18.164407 19.419872 18.071868 14.581305 14.566913 15.969312 12.454707 11.267085 10.963203 10.110631 8.550668 6.9018319 5.3285584 5.0477566 4.8504964 5.7323554 4.6567029 3.1964923 2.7755711 -1.4407517 -3.4657921 -3.8105922 -4.1610853 -4.5276118 -4.7195086 -4.8125813 -4.9064317 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 161.12722 159.15002 154.80185 151.73126 148.90389 151.76248 155.94842 150.17662 144.03842 151.00139 149.22331 147.63647 145.9053 143.64525 141.62447 139.45874 135.99424 125.90073 117.18934 116.06686 126.53451 130.60562 127.67175 126.25597 119.29194 117.49464 117.53216 114.27026 107.22805 107.20512 106.2175 109.65585 105.19145 103.89753 95.237454 93.054011 92.5305 94.187881 99.167437 98.986774 98.360668 96.887528 95.466634 94.519792 93.840853 93.575714 93.548994 92.849322 87.156029 80.922379 78.812283 77.970885 76.348243 76.168578 75.939155 75.673558 74.378628 74.216744 73.689581 73.915684 74.413384 74.756951 74.178504 73.429809 72.737555 102.23635 120.04217 129.68832 122.78712 127.21534 133.40295 124.57297 113.66253 95.19522 69.761001 66.72892 66.189728 65.645809 65.263123 64.86808 64.400579 63.923256 63.382466 62.960393 62.578502 62.251513 61.840905 61.378124 61.019593 60.570221 60.407408 60.006357 59.520643 58.976545 58.81128 58.533668 58.415816 57.65443 57.084123 56.520503 56.237661 55.933264 56.310889 56.098092 55.91644 56.073822 55.718126 55.316359 54.915884 54.303373 53.765674 53.157833 53.024481 52.372429 52.278109 52.209017 51.594676 51.170035 50.821571 50.486308 49.850043 49.356464 48.969929 48.010865 47.740174 46.956437 46.659023 45.806386 45.141672 44.401977 43.724849 43.046342 42.301915 41.595357 41.007009 40.353433 39.756867 39.059534 38.446111 37.753002 37.002482 36.55641 35.843476 35.160685 34.377017 33.683756 33.129281 32.787479 32.065641 31.388328 30.769098 30.14883 29.586255 29.054433 28.621544 28.006015 27.405897 26.842226 26.355176 25.857574 25.593955 25.40342 24.824402 24.339842 23.775517 23.47823 23.373964 22.984833 22.496227 23.118566 22.349129 22.070952 22.68729 22.334818 22.441237 22.377811 21.792908 21.255236 21.015932 20.875927 21.121919 20.944749 20.680235 21.311526 21.255906 20.532196 19.945919 19.444281 18.965008 18.431366 17.950077 17.562211 17.052694 16.713196 16.834404 16.954074 16.54726 15.634591 15.49661 15.320598 15.220191 15.236961 15.576346 15.639323 15.647507 15.389773 14.803575 14.541669 14.301231 14.041706 13.75693 13.311941 13.806774 13.854864 13.524595 13.052025 12.454293 11.920142 11.129127 10.880478 10.728428 10.423945 10.110476 9.81771 9.6441445 9.6564896 9.7476263 10.543883 11.352027 11.836792 12.2779 12.640652 13.056647 13.474956 13.989843 14.703154 15.252612 15.068513 15.170059 14.954773 14.36333 13.674489 13.108778 12.391911 11.833829 11.454393 11.134617 10.859804 10.482588 10.462355 10.474525 10.758949 10.595821 11.108616 10.743809 10.404451 10.190188 9.8593055 9.4832523 8.7884788 8.7003203 8.3092945 8.0713147 7.9806924 7.9142875 7.8016599 7.6686773 7.583857 7.58376 7.5823255 7.5576548 7.7211465 8.1615188 8.1799407 7.9716144 7.7516057 7.1254437 6.4760151 6.0812282 5.8714715 5.6780306 5.8499291 6.2759945 6.2246209 6.3016272 6.7624485 6.9519866 6.7044082 7.273289 6.5982593 4.3342474 6.6684756 6.7415153 8.3661921 7.897617 7.57714 6.9334702 6.393583 6.3161984 6.3871273 6.5391549 6.7527851 7.1773756 7.7354278 8.5087484 8.8108091 9.4047783 8.8136025 8.9022298 8.5632136 9.4920135 8.5617927 7.4690559 7.3603415 6.5946592 8.254827 8.3988185 7.30868 6.9815611 6.8382917 6.6899937 6.5237297 6.3002577 6.2209641 6.123354 6.0463267 6.0303527 6.1564172 6.0044493 5.5978985 5.4156794 5.3541519 5.5124992 6.2943418 7.9339026 8.8973038 10.453833 12.133861 14.242055 16.257772 18.100176 20.086913 19.161065 16.184833 14.206426 15.11384 15.206912 14.07648 13.189438 12.453645 11.278804 9.9094354 7.7031884 6.7197772 6.8099494 6.1836143 5.9548335 5.5008453 3.4319577 3.471476 2.9178173 -1.2319397 -3.2303334 -3.8472422 -4.2292231 -4.6210085 -4.7636014 -4.8304192 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 163.12244 161.52877 157.20626 150.15858 144.42254 143.69473 151.5544 142.33724 145.77264 148.43586 145.96443 145.93397 144.35334 140.53779 138.53026 136.50055 131.03693 120.82385 114.53608 113.43262 121.37575 127.12244 118.77553 112.65757 103.18589 101.21987 100.07435 98.951869 97.975803 97.645563 96.757733 95.278197 94.289011 93.476128 92.749189 91.942485 91.390608 94.357131 98.38541 97.966729 97.376121 96.026401 94.020493 93.434289 93.061316 92.969699 92.667116 92.105163 86.428416 80.956914 80.259679 79.881474 76.042617 75.334534 75.137407 74.379578 73.786402 73.411939 73.432546 73.569638 73.548524 73.751842 73.441979 72.861307 72.127223 87.709326 94.055045 91.748438 86.808897 88.928503 98.614735 93.135563 73.540169 67.655001 67.011732 66.359046 66.009822 65.449589 65.002389 64.600787 64.21985 63.764577 63.203462 62.71475 62.277643 61.777693 61.297061 60.965025 60.602299 60.473425 59.920376 59.302198 59.490949 59.391393 58.88771 58.020669 57.448188 56.999396 56.511828 56.635975 57.088678 56.917969 56.653943 56.415214 56.269817 55.659513 54.632752 54.190099 53.637891 53.420801 53.121976 53.305958 53.04616 52.72641 52.248349 52.044653 51.388455 50.673723 50.145252 49.846553 49.243845 48.805686 48.347657 47.551145 46.568174 46.540481 46.001334 45.356004 44.631159 43.947212 43.23485 42.626486 41.855995 41.152441 40.369561 39.683061 38.965372 38.291626 37.61705 36.967304 36.315326 35.712457 35.168639 34.634472 33.7674 32.956925 32.392524 31.92629 31.440484 30.697102 30.174927 29.688697 28.863376 28.34153 27.932857 27.474132 27.139518 26.731407 26.345535 25.831467 25.346285 24.825422 24.122934 23.68021 23.434318 23.265697 23.147828 22.159645 22.888149 22.262938 21.743214 22.43001 22.055574 22.114656 21.98296 22.11474 21.869334 21.127848 20.604908 20.857753 20.660486 20.69852 20.295094 21.072096 20.832406 20.12433 19.551339 19.288039 18.770747 18.266835 17.609948 17.097991 16.943654 16.987703 16.93464 16.701577 15.365237 15.021596 15.139085 14.97987 14.756185 14.776823 14.71867 14.869959 14.96323 15.108143 14.698475 14.524451 14.343909 13.874518 13.376109 12.908875 13.643505 13.520873 13.058551 12.603719 12.241815 11.496188 10.830955 10.472446 10.146457 9.9379881 9.7213432 9.6103704 9.6789791 9.7583477 10.241109 11.164284 11.693228 12.227743 12.538488 12.765585 13.352483 13.723323 14.378543 15.174463 15.174533 14.739301 14.963822 14.511266 13.662566 12.904519 12.392368 11.951145 11.517098 11.191824 10.829966 10.576474 10.415135 10.434456 10.524649 10.736546 10.440443 10.149468 10.227536 10.228739 9.8375118 9.4606304 9.315099 8.8374051 8.4306466 8.2918264 8.2411993 8.1287313 7.8264107 7.8212451 7.9450727 7.888786 7.8715887 7.8536976 8.0074431 8.2949704 8.3909617 8.0476603 7.5368109 7.1160122 6.6586661 6.3788643 6.0965698 5.7725691 5.7541131 6.2074918 6.405512 6.3403717 6.5696058 6.9559608 6.9935262 7.1515841 7.2627564 4.1665844 6.8544706 7.5504401 8.4590536 7.9737792 7.5611879 6.8929637 6.6608943 6.6063656 6.6488696 6.7439997 6.9637321 7.3481234 7.8209808 8.6062294 9.0616517 9.5763297 9.435261 9.0175315 8.6347785 9.6316345 8.265925 6.8923938 7.3063734 7.2690484 8.7419849 8.0903959 7.4933768 7.3387911 7.0956699 6.9144831 6.7346935 6.7403489 6.5348438 6.4150791 6.352427 6.1808513 6.2577088 5.9556193 5.6498167 5.6027331 5.7171112 6.2997881 10.156755 10.392068 10.824586 12.231064 13.631018 16.380471 18.359787 20.562805 21.040728 19.721949 16.858164 17.51151 19.236708 16.578929 16.224163 14.85864 13.901356 12.552567 11.081261 9.5228881 8.9254625 8.2836519 7.2559652 5.6597389 5.843957 4.8495047 4.3867549 3.3946077 2.9529861 -1.1505646 -3.6809788 -4.1078518 -4.3498515 -4.4844598 -4.5634802 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 164.65756 163.24808 159.96669 155.80142 147.89795 141.85461 139.20372 141.36956 140.07567 147.18312 143.6259 143.54655 143.57303 141.08454 137.17763 134.8085 132.36417 129.55026 120.73776 113.01053 110.79895 111.70114 106.33003 102.01665 99.744797 99.537315 98.617746 98.025483 97.508591 96.547163 95.362254 95.268647 94.570708 93.320991 92.033185 91.49752 90.945738 91.158908 98.333126 97.234454 97.065577 96.349779 94.457448 93.097999 92.515338 92.266709 92.243411 91.485669 90.801954 83.277931 80.378999 79.356413 78.366475 75.247991 74.70463 74.643032 73.702833 73.009531 72.996274 72.897378 72.584435 72.990206 73.047288 72.362402 71.612756 70.74139 70.581759 70.521778 69.970275 69.318365 68.965443 68.568818 67.970461 67.723768 67.181248 66.852907 66.357101 65.789383 65.424128 65.097276 64.572995 63.883343 63.239114 62.713974 62.295375 61.808732 61.475973 61.194728 61.124069 60.69132 60.540107 60.138489 60.038798 59.432227 58.607816 58.080234 57.304881 56.774595 57.353234 58.185379 57.637233 57.378282 56.880826 56.539908 55.673424 55.185075 54.821165 54.178399 53.752514 53.429542 53.739783 54.186502 53.451814 52.969054 52.513267 51.916046 51.425162 50.87016 50.167478 49.455868 49.167212 48.680264 48.17163 47.631971 46.41923 45.709566 45.566519 45.278351 44.709615 44.00883 43.549405 42.903283 42.072754 41.320331 40.628234 39.924958 39.139192 38.508475 37.741906 37.093992 36.518925 35.81323 35.072534 34.469801 33.986945 33.416928 32.791016 32.129278 31.49907 31.13109 30.516217 30.038157 29.438927 28.930235 28.458572 28.021133 27.632389 27.188204 26.551921 26.030954 25.532407 24.872165 24.211225 23.625098 23.321003 23.000272 23.069611 22.048938 22.783548 22.796351 21.663651 22.251966 22.18296 21.614537 21.518624 21.897844 22.086915 22.045431 21.815873 20.63316 20.500738 20.353129 20.342957 20.214521 20.704426 20.128433 19.770958 19.492061 18.758781 18.279891 17.633745 17.021083 17.030629 17.19633 17.03139 16.742316 15.861088 14.907847 14.753399 14.674288 14.353984 14.29974 14.605565 14.960198 14.886798 14.663849 14.484572 14.183663 14.010966 13.847897 13.35817 12.687945 13.393953 13.260873 12.927426 12.707973 12.377348 11.904856 11.116883 10.756879 10.215258 9.8644969 9.5723486 9.4464659 9.4759008 9.4955356 9.8645545 10.758378 11.648758 12.179563 12.428845 12.700234 13.253888 13.718814 14.380435 15.114967 14.563622 14.582917 14.93507 14.483359 13.539032 12.750158 12.14095 11.779669 11.439672 11.170242 11.017232 10.633571 10.418626 10.288472 10.235563 10.380065 10.376035 10.272243 10.065658 9.9419316 9.4229685 9.5161077 9.2525782 8.9352404 8.8083662 8.6156738 8.3066822 8.0701392 7.988579 8.1496988 8.1138866 7.961514 7.9716729 8.1666138 8.2271216 8.3562294 8.6808192 8.3598235 7.8482818 7.4759328 6.6706918 6.2232473 6.1507981 5.9676689 5.9170601 5.9074623 6.3025567 6.5385605 6.51902 6.9227365 7.2186182 7.3243572 7.6438959 3.8122207 6.4887222 7.1327516 8.4556178 7.9588647 7.1496926 6.5918159 6.5000228 6.684695 6.8983931 7.0996326 7.2724528 7.7659059 8.4459942 8.9162893 9.0014471 9.3130349 9.2811251 9.1726737 8.5961118 9.6809729 8.3257869 6.8311489 6.9407802 8.2452396 9.0698686 8.2182126 7.8753434 7.5679301 7.4212728 7.1972741 7.0387133 6.9323472 6.7908053 6.5904414 6.5410023 6.51043 6.1949145 6.0178618 5.9420605 5.9631059 6.716819 8.2682012 10.705527 12.300025 13.834668 15.739437 18.039717 20.70407 21.167088 22.758677 22.740926 20.34475 18.144013 20.271948 20.040131 18.954291 17.827862 17.187889 15.647201 14.110952 12.69876 11.653027 10.472386 8.7658251 6.8409769 5.5154041 4.5557846 4.4934918 5.2379141 4.2006751 3.3260846 3.2038508 -1.3030626 -3.7297663 -3.9731458 -4.1381789 -4.2388611 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 163.65393 160.49067 158.0611 155.5916 152.83058 142.06439 134.47243 133.34324 140.69519 140.17239 135.79346 142.13887 139.7055 137.35928 131.886 128.36048 124.54201 127.17854 113.35546 109.76051 107.35682 105.55828 100.35668 99.515012 99.106737 98.652225 98.174751 97.237195 95.990045 95.143678 94.332994 94.149814 92.668046 91.930172 91.340782 90.955874 90.282274 91.585157 95.993071 96.727818 96.371187 95.40226 92.489531 92.333398 91.648704 91.717634 90.942155 90.171692 89.206748 81.513134 79.31658 78.385434 77.30103 74.970799 74.107438 74.111013 73.01344 72.541386 72.448604 72.059558 71.747018 71.349899 70.970087 70.920177 70.88827 70.48724 69.92623 69.574284 69.232835 68.739412 68.382926 67.866252 67.332778 67.027012 66.662035 66.225289 65.638234 65.321319 64.940775 64.579423 64.387693 63.891636 63.124456 62.554612 62.36686 62.007862 61.997487 61.909575 61.786818 61.068725 60.367953 59.556809 59.080842 58.78221 57.886528 57.367647 57.80439 58.424355 58.086523 57.715442 57.221152 57.057172 56.361017 55.88757 54.959089 54.283487 54.34014 54.209528 54.346724 54.547417 54.224372 53.593349 53.330515 52.830113 52.264502 51.775603 51.115485 50.371362 49.676837 48.992913 48.41061 47.865011 47.2558 46.538893 45.742429 45.131563 44.801007 44.481536 43.899506 43.188414 42.65706 42.251104 41.286163 40.597076 39.961199 39.3264 38.701346 38.015581 37.432194 36.675466 35.93928 35.356365 34.757231 34.144415 33.583097 33.056206 32.466673 31.858013 31.319713 30.699001 30.25346 29.83855 29.290726 28.769839 28.295758 27.767253 27.345875 26.919076 26.180719 25.56352 24.963873 24.369028 23.746231 23.277974 23.021731 22.792785 22.347235 22.223864 22.655295 21.549021 21.681869 22.434362 21.41255 21.351857 21.718693 21.816188 21.698977 21.861885 21.467839 20.686915 19.780749 19.769079 19.733786 19.774991 20.341244 20.443731 19.611196 19.153486 18.43307 17.890436 17.446889 17.517263 17.776935 17.476371 16.830286 16.186669 15.743781 15.573553 15.308621 15.171156 15.224799 15.008489 14.585876 14.29582 14.349486 14.263241 14.156275 13.82452 13.441339 13.16386 12.524636 13.196827 13.11867 13.02595 12.918495 12.455131 12.059535 11.594664 10.867454 10.8563 10.404922 9.801189 9.7309562 9.7262308 9.5805331 9.7452915 10.424198 11.396792 12.13664 12.494391 12.917741 13.290263 13.893831 14.56588 14.829362 13.633937 14.157115 14.864566 14.192729 13.222195 12.588831 12.070984 11.68138 11.378215 11.133169 10.835623 10.55413 10.320512 10.155687 10.099207 10.08413 10.075572 9.9594841 9.8525953 9.6623744 9.8325102 9.5237541 9.2863628 9.3259936 9.0030692 8.7074969 8.3138187 8.2870073 8.3487573 8.4185663 8.3073754 8.2546043 8.1523024 8.4582092 8.595478 8.5473094 8.9371744 8.7469011 8.1684862 7.7432938 6.8504525 6.565178 6.6580662 6.6863463 6.3606487 6.0212983 6.2312644 6.5754432 6.8251755 7.0365684 7.2356713 7.4675078 7.8934304 6.0525717 3.5842882 7.5276717 8.3035691 7.3780976 6.3225732 6.2539956 6.5156814 6.7710209 6.9961245 7.2424259 7.6121607 8.1457891 8.7987352 9.0078941 9.379886 9.7741536 9.4794387 9.1268542 9.022451 9.8463281 9.0362194 6.7500475 7.1354301 8.8820457 9.3937296 8.4410811 8.0982051 7.7038059 7.582261 7.3558187 7.3150011 7.106554 7.0656298 7.1097063 6.9731799 6.6086109 6.204602 6.2734557 6.4119689 7.2563818 8.9827289 15.821661 19.091107 17.372419 19.26709 21.756515 24.046237 26.183539 26.881733 26.41395 23.783015 20.401728 20.706895 22.97376 21.090987 20.403484 19.306353 17.93421 17.135838 16.015927 14.284006 12.844276 10.636257 8.2558467 6.2037789 4.7062899 4.9997635 5.281494 6.7496237 5.5745381 4.0159932 3.2158636 3.2095647 -0.94790144 -3.420397 -3.651492 -3.8047713 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 167.34865 164.70689 159.58802 156.58091 154.65033 151.21306 143.18828 133.31197 128.60577 128.77452 128.13494 134.57771 136.16142 133.18793 128.66809 128.80518 117.34868 113.65496 110.09236 107.8478 106.55416 104.36399 100.29571 100.06716 99.280425 98.356314 97.336696 96.339269 95.268119 94.867179 94.226347 93.559153 92.950198 92.103288 91.39123 90.725442 90.236863 89.903833 89.303528 93.20678 96.03658 95.515323 93.156651 91.793074 91.035475 90.961994 90.45508 89.352064 88.885286 87.451551 83.947488 81.957964 81.200644 80.620783 77.679293 73.490556 73.69051 72.996311 72.58288 71.732767 71.317185 71.010271 70.662026 70.306681 69.977839 69.799635 69.701528 69.283968 69.290644 68.95078 68.489418 67.94484 67.505356 67.029346 66.668841 66.397339 65.885261 65.51271 65.117436 64.572847 63.94897 63.593147 63.230105 62.620069 62.171717 61.944717 61.675172 61.286585 61.089762 60.370167 60.04935 59.536162 58.940948 58.383648 57.891189 58.342939 58.728192 58.193443 57.880354 57.869068 57.765673 57.069323 56.302792 55.460509 55.234779 55.03849 55.203249 55.038969 55.437406 54.841512 54.401508 54.063758 53.495551 52.918197 52.168412 51.511026 50.923081 50.294168 49.81437 48.9467 48.219752 47.716439 47.294093 46.728426 45.980673 45.160116 44.387992 44.056307 43.689659 43.112579 42.396533 41.988275 41.563072 40.570031 39.945299 39.284597 38.651664 38.063004 37.566251 36.883738 36.134664 35.514706 34.963602 34.339354 33.805466 33.307355 32.610256 31.980602 31.58183 31.05832 30.509947 30.006381 29.493888 28.9614 28.414703 27.803985 27.237785 26.672094 26.242072 25.588125 25.150875 24.599926 23.885311 23.267033 22.854831 22.649321 22.522865 21.730071 22.234064 21.492578 21.376881 22.383137 21.779839 21.143048 21.468264 21.623988 21.495399 21.534957 21.398051 21.792282 21.104641 19.786438 19.252224 19.451566 19.503011 20.509248 19.978855 19.318305 18.848864 18.547983 17.857103 18.339799 17.876678 16.848264 16.083639 15.508278 15.430782 15.195548 15.072847 15.011436 14.898886 14.734877 14.398476 13.977264 13.866363 13.815335 13.834982 13.623792 13.537878 13.322518 12.365453 12.943697 13.086066 12.587677 12.369774 12.504484 12.299286 11.953288 11.390118 11.150352 11.248889 10.818849 10.667717 10.964136 10.323747 9.8044897 10.350304 11.061251 12.122924 12.551486 12.912328 13.37529 13.939267 14.654157 14.867125 14.144095 13.623625 14.425775 13.774718 12.963325 12.406064 11.993028 11.570617 11.355303 10.92755 10.582778 10.396737 10.145225 10.080418 10.002918 9.9541801 9.813601 9.8823994 9.9608479 10.132756 9.9778994 9.7728214 9.6359133 9.4062603 9.0497223 8.5701275 8.6257696 8.6557444 8.4493063 8.665507 8.4756356 8.5396736 8.3705638 8.3662262 8.8058554 8.8113856 9.023142 8.9946336 8.3638951 8.1005861 7.1838578 7.2667868 7.3141689 6.9684609 6.4775833 6.2515354 6.41976 6.6900802 6.9820327 7.3230465 7.4409745 7.6951422 8.0336071 7.0768511 4.7173565 5.4174299 7.7699453 7.0149715 6.4966783 6.3493807 6.5649136 6.8724681 7.2230787 7.5441955 7.8148722 8.3705744 8.9210998 9.1633012 9.5191036 9.7210524 9.466486 9.2531325 9.21953 9.8423789 8.559359 6.962215 7.3389282 9.1644689 9.7021373 8.5934614 8.1997475 8.0363724 7.9261949 7.7399712 7.5707233 7.3839261 7.4705971 7.4347424 7.1435596 6.7101527 6.6796271 7.0187094 10.597488 10.771387 11.815709 17.66474 22.957438 23.15364 23.2895 25.0793 28.368746 31.526701 32.815592 32.559149 27.789394 24.761707 26.708186 23.723488 22.91245 21.652269 20.727986 19.648421 18.726796 16.477742 14.698902 12.746956 10.124896 7.0205855 6.0254539 6.0027315 5.999042 7.3271733 8.0242227 7.4954887 5.3786776 3.9974235 3.3128632 3.2507873 2.9274462 -2.9019872 -3.2602713 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 163.39521 161.60648 156.62164 154.46829 152.25071 148.75738 146.70284 140.65872 134.63504 126.17496 122.69885 125.70632 125.28553 118.73202 115.81025 113.11146 111.62528 109.73979 107.58307 106.93589 101.59559 100.66319 100.47764 99.330421 98.283744 96.664873 95.680033 95.124667 94.581197 93.940306 93.334358 92.762271 91.938661 91.156239 90.619386 90.021136 89.235228 88.676688 87.844068 89.638933 94.315198 94.365997 92.140225 90.803961 90.170399 89.716182 88.826983 88.031266 87.277615 86.625045 86.059629 85.592506 85.042161 84.204473 78.34221 73.169986 72.991615 72.966925 72.284923 71.55393 71.023906 70.597967 70.210522 69.720245 69.559479 69.05855 68.725758 68.301289 67.800037 67.93896 67.807098 67.298267 66.970007 66.452173 65.994296 65.701875 64.948715 64.219765 63.959512 63.453192 63.035826 62.900914 62.580441 62.03081 61.636856 61.186378 60.842787 60.595563 60.207508 59.692893 59.324337 58.746238 58.381965 58.697356 59.257076 58.727374 58.737568 58.799488 58.382398 57.61975 56.984499 56.537084 55.851525 55.525782 55.948431 55.801645 55.927897 55.694239 55.237928 54.845131 54.390047 53.54753 52.990911 52.357502 51.61059 50.802945 50.330755 49.592575 48.974058 48.366191 47.580355 46.99439 46.625313 46.019575 45.397597 44.511343 43.468716 43.193374 42.682793 42.225584 41.463549 41.102713 40.939598 40.1198 39.274929 38.632108 37.99769 37.25107 36.710988 36.092991 35.663745 35.201035 34.560301 33.8962 33.325555 32.768685 32.325375 31.761841 31.211936 30.58263 30.042453 29.551792 28.90805 28.363789 27.929298 27.449561 26.765966 26.118705 25.544232 25.124072 24.717884 23.970111 23.286265 22.81303 22.502728 22.477613 21.88984 21.806078 21.658907 21.262185 21.961366 21.877502 20.91043 21.145816 21.456652 21.136853 21.031398 21.27693 21.381522 21.686059 21.679561 20.01379 19.003175 18.916703 18.828832 19.958463 19.588209 18.712638 18.093706 17.208665 16.927215 16.594595 16.313277 15.679715 15.321417 15.024666 14.78265 14.543174 14.570412 14.521743 14.401725 14.123002 13.836391 13.520442 13.168909 13.261417 13.28074 13.171391 13.010549 12.120189 12.478118 12.453043 12.71819 12.451456 12.288582 12.60534 12.161438 11.546416 10.975087 11.182818 11.424789 11.47305 11.305997 10.535794 9.8326416 10.225522 10.92174 11.871909 12.540372 12.847874 13.352268 14.16709 14.573046 14.707852 14.532902 14.354656 14.144735 13.471148 12.809612 12.357673 11.934073 11.568965 11.320641 10.897546 10.478891 10.375671 10.147675 9.9489874 9.8922073 10.114631 10.331501 10.270597 10.408766 10.499245 10.47802 10.16032 9.7292783 9.3583645 9.0177622 8.8534029 8.8460808 8.7725361 8.6975998 8.7847385 8.8406272 8.7324542 8.6469709 8.3423718 8.6030351 9.1123273 9.165796 9.0908549 8.9022686 8.5113273 7.9605216 7.8181723 7.5975132 6.9616845 6.7108679 6.6624692 6.7425989 7.1019739 7.4492437 7.4955131 7.6664023 7.8464629 8.1467374 7.4078897 6.3687256 3.5601741 7.3713802 6.7333024 6.6661257 6.52996 6.6683271 6.8810129 7.4524902 7.9313672 8.2110921 8.4382711 9.0671264 9.333997 9.5740308 9.604662 9.3746311 9.4832568 9.491916 10.081675 8.6125431 7.0440603 7.7621502 9.297693 10.002971 8.9157093 8.6322195 8.2596918 8.1074284 7.9178319 7.792922 7.7641027 7.8418971 7.6951857 7.4454392 7.4211585 7.5707413 10.248512 17.902818 15.830833 17.685116 19.141072 24.173542 32.488582 36.588467 37.349507 36.426453 38.78339 42.804507 40.835147 34.460406 30.207785 27.462182 25.574954 24.563092 23.602592 21.750264 23.566792 23.01931 17.278174 14.680393 12.284171 9.6326303 7.8611268 6.4992903 5.9696044 7.4408225 8.3350328 8.4528497 8.4211817 7.3694581 5.2054454 4.1153584 3.4848358 3.2747523 3.3804972 -1.2140401 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 146.96692 148.9043 154.71184 153.46458 150.70198 147.71497 144.30604 139.66304 139.13873 139.4842 129.69999 120.87964 118.70862 116.88408 115.55147 113.64336 112.50655 110.33591 106.07739 102.88319 101.27241 101.14943 100.62429 98.822275 97.206014 96.80626 96.134223 95.597623 94.939725 94.145139 93.074489 92.678846 92.080281 90.787192 89.898005 89.458859 88.732602 88.238635 87.897083 87.21215 87.195247 91.875074 93.754393 92.42328 90.374352 88.833115 88.363003 87.716648 86.719231 86.179262 85.508902 85.149273 84.565404 84.203768 82.829946 74.14101 72.549871 72.422931 72.271583 71.659166 70.938156 70.604388 70.361425 69.963544 69.375954 68.996103 68.691711 68.255867 67.782712 67.257932 66.919125 66.626308 66.374825 66.067528 65.758036 65.38282 64.837336 64.55235 64.227545 63.846868 63.421584 63.013084 62.564923 62.259323 61.717742 61.228769 60.66458 60.138085 59.830616 59.579426 59.583657 59.62596 59.411957 59.577819 59.370949 59.887004 59.217078 58.2543 57.690932 57.347998 57.223429 56.773521 56.235493 56.184588 56.368395 56.433675 56.383544 55.981166 55.637854 55.13018 54.50997 53.72297 53.026312 52.383001 51.732388 51.251169 50.669747 49.898079 49.02177 48.364399 47.68395 47.101951 46.370595 45.956615 45.337941 44.70726 43.957447 42.745557 42.227695 41.793826 41.239056 40.720165 40.122485 39.991997 39.306066 38.479278 37.831367 37.24346 36.525026 35.966295 35.527645 35.070825 34.521317 34.031194 33.457672 32.868164 32.31202 31.704589 31.128089 30.539515 29.983083 29.52489 28.9939 28.52906 27.970886 27.393272 26.794788 26.305027 25.837239 25.27279 24.732115 24.232873 23.480536 22.767741 22.428506 22.296634 22.115135 21.608073 21.553012 21.383882 21.755688 21.803935 20.95448 20.912093 21.119768 20.960163 20.884556 21.010503 21.161378 21.315481 21.595264 21.146699 19.422457 18.638874 18.219897 18.6862 19.016725 18.225949 17.274244 17.267566 17.154127 16.713261 16.117512 15.925427 15.579203 15.288566 14.843756 14.516089 14.223984 14.110234 14.049798 13.857366 13.636007 13.264122 13.10192 12.716675 12.795182 12.977003 13.057929 12.865432 11.972064 11.976557 12.001395 11.907952 11.625274 11.807647 12.129827 11.597611 10.895119 10.720859 11.142869 11.353457 11.504149 11.097662 10.009574 10.520355 11.439139 11.787155 12.529689 13.14815 13.831547 14.184951 14.459845 14.417655 14.32808 14.359384 13.906181 13.334909 12.720105 12.19936 11.805311 11.601437 11.147793 10.854969 10.701982 10.323885 10.084817 9.8293392 10.287295 10.71948 10.769657 10.670374 10.777415 10.840159 10.482452 10.346705 9.7390641 9.2979326 9.0871914 8.8950843 8.7520509 8.8327766 8.973313 8.9783056 9.0988679 9.0583738 8.9723183 8.6593039 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 140.53417 139.32531 141.43182 144.62534 145.85533 142.20045 142.88498 136.21055 129.19801 126.28261 120.87129 118.50024 116.56568 114.02665 112.43333 111.44744 109.44668 105.53338 103.54971 102.17962 101.59428 99.218004 98.051996 98.477495 97.800228 96.982784 96.090854 95.068568 94.365711 94.299879 92.854698 93.404287 94.230682 92.113884 89.575046 88.767919 88.166512 87.314093 86.814058 86.398029 85.41438 87.54008 91.099616 92.105857 90.111597 88.115284 87.119188 86.322378 85.519273 84.92761 84.406157 84.202574 83.975608 79.253042 73.005037 72.819332 72.867275 72.538316 72.133133 71.501173 71.333951 70.707158 70.581201 70.365029 69.749862 69.25758 68.900807 68.249835 67.636621 67.059357 66.70495 66.407211 65.981596 65.414758 65.154097 64.684831 64.624505 64.406762 64.096865 63.801484 63.540906 63.13998 62.715421 62.329524 62.030301 61.605387 61.197873 61.159493 60.421609 60.166638 59.995724 60.12055 60.595114 60.900128 59.829918 58.786833 58.256212 57.853798 57.680825 57.15183 57.00192 57.132033 57.54427 57.604423 57.130731 56.731739 56.24415 55.539261 54.918219 54.482445 53.808688 53.224178 52.517558 51.831474 51.165171 50.618387 49.980677 49.60111 49.097288 48.331795 47.422869 46.582802 45.777529 45.504786 44.469903 43.794409 43.028233 42.024079 41.250539 40.835458 40.347469 39.668473 39.0103 38.62966 38.42137 37.743373 37.070662 36.48574 35.907249 35.341708 34.823582 34.398968 33.949346 33.588654 32.891213 32.247856 31.721017 31.325276 30.641021 30.054952 29.567461 29.121738 28.631019 28.16882 27.60664 27.014796 26.444088 26.053749 25.570792 24.887493 24.18726 23.617692 22.737684 22.304863 22.143694 22.083902 21.599455 21.393108 21.29488 21.482949 21.499332 20.948637 21.111035 20.715888 20.691735 20.574533 20.753381 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7.745469 7.467215 7.5244599 7.2731533 5.2106879 5.5266168 7.0431493 6.5833713 5.7308453 4.9800669 6.0325791 7.3318525 7.0010198 6.6914539 6.7608026 7.1230803 7.4310114 7.6342287 8.178522 8.6844584 8.8867086 9.2941745 9.3347795 10.01471 9.8432928 9.809739 9.552219 9.8673817 10.406435 9.9534544 7.7132307 7.2654149 8.8164023 10.110034 9.7281197 9.3602584 8.8885874 8.9350845 8.7818537 8.7236472 8.567558 8.3908649 8.2088064 7.9668799 8.4031836 11.920673 15.101445 18.818199 26.463023 25.440314 32.749509 34.673879 33.418492 37.104986 38.508547 39.692606 47.169705 47.486165 45.664561 40.870028 32.848414 33.417943 33.43145 29.497788 27.09689 43.7709 42.290696 29.287593 17.197655 19.453026 15.39991 13.313965 10.198735 8.9397118 8.1504072 7.8740153 7.6791981 7.2841298 7.2066739 7.5273613 8.2974328 8.0322909 5.3011626 4.0849724 3.4886253 3.1034974 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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62.297667 62.680755 62.99589 62.260454 60.821744 59.545744 58.903197 58.568977 58.383219 58.065392 57.862288 58.403288 58.580204 58.064904 57.628508 57.18685 56.662529 56.208151 55.687828 55.109229 54.397275 53.72943 53.036691 52.471313 51.918251 51.523589 50.852691 50.454577 49.872432 49.223423 48.815003 48.12578 47.217209 46.044856 45.083122 44.407154 43.701107 42.986488 42.490358 41.846668 41.204729 40.605173 40.18956 39.536187 38.588491 38.082793 38.121742 37.391658 36.58825 35.912462 35.328887 34.639534 34.14758 33.848929 33.372145 32.850885 32.299207 31.619912 31.158014 30.621157 30.080301 29.539272 29.085616 28.462548 28.107754 27.641214 27.195878 26.500594 25.905117 25.456276 24.88697 24.054758 23.239384 22.547448 22.245005 22.077833 21.900688 21.373433 21.249129 21.186754 21.317738 20.780954 20.980678 20.950693 20.59209 20.513152 20.390962 20.472465 20.623069 20.767473 20.788805 21.231099 20.566013 19.462734 19.148418 18.491795 18.084149 18.17097 18.610555 17.946289 17.168486 17.014341 16.877709 16.204693 15.856247 15.452381 15.126726 14.603209 13.997152 13.621154 13.574546 13.45792 13.203306 12.910456 12.353792 11.833302 11.552981 11.868027 12.662445 12.755472 12.945832 12.897685 12.519897 12.324983 12.18069 11.591885 11.178119 11.701204 11.220888 10.981909 10.681968 10.630939 10.961472 11.440126 11.427081 11.864995 12.121525 12.359087 12.929323 13.365582 13.626053 13.670213 13.975277 14.296797 13.904004 13.556029 13.294744 12.906426 12.519032 12.086601 11.744556 11.519387 11.247751 10.999401 10.797968 10.521077 10.215872 10.144169 10.548536 11.060751 11.262166 11.267706 11.054778 12.83462 11.148011 9.9329022 9.7623587 9.4502126 9.3586828 9.2435842 9.4570011 9.114748 9.2479027 9.543287 9.4295615 9.1458725 9.3896881 9.3450021 9.2072656 9.2742926 9.5988294 9.5786716 9.4523846 9.0743538 8.6313759 8.2517943 7.7244991 7.5974693 7.4453467 7.4031423 7.6107479 7.1138052 7.4090752 5.4109606 5.8550614 8.1477378 8.0525198 8.0818251 7.7440837 7.6572684 7.3770171 7.279903 6.8217537 6.7869251 6.9219141 7.2907273 7.760695 8.3668215 8.6004926 9.1699717 9.5331585 9.5693189 10.354665 10.081195 10.049387 9.662205 9.6372959 10.296374 9.9778681 8.8768146 7.6123623 8.0653062 9.9105494 10.569905 9.7628585 9.5504783 9.3399461 9.0826083 8.8946286 8.8249582 8.6857183 8.4653229 8.0709676 8.7248076 16.191723 22.860937 16.735414 21.106582 32.356581 35.640994 39.661699 43.622728 47.405704 54.143689 46.754602 52.78584 54.583833 48.426004 42.25348 33.53254 41.281951 40.385459 30.031118 38.27378 45.335585 32.94274 29.874359 23.65902 24.869071 20.760137 17.533919 14.74362 9.5033173 8.2804441 8.0097463 7.3493927 6.7691104 6.5634239 7.271188 8.4426 9.2912333 8.9953408 5.6388965 3.9971445 3.019776 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 143.26137 137.90044 131.71518 129.64507 129.36694 131.53419 135.25763 135.43101 136.53849 121.08531 117.52447 115.82845 111.81291 105.57883 105.39369 106.30544 105.31527 102.398 102.43275 104.20269 102.68591 102.13096 102.37374 105.84365 107.07816 106.72712 106.03223 104.60603 103.8462 102.7622 101.57723 100.38809 99.229666 98.481598 96.971031 90.178293 87.256715 86.280962 85.53129 85.022724 84.409925 83.868734 83.16956 82.661339 83.286882 85.337841 88.342566 86.351041 84.530081 83.530169 83.229716 82.871962 77.821879 73.414749 73.660736 73.646593 73.05518 72.108201 71.379255 70.804517 70.377372 70.136967 69.90616 69.831864 69.350837 68.870216 68.407956 67.960522 67.793677 67.396531 66.922891 66.678983 66.483135 66.086486 65.684921 65.280845 65.059059 64.6516 63.767031 63.331166 63.244842 62.941594 62.094658 62.399953 63.409285 64.633333 64.59619 64.274547 63.934266 63.794293 63.444185 61.919027 60.25993 59.474042 59.227763 58.914299 58.531655 58.051331 58.957336 58.874267 58.559036 58.046921 57.540268 57.18633 56.692602 56.165431 55.652423 55.102289 54.518749 53.706751 53.107338 52.675444 52.294849 52.035426 51.609314 50.539205 50.077366 49.435341 48.461069 47.646098 46.723873 45.538337 44.901608 44.417538 43.894437 43.048582 42.259898 41.692038 41.156855 40.476945 39.795848 39.149222 38.291459 37.808301 37.867667 37.381245 36.597546 35.980002 35.376211 34.633912 34.12205 33.675513 33.239068 32.737129 32.204484 31.616769 31.048345 30.588232 30.074044 29.556879 29.187745 28.847167 28.467069 27.986976 27.326569 26.613424 25.860737 25.302439 24.897538 24.061771 23.241839 22.451253 22.202335 22.030017 21.864671 21.353535 21.113329 21.073102 20.844701 20.709669 20.912696 20.799829 20.342037 20.445704 20.080848 20.26496 20.442199 20.456601 20.481369 21.710613 20.402056 19.865951 18.415982 18.352995 18.813908 19.560192 18.4814 18.030274 17.358206 17.578956 16.677142 16.12761 16.237671 15.992719 15.131398 14.020955 13.699941 13.69746 13.535962 13.2941 12.941924 12.591979 12.106344 11.716361 11.461235 11.706393 12.359905 12.476809 12.685066 12.689173 12.567109 12.275547 12.515867 11.808323 11.023399 11.805059 11.051942 11.078138 10.463749 10.129234 11.114978 11.572215 11.609088 11.716026 11.795258 12.180766 12.86074 13.310882 13.510454 13.585279 14.117124 14.137719 13.901589 13.523414 13.08847 12.727534 12.406258 12.11088 11.721612 11.469357 11.310655 11.054196 10.901898 10.61393 10.395374 10.518175 10.819404 11.298546 11.018904 10.970807 10.644852 10.459418 10.317813 10.317512 10.195033 9.9634643 9.6625123 9.5735217 9.5448028 9.4752821 9.6681119 9.9446529 9.7161808 9.4015722 9.3591616 9.7260703 9.7221216 9.8115971 9.7518693 9.6146797 9.3153878 8.9273818 8.7127537 8.2588756 7.9301012 7.8489576 7.5964943 7.701307 7.401681 7.1853774 7.4455603 6.8713615 4.1154944 5.8461291 8.3764651 8.3865427 8.2074153 8.0264103 7.8511931 7.2043497 6.7985678 6.720845 6.8616934 7.2059479 7.7767411 8.347843 8.6412138 9.4194098 9.8309371 9.9815631 10.389579 10.716881 10.178304 10.154291 9.5412025 9.8371718 10.563328 10.16929 7.7046644 7.9319223 8.7911048 10.161641 10.769957 10.063694 9.660189 9.285779 9.1992882 9.1608009 8.9192171 8.6748482 8.4522388 9.6441385 15.840823 26.194782 25.157076 20.87708 28.367948 41.27635 47.483408 53.41957 55.121256 64.270612 63.687693 54.142602 57.792258 50.588973 42.909805 38.694343 45.332649 42.140349 32.316334 36.957841 46.621318 43.565704 46.654612 32.304763 22.533304 28.506668 23.631963 23.471588 14.656524 9.5078865 7.4604899 6.6835795 6.1254794 6.0213025 6.7465784 8.4815935 9.7834594 11.102866 11.122922 6.3558198 4.0476536 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 163.00985 155.59382 139.69569 129.13092 125.45143 125.96903 124.6107 123.90091 122.13833 119.82545 117.46423 110.85497 105.92831 104.66558 104.42495 106.36297 103.58612 103.48086 107.14953 110.8128 113.45635 112.84533 109.76278 111.17439 109.57822 105.8729 103.81638 102.6957 102.61921 102.12533 101.1793 100.34971 99.610064 98.6067 98.032122 96.548825 91.508256 86.40723 85.457577 84.628613 83.732025 83.315132 82.685062 82.201356 81.479153 81.492971 82.444039 84.948583 82.137349 79.232892 79.196581 82.679274 80.503436 73.937951 73.928489 73.959342 72.869014 72.166577 71.494587 70.771288 70.157827 69.760821 69.355658 69.154682 69.077498 68.684448 68.255823 67.905148 67.59091 67.22861 66.873695 66.384432 65.965436 65.805347 65.492075 65.035074 64.537468 64.017647 63.607827 63.388725 63.07581 63.42189 64.668247 65.842233 65.746569 65.505882 65.117597 64.776599 64.682347 64.592272 63.369278 61.122059 59.937502 59.766364 59.649891 59.1499 58.544622 58.757702 59.353772 58.979274 58.531438 58.013201 57.437397 57.193495 56.687871 56.202491 55.546705 54.91704 54.404135 53.894117 53.418315 53.315871 52.520773 51.923151 50.907926 50.366216 49.694943 49.070595 48.45523 47.678863 46.952831 45.829 44.625202 43.964219 43.466572 42.892964 41.987175 41.3247 40.750027 40.157154 39.918587 39.381689 38.619762 37.397979 37.541378 37.215054 36.606525 35.953117 35.283413 34.624671 33.985064 33.429037 33.058215 32.698785 32.114217 31.614213 31.261962 30.858148 30.275568 29.813293 29.33739 28.965623 28.475151 28.027907 27.280974 26.608205 25.866168 25.326759 24.889734 24.045039 23.334022 22.716508 22.377488 22.091834 21.796856 21.658036 20.984069 20.931288 20.810754 20.547596 20.747142 20.511306 20.045223 20.483706 20.023758 19.842834 20.193529 20.109406 20.227892 21.144609 20.14077 20.143692 18.949034 18.483988 18.099331 18.700957 18.608642 18.315273 17.92741 17.366477 16.66322 16.673133 16.927694 15.792022 14.396819 14.118205 13.911843 13.656381 13.464368 13.109849 12.797541 12.428317 11.942303 11.526685 11.301564 11.710922 12.133119 12.082167 12.476698 12.420233 12.341965 12.064439 11.940379 11.302207 10.783153 11.685441 11.10715 10.750151 10.508077 10.10296 10.568745 11.153923 11.078309 11.241085 11.322317 11.838739 12.529771 13.171045 13.320099 13.43815 13.33057 13.90511 13.769724 13.645352 13.388162 12.893854 12.625277 12.378872 12.105722 11.602441 11.28566 11.344476 11.349479 11.008526 10.760805 11.012473 11.424255 11.176533 10.823151 10.715529 10.68123 10.717019 10.886721 10.91496 10.773763 10.516201 10.145387 9.9658363 9.8448894 9.8855963 10.021195 9.7132451 10.027812 10.026265 9.8014847 9.4569854 9.9342599 9.9775057 9.8799857 9.7685761 9.4811385 8.9942583 8.7067177 7.8899918 6.9935739 6.6808182 7.4617611 7.3919339 6.9960116 7.1059375 7.1473608 7.1393867 7.8592706 6.0331738 5.2863507 8.8013372 8.6922818 8.3739016 7.742435 6.9266928 6.5629789 6.7030304 6.8019031 7.1076688 7.6219982 8.30319 8.8578493 9.4191132 9.870402 10.287178 10.555652 11.186144 10.769385 10.294654 9.9903218 9.6065399 10.818146 10.500762 9.3053119 8.1479839 8.1121844 9.7382892 10.211858 10.965065 10.082326 9.974315 9.4924347 9.3970272 9.1823366 9.0207659 8.7599994 10.548164 16.492213 29.048294 28.068906 32.829326 28.930783 32.180621 43.015463 48.091993 55.561587 60.964669 70.653452 69.080047 59.062906 51.058413 45.33106 43.331088 48.945393 43.407955 37.675196 36.334092 53.905746 57.057944 47.125303 36.08696 24.472311 32.280423 32.914077 25.526491 19.022073 12.702562 7.6969971 5.9642385 5.579211 5.620507 6.4515111 8.3803467 10.943275 12.675939 13.896134 14.432836 9.2830359 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 163.7348 145.62407 132.42894 127.23462 122.95959 120.22212 117.70681 117.96105 115.4743 109.2676 104.20491 103.01294 102.73582 103.88515 103.02413 103.58902 109.73425 117.34457 120.83984 118.61479 114.21689 113.82553 106.8521 104.05858 103.35754 102.38724 101.70822 101.38218 101.37645 100.7591 100.09463 99.301249 98.225054 97.412142 96.668398 95.944701 90.174474 85.53848 84.771289 84.153337 83.402574 82.869216 82.28755 81.580464 81.03694 80.253755 79.536002 78.779218 78.285238 77.944927 81.409717 80.226124 74.553652 74.667414 74.23159 73.012914 72.158957 71.671288 71.136742 70.614004 70.502384 70.09108 68.71522 68.526312 68.332615 68.030109 67.626756 67.188402 66.966238 66.921262 66.574826 66.261479 65.659509 65.303778 64.832545 64.471589 64.164312 64.213044 64.499047 65.74164 67.095535 66.803063 66.469008 66.183085 65.86247 65.566055 65.537401 65.637978 64.579481 62.032253 60.833102 60.320205 59.842517 59.357002 59.039408 58.863819 59.550203 59.295147 58.978067 58.634119 58.138274 57.70777 57.294904 56.612077 55.959637 55.41069 54.971983 54.818339 54.328286 53.747586 52.845009 52.132913 51.493111 50.913236 50.219792 49.588445 48.982891 48.292688 47.644465 46.916039 46.235193 45.390658 43.693124 43.074715 42.546521 41.936908 41.233084 40.602739 40.010855 39.505153 39.129855 38.51628 37.27728 37.356279 37.050133 36.566727 35.994129 35.282478 34.662363 33.972555 33.277875 32.809517 32.36045 31.932282 31.43635 31.098556 30.705411 30.281971 30.015887 29.401799 29.000366 28.464227 27.898359 27.251504 26.564978 25.89557 25.442964 24.909971 24.435302 23.749705 23.086896 22.498978 22.182345 21.884219 21.642736 21.358776 20.884185 20.655164 20.386461 20.227474 20.28251 19.924306 20.305353 19.882599 19.601758 19.655189 19.608445 20.350178 20.268749 19.827905 19.944831 18.985673 20.508559 20.741756 18.927981 18.033589 18.450518 17.880089 17.323887 16.950785 16.905311 17.025139 15.600837 14.443948 14.135436 13.574344 13.224648 13.067412 12.863631 12.583758 12.032336 11.675492 11.431003 11.247081 11.490445 11.747702 12.115677 12.343112 12.21932 12.034672 11.815623 11.525416 11.117208 10.755874 11.535603 11.135368 10.728574 10.312829 9.970655 9.7937573 10.249424 10.330877 10.499049 10.927894 11.391546 12.082018 12.461517 12.734636 13.107558 13.205643 13.088755 13.233173 13.360801 13.030939 12.471472 12.518115 12.347361 12.619612 11.992107 11.856336 11.826146 11.775189 11.336642 11.249154 11.595086 11.454912 11.115202 10.87163 10.927311 11.075792 11.245478 11.288274 11.05637 10.866385 10.776642 10.61604 10.345011 10.181127 10.097419 10.012476 10.102657 9.9905376 9.686141 9.8101023 9.8514119 9.7416798 9.6403849 9.5262883 9.7036039 9.5879012 9.1528276 8.7440896 8.248161 7.5416624 6.9183601 7.1524096 7.2454501 6.3382112 7.6296813 7.0700689 6.9973756 7.7472063 8.6468145 6.3601366 7.3474594 8.8032709 8.4969911 7.5638502 7.1758917 6.9515518 6.5863439 6.782502 7.2602767 7.6810322 8.3254821 8.9906583 9.3490863 9.8999991 10.415123 10.760592 11.249797 11.189081 10.47819 10.323697 9.9309618 10.372269 11.36733 10.695006 9.3412115 8.2780798 9.1143293 10.165848 10.707897 10.792983 10.329468 10.034533 9.6475928 9.3750763 9.2344178 9.3513441 12.441743 18.782827 30.167782 30.739313 38.764169 47.276957 36.455611 33.533555 36.51974 43.201981 53.493731 68.392438 77.353406 69.484307 57.949952 44.744115 53.049097 55.939104 39.67694 48.342258 39.217017 53.659591 66.701449 54.646895 36.718883 33.494464 36.118416 37.466461 35.306162 23.409169 13.850131 7.5445125 5.5069779 5.4409127 5.4202285 6.6248078 9.0209714 12.224941 14.285047 15.815263 16.958777 19.228996 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 151.19048 140.55582 142.3104 129.52829 124.74176 122.77073 121.3459 116.66392 108.91937 104.64123 104.09593 103.51875 102.82657 103.58853 107.89125 111.03063 123.53974 126.10329 122.79594 119.47627 119.0019 110.34797 105.27219 105.34923 105.84237 102.56924 101.60815 101.05192 100.98943 100.86373 99.175113 98.756542 97.757088 97.013229 96.223681 95.593844 94.522593 90.081716 84.802044 83.910062 82.832317 82.25822 81.549077 80.975937 80.536658 79.996292 79.185999 78.644243 78.270228 77.757507 77.639933 78.214736 76.619611 75.467129 74.598022 73.156721 72.230548 72.485409 73.827704 75.809174 77.895503 76.872853 74.428378 72.716386 72.456042 71.2911 69.625234 68.492773 67.814753 66.887417 66.207179 65.79254 65.559909 65.990551 66.439791 67.100737 67.746126 68.429536 68.518503 68.263945 68.038168 68.335097 69.347286 69.525056 67.002725 66.641453 66.115791 65.411322 63.420642 61.671536 60.85827 60.470465 60.229589 59.62581 59.549278 60.012519 59.898661 59.560149 59.457112 58.9214 58.446178 57.880744 57.556778 56.929 56.345913 55.829996 55.545467 55.498778 54.105082 53.324407 52.659732 51.984341 51.435432 50.959103 50.310947 49.617163 48.984721 48.247537 47.200989 46.538049 45.867389 45.10402 43.843835 42.898292 42.14795 41.66275 41.061294 40.510568 39.598638 39.301125 38.856346 38.349222 37.428332 36.844838 36.540123 35.950198 35.703698 35.196649 34.557254 34.039673 33.323854 32.766423 32.231318 31.862252 31.409407 30.932732 30.650719 30.148665 29.822131 29.507385 29.045044 28.643159 28.032041 27.547651 26.841538 26.084396 25.556183 25.106377 24.704508 24.195853 23.506372 22.884229 22.366054 22.117739 21.833534 21.594536 21.329615 20.77064 20.162222 19.970712 19.68809 19.711633 19.944309 19.715831 19.406974 19.201979 19.412617 19.689547 19.720098 19.612082 19.662953 19.357968 21.220212 20.681635 20.168223 18.196972 17.708911 17.004617 16.812892 16.369178 16.401105 15.928186 15.387238 14.600175 14.422501 14.347429 13.740102 12.806591 12.240639 11.844827 11.568452 11.380286 11.211761 11.109652 11.326132 11.51581 12.006256 12.029203 12.015125 11.694314 11.580273 11.132368 10.685711 11.269672 11.357208 11.175061 10.758152 10.238654 10.175096 9.9084723 9.7785537 9.760955 9.8609115 10.494605 11.009222 11.461192 11.771879 12.155789 12.478393 12.911587 13.045092 12.987237 12.903836 12.608951 12.642943 12.949347 12.958655 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45.114357 55.924857 52.202037 46.60978 64.389246 56.891717 41.012674 42.945161 36.713193 40.828383 48.047328 37.473588 23.138879 8.2352299 5.4528466 5.4366505 5.3857894 6.4915598 10.24839 14.336289 16.855491 17.180744 19.558831 17.396569 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 152.13971 148.46637 133.46777 125.93809 122.27837 119.56333 112.34079 106.54574 104.45272 104.55793 104.26371 103.62946 111.05895 112.58317 128.669 152.08828 147.70723 136.95584 127.53881 117.77771 107.12648 110.15671 112.31801 107.27857 105.46236 103.29085 102.59028 106.08493 103.9744 100.26064 97.353405 95.8078 96.024757 95.160381 94.610772 92.682845 92.110325 87.877092 84.869889 83.526317 82.516186 81.572328 80.421033 80.01976 79.854757 79.420902 79.759649 78.442963 77.93713 77.17842 76.96219 76.133561 77.284532 76.603888 75.440578 74.623104 75.531055 77.938949 79.737044 79.498404 78.902484 78.37556 77.75794 76.852686 76.184504 75.47806 74.701451 73.82055 72.66547 71.389077 70.366673 70.336104 70.155339 69.920234 69.84324 69.593799 69.779271 71.749192 75.566231 82.440655 100.43007 108.07629 104.80989 90.379403 76.976846 68.444582 65.354236 62.934149 62.303632 62.149295 61.732298 61.232155 60.840966 60.968848 60.340342 59.681519 59.302914 58.812714 58.424655 57.839198 57.379704 56.87673 56.388879 55.92913 55.523597 55.138209 54.354789 53.66208 53.111724 52.532663 51.896161 51.368477 50.860452 50.189772 49.548436 48.88179 48.272049 47.551494 46.83553 45.902494 45.119676 44.605095 43.771128 42.341702 41.440828 40.777045 40.233887 39.588231 38.992574 38.490358 38.201636 37.623964 36.87306 36.873465 36.26399 35.645216 34.951956 34.51364 33.945548 33.307531 32.726182 32.212002 31.745865 31.460147 31.051272 30.693891 30.376557 30.009635 29.662821 29.123187 28.630242 28.10679 27.731255 27.071722 26.291686 25.728278 25.185857 24.714639 24.450354 23.800986 23.105407 22.697665 22.438612 22.157004 21.864168 21.444807 20.860938 20.353663 19.709384 19.308939 19.565061 19.475235 19.790249 19.342059 19.013044 19.228997 19.308774 19.357041 19.564494 19.055906 19.096885 21.105623 20.357461 19.817453 18.144087 18.522691 18.322736 17.855351 17.602352 17.15377 16.779477 16.598328 14.449768 14.279982 14.094514 13.479267 12.737723 12.409148 11.754897 11.314006 11.175355 10.838091 10.656173 11.066636 11.427089 11.941012 11.829686 11.891248 11.428195 11.333194 10.855096 10.516824 11.798288 11.461525 11.01018 10.9499 10.553787 10.257512 10.08177 9.9208382 9.7448577 9.5507456 9.8626619 10.432097 10.810459 11.286375 11.671009 11.879609 12.455103 12.581087 12.54768 12.419041 12.246299 12.367778 12.594729 12.772822 12.592644 12.008356 12.108212 11.92747 11.573465 11.479249 11.400153 11.311459 11.482676 11.492884 11.446025 11.441381 11.346445 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50.010423 25.519212 8.2515627 5.8210945 5.6010219 7.2543787 13.134809 17.485003 21.417003 20.21751 22.054585 18.918463 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 144.93837 150.49453 141.1551 128.08628 120.02711 109.45217 108.35935 106.72736 105.32583 105.01932 104.23585 107.90516 113.84582 122.84034 152.06494 158.35215 153.47999 147.54772 135.56471 115.6043 107.46338 115.84807 115.76416 111.17129 110.05287 103.63052 104.18574 107.82644 105.01506 101.10669 100.11602 98.757557 95.288737 95.141925 94.092048 93.750335 91.440226 91.223759 90.734903 93.738047 87.566141 88.38267 89.53938 93.97475 100.30271 102.93555 101.62481 92.207231 84.674215 83.338602 82.803468 86.198268 83.755678 80.594666 80.164299 80.555695 81.162261 80.259033 79.369339 78.826806 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42.493017 49.360073 58.753307 65.645084 71.443581 74.490416 81.055873 77.105303 72.633141 75.113628 70.427946 62.862948 78.431724 82.114904 96.414618 93.086851 88.087734 73.614054 83.679389 60.338347 71.54243 54.426559 56.703331 35.099324 18.428456 15.833632 16.578764 13.594025 17.36795 29.894296 31.567967 30.158706 35.08936 40.313508 34.354569 30.857714 28.310765 30.569482 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 120.64708 110.86677 110.01607 109.70161 116.75924 115.7873 113.43346 118.61622 131.46017 148.16366 165.81792 172.41024 164.6253 164.49055 151.4632 131.92983 120.02959 126.16483 147.70233 132.71351 146.15348 150.54456 142.77279 136.02165 118.77278 112.2852 126.9753 134.69165 151.64632 151.04989 141.78935 119.22408 107.73229 115.6783 130.85177 148.93696 140.61772 167.20355 182.61995 171.9938 163.57657 176.48541 185.47598 202.4914 187.73041 197.99207 202.7159 188.45267 185.61868 189.69037 184.95264 181.46758 170.38083 166.37212 166.08207 164.88391 150.79033 147.65718 164.35256 183.05825 176.30896 174.53034 172.43066 159.53154 162.759 151.0145 151.237 163.58609 163.95114 149.78879 135.77935 129.12122 122.65355 111.32254 99.68558 87.05109 87.675999 90.363247 91.947103 79.840059 60.918624 58.634051 58.103122 58.014234 58.087285 58.088999 58.284 58.662154 59.0354 59.622395 60.877567 62.040195 67.14613 67.646774 65.501493 54.878062 54.011665 54.475174 54.614831 54.431936 53.969914 53.706945 53.261 52.657458 52.04336 51.479524 51.030561 50.711826 50.390817 49.933911 49.608985 49.144294 48.479703 47.959259 47.874344 47.341755 46.464928 45.732178 44.950404 43.25749 42.239766 41.978391 41.624793 41.04483 40.6889 39.85405 39.272243 38.948066 38.821336 37.89335 37.052061 36.89826 37.456114 36.138711 35.441786 35.019978 34.409222 33.927309 33.535669 33.068834 32.452715 32.297756 31.8412 31.466213 31.019679 32.180948 38.496178 41.408535 30.618355 28.3375 27.652212 27.156908 26.711929 26.326976 25.767893 25.698505 25.251123 25.029977 24.549655 23.94833 23.17635 21.853272 21.28136 21.703705 19.699614 19.032842 18.000689 18.475394 18.326875 18.682168 18.652219 18.155932 18.059822 17.591137 17.778438 17.979943 18.032251 17.05936 17.103101 17.225396 17.058109 17.134943 16.912573 16.988436 16.285601 14.845314 14.666628 14.798818 14.529421 14.298202 13.928295 13.486527 13.006342 12.533145 12.03042 11.934146 11.639322 11.549098 11.410791 11.069977 10.216991 10.558887 10.840067 10.892075 10.759281 10.774192 11.063931 10.141265 9.801449 10.64177 11.143148 11.84989 11.524072 11.305466 10.981086 10.920498 10.865699 10.678714 10.410098 10.309738 10.161196 9.8180722 9.671185 9.6326183 9.5665944 9.7613576 9.9488578 9.8785276 9.7808301 9.7731683 9.6709118 10.098847 10.400809 10.786579 11.003923 11.120849 11.288484 11.349167 11.14503 11.149074 11.166763 11.027275 10.575541 10.210406 10.018211 9.9426994 9.7998842 9.8559312 9.8087988 9.4613253 9.4865056 9.8182986 10.244608 10.200947 9.7238322 9.9261319 9.2761516 9.7669626 9.3417857 9.059919 9.0892542 9.02495 8.6006308 7.9644264 8.3001452 8.577369 6.9111153 8.0071218 8.7395502 8.2585594 8.3966007 8.4831809 8.1520186 7.7894416 7.5362896 7.4009103 7.4067387 7.6123125 8.513573 6.3697166 6.7855767 8.0841852 7.7523854 7.1172418 6.71623 6.6329041 6.6820549 7.2313903 7.0728393 7.089705 7.5317817 8.0374232 8.6037432 9.2968179 10.15181 10.847522 11.686605 12.477115 12.708412 12.309044 11.992407 11.875819 11.842231 12.111855 12.858481 11.6688 11.628174 9.532665 9.7532405 11.632426 12.182672 12.625853 14.447594 17.277742 20.171347 22.424901 24.347884 26.421919 28.837694 32.022297 36.705311 44.275775 52.445591 55.195833 59.33065 70.757124 82.759337 89.197155 81.06268 85.316887 81.926276 72.189463 91.428622 102.15496 102.20744 93.995496 101.02549 106.27969 86.10861 81.083169 59.973803 64.681527 61.094468 34.807288 24.035367 19.504557 20.577184 30.973275 24.722185 43.947027 58.917906 48.48689 35.336278 40.814439 38.682465 32.639853 35.030962 27.776761 32.822689 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 109.97967 110.393 116.51711 119.1005 118.7846 118.17991 123.01146 135.36536 155.8061 172.60555 174.03267 162.33651 158.24653 153.38471 135.59319 123.39191 123.47742 145.23285 140.62198 148.7202 155.84199 164.46078 149.26584 135.89048 116.67859 123.08679 140.55224 153.70244 159.15164 134.18721 127.86046 123.84776 117.87296 137.4211 163.4393 166.37217 172.93736 183.7315 178.07619 168.05437 180.74498 206.34101 203.00601 191.6824 203.2824 203.11549 188.8283 195.4103 191.31136 198.37263 209.79582 206.69757 204.91746 201.39365 204.06789 189.46184 187.72534 189.18807 184.96809 177.32859 164.99293 168.92969 154.34899 123.34819 111.62097 123.1233 139.79267 137.7672 120.25681 110.01405 94.092297 89.843398 82.268887 74.739391 68.528154 65.895625 65.412119 67.776149 63.80068 59.454897 58.423228 57.95815 57.755372 57.775522 57.84419 58.051114 58.2741 58.490256 67.129687 80.551193 84.717194 79.47578 79.228606 86.766465 63.405309 61.084392 58.756166 54.409645 53.774638 53.449729 53.079819 52.691831 52.407771 51.790029 51.420499 50.8482 50.427789 50.011379 49.598345 49.152374 48.734662 48.423281 48.034462 46.078284 46.447356 46.008208 44.743106 43.593656 43.402924 43.312148 42.804607 41.955887 41.325823 40.658764 40.238434 39.877588 39.031458 38.654204 38.692525 40.822639 44.774981 51.136509 40.215923 35.894221 35.360445 34.930552 34.44863 37.159516 44.185777 43.061863 38.374336 34.373032 33.076241 34.686542 38.20752 48.911615 54.360771 31.603332 28.741293 28.008326 27.370847 27.136305 26.576747 26.269222 25.837634 25.36905 25.081426 24.66035 24.168751 23.299687 23.379118 23.342384 22.60921 20.367084 19.692234 19.298145 17.557189 18.043735 18.141189 18.025517 17.691201 17.803159 17.365724 17.926494 18.388494 18.777308 17.569744 16.811921 16.863547 16.283273 15.838179 15.348482 15.246573 15.149331 14.830724 13.804556 13.705283 14.026238 14.028206 13.650769 13.317763 12.934626 12.435908 12.103763 11.892338 11.566235 11.371236 11.272219 11.436831 10.103547 10.394929 10.719834 10.413914 10.411673 10.597213 10.895282 9.8091609 10.544488 10.819648 11.186383 11.848533 11.548787 11.113058 11.043914 10.928365 10.8518 10.736273 10.854123 10.596965 10.356665 9.9869246 9.7022609 9.6157921 9.537844 9.5081521 9.6433678 9.6617989 9.4126821 9.420347 9.4947807 9.7425304 10.139345 10.58958 10.497809 10.635228 10.691579 10.78697 10.584239 10.673491 10.681326 10.375706 10.235051 9.8803678 9.6925161 9.5421459 9.4740894 9.527647 9.5705428 9.4954974 9.4773743 9.8463854 10.158829 10.154488 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35.771449 34.120171 28.41698 30.574778 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 110.60527 114.20582 119.8696 118.62646 120.17524 119.37039 128.64939 144.43662 152.24285 161.97282 165.86361 164.23144 148.21309 136.63981 132.28701 128.23672 122.14401 130.05147 140.09655 139.96774 150.23234 173.35788 169.65752 140.40579 120.69867 122.62445 135.52562 157.72341 163.24827 147.55611 151.41755 134.9618 121.30869 146.78881 171.18616 174.70835 169.9114 171.7339 179.36272 178.98433 196.66784 207.49415 196.03071 200.57931 210.63941 198.65719 195.38342 208.37844 197.20181 201.2674 208.89244 207.30783 201.15244 199.71918 200.78258 191.24299 193.04209 182.07814 183.34193 187.4508 181.70531 166.44819 148.96489 119.88385 90.452132 94.035019 106.61342 110.24543 104.45446 95.576086 76.538199 67.833027 64.993652 63.607964 62.923574 62.221527 61.472289 60.520446 59.494375 58.806652 58.176721 57.845041 57.690768 57.658217 57.789034 57.97667 58.069091 58.762406 63.741612 80.534995 85.959755 79.521522 89.09 97.335053 78.589925 65.671227 56.999638 54.219679 53.909912 56.406083 52.641146 52.147284 51.725451 51.324211 51.073102 50.61179 50.369032 49.868995 49.196476 48.669161 48.454962 48.222709 47.498994 46.420948 45.496551 44.821582 43.927671 44.97036 44.385175 43.90378 43.423673 42.763039 42.132375 41.556131 40.884048 40.30215 39.88295 39.78071 41.101862 41.223482 45.828308 58.05794 50.417313 37.123643 35.313803 35.079164 34.906733 38.448901 51.752499 66.469533 53.838379 39.130182 44.372691 53.334048 58.85202 67.155631 58.385185 33.467611 28.299338 28.061519 27.919237 27.980195 27.78216 26.802664 26.502847 26.018262 25.398867 24.950364 25.151983 23.982653 23.528742 22.870505 22.092526 22.390913 20.428606 19.456484 19.463176 17.075617 17.257216 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49.763133 49.69792 49.446218 49.388412 49.037858 48.752316 48.434864 47.394504 51.382066 48.800818 46.871577 46.68449 50.541628 65.722462 64.739089 45.378787 42.796502 42.23658 41.85049 41.327903 40.788968 40.189052 42.424719 45.811263 57.032848 58.204349 50.023133 62.098559 51.347584 37.754875 35.553649 36.198957 52.663083 51.040487 55.483428 76.066677 67.696891 64.707148 73.528712 73.708475 61.493383 43.974011 32.199169 35.001174 35.773618 43.251042 38.811859 31.555824 35.367259 26.626448 25.649363 25.44961 25.250636 25.147631 24.500422 24.162229 24.546835 24.610902 23.819038 22.499608 21.313716 20.120062 18.532964 17.225084 15.578102 16.190108 16.642472 17.077189 17.332849 17.590694 17.68468 17.628266 17.80957 18.260049 18.113004 17.200597 16.435572 15.971564 15.514605 14.456101 13.676047 13.363484 13.218519 12.389959 11.825284 11.355315 11.148435 10.782298 10.195139 10.912287 11.292529 10.631526 10.022097 10.429092 9.698205 10.023845 10.021504 10.045098 9.9786353 10.146446 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41.960836 41.427292 41.015037 40.493233 40.670578 40.786181 43.306874 41.972013 47.114239 68.244767 63.948786 43.406467 36.753125 36.900216 38.997946 41.168064 58.719236 66.276278 64.129869 65.377263 70.380062 71.846488 55.044738 49.163354 50.488131 51.345163 47.72207 49.970764 32.625807 25.3397 25.768242 24.85448 25.033308 25.196703 25.047869 24.970265 24.331481 24.43544 24.367316 23.578064 22.851686 22.011023 20.82175 19.202511 18.030038 16.972831 15.602474 15.871986 16.574894 16.88663 16.973314 17.25013 17.209504 17.261227 17.420841 17.723767 18.154281 17.082947 16.354619 15.92706 15.430476 15.005297 14.545599 14.106529 13.277502 12.646518 11.958672 11.469967 10.942818 10.50718 10.11541 9.8659535 10.157142 9.8101922 10.62303 9.7137983 9.7539649 9.8585889 10.099096 9.9514225 9.6844385 9.7764074 9.9624818 9.7523914 10.470911 10.583041 10.410846 10.332256 11.133903 11.663937 11.617703 11.489796 11.358211 11.278568 11.149512 11.130287 10.928326 10.857774 10.874188 10.79849 10.564489 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92.930057 107.07612 112.34409 111.66578 95.139035 120.55439 126.13998 119.15841 104.49623 94.837045 82.305871 72.253484 66.034527 68.355513 54.973581 51.416682 62.724139 59.618888 59.30146 50.733284 54.743881 82.552519 80.243744 71.784969 67.316762 69.247587 66.326142 53.855492 37.412144 36.078668 36.886812 43.860698 27.746208 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 122.60291 123.59161 126.35096 132.17198 137.12637 146.59444 153.60845 167.60998 168.46455 173.04484 152.94396 138.00534 137.47153 136.93622 133.44581 134.43789 139.0356 140.99373 144.78954 144.49396 152.08928 161.05 159.18416 167.6853 183.83304 192.55379 205.42041 210.48226 212.75524 214.44427 213.59845 217.38907 211.19962 211.00971 212.0084 210.60607 214.55964 211.16353 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7.9410743 8.5141924 8.9746936 8.9148361 8.7252191 8.9672216 8.8476776 8.453412 8.152823 8.1232017 8.2048281 9.4690934 6.1993936 8.4701249 9.2489345 8.9292654 8.7396235 8.0287158 8.3374317 8.4007738 8.1040292 8.5071491 7.5358236 7.6169319 7.855476 8.011479 8.7732868 8.613797 8.9869727 9.5217845 9.9084755 10.594879 11.758763 12.444727 13.224426 14.094567 14.861953 13.243407 13.552721 14.071154 12.648293 13.171362 13.010342 11.270175 10.955847 12.13701 16.853676 20.083322 22.619902 25.65874 29.150619 33.221747 37.761058 42.683984 47.540995 51.923204 56.986189 62.702579 70.657823 77.032536 83.281754 94.001223 106.06654 117.06951 131.23622 136.65295 127.3232 107.2732 94.405076 91.590745 77.293625 67.63682 58.630113 63.892167 60.927093 51.277132 52.233425 62.431 59.657958 75.306857 79.826077 81.100861 65.514135 49.421416 51.309481 48.041777 48.417951 45.516938 41.614018 41.01775 40.343174 47.177894 48.609173 41.93556 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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58.29445 58.751286 61.425647 67.581507 76.153054 93.888778 123.76847 127.09416 121.16912 95.81837 69.884596 60.102298 54.213074 51.662403 49.212156 48.956002 49.081986 48.524195 48.210046 48.045887 47.949437 48.133741 48.062855 48.084374 47.935236 47.731402 49.052203 49.052437 49.311647 58.891389 63.52255 48.987529 54.31136 66.307782 73.161127 72.700737 50.959732 43.042761 43.079377 50.663311 67.374758 71.737629 50.786733 43.603496 43.226326 46.046157 56.165207 75.070702 79.896772 85.584885 57.931781 42.079734 53.055971 66.687158 74.335001 82.101749 79.159973 84.446039 63.591871 39.117778 34.984797 52.725332 37.12077 31.11695 26.635574 26.462623 22.733593 22.687022 23.069367 23.487018 23.81486 23.812988 23.587986 23.006791 22.865262 22.604573 22.217076 21.350685 20.411454 19.471367 18.467348 17.667633 17.271854 15.799802 15.93576 15.826064 16.081555 16.045134 16.022651 15.934052 16.017049 15.972323 15.983007 16.263008 16.834305 16.919761 15.995536 15.536324 15.063997 14.414922 13.918042 13.505879 13.006612 12.707954 12.302082 11.992187 11.767212 11.450527 11.343604 11.290973 10.958416 10.929868 10.808748 10.870136 11.015158 11.19253 10.712148 9.3454282 9.3071261 9.2647262 8.9830948 8.6399721 9.364934 9.2228213 9.7129028 9.6887492 9.2089752 8.8521903 9.1535486 11.121675 11.639943 11.430078 11.293346 11.160364 10.994883 10.904113 10.816029 10.611468 10.641371 10.506531 10.638379 10.454481 10.421121 10.451304 10.103383 9.8526841 9.7140646 9.7106641 9.6348744 9.2720894 9.1554813 8.7296632 7.7170015 7.7434923 7.7119582 7.8604548 7.9567818 7.8313859 8.1261577 8.6151517 9.0901358 9.5029913 9.389401 8.5499981 9.5806581 9.7359771 9.3167432 9.2843725 8.8758013 8.2454303 7.5403788 7.8280766 8.2503235 8.6828023 8.3253879 7.5883606 7.818433 8.7089679 8.4034631 7.8451352 7.8341397 8.6612114 8.7167985 8.8719636 8.9141242 8.8517284 8.5789526 8.3573727 8.4576566 8.1925351 9.2832343 6.8394987 8.458437 9.0203934 9.0333142 8.7608152 8.595283 8.3162 8.3024463 8.5511926 8.5051406 8.3691885 7.7710297 7.8507034 8.1625609 8.7413211 9.2519939 9.3688075 9.7779388 10.504316 10.384924 11.361419 12.552721 13.334325 14.109134 14.947238 15.040206 13.441142 13.872716 13.705152 12.785395 13.191191 11.897391 11.270825 11.990061 17.660393 21.32916 23.432662 25.925996 29.380782 33.267776 37.572164 42.176821 46.632063 51.425457 57.235252 61.983304 69.384925 79.523124 86.000581 96.056401 121.34233 134.05281 139.88926 152.41008 141.95451 114.74294 96.032042 82.390916 70.647516 63.8395 58.489984 58.075268 59.982943 64.080588 51.269025 60.167898 66.098317 67.074622 91.110455 78.352247 55.671937 58.036472 57.249144 62.369822 65.821557 55.763048 46.842691 46.118939 45.008307 50.093298 40.621332 33.566309 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 135.87788 152.2961 165.76408 166.23277 163.01975 161.36941 151.04211 153.45685 163.4989 167.05618 168.03917 151.12273 151.72038 155.37101 155.60506 176.18751 183.70833 196.94698 224.17231 232.35653 225.88031 216.94113 218.84634 217.52709 211.56811 200.76683 198.41565 196.20204 190.10136 190.87309 197.77902 197.45867 208.63697 205.08422 202.25685 205.23912 216.80093 213.51865 192.00563 170.42624 152.96598 133.43242 115.1439 106.36081 101.64555 86.884354 80.195231 78.40689 77.286906 80.324328 77.746039 73.627491 72.118415 71.467642 70.620624 69.760401 69.24161 68.893091 70.314181 74.097763 72.651954 89.525145 98.336829 105.49427 110.25778 106.21202 100.93865 88.261697 71.00478 87.848619 91.355504 85.018537 73.56095 64.046298 65.208386 68.862792 77.019664 62.456713 64.326287 68.854774 73.425643 90.372923 102.55334 124.63643 110.23905 104.56561 85.592497 67.669676 54.042216 51.75355 51.760123 51.176782 50.431487 48.771874 48.160872 47.743936 47.60933 47.55691 47.375949 47.214351 47.28481 47.331778 48.468708 49.079757 49.051226 53.875115 63.359437 56.46724 56.608885 53.151299 61.319831 75.375346 76.004121 54.734079 44.294597 44.751209 54.696328 62.807328 71.431373 68.701426 63.51331 48.236306 46.90562 53.622883 60.709897 77.85216 84.088946 57.506368 44.075985 48.352467 61.747291 80.657486 97.248744 68.220854 59.863364 55.180371 35.690348 27.775337 39.338097 32.710142 22.17588 22.217384 21.513835 21.686693 22.098422 22.583864 22.949206 23.174283 23.055165 22.698129 22.314086 22.17873 21.925761 21.485593 20.904465 19.866945 18.88957 18.052061 17.479109 16.838321 15.963181 15.884671 15.8661 15.481278 15.7556 15.561669 15.611277 15.4713 15.536573 15.588952 15.810532 16.237201 16.730194 16.142738 15.471435 14.881141 14.205348 13.796084 13.323367 12.952114 12.593866 12.222015 11.908057 11.693193 11.464102 11.3887 11.256939 11.013364 10.965701 11.054526 11.018298 10.690814 10.952149 11.249122 10.963768 9.7084014 9.1825924 8.6414497 8.7288623 9.3221525 9.1621927 8.8563132 9.5771179 9.5698826 9.005362 8.5800604 9.8453582 11.641909 11.592502 11.351891 11.127658 11.083564 10.97275 10.793708 10.66874 10.627501 10.60881 10.312414 10.323409 10.273969 10.534471 10.504079 10.329493 10.123694 9.9401093 9.8443756 9.8045509 9.6085665 9.3656757 8.6162155 8.0377897 8.7989837 8.5651079 8.7373073 8.4915216 8.6108952 8.6342314 8.9610147 9.2820237 9.5566786 9.6578717 9.7897192 9.4118617 8.7777065 8.4638015 7.7542138 7.8398717 7.41706 7.4045841 8.0895301 8.7450611 8.7079981 8.1803062 7.6049248 8.9108102 8.2446352 8.0936365 8.0260149 8.6510451 8.9083109 8.9039244 8.8466463 8.705292 8.4836739 8.5687766 8.5613521 8.2498066 9.222543 5.7846574 9.5532165 9.364855 9.0117616 8.609026 7.855516 8.4343874 8.5961245 8.5281343 8.8051353 8.3719322 8.2306158 9.2373235 8.4297251 8.8555622 9.5598895 9.9846859 10.29255 10.748452 10.890471 11.905507 12.688755 13.425195 14.15996 15.044341 15.86798 14.883686 13.656454 14.402508 13.078443 13.252335 12.749639 11.279973 12.429886 16.833118 22.483701 24.970182 27.07084 30.49407 33.548496 36.825423 40.520629 45.30584 50.621126 55.480377 63.650379 77.409894 94.101957 108.4206 97.200575 109.86875 118.93523 141.0816 144.51343 147.43306 123.95467 108.03412 86.357745 73.1379 62.664468 68.186892 65.219535 73.019573 82.440646 61.342091 53.603653 57.247107 65.594016 75.438904 56.130083 56.784426 68.778787 57.696629 71.322804 94.457968 74.29982 57.213229 57.613134 54.174444 40.660784 36.546093 43.859146 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 162.36053 162.8635 158.98191 157.9338 166.65198 166.56404 182.86012 187.96068 175.67729 167.9485 161.45823 174.87897 174.61219 187.98487 199.5314 216.4053 235.15877 229.00189 222.23781 227.16187 223.78764 215.88319 208.85074 203.81432 203.29988 199.54742 193.90775 182.59483 187.61 195.05712 207.22845 197.85206 200.10768 206.71125 212.13676 205.39627 168.77573 151.60162 138.51453 109.64739 93.458773 87.636941 85.598186 82.278877 87.662992 78.119076 76.504285 75.706373 74.533215 73.854293 72.942058 72.198845 71.177248 70.326692 69.773322 69.596687 77.217904 84.174545 77.033305 91.68106 112.4838 127.93397 142.20117 127.42682 102.83281 85.950224 75.087361 96.332772 112.83654 119.58331 102.75729 90.056901 78.824666 74.542678 87.084683 70.911498 75.162137 93.617408 94.391599 108.47447 117.1832 113.67802 94.270395 84.06527 73.981313 59.909452 52.28397 51.753776 51.716603 53.725362 62.813174 51.062007 48.087028 47.493043 47.397988 47.398531 47.058796 47.597582 47.692251 47.954338 48.002742 48.945638 49.012138 52.745929 51.752935 53.153174 67.529569 56.740725 63.160529 76.705742 81.606663 60.355688 45.088405 45.276362 46.545477 52.730885 69.943554 87.442227 85.314372 61.559898 54.575695 57.440715 71.852896 90.584896 87.83085 60.968248 49.130047 49.325082 60.804007 78.136108 94.505479 67.532699 46.308147 47.51886 32.182729 23.885686 26.915523 28.266095 20.583097 20.7597 20.936681 21.226262 21.72186 22.004707 22.357178 22.411015 22.183075 22.058803 21.770049 21.606067 21.37148 20.89728 20.18498 19.419033 18.459531 17.873813 17.487529 16.777341 16.36539 15.197435 15.955485 15.11758 15.201359 15.160202 15.101525 14.95838 15.052214 15.215937 15.448946 15.80011 16.22738 16.26584 15.542318 15.151984 14.220159 13.6787 13.348216 13.020853 12.677157 12.444356 12.182789 11.929242 11.711377 11.57986 11.352239 11.011686 10.840837 10.995901 11.042062 10.670792 10.591251 10.962521 10.962484 11.027847 9.5876307 8.9532424 8.3184206 8.885484 8.9331449 8.798729 8.6420286 8.8668219 8.9902203 8.9347467 8.7226569 10.003344 11.472312 11.383515 11.209521 10.949154 10.916575 10.758245 10.559252 10.553392 10.487689 10.193308 10.276812 10.31328 10.23116 10.353795 10.360075 10.148899 9.8833231 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74.868997 66.158496 80.512164 68.273969 70.024889 97.385271 94.464858 74.794183 74.081236 58.140787 57.292852 62.62101 72.702755 68.604389 72.610086 81.6539 97.752597 70.547792 75.992918 72.12015 55.083242 41.534758 50.643118 59.900562 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 158.82837 166.24249 179.57218 177.04951 194.85378 204.34418 192.0582 189.94368 181.02159 192.61941 194.26778 204.28264 223.29388 235.94351 234.01617 224.69039 224.81423 228.77417 226.69301 219.16814 208.00459 211.8824 209.0453 203.83135 197.43737 183.22299 172.20063 186.17703 195.04715 193.22396 206.36686 206.14239 204.96491 182.83664 160.67979 130.37267 102.6869 88.087912 88.966554 85.361994 90.205341 103.55448 109.62625 90.354823 77.876541 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 217.74353 219.54796 219.73392 221.54404 219.18714 195.20239 178.86736 156.64018 146.10815 154.04795 158.77686 147.40964 166.3968 195.79498 192.09704 153.65422 124.47457 103.22138 109.17087 139.32597 145.32987 121.22988 153.09501 168.87874 170.36494 155.19243 157.98575 148.48453 142.82702 150.01724 150.97353 134.3006 95.763885 90.953054 122.47404 151.47573 155.06827 138.18837 147.95207 155.25091 164.31747 168.88472 167.57448 166.30189 169.59038 158.00158 149.05349 114.22271 84.011939 84.484636 63.681118 55.241023 50.910039 56.419939 66.116977 71.584793 58.469607 47.822395 47.411775 47.095142 46.935108 46.92349 46.744269 46.625638 46.554927 46.563875 46.818914 45.936798 45.662293 45.39382 46.30803 49.546562 48.42216 45.046982 45.135512 45.552569 45.29607 46.275589 50.213588 56.104471 58.556305 53.613959 57.315638 67.069407 83.463968 80.058734 72.375241 58.41781 64.178811 74.423362 89.493744 94.924136 102.73923 94.892206 88.446547 55.14697 44.93956 44.863511 42.313879 29.380317 27.434439 28.981916 30.278793 44.001163 29.61505 19.13521 19.092617 16.35232 14.010328 13.08618 14.120924 18.221382 14.282577 14.676458 15.29467 17.667908 16.769171 16.904196 17.319874 17.473886 17.580749 17.299736 17.214172 16.86993 16.87636 16.723465 16.299173 15.82782 15.360051 15.112463 14.922432 15.202089 14.943321 15.076486 14.626403 14.229421 13.547619 12.652632 12.491234 12.002232 12.142039 12.233288 12.252583 12.251844 12.443005 12.803035 12.65738 12.822759 12.940724 13.072706 13.354581 13.265398 13.357473 13.354798 13.532871 13.655669 13.813596 14.039366 13.980078 13.253352 12.389506 11.844827 11.37519 11.038118 10.5867 10.189377 9.9485573 9.9737054 9.8854502 10.000038 10.064838 9.9411351 9.7762309 9.7962893 10.618426 10.730718 10.632439 10.987269 10.898022 10.875302 10.449017 9.9750163 10.140557 10.553533 8.9794459 8.7773289 11.174811 10.687942 10.376623 10.258086 10.289247 10.251967 10.058737 10.04541 9.9922847 9.8643623 9.6731485 9.1698406 8.880954 8.5493603 8.075268 8.1326165 8.091032 7.9904034 7.9835672 8.1436272 8.2692988 8.6255301 8.8122139 8.6405319 9.1997406 9.1850578 9.3081533 9.6831912 9.5522231 8.9017376 8.5368232 9.7305954 9.3026521 9.1518423 9.116016 9.0469257 8.8424561 8.5500682 8.5542014 8.7686925 8.3184863 8.6145188 9.5435113 9.6781461 10.012231 7.5672204 7.599854 8.7161991 8.3578313 8.3677378 8.2448443 9.2924209 11.317168 13.401373 15.505835 16.84741 18.196848 19.825253 20.530755 21.175257 21.749025 22.155527 22.840142 24.281737 25.042039 26.763606 28.273021 29.761025 30.095768 30.348953 30.88975 32.298128 33.676539 35.139504 36.551986 32.730455 28.623607 20.164332 19.634093 20.203148 20.566759 20.972375 20.906051 16.803756 16.795661 17.463302 17.977899 18.526188 18.586301 15.984745 16.602704 14.80652 12.854587 14.602927 15.722737 30.057883 58.184986 68.179279 91.314899 88.806048 86.162436 84.907709 96.46533 109.14728 115.00922 139.00666 133.46733 106.04137 96.484292 76.763386 77.801643 93.394784 113.11539 116.55225 131.25555 125.52701 112.93393 97.681681 89.200906 84.196422 80.480386 82.889564 92.995736 91.603559 84.038738 87.05576 76.845778 81.632765 111.03128 110.18873 110.06783 134.15708 113.45518 126.8898 138.15505 107.78021 89.797515 86.868635 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 224.29282 227.31509 226.03388 212.51174 188.73374 173.40359 165.82893 169.24728 175.89253 168.34691 158.34619 176.21738 197.95873 195.86107 160.96146 124.83344 104.06179 108.48386 136.13163 157.61427 125.16932 144.54249 164.20787 162.66996 158.8848 153.6624 156.27998 151.99699 147.56396 156.35594 140.098 101.08485 92.38637 120.90342 144.81623 155.43126 142.19852 160.12395 165.72221 167.90833 161.87053 159.05947 131.90688 153.44953 133.31664 114.30751 104.796 77.117993 64.782638 57.720141 51.836486 49.540659 56.90037 60.000699 51.361846 48.586207 47.389752 47.034187 46.803513 46.733577 46.740719 46.707432 46.563021 46.320059 46.959217 47.797536 45.462816 45.097133 45.98979 47.378908 49.837307 55.062877 45.759847 45.203421 45.868511 46.111681 46.790963 50.351588 58.632475 66.806471 64.580028 59.052314 65.86312 83.369892 95.746048 89.036835 66.428422 67.975166 71.591698 80.205032 95.017879 88.34274 74.696362 86.23123 62.526376 37.058505 34.847818 42.068038 35.177334 22.493633 22.780567 29.94644 35.128153 34.314159 18.59008 16.877015 14.969389 13.536215 12.993222 13.615969 14.184546 13.88192 14.331172 14.800832 15.573785 15.755639 16.129005 16.45199 16.73925 16.869134 16.685531 16.742529 16.41528 16.235257 16.240761 15.811584 15.418692 15.209057 14.977763 15.282823 15.27876 14.790276 14.842859 14.264863 13.800385 12.945871 12.354652 12.410719 11.878071 12.075998 12.302087 12.009164 12.206874 12.241451 12.323445 12.553808 12.617128 12.55807 12.759217 12.856011 12.880849 13.008162 13.131149 13.37634 13.35169 13.567823 13.781397 14.052705 13.58507 12.670699 12.116617 11.761667 11.544718 11.120352 10.615015 10.107366 9.8544221 9.8192753 9.7675379 9.6392832 9.5908891 9.5435931 9.4758489 9.8812649 10.52061 10.317441 10.980238 11.101311 10.639745 10.150469 9.7655326 9.6174164 10.209517 10.640709 8.1518482 9.4491965 11.229213 11.198577 10.818402 10.571581 10.524127 10.324805 10.153444 10.000064 9.9197137 9.4903714 9.0583194 8.8147916 8.5000595 8.2127843 8.2816616 8.1927853 7.9868836 7.9455816 8.11475 8.6684643 8.962494 8.7018344 8.6928264 8.9272318 9.2617973 9.4625854 8.9008188 7.3341013 7.5307349 6.7368648 8.5244407 9.6235567 9.3780538 9.1358093 8.8648707 8.7471444 8.7374416 8.8434274 6.0476127 5.8598709 5.7698524 6.2513135 7.2888407 6.68086 7.5976254 9.2374606 8.4010205 8.06306 8.1912552 8.8292711 11.014109 13.026727 15.152406 17.287447 19.063816 19.594223 20.815759 22.680692 23.547495 24.829924 25.492811 24.922831 25.251305 26.659753 29.03585 30.347433 31.343843 31.987779 32.38587 33.032584 34.180185 35.509354 36.582373 38.099388 38.808185 37.550767 24.93948 21.185907 21.331108 21.836189 21.952097 22.412998 21.451846 16.849006 17.532772 17.992869 18.490428 19.092509 18.683055 16.386244 16.108358 13.05532 13.81238 15.910309 30.163692 50.519714 78.925454 108.05196 121.65236 118.72168 112.44944 126.323 140.21265 145.73443 145.49359 121.0433 98.737138 87.17151 64.872675 78.810717 101.50699 109.05278 110.41798 132.84462 115.83676 111.47755 101.8204 93.958183 103.40498 96.422513 102.81402 127.09276 103.47566 112.07012 108.84958 82.99099 85.587258 117.40305 133.36314 120.18297 144.76313 139.55893 134.65116 107.79828 99.244843 96.10021 99.021091 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 223.80816 218.80121 200.99727 187.29099 173.57283 174.81952 190.31071 192.14085 170.88067 163.33371 192.36806 206.09397 200.11007 169.21835 133.47299 109.98845 104.68927 131.86561 160.94154 151.68574 136.8394 138.20291 157.45224 159.40097 158.36512 160.53947 161.53415 153.9284 157.69813 137.63721 95.332342 97.612156 134.78853 155.18103 158.09017 149.99633 168.19412 167.08944 171.73132 140.51974 121.90959 102.17644 113.84128 108.77909 95.151823 96.148243 72.694584 58.431498 53.89978 49.897883 48.477378 51.468826 49.502941 47.947268 47.667355 47.054059 46.842971 46.59643 46.892714 46.755909 46.148392 45.16361 45.239076 45.255848 46.592673 48.210221 46.040982 46.121491 46.346503 53.112066 55.853776 45.829526 45.427403 47.433238 48.464456 47.358557 55.352364 67.097621 74.812815 77.913009 74.205775 67.591317 75.689657 97.169148 105.30921 82.466143 91.667188 91.80052 97.732052 91.209105 64.872249 58.301619 76.133275 59.653854 35.234881 28.520874 38.023708 48.120325 26.455947 21.218896 33.767353 23.951917 28.954294 17.662308 15.547193 14.190607 12.973552 12.765232 13.169346 13.555673 13.630991 13.9378 14.351726 14.857489 15.112537 15.426456 15.766297 15.936644 16.18452 15.79334 16.015568 15.862065 15.706505 15.632918 15.366844 15.132036 15.118055 15.026541 15.256145 14.861893 14.447375 14.399916 13.874685 13.143279 12.561528 12.235625 12.285327 11.5894 12.075239 12.33229 11.961868 12.05629 12.078703 12.049979 12.394764 12.25958 12.344623 12.381942 12.601851 12.735293 12.812771 12.984736 13.161329 13.070381 13.302949 13.501501 13.847226 13.483128 12.852823 12.403265 12.083387 11.722079 11.430681 10.962397 10.49393 10.054809 9.8486716 9.6260725 9.5671681 9.482756 9.4466216 9.4195102 9.5314913 9.5573937 10.084264 10.8314 10.856164 10.789532 10.583185 10.118213 9.3312948 9.6923223 10.487539 11.079331 8.2598669 7.9785269 9.7692123 10.211249 10.955532 11.042515 10.752977 10.246452 9.9267231 9.9165126 9.9110997 9.0639166 8.8308215 8.5934329 8.6025486 8.5764612 8.2776942 8.0081177 7.9906982 8.0226288 8.589371 9.2634669 9.4950061 9.3998454 9.3601622 9.7041799 8.8131376 6.8178738 6.5245618 6.6127551 6.8272272 6.9122225 9.7395451 9.5564181 8.9970311 8.8783926 9.1404648 9.4521506 7.0038581 8.0960234 9.0362494 8.9038636 9.2756769 9.1083035 8.4579441 9.4418022 8.9835953 8.3954957 8.0960839 8.1917032 9.7788311 12.077083 14.595539 17.399264 19.907995 21.20285 22.286745 21.626533 24.205728 25.872211 27.318012 28.283252 27.980014 27.816982 28.050527 30.811802 32.569107 33.533632 33.618587 34.476171 35.166262 36.267091 37.400452 38.517747 39.795779 41.183539 41.665392 35.871525 23.371919 23.052279 23.146288 23.278757 23.322171 23.526006 19.544686 17.764714 18.357586 18.801585 19.2143 19.78518 18.018687 16.870235 14.29261 13.114346 15.051214 28.659033 50.652071 78.079369 96.546719 110.22612 116.55768 102.55284 116.52468 128.30883 134.64802 119.80262 100.33475 85.965846 57.224028 67.135409 82.339799 97.09332 97.225934 110.74752 117.37126 117.39317 118.12659 122.63602 121.27952 107.61673 93.698101 113.571 134.28648 113.95796 119.71948 101.28182 81.632883 103.75389 105.225 130.33119 140.43104 148.53536 130.97571 115.15872 112.4797 109.75553 110.19612 110.62148 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 219.34723 204.06871 191.07257 181.10788 182.22563 198.12246 202.47128 180.53897 177.80373 177.64831 190.83532 195.06099 197.10872 178.27996 148.98195 122.27901 101.0221 108.33604 138.29419 168.798 159.08825 144.90819 137.13715 151.73021 158.23569 153.61957 153.76994 158.96036 157.66743 130.24804 95.168627 97.235369 127.24421 154.24926 161.4285 150.31338 154.62413 161.4983 166.74792 130.34483 93.552833 82.881832 93.240828 90.491665 80.924562 76.952031 66.741389 58.556475 50.88813 48.940962 48.544447 48.440538 48.008634 47.909393 46.965542 46.869483 46.654982 46.956604 46.947087 45.882345 45.515179 44.960702 45.026731 45.380205 44.403173 46.899275 46.151062 44.196404 45.854392 49.433101 48.96889 47.325734 47.695171 52.531417 56.688889 49.989273 53.625682 64.887081 74.038161 86.46083 89.415418 91.601743 78.798425 95.962119 114.53294 97.058501 83.470006 73.354178 90.389496 73.090851 64.571931 46.738769 54.833467 65.18903 35.241203 25.291358 28.464297 41.229748 30.616452 19.059506 20.253588 16.815005 15.488321 14.626605 14.177142 13.602524 12.6193 12.653787 12.950769 13.208811 13.299866 13.655716 13.930101 14.291059 14.371742 14.726114 15.048693 15.382721 15.351477 15.224304 15.573997 15.314007 15.017195 15.086342 15.031002 14.99756 14.862459 14.909899 15.13661 14.473495 14.155081 13.872351 13.475521 12.813016 12.190782 12.130723 11.962533 11.334473 12.066953 12.232404 11.980128 11.490574 11.723904 11.812746 11.992139 12.004707 12.007145 12.058729 12.471957 12.647116 12.675136 12.777209 12.810667 12.911949 12.972884 13.35587 13.800472 13.725369 12.925485 12.671946 12.252119 11.879951 11.398409 11.033887 10.625919 10.164962 9.7906962 9.555643 9.4799137 9.4155962 9.4175202 9.365338 9.087161 9.1993456 9.8130192 10.495216 10.675977 10.629859 10.467081 10.319625 9.574823 9.4243418 10.483124 10.816644 10.970884 10.077871 8.8818938 7.823633 7.3035481 7.167382 8.4446065 10.503118 10.738781 10.526179 10.354478 9.5481235 9.3470177 8.7439711 9.1830845 9.5520152 8.5536653 8.1001524 8.1665772 8.2719126 9.2640833 9.7447649 9.707677 9.5655565 9.4308096 9.9627031 8.1542459 6.9156413 6.0052921 5.6941978 6.2605115 6.758214 9.7889224 9.4130255 9.10897 9.5375349 10.017318 8.945715 6.5470398 9.9358788 9.6227311 9.6348505 10.029892 9.6702888 8.5018769 9.3088789 8.735141 8.8952014 8.7404935 9.8370758 12.212966 15.275509 17.030275 18.779195 21.24166 24.294048 25.435878 25.16313 24.57921 30.246189 33.147356 31.902382 31.111337 31.648219 31.894104 32.563288 34.214398 35.622689 35.802487 36.402561 37.654905 38.68315 39.620461 40.588916 41.542374 42.933362 44.518443 41.38519 25.83664 25.183955 25.103647 24.901803 24.905585 24.789499 25.229295 22.216723 18.999102 19.270386 19.768705 20.173832 20.444488 17.212257 15.087287 14.018415 14.585278 39.719379 66.500022 65.582369 75.398277 81.510369 87.991706 87.215132 84.416247 91.017492 97.1567 88.514284 75.122659 59.708901 52.113969 68.660801 83.947652 86.84782 84.654454 103.67114 96.578681 119.90375 138.7827 124.59176 107.11711 113.10001 101.79674 127.11159 142.32852 124.70908 108.92121 88.326063 92.7684 103.16335 125.51258 125.86508 151.87331 139.03997 118.42539 123.14636 124.81186 121.31043 122.38405 124.45118 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 183.60905 179.6 188.46909 209.32195 215.11661 193.8443 194.10658 204.44242 192.72876 185.18355 198.55034 199.36263 192.58662 166.33437 138.6807 106.7605 98.943175 119.31474 160.2699 164.98823 164.18925 146.403 139.97632 146.18406 150.67219 157.71543 160.28087 152.01122 113.78564 94.880229 119.80326 131.7608 148.42877 158.50386 155.33132 164.63536 169.83387 155.57886 111.32447 85.089742 71.086058 69.857985 80.981369 74.579714 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45.202434 42.234349 28.891865 20.314663 19.617509 23.487989 17.887385 17.959628 15.615629 14.329836 13.399714 12.732633 12.481769 12.274579 12.38532 12.565188 12.633031 12.761295 12.931022 12.918253 12.919459 13.095451 13.511254 13.909035 14.108938 14.298525 14.387806 14.448956 14.541819 14.626341 14.699202 14.839825 14.660333 14.568044 14.650473 14.760945 15.004981 14.328922 13.527479 13.013383 12.638644 12.215572 11.768526 11.306872 10.967 10.946118 11.499368 11.624778 11.290966 11.186428 11.332237 11.486483 11.399511 11.565989 11.427479 11.619454 11.923362 12.259691 12.365271 12.335406 12.244308 12.287085 12.549678 13.084571 13.553546 13.674224 13.415976 12.871612 12.223244 11.649741 11.361478 11.037599 10.696218 10.268307 9.9589181 9.6642794 9.4834064 9.451023 9.2637711 9.0850213 8.7460138 8.8573838 9.3887574 10.005791 10.46258 10.51216 10.599666 10.338181 10.266064 10.137504 9.929728 9.7186966 9.8218139 10.152702 9.9901994 10.375479 10.514006 10.831957 11.129201 9.6613714 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63.856882 51.092585 53.153586 68.017667 65.515859 72.901748 87.33996 122.40189 146.07106 132.9324 139.24369 143.64012 132.91446 111.44497 97.92653 88.739074 97.945157 110.45085 119.5429 136.78141 149.9368 150.82777 137.55455 141.00583 141.53818 131.57411 133.15339 133.1964 140.91657 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 225.18283 222.17458 205.32059 183.27954 198.91793 206.92298 207.22442 190.52398 183.00089 189.73901 186.35036 183.18537 162.21098 131.98875 102.85091 102.74778 117.98776 142.77282 162.53251 163.15521 151.53929 147.94724 160.97401 164.21162 162.57041 150.52427 117.60644 100.3628 129.57621 156.51675 167.57343 167.849 167.66997 174.57625 172.83346 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35.600418 33.437179 36.567804 42.857273 45.438871 47.360023 43.720489 42.350259 35.502616 41.306836 55.25109 53.645749 45.560729 49.457267 49.9327 49.772433 46.830418 45.998775 48.943306 52.002603 51.858358 52.152524 54.64759 57.845917 55.421706 53.766176 47.910168 51.980352 40.641537 38.294469 33.676882 32.10294 32.304015 32.998535 34.793221 35.415705 31.720823 31.081318 38.710401 29.243637 16.63257 15.329997 14.608937 16.635382 23.162078 27.140381 30.640722 47.906152 70.667727 77.313545 84.042797 89.883193 101.43698 94.02984 87.006019 88.843856 105.40568 88.289501 67.511357 56.770282 76.181767 84.44536 89.245632 106.2869 136.42119 146.94882 128.63175 114.78114 109.10347 115.62209 111.27585 130.78169 124.67511 121.99529 146.1384 142.2321 153.09455 136.06685 136.84819 141.54041 131.99291 120.74854 106.24178 119.28655 100.65716 105.77737 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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79.748971 86.153952 66.22519 42.745723 27.051395 24.572582 25.421691 24.221122 19.866386 20.973149 18.480939 15.814917 15.017546 14.086911 13.404559 12.756624 12.183177 12.020249 11.969795 11.909514 11.881699 11.914718 12.030893 12.066054 12.693781 12.803767 12.737498 13.006348 12.601615 12.770632 13.246228 13.489175 13.661195 13.878559 13.948019 14.064078 14.09871 14.163898 14.231968 14.260327 14.038664 14.113998 14.009298 13.920127 13.864128 13.506718 13.526056 12.264801 11.403792 11.055182 10.906486 10.688477 10.403249 10.368754 10.610835 10.761094 10.538406 10.432307 10.632512 10.647505 10.669838 10.725691 10.632408 10.960454 11.366149 11.532891 11.429654 11.270684 11.536579 11.946874 12.381658 12.820682 13.187222 13.548284 13.647165 13.354759 12.864636 12.279647 11.825314 11.338077 10.918675 10.643519 10.33992 10.159849 9.8913861 9.7849316 9.7068869 9.4303826 9.1697296 9.3282564 9.7591158 10.165468 10.460058 10.608232 10.083507 10.035953 9.6699719 8.7792622 9.9153802 10.176548 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65.466875 75.933517 75.983604 75.511697 72.568569 77.146178 100.63326 118.83192 105.92708 79.776967 62.12708 74.401024 68.233725 89.612201 115.70017 143.56246 137.96692 121.43941 126.99086 125.88572 122.07096 127.03717 140.2621 133.58519 0 0 0 151.02841 125.846 126.96116 129.64627 121.37376 115.6602 101.32165 113.27152 93.082474 100.07624 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 209.54624 208.92485 203.64363 189.82063 183.39856 187.40525 178.89718 162.77452 136.06891 113.52906 103.49846 106.16324 137.98805 156.09006 153.78292 157.0477 160.51324 162.2027 160.52022 162.26897 161.97707 153.63195 129.7179 112.9008 124.2515 142.44563 157.24917 160.753 166.99071 170.69973 152.15396 116.8475 82.23715 70.329606 73.037855 78.39484 61.616508 54.602004 52.318078 49.283159 49.123735 47.631479 48.446715 49.6731 50.317966 50.380142 49.965605 49.396709 50.285508 50.13735 47.59473 47.200849 49.324559 47.513861 48.178678 50.224987 58.738363 64.066548 50.289733 60.421841 70.435893 54.681981 56.904562 66.776562 76.60172 90.662627 97.180268 96.841161 87.995301 79.441707 71.456323 74.917466 82.039868 92.987083 108.95473 101.16255 65.854748 65.815693 69.465801 62.611723 62.037294 44.855771 26.219758 19.994827 18.528332 18.186053 18.080967 17.650571 15.794316 14.484152 13.778836 13.144054 12.476817 12.124313 12.075626 12.099059 12.138473 11.931446 11.95678 12.053545 12.098864 12.363944 12.207821 12.113891 12.525665 12.487101 12.475972 12.476324 13.07664 13.506634 13.572909 13.830239 13.950934 13.968839 14.029221 14.080988 14.077671 13.820278 13.782059 13.843557 13.722311 13.655241 13.377606 13.25197 12.710815 11.555042 10.900004 10.580971 10.46064 10.163659 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43.166399 48.709376 49.094712 53.187659 52.693044 54.456966 55.172839 49.359624 42.125262 43.123803 60.881223 49.618318 59.041227 58.788919 56.332659 56.733022 57.088101 55.981982 54.093569 57.991944 60.636885 62.33042 64.611788 61.463064 56.331048 62.236916 65.647333 58.21325 61.556213 68.253597 63.67407 62.485515 67.781854 48.496095 38.107678 41.096365 43.66203 35.62914 17.358851 17.064048 15.536206 15.565883 24.199679 25.453207 27.696666 29.767982 33.000722 43.848381 48.224963 50.741971 54.628231 61.487343 78.3016 97.465555 124.04416 135.89439 118.93548 77.733011 74.400602 100.47723 83.283609 98.269946 122.99906 136.00696 130.79882 125.08188 113.47631 115.55638 122.27351 126.60724 127.48827 128.92145 128.3167 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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12.862093 12.400205 12.16212 12.191078 12.731284 17.296291 13.232205 12.108133 11.973866 12.131143 12.065474 11.904399 11.99958 12.133304 12.273653 12.259186 12.205204 12.611045 13.217494 13.432832 13.531522 13.686254 13.757006 13.837433 13.841865 13.852342 13.717922 13.522414 13.53461 13.387761 13.246047 12.941737 12.918854 12.541415 11.614423 10.77554 10.446494 10.206999 10.110426 10.095562 10.127926 10.236978 10.395071 10.458768 10.309797 10.240659 10.57404 10.421766 10.477084 10.66668 10.995474 11.102538 11.206596 11.378982 11.537276 11.811136 12.080288 12.476201 12.672967 13.274041 13.373207 13.028096 12.38712 11.923179 11.678526 11.347786 10.821421 10.533069 10.271497 10.088692 9.8956469 9.7112598 9.56128 9.3703771 9.1355182 9.4591688 9.8749328 10.143423 10.258929 10.251402 9.6868963 8.7201996 8.0216937 8.4211242 8.7938232 8.325598 7.5181161 7.3025703 8.0502183 9.0027734 8.7564127 8.2618001 8.2834253 9.310627 10.058471 10.196765 9.916092 9.1966236 9.5700637 10.364399 10.314892 9.0564775 5.8038474 9.7313577 9.8382918 9.6181424 9.0298616 6.8101372 7.9710199 9.9562197 9.894008 9.4698668 9.2431218 9.3186898 9.3914416 9.9917609 9.2641803 6.9267931 5.2639772 5.188388 8.4166047 8.7921832 8.5192238 8.5942632 8.7749262 8.7818876 9.3430043 10.577693 11.254609 13.525623 15.53998 19.391234 23.32305 23.225615 20.449701 19.898814 19.265246 19.560972 28.612236 42.203799 42.794057 46.355533 50.874536 53.476148 55.940777 58.801169 55.281667 49.147119 42.376428 62.900451 62.789839 53.191597 60.196996 67.545622 71.440903 68.53695 70.335973 61.182491 56.34591 65.56598 67.796906 67.768391 66.351953 60.515836 67.91036 68.919177 79.520018 77.773959 75.541739 73.204501 79.852357 84.368668 53.435837 43.674631 45.205038 45.140107 22.831264 17.557553 17.223094 15.450687 18.261353 24.564722 26.556012 27.430461 28.77231 30.806476 37.89652 50.483858 55.178701 61.107402 73.597284 103.02184 125.84915 138.86497 127.73939 92.356255 68.294676 75.207252 89.88704 94.784874 112.23027 131.37406 128.66219 121.51433 122.6832 111.72073 104.07973 114.85081 111.61315 110.62296 115.20306 113.08868 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 188.27243 194.25528 191.69634 184.81659 172.76978 149.54995 114.30321 103.69039 106.97851 133.06589 144.88846 150.9893 153.54232 160.25011 170.62133 171.44239 167.7238 168.0046 152.05486 138.50391 145.29807 143.99701 154.67864 156.62712 158.38367 167.88648 171.07348 170.54686 139.67886 92.134673 70.500207 66.72954 62.103171 58.259252 52.567574 50.808201 49.145245 48.836424 55.539348 50.531753 48.712507 48.417891 46.811266 47.770797 49.150178 49.984603 50.976764 54.77826 66.643211 61.101631 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axes[0, 0].plot(x, y, 'ko', markersize=5, markeredgecolor='white', markeredgewidth=1) - - # Draw bearing direction - scale arrow based on domain size - domain_scale = max(xcentergrid.max() - xcentergrid.min(), - ycentergrid.max() - ycentergrid.min()) - arrow_length = domain_scale * 0.02 # 2% of domain size - - bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting - dx = arrow_length * np.cos(bearing_rad) - dy = arrow_length * np.sin(bearing_rad) - - axes[0, 0].arrow(x, y, dx, dy, - head_width=domain_scale * 0.01, head_length=domain_scale * 0.01, - fc='red', ec='red', alpha=0.8, linewidth=2) - - axes[0, 0].set_title('Field with Conceptual Point Vectors') + # for idx, row in cp_df.iterrows(): + # x, y = row['x'], row['y'] + # + # # Plot point + # axes[0, 0].plot(x, y, 'ko', markersize=5, markeredgecolor='white', markeredgewidth=1) + # + # # Draw bearing direction - scale arrow based on domain size + # domain_scale = max(xcentergrid.max() - xcentergrid.min(), + # ycentergrid.max() - ycentergrid.min()) + # arrow_length = domain_scale * 0.02 # 2% of domain size + # + # bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + # dx = arrow_length * np.cos(bearing_rad) + # dy = arrow_length * np.sin(bearing_rad) + # + # axes[0, 0].arrow(x, y, dx, dy, + # head_width=domain_scale * 0.01, head_length=domain_scale * 0.01, + # fc='red', ec='red', alpha=0.8, linewidth=2) + + axes[0, 0].set_title('Field')# with Conceptual Point Vectors') axes[0, 0].set_xlabel('X Coordinate') axes[0, 0].set_ylabel('Y Coordinate') axes[0, 0].set_aspect('equal') diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index 7f48e7b46..6b43e0fbd 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -226,7 +226,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, layer_zones = zones # Use generate_single_layer which internally calls apply_nsaf_hyperpars - field, results = generate_single_layer( + results = generate_single_layer( conceptual_points=layer_cp, modelgrid=modelgrid, iids=layer_iids, @@ -241,6 +241,8 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, n_realizations=n_realizations ) + field = results['fields'] + # Store results result_key = f"{fn}_layer_{target_layer}" all_results[result_key] = results @@ -255,16 +257,16 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, xcentergrid = grid_info['xcentergrid'] ycentergrid = grid_info['ycentergrid'] fname = f"layer{target_layer}.arr" - np.savetxt(os.path.join(save_dir, fname), field, fmt="%20.8E") + np.savetxt(os.path.join(save_dir, fname), results['fields'][0], fmt="%20.8E") fig_path = os.path.join(save_dir, 'figure') if not os.path.exists(fig_path): os.mkdir(fig_path) pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones[target_layer-1], - conceptual_points=layer_cp, subsample=4, max_ellipse_size=0.1, + conceptual_points=layer_cp, subsample=10, max_ellipse_size=0.1, figsize=(14, 12), title_suf=mean_col, save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) - pu.visualize_nsaf(field, layer_cp, xcentergrid, ycentergrid, + pu.visualize_nsaf(results, layer_cp, xcentergrid, ycentergrid, transform='log', title_suf=mean_col, save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) diff --git a/pyemu/utils/nsaf_useage.py b/pyemu/utils/nsaf_useage.py index 90bf36413..f7dca16b9 100644 --- a/pyemu/utils/nsaf_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -25,7 +25,7 @@ data_dir = r'..\..\examples\HBHP\hpm_mf6' # need to ensure proper name structure for zone files here zone_files = None #[f'{model_name}.geoclass_layer{i+1}.arr' for i in range(0,13)] - con_pts_file = 'conceptual_points.csv' + con_pts_file = 'Bridgpa_HU2_sva.csv' save_dir = os.path.join(data_dir) if not os.path.exists(save_dir): From 30ca942d94645aa452d3cef6425320c3cab99c86 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 2 Sep 2025 17:52:04 +1200 Subject: [PATCH 27/36] cleaned up some figures --- pyemu/plot/plot_utils.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 6c21c1c44..861f05312 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2360,18 +2360,15 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, transform=None, title_suf=None, save_path='nsaf_visualization.png'): # Apply transform if specified + field = np.where(results['fields'][0] == 0, np.nan, results['fields'][0]) + sd = np.where(results['fields'][0] == 0, np.nan, results['sd']) + mean = np.where(results['fields'][0] == 0, np.nan, results['mean']) if transform == 'log': - field = np.where(results['fields'][0]==0,np.nan,results['fields'][0]) field = np.log10(field) - sd = np.where(results['sd'] == 0, np.nan, results['sd']) sd = np.log10(sd) - mean = np.where(results['mean'] == 0, np.nan, results['mean']) mean = np.log10(mean) field_label = 'log10(Field)' else: - field = results['field'][0] - sd = results['sd'] - mean = results['mean'] field_label = 'Field' # Visualize results @@ -2407,7 +2404,7 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, plt.colorbar(im1, ax=axes[0, 0], label=field_label) # mean field - im2 = axes[0, 1].imshow(results['mean'], origin='upper', cmap='RdYlBu_r', + im2 = axes[0, 1].imshow(mean, origin='upper', cmap='RdYlBu_r', extent=[xcentergrid.min(), xcentergrid.max(), ycentergrid.min(), ycentergrid.max()]) axes[0, 1].set_title(f'Mean field for {field_label} {title_suf or ""}') @@ -2416,7 +2413,7 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, plt.colorbar(im2, ax=axes[0, 1], label=field_label) # sd field - im3 = axes[1, 0].imshow(results['sd'], origin='upper', cmap='RdYlBu_r', alpha=0.8, + im3 = axes[1, 0].imshow(sd, origin='upper', cmap='RdYlBu_r', alpha=0.8, extent=[xcentergrid.min(), xcentergrid.max(), ycentergrid.min(), ycentergrid.max()]) axes[1, 0].set_title(f'Standard Deviation field for {field_label} {title_suf or ""}') From 5fee6db10f59b0f9611312a888150bbdf93be0f2 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 2 Sep 2025 18:04:04 +1200 Subject: [PATCH 28/36] figure arrow tweaks --- pyemu/plot/plot_utils.py | 110 ++++++++++++++++++------------------ pyemu/utils/nsaf_helpers.py | 8 +-- 2 files changed, 59 insertions(+), 59 deletions(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 861f05312..af2a98abf 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2378,24 +2378,24 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, im1 = axes[0, 0].imshow(field, origin='upper', cmap='RdYlBu_r', extent=[xcentergrid.min(), xcentergrid.max(), ycentergrid.min(), ycentergrid.max()]) - # for idx, row in cp_df.iterrows(): - # x, y = row['x'], row['y'] - # - # # Plot point - # axes[0, 0].plot(x, y, 'ko', markersize=5, markeredgecolor='white', markeredgewidth=1) - # - # # Draw bearing direction - scale arrow based on domain size - # domain_scale = max(xcentergrid.max() - xcentergrid.min(), - # ycentergrid.max() - ycentergrid.min()) - # arrow_length = domain_scale * 0.02 # 2% of domain size - # - # bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting - # dx = arrow_length * np.cos(bearing_rad) - # dy = arrow_length * np.sin(bearing_rad) - # - # axes[0, 0].arrow(x, y, dx, dy, - # head_width=domain_scale * 0.01, head_length=domain_scale * 0.01, - # fc='red', ec='red', alpha=0.8, linewidth=2) + for idx, row in cp_df.iterrows(): + x, y = row['x'], row['y'] + + # Plot point + axes[0, 0].plot(x, y, 'ko', markersize=2) #, markeredgecolor='white', markeredgewidth=0.1) + + # Draw bearing direction - scale arrow based on domain size + domain_scale = max(xcentergrid.max() - xcentergrid.min(), + ycentergrid.max() - ycentergrid.min()) + arrow_length = domain_scale * 0.02 # 2% of domain size + + bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + dx = arrow_length * np.cos(bearing_rad) + dy = arrow_length * np.sin(bearing_rad) + + axes[0, 0].arrow(x, y, dx, dy, + head_width=domain_scale * 0.005, head_length=domain_scale * 0.005, + fc='black', ec='black', alpha=0.8, linewidth=0.5) axes[0, 0].set_title('Field')# with Conceptual Point Vectors') axes[0, 0].set_xlabel('X Coordinate') @@ -2440,43 +2440,43 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, axes[1, 0].legend() # Conceptual points layout - show bearing vectors in domain coordinates - axes[1, 1].set_xlim(xcentergrid.min(), xcentergrid.max()) - axes[1, 1].set_ylim(ycentergrid.min(), ycentergrid.max()) - - for idx, row in cp_df.iterrows(): - x, y = row['x'], row['y'] - - # Plot point - axes[1, 1].plot(x, y, 'bo', markersize=6, markeredgecolor='black', markeredgewidth=1, alpha=0.8) - - # Draw bearing direction - domain_scale = max(xcentergrid.max() - xcentergrid.min(), - ycentergrid.max() - ycentergrid.min()) - arrow_length = domain_scale * 0.05 # 5% of domain size for visibility - - bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting - dx = arrow_length * np.cos(bearing_rad) - dy = arrow_length * np.sin(bearing_rad) - - axes[1, 1].arrow(x, y, dx, dy, - head_width=domain_scale * 0.015, head_length=domain_scale * 0.015, - fc='blue', ec='blue', alpha=0.7, linewidth=2) - - # Add bearing label - axes[1, 1].annotate(f'{row["bearing"]:.0f}°', - (x, y), xytext=(5, 5), textcoords='offset points', - fontsize=8, alpha=0.8) - - # Fix the title string formatting - stats_title = (f'Conceptual Points Layout\n' - f'Stats: mean={mean_val:.3f}, std={std_val:.3f}\n' - f'Number of conceptual points: {len(cp_df)}') - - axes[1, 1].set_title(stats_title) - axes[1, 1].set_xlabel('X Coordinate') - axes[1, 1].set_ylabel('Y Coordinate') - axes[1, 1].set_aspect('equal') - axes[1, 1].grid(True, alpha=0.3) + # axes[1, 1].set_xlim(xcentergrid.min(), xcentergrid.max()) + # axes[1, 1].set_ylim(ycentergrid.min(), ycentergrid.max()) + # + # for idx, row in cp_df.iterrows(): + # x, y = row['x'], row['y'] + # + # # Plot point + # axes[1, 1].plot(x, y, 'bo', markersize=6, markeredgecolor='black', markeredgewidth=1, alpha=0.8) + # + # # Draw bearing direction + # domain_scale = max(xcentergrid.max() - xcentergrid.min(), + # ycentergrid.max() - ycentergrid.min()) + # arrow_length = domain_scale * 0.05 # 5% of domain size for visibility + # + # bearing_rad = np.radians(90 - row['bearing']) # Convert geo to math for plotting + # dx = arrow_length * np.cos(bearing_rad) + # dy = arrow_length * np.sin(bearing_rad) + # + # axes[1, 1].arrow(x, y, dx, dy, + # head_width=domain_scale * 0.01, head_length=domain_scale * 0.01, + # fc='blue', ec='blue', alpha=0.7, linewidth=0.7) + # + # # Add bearing label + # axes[1, 1].annotate(f'{row["bearing"]:.0f}°', + # (x, y), xytext=(5, 5), textcoords='offset points', + # fontsize=8, alpha=0.8) + # + # # Fix the title string formatting + # stats_title = (f'Conceptual Points Layout\n' + # f'Stats: mean={mean_val:.3f}, std={std_val:.3f}\n' + # f'Number of conceptual points: {len(cp_df)}') + # + # axes[1, 1].set_title(stats_title) + # axes[1, 1].set_xlabel('X Coordinate') + # axes[1, 1].set_ylabel('Y Coordinate') + # axes[1, 1].set_aspect('equal') + # axes[1, 1].grid(True, alpha=0.3) plt.tight_layout() diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index 6b43e0fbd..deca20286 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -261,10 +261,10 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, fig_path = os.path.join(save_dir, 'figure') if not os.path.exists(fig_path): os.mkdir(fig_path) - pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones[target_layer-1], - conceptual_points=layer_cp, subsample=10, max_ellipse_size=0.1, - figsize=(14, 12), title_suf=mean_col, - save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) + # pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones[target_layer-1], + # conceptual_points=layer_cp, subsample=20, max_ellipse_size=0.1, + # figsize=(14, 12), title_suf=mean_col, + # save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) pu.visualize_nsaf(results, layer_cp, xcentergrid, ycentergrid, transform='log', title_suf=mean_col, From 55a50acca2ffd6252427c068558c718f2801fa7a Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Tue, 2 Sep 2025 18:07:42 +1200 Subject: [PATCH 29/36] no need for points at the tail --- pyemu/plot/plot_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index af2a98abf..d4e1c9f11 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2382,7 +2382,7 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, x, y = row['x'], row['y'] # Plot point - axes[0, 0].plot(x, y, 'ko', markersize=2) #, markeredgecolor='white', markeredgewidth=0.1) + # axes[0, 0].plot(x, y, 'ko', markersize=2) #, markeredgecolor='white', markeredgewidth=0.1) # Draw bearing direction - scale arrow based on domain size domain_scale = max(xcentergrid.max() - xcentergrid.min(), From e13f979517dcd16206e15c36af170407f95516b2 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 3 Sep 2025 15:32:55 +1200 Subject: [PATCH 30/36] function to output raster file --- pyemu/utils/nsaf_helpers.py | 15 ++++++++++-- pyemu/utils/nsaf_utils.py | 48 ++++++++++++++++++++++++++++++++++++- 2 files changed, 60 insertions(+), 3 deletions(-) diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index deca20286..cd329b617 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -10,7 +10,8 @@ from nsaf_utils import ( generate_single_layer, detect_zone_boundaries, - _extract_grid_info + _extract_grid_info, + arr_to_rast ) @@ -253,11 +254,21 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # todo: better file name handling if save_dir is not None: from pyemu import plot_utils as pu + xul = modelgrid.xoffset + yul = modelgrid.yoffset # + np.sum(modelgrid.delr) + if modelgrid.epsg is None: + epsg = 2193 + else: + epsg = modelgrid.epsg grid_info = _extract_grid_info(modelgrid) xcentergrid = grid_info['xcentergrid'] ycentergrid = grid_info['ycentergrid'] fname = f"layer{target_layer}.arr" np.savetxt(os.path.join(save_dir, fname), results['fields'][0], fmt="%20.8E") + arr_to_rast(results['fields'][0], + os.path.join(save_dir, fname.replace('.arr','.tif')), + xul, yul, + np.mean(modelgrid.delc), np.mean(modelgrid.delr), epsg) fig_path = os.path.join(save_dir, 'figure') if not os.path.exists(fig_path): os.mkdir(fig_path) @@ -574,7 +585,7 @@ def detect_zone_boundaries(zones): return boundary_mask, boundary_directions -def save_results(result, out_filename, transform): +def save_results(result, out_filename): """Save results to files in PyEMU-compatible format.""" # todo: better file name handling diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index e837e360d..7d1aec7f9 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -1468,4 +1468,50 @@ def save_results(results, out_filename): field_file = f"{out_filename}_real_{i + 1:03d}.txt" np.savetxt(field_file, field, fmt="%20.8E") - print(f" Saved {len(results['fields'])} realizations") \ No newline at end of file + print(f" Saved {len(results['fields'])} realizations") + + +import numpy as np +from osgeo import gdal, osr + + +def arr_to_rast(arr, outrast, x_min, y_min, dx, dy, epsg): + ''' + Create a georeferenced raster from a NumPy array. + + Parameters: + - arr: 2D or 3D numpy array (if 3D: (bands, rows, cols)) + - outrast: output raster filepath + - x_min, y_min: LOWER-LEFT corner coordinates # Changed from y_max + - dx, dy: cell size (both positive for standard coordinate system) + - epsg: EPSG code for coordinate reference system + ''' + # Calculate top-left corner from lower-left + rows = arr.shape[-2] if arr.ndim == 3 else arr.shape[0] + y_max = y_min + (rows * dy) # Top edge = bottom + (rows * cell_height) + + # Build affine transform (dy becomes negative for north-up raster) + geotransform = (x_min, dx, 0, y_max, 0, -dy) # Note: -dy here + + # Rest of function stays the same... + driver = gdal.GetDriverByName("GTiff") + if arr.ndim == 2: + bands, rows, cols = 1, *arr.shape + arr = np.expand_dims(arr, 0) + else: + bands, rows, cols = arr.shape + + ds = driver.Create(outrast, cols, rows, bands, gdal.GDT_Float32) + ds.SetGeoTransform(geotransform) + + srs = osr.SpatialReference() + srs.ImportFromEPSG(epsg) + ds.SetProjection(srs.ExportToWkt()) + + for i in range(bands): + band = ds.GetRasterBand(i + 1) + band.SetNoDataValue(np.nan) + band.WriteArray(arr[i]) + + ds.FlushCache() + ds = None From 99869b070a9d10eb946f6ca4bd8aed9b124c73b2 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Wed, 3 Sep 2025 16:21:08 +1200 Subject: [PATCH 31/36] save tif to figures dir --- pyemu/utils/nsaf_helpers.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index cd329b617..ce70848e9 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -253,6 +253,9 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, print(f" SD field: [{results['sd'].min():.3f}, {results['sd'].max():.3f}]") # todo: better file name handling if save_dir is not None: + fig_path = os.path.join(save_dir, 'figure') + if not os.path.exists(fig_path): + os.mkdir(fig_path) from pyemu import plot_utils as pu xul = modelgrid.xoffset yul = modelgrid.yoffset # + np.sum(modelgrid.delr) @@ -266,12 +269,10 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, fname = f"layer{target_layer}.arr" np.savetxt(os.path.join(save_dir, fname), results['fields'][0], fmt="%20.8E") arr_to_rast(results['fields'][0], - os.path.join(save_dir, fname.replace('.arr','.tif')), + os.path.join(fig_path, fname.replace('.arr','.tif')), xul, yul, np.mean(modelgrid.delc), np.mean(modelgrid.delr), epsg) - fig_path = os.path.join(save_dir, 'figure') - if not os.path.exists(fig_path): - os.mkdir(fig_path) + # pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones[target_layer-1], # conceptual_points=layer_cp, subsample=20, max_ellipse_size=0.1, # figsize=(14, 12), title_suf=mean_col, From acca03035a6a55b39a2a76398598a165cea516ea Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 28 Nov 2025 00:16:30 +1300 Subject: [PATCH 32/36] new python implementation of fieldgen, good on tangential but wairau is messed up, probably generate_from_files --- pyemu/plot/plot_utils.py | 48 +- pyemu/utils/nsaf_helpers.py | 752 +++++------- pyemu/utils/nsaf_useage.py | 21 +- pyemu/utils/nsaf_utils.py | 2279 +++++++++++++++++------------------ 4 files changed, 1497 insertions(+), 1603 deletions(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index d4e1c9f11..6ec016de9 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2,6 +2,7 @@ import os import numpy as np import pandas as pd +from scipy import ndimage import warnings from datetime import datetime import string @@ -2093,7 +2094,7 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, Examples -------- >>> visualize_tensors(tensors, xcentergrid, ycentergrid, - ... zones=zones, subsample=8, max_ellipse_size=0.05) + ... zones=zones, subsample=12, max_ellipse_size=0.05) """ from matplotlib.patches import Ellipse from matplotlib.patches import Rectangle @@ -2358,26 +2359,34 @@ def visualize_tensors(tensors, xcentergrid, ycentergrid, zones=None, def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, - transform=None, title_suf=None, save_path='nsaf_visualization.png'): + transform=None, domain=None, title_suf=None, + save_path='nsaf_visualization.png'): # Apply transform if specified - field = np.where(results['fields'][0] == 0, np.nan, results['fields'][0]) - sd = np.where(results['fields'][0] == 0, np.nan, results['sd']) - mean = np.where(results['fields'][0] == 0, np.nan, results['mean']) + field = results['field'] + sd = results['sd'] + mean = results['mean'] + if domain is not None: + field = np.where(domain == 0, np.nan, results['field']) + sd = np.where(domain == 0, np.nan, results['sd']) + mean = np.where(domain == 0, np.nan, results['mean']) if transform == 'log': field = np.log10(field) sd = np.log10(sd) mean = np.log10(mean) - field_label = 'log10(Field)' + field_label = f'log10({title_suf})' else: field_label = 'Field' # Visualize results fig, axes = plt.subplots(2, 2, figsize=(16, 12)) + vmin = -4 + vmax = 4 # Field with conceptual points using real coordinates im1 = axes[0, 0].imshow(field, origin='upper', cmap='RdYlBu_r', extent=[xcentergrid.min(), xcentergrid.max(), - ycentergrid.min(), ycentergrid.max()]) + ycentergrid.min(), ycentergrid.max()], + vmin=vmin, vmax=vmax) for idx, row in cp_df.iterrows(): x, y = row['x'], row['y'] @@ -2406,7 +2415,8 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, # mean field im2 = axes[0, 1].imshow(mean, origin='upper', cmap='RdYlBu_r', extent=[xcentergrid.min(), xcentergrid.max(), - ycentergrid.min(), ycentergrid.max()]) + ycentergrid.min(), ycentergrid.max()], + vmin=vmin, vmax=vmax) axes[0, 1].set_title(f'Mean field for {field_label} {title_suf or ""}') axes[0, 1].set_xlabel('X Coordinate') axes[0, 1].set_ylabel('Y Coordinate') @@ -2415,29 +2425,29 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, # sd field im3 = axes[1, 0].imshow(sd, origin='upper', cmap='RdYlBu_r', alpha=0.8, extent=[xcentergrid.min(), xcentergrid.max(), - ycentergrid.min(), ycentergrid.max()]) + ycentergrid.min(), ycentergrid.max()], + vmin=vmin, vmax=vmax) axes[1, 0].set_title(f'Standard Deviation field for {field_label} {title_suf or ""}') axes[1, 0].set_xlabel('X Coordinate') axes[1, 0].set_ylabel('Y Coordinate') plt.colorbar(im3, ax=axes[1, 0], label=field_label) - # Field statistics histogram - axes[1, 1].hist(field.flatten(), bins=50, alpha=0.7, color='purple', edgecolor='black') + axes[1, 1].hist(field.flatten(), bins=50, alpha=0.7, color='purple', edgecolor='black', range=(vmin, vmax)) axes[1, 1].set_title(f'{field_label} Distribution') axes[1, 1].set_xlabel(f'{field_label} Value') axes[1, 1].set_ylabel('Frequency') axes[1, 1].grid(True, alpha=0.3) # Add statistics text - mean_val = np.mean(field) - std_val = np.std(field) - axes[1, 0].axvline(mean_val, color='red', linestyle='--', alpha=0.7, label=f'Mean: {mean_val:.3f}') - axes[1, 0].axvline(mean_val + std_val, color='orange', linestyle='--', alpha=0.7, - label=f'+1σ: {mean_val + std_val:.3f}') - axes[1, 0].axvline(mean_val - std_val, color='orange', linestyle='--', alpha=0.7, - label=f'-1σ: {mean_val - std_val:.3f}') - axes[1, 0].legend() + # mean_val = np.nanmean(field) + # std_val = np.nanstd(field) + # axes[1, 0].axvline(mean_val, color='red', linestyle='--', alpha=0.7, label=f'Mean: {mean_val:.3f}') + # axes[1, 0].axvline(mean_val + std_val, color='orange', linestyle='--', alpha=0.7, + # label=f'+1σ: {mean_val + std_val:.3f}') + # axes[1, 0].axvline(mean_val - std_val, color='orange', linestyle='--', alpha=0.7, + # label=f'-1σ: {mean_val - std_val:.3f}') + # axes[1, 0].legend() # Conceptual points layout - show bearing vectors in domain coordinates # axes[1, 1].set_xlim(xcentergrid.min(), xcentergrid.max()) diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index ce70848e9..ffc03a059 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -9,20 +9,20 @@ # Import from refactored nsaf_utils from nsaf_utils import ( generate_single_layer, - detect_zone_boundaries, - _extract_grid_info, - arr_to_rast + _detect_zone_boundaries, + _extract_grid_info ) def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, - zone_file=None, iids_file=None, field_name=['field'], + zone_files=None, iid_files=None, field_name=['field'], layer_mode=True, save_dir=None, tensor_interp='krig', transform='log', n_realizations=1, ml=None, sr=None, - boundary_enhance=False, boundary_smooth=False): + boundary_enhance=False, boundary_smooth=False, + cp_0based=False, use_depth=False): """ - Generate spatially correlated fields using tensor-based geostatistics. - REFACTORED to use modelgrid consistently and call generate_single_layer. + Generate spatially correlated fields using tensor-based geostatistics + from modelgrid and files, call generate_single_layer for each layer. Parameters ---------- @@ -31,13 +31,24 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, model_name : str Name of the flopy model (e.g., 'freyberg6') conceptual_points_file : str - CSV file containing conceptual points - zone_file : str or list of str, optional - zone file(s) - iids_file : str or list of str, optional - noise file(s) - field_name : list of str, default ['field'] - Name(s) of field(s) to generate + CSV file containing conceptual point locations and parameters + zone_files : None, str, or dict, optional + Zone file(s). Can be: + - None: uses modelgrid.idomain + - str: pattern with {} placeholder, e.g., 'zones_layer{}.arr' (replaced with 0-based layer number) + - dict: explicit mapping of 0-based layer numbers to filenames, e.g., {0: 'zone_layer0.arr', 1: 'zone_layer1.arr'} + If dict is provided, it must contain entries for all layers (will error if layers are missing). + iid_files : None, str, or dict, optional + IID file(s) containing noise arrays. Can be: + - None: generates default files 'iids_layer{n}.arr' where n is 0-based layer number + - str: pattern with {} placeholder, e.g., 'iids_layer{}.arr' (replaced with 0-based layer number) + - dict: explicit mapping of 0-based layer numbers to filenames, e.g., {0: 'iid_layer0.arr', 1: 'iid_layer1.arr'} + If dict is provided, it must contain entries for all layers (will error if layers are missing). + If files don't exist, they will be generated and saved. + field_name : list of str, optional, default ['field'] + Name(s) of field(s) to generate. Looks for field-specific mean/sd columns + (e.g., 'mean_{field}' or '{field}_mean'). If empty/None, uses generic + 'mean' and 'sd' columns instead. layer_mode : bool, default True If True, process each layer independently save_dir : str, optional @@ -52,11 +63,21 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, A flopy mbase derived type. If None, sr must not be None. sr : flopy.utils.reference.SpatialReference, optional A spatial reference to locate the model grid. If None, ml must not be None. + boundary_enhance : bool, default False + Whether to enhance boundaries in field generation + boundary_smooth : bool, default False + Whether to smooth boundaries in field generation + cp_0based : bool, default False + If False (default), conceptual points 'layer' column uses 1-based indexing (MODFLOW convention) + and will be converted to 0-based internally. If True, 'layer' column is already 0-based. + use_depth : bool, default False + If True, use 'z' or 'depth' coordinate from conceptual points to determine layer by + intersecting with model grid. Requires gridit package. Not yet implemented. Returns ------- dict - fieldresult from workflow + Dictionary of results for each field and layer, keyed by '{field}_layer{n}' where n is 0-based """ try: import flopy @@ -64,6 +85,9 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, except ImportError as e: raise ImportError(f"flopy and pyemu are required: {e}") + if use_depth: + raise NotImplementedError("use_depth=True requires gridit package integration - not yet implemented") + print(f"=== Tensor-Based Field Generation ===") print(f"Model workspace: {tmp_model_ws}") print(f"Model name: {model_name}") @@ -110,29 +134,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, if 'transverse' not in conceptual_points.columns and 'anisotropy' in conceptual_points.columns: conceptual_points['transverse'] = conceptual_points['major'] / conceptual_points['anisotropy'] - # Load zones - zones = modelgrid.idomain - if zone_file: - if isinstance(zone_file, str): - zones = np.loadtxt(os.path.join(tmp_model_ws, zone_file)).astype(np.int64) - if zones.shape != (ny, nx): - if zones.size == ny * nx: - zones = zones.reshape(ny, nx) - else: - raise ValueError(f"Zone file shape {zones.shape} doesn't match grid {(ny, nx)}") - print(f"Loaded zones from {zone_file}, shape: {zones.shape}") - else: - # Handle list of zone files for multiple layers - zones = [] - for zf in zone_file: - z = np.loadtxt(os.path.join(tmp_model_ws, zf)).astype(np.int64) - if z.shape != (ny, nx): - if z.size == ny * nx: - z = z.reshape(ny, nx) - zones.append(z) - print(f"Loaded {len(zones)} zone arrays from multiple files") - - # Determine layers + # Determine total layers if hasattr(ml, 'dis'): if hasattr(ml.dis, 'nlay'): total_layers = ml.dis.nlay.array if hasattr(ml.dis.nlay, 'array') else ml.dis.nlay @@ -145,50 +147,188 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, print(f"Model has {total_layers} layers") - # Handle layers in conceptual points - if 'layer' in conceptual_points.columns: + # Handle layer assignment in conceptual points + if use_depth: + # Future implementation: use z/depth coordinate to intersect with grid + # This would call a function using gridit package + conceptual_points = _assign_layers_from_depth(conceptual_points, modelgrid) + elif 'layer' in conceptual_points.columns: + # Convert layer numbering to 0-based if needed + if not cp_0based: + print("Converting conceptual points 'layer' column from 1-based to 0-based indexing") + conceptual_points['layer'] = conceptual_points['layer'] - 1 + print(f" Layer range after conversion: [{conceptual_points['layer'].min()}, " + f"{conceptual_points['layer'].max()}]") + available_cp_layers = sorted(conceptual_points['layer'].unique()) - print(f"Conceptual points available for layers: {available_cp_layers}") + print(f"Conceptual points available for layers (0-based): {available_cp_layers}") + + # Validate layer numbers + if conceptual_points['layer'].min() < 0: + raise ValueError(f"Conceptual points contain negative layer numbers after conversion. " + f"Check cp_0based parameter.") + if conceptual_points['layer'].max() >= total_layers: + raise ValueError(f"Conceptual points contain layer {conceptual_points['layer'].max()} " + f"but model only has {total_layers} layers (0-{total_layers - 1})") else: - available_cp_layers = [1] - print("No 'layer' column in conceptual points - using layer 1") - - # Generate or load IIDs for all model layers - iids_dict = {} - for layer_num in range(total_layers): - layer_1based = layer_num + 1 - iids_dict[layer_1based] = _load_or_generate_iids( - tmp_model_ws, iids_file, layer_num, ny, nx, n_realizations - ) + available_cp_layers = [0] + conceptual_points['layer'] = 0 + print("No 'layer' column in conceptual points - assigning all points to layer 0") - # Process each field - all_results = {} + # Load zones + zone_paths = {} + if zone_files is None: + # Use modelgrid idomain for all layers + print("Using modelgrid.idomain for zones") + for layer_num in range(total_layers): + zone_paths[layer_num] = None # Signal to use idomain + elif isinstance(zone_files, str): + # String pattern - must contain {} placeholder + if "{}" not in zone_files: + raise ValueError(f"zone_files pattern must contain '{{}}' placeholder, got: {zone_files}") + for layer_num in range(total_layers): + zone_paths[layer_num] = os.path.join(tmp_model_ws, zone_files.format(layer_num)) + elif isinstance(zone_files, dict): + # Explicit dict mapping - must contain all layers + for layer_num in range(total_layers): + if layer_num not in zone_files: + raise ValueError(f"zone_files dict missing entry for layer {layer_num}. " + f"Dict must contain all layers 0-{total_layers - 1}") + zone_paths[layer_num] = os.path.join(tmp_model_ws, zone_files[layer_num]) + else: + raise ValueError(f"zone_files must be None, str, or dict. Got: {type(zone_files)}") + + # Load zone arrays + zones_dict = {} + for layer_num, zone_path in zone_paths.items(): + if zone_path is None: + # Use idomain + if hasattr(modelgrid, 'idomain') and modelgrid.idomain is not None: + if len(modelgrid.idomain.shape) == 3: + zones_dict[layer_num] = modelgrid.idomain[layer_num] + else: + zones_dict[layer_num] = modelgrid.idomain + else: + zones_dict[layer_num] = None + else: + # Load from file + z = np.loadtxt(zone_path).astype(np.int64) + if z.shape != (ny, nx): + if z.size == ny * nx: + z = z.reshape(ny, nx) + else: + raise ValueError(f"Zone file {zone_path} shape {z.shape} doesn't match grid {(ny, nx)}") + zones_dict[layer_num] = z + print(f" Loaded zones for layer {layer_num} from {os.path.basename(zone_path)}") + + # Resolve file paths for iids + iids_dict = {} + if iid_files is None: + # No IID files specified - pass None to trigger FIELDGEN2D_SVA internal generation + print("No IID files specified - will use FIELDGEN2D_SVA internal random generation") + for layer_num in range(total_layers): + iids_dict[layer_num] = None + else: + # IID files specified - resolve paths and load/generate + iid_paths = {} + for layer_num in range(total_layers): + if isinstance(iid_files, str): + # String pattern - must contain {} placeholder + if "{}" not in iid_files: + raise ValueError(f"iid_files pattern must contain '{{}}' placeholder, got: {iid_files}") + iid_path = os.path.join(tmp_model_ws, iid_files.format(layer_num)) + elif isinstance(iid_files, dict): + # Explicit dict mapping - must contain all layers + if layer_num not in iid_files: + raise ValueError(f"iid_files dict missing entry for layer {layer_num}. " + f"Dict must contain all layers 0-{total_layers - 1}") + iid_path = os.path.join(tmp_model_ws, iid_files[layer_num]) + else: + raise ValueError(f"iid_files must be None, str, or dict. Got: {type(iid_files)}") + + iid_paths[layer_num] = iid_path - for fn in field_name: - print(f"\n=== Processing field: {fn} ===") + print(f"IID file paths resolved for {len(iid_paths)} layers") - # SIMPLIFIED: Look for columns with field name pattern - mean_col = None - sd_col = None + # Now load or generate IIDs using the resolved paths + for layer_num, iid_path in iid_paths.items(): + iids_dict[layer_num] = _load_or_generate_iids( + iid_path, ny, nx, 1 # Single realization per call + ) - # First, try field-specific columns - if f'mean_{fn}' in conceptual_points.columns: - mean_col = f'mean_{fn}' - sd_col = f'sd_{fn}' - elif f'{fn}_mean' in conceptual_points.columns: - mean_col = f'{fn}_mean' - sd_col = f'{fn}_sd' + # Process each field + all_results = {} + # If no field names provided, use generic columns once + if not field_name or len(field_name) == 0: + mean_candidates = [col for col in conceptual_points.columns if 'mean' in col.lower()] + sd_candidates = [col for col in conceptual_points.columns if 'sd' in col.lower()] + + if mean_candidates and sd_candidates: + mean_col = mean_candidates[0] + sd_col = sd_candidates[0] + print(f"Using generic columns: mean='{mean_col}', sd='{sd_col}'") + field_name = [''] # Empty string for generic processing else: - # Look for generic columns - mean_candidates = [col for col in conceptual_points.columns if 'mean' in col.lower()] - sd_candidates = [col for col in conceptual_points.columns if 'sd' in col.lower()] - - if mean_candidates and sd_candidates: - mean_col = mean_candidates[0] # Use first match - sd_col = sd_candidates[0] - print(f"Using generic columns: mean='{mean_col}', sd='{sd_col}'") + raise ValueError("No mean/sd columns found") + + # Process each specific field + for fn, trans in zip(field_name, transform): + print(f"\n=== Processing field: {fn if fn else 'generic'} ===") + + # Try field-specific columns only if fn is not empty + if fn: # If we have a specific field name + sd_is_log_space = False # Default assumption + + if trans == 'log': + # For log-transformed fields, prefer log-space sd columns + if f'mean_{fn}' in conceptual_points.columns: + mean_col = f'mean_{fn}' + # Look for log-space sd (multiple naming conventions) + if f'logsd{fn}' in conceptual_points.columns: + sd_col = f'logsd{fn}' + sd_is_log_space = True + elif f'log_sd_{fn}' in conceptual_points.columns: + sd_col = f'log_sd_{fn}' + sd_is_log_space = True + elif f'sd_{fn}' in conceptual_points.columns: + sd_col = f'sd_{fn}' + sd_is_log_space = False + print(f" WARNING: Using native-space 'sd_{fn}' for log-transformed field '{fn}'. " + f"Consider providing 'logsd{fn}' or 'log_sd_{fn}' instead for better interpretation.") + else: + raise ValueError( + f"No sd column found for field '{fn}'. Expected 'sd_{fn}', 'logsd{fn}', or 'log_sd_{fn}'") + elif f'{fn}_mean' in conceptual_points.columns: + mean_col = f'{fn}_mean' + # Look for log-space sd (multiple naming conventions) + if f'{fn}_logsd' in conceptual_points.columns: + sd_col = f'{fn}_logsd' + sd_is_log_space = True + elif f'{fn}_log_sd' in conceptual_points.columns: + sd_col = f'{fn}_log_sd' + sd_is_log_space = True + elif f'{fn}_sd' in conceptual_points.columns: + sd_col = f'{fn}_sd' + sd_is_log_space = False + print(f" WARNING: Using native-space '{fn}_sd' for log-transformed field '{fn}'. " + f"Consider providing '{fn}_logsd' or '{fn}_log_sd' instead for better interpretation.") + else: + raise ValueError( + f"No sd column found for field '{fn}'. Expected '{fn}_sd', '{fn}_logsd', or '{fn}_log_sd'") + else: + raise ValueError(f"No mean column found for field '{fn}'. Expected 'mean_{fn}' or '{fn}_mean'") else: - raise ValueError(f"No mean/sd columns found for field '{fn}'") + # Normal space - use regular sd columns + if f'mean_{fn}' in conceptual_points.columns: + mean_col = f'mean_{fn}' + sd_col = f'sd_{fn}' + elif f'{fn}_mean' in conceptual_points.columns: + mean_col = f'{fn}_mean' + sd_col = f'{fn}_sd' + else: + raise ValueError(f"No mean/sd columns found for field '{fn}'. " + f"Expected 'mean_{fn}' or '{fn}_mean'") + # else: mean_col and sd_col already set from generic case # Verify columns exist if mean_col not in conceptual_points.columns: @@ -200,7 +340,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, if layer_mode and 'layer' in conceptual_points.columns: # Process each available layer for target_layer in available_cp_layers: - print(f"\n--- Processing layer {target_layer} ---") + print(f"\n--- Processing layer {target_layer} (0-based) ---") # Filter conceptual points for this layer layer_cp = conceptual_points[conceptual_points['layer'] == target_layer].copy() @@ -214,17 +354,11 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, print(f" {mean_col}: [{layer_cp[mean_col].min():.2f}, {layer_cp[mean_col].max():.2f}]") print(f" {sd_col}: [{layer_cp[sd_col].min():.2f}, {layer_cp[sd_col].max():.2f}]") - # Get IIDs for this layer - layer_iids = iids_dict.get(target_layer, iids_dict[1]) + # Get IIDs for this layer (now directly indexed with 0-based) + layer_iids = iids_dict[target_layer] - # Get zones for this layer if available - layer_zones = None - if zones is not None: - if isinstance(zones, list) or (isinstance(zones, np.ndarray) & (len(zones.shape)==3)): - if target_layer - 1 < len(zones): - layer_zones = zones[target_layer - 1] - else: - layer_zones = zones + # Get zones for this layer (now directly indexed with 0-based) + layer_zones = zones_dict.get(target_layer, None) # Use generate_single_layer which internally calls apply_nsaf_hyperpars results = generate_single_layer( @@ -234,286 +368,81 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, zones=layer_zones, layer=target_layer, tensor_interp=tensor_interp, - transform=transform, + transform=trans, boundary_smooth=boundary_smooth, boundary_enhance=boundary_enhance, mean_col=mean_col, sd_col=sd_col, - n_realizations=n_realizations + n_realizations=n_realizations, + sd_is_log_space=sd_is_log_space ) - field = results['fields'] + results[fn] = results['field'] - # Store results - result_key = f"{fn}_layer_{target_layer}" + # Store results with 0-based layer numbering + if fn: + result_key = f"{fn}_layer{target_layer}" + else: + result_key = f"field_layer{target_layer}" all_results[result_key] = results - print(f"fieldresult for layer {target_layer}:") + print(f"Field result for layer {target_layer}:") print(f" Mean field: [{results['mean'].min():.3f}, {results['mean'].max():.3f}]") print(f" SD field: [{results['sd'].min():.3f}, {results['sd'].max():.3f}]") - # todo: better file name handling + + # Save outputs if requested if save_dir is not None: fig_path = os.path.join(save_dir, 'figure') if not os.path.exists(fig_path): - os.mkdir(fig_path) + os.makedirs(fig_path) + from pyemu import plot_utils as pu - xul = modelgrid.xoffset - yul = modelgrid.yoffset # + np.sum(modelgrid.delr) - if modelgrid.epsg is None: - epsg = 2193 - else: - epsg = modelgrid.epsg - grid_info = _extract_grid_info(modelgrid) - xcentergrid = grid_info['xcentergrid'] - ycentergrid = grid_info['ycentergrid'] - fname = f"layer{target_layer}.arr" - np.savetxt(os.path.join(save_dir, fname), results['fields'][0], fmt="%20.8E") - arr_to_rast(results['fields'][0], - os.path.join(fig_path, fname.replace('.arr','.tif')), - xul, yul, - np.mean(modelgrid.delc), np.mean(modelgrid.delr), epsg) - # pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones[target_layer-1], - # conceptual_points=layer_cp, subsample=20, max_ellipse_size=0.1, - # figsize=(14, 12), title_suf=mean_col, - # save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) + # Prepare filename + fname = f"layer{target_layer}.arr" + np.savetxt(os.path.join(save_dir, fname), results[fn], fmt="%20.8E") + # Visualization pu.visualize_nsaf(results, layer_cp, xcentergrid, ycentergrid, - transform='log', title_suf=mean_col, + transform=transform, + domain=ml.dis.idomain[target_layer], + title_suf=mean_col.split('_')[-1] if '_' in mean_col else mean_col, save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) - - else: - # todo 3d - pass + # Non-layer mode - process all conceptual points together + # TODO: Implement 3D field generation + raise NotImplementedError("Non-layer mode (3D field generation) not yet implemented") + print("\n=== Complete ===") return all_results -""" -Final helper functions for NSAF Utils -These complete the implementation with field generation and boundary functions -""" -# Add these to nsaf_utils.py - -def _generate_stochastic_field(grid_coords, mean_field, sd_field, - bearing, anisotropy, corrlen, - zones, n_realizations, transform, - config_dict, ny, nx, iids=None, area=None, - active=None): - """ - Generate stochastic field using pypestutils FIELDGEN2D_SVA. +def _assign_layers_from_depth(conceptual_points, modelgrid): """ - try: - from pypestutils.pestutilslib import PestUtilsLib - except Exception as e: - raise Exception(f"Error importing pypestutils: {e}") - - print(" Generating stochastic field using FIELDGEN2D_SVA...") - - # Prepare grid data - n_points = ny * nx - x_coords = grid_coords[:, 0] - y_coords = grid_coords[:, 1] - - # Flatten fields - ENSURE PROPER ORDERING - mean_flat = mean_field.flatten().clip(1e-8, None) - variance_native = sd_field.flatten() ** 2 - log_variance_flat = np.log(1 + variance_native / mean_flat ** 2) - trans_flag = 1 # Log domain - - # Geostatistical parameters from tensors - aa_values = corrlen.flatten() - aniso_values = anisotropy.flatten() - bearing_values = bearing.flatten() - - # Set defaults - if active is None: - active = np.ones(n_points, dtype=int) - if area is None: - area = np.ones(n_points) - else: - if area.ndim > 1: - area = area.flatten() - - # Generate or use provided IIDs - if iids is None: - print(" Generating new IIDs...") - iids = np.random.normal(0, 1, size=(n_points, n_realizations)) - else: - print(" Using provided IIDs...") - if iids.shape != (n_points, n_realizations): - raise ValueError(f"IIDs shape {iids.shape} doesn't match expected {(n_points, n_realizations)}") - - try: - # Set up pypestutils parameters - lib = PestUtilsLib() - lib.initialize_randgen(11235813) - - # Averaging function type - avg_func = config_dict.get('averaging_function', 'gaussian') - if isinstance(avg_func, dict): - if 'pow' in avg_func: - avg_flag = 4 - power_value = avg_func['pow'] - else: - avg_flag, power_value = next(iter(avg_func.items())) - else: - power_value = 0 - if avg_func == 'exponential': - avg_flag = 2 - elif avg_func == 'gaussian': - avg_flag = 3 - elif avg_func == 'spherical': - avg_flag = 1 - else: - avg_flag = 2 # Default to exponential - - # Call FIELDGEN2D_SVA through pypestutils interface - print(f" Calling FIELDGEN2D_SVA for {n_realizations} realizations...") - - field = lib.fieldgen2d_sva( - x_coords, y_coords, - area=area, - active=active, - mean=mean_flat, - var=log_variance_flat, - aa=aa_values, - anis=aniso_values, - bearing=bearing_values, - transtype=trans_flag, - avetype=avg_flag, - power=power_value, - nreal=n_realizations, - ) - - # Handle the output format from FIELDGEN2D_SVA - if field.shape == (n_points, n_realizations): - # Output is (n_points, n_realizations) - transpose to (n_realizations, n_points) - field = field.T # Now shape is (n_realizations, n_points) - # Reshape each realization to (ny, nx) - field = field.reshape((n_realizations, ny, nx)) - - elif field.shape == (n_realizations * n_points,): - # Output is flattened - reshape directly - field = field.reshape((n_realizations, ny, nx)) - - elif field.shape == (n_realizations, n_points): - # Output is already (n_realizations, n_points) - just reshape spatial dimensions - field = field.reshape((n_realizations, ny, nx)) - - else: - raise ValueError(f"Unexpected fieldgen2d_sva output shape: {field.shape}") + Assign layer numbers to conceptual points based on z/depth coordinates + by intersecting with model grid elevations. - print(f" Successfully generated {n_realizations} realizations with shape {field.shape}") - - # Clean up - lib.free_all_memory() - - return field - - except Exception as e: - print(f" Warning: FIELDGEN2D_SVA failed: {e}") - print(f" Using fallback method...") - - # Fallback to simple field generation - field = np.zeros((n_realizations, ny, nx)) - for real in range(n_realizations): - current_iids = iids[:, real] - field_values = _generate_simple_field( - grid_coords, mean_flat, log_variance_flat, - aa_values, aniso_values, bearing_values, - transform, current_iids, ny, nx - ) - field[real] = field_values.reshape(ny, nx) - - return field + This function will use the gridit package to perform the intersection. + Parameters + ---------- + conceptual_points : pd.DataFrame + Conceptual points with 'z' or 'depth' column + modelgrid : flopy.discretization.Grid + Model grid with layer elevation information -def _generate_simple_field(grid_coords, mean_flat, variance_flat, - aa_values, aniso_values, bearing_values, - transform, iids, ny, nx): - """ - Simple fallback field generation if FIELDGEN2D_SVA fails. + Returns + ------- + pd.DataFrame + Conceptual points with 'layer' column added (0-based) """ - print(" Using improved simple correlated field generation fallback...") - - n_points = len(grid_coords) - - # Convert bearing from degrees to radians for calculations - bearing_rad = np.radians(bearing_values) - - # Create spatially correlated field using moving average approach - correlated_field = np.zeros(n_points) - - # For computational efficiency, use a subset of points for correlation - max_correlation_points = min(2000, n_points) - - if n_points > max_correlation_points: - # Sample points for correlation calculation - correlation_indices = np.random.choice(n_points, max_correlation_points, replace=False) - correlation_indices = np.sort(correlation_indices) - else: - correlation_indices = np.arange(n_points) - - # Apply spatial correlation - for i in correlation_indices: - # Get local correlation parameters - local_aa = aa_values[i] - local_aniso = aniso_values[i] - local_bearing = bearing_rad[i] - - # Calculate distances to all points - dx = grid_coords[:, 0] - grid_coords[i, 0] - dy = grid_coords[:, 1] - grid_coords[i, 1] - - # Apply anisotropic transformation - cos_bear = np.cos(local_bearing) - sin_bear = np.sin(local_bearing) - - # Rotate to principal axes (geological bearing: 0° = North, clockwise positive) - dx_rot = dx * sin_bear + dy * cos_bear # East component (major axis) - dy_rot = -dx * cos_bear + dy * sin_bear # North component (minor axis) - - # Scale by anisotropy - minor_aa = local_aa / local_aniso - dx_scaled = dx_rot / local_aa - dy_scaled = dy_rot / minor_aa - - # Calculate anisotropic distance - aniso_distances = np.sqrt(dx_scaled ** 2 + dy_scaled ** 2) - - # Exponential correlation function - correlation_weights = np.exp(-aniso_distances) - - # Normalize weights - correlation_weights = correlation_weights / np.sum(correlation_weights) - - # Apply correlation to the random field - correlated_field[i] = np.sum(correlation_weights * iids) - - # For points not in correlation_indices, use interpolation - if n_points > max_correlation_points: - remaining_indices = np.setdiff1d(np.arange(n_points), correlation_indices) - - for i in remaining_indices: - # Find nearest correlation points - distances_to_corr = np.sum((grid_coords[correlation_indices] - grid_coords[i]) ** 2, axis=1) - nearest_corr_idx = correlation_indices[np.argmin(distances_to_corr)] - correlated_field[i] = correlated_field[nearest_corr_idx] - - # Scale by local standard deviation - stochastic_component = correlated_field * np.sqrt(variance_flat) - - # Combine with mean field - if transform == 'log': - # Log domain: mean * exp(stochastic_component) - field_values = mean_flat * np.exp(stochastic_component) - else: - # Normal domain: mean + stochastic_component - field_values = mean_flat + stochastic_component - - return field_values + # TODO: Implement using gridit package + # Expected workflow: + # 1. Extract z coordinate from conceptual_points ('z' or 'depth' column) + # 2. Get layer elevations from modelgrid (top and botm arrays) + # 3. Use gridit to find which layer each point falls into + # 4. Add 'layer' column to conceptual_points (0-based) + pass def create_boundary_modified_scalar(base_field, zones, @@ -530,7 +459,7 @@ def create_boundary_modified_scalar(base_field, zones, if zones.shape != base_field.shape: raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") - boundary_mask, _ = detect_zone_boundaries(zones) + boundary_mask, _ = _detect_zone_boundaries(zones) distance = distance_transform_edt(~boundary_mask) transition_mask = distance <= transition_cells @@ -615,19 +544,15 @@ def save_results(result, out_filename): print(f" Saved {len(result['field'])} realizations") -def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realizations): + +def _load_or_generate_iids(iid_path, ny, nx, n_realizations): """ - Load IIDs from file or generate and save new ones for PEST parameterization. - FIXED version with proper array handling. + Load IIDs from file or generate and save new ones. Parameters ---------- - tmp_model_ws : str - Model workspace path - iids_file : str or list or None - IID file specification - layer_num : int - Layer number (0-based) + iid_path : str + Explicit path to IID file (including directory) ny, nx : int Grid dimensions n_realizations : int @@ -639,37 +564,11 @@ def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realiza IIDs array with shape (ny*nx, n_realizations) """ - # Convert to 1-based for file naming - layer_1based = layer_num + 1 - - # Determine the IID file path - if iids_file is None: - # Default pattern - iid_path = os.path.join(tmp_model_ws, f"iids_layer_{layer_1based}.arr") - elif isinstance(iids_file, str): - # Single string - assume pattern or explicit path - if "{" in iids_file or "layer" in iids_file.lower(): - # Pattern with layer number - iid_path = os.path.join(tmp_model_ws, iids_file.format(layer_1based)) - else: - # Explicit path - append layer number - base_name, ext = os.path.splitext(iids_file) - iid_path = os.path.join(tmp_model_ws, f"{base_name}_layer_{layer_1based}{ext}") - elif isinstance(iids_file, list): - # List of explicit paths - if layer_num < len(iids_file): - iid_path = os.path.join(tmp_model_ws, iids_file[layer_num]) - else: - # Generate default name if not enough files specified - iid_path = os.path.join(tmp_model_ws, f"iids_layer_{layer_1based}.arr") - else: - raise ValueError(f"Invalid iids_file specification: {iids_file}") - # Try to load existing IIDs if os.path.exists(iid_path): - print(f" Loading existing IIDs from {iid_path}") + print(f" Loading existing IIDs from {os.path.basename(iid_path)}") try: - # Try to load as text array + # Load as text array iids_data = np.loadtxt(iid_path) # Handle different shapes @@ -692,30 +591,30 @@ def _load_or_generate_iids(tmp_model_ws, iids_file, layer_num, ny, nx, n_realiza # Expand or truncate to match n_realizations if iids.shape[1] < n_realizations: - print(f" Expanding IIDs from {iids.shape[1]} to {n_realizations} realizations") + print(f" Expanding IIDs from {iids.shape[1]} to {n_realizations} realizations") additional_iids = np.random.normal(0, 1, size=(ny * nx, n_realizations - iids.shape[1])) iids = np.column_stack([iids, additional_iids]) elif iids.shape[1] > n_realizations: - print(f" Truncating IIDs from {iids.shape[1]} to {n_realizations} realizations") + print(f" Truncating IIDs from {iids.shape[1]} to {n_realizations} realizations") iids = iids[:, :n_realizations] + return iids + except Exception as e: - print(f" Warning: Could not load IID file {iid_path}: {e}") + print(f" Warning: Could not load IID file: {e}") print(f" Generating new IIDs...") - iids = None else: - print(f" IID file {iid_path} not found, generating new IIDs...") - iids = None + print(f" IID file not found: {os.path.basename(iid_path)}") + print(f" Generating new IIDs...") - # Generate new IIDs if loading failed - if iids is None: - iids = np.random.normal(0, 1, size=(ny * nx, n_realizations)) + # Generate new IIDs + iids = np.random.normal(0, 1, size=(ny * nx, n_realizations)) - # Save for PEST parameterization - print(f" Saving IIDs to {iid_path} for PEST parameterization") - os.makedirs(os.path.dirname(iid_path), exist_ok=True) - np.savetxt(iid_path, iids, fmt="%.8f", - header=f"IIDs for layer {layer_1based}, shape: {ny * nx} x {n_realizations}") + # Save for future use + print(f" Saving IIDs to {os.path.basename(iid_path)}") + os.makedirs(os.path.dirname(iid_path), exist_ok=True) + np.savetxt(iid_path, iids, fmt="%.8f", + header=f"IIDs shape: {ny * nx} x {n_realizations}") return iids @@ -809,7 +708,7 @@ def example_field_generation(): 'x': cp_x, 'y': cp_y, 'mean': mean_kh, - 'sd': mean_kh * 0.2, + 'sd': 1.0, 'major': major_length, 'anisotropy': 4.0, 'bearing': bearing, @@ -821,41 +720,48 @@ def example_field_generation(): # Generate field print("=== Testing tangential field generation ===") - results = generate_single_layer( - cp_df, modelgrid, - zones=zones, - tensor_interp='krig', - #transform='log', - boundary_smooth={'transition_cells': 5}, - boundary_enhance={'transition_cells': 5, - 'peak_increase': 2} - ) + for scen in ['fieldgen', 'python']: + if scen=='fieldgen': + iids = None # let fieldgen create them + else: + iids = np.random.normal(0, 1, size=(ny * nx)) + results = generate_single_layer( + cp_df, modelgrid, + iids=iids, + zones=zones, + tensor_interp='krig', + transform='log', + boundary_smooth={'transition_cells': 5}, + boundary_enhance={'transition_cells': 5, + 'peak_increase': 2}, + sd_is_log_space=True + ) - print(f"Field statistics: mean={np.mean(results['fields'][0]):.3f}, std={np.std(results['fields'][0]):.3f}") - print(f"Number of conceptual points: {len(cp_df)}") - print("Tangential field generation complete!") - - save_dir = os.path.join('..','..','examples','tangential_nsaf') - if not os.path.exists(save_dir): - os.mkdir(save_dir) - if save_dir is not None: - from pyemu import plot_utils as pu - grid_info = _extract_grid_info(modelgrid) - xcentergrid = grid_info['xcentergrid'] - ycentergrid = grid_info['ycentergrid'] - fname = f"tangential_example.arr" - np.savetxt(os.path.join(save_dir, fname), results['fields'][0], fmt="%20.8E") - fig_path = os.path.join(save_dir, 'figure') - if not os.path.exists(fig_path): - os.mkdir(fig_path) - pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones, - conceptual_points=cp_df, subsample=4, max_ellipse_size=0.1, - figsize=(14, 12), title_suf='tangential', - save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) - - pu.visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, - transform='log', title_suf='tangential', - save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) + print(f"Field statistics: mean={np.mean(results['field']):.3f}, std={np.std(results['field']):.3f}") + print(f"Number of conceptual points: {len(cp_df)}") + print("Tangential field generation complete!") + + save_dir = os.path.join('..','..','examples',f'tangential_nsaf_{scen}') + if not os.path.exists(save_dir): + os.mkdir(save_dir) + if save_dir is not None: + from pyemu import plot_utils as pu + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + fname = f"tangential_example_{scen}.arr" + np.savetxt(os.path.join(save_dir, fname), results['field'], fmt="%20.8E") + fig_path = os.path.join(save_dir, 'figure') + if not os.path.exists(fig_path): + os.mkdir(fig_path) + pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones, + conceptual_points=cp_df, subsample=4, max_ellipse_size=0.1, + figsize=(14, 12), title_suf='tangential', + save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) + + pu.visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, + transform='log', title_suf='tangential', + save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) return results, cp_df diff --git a/pyemu/utils/nsaf_useage.py b/pyemu/utils/nsaf_useage.py index f7dca16b9..fc7ede61a 100644 --- a/pyemu/utils/nsaf_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -21,25 +21,28 @@ import time start_time = time.perf_counter() - model_name = 'hpm_mf6' - data_dir = r'..\..\examples\HBHP\hpm_mf6' + model_name = 'waq_arr' + data_dir = r'..\..\examples\Wairau\waq_arr' # need to ensure proper name structure for zone files here - zone_files = None #[f'{model_name}.geoclass_layer{i+1}.arr' for i in range(0,13)] - con_pts_file = 'Bridgpa_HU2_sva.csv' - save_dir = os.path.join(data_dir) + zone_files = {} + for i in range(0, 13): + zone_files[i] = f'{model_name}.geoclass_layer{i+1}.arr' + con_pts_file = 'conceptual_points.csv' + save_dir = os.path.join(data_dir) if not os.path.exists(save_dir): os.mkdir(save_dir) nsaf_helpers.generate_fields_from_files(data_dir, model_name, con_pts_file, - zone_file=zone_files, + zone_files=zone_files, field_name=['kh'], layer_mode=True, save_dir=save_dir, tensor_interp='idw', - boundary_smooth={'transition_cells': 5}, - boundary_enhance={'transition_cells': 5, - 'peak_increase': 0.1} + transform=['log'], + boundary_smooth={'transition_cells': 2}, + boundary_enhance={'transition_cells': 2, + 'peak_increase': 2.0} ) diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index 7d1aec7f9..f780f4304 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -8,223 +8,396 @@ import matplotlib.pyplot as plt -def get_tensor_bearing(tensor, expected_bearing=None): - """Extract geological bearing from tensor - NO CHANGES to math.""" - eigenvals, eigenvecs = np.linalg.eigh(tensor) - max_idx = np.argmax(eigenvals) - major_eigenvec = eigenvecs[:, max_idx] - - # Swap components to match geological convention: [north, east] instead of [east, north] - north_component = major_eigenvec[1] # y-component is north - east_component = major_eigenvec[0] # x-component is east - - # Calculate bearing from north (geological convention) - angle1 = np.degrees(np.arctan2(east_component, north_component)) - if angle1 < 0: - angle1 += 360 - angle2 = (angle1 + 180) % 360 - - # Choose correct direction if expected bearing provided - if expected_bearing is not None: - diff1 = min(abs(angle1 - expected_bearing), abs(angle1 - expected_bearing + 360), - abs(angle1 - expected_bearing - 360)) - diff2 = min(abs(angle2 - expected_bearing), abs(angle2 - expected_bearing + 360), - abs(angle2 - expected_bearing - 360)) - return angle1 if diff1 < diff2 else angle2 - - return angle1 - - -def _ensure_positive_definite(tensor, min_eigenval=1e-8): - """Ensure tensor is positive definite - NO CHANGES.""" - eigenvals, eigenvecs = np.linalg.eigh(tensor) - eigenvals_reg = np.maximum(eigenvals, min_eigenval) - return eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T - - -def create_2d_tensors(theta, major, minor): - """Create 2x2 anisotropy tensors from geostatistical parameters - NO CHANGES to math. +def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, + tensor_interp='idw', transform='log', layer=1, + boundary_smooth=False, boundary_enhance=False, + mean_col='mean', sd_col='sd', sd_is_log_space=False, + n_realizations=1, save_dir='.'): + """ + Generate single layer using NSAF approach with boundary adjustments. + REFACTORED to use modelgrid and call apply_nsaf_hyperpars internally. Parameters ---------- - theta : array-like - Bearing angles in radians (0 = North, clockwise positive) - major : array-like - Major axis correlation lengths - minor : array-like - Minor axis correlation lengths + conceptual_points : pd.DataFrame + Conceptual points DataFrame + modelgrid : flopy.discretization.grid + Flopy modelgrid object + iids : np.ndarray, optional + Pre-generated noise, shape (ny*nx, n_realizations) + zones : np.ndarray, optional + Zone IDs, shape (ny, nx) + tensor_interp : str, default 'idw' + Tensor interpolation method + transform : str, default 'log' + Transformation: 'log' or 'none' + boundary_smooth : bool, default True + Apply smoothing to mean at zone boundaries + boundary_enhance : bool, default True + Apply variance enhancement at zone boundaries + mean_col : str, default 'mean' + Name of mean column in conceptual points + sd_col : str, default 'sd' + Name of sd column in conceptual points + n_realizations : int, default 1 + Number of realizations to generate Returns ------- - np.ndarray - Tensors with shape (n, 2, 2) + tuple + (fields, results_dict) where: + - fields: Generated field(s), shape (n_realizations, ny, nx) or (ny, nx) if n_realizations=1 + - results_dict: Full results dictionary from apply_nsaf_hyperpars """ - theta = np.atleast_1d(theta) - major = np.atleast_1d(major) - minor = np.atleast_1d(minor) - - n = len(theta) - tensors = np.zeros((n, 2, 2)) - - for i in range(n): - bearing_rad = theta[i] - - # Direction vector for this bearing (geological convention) - east_comp = np.sin(bearing_rad) # X component - north_comp = np.cos(bearing_rad) # Y component - # Create rotation matrix to align [1,0] with bearing direction - cos_t = north_comp - sin_t = east_comp + # Call the main function with single layer settings + results = apply_nsaf_hyperpars( + conceptual_points=conceptual_points, + modelgrid=modelgrid, + zones=zones, + out_filename=None, + n_realizations=n_realizations, + layer=layer, + transform=transform, + boundary_smooth=boundary_smooth, + boundary_enhance=boundary_enhance, + tensor_method=tensor_interp, + config_dict=None, + iids=iids, + mean_col=mean_col, + sd_col=sd_col, + sd_is_log_space=sd_is_log_space + ) - # Clockwise rotation by angle theta - R = np.array([[cos_t, sin_t], - [-sin_t, cos_t]]) - S = np.diag([minor[i] ** 2, major[i] ** 2]) - tensors[i] = R @ S @ R.T + return results - return tensors.squeeze() if n == 1 else tensors +def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename=None, + n_realizations=1, layer=0, transform="none", + boundary_smooth=False, boundary_enhance=False, + tensor_method='krig', config_dict=None, iids=None, + mean_col='mean', sd_col='sd', + sd_is_log_space=False, **kwargs): + """ + Parameter interpolation and field generation combining NSAF tensor mathematics + with pypestutils FIELDGEN2D_SVA for spatially correlated noise. -def load_conceptual_points(cp_file): - """Load conceptual points from CSV file or DataFrame - SIMPLIFIED. + REFACTORED to use modelgrid consistently throughout. Parameters ---------- - cp_file : str or pd.DataFrame - Path to CSV file or DataFrame with columns: name,x,y,z,mean_kh,sd_kh,major,anisotropy,bearing,layer + conceptual_points : pd.DataFrame + Conceptual points DataFrame + modelgrid : flopy.discretization.grid + Flopy modelgrid object containing grid information + zones : np.ndarray, optional + Zone array, shape (ny, nx) + out_filename : str, optional + Base filename for output (if None, returns arrays only) + n_realizations : int, default 1 + Number of stochastic realizations to generate + layer : int, default 0 + Layer number to process + transform : str, default "none" + Transformation: "none" or "log" + boundary_smooth : bool, or dict default False + Apply smoothing to mean at zone boundaries + boundary_enhance : bool, default True + Apply variance enhancement at zone boundaries + tensor_method : str, default 'krig' + Tensor interpolation method: 'krig', 'idw', or 'nearest' + config_dict : dict, optional + Configuration parameters for pypestutils + iids : np.ndarray, optional + Pre-generated IIDs with shape (ny*nx, n_realizations) + mean_col : str, default 'mean' + Name of the mean column in conceptual points + sd_col : str, default 'sd' + Name of the standard deviation column in conceptual points Returns ------- - pd.DataFrame - Conceptual points with added 'transverse' column if missing + dict + Dictionary containing: + - 'fields': Generated parameter fields, shape (n_realizations, ny, nx) + - 'mean': Interpolated mean field, shape (ny, nx) + - 'sd': Interpolated standard deviation field, shape (ny, nx) + - 'tensors': Interpolated tensors, shape (ny*nx, 2, 2) """ - if isinstance(cp_file, str): - cp_df = pd.read_csv(cp_file) - elif isinstance(cp_file, pd.DataFrame): - cp_df = cp_file.copy() + + print("=== Parameter Interpolation and Field Generation ===") + + # Extract grid information from modelgrid + grid_info = _extract_grid_info(modelgrid) + ny = grid_info['ny'] + nx = grid_info['nx'] + + # Load conceptual points + if 'layer' in conceptual_points.columns: + layer_cp = conceptual_points[conceptual_points['layer'] == layer].copy() else: - raise TypeError(f"cp_file must be string path or pandas DataFrame, got {type(cp_file)}") + layer_cp = conceptual_points.copy() - # Validate required columns (excluding anisotropy/transverse for now) - required_cols = ['x', 'y', 'major', 'bearing'] - missing_cols = [col for col in required_cols if col not in cp_df.columns] + if len(layer_cp) == 0: + raise ValueError(f"No conceptual points found for layer {layer}") + + # Validate required columns + required_base_cols = ['x', 'y', 'major', 'bearing'] + missing_cols = [col for col in required_base_cols if col not in layer_cp.columns] if missing_cols: raise ValueError(f"Missing required columns: {missing_cols}") - # Check for anisotropy OR transverse (at least one must exist) - has_anisotropy = 'anisotropy' in cp_df.columns - has_transverse = 'transverse' in cp_df.columns - - if not has_anisotropy and not has_transverse: - raise ValueError("Must have either 'anisotropy' OR 'transverse' column") + # Check for transverse/anisotropy + if 'transverse' not in layer_cp.columns: + if 'anisotropy' in layer_cp.columns: + layer_cp['transverse'] = layer_cp['major'] / layer_cp['anisotropy'] + else: + raise ValueError("Must have either 'transverse' or 'anisotropy' column") - # Add transverse column if missing (derive from anisotropy) - if not has_transverse and has_anisotropy: - cp_df['transverse'] = cp_df['major'] / cp_df['anisotropy'] - print(f" Added 'transverse' column derived from anisotropy") + # Validate mean and sd columns + if mean_col not in layer_cp.columns: + raise ValueError(f"Mean column '{mean_col}' not found in conceptual points") + if sd_col not in layer_cp.columns: + raise ValueError(f"Standard deviation column '{sd_col}' not found in conceptual points") - # Add anisotropy column if missing (derive from transverse) - elif not has_anisotropy and has_transverse: - cp_df['anisotropy'] = cp_df['major'] / cp_df['transverse'] - print(f" Added 'anisotropy' column derived from transverse") + print(f"Processing layer {layer} with {len(layer_cp)} conceptual points") + print(f"Grid dimensions: {ny} x {nx} = {ny * nx} cells") + print(f"Using columns: mean='{mean_col}', sd='{sd_col}'") - # If both exist, validate they're consistent - elif has_anisotropy and has_transverse: - expected_transverse = cp_df['major'] / cp_df['anisotropy'] - if not np.allclose(cp_df['transverse'], expected_transverse, rtol=1e-3): - print(" Warning: 'transverse' and 'anisotropy' columns are inconsistent") - print(" Using 'transverse' values and recalculating 'anisotropy'") - cp_df['anisotropy'] = cp_df['major'] / cp_df['transverse'] + # Step 1: Interpolate tensors + print("\nStep 1: Interpolating tensor field...") + tensors = interpolate_tensors( + layer_cp, modelgrid, zones=zones, + method=tensor_method, layer=layer, + config_dict=config_dict + ) - return cp_df + # Step 2: Convert tensors to geostatistical parameters + print("\nStep 2: Converting tensors to geostatistical parameters...") + bearing_deg, anisotropy, corrlen_major = _tensors_to_geostat_params(tensors) + # Step 3: Prepare conceptual point data + cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) + cp_means = layer_cp[mean_col].values + cp_sd = layer_cp[sd_col].values.clip(1e-8, None) -def _extract_grid_info(modelgrid): - """Extract grid information from flopy modelgrid object. + print(f"Conceptual point data ranges:") + print(f" {mean_col}: [{cp_means.min():.3f}, {cp_means.max():.3f}]") + print(f" {sd_col}: [{cp_sd.min():.3f}, {cp_sd.max():.3f}]") - Parameters - ---------- - modelgrid : flopy.discretization.grid - Flopy modelgrid object + # Step 4: Interpolate mean and sd using tensor-aware kriging + print("\nStep 4: Interpolating mean and standard deviation fields...") - Returns - ------- - dict - Dictionary with grid information including: - - xcentergrid: X coordinates of cell centers - - ycentergrid: Y coordinates of cell centers - - nx, ny: Grid dimensions - - area: Cell areas - - grid_type: Type of grid - """ - grid_info = {} + # Configure kriging parameters + if config_dict is None: + config_dict = { + 'vartype': 'gaussian', + 'krigtype': 1, + 'search_radius': 1e10, + 'maxpts_interp': 20, + 'minpts_interp': 1 + } - # Get grid type - grid_info['grid_type'] = modelgrid.grid_type + # Transform for log-domain if needed + transform = 'log' if transform == 'log' else None + min_mean = 1e-8 if transform == 'log' else None + max_mean = 1e8 if transform == 'log' else None + bounds = (min_mean, max_mean) - # Get cell centers - if hasattr(modelgrid, 'xcellcenters'): - grid_info['xcentergrid'] = modelgrid.xcellcenters - grid_info['ycentergrid'] = modelgrid.ycellcenters - else: - raise AttributeError("modelgrid missing xcellcenters/ycellcenters") + # Interpolate mean + print(" Interpolating mean...") + interp_means_2d = tensor_aware_kriging( + cp_coords, cp_means, modelgrid, tensors, + variogram_model='exponential', + background_value=np.mean(cp_means), max_search_radius=1e20, + min_points=3, transform=transform, bounds=bounds, + max_neighbors=4, zones=zones + ) - # Get dimensions - if modelgrid.grid_type == 'structured': - grid_info['ny'] = modelgrid.nrow - grid_info['nx'] = modelgrid.ncol - # Calculate areas from delr, delc - if hasattr(modelgrid, 'delr') and hasattr(modelgrid, 'delc'): - delr = modelgrid.delr - delc = modelgrid.delc - grid_info['area'] = np.outer(delc, delr) - else: - # Uniform area assumption - grid_info['area'] = np.ones((grid_info['ny'], grid_info['nx'])) - elif modelgrid.grid_type == 'vertex': - # Vertex grid - reshape if needed - xc = grid_info['xcentergrid'] - yc = grid_info['ycentergrid'] - if len(xc.shape) == 1: - grid_info['xcentergrid'] = xc.reshape(-1, 1) - grid_info['ycentergrid'] = yc.reshape(-1, 1) - grid_info['nx'] = 1 - grid_info['ny'] = xc.shape[0] - else: - grid_info['ny'], grid_info['nx'] = xc.shape - # Calculate areas for vertex grid - grid_info['area'] = _calculate_vertex_areas(modelgrid) + # Apply boundary smoothing to mean + if boundary_smooth and zones is not None: + if isinstance(boundary_smooth, dict): + smooth_params = {**boundary_smooth, **boundary_smooth} + else: # Must be True (boolean) + smooth_params = boundary_smooth + + print(" Smoothing mean at geological boundaries...") + interp_means_2d = create_boundary_modified_scalar( + interp_means_2d, zones, transform=transform, + transition_cells=smooth_params['transition_cells'], + mode='smooth' + ) + + min_sd = 1e-8 if transform == 'log' else None + max_sd = 1e8 if transform == 'log' else None + bounds = (min_sd, max_sd) + + # Interpolate standard deviation + print(" Interpolating standard deviation...") + interp_sd_2d = tensor_aware_kriging( + cp_coords, cp_sd, modelgrid, tensors, + variogram_model='exponential', + background_value=np.mean(cp_sd), max_search_radius=1e20, + min_points=3, transform=transform, bounds=bounds, + max_neighbors=4, zones=zones, + ) + + # Apply boundary enhancement to sd + if boundary_enhance and zones is not None: + if isinstance(boundary_smooth, dict): + smooth_params = {**boundary_enhance, **boundary_smooth} + else: # Must be True (boolean) + smooth_params = boundary_enhance + print(" Enhancing variance at geological boundaries...") + interp_sd_2d = create_boundary_modified_scalar( + interp_sd_2d, zones, transform=transform, + peak_increase=smooth_params['peak_increase'], + transition_cells=smooth_params['transition_cells'], + mode='enhance' + ) + + print(f"Interpolated field ranges:") + print(f" Mean: [{interp_means_2d.min():.3f}, {interp_means_2d.max():.3f}]") + print(f" SD: [{interp_sd_2d.min():.3f}, {interp_sd_2d.max():.3f}]") + + # Step 5: Generate stochastic fields using FIELDGEN2D_SVA approach + print(f"\nStep 5: Generating {n_realizations} stochastic realizations...") + + if n_realizations > 0: + # Get active cells if available + active = kwargs.get('active', None) + if active is None and hasattr(modelgrid, 'idomain'): + active = modelgrid.idomain[layer-1].flatten() if modelgrid.idomain.ndim == 3 else (modelgrid.idomain.flatten()) + + field = _generate_stochastic_field( + modelgrid=modelgrid, + mean_field=interp_means_2d, + sd_field=interp_sd_2d, + iids=iids, + bearing=bearing_deg, + anisotropy=anisotropy, + corrlen=corrlen_major, + sd_is_log_space=sd_is_log_space, + config_dict=config_dict, + active=active + ) else: - # Fallback for other grid types - xc = grid_info['xcentergrid'] - grid_info['ny'], grid_info['nx'] = xc.shape - grid_info['area'] = np.ones((grid_info['ny'], grid_info['nx'])) + field = None - return grid_info + # Prepare output + results = { + 'field': field, + 'mean': interp_means_2d, + 'sd': interp_sd_2d, + 'tensors': tensors, + 'bearing': bearing_deg.reshape(ny, nx), + 'anisotropy': anisotropy.reshape(ny, nx), + 'corrlen': corrlen_major.reshape(ny, nx) + } + # Save to files if requested + if out_filename is not None: + save_results(results, out_filename, transform) -def _calculate_vertex_areas(modelgrid): - """Calculate cell areas for vertex grid using shoelace formula.""" - ncpl = modelgrid.ncpl if hasattr(modelgrid, 'ncpl') else len(modelgrid.xcellcenters) - areas = np.zeros(ncpl) + print("=== Interpolation complete ===") + return results - for i in range(ncpl): - try: - verts = modelgrid.get_cell_vertices(i) - # Shoelace formula - x = [v[0] for v in verts] - y = [v[1] for v in verts] - area = 0.5 * abs(sum(x[j] * y[j + 1] - x[j + 1] * y[j] for j in range(-1, len(x) - 1))) - areas[i] = area - except: - areas[i] = 1.0 # Default area +def load_conceptual_points(cp_file): + """Load conceptual points from CSV file or DataFrame - SIMPLIFIED. - # Reshape if needed - if hasattr(modelgrid, 'ncpl'): - return areas.reshape(-1, 1) + Parameters + ---------- + cp_file : str or pd.DataFrame + Path to CSV file or DataFrame with columns: name,x,y,z,mean_kh,sd_kh,major,anisotropy,bearing,layer + + Returns + ------- + pd.DataFrame + Conceptual points with added 'transverse' column if missing + """ + if isinstance(cp_file, str): + cp_df = pd.read_csv(cp_file) + elif isinstance(cp_file, pd.DataFrame): + cp_df = cp_file.copy() else: - return areas.reshape(modelgrid.nrow, modelgrid.ncol) + raise TypeError(f"cp_file must be string path or pandas DataFrame, got {type(cp_file)}") + + # Validate required columns (excluding anisotropy/transverse for now) + required_cols = ['x', 'y', 'major', 'bearing'] + missing_cols = [col for col in required_cols if col not in cp_df.columns] + if missing_cols: + raise ValueError(f"Missing required columns: {missing_cols}") + + # Check for anisotropy OR transverse (at least one must exist) + has_anisotropy = 'anisotropy' in cp_df.columns + has_transverse = 'transverse' in cp_df.columns + + if not has_anisotropy and not has_transverse: + raise ValueError("Must have either 'anisotropy' OR 'transverse' column") + + # Add transverse column if missing (derive from anisotropy) + if not has_transverse and has_anisotropy: + cp_df['transverse'] = cp_df['major'] / cp_df['anisotropy'] + print(f" Added 'transverse' column derived from anisotropy") + + # Add anisotropy column if missing (derive from transverse) + elif not has_anisotropy and has_transverse: + cp_df['anisotropy'] = cp_df['major'] / cp_df['transverse'] + print(f" Added 'anisotropy' column derived from transverse") + + # If both exist, validate they're consistent + elif has_anisotropy and has_transverse: + expected_transverse = cp_df['major'] / cp_df['anisotropy'] + if not np.allclose(cp_df['transverse'], expected_transverse, rtol=1e-3): + print(" Warning: 'transverse' and 'anisotropy' columns are inconsistent") + print(" Using 'transverse' values and recalculating 'anisotropy'") + cp_df['anisotropy'] = cp_df['major'] / cp_df['transverse'] + + return cp_df + + +def create_2d_tensors(theta, major, minor): + """Create 2x2 anisotropy tensors from geostatistical parameters - NO CHANGES to math. + + Parameters + ---------- + theta : array-like + Bearing angles in radians (0 = North, clockwise positive) + major : array-like + Major axis correlation lengths + minor : array-like + Minor axis correlation lengths + + Returns + ------- + np.ndarray + Tensors with shape (n, 2, 2) + """ + theta = np.atleast_1d(theta) + major = np.atleast_1d(major) + minor = np.atleast_1d(minor) + + n = len(theta) + tensors = np.zeros((n, 2, 2)) + + for i in range(n): + bearing_rad = theta[i] + + # Direction vector for this bearing (geological convention) + east_comp = np.sin(bearing_rad) # X component + north_comp = np.cos(bearing_rad) # Y component + + # Create rotation matrix to align [1,0] with bearing direction + cos_t = north_comp + sin_t = east_comp + + # Clockwise rotation by angle theta + R = np.array([[cos_t, sin_t], + [-sin_t, cos_t]]) + S = np.diag([minor[i] ** 2, major[i] ** 2]) + tensors[i] = R @ S @ R.T + + return tensors.squeeze() if n == 1 else tensors def interpolate_tensors(cp_file, modelgrid, zones=None, method='idw', @@ -257,6 +430,7 @@ def interpolate_tensors(cp_file, modelgrid, zones=None, method='idw', from pypestutils.pestutilslib import PestUtilsLib except Exception as e: raise Exception(f"Error importing pypestutils: {e}") + # todo change to warning, use other method # Extract grid info grid_info = _extract_grid_info(modelgrid) @@ -336,1182 +510,983 @@ def interpolate_tensors(cp_file, modelgrid, zones=None, method='idw', return interp_tensors -def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, - xcentergrid, ycentergrid, zones_flat, config_dict, nx, ny): - """Interpolate tensors using pypestutils kriging with log-Euclidean approach.""" + +def tensor_aware_kriging(cp_coords, cp_values, modelgrid, interp_tensors, + variogram_model='exponential', + background_value=0.0, max_search_radius=1e20, min_points=1, + max_neighbors=4, transform=None, bounds=(1e-8, 1e8), + zones=None, n_dims=2): + """ + Tensor-aware kriging using pypestutils + """ try: from pypestutils.pestutilslib import PestUtilsLib except Exception as e: raise Exception(f"Error importing pypestutils: {e}") + # Get grid shape + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] n_grid = np.prod(xcentergrid.shape) - result_tensors = np.zeros((n_grid, 2, 2)) - - # Convert tensors to log space - log_tensors = np.array([logm(_ensure_positive_definite(tensor)) - for tensor in cp_tensors]) - # Process each zone separately - unique_zones = np.unique(zones_flat) + print(f" Tensor-aware kriging: {len(cp_coords)} conceptual points -> {len(xcentergrid)} grid points") + print( + f" Conceptual points range: X=[{cp_coords[:, 0].min():.1f}, {cp_coords[:, 0].max():.1f}], Y=[{cp_coords[:, 1].min():.1f}, {cp_coords[:, 1].max():.1f}]") + print( + f" Grid points range: X=[{xcentergrid[0].min():.1f}, {xcentergrid[0].max():.1f}], Y=[{ycentergrid[1].min():.1f}, {ycentergrid[1].max():.1f}]") + print(f" Value range: [{cp_values.min():.6f}, {cp_values.max():.6f}], background: {background_value:.6f}") + print(f" Transform requested: {transform}") - for zone_id in unique_zones: - zone_mask = zones_flat == zone_id - zone_indices = np.where(zone_mask)[0] + # Handle zones shape inference + if zones is not None: + shape = zones.shape + if n_dims == 2: + ny, nx = shape + else: + nz, ny, nx = shape + print(f" Using zones with shape: {shape}") + else: + unique_x = np.unique(xcentergrid) + unique_y = np.unique(ycentergrid) + nx = len(unique_x) + ny = len(unique_y) - if len(zone_indices) == 0: - continue + if nx * ny != n_grid: + side_length = int(np.sqrt(n_grid)) + nx = ny = side_length + print(f" Warning: Could not infer grid dimensions, using square grid {nx}x{ny}") + else: + print(f" Inferred grid dimensions: ny={ny}, nx={nx}") - # Filter conceptual points for this zone - cp_mask = cp_zones == zone_id - if np.sum(cp_mask) == 0: - # No conceptual points in this zone - use default tensor - default_tensor = np.eye(2) * 1000 ** 2 - result_tensors[zone_indices] = default_tensor - continue + shape = (ny, nx) - zone_cp_coords = cp_coords[cp_mask] - zone_log_tensors = log_tensors[cp_mask] - zone_xcentergrid = xcentergrid[zone_indices] - zone_ycentergrid = ycentergrid[zone_indices] + # Convert tensors to geostatistical parameters for pypestutils + print(" Converting tensors to geostatistical parameters...") + bearing_deg, anisotropy_ratios, corrlen_major = _tensors_to_geostat_params(interp_tensors) - # Interpolate each tensor component separately - zone_tensors = np.zeros((len(zone_indices), 2, 2)) + # Handle zones + if zones is not None: + zones_flat = zones.flatten().astype(int) + cp_zones = _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx) + else: + zones_flat = np.ones(n_grid, dtype=int) + cp_zones = np.ones(len(cp_coords), dtype=int) - for i in range(2): - for j in range(i, 2): # Only upper triangle, then mirror - component_values = zone_log_tensors[:, i, j].real + # Handle transformation - NO automatic detection, just pass through + cp_values_transformed = cp_values.copy() # Always use values as-is - # Use pypestutils for kriging - interp_component = _krig_component_pypestutils( - zone_cp_coords[:, 0], zone_cp_coords[:, 1], - component_values, zone_xcentergrid, zone_ycentergrid, - config_dict - ) + # Set pypestutils transform flag + if transform == 'log': + transform_flag = "log" + print(f" Using log transform in pypestutils") + else: + transform_flag = "none" + print(f" Using no transform in pypestutils") - zone_tensors[:, i, j] = interp_component - if i != j: - zone_tensors[:, j, i] = interp_component + print(f" Input values to pypestutils: [{cp_values_transformed.min():.6f}, {cp_values_transformed.max():.6f}]") - # Convert back from log space and ensure positive definiteness - for k in range(len(zone_indices)): - try: - tensor_exp = expm(zone_tensors[k]) - result_tensors[zone_indices[k]] = _ensure_positive_definite(tensor_exp) - except: - # Fallback to default tensor - result_tensors[zone_indices[k]] = np.eye(2) * 1000 ** 2 + # Use pypestutils for kriging + print(" Calling pypestutils for tensor-aware kriging...") - return result_tensors + def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): + """ + Ensure every zone in the grid either: + 1) Has >= min_points control points, OR + 2) Gets merged with a viable neighboring zone + """ + zones_fixed = zones_flat.copy() + cp_zones_fixed = cp_zones.copy() + unique_grid_zones = np.unique(zones_flat) -def _krig_component_pypestutils(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, config_dict): - """Use pypestutils to krig a single tensor component.""" - try: - from pypestutils.pestutilslib import PestUtilsLib - except Exception as e: - raise Exception(f"Error importing pypestutils: {e}") + zones_to_eliminate = [] - lib = PestUtilsLib() + for zone in unique_grid_zones: + cp_count = np.sum(cp_zones == zone) + grid_count = np.sum(zones_flat == zone) - # Create temporary factor file - fac_fname = "temp_tensor_component.fac" - if os.path.exists(fac_fname): - os.remove(fac_fname) - fac_ftype = "text" + if cp_count < min_points: + print(f" Zone {zone}: {cp_count} CPs, {grid_count} grid cells - ELIMINATING") + zones_to_eliminate.append(zone) - # All points in same zone for component interpolation - cp_zones = np.ones(len(cp_x), dtype=int) - grid_zones = np.ones(len(xcentergrid), dtype=int) + if zones_to_eliminate: + # Find the zone with the most control points to use as the "receiver" + viable_zones = [z for z in unique_grid_zones if z not in zones_to_eliminate] + if viable_zones: + # Pick the zone with most control points + best_zone = max(viable_zones, key=lambda z: np.sum(cp_zones == z)) + print(f" Consolidating {len(zones_to_eliminate)} bad zones into zone {best_zone}") - # Use isotropic parameters for component interpolation - x_extent = np.max(xcentergrid) - np.min(xcentergrid) - y_extent = np.max(ycentergrid) - np.min(ycentergrid) - domain_extent = max(x_extent, y_extent) - corrlen = np.full(len(xcentergrid), np.max([10000.0, domain_extent/3])) - aniso = np.ones(len(xcentergrid)) # Isotropic - bearing = np.zeros(len(xcentergrid)) # No rotation + for bad_zone in zones_to_eliminate: + # Move all grid cells from bad zone to best zone + zones_fixed[zones_flat == bad_zone] = best_zone + # Move any control points (though there shouldn't be many) + cp_zones_fixed[cp_zones == bad_zone] = best_zone + else: + # All zones are bad - just make everything zone 1 + print(" All zones insufficient - making single zone") + zones_fixed = np.ones_like(zones_flat) + cp_zones_fixed = np.ones_like(cp_zones) + + # Final verification + final_zones = np.unique(zones_fixed) + print(f" Final zones: {final_zones}") + for zone in final_zones: + cp_count = np.sum(cp_zones_fixed == zone) + grid_count = np.sum(zones_fixed == zone) + print(f" Zone {zone}: {cp_count} CPs, {grid_count} grid cells") + + if cp_count < min_points: + print(f" WARNING: Zone {zone} still has insufficient points!") + + return zones_fixed, cp_zones_fixed + + # Use it: + zones_for_kriging, cp_zones_for_kriging = fix_zones_for_pypestutils( + zones_flat, cp_zones, min_points + ) try: + lib = PestUtilsLib() + + # Create temporary factor file + fac_fname = "temp_tensor_aware_kriging.fac" + if os.path.exists(fac_fname): + os.remove(fac_fname) + fac_ftype = "text" + + # Set up variogram type + if variogram_model == 'exponential': + vartype = 2 + elif variogram_model == 'gaussian': + vartype = 3 + elif variogram_model == 'spherical': + vartype = 1 + else: + vartype = 2 # Default to exponential + + print(f" Pypestutils parameters:") + print(f" Variogram: {variogram_model} (type {vartype})") + print(f" Transform: {transform_flag}") + print(f" Search radius: {max_search_radius}") + print(f" Max neighbors: {max_neighbors}") + + # Then use the modified zones in kriging npts = lib.calc_kriging_factors_2d( - cp_x, cp_y, cp_zones, - xcentergrid, ycentergrid, grid_zones, - int(config_dict.get("vartype", 2)), # Exponential - int(config_dict.get("krigtype", 1)), # Ordinary kriging - corrlen, aniso, bearing, - config_dict.get("search_radius", 1e20), - config_dict.get("maxpts_interp", 20), - config_dict.get("minpts_interp", 1), + cp_coords[:, 0], cp_coords[:, 1], cp_zones_for_kriging, # Modified zones + xcentergrid.flatten(), ycentergrid.flatten(), zones_for_kriging, # Modified zones + vartype, 1, corrlen_major, anisotropy_ratios, bearing_deg, + max_search_radius, max_neighbors, min_points, fac_fname, fac_ftype ) - # Apply kriging using the component values + print(f" Pypestutils kriging factors calculated for {npts} interpolation points") + + # Apply kriging result = lib.krige_using_file( fac_fname, fac_ftype, - len(xcentergrid), - int(config_dict.get("krigtype", 1)), - "none", # No transformation for individual components - cp_values, - np.mean(cp_values), # Fill value - np.mean(cp_values) # No-interpolation value + len(xcentergrid.flatten()), + 1, # Ordinary kriging + transform_flag, # Pass through the transform flag as specified + cp_values_transformed, # Use values exactly as provided + background_value, # Use background exactly as provided + background_value # Use no-interpolation value exactly as provided ) + # Clean up if os.path.exists(fac_fname): os.remove(fac_fname) - lib.free_all_memory() - return result["targval"] + # Get results - NO back-transformation here, pypestutils handles it + interp_values_1d = result["targval"] - except Exception as e: - # Cleanup and fallback - if os.path.exists(fac_fname): - os.remove(fac_fname) - lib.free_all_memory() + print(f" Pypestutils kriging complete") + print(f" Result range: [{interp_values_1d.min():.6f}, {interp_values_1d.max():.6f}]") - # Simple IDW fallback - return _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid) + if bounds is not None: + interp_values_1d = np.clip(interp_values_1d, min=bounds[0], max=bounds[1]) + return interp_values_1d.reshape(shape) -def _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, power=2): - """Inverse distance weighting fallback for component interpolation.""" - result = np.zeros(len(xcentergrid)) + except Exception as e: + print(f" Error: Pypestutils kriging failed: {e}") + print(f" Falling back to simple IDW interpolation...") - for i in range(len(xcentergrid)): - distances = np.sqrt((cp_x - xcentergrid[i]) ** 2 + (cp_y - ycentergrid[i]) ** 2) - distances = np.maximum(distances, 1e-10) # Avoid division by zero + # Fallback to simple IDW + interp_values_1d = np.full(n_grid, background_value) - weights = 1.0 / (distances ** power) - result[i] = np.sum(weights * cp_values) / np.sum(weights) + for i in range(len(ycentergrid.flatten())): + distances = np.linalg.norm(cp_coords - np.stack([xcentergrid.flatten(), ycentergrid.flatten()]).T[i], axis=1) + distances = np.maximum(distances, 1e-10) - return result + # Simple IDW weights + weights = 1.0 / (distances ** 2) + weights = weights / np.sum(weights) + interp_values_1d[i] = np.sum(weights * cp_values_transformed) -def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, - xcentergrid, ycentergrid, zones_flat, nx, ny): - """IDW interpolation using log-Euclidean approach.""" - n_grid = np.prod(xcentergrid.shape) - result_tensors = np.zeros((n_grid, 2, 2)) + print(f" IDW fallback complete") + print(f" Result range: [{interp_values_1d.min():.6f}, {interp_values_1d.max():.6f}]") - # Convert to log space - log_tensors = np.array([logm(_ensure_positive_definite(tensor)) - for tensor in cp_tensors]) - - # Process each zone - unique_zones = np.unique(zones_flat) - - for zone_id in unique_zones: - zone_mask = zones_flat == zone_id - zone_indices = np.where(zone_mask)[0] - - if len(zone_indices) == 0: - continue - - cp_mask = cp_zones == zone_id - if np.sum(cp_mask) == 0: - default_tensor = np.eye(2) * 1000 ** 2 - result_tensors[zone_indices] = default_tensor - continue + return interp_values_1d.reshape(shape) - zone_cp_coords = cp_coords[cp_mask] - zone_log_tensors = log_tensors[cp_mask] - for idx in zone_indices: - gx, gy = xcentergrid[idx], ycentergrid[idx] - distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + - (zone_cp_coords[:, 1] - gy) ** 2) - distances = np.maximum(distances, 1e-10) +def create_boundary_modified_scalar(base_field, zones, + peak_increase=0.3, transition_cells=5, mode="enhance", + transform=None): + """ + Modify scalar field values near geological zone boundaries. + """ + from scipy.ndimage import distance_transform_edt, gaussian_filter - weights = 1.0 / (distances ** 2) - weights /= np.sum(weights) + if mode not in ("enhance", "smooth"): + raise ValueError("mode must be 'enhance' or 'smooth'") - # Weighted average in log space - log_result = np.sum(weights[:, np.newaxis, np.newaxis] * zone_log_tensors, axis=0) + if zones.shape != base_field.shape: + raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") - try: - tensor_exp = expm(log_result) - result_tensors[idx] = _ensure_positive_definite(tensor_exp) - except: - result_tensors[idx] = np.eye(2) * 1000 ** 2 + boundary_mask, _ = _detect_zone_boundaries(zones) + distance = distance_transform_edt(~boundary_mask) + transition_mask = distance <= transition_cells - return result_tensors + modified = base_field.copy() + smoothed_field = gaussian_filter(base_field, sigma=transition_cells) + if mode == "enhance": + factor = 1 - distance[transition_mask] / transition_cells + modified[transition_mask] = smoothed_field[transition_mask] * (1 + peak_increase * factor) -def _interpolate_tensors_nearest(cp_coords, cp_tensors, cp_zones, - xcentergrid, ycentergrid, zones_flat, nx, ny): - """Nearest neighbor interpolation.""" - n_grid = np.prod(xcentergrid.shape) - result_tensors = np.zeros((n_grid, 2, 2)) + elif mode == "smooth": + # Smooth field with Gaussian filter + #smoothed_field = gaussian_filter(base_field, sigma=transition_cells) + weight = 1 - distance[transition_mask] / transition_cells + modified[transition_mask] = ( + weight * smoothed_field[transition_mask] + + (1 - weight) * base_field[transition_mask] + ) - # Process each zone - unique_zones = np.unique(zones_flat) + print(f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") + return modified - for zone_id in unique_zones: - zone_mask = zones_flat == zone_id - zone_indices = np.where(zone_mask)[0] - if len(zone_indices) == 0: - continue +def save_results(results, out_filename): + """Save results to files in PyEMU-compatible format.""" - cp_mask = cp_zones == zone_id - if np.sum(cp_mask) == 0: - default_tensor = np.eye(2) * 1000 ** 2 - result_tensors[zone_indices] = default_tensor - continue + # Save mean field + mean_file = f"{out_filename}_mean.txt" + np.savetxt(mean_file, results['mean'], fmt="%20.8E") + print(f" Saved mean field to {mean_file}") - zone_cp_coords = cp_coords[cp_mask] - zone_cp_tensors = cp_tensors[cp_mask] + # Save standard deviation field + sd_file = f"{out_filename}_sd.txt" + np.savetxt(sd_file, results['sd'], fmt="%20.8E") + print(f" Saved SD field to {sd_file}") - for idx in zone_indices: - gx, gy = xcentergrid[idx], ycentergrid[idx] - distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + - (zone_cp_coords[:, 1] - gy) ** 2) - nearest_idx = np.argmin(distances) - result_tensors[idx] = zone_cp_tensors[nearest_idx] + # Save geostatistical parameter field + for param in ['bearing', 'anisotropy', 'corrlen']: + param_file = f"{out_filename}_{param}.txt" + np.savetxt(param_file, results[param], fmt="%20.8E") + print(f" Saved {param} field to {param_file}") - return result_tensors + # Save stochastic realizations + if results['field'] is not None: + field_file = f"{out_filename}.arr" + np.savetxt(field_file, results['field'], fmt="%20.8E") + print(f" Saved realization to {field_file}") -def _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx): - """Assign conceptual points to zones based on nearest grid point.""" - grid_coords = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) - cp_zones = np.zeros(len(cp_coords), dtype=int) +def _detect_zone_boundaries(zones): + """Detect boundaries between geological zones.""" + ny, nx = zones.shape + boundary_mask = np.zeros((ny, nx)).astype(bool) + boundary_directions = np.zeros((ny, nx, 2)) - for i, cp_coord in enumerate(cp_coords): - distances = np.sum((grid_coords - cp_coord) ** 2, axis=1) - closest_idx = np.argmin(distances) - cp_zones[i] = zones_flat[closest_idx] + # Find boundary points + for i in range(ny): + for j in range(nx): + current_zone = zones[i, j] - return cp_zones + # Check 8-connected neighbors + for di in [-1, 0, 1]: + for dj in [-1, 0, 1]: + if di == 0 and dj == 0: + continue + ni, nj = i + di, j + dj + if 0 <= ni < ny and 0 <= nj < nx: + if zones[ni, nj] != current_zone: + boundary_mask[i, j] = True + break + if boundary_mask[i, j]: + break + return boundary_mask, boundary_directions -""" -NSAF Utils - Main API functions refactored to use modelgrid consistently -""" +def _ensure_positive_definite(tensor, min_eigenval=1e-8): + """Ensure tensor is positive definite - NO CHANGES.""" + eigenvals, eigenvecs = np.linalg.eigh(tensor) + eigenvals_reg = np.maximum(eigenvals, min_eigenval) + return eigenvecs @ np.diag(eigenvals_reg) @ eigenvecs.T -def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename=None, - n_realizations=1, layer=0, transform="none", - boundary_smooth=False, boundary_enhance=False, - tensor_method='krig', config_dict=None, iids=None, - mean_col='mean', sd_col='sd', **kwargs): - """ - Parameter interpolation and field generation combining NSAF tensor mathematics - with pypestutils FIELDGEN2D_SVA for spatially correlated noise. - REFACTORED to use modelgrid consistently throughout. +def _extract_grid_info(modelgrid): + """Extract grid information from flopy modelgrid object. Parameters ---------- - conceptual_points : pd.DataFrame - Conceptual points DataFrame modelgrid : flopy.discretization.grid - Flopy modelgrid object containing grid information - zones : np.ndarray, optional - Zone array, shape (ny, nx) - out_filename : str, optional - Base filename for output (if None, returns arrays only) - n_realizations : int, default 1 - Number of stochastic realizations to generate - layer : int, default 0 - Layer number to process - transform : str, default "none" - Transformation: "none" or "log" - boundary_smooth : bool, or dict default False - Apply smoothing to mean at zone boundaries - boundary_enhance : bool, default True - Apply variance enhancement at zone boundaries - tensor_method : str, default 'krig' - Tensor interpolation method: 'krig', 'idw', or 'nearest' - config_dict : dict, optional - Configuration parameters for pypestutils - iids : np.ndarray, optional - Pre-generated IIDs with shape (ny*nx, n_realizations) - mean_col : str, default 'mean' - Name of the mean column in conceptual points - sd_col : str, default 'sd' - Name of the standard deviation column in conceptual points + Flopy modelgrid object Returns ------- dict - Dictionary containing: - - 'fields': Generated parameter fields, shape (n_realizations, ny, nx) - - 'mean': Interpolated mean field, shape (ny, nx) - - 'sd': Interpolated standard deviation field, shape (ny, nx) - - 'tensors': Interpolated tensors, shape (ny*nx, 2, 2) + Dictionary with grid information including: + - xcentergrid: X coordinates of cell centers + - ycentergrid: Y coordinates of cell centers + - nx, ny: Grid dimensions + - area: Cell areas + - grid_type: Type of grid """ + grid_info = {} - print("=== Parameter Interpolation and Field Generation ===") - - # Extract grid information from modelgrid - grid_info = _extract_grid_info(modelgrid) - ny = grid_info['ny'] - nx = grid_info['nx'] + # Get grid type + grid_info['grid_type'] = modelgrid.grid_type - # Load conceptual points - if 'layer' in conceptual_points.columns: - layer_cp = conceptual_points[conceptual_points['layer'] == layer].copy() + # Get cell centers + if hasattr(modelgrid, 'xcellcenters'): + grid_info['xcentergrid'] = modelgrid.xcellcenters + grid_info['ycentergrid'] = modelgrid.ycellcenters else: - layer_cp = conceptual_points.copy() - - if len(layer_cp) == 0: - raise ValueError(f"No conceptual points found for layer {layer}") - - # Validate required columns - required_base_cols = ['x', 'y', 'major', 'bearing'] - missing_cols = [col for col in required_base_cols if col not in layer_cp.columns] - if missing_cols: - raise ValueError(f"Missing required columns: {missing_cols}") + raise AttributeError("modelgrid missing xcellcenters/ycellcenters") - # Check for transverse/anisotropy - if 'transverse' not in layer_cp.columns: - if 'anisotropy' in layer_cp.columns: - layer_cp['transverse'] = layer_cp['major'] / layer_cp['anisotropy'] + # Get dimensions + if modelgrid.grid_type == 'structured': + grid_info['ny'] = modelgrid.nrow + grid_info['nx'] = modelgrid.ncol + # Calculate areas from delr, delc + if hasattr(modelgrid, 'delr') and hasattr(modelgrid, 'delc'): + delr = modelgrid.delr + delc = modelgrid.delc + grid_info['area'] = np.outer(delc, delr) else: - raise ValueError("Must have either 'transverse' or 'anisotropy' column") + # Uniform area assumption + grid_info['area'] = np.ones((grid_info['ny'], grid_info['nx'])) + elif modelgrid.grid_type == 'vertex': # untested + # Vertex grid - reshape if needed + xc = grid_info['xcentergrid'] + yc = grid_info['ycentergrid'] + if len(xc.shape) == 1: + grid_info['xcentergrid'] = xc.reshape(-1, 1) + grid_info['ycentergrid'] = yc.reshape(-1, 1) + grid_info['nx'] = 1 + grid_info['ny'] = xc.shape[0] + else: + grid_info['ny'], grid_info['nx'] = xc.shape + # Calculate areas for vertex grid + grid_info['area'] = _calculate_vertex_areas(modelgrid) + else: + # Fallback for other grid types + xc = grid_info['xcentergrid'] + grid_info['ny'], grid_info['nx'] = xc.shape + grid_info['area'] = np.ones((grid_info['ny'], grid_info['nx'])) - # Validate mean and sd columns - if mean_col not in layer_cp.columns: - raise ValueError(f"Mean column '{mean_col}' not found in conceptual points") - if sd_col not in layer_cp.columns: - raise ValueError(f"Standard deviation column '{sd_col}' not found in conceptual points") + return grid_info - print(f"Processing layer {layer} with {len(layer_cp)} conceptual points") - print(f"Grid dimensions: {ny} x {nx} = {ny * nx} cells") - print(f"Using columns: mean='{mean_col}', sd='{sd_col}'") - # Step 1: Interpolate tensors - print("\nStep 1: Interpolating tensor field...") - tensors = interpolate_tensors( - layer_cp, modelgrid, zones=zones, - method=tensor_method, layer=layer, - config_dict=config_dict - ) - - # Step 2: Convert tensors to geostatistical parameters - print("\nStep 2: Converting tensors to geostatistical parameters...") - bearing_deg, anisotropy, corrlen_major = _tensors_to_geostat_params(tensors) - - # Step 3: Prepare conceptual point data - cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) - cp_means = layer_cp[mean_col].values - cp_sd = layer_cp[sd_col].values.clip(1e-8, None) +def _calculate_vertex_areas(modelgrid): + """Calculate cell areas for vertex grid using shoelace formula.""" + ncpl = modelgrid.ncpl if hasattr(modelgrid, 'ncpl') else len(modelgrid.xcellcenters) + areas = np.zeros(ncpl) - print(f"Conceptual point data ranges:") - print(f" {mean_col}: [{cp_means.min():.3f}, {cp_means.max():.3f}]") - print(f" {sd_col}: [{cp_sd.min():.3f}, {cp_sd.max():.3f}]") + for i in range(ncpl): + try: + verts = modelgrid.get_cell_vertices(i) + # Shoelace formula + x = [v[0] for v in verts] + y = [v[1] for v in verts] + area = 0.5 * abs(sum(x[j] * y[j + 1] - x[j + 1] * y[j] for j in range(-1, len(x) - 1))) + areas[i] = area + except: + areas[i] = 1.0 # Default area - # Step 4: Interpolate mean and sd using tensor-aware kriging - print("\nStep 4: Interpolating mean and standard deviation fields...") + # Reshape if needed + if hasattr(modelgrid, 'ncpl'): + return areas.reshape(-1, 1) + else: + return areas.reshape(modelgrid.nrow, modelgrid.ncol) - # Configure kriging parameters - if config_dict is None: - config_dict = { - 'vartype': 'gaussian', - 'krigtype': 1, - 'search_radius': 1e10, - 'maxpts_interp': 20, - 'minpts_interp': 1 - } - # Transform for log-domain if needed - transform = 'log' if transform == 'log' else None - min_mean = 1e-8 if transform == 'log' else None - max_mean = 1e8 if transform == 'log' else None - bounds = (min_mean, max_mean) +def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, + xcentergrid, ycentergrid, zones_flat, config_dict, nx, ny): + """Interpolate tensors using pypestutils kriging with log-Euclidean approach.""" + n_grid = np.prod(xcentergrid.shape) + result_tensors = np.zeros((n_grid, 2, 2)) - # Interpolate mean - print(" Interpolating mean...") - interp_means_2d = tensor_aware_kriging( - cp_coords, cp_means, modelgrid, tensors, - variogram_model='exponential', - background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform=transform, bounds=bounds, - max_neighbors=4, zones=zones - ) + # Convert tensors to log space + log_tensors = np.array([logm(_ensure_positive_definite(tensor)) + for tensor in cp_tensors]) - # Apply boundary smoothing to mean - if boundary_smooth and zones is not None: - if isinstance(boundary_smooth, dict): - smooth_params = {**boundary_smooth, **boundary_smooth} - else: # Must be True (boolean) - smooth_params = boundary_smooth + # Process each zone separately + unique_zones = np.unique(zones_flat) - print(" Smoothing mean at geological boundaries...") - interp_means_2d = create_boundary_modified_scalar( - interp_means_2d, zones, transform=transform, - transition_cells=smooth_params['transition_cells'], - mode='smooth' - ) + for zone_id in unique_zones: + zone_mask = zones_flat == zone_id + zone_indices = np.where(zone_mask)[0] - min_sd = 1e-8 if transform == 'log' else None - max_sd = 1e8 if transform == 'log' else None - bounds = (min_sd, max_sd) + if len(zone_indices) == 0: + continue - # Interpolate standard deviation - print(" Interpolating standard deviation...") - interp_sd_2d = tensor_aware_kriging( - cp_coords, cp_sd, modelgrid, tensors, - variogram_model='exponential', - background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform=transform, bounds=bounds, - max_neighbors=4, zones=zones, - ) + # Filter conceptual points for this zone + cp_mask = cp_zones == zone_id + if np.sum(cp_mask) == 0: + # No conceptual points in this zone - use default tensor + default_tensor = np.eye(2) * 1000 ** 2 + result_tensors[zone_indices] = default_tensor + continue - # Apply boundary enhancement to sd - if boundary_enhance and zones is not None: - if isinstance(boundary_smooth, dict): - smooth_params = {**boundary_enhance, **boundary_smooth} - else: # Must be True (boolean) - smooth_params = boundary_enhance - print(" Enhancing variance at geological boundaries...") - interp_sd_2d = create_boundary_modified_scalar( - interp_sd_2d, zones, transform=transform, - peak_increase=smooth_params['peak_increase'], - transition_cells=smooth_params['transition_cells'], - mode='enhance' - ) + zone_cp_coords = cp_coords[cp_mask] + zone_log_tensors = log_tensors[cp_mask] + zone_xcentergrid = xcentergrid[zone_indices] + zone_ycentergrid = ycentergrid[zone_indices] - print(f"Interpolated field ranges:") - print(f" Mean: [{interp_means_2d.min():.3f}, {interp_means_2d.max():.3f}]") - print(f" SD: [{interp_sd_2d.min():.3f}, {interp_sd_2d.max():.3f}]") + # Interpolate each tensor component separately + zone_tensors = np.zeros((len(zone_indices), 2, 2)) - # Step 5: Generate stochastic fields using FIELDGEN2D_SVA approach - print(f"\nStep 5: Generating {n_realizations} stochastic realizations...") + for i in range(2): + for j in range(i, 2): # Only upper triangle, then mirror + component_values = zone_log_tensors[:, i, j].real - if n_realizations > 0: - # Get active cells if available - active = kwargs.get('active', None) - if active is None and hasattr(modelgrid, 'idomain'): - active = modelgrid.idomain[layer-1].flatten() if modelgrid.idomain.ndim == 3 else (modelgrid.idomain.flatten()) + # Use pypestutils for kriging + try: + from pypestutils.pestutilslib import PestUtilsLib + interp_component = _krig_component_pypestutils( + zone_cp_coords[:, 0], zone_cp_coords[:, 1], + component_values, zone_xcentergrid, zone_ycentergrid, + config_dict + ) + except Exception as e: + raise Exception(f"Error importing pypestutils, using python based method: {e}") + # todo: change to warning use other method - fields = _generate_stochastic_fields( - modelgrid=modelgrid, - mean_field=interp_means_2d, - sd_field=interp_sd_2d, - bearing=bearing_deg, - anisotropy=anisotropy, - corrlen=corrlen_major, - n_realizations=n_realizations, - transform='n', - config_dict=config_dict, - active=active - ) - else: - fields = None + zone_tensors[:, i, j] = interp_component + if i != j: + zone_tensors[:, j, i] = interp_component - # Prepare output - results = { - 'fields': fields, - 'mean': interp_means_2d, - 'sd': interp_sd_2d, - 'tensors': tensors, - 'bearing': bearing_deg.reshape(ny, nx), - 'anisotropy': anisotropy.reshape(ny, nx), - 'corrlen': corrlen_major.reshape(ny, nx) - } + # Convert back from log space and ensure positive definiteness + for k in range(len(zone_indices)): + try: + tensor_exp = expm(zone_tensors[k]) + result_tensors[zone_indices[k]] = _ensure_positive_definite(tensor_exp) + except: + # Fallback to default tensor + result_tensors[zone_indices[k]] = np.eye(2) * 1000 ** 2 - # Save to files if requested - if out_filename is not None: - save_results(results, out_filename, transform) + return result_tensors - print("=== Interpolation complete ===") - return results -def _generate_stochastic_fields(modelgrid, mean_field, sd_field, - bearing, anisotropy, corrlen, - transform, config_dict, iids=None, area=None, - n_realizations=1, active=None): - """ - Generate stochastic fields using pypestutils FIELDGEN2D_SVA. - FINAL FIXED version with correct reshaping logic. - """ +def _krig_component_pypestutils(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, config_dict): + """Use pypestutils to krig a single tensor component.""" try: from pypestutils.pestutilslib import PestUtilsLib except Exception as e: raise Exception(f"Error importing pypestutils: {e}") + # todo: change to warning use other method - print(" Generating stochastic fields using FIELDGEN2D_SVA...") - - # Prepare grid data - grid_info = _extract_grid_info(modelgrid) - xcentergrid = grid_info['xcentergrid'] - ycentergrid = grid_info['ycentergrid'] - ny = grid_info['ny'] - nx = grid_info['nx'] - area = grid_info['area'] - - n_points = ny * nx - - # Flatten fields - ENSURE PROPER ORDERING - mean_flat = mean_field.flatten().clip(1e-8, None) - variance_native = sd_field.flatten() ** 2 - log_variance_flat = np.log(1 + variance_native / mean_flat ** 2) - trans_flag = 1 # Log domain + lib = PestUtilsLib() - # Geostatistical parameters from tensors - aa_values = corrlen.flatten() - aniso_values = anisotropy.flatten() - bearing_values = bearing.flatten() + # Create temporary factor file + fac_fname = "temp_tensor_component.fac" + if os.path.exists(fac_fname): + os.remove(fac_fname) + fac_ftype = "text" - # FIXED: domain based on zones - if active is None: - active = np.ones(n_points) - active = active.astype(int) - if area is None: - area = np.ones(n_points) - else: - area = area.flatten() + # All points in same zone for component interpolation + cp_zones = np.ones(len(cp_x), dtype=int) + grid_zones = np.ones(len(xcentergrid), dtype=int) - # Generate or use provided IIDs - if iids is None: - print(" Generating new IIDs...") - iids = np.random.normal(0, 1, size=(n_points, n_realizations)) - else: - print(" Using provided IIDs...") - if iids.shape != (n_points, n_realizations): - raise ValueError(f"IIDs shape {iids.shape} doesn't match expected {(n_points, n_realizations)}") + # Use isotropic parameters for component interpolation + x_extent = np.max(xcentergrid) - np.min(xcentergrid) + y_extent = np.max(ycentergrid) - np.min(ycentergrid) + domain_extent = max(x_extent, y_extent) + corrlen = np.full(len(xcentergrid), np.max([10000.0, domain_extent/3])) + aniso = np.ones(len(xcentergrid)) # Isotropic + bearing = np.zeros(len(xcentergrid)) # No rotation try: - # Set up pypestutils parameters - lib = PestUtilsLib() - lib.initialize_randgen(11235813) - - # Averaging function type - avg_func = config_dict.get('averaging_function', 'gaussian') - if isinstance(avg_func, dict): - if 'pow' in avg_func: - avg_flag = 4 - power_value = avg_func['pow'] - else: - avg_flag, power_value = next(iter(avg_func.items())) - else: - power_value = 0 - if avg_func == 'exponential': - avg_flag = 2 - elif avg_func == 'gaussian': - avg_flag = 3 - elif avg_func == 'spherical': - avg_flag = 1 - else: - avg_flag = 2 # Default to exponential - - # Call FIELDGEN2D_SVA through pypestutils interface, does not take iid file - # Need FIELDGEN2D_SVA1 to pass iid files, but can't access in current ppu - print(f" Calling FIELDGEN2D_SVA for {n_realizations} realizations...") - - fields = lib.fieldgen2d_sva( - xcentergrid.flatten(), ycentergrid.flatten(), - area=area, - active=active, - mean=mean_flat, - var=log_variance_flat, - aa=aa_values, - anis=aniso_values, - bearing=bearing_values, - transtype=trans_flag, - avetype=avg_flag, - power=power_value, - nreal=n_realizations, + npts = lib.calc_kriging_factors_2d( + cp_x, cp_y, cp_zones, + xcentergrid, ycentergrid, grid_zones, + int(config_dict.get("vartype", 2)), # Exponential + int(config_dict.get("krigtype", 1)), # Ordinary kriging + corrlen, aniso, bearing, + config_dict.get("search_radius", 1e20), + config_dict.get("maxpts_interp", 20), + config_dict.get("minpts_interp", 1), + fac_fname, fac_ftype ) - # FIELDGEN2D_SVA returns n_realizations, we use 1 - if fields.shape == (n_points, n_realizations): - # Output is (n_points, n_realizations) - transpose to (n_realizations, n_points) - fields = fields.T # Now shape is (n_realizations, n_points) - # Reshape each realization to (ny, nx) - fields = fields.reshape((n_realizations, ny, nx)) - - elif fields.shape == (n_realizations * n_points,): - # Output is flattened - reshape directly - fields = fields.reshape((n_realizations, ny, nx)) - - elif fields.shape == (n_realizations, n_points): - # Output is already (n_realizations, n_points) - just reshape spatial dimensions - fields = fields.reshape((n_realizations, ny, nx)) - - else: - raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") + # Apply kriging using the component values + result = lib.krige_using_file( + fac_fname, fac_ftype, + len(xcentergrid), + int(config_dict.get("krigtype", 1)), + "none", # No transformation for individual components + cp_values, + np.mean(cp_values), # Fill value + np.mean(cp_values) # No-interpolation value + ) - print(f" Successfully generated {n_realizations} realizations with shape {fields.shape}") + if os.path.exists(fac_fname): + os.remove(fac_fname) - # Clean up lib.free_all_memory() - return fields + return result["targval"] except Exception as e: - print(f" Warning: FIELDGEN2D_SVA failed: {e}") - print(f" Using fallback method...") + # Cleanup and fallback + if os.path.exists(fac_fname): + os.remove(fac_fname) + lib.free_all_memory() - # Fallback to simple field generation - fields = np.zeros((n_realizations, ny, nx)) - for real in range(n_realizations): - current_iids = iids[:, real] - field_values = _generate_simple_field( - modelgrid, mean_flat, log_variance_flat, - aa_values, aniso_values, bearing_values, - transform, current_iids - ) - fields[real] = field_values.reshape(ny, nx) + # Simple IDW fallback + return _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid) - return fields -def _generate_simple_field(modelgrid, mean_flat, variance_flat, - aa_values, aniso_values, bearing_values, - transform, iids): - """ - IMPROVED simple fallback field generation if FIELDGEN2D_SVA fails. - Fixed to eliminate checkering and provide proper spatial correlation. - """ - print(" Using improved simple correlated field generation fallback...") - grid_info = _extract_grid_info(modelgrid) - xcentergrid = grid_info['xcentergrid'] - ycentergrid = grid_info['ycentergrid'] - grid_coords = np.stack([xcentergrid, ycentergrid]) - n_points = len(xcentergrid) - - # Convert bearing from degrees to radians for calculations - bearing_rad = np.radians(bearing_values) - - # Create spatially correlated field using moving average approach - correlated_field = np.zeros(n_points) - - # For computational efficiency, use a subset of points for correlation - # This prevents the O(n²) complexity from being too slow - max_correlation_points = min(2000, n_points) - - if n_points > max_correlation_points: - # Sample points for correlation calculation - correlation_indices = np.random.choice(n_points, max_correlation_points, replace=False) - correlation_indices = np.sort(correlation_indices) - else: - correlation_indices = np.arange(n_points) - - # Apply spatial correlation - for i in correlation_indices: - # Get local correlation parameters - local_aa = aa_values[i] - local_aniso = aniso_values[i] - local_bearing = bearing_rad[i] - - # Calculate distances to all points - dx = xcentergrid[:] - xcentergrid[i] - dy = ycentergrid[:] - ycentergrid[i] - - # Apply anisotropic transformation - # Rotate coordinates to align with anisotropy ellipse - cos_bear = np.cos(local_bearing) - sin_bear = np.sin(local_bearing) - - # Rotate to principal axes (geological bearing: 0° = North, clockwise positive) - dx_rot = dx * sin_bear + dy * cos_bear # East component (major axis) - dy_rot = -dx * cos_bear + dy * sin_bear # North component (minor axis) - - # Scale by anisotropy (major axis has length aa, minor axis has length aa/aniso) - minor_aa = local_aa / local_aniso - dx_scaled = dx_rot / local_aa - dy_scaled = dy_rot / minor_aa - - # Calculate anisotropic distance - aniso_distances = np.sqrt(dx_scaled ** 2 + dy_scaled ** 2) - - # Exponential correlation function - correlation_weights = np.exp(-aniso_distances) - - # Normalize weights - correlation_weights = correlation_weights / np.sum(correlation_weights) +def _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, power=2): + """Inverse distance weighting fallback for component interpolation.""" + result = np.zeros(len(xcentergrid)) - # Apply correlation to the random field - correlated_field[i] = np.sum(correlation_weights.flatten() * iids) + for i in range(len(xcentergrid)): + distances = np.sqrt((cp_x - xcentergrid[i]) ** 2 + (cp_y - ycentergrid[i]) ** 2) + distances = np.maximum(distances, 1e-10) # Avoid division by zero - # For points not in correlation_indices, use interpolation - if n_points > max_correlation_points: - # Simple interpolation for remaining points - remaining_indices = np.setdiff1d(np.arange(n_points), correlation_indices) + weights = 1.0 / (distances ** power) + result[i] = np.sum(weights * cp_values) / np.sum(weights) - for i in remaining_indices: - # Find nearest correlation points - distances_to_corr = np.sum((grid_coords[correlation_indices] - grid_coords[i]) ** 2, axis=1) - nearest_corr_idx = correlation_indices[np.argmin(distances_to_corr)] - correlated_field[i] = correlated_field[nearest_corr_idx] + return result - # Scale by local standard deviation - stochastic_component = correlated_field * np.sqrt(variance_flat) - # Combine with mean field - if transform == 'log': - # Log domain: mean * exp(stochastic_component) - field_values = mean_flat * np.exp(stochastic_component) - else: - # Normal domain: mean + stochastic_component - field_values = mean_flat + stochastic_component +def _interpolate_tensors_idw(cp_coords, cp_tensors, cp_zones, + xcentergrid, ycentergrid, zones_flat, nx, ny): + """IDW interpolation using log-Euclidean approach.""" + n_grid = np.prod(xcentergrid.shape) + result_tensors = np.zeros((n_grid, 2, 2)) - return field_values + # Convert to log space + log_tensors = np.array([logm(_ensure_positive_definite(tensor)) + for tensor in cp_tensors]) + # Process each zone + unique_zones = np.unique(zones_flat) -def _tensors_to_geostat_params(tensors): - """Convert tensor field to geostatistical parameters.""" - n_points = len(tensors) - bearing_deg = np.zeros(n_points) - anisotropy = np.zeros(n_points) - corrlen_major = np.zeros(n_points) + for zone_id in unique_zones: + zone_mask = zones_flat == zone_id + zone_indices = np.where(zone_mask)[0] - for i, tensor in enumerate(tensors): - # Skip default tensors - if np.allclose(tensor, np.eye(2) * 1000000): - bearing_deg[i] = 0.0 - anisotropy[i] = 1.0 - corrlen_major[i] = 1000.0 + if len(zone_indices) == 0: continue - try: - # Eigendecomposition - eigenvals, eigenvecs = np.linalg.eigh(tensor) - - # Sort by eigenvalue magnitude - idx = np.argsort(eigenvals)[::-1] - eigenvals = eigenvals[idx] - eigenvecs = eigenvecs[:, idx] - - # Extract parameters - major_length = np.sqrt(eigenvals[0]) - minor_length = np.sqrt(eigenvals[1]) - - corrlen_major[i] = major_length - anisotropy[i] = major_length / minor_length - - # Calculate bearing from major eigenvector - major_vec = eigenvecs[:, 0] - # Convert to geological bearing (0° = North, clockwise positive) - bearing_rad = np.arctan2(major_vec[0], major_vec[1]) - bearing_deg[i] = np.degrees(bearing_rad) % 360 - - except: - # Fallback values - bearing_deg[i] = 0.0 - anisotropy[i] = 1.0 - corrlen_major[i] = 1000.0 + cp_mask = cp_zones == zone_id + if np.sum(cp_mask) == 0: + default_tensor = np.eye(2) * 1000 ** 2 + result_tensors[zone_indices] = default_tensor + continue - return bearing_deg, anisotropy, corrlen_major + zone_cp_coords = cp_coords[cp_mask] + zone_log_tensors = log_tensors[cp_mask] -def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, - tensor_interp='idw', transform='log', layer=1, - boundary_smooth=False, boundary_enhance=False, - mean_col='mean', sd_col='sd', n_realizations=1, save_dir='.'): - """ - Generate single layer using NSAF approach with boundary adjustments. - REFACTORED to use modelgrid and call apply_nsaf_hyperpars internally. + for idx in zone_indices: + gx, gy = xcentergrid[idx], ycentergrid[idx] + distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + + (zone_cp_coords[:, 1] - gy) ** 2) + distances = np.maximum(distances, 1e-10) - Parameters - ---------- - conceptual_points : pd.DataFrame - Conceptual points DataFrame - modelgrid : flopy.discretization.grid - Flopy modelgrid object - iids : np.ndarray, optional - Pre-generated noise, shape (ny*nx, n_realizations) - zones : np.ndarray, optional - Zone IDs, shape (ny, nx) - tensor_interp : str, default 'idw' - Tensor interpolation method - transform : str, default 'log' - Transformation: 'log' or 'none' - boundary_smooth : bool, default True - Apply smoothing to mean at zone boundaries - boundary_enhance : bool, default True - Apply variance enhancement at zone boundaries - mean_col : str, default 'mean' - Name of mean column in conceptual points - sd_col : str, default 'sd' - Name of sd column in conceptual points - n_realizations : int, default 1 - Number of realizations to generate + weights = 1.0 / (distances ** 2) + weights /= np.sum(weights) - Returns - ------- - tuple - (fields, results_dict) where: - - fields: Generated field(s), shape (n_realizations, ny, nx) or (ny, nx) if n_realizations=1 - - results_dict: Full results dictionary from apply_nsaf_hyperpars - """ + # Weighted average in log space + log_result = np.sum(weights[:, np.newaxis, np.newaxis] * zone_log_tensors, axis=0) - # Call the main function with single layer settings - results = apply_nsaf_hyperpars( - conceptual_points=conceptual_points, - modelgrid=modelgrid, - zones=zones, - out_filename=None, - n_realizations=n_realizations, - layer=layer, - transform=transform, - boundary_smooth=boundary_smooth, - boundary_enhance=boundary_enhance, - tensor_method=tensor_interp, - config_dict=None, - iids=iids, - mean_col=mean_col, - sd_col=sd_col - ) + try: + tensor_exp = expm(log_result) + result_tensors[idx] = _ensure_positive_definite(tensor_exp) + except: + result_tensors[idx] = np.eye(2) * 1000 ** 2 - return results + return result_tensors -def tensor_aware_kriging(cp_coords, cp_values, modelgrid, interp_tensors, - variogram_model='exponential', - background_value=0.0, max_search_radius=1e20, min_points=1, - max_neighbors=4, transform=None, bounds=(1e-8, 1e8), - zones=None, n_dims=2): - """ - Tensor-aware kriging using pypestutils with anisotropic correlation structures. - Clean version without automatic transform detection. - """ - try: - from pypestutils.pestutilslib import PestUtilsLib - except Exception as e: - raise Exception(f"Error importing pypestutils: {e}") - # Get grid shape - grid_info = _extract_grid_info(modelgrid) - xcentergrid = grid_info['xcentergrid'] - ycentergrid = grid_info['ycentergrid'] +def _interpolate_tensors_nearest(cp_coords, cp_tensors, cp_zones, + xcentergrid, ycentergrid, zones_flat, nx, ny): + """Nearest neighbor interpolation.""" n_grid = np.prod(xcentergrid.shape) + result_tensors = np.zeros((n_grid, 2, 2)) - print(f" Tensor-aware kriging: {len(cp_coords)} conceptual points -> {len(xcentergrid)} grid points") - print( - f" Conceptual points range: X=[{cp_coords[:, 0].min():.1f}, {cp_coords[:, 0].max():.1f}], Y=[{cp_coords[:, 1].min():.1f}, {cp_coords[:, 1].max():.1f}]") - print( - f" Grid points range: X=[{xcentergrid[0].min():.1f}, {xcentergrid[0].max():.1f}], Y=[{ycentergrid[1].min():.1f}, {ycentergrid[1].max():.1f}]") - print(f" Value range: [{cp_values.min():.6f}, {cp_values.max():.6f}], background: {background_value:.6f}") - print(f" Transform requested: {transform}") - - # Handle zones shape inference - if zones is not None: - shape = zones.shape - if n_dims == 2: - ny, nx = shape - else: - nz, ny, nx = shape - print(f" Using zones with shape: {shape}") - else: - unique_x = np.unique(xcentergrid) - unique_y = np.unique(ycentergrid) - nx = len(unique_x) - ny = len(unique_y) - - if nx * ny != n_grid: - side_length = int(np.sqrt(n_grid)) - nx = ny = side_length - print(f" Warning: Could not infer grid dimensions, using square grid {nx}x{ny}") - else: - print(f" Inferred grid dimensions: ny={ny}, nx={nx}") - - shape = (ny, nx) - - # Convert tensors to geostatistical parameters for pypestutils - print(" Converting tensors to geostatistical parameters...") - bearing_deg, anisotropy_ratios, corrlen_major = _tensors_to_geostat_params(interp_tensors) - - # Handle zones - if zones is not None: - zones_flat = zones.flatten().astype(int) - cp_zones = _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx) - else: - zones_flat = np.ones(n_grid, dtype=int) - cp_zones = np.ones(len(cp_coords), dtype=int) - - # Handle transformation - NO automatic detection, just pass through - cp_values_transformed = cp_values.copy() # Always use values as-is - - # Set pypestutils transform flag - if transform == 'log': - transform_flag = "log" - print(f" Using log transform in pypestutils") - else: - transform_flag = "none" - print(f" Using no transform in pypestutils") - - print(f" Input values to pypestutils: [{cp_values_transformed.min():.6f}, {cp_values_transformed.max():.6f}]") - - # Use pypestutils for kriging - print(" Calling pypestutils for tensor-aware kriging...") - - def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): - """ - Ensure every zone in the grid either: - 1) Has >= min_points control points, OR - 2) Gets merged with a viable neighboring zone - """ - zones_fixed = zones_flat.copy() - cp_zones_fixed = cp_zones.copy() - - unique_grid_zones = np.unique(zones_flat) - - zones_to_eliminate = [] + # Process each zone + unique_zones = np.unique(zones_flat) - for zone in unique_grid_zones: - cp_count = np.sum(cp_zones == zone) - grid_count = np.sum(zones_flat == zone) + for zone_id in unique_zones: + zone_mask = zones_flat == zone_id + zone_indices = np.where(zone_mask)[0] - if cp_count < min_points: - print(f" Zone {zone}: {cp_count} CPs, {grid_count} grid cells - ELIMINATING") - zones_to_eliminate.append(zone) + if len(zone_indices) == 0: + continue - if zones_to_eliminate: - # Find the zone with the most control points to use as the "receiver" - viable_zones = [z for z in unique_grid_zones if z not in zones_to_eliminate] - if viable_zones: - # Pick the zone with most control points - best_zone = max(viable_zones, key=lambda z: np.sum(cp_zones == z)) - print(f" Consolidating {len(zones_to_eliminate)} bad zones into zone {best_zone}") + cp_mask = cp_zones == zone_id + if np.sum(cp_mask) == 0: + default_tensor = np.eye(2) * 1000 ** 2 + result_tensors[zone_indices] = default_tensor + continue - for bad_zone in zones_to_eliminate: - # Move all grid cells from bad zone to best zone - zones_fixed[zones_flat == bad_zone] = best_zone - # Move any control points (though there shouldn't be many) - cp_zones_fixed[cp_zones == bad_zone] = best_zone - else: - # All zones are bad - just make everything zone 1 - print(" All zones insufficient - making single zone") - zones_fixed = np.ones_like(zones_flat) - cp_zones_fixed = np.ones_like(cp_zones) + zone_cp_coords = cp_coords[cp_mask] + zone_cp_tensors = cp_tensors[cp_mask] - # Final verification - final_zones = np.unique(zones_fixed) - print(f" Final zones: {final_zones}") - for zone in final_zones: - cp_count = np.sum(cp_zones_fixed == zone) - grid_count = np.sum(zones_fixed == zone) - print(f" Zone {zone}: {cp_count} CPs, {grid_count} grid cells") + for idx in zone_indices: + gx, gy = xcentergrid[idx], ycentergrid[idx] + distances = np.sqrt((zone_cp_coords[:, 0] - gx) ** 2 + + (zone_cp_coords[:, 1] - gy) ** 2) + nearest_idx = np.argmin(distances) + result_tensors[idx] = zone_cp_tensors[nearest_idx] - if cp_count < min_points: - print(f" WARNING: Zone {zone} still has insufficient points!") + return result_tensors - return zones_fixed, cp_zones_fixed - # Use it: - zones_for_kriging, cp_zones_for_kriging = fix_zones_for_pypestutils( - zones_flat, cp_zones, min_points - ) +def _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx): + """Assign conceptual points to zones based on nearest grid point.""" + grid_coords = np.column_stack([xcentergrid.flatten(), ycentergrid.flatten()]) + cp_zones = np.zeros(len(cp_coords), dtype=int) - try: - lib = PestUtilsLib() + for i, cp_coord in enumerate(cp_coords): + distances = np.sum((grid_coords - cp_coord) ** 2, axis=1) + closest_idx = np.argmin(distances) + cp_zones[i] = zones_flat[closest_idx] - # Create temporary factor file - fac_fname = "temp_tensor_aware_kriging.fac" - if os.path.exists(fac_fname): - os.remove(fac_fname) - fac_ftype = "text" + return cp_zones - # Set up variogram type - if variogram_model == 'exponential': - vartype = 2 - elif variogram_model == 'gaussian': - vartype = 3 - elif variogram_model == 'spherical': - vartype = 1 - else: - vartype = 2 # Default to exponential - print(f" Pypestutils parameters:") - print(f" Variogram: {variogram_model} (type {vartype})") - print(f" Transform: {transform_flag}") - print(f" Search radius: {max_search_radius}") - print(f" Max neighbors: {max_neighbors}") +def _generate_stochastic_field(modelgrid, mean_field, sd_field, + bearing, anisotropy, corrlen, + sd_is_log_space, config_dict, iids=None, area=None, + active=None): + """ + Generate a single stochastic field realization. - # Then use the modified zones in kriging - npts = lib.calc_kriging_factors_2d( - cp_coords[:, 0], cp_coords[:, 1], cp_zones_for_kriging, # Modified zones - xcentergrid.flatten(), ycentergrid.flatten(), zones_for_kriging, # Modified zones - vartype, 1, corrlen_major, anisotropy_ratios, bearing_deg, - max_search_radius, max_neighbors, min_points, - fac_fname, fac_ftype - ) + Note: FIELDGEN2D_SVA1 is required for external iid files but not yet in pypestutils. + When iids are provided, uses Python fallback implementation. + When iids are None, uses FIELDGEN2D_SVA but only returns first realization. - print(f" Pypestutils kriging factors calculated for {npts} interpolation points") + Parameters + ---------- + modelgrid : flopy modelgrid + mean_field : np.ndarray + Mean field values, shape (ny, nx) + sd_field : np.ndarray + Standard deviation field values, shape (ny, nx) + bearing : np.ndarray + Bearing field values in degrees, shape (ny, nx) + anisotropy : np.ndarray + Anisotropy field values, shape (ny, nx) + corrlen : np.ndarray + Correlation length field values, shape (ny, nx) + sd_is_log_space : str + how stochasticity is expressed, 0 for native, 1 for log + config_dict : dict + Configuration dictionary with averaging function settings + iids : np.ndarray, optional + Independent identically distributed random values, shape (ny*nx,) + If provided, uses fallback method. If None, uses FIELDGEN2D_SVA. + area : np.ndarray, optional + Cell areas, shape (ny*nx,) + active : np.ndarray, optional + Active domain mask, shape (ny*nx,) - # Apply kriging - result = lib.krige_using_file( - fac_fname, fac_ftype, - len(xcentergrid.flatten()), - 1, # Ordinary kriging - transform_flag, # Pass through the transform flag as specified - cp_values_transformed, # Use values exactly as provided - background_value, # Use background exactly as provided - background_value # Use no-interpolation value exactly as provided - ) + Returns + ------- + np.ndarray + Generated field, shape (ny, nx) + """ + try: + from pypestutils.pestutilslib import PestUtilsLib + plib = PestUtilsLib() + plib.initialize_randgen(11235813) + except Exception as e: + raise Exception(f"Error importing pypestutils: {e}") - # Clean up - if os.path.exists(fac_fname): - os.remove(fac_fname) - lib.free_all_memory() + # Prepare grid data + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + ny = grid_info['ny'] + nx = grid_info['nx'] - # Get results - NO back-transformation here, pypestutils handles it - interp_values_1d = result["targval"] + if area is None: + area = grid_info['area'] - print(f" Pypestutils kriging complete") - print(f" Result range: [{interp_values_1d.min():.6f}, {interp_values_1d.max():.6f}]") + n_points = ny * nx - if bounds is not None: - interp_values_1d = np.clip(interp_values_1d, min=bounds[0], max=bounds[1]) + # Flatten fields + mean_flat = mean_field.flatten().clip(1e-8, None) # Always native space + sd_flat = sd_field.flatten() - return interp_values_1d.reshape(shape) + # Compute variance and set transtype based on what space sd_field is in + if sd_is_log_space: + # SD is in log space, variance is sd^2 + variance_flat = sd_flat ** 2 + trans_flag = 1 # Tell FIELDGEN2D_SVA: variance is in log space + else: + # SD is in native space, variance is sd^2 + variance_flat = sd_flat ** 2 + trans_flag = 0 # Tell FIELDGEN2D_SVA: variance is in native space - except Exception as e: - print(f" Error: Pypestutils kriging failed: {e}") - print(f" Falling back to simple IDW interpolation...") + # Geostatistical parameters from tensors + aa_values = corrlen.flatten() + aniso_values = anisotropy.flatten() + bearing_values = bearing.flatten() - # Fallback to simple IDW - interp_values_1d = np.full(n_grid, background_value) + # Domain based on zones + if active is None: + active = np.ones(n_points, dtype=int) + else: + active = active.astype(int).flatten() - for i in range(len(ycentergrid.flatten())): - distances = np.linalg.norm(cp_coords - np.stack([xcentergrid.flatten(), ycentergrid.flatten()]).T[i], axis=1) - distances = np.maximum(distances, 1e-10) + if area is not None: + area = area.flatten() - # Simple IDW weights - weights = 1.0 / (distances ** 2) - weights = weights / np.sum(weights) + # Handle IID-based generation (external control for PEST) + if iids is not None: + print(" Using provided IIDs for single realization...") + if iids.shape[0] != n_points: + raise ValueError(f"IIDs shape {iids.shape} doesn't match expected ({n_points},)") - interp_values_1d[i] = np.sum(weights * cp_values_transformed) + print(f" FIELDGEN2D_SVA1 (with external iids) not available in pypestutils, using fallback method...") - print(f" IDW fallback complete") - print(f" Result range: [{interp_values_1d.min():.6f}, {interp_values_1d.max():.6f}]") + # Use fallback method for single realization + field_values = _generate_simple_field( + modelgrid, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + sd_is_log_space, iids + ) + field = field_values.reshape(ny, nx) - return interp_values_1d.reshape(shape) + return field -def create_boundary_modified_scalar(base_field, zones, - peak_increase=0.3, transition_cells=5, mode="enhance", - transform=None): - """ - Modify scalar field values near geological zone boundaries. - """ - from scipy.ndimage import distance_transform_edt, gaussian_filter + # Handle FIELDGEN2D_SVA generation (internal random generation) + else: + try: + # Averaging function type + avg_func = config_dict.get('averaging_function', 'gaussian') + if isinstance(avg_func, dict): + if 'pow' in avg_func: + avg_flag = 4 + power_value = avg_func['pow'] + else: + avg_flag, power_value = next(iter(avg_func.items())) + else: + power_value = 0 + if avg_func == 'exponential': + avg_flag = 2 + elif avg_func == 'gaussian': + avg_flag = 3 + elif avg_func == 'spherical': + avg_flag = 1 + else: + avg_flag = 2 # Default to exponential + fields = plib.fieldgen2d_sva( + xcentergrid.flatten(), ycentergrid.flatten(), + area=area, + active=active, + mean=mean_flat, + var=variance_flat, + aa=aa_values, + anis=aniso_values, + bearing=bearing_values, + transtype=sd_is_log_space, + avetype=avg_flag, + power=power_value, + nreal=1, # Only request 1 realization + ) - if mode not in ("enhance", "smooth"): - raise ValueError("mode must be 'enhance' or 'smooth'") + # Handle different possible output shapes from FIELDGEN2D_SVA + if fields.shape == (n_points, 1): + # Output is (n_points, 1) - extract and reshape + field = fields[:, 0].reshape(ny, nx) + elif fields.shape == (1, n_points): + # Output is (1, n_points) - extract and reshape + field = fields[0, :].reshape(ny, nx) + elif fields.shape == (n_points,): + # Output is flattened single realization + field = fields.reshape(ny, nx) + else: + raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") - if zones.shape != base_field.shape: - raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") + print(f" Successfully generated field with shape {field.shape}") - boundary_mask, _ = detect_zone_boundaries(zones) - distance = distance_transform_edt(~boundary_mask) - transition_mask = distance <= transition_cells + # Clean up + plib.free_all_memory() - modified = base_field.copy() + return field - if mode == "enhance": - factor = 1 - distance[transition_mask] / transition_cells - modified[transition_mask] *= (1 + peak_increase * factor) + except Exception as e: + print(f" Error: FIELDGEN2D_SVA failed: {e}") + print(f" Falling back to simple field generation with random iids...") - elif mode == "smooth": - # Smooth field with Gaussian filter - smoothed_field = gaussian_filter(base_field, sigma=transition_cells) - weight = 1 - distance[transition_mask] / transition_cells - modified[transition_mask] = ( - weight * smoothed_field[transition_mask] + - (1 - weight) * base_field[transition_mask] - ) + # Generate random iids and use fallback + random_iids = np.random.normal(0, 1, size=n_points) + field_values = _generate_simple_field( + modelgrid, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + sd_is_log_space, random_iids + ) + field = field_values.reshape(ny, nx) - print(f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} {np.count_nonzero(transition_mask)} points near boundaries") - return modified + return field -def detect_zone_boundaries(zones): - """Detect boundaries between geological zones.""" - ny, nx = zones.shape - boundary_mask = np.zeros((ny, nx)).astype(bool) - boundary_directions = np.zeros((ny, nx, 2)) +def _generate_simple_field(modelgrid, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + sd_is_log_space, iids): + """ + Generate field using moving average (convolution) method - matches FIELDGEN2D_SVA approach. + """ + print(" Using moving average convolution (FIELDGEN_SVA method)...") - # Find boundary points - for i in range(ny): - for j in range(nx): - current_zone = zones[i, j] + grid_info = _extract_grid_info(modelgrid) + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] + ny, nx = xcentergrid.shape - # Check 8-connected neighbors - for di in [-1, 0, 1]: - for dj in [-1, 0, 1]: - if di == 0 and dj == 0: - continue - ni, nj = i + di, j + dj - if 0 <= ni < ny and 0 <= nj < nx: - if zones[ni, nj] != current_zone: - boundary_mask[i, j] = True - break - if boundary_mask[i, j]: - break + xcentergrid_flat = xcentergrid.flatten() + ycentergrid_flat = ycentergrid.flatten() + n_points = len(xcentergrid_flat) - return boundary_mask, boundary_directions + # Build KD-tree for neighbor search + from scipy.spatial import cKDTree + coords = np.column_stack([xcentergrid_flat, ycentergrid_flat]) + tree = cKDTree(coords) + # Initialize output + Z_field = np.zeros(n_points) + # For each target point, apply moving average + for i in range(n_points): + if i % 2000 == 0: + print(f" Progress: {i}/{n_points} points") -def save_results(results, out_filename): - """Save results to files in PyEMU-compatible format.""" + # Get local parameters for this target point + x0 = xcentergrid_flat[i] + y0 = ycentergrid_flat[i] + local_aa = aa_values[i] + local_aniso = aniso_values[i] + local_bearing = np.radians(bearing_values[i]) + local_var = variance_flat[i] + + # Find all points within practical range + # search_radius = 4 * local_aa + # Cutoff where exp(-h/a) = 1e-5 + cutoff_threshold = 1e-5 + h_cutoff = -np.log(cutoff_threshold) # ≈ 11.51 + + # Search radius in actual distance units + search_radius = h_cutoff * local_aa # About 11.5 * local_aa + neighbor_indices = tree.query_ball_point([x0, y0], search_radius) + + if len(neighbor_indices) == 0: + neighbor_indices = [i] + + # Compute anisotropic distances and weights (exponential function) + weights = np.zeros(len(neighbor_indices)) + for j, idx in enumerate(neighbor_indices): + dx = xcentergrid_flat[idx] - x0 + dy = ycentergrid_flat[idx] - y0 + + # Rotate to principal axes + cos_bear = np.cos(local_bearing) + sin_bear = np.sin(local_bearing) + dx_rot = dx * sin_bear + dy * cos_bear + dy_rot = -dx * cos_bear + dy * sin_bear + + # Scale by anisotropy + minor_aa = local_aa / local_aniso + h_major = dx_rot / local_aa + h_minor = dy_rot / minor_aa + + # Anisotropic distance + h = np.sqrt(h_major ** 2 + h_minor ** 2) + + # Exponential averaging function: exp(-h) + weights[j] = np.exp(-h) + # with cutoff + if weights[j] < cutoff_threshold: + weights[j] = 0.0 # Exclude points beyond cutoff + + # Apply weighted sum (NOT normalized) + weighted_sum = np.sum(weights * iids[neighbor_indices]) + + # Compute variance scaling factor + # The variance of the weighted sum is: Var(weighted_sum) = sum(w_i^2) * Var(iids) + # Since Var(iids) = 1, we have: Var(weighted_sum) = sum(w_i^2) + variance_of_weighted_sum = np.sum(weights ** 2) + + # Scale to achieve target variance + scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) + + Z_field[i] = weighted_sum * scaling_factor + + print(f" Convolution complete. Z-field stats: mean={Z_field.mean():.3f}, std={Z_field.std():.3f}") + + # Apply transformation + if sd_is_log_space: + field_values = mean_flat * np.power(10.0, Z_field) + else: + field_values = mean_flat + Z_field - # Save mean field - mean_file = f"{out_filename}_mean.txt" - np.savetxt(mean_file, results['mean'], fmt="%20.8E") - print(f" Saved mean field to {mean_file}") + return field_values - # Save standard deviation field - sd_file = f"{out_filename}_sd.txt" - np.savetxt(sd_file, results['sd'], fmt="%20.8E") - print(f" Saved SD field to {sd_file}") - # Save geostatistical parameter fields - for param in ['bearing', 'anisotropy', 'corrlen']: - param_file = f"{out_filename}_{param}.txt" - np.savetxt(param_file, results[param], fmt="%20.8E") - print(f" Saved {param} field to {param_file}") +def _tensors_to_geostat_params(tensors): + """Convert tensor field to geostatistical parameters.""" + n_points = len(tensors) + bearing_deg = np.zeros(n_points) + anisotropy = np.zeros(n_points) + corrlen_major = np.zeros(n_points) - # Save stochastic realizations - if results['fields'] is not None: - for i, field in enumerate(results['fields']): - field_file = f"{out_filename}_real_{i + 1:03d}.txt" - np.savetxt(field_file, field, fmt="%20.8E") + for i, tensor in enumerate(tensors): + # Skip default tensors + if np.allclose(tensor, np.eye(2) * 1000000): + bearing_deg[i] = 0.0 + anisotropy[i] = 1.0 + corrlen_major[i] = 1000.0 + continue - print(f" Saved {len(results['fields'])} realizations") + try: + # Eigendecomposition + eigenvals, eigenvecs = np.linalg.eigh(tensor) + # Sort by eigenvalue magnitude + idx = np.argsort(eigenvals)[::-1] + eigenvals = eigenvals[idx] + eigenvecs = eigenvecs[:, idx] -import numpy as np -from osgeo import gdal, osr - - -def arr_to_rast(arr, outrast, x_min, y_min, dx, dy, epsg): - ''' - Create a georeferenced raster from a NumPy array. - - Parameters: - - arr: 2D or 3D numpy array (if 3D: (bands, rows, cols)) - - outrast: output raster filepath - - x_min, y_min: LOWER-LEFT corner coordinates # Changed from y_max - - dx, dy: cell size (both positive for standard coordinate system) - - epsg: EPSG code for coordinate reference system - ''' - # Calculate top-left corner from lower-left - rows = arr.shape[-2] if arr.ndim == 3 else arr.shape[0] - y_max = y_min + (rows * dy) # Top edge = bottom + (rows * cell_height) - - # Build affine transform (dy becomes negative for north-up raster) - geotransform = (x_min, dx, 0, y_max, 0, -dy) # Note: -dy here - - # Rest of function stays the same... - driver = gdal.GetDriverByName("GTiff") - if arr.ndim == 2: - bands, rows, cols = 1, *arr.shape - arr = np.expand_dims(arr, 0) - else: - bands, rows, cols = arr.shape + # Extract parameters + major_length = np.sqrt(eigenvals[0]) + minor_length = np.sqrt(eigenvals[1]) - ds = driver.Create(outrast, cols, rows, bands, gdal.GDT_Float32) - ds.SetGeoTransform(geotransform) + corrlen_major[i] = major_length + anisotropy[i] = major_length / minor_length - srs = osr.SpatialReference() - srs.ImportFromEPSG(epsg) - ds.SetProjection(srs.ExportToWkt()) + # Calculate bearing from major eigenvector + major_vec = eigenvecs[:, 0] + # Convert to geological bearing (0° = North, clockwise positive) + bearing_rad = np.arctan2(major_vec[0], major_vec[1]) + bearing_deg[i] = np.degrees(bearing_rad) % 360 - for i in range(bands): - band = ds.GetRasterBand(i + 1) - band.SetNoDataValue(np.nan) - band.WriteArray(arr[i]) + except: + # Fallback values + bearing_deg[i] = 0.0 + anisotropy[i] = 1.0 + corrlen_major[i] = 1000.0 - ds.FlushCache() - ds = None + return bearing_deg, anisotropy, corrlen_major \ No newline at end of file From 7d0bc8d086db2eb65fe474e10dcac972994ff9a7 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 28 Nov 2025 10:18:43 +1300 Subject: [PATCH 33/36] working python version, moving average options, multiple realizations --- pyemu/utils/nsaf_helpers.py | 87 ++++++---- pyemu/utils/nsaf_utils.py | 306 ++++++++++++++++++++++++++---------- 2 files changed, 277 insertions(+), 116 deletions(-) diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index ffc03a059..4323ba60d 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -720,49 +720,72 @@ def example_field_generation(): # Generate field print("=== Testing tangential field generation ===") - for scen in ['fieldgen', 'python']: - if scen=='fieldgen': - iids = None # let fieldgen create them - else: - iids = np.random.normal(0, 1, size=(ny * nx)) - results = generate_single_layer( - cp_df, modelgrid, - iids=iids, - zones=zones, - tensor_interp='krig', - transform='log', - boundary_smooth={'transition_cells': 5}, - boundary_enhance={'transition_cells': 5, - 'peak_increase': 2}, - sd_is_log_space=True - ) - print(f"Field statistics: mean={np.mean(results['field']):.3f}, std={np.std(results['field']):.3f}") - print(f"Number of conceptual points: {len(cp_df)}") - print("Tangential field generation complete!") + n_realizations = 5 # Generate 5 realizations for comparison + + for scen in ['fieldgen', 'python']: + print(f"\n=== Scenario: {scen} ===") - save_dir = os.path.join('..','..','examples',f'tangential_nsaf_{scen}') + # Create save directory + save_dir = os.path.join('..', '..', 'examples', f'tangential_nsaf_{scen}') if not os.path.exists(save_dir): - os.mkdir(save_dir) - if save_dir is not None: + os.makedirs(save_dir) + fig_path = os.path.join(save_dir, 'figure') + if not os.path.exists(fig_path): + os.makedirs(fig_path) + + # Generate realizations + for real_num in range(n_realizations): + print(f"\n Generating realization {real_num + 1}/{n_realizations}...") + + if scen == 'fieldgen': + iids = None # Let fieldgen create them + random_seed = 42 + real_num # Different seed for each realization + else: + # Generate different random IIDs for each realization + np.random.seed(42 + real_num) # For reproducibility + iids = np.random.normal(0, 1, size=(ny * nx)) + random_seed = None # Not used when iids provided + + results = generate_single_layer( + cp_df, modelgrid, + iids=iids, + zones=zones, + tensor_interp='krig', + transform='log', + boundary_smooth={'transition_cells': 5}, + boundary_enhance={'transition_cells': 5, 'peak_increase': 2}, + sd_is_log_space=True, + n_realizations=1, # Single realization per call + random_seed=random_seed + ) + + print(f" Field statistics: mean={np.mean(results['field']):.3f}, std={np.std(results['field']):.3f}") + + # Save array file + fname = f"tangential_example_{scen}_real{real_num}.arr" + np.savetxt(os.path.join(save_dir, fname), results['field'], fmt="%20.8E") + + # Visualize this realization from pyemu import plot_utils as pu grid_info = _extract_grid_info(modelgrid) xcentergrid = grid_info['xcentergrid'] ycentergrid = grid_info['ycentergrid'] - fname = f"tangential_example_{scen}.arr" - np.savetxt(os.path.join(save_dir, fname), results['field'], fmt="%20.8E") - fig_path = os.path.join(save_dir, 'figure') - if not os.path.exists(fig_path): - os.mkdir(fig_path) - pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones, - conceptual_points=cp_df, subsample=4, max_ellipse_size=0.1, - figsize=(14, 12), title_suf='tangential', - save_path=os.path.join(fig_path, fname.replace('.arr', '_tensors.png'))) + # Only visualize tensors once (same for all realizations) + if real_num == 0: + pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones, + conceptual_points=cp_df, subsample=4, max_ellipse_size=0.1, + figsize=(14, 12), title_suf='tangential', + save_path=os.path.join(fig_path, f'{scen}_tensors.png')) + + # Visualize each realization pu.visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, - transform='log', title_suf='tangential', + transform='log', title_suf=f'tangential_real{real_num}', save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) + print(f"\n{scen.upper()} complete! Generated {n_realizations} realizations") + print(f"Files saved to: {save_dir}") return results, cp_df if __name__ == "__main__": diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index f780f4304..8f940ab81 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -12,10 +12,9 @@ def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, tensor_interp='idw', transform='log', layer=1, boundary_smooth=False, boundary_enhance=False, mean_col='mean', sd_col='sd', sd_is_log_space=False, - n_realizations=1, save_dir='.'): + n_realizations=1, random_seed=None, save_dir='.'): """ Generate single layer using NSAF approach with boundary adjustments. - REFACTORED to use modelgrid and call apply_nsaf_hyperpars internally. Parameters ---------- @@ -24,7 +23,7 @@ def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, modelgrid : flopy.discretization.grid Flopy modelgrid object iids : np.ndarray, optional - Pre-generated noise, shape (ny*nx, n_realizations) + Pre-generated noise, shape (ny*nx, ) zones : np.ndarray, optional Zone IDs, shape (ny, nx) tensor_interp : str, default 'idw' @@ -40,23 +39,28 @@ def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, sd_col : str, default 'sd' Name of sd column in conceptual points n_realizations : int, default 1 - Number of realizations to generate + Number of realizations. Only used when iids=None (FIELDGEN2D_SVA mode). + When iids is provided (PEST mode), only 1 realization is generated. + random_seed : int, optional + Random seed for reproducibility. Only used when iids=None. + When iids is provided, the iids themselves control the realization. Returns ------- - tuple - (fields, results_dict) where: - - fields: Generated field(s), shape (n_realizations, ny, nx) or (ny, nx) if n_realizations=1 - - results_dict: Full results dictionary from apply_nsaf_hyperpars + dict + Results dictionary containing: + - 'field': Single field (ny, nx) if n_realizations=1 or iids provided + - 'fields': Multiple fields (n_realizations, ny, nx) if n_realizations>1 and iids=None + - 'mean', 'sd', 'tensors', etc. """ - # Call the main function with single layer settings results = apply_nsaf_hyperpars( conceptual_points=conceptual_points, modelgrid=modelgrid, zones=zones, out_filename=None, n_realizations=n_realizations, + random_seed=random_seed, layer=layer, transform=transform, boundary_smooth=boundary_smooth, @@ -73,7 +77,7 @@ def generate_single_layer(conceptual_points, modelgrid, iids=None, zones=None, def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename=None, - n_realizations=1, layer=0, transform="none", + n_realizations=1, random_seed=None, layer=0, transform="none", boundary_smooth=False, boundary_enhance=False, tensor_method='krig', config_dict=None, iids=None, mean_col='mean', sd_col='sd', @@ -95,7 +99,9 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= out_filename : str, optional Base filename for output (if None, returns arrays only) n_realizations : int, default 1 - Number of stochastic realizations to generate + Number of stochastic realizations. Only used when iids=None. + random_seed : int, optional + Random seed for reproducibility. Only used when iids=None. layer : int, default 0 Layer number to process transform : str, default "none" @@ -260,16 +266,21 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= print(f" Mean: [{interp_means_2d.min():.3f}, {interp_means_2d.max():.3f}]") print(f" SD: [{interp_sd_2d.min():.3f}, {interp_sd_2d.max():.3f}]") - # Step 5: Generate stochastic fields using FIELDGEN2D_SVA approach - print(f"\nStep 5: Generating {n_realizations} stochastic realizations...") + # Step 5: Generate stochastic fields + print(f"\nStep 5: Generating stochastic realization(s)...") + + if iids is not None: + print(" Note: iids provided - generating single realization (n_realizations parameter ignored)") + n_realizations = 1 # Force to 1 when iids provided if n_realizations > 0: # Get active cells if available active = kwargs.get('active', None) if active is None and hasattr(modelgrid, 'idomain'): - active = modelgrid.idomain[layer-1].flatten() if modelgrid.idomain.ndim == 3 else (modelgrid.idomain.flatten()) + active = modelgrid.idomain[ + layer - 1].flatten() if modelgrid.idomain.ndim == 3 else modelgrid.idomain.flatten() - field = _generate_stochastic_field( + fields = _generate_stochastic_field( modelgrid=modelgrid, mean_field=interp_means_2d, sd_field=interp_sd_2d, @@ -279,14 +290,15 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= corrlen=corrlen_major, sd_is_log_space=sd_is_log_space, config_dict=config_dict, - active=active + active=active, + n_realizations=n_realizations, + random_seed=random_seed ) else: - field = None + fields = None - # Prepare output + # Prepare output - handle both single and multiple realizations results = { - 'field': field, 'mean': interp_means_2d, 'sd': interp_sd_2d, 'tensors': tensors, @@ -295,6 +307,14 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= 'corrlen': corrlen_major.reshape(ny, nx) } + # Add field(s) to results + if fields is not None: + if n_realizations == 1: + results['field'] = fields # Single field (ny, nx) + else: + results['fields'] = fields # Multiple fields (n_realizations, ny, nx) + results['field'] = fields[0] # Also provide first one as 'field' for backward compat + # Save to files if requested if out_filename is not None: save_results(results, out_filename, transform) @@ -1170,9 +1190,12 @@ def _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx): def _generate_stochastic_field(modelgrid, mean_field, sd_field, bearing, anisotropy, corrlen, sd_is_log_space, config_dict, iids=None, area=None, - active=None): + active=None, n_realizations=1, random_seed=None): """ - Generate a single stochastic field realization. + Generate stochastic field realization(s). + + Parameters + ---------- Note: FIELDGEN2D_SVA1 is required for external iid files but not yet in pypestutils. When iids are provided, uses Python fallback implementation. @@ -1202,16 +1225,25 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, Cell areas, shape (ny*nx,) active : np.ndarray, optional Active domain mask, shape (ny*nx,) + n_realizations : int, default 1 + Number of realizations to generate. Only used when iids=None (FIELDGEN2D_SVA mode). + random_seed : int, optional + Random seed for reproducibility. Only used when iids=None (FIELDGEN2D_SVA mode). Returns ------- np.ndarray - Generated field, shape (ny, nx) + - If iids provided OR n_realizations=1: shape (ny, nx) + - If iids=None AND n_realizations>1: shape (n_realizations, ny, nx) """ try: from pypestutils.pestutilslib import PestUtilsLib plib = PestUtilsLib() - plib.initialize_randgen(11235813) + # Initialize with seed if provided + if random_seed is not None: + plib.initialize_randgen(random_seed) + else: + plib.initialize_randgen(11235813) # Default seed except Exception as e: raise Exception(f"Error importing pypestutils: {e}") @@ -1255,45 +1287,55 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, if area is not None: area = area.flatten() - # Handle IID-based generation (external control for PEST) + # Averaging function type + avg_func = config_dict.get('averaging_function', 'gaussian') + if isinstance(avg_func, dict): + if 'pow' in avg_func: + avg_flag = 4 + power_value = avg_func['pow'] + else: + avg_flag, power_value = next(iter(avg_func.items())) + else: + power_value = 0 + if avg_func == 'exponential': + avg_flag = 2 + elif avg_func == 'gaussian': + avg_flag = 3 + elif avg_func == 'spherical': + avg_flag = 1 + else: + avg_flag = 2 # Default to exponential + + # Handle IID-based generation (PEST mode - always single realization) if iids is not None: - print(" Using provided IIDs for single realization...") + print(" Using provided IIDs for single realization (PEST mode)...") + if n_realizations > 1: + print(" Note: n_realizations>1 ignored when iids are provided") + if iids.shape[0] != n_points: raise ValueError(f"IIDs shape {iids.shape} doesn't match expected ({n_points},)") - print(f" FIELDGEN2D_SVA1 (with external iids) not available in pypestutils, using fallback method...") + print(f" FIELDGEN2D_SVA1 (with external iids) not available, using Python fallback...") # Use fallback method for single realization - field_values = _generate_simple_field( + field_values = _moving_average_field( modelgrid, mean_flat, variance_flat, aa_values, aniso_values, bearing_values, - sd_is_log_space, iids + sd_is_log_space, iids, + avg_flag=avg_flag, + power_value=power_value ) field = field_values.reshape(ny, nx) - return field + return field # Shape: (ny, nx) # Handle FIELDGEN2D_SVA generation (internal random generation) else: try: - # Averaging function type - avg_func = config_dict.get('averaging_function', 'gaussian') - if isinstance(avg_func, dict): - if 'pow' in avg_func: - avg_flag = 4 - power_value = avg_func['pow'] - else: - avg_flag, power_value = next(iter(avg_func.items())) - else: - power_value = 0 - if avg_func == 'exponential': - avg_flag = 2 - elif avg_func == 'gaussian': - avg_flag = 3 - elif avg_func == 'spherical': - avg_flag = 1 - else: - avg_flag = 2 # Default to exponential + print(f" Generating {n_realizations} realization(s) using FIELDGEN2D_SVA...") + if random_seed is not None: + print(f" Using random seed: {random_seed}") + fields = plib.fieldgen2d_sva( xcentergrid.flatten(), ycentergrid.flatten(), area=area, @@ -1306,19 +1348,59 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, transtype=sd_is_log_space, avetype=avg_flag, power=power_value, - nreal=1, # Only request 1 realization + nreal=n_realizations, # Pass n_realizations to FIELDGEN2D_SVA ) - # Handle different possible output shapes from FIELDGEN2D_SVA - if fields.shape == (n_points, 1): - # Output is (n_points, 1) - extract and reshape - field = fields[:, 0].reshape(ny, nx) - elif fields.shape == (1, n_points): - # Output is (1, n_points) - extract and reshape - field = fields[0, :].reshape(ny, nx) - elif fields.shape == (n_points,): - # Output is flattened single realization - field = fields.reshape(ny, nx) + # Handle output shapes + if n_realizations == 1: + # Single realization - return (ny, nx) + if fields.shape == (n_points, 1): + field = fields[:, 0].reshape(ny, nx) + elif fields.shape == (1, n_points): + field = fields[0, :].reshape(ny, nx) + elif fields.shape == (n_points,): + field = fields.reshape(ny, nx) + else: + raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") + + print(f" Successfully generated field with shape {field.shape}") + plib.free_all_memory() + return field # Shape: (ny, nx) + + else: + # Multiple realizations - return (n_realizations, ny, nx) + if fields.shape == (n_points, n_realizations): + # Transpose and reshape + fields_3d = fields.T.reshape(n_realizations, ny, nx) + elif fields.shape == (n_realizations, n_points): + # Just reshape + fields_3d = fields.reshape(n_realizations, ny, nx) + else: + raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") + + print(f" Successfully generated {n_realizations} fields with shape {fields_3d.shape}") + plib.free_all_memory() + return fields_3d # Shape: (n_realizations, ny, nx) + + except Exception as e: + print(f" Error: FIELDGEN2D_SVA failed: {e}") + + if iids is None: + print(f" Falling back to Python with random iids...") + # Generate random iids for fallback + if random_seed is not None: + np.random.seed(random_seed) + random_iids = np.random.normal(0, 1, size=n_points) + + field_values = _moving_average_field( + modelgrid, mean_flat, variance_flat, + aa_values, aniso_values, bearing_values, + sd_is_log_space, random_iids, + avg_flag=avg_flag, + power_value=power_value + ) + field = field_values.reshape(ny, nx) + return field # Shape: (ny, nx) else: raise ValueError(f"Unexpected fieldgen2d_sva output shape: {fields.shape}") @@ -1335,23 +1417,57 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, # Generate random iids and use fallback random_iids = np.random.normal(0, 1, size=n_points) - field_values = _generate_simple_field( + field_values = _moving_average_field( modelgrid, mean_flat, variance_flat, aa_values, aniso_values, bearing_values, - sd_is_log_space, random_iids + sd_is_log_space, random_iids, + avg_flag=avg_flag, + power_value=power_value ) field = field_values.reshape(ny, nx) return field -def _generate_simple_field(modelgrid, mean_flat, variance_flat, +def _moving_average_field(modelgrid, mean_flat, variance_flat, aa_values, aniso_values, bearing_values, - sd_is_log_space, iids): + sd_is_log_space, iids, avg_flag=3, power_value=0): """ Generate field using moving average (convolution) method - matches FIELDGEN2D_SVA approach. + + Parameters + ---------- + modelgrid : flopy modelgrid + mean_flat : np.ndarray + Flattened mean values in native space, shape (ny*nx,) + variance_flat : np.ndarray + Flattened variance values (log-space if sd_is_log_space=True), shape (ny*nx,) + aa_values : np.ndarray + Flattened correlation length values, shape (ny*nx,) + aniso_values : np.ndarray + Flattened anisotropy values, shape (ny*nx,) + bearing_values : np.ndarray + Flattened bearing values in degrees, shape (ny*nx,) + sd_is_log_space : bool + True if variance_flat is in log space + iids : np.ndarray + Independent identically distributed random values, shape (ny*nx,) + avg_flag : int, default 3 + Averaging function type: 1=spherical, 2=exponential, 3=gaussian, 4=power + power_value : float, default 0 + Power value if avg_flag=4 + + Returns + ------- + np.ndarray + Generated field values in native space, shape (ny*nx,) """ - print(" Using moving average convolution (FIELDGEN_SVA method)...") + from scipy.spatial import cKDTree + + # Map avg_flag to function name for display + avg_names = {1: 'spherical', 2: 'exponential', 3: 'gaussian', 4: 'power'} + avg_name = avg_names.get(avg_flag, 'unknown') + print(f" Using moving average convolution with {avg_name} function...") grid_info = _extract_grid_info(modelgrid) xcentergrid = grid_info['xcentergrid'] @@ -1363,10 +1479,24 @@ def _generate_simple_field(modelgrid, mean_flat, variance_flat, n_points = len(xcentergrid_flat) # Build KD-tree for neighbor search - from scipy.spatial import cKDTree coords = np.column_stack([xcentergrid_flat, ycentergrid_flat]) tree = cKDTree(coords) + # Determine cutoff threshold and search radius based on averaging function + cutoff_threshold = 1e-5 + + if avg_flag == 1 or avg_flag == 4: # spherical or power + # Compact support - cutoff at h = 1.0 + h_cutoff = 1.0 + elif avg_flag == 2: # exponential + # exp(-h) = 1e-5 => h = -ln(1e-5) ≈ 11.51 + h_cutoff = -np.log(cutoff_threshold) + elif avg_flag == 3: # gaussian + # exp(-h²) = 1e-5 => h = sqrt(-ln(1e-5)) ≈ 3.39 + h_cutoff = np.sqrt(-np.log(cutoff_threshold)) + else: + raise ValueError(f"Unknown avg_flag: {avg_flag}") + # Initialize output Z_field = np.zeros(n_points) @@ -1383,20 +1513,14 @@ def _generate_simple_field(modelgrid, mean_flat, variance_flat, local_bearing = np.radians(bearing_values[i]) local_var = variance_flat[i] - # Find all points within practical range - # search_radius = 4 * local_aa - # Cutoff where exp(-h/a) = 1e-5 - cutoff_threshold = 1e-5 - h_cutoff = -np.log(cutoff_threshold) # ≈ 11.51 - # Search radius in actual distance units - search_radius = h_cutoff * local_aa # About 11.5 * local_aa + search_radius = h_cutoff * local_aa neighbor_indices = tree.query_ball_point([x0, y0], search_radius) if len(neighbor_indices) == 0: neighbor_indices = [i] - # Compute anisotropic distances and weights (exponential function) + # Compute anisotropic distances and weights weights = np.zeros(len(neighbor_indices)) for j, idx in enumerate(neighbor_indices): dx = xcentergrid_flat[idx] - x0 @@ -1413,27 +1537,41 @@ def _generate_simple_field(modelgrid, mean_flat, variance_flat, h_major = dx_rot / local_aa h_minor = dy_rot / minor_aa - # Anisotropic distance + # Anisotropic distance in "range units" h = np.sqrt(h_major ** 2 + h_minor ** 2) - # Exponential averaging function: exp(-h) - weights[j] = np.exp(-h) - # with cutoff - if weights[j] < cutoff_threshold: - weights[j] = 0.0 # Exclude points beyond cutoff + # Apply averaging function based on avg_flag + if avg_flag == 1: # spherical + if h <= 1.0: + weights[j] = 1.0 - 1.5 * h + 0.5 * h ** 3 + else: + weights[j] = 0.0 + elif avg_flag == 2: # exponential + weights[j] = np.exp(-h) + if weights[j] < cutoff_threshold: + weights[j] = 0.0 + elif avg_flag == 3: # gaussian + weights[j] = np.exp(-h ** 2) + if weights[j] < cutoff_threshold: + weights[j] = 0.0 + elif avg_flag == 4: # power + if h <= 1.0: + weights[j] = (1.0 - h) ** power_value + else: + weights[j] = 0.0 # Apply weighted sum (NOT normalized) weighted_sum = np.sum(weights * iids[neighbor_indices]) # Compute variance scaling factor - # The variance of the weighted sum is: Var(weighted_sum) = sum(w_i^2) * Var(iids) - # Since Var(iids) = 1, we have: Var(weighted_sum) = sum(w_i^2) variance_of_weighted_sum = np.sum(weights ** 2) - # Scale to achieve target variance - scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) - - Z_field[i] = weighted_sum * scaling_factor + if variance_of_weighted_sum > 0: + scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) + Z_field[i] = weighted_sum * scaling_factor + else: + # Fallback: unconditional simulation + Z_field[i] = np.sqrt(local_var) * iids[i] print(f" Convolution complete. Z-field stats: mean={Z_field.mean():.3f}, std={Z_field.std():.3f}") From 509c5109800996262eab2120bff7eac938430f00 Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Fri, 28 Nov 2025 20:28:19 +1300 Subject: [PATCH 34/36] fixed bug in iid generation, python similar to fieldgen for all tested examples. normalize weights in _moving_average_field still open question. --- pyemu/plot/plot_utils.py | 28 ++-- pyemu/utils/nsaf_helpers.py | 318 +++++++++++++++++++++++------------- pyemu/utils/nsaf_useage.py | 1 + pyemu/utils/nsaf_utils.py | 147 +++++++++++++---- 4 files changed, 329 insertions(+), 165 deletions(-) diff --git a/pyemu/plot/plot_utils.py b/pyemu/plot/plot_utils.py index 6ec016de9..070f47419 100644 --- a/pyemu/plot/plot_utils.py +++ b/pyemu/plot/plot_utils.py @@ -2412,32 +2412,32 @@ def visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, axes[0, 0].set_aspect('equal') plt.colorbar(im1, ax=axes[0, 0], label=field_label) + # Field statistics histogram + axes[1, 0].hist(field.flatten(), bins=50, alpha=0.7, color='purple', edgecolor='black', range=(vmin, vmax)) + axes[1, 0].set_title(f'{field_label} Distribution') + axes[1, 0].set_xlabel(f'{field_label} Value') + axes[1, 0].set_ylabel('Frequency') + axes[1, 0].grid(True, alpha=0.3) + # mean field im2 = axes[0, 1].imshow(mean, origin='upper', cmap='RdYlBu_r', extent=[xcentergrid.min(), xcentergrid.max(), ycentergrid.min(), ycentergrid.max()], - vmin=vmin, vmax=vmax) + ) axes[0, 1].set_title(f'Mean field for {field_label} {title_suf or ""}') axes[0, 1].set_xlabel('X Coordinate') axes[0, 1].set_ylabel('Y Coordinate') plt.colorbar(im2, ax=axes[0, 1], label=field_label) # sd field - im3 = axes[1, 0].imshow(sd, origin='upper', cmap='RdYlBu_r', alpha=0.8, + im3 = axes[1, 1].imshow(sd, origin='upper', cmap='RdYlBu_r', alpha=0.8, extent=[xcentergrid.min(), xcentergrid.max(), ycentergrid.min(), ycentergrid.max()], - vmin=vmin, vmax=vmax) - axes[1, 0].set_title(f'Standard Deviation field for {field_label} {title_suf or ""}') - axes[1, 0].set_xlabel('X Coordinate') - axes[1, 0].set_ylabel('Y Coordinate') - plt.colorbar(im3, ax=axes[1, 0], label=field_label) - - # Field statistics histogram - axes[1, 1].hist(field.flatten(), bins=50, alpha=0.7, color='purple', edgecolor='black', range=(vmin, vmax)) - axes[1, 1].set_title(f'{field_label} Distribution') - axes[1, 1].set_xlabel(f'{field_label} Value') - axes[1, 1].set_ylabel('Frequency') - axes[1, 1].grid(True, alpha=0.3) + ) + axes[1, 1].set_title(f'Standard Deviation field for {field_label} {title_suf or ""}') + axes[1, 1].set_xlabel('X Coordinate') + axes[1, 1].set_ylabel('Y Coordinate') + plt.colorbar(im3, ax=axes[1, 1], label=field_label) # Add statistics text # mean_val = np.nanmean(field) diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index 4323ba60d..ec41179cd 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -40,7 +40,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, If dict is provided, it must contain entries for all layers (will error if layers are missing). iid_files : None, str, or dict, optional IID file(s) containing noise arrays. Can be: - - None: generates default files 'iids_layer{n}.arr' where n is 0-based layer number + - None: defaults to fieldgen2d_sva internal iid generator - str: pattern with {} placeholder, e.g., 'iids_layer{}.arr' (replaced with 0-based layer number) - dict: explicit mapping of 0-based layer numbers to filenames, e.g., {0: 'iid_layer0.arr', 1: 'iid_layer1.arr'} If dict is provided, it must contain entries for all layers (will error if layers are missing). @@ -100,14 +100,16 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws, load_only=['dis']) ml = sim.get_model(model_name) print(f"Loaded MF6 model: {model_name} (DIS package only)") - except: + except Exception as e_mf6: try: # Fall back to MODFLOW-2005 - only load DIS package ml = flopy.modflow.Modflow.load(f"{model_name}.nam", model_ws=tmp_model_ws, - load_only=['dis']) + load_only=['dis'], check=False) print(f"Loaded MODFLOW-2005 model: {model_name} (DIS package only)") - except Exception as e: - raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}: {e}") + except Exception as e_mf2005: + raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}.\n" + f"MF6 error: {e_mf6}\n" + f"MF2005 error: {e_mf2005}") # Get modelgrid from the model modelgrid = ml.modelgrid @@ -404,7 +406,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # Visualization pu.visualize_nsaf(results, layer_cp, xcentergrid, ycentergrid, - transform=transform, + transform=trans, domain=ml.dis.idomain[target_layer], title_suf=mean_col.split('_')[-1] if '_' in mean_col else mean_col, save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) @@ -561,7 +563,7 @@ def _load_or_generate_iids(iid_path, ny, nx, n_realizations): Returns ------- np.ndarray - IIDs array with shape (ny*nx, n_realizations) + IIDs array with shape (ny*nx,) for single realization or (ny*nx, n_realizations) for multiple """ # Try to load existing IIDs @@ -573,23 +575,39 @@ def _load_or_generate_iids(iid_path, ny, nx, n_realizations): # Handle different shapes if iids_data.ndim == 1: - # Single realization + # Single realization - keep as 1D if n_realizations==1 if len(iids_data) == ny * nx: - iids = iids_data.reshape(-1, 1) + if n_realizations == 1: + return iids_data # Return 1D array (ny*nx,) + else: + iids = iids_data.reshape(-1, 1) else: raise ValueError(f"IID file has wrong size: {len(iids_data)} vs {ny * nx}") elif iids_data.ndim == 2: - # Multiple realizations - if iids_data.shape[0] == ny * nx: + # Could be (ny, nx) or (ny*nx, n_real) + if iids_data.shape == (ny, nx): + # Saved as 2D grid - flatten it + iids_data = iids_data.flatten() + if n_realizations == 1: + return iids_data # Return 1D array (ny*nx,) + else: + iids = iids_data.reshape(-1, 1) + elif iids_data.shape[0] == ny * nx: + # Already in (ny*nx, n_real) format iids = iids_data elif iids_data.shape[1] == ny * nx: + # Transposed - fix it iids = iids_data.T else: raise ValueError(f"IID file has wrong shape: {iids_data.shape}") else: raise ValueError(f"IID file has too many dimensions: {iids_data.ndim}") - # Expand or truncate to match n_realizations + # If we get here and n_realizations==1, extract the single column + if n_realizations == 1 and iids.ndim == 2: + return iids[:, 0] # Return 1D array (ny*nx,) + + # Expand or truncate to match n_realizations (only for multi-realization case) if iids.shape[1] < n_realizations: print(f" Expanding IIDs from {iids.shape[1]} to {n_realizations} realizations") additional_iids = np.random.normal(0, 1, size=(ny * nx, n_realizations - iids.shape[1])) @@ -608,98 +626,156 @@ def _load_or_generate_iids(iid_path, ny, nx, n_realizations): print(f" Generating new IIDs...") # Generate new IIDs - iids = np.random.normal(0, 1, size=(ny * nx, n_realizations)) + if n_realizations == 1: + # Generate single realization as 1D array + iids = np.random.normal(0, 1, size=(ny * nx)) + else: + # Generate multiple realizations as 2D array + iids = np.random.normal(0, 1, size=(ny * nx, n_realizations)) # Save for future use print(f" Saving IIDs to {os.path.basename(iid_path)}") os.makedirs(os.path.dirname(iid_path), exist_ok=True) - np.savetxt(iid_path, iids, fmt="%.8f", - header=f"IIDs shape: {ny * nx} x {n_realizations}") + + if n_realizations == 1: + # Save as 2D grid for easier viewing + np.savetxt(iid_path, iids.reshape(ny, nx), fmt="%.8f") + else: + np.savetxt(iid_path, iids, fmt="%.8f", + header=f"IIDs shape: {ny * nx} x {n_realizations}") return iids -def example_field_generation(): - """Example using concentric circles of tangentially-oriented anisotropic tensors.""" +def example_field_generation_save_files(): + """Modified tangential example that saves files for testing generate_fields_from_files().""" import flopy - import matplotlib.pyplot as plt import numpy as np import pandas as pd + import os + + # Create test directory + test_ws = os.path.join('..', '..', 'examples', 'tangential_test') + if not os.path.exists(test_ws): + os.makedirs(test_ws) - # Create example PyEMU grid - square domain + # Create example grid parameters nx, ny = 100, 100 xmax = 5000 ymax = 5000 - # Correct: delr is row spacing (y-direction), delc is column spacing (x-direction) - delr = np.full(ny, ymax / ny) # Cell height (y-direction, row spacing) - delc = np.full(nx, xmax / nx) # Cell width (x-direction, column spacing) - - modelgrid = flopy.discretization.StructuredGrid( - delc=delc, delr=delr, - top=np.ones((ny, nx)), - botm=np.zeros((1, ny, nx)), - xoff=0, - yoff=0, - angrot=0.0, - idomain=np.ones((ny, nx)), + delr = np.full(ny, ymax / ny) + delc = np.full(nx, xmax / nx) + + # Create a minimal MODFLOW 6 model + sim = flopy.mf6.MFSimulation( + sim_name='tangential_test', + sim_ws=test_ws, + exe_name='mf6' + ) + + # Add TDIS + tdis = flopy.mf6.ModflowTdis( + sim, + nper=1, + perioddata=[(1.0, 1, 1.0)] ) + # Create groundwater flow model + gwf = flopy.mf6.ModflowGwf( + sim, + modelname='tangential_test', + save_flows=True + ) + + # Add IMS (solver) - required for valid simulation + ims = flopy.mf6.ModflowIms( + sim, + print_option='SUMMARY', + complexity='SIMPLE' + ) + + # Add DIS package + dis = flopy.mf6.ModflowGwfdis( + gwf, + nlay=1, + nrow=ny, + ncol=nx, + delr=delc, + delc=delr, + top=1.0, + botm=0.0, + idomain=1 + ) + + # Add minimal IC (initial conditions) - required + ic = flopy.mf6.ModflowGwfic(gwf, strt=1.0) + + # Add minimal NPF (node property flow) - required + npf = flopy.mf6.ModflowGwfnpf(gwf, icelltype=1, k=1.0) + + # Write the simulation + sim.write_simulation() + print(f"Saved MODFLOW 6 model to: {test_ws}") + + # Get modelgrid for generating conceptual points + modelgrid = gwf.modelgrid grid_info = _extract_grid_info(modelgrid) xcentergrid = grid_info['xcentergrid'] ycentergrid = grid_info['ycentergrid'] center_x = 2500 center_y = 2500 - zones = np.ones((ny, nx)) + + # Create zones + zones = np.ones((ny, nx), dtype=int) zones[(ycentergrid < center_y) & (xcentergrid > center_x)] = 2 # SE zones[(ycentergrid < center_y) & (xcentergrid < center_x)] = 3 # SW zones[(ycentergrid > center_y) & (xcentergrid < center_x)] = 4 # NE - # Define circle center and radius - max_radius = 2500*np.sqrt(2) + # Save zones + zone_file = 'zones_layer0.arr' + np.savetxt(os.path.join(test_ws, zone_file), zones, fmt='%d') + print(f"Saved zones to: {zone_file}") + + max_radius = 2500 * np.sqrt(2) - # Create conceptual points on concentric circles + # Create conceptual points cp_list = [] n_circles = 4 - # Define zone-specific mean values zone_mean_multipliers = { - 1: 1.0, # NW quadrant - baseline - 2: 2.0, # SE quadrant - 2x higher - 3: 3.0, # SW quadrant - 2x lower - 4: 4.0 # NE quadrant - 4x higher + 1: 1.0, + 2: 10.0, + 3: 100.0, + 4: 0.1 } for circle_idx in range(n_circles): radius_fraction = (circle_idx + 0.3) / n_circles cp_radius = max_radius * radius_fraction n_points = 6 + circle_idx * 4 - base_mean_kh = 2 ** (circle_idx - n_circles) # Base value from circle + base_mean_kh = 4 ** (circle_idx - n_circles) major_length = 500 + 50 * radius_fraction for i in range(n_points): angle = 2 * np.pi * i / n_points - # Position on circle cp_x = center_x + cp_radius * np.cos(angle) cp_y = center_y + cp_radius * np.sin(angle) - # Determine which zone this point is in if cp_y > center_y and cp_x > center_x: - point_zone = 1 # NW + point_zone = 1 elif cp_y < center_y and cp_x > center_x: - point_zone = 2 # SE + point_zone = 2 elif cp_y < center_y and cp_x < center_x: - point_zone = 3 # SW - else: # cp_y > center_y and cp_x < center_x - point_zone = 4 # NE + point_zone = 3 + else: + point_zone = 4 - # Apply zone-specific multiplier zone_multiplier = zone_mean_multipliers[point_zone] mean_kh = base_mean_kh * zone_multiplier - # Tangential bearing calculation geo_radial = np.degrees(np.arctan2(cp_x - center_x, cp_y - center_y)) bearing = (geo_radial + 90) % 360 @@ -707,86 +783,92 @@ def example_field_generation(): 'name': f'cp_circle{circle_idx}_point{i}_zone{point_zone}', 'x': cp_x, 'y': cp_y, - 'mean': mean_kh, - 'sd': 1.0, + 'layer': 0, # 0-based layer column + 'mean_kh': mean_kh, + 'logsdkh': 0.5, # Log-space SD 'major': major_length, 'anisotropy': 4.0, 'bearing': bearing, - 'zone': point_zone # Optional: track which zone + 'zone': point_zone }) cp_df = pd.DataFrame(cp_list) cp_df['transverse'] = cp_df['major'] / cp_df['anisotropy'] - # Generate field - print("=== Testing tangential field generation ===") + # Save conceptual points + cp_file = 'conceptual_points.csv' + cp_df.to_csv(os.path.join(test_ws, cp_file), index=False) + print(f"Saved conceptual points to: {cp_file}") + print(f"Columns: {list(cp_df.columns)}") + + # Generate and save IIDs + np.random.seed(42) + iids = np.random.normal(0, 1, size=(ny * nx)) + iid_file = 'iids_layer0.arr' + np.savetxt(os.path.join(test_ws, iid_file), iids.reshape(ny, nx), fmt='%.6f') + print(f"Saved IIDs to: {iid_file}") + + print(f"\n=== Files ready for testing ===") + print(f"Test workspace: {test_ws}") + + return test_ws + + +# Then test with generate_fields_from_files(): +def test_tangential_via_files(): + """Test tangential example through generate_fields_from_files().""" + + # Generate the files + test_ws = example_field_generation_save_files() + + print("\n=== Testing with generate_fields_from_files() ===") + + # Test with fieldgen (iids=None) + print("\n--- Test 1: FIELDGEN2D_SVA (iids=None) ---") + results_fieldgen = generate_fields_from_files( + tmp_model_ws=test_ws, + model_name='tangential_test', + conceptual_points_file='conceptual_points.csv', + zone_files='zones_layer{}.arr', + iid_files=None, # Use FIELDGEN2D_SVA internal generation + field_name=['kh'], + layer_mode=True, + save_dir=os.path.join(test_ws, 'output_fieldgen'), + tensor_interp='krig', + transform=['log'], + boundary_smooth={'transition_cells': 5}, + boundary_enhance={'transition_cells': 5, 'peak_increase': 2}, + cp_0based=True # Our layer column is already 0-based + ) - n_realizations = 5 # Generate 5 realizations for comparison + # Test with Python fallback (iids provided) + print("\n--- Test 2: Python fallback (iids provided) ---") + results_python = generate_fields_from_files( + tmp_model_ws=test_ws, + model_name='tangential_test', + conceptual_points_file='conceptual_points.csv', + zone_files='zones_layer{}.arr', + iid_files='iids_layer{}.arr', # Use provided IIDs + field_name=['kh'], + layer_mode=True, + save_dir=os.path.join(test_ws, 'output_python'), + tensor_interp='krig', + transform=['log'], + boundary_smooth={'transition_cells': 5}, + boundary_enhance={'transition_cells': 5, 'peak_increase': 2}, + cp_0based=True + ) - for scen in ['fieldgen', 'python']: - print(f"\n=== Scenario: {scen} ===") + print("\n=== Comparison ===") + field_fieldgen = results_fieldgen['kh_layer0']['field'] + field_python = results_python['kh_layer0']['field'] - # Create save directory - save_dir = os.path.join('..', '..', 'examples', f'tangential_nsaf_{scen}') - if not os.path.exists(save_dir): - os.makedirs(save_dir) - fig_path = os.path.join(save_dir, 'figure') - if not os.path.exists(fig_path): - os.makedirs(fig_path) + print(f"FIELDGEN: mean={field_fieldgen.mean():.3f}, std={field_fieldgen.std():.3f}") + print(f"Python: mean={field_python.mean():.3f}, std={field_python.std():.3f}") - # Generate realizations - for real_num in range(n_realizations): - print(f"\n Generating realization {real_num + 1}/{n_realizations}...") + return results_fieldgen, results_python - if scen == 'fieldgen': - iids = None # Let fieldgen create them - random_seed = 42 + real_num # Different seed for each realization - else: - # Generate different random IIDs for each realization - np.random.seed(42 + real_num) # For reproducibility - iids = np.random.normal(0, 1, size=(ny * nx)) - random_seed = None # Not used when iids provided - - results = generate_single_layer( - cp_df, modelgrid, - iids=iids, - zones=zones, - tensor_interp='krig', - transform='log', - boundary_smooth={'transition_cells': 5}, - boundary_enhance={'transition_cells': 5, 'peak_increase': 2}, - sd_is_log_space=True, - n_realizations=1, # Single realization per call - random_seed=random_seed - ) - - print(f" Field statistics: mean={np.mean(results['field']):.3f}, std={np.std(results['field']):.3f}") - - # Save array file - fname = f"tangential_example_{scen}_real{real_num}.arr" - np.savetxt(os.path.join(save_dir, fname), results['field'], fmt="%20.8E") - - # Visualize this realization - from pyemu import plot_utils as pu - grid_info = _extract_grid_info(modelgrid) - xcentergrid = grid_info['xcentergrid'] - ycentergrid = grid_info['ycentergrid'] - - # Only visualize tensors once (same for all realizations) - if real_num == 0: - pu.visualize_tensors(results['tensors'], xcentergrid, ycentergrid, zones=zones, - conceptual_points=cp_df, subsample=4, max_ellipse_size=0.1, - figsize=(14, 12), title_suf='tangential', - save_path=os.path.join(fig_path, f'{scen}_tensors.png')) - - # Visualize each realization - pu.visualize_nsaf(results, cp_df, xcentergrid, ycentergrid, - transform='log', title_suf=f'tangential_real{real_num}', - save_path=os.path.join(fig_path, fname.replace('.arr', '.png'))) - - print(f"\n{scen.upper()} complete! Generated {n_realizations} realizations") - print(f"Files saved to: {save_dir}") - return results, cp_df +# Run the test if __name__ == "__main__": - example_field_generation() \ No newline at end of file + results_fieldgen, results_python = test_tangential_via_files() \ No newline at end of file diff --git a/pyemu/utils/nsaf_useage.py b/pyemu/utils/nsaf_useage.py index fc7ede61a..23d3ed2b4 100644 --- a/pyemu/utils/nsaf_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -35,6 +35,7 @@ nsaf_helpers.generate_fields_from_files(data_dir, model_name, con_pts_file, zone_files=zone_files, + iid_files='iids_layer{}.arr', field_name=['kh'], layer_mode=True, save_dir=save_dir, diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index 8f940ab81..63a95171c 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -137,8 +137,10 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= grid_info = _extract_grid_info(modelgrid) ny = grid_info['ny'] nx = grid_info['nx'] + xcentergrid = grid_info['xcentergrid'] + ycentergrid = grid_info['ycentergrid'] - # Load conceptual points + # Load conceptual points for this layer if 'layer' in conceptual_points.columns: layer_cp = conceptual_points[conceptual_points['layer'] == layer].copy() else: @@ -170,6 +172,34 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= print(f"Grid dimensions: {ny} x {nx} = {ny * nx} cells") print(f"Using columns: mean='{mean_col}', sd='{sd_col}'") + # === ZONE ASSIGNMENT - DO THIS ONCE, EARLY === + n_grid = ny * nx + cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) + + if zones is not None: + # Zones array provided - assign CPs to zones by nearest grid point + print(" Assigning conceptual points to zones from zone array...") + zones_flat = zones.flatten().astype(int) + cp_zones = _assign_cp_to_zones(cp_coords, xcentergrid, ycentergrid, zones_flat, nx) + layer_cp['zone'] = cp_zones # Add to dataframe + print(f" Assigned CPs to zones: {np.unique(cp_zones)}") + elif 'zone' in layer_cp.columns: + # Use existing zone column from conceptual points + print(" Using 'zone' column from conceptual points...") + zones_flat = None # No grid-based zones + cp_zones = layer_cp['zone'].values.astype(int) + print(f" CP zones: {np.unique(cp_zones)}") + else: + # No zones - use zone=1 for everything + print(" No zones provided - using zone=1 for all points") + zones_flat = np.ones(n_grid, dtype=int) + cp_zones = np.ones(len(layer_cp), dtype=int) + layer_cp['zone'] = cp_zones + + # Verify we have zone information + if 'zone' not in layer_cp.columns: + layer_cp['zone'] = cp_zones + # Step 1: Interpolate tensors print("\nStep 1: Interpolating tensor field...") tensors = interpolate_tensors( @@ -183,13 +213,13 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= bearing_deg, anisotropy, corrlen_major = _tensors_to_geostat_params(tensors) # Step 3: Prepare conceptual point data - cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) cp_means = layer_cp[mean_col].values cp_sd = layer_cp[sd_col].values.clip(1e-8, None) print(f"Conceptual point data ranges:") print(f" {mean_col}: [{cp_means.min():.3f}, {cp_means.max():.3f}]") print(f" {sd_col}: [{cp_sd.min():.3f}, {cp_sd.max():.3f}]") + print(f" Zones: {sorted(np.unique(layer_cp['zone']))}") # Step 4: Interpolate mean and sd using tensor-aware kriging print("\nStep 4: Interpolating mean and standard deviation fields...") @@ -205,7 +235,7 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= } # Transform for log-domain if needed - transform = 'log' if transform == 'log' else None + transform_str = 'log' if transform == 'log' else None min_mean = 1e-8 if transform == 'log' else None max_mean = 1e8 if transform == 'log' else None bounds = (min_mean, max_mean) @@ -216,21 +246,21 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= cp_coords, cp_means, modelgrid, tensors, variogram_model='exponential', background_value=np.mean(cp_means), max_search_radius=1e20, - min_points=3, transform=transform, bounds=bounds, + min_points=3, transform=transform_str, bounds=bounds, max_neighbors=4, zones=zones ) # Apply boundary smoothing to mean if boundary_smooth and zones is not None: if isinstance(boundary_smooth, dict): - smooth_params = {**boundary_smooth, **boundary_smooth} - else: # Must be True (boolean) smooth_params = boundary_smooth + else: + smooth_params = {'transition_cells': 2} print(" Smoothing mean at geological boundaries...") interp_means_2d = create_boundary_modified_scalar( - interp_means_2d, zones, transform=transform, - transition_cells=smooth_params['transition_cells'], + interp_means_2d, zones, transform=transform_str, + transition_cells=smooth_params.get('transition_cells', 2), mode='smooth' ) @@ -240,25 +270,27 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= # Interpolate standard deviation print(" Interpolating standard deviation...") + sd_transform = None if sd_is_log_space else transform_str interp_sd_2d = tensor_aware_kriging( cp_coords, cp_sd, modelgrid, tensors, variogram_model='exponential', background_value=np.mean(cp_sd), max_search_radius=1e20, - min_points=3, transform=transform, bounds=bounds, + min_points=3, transform=sd_transform, bounds=bounds, max_neighbors=4, zones=zones, ) # Apply boundary enhancement to sd if boundary_enhance and zones is not None: - if isinstance(boundary_smooth, dict): - smooth_params = {**boundary_enhance, **boundary_smooth} - else: # Must be True (boolean) + if isinstance(boundary_enhance, dict): smooth_params = boundary_enhance + else: + smooth_params = {'peak_increase': 2.0, 'transition_cells': 2} + print(" Enhancing variance at geological boundaries...") interp_sd_2d = create_boundary_modified_scalar( - interp_sd_2d, zones, transform=transform, - peak_increase=smooth_params['peak_increase'], - transition_cells=smooth_params['transition_cells'], + interp_sd_2d, zones, transform=transform_str, + peak_increase=smooth_params.get('peak_increase', 2.0), + transition_cells=smooth_params.get('transition_cells', 2), mode='enhance' ) @@ -553,8 +585,7 @@ def tensor_aware_kriging(cp_coords, cp_values, modelgrid, interp_tensors, print(f" Tensor-aware kriging: {len(cp_coords)} conceptual points -> {len(xcentergrid)} grid points") print( f" Conceptual points range: X=[{cp_coords[:, 0].min():.1f}, {cp_coords[:, 0].max():.1f}], Y=[{cp_coords[:, 1].min():.1f}, {cp_coords[:, 1].max():.1f}]") - print( - f" Grid points range: X=[{xcentergrid[0].min():.1f}, {xcentergrid[0].max():.1f}], Y=[{ycentergrid[1].min():.1f}, {ycentergrid[1].max():.1f}]") + print(f" Grid points range: X=[{xcentergrid.min():.1f}, {xcentergrid.max():.1f}], Y=[{ycentergrid.min():.1f}, {ycentergrid.max():.1f}]") print(f" Value range: [{cp_values.min():.6f}, {cp_values.max():.6f}], background: {background_value:.6f}") print(f" Transform requested: {transform}") @@ -912,6 +943,12 @@ def _extract_grid_info(modelgrid): grid_info['ny'], grid_info['nx'] = xc.shape grid_info['area'] = np.ones((grid_info['ny'], grid_info['nx'])) + # get active domain + if hasattr(modelgrid, 'idomain'): + grid_info['idomain'] = modelgrid.idomain + else: + raise AttributeError("modelgrid missing idomain") + return grid_info @@ -1253,7 +1290,7 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, ycentergrid = grid_info['ycentergrid'] ny = grid_info['ny'] nx = grid_info['nx'] - + active = active.astype(int) if area is None: area = grid_info['area'] @@ -1278,12 +1315,6 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, aniso_values = anisotropy.flatten() bearing_values = bearing.flatten() - # Domain based on zones - if active is None: - active = np.ones(n_points, dtype=int) - else: - active = active.astype(int).flatten() - if area is not None: area = area.flatten() @@ -1323,7 +1354,8 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, aa_values, aniso_values, bearing_values, sd_is_log_space, iids, avg_flag=avg_flag, - power_value=power_value + power_value=power_value, + active=active ) field = field_values.reshape(ny, nx) @@ -1431,7 +1463,8 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, def _moving_average_field(modelgrid, mean_flat, variance_flat, aa_values, aniso_values, bearing_values, - sd_is_log_space, iids, avg_flag=3, power_value=0): + sd_is_log_space, iids, avg_flag=3, power_value=0, + active=None): """ Generate field using moving average (convolution) method - matches FIELDGEN2D_SVA approach. @@ -1500,8 +1533,18 @@ def _moving_average_field(modelgrid, mean_flat, variance_flat, # Initialize output Z_field = np.zeros(n_points) + # Ensure we have active mask + if active is None: + active = np.ones(n_points, dtype=bool) + else: + active = active.astype(bool) + # For each target point, apply moving average for i in range(n_points): + # Skip inactive cells + if not active[i]: + Z_field[i] = 0.0 + continue if i % 2000 == 0: print(f" Progress: {i}/{n_points} points") @@ -1515,11 +1558,29 @@ def _moving_average_field(modelgrid, mean_flat, variance_flat, # Search radius in actual distance units search_radius = h_cutoff * local_aa + # Find neighbors neighbor_indices = tree.query_ball_point([x0, y0], search_radius) if len(neighbor_indices) == 0: neighbor_indices = [i] + # CRITICAL: Filter to only active neighbors + neighbor_indices = [idx for idx in neighbor_indices if active[idx]] + + # MAX_NEIGHBORS = 2 # Reasonable limit + # + # if len(neighbor_indices) > MAX_NEIGHBORS: + # # Keep only the closest MAX_NEIGHBORS points + # neighbor_coords = coords[neighbor_indices] + # distances = np.sqrt(np.sum((neighbor_coords - [x0, y0]) ** 2, axis=1)) + # closest_indices = np.argsort(distances)[:MAX_NEIGHBORS] + # neighbor_indices = [neighbor_indices[i] for i in closest_indices] + + if len(neighbor_indices) == 0: + # No active neighbors - use unconditional simulation + Z_field[i] = np.sqrt(local_var) * iids[i] + continue + # Compute anisotropic distances and weights weights = np.zeros(len(neighbor_indices)) for j, idx in enumerate(neighbor_indices): @@ -1560,18 +1621,38 @@ def _moving_average_field(modelgrid, mean_flat, variance_flat, else: weights[j] = 0.0 - # Apply weighted sum (NOT normalized) + # # Apply weighted sum (NOT normalized) + # weighted_sum = np.sum(weights * iids[neighbor_indices]) + # + # # Compute variance scaling factor + # variance_of_weighted_sum = np.sum(weights ** 2) + # + # if variance_of_weighted_sum > 0: + # scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) + # # if i < 10 or scaling_factor > 10 or scaling_factor < 0.1: + # # print(f"Point {i}: n_neighbors={len(neighbor_indices)}, " + # # f"sum(w²)={variance_of_weighted_sum:.6f}, " + # # f"local_var={local_var:.6f}, " + # # f"scaling={scaling_factor:.6f}") + # Z_field[i] = weighted_sum * scaling_factor + # else: + # # Fallback: unconditional simulation + # Z_field[i] = np.sqrt(local_var) * iids[i] + + # After computing all weights, normalize them + weight_sum = np.sum(weights) + if weight_sum > 0: + weights = weights / weight_sum # Now they sum to 1 + + # Apply weighted sum weighted_sum = np.sum(weights * iids[neighbor_indices]) # Compute variance scaling factor + # After normalization, sum(w²) will be much smaller variance_of_weighted_sum = np.sum(weights ** 2) - if variance_of_weighted_sum > 0: - scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) - Z_field[i] = weighted_sum * scaling_factor - else: - # Fallback: unconditional simulation - Z_field[i] = np.sqrt(local_var) * iids[i] + scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) + Z_field[i] = weighted_sum * scaling_factor print(f" Convolution complete. Z-field stats: mean={Z_field.mean():.3f}, std={Z_field.std():.3f}") From d4ee72da8e40c6ba34fe774a694533bb591d29ce Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Sat, 29 Nov 2025 13:29:21 +1300 Subject: [PATCH 35/36] cleaned up a bit --- .../Wairau/waq_arr/20240718_hipor_300.ims | 18 + .../Wairau/waq_arr/20240718_hipor_300.tdis | 13 + examples/Wairau/waq_arr/conceptual_points.csv | 603 ++++++++++++++++++ examples/Wairau/waq_arr/mfsim.nam | 19 + examples/Wairau/waq_arr/waq_arr.bottom.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer1.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer10.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer11.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer12.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer13.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer2.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer3.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer4.arr | 67 ++ .../Wairau/waq_arr/waq_arr.bottom_layer5.arr | 67 ++ 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--- pyemu/utils/nsaf_utils.py | 214 +++---- 54 files changed, 3575 insertions(+), 229 deletions(-) create mode 100644 examples/Wairau/waq_arr/20240718_hipor_300.ims create mode 100644 examples/Wairau/waq_arr/20240718_hipor_300.tdis create mode 100644 examples/Wairau/waq_arr/conceptual_points.csv create mode 100644 examples/Wairau/waq_arr/mfsim.nam create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer1.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer10.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer11.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer12.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer13.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer2.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer3.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer4.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer5.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer6.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer7.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer8.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.bottom_layer9.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.dis create mode 100644 examples/Wairau/waq_arr/waq_arr.dis.grb create mode 100644 examples/Wairau/waq_arr/waq_arr.dis_delc.txt create mode 100644 examples/Wairau/waq_arr/waq_arr.dis_delr.txt create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer1.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer1.tif create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer10.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer11.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer12.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer13.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer2.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer3.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer4.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer5.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer6.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer7.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer8.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.domain_layer9.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer1.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer10.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer11.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer12.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer13.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer2.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer3.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer4.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer5.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer6.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer7.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer8.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.geoclass_layer9.arr create mode 100644 examples/Wairau/waq_arr/waq_arr.nam create mode 100644 examples/Wairau/waq_arr/waq_arr.top.arr diff --git a/examples/Wairau/waq_arr/20240718_hipor_300.ims b/examples/Wairau/waq_arr/20240718_hipor_300.ims new file mode 100644 index 000000000..b6370bf06 --- /dev/null +++ b/examples/Wairau/waq_arr/20240718_hipor_300.ims @@ -0,0 +1,18 @@ +# File generated by Flopy version 3.8.0.dev0 on 07/18/2024 at 09:45:52. +BEGIN options + PRINT_OPTION all + COMPLEXITY moderate + CSV_OUTER_OUTPUT FILEOUT mfsim_outer.out + CSV_INNER_OUTPUT FILEOUT mfsim_inner.out +END options + +BEGIN nonlinear + OUTER_DVCLOSE 1.00000000E-04 + OUTER_MAXIMUM 1000 +END nonlinear + +BEGIN linear + INNER_MAXIMUM 200 + INNER_DVCLOSE 1.00000000E-04 +END linear + diff --git a/examples/Wairau/waq_arr/20240718_hipor_300.tdis b/examples/Wairau/waq_arr/20240718_hipor_300.tdis new file mode 100644 index 000000000..a6ade8e51 --- /dev/null +++ b/examples/Wairau/waq_arr/20240718_hipor_300.tdis @@ -0,0 +1,13 @@ +# File generated by Flopy version 3.8.0.dev0 on 07/18/2024 at 09:45:52. +BEGIN options + TIME_UNITS days +END options + +BEGIN dimensions + NPER 1 +END dimensions + +BEGIN perioddata + 1.00000000 1 1.00000000 +END perioddata + diff --git a/examples/Wairau/waq_arr/conceptual_points.csv b/examples/Wairau/waq_arr/conceptual_points.csv new file mode 100644 index 000000000..94bed6652 --- /dev/null +++ b/examples/Wairau/waq_arr/conceptual_points.csv @@ -0,0 +1,603 @@ 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diff --git a/examples/Wairau/waq_arr/mfsim.nam b/examples/Wairau/waq_arr/mfsim.nam new file mode 100644 index 000000000..00d2c4c6c --- /dev/null +++ b/examples/Wairau/waq_arr/mfsim.nam @@ -0,0 +1,19 @@ +# File generated by Flopy version 3.8.0.dev0 on 07/18/2024 at 09:45:52. +BEGIN options +END options + +BEGIN timing + TDIS6 20240718_hipor_300.tdis +END timing + +BEGIN models + gwf6 waq_arr.nam waq_arr +END models + +BEGIN exchanges +END exchanges + +BEGIN solutiongroup 1 + ims6 20240718_hipor_300.ims waq_arr +END solutiongroup 1 + diff --git a/examples/Wairau/waq_arr/waq_arr.bottom.arr b/examples/Wairau/waq_arr/waq_arr.bottom.arr new file mode 100644 index 000000000..f649a2a8e --- /dev/null +++ b/examples/Wairau/waq_arr/waq_arr.bottom.arr @@ -0,0 +1,67 @@ + -1.55045630999999986e+02 -1.55045630999999986e+02 -1.55045630999999986e+02 -1.55045630999999986e+02 -1.55045630999999986e+02 -1.55045630999999986e+02 -1.55045630999999986e+02 -1.55045630999999986e+02 -1.55045630999999986e+02 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+ OPEN/CLOSE 'waq_arr.dis_delr.txt' FACTOR 1.0 + delc + OPEN/CLOSE 'waq_arr.dis_delc.txt' FACTOR 1.0 + top + OPEN/CLOSE 'waq_arr.top.arr' FACTOR 1.0 IPRN -1 + botm LAYERED + OPEN/CLOSE 'waq_arr.bottom_layer1.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer2.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer3.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer4.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer5.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer6.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer7.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer8.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer9.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer10.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer11.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer12.arr' FACTOR 1.0 IPRN -1 + OPEN/CLOSE 'waq_arr.bottom_layer13.arr' FACTOR 1.0 IPRN -1 + idomain LAYERED + OPEN/CLOSE 'waq_arr.domain_layer1.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer2.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer3.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer4.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer5.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer6.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer7.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer8.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer9.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer10.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer11.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer12.arr' FACTOR 1 IPRN -1 + OPEN/CLOSE 'waq_arr.domain_layer13.arr' FACTOR 1 IPRN -1 +END griddata + diff --git a/examples/Wairau/waq_arr/waq_arr.dis.grb b/examples/Wairau/waq_arr/waq_arr.dis.grb new file mode 100644 index 0000000000000000000000000000000000000000..9d9b1e4343f55523e3bfc8a211cfdfabc73bb8c6 GIT binary patch literal 3500684 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zHA#7BpZ6bE&5|w8*^hneEt-a{w`{%NTQKXk{w~>s{cBx0`hR%(z>fUAYRvj=W8OS5 z=3vjZzu0!)wtw38@8O%~d$%>HxpLHs)~yhOzJ+?f z*G$Hj`@I#@G)q>3n)$S5R+IAca(z%Cx=-6LUvwMEw0$>!Kl!-*JA6xt4)SyG=gu(4vzG9{eZYNC+ zGOu}X3a3@%bY8qo`&~GN(<*T~Ehn+O2e0s|k=I!G3bhY7h0`KA&70F%bC~x#Qs>R8 z5AVbIB+jMs5c|I24c;d4mNyr%=UdDE^7_F%cpm3jOa7&t1ODJ|oIhV4q~u!bK7IZ$ zUTvPW=3d&l;IP^pX8A8I$J+Os<2kj>UKOA(MA_49jDdp70@)k63)m$`$=#_>IM9_96S8fJb;t6o6wH z9K-AJKKb+^=f~@ZK4J9n~ne4uw6 zdiyvDJ&x~j_>K=UeF)8C)_UYaW^Gzq}=W#_cEm==3KqpKcyG(3t~#mEo)7&{yK8 z3_tNnr%ygU-8la1^dDbk`06q$Xn(bEAmtoI`^ow` z?*l3M*1Au)(#cg(zG~$_+PRAImvwdA2h#GaeZO#}gR9U!YR|p&^M&@C^<>`z((|l+ z|L`T7uTcJJ&$+hqh4P(sWbv;p$5lBGoXO%Wv|m-pIp-Ya-02W1M{Ye=rq^}-S!H@Y z?aB91mVWdf%{6+IqX(SAX`%qUmBZV#I>RZP!m0dws;V4Md++gH+`mg&{ee?*$M3DY z-`lEitM%UHT-CaFrSVEWtCdgup6qul+~Ut 0), field, np.nan)) + sd = np.log10(np.where(np.isfinite(sd) & (sd > 0), sd, np.nan)) + mean = np.log10(np.where(np.isfinite(mean) & (mean > 0), mean, np.nan)) field_label = f'log10({title_suf})' else: field_label = 'Field' # Visualize results fig, axes = plt.subplots(2, 2, figsize=(16, 12)) - vmin = -4 - vmax = 4 + vmin = np.floor(np.nanmin(field)) + vmax = np.ceil(np.nanmax(field)) # Field with conceptual points using real coordinates im1 = axes[0, 0].imshow(field, origin='upper', cmap='RdYlBu_r', diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index ec41179cd..e3ee371ba 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -447,106 +447,6 @@ def _assign_layers_from_depth(conceptual_points, modelgrid): pass -def create_boundary_modified_scalar(base_field, zones, - peak_increase=0.3, transition_cells=5, mode="enhance"): - """ - Modify scalar field values near geological zone boundaries. - """ - from scipy.ndimage import distance_transform_edt, gaussian_filter - - if mode not in ("enhance", "smooth"): - raise ValueError("mode must be 'enhance' or 'smooth'") - - # 2D case only for now - if zones.shape != base_field.shape: - raise ValueError(f"Zones shape {zones.shape} must match field shape {base_field.shape}") - - boundary_mask, _ = _detect_zone_boundaries(zones) - distance = distance_transform_edt(~boundary_mask) - transition_mask = distance <= transition_cells - - modified = base_field.copy() - - if mode == "enhance": - # Linear enhancement near boundaries - factor = 1 - distance[transition_mask] / transition_cells - enhancement = peak_increase * factor - modified[transition_mask] += enhancement - - elif mode == "smooth": - # Smooth field with Gaussian filter - smoothed_field = gaussian_filter(base_field, sigma=transition_cells / 2) - - # Blend with original based on distance to boundary - weight = 1 - distance[transition_mask] / transition_cells - modified[transition_mask] = ( - weight * smoothed_field[transition_mask] + - (1 - weight) * base_field[transition_mask] - ) - - print(f" {'Enhanced' if mode == 'enhance' else 'Smoothed'} " - f"{np.count_nonzero(transition_mask)} points near boundaries") - - return modified - - -def detect_zone_boundaries(zones): - """Detect boundaries between geological zones.""" - ny, nx = zones.shape - boundary_mask = np.zeros((ny, nx)).astype(bool) - boundary_directions = np.zeros((ny, nx, 2)) - - # Find boundary points - for i in range(ny): - for j in range(nx): - current_zone = zones[i, j] - - # Check 8-connected neighbors - for di in [-1, 0, 1]: - for dj in [-1, 0, 1]: - if di == 0 and dj == 0: - continue - ni, nj = i + di, j + dj - if 0 <= ni < ny and 0 <= nj < nx: - if zones[ni, nj] != current_zone: - boundary_mask[i, j] = True - break - if boundary_mask[i, j]: - break - - return boundary_mask, boundary_directions - - -def save_results(result, out_filename): - """Save results to files in PyEMU-compatible format.""" - - # todo: better file name handling - - # Save mean field - mean_file = f"{out_filename}_mean.txt" - np.savetxt(mean_file, result['mean'], fmt="%20.8E") - print(f" Saved mean field to {mean_file}") - - # Save standard deviation field - sd_file = f"{out_filename}_sd.txt" - np.savetxt(sd_file, result['sd'], fmt="%20.8E") - print(f" Saved SD field to {sd_file}") - - # Save geostatistical parameter field - for param in ['bearing', 'anisotropy', 'corrlen']: - param_file = f"{out_filename}_{param}.txt" - np.savetxt(param_file, result[param], fmt="%20.8E") - print(f" Saved {param} field to {param_file}") - - # Save stochastic realizations - if result['field'] is not None: - for i, field in enumerate(result['field']): - field_file = f"{out_filename}_real_{i + 1:03d}.txt" - np.savetxt(field_file, field, fmt="%20.8E") - - print(f" Saved {len(result['field'])} realizations") - - def _load_or_generate_iids(iid_path, ny, nx, n_realizations): """ Load IIDs from file or generate and save new ones. diff --git a/pyemu/utils/nsaf_utils.py b/pyemu/utils/nsaf_utils.py index 63a95171c..7660f4982 100644 --- a/pyemu/utils/nsaf_utils.py +++ b/pyemu/utils/nsaf_utils.py @@ -172,7 +172,7 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= print(f"Grid dimensions: {ny} x {nx} = {ny * nx} cells") print(f"Using columns: mean='{mean_col}', sd='{sd_col}'") - # === ZONE ASSIGNMENT - DO THIS ONCE, EARLY === + # zone assignment n_grid = ny * nx cp_coords = np.column_stack([layer_cp['x'].values, layer_cp['y'].values]) @@ -212,7 +212,10 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= print("\nStep 2: Converting tensors to geostatistical parameters...") bearing_deg, anisotropy, corrlen_major = _tensors_to_geostat_params(tensors) - # Step 3: Prepare conceptual point data + + # Step 3: Interpolate mean and sd using tensor-aware kriging + print("\nStep 3: Interpolating mean and standard deviation fields...") + cp_means = layer_cp[mean_col].values cp_sd = layer_cp[sd_col].values.clip(1e-8, None) @@ -221,9 +224,6 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= print(f" {sd_col}: [{cp_sd.min():.3f}, {cp_sd.max():.3f}]") print(f" Zones: {sorted(np.unique(layer_cp['zone']))}") - # Step 4: Interpolate mean and sd using tensor-aware kriging - print("\nStep 4: Interpolating mean and standard deviation fields...") - # Configure kriging parameters if config_dict is None: config_dict = { @@ -298,8 +298,8 @@ def apply_nsaf_hyperpars(conceptual_points, modelgrid, zones=None, out_filename= print(f" Mean: [{interp_means_2d.min():.3f}, {interp_means_2d.max():.3f}]") print(f" SD: [{interp_sd_2d.min():.3f}, {interp_sd_2d.max():.3f}]") - # Step 5: Generate stochastic fields - print(f"\nStep 5: Generating stochastic realization(s)...") + # Step 4: Generate stochastic fields + print(f"\nStep 4: Generating stochastic realization(s)...") if iids is not None: print(" Note: iids provided - generating single realization (n_realizations parameter ignored)") @@ -723,7 +723,7 @@ def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): print(f" Search radius: {max_search_radius}") print(f" Max neighbors: {max_neighbors}") - # Then use the modified zones in kriging + # use the modified zones in kriging npts = lib.calc_kriging_factors_2d( cp_coords[:, 0], cp_coords[:, 1], cp_zones_for_kriging, # Modified zones xcentergrid.flatten(), ycentergrid.flatten(), zones_for_kriging, # Modified zones @@ -739,10 +739,10 @@ def fix_zones_for_pypestutils(zones_flat, cp_zones, min_points): fac_fname, fac_ftype, len(xcentergrid.flatten()), 1, # Ordinary kriging - transform_flag, # Pass through the transform flag as specified - cp_values_transformed, # Use values exactly as provided - background_value, # Use background exactly as provided - background_value # Use no-interpolation value exactly as provided + transform_flag, + cp_values_transformed, + background_value, + background_value ) # Clean up @@ -810,8 +810,6 @@ def create_boundary_modified_scalar(base_field, zones, modified[transition_mask] = smoothed_field[transition_mask] * (1 + peak_increase * factor) elif mode == "smooth": - # Smooth field with Gaussian filter - #smoothed_field = gaussian_filter(base_field, sigma=transition_cells) weight = 1 - distance[transition_mask] / transition_cells modified[transition_mask] = ( weight * smoothed_field[transition_mask] + @@ -960,7 +958,6 @@ def _calculate_vertex_areas(modelgrid): for i in range(ncpl): try: verts = modelgrid.get_cell_vertices(i) - # Shoelace formula x = [v[0] for v in verts] y = [v[1] for v in verts] area = 0.5 * abs(sum(x[j] * y[j + 1] - x[j + 1] * y[j] for j in range(-1, len(x) - 1))) @@ -975,74 +972,6 @@ def _calculate_vertex_areas(modelgrid): return areas.reshape(modelgrid.nrow, modelgrid.ncol) -def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, - xcentergrid, ycentergrid, zones_flat, config_dict, nx, ny): - """Interpolate tensors using pypestutils kriging with log-Euclidean approach.""" - n_grid = np.prod(xcentergrid.shape) - result_tensors = np.zeros((n_grid, 2, 2)) - - # Convert tensors to log space - log_tensors = np.array([logm(_ensure_positive_definite(tensor)) - for tensor in cp_tensors]) - - # Process each zone separately - unique_zones = np.unique(zones_flat) - - for zone_id in unique_zones: - zone_mask = zones_flat == zone_id - zone_indices = np.where(zone_mask)[0] - - if len(zone_indices) == 0: - continue - - # Filter conceptual points for this zone - cp_mask = cp_zones == zone_id - if np.sum(cp_mask) == 0: - # No conceptual points in this zone - use default tensor - default_tensor = np.eye(2) * 1000 ** 2 - result_tensors[zone_indices] = default_tensor - continue - - zone_cp_coords = cp_coords[cp_mask] - zone_log_tensors = log_tensors[cp_mask] - zone_xcentergrid = xcentergrid[zone_indices] - zone_ycentergrid = ycentergrid[zone_indices] - - # Interpolate each tensor component separately - zone_tensors = np.zeros((len(zone_indices), 2, 2)) - - for i in range(2): - for j in range(i, 2): # Only upper triangle, then mirror - component_values = zone_log_tensors[:, i, j].real - - # Use pypestutils for kriging - try: - from pypestutils.pestutilslib import PestUtilsLib - interp_component = _krig_component_pypestutils( - zone_cp_coords[:, 0], zone_cp_coords[:, 1], - component_values, zone_xcentergrid, zone_ycentergrid, - config_dict - ) - except Exception as e: - raise Exception(f"Error importing pypestutils, using python based method: {e}") - # todo: change to warning use other method - - zone_tensors[:, i, j] = interp_component - if i != j: - zone_tensors[:, j, i] = interp_component - - # Convert back from log space and ensure positive definiteness - for k in range(len(zone_indices)): - try: - tensor_exp = expm(zone_tensors[k]) - result_tensors[zone_indices[k]] = _ensure_positive_definite(tensor_exp) - except: - # Fallback to default tensor - result_tensors[zone_indices[k]] = np.eye(2) * 1000 ** 2 - - return result_tensors - - def _krig_component_pypestutils(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, config_dict): """Use pypestutils to krig a single tensor component.""" try: @@ -1112,6 +1041,74 @@ def _krig_component_pypestutils(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, return _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid) +def _interpolate_tensors_kriging(cp_coords, cp_tensors, cp_zones, + xcentergrid, ycentergrid, zones_flat, config_dict, nx, ny): + """Interpolate tensors using pypestutils kriging with log-Euclidean approach.""" + n_grid = np.prod(xcentergrid.shape) + result_tensors = np.zeros((n_grid, 2, 2)) + + # Convert tensors to log space + log_tensors = np.array([logm(_ensure_positive_definite(tensor)) + for tensor in cp_tensors]) + + # Process each zone separately + unique_zones = np.unique(zones_flat) + + for zone_id in unique_zones: + zone_mask = zones_flat == zone_id + zone_indices = np.where(zone_mask)[0] + + if len(zone_indices) == 0: + continue + + # Filter conceptual points for this zone + cp_mask = cp_zones == zone_id + if np.sum(cp_mask) == 0: + # No conceptual points in this zone - use default tensor + default_tensor = np.eye(2) * 1000 ** 2 + result_tensors[zone_indices] = default_tensor + continue + + zone_cp_coords = cp_coords[cp_mask] + zone_log_tensors = log_tensors[cp_mask] + zone_xcentergrid = xcentergrid[zone_indices] + zone_ycentergrid = ycentergrid[zone_indices] + + # Interpolate each tensor component separately + zone_tensors = np.zeros((len(zone_indices), 2, 2)) + + for i in range(2): + for j in range(i, 2): # Only upper triangle, then mirror + component_values = zone_log_tensors[:, i, j].real + + # Use pypestutils for kriging + try: + from pypestutils.pestutilslib import PestUtilsLib + interp_component = _krig_component_pypestutils( + zone_cp_coords[:, 0], zone_cp_coords[:, 1], + component_values, zone_xcentergrid, zone_ycentergrid, + config_dict + ) + except Exception as e: + raise Exception(f"Error importing pypestutils, using python based method: {e}") + # todo: change to warning use other method + + zone_tensors[:, i, j] = interp_component + if i != j: + zone_tensors[:, j, i] = interp_component + + # Convert back from log space and ensure positive definiteness + for k in range(len(zone_indices)): + try: + tensor_exp = expm(zone_tensors[k]) + result_tensors[zone_indices[k]] = _ensure_positive_definite(tensor_exp) + except: + # Fallback to default tensor + result_tensors[zone_indices[k]] = np.eye(2) * 1000 ** 2 + + return result_tensors + + def _idw_component(cp_x, cp_y, cp_values, xcentergrid, ycentergrid, power=2): """Inverse distance weighting fallback for component interpolation.""" result = np.zeros(len(xcentergrid)) @@ -1297,7 +1294,7 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, n_points = ny * nx # Flatten fields - mean_flat = mean_field.flatten().clip(1e-8, None) # Always native space + mean_flat = mean_field.flatten().clip(1e-8, None) # native space sd_flat = sd_field.flatten() # Compute variance and set transtype based on what space sd_field is in @@ -1361,7 +1358,7 @@ def _generate_stochastic_field(modelgrid, mean_field, sd_field, return field # Shape: (ny, nx) - # Handle FIELDGEN2D_SVA generation (internal random generation) + # Handle FIELDGEN2D_SVA generation (internal iid generation) else: try: print(f" Generating {n_realizations} realization(s) using FIELDGEN2D_SVA...") @@ -1533,7 +1530,7 @@ def _moving_average_field(modelgrid, mean_flat, variance_flat, # Initialize output Z_field = np.zeros(n_points) - # Ensure we have active mask + # Ensure active mask if active is None: active = np.ones(n_points, dtype=bool) else: @@ -1564,18 +1561,9 @@ def _moving_average_field(modelgrid, mean_flat, variance_flat, if len(neighbor_indices) == 0: neighbor_indices = [i] - # CRITICAL: Filter to only active neighbors + # Filter to only active neighbors neighbor_indices = [idx for idx in neighbor_indices if active[idx]] - # MAX_NEIGHBORS = 2 # Reasonable limit - # - # if len(neighbor_indices) > MAX_NEIGHBORS: - # # Keep only the closest MAX_NEIGHBORS points - # neighbor_coords = coords[neighbor_indices] - # distances = np.sqrt(np.sum((neighbor_coords - [x0, y0]) ** 2, axis=1)) - # closest_indices = np.argsort(distances)[:MAX_NEIGHBORS] - # neighbor_indices = [neighbor_indices[i] for i in closest_indices] - if len(neighbor_indices) == 0: # No active neighbors - use unconditional simulation Z_field[i] = np.sqrt(local_var) * iids[i] @@ -1621,38 +1609,18 @@ def _moving_average_field(modelgrid, mean_flat, variance_flat, else: weights[j] = 0.0 - # # Apply weighted sum (NOT normalized) - # weighted_sum = np.sum(weights * iids[neighbor_indices]) - # - # # Compute variance scaling factor - # variance_of_weighted_sum = np.sum(weights ** 2) - # - # if variance_of_weighted_sum > 0: - # scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) - # # if i < 10 or scaling_factor > 10 or scaling_factor < 0.1: - # # print(f"Point {i}: n_neighbors={len(neighbor_indices)}, " - # # f"sum(w²)={variance_of_weighted_sum:.6f}, " - # # f"local_var={local_var:.6f}, " - # # f"scaling={scaling_factor:.6f}") - # Z_field[i] = weighted_sum * scaling_factor - # else: - # # Fallback: unconditional simulation - # Z_field[i] = np.sqrt(local_var) * iids[i] - - # After computing all weights, normalize them - weight_sum = np.sum(weights) - if weight_sum > 0: - weights = weights / weight_sum # Now they sum to 1 - - # Apply weighted sum + # Apply weighted sum (NOT normalized) weighted_sum = np.sum(weights * iids[neighbor_indices]) # Compute variance scaling factor - # After normalization, sum(w²) will be much smaller variance_of_weighted_sum = np.sum(weights ** 2) - scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) - Z_field[i] = weighted_sum * scaling_factor + if variance_of_weighted_sum > 0: + scaling_factor = np.sqrt(local_var / variance_of_weighted_sum) + Z_field[i] = weighted_sum * scaling_factor + else: + # Fallback: unconditional simulation + Z_field[i] = np.sqrt(local_var) * iids[i] print(f" Convolution complete. Z-field stats: mean={Z_field.mean():.3f}, std={Z_field.std():.3f}") From bc682408d1c96ce9db12175be62fbf462f88e57e Mon Sep 17 00:00:00 2001 From: Wes Kitlasten Date: Thu, 4 Dec 2025 13:04:19 +1300 Subject: [PATCH 36/36] model_ws change --- pyemu/utils/nsaf_helpers.py | 29 +++++++++++++++-------------- pyemu/utils/nsaf_useage.py | 1 + 2 files changed, 16 insertions(+), 14 deletions(-) diff --git a/pyemu/utils/nsaf_helpers.py b/pyemu/utils/nsaf_helpers.py index e3ee371ba..1c799457d 100644 --- a/pyemu/utils/nsaf_helpers.py +++ b/pyemu/utils/nsaf_helpers.py @@ -14,7 +14,8 @@ ) -def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, +def generate_fields_from_files(model_name, conceptual_points_file, + model_ws='.', zone_files=None, iid_files=None, field_name=['field'], layer_mode=True, save_dir=None, tensor_interp='krig', transform='log', n_realizations=1, ml=None, sr=None, @@ -26,7 +27,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, Parameters ---------- - tmp_model_ws : str + model_ws : str Path to model workspace containing flopy model files model_name : str Name of the flopy model (e.g., 'freyberg6') @@ -52,7 +53,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, layer_mode : bool, default True If True, process each layer independently save_dir : str, optional - Directory path to save output files (default: tmp_model_ws) + Directory path to save output files (default: model_ws) tensor_interp : str, default 'krig' Tensor interpolation method transform : str, default 'log' @@ -89,7 +90,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, raise NotImplementedError("use_depth=True requires gridit package integration - not yet implemented") print(f"=== Tensor-Based Field Generation ===") - print(f"Model workspace: {tmp_model_ws}") + print(f"Model workspace: {model_ws}") print(f"Model name: {model_name}") # Setup spatial reference following PyEMU pattern @@ -97,17 +98,17 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, print("Loading flopy model...") try: # Try MF6 first - only load DIS package for efficiency - sim = flopy.mf6.MFSimulation.load(sim_ws=tmp_model_ws, load_only=['dis']) + sim = flopy.mf6.MFSimulation.load(sim_ws=model_ws, load_only=['dis']) ml = sim.get_model(model_name) print(f"Loaded MF6 model: {model_name} (DIS package only)") except Exception as e_mf6: try: # Fall back to MODFLOW-2005 - only load DIS package - ml = flopy.modflow.Modflow.load(f"{model_name}.nam", model_ws=tmp_model_ws, + ml = flopy.modflow.Modflow.load(f"{model_name}.nam", model_ws=model_ws, load_only=['dis'], check=False) print(f"Loaded MODFLOW-2005 model: {model_name} (DIS package only)") except Exception as e_mf2005: - raise ValueError(f"Could not load model {model_name} from {tmp_model_ws}.\n" + raise ValueError(f"Could not load model {model_name} from {model_ws}.\n" f"MF6 error: {e_mf6}\n" f"MF2005 error: {e_mf2005}") @@ -126,7 +127,7 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, f"Y=[{ycentergrid.min():.1f}, {ycentergrid.max():.1f}]") # Load conceptual points - conceptual_points = pd.read_csv(os.path.join(tmp_model_ws, conceptual_points_file)) + conceptual_points = pd.read_csv(os.path.join(model_ws, conceptual_points_file)) print(f"Loaded {len(conceptual_points)} conceptual points") print(f"Conceptual points extent: X=[{conceptual_points['x'].min():.1f}, " f"{conceptual_points['x'].max():.1f}], Y=[{conceptual_points['y'].min():.1f}, " @@ -189,14 +190,14 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, if "{}" not in zone_files: raise ValueError(f"zone_files pattern must contain '{{}}' placeholder, got: {zone_files}") for layer_num in range(total_layers): - zone_paths[layer_num] = os.path.join(tmp_model_ws, zone_files.format(layer_num)) + zone_paths[layer_num] = os.path.join(model_ws, zone_files.format(layer_num)) elif isinstance(zone_files, dict): # Explicit dict mapping - must contain all layers for layer_num in range(total_layers): if layer_num not in zone_files: raise ValueError(f"zone_files dict missing entry for layer {layer_num}. " f"Dict must contain all layers 0-{total_layers - 1}") - zone_paths[layer_num] = os.path.join(tmp_model_ws, zone_files[layer_num]) + zone_paths[layer_num] = os.path.join(model_ws, zone_files[layer_num]) else: raise ValueError(f"zone_files must be None, str, or dict. Got: {type(zone_files)}") @@ -238,13 +239,13 @@ def generate_fields_from_files(tmp_model_ws, model_name, conceptual_points_file, # String pattern - must contain {} placeholder if "{}" not in iid_files: raise ValueError(f"iid_files pattern must contain '{{}}' placeholder, got: {iid_files}") - iid_path = os.path.join(tmp_model_ws, iid_files.format(layer_num)) + iid_path = os.path.join(model_ws, iid_files.format(layer_num)) elif isinstance(iid_files, dict): # Explicit dict mapping - must contain all layers if layer_num not in iid_files: raise ValueError(f"iid_files dict missing entry for layer {layer_num}. " f"Dict must contain all layers 0-{total_layers - 1}") - iid_path = os.path.join(tmp_model_ws, iid_files[layer_num]) + iid_path = os.path.join(model_ws, iid_files[layer_num]) else: raise ValueError(f"iid_files must be None, str, or dict. Got: {type(iid_files)}") @@ -726,7 +727,7 @@ def test_tangential_via_files(): # Test with fieldgen (iids=None) print("\n--- Test 1: FIELDGEN2D_SVA (iids=None) ---") results_fieldgen = generate_fields_from_files( - tmp_model_ws=test_ws, + model_ws=test_ws, model_name='tangential_test', conceptual_points_file='conceptual_points.csv', zone_files='zones_layer{}.arr', @@ -744,7 +745,7 @@ def test_tangential_via_files(): # Test with Python fallback (iids provided) print("\n--- Test 2: Python fallback (iids provided) ---") results_python = generate_fields_from_files( - tmp_model_ws=test_ws, + model_ws=test_ws, model_name='tangential_test', conceptual_points_file='conceptual_points.csv', zone_files='zones_layer{}.arr', diff --git a/pyemu/utils/nsaf_useage.py b/pyemu/utils/nsaf_useage.py index 23d3ed2b4..549f500c4 100644 --- a/pyemu/utils/nsaf_useage.py +++ b/pyemu/utils/nsaf_useage.py @@ -34,6 +34,7 @@ os.mkdir(save_dir) nsaf_helpers.generate_fields_from_files(data_dir, model_name, con_pts_file, + model_ws=data_dir, zone_files=zone_files, iid_files='iids_layer{}.arr', field_name=['kh'],