From 4b05f9871b1e5a814e0afd61a222d5b48325cb08 Mon Sep 17 00:00:00 2001 From: Gerd Duscher <50049264+gduscher@users.noreply.github.com> Date: Sun, 15 Dec 2024 17:35:21 -0500 Subject: [PATCH] infoWidget with plotly --- notebooks/0_pyTEMlib.ipynb | 2 +- notebooks/Spectroscopy/Analyse_Low_Loss.ipynb | 2798 ++++++++++++++++- notebooks/Spectroscopy/EDS.ipynb | 118 +- pyTEMlib/core_loss_widget.py | 10 +- pyTEMlib/eels_tools.py | 80 +- pyTEMlib/file_tools.py | 60 +- pyTEMlib/info_widget.py | 90 +- pyTEMlib/info_widget3.py | 1153 +++++++ pyTEMlib/low_loss_widget.py | 276 +- 9 files changed, 4391 insertions(+), 196 deletions(-) create mode 100644 pyTEMlib/info_widget3.py diff --git a/notebooks/0_pyTEMlib.ipynb b/notebooks/0_pyTEMlib.ipynb index 3ea47dbd..ce7a6768 100644 --- a/notebooks/0_pyTEMlib.ipynb +++ b/notebooks/0_pyTEMlib.ipynb @@ -63,7 +63,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.13.0" }, "toc": { "base_numbering": "1", diff --git a/notebooks/Spectroscopy/Analyse_Low_Loss.ipynb b/notebooks/Spectroscopy/Analyse_Low_Loss.ipynb index 6dd023d2..28763990 100644 --- a/notebooks/Spectroscopy/Analyse_Low_Loss.ipynb +++ b/notebooks/Spectroscopy/Analyse_Low_Loss.ipynb @@ -143,8 +143,6 @@ } ], "source": [ - "%matplotlib widget\n", - "import matplotlib.pylab as plt\n", "import numpy as np\n", "import sys\n", "\n", @@ -164,11 +162,10 @@ "import pyTEMlib\n", "import pyTEMlib.file_tools as ft # File input/ output library\n", "from pyTEMlib import eels_tools \n", - "import pyTEMlib.info_widget\n", + "import pyTEMlib.info_widget3\n", "import pyTEMlib.eels_dialog\n", "import pyTEMlib.peak_dialog\n", "\n", - "\n", "import pyTEMlib.kinematic_scattering as ks # Kinematic sCattering Library\n", " # Atomic form factors from Kirklands book\n", "\n", @@ -176,6 +173,13 @@ "print('pyTEM version: ',pyTEMlib.__version__)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": { @@ -202,12 +206,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "14d4d0a343d241c7ac7e9363ae2121a5", + "model_id": "24d5e4408b284b4b8f51f8bcbdf62af4", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "AppLayout(children=(VBox(children=(ToggleButtons(layout=Layout(width='auto'), options=('File', 'Spec.', 'LowLo…" + "AppLayout(children=(Tab(children=(GridspecLayout(children=(Dropdown(description='directory:', layout=Layout(gr…" ] }, "metadata": {}, @@ -217,39 +221,2785 @@ "source": [ "if 'google.colab' in sys.modules:\n", " drive.mount(\"/content/drive\")\n", - " \n", + "import pyTEMlib.info_widget \n", "# filename = '../../example_data/AL-DFoffset0.00.dm3'\n", - "infoWidget= pyTEMlib.info_widget.EELSWidget()" + "infoWidget= pyTEMlib.info_widget3.EELSWidget()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 47, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Channel_000 Channel_001 0\n", - "Channel_000 Channel_001 0\n", - "Channel_001 Channel_001 0\n", - "Channel_001 Channel_001 1\n", - "resolution_function Channel_001 1\n", - "resolution_function Channel_001 1\n", - "plasmon Channel_001 1\n", - "plasmon Channel_001 1\n", - "multiple_scattering Channel_001 1\n", - "multiple_scattering Channel_001 1\n" + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "#infoWidget.datasets['_dif'] = infoWidget.spectrum\n", + "\n", + "infoWidget.x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "infoWidget.low_loss.get_drude()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'go' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[22], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39m_update()\n\u001b[0;32m 2\u001b[0m resolution_function \u001b[38;5;241m=\u001b[39m infoWidget\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39mget_additional_spectrum(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzero_loss\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39mspectrum_plot\u001b[38;5;241m.\u001b[39madd_trace(\u001b[43mgo\u001b[49m\u001b[38;5;241m.\u001b[39mScatter(x\u001b[38;5;241m=\u001b[39minfoWidget\u001b[38;5;241m.\u001b[39menergy_scale, y\u001b[38;5;241m=\u001b[39mresolution_function))\n", + "\u001b[1;31mNameError\u001b[0m: name 'go' is not defined" ] } ], "source": [ - "infoWidget.tab_buttons.index = 2\n", + "infoWidget._update()\n", + "resolution_function = infoWidget.low_loss.get_additional_spectrum('zero_loss')\n", + "infoWidget.spectrum_plot.add_trace(go.Scatter(x=infoWidget.energy_scale, y=resolution_function))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "infoWidget.energy_scale[:3].values\n", + "k = infoWidget.key\n", + "infoWidget.datasets['Channel_000'].energy_loss[:3].values\n", + "infoWidget.spectrum_plot.data[1].x[1]-infoWidget.spectrum_plot.data[1].x[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "infoWidget.canvas_plot.children = [infoWidget.image_plot]\n", + "infoWidget.canvas_plot.children" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "infoWidget.image_plot.data[0].x = infoWidget.datasset." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ce46460f48b9408183cba716aa5c6c35", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FigureWidget({\n", + " 'data': [{'type': 'heatmap',\n", + " 'uid': '591bccf3-96de-4ed2-9089-c1b5eefd9fa1',\n", + " 'x': array([0.00000000e+00, 1.48742899e-01, 2.97485799e-01, ..., 1.51866500e+02,\n", + " 1.52015243e+02, 1.52163986e+02]),\n", + " 'y': array([0.00000000e+00, 1.48742899e-01, 2.97485799e-01, ..., 1.51866500e+02,\n", + " 1.52015243e+02, 1.52163986e+02]),\n", + " 'z': array([[ 13136, 42313, 29516, ..., 21711, 60883, 13305],\n", + " [ 59442, 43388, 9402, ..., 19257, 31239, 35096],\n", + " [ 59924, 52782, 17703, ..., 16142, 25584, 31181],\n", + " ...,\n", + " [ 826035, 883774, 1075301, ..., 952118, 767503, 889465],\n", + " [ 884179, 949242, 1025625, ..., 724397, 829963, 864289],\n", + " [ 975021, 817816, 936112, ..., 850066, 878260, 819927]],\n", + " dtype=uint32)}],\n", + " 'layout': {'autosize': True,\n", + " 'height': 500,\n", + " 'plot_bgcolor': 'white',\n", + " 'template': '...',\n", + " 'width': 500,\n", + " 'xaxis': {'showgrid': False, 'title': {'text': 'distance (nm)'}},\n", + " 'yaxis': {'scaleanchor': 'x', 'showgrid': False, 'title': {'text': 'distance (nm)'}}}\n", + "})" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "image_dims = infoWidget.dataset.get_image_dims(return_axis=True)\n", + " \n", + "if len(infoWidget.image_plot.data) == 0:\n", + " infoWidget.image_plot.add_trace(go.Heatmap(z=self.dataset))\n", + "else:\n", + " infoWidget.image_plot.data[0].z=infoWidget.dataset\n", + "infoWidget.image_plot.data[0].x = image_dims[0].values\n", + "infoWidget.image_plot.data[0].y = image_dims[1].values\n", "\n", - "infoWidget.low_loss.update_ll_sidebar()\n" + "infoWidget.image_plot.update_layout(xaxis_title = f\"{image_dims[0].quantity} ({image_dims[0].units})\", \n", + " yaxis_title = f\"{image_dims[0].quantity} ({image_dims[0].units})\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "plasmon = eels_tools.fit_plasmon(infoWidget.dataset, 12, 20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "plasmon = np.array(plasmon)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.47891069e+01, 5.24804751e+00, 1.59869663e+04])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plasmon[23,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[126], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m res \u001b[38;5;241m=\u001b[39m eels_tools\u001b[38;5;241m.\u001b[39mget_resolution_functions(infoWidget\u001b[38;5;241m.\u001b[39mdataset, startFitEnergy\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m.5\u001b[39m, endFitEnergy\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.5\u001b[39m, n_workers \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8\u001b[39m, n_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\eels_tools.py:494\u001b[0m, in \u001b[0;36mget_resolution_functions\u001b[1;34m(dataset, startFitEnergy, endFitEnergy, n_workers, n_threads)\u001b[0m\n\u001b[0;32m 490\u001b[0m \u001b[38;5;66;03m# apply to all spectra\u001b[39;00m\n\u001b[0;32m 491\u001b[0m zero_loss_fitter \u001b[38;5;241m=\u001b[39m SidFitter(fit_dset, zl_func, num_workers\u001b[38;5;241m=\u001b[39mn_workers, guess_fn\u001b[38;5;241m=\u001b[39mguess_function, threads\u001b[38;5;241m=\u001b[39mn_threads,\n\u001b[0;32m 492\u001b[0m return_cov\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, return_fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, return_std\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, km_guess\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, num_fit_parms\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m)\n\u001b[1;32m--> 494\u001b[0m [z_loss_params] \u001b[38;5;241m=\u001b[39m zero_loss_fitter\u001b[38;5;241m.\u001b[39mdo_fit()\n\u001b[0;32m 495\u001b[0m z_loss_dset \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 496\u001b[0m z_loss_dset \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\proc\\fitter.py:279\u001b[0m, in \u001b[0;36mSidFitter.do_fit\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 273\u001b[0m lazy_result \u001b[38;5;241m=\u001b[39m dask\u001b[38;5;241m.\u001b[39mdelayed(SidFitter\u001b[38;5;241m.\u001b[39mdefault_curve_fit)(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_fn, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdep_vec,\n\u001b[0;32m 274\u001b[0m ydata, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_fit_parms,\n\u001b[0;32m 275\u001b[0m return_cov\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_cov \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_std),\n\u001b[0;32m 276\u001b[0m p0\u001b[38;5;241m=\u001b[39mp0, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 277\u001b[0m fit_results\u001b[38;5;241m.\u001b[39mappend(lazy_result)\n\u001b[1;32m--> 279\u001b[0m fit_results_comp \u001b[38;5;241m=\u001b[39m dask\u001b[38;5;241m.\u001b[39mcompute(\u001b[38;5;241m*\u001b[39mfit_results)\n\u001b[0;32m 280\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:661\u001b[0m, in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m 658\u001b[0m postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m 660\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m shorten_traceback():\n\u001b[1;32m--> 661\u001b[0m results \u001b[38;5;241m=\u001b[39m schedule(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 663\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\threading.py:655\u001b[0m, in \u001b[0;36mEvent.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 653\u001b[0m signaled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flag\n\u001b[0;32m 654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m signaled:\n\u001b[1;32m--> 655\u001b[0m signaled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cond\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[0;32m 656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m signaled\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\threading.py:359\u001b[0m, in \u001b[0;36mCondition.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 359\u001b[0m gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mTrue\u001b[39;00m, timeout)\n\u001b[0;32m 360\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 361\u001b[0m gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "res = eels_tools.get_resolution_functions(infoWidget.dataset, startFitEnergy=-.5, endFitEnergy=.5, n_workers = 8, n_threads=32)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "73e8726e4ea64fc48f95af4652debd0b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "005523bfdd4b440f895b8f045c99dca8", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "01a276afbc2d4c8a852038d26a010308", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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Using: 11_si-1.hf5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels to 11_eels\n", + " warn('main_data_name should not contain the \"-\" character. Reformatted'\n", + "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n", + " warn('validate_h5_dimension may be removed in a future version',\n", + "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels_new to 11_eels_new\n", + " warn('main_data_name should not contain the \"-\" character. Reformatted'\n", + "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (0, 0) from type: to str\n", + " warn('Converted key: {} from type: {} to str'\n", + "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (19, 2) from type: to str\n", + " warn('Converted key: {} from type: {} to str'\n", + "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n", + " warn('validate_h5_dimension may be removed in a future version',\n", + "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels_new_new to 11_eels_new_new\n", + " warn('main_data_name should not contain the \"-\" character. Reformatted'\n", + "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (19, 2) from type: to str\n", + " warn('Converted key: {} from type: {} to str'\n", + "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n", + " warn('validate_h5_dimension may be removed in a future version',\n", + "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels_new_new to 11_eels_new_new\n", + " warn('main_data_name should not contain the \"-\" character. 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infoWidget\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39mget_multiple_scattering()\n", + "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\low_loss_widget.py:281\u001b[0m, in \u001b[0;36mget_multiple_scattering\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 0\u001b[0m \n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:716\u001b[0m, in \u001b[0;36mTraitType.__set__\u001b[1;34m(self, obj, value)\u001b[0m\n\u001b[0;32m 714\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread_only:\n\u001b[0;32m 715\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TraitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m trait is read-only.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m--> 716\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset(obj, value)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:706\u001b[0m, in \u001b[0;36mTraitType.set\u001b[1;34m(self, obj, value)\u001b[0m\n\u001b[0;32m 702\u001b[0m silent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 703\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m silent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m 704\u001b[0m \u001b[38;5;66;03m# we explicitly compare silent to True just in case the equality\u001b[39;00m\n\u001b[0;32m 705\u001b[0m \u001b[38;5;66;03m# comparison above returns something other than True/False\u001b[39;00m\n\u001b[1;32m--> 706\u001b[0m obj\u001b[38;5;241m.\u001b[39m_notify_trait(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, old_value, new_value)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:1513\u001b[0m, in \u001b[0;36mHasTraits._notify_trait\u001b[1;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[0;32m 1512\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_notify_trait\u001b[39m(\u001b[38;5;28mself\u001b[39m, name: \u001b[38;5;28mstr\u001b[39m, old_value: t\u001b[38;5;241m.\u001b[39mAny, new_value: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1513\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnotify_change(\n\u001b[0;32m 1514\u001b[0m Bunch(\n\u001b[0;32m 1515\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m 1516\u001b[0m old\u001b[38;5;241m=\u001b[39mold_value,\n\u001b[0;32m 1517\u001b[0m new\u001b[38;5;241m=\u001b[39mnew_value,\n\u001b[0;32m 1518\u001b[0m owner\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 1519\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchange\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 1520\u001b[0m )\n\u001b[0;32m 1521\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\ipywidgets\\widgets\\widget.py:687\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[1;34m(self, change)\u001b[0m\n\u001b[0;32m 684\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[0;32m 685\u001b[0m \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[0;32m 686\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[1;32m--> 687\u001b[0m \u001b[38;5;28msuper\u001b[39m(Widget, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mnotify_change(change)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:1525\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[1;34m(self, change)\u001b[0m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change: Bunch) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1525\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_notify_observers(change)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:1568\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[1;34m(self, event)\u001b[0m\n\u001b[0;32m 1565\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1566\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m-> 1568\u001b[0m c(event)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\info_widget.py:507\u001b[0m, in \u001b[0;36mEELSBaseWidget._update\u001b[1;34m(self, ev)\u001b[0m\n\u001b[0;32m 505\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mylabel)\n\u001b[0;32m 506\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchange_y_scale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m--> 507\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_tab_spectra()\n\u001b[0;32m 508\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle()\n", + "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\info_widget.py:818\u001b[0m, in \u001b[0;36mEELSWidget.update_tab_spectra\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 816\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_tab_spectra\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 817\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtabval \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m--> 818\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39m_update()\n", + "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\low_loss_widget.py:334\u001b[0m, in \u001b[0;36mLowLoss._update\u001b[1;34m(self, ev)\u001b[0m\n\u001b[0;32m 332\u001b[0m difference \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m plasmon\n\u001b[0;32m 333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss_tab[\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss_tab[\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss_tab[\u001b[38;5;241m15\u001b[39m, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 334\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39menergy_scale, difference, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifference\u001b[39m\u001b[38;5;124m'\u001b[39m) \n\u001b[0;32m 335\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mlegend()\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1779\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1536\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1537\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m 1538\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1776\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m 1777\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1778\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[1;32m-> 1779\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[0;32m 1780\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m 1781\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_base.py:296\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 294\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m 295\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m--> 296\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_plot_args(\n\u001b[0;32m 297\u001b[0m axes, this, kwargs, ambiguous_fmt_datakey\u001b[38;5;241m=\u001b[39mambiguous_fmt_datakey)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_base.py:483\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[0;32m 481\u001b[0m axes\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mupdate_units(x)\n\u001b[0;32m 482\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axes\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 483\u001b[0m axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[0;32m 485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m 486\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 487\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axis.py:1750\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 1744\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_units\u001b[39m(\u001b[38;5;28mself\u001b[39m, data):\n\u001b[0;32m 1745\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1746\u001b[0m \u001b[38;5;124;03m Introspect *data* for units converter and update the\u001b[39;00m\n\u001b[0;32m 1747\u001b[0m \u001b[38;5;124;03m ``axis.converter`` instance if necessary. Return *True*\u001b[39;00m\n\u001b[0;32m 1748\u001b[0m \u001b[38;5;124;03m if *data* is registered for unit conversion.\u001b[39;00m\n\u001b[0;32m 1749\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1750\u001b[0m converter \u001b[38;5;241m=\u001b[39m munits\u001b[38;5;241m.\u001b[39mregistry\u001b[38;5;241m.\u001b[39mget_converter(data)\n\u001b[0;32m 1751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m converter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1752\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\units.py:183\u001b[0m, in \u001b[0;36mRegistry.get_converter\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 181\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 182\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# If cache lookup fails, look up based on first element...\u001b[39;00m\n\u001b[1;32m--> 183\u001b[0m first \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39m_safe_first_finite(x)\n\u001b[0;32m 184\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mStopIteration\u001b[39;00m):\n\u001b[0;32m 185\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\cbook.py:1782\u001b[0m, in \u001b[0;36m_safe_first_finite\u001b[1;34m(obj)\u001b[0m\n\u001b[0;32m 1780\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1781\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m obj:\n\u001b[1;32m-> 1782\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m safe_isfinite(val):\n\u001b[0;32m 1783\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m val\n\u001b[0;32m 1784\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m safe_first_element(obj)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\cbook.py:1762\u001b[0m, in \u001b[0;36m_safe_first_finite..safe_isfinite\u001b[1;34m(val)\u001b[0m\n\u001b[0;32m 1760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 1761\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1762\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m math\u001b[38;5;241m.\u001b[39misfinite(val)\n\u001b[0;32m 1763\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[0;32m 1764\u001b[0m \u001b[38;5;66;03m# if the outer object is 2d, then val is a 1d array, and\u001b[39;00m\n\u001b[0;32m 1765\u001b[0m \u001b[38;5;66;03m# - math.isfinite(numpy.zeros(3)) raises TypeError\u001b[39;00m\n\u001b[0;32m 1766\u001b[0m \u001b[38;5;66;03m# - math.isfinite(torch.zeros(3)) raises ValueError\u001b[39;00m\n\u001b[0;32m 1767\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\array\\core.py:1883\u001b[0m, in \u001b[0;36mArray.__float__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1882\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__float__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m-> 1883\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_scalarfunc(\u001b[38;5;28mfloat\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\array\\core.py:1875\u001b[0m, in \u001b[0;36mArray._scalarfunc\u001b[1;34m(self, cast_type)\u001b[0m\n\u001b[0;32m 1873\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly length-1 arrays can be converted to Python scalars\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1874\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1875\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast_type(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute()\u001b[38;5;241m.\u001b[39mitem())\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:375\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 351\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 352\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[0;32m 353\u001b[0m \n\u001b[0;32m 354\u001b[0m \u001b[38;5;124;03m This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[38;5;124;03m dask.compute\u001b[39;00m\n\u001b[0;32m 374\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 375\u001b[0m (result,) \u001b[38;5;241m=\u001b[39m compute(\u001b[38;5;28mself\u001b[39m, traverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 376\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:654\u001b[0m, in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m args\n\u001b[0;32m 648\u001b[0m schedule \u001b[38;5;241m=\u001b[39m get_scheduler(\n\u001b[0;32m 649\u001b[0m scheduler\u001b[38;5;241m=\u001b[39mscheduler,\n\u001b[0;32m 650\u001b[0m collections\u001b[38;5;241m=\u001b[39mcollections,\n\u001b[0;32m 651\u001b[0m get\u001b[38;5;241m=\u001b[39mget,\n\u001b[0;32m 652\u001b[0m )\n\u001b[1;32m--> 654\u001b[0m dsk \u001b[38;5;241m=\u001b[39m collections_to_dsk(collections, optimize_graph, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 655\u001b[0m keys, postcomputes \u001b[38;5;241m=\u001b[39m [], []\n\u001b[0;32m 656\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m collections:\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:427\u001b[0m, in \u001b[0;36mcollections_to_dsk\u001b[1;34m(collections, optimize_graph, optimizations, **kwargs)\u001b[0m\n\u001b[0;32m 425\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m opt, val \u001b[38;5;129;01min\u001b[39;00m groups\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 426\u001b[0m dsk, keys \u001b[38;5;241m=\u001b[39m _extract_graph_and_keys(val)\n\u001b[1;32m--> 427\u001b[0m dsk \u001b[38;5;241m=\u001b[39m opt(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 429\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m opt_inner \u001b[38;5;129;01min\u001b[39;00m optimizations:\n\u001b[0;32m 430\u001b[0m dsk \u001b[38;5;241m=\u001b[39m opt_inner(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\array\\optimization.py:51\u001b[0m, in \u001b[0;36moptimize\u001b[1;34m(dsk, keys, fuse_keys, fast_functions, inline_functions_fast_functions, rename_fused_keys, **kwargs)\u001b[0m\n\u001b[0;32m 49\u001b[0m dsk \u001b[38;5;241m=\u001b[39m optimize_blockwise(dsk, keys\u001b[38;5;241m=\u001b[39mkeys)\n\u001b[0;32m 50\u001b[0m dsk \u001b[38;5;241m=\u001b[39m fuse_roots(dsk, keys\u001b[38;5;241m=\u001b[39mkeys)\n\u001b[1;32m---> 51\u001b[0m dsk \u001b[38;5;241m=\u001b[39m dsk\u001b[38;5;241m.\u001b[39mcull(\u001b[38;5;28mset\u001b[39m(keys))\n\u001b[0;32m 53\u001b[0m \u001b[38;5;66;03m# Perform low-level fusion unless the user has\u001b[39;00m\n\u001b[0;32m 54\u001b[0m \u001b[38;5;66;03m# specified False explicitly.\u001b[39;00m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptimization.fuse.active\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\highlevelgraph.py:738\u001b[0m, in \u001b[0;36mHighLevelGraph.cull\u001b[1;34m(self, keys)\u001b[0m\n\u001b[0;32m 736\u001b[0m output_keys \u001b[38;5;241m=\u001b[39m keys_set\u001b[38;5;241m.\u001b[39mintersection(layer\u001b[38;5;241m.\u001b[39mget_output_keys())\n\u001b[0;32m 737\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_keys:\n\u001b[1;32m--> 738\u001b[0m culled_layer, culled_deps \u001b[38;5;241m=\u001b[39m layer\u001b[38;5;241m.\u001b[39mcull(output_keys, all_ext_keys)\n\u001b[0;32m 739\u001b[0m \u001b[38;5;66;03m# Update `keys` with all layer's external key dependencies, which\u001b[39;00m\n\u001b[0;32m 740\u001b[0m \u001b[38;5;66;03m# are all the layer's dependencies (`culled_deps`) excluding\u001b[39;00m\n\u001b[0;32m 741\u001b[0m \u001b[38;5;66;03m# the layer's output keys.\u001b[39;00m\n\u001b[0;32m 742\u001b[0m external_deps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\highlevelgraph.py:154\u001b[0m, in \u001b[0;36mLayer.cull\u001b[1;34m(self, keys, all_hlg_keys)\u001b[0m\n\u001b[0;32m 152\u001b[0m k \u001b[38;5;241m=\u001b[39m work\u001b[38;5;241m.\u001b[39mpop()\n\u001b[0;32m 153\u001b[0m out[k] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m[k]\n\u001b[1;32m--> 154\u001b[0m ret_deps[k] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dependencies(k, all_hlg_keys)\n\u001b[0;32m 155\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m ret_deps[k]:\n\u001b[0;32m 156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m seen:\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\highlevelgraph.py:178\u001b[0m, in \u001b[0;36mLayer.get_dependencies\u001b[1;34m(self, key, all_hlg_keys)\u001b[0m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_dependencies\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: Key, all_hlg_keys: Collection[Key]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mset\u001b[39m:\n\u001b[0;32m 164\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Get dependencies of `key` in the layer\u001b[39;00m\n\u001b[0;32m 165\u001b[0m \n\u001b[0;32m 166\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 176\u001b[0m \u001b[38;5;124;03m A set of dependencies\u001b[39;00m\n\u001b[0;32m 177\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 178\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keys_in_tasks(all_hlg_keys, [\u001b[38;5;28mself\u001b[39m[key]])\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\core.py:199\u001b[0m, in \u001b[0;36mkeys_in_tasks\u001b[1;34m(keys, tasks, as_list)\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 198\u001b[0m tasks \u001b[38;5;241m=\u001b[39m work\n\u001b[1;32m--> 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret \u001b[38;5;28;01mif\u001b[39;00m as_list \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m(ret)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "infoWidget.low_loss.get_multiple_scattering()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": 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"cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9e29c5f2893d49c79893d5d23ee0a277", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", 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{'microscope': '', 'acceleration_voltage': 199990.28125},\n", + " 'annotations': {}}" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a0063153df364ce3bceef64164623036", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "def read_annotation(image):\n", + " scale_x = np.abs(image.x[1]-image.x[0])\n", + " scale_y = np.abs(image.y[1]-image.y[0])\n", + " rec_scale = np.array([scale_x, scale_y,scale_x, scale_y])\n", + " if 'DocumentObjectList' not in image.original_metadata:\n", + " return {}\n", + " if '0' not in image.original_metadata['DocumentObjectList']:\n", + " return {}\n", + " annotations = {} \n", + " tags = image.original_metadata['DocumentObjectList']['0'] \n", + " for key in tags:\n", + " if 'AnnotationGroupList' in key:\n", + " an_tags = tags[key]\n", + " for key2 in an_tags:\n", + " if isinstance(an_tags[key2], dict):\n", + " if an_tags[key2]['AnnotationType'] == 13: #type 'text'\n", + " annotations[key2] = {'type': 'text'}\n", + " if 'Label' in an_tags:\n", + " annotations[key2]['label'] = an_tags['Label']\n", + " rect = np.array(an_tags[key2]['Rectangle']) * rec_scale\n", + " annotations[key2]['position'] = [rect[1],rect[0]]\n", + " annotations[key2]['text'] = an_tags['Text'] \n", + " \n", + " elif an_tags[key2]['AnnotationType']==6:\n", + " annotations[key2] = {'type': 'circle'}\n", + " if 'Label' in an_tags:\n", + " annotations[key2]['label'] = an_tags['Label']\n", + " rect = np.array(an_tags[key2]['Rectangle']) * rec_scale\n", + " \n", + " annotations[key2]['radius'] =rect[3]-rect[1]\n", + " annotations[key2]['position'] = [rect[1],rect[0]]\n", + " \n", + " elif an_tags[key2]['AnnotationType'] == 23:\n", + " print('1')\n", + " annotations[key2] = {'type': 'spectral_image'}\n", + " if 'Label' in an_tags[key2]:\n", + " annotations[key2]['label'] = an_tags[key2]['Label']\n", + " rect = np.array(an_tags[key2]['Rectangle']) * rec_scale\n", + " \n", + " annotations[key2]['width'] =rect[3]-rect[1]\n", + " annotations[key2]['height'] =rect[2]-rect[0]\n", + " annotations[key2]['position'] = [rect[1],rect[0]]\n", + " annotations[key2]['Rectangle'] = np.array(an_tags[key2]['Rectangle'])\n", + " \n", + " image.metadata['annotations'] = annotations \n", + " return annotations\n", + "\n", + "\n", + "dset = infoWidget.datasets['Channel_001']\n", + "read_annotation(dset)\n", + "\n", + "\n", + "dset.plot()\n", + "if 'annotations' in dset.metadata:\n", + " annotations = dset.metadata['annotations']\n", + " for key in annotations:\n", + " if annotations[key]['type'] == 'spectral_image':\n", + " kwargs={'edgecolor': 'red', 'facecolor': 'None'}\n", + " \n", + " r = matplotlib.patches.Rectangle(annotations[key]['position'], annotations[key]['width'], annotations[key]['height'], **kwargs)\n", + " plt.gca().text(annotations[key]['position'][0], annotations[key]['position'][1], annotations[key]['label'], color='r')\n", + " plt.gca().add_artist(r)\n", + "dset.metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'AnnotationType': 23,\n", + " 'BackgroundColor': [-1, -1, -1],\n", + " 'BackgroundMode': 2,\n", + " 'Color': [-258, 0, 0],\n", + " 'FillMode': 2,\n", + " 'ForegroundColor': [0, -1, 0],\n", + " 'HasBackground': 0,\n", + " 'IsDeletable': 1,\n", + " 'IsMoveable': 1,\n", + " 'IsResizable': 1,\n", + " 'IsSelectable': 1,\n", + " 'IsTranslatable': 1,\n", + " 'IsVisible': 1,\n", + " 'IsVolatile': 0,\n", + " 'Label': '1',\n", + " 'Name': 'SICursor',\n", + " 'ObjectTags': {},\n", + " 'Rectangle': [1.0, 26.0, 10.0, 27.0],\n", + " 'SelectionStyle': 1,\n", + " 'UniqueID': 13}" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "expected an indented block after 'for' statement on line 4 (3538655079.py, line 5)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m Cell \u001b[1;32mIn[41], line 5\u001b[1;36m\u001b[0m\n\u001b[1;33m if annotations[key]['AnnotationType']==13:\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block after 'for' statement on line 4\n" + ] + } + ], + "source": [ + " \n", + " if split_keys[5] in ['AnnotationType','Text','Rectangle','Name', 'Label']:\n", + " \n", + " tags['annotations'] = {}\n", + " for key in annotations:\n", + " if annotations[key]['AnnotationType']==13: \n", + " if 'Label' in annotations[key]:\n", + " tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n", + " tags['annotations']['annotations_'+str(key)+'_type'] = 'text'\n", + " rect = np.array(annotations[key]['Rectangle'])* rec_scale\n", + " tags['annotations']['annotations_'+str(key)+'_x'] = rect[1]\n", + " tags['annotations']['annotations_'+str(key)+'_y'] = rect[0]\n", + " tags['annotations']['annotations_'+str(key)+'_text'] = annotations[key]['Text']\n", + " \n", + " elif annotations[key]['AnnotationType']==6:\n", + " #out_tags['annotations'][key] = {}\n", + " if 'Label' in annotations[key]:\n", + " tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n", + " tags['annotations']['annotations_'+str(key)+'_type'] = 'circle'\n", + " rect = np.array(annotations[key]['Rectangle'])* rec_scale\n", + " \n", + " tags['annotations']['annotations_'+str(key)+'_radius'] =rect[3]-rect[1]\n", + " tags['annotations']['annotations_'+str(key)+'_position'] = [rect[1],rect[0]]\n", + " \n", + " elif annotations[key]['AnnotationType']==6:\n", + " #out_tags['annotations'][key] = {}\n", + " if 'Label' in annotations[key]:\n", + " tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n", + " tags['annotations']['annotations_'+str(key)+'_type'] = 'circle'\n", + " rect = np.array(annotations[key]['Rectangle'])* rec_scale\n", + " \n", + " tags['annotations']['annotations_'+str(key)+'_radius'] =rect[3]-rect[1]\n", + " tags['annotations']['annotations_'+str(key)+'_position'] = [rect[1],rect[0]]\n", + "\n", + " \n", + "\n", + " elif annotations[key]['AnnotationType']==23:\n", + " if 'Name' in annotations[key]:\n", + " if annotations[key]['Name'] == 'Spectrum Image':\n", + " #tags['annotations'][key] = {}\n", + " if 'Label' in annotations[key]:\n", + " tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n", + " tags['annotations']['annotations_'+str(key)+'_type'] = 'spectrum image'\n", + " rect = np.array(annotations[key]['Rectangle'])* rec_scale\n", + " \n", + " tags['annotations']['annotations_'+str(key)+'_width'] =rect[3]-rect[1]\n", + " tags['annotations']['annotations_'+str(key)+'_height'] =rect[2]-rect[0]\n", + " tags['annotations']['annotations_'+str(key)+'_position'] = [rect[1],rect[0]]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'EELSWidget' object has no attribute 'tab_buttons'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[20], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39mtab_buttons\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m 3\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39mupdate_ll_sidebar()\n", + "\u001b[1;31mAttributeError\u001b[0m: 'EELSWidget' object has no attribute 'tab_buttons'" + ] + } + ], + "source": [ + "infoWidget.tab_buttons.index = 2\n", + "\n", + "infoWidget.low_loss.update_ll_sidebar()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 121, diff --git a/notebooks/Spectroscopy/EDS.ipynb b/notebooks/Spectroscopy/EDS.ipynb index 0c1a694a..7140a023 100644 --- a/notebooks/Spectroscopy/EDS.ipynb +++ b/notebooks/Spectroscopy/EDS.ipynb @@ -111,17 +111,19 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { + "ExecuteTime": { + "end_time": "2024-09-22T22:38:39.738354Z", + "start_time": "2024-09-22T22:38:23.930683Z" + }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "tqjQhosvEN4G", - "outputId": "66fe965d-d5b9-4b36-9c39-e9f8f6d0b69a", - "ExecuteTime": { - "end_time": "2024-09-22T22:38:39.738354Z", - "start_time": "2024-09-22T22:38:23.930683Z" - } + "outputId": "66fe965d-d5b9-4b36-9c39-e9f8f6d0b69a" }, + "outputs": [], "source": [ "%matplotlib widget\n", "import numpy as np\n", @@ -155,21 +157,7 @@ "print('pyTEM version: ',pyTEMlib.__version__)\n", "__notebook__ = '2_Image_Registration'\n", "__notebook_version__ = '2024_6_28'" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You don't have igor2 installed. If you wish to open igor files, you will need to install it (pip install igor2) before attempting.\n", - "You don't have gwyfile installed. If you wish to open .gwy files, you will need to install it (pip install gwyfile) before attempting.\n", - "0.11.6\n", - "Symmetry functions of spglib enabled\n", - "pyTEM version: 0.2024.09.0\n" - ] - } - ], - "execution_count": 1 + ] }, { "cell_type": "markdown", @@ -186,7 +174,12 @@ }, { "cell_type": "code", + "execution_count": 4, "metadata": { + "ExecuteTime": { + "end_time": "2024-09-22T22:44:51.872263Z", + "start_time": "2024-09-22T22:44:51.366797Z" + }, "colab": { "base_uri": "https://localhost:8080/", "height": 325, @@ -224,46 +217,41 @@ ] }, "id": "udl5pqHAEN4G", - "outputId": "638ff70e-ebfd-4cbc-f158-e5a6630444f2", - "ExecuteTime": { - "end_time": "2024-09-22T22:44:51.872263Z", - "start_time": "2024-09-22T22:44:51.366797Z" - } + "outputId": "638ff70e-ebfd-4cbc-f158-e5a6630444f2" }, - "source": [ - "fileWidget = pyTEMlib.file_tools.FileWidget3()" - ], "outputs": [ { "data": { - "text/plain": [ - "VBox(children=(Dropdown(description='directory:', layout=Layout(width='90%'), options=('C:\\\\Users\\\\gduscher\\\\D…" - ], "application/vnd.jupyter.widget-view+json": { + "model_id": "d4b649d5acc2419283d69fffd4b83b65", "version_major": 2, - "version_minor": 0, - "model_id": "d4b649d5acc2419283d69fffd4b83b65" - } + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Dropdown(description='directory:', layout=Layout(width='90%'), options=('C:\\\\Users\\\\gduscher\\\\D…" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": [ - "HBox(children=(Button(description='Select Main', layout=Layout(grid_area='header', width='auto'), style=Button…" - ], "application/vnd.jupyter.widget-view+json": { + "model_id": "3f9c09286a9f4e6f818312ec2f8f1999", "version_major": 2, - "version_minor": 0, - "model_id": "3f9c09286a9f4e6f818312ec2f8f1999" - } + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(Button(description='Select Main', layout=Layout(grid_area='header', width='auto'), style=Button…" + ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 4 + "source": [ + "fileWidget = pyTEMlib.file_tools.FileWidget3()" + ] }, { "cell_type": "markdown", @@ -276,35 +264,35 @@ }, { "cell_type": "code", + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-09-22T22:46:44.066830Z", "start_time": "2024-09-22T22:46:42.337887Z" } }, - "source": [ - "v = fileWidget.selected_dataset.plot()" - ], "outputs": [ { "data": { - "text/plain": [ - "HBox(children=(Play(value=0, description='Press play', interval=500, max=10), IntSlider(value=0, continuous_up…" - ], "application/vnd.jupyter.widget-view+json": { + "model_id": "8848b344a6b64e3581795602d28b10b7", "version_major": 2, - "version_minor": 0, - "model_id": "8848b344a6b64e3581795602d28b10b7" - } + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(Play(value=0, description='Press play', interval=500, max=10), IntSlider(value=0, continuous_up…" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": [ 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"text/html": [ "\n", @@ -316,17 +304,17 @@ " \n", " " ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "6f8c899bdb0f420ebad417c0a49cb40a" - } + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 6 + "source": [ + "v = fileWidget.selected_dataset.plot()" + ] }, { "cell_type": "code", @@ -495,11 +483,11 @@ "evalue": "Datasets with data_type DataType.SPECTRAL_IMAGE cannot be plotted, yet.", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNotImplementedError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[13], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m spectrum\u001B[38;5;241m.\u001B[39mdata_type \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mspectral_image\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m----> 2\u001B[0m view \u001B[38;5;241m=\u001B[39m spectrum\u001B[38;5;241m.\u001B[39msum(axis\u001B[38;5;241m=\u001B[39m[\u001B[38;5;241m1\u001B[39m])\u001B[38;5;241m.\u001B[39mplot()\n", - "File \u001B[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\sidpy\\sid\\dataset.py:651\u001B[0m, in \u001B[0;36mDataset.plot\u001B[1;34m(self, verbose, figure, **kwargs)\u001B[0m\n\u001B[0;32m 649\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mview \u001B[38;5;241m=\u001B[39m PointCloudVisualizer(\u001B[38;5;28mself\u001B[39m, figure\u001B[38;5;241m=\u001B[39mfigure, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 650\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 651\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mDatasets with data_type \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m cannot be plotted, yet.\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata_type))\n\u001B[0;32m 652\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mshape) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m3\u001B[39m:\n\u001B[0;32m 653\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m verbose:\n", - "\u001B[1;31mNotImplementedError\u001B[0m: Datasets with data_type DataType.SPECTRAL_IMAGE cannot be plotted, yet." + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[13], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m spectrum\u001b[38;5;241m.\u001b[39mdata_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspectral_image\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 2\u001b[0m view \u001b[38;5;241m=\u001b[39m spectrum\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m1\u001b[39m])\u001b[38;5;241m.\u001b[39mplot()\n", + "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\sidpy\\sid\\dataset.py:651\u001b[0m, in \u001b[0;36mDataset.plot\u001b[1;34m(self, verbose, figure, **kwargs)\u001b[0m\n\u001b[0;32m 649\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mview \u001b[38;5;241m=\u001b[39m PointCloudVisualizer(\u001b[38;5;28mself\u001b[39m, figure\u001b[38;5;241m=\u001b[39mfigure, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 650\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 651\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatasets with data_type \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m cannot be plotted, yet.\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_type))\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n", + "\u001b[1;31mNotImplementedError\u001b[0m: Datasets with data_type DataType.SPECTRAL_IMAGE cannot be plotted, yet." ] } ], @@ -1098,7 +1086,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.13.0" }, "toc": { "base_numbering": "2", diff --git a/pyTEMlib/core_loss_widget.py b/pyTEMlib/core_loss_widget.py index 159729de..d3ae1a35 100644 --- a/pyTEMlib/core_loss_widget.py +++ b/pyTEMlib/core_loss_widget.py @@ -191,14 +191,16 @@ def update_cl_sidebar(self): cl_index = index+1 self.core_loss_tab[0, 0].options = spectrum_list self.core_loss_tab[0, 0].value = spectrum_list[cl_index] - self.update_cl_dataset() - self.set_fit_start() - self.parent.plot() + if '_relationship' in self.parent.datasets.keys(): + self.update_cl_dataset() + self.set_fit_start() + self.parent.plot() def update_cl_dataset(self, value=0): self.cl_key = self.core_loss_tab[0, 0].value.split(':')[0] self.parent.coreloss_key = self.cl_key - self.parent.datasets['_relationship']['core_loss'] = self.cl_key + if '_relationship' in self.parent.datasets.keys(): + self.parent.datasets['_relationship']['core_loss'] = self.cl_key if 'None' in self.cl_key: return diff --git a/pyTEMlib/eels_tools.py b/pyTEMlib/eels_tools.py index fedaa07b..d88251f1 100644 --- a/pyTEMlib/eels_tools.py +++ b/pyTEMlib/eels_tools.py @@ -395,6 +395,15 @@ def align_zero_loss(dataset: sidpy.Dataset) -> sidpy.Dataset: new_si.metadata.update({'zero_loss': {'shifted': shifts}}) return new_si +from numba import jit + +def get_zero_losses(energy, z_loss_params): + z_loss_dset = np.zeros((z_loss_params.shape[0], z_loss_params.shape[1], energy.shape[0])) + for x in range(z_loss_params.shape[0]): + for y in range(z_loss_params.shape[1]): + z_loss_dset[x, y] += zl_func(energy, *z_loss_params[x, y]) + return z_loss_dset + @@ -488,11 +497,12 @@ def guess_function(xvec, yvec): z_loss_dset = dataset.copy() z_loss_dset *= 0.0 - energy_grid = np.broadcast_to(energy.reshape((1, 1, -1)), (z_loss_dset.shape[0], - z_loss_dset.shape[1], energy.shape[0])) - z_loss_peaks = zl_func(energy_grid, *z_loss_params) - z_loss_dset += z_loss_peaks - + #energy_grid = np.broadcast_to(energy.reshape((1, 1, -1)), (z_loss_dset.shape[0], + # z_loss_dset.shape[1], energy.shape[0])) + #z_loss_peaks = zl_func(energy_grid, *z_loss_params) + z_loss_params = np.array(z_loss_params) + z_loss_dset += get_zero_losses(np.array(energy), np.array(z_loss_params)) + shifts = z_loss_params[:, :, 0] * z_loss_params[:, :, 3] widths = z_loss_params[:, :, 2] * z_loss_params[:, :, 5] @@ -522,7 +532,15 @@ def drude_lorentz(eps_inf, leng, ep, eb, gamma, e, amplitude): return eps -def fit_plasmon(dataset: Union[sidpy.Dataset, np.ndarray], startFitEnergy: float, endFitEnergy: float, plot_result: bool = False, number_workers: int = 4, number_threads: int = 8) -> Union[sidpy.Dataset, np.ndarray]: +def get_plasmon_losses(energy, params): + dset = np.zeros((params.shape[0], params.shape[1], energy.shape[0])) + for x in range(params.shape[0]): + for y in range(params.shape[1]): + dset[x, y] += energy_loss_function(energy, params[x, y]) + return dset + + +def fit_plasmon(dataset: Union[sidpy.Dataset, np.ndarray], startFitEnergy: float, endFitEnergy: float, number_workers: int = 4, number_threads: int = 8) -> Union[sidpy.Dataset, np.ndarray]: """ Fit plasmon peak positions and widths in a TEM dataset using a Drude model. @@ -567,8 +585,6 @@ def energy_loss_function(E: np.ndarray, Ep: float, Ew: float, A: float) -> np.nd elf = (-1/eps).imag return A*elf - - # define window for fitting energy = dataset.get_spectral_dims(return_axis=True)[0].values start_fit_pixel = np.searchsorted(energy, startFitEnergy) @@ -589,18 +605,26 @@ def energy_loss_function(E: np.ndarray, Ep: float, Ew: float, A: float) -> np.nd guess_pos = energy[guess_pos] if guess_width >8: guess_width=8 - popt, pcov = curve_fit(energy_loss_function, energy[start_fit_pixel:end_fit_pixel], fit_dset, - p0=[guess_pos, guess_width, guess_amplitude]) + try: + popt, pcov = curve_fit(energy_loss_function, energy[start_fit_pixel:end_fit_pixel], fit_dset, + p0=[guess_pos, guess_width, guess_amplitude]) + except: + end_fit_pixel = np.searchsorted(energy, 30) + fit_dset = np.array(dataset[start_fit_pixel:end_fit_pixel]/ anglog[start_fit_pixel:end_fit_pixel]) + try: + popt, pcov = curve_fit(energy_loss_function, energy[start_fit_pixel:end_fit_pixel], fit_dset, + p0=[guess_pos, guess_width, guess_amplitude]) + except: + popt=[0,0,0] plasmon = dataset.like_data(energy_loss_function(energy, popt[0], popt[1], popt[2])) plasmon *= anglog start_plasmon = np.searchsorted(energy, 0)+1 - - plasmon[:start_plasmon] = 0. + epsilon = drude(energy, popt[0], popt[1], 1) * popt[2] epsilon[:start_plasmon] = 0. - + plasmon.metadata['plasmon'] = {'parameter': popt, 'epsilon':epsilon} return plasmon @@ -608,18 +632,19 @@ def energy_loss_function(E: np.ndarray, Ep: float, Ew: float, A: float) -> np.nd fitter = SidFitter(fit_dset, energy_loss_function, num_workers=number_workers, threads=number_threads, return_cov=False, return_fit=False, return_std=False, km_guess=False, num_fit_parms=3) - [fitted_dataset] = fitter.do_fit() + [fit_parameter] = fitter.do_fit() + + plasmon_dset = dataset * 0.0 + fit_parameter = np.array(fit_parameter) + plasmon_dset += get_plasmon_losses(np.array(energy), fit_parameter) + if 'plasmon' not in plasmon_dset.metadata: + plasmon_dset.metadata['plasmon'] = {} + plasmon_dset.metadata['plasmon'].update({'startFitEnergy': startFitEnergy, + 'endFitEnergy': endFitEnergy, + 'fit_parameter': fit_parameter, + 'original_low_loss': dataset.title}) - if plot_result: - fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex=True, sharey=True) - ax1.imshow(fitted_dataset[:, :, 0], cmap='jet') - ax1.set_title('Ep - Peak Position') - ax2.imshow(fitted_dataset[:, :, 1], cmap='jet') - ax2.set_title('Ew - Peak Width') - ax3.imshow(fitted_dataset[:, :, 2], cmap='jet') - ax3.set_title('A - Amplitude') - plt.show() - return fitted_dataset + return plasmon_dset def angle_correction(spectrum): @@ -722,8 +747,11 @@ def multiple_scattering(energy_scale: np.ndarray, p: list, core_loss=False)-> np ssd = ssd * ssd2 PSD /=tmfp*np.exp(-tmfp) - BGDcoef = scipy.interpolate.splrep(LLene, PSD, s=0) - return scipy.interpolate.splev(energy_scale, BGDcoef) + BGDcoef = scipy.interpolate.splrep(LLene, PSD, s=0) + msd = scipy.interpolate.splev(energy_scale, BGDcoef) + start_plasmon = np.searchsorted(energy_scale, 0)+1 + msd[:start_plasmon] = 0. + return msd def fit_multiple_scattering(dataset: Union[sidpy.Dataset, np.ndarray], startFitEnergy: float, endFitEnergy: float,pin=None, number_workers: int = 4, number_threads: int = 8) -> Union[sidpy.Dataset, np.ndarray]: """ diff --git a/pyTEMlib/file_tools.py b/pyTEMlib/file_tools.py index 11fd8796..66643562 100644 --- a/pyTEMlib/file_tools.py +++ b/pyTEMlib/file_tools.py @@ -786,6 +786,55 @@ def h5_group_to_dict(group, group_dict={}): return group_dict +def read_annotation(image): + if 'MAGE' not in image.data_type.name: + return {} + scale_x = np.abs(image.x[1]-image.x[0]) + scale_y = np.abs(image.y[1]-image.y[0]) + rec_scale = np.array([scale_x, scale_y,scale_x, scale_y]) + if 'DocumentObjectList' not in image.original_metadata: + return {} + if '0' not in image.original_metadata['DocumentObjectList']: + return {} + annotations = {} + tags = image.original_metadata['DocumentObjectList']['0'] + for key in tags: + if 'AnnotationGroupList' in key: + an_tags = tags[key] + for key2 in an_tags: + if isinstance(an_tags[key2], dict): + if an_tags[key2]['AnnotationType'] == 13: #type 'text' + annotations[key2] = {'type': 'text'} + if 'Label' in an_tags: + annotations[key2]['label'] = an_tags['Label'] + rect = np.array(an_tags[key2]['Rectangle']) * rec_scale + annotations[key2]['position'] = [rect[1],rect[0]] + annotations[key2]['text'] = an_tags['Text'] + + elif an_tags[key2]['AnnotationType']==6: + annotations[key2] = {'type': 'circle'} + if 'Label' in an_tags: + annotations[key2]['label'] = an_tags['Label'] + rect = np.array(an_tags[key2]['Rectangle']) * rec_scale + + annotations[key2]['radius'] =rect[3]-rect[1] + annotations[key2]['position'] = [rect[1],rect[0]] + + elif an_tags[key2]['AnnotationType'] == 23: + annotations[key2] = {'type': 'spectral_image'} + if 'Label' in an_tags[key2]: + annotations[key2]['label'] = an_tags[key2]['Label'] + rect = np.array(an_tags[key2]['Rectangle']) * rec_scale + + annotations[key2]['width'] =rect[3]-rect[1] + annotations[key2]['height'] =rect[2]-rect[0] + annotations[key2]['position'] = [rect[1],rect[0]] + annotations[key2]['Rectangle'] = np.array(an_tags[key2]['Rectangle']) + if len(annotations)>0: + image.metadata['annotations'] = annotations + return annotations + + def open_file(filename=None, h5_group=None, write_hdf_file=False, sum_frames=False): # save_file=False, """Opens a file if the extension is .hf5, .ndata, .dm3 or .dm4 @@ -849,11 +898,16 @@ def open_file(filename=None, h5_group=None, write_hdf_file=False, sum_frames=Fa dataset_dict[key] = h5_group_to_dict(master_group[key]) if not write_hdf_file: file.close() + for dset in dataset_dict.values(): + if isinstance(dset, sidpy.Dataset): + if 'Measurement' in dset.title: + dset.title = dset.title.split('/')[-1] return dataset_dict elif extension in ['.dm3', '.dm4', '.ndata', '.ndata1', '.h5', '.emd', '.emi', '.edaxh5']: # tags = open_file(filename) if extension in ['.dm3', '.dm4']: reader = SciFiReaders.DMReader(filename) + elif extension in ['.emi']: try: import hyperspy.api as hs @@ -899,8 +953,10 @@ def open_file(filename=None, h5_group=None, write_hdf_file=False, sum_frames=Fa if not isinstance(dset, dict): print('Please use new SciFiReaders Package for full functionality') if isinstance(dset, sidpy.Dataset): - dset = [dset] - + dset = {'Channel_000': dset} + for key in dset: + read_annotation(dset[key]) + if isinstance(dset, dict): dataset_dict = dset diff --git a/pyTEMlib/info_widget.py b/pyTEMlib/info_widget.py index 7b284093..382f8e24 100644 --- a/pyTEMlib/info_widget.py +++ b/pyTEMlib/info_widget.py @@ -190,7 +190,7 @@ def get_info_sidebar() -> Any: side_bar[row, :2] = ipywidgets.IntText(value=1, description='bin X:', disabled=False, color='black', layout=ipywidgets.Layout(width='200px')) row += 1 - side_bar[row, :2] = ipywidgets.IntText(value=1, description='bin X:', disabled=False, color='black', + side_bar[row, :2] = ipywidgets.IntText(value=1, description='bin Y:', disabled=False, color='black', layout=ipywidgets.Layout(width='200px')) for i in range(15, 18): @@ -233,13 +233,18 @@ def get_file_widget_ui(): side_bar[row, 0] = ipywidgets.Button(description='Save', layout=ipywidgets.Layout(width='100px'), style=ipywidgets.ButtonStyle(button_color='lightblue')) - + side_bar[row, 1] = ipywidgets.Text( + value='Test.hf5', + placeholder='Type something', + description='File:', + disabled=False + ) return side_bar class EELSBaseWidget(object): - def __init__(self, datasets, sidebar, tab_title=None): + def __init__(self, datasets=None, sidebar=None, tab_title=None): self.datasets = datasets self.dataset = None @@ -258,7 +263,7 @@ def __init__(self, datasets, sidebar, tab_title=None): self.google = True else: self.google = False - self.google = True + # self.google = True self.save_path = True @@ -284,13 +289,15 @@ def __init__(self, datasets, sidebar, tab_title=None): children = [self.file_bar] titles = ['File'] if isinstance(sidebar, dict): - for sidebar_key, sidebar_gui in sidebar.items(): children.append(sidebar_gui) titles.append(sidebar_key) elif not isinstance(sidebar, list): children = [self.file_bar, sidebar] titles = ['File', 'Info'] + if sidebar is None: + children = [self.file_bar] + titles = ['File'] if self.google: self.buttons = [] @@ -364,11 +371,24 @@ def __init__(self, datasets, sidebar, tab_title=None): self.loaded_datasets.observe(self.select_dataset, names='value') self.file_bar[4, 0].observe(self.set_image, names='value') self.file_bar[5, 0].observe(self.set_survey_image, names='value') + + self.file_bar[6, 0].observe(self.save_datasets) + def save_datasets(self, value=0): + import warnings + file_name = self.file_bar[6, 1].value + path = self.path_choice.options[0] + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + h5_group = file_tools.save_dataset(self.datasets, os.path.join(path, file_name)) + h5_group.file.close() + self.status_message(' File saved') + def status_message(self, out: str): self.statusbar.value = out def set_survey_image(self, key=None): + self.datasets['_relationship']['survey_image'] = self.file_bar[5, 0].value # ToDo: Find boundaries of scan. @@ -421,13 +441,27 @@ def plot_image(self): for dim, axis in self.dataset._axes.items(): if axis.dimension_type in [sidpy.DimensionType.SPATIAL, sidpy.DimensionType.RECIPROCAL]: image_dims.append(dim) - self.img = self.axis.imshow(self.dataset, extent=self.dataset.get_extent(image_dims)) + self.img = self.axis.imshow(self.dataset.T, extent=self.dataset.get_extent(image_dims)) self.axis.set_xlabel(self.dataset.labels[image_dims[0]]) self.axis.set_ylabel(self.dataset.labels[image_dims[1]]) cbar = self.figure.colorbar(self.img) cbar.set_label(self.dataset.data_descriptor) - + if 'annotations' in self.dataset.metadata: + kwargs={'edgecolor': 'red', 'facecolor': 'None'} + + annotations = self.dataset.metadata['annotations'] + for key in annotations: + if annotations[key]['type'] == 'spectral_image': + rectangle = matplotlib.patches.Rectangle(annotations[key]['position'], annotations[key]['width'], annotations[key]['height'], **kwargs) + self.axis.text(annotations[key]['position'][0], annotations[key]['position'][1], annotations[key]['label'], color='r') + self.axis.add_artist(rectangle) + elif annotations[key]['type'] == 'text': + self.axis.text(annotations[key]['position'][0], annotations[key]['position'][1], annotations[key]['label'], color='r') + elif annotations[key]['type'] == 'circle': + circle = matplotlib.patches.Circle(annotations[key]['position'], annotations[key]['radius'], **kwargs) + self.axis.add_artist(circle) + self.figure.tight_layout() self.axis.ticklabel_format(style='sci', scilimits=(-2, 3)) self.figure.tight_layout() self.figure.canvas.draw_idle() @@ -472,7 +506,8 @@ def _update(self, ev=None): self.get_spectrum() if len(self.energy_scale) != self.spectrum.shape[0]: self.spectrum = self.spectrum.T - self.axis.plot(self.energy_scale, self.spectrum.compute(), label='experiment') + # self.axis.plot(self.energy_scale, self.spectrum.compute(), label='experiment') + self.axis.plot(self.energy_scale, self.spectrum, label='experiment') self.axis.set_title(f'spectrum {self.x}, {self.y}') self.figure.tight_layout() @@ -486,9 +521,12 @@ def _update(self, ev=None): self.axis.set_xlabel(self.xlabel) self.axis.set_ylabel(self.ylabel) self.change_y_scale = 1.0 - + self.update_tab_spectra() self.figure.canvas.draw_idle() + def update_tab_spectra(self): + pass + def _onclick(self, event): self.event = event if event.inaxes in [self.axes[0]]: @@ -635,6 +673,7 @@ def select_main(self, value=0): # self.loaded_datasets.options = self.dataset_list self.datasets = file_tools.open_file(self.file_name) + file_tools.save_path(self.file_name) self.dataset_list = [] self.image_list = ['Sum'] @@ -668,6 +707,9 @@ def select_main(self, value=0): self.file_bar[3, 0].options = self.dataset_list self.loaded_datasets.options = self.dataset_list self.loaded_datasets.value = self.dataset_list[0] + path, filename = os.path.split(self.file_name) + name, extension = os.path.splitext(filename) + self.file_bar[6, 1].value = name+'.hf5' self.status_message(' New file loaded') def add_dataset(self, value=0): @@ -675,6 +717,17 @@ def add_dataset(self, value=0): self.dataset_list.append(f'{key}: {self.datasets[key].title}') self.loaded_datasets.options = self.dataset_list self.loaded_datasets.value = self.dataset_list[-1] + + if 'SPECTR' in self.datasets[key].data_type.name: + self.spectral_list.append(f'{key}: {self.datasets[key].title}') + if 'IMAGE' == self.datasets[key].data_type.name: + if 'survey' in self.datasets[key].title.lower(): + self.survey_list.append(f'{key}: {self.datasets[key].title}') + self.file_bar[5, 0].options = self.survey_list + else: + self.image_list.append(f'{key}: {self.datasets[key].title}') + self.file_bar[4, 0].options = self.image_list + self.status_message(' image list file loaded') def get_directory(self, directory='.'): self.dir_name = directory @@ -744,7 +797,8 @@ def __init__(self, datasets=None): self.lowloss_key = 'None' self.coreloss_key = 'None' self.info_key = 'None' - + self.tabval = 0 + sidebar = {'Spec.': get_info_sidebar(), 'LowLoss': get_low_loss_sidebar(), 'CoreLoss': get_core_loss_sidebar()} @@ -766,7 +820,7 @@ def set_action(self): def tab_activated(self, val=0): if self.google: - self.tab.children = [self.tab_buttons, self.children[self.tab_buttons.index]] + self.tab.children = [self.tab_buttons, self.children[self.tab_buttons.index]] # update sidebar gui self.tabval = self.tab_buttons.index else: if isinstance(val.new, int): @@ -779,6 +833,11 @@ def tab_activated(self, val=0): elif self.tabval == 3: self.core_loss.update_cl_sidebar() + + def update_tab_spectra(self): + if self.tabval == 2: + self.low_loss._update() + def update_sidebars(self): if hasattr(self, 'info'): @@ -969,14 +1028,19 @@ def update_dataset(self, value=0): if self.info_key != 'None': self.parent.set_dataset(self.info_key) self.parent.status_message(self.key+' , '+ self.parent.info_key) - self.parent.datasets['_relationship']['spectrum'] = self.info_key + if '_relationship' in self.parent.datasets.keys(): + self.parent.datasets['_relationship']['spectrum'] = self.info_key self.update_sidebar() self.parent._update(0) def shift_low_loss(self, value=0): if 'low_loss' in self.parent.datasets['_relationship']: low_loss = self.parent.datasets[self.parent.datasets['_relationship']['low_loss']] - self.parent.datasets[self.parent.datasets['_relationship']['low_loss']] = eels_tools.align_zero_loss(low_loss) + + self.parent.datasets['shifted_low_loss'] = eels_tools.align_zero_loss(low_loss) + self.parent.datasets['shifted_low_loss'].title = self.parent.dataset.title + '_shifted' + self.parent.datasets['_relationship']['low_loss'] = 'shifted_low_loss' + self.update_sidebar() if 'low_loss' in self.parent.datasets['_relationship']: if 'zero_loss' in self.parent.datasets[self.parent.datasets['_relationship']['low_loss']].metadata: diff --git a/pyTEMlib/info_widget3.py b/pyTEMlib/info_widget3.py new file mode 100644 index 00000000..9b15e10b --- /dev/null +++ b/pyTEMlib/info_widget3.py @@ -0,0 +1,1153 @@ +from typing import Any + +import numpy as np +import os +import sys +import ipywidgets +import matplotlib.pylab as plt +import matplotlib +from IPython.display import display +import plotly.graph_objects as go + +import sidpy +# from pyTEMlib.microscope import microscope +from pyTEMlib import file_tools +from pyTEMlib import eels_tools +from pyTEMlib.core_loss_widget import get_core_loss_sidebar, CoreLoss +from pyTEMlib.low_loss_widget import get_low_loss_sidebar, LowLoss + +def get_image_sidebar() -> Any: + side_bar = ipywidgets.GridspecLayout(14, 3, width='auto', grid_gap="0px") + + side_bar[0, :2] = ipywidgets.Dropdown( + options=[('None', 0)], + value=0, + description='Main Dataset:', + disabled=False) + row = 1 + side_bar[row, :3] = ipywidgets.Button(description='Image Scale', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=7.5, description='x dim:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="nm", layout=ipywidgets.Layout(width='20px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=7.5, description='y dim:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="nm", layout=ipywidgets.Layout(width='20px')) + row += 1 + + side_bar[row, :3] = ipywidgets.Button(description='Microscope', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=-1, description='Conv.Angle:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="mrad", layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=-0.1, description='Coll.Angle:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="mrad", layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=.1, description='Acc Voltage:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="keV", layout=ipywidgets.Layout(width='100px')) + + row += 1 + side_bar[row, :3] = ipywidgets.Button(description='Calibration', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=0.1, description='Pixel_Time:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="μs", layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=7.5, description='Screen Curr:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="pA", layout=ipywidgets.Layout(width='50px')) + row += 1 + + side_bar[row, 0] = ipywidgets.Button(description='FFT', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 1] = ipywidgets.Button(description='LR-Decon', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 2] = ipywidgets.Button(description='Find atoms', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + + row += 1 + side_bar[row, :3] = ipywidgets.Button(description='Image Stack', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + row += 1 + side_bar[row, 0] = ipywidgets.Button(description='Rig Reg.', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 1] = ipywidgets.Button(description='Demon', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 2] = ipywidgets.Button(description='Sum', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + + side_bar[-2, 0].layout.display = "none" + for i in range(3): + side_bar[-1, i].layout.display = "none" + return side_bar + + +def get_info_sidebar() -> Any: + side_bar = ipywidgets.GridspecLayout(18, 3, width='auto', grid_gap="0px") + + side_bar[0, :2] = ipywidgets.Dropdown( + options=[('None', 0)], + value=0, + description='Main Dataset:', + disabled=False) + + row = 1 + side_bar[row, :3] = ipywidgets.Button(description='Energy Scale', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=7.5, description='Offset:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="eV", layout=ipywidgets.Layout(width='20px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=0.1, description='Dispersion:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="eV", layout=ipywidgets.Layout(width='20px')) + row += 1 + side_bar[row, :3] = ipywidgets.Button(description='Microscope', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=-1, description='Conv.Angle:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="mrad", layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=-0.1, description='Coll.Angle:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="mrad", layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=0.1, description='Acc Voltage:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="keV", layout=ipywidgets.Layout(width='100px')) + + row += 1 + side_bar[row, :3] = ipywidgets.Button(description='Calibration', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + row += 1 + side_bar[row, :2] = ipywidgets.Dropdown( + options=[('None', 0)], + value=0, + description='Reference:', + disabled=False) + side_bar[row, 2] = ipywidgets.ToggleButton(description='Probability', + disabled=False, + button_style='', # 'success', 'info', 'warning', 'danger' or '' + tooltip='Changes y-axis to probability if flux is given', + layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=0.1, description='Exp_Time:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="s", layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=7.5, description='Flux:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="Mcounts", layout=ipywidgets.Layout(width='50px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=0.1, description='Conversion:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="e⁻/counts", layout=ipywidgets.Layout(width='100px')) + row += 1 + side_bar[row, :2] = ipywidgets.FloatText(value=0.1, description='Current:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + side_bar[row, 2] = ipywidgets.widgets.Label(value="pA", layout=ipywidgets.Layout(width='100px')) + + row += 1 + side_bar[row, 0] = ipywidgets.Button(description='Get Shift', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 1] = ipywidgets.Button(description='Shift Spec', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + """ + side_bar[row, 2] = ipywidgets.Button(description='Res.Fct.', disabled=True, + layout=ipywidgets.Layout(width='auto'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + """ + + row += 1 + side_bar[row, :3] = ipywidgets.Button(description='Spectrum Image', + layout=ipywidgets.Layout(width='auto', grid_area='header'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + + row += 1 + side_bar[row, :2] = ipywidgets.IntText(value=1, description='bin X:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + row += 1 + side_bar[row, :2] = ipywidgets.IntText(value=1, description='bin Y:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + + for i in range(15, 18): + side_bar[i, 0].layout.display = "none" + return side_bar + + + +def get_file_widget_ui(): + side_bar = ipywidgets.GridspecLayout(7, 3, height='500px', width='auto', grid_gap="0px") + row = 0 + side_bar[row, :3] = ipywidgets.Dropdown(options=['None'], value='None', description='directory:', disabled=False, + button_style='', layout=ipywidgets.Layout(width='auto', grid_area='header')) + row += 1 + side_bar[row, :3] = ipywidgets.Select(options=['.'], value='.', description='Select file:', disabled=False, + rows=10, layout=ipywidgets.Layout(width='auto')) + row += 1 + side_bar[row, 0] = ipywidgets.Button(description='Select Main', + layout=ipywidgets.Layout(width='100px'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 1] = ipywidgets.Button(description='Add', + layout=ipywidgets.Layout(width='50px'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 2] = ipywidgets.Dropdown(options=['None'], value='None', description='loaded:', disabled=False, + button_style='') + + row += 1 + side_bar[row, :3] = ipywidgets.Select(options=['None'], value='None', description='Spectral:', + disabled=False, rows=3, layout=ipywidgets.Layout(width='auto')) + row += 1 + side_bar[row, :3] = ipywidgets.Select(options=['Sum'], value='Sum', description='Images:', + disabled=False, rows=3, layout=ipywidgets.Layout(width='auto')) + row += 1 + side_bar[row, :3] = ipywidgets.Select(options=['None'], value='None', description='Survey:', + disabled=False, rows=2, layout=ipywidgets.Layout(width='auto')) + for i in range(3, 6): + side_bar[i, 0].layout.display = "none" + + row += 1 + side_bar[row, 0] = ipywidgets.Button(description='Save', + layout=ipywidgets.Layout(width='100px'), + style=ipywidgets.ButtonStyle(button_color='lightblue')) + side_bar[row, 1] = ipywidgets.Text( + value='Test.hf5', + placeholder='Type something', + description='File:', + disabled=False + ) + return side_bar + +out = ipywidgets.Output() + +class EELSBaseWidget(object): + + def __init__(self, datasets=None, sidebar=None, tab_title=None): + + self.datasets = datasets + self.dataset = None + self.save_path = False + self.dir_dictionary = {} + self.dir_list = ['.', '..'] + self.display_list = ['.', '..'] + self.dataset_list = ['None'] + self.image_list = ['Sum'] + self.added_spectra = {} + self.dir_name = file_tools.get_last_path() + + self.key = None + self.new_info = False + self.image = 'Sum' + if 'google.colab' in sys.modules: + self.google = True + else: + self.google = False + # self.google = True + + self.save_path = True + + if not os.path.isdir(self.dir_name): + self.dir_name = '.' + + self.get_directory(self.dir_name) + self.dir_list = ['.'] + self.extensions = '*' + self.file_name = '' + self.datasets = {} + self.dataset = None + self.sd0 = 0 + self.sds = 0 + + self.bin_x = 0 + self.bin_y = 0 + + self.start_channel = -1 + self.end_channel = -2 + + self.file_bar = get_file_widget_ui() + children = [self.file_bar] + titles = ['File'] + if isinstance(sidebar, dict): + for sidebar_key, sidebar_gui in sidebar.items(): + children.append(sidebar_gui) + titles.append(sidebar_key) + elif not isinstance(sidebar, list): + children = [self.file_bar, sidebar] + titles = ['File', 'Info'] + if sidebar is None: + children = [self.file_bar] + titles = ['File'] + + if self.google: + self.buttons = [] + for i in range(len(children)): + self.buttons.append(ipywidgets.Button(description=titles[i], + disabled=False, + button_style='', # 'success', 'info', 'warning', 'danger' or '' + layout=ipywidgets.Layout(width='800px'))) + + + self.tab_buttons = ipywidgets.ToggleButtons(options=titles, description='', disabled=False, + layout=ipywidgets.Layout(width='auto'), + style={"button_width": "auto"}) + tab = ipywidgets.VBox([self.tab_buttons, self.file_bar]) + self.children = children + + else: + tab = ipywidgets.Tab() + tab.children = children + tab.titles = titles + + + + self.spectrum_plot = go.FigureWidget() + self.spectrum_plot.update_xaxes(showgrid=True, zeroline=True, showticklabels=True, + showspikes=True, spikemode='across', spikesnap='cursor', showline=False, spikedash='solid') + self.spectrum_plot['layout'].update(height=500) + self.image_plot = go.FigureWidget() + self.image_plot['layout'].update(height=500, + width=500, + autosize=True, + xaxis_showgrid=False, + yaxis_showgrid=False, + yaxis = dict(scaleanchor = 'x', autorange = "reversed"), + plot_bgcolor="white") + + self.tab =tab + self.canvas_plot = ipywidgets.HBox([self.spectrum_plot]) + self.canvas = ipywidgets.VBox([self.canvas_plot, out]) + + self.start_cursor = ipywidgets.FloatText(value=0, description='Start:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + self.end_cursor = ipywidgets.FloatText(value=0, description='End:', disabled=False, color='black', + layout=ipywidgets.Layout(width='200px')) + self.statusbar = ipywidgets.Text(value='Starting', + placeholder='Type something', + description='Status:', + disabled=True, + layout=ipywidgets.Layout(width='100%')) + self.panel = ipywidgets.VBox([ipywidgets.HBox([ipywidgets.Label('', layout=ipywidgets.Layout(width='100px')), + ipywidgets.Label('Cursor:'), + self.start_cursor, ipywidgets.Label('eV'), + self.end_cursor, ipywidgets.Label('eV')]), + self.canvas, + self.statusbar]) + + self.app_layout = ipywidgets.AppLayout( + left_sidebar=tab, + center=self.panel, + footer=None, # message_bar, + pane_heights=[0, 10, 0], + pane_widths=[4, 10, 0], + ) + # self.set_dataset() + self.change_y_scale = 1.0 + self.x = 0 + self.y = 0 + self.bin_x = 1 + self.bin_y = 1 + self.count = 0 + display(self.app_layout) + + self.select_files = self.file_bar[1, 0] + self.path_choice = self.file_bar[0, 0] + self.set_file_options() + select_button = self.file_bar[2, 0] + add_button = self.file_bar[2, 1] + self.loaded_datasets = self.file_bar[2, 2] + self.select_files.observe(self.get_file_name, names='value') + self.path_choice.observe(self.set_dir, names='value') + + select_button.on_click(self.select_main) + add_button.on_click(self.add_dataset) + self.loaded_datasets.observe(self.select_dataset, names='value') + self.file_bar[4, 0].observe(self.set_image, names='value') + self.file_bar[5, 0].observe(self.set_survey_image, names='value') + + self.file_bar[6, 0].observe(self.save_datasets) + + def save_datasets(self, value=0): + import warnings + file_name = self.file_bar[6, 1].value + path = self.path_choice.options[0] + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + h5_group = file_tools.save_dataset(self.datasets, os.path.join(path, file_name)) + h5_group.file.close() + self.status_message(' File saved') + + def status_message(self, out: str): + self.statusbar.value = out + + def set_survey_image(self, key=None): + + self.datasets['_relationship']['survey_image'] = self.file_bar[5, 0].value + # ToDo: Find boundaries of scan. + + def get_image(self): + if self.file_bar[4, 0].value == 'Sum': + spec_dim = self.dataset.get_dimensions_by_type(sidpy.DimensionType.SPECTRAL) + if len(spec_dim) != 1: + raise ValueError('Only one spectral dimension') + + channel_dim = self.dataset.get_dimensions_by_type(sidpy.DimensionType.CHANNEL) + + if len(channel_dim) > 1: + raise ValueError('Maximal one channel dimension') + + if len(channel_dim) > 0: + self.image = self.dataset.mean(axis=(spec_dim[0], channel_dim[0])) + else: + self.image = self.dataset.mean(axis=(spec_dim[0])) + image_key = 'Sum' + else: + image_key = self.file_bar[4, 0].value.split(':')[0] + self.image = self.datasets[image_key] + self.datasets['_relationship']['image'] = image_key + + def set_image(self, key=None): + self.get_image() + self.plot() + + def plot(self, scale=True): + + spec_dims = self.dataset.get_spectral_dims(return_axis=True) + if len(spec_dims)>0: + self.energy_scale = spec_dims[0] + if self.dataset.data_type.name == 'SPECTRUM': + self.canvas_plot.children = [self.spectrum_plot] + else: + self.get_image() + self.canvas_plot.children = [self.image_plot, self.spectrum_plot] + self.plot_spectrum_image() + # self.axis = self.axes[-1] + self.spectrum = self.get_spectrum() + self.plot_spectrum() + + else: + self.canvas_plot.children = [self.image_plot] + self.image = self.dataset + self.plot_image() + + def plot_image(self, add_annotations=True): + image_dims = self.dataset.get_image_dims(return_axis=True) + + if len(self.image_plot.data) == 0: + self.image_plot.add_trace(go.Heatmap(z=self.image.T)) + else: + self.image_plot.data[0].z=np.array(self.image).T + self.image_plot.data[0].x = image_dims[0].values + self.image_plot.data[0].y = image_dims[1].values + + self.image_plot.update_layout(xaxis_title = f"{image_dims[0].quantity} ({image_dims[0].units})", + yaxis_title = f"{image_dims[1].quantity} ({image_dims[1].units})") + + if 'annotations' in self.dataset.metadata and add_annotations: + annotations = self.dataset.metadata['annotations'] + for key in annotations: + if annotations[key]['type'] == 'spectral_image': + pos, w, h = annotations[key]['position'], annotations[key]['width'], annotations[key]['height'] + self.image_plot.add_trace(go.Scatter(x= [pos[0], pos[0], pos[0]+w, pos[0]+w, pos[0]], y = [pos[1], pos[1]+h, pos[1]+h, pos[1], pos[1]], mode='lines')) + self.image_plot.add_trace(go.Scatter(x= [pos[0]], y = [pos[1]], mode='text', text=['spectrum image'], + textfont=dict(color="red"), + textposition="top right")) + + elif annotations[key]['type'] == 'text': + self.image_plot.add_trace(go.Scatter(y=[annotations[key]['position'][0]], x=[annotations[key]['position'][1]], + text=[annotations[key]['label']])) + elif annotations[key]['type'] == 'circle': + [x, y] = annotations[key]['position'] + r = annotations[key]['radius'] + self.image_plot.add_shape(type="circle", xref="x", yref="y", x0=x-r, y0 = y-r, x1=x+r, y1=y+r, + line_color="LightSeaGreen") + + + def plot_spectrum(self): + if len(self.spectrum_plot.data) == 0: + self.spectrum_plot.add_trace(go.Scatter(x=self.energy_scale, y=self.spectrum, mode='markers+lines', marker_size=.1, name=self.dataset.title)) + self.spectrum_plot.data = [self.spectrum_plot.data[0]] + + self.xlabel = self.datasets[self.key].labels[0] + self.ylabel = self.datasets[self.key].data_descriptor + self.change_y_scale = 1.0 + if self.y_scale != 1.: + self.ylabel('scattering probability (ppm/eV)') + + self.spectrum_plot.update_layout(xaxis_title=self.xlabel, yaxis_title=self.ylabel) + self.spectrum_plot.data[0].y=self.spectrum + self.spectrum_plot.data[0].x=self.energy_scale + self.spectrum_plot.data[0].on_selection(self.selection_cursor) + + if self.dataset.data_type.name != 'SPECTRUM': + self.spectrum_plot.data[0].name = f'spectrum {self.x}, {self.y}' + + for key in self.added_spectra: + self.added_spectra[key] + spectrum = self.get_additional_spectrum(key) + self.spectrum_plot.add_trace(go.Scatter(x=self.energy_scale, y=spectrum, mode='markers+lines', marker_size=.1, name=self.added_spectra[key])) + + + def _update(self, ev=None): + self.get_spectrum() + self.plot_spectrum() + + def update_tab_spectra(self): + pass + + def _onclick(self, event): + self.event = event + if event.inaxes in [self.axes[0]]: + x = int(event.xdata) + y = int(event.ydata) + + x = int(x - self.rectangle[0]) + y = int(y - self.rectangle[2]) + + if x >= 0 and y >= 0: + if x <= self.rectangle[1] and y <= self.rectangle[3]: + self.x = int(x / (self.rect.get_width() / self.bin_x)) + self.y = int(y / (self.rect.get_height() / self.bin_y)) + image_dims = self.dataset.get_image_dims() + + if self.x + self.bin_x > self.dataset.shape[image_dims[0]]: + self.x = self.dataset.shape[image_dims[0]] - self.bin_x + if self.y + self.bin_y > self.dataset.shape[image_dims[1]]: + self.y = self.dataset.shape[image_dims[1]] - self.bin_y + + self.rect.set_xy([self.x * self.rect.get_width() / self.bin_x + self.rectangle[0], + self.y * self.rect.get_height() / self.bin_y + self.rectangle[2]]) + # self.get_spectrum() + self._update() + else: + if event.dblclick: + bottom = float(self.spectrum.min()) + if bottom < 0: + bottom *= 1.02 + else: + bottom *= 0.98 + top = float(self.spectrum.max()) + if top > 0: + top *= 1.02 + else: + top *= 0.98 + self.axis.set_ylim(bottom=bottom, top=top) + + def get_spectrum(self): + if self.dataset.data_type.name == 'SPECTRUM': + self.spectrum = self.dataset.copy() + else: + image_dims = self.dataset.get_dimensions_by_type(sidpy.DimensionType.SPATIAL) + if self.x > self.dataset.shape[image_dims[0]] - self.bin_x: + self.x = self.dataset.shape[image_dims[0]] - self.bin_x + if self.y > self.dataset.shape[image_dims[1]] - self.bin_y: + self.y = self.dataset.shape[image_dims[1]] - self.bin_y + selection = [] + for dim, axis in self.dataset._axes.items(): + if axis.dimension_type == sidpy.DimensionType.SPATIAL: + if dim == image_dims[0]: + selection.append(slice(self.x, self.x + self.bin_x)) + else: + selection.append(slice(self.y, self.y + self.bin_y)) + + elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: + selection.append(slice(None)) + elif axis.dimension_type == sidpy.DimensionType.CHANNEL: + selection.append(slice(None)) + else: + selection.append(slice(0, 1)) + + self.spectrum = self.dataset[tuple(selection)].mean(axis=tuple(image_dims)) + + self.spectrum *= self.y_scale + self.spectrum.squeeze() + self.spectrum.data_type = 'spectrum' + return self.spectrum + + @out.capture(clear_output=True) + def click_callback(self, trace, points, selector): + self.status_message(f'click_callback: {selector}') + if selector.shift: + self.spectrum_plot.add_trace(go.Scatter(x=self.energy_scale, + y=self.dataset[points.point_inds[0][1], points.point_inds[0][0]], + mode='lines', + name='spectrum'+str(points.point_inds[0]))) + else: + if selector.ctrl: + self.spectrum_plot.data =[self.spectrum_plot.data[0]] + self.spectrum_plot.data[0].y= self.dataset[points.point_inds[0][1],points.point_inds[0][0]] + self.spectrum_plot.data[0].name = 'spectrum'+str(points.point_inds[0]) + + self.image_plot.data[1].x = [points.point_inds[0][1]] + self.image_plot.data[1].y = [points.point_inds[0][0]] + + @out.capture(clear_output=True) + def selection_fn(self, trace,points,selector): + if selector.type == 'box': + xr = np.array(selector.xrange) + if xr[0]<0: + xr[0] = 0 + yr = np.array(selector.yrange) + if yr[0]<1: + yr[0] = 0 + size_sel = (int(xr[1])-int(xr[0]))*(int(yr[1])-int(yr[0])) + + self.spectrum_plot.data[0].y= self.dataset[int(xr[0]):int(xr[1]), int(yr[0]):int(yr[1]),:].sum(axis=[0,1]).compute()/ size_sel + self.spectrum_plot.data[0].name = str(size_sel)+ ' spectra' + else: + print(selector) + + def plot_spectrum_image(self): + if len(self.image_plot.data) == 0: + self.image_plot.add_trace(go.Heatmap(z=self.image.T)) + else: + self.image_plot.data[0].z=np.array(self.image).T + self.plot_spectrum() + self.image_plot.add_trace( + go.Scatter(mode="markers", x=[0], y=[0], marker_symbol=[101], + marker_color="darkgray", + marker_line_width=1, marker_size=11, hovertemplate= 'x: %{x}
y: %{y}')) + + self.image_plot.data[0].on_selection(self.selection_fn) + self.image_plot.data[0].on_click(self.click_callback) + + @out.capture(clear_output=True) + def selection_cursor(self, trace, points, selector): + if selector.type == 'box': + self.start_cursor.value = np.round(selector.xrange[0], 3) + self.end_cursor.value = np.round(selector.xrange[1], 3) + + energy_scale = self.dataset.get_spectral_dims(return_axis=True)[0] + self.start_channel = np.searchsorted(energy_scale, self.start_cursor.value) + self.end_channel = np.searchsorted(energy_scale, self.end_cursor.value) + + def set_dataset(self, key=None): + + if len(self.datasets) == 0: + data_set = sidpy.Dataset.from_array([0, 1], name='generic') + data_set.set_dimension(0, sidpy.Dimension([0, 1], 'energy_loss', units='channel', quantity='generic', + dimension_type='spectral')) + data_set.data_type = 'spectrum' + data_set.metadata = {'experiment': {'convergence_angle': 0, + 'collection_angle': 0, + 'acceleration_voltage': 0, + 'exposure_time': 0}} + self.datasets = {'Nothing': data_set} + key = 'Nothing' + + dataset_key = key + + self.dataset_list = [] + dataset_keys = [] + for key in self.datasets.keys(): + if isinstance(self.datasets[key], sidpy.Dataset): + self.dataset_list.append(f'{key}: {self.datasets[key].title}') + dataset_keys.append(key) + if dataset_key not in dataset_keys: + dataset_key = dataset_keys[0] + self.key = dataset_key + + self.dataset = self.datasets[self.key] + + spectral_dims = self.dataset.get_spectral_dims(return_axis=True) + if len(spectral_dims) >0: + self.energy_scale = spectral_dims[0] + self.y_scale = 1.0 + self.change_y_scale = 1.0 + self.x = 0 + self.y = 0 + self.bin_x = 1 + self.bin_y = 1 + self.count = 0 + + self.update_sidebars() + #self.update_sidebar() + self.plot() + + def update_sidebars(self): + pass + + def select_main(self, value=0): + self.sds +=1 + self.datasets = {} + # self.loaded_datasets.options = self.dataset_list + + self.datasets = file_tools.open_file(self.file_name) + + file_tools.save_path(self.file_name) + self.dataset_list = [] + self.image_list = ['Sum'] + self.survey_list = ['None'] + self.spectral_list = ['None'] + for key in self.datasets.keys(): + if isinstance(self.datasets[key], sidpy.Dataset): + self.dataset_list.append(f'{key}: {self.datasets[key].title}') + if 'SPECTR' in self.datasets[key].data_type.name: + self.spectral_list.append(f'{key}: {self.datasets[key].title}') + if 'IMAGE' == self.datasets[key].data_type.name: + if 'survey' in self.datasets[key].title.lower(): + self.survey_list.append(f'{key}: {self.datasets[key].title}') + else: + self.image_list.append(f'{key}: {self.datasets[key].title}') + + + self.key = self.dataset_list[0].split(':')[0] + self.dataset = self.datasets[self.key] + if len(self.image_plot.data)>0: + self.image_plot.data = [self.image_plot.data[0]] + self.spectrum_plot.data = [self.spectrum_plot.data[0]] + self.new_info = True + + self.selected_dataset = self.dataset + if len(self.image_list) > 0: + self.file_bar[4, 0].options = self.image_list + self.file_bar[5, 0].options = self.survey_list + self.file_bar[4, 0].layout.display = "flex" + self.file_bar[4, 0].value = self.image_list[0] + self.file_bar[5, 0].layout.display = "flex" + self.file_bar[5, 0].value = self.survey_list[0] + + self.file_bar[3, 0].options = self.dataset_list + self.loaded_datasets.options = self.dataset_list + self.loaded_datasets.value = self.dataset_list[0] + path, filename = os.path.split(self.file_name) + name, extension = os.path.splitext(filename) + self.file_bar[6, 1].value = name+'.hf5' + self.status_message(' New file loaded') + + def add_dataset(self, value=0): + key = file_tools.add_dataset_from_file(self.datasets, self.file_name, 'Channel') + self.dataset_list.append(f'{key}: {self.datasets[key].title}') + self.loaded_datasets.options = self.dataset_list + self.loaded_datasets.value = self.dataset_list[-1] + + if 'SPECTR' in self.datasets[key].data_type.name: + self.spectral_list.append(f'{key}: {self.datasets[key].title}') + energy = self.datasets[key].get_spectral_dims(return_axis=True)[0] + self.spectrum_plot.add_trace(go.Scatter(x=energy, y=self.datasets[key], mode='markers+lines', marker_size=.1, name=key)) + if 'IMAGE' == self.datasets[key].data_type.name: + if 'survey' in self.datasets[key].title.lower(): + self.survey_list.append(f'{key}: {self.datasets[key].title}') + self.file_bar[5, 0].options = self.survey_list + else: + self.image_list.append(f'{key}: {self.datasets[key].title}') + self.file_bar[4, 0].options = self.image_list + self.status_message(' image list file loaded') + + def get_directory(self, directory='.'): + self.dir_name = directory + self.dir_dictionary = {} + self.dir_list = [] + self.dir_list = ['.', '..'] + os.listdir(directory) + + def set_dir(self, value=0): + self.dir_name = self.path_choice.value + self.select_files.index = 0 + self.set_file_options() + + def select_dataset(self, value=0): + key = self.loaded_datasets.value.split(':')[0] + if key != 'None': + self.selected_dataset = self.datasets[key] + self.selected_key = key + self.key = key + self.datasets['_relationship'] = {'main_dataset': self.key} + + self.set_dataset() + + def set_file_options(self): + self.dir_name = os.path.abspath(os.path.join(self.dir_name, self.dir_list[self.select_files.index])) + dir_list = os.listdir(self.dir_name) + file_dict = file_tools.update_directory_list(self.dir_name) + + sort = np.argsort(file_dict['directory_list']) + self.dir_list = ['.', '..'] + self.display_list = ['.', '..'] + for j in sort: + self.display_list.append(f" * {file_dict['directory_list'][j]}") + self.dir_list.append(file_dict['directory_list'][j]) + + sort = np.argsort(file_dict['display_file_list']) + + for i, j in enumerate(sort): + if '--' in dir_list[j]: + self.display_list.append(f" {i:3} {file_dict['display_file_list'][j]}") + else: + self.display_list.append(f" {i:3} {file_dict['display_file_list'][j]}") + self.dir_list.append(file_dict['file_list'][j]) + + self.dir_label = os.path.split(self.dir_name)[-1] + ':' + self.select_files.options = self.display_list + + path = self.dir_name + old_path = ' ' + path_list = [] + while path != old_path: + path_list.append(path) + old_path = path + path = os.path.split(path)[0] + self.path_choice.options = path_list + self.path_choice.value = path_list[0] + + def get_file_name(self, b): + + if os.path.isdir(os.path.join(self.dir_name, self.dir_list[self.select_files.index])): + self.set_file_options() + + elif os.path.isfile(os.path.join(self.dir_name, self.dir_list[self.select_files.index])): + self.file_name = os.path.join(self.dir_name, self.dir_list[self.select_files.index]) + +class EELSWidget(EELSBaseWidget): + def __init__(self, datasets=None): + self.lowloss_key = 'None' + self.coreloss_key = 'None' + self.info_key = 'None' + self.tabval = 0 + + sidebar = {'Spec.': get_info_sidebar(), + 'LowLoss': get_low_loss_sidebar(), + 'CoreLoss': get_core_loss_sidebar()} + super().__init__(datasets, sidebar) + self.info_tab = sidebar['Spec.'] + self.core_loss_tab = sidebar['CoreLoss'] + self.low_loss_tab = sidebar['LowLoss'] + super().set_dataset() + self.info = Info(self.info_tab, self) + self.low_loss = LowLoss(self.low_loss_tab, self) + self.core_loss = CoreLoss(self.core_loss_tab, self) + + self.set_action() + + def set_action(self): + if self.google: + self.tab_buttons.observe(self.tab_activated) + self.tab.observe(self.tab_activated) + + def tab_activated(self, val=0): + if self.google: + self.tab.children = [self.tab_buttons, self.children[self.tab_buttons.index]] # update sidebar gui + self.tabval = self.tab_buttons.index + else: + if isinstance(val.new, int): + self.tabval = val.new + # self.update_sidebars() + if self.tabval == 1: + self.info.update_dataset() + elif self.tabval == 2: + self.low_loss.update_ll_sidebar() + elif self.tabval == 3: + self.core_loss.update_cl_sidebar() + + def update_tab_spectra(self): + if self.tabval == 2: + self.low_loss._update() + + def update_sidebars(self): + if hasattr(self, 'info'): + self.info.update_sidebar() + #if hasattr(self, 'low_loss'): + # self.low_loss.update_ll_sidebar() + #if hasattr(self, 'core_loss'): + # self.core_loss.update_cl_sidebar() + + + def get_additional_spectrum(self, key): + if key not in self.datasets.keys(): + return + if isinstance(self.datasets[key], np.ndarray): + return self.datasets[key]*self.y_scale + + if isinstance(self.datasets[key], sidpy.Dataset): + if self.datasets[key].data_type == sidpy.DataType.SPECTRUM: + spectrum = self.datasets[key].copy() + else: + image_dims = self.datasets[key].get_dimensions_by_type(sidpy.DimensionType.SPATIAL) + selection = [] + x = self.x + y = self.y + bin_x = self.bin_x + bin_y = self.bin_y + for dim, axis in self.datasets[key]._axes.items(): + # print(dim, axis.dimension_type) + if axis.dimension_type == sidpy.DimensionType.SPATIAL: + if dim == image_dims[0]: + selection.append(slice(x, x + bin_x)) + else: + selection.append(slice(y, y + bin_y)) + + elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: + selection.append(slice(None)) + elif axis.dimension_type == sidpy.DimensionType.CHANNEL: + selection.append(slice(None)) + else: + selection.append(slice(0, 1)) + + spectrum = self.datasets[key][tuple(selection)].mean(axis=tuple(image_dims)) + spectrum.data_type = 'spectrum' + + spectrum *= self.y_scale + return spectrum.squeeze() + + +class Info(object): + def __init__(self, sidebar=None, parent=None): + self.parent = parent + self.info_tab = sidebar + self.key = self.parent.info_key + self.update_sidebar() + self.set_action() + self.count =0 + + def set_energy_scale(self, value): + self.ens = 1 + self.energy_scale = self.parent.datasets[self.key].get_spectral_dims(return_axis=True)[0] + dispersion = self.parent.datasets[self.key].get_dimension_slope(self.energy_scale) + self.ens = dispersion + self.energy_scale *= (self.info_tab[3, 0].value / dispersion) + self.energy_scale += (self.info_tab[2, 0].value - self.energy_scale[0]) + self.parent.plot() + + def set_y_scale(self, value): + self.count += 1 + self.parent.change_y_scale = 1.0 / self.parent.y_scale + if self.parent.datasets[self.parent.key].metadata['experiment']['flux_ppm'] > 1e-12: + if self.info_tab[9, 2].value: + dispersion = self.parent.datasets[self.parent.key].get_dimension_slope(self.parent.energy_scale) + self.parent.y_scale = 1 / self.parent.datasets[self.parent.key].metadata['experiment']['flux_ppm'] * dispersion + self.parent.ylabel = 'scattering probability (ppm)' + else: + self.parent.y_scale = 1.0 + self.parent.ylabel = 'intensity (counts)' + self.parent.change_y_scale *= self.parent.y_scale + self.parent._update() + + def set_flux(self, value): + # self.parent.datasets[self.key].metadata['experiment']['exposure_time'] = self.info_tab[10, 0].value + if self.info_tab[9, 0].value == 'None': + self.parent.datasets[self.parent.key].metadata['experiment']['flux_ppm'] = 0. + else: + ll_key = self.info_tab[9, 0].value.split(':')[0] + self.parent.datasets['_relationship']['low_loss'] = ll_key + self.parent.lowloss_key = ll_key + spectrum_dimensions = self.parent.dataset.get_spectral_dims() + + number_of_pixels = 1 + for index, dimension in enumerate(self.parent.dataset.shape): + if index not in spectrum_dimensions: + number_of_pixels *= dimension + if self.parent.datasets[ll_key].metadata['experiment']['exposure_time'] == 0.0: + if self.parent.datasets[ll_key].metadata['experiment']['single_exposure_time'] == 0.0: + return + else: + self.parent.datasets[ll_key].metadata['experiment']['exposure_time'] = (self.parent.datasets[ll_key].metadata['experiment']['single_exposure_time'] * + self.parent.datasets[ll_key].metadata['experiment']['number_of_frames']) + + self.parent.datasets[self.parent.key].metadata['experiment']['flux_ppm'] = ((np.array(self.parent.datasets[ll_key])*1e-6).sum() / + self.parent.datasets[ll_key].metadata['experiment']['exposure_time'] / + number_of_pixels) + self.parent.datasets[self.parent.key].metadata['experiment']['flux_ppm'] *= self.parent.datasets[self.parent.key].metadata['experiment']['exposure_time'] + if 'SPECT' in self.parent.datasets[ll_key].data_type.name: + self.info_tab[14, 0].disabled = False + self.info_tab[11, 0].value = np.round(self.parent.datasets[self.parent.key].metadata['experiment']['flux_ppm'], 2) + + def set_microscope_parameter(self, value): + if not self.parent.new_info: + self.parent.datasets[self.key].metadata['experiment']['convergence_angle'] = self.info_tab[5, 0].value + self.parent.datasets[self.key].metadata['experiment']['collection_angle'] = self.info_tab[6, 0].value + self.parent.datasets[self.key].metadata['experiment']['acceleration_voltage'] = self.info_tab[7, 0].value*1000 + + def cursor2energy_scale(self, value): + self.energy_scale = self.parent.datasets[self.key].get_spectral_dims(return_axis=True)[0] + dispersion = (self.parent.end_cursor.value - self.parent.start_cursor.value) / (self.parent.end_channel - self.parent.start_channel) + + self.energy_scale *= (self.info_tab[3, 0].value/dispersion) + + offset = self.parent.start_cursor.value - self.parent.start_channel * dispersion + self.parent.energy_scale += (self.info_tab[2, 0].value-self.parent.energy_scale[0]) + self.info_tab[2, 0].value = np.round(offset,4) + self.info_tab[3, 0].value = np.round(dispersion,4) + self.parent.plot() + + def set_binning(self, value): + if 'SPECTRAL' in self.parent.dataset.data_type.name: + image_dims = self.parent.dataset.get_image_dims() + + self.bin_x = int(self.info_tab[16, 0].value) + self.bin_y = int(self.info_tab[17, 0].value) + if self.bin_x < 1: + self.bin_x = 1 + self.info_tab[16, 0].value = self.bin_x + if self.bin_y < 1: + self.bin_y = 1 + self.info_tab[17, 0].value = self.bin_y + if self.bin_x > self.parent.dataset.shape[image_dims[0]]: + self.bin_x = self.parent.dataset.shape[image_dims[0]] + self.info_tab[16, 0].value = self.bin_x + if self.bin_y > self.parent.dataset.shape[image_dims[1]]: + self.bin_y = self.parent.dataset.shape[image_dims[1]] + self.info_tab[17, 0].value = self.bin_y + self.parent.bin_x = self.bin_x + self.parent.bin_y = self.bin_y + + self.parent.datasets[self.key].metadata['experiment']['SI_bin_x'] = self.bin_x + self.parent.datasets[self.key].metadata['experiment']['SI_bin_y'] = self.bin_y + self.parent.plot() + + + + def update_sidebar(self): + spectrum_list = ['None'] + reference_list = ['None'] + data_list = [] + + self.key = self.info_key = self.parent.info_key + + spectrum_data = False + info_index= 0 + for key in self.parent.datasets.keys(): + if isinstance(self.parent.datasets[key], sidpy.Dataset): + if key[0] != '_' : + data_list.append(f'{key}: {self.parent.datasets[key].title}') + if 'SPECTR' in self.parent.datasets[key].data_type.name: + spectrum_data = True + spectrum_list.append(f'{key}: {self.parent.datasets[key].title}') + if self.info_key == key: + info_index = len(spectrum_list)-1 + reference_list.append(f'{key}: {self.parent.datasets[key].title}') + self.info_tab[0, 0].options = spectrum_list + self.info_tab[9, 0].options = reference_list + self.info_tab[0, 0].value = spectrum_list[info_index] + + if 'SPECTRUM' in self.parent.dataset.data_type.name: + for i in range(15, 18): + self.info_tab[i, 0].layout.display = "none" + else: + for i in range(15, 18): + self.info_tab[i, 0].layout.display = "flex" + + if 'None' not in self.key: + self.parent.new_info = True + energy_scale = self.parent.datasets[self.key].get_spectral_dims(return_axis=True) + if len(energy_scale) == 0: + return + energy_scale = energy_scale[0] + offset = energy_scale[0] + # dispersion = self.parent.datasets[self.key].get_dimension_slope(energy_scale) + dispersion = energy_scale[1] - offset + + # self.info_tab[0,0].value = dataset_index #f'{self.key}: {self.parent.datasets[self.key].title}' + self.info_tab[2, 0].unobserve_all() + self.info_tab[2, 0].value = np.round(offset, 3) + self.info_tab[3, 0].value = np.round(dispersion, 4) + self.info_tab[5, 0].value = np.round(self.parent.datasets[self.key].metadata['experiment']['convergence_angle'], 1) + self.info_tab[6, 0].value = np.round(self.parent.datasets[self.key].metadata['experiment']['collection_angle'], 1) + self.info_tab[7, 0].value = np.round(self.parent.datasets[self.key].metadata['experiment']['acceleration_voltage']/1000, 1) + self.info_tab[2, 0].observe(self.set_energy_scale, names='value') + # print(self.parent.datasets[self.key].metadata['experiment']['acceleration_voltage']) + self.info_tab[10, 0].value = np.round(self.parent.datasets[self.key].metadata['experiment']['exposure_time'], 4) + if 'flux_ppm' not in self.parent.datasets[self.key].metadata['experiment']: + self.parent.datasets[self.key].metadata['experiment']['flux_ppm'] = 0 + self.info_tab[11, 0].value = self.parent.datasets[self.key].metadata['experiment']['flux_ppm'] + if 'count_conversion' not in self.parent.datasets[self.key].metadata['experiment']: + self.parent.datasets[self.key].metadata['experiment']['count_conversion'] = 1 + self.info_tab[12, 0].value = self.parent.datasets[self.key].metadata['experiment']['count_conversion'] + if 'beam_current' not in self.parent.datasets[self.key].metadata['experiment']: + self.parent.datasets[self.key].metadata['experiment']['beam_current'] = 0 + self.info_tab[13, 0].value = self.parent.datasets[self.key].metadata['experiment']['beam_current'] + ll_key = 'None' + if '_relationship' in self.parent.datasets: + if 'low_loss' in self.parent.datasets['_relationship']: + ll_key = self.parent.datasets['_relationship']['low_loss'] + ll_key = f'{ll_key}: {self.parent.datasets[ll_key].title}' + self.lowloss_key = ll_key + self.info_tab[9, 0].value = ll_key + self.parent.new_info = False + + def update_dataset(self, value=0): + self.key = self.info_tab[0, 0].value.split(':')[0] + + self.info_key = self.key + self.parent.info_key = self.key + + if self.info_key != 'None': + self.parent.set_dataset(self.info_key) + self.parent.status_message(self.key+' , '+ self.parent.info_key) + if '_relationship' in self.parent.datasets.keys(): + self.parent.datasets['_relationship']['spectrum'] = self.info_key + self.update_sidebar() + self.parent._update(0) + + def shift_low_loss(self, value=0): + if 'low_loss' in self.parent.datasets['_relationship']: + low_loss = self.parent.datasets[self.parent.datasets['_relationship']['low_loss']] + + self.parent.datasets['shifted_low_loss'] = eels_tools.align_zero_loss(low_loss) + self.parent.datasets['shifted_low_loss'].title = self.parent.dataset.title + '_shifted' + self.parent.datasets['_relationship']['low_loss'] = 'shifted_low_loss' + self.update_sidebar() + + if 'low_loss' in self.parent.datasets['_relationship']: + if 'zero_loss' in self.parent.datasets[self.parent.datasets['_relationship']['low_loss']].metadata: + if 'shifted' in self.parent.datasets[self.parent.datasets['_relationship']['low_loss']].metadata['zero_loss'].keys(): + self.info_tab[14, 1].disabled = False + + + def shift_spectrum(self, value=0): + shifts = self.parent.dataset.shape + if 'low_loss' in self.parent.datasets['_relationship']: + if 'zero_loss' in self.parent.datasets[self.parent.datasets['_relationship']['low_loss']].metadata: + if 'shifted' in self.parent.datasets[self.parent.datasets['_relationship']['low_loss']].metadata['zero_loss'].keys(): + shifts = self.parent.datasets[self.parent.datasets['_relationship']['low_loss']].metadata['zero_loss']['shifted'] + shifts_new = shifts.copy() + if 'zero_loss' in self.parent.dataset.metadata: + if 'shifted' in self.parent.dataset.metadata['zero_loss'].keys(): + shifts_new = shifts-self.parent.dataset.metadata['zero_loss']['shifted'] + else: + self.parent.dataset.metadata['zero_loss'] = {} + + + self.parent.dataset = eels_tools.shift_energy(self.parent.dataset, shifts_new) + self.parent.dataset.metadata['zero_loss']['shifted'] = shifts + self.parent.plot() + + + def set_action(self): + self.info_tab[0, 0].observe(self.update_dataset, names='value') + self.info_tab[1, 0].on_click(self.cursor2energy_scale) + self.info_tab[2, 0].observe(self.set_energy_scale, names='value') + self.info_tab[3, 0].observe(self.set_energy_scale, names='value') + self.info_tab[5, 0].observe(self.set_microscope_parameter) + self.info_tab[6, 0].observe(self.set_microscope_parameter) + self.info_tab[7, 0].observe(self.set_microscope_parameter) + self.info_tab[9, 0].observe(self.set_flux, names='value') + self.info_tab[9, 2].observe(self.set_y_scale, names='value') + self.info_tab[10, 0].observe(self.set_flux) + self.info_tab[14, 0].on_click(self.shift_low_loss) + self.info_tab[14, 1].on_click(self.shift_spectrum) + self.info_tab[14, 1].on_click(self.shift_spectrum) + + self.info_tab[16, 0].observe(self.set_binning) + self.info_tab[17, 0].observe(self.set_binning) diff --git a/pyTEMlib/low_loss_widget.py b/pyTEMlib/low_loss_widget.py index b5901f9d..3527b2f9 100644 --- a/pyTEMlib/low_loss_widget.py +++ b/pyTEMlib/low_loss_widget.py @@ -14,12 +14,12 @@ def get_low_loss_sidebar() -> Any: - side_bar = ipywidgets.GridspecLayout(16, 3, width='auto', grid_gap="0px") + side_bar = ipywidgets.GridspecLayout(17, 3, width='auto', grid_gap="0px") side_bar[0, :2] = ipywidgets.Dropdown( options=[('None', 0)], value=0, - description='Main Dataset:', + description='Low-Loss:', disabled=False) row = 1 @@ -81,6 +81,11 @@ def get_low_loss_sidebar() -> Any: button_style='', # 'success', 'info', 'warning', 'danger' or '' tooltip='Changes y-axis to probability if flux is given', layout=ipywidgets.Layout(width='100px')) + side_bar[row, 1] = ipywidgets.ToggleButton(description='Do All', + disabled=False, + button_style='', # 'success', 'info', 'warning', 'danger' or '' + tooltip='Changes y-axis to probability if flux is given', + layout=ipywidgets.Layout(width='100px')) row += 1 side_bar[row, :3] = ipywidgets.Button(description='Multiple Scattering', layout=ipywidgets.Layout(width='auto', grid_area='header'), @@ -105,13 +110,9 @@ def get_low_loss_sidebar() -> Any: tooltip='Plots resolution function on right', layout=ipywidgets.Layout(width='100px')) - side_bar[row, 2] = ipywidgets.ToggleButton(description='Nix', - disabled=False, - button_style='', # 'success', 'info', 'warning', 'danger' or '' - tooltip='Changes y-axis to probability if flux is given', - layout=ipywidgets.Layout(width='100px')) - + side_bar[row, 1:3] = ipywidgets.IntProgress(value=0, min=0, max=10, description=' ', bar_style='', # 'success', 'info', 'warning', 'danger' or '' + style={'bar_color': 'maroon'}, orientation='horizontal') return side_bar class LowLoss(object): @@ -133,10 +134,9 @@ def update_ll_sidebar(self): energy_offset = self.parent.datasets[key].get_spectral_dims(return_axis=True)[0][0] if energy_offset < 0: spectrum_list.append(f'{key}: {self.parent.datasets[key].title}') - print(key, self.ll_key, ll_index) if key == self.ll_key: ll_index = index-1 - print(key, self.ll_key, ll_index) + if ll_index >len(spectrum_list) - 1: ll_index = len(spectrum_list) - 1 @@ -158,42 +158,96 @@ def update_ll_dataset(self, value=0): def get_resolution_function(self, value=0): - self.low_loss_tab[4, 0].value = False + zero_loss_fit_width=self.low_loss_tab[2, 0].value spectrum = self.parent.spectrum - self.parent.datasets['resolution_function'] = eels_tools.get_resolution_functions(spectrum, - startFitEnergy=-zero_loss_fit_width, - endFitEnergy=zero_loss_fit_width) - self.parent.datasets['_relationship']['resolution_function'] = 'resolution_function' - if 'low_loss' not in self.dataset.metadata: - self.dataset.metadata['zero_loss'] = {} - self.dataset.metadata['zero_loss'].update(self.parent.datasets['resolution_function'].metadata['zero_loss']) - self.low_loss_tab[3, 0].value = True - self.low_loss_tab[14, 1].value = np.round(np.log(self.parent.dataset.sum()/self.parent.datasets['resolution_function'].sum()), 4) + if 'zero_loss' not in self.parent.datasets.keys(): + self.parent.datasets['zero_loss'] = self.parent.dataset.copy()*0 + # if 'zero_loss' not in self.parent.datasets['zero_loss'].metadata.keys(): + self.parent.datasets['zero_loss'].metadata['zero_loss']={} + self.parent.datasets['zero_loss'].metadata['zero_loss']['parameter'] = np.zeros([self.dataset.shape[0], self.dataset.shape[1], 6]) + + + res = eels_tools.get_resolution_functions(spectrum, startFitEnergy=-zero_loss_fit_width, endFitEnergy=zero_loss_fit_width) + if len(self.parent.datasets['zero_loss'].shape) > 2: + self.parent.datasets['zero_loss'][self.parent.x, self.parent.y] = np.array(res) + self.parent.datasets['zero_loss'].metadata['zero_loss'][self.parent.x, self.parent.y] = res.metadata['zero_loss']['fit_parameter'] + else: + self.parent.datasets['zero_loss'] = res + self.parent.datasets['zero_loss'].metadata['zero_loss'].update(res.metadata['zero_loss']) + + self.parent.datasets['_relationship']['resolution_function'] = 'zero_loss' + + self.parent.dataset.metadata['zero_loss'].update(self.parent.datasets['zero_loss'].metadata['zero_loss']) + + if self.low_loss_tab[3, 0].value: + self.parent._update() + else: + self.low_loss_tab[3, 0].value = True + self.low_loss_tab[14, 1].value = np.round(np.log(self.parent.spectrum.sum()/res.sum()), 4) self.parent.status_message('Fitted zero-loss peak') def get_drude(self, value=0): self.low_loss_tab[8, 0].value = False fit_start = self.low_loss_tab[5, 0].value fit_end = self.low_loss_tab[6, 0].value + if 'plasmon' not in self.parent.datasets.keys(): + self.parent.datasets['plasmon'] = self.parent.dataset.copy()*0 + if 'plasmon' not in self.parent.datasets['plasmon'].metadata.keys(): + self.parent.datasets['plasmon'].metadata['plasmon'] = {} + if 'fit_parameter' not in self.parent.datasets['plasmon'].metadata['plasmon'].keys(): + if len(self.dataset.shape) > 2: + self.parent.datasets['plasmon'].metadata['plasmon']['fit_parameter'] = np.zeros([self.dataset.shape[0], self.dataset.shape[1], 4]) + self.parent.datasets['plasmon'].metadata['plasmon']['IMFP'] = np.zeros([self.dataset.shape[0], self.dataset.shape[1]]) + + if 'low_loss_model' not in self.parent.datasets.keys(): + self.parent.datasets['low_loss_model'] = self.parent.dataset.copy()*0 plasmon = eels_tools.fit_plasmon(self.parent.spectrum, fit_start, fit_end) + p = plasmon.metadata['plasmon']['parameter'] + p = list(np.abs(p)) + p.append(self.low_loss_tab[14, 0].value) + + + anglog, _, _ = eels_tools.angle_correction(self.parent.spectrum) + + low_loss = eels_tools.multiple_scattering(self.parent.energy_scale, p) * anglog + - self.parent.datasets['plasmon'] = plasmon + if len(self.parent.datasets['plasmon'].shape) > 2: + self.parent.datasets['plasmon'][self.parent.x, self.parent.y] = np.array(plasmon) + self.parent.datasets['low_loss_model'][self.parent.x, self.parent.y] = np.array(low_loss) + self.parent.datasets['plasmon'].metadata['plasmon']['fit_parameter'][self.parent.x, self.parent.y] = p + + if 'zero_loss' in self.parent.datasets: + res = self.parent.datasets['zero_loss'][self.parent.x, self.parent.y] + + else: + self.parent.datasets['plasmon'] = plasmon + self.parent.datasets['low_loss_model'] = low_loss + if 'zero_loss' in self.parent.datasets: + res = self.parent.datasets['zero_loss'] self.parent.datasets['_relationship']['plasmon'] = 'plasmon' + self.parent.datasets['_relationship']['low_loss_model'] = 'low_loss_model' #self.dataset.metadata['plasmon'].update(self.parent.datasets['plasmon'].metadata['zero_loss']) - self.low_loss_tab[10, 0].value = True - p = plasmon.metadata['plasmon']['parameter'] - self.low_loss_tab[7, 0].value = np.round(p[0],3) + if self.low_loss_tab[10, 0].value: + self.parent._update() + self._update() + else: + self.low_loss_tab[10, 0].value = True + + self.low_loss_tab[7, 0].value = np.round(np.abs(p[0]),3) self.low_loss_tab[8, 0].value = np.round(p[1],3) self.low_loss_tab[9, 0].value = np.round(p[2],1) _, dsdo, _ = eels_tools.angle_correction(self.parent.spectrum) - - I0 = self.parent.datasets['resolution_function'].sum() + p[2] + if 'zero_loss' in self.parent.datasets: + I0 = res.sum() + p[2] + else: + I0 = self.parent.spectrum.sum() # I0 = self.parent.spectrum.sum() # print(I0) # T = m_0 v**2 !!! a_0 = 0.05292 nm p[2] = S(E)/elf @@ -204,13 +258,90 @@ def get_drude(self, value=0): # print(t_nm, relative_thickness, imfp) self.parent.status_message(f'Fitted plasmon peak: thickness :{t_nm:.1f} nm and IMFP: {t_nm/relative_thickness:.1f} nm in free electron approximation') - plasmon.metadata['plasmon']['thickness'] = t_nm - plasmon.metadata['plasmon']['relative_thickness'] = relative_thickness - plasmon.metadata['plasmon']['IMFP'] = t_nm/relative_thickness + if self.dataset.ndim>1: + # self.parent.datasets['plasmon'].metadata['plasmon'][self.parent.x, self.parent.y]['thickness'] = t_nm + # self.parent.datasets['plasmon'].metadata['plasmon'][self.parent.x, self.parent.y]['relative_thickness'] = relative_thickness + self.parent.datasets['plasmon'].metadata['plasmon']['IMFP'][self.parent.x, self.parent.y] = t_nm/relative_thickness + + else: + self.parent.datasets['plasmon'].metadata['plasmon']['thickness'] = t_nm + self.parent.datasets['plasmon'].metadata['plasmon']['relative_thickness'] = relative_thickness + self.parent.datasets['plasmon'].metadata['plasmon']['IMFP'] = t_nm/relative_thickness + + + def multiple_scattering(self, value=0): + if self.dataset.ndim >1: + anglog, dsdo, _ = eels_tools.angle_correction(self.parent.spectrum) + par = np.array(self.parent.datasets['plasmon'].metadata['plasmon']['fit_parameter']) + for x in range(self.parent.dataset.shape[0]): + for y in range(self.parent.dataset.shape[1]): + self.parent.datasets['low_loss_model'][x, y] = eels_tools.multiple_scattering(self.parent.energy_scale, par[x, y]) * anglog + + + def do_all(self, value=0): + if len(self.parent.dataset.shape) < 3: + return + + zero_loss_fit_width=self.low_loss_tab[2, 0].value + fit_start = self.low_loss_tab[5, 0].value + fit_end = self.low_loss_tab[6, 0].value + + + if 'low_loss_model' not in self.parent.datasets.keys(): + self.parent.datasets['low_loss_model'] = self.parent.dataset.copy()*0 + self.parent.datasets['low_loss_model'].title = self.parent.dataset.title + ' low_loss_model' + + self.low_loss_tab[15,1].max = self.parent.dataset.shape[0]*self.parent.dataset.shape[1] + + self.parent.datasets['zero_loss'] = eels_tools.get_resolution_functions(self.dataset, startFitEnergy=-zero_loss_fit_width, endFitEnergy=zero_loss_fit_width) + self.parent.datasets['zero_loss'].title = self.parent.dataset.title + ' zero_loss' + self.parent.status_message('Fitted zero-loss peak') + + self.parent.datasets['plasmon'] = eels_tools.fit_plasmon(self.dataset, fit_start, fit_end) + self.parent.datasets['plasmon'].title = self.parent.dataset.title + ' plasmon' + + self.parent.status_message('Fitted zero-loss + plasmon peak') - self.parent.spectrum.metadata['plasmon'] = plasmon.metadata['plasmon'] + """ + anglog, _, _ = eels_tools.angle_correction(self.parent.spectrum) + i = 0 + for x in range(self.parent.dataset.shape[0]): + for y in range(self.parent.dataset.shape[1]): + self.low_loss_tab[15,1].value = i + i+= 1 + spectrum = self.parent.dataset[x, y] + + plasmon = eels_tools.fit_plasmon(spectrum, fit_start, fit_end) + p =np.abs(plasmon.metadata['plasmon']['parameter']) + p = list(np.abs(p)) + + p.append(np.log(spectrum.sum()/self.parent.datasets['zero_loss'][x,y].sum())) + if p[-1] is np.nan: + p[-1] = 0 + low_loss = eels_tools.multiple_scattering(self.parent.energy_scale, p) * anglog + self.parent.datasets['plasmon'][x, y] = np.array(plasmon.compute()) + self.parent.datasets['low_loss_model'][x, y] = np.array(low_loss) + drude_p[x, y, :] = np.array(p) + + + + self.parent.datasets['plasmon'].metadata['plasmon'].update({'parameter': drude_p}) + self.parent.datasets['low_loss_model'].metadata['low_loss'] = ({'parameter': drude_p}) + """ + + imfp = np.log(self.parent.dataset.sum(axis=2)/self.parent.datasets['zero_loss'].sum(axis=2)) + self.parent.datasets['plasmon'].metadata['plasmon']['fit_parameter'] = np.append(self.parent.datasets['plasmon'].metadata['plasmon']['fit_parameter'], imfp[..., np.newaxis], axis=2) + E_p = self.parent.datasets['plasmon'].metadata['plasmon']['fit_parameter'][:,:,0] + self.parent.datasets['plasmon'].metadata['plasmon']['IMFP'], _ = eels_tools.inelatic_mean_free_path(E_p, self.parent.spectrum) + self.parent.datasets['_relationship']['zero_loss'] = 'zero_loss' + self.parent.datasets['_relationship']['plasmon'] = 'plasmon' + self.multiple_scattering() + self.parent.datasets['_relationship']['low_loss_model'] = 'low_loss_model' + + self.low_loss_tab[10, 1].value = False + def get_multiple_scattering(self, value=0): self.low_loss_tab[15, 0].value = False fit_start = self.low_loss_tab[12, 0].value @@ -242,46 +373,65 @@ def set_ll_action(self): self.low_loss_tab[3, 0].observe(self._update, names='value') self.low_loss_tab[4, 0].on_click(self.get_drude) self.low_loss_tab[10, 0].observe(self._update, names='value') + self.low_loss_tab[10, 1].observe(self.do_all, names='value') self.low_loss_tab[10, 2].observe(self._update, names='value') self.low_loss_tab[11, 0].on_click(self.get_multiple_scattering) self.low_loss_tab[15, 0].observe(self._update, names='value') - + def _update(self, ev=0): - - self.parent._update(ev) - spectrum = self.parent.spectrum - anglog, _, _ = eels_tools.angle_correction(spectrum) + low_loss = None + plasmon = None resolution_function = None + if 'zero_loss' in self.get_additional_spectrum.keys(): + del self.get_additional_spectrum['zero_loss'] + if 'plasmon' in self.get_additional_spectrum.keys(): + del self.get_additional_spectrum['plasmon'] + if 'low_loss_model' in self.get_additional_spectrum.keys(): + del self.get_additional_spectrum['low_loss_model'] + if self .low_loss_tab[3, 0].value: - if 'resolution_function' in self.parent.datasets: - resolution_function = self.get_additional_spectrum('resolution_function') - self.parent.axis.plot(self.parent.energy_scale, resolution_function, label='resolution function') + if 'zero_loss' in self.parent.datasets.keys(): + resolution_function = np.array(self.parent.get_additional_spectrum('zero_loss')) + self.parent.added_spectra.update({'zero_loss': 'resolution'}) if self.low_loss_tab[10, 0].value: - p = [self.low_loss_tab[7, 0].value, self.low_loss_tab[8, 0].value, self.low_loss_tab[9, 0].value] - self.parent.datasets['plasmon'] = self.parent.datasets['plasmon'].like_data(eels_tools.energy_loss_function(spectrum.energy_loss, p))*anglog - plasmon = self.get_additional_spectrum('plasmon') - self.parent.axis.plot(self.parent.energy_scale, plasmon, label='plasmon') - else: - plasmon = None + if 'plasmon' in self.parent.datasets.keys(): + plasmon = self.parent.get_additional_spectrum('plasmon') + if len(self.dataset.shape) > 1: + p = np.round(plasmon.metadata['plasmon']['fit_parameter'][self.parent.x, self.parent.y], 3) + imfp = np.array(plasmon.metadata['plasmon']['IMFP'][self.parent.x, self.parent.y]) + else: + p = np.round(plasmon.metadata['plasmon']['fit_parameter'], 3) + imfp = plasmon.metadata['plasmon']['IMFP'] + + self.parent.added_spectra.update({'plasmon': 'plasmon'}) + self.low_loss_tab[7, 1].value =p[0] + self.low_loss_tab[8, 1].value = p[1] + self.low_loss_tab[8, 1].value = p[2] + + self.low_loss_tab[14, 1].value =p[-1] + t_nm = float(p[-1] * imfp) + # print(t_nm, p[-1], imfp) + self.parent.status_message(f'Fitted plasmon peak: thickness :{t_nm:.1f} nm and IMFP: {imfp:.1f} nm in free electron approximation') + if self.low_loss_tab[15, 0].value: - p = [self.low_loss_tab[7, 0].value, self.low_loss_tab[8, 0].value, self.low_loss_tab[9, 0].value, self.low_loss_tab[14, 0].value] - low_loss = eels_tools.multiple_scattering(self.parent.energy_scale, p) * anglog - self.parent.axis.plot(self.parent.energy_scale, low_loss*self.parent.y_scale, label='multiple scattering') - else: - low_loss = None + low_loss = np.array(self.parent.get_additional_spectrum('low_loss_model')) + self.parent.added_spectra.update({'low_loss': 'low_loss'}) - difference = spectrum - if resolution_function is not None: - difference -= resolution_function - if low_loss is not None: - difference -= low_loss *self.parent.y_scale - else: - if plasmon is not None: - difference -= plasmon if self.low_loss_tab[3, 0].value + self.low_loss_tab[10, 0].value + self.low_loss_tab[15, 0].value > 0: - self.parent.axis.plot(self.parent.energy_scale, difference, label='difference') - self.parent.axis.legend() + self.parent.datasets['_difference'] = np.array(self.parent.spectrum) + if resolution_function is not None: + self.parent.datasets['_difference'] -= resolution_function + if low_loss is not None: + self.parent.datasets['_difference'] -= low_loss + else: + if plasmon is not None: + self.parent.datasets['_difference'] -= np.array(plasmon) + self.parent.added_spectra.update({'_difference': 'difference'}) + else: + if '_difference' in self.parent.datasets.keys(): + del self.parent.datasets['_difference'] + self.parent._update() def get_additional_spectrum(self, key): if key not in self.parent.datasets.keys(): @@ -292,13 +442,17 @@ def get_additional_spectrum(self, key): else: image_dims = self.parent.datasets[key].get_dimensions_by_type(sidpy.DimensionType.SPATIAL) selection = [] + x = self.parent.x + y = self.parent.y + bin_x = self.parent.bin_x + bin_y = self.parent.bin_y for dim, axis in self.parent.datasets[key]._axes.items(): # print(dim, axis.dimension_type) if axis.dimension_type == sidpy.DimensionType.SPATIAL: if dim == image_dims[0]: - selection.append(slice(self.x, self.x + self.bin_x)) + selection.append(slice(x, x + bin_x)) else: - selection.append(slice(self.y, self.y + self.bin_y)) + selection.append(slice(y, y + bin_y)) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(None))