From f43ef2df4faea0d04e45a0de53a95b32b7a6f2fe Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Wed, 23 Dec 2020 13:54:51 +0100 Subject: [PATCH 01/36] Update README.rst --- README.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/README.rst b/README.rst index 471ee27c..ec151032 100644 --- a/README.rst +++ b/README.rst @@ -73,6 +73,7 @@ This project is partly supported by grants DGAPA/PAPIIT-107215 and CONACyT-CB201 PyNeb uses part of Chiantipy: * Utility functions, many for reading the CHIANTI database files: + Copyright 2009, 2010 Kenneth P. Dere This software is distributed under the terms of the GNU General Public License that is found in the LICENSE file From 4eb749e74c1541a71959fe14383a46aa8cdb027e Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Wed, 23 Dec 2020 13:55:05 +0100 Subject: [PATCH 02/36] Adding eval_diag method --- pyneb/core/diags.py | 36 +++++++++++++++++++++++++++++++++++- 1 file changed, 35 insertions(+), 1 deletion(-) diff --git a/pyneb/core/diags.py b/pyneb/core/diags.py index fbb87661..e0e28378 100644 --- a/pyneb/core/diags.py +++ b/pyneb/core/diags.py @@ -862,4 +862,38 @@ def getDiagLimits(self, diag): LDR = atom.getLowDensRatio(to_eval = to_eval) return(np.sort((LDR, HDR))) - \ No newline at end of file + def eval_diag(self, label): + """ + + Parameters + ---------- + label : diagnostic label(e.g. '[OIII] 4363/5007') + A string of a key included in the self.diags dictionnary. + + Returns + ------- + np.array + The evaluation of the diagnostic corresponding to the label. + + """ + if label not in self.diags: + self.log_.error('Unknown diagnostic: {}'.format(label), calling='eval_diag') + atom, diag_expression, error = self.diags[label] + sym, spec, rec = parseAtom2(atom) + def I(i, j): + wave = atom.wave_Ang[i - 1, j - 1] + corrIntens = obs.getLine(sym, spec, wave).corrIntens + return corrIntens + def L(wave): + corrIntens = obs.getLine(sym, spec, wave).corrIntens + return corrIntens + def B(label): + full_label = atom + '_' + label + corrIntens = obs.getLine(label=full_label).corrIntens + return corrIntens + def S(label): + full_label = atom + '_' + label + 'A' + corrIntens = obs.getLine(label=full_label).corrIntens + return corrIntens + diag_value = eval(diag_expression) + return diag_value \ No newline at end of file From 7fa5dce33ceeafe47079b8b43171a54980418b32 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Wed, 23 Dec 2020 13:55:14 +0100 Subject: [PATCH 03/36] 1.1.15b2 --- pyneb/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/version.py b/pyneb/version.py index 2e6956e1..606e2ff3 100644 --- a/pyneb/version.py +++ b/pyneb/version.py @@ -1,2 +1,2 @@ # PyNeb version -__version__ = '1.1.14' +__version__ = '1.1.15b2' From 15f5bf11ef5d73c88b26e36e7ecd37f08b76d6e7 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 29 Mar 2021 20:03:36 +0200 Subject: [PATCH 04/36] Correct bug using Chianti 7.0 with python 3 (!) --- pyneb/utils/_chianti_tools.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/pyneb/utils/_chianti_tools.py b/pyneb/utils/_chianti_tools.py index 2bdc6252..18e7650f 100644 --- a/pyneb/utils/_chianti_tools.py +++ b/pyneb/utils/_chianti_tools.py @@ -462,7 +462,7 @@ def elvlcRead(ions, filename = None, verbose=0, useTh=1): fstring='i3,i6,a15,i3,i3,a3,f4.1,i3,4f15.2' elvlcFormat=FortranFormat(fstring) # - if type(filename) == NoneType: + if type(filename) == type(None): fname=ion2filename(ions) elvlname=fname+'.elvlc' else: @@ -958,11 +958,13 @@ def splupsRead(ions, filename=0, prot=0, ci=0, diel=0): else: as1 = s1[i][45:].rstrip() nspl[i] = len(as1)/10 - splupsFormat3 = FortranFormat(str(nspl[i])+'E10.2') + splupsFormat3 = FortranFormat(str(nspl[i])+'e10.2') # splupsFormat3 = '(' + str(nspl[i]) + 'e10.3' + ')' inpt = FortranLine(as1, splupsFormat3) - spl1 = np.asarray(inpt[:], 'float64') +# spl1 = np.asarray(inpt[:], 'float64') + spl1 = np.asarray([ii for ii in as1.split()], 'float64') splups[i] = spl1 + print(i, '::', type(as1), '::', nspl[i], '::', inpt, '::', spl1) # ref=[] for i in range(nsplups+1,len(s1)-1): From f2b2a4e48530058b1fb81f56fd5862226db7945c Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 5 Apr 2021 17:41:16 +0100 Subject: [PATCH 05/36] Update _chianti_tools.py --- pyneb/utils/_chianti_tools.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pyneb/utils/_chianti_tools.py b/pyneb/utils/_chianti_tools.py index 18e7650f..5b4c67b6 100644 --- a/pyneb/utils/_chianti_tools.py +++ b/pyneb/utils/_chianti_tools.py @@ -964,7 +964,6 @@ def splupsRead(ions, filename=0, prot=0, ci=0, diel=0): # spl1 = np.asarray(inpt[:], 'float64') spl1 = np.asarray([ii for ii in as1.split()], 'float64') splups[i] = spl1 - print(i, '::', type(as1), '::', nspl[i], '::', inpt, '::', spl1) # ref=[] for i in range(nsplups+1,len(s1)-1): From 48b7e169676d9f66b7fef2b638cb0371a8c87755 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 5 Apr 2021 17:41:23 +0100 Subject: [PATCH 06/36] Update fe_iii_coll_Q96.dat --- pyneb/atomic_data/deprecated/fe_iii_coll_Q96.dat | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/atomic_data/deprecated/fe_iii_coll_Q96.dat b/pyneb/atomic_data/deprecated/fe_iii_coll_Q96.dat index 70d5a81f..d9525e1a 100644 --- a/pyneb/atomic_data/deprecated/fe_iii_coll_Q96.dat +++ b/pyneb/atomic_data/deprecated/fe_iii_coll_Q96.dat @@ -567,5 +567,5 @@ *** SPECTRUM 3 *** N_LEVELS 34 *** GSCONFIG d6 -*** SOURCE Was wrogly refered to Quinet 1996. Data are actually a subset of Z96 +*** SOURCE Was wrongly referred to Quinet 1996. Data are actually a subset of Z96 *** NOTE Collision strengths From 0c71c6f4285125aaf4670b9f3f3fa10fd80aa96b Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 5 Apr 2021 17:41:43 +0100 Subject: [PATCH 07/36] update notebooks --- docs/Notebooks/OIII_diag.pdf | Bin 23227 -> 23232 bytes docs/Notebooks/PyNeb_manual_7.ipynb | 47 ++++++-- docs/Notebooks/PyNeb_manual_7b.ipynb | 2 +- docs/Notebooks/PyNeb_manual_8.ipynb | 2 +- docs/Notebooks/PyNeb_manual_9.ipynb | 171 +++++++++++---------------- 5 files changed, 108 insertions(+), 114 deletions(-) diff --git a/docs/Notebooks/OIII_diag.pdf b/docs/Notebooks/OIII_diag.pdf index a0c262d88fdc1849726be854eb102f3c59651602..90219bc34fe0a804c266fd7e1ce33306aa73e208 100644 GIT binary patch delta 12808 zcmZXZWmr`~yZ333MxP0OZx)5xMhTIufY?(PQZ?hffjU(b8qxX$DH zFl**F|9j?+`LM28Ep_mnb?}ux;Q>qrc!Xs{Gdy=A#%g16+8#XeKQFCz1QRr2PO{0L zSc#__Y}cpv&$r78LZw~2yq+%mMEeK$M1{Mzb)WzIin_m_-3~MGe(3;yA04m%J|c>$ 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b/docs/Notebooks/PyNeb_manual_7.ipynb index b8e2c61a..588cceeb 100644 --- a/docs/Notebooks/PyNeb_manual_7.ipynb +++ b/docs/Notebooks/PyNeb_manual_7.ipynb @@ -137,9 +137,17 @@ } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/core/emisGrid.py:216: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", + " emap = plt.pcolor(X, Y, Z, **kwargs)\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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SZ7URZ7GSYLWRYLNj0CLmY4LRydkIYFHx7k4TUJffR0lzHWm2GFU+kloE1F2jyj7OFKNKQKONcQDUeauUC6ghgXLX1qOfeBishgzKm+artm+NyRQUULd3oyagJyKi1VBsqKHb+nK8I6XEEwgKa7PXS5PPS5PXS7PPS6PXS6PXQ6PHQ4PXQ6PXQ4PHQ53HTb3HTZ3bTa3bRVFdLTVuF7VuF4F2ps4FEGexkmizk2x3kOpwkmJ3kupwku6MIisqmlS7E6MW3XY6MWYr/eNTWVSym18NPNyObOrJdQQTanY3VKoWULvBgsNgpVRFBAotAqo8GSiqlYCmWrOPcvah2A2JNPur8Qc86HXKl6JYjZl4ApX4Ao0YdHbF9q3R61LQ6eLweNeH5acz0QRU47hACIFZHxzSjTZbjm5wBAJSUud2UdncTLWrmUpXExVNTZQ3NVLW1EhZUwNljQ1sqCijornpEFudEKQ6nORGx9IjJpbc6Dh6xMTSMy6BZLtD8ZyUxuE5KSWHZzcuod7jxmkyR9R3D2dQQHc1VDAmqe3Wph0n2RJDiUu5CEIwE3djnfJosCUCrfUqTyRyGIPL8Bt8FUSb0o5y9k+xGn7MxHWaOrp9bfsIITAb++HxbDj6yd2EJqAaGm3QCUGMxUqM5eiLyT1+P6WN9eyrr6OorpZ99bXsratld201H2zdSL3Hc/DcaLOFXvEJ9I5PpG9CMv0Tk8mPjdeGhVUyNjWHpzcsZlnZXiZlFBzdQAEJZgd2gynsRKJkSwxlKgU03hxLjbdW8Tys0xADqBRQQ4uAlqkT0JalLN6isAUUwGTsS13DS0jpI7gy8dji2OuRhsbPCJNeT2ZUDJlRMT/WwwohpaS8uYmdNVVsrapgc2UFmyvL+d/m9cz0BncYsxgM9E1IYmhyGsNTMxiWmk5sB4RbA4YkpGPS6Vlaui/iAiqEoIcjgZ0N4QpoLGtrdquyjTPF4JcBar31xJqiO2xn0BlxGKKp9SpfhuMwBIvvNKhMJLIagvPRzb69quzbYjL1ReLG69uGydg7Ij4jiSagGhqdhBCCJJudJJudUWk/JroEpGRXTTXryktYW17KmrJiZq77gefWBCtiFcTGMzYjmwmZOYxKy8RqVF9O7njGYjAyMCGNpWWR+WPdllxnAssqdoflI9kSQ4OvmUafC7tB2dRCvClYiq/KU6NIQCE4jFunagg3JKA+ddubGnUxGIQjYktZzMb+ALg96zQB1dDQCA4R58XGkRcbx7TCPgC4fD7WlpWwvLiIJcVFvLVxLTPXrcKk1zMiNYOpufmclltAkt3Rzb0/thiZlMkzGxZ3yjxoriOBj/etpdHnxm5Q5zvZEhTBElc1eY5URbZxBwW0mjyUJQNFG+Op9iiPIo06KxZ9FA1edQIqhMBqzKTJG5mHGqMhDyGsuL3rcHJRRHxGEk1ANTSOASwGAyPSMhiRlsGtgMvnZVnxfhbu28XXe3bx+2/n8Ydv5zEsJZ3T8wo5O78XibbwshyPB0YmZ/Hv9d+zsryIk9PVJ/u0R44jWAR+b0MVvWOUiV8LKSEBLVUhoPGhQgwVbuVzqNHGOHY3blFsB8F5ULURKASHcRu86pfCtEYIPSZjPzyedRHxF2m07AUNjWMQi8HI+Mwc7h9zCl9fei1zLr6GXw0fQ73Xw58WzWf0a89y/ewP+XLXNjx+5bVWjxeGJKZjELpOGcZtEdBwauIejECblYug0+DAIAyqlrJEG+Np8tfjDbgV2zqMSdSrjEABbMZMmr37+bFwXHiYjf1xe9cjQ2vVjyW0CFRD42dAQVwCd8QlcMewMWyvruR/m9cza8sG5uzeTpzFyqV9BnBl30GkOJQtnP+5YzOYGJiQypLSyAtolj24HGRXGIlEcSYHJp1B1VIWndARZ4qhUlUEGhT/Gm8ViWZlka/TkMyBpjWKr9mC1ZCFxIvLV4LVmH50g6NgNvWnrvElvL6dmIz5YfuLJFoEqqHxMyM/Np7fjp7Akqtu4uUzzmNoSjpPr1rKSW88zy/nfMqasuLu7mKXMio5m3WVxTR4lUdbR8JqMJFqjWZXGBGoTuhItsRSqnYpiymWSjURqCkooLUe5eLvMCbhCTTi9jcotgWwGYPztU0RysQ1m4Jbynm8ayPiL5JoAqqh8TPFoNNxSnYPnj99Ggsuv46r+w1m/t6dnDvrDa7+9D1Wl54YQjoqOQu/lKwoi0zmZ2tyHfHsCXNbsxRLLMVhrAWt9CjPpv0xAlXed6chuP+o2nlQmzELgOaIJRIVIIQFl2d1RPxFEk1ANTSOA7KiYnhg7CksvvIm7h01nrXlpUx7/w2u+/wD1peXdnf3OpWhiRkYdToWl3ZoD2RF5DgS2NVQ8ZM61kpIscRS0qxuW+J4UyxVnhoCCuf/Yg5WI1KxFtQYFNB6r7p/N2Z9MjphjlgEKoQBk7E/bk1ANTQ0OhOHycRNg0fw7eXX838jTmJZcRFnv/ca98z/kvKmxu7uXqdgNRgZlJDOkpLIC2iuM55Gn4dyl/I9PVtIscZS422k2e85+sltiDfH4pcBarx1iuyMOjN2vVNdBBoS0AaVAiqEDqshkyZv5H4fFtNQPJ61SKn8Z9iZaAKqoXEc4jCZuG3oKL694nquHziMWVs3MPGtF3l+9fLjMmt3dHIW66tLqfO4Iuq3pah8OPOgKZZgNFiqIhM3wRy0VZVIZEpQNQdq08eiE0bqfepHLmzGbJq8u1Xbt8VsGorEjdtzbBWW1wRUQ+M4Jtps4XdjTubLi65haEo6Dy9ewGnvvsLKkgPd3bWIMjolh4CULC3dF1G/rYvKqyXVGlzKUuxSPoybYAoKaIWKedBYYwI1XuX9FkKH05BMnbdEsW0LNmM2zb59EVvKYjEPB8DlWR4Rf5FCE1ANjROAvNg4Zp55Pi+dMR23z8uFH77FY0sW4vb7urtrEWFwQhpmvYHFpbsj6jfZEoVVb2RnGEXlU0MR6AEV86AtEWiFW7ltjCmBGo+6yNlpTKY+DAG1G3MJSA/Nvsg8qBn0yRj0Wbg9KyLiL1JoAqqhcQIxMTuPLy6+hgt79uOZH5ZxznuvHxdJRma9gWGJGXwf4XlQIQQ9nAlhrgV1YtYZVUWgNr0Vq95CpRoBNSbgCjTR7Fc+9+00pqhOIgKwGXMBaPLuUu2jLRbzMFzuFWEldEUaTUA1NE4wnCYzj51yKi+dMZ1qVzPT33+D19b/cEz9YVLDmJRsttSUU+GKbLJUriOBHfXqdieBoAinWGIpVhGBCiGIN8VRrjICBahRMQ/qNKbQ7K/GG1A3p2w39gCg0btTlX17mE3D8QdK8Pkjv1xJLZqAamicoEzMzuPLi6/hpIxsHvh2Hr+e9zlN3mMry1EJo1OCC/gjnY3bw5lISXMdjT71hRrSrPGql7IkmGNVzYHGGEMCqmIeNMqYAqB6GNekj8Woi6ExkhGoKTQP6l4aMZ/hogmohsYJTKzFyotnnMdvRozlo22bOHfWG2yvDq9wQHfRLy4Fm8HIsrLIJhLltSQShTEPmmaN40BzlaooP9Ecr2oONNYU3JqsWkUEGmUMlv+r86ovxmE39qDRs0O1fVtMxl7oRBQujyagGhoaxwg6Ibh96GhePesCKpubmP7+G3y/v3P22OxMjDo9QxLTWR7hikR5zqAQhZVIZI2n0e+iztek2DbBHEeDrxGXX9lwqsMQjV4YqFGxrVnEBDSCEagQeszmYbjcyyLmM1w0AdXQ0ABgXGYOn1xwJSl2J9d8OotPt2/u7i4pZkRSFltqyqhxN0fMZ6Y9DoPQsaNB/Txoy1IWNZm4ieZgWT6l86A6oSPGGE+1V3m/rfpYDMISnoCa8vAGqvD41ZUxbA+LaSRe31b8fnXD4ZFGE1ANDY2DpDuj+N+0SxiYlMLtcz7l+dXLf1bJRcOTMpEQ0SjUqNOT7Yhne516AU2zBkXwQJPy4fEfl7Iot401JanaWFsIQZQxhTqv+mUoPyYSRW4Y12oeiV6fhs8f2WF6tWgCqqGhcQgxFiuvnX0Bp/co5OHFC3hw0fyfjYgOTkjDpNNHfH/QPGciO8PIxD0ooC7lIqg2AoXgPKiaOVCAKFMadZ5whnCDG5w3eiKZiTuCrJQVB3do6W40AdXQ0PgJFoORf089m2sHDGXmulX88buvfxYiatYbGJyQzrII7w+a50xkX2M1br9Xlb1VbyLO5GS/igg0xhiFQRgoVxWBJlLvq8YbUJ5dHWVMo85brPr3bjGkohc2Gr3bVdm3hxACIUTE/IWLJqAaGhrtohOCB8aczHUDh/LK+h/4088kEh2ZnMmGCNfFzXcmEkCGVRM33RrPgWbl9jqhI9Ecp05AjcEEKDVLWaKNafikiyaV841C6LAbe9AQwUzcY40uE1AhxEtCiDIhxPpWbYOEEEuEEKuFECuEECNaHfutEGK7EGKLEOLUVu1DhRDrQsf+KUKPI0IIsxDinVD7UiFETlfdm4bG8YoQgvtGn8wv+g/h5XWreGTxgmNeREclZxOQkuURXM7Skom7vU7dHpnw41IWNSSoFNA4UxIAVW7l/Y4ypgFQ5wljHtSUF9EI9FijKyPQmcBpbdr+CjwopRwE/D70HSFEH+ASoG/I5mkhhD5k8wxwA1AQerX4nAFUSynzgSeAxzrrRjQ0TiSEEPx+7Clc3W8wz69ZwaNLFnZ3l47IwXnQCBaWz3HEoxcirIpEqdZ4yt21eALK6w8nmuNVD+ECqjJxo01BAa317lds24LdmIfbX4bXr2w7tp8LXSagUsqFQNvHLwlEhT5HAy2POucCb0sp3VLKXcB2YIQQIhWIklIulsHH4FeBaa1sXgl9fg+YJI6lwXINjZ8xQgj+eNJEruw7iGdXL+fltau6u0uHxWIwMighjSUR3GDbpDeQbY9nWxgRaLo1HolUVZEoyZxArbde8VrQKGMseqGnyq28rq3TmIJAR20YmbgOUwHAcRuFdvcc6K+Avwkh9gGPA78NtacDrR8fi0Jt6aHPbdsPsZFS+oBaIL69iwohbggNGa8oL1f/RKmhcSLRIqKn5hbwp0Vf8+Wubd3dpcMyOjk78vOgUUlsqw9nCDf452i/innQJEuwGlKZwihUJ/TEGBOp8ijvt14YcRpTqPWoj0AdxqCANni69t9KV00zdLeA3gz8WkqZCfwaeDHU3l7kKI/QfiSbnzZK+ZyUcpiUclhiYqLCLmtonLjodTqenHQGA5NS+eWcz/ihVP0yh85kVHIWASkjWtYvz5nI/sYaXCozcdNblrKoENCDS1lULIOJMyVRrUJAIZhIFM4QrsWQhl7YaPB2joD6fD42bdrEs88+y8UXX8wtt9zCqlWr8HfRpvHdLaBXA++HPv8PaEkiKgIyW52XQXB4tyj0uW37ITZCCAPBIeFjo1yFhsZxhNVo5IXTp5FstzPj8/fZXRu5SjORYnBienB/0AgWlm/JxN2tMhM31uTAqjepikCTD0agyrNp40xJqiJQgGhTOrWeItURnRAChymfBs9WVfZHYunSpZx77rlMnjyZxx9/nOjoaLxeLzNmzODFF188uoMI0N0CegCYEPo8EWh5TPkYuCSUWZtLMFlomZSyGKgXQowKzW9eBXzUyubq0OcLgK/lsZ4uqKHxMyXBZmfmmecDcO1n71PvUb9TSWdg1hsYmpge0f1BWzJx1SYSCSFIt8arWgsaZXBi1pnVCag5mUZ/PS6/8vKG0cYMPIFGXGEkATmMhTR4tkV0WPXNN9/k7LPPJjExkS+++IJt27bx3HPP8fzzz/PAAw/w17/+NWLXOhJduYzlLWAx0FMIUSSEmAFcD/xdCLEGeIRgdi1Syg3Au8BG4AvgVillS0x+M/ACwcSiHcDsUPuLQLwQYjtwJ3Bvl9yYhsYJSo+YOJ459Rz21NVw9/wvjrnlLWNScthcU0alS3kB9/ZoycQNZylLujWB/c3KRVAIQZI5njIVQ7jxpmQAqjzKE4miTcEUkxqv+qFwh6kQb6AGjz8y+SZVVVW899573HPPPcycOZP+/fsfcjwmJob8/HyamiLzez8SXZmFe6mUMlVKaZRSZkgpX5RSfielHCqlHCilHCmlXNnq/IellHlSyp5Sytmt2ldIKfuFjt3WEmVKKV1SygullPlSyhFSysjVj9LQ0GiXkWmZ3DNqPLN3buPFtSuPbtCFjE7OAmBZhMr6tWTihrOUJd2WwIHmKvwyoNg22ZKgeggX1AlojCk4Y1brUV9b2GEqBCKXSGQ0Gvnuu++44YYbDmk/cOAAb7zxBldccQVTp07FZrNF5HpHwtDpV9DQ0DiuuX7gMFaVHOAvixfQPzGZkWmZRzfqAvrFpWLRG1heto/Ts3pFxGd+VCJbw4hAM6zx+KSfMlcNqdY4RbaJ5gTW1W5BSqmonF1cKAKtVFFMwWlMDS5lCScTNySg9d4txDNWtZ+DfXI6yc7O5i9/+QunnHIKPp+PkpISfvjhB3bu3MlvfvMbfvOb34R9nY7Q3XOgGhoaP3OEEPxt4mlkR8Vw61efUNJQ391dAsCk1zM4IbL7g+Y5k9jbUKW6Jm6GLZgMtK9JeRSbbEnAHXBT61X287UZHFj1dqo8JYqvqRcGooypYQ3hmvSxmPSJNHi2qPbRlhdffJGysjKuv/56nnvuOd58800aGhq48cYbufXWWyN2naOhRaAaGhph4zSZefa0c5n2/hvcPudT3j73YvS67n8+H5GUyb/WL6LO4yLKZAnbX35UMBN3Z30FvWNSFdu3CGhRUwUj4nsqsk22BJOYytwVxJiijnL2ocSbUqhUUUwBIMaUSY0nvOVATlMv6iMooAMGDOCZZ57B4/GwZcsWsrKySEhIOOQcpZG6Grr/X7iGhsZxQUFcAn8eN5nlJft5fs2K7u4OACOSMwlIycryyEShBc7gfKLaggrxpiisehNFKhKJWgS01KU8eo03J1OpIgKF4DxorWc/UsW8bQtOU28aPTsISOW7wrSHlBKj0UhNTQ1DhgwhISEBKSWBwI997IpCdJqAamhoRIzphX04LbeAfyxbxKbK7q/yNTghHaNOF7G6uNmOeAxCpzoTt2UpS1GTcgFNMscjEJSoEVBTMtWecvxSeR3eGFMmPummwaf+9xll7oPEF7FEIiEEn376KWeccQbwY7SpazXqsX37dr744gsqK9XvoHM0NAHV0NCIGEIIHpkwhSizmV/P/QyXT/kf7EhiNRgZGJ8WsQ22jTo9uc6E8BKJbInsUyGgRp2ROFOMygg0lQABalRsrh1jCmYzhzOM6zT1AaDOvUG1j7acddZZfPHFF0D70WZtbS0ffPAB//jHPyJ2zbZoAqqhoRFR4qw2/nbKaWyuquDxZd92d3cYmZzFuspiGryRKfZQGJUUVlH5TFsCJa4qfAHl5eZSLEmUupULaIIpBYAKt/Jh3NgICKjVkIFBF0WdJ3ICKqUkNTWV/fsPzRBuKeM3dOhQZsyYwXPPPRexa7ZFE1ANDY2Ic0p2D67sO4gX1qxkUVHkqgGpYVRyFv5IzoNGJVPcXEu9V12h+kxbIn4ZUFUTN8WSqDICDQqomnlQqz4Wk85OtUd9FC+EIMrUJ6IRqBCCdevWMXr0aK655ho2bdpEIBBAr9cfPGfEiBGkpaUxf/78iF23NZqAamhodAq/Gz2BHjFx3PvNVzR71S37iARDEoPzoEtKIzOMWxgVTCRSOw+aaQsmA6kZxk22JFLrrafJp6wsn9MQg0lnpsKtvPi/EIJYUxY1YQgoQJS5Lw2erRFLJALo378/gUCA4uJiLr/8cq688kref/99pJR4PB6++uorbDYbBkPnLDjRBFRDQ6NTsBqNPDJhCvvqa/nXyiXd1g+bwcTA+LSICWhBVLAwgdp50BYB3duk3D7FEhTvEpcyWyEE8aYUVQIKwUSicCJQAKepLxIv9REuLH/qqacyePBgVq1aRf/+/XniiSfIy8tjxowZPPvss4wcOZJx48ZF9JotaAKqoaHRaYxKy+T8nn15bs1yNndjVm4k50HTrNE4DGa21qlbVxlltBFttKsqppASWsqiJhM30Zymag4UINaUTaOvAo+/UZU9QJS5HwB17vWqfbTHDTfcwCuvvALAvffeyzfffMPTTz/N4MGDGTFiBL/+9a8jer3WaAKqoaHRqdw3egJRJjO/XfAVgW4qOD8mJRu/lCyPwP6gQgj6xqTR7FM/FJltS2KvKgENRqDFCiNQgARzKlWeUnwB5cPpseZsAKo86uezrYYMjLoY6tzrVPtoj5EjR2K1Wlmx4se1x6eddhp33nkn99xzD9nZ2bhcLvbti9zesC1oAqqhodGpxFlt3D/mZH4oLeaNDau7pQ9DEtIx6fQR297spbFX8cjQ6artM+2J7GtULqBmvYk4U4ziIVwIRqABAqr2Bo0z5QBQ7dmt2LYFIQRR5v4Rj0ABnnrqKaxWK8AhSUQALpeLRx55hOuuuy7iO7RoAqqhodHpTC/sw0kZ2Ty25NtuqZVrMRgZmpgRsQ22w61yk2VLpNrbQL1X+R/0FEsSJc3qIlBA1Tyo05iCXpiocu9WbNuaaHN/Grzb8QciK2Rnn302ffv2/Um7lBKLxcLdd99NIBDgpZdeiuh1NQHV0NDodIQQPDx+Ct5AgD9//0239GFUchYbq0updnf+PpFHI9seHIrdoyKRKNWSTLFL+fxrYkhAy90HFNvqhJ5YUzZV7l2KbVsTnAcNRHQ5SwsPPvgg06dP5/vvvz+4FlQIQVNTEw6Hg6uvvprXX389otfUBFRDQ6NLyI6O4abBw/lsxxaWF0duh5SOMjolGwkR3Z1FLVm2kIA2qhBQaxL1vkYavMoSemwGJ3a9U5WAAsSZc6gKYwgXINo8EIBa99qw/LSHwWDgo48+4uGHH+b000/njTfeIBAIHNwXNBAI0Lt3b7wRXFKlCaiGhkaXceOg4aTYHfx50fwuTygaEJ+KWW9gWYSWs4RDqjUOo9CzV4WAplmCy2gOqIlCLemqBTTelEujrwKXX/0QvEkfh9WQ0SkCet111xEdHc1HH33EaaedxtNPP82wYcP49a9/zWWXXcatt97KKaecgtFojNg1NQHV0NDoMmxGE3ePHMfa8lI+2LqxS69t1hsYnJDG0ghk4oaLXujIsCWqG8K1BgVUTSZuojmNMre6zbHjzLkAVIc5DxplHtApApqcnEx2djbvvvsud955J5988gmPPPIIFRUVJCUl8fbbb3PVVVdF9JqagGpoaHQp0wr7MDAphb8u/ZZGb+Sq0nSEEUmZbKwupc6jrgxfJMm2J7FHRSZukjkBvdBR3Kx8TWeiOY1GXx1NPuVRZIuAVrp3KrZtTYx5IG5/CS6fujWpR+Kyyy7jmWeeASA6OprTTjuN1157jSeffPLgzi2RRBNQDQ2NLkUnBL8fO5HSxgaeXrW0S689KjmbgJSsOAbmQbNtSRQ3V+IJKNuxxqDTk2xO5ECz8iHcJEsGoC6RyGFIwqSzU+kJT0B/nAddE5af9rj99tu56KKLgJ8uZ2nJnG5JMIoEmoBqaGh0OUNT0phe2Ifn16xgb11Nl113cEIaJp0+YmX9wiHbnkQAqaoiUZo1mf1qBNScBkCZS/kwrhCCeHNe2BGo09wbnTBR4/ohLD/tYbVaufnmmwFYuHAh559/Pq+99hqvv/46VVVVNDU1/URYw0ETUA0NjW7h3lHjMQgdf13adVueWQxGBiWksbQ0cjvEtOxJqZSDS1nUJBJZUyhxlRGQAUV2caYkDMKoeh403tyDKvcuZBgJYDphIsrUj1p35AUUOFg4vqmpiXnz5lFSUsL777/PFVdcwXnnncc///lPnn/++YhstK0JqIaGRreQbHdwTf8hfLp9C5u6sE7uqOQs1kdgHrRFRH7961+zdOnSQ9o6QpYtCYFgd6PySDLNmoxP+ihzKdvRRSf0JJhTKXWpG8KON/fAE2ik3qeuDnALMZYh1Lk34g8o21VGCWPHjiUuLo7TTz+d999/nw8//JBnn32Wt99+mxtvvJFHHnkk7GtoAqqhodFt3Dh4OE6Tmb8v/a7Lrjk6NA+6LMxs3Ja5tPz8fBYuXAgE1xp2FLPeSKo1VnUECrDfpTwRJ8mcHkYEmgdApXuHKvsWYi3Dkfioca8Oy8/hkFLidDo544wzePHFF1m2bBk33XQTkyZNwmAwcM8993DmmWeGfR1NQDU0NLqNaLOFmwYNZ+6eHawsUbc+USmDE4N1ccMt69eSlHLxxRfT2BgsaqDX6/F6vezcuZNt27Yd1UeOPUVVBJreIqBNygU02ZJJtacMT0D5zjTx5lxAUOHarti2NTGWIYCOatfysPwcDiEEixcv5sCBAzzzzDPMmDEDnU7HY489xsyZM7nvvvuYOHFi2NfpnF1GNTQ0NDrINQOG8PK6Vfxt6be8dc5FYdeZPRpmvYFhSRksDnMetCUZ5ZJLLsFgMCClPNj3zz//nNmzZ/PZZ58d0UeOPZlllVvwBfwYdB1PbnEY7EQbo9ivYilLsiUDiaTcdYB0W64iW6POSrQxPewI1KBzEGXq02kCCnDbbbeRm5uL0+nk/vvv5+KLLyYQCKDTRS5u1CJQDQ2NbsVuNHHb0FEsObCPb4sil9xzJEYlZ7Opuowql7q6uIFAgEceeQS/338waaVFPI1GI2PHjmXx4sXs2XPk+8m1J+OTfvY3K09oSbemsL9ZeWH4lqUspW51Q9gJljwqwhRQCA7j1rrW4A90zprcjz/+mLfeeosnn3ySxMTgPqqRFE/QBFRDQ+MY4NI+A8hwRvHYkoVdUuJvbEpwf0u1UahOp+P+++/nhx+CmaTbt2/nvffe44477mDkyJFMmTKFmpoaduw4stDk2INVhXY1Ko8kgwJaojgjNsGUgg696kSiBHM+dd4DuP0NquxbiLUOR+KltpPmQdPT0zEajVx66aURGa5tjy4TUCHES0KIMiHE+jbttwshtgghNggh/tqq/bdCiO2hY6e2ah8qhFgXOvZPEXrsE0KYhRDvhNqXCiFyuureNDQ0wsOsN3Dn8LFsqCjj0+1bOv16A+LTcBhNYe0POm3aNE477TRSUlIoLCzk1ltvZevWrZx//vnMnj0bv9/PxIkTjyhw2fZgJu6uBuUCmmlLo8nfTLW3VpGdQWck0ZxKqUtlBGouACKTSCTQU+Xq3GIakY46D/HdaZ5/ykzgtNYNQohTgHOBAVLKvsDjofY+wCVA35DN00KIlgmCZ4AbgILQq8XnDKBaSpkPPAE81pk3o6GhEVnOLehNQWw8/1m1pNOjUINOx4ikLL4v2a3ax4QJEzAajcyfP5/6+npKS0uZPXs2d999N8OHD0cIQSAQOOKcrkVvIs0arzKRKLg9WVGT8mHcZEsmJWoF1JIPQLn76ElSR8Kgc+A096WquWurUUWSLhNQKeVCoKpN883Ao1JKd+iclnzuc4G3pZRuKeUuYDswQgiRCkRJKRfL4GPdq8C0VjavhD6/B0wSnZ2NoKGhETH0Oh23DBnJlqoK5u4Of47taIxNyWZ3fTX7G5VFcC0MGjQIv99P7969sdvtBAIBfD4ffr//YNTZkegn157MDhURaEaLgDYrz14+mInrVz7/aDfEY9XHUuEKT0AB4iwjqXOvwxdQtjVbOPj8kVtz3N1zoIXAuNCQ6wIhxPBQezrQ+vGoKNSWHvrctv0QGymlD6gF4tu7qBDiBiHECiHEivLyrlvAraGhcWTOzu9FVlQ0/1q5OKxqNx1hVGgeVG1Zv5NOOok//OEP+HzBWrY6nQ6DwYBer1eUSdzDkcL+5grcfmX7VEYbnTgMdlURaKo1C4mkRGUiUaKlkPIwl7IAxFlHIvFR7VoRtq+OUNfwKvuKR+DzRaYWcncLqAGIBUYBdwHvhqLG9v71ySO0c5RjhzZK+ZyUcpiUclhLdpaGhkb3Y9DpuHXIKNaVl/LN3l2deq1eMUnEmCwsUTkPKoTg1ltvPZiFq5YejlT8MqB4azMhBJnWNPapiEBTLcGHh5JmdQ8PiZZCqj278YaZQRtjHopOmKhqXhyWn45is0wEJFV1f4+Iv+4W0CLgfRlkGRAAEkLtma3OywAOhNoz2mmntY0QwgBE89MhYw0NjWOc6YV9SHdG8dSKzo1CdUIwMjmLxSojUJ1Ox7x58/jmm2+AH6sQSSkV9buHI1gUYWeD8kgy05bGvqYDin9OsaYkTDozxS51Dw9Jlp5IAlS4w4tC9ToLMeYhVDV/H5afjmIwZBDluIaGpnfxeDeH7a+7BfRDYCKAEKIQMAEVwMfAJaHM2lyCyULLpJTFQL0QYlQoUr0K+Cjk62Pg6tDnC4CvZWePAWloaEQck17PzYNHsLqsmO86eV3oqORs9jfWsq+hRpV9ZWUlixYtAn6c7xRCKBrCzbAmYNIZ2KFSQJv9Lio91YrsdEJHiiWL4ma1AtoLgDJX+CIUZx1Dg3cbbp/ykoZqiHH+Ep2IoqLmvrAf0LpyGctbwGKgpxCiSAgxA3gJ6BFa2vI2cHUoGt0AvAtsBL4AbpVStmzidjPwAsHEoh3A7FD7i0C8EGI7cCdwbxfdmoaGRoS5sFc/Uu1OnlzxfadGoWNC86Bql7OceeaZXH755Qe/V1dXs27dOr799ls2bdp0sMTfkTDo9OTYk1UJaJYtmAKyr0l5bdtUSzbFrj2qfr52Qzx2QyJlzZsU27Yl3joGgMouikL1+jjion+Hy/09DU3/C8tXl5Xyk1JeephDVxzm/IeBh9tpXwH0a6fdBVwYTh81NDSODcx6A7cMGckD387l26I9jM/M6ZTrFEQnkGix833Jbi7OH6jY3m63Y7fbAZg7dy6zZ89m//791NbWsnXrVqqqqvjzn//M1VdfjdPpPKyffEcaiyuUi1GLgO5p2s/g2P6KbNOsOSytmkuNt4JYk/JckGRLb0ojEIE6Tb0x6uKobF5EmnNa2P46dE375dQ3/Y/K2j9is0xCr2833/SodPcQroaGhka7XNS7H8l2B8/+sKzTriGEYExKNt+XqIvEIDjnee2113LhhRdSWlrKhAkTuPPOO1mwYAGffvopr776Kq+//voRfeQ706j2NlDprlN0bZvBSoIpjr2NyiPQNGsOAAeadyu2BUi29qHOe4BmX40q+xaE0JFgG0dl87cEpC8sX0qumRj7VwKBeipr/6TajyagGhoaxyRmvYFr+g9m0f69bKjovPmxsam5VLga2VqrbG/NFmbPns2+fftYtGgRr7/+OjfffDNTpkwhIyODsWPHctZZZzF37twj+sh3BNd0bm9QnlGbZU9nj8ohXIFQL6CW3gCUujaqsm9Nou1kvIHaTivr1x4mYy9inLfQ0PQuzS512+lpAqqhoXHMclmfgdiNRp5b3Xm7drTMgy4qVrdsZv/+/TQ3N9OnT5+fHHO73WzdupVhw4Yd0UeeIw2AbfXKBTTbls6B5hI8AWXrSE16CwnmVA40q7vvREshOvSUNIcvoPHWkxAYKW+aH7YvJcRE/QqDIZfy6v8jEFC+sYAmoBoaGscs0WYLl/UZyKfbN7OvTl3FoKORbo8m1xnHd8W7VdlPmDCB3bt38/nnn1NRUcGSJUt48803+dWvfkXv3r3ZtGkTN9544xF9OI1WUiyxKgU0gwABVQUV0q257FcpoEadhXhzXkQiUIPOQZx1JGWNczu9gEZrdMJKYuzj+Px7qaz9vXL7TuiTxs+YnTt3cuGFF5KZmYler+9QFuELL7zAgAEDSEtLY/To0ZSV/Tjc9txzzzFixAh69uzJQw89dIjdqFGj6Nu3L4MHD2bw4MFMnDiRRx555GBlFw0NgGsHDEUnBC+s6bxqNSel5rC0bC8ev//oJ7ehsLCQp556ittvv51BgwZx//33M2vWLBobG/n973/PypUriYuLO6qfAmcaO1QM4Wbbg0vm9zQpr66Tbu1BjbeCRp+yudcWUqx9KW3eREAq/7m1JdE2iWbfXhq9nV/GsTVW8xhinLdR3/gGDU0fHd2gFdqG2ho/YfLkyVxxxRVMnz794OLww/H888/z0ksv8dhjj5GXl8fWrVuxWCwAvPHGG7zwwgs8+OCD2Gw2fvOb3xAfH8+NN96ITqfj0UcfxePxEAgEMBgMTJ06lXPOOefgRsUaGgCpDifnFvTmnc3ruGPYaOKstohf46TUXF7buopVFfsZlZyl2P78889n6tSplJWVsW/fPux2Ozk5OSQmJuLz+aipqcFiseBwOA7rI9+RxnflG2n2e7DqTR2+doolEbPOxJ5G5QKaZg1uqL2/eReFTuVZyCnWvqyr+YBK904SLQWK7VuTaJvI5soHKW+ah8OUH5YvpcRG3UWzezHl1XdhNg3osJ0moBqH0KNHD2644QZKS0sRQhyxGHYgEOCBBx7g/fffZ8yY4FquwsLCg6L78ssvc8kll3D66acDcP311/PUU09x7bXXYjabOfnkkw/62rZtGykpKVxxxRWKFqFrnBjcNHgE723ZwMx1q7hzxEkR9z8qOQu9EHxbvEuVgAI4nU4cDgd5eXkA7Nu3jw8//JDi4mJ++OEH8vLyuOeeew5rX+BMRyLZ0XCAftE5Hb6uTujIsqWzp0l5Xdv0FgFt2qlaQAFKmteHLaAWQxLR5gGUNc4lN+bIQ96RRggjSXFPs790KqWVN3TY7oQX0D3VNVw/64OOG7T54360P/VtxaDt+T/5Hjpf/NjQ7vltz2v9XQgQoSO/PXkCiQ77UXr50z60lCM7koCuW7cOt9vN+vXruf766zEajfz2t7/l4osvBqCqqor8/HwCgQA6nY5evXqxZcsWGhoaMJvNh1zjkUce4ZRTTiE+/ujrsQ7U1dPs9WLU6zDo9Jj0Oox6PUadHpNBj15hJRiNY5/82Him5uYzc90P3DBoBA5TxyO0jhBlsjA4IZ3vDuzkrkETFNsXFRXxxhtvcPbZZ9OnTx98Ph8rV67kyiuvZPLkyTQ3N/Pee+8dUUALncE1nVvr9isSUIAceyaLKpYjpVT0b99mcBBnSqaoWd2wqcOQjN2QSHHzevrHTlflozVJtilsq/47zb4DWA1pYftTgtGQSVLcvyipvLLDNie8gHoDAcobO5Z9JdvUpj/aXHfb83/6tX1/Le1t3bc9TtvzZfBz6/PcfnXziS1R5JH+M+7atYumpibmzp3LO++8w4EDB5gxYwZpaWmMGzeOcePG8Z///IdzzjkHgMcffxwpJfX19cTHxx8seeZ2u5k1axaffvpph/7zP/rNAj7fsvWwxwVgMugx6w2Y9HrMBgPm0HezwYDFaMBqMGAxGg++24wGbEYTNpMRu9GE3WTEYTbjMJlwmEw4zWaiLGacZjM6TZy7hVsGj+SrXdt5Z9M6ZgwcGnH/41JzeXLtt1S5moizKBsm1ul0vPDCC/zmN79BSonBYGD8+PHo9Xo++CD4gJ6UlERDQ8Nhh3ETzdHEGO1srVe+JCXHnsmc0oWUuytJsiQoss2w9mBfs7qatkIIUq39KG5ep8q+LYn2yWyr/jvljXPJir4qIj6VYLNOJsb5S+B3HTr/hBfQ/Pg4Przq8qOfeJzywQcfcO211+L1ehkzZgyzZ89Gr9d3aD9DvV6P1+vlrrvuol+/fvTr148pU6bw6aefctJJJ3H//fdz++23069fPwwGAxMnTgT4SZT54YcfkpyczIgRIzrU52uGDmFKQT4+fwBPwB989/vx+v14A348fj8eX/Dd7fPhbvXu8vpw+3yUu5to9nlp9npp9vpo8npw+zqWCOEwmYi2WIixWoixWIixWomzWom32YizBd8T7XaSHHYS7XbMYe7WoRFkUHIqQ1PSeGX9Kq7pPxh9B/baVMJJqTk8sfZbFpfu4czs3ops09LSKCsro6amhoSEoIDFxcWRnp7OunXr6N+/P3q9nkWLFnHqqae260MIQYEznW0qBRRgV+Ne5QJqy2Nt7WIaffXYDYevlnQ4Uqz92F4/n3pvCU5jimL71tiNOTiMhZQ2ftUtAgoQG3UvmoBqdIjp06cfkizUIpj+UDbikSLClnVv2dnZB4dpMzIyaNljNTExkX//+9/s2bMHk8nEtm3b+OSTTw6WPmvhmWee4fLLLz+YfHQ0hqSnMSQ98sM7/kCAJq+XRo+HJq+XBreHRo+Herebeo+HWpeLerebOpebWpeLGpeLmmYX++vqqGpqps7tbtdvjMVCitNBalQUaU4nqVFOMqOjyYqJJismhugO3rcG/KL/EG6b8ylf79nJlNzIJpoMiE/DYTTxfYlyAQUYOHAg7733Hpdddhk2m42nnnqKpKSkg0lxd9xxx8H/V4ej0JnOO3sX4vZ7MeuNHb52li0dvdCxq3EfI+OHKOp3hrUHAEVNO+gZNUiRLUCaNZh0U9y8LmwBBUiyT2VnzX9w+cqwGJLC9qcUJUPgmoBqAD+NNFsSiIzGw/8nzsvLY+DAgfz973/nscceo7a2lhdffJGnnnoKIQRVVVUkJCSQkJBAQ0MDl156Kffcc88h1yoqKmLZsmXMnDmzs26tw+h1Opzm4DCtGjx+P9XNzVQ2NlHe1Eh5QyNlDY2UNjRQ0tDAgbo6fth/gBrXoXsoRlvM5MbGkR8fR158HHnx8fRKTCTV6dDmcttwWo9C0hxOXlq7MuICatDpGJWcrbqgwt13382jjz7KZ599Rp8+fVi0aBHXXnvtwQfNO+6446hZ7T2dGfikn52NJfSOyjziua0x6YxkWNPY1ah8a7Z0W0hAm9UJaJw5F5POzoGmtRRGTVFs35Zk+2nsrPk3ZY1fkRXdbqn0YwZNQDUOYd++feTm5h78j240GsnKymLHjmCSwbnnnsuYMWMOJkO88cYb3HTTTaSkpBATE8OMGTMOznlu3LiRW265hZqaGqKjoznvvPO48spDJ+j/+te/MnDgQHJycrruJjsJk15PssNB8hGWKgA0eDwU1dSyt6aGvaH3HVVVfLNzF++t33DwvBiLhd5JifROSmJgagqDUlNJi3Ke0KJq0Om4ut9g/rJkIRsryuiTENkIZWxKDnOLtrGvoYZMR4wi27POOouePXvyyiuvUFJSwu23387ZZ5998LjVaj2qj8Ko4HbHW+uKFAkoQK49i1XV6xQnEln1dhLNaexrUjcPqhN6UiI4D+ow5eEwFlDS+LkmoBo/LzIzM2lsbDw4B+r1Hloe7NVXX8XQak6vT58+fPDBB9TW1qLX60lJScEUypAcOnQob731Fi6XC7PZTH5+PuY20d2tt9568PwTBYfJRK+kRHol/XQHjJrmZnZUVrGpvJxNZeVsKivjjdWreWlFcOgvwWZjYFoqIzIyGJmVQe/ExIjPBR7rXNJnAE+u+J4X167k7xNPj6jvk1JzAPi2eBeXFQxWbF9QUPCTgiGtue+++7jnnnuIiopq93iqJZYog43N9UWcq/DaufZMvin/nkpPNQnmoxduaE2mLZ9t9WsVi28LadaBLGlcSpOvCptB2bXbI8VxNtur/0GTdy82o7plRV2BJqAaP6G1yLWdl4yOjv7J+fHx8e0uP7FarfTt2/eI1+rZs6fKXh6fxFitDM1IZ2hG+sE2r9/P5vJy1hSXsKa4hB8OHGDe9uCIgNNsZnhGOuNzczilRw/So9v/w3w8EW22cFGv/ry5cQ33jBxHkv3IEb8S8qLiSbU5+a54tyoBbcHr9SKlxGg0IoTA5/NhMBiYOXMmw4YNY/r09pd8CCHoGZXBljrlazp7OII1fXc17lUloKuqF6re2izdNgiA/U2rKYiaqNi+LamOs9le/QTFDR+RF3t72P46ixPr0VVD42eIUa+nf0oKVwwexN/OOI25113Loptv4ImzzuDMnoVsr6zkj3O/ZsJzL3D6y6/w1wULWV9S2qU1RbuaXwwYgi8Q4JX1P0TUrxCCcam5LCrZje8o85WHIxAIYDQaMZlMB9dUtyQSnXnmmXz//ZE3ju4VlcGuxlLcfmXF4XNsGeiFjh0NyjcHz7IVAqgexk20FGLS2dnftEqVfVsshhTiLKM50PAhUqr7PXQFWgSqofEzJNnh4OzevTi7dy+klOyurmH+zp18s2MXL61YxXPLVpAVE82ZvXpyZs+e7Q4X/5zJiY5lam4Br29Yw61DRmIzRm4aYFxqLu/uWMu6ymIGJ6Yf3aAVs2fPZu7cuYwaNQqdTsegQYPIy8ujqakJm83GhRdeyAMPPHBEH72iMvHLANsalBVUMOlNZFrTVQloqiULgzCyp2krA2JGK7bXCT1ptoEUNUXugSbNOZ315XdR7VpBnLVjS9y6Gk1ANTR+5gghyI2LJTduKNcOG0pNczNfbdvOZ5u38uzS5TyzZBl9k5O4sH8/zundi6jjZNnM9QOH8eWubby/dSNX9B0UMb9jU3MQwIIDOxULaG5uLqtWreKjjz4iNTWV5uZmdu/ezciRIxk1ahRlZWVs3rz5iD56OYPJQ5vrihRXJOrhyGJZ1WrFc5kGnZEMaw/2Nh6+QMnRyLANYXfD99R5S4iKxHIW2yQMwsGBhlmagGpoaHQNMVYrFw3oz0UD+lPZ2MRnW7bwv7Xr+ePcr/nLNws4vbCQXwwbSt/krl9jF0mGpqTRNyGJ19av5vI+AyOWnRxrtjEwIY2FxTv51cBximx79erF/PnBPS03b95MUVERUkpWr17NggULcLlc5OfnU1tb224+AUCiJZoEcxSb6pQvSclz5PB12SJK3RWkWJSNOmTbe/Jdxef4Al4Muo6vQW0hwxasDlXUuII+MWcptm+LXmcl2XEmxQ0f0TP+fow65UUeOhttDlRD4zgm3m7jqiGD+fjqK/jwyss5v18/5mzbzrmvvs6V7/yPBTt3/WznSoUQXNVvMFuqKlhWrHwnkiMxIbUHayqLqXE3q7IPBAL06tWLyZMnM2XKFO666y4+/fRT5s6dy1tvvfWTYiJt6R2VyaZa5YlE+Y4cAHY07FZsm23viV/62N+8U7EtQKwpG7shgX1NK1XZt0e683wC0kVJw2cR8xlJNAHV0DgBEELQLyWZP02ZxLc3Xc89E8axs6qaGbM+4MyZr/LFlq0EfoZCek5+L6JMZl5dvzqifiek9SAgJd+pLKrQUiyk9cNJIBBASklBQcEhS8Hao3dUFkXNFdR5O1anu4UMaxomnZFt9cr7nR1KJNrduEWxLQT/jWXYhrC/aVXEEn+iTP1wGAs5UD8rIv4ijSagGhonGFEWC9ePGM78G2bwtzNOwxcIcNvHn3Luq68zb/uOn1VEajUauah3P77YuZWShvqI+R0Qn0qMycI3B9RFYy20HlbW6XQdHmbuHRVc+7hZ4XIWg05Prj1LVQTqNMYQb0pmT5M6AQXItA/D5a+j3L1NtY/WCCFId15AnWc9de6NEfEZSTQB1dA4QTHp9Uzv24fPf3E1fzvjNBo9Hm784CMufONt1pWUdHf3OsxV/QYTkJI3Nq6JmE+9Tsf4tB58c2BHt0TmvaMy0CFYX6s8o7bAkcuuxr34Asp3Ysqx92JX42bVD1EZtmEA7G1cpsq+PVId56ATZorq34mYz0ihCaiGxgmOQadjet8+fHntNTx86hT219Vx3mtvct+Xc6hsUjaE2B1kRcUwKSePNzesweVTt31fe4xP60Glq4lN1aUR89lRbAYL2fZkVYlE+c5cvNLH7ibl88I59t40+uoodx9QbAtgM8SSaC5gX+NyVfbtYdRHk2I/i+KGj/H6ayLmNxJoAqqhoQEECzZcPKA/X824hmuHDWXW+g1MeeFlXlu1+pifH72m/xAqXc18uv3IS0SUMD41F4CFB9TNg4ZL3+gsNtbuVRwNFjiC/VYzD5pr7wXArkb1P8dM+3BKmjfg9jeo9tGWrOirCEgXRfXvRsxnJNAEVEND4xCcZjO/PWUCn1x9Jf1Tknlw3tdc/va77Kmu6e6uHZax6VkUxMbz8rpVEZvDTbQ66B2bxMLi8OZB1dInOpt6XzP7msoV2cWbYok1RrOtQXm/E81p2PVOdjduUmzbQpZ9JJIARRHMxnWaComzjGFv3esEpCdifsNFE1ANDY12KUiIZ+aF5/PoaVPZVFbOWa+8yqurfjgmo1EhBNcOGMqGijKWFyvfkPpwTEjrwYqyIhq87e/12hECMkCNR/lQeL+oYG1bpfOgQggKnXlsrVcuoEIIch192BWGgKZY+2LS2dnTuFS1j/bIib4Wj7/8mFrS0mUCKoR4SQhRJoRY386x/xNCSCFEQqu23wohtgshtgghTm3VPlQIsS507J8ilNYmhDALId4JtS8VQuR0yY1paBzHCCG4oH8/Pv/F1QxLT+dP8+ZzzbuzqGhs7O6u/YRpBb1xmswRTSaakNoDnwzwfYnyZJ4WrvpuJv+3/D3Fdln2RBwGKxtUJBIVOntQ7q6kxlOr2LaHvTdVnjJqPBWKbSFY1i/TPoy9DUsjWsc2zjoGh7GQ3bUvHTOZ4l0Zgc4ETmvbKITIBKYAe1u19QEuAfqGbJ4WQuhDh58BbgAKQq8WnzOAaillPvAE8Fin3IWGxglIWpSTly44j4emTmblgf2c88rrLN2nfKF/Z2I1Gjm/Zx9m79hKZXNkkp+GJGbgMJr4Zv8O1T56RSXzQ9U+vAG/Ijud0NE3Oov1tbsVX7PQGdwke0u98n7n2oMbgO8MIwrNsY+hyV9FmUv9kpi2CCHIjv4Fjd7tVDQviJjfcOgyAZVSLgSq2jn0BHA30PqR4lzgbSmlW0q5C9gOjBBCpAJRUsrFMvgI8iowrZXNK6HP7wGTWqJTDQ2N8BFCcMnAAcy6/DLsJhNXvvMeTy9eekwN6V7eZyCegJ//bf7JQJcqTHo9J6XksuDATtVRz9CEbJr9XjbWFCu27Redw67GUuoVFlToYc/CKIyqBDTVmoVVb2dnw4ajn3wYsh2jEOjY1fCdah/tkeI4E4s+ld01L0TUr1q6dQ5UCHEOsF9K2XbMJR1o/XhbFGpLD31u236IjZTSB9QCP92kMnjdG4QQK4QQK8rLlU3Qa2ic6PRKSuSDqy7nzF6F/OO7Rdz4/oc0eI6NxI6CuARGpWXyxoY1ERP2k9PzONBUx5YadX8rhsUH5zJXVO5WbNs/VExe6TyoQWcg35HD5jrl25PphJ5ce292NqgvXGDRR5FmG8iuhkWqfbSHThjJjv4FNe6VVLsil6Skuj/ddWEhhA24D/h9e4fbaZNHaD+SzU8bpXxOSjlMSjksMfH42uZJQ6MrcJhM/OPMM/jj5Iks3LWbS998hwN1kasEFA5X9B3IvvpaFuyNzPKTk9OCw6HzVQ7jJlqc5DriWV6hfC6zd3QWeqFTNYzbKyqPXY37cPmVJ0D1cPSlwlNMjadSse2PPsZR7dlDtUf5WtYjke68AKMulp3V/4moXzV0ZwSaB+QCa4QQu4EMYJUQIoVgZJnZ6twM4ECoPaOddlrbCCEMQDTtDxlraGhEACEEVwwexPPnT2dfbS0XvP4ma4u7v4LRqbkFJNnsvBqhzbaTbU76xibz9X51m00DDE/IYWXlHnwK50GtehMFznTW1uxWfM2eznwCBNjWoPxBIt/RD4DtDesU27aQ6zgJgJ31C1X7aA+9zkpuzA1UuRZT2bw4or6V0m0CKqVcJ6VMklLmSClzCArgECllCfAxcEkoszaXYLLQMillMVAvhBgVmt+8Cvgo5PJj4OrQ5wuAr+WxkqqloXEcMz43h3cvvwSTQc+lb7/Dl1sjUwdVLUa9nsv6DOSbvbvYXVsdEZ8TM/JZVbGfare65KSRCbk0+jxsUDEPOiAml011e/EoLM3X05mHQLCpTvnvI8WShd0QxfaGtYptW3AYE0m29I64gAJkOC/Fok9le9U/ujUjtyuXsbwFLAZ6CiGKhBAzDneulHID8C6wEfgCuFVK2fLodjPwAsHEoh3A7FD7i0C8EGI7cCdwb6fciIaGxk8oTEhg1uWX0Tsxids//pSPNqrP4IwEl/UZgF6ni9guLRPT8wlIyQKVxeWHJ+QAsKxCeTQ4IDoHT8DHljplpflsBis59kyV86A68h392Va/LiyB6uEcT7l7G3Ue5Q8OR0KvM5MXezt1nvWUNn4RUd9K6Mos3EullKlSSqOUMkNK+WKb4zlSyopW3x+WUuZJKXtKKWe3al8hpewXOnZbS5QppXRJKS+UUuZLKUdIKbunfIiGxglKvN3GKxedz4jMDP7vs9m8tVp99BIuSXYHp/co5H+b19PoDT/BaUB8KgkWO3OL1A3jJlgc5DsTWVaxW/m1Y4Kl+dbUKP+T1suZz9b6nXgDXsW2BY7+1PtqKHGpn8Ps4RgPwI6GyEehqY5zsBvz2V79ZLdVJ9IqEWloaEQMu8nEC+dN4+QeuTwwZy7PL4tcUXGlXNN/MPUeNx9vC78+rk4IJqbns/DATsXrOVsYkZDLqkrlQ7ExJgc59mTW1CiPXvtGF+KVXnY0qCnGMBCAbWEM40ab0kg0F7Kj/hvVPg6HEHoK4+6i2beXorq3I+6/I2gCqqGhEVEsRiP/mXYOZ/Qs5LEF3/Kv77sn0WNIcho94xJ4K0KViSZl5FPvdbOiTPkuJwAjE3No9ntZX618p5OBMbmsq9mlOAmplzMfgI11WxVfM8aUQJI5na314f388p0nU+baTJ038glm8dZxxFlGs7Pmabx+5VWXwkUTUA0NjYhj0ut54qwzOK9vH55atJiXV6zq8j4IIbi0zwDWlpeyvjz8LcnGpuRg0ulVZ+MOT8hBAEvLlUeSg2LyaPK72d6gTHydRgdZtnRVAgrBKHRnw0a8AfW1gPOcJwOwo26+ah+HQwhBYdzdeAN17Kjp+mUtmoBqaGh0CnqdjkdOm8qphQU8PP8b3lmrfkmEWqYX9sFqMPD6htVh+7IbTYxMzlItoDEmG72iU1iqIpFoYGxwLeoP1crXovaN6smW+h2q5kELnYPwSS87G9QnhUWZUkm29GZr/TzVPo6E09yLDOcl7Kt7nVp31867awKqoaHRaRh0Op446wwm5OZw/5dz+GRT5Pbr7AjRZgvn5Pfmo22bqHOrj6JamJSRz866KnbVqVtiPiqxBz9U7aPZpyzpJcEcRZYtkdXVyhOJ+kYX4gl42dawW7FtnqMPBmFkS314a2oLo6ZQ6d5BpbtzcjsL4u7ErE9iY/kDBKTyBwW1aAKqoaHRqZj0ev5z7tkHs3PnbldfmF0NV/QbSLPPx/tb1dd2bWFSenBOcZ7KbNxRibl4A35WVSnPbB0Um8eamp2K50H7RBUiEGyoVf7wYtSZyXP0ZXOYAprvnIgOPZtrvwzLz+Ew6Bz0in+ABu9W9tS+1CnXaA9NQDU0NDodi9HIs+dNo29yEr/65DPWlXRdxaL+iSkMTErhtfWrw150n+GIoWdMInOL1BWLGBqfjVGnZ3GZ8khscGgedGu9sv1O7QYbufYs1teq2xmlp3MwFe5iKt3qf2dWQzTZjtFsrZuDXyrLQu4oSfZJJNlOZUf1f2jwdM1DmiagGhoaXYLDZOK586YRb7Nx/awP2V9b12XXvqrfYHbUVPH9/vDrsk7OKGB5+T5VVYlsBhOD4zJZVKb8D/zg2DwAVlUrj377R/diW8NOXH6XYtveUUMA2FQXXiJY7+jTafZXszfCG223plfC/Rh0djZW3MePtXc6D01ANTQ0uowEu50Xzp+O2+/nuvc/oD4C85Id4cy8nsRZrBGpTDQlo4CAlHytsrj8mKQ8ttSVUuFqUGQXZ3aSa09RJ6AxvfHLABtVlPWLN6eQaE5jc5gCmmUfiVUfy+ba2Uc/WSVmfQI94++j1r2G3V0wlHtUARVCxHXgFdPpPdXQ0DguKEiI5z/nns2uqmpu/fATPP7OjxQsBgMX9+7PnN3b2V8fXuTbPz6VZKuDOfvULQ0ZkxjMqF1crnwYd0hsPmtrduH2K0uU6enMwyiMrK1Rl03bO2ooOxo34PI3q7KH4DZpPaOmsrthMU2+ztvnI8V+Jsn209hR/VSnb3nWkQj0ALACWHmEV/fV7NLQ0PjZMSY7i7+cOpXv9+7lwblfd8k1L+szECkl72wK78+VTgimZBay8MAumn3KMz77xKQSa7KxqEx5JDksrgBPwKd4f1CTzkifqALW1Krb47NP1DD80sfW+tWq7FvoHXMGkgCbazuvfq0Qgt4Jf8JiSGdd2Z14/Oq3ZDsaHRHQTVLKHlLK3MO9gM7roYaGxnHJ9H59uHnUCN5Zu453u2CNaGZUNOMzc3hn83p8gUBYvk7NLKTZ7+XbYuVrOnVCx5ikHiwq20FAKuvH4Nge6IWOlVXKh2IHxPThQHMJFW7l0V+2vSd2vZP1tcsU27Ym1pRFmnUAG2s/Qyq8dyUYdU4GJj2JN1DLurK7Om0+tCMCOjpC52hoaGgcwq/GjmFsdhZ/nPt1l+wlelnfgZQ2NjBvT3hZmiOTs4g2Wfhyn7rM1rFJ+VS6G9lcq6xCks1goU9UFiuqlA8fD4zpA8CaGuXLefRCT++oYWyuW4lPRUGG1vSJOYs67wGKmiKzX+vhcJp70yv+fqpci9lR8+9OuUZHBDThcAeEEGdDcCeUiPVIQ0PjhEGv0/HEWWeSaLdzy0cfU9HY2KnXm5SdR4rdwRsbwqvvatTpmZxRwNyi7aqKy49NCmbUfluqPJIcFlfAlvr91HqV/awyrKnEm2L5QYWAAvSLGYkr0Mz2hvWq7Fvo4ZiAWRfFhpqPw/LTEdIc55PmOI9dNf+ltPGriPvviIDOE0LktG0UQlwLPBnpDmloaJxYxNmsPD3tHGqaXdz+8ad4OzGpyKDTcWmfASzctzvszbanZhZS53GxtFT50phEi5M+0amqBHREfE8kkhUKh3GFEAyK6cf62s34FO4IA1DoGIBZZ2Vd7RLFtq0x6Ez0iTmDXQ3fUd8JBeZbI4SgV/zviTYPYn35XRFPKuqIgP4amCOEKGjVqd+G2idEtDcaGhonJH2Tk3jk1CksL9rP3xZ+16nXuqT3APRC8GaYUei41FwsegNfqszGPSk5nzXVRdR5lGW29orKxGGwsrxS+XUHx/al2e9ic73yIWyDzkjvqCFsqF2OP8w5xX4x0wBYV/1hWH46gl5nZlDy01j0qawuvZUGj7oqUu1xVAGVUn4O3ATMFkL0E0I8CZwFjJdSqtvXR0NDQ6MN5/TpzeWDBvLSipUs2Kk8OaejJNsdnJpbwDub19PsVT+fZzUYmZDWgzn7thJQUeFoXHI+fikVL2fRCx3D4wpYXrVVcWWlftG90As9q6vVDcP2jx5Nk7+enQ3hlUV0GpPJc05gY+2nePydO2wPYNLHMiTlBXTCyKqS62n2Kd9Srj06VEhBSjkPuAb4BugBTJJShjf+oaGhodGG350ygV6JCfzf519Q2qCs0IASru4/mFq3i0+2h1fc/tTMnpQ2N7CmQsUen7EZRBktLFQ5jFvurmVnQ7EiO6veQt+oQlbWqMt67hU1CJPOzJqa71XZt2ZQ3MV4Ao1srP0sbF8dwWrMYEjy8/hlI6tKrovI8paOFFKoF0LUAbOBKGASUNaqXUNDQyMimA0Gnjz7TFw+L//32Wz8YS43ORwjUjPoFZfAzPU/hFUfd1JGPkadjtl7lWfjGnR6TkrKZ2HpNsXLWUbG9wRgSaXy6w6O7ceB5hJKXOWKbY06M32ihrG+dlnYNW2TLD1Jsw5kbfWsTquP2xanuReDkp/G5StmRfFVuHzh7RPbkSFcp5QyKvRuklLaW32PCuvqGhoaGm3Ij4/nD5MmsnjvPp5e0jl1U4UQXNlvMBsrylhVqn44L8pkYUxKDrP3blYlxOOTC6h0N7KhRlkkmWCOJt+RypJK5RH0kNgBAKysVldQYkDMGJr89WyvD3/t7qC4i2nwlbG9rmuKaQDEWoYxJPk5XL4SlhdfTqN3t2pfHYlAj1oAsSPnaGhoaHSU8/v1ZVqf3vxz0WIW7w2/AHx7TCvsjcNo4vUwk4nOzO5NUWMtayuViSDA+JQCdAjmFyuPJEcn9GF97W7qvMqK2qdYEsmwprGiSt1993IOxqq3s7pmkSr71mTbRxJn6sGqqjc7tbBCW2Ktwxma+gr+QBMrDlxBvVtdicOOzIH2FkKsPcJrHUdYK6qhoaGhFCEEf5oymZzYWO6Z/WWnFJ23G02c37Mvn23fQlWz8p1VWpiaUYBRp+OzPcqjwRiTjSHxWcwvUS6gYxKCBeKXqhjGHR43kM1126n3Kp9nNuiM9I8exbrapXgC4f1ehNAxNP4yqj172NUQ/ryqEqLN/Rie+jo6YWJF8VVUNit/IDB04JxeHTin86tBdxIHaur4w8dzD34XSh2II1sczZ9oY9/2/KMfP/RI2+6INu0Cccg5QoiDPls+t1zz4HvI/kdbcch3XYsPIQ5+1gkR+h46HnrpDtoFz9XrWn0OndPSphc6dLpgu16nC52vQ68Lvht0Ap1OhyHUZtDpMegEBr0evU5g1Osx6HQY9TqMev1PfpYaxzY2k5G/nnEqF7/5Dg99/Q2PnX5qxK9xRd+BvLL+B97ZvI6bB49U5SPabGVcai6f7dnEvUNOCf7bV8ApKT3524av2N9YTbo9tsN2vaIyiTHa+b5iI1NSBiu65vC4QXywfzYrq9dyctIYRbYAg2PHsaxqHutrlzEkdpxi+9bkOU9macVLrKh8lVzHGITouk3C7KYeDEt7ndUlN7Gq5AbyY39FTvR1HbY/qoBKKZVVLf6ZUe9y8/VmdWW9jjblITnyCW3t257/U//tH5cHv8t2z27dLqU8pP3Hz6EeyGA/fvQtj3qfPxcMOh0mg/6goJoMBkx6PWaDHrPBgCn0bjEaMBuD71aj8eC7zRR8WU1G7CYTdrMRh9mE3WzGaTERZTFjMnTkmVSjowxOS+PmUSP4z+KlTMzrwamFBUc3UkBBXAKj0zJ5ff1qbhg4HL1O3R/vs7L78PX+T1hVvp9hSRmKbCemBgV0XvFmrsrveFVUvdAxJqEPC8rW4Q34MOo6/m+vhz2LBFMcS6t+UCWgufbexJmSWFE1P2wB1Qk9w+OvZl7JX9hRv4D8qFPC8qcUqyGN4WlvsbHifrZX/4M6d8eX+Jzw/9t7piTy7d03dnc3fhZIeajA/iiukoD8UZCllASkDL23nBc898d2echnf+izP/DjMX8gEPwcCH72hz77AoGD3/2hz77QOT5/AF8ggNfvP/jZ5w9+D74CePx+PD5f6D34cvt8uH1+6t1uKhubaPZ6cXt9NHt9NHu9eHwdG2QxG/Q4LWairRZirBaibRZirFZi7Vbi7TbiQu8JTjuJDjuxNis6nRYZH4nbRo9iwc7d3P/VHAanpZLkcETU/9X9B3PTlx8zd88OTs1VJ9BTMgsw6w18vHuDYgHNdsRTEJWkWEABTkrsy+fFy1lTs5NhcYUdthNCMDJ+CF+WfEOTrxmbwaroujqhY2jsycwt/R9VnjLiTEmK7NtSEDWJH6reYmnFS+Q6x6EXXStNBp2d/on/IMo8gO1Vf++4XSf2SeM4IzgMCyoGun/2+AMBmj1emrxeGt1emjweGt3BV73bQ4PbQ32zmzqXi3qXm9pmFzVNLvZX17F+fynVTa52S9QZdDoSHDaSo5ykRjtIiXaSGu0kIzb64MtmMnbDHR87GPV6/n7m6Zz76uvcM/tLXrzgPMXDpEdick4+aQ4nr6z7QbWAOoxmJqXn8/mezfx+2BQMCiPZSSm9eG7rt1S5G4kz2ztsNyyuALPOyLflGxQJKMCo+CF8VjyXldVrGZeofPh6WFxQQFdUfcPUlIsU27dGJ/SMTLyO2fvvZ1Pt5/SLOScsf2oQQpAT/QuiTH2AUR2y0QRUQ6MD6HU6HBYzDosZnMrtpZQ0uD1UNjZR2dBEeX0j5Q2NVNQ3UlbfSEldPZtLKvhm6y5c3kPXxCU4bOQmxNEjITb4nhhHz+QEEp32E2ZeNy8+jntPHs8f537Nm6vXcMXgQRHzbdDpuLLfIB5b8i2bK8vpFZ+oys/ZOX34fO9mFpfuYVxqriLbyWm9+e/WhXxdvJkLcoZ22M6iNzEivpDvyjdwR+G56BTMH+Y7cog3xbKkcqUqAY01JVLgGMCKqvlMTj4fndAr9tGaHPsYUqz9WF4xk8KoyZh0trD8qSXO2vGfhSoBFULcAfSTUl4vhHhASvnnDti8RLAEYJmUsl+o7W/A2YAH2AH8QkpZEzr2W2AGwQSlX0opvwy1DwVmAlbgc+AOKaUUQpiBV4GhBPcnvVhKuVvN/WloRBohBE6LGafFTE784RNFpJRUNzVTVF3Hvupaiqpq2Vtdw+6Kar7YsI3a5h83PoqxWeiVnEiv1ET6pacwID2ZjNjo41ZULx80kLnbd/C3Bd8yMa8HaVGRW4Z+ae8BPLViMTPXreLRk9UlK52SnofDaOKT3RsVC2jv6BQybDF8dWCTIgEFGJfYj2/LN7C5rog+0VkdttMJHaPih/BlyQIafU3YDcoFa0T8JF7f8w+21a+lZ5SyRKa2CCEYk3gz7++9ldVV7zIi4Zqw/HUFaiPQPGBf6HNHn8dnAv8mKHItzAF+K6X0CSEeA34L3COE6ANcAvQF0oC5QohCGdwV9RngBmAJQQE9jWCVpBlAtZQyXwhxCfAYcLHK+9PQ6BaEEMTZbcTZbQzISPnJ8erGZraXV7K1tILNJeVsKSnnrWVrcPuCS7FjbVb6pSczLDudYdnp9EtPPm4Sm4QQPDR1Mme8/Cr3fTmHly44L2IPCzEWK9ML+/D+lo3cM2o8sRZlc4IAZr2BqZmFfLF3C38ecSpmfcd/7kIIJqf15vUdS6n3unAaLR22HZPQB73QsbB8vSIBBRgdP4zPiuexomoNE5KUb+vcJ2oYdkMUS6vmhS2gACnWPuQ5J7C66h36xpyF3XBsr5BUmy8sAasQoh9BgTu6gZQLgao2bV9JebCG0xKgZfb9XOBtKaVbSrkL2A6MEEKkAlFSysUymFr6KjCtlc0roc/vAZPE8foornHCEmu3Mjwng8tHDuLP507h3RsvY/l9tzLr5sv5w9kTOaVXDw7U1PHE3EVc/uK7jHjkaa55+T1e+HY5W0rKwypbdyyQER3N/40/iW9372HW+vAKmrfl6n6Dcft9vLtJfYWdc3L6Uu91M3+/8sz+U9P64JMBxWtCo4w2hsTms6BsneLfb74jh0RzPIsqliuya8GgMzIs9mQ21i6n1lt11PM7wqiE6wngZ3H5cxHx15l0pBJRe49RfyeYSXIl8LsI9eVagpEkQDo/RrgARaG29NDntu2H2IREuRaIb+9CQogbhBArhBArysuV14PU0DiWMOr19ElN4pLhA3l42lQ+vf1qvr/nJv516dlcMnwgNU3N/H3Od0x7+nVOfvx57vvwKxZs3dXhzOJjjSsGD2J4RjoPz19AcX19xPz2ik9kZFoGr21YrboG79iUHOItNj7apVzc+8emk2KN4qv9GxXbnpw0gP3NFexQWFxeCMGY+GGsq91MnVfdz3Jk/GQCBFhRNV+VfVuiTekMir2QrXVz2N+0OiI+O4uORKDL2jZIKfdKKe+VUt4jpQy7zpYQ4j7AB7zR0tTOafII7Uey+WmjlM9JKYdJKYclJqpLGNDQOJaJtVuZ3Dufe0+fwIe3XsmC/7ueh6dNZUh2Ol9t2MZNr3/I2Mf+y93vzWbOxu14fF1TzDsS6ITg0dNOxev3c/+XcyIaVV/TbwhF9XXM26Nsi7EWDDodZ2f34ev926nzuI5u0Aqd0DEltTfflW2nwavMdlxiX/RCx/wy5fVtT0oYQYAAiyvVbTadYE6lwDGAJZVzwt4ntIWh8VfiNKawoPQJ/AFPRHx2Bh0R0E4dBhVCXE0wuehy+eP/hCIgs9VpGcCBUHtGO+2H2ISi5mjaDBlraJyoJEU5OG9IX5646EwW3XMjz1x+LlP7FvDt9t388u1PGP+35/nzp1+ztqjkZzHMmx0bw90TxrFg127e36A8YjscU3KDS1pmrlNf3nt6j354An5Vpf1OS++LJ+Dna4XDuDEmB4Nj8/i6dI3i31+WPZ0sWzrflf8kVuowYxJOo9ZbyfrayBT/N+osTEj+NTWevaysejMiPjuDjghoohDizsO9wrm4EOI04B7gHCll62KUHwOXCCHMQohcoABYJqUsBuqFEKNC85tXAR+1srk69PkC4Gv5c/hLoKHRxZgMBk7u2YOHp03l27tu5LkrpzM2P5tZq9Zz8XNvcc5/XuPt5Wto8qjfbLoruGLwIIamp/HoNwuoamqOiM+WJS3f79/L5kp10zv941LIj47n/Z3K51IHxmWQYo3iy/3Kh4BPSRrI/uYKtjUo313mpIQRbG3YSUlzmWJbgN5RQ4g3JfNt+aeq7Nsjyz6CAudkVlW+QZW78zZYD4eOCKgecBDMtm3v1SGEEG8Bi4GeQogiIcQMglm5TmCOEGK1EOK/AFLKDcC7wEbgC+DWUAYuwM3ACwQTi3bw47zpi0C8EGI7cCdwb0f7pqFxomLQ6xhXkMPfLzyDhXfdwIPnTMak1/PgJ19z8uPP85fZ37C7srq7u9kuOiH485TJ1Ls9PLZgYcT8Xtp7ABaDgZfXqotChRCcl9ufFeVF7KlX9rPTCR2npvXl29Lt1HqUPRSMT+qHXuiYV7pakR0EBVQgWFi+RLEtBAshnJRwJnubtrGncasqH+32K+lWTHob80v+TiBCw8ORpCMCWiyl/JOU8sH2Xh29kJTyUillqpTSKKXMkFK+KKXMl1JmSikHhV43tTr/YSllnpSyp5Rydqv2FVLKfqFjt7VEmVJKl5TywpDPEVJKdZMYGhonKFFWCxcN6897N13Gm9ddzPiCHN5atoYz//kKv/nf52wtrejuLv6EwsQErhs+jFnrN/D9nshsexZjsXJ+YV8+2LaRiqZGVT6m5fZFgKoo9IyMfvhkgLkHlG2xFW20MzyukK9LVyveoDveHEv/6N4sKF+i2LaFYXEnY9XbWVj+sSr79rAaYjgp6TZKXRtYWz0rYn4jRbfPgWpoaBxbCCEYnJXG4xeewbw7r+PasUP5ZstOzv3Pa9z25sdsOFDa3V08hNtGjyQnNob7v5pDszcyw87XDhiKx+9XvVdoqj2Kk1JzmbVzPQGFM0n9YtLIssfx2X7l4js5ZTClrho21CrfA+SUpDFUeKpYX6t8ezQAs97KqPiprK9dRqW7RJWP9ihwTibXcRJLyp+n3LUtYn4jQUcEdFKn90JDQ+OYJNFp5zdTxzHvzuu47ZRRrNhTxAX/fZPf/O9z9lXVdHf3ALAYjTw0dQp7a2r556LFEfGZFxvHpOwevLb+B1wqM5TP69GP/Y21LCvbd/STWyGE4MyMfiwr3025S9nSkpMS+mLWGZlT8oMiO4BhcQOx623ML1O/UfbYhNPRCT0Lyz9R7aMtQghOSfk/rIYY5hz4M56A+r1bI81RBVRKqWWyamic4MTYLNx6ymjm/noGN44fwdebd3Dmv17h4c/mU9XY/X/QRmVlcvGA/ry4YiXrSyITIV87YCiVrmY+2a48mxbg1MyeOIwm3tuhfGnJmRn9CSD5rEhZFGozmBmX2JevS9fgDSgTfpPOyLjEkSyrWq1qo22AKGMsQ2MnsLxqPvXeGlU+2sOij2Zy6n3UevezoOQfx0ymeNftXKqhofGzx2Ex86vJY/nyjl9w3uC+vLV8Dac/NZN3lq8lEOjeP2r3njyeOKuVP8ydp3jYtD3GpGfRMy6Bl9auVPUH22owcmZ2b2bv3UyjV9laxh7ORPrFpPHpPuXDuFNShlDna2JJpXLhn5g0Fp/08W2F+uUoE5LOwS99fFfxuWof7ZFuG8TwhGvYVj+PTbWR9a0WTUA1NDQUkxTl4I/nTOajW6+kV0oif/xkHpe+8Ha3zo86zWbuPXk8a4pLeGeN+nJ8LQgh+EX/IWyqLGfJAWXDsC1cmDeAJp+Xz/YoSwgCOCujPxtri9lep2xpyfC4QmKNDr4sVl4YIdueQZ49m3ml36mO8hLNafSPHsXiii9o8kWuUhTAkLjLyLAN49uyfx4T86GagGpoaKgmLzGemb+4gEfPO5X91XVc9Oxb/GX2Nz/Zkq2rOLdPb0ZmZvC3hd9SGYGh5WmFvYm3WHlhzQpV9kMS0smLiufdHcqTkc7I6I9eCD7ep2wI2KDTMzllMN9XbKLWqzyLeHLyeIqai9lSr7yebwuTks/HHXBFdC4UgstlJqf+Dos+mtn776fRVxlR/4r7061X19DQ+NkjhODcQX34/JdXc8nwAby6+AfOe+Z11hZFLhNTSV8enDKJZq+Xv3yzIGx/FoORK/oNYt6enWyvVv7HWgjBRfkDWVm+n+21ypYBJVgcnJSUz6dFa/ErXFpyeuowfNLP3JLViuwAxiQMw6q3MKdU/draVGs2A2LG8F3F5zR4a1X7aQ+bIZYz0x/B5a/j86Lf4Q1EpoiGGjQB1dDQiAhRVgsPnDWRl64+n2aPl8teeJsn5y7q8jq7+fHxXD9iOB9u3BSRtaFX9h2ESa/nxTXqasWe16MfRp2Ot7etVmx7btYgSprrWFqurBJPvjONAkcanxcr32XFojczPnEUSypXUeutU2zfwpTkC/EGPHxd9oFqH4cjwZLP1LTfU+HezlcH/txtRRY0AdXQ0Igoo/Oy+OjWKzlnYG+eXbiMS59/p8uXvNwyagTZMTE88NVcXGGuDU2w2bmgZz9mbd1AmYrCCgkWO1MyCpm1cx1uv7KHiVNSCokyWvhgr/JlKWekjWBb/X621u9XbHtqysn4pI+5pd8ptm0hyZLOsLhTWFz5JdWeyO96leMYzbik29nTuJiFpU91S2auJqAaGhoRJ8pq4ZHpp/KvS8+mqLqW8//7JvM2qZ9TU4rFaOTPUyezp6aG55erm79szfUDh+H1+3lFZZH5SwsGUeNx8cVeZUUKzHojZ2X0Z+6BzdQpLO03NWUwJp2Bzw4oLxKfbk1hYHQf5pQswKdwOUxrpiRfiEDwVck7qn0ciX6x0xgcdxkbaz9heeXLnXKNI6EJqIaGRqcxuXc+s26+nKy4aG5762P+MvubLtuHdEx2Fqf3LOS/S5exrya8ebjcmFhOzS3gtfWrafAo315rTEoO2Y4Y3timPJKcnj0Yd8CneE2o02hjQlJ/5pSswuVX3ufTUydS7a1Vvc0ZQIwpgTEJp7GqeiHFzcqrI3WEUQnX0Sv6dFZUvsayipe6NBLVBFRDQ6NTyYiN5s3rLubykYN4dfEP/GLme11WfOF3p0xAL3Q8NP+bsH3dMGg4dR43725WvkRGJwSXFAxmedk+ttUoSybqE51Kr+gU3tujPPo9O20UDT4X80uVZwEPjOlDujWFz4rnhSVKE5OmE2dKpsqjbqeXoyGE4OTk3xwU0SUVL3SZiGoCqqGh0emYDAbuP/MU/n7hGWw4UMpFz77VJcXpU51Obhs9innbd/D1jvD2lxiSksbwlHReXLsSX0B5wfUL8vpj1Ol4a7uyKFQIwQXZQ9hUW8KGGmVblQ2MySXLlsTH+5XvsqITOs5MncSuxr1srFO/w4rN4OSuXk/SN3q4ah9HQyf0nJL8f/SJPpsfqt7k+/L/domIagKqoaHRZZzRvyevzbgIj9/Ppc+/3SXzotcMG0JefBx/mvd12MXmbxg0nP31dXy2XXnB9QSLndMye/LejnU0+ZQNqZ6V0R+L3sD/disbThVCcE76SDbU7WWbimSicQkjiTI4+eTAHMW2rdEJfVj2HUEIHROSf03/mOmsqX6X+SV/xS87d09bTUA1NDS6lP7pKfzvxsvokRDHbW99zEvfhZ/kcyRMej1/njKZoto6/rNYfYk6gEk5eRTExvPM6mWqIpwrew6l3uvmk93KKhNFmaycltaXT4vW0eh1K7I9PXUYZp2RD4uUF9o36U2clnoKP9SsZ2+jcgHuaoQQnJR0O8Pir2Zz3Rd8su8uXP7IrkNtjSagGhoaXU5ylIPXZlzEaf0K+dtX3/K3Lxd2ai3dEZkZnN+vLy8sX8H2SvXVa3RCcMOg4WyuLOebvcrWZgIMS8ygZ0wir21RXl/3otxhNPk8fKoimWhS8iDmlKyi3qu86MCpyRMw68x8fOArxbbdgRCCEQnXMDn1d5S4NjJrz61UuyOzV2xbNAHV0NDoFixGA49fcDqXjRjIS4tWcu8HX3Rqhu7dE8ZhMxr587z5Yc2PnVvQmzSHk6dXKY9mhRBc1XMoG6pLWVmuLKIbGJtBz6hk3t61XHH/p2eMwRXwMltFYQWH0c7k5JNYVLGcUlfk13N2FoVRUzg34+94Ak3M2nsLO+vVr2k9HJqAamhodBt6nY77zzyFOyaN4ZM1m7nljY9odCtfctER4m02fnXSGBbt2cuXW9UXIjfp9dwwaDjLS/azVEWR+Wm5fYkyWZi5RdnQtRCCS3sMZ0tdKaurlF23Z1QGA6JzmbVvkeKygABnpU1BL3R8uP8LxbbdSaqtPxdkP0O0MZ0vDjzAwtIn8QWUDYEfCU1ANTQ0uhUhBDdNGMlD06awZNdernv1fepdkfsj15rLBg2kV2ICD89fQJNHfYLJJb37k2C18e+VyrNbbQYTF+UN4Iu9mznQqKxU3pkZ/XEYzLy5S3kkeUHmSRS7qlhUvkGxbZwphonJJ7GgfDFlrs7Pno4kTmMy52X/m4GxF7G+5iPe23MTFa7tEfGtCaiGhsYxwflD+vGPi85k/f5SZrwyi9pmV8SvYdDpeHDyJIrr6/n3YuXi14LFYOS6gcP4tmgPa8uUF82/uudQJPDaVmVZtXaDmelZg/hy/wbKXcq2CjspsS8pllje3fetIrsWpqWdig4ds4oitxdnV63X1AsjY5Nu5qyMx3D563hv7y2sqHgVfyC80Q5NQDU0NI4ZpvYp4J+XnMXmkgp+MXMW1U2R32ljaEY6F/Try0srVrKtQn1C0eV9BxJlMvMfFXOhGY4YpmYW8ta21YqXtFzaYwQ+GeCdXcqGgA06PRdkjmNtzS421Skfeo4zxzI5eTwLy5dQ3Bzevq/V1dVAcPTB7Q6ONni9Xurq6ti5c2enCWuWfQQX57xED8dJLKt8mXf2XMf+ptWq/YnuKMB7LJFZ0Ev++h/Pdfh8gVDkXyg7/af+j2Iv2lxAtGn/ibk49DpCHPq5XZ9tfB20Cb0LETxHtD639bHW54hgJiMIdK2/h96DbeKnn3WHvut1wWN6oUOnC37XCR16vcCga2nTYdTr0Ot+fLV8N+h1GPV6dDqFvyCNLuHbbbu57a2PyY6L4aVrzifBYY+o/8qmJqa++DKFCQm8eclFP/k331H+sew7/rlyCV9dfA2FcQmKbJeV7ePir17noRGncnnhEEW2Ny1+gw01B5g39deY9IYO2zX5XFyw6GGGxxXyYP8rFV0ToMZTyy9/eIBhsQP5ZeEMxfYtPPbYYyxbtoxZs2YBsH37dv773/8yb948LBYLXq+XBQsWYLdH9vfemr2Ny1hY+iR13mIKnBMZlXgDTmMyAEKIlVLKYUfzccILqD0lS/a9+q4Onav0Z6X0J9vWvTyah8Oc3+Knrf0J/qtuF50QGPU6jAY9Rr3+4GeTwYDJoD/4MhsNP74MeiwmIxajAavJiMVkwGY2YTMZsZmN2Mwm7GYTDosJuyX4bjOZNLFWyJKde7nljY/IjIvhlV9cSIzNElH/76xdx31fzuHxM05jWt8+qnxUu5o56fXnmJSdxz+nnKXIVkrJtNkzqfd6mHvODaEHy47xfdkOrvv+NR4efC7Tswcruu6z2z/nrT3f8Prou8mwKRN9gLf2fsiH+7/gsQH3kWPPVGzfQr9+/Tj55JO5//77mTZtGr1792bMmDFceeWV3H777ZSUlPD+++9jNBpVX+No+AJuVlW9yQ9VbyGBfjHnMCTucuzGOE1AO8KwYcPkihWdu5D7WEVK+ROx/Yn4tnNcyuD34LnykO8SCW3OaX1uIHTNQPBA8DvyYF8CMnhOIBBsDwQgIAMEAvKQY34ZQAbAHzrmD0gCMoA/IPEHAqGXPPju8/vxtbz7AwdfXr8/+PK1fA6+e7w+PD7/wZfb6wu+fD5coc8uT/BzR9AJgcNiIspmIcpqJtpmIdpuJcZmIdpuId5hI67l5bSSEGXHaTGrjoyOFxbv2MtNb3xIz+QEXrr6fBwWc8R8B6TkgtffpKS+gS9nXIPTrM73o0sW8uwPy5hzyS/Ij41XZPvJ7o388ruPeG7CBUzJLOiwnZSSafOfQYfg/VNuUvTvpMJdx8WLHuGMtOH8ptf5ivoL0OBr5JerHqDAmctve9+u2N7n82EwBKPm2267jfnz5zNixAjuvfdeevbsCcBnn33GP//5T2bPno1O1/kzjfXeUpZXvsKW2i8xCDM39JzdIQHteOyvcdzRMqQa+tadXfnZEghIXF4vzR4vTe7gq9HtCb5cHhpcwc/1zW7qmlzUN7upbXJR2+Rif1UdtU0u6ppd7Y4OWIwGEqPsJEY7SI1xkhLrJC02ipRYJ5nxMaTHRWE0dH6JtO5kdF4WT150Jr98+1NueO0Dnr/qPOxmU0R864TgD5MncsHrb/Gv75fwu1MmqPJz/cBhvLJuFf9auYSnJp+pyPa0rJ6k2aN4buMSRQIqhODqvNHc/8NHLC7fyZikvA7bJpijOC11GLOLV3B17hQSzFGK+uww2Dk3/VTe3PsBa2s2MSCmtyJ7g8GA3+9Hr9fjdDoZPXo0v/rVrw6KJ8CuXbswm800NTVhs9k6XUSdxmQmptzN4NhLWFbxEjC7Q3ZaBHoCR6Aaxwb+QICaRhdVDU1U1gdfFfWNlNc2UFbXSFltAyU19ZTVNBxSxFwnBCmxTrISYuiRFEduchw9kuPokRxPvNPWjXcUeb7asI07//cZQ7LSefaKaVhNkRvWu+/LOby3bj2fXH0lhYnKhzQB/rJ4Ac+tXq4qCn1583L+tGIus069kiGJGR228/h9TJnzJAXOJF4Ye5Wia+5vquDyxX/lgqxx3FZwtiJbAG/Ay29WP4hJZ+KxgfehV1Hr1uVycfrpp3Peeedx++0/RrIbN25k5MiR/PWvf+Xmm29W7DcSaHOgHUQTUI2fC/5AgPK6Rg5U1VFUWcu+ihr2Vdayp7yaXWVVNLl/XNcY77TRMy2RwrQEeqYl0T8rhcyE6J/1kPCnazdz96zZTCjI5V+XnoNBH5mopLq5makvvkxeXDxvXnqRornIFiqbmxj3+vNMzOnBv6coE6RGr4exH/yHkclZPDtB2ZDq81u/5YmN85h18o30jklVZPvQhrdYWLaOd8b+jliTQ5EtwNLKH/jH1me5NvcSTk05WbF9SUkJ06ZN49133yUrKwuAnTt3MnLkSM477zyeffZZIDhc3dX/bo85ARVCvAScBZRJKfuF2uKAd4AcYDdwkZSyOnTst8AMwA/8Ukr5Zah9KDATsAKfA3dIKaUQwgy8CgwFKoGLpZS7j9YvTUA1jgeklJTWNLCzrIodJZVsPVDOlgMV7CipxOsPlseLtlnol5XCgOwUhuVlMCA7FbPx5zWL8/byNTz4ydecN7gvD02bErE/rP9bt57ffvEVj5w6hYsG9Ffl469LvuWZH5bypYqM3CfWLOSf6xbx1VnXUxDTcds6TzOTvnqC8ckF/H34hYquubexjKuWPM5FWeO5pUBZAhQE/839eeOT7G7cx5OD/0SUUZkIe71eJk+eTI8ePbjlllvYsGED99xzD6effjozZ848eI3ueOg7FgV0PNAAvNpKQP8KVEkpHxVC3AvESinvEUL0Ad4CRgBpwFygUErpF0IsA+4AlhAU0H9KKWcLIW4BBkgpbxJCXAJMl1JefLR+aQKqcTzj9fvZWVLFur0lrN9bwrq9JWwvqUBKMOr1QTHNz2Bszxz6ZaVELKrrTP799WL+880Srh83nDunnBQRnwEpufztd9lWUclXM35BnM2q2EdLRu7JWT34z1RlUWi1u4mx7z/NaVk9+cdYZbb/2DCHl7Z9z2eTbyPboWz4+KENb7GgbB3vjPktcWanIluAfU0HuHvNQ0xMHsv1PS5XbO9yuTj77LNxu91ERUUxatQo7r///sOe31WCeswJKIAQIgf4tJWAbgFOllIWCyFSgW+klD1D0SdSyr+EzvsS+CPBKHW+lLJXqP3SkP2NLedIKRcLIQxACZAoj3KDmoBqnGjUNbv4YecBVuwoYsWOIjYVlRGQEqfVzKjCLE7qlcPJffOIdSgXka5ASsmDn8zjnRXruGvqOK496ah/5zrE1vIKzn7lNc7v35dHTp2qysfjS7/j36uW8PmFV9EnIUmR7UMr5jJzywrmn3sTmY6YDtuVu+qZ8tWTnJ05gD8PPlfRNfc1lXPVkseZnjGGXxYqs23hld3vMrt4Pg/1u5t8Z64qH7W1teh0OpzOoIi3ztQNBAI/SSKqrKzE5/ORnJys6npHo6MC2t2Pm8lSymKA0HvLv7h0oHWpjKJQW3roc9v2Q2yklD6gFmj3cUwIcYMQYoUQYkV5+c9ndwENjUgQZbUwoW8PfnPOeN769WUs+NNNPH7VmUzun8+a3cX84Z05TPzjs1z3zHu8vWgNFXWN3d3lQxBC8MBZEw9uhfbh6o0R8VuYmMA1Q4fw7tr1rNp/QJWP6wcNI8pk5h/LFim37TMSvdDxzHpl+3YmWpxckD2Uj/auYX9TjSLbTFsip6cO46OixZS6qhXZtnBhxtnEGKN4fteb+KW63XSio6MPiidwUDwBdDod9fX1VFZW8uijj/LEE08wfvx4srOzWbhwoarrRYruFtDD0V6MLo/QfiSbnzZK+ZyUcpiUclhiYqLKLmpoHB9E2y1MHVTIg5dMZc7vr+PtOy/jFxOHU1bbwCOzvmbyg89z07Pv8+mKTYckKnUnep2Ox847jVG5mfz+ozks3am8NF173D52NMkOB3+YM+/g3LESos0Wbhw0nLl7drCyRNl2Zck2JxflD+S9nWvZ36hsE+gZBWMBeGGr8i27rs6dDMDLO+cotgWwGaxck3sRuxv38emBuap8tPDBBx9w4403Hvw+Z84cHn/8cU4//XRuuukmdu/eTUZGBna7nalTp+JX8TuKJN0toKWhoVtC72Wh9iKgdYmLDOBAqD2jnfZDbEJDuNFAVaf1XEPjOEQIQZ+MZH55xlg+vvcaPrj7KmZMGs7usmp+9+YXnPKHZ3ngrS9Zt0d5AfVIYzLoefKSs8iKi+H2tz9ha2n4u4Q4TCZ+P+kUNpWXM3PlKlU+fjFgCIk2O48uXqi4etnNfUcjEDytMApNtUVzQc4QZu1Zxf5GZZFksiWW6Rlj+KJ4BTsaihXZtjAybggj4gbz7r5PKGpSF70DTJ8+nfHjx1NWVobP5+Ojjz7iiSee4KGHHuJ///sf//3vf/nuu+/Iycnh5ptvZvz48UDXFaVvS3cL6MfA1aHPVwMftWq/RAhhFkLkAgXAstAwb70QYpQIziRf1camxdcFwNdHm//U0NA4Mnkp8dx+xlg+v+9aXr7tQs4Y0pM5a7dx+VNvcekTb/LB0vU0h7EtWLhEWy08d+V0LEYDN772AaV1DWH7nFqQz6T8PJ5atJh9NcoiQQCb0cQdw0azvGQ/c3fvUGSbZo/i4vyBvLt9DUUNNYpsbygch04IntmifFjzqtzJ2A0Wntn2qWJbCD54zci9FKvezH+2v6J6KBfg8ssvJykpCYPBwEMPPUSvXr0OLmm544472Lx5M1dddRVTpkxBr9fjcrlYuXIlc+aoi6DDocsEVAjxFrAY6CmEKBJCzAAeBaYIIbYBU0LfkVJuAN4FNgJfALdKefA3cjPwArAd2MGPJSNeBOKFENuBO4F7u+TGNDROAHQ6wdAeGfzhoinM/cP1/O78ibi8Pv7wzhym/ukF/vPF91Q1NHVL39Jionj2imnUuz1c/+r71IW5DZoQgj9MmoheCO7/aq6q6ObiXv3pER3LY0sXHlL8oiPc0m8MOiH45zpl86gp1mguzhnGR/tWs6teWTQeZbRxde4UllVtZXHFJkW2LcSYopiReyk7G/fwQVH4G29LKYmJiWHevHnU19eTk5PD1q1bueuuu5g0adLBeVK3283ixYu54447ePDBB8O+rhK6TECllJdKKVOllEYpZYaU8kUpZaWUcpKUsiD0XtXq/IellHlSyp5Sytmt2ldIKfuFjt3WEmVKKV1SygullPlSyhFSyp1ddW8aGicSDouZS8YO5P27ruTl2y5kSI90nv1qKaf9+UUemfU1+6uUR23h0js1iX9dcja7Kqq5893P8fmViVZb0qKc/Gb8SSzas4dPN29RbG/U67l71Di2V1fx3ub1imxTbE6uLBzCrJ3r2FGrbLu1G3qOw6Qz8K/N8xXZAUzPGE2mLZH/bPsEb6BjNZ7bMjphGGPjhzOr6DN2NOxR5aOF1stVCgoKaG5upqioiFGjRmG1Wg8+2ERH/3975x1eRZX+8c/cm957JQVIhVBCh9B7FwEFREUUe0HRta67+hPb6rq6uhZERakivffeQgsEQgqkQ3rvue38/rg3EFBgbhJEdD7PM8/MnTnvmTM35Z1T3u/rzKOPPsq0adN4++23iY+P/92GdG/3EK6CgsIdiiQZe6WfPTyeta/MYFR0OCuOnGHc+wuYu2In+WXNH041h95tA/nnuCEcTM3kvc17mv1PdHrnTnTw8Wburj2U15nfqx3ROpQu3n58cuwgNVrzcn4+GdUHG7UFn5w2bzjW3dqBGW17s+VSAgll5s1FWqoseCZ0HFk1hazMNn8VcQMPt5mKi5UTn5//nlp985Oi79u3j4MHD/LRRx8xceJEkpKSAOPvX8PPODY2lo8++oi5c+fSsWPH3018QXGgCgoKzaa1txtvTx3OpjceZmLPKFbFnmXMe9/z4eo9FFf+fmEwk7tG8XBMV5YePc3CI3HNqkutUjF3+DDKamv5cK/584qSJPFGnwEU1FQz75R5sebuNnY8EtmDTVlJnCk2b2HPzNA+uFjZ8vHZ7Wa/RPT2iKS3eyQL0rdTVF9hlm0DDhb2PBMyk7y6Ar5NXdzsF5n+/fvz448/MmnSJN5++226dbsSnilJEkeOHGHs2LE888wzvP766wBoNOa9sDQVxYEqKCi0GD4ujvx98hDWv/YQY7pEsOzgKca+t4Dvdx1Do2vasKC5vDisH0MjQ/hgy152JZm3iOda2nt78XC3riyPP8uRLPNDZbr6+DO6TRjfnDpKfrV5PfJHI3viam3Lh3F7zLJztLThyfABxBals7/gglm2AM+GjUdr0PH1hY1m2zbQ3jmcKQHjOVh8jB35+5tcTwORkZGXk2s3Dl2Ji4tj9OjRPPLII7z33nuAUXjBysqK0tJSnnjiiWbf+0YoDlRBQaHF8Xdz5u2pw1n98gy6tvXn0w0HuOuDH9l2OuWWz0+pVBL/mjSSKD9vXvxlEwk5+c2q77mY3gQ4O/PG1u3Uas1fcfxq7/7oDYJ/xZrnSBytrHkmqg8H8zLYm2Peko4prbsRYOfKR2e3oTOYtyK2lZ0H04IGsi3vJCdKzptl25i7/EfQ2aU9P2T8zPnK9CbX0xitVsuDDz7Ijh07KCkpYeTIkUyaNIlPP/0UuFq1yNXVlb179zJunPnZZuSiOFAFBYVbRrCXK1/MmsA3j0/E1sqSl37cyKNfrySrsOyW3tfWypIvp9+Fq50tzyxZR2EzhpFtLS15b+QwMsvK+OzgIbPtA51cmNmxCyuTEzhdYN5w7PSwLgQ4uPDByd3ozVjNa6Wy4KWoYaRWFrIi0/x41geCh+Bv68HHSSup1zctTEklqXg25GHcrFz4JOUbyrVNGxJujKWlJT169GD48OH4+/tz99138+233wLGnmmD82zopSYmJlJTU8OAAU3L9XozFAeqoKBwy+kdHsTyF+/njUmDOZedz8SPfmLe9thbOqzr4WDPF/eNp6y2jueWrade2/R79Q4MZGrHDnx//CRn8swXkXimay88bO1468Aus3rg1moLXu48gKSyAlamnTHrnkN9I+nuHsR/E3dRrqk1y9ZabclLEZO4VFvM9+nbzLJtjIOlPS+GP06ltppPkuehNTQ/Znj27Nls2LCB+vp6xo41ZpHR6XSo1VdykjZeRLRz504uXbrE+PHjm33va1EcqIKCwu+ChVrFlJhOrHllBgPbt+GLzYe45+PFxKWbJ3lnDu18vfhg4ghOZefy97XmL6ppzMsD+uFhZ8drW7aZLfPnaGXNy736EZefy5rz5sVZjgmKpIuHPx+f2kuVtl62nSRJvNZxFBWaOr5oQlhLV7cQxvr14OfMvSRWNF0qsbV9IE+GPEhS5QW+SV3UIkP4o0ePZuXKlUydOpXk5OTLztNg6qU39ERXrFjBa6+9RlFRESqVihMnTjT73o1RHKiCgsLvipezAx/PGMv/Hp1AvU7HzC9+4dMN+29Zb3RE+zBmD+nDhvgkvtob2+R6nGxseHvYEJIKi/j2qPkZnCaHR9HJy4f3D++lyoxVopIk8fduQyisq+arhCNm3TPC2Yd7W3dlWfoxUsrNnwt+KnQs7tZOfHBuOZomxoYCxHh0596A8ewviuWX7PVNrqcxd999N8nJyYSHh1/ucapUKqqrqzl8+DBjxoxh7ty5JCUlsX79etasWUPXrl1b5N4NKA5UQUHhttAvsjUrX3qACT3a8/2u40z5ZAnnspu34Od6PN6/B+M7RfL5rsNsTUhpcj3DQkMYHR7G54cOk1JontqPSpJ4q+8QCmqq+fyEeVq30R7+3BXcnvnnYsmsNE/r9rnIwTha2vBO/Eaze38OFra8FDGJ9Oo8fkxvnlTeRP9RDPLsw8pLm9ie3zJZVPz8/C4f5+TkcPLkSUaNGsXrr7+OTqdj+fLl/PDDD/Tr1++WLF5THKiCgsJtw97GiremDON/j06gsqaO6Z8t5dsdRzEYWvafnSRJvHPXUDoH+PLaqq0k5zU9jeE/hw7GwdqaV7ZsNVumL9rbl3sjovgu/gQXSs1TGXq1yyAsVCrePbHTLDsXKzvmtBvKieIs1mWfNssWjLGho327szhjN2fKMsy2b0CSJGa1mU60SxTfpS3lWMmpJtfVuE4wJuZ+5513GDNmDGFhYbz33nts2bKFsLAwXFxcrirbkigOVEFB4bbTL7I1q15+kGEdQ/l800GemLeqxfOQWllY8NnUsTjaWPPUknUUN1G7193OjreHDeFMXj7zYo+Zbf9Kr/7YWVjy933m6ez62DnyTFQM2y+eZ88l8+JbJwZF08m1Ff86u43SevO/12fDxuNl48I7CUuo1Jq3IKkxFio1z4c9SluHID5NmU9cqXkyh9fDxsaG//u//2PRokXMnz+f3r17I0nS5TnRG9Ec4Xvpr56wxN4zQETdPaflKmzmW87NrK99i7p5+d8+IV3+eKXAr85d3klX1fVb5SSkRtdNx43uJUnG8pKpIslkr5Ikk70xfg+M5xrKqyQJlUq6bNtwbCxjvKb6jb1apUKlklCrVahN543HKtRq43W1WoVapcJCfWVTm/aWajWWFmosLUzHlmos1WqsLI3nrS0tsLJUY2VpgbWFGmsrC6wtLS7vfy8psT8bQghWxybwwerd2Flb8e59I4iJCG7Re5y9lMf93y2nvZ83Pzw0CatGyZvNYfb6jWxLOc+aB6cTbmZe4cUJp3lj33Y+GTyKieHtZdvV63WM2vAdBmFg67hHsVbLb3tKeT6T93zD2FYdeK/r3Wa1FyChPJNnTnzJAM8O/DNqerN+x6t01cw99xkXa3J4KfxJOrvK/w7k0Dge9EaUa0v4b8ordHUdQA/3IXhY+wIgSdIJIUS3m5grDjQoJEK88tF3LVLXrf4ur63/ZrcTXK+8+JV9w7H41bWrPze0QXDN50bXxTVtFUIghLFuIYzGDccCgTBcsTEYDzA01GMwHhsM4oqNEOgNwlTeeO3K3oDBYLxuMBjQGwR6g/GcwSDQGQwYDAZ0egN6vfG6rtHnlsLaygI7a0tsrS2xsbLEzsYSO2sr497GCgdba9NmPHaytzFudsZjFwdbbK0t/7KOODWvmJcXbuJ8bhGPDO7OM6P7oJbxD1Eum84k8+Ivm7ircyTv3z2iSd9zSU0tI39YgI+DIyvvn4ZlozCKm2EQgomrlpBdUcbOaQ/jYmMr23Z/bjoP7lzG8x37MrtjP7Pa/Om5ncxL2c+3fe4nxivELFuARRm7mJe6mZciJjHev5fZ9o2p0lbzTuKnXKzJ5cXwx+ni2qFZ9TWF4vo8NuUu4mz5UQSCILtwuroNoLfHcMWByqFbt27i+HHzV9Qp/PkQQqDXG52pVq9HqzOg1ekvb5prjjVaPfVaHfVaHRqtjnqNjjqNjjqNljqNjtp6LbUarXFfr6WmTkN1nYaaOi1VtfVU12ow3ODvz8pCjaujLc4Otng42+PuZI+7sx3uzvZ4uTrg7eqIl6sD7s72Lepc/ijUaXR8uGY3K4+cpXdYIB/cPxpXB/mO5mb8b/cRvth9mFdG9OehmKatztyacp6n167nuT69eS6mt1m2CUUFjF+xkHsjOvD+wOFm2T67fw3bslPYMnYWrZ3cZNvV67VM3P019Xoda4c8hb2FtVn3NQgDL52aT3xZOt90e5a2jn43N7oBVdpq3k38L5k1F3k+dBY93KObVV9TKdcWc7J0PydK9lBQf4mPOq9QHKgcFAeqcLsQQlBbr6Wypp7K2noqquuoqK6jvLqOsspayqpqKauspbSqhpKKGorLqymqqPlVT1mtkvB2c8THzQk/Dyf8PJwJ8HIhwMuFQG9XnOxtbtMTtgwrj5zhvZW78XCy498zxhIV6NMi9QoheGH5Rrafu8C8B+4mJiSoSfXM2bCJTckprJg+jSgfb7Ns3zu0h3mnj/PzXVPo6Rcg266gpoqh6+fR0d2XhUOmmtWDPlmcxQP7v2dq6+682WmMWe0FKNVU8XDsf7BVWzGvx3M4WDTvpaZGV8t7if/lQlUGD7eeynCfW6MaJAchBBdr0wi0D1EcqBwUB6pwJ2EwCMqraykoraKgtIr80krySyrJK64gp7iC3OIKCsuqrhqed3GwpY2fO2393Wnj506IvwehAZ442JrX+7idnM3K48UfN1BUUcM/7hnCXT1aZs6sul7D9Pk/k1Neyc+PTaW1h/zeXAPldXWM/uEnHKysWPvgdGwsLWXb1mg1jPj5RyxVKjbdOwMbM+Zjl6TE8cbRLXzUewyT23Y0q80fnNnCT6lH+C7mQXp7tjHLFuB0aRrPx31DD7dw3uv0EGqpeSMgdfp6PkuZz8myM4zzG8Z9gXejamadzUGZA5WJ4kAV/mzUa3VcKiwnK7+U7IIyMvNKSMspJjWnmOraKwH8gd4uhAd4ER7kRYc2vrQL9sHWWv4//9+bsupaXl64iSMpWcwc1I3nxsS0yND1pbIK7v1mCU42Nix9dCoudub32A9mZDLjl5U82KUz/xgy2CzbAxczuX/9LzwZ3YNXevWXbWcQgqnbF5NSVsj2cY/iaesg27ZWp2Hynm+o1WtZPehJnK3M70WuuXiIT5JXMz1oEI+HjDbb/lr0Qs8P6T+zPX8fvdy78HTbh7BSWzW73qagOFCZKA5U4a+CEIKC0irOXywkKauA5MwCUrILuVRUDhiHgkNaedKxrS892wUxMNr8RSapqaksX74ctVrNc889h41Nyw4fa/V6Pli9h18OxdO/XWs+uH8UDjbN70mfyLzEzAUr6RLox7cP3m3WgqAG5u7azYITcfwweSL9WgebZfu33VtYlZzA6onT6eglf4g6tbyY0Ru/Y5B/W77qP9GsodwzpZeYvu87hvlF8nG3yWYvpBJC8O/kVay7dIS/t5vKcN/mq/wIIdiQu4PFmasItg9gTthjeNl4NLtec5HrQP98Kw8UFBR+E0kyzpX27diGWWN78dHT41n7wSPs+M+TfPrcBGaO7oGzvQ0bD53jpy3y4xt1jST4UlNTcXBwYM+ePfj6+vLFF1+YVmG3zIu6pVrNm5OH8MakwRxMyuD+z5Zxsbis2fV2DfLnnbuGEpuezQeb9zapjpf69SXE3Z1XNm+ltNa8WMm/9xmIh509f9u9BY0ZOrttnd15oVN/tmansCHTPI3dDq7+PB0xkM2XEpoksCBJEs+HTSDapS0fJv7SLJGFxnWO8xvG3yKeJL+ukFfj3+NESXyz671VKD1QpQeqoHAVOp2eksoavFwdb1iupKQEN7frzxnGxsbyt7/9jV9++QVvb/MW18jh6Pls5ixYj4VazeeP3EWHoOYvLvpwy14WHDrJ2+OHcG838+YVAc7lFzBp0RIGtmnNlxPGm9Wr25mRyiObV/NMl1681LOvbDudwcDkrT+RWVnKNjOHcvXCwMMHfuRsWQ4rBz1OsIP5vb0KbQ2PH/ucKl0N/+v6NIH2XmbX8Vvk1xXySco8MqqzGeM7lPsCJ2ChalrMrrkoPVAFBYUmYWGhvqnzFEKwdOlSvLy8+N///nfVNY1JKN3FxQWtVtviGTAa6BEawMLnpmJrZcEjX/7CrjMXml3nS8P70S8kmHc27CY2zfwMJO28vXixf1+2X0hl2Wnz0o8NCW7L5PD2fBUXy6l8+XlDLVQq/t1nHLV6Ha8e2WRWb18tqfiw20SsVBa8eGwFGr35gvFOlnZ81PkRVKh48dS3FNSVmV3Hb+Ft48k7US8zwnsAG3N38Pez/yK7JqdF6m4pFAeqoKBgNpIk8fTTTzN//nyWLl3Kp59+evmalZVx4cfmzZtxdHTE0dHojH8tBNL80a/W3m4smj2VEF8PXliwnp/2nGhWvWqVin/fO5ogdxdm/7ye9CLzhNsBHu7Wlb7BQby7ew8pReYJzv8jZjBedg7M2bWZWq383Jltnd15JXoguy6lsvT8KbPu6WPrzLtd7iKxPI/3zmw2y7aBVnYe/KvzI1Rqa3kx7lvKNFVNqudarFSWPNxmGnPCHqeovoRX4t9l5cVN6AxNl99rSRQHqqCgYDYNTmr8+PEcOHCAZ599FoDKykoWL17MtGnTmD9/PlOmTKFfP6NazrXDmQsWLOCJJ54gKyurWW1xd7Tnu6cmM6RDCB+v28e/1uxtlhi9o401X02/C5Wk4snFayivrTPLXiVJfDx6JA5WVjy3bqNZjtDJ2pqPB48krayED46Yl7FkRng3+voG886JHaSWmydUP9g3glmhMSzPOMGarFNm2TYQ7tSKDzrNJLeuhJdOzadK13TN3Gvp6R7NJ53/SQ+3aJZnr+ONMx+QWpXZYvU3FcWBKigomI0kSehNi11qa2s5fPgw77//Pu3bt+ff//43gwcPZs2aNTzyyCPAr3ubBoOBjh07otVq6dSpEzNnzqSysrLJ7bG1suTjB8cyvV80i/fH8fqSLWh1Te+lBLi58MW0ceSUVfDCzxvNTqDtYW/Px2NGcaG4mLm79phlG9MqiEc6duXHs3HszkyTbaeSJD7uPRZbC0tmH1hLvZnDsc9FDqanR2vePrWBhLKmDZV2dm3LOx0eJLUql5dPfUe1zryXjxvhZOnI82GzeCn8Ccq05bxx5gO+uvATZZryFruHuSgOVEFBoUmoTaEer7zyCv379+fcuXPs37+fkydP8uijj9K6devLZRv3PhuEvrt27cp3331HamoqOp2OffualyNSpZJ4ecIAZo+JYdPJJJ6ev4aquvom19clyJ+3xw/lcFoW723aY/bQcN/gIB7r0Z2f48+wITHJLNu/9exHhLsnf9u9hcIa+dlTvO0c+bDXGBJK8/kwbrdZ97RQqfl398m4WdvzbOwyiuubNgzb2yOSt6LuJ7Eimzlx86jUNi3rzfXo7taZ/3R+m7G+Q9lfFMvsuH+w5tIWNAb5Pf2WQnGgCgoKzeKNN97g2WefZe3atbzzzjvk5eUBVxzstTRkyZgzZw4bN27Ezc2N4uJilixZAkB9fT07duwgJcX8xNeSJPHIkB7MnTaC4xcuMvOLXyisaPp83N3R7Xk4pivLjsWz8Eic2fYv9O1DFz8/3ti2g/QS+fOpNhYW/HfoGCo1Gl7ctfmGmsnXMiwglBnhXfkh6Tjbs8+b1V43a3s+7zmF0voaZh9d3qRFRQADvDowt8MMLlTmMPvk15TUN3104bews7Dl/uBJ/LvTP4hyDmdp1hqeO/l3tuTu/l0dqeJAFRQUmoW3tzefffYZycnJaDQaIiMj+fzzz68q07j31pCjMTw8nAceeICAgACOHTvGww8/DEB5eTnJycn069ePoUOHEh9vfhzg+O7t+OLRCWQVlTHj8+VcLG76MN+cYX0ZGhnCB1v2sivJvDyclmo1n44bg5VaxTNr15s1Hxrm5sGbMQPZl53BvFPm5R19rctgotx8eOnwBrIqzVsI1c7Fj3e73MXJ4iz+cWpdkxdlxXi24/1OM7lYU8QzJ74kv878BVk3w9fWm79FPMWb7V7A28aTHzJ+5rmTf2dz7m40es3NK2gmShyoEgeqoNCilJSUcPHiRTp27MipU6fw9PTE39//N8umpKTwn//8h3HjxjF69NVycFqtlrlz57Jp0yYWLlxIRESE2W05k5nHU9+uxspCzVePTSTMr2mqNrUaLTN++IULBcUsfORe2vuZF9e6Nz2dWStWM6F9O/41Sn76NCEET29bz9b08yyfMJWuPr/9Pf4W2VVljN30AwH2zqwY8QA2FubJNH6VtJfPk3bzZPgAno0cZJZtY86UpfPK6e+xVVvzfqeZhDnKfwZzEEKQUJHCiuwNJFaex8HCniFefRnuMwAPa/M0ju+oOFBJkl6QJClBkqSzkiQtlSTJRpIkN0mStkuSdN60d21U/jVJki5IkpQsSdKIRue7SpJ0xnTtv9JfNZmigsJtxM3NjY4djSIEcXFxvPHGGxQVFZGbm8u5c+eAK7GiYWFhAPTqdSW3ZG1tLSdOnMDS0pK3336b4ODgy71Qc1/4OwT58MMz9yBJEjP/t5yTaZea9Ey2Vpb87767cLGz5anFa8krN29IckDr1jzTpxerE87xc7z8+FBJknh/4HD8HZ14ZtsGSmrlzycGOLjwSZ+xJJTm8+bRrWZ/d0+E9+fuwM58lbyXXzKaHsvbwaU1n3d5EoBnT3zJwcJzTa7rRkiSRJRzOG9Fvcg/279IO6cw1uVs49mTf+eT5HnElyViEC2X8xf+AA5UkiR/4DmgmxAiClADU4FXgZ1CiFBgp+kzkiS1M11vD4wEvpQkqWGy5SvgMSDUtI38HR9FQUHhGmbOnMn8+fPx8PAgJyeHf/7zn+zbt+9yrOgPP/zAkSNHLs+LgtGBzpkzBy8vL6ZPn05aWhqXLhkdX1lZGfv27eP1119n9255i2RCfDz46dkpuDva8fjXK9l/Lr1Jz+LpaM/X90+gWqPlycVrzV6g9EzvXvQLDuLtnbs5nStfKMHZ2oYvh4+nuLaG53duQm+Q7wSGtApldoe+rEg7w6KUk2a1V5Ik3uo8jr5eIfzf6Q3szk02y74xbR39+Kb7cwTZefF6/AKWZpq/KMsc2jmF8mL443zeZS5j/YZytjyJdxM/45mTb7Asaw05tXktcp/bPoRrcqBHgE5ABbAG+C/wOTBQCJErSZIvsEcIES5J0msAQoj3TfZbgbeADGC3ECLCdH6ayf7xG90/qE2EeHXut9dvX3MeTg5mdpJvVly6psXXlv+1vXTdaw0deOk6BRo+Ntzz8mXJeO7ydclUQpKuspEk0zmTrSRJpu3K9cufG11TXT42fVZJl8+pVNI1n1WoVMaFK+qGayoVatNntVqFyrRXq1So1Sos1BJqlQoLCxVqtRoLten4T5i0+vfEYDDw7rvv8sknn9C9e3ciIyP58ssvef/993nyySext7e/qvw777zDd999x5tvvsmkSZNwcXFhxowZJCUlERISwr59+4iOjmbevHn4+Nxcxq+0qpYn560iJaeI96aPZGR0eJOeY//5DJ5cvIbebQL5cvpdZgnPl9bWcvfCxWj1BtY+OB2Pa575Riw5d5rX927nua69mNNDvtSfQQge3bOCfTlp/DRkKr19zMt7Wq2r5+EDP5JSUcA3vafTw7P1zY2uQ51ew/vnfmZ3QTzDfKJ5KWIytr9DxhWNQcuxklPsKzzC6bJzCATBdgH0cI+mp1s0rex8ryp/R2VjkSRpNvAuUAtsE0JMlySpTAjh0qhMqRDCVZKkL4AjQohFpvPfAZsxOtAPhBBDTef7Aa8IIcb+xv0ew9hTxd61VdeuI1/8zXbd6u/G7Npv0p5rLwuuVX4x94YKjVFJEhYWKiws1FhaqLC0UF/ZLNVYWVpgZak2bRZYWzXarC2xsbbAxsoSGxtL7GwssbWxxNbGCjsbS+xsrbC3tcbezgp7WyusLH8fzc/bgV6v5z//+Q+1tbVERERwzz33oNFoKCsrw8vLi7q6OmxsbEhLS2Pv3r3MnDkTgOLiYqZOncrzzz/PmDHGRNB9+vTh9ddfZ+zYX/2Z/yaVtfU8M38NpzJyeHPyECb3Nl/vFmDFibO8uXY7d0e3490Jw83SvD2XX8C9S5YR5e3NT1MmYyXTAQsheHnPVn5JOsu3IycwrLX8bDkVmjombf2JotpqVo2cQWsn8+YES+urefDAAnJqyvi2zwN0cQ80y74xQgh+ytjJ92nbCLTz5K0O99PWwffmhi1Eqaacg0XHiC05SUqlMc7Wz9aHaJf2dHRpRzvHUKwtrO8MB2qa21wJTAHKgF+AFcAX13Gg/wMOX+NANwFZwPvXONCXhRDjbnR/ZRGRkV/LrJn2Jif8q18T0wlx+WPjcgIhrq5DiKttjBk6GpcVNBRpyN7RcN1g+PU1g0FcsTEI9AYBGPcNn4UQGITAYDBgMJ27fKw3mD43HBs/6/UG9HoDOtOm1+kvH+t0BrQ6PTrTOY3WeKzR6dFq9Wh1euo1OjRaHRqt8fjyVq+ltl4r+yXG2soCR3trHO1tcLS3xsnRFmcHG1ycbHF2tMXV2Q5XJzvcXOxxc7bDzdkOCwvzU3D9Udi0aRNJSUnMmTPn8rnTp09z//33s3v3bjw8PBBCMHv2bIYOHcr48ePJy8tj1qxZDBkyhBdeeEH2vWo1Wl76cSP7E9N5ZlQfHh3aw+xUXgBf7DrM//Yc4amBPXl2cB+zbDcmJTN7/Ubu6RDFeyOGyb5/nU7LvWuWkVpWwuqJ0wlzk78oKrOylIlbfsTJyoZVIx/E1drOrDYX1lXy4P4fKKyvYn6fB+jsFmCW/bWcKDnPOwlLqNLV8WzoeMb792rSz6E5lNSXcrTkFMdLT5NUcQGt0GEpWbK49xeyHOgf4TV3KJAuhCgEkCRpFdAHyJckybfREG6BqfxFoPFPrhWQYzrf6jfOK8jg2l/cKx+VdVgthRACjVZPXb2WunottXVaauq01NRpqKnVUF2robqmnqqaeqqq66msrqOyup6KqjryCytITsunvLIOjfbXsXmSBK5Odni4OeDh6oC3uyPeHo54ezjh4+mIn5cL7i72qFR/zJ+nu7s7P/74I8uWLeP9998nNTWV77//ntDQUDw8PEhMTOTjjz8mOTmZjRs3otPp2Lx5MxYWFpcFG4QQsv4B21pZ8unD4/jnz9v5YvMhKmvrmTOun9n/vJ8e1Ivcikq+3BOLt5ODWdlbxkSEk1JUxP8OxxLu6cFDXbvIsrOxsOSbkRMYt2Ihj25ew+qJ9+FmK88RBjm6Mm/gZO7bvoTH9qxk0dBpWKvluwBPG0cW9H2Ihw4s4NFDC5nX+36im9ET7eoWyvc95vDeuWX8O3kVscXJzImYiIe1U5PrNBc3a1dG+g5ipO8g6vUaEivOc7o8gcV8Icv+j9AD7Ql8D3THOIS7ADgOBALFQogPJEl6FXATQrwsSVJ7YAnQA/DDuMAoVAihlyTpGPAsEIuxV/q5EGLTje6v9EAV7jRq67SUVtRQUlZNaUUNxWU1FJVUUVRaRXFpNQUllRQUV1JeebWMmpWlBf7ezvh7OxPk70aQvztB/m4E+7vhaN+yia+byieffMLChQvx9vYmKiqK5557jsDAQHr37o2bmxtz587l+PHjvPXWW4wZM4Y5c+YQGhp6XdGGG2EwCD5YvZtlB08zqVcUf588xOx5bq1ez9NL1nHwQiafThnLsHbyh1UNQvDM2vXsuJDKvIl3MbBNG9m2J/NymLruZzp7+bJo3D2yh4EBNmYm8uz+NYwICOeLfhPMfub82gpmHvyR/NoKPu85lT5ebc2yvxaDMLA8az/z07ZgpbLk2dBxjPTt9rv3Rhtzp82Bvo1xCFcHxAGzAAdgOUZHmgXcI4QoMZV/A3jYVP55IcRm0/luGB2wLcZ50WfFTR5QcaAKf1Zq67TkF1eQV1jBpfxycvLLuJRfTnZeKRdzy67SivV2d6RNoAdtAz0ICfIksq0P/t4ut63HWlRUhIeHx+Xj0NBQ9u7dezk85uOPP6awsJAPP/zwsjRgUxBC8PnmQ8zfcZQRncN4974RWFmYNzBXo9Hy8IKVJOYVMO/+u+nZRv7QZo1Gy9SlP5NZWsqy+6YS6eUp23ZtSiKzd25kUnh7Ph400iyH813iUeae2Ml9odHM7SE/LrWBwrpKHj20kPSqYv7VdSIj/NubZf9bZNcU8uG5X4gvT6ebWygvRUzCz9a92fU2hTvKgd5OFAeq8FdEpzeQV1hO5qUS0rKLSc0qIi27iIyLxej0xjAJBztrwtt4E9nWm04R/nQI98PJwfZ3bWfDnPfTTz+Nh4cH77zzDgA//vgj8+fPZ+vWrdjZmTeX91ss2H2cT9bvp1dYIJ/OHIedtXkrQ0tranngu+XkVVTx48zJZgkt5FVWMmnRUgBW3D8NX8cb52JtzKfHDvHp8UO80L0Ps7uZNw/7Udwevkw4zBPte/FKtPlCCeWaWp4+soS4kmze6Dia+9r0MLuOazEIA2suHmZe6mZ0Qs+UwP5MDxqMnYV1s+s2B8WBykRxoAoKV9Dp9KRfLCY5LZ/EtHwSU/O4kFF42akG+7vRKbIVXaMC6NI+EDfn5jsvOezfv58nnngCSZJo164d6enpjB49mrfffrtZPdDGrD2WwD+XbadDkA//mzUBJzvzhrXzK6qY9u0y6nU6Fj0yhdYerjc3MpFUUMiUpT8T4OzE0mlTcLSW5zCEELy0ewsrkxP416AR3BvRQfY9hRD849hWFqXEMadTf57tECPbtoE6vZYXj61gd14ys0L78ny7waik5v8siurL+ebCZrbmncDNypFH245kpG831C1QtxwUByoTxYEqKNyYunotial5nE66xJnkHOKTLlFda1QSahvoQfeOQcR0aUOnCP9bvhJ45cqVJCQkMG7cOCIjI7GxsZG9eEgOO+LP88rCzbT2cuXrxyfi4SQ/ThMgvaiE+79bjrWFBYseuRc/F/kLYvanZ/DoqjX0aNWK+ZPvlj2vqdHreWTTKg5dymL+qLsZFCR/LtUgBC8d2sDq9LO81mUwj7XrKdu2AZ1Bz9z4TSzPOMEQ3wg+6Ho39i3UYzxXnsXn59eRUJ5Ja3tvZrYeTn+vqBZx0jdCcaAyURyogoJ56PQGUtLzOX4mi+NnszideAmtTo+9rRXdOwTRt1tb+nZri5PDrV2Y1JKOszGHkzOZ/cM6PBzt+ebxiQR4uJhlfy63gBnf/4K7gx0LH74XT0f5Tnj12XP8bfMWxkWG8+8xo1HJfL4qjYapa3/mQlkxi8beQzdf+XqzOoOB5w+uY2NmIq93GcyjTXCiQggWpcXy4ZmthDh58nnPqQTYmxdreqO6dxfE833aNrJqCmjj4MOM4KEM8Opwyxyp4kBlojhQBYXmUVOn4Vh8Jofj0jlyKp2C4irUahXdogIZ1CuUAT1CcXb8/edOm+NcT2fk8sz8NVioVXzz+ETC/OQv7gE4mXmJRxeuxs/FiZ8evgdXO/nP/03sUT7ad4AHu3TmzcGDZD9HUU01965ZRmFtDT/fNYV2Hl6y76k16Hnh4Ho2ZibyUucBPB1l3nxqA4cKUplz7BcAPu42mb7e8lcl3wy9MLAz/xQ/pe8kq6aAADtP7gnoywjfbi2uZqQ4UJkoDlRBoeUQQpCYms/uIynsiT3Ppfwy1GoVvTsHM6JfO/p2a4O1lXlZQZrCuz9tx93JjlljezV5WDktv5jHvl5FrUbL/2ZNoHNrP7Psj6Rl8fiiNYR5efD9Q5NwtJE/r/n+nn18f/wEz/bpxewY+c7sUmUFk1cvRaPX8fOEqYS4yl/FqjMY+NvhDaxJT+Cp9r15qfOAJr2EZFeX8GzsMs5XFDArrC/PRAzCUtVyQ/t6YWBvwRmWZe0lqSIbBwtbxvr14C7/XvjbNS3bzrUoDlQmigNVULg1CCFIyShgx8Ekth1IorCkCjtbK4b2CeeuoR2JaON9S4ZgdXoDc3/cxoZD52gX7M27j44mwFv+gp7GXCop54lvVpFfVsW/HxpLv0jzdGD3JKfx3LL1tPP15rsZE7GXubrXIASvbdnGyrMJvDFoIDO7yRNaAEgrK+HeNctQSRLLJ0wl2Fn+s+sNBv5xbBtLzsfxQFgX3uo+XPYwcmNqdBreP7OZlZlxdHJtxcfdJuFv37SfwfUQQnCmPIOV2QfZV3gGvTAQ7dKWMX7d6e/VAZtm9EoVByoTxYEqKNx69AYDcecusmXfOXYdTqauXkdosCfjB3dgRP92ONi1fJjCjuMpvPfTdnR6A689MJRRvSKbVE9xZQ1Pfbua8zlF/HPKUO7qbl7M4/ZzF3hh+QY6tfJl3gN3y3aiOoOB2es3sjXlPHOHD2VqJ/lKR8nFhUxdtxwbtQVL77rXLCcqhOCDuN3MOxfLqMAI/hMzzizFosZsvniWf55aD8DfO45mXEDHW/LSVFhXzpa842zKOcal2mLs1Nb082zPQK9OdHcPw0plXvsVByoTxYEqKPy+VNXUs/1AEut2xpOcXoCdjSWjB7Zn8shoAv1aZuFJA3nFFfx9/mZOnb/EmN7teHn6YOxtzO+ZVNXV88IP64k9n82L4/szY2BXs+y3JqTw4i+b6NzKl2/McKIavZ6n1qxjb1o6H44awcQo+c77XFEB09f/grVazZLx99LGxbzvtkFsoZtnK+YNnGS2dm4Dl6pLeeXkak4WZzHIJ4x/dBqLt+2tkeszCAPxZelsyT3B/sKzVOpqsVfbEOPZjhiPdnRzC8PR8ubz0YoDlUlUh05i5aqt173+e+uwmPt2dvPiNy7Q2P76erjX2lydvuxyCjXp5tcbX5OMec8u20iNbBpSn6mMucyuPm4or5JQXZsGzZTSrPFe4Y9LYmoeKzbHsf1gEjq9gZ6dgpk2rivdOwS12M9Opzfw3YYjfLchFl8PJ95/fAztgm+e/uxaNDodry3ewvbT55kxsCsvjO1nllLTlrMpvLRiE51a+fLN/RNwkDknWq/T8diqNRzKzOJfo0dyd/t2su+ZWFzI9HXLUatULBl3D6FmiM+DUfZvzsH1+Nk78d2ge2jj1DRlIL0wsCg1ls8Sd2IhqZnTfij3Bne9peEoWoOOk6UX2J0ff9mZqiUV7Z2D6OUeQRfXEMIc/bH4jflZxYHKxNG5lejW69nb3QyFW0hjh9qQD1QlGXOEqtTG3KEqlQq1uiF/qCk/aKOtIR+ohYUKtUVD3lD11enNLC2wsLxybGmpxsrqyt7aygIrK+OxjbUl1tYW2NhYNtqssLWxRK3+6+UdLSmrZu2OeFZvP01RaTURbbyZPr47A3qGYtFC38ep85d449tNlFTUMPue/kwZ3NlsJ603GPhg9R5+PniasV0jeHvKcCzNWKS0LeE8L/6yifZ+3sx7YAJOtvJCfWq1Wh5ftZbDWeY70fMlRdy3/hf0BgM/jZ1MlKd8lSSAE4UXeWzPCvRC8Hm/CfTzbXo+0KzqEt46tZ4jhelEuwXwWoeRRLnKD7lpKjqDnsSKLI4UJ3OkOInzlcYE7fZqGzq4BNPZtQ3tnIIId2qFrdpKcaByiYjsIL7/YfVvXvu9vxvzb2deftBfXxeNjm9c9+XUZFelLbtS7sr1hrOi8eVG6clM10SjuhrqbVy2oS5TSrLfOm5IZWYQxhRmAqNAOOJKerOGtGd6/ZU0aIZGac0MQmDQC/QGA4ZrU5w13gwG9DpjSjOdXo9eL4xpzXR64zmdHq3OgE5rTG2m0eqMbWkCVlYW2NlaYWfXsFnjYG+Ng4MNDg7GY0dHW5ydbHFyssHJ0RYXFztcXOywbcLw5B8JjVbHln3nWLzuONm5pfh5OfPAhB6MGdi+RUQayqpqefv7reyPT2NA57a8+dBwXMyUJxRC8O2Oo3yx+RA9QwP4z8xxsnuTADsSLzBn+UZCvTyYP2Oi7BCXxk703RHDuLejfNWh9LJS7l+/nPL6euaNmkAff/OyqGRVlvLY3pWcLy/ib50H8Hi7pqceE0KwJusUn5zbQUl9NXcFduaFdkPwtJEvYdhcSuoriStL5VRpKnGlaWTVGJN9qSUVre29+aHXi4oDlYMyB6pwq9DrDUZnqtGhbcgPWq9Fo9FRX6+jznRcW6elrk5Lba2G2tqGvYbqGuO+qrqe6up6qqrrqKoyHl8Pa2sLXFzscHO1x93Nwbh3d8DTwxFPD0e8PJ3w9HL8wztavcHAgeOpLFxzlHMX8vD1dOKBu3syZmB7s3p8v4UQgqU74vjvin24O9nx7mNj6Bxqfi9o7bEE3v55ByG+7vzv0Ql4OjnItt2bks5zy9YT5ObC/BkT8XKUZ1un1fLU2vXsS8/gH0MG8WCXaNn3zKuq5IENK8gsL+PToaMZ3TZcti1AtVbDK0c2sTEzkZGB4fyr1xgcrZq++KtSW8fXyftYmHoEK7UFD4f04cG2vXCw/P0zA5VpqjhXkcW58izOVWTxny6PKw5UDooDVbjT0OsNVFXXU1FRS2VlLeXltZSV11BWVkNpWQ1l5TWUlFRRUlpNSWk15eW1v6rDxdkOH29nfHyc8fN1IaCVG638XWnl74azs+0fZu5YCMHhuHR+WHmEhPO5eLs78tCkXowZFNXsod3EjHxe+2YDucUVPHV3Xx4Y0c3s7DP7E9N56ceNuNjb8NVjd9PGW/4c4ZG0LJ5esg4PBzu+nzEJf1dnWXb1Oh2z129kx4VU5vSL4cme8hOCl9XV8sjm1ZzMy+HNmEE83NG8xVBCCOYnHuXDuN0EOLjwed8JRLmbP5/cmMyqYv6dsJ0duUk4W9rycGgf7mvTo8XkAJuCMoQrE8WBKvzZ0Wh0FBVXUVhUSWFhJfkF5eTmlZOXV0ZuXjn5BRXoTWLxAI4ONgQFuRMc5EFQoAetgz0IbeuNk9PvqybUGCEER+Mzmb/8EAnncwn0deXRKTEM6hXWrJRrVTX1zP1pOzuOp9AnKpi3Hh6Jm5N5q03PZefz9Pw1aHR6PnloLD1D5Q+PxmXl8MSiNdhaWfLtg3cT6iVvkY9Wr+eVzVtZl5jEw9268urA/rLjNet0Wp7bsZFt6Rd4qEMX/t5nIBZmivHH5mfx/MF1FNdV81LngcyK7NGkeNHGnC29xOdJu9mffwFXKzsebNuLqa2742z1+//eKQ5UJooDVfiro9cbyMsrJ+tiCZculZCVXUJmVhEZmUVUNErK7e3lREhbb8JCvYmM8CMizBdHx993uE0Iwb5jF5j380HSs4sJDfbk6fv706NjcLPqXLHnNP/5eS+O9ja8//gYuoS1MquOnJIKnp6/hszCUt66dxjju8tf5JOcV8isn1ah1ev5avoEogPlKR4ZhGDurt38dPIUd7WL5P2Rw2UL0OsNBt49vJfv408wICCYz4eNw0lmBpgGSutreO3IZrZmpxDjE8xHvcfga9/88JRTJdl8mbSHAwWp2KotmRTUhQfa9mwxbV05KA5UJooDVVD4bYQQlJZWk5peyIUL+VxIK+D8hXyyL5ZcLhPQyo12kX507BBAp6gA/PxcfpfhX73BwPYDSXz780FyCyvo1TmYp+/vT9tA8zRrG3M+u5BXvl7PxYJyHr+rDzNH9zCrd1tZW8+cBcZY0ceH9eSpkb1lfxfZJWXM+mk1BZVVfHLvGAaFy8uoIoTgqyNH+eTAQWKCgvjirrGyU6EBLDl3mn/s30mQkwvfjppgdqyoEIKfL5zm/47vwEKl4o2uQ7i3bcuIJSSX5/HjhcNsuGhUGRrgHcY9wV3p5x3ym6EnLYniQGWiOFAFBfOoqq4nOSWXpORcEpNyOXvu4uV5Vnc3Bzp1DKBrl2C6Rgfj7XVrAuYb0Gh1rNhyih9XHaG6RsPoge15bGoMHq7yF/Q0prpOw3s/bWfr0WR6tgvi7UdG4uEsP5uKVqdn7oqdrD6awMjocP5vynBsrOSp4BRX1fDEojWcyy3gn+OGcG83+atsV5w5yxtbtxPm4cG8SRPMSsp9JCebp7auQ2cw8J8hoxkS3Fa2bQMZlSW8engzsQVZxPgEM7fnCIIdW6bHWFBbwbL046zIPElRfRXeNo7cHRjN+MBOBDs0LS71ZigOVCaKA72aRYsW8d///peUlBQcHR2ZPn067777LurrDA1t27aNl19+mbS0NDp16sR3331HWFgYAM888wwLFy5sFPoisLS0pKTE2IPJy8vj6aefZu/evVhbWzNt2jQ+/vjj3+dBFVoMIQRZ2cWcPnOR0/FZnIrPoqSkGjD2ULt1bU1MrxA6dgjA0vLW9BwqqmpZsCqWlVtOYWmh4sGJPZkyuivWMp1XY4QQrNl/ho+X7sbexpp3HxtN90j585pCCH7YdZzPNh2gfStvPntkvOwVutX1Gl5YvpH95zN4rF93Zg+Jkd0L3p+ewTPrNmBvacm8iROI8pEf75ldUc4TW9eSUFTAk9E9eLFHX7PnRQ1CsPR8HB/G7UFj0PNshxhmRfZosgzgtWgNevbmpbA84wQHCy4ggPYuvoz278CoVu3xsZW3CEsOigOVieJAr+abb74hMjKSXr16UVxczPjx45k8eTKvvPLKr8pmZGQQHR3NypUriYmJ4eOPP2bhwoUkJCSgVqvR6/XodDrAqBD0+OOPo9VqWbRoEQCTJk3CxsaGzz//nIqKCsaNG8fTTz/NE0888bs+s0LLIoQgI7OI4yczOHEyg7jTWWg0OuzsrOgaHUxM71BieofgcAvyhV7MK+Pzn/aw/3gqfl7OPHV/fwb1DG3SkGLqpSJe/XoDGXklzBrbi1njeqE2w6nsPpvKq4s242hrzX8fHk+7AHkOTac38M7GXSw/foZRUWG8d/cIbCzlOaGUwiIeXbWG4poa/jV6JKPDw2S3t06n4+0Du1iaGE9Pv1b8d+hYvO3N78nn1VTy9rHtbMlOJtjRlde6DGZYq6b9DK57j9pyNl9MYNOlMySU5SIBHVz9GegTziCfMMKcmpeoQHGgMlEc6I359NNP2bVrF+vWrfvVta+//pp169axadMmwPiP08HBgQ0bNjBo0KCryup0Ory8vFixYgWDBw8GICIigk8++YTRo0cD8Morr1BaWsq8efNu8VMp/J7U1Wk5EZfBkaOpHDmaSlFRFRYWKrpGB9O/Xzh9e4e2+ArfY/GZ/PenPaRmFdE1KpAXHx5McCvzh/tq6jT8a8kuNhw6R5cwf96ZNRpvN/nDo0mXCpj9/TpKKmv4v2kjGBUtL/ZSCMF3B47zyY4DdPD34Ytp42Un5i6qrubJNeuIy8nl6d49mR3Tx6wVsquSE3hj33ZsLSz516CRDG3CkC7A3pw05p7YwYXyYmJ8gnmj6xAiXeXnKJVLRlUxWy6dZVduMmfLcgDws3Wmr3cIvT3b0NOzNS5W5q2sVhyoTBQHemMmT55M27Zt+fDDD3917YsvvmDr1q2sX7/+8jl7e3vef/99nnvuuavKLlu2jFdffZW0tDRUprf4L774glOnTvHtt99SUVHBgAEDeO2115gyZcqtfSiF24YQgqTkXPYeSGbf/mRy88qxsFDRo1sbhg5uR59eIVhbt0y+UL3BwJrt8cxbdoCaOi2TR0bzyD29m5T5ZePhc3ywaCdWFmr+OXME/TvLdyrFlTXMWbCeuPQcZg7qxnNjYmT3ZLefu8ArKzfjbGvDF/eNp72fvF5svU7HP3fsZMWZBAa2ac2/x4zC2UZ+j/9CaTHP7djIuaICprXryN/7DMTe0nzxDa1Bz+KUOD6N30+Fpo5xwe2Y3bFvkzV1b0ZBbQV788+zJy+Fo0XpVOs0SECEsw9d3YOIdg8g2i3gpsO9igOViad7kJg0+vXbdv/mDmvczPqtj6bg5d20uYGffvqJ119/nVOnTuHh8ev4tOTkZLp06cK2bduIiYnhgw8+4I033uDdd9/l1VdfvarsqFGj6Nq1K3Pnzr18LjU1lSlTpnDmzBn0ej3Tp0/nxx9/vGm7lv10gLOnsq4WkVdJSBgF5hsLzzfMH6lUDeLzV/YqtQpJklA3XFMZz135fEUXV3WNNm5jjVyV2qiHa2Fp0sa1VGNp+mxpqb58zcrKAkuTFq7x2KJZMYx3OkIIUi7ks2v3OXbtSaSouAo7OysG9o9g1PAOtG/n3yLDfqUVNXyz9ADrd53BxcmOZx8YwIh+kWbXnZlXyuvzNpKcVcC0oV14dlJfrGQOrWp1ej5Ys4dfDsXTJzyID+8fjbO9PIeWmFvA00vWUVpTy9wJwxnTQX4vdsmpeObu2o2PoyNf3DWO9t7ye4D1eh3/OXaIb+KOEujkwseDR9Ld17zwngbK6muZdy6WBUnH0Rh0TGzTgWei+hDo2LI5QhujNeg5W3qJ2KJ0YgvTOV16kTq9cUrJ19aZDq7+tHPxpZ2zL+1cfHGzvtLDVxyoTPx82opHH3zv9ty8md+9HPPZr4zB3fP6Q05Lly7l8ccfRwhBv379Lg/Hrl+/nkcffZRt27bRseP18xAuX76cd955h7y8PKZOncqePXt4+eWXeeCBBy6XycvLIzAwkHPnzhESEnL5fEREBFOmTOH555+nqqqKZ555hujoaN56660bPtN3X+7k5NE0hGjQ2L2ihdtwbBBX9HYbNHAbyjdo4QrDb2jj6g1X9HEbiQvcKiwt1VhZW2BtY4m1taVpb4GNrRW2tlbY2llhY9LFtTXp4trZW+PgaIOdg0kf19EGRydbHBxsUFvcmUL0er2B02ey2b4zgT37kqir0xLQyo1RIzoycngUri7yV8Jej6S0fD6ev4NzF/LoHNmKFx8ZQttA87KTaLQ6PvtlHz/vOkVYgCfvPTaGYF/5q01XHD7D+6t24+VszycPjSOylTyHVlRVzfPLNnAiK4eZfboyZ1hf2UpMJy/l8Oy6DZTW1vL3wQOZ1sm8MJOjOReZs2sTlyoreDAqmr/17IeDVdOkIAtrq/kq4RCLU+LQCQNjAiN4vH0v2rs1T81IDlqDnuTyPOJKsokrySahNIfsmtLL1z2sHQh18iLUyYvXOo5SHKgclCHcX7N9+3amT5/O+vXr6dmz503LNywUqqmpISAggCNHjhAZeSV58UcffcTatWs5cODA5XMlJSV4eHhQWlqKs7Oxh7x27VrefPNN4uPjW/iJms5l59pIcF6nM+31RhF5vc4oOK/TmsTltUaBea3pWKsxntdqdJd1cTX1xmNNvZb6eqM2bn2dlvo6LXV1GurrtNTWaqkz6eLW1hi3m2HvYI2jky3OLnY4u9jhZBKZd3V3wNXNATd3B9w8HPD0csLO3voPI9nXmNpaDXv2JbF56xnOJFzE0lJNv5gw7hobTYeoVs1qs8EgWL/rDF8t2U91TT2TR3Vh1j29sTdzWHffqVTeXrCVeo2Ol6YN4q6+UbLbFZ+Zy5wFGyivruX1SYO5u2eULDuNTs+/tu5jcewpegS34uN7RsueFy2uqeHFjZs5kJHJyLBQ3h0xzKwh3Wqtho9i9/PjmTh8HRx5q+8QhrcOubnhdcivqeT7pGMsOR9HlVZDjE8wD0d0Z4BfG7MWajWXck0tSeV5nCvL5XxlARcqCrhQWUDc+DcVByoHxYFezZ49e7j33ntZtWoVffv2vWn5+Ph4OnbsSGVlJU899RQajYaff/75qjJRUVE8//zzzJo166rzgYGBvPjii8yePRuNRsODDz6IwWBg+fLlLfpMfxYMBkFtrYaa6nqqq+qorqyjqqqOyoo6Kitqqaqso6qyjoryGsrLrmxlpdVo6nW/qs/G1hIPLyc8vZzw8XXBy8cZb18XfHxd8Atww9XN/rY72MysItZtPMXW7Weprq4nOMiDu+/qyvAh7bGxafpcaVlFDd8sO8i6nfF4uDow+6FBZq/WLSyr4p/fbeFoYhZDu4Xx2v1DcJaZ2aW4soZXF20i9nw2d/doz6sTB2FrJe951p46x1vrd+JgbcW/7xlDj9byhlUNQvDt0WP858AhvBzs+feYUXRvZd6Q7Im8HN7Yu42kkiKGBYfwZsxAAp1czKqjMRWaOpacj2NB0nHya6toZe/MtNDO3NO2E562zR91aAoGYUCtUisOVA6KA72awYMHs3//fmxtr/wj6NevHxs3bgRgzJgx9OvX7/IcZ79+/YiPj8fCwoKJEyfyySef4NgoiDs2Npb+/ftTVFR01XmAY8eOMWfOHBITE1GpVPTp04cvv/wSPz95UmYK8hBCUFOjobS4ipLiKoqLKikuqKSwsILiwkoK8srJzy2j1BS72YCNrSV+/m74BbgRGOxxeQsI8sC6Gc6rKdTWadi9N4k1605y/kI+jg42jBnViQnjuzRLrCHhfC7/+nY75zMK6dEpiDkzBxPoJ39I1mAQLNx6nC/XHMTdyY5/zhxBz3ZBsmz1BgNfbT3CvO2xhPi48/GMMbLF6FPyi3hu2XqyS8p5elAvHu/fQ3bP7XRuLi9s2ER2WTmP9ujG7Jg+WFvIj9XU6vV8F3+Cz44fQm8QzOrUjae69GzysC4Yh1e3ZaewKOUkR/KzsJBUDGkVwt2toxjo37bFYknlosyBykRxoOYhhLjqLd1gMFyeZ1Sr1b/5Bq/VarG0/O1/uBqNBr1ef1lk4XrlFG499XVaCvLLycspI/dSKZculpCTXcLFrGJyL5Vezm+qUkn4B7jTOsSL4LZetA31JizSD3ePW5/PUQjB2YRLrFh9jAOHzgMwsH8EU+/pSWiIeYmiG9DpDazedop5yw6i0ep5YEJ3HpjQ0ywRhsSMfN6cv5mMvBKmD+vKUxNjsJa5wOhgUgavL95CrVbL6xMHc1f3drJ6wtX1Gt5ev5P18Un0bB3Ah5NG4i1TsKFKo+GD3XtZFn+GEHd3Phw1nE6+vrJsG8irquRfsftZlXIOTzt75nSP4Z6IKLMFGK4ltbyYpRdOsSb9LMV1NbhY2TA6KJKxQZF09wpodv1yuKMcqCRJLsB8IApjWuWHgWTgZyAYyADuFUKUmsq/BjwC6IHnhBBbTee7AgsAW2ATMFvc5AEVB6qgcHM0Gh2XskvIzigiI62AtAv5pF8oIPfSlUUY7p6OhEX4Et7On3YdWxEe6Y+t3a3LO5qXX87qtSfYsPk0NTUaunQOYsrkHnTv1rpJQ8/FZdV8/tMeth1Iwt/bhRcfGUKvzsGy7evqtXy2Yh+/7D5NGz933pk1ivBAeYuECsqreG3RZo6lXmRUdDh/nzwER9ubz8sKIVgdd453N+3GUq3m/+4ayvB2obLbvDc9nde3bKewuppZ3bvyXJ/e2Jj5EhuXn8vcQ7s5kZdDG2dXXugRw5i24c3OzqIzGDiQm87q9LNszz5PrV6Lm7UtQ1uFMjwgjBifYGwsbs0L953mQH8E9gsh5kuSZAXYAa8DJUKIDyRJehVwFUK8IklSO2Ap0APwA3YAYUIIvSRJR4HZwBGMDvS/QojNN7q34kAVFJpOTXU9aefzSUnK4XxSLsmJOVzMLAaMPdXWId5EdQqkU5cgOkQH4exiXkC7HKqq69mw6RQrVx+nqLiKtm28mD61F/37hqNuQs7Q42cy+Xj+TrJySxnQI4TnHxqEt4f8YeKDZ9L5vwXbKK+qZda4Xjw0qoesFbN6g4Hvdh7jq62H8XZ25L3pI+nSRl6i7/SiUv62YhMJOQVM6NyO10cPxNFG3sKoyvp63t+zl+XxZwlwdubtYUPo3zpYlm0DQgh2ZKTy8dEDJJcUEeHmwfPd+zC8dWizHSlAjU7Dvpx0tmQls+vSBSq19VirLejpFUB/vzYM8GtDWyf3Fpuzv2McqCRJTsBpoE3j3qIkScnAQCFEriRJvsAeIUS4qfeJEOJ9U7mtwFsYe6m7hRARpvPTTPaP3+j+igNVUGhZKsprSUq4SOLZi5yLv8i5sxepr9MC0LqtF527taZrzzZ0iA7C1rbleqharZ6du8+x5OcjZF8sIaCVG1Mm92D40CizNXg1Wh3LNpzgh5VHUEkSj9zbh3tHRWNhIa+esqpaPly8i+3Hkmnf2oe3Hx4pO9zldEYOry3eQk5JBQ8P6c6Tw3thKeO+Gp2er/fGMm//UbwcHZg7YRh92sqbjwU4nJXFP7btJL20lNHhYbw+aAA+ZojSg/ElYENqMp8dO0RaeSkhrm481rk7E0LbyU61djPq9TqO5GexLyeNvTlppFYYX9i8bR3o4RVAD+9AengFEOLs0WTnfSc50M7APOAc0Ak4gbEXeUkI4dKoXKkQwlWSpC+AI0KIRabz3wGbMTrQD4QQQ03n+wGvCCHG/sY9HwMeAwgMDOyamZl5y55PQeGvjlarJyUxh/iTGZw6kUFCfDaaeh0WFiradwqke+8QevUNIyCoZXoQer2BA4dSWLzsCOcv5OPt5cR9U3oxcngHrMwUl88pKOeT73dx6GQarQPceemRoUS3k79ydevRJP61eBe19VqevDuG+4Z1kbXYp7pOw4dr9rDmaAIR/p7MnTaSMD95Mauns3N5bfVW0otKubdbB14a3k92b7Rep2Pe0WN8HXsUtaTiyV49ebhbF7MWGYFx+HVjajJfxR0lqbgQH3sHZnbowpTIDrjYtKxs48WqcvblpnEkL5OjBdnk11YB4GhpTScPXzq7+9HJw48oNx+8bR1k/Y7dSQ60G8Yh1xghRKwkSZ8BFcCz13Gg/wMOX+NANwFZwPvXONCXhRDjbnT/AN+24sWZv5apa/rztFhVt+QGNzL/1S/WNZ9/dZkbX2840XD+cv3XfJa4/nVJkkAy3ssoLnSlTIMKEZKxJSqVUX3IqEykuuqcSnW1apFKZVQhalAokiQJtbrhWGVSJTKpEZn2arVJmchChUqtRm1hUiSyaFAlUqOyMKkSWahQN6gTWV5RJWpQP/oro6nXcfZ0FidiUzkRm0bahXwA/Fq50jMmjD79w4nqHNik4dfGCCE4djydHxcf5FxiDh4eDkyZ3JNxozuZJRcohGD/8VQ+XbCbvMIKRvZvxzP398dNprhDUXk1HyzawZ64VKLa+PCPh0bQxk/eatvdZ1N5e/kOKmrreHJEb2YO6iZrOLhOq+PzXYdYcOgkHg52/GPsEIZEypcfzCor4/3de9l+IZVWzk78rX8/RoeHmf27K4Rgb3YGX8cd5UhONjYWFowPieDBqGiiPJu26Otm98uuKuNoQTZxRTmcKsohuawAvcnPuVrbEunqRaSLF2EunoQ4uxPi7IGT1dUxsXeSA/XB2KMMNn3uB7wKhPA7DOG6O/qLUZ2fapmHucVfpWjmDW74o77m4rVlr/09+VVd116/1k5c/fnK/moL0bjcZSWhq+/fcI7fuPZHR5IkozO1UmNpZYGVtVHSz8rG0ijxZ22JpbUFNjaWWNlYYm1ribWNUY3Ixs4SWztrrG2tsHOwxtbeuNk5WGPvYIO9o3FTyxxm/KNQkFdO7MHzxB5MIe54OlqNHmdXO/r0CydmYATR3ds0Kw2aEIKTcZksXHqI0/HZeLg7cN+UXowZ1cmsHmldvZYfV8eyeO0xbKwteXRKH+4e3lmWQxNCsPVoMh8t3UVNnZZZY3sxY2Q3WUPCJVU1vL9qN1tPpdCulRdvTxlOuL+8xOFnLuXx5prtJOcXMaxdCK+PGoiPs/xh2UOZWby3ew9JhUV09vXlpf596RUYINu+MeeKClh49hRrzp+jVqejk5cP90REMS4kAmfrls/M00CNTkNCST7nSvNJLC0gsSSf5PIi6vVXYqO9bB0IdnQl2NGVIEdXnu4Qc2c4UABJkvYDs4QQyZIkvQU0vNoVN1pE5CaEeFmSpPbAEq4sItoJhJoWER0DngViMfZKPxdCbLrRvZU50D8PDTJ9NEj3GU8ajy9L+RkQAgx6w9XSfnrDFcm/31AfMugbndPrL0v96fV6dFpjGb2uQZmoQZHIYDxupFCk1RoViYybHm29Fq1Gj6Zea1QnqjeqEWnqjMd1tUZVoroaDTqtXtb3YGtvhYOTLY7Odji62OHgbIuzqz3ObvY4udrj4u6Ai7sDrp6OuLo74OBse1ng/3ZTW6vh2OELHNidyNGD56mp0eDoZEPfgZEMHB5Fx+igZvVMT53O4oef9hN/9iIe7g5Mm9KLsWY60sycEj75fhfH4jNpG+jBS7OG0ClC3rBucXk1Hy3dzY7jKYT4e/D3GcOIaiMvfGTb6RTeW7mbipo6Zg7uxmPDesoKldHq9Sw4eIIv98aikiSeGtiLB3pFYyXzRUtvMLDybAKfHTxMflUVMUGBvNA3hs5+5oW9NFBeX8eKpASWJ50huaQIK7Wa4a1DmBjWjr6tgltsrvRG6A0GsqvLuFBezIXyIi6UF5NZWUpGZSlFddVkPPD6HeVAO2MMY7EC0oCZgApYDgRiHJ69RwhRYir/BsZQFx3wfMNKW9Nw8AKMYSybMQ4DK2EsCn8K9Dq9UdqvWkNtdT011fXGfVU91ZV11FTVUVVRS3VFLVUVtVSW11JZVkNFWQ0VpdVUltVcjuVsjIWlGjcvJ9y9HHH3dsbDxxkvPxe8/Fzx8nPBp5Ubjrdg9ezN0Gh0xB1LY8/2BA7tS6a2RoOrmz0Dh0UxbHRH2ob5NGk4XAjBqdNZLFh4gPizF/H0cOT+ab0ZNaKj7J6uEIK9Ry/w2YLd5BdXMqJfJE/f3x8PV3lxmHviLvCvJbsoLKvi3kGdeWpiX+xtbr6gqqy6lo/W7mX98USCPF34++Qh9AyVl+z7Ymk5723aw+7kNFp7uPLG6EHEhMhfZFSn1bLkdDxfHTlKaW0tA9u05qlePeni3zThEyEECUUFrEg+y5qURMrq63CysmZY6xDGtg0nplXQ7+JMr6Vaq8HByvrOcaC3E8WBKvxV0OsNVFfUUlZcRVlxFaVFVZQWVVJSUElJQQVF+eWUFFRQmFdO3TW6uw5OtvgGuuET4IZfoAf+rT1p1caDVq09cXS+9c61rk7L0UPn2bPtLLEHz6PV6glu68XQkR0YMrLjDRMmXA8hBHGns/j+x/0knLuEt7cT06f2ZtTwDrJX29bWaflpTSxL1h3H0kLFjIm9mDKmi6wsLVW19Xy5+iC/7D6Fp4sDL04dyOAu8uQEDydnMnfFTrKLyxnbNYI54/rj4SRvTnZPchrvb95LVkkZ/UNb87cR/Qjxkp9erEqjYeHJU3x//DiltXX0DGjFYz270z84uMnz+xq9ngMXM9hwIZntGalUaupxsLSiX0AQg4PaMjCwNZ52v5+03x0zB3q7URyogsLVCCGoLKuhIKeMgpwy8rKLyc0qMe6zS8i7WIJedyVTjauHA8FhPgSF+RAU4k3rcF+Cw31umdxfRXkte3cksHNLPOfOXESllujRO5SR46Pp0SdEtvNroGGx0YJFB0hMysXP14UZ98cwZFA72cPFF/PK+O9PezhwPJUAX1eemzGQPtHyBB3OpuXy3sIdpGQX0icqmL9NG0SA983TfNVpdMzfeZQfdh3H2lLN0yP7MCWmk6w5WY1Ox8Ijp/hm31Gq6zXc0zWKpwf1li1OD1Ct0fBz/Bm+O3aC/KoqIj09mdE1mnGREWav2m1MvV7HwYuZbEu/wO6sdPKrq5CAKE9vYloFEeMfSDcff2xvoWqZ4kBlojhQBQXz0Gn15F0s4WJ6IRfTC8m6UEBGch5ZF/Ivx3uq1CoC2njSNtKP0A6tCO8UQNtIP6xaKFl2Axezitm6Po7tm+IpKa7CxdWeEWM7MXpCV3z9zcs1KYQg9lga3/+4n/MX8glo5caM+2MYNCBSdt7WI6fS+XTBbrJySunZKZjnZgykdaub9+50egO/7D7F12sOodHpeXBkN2aO6oGNjO8ro6CUD1bv5lByJm193HllwgB6hckbmi2truWrvUdYejQeS7WKB3pF83Dfbjjbyl/Uo9HrWXcuke+PnyClqBhXW1umdOzAfZ074efUPHnHhmHe3Vlp7M/OJC4/B63BgJVKTQcvb7r5+Bs3X39cWzA8RnGgMlEcqIJCy6DXG8i/VEp6Yi6piZdITcwlNeESxQUVgHGutXW4DxGdg2jXJYj23YLx9HFpkXvrdHqOH0ll87o4Yg+kIIQgunsbRk/oQp/+4Wb1Sg0GwYFDKfy46CBp6YW0DvbgoQf60i9GXhiHVqdn1dZTfL/iMDW1GiYM68Qj9/TGxenmQ92FZVX895d9bI5NwtvNkdmT+zOs+83vK4Rg15lU/r1+HxeLyxnYvg0vjOtHay954g2ZxWX8d9chNp1JxtHGmhm9o3mwdxfZ8aMNbTiSlc1PJ+PYmZoGQL/gIO7pEMXgkLYtMp9ZrdVwPPcShy5lcSz3EmcK89AajKMhQU4udPDypqOnD1Ge3rT38Gry6l7Fgcqka5eu4uD+Q7e7GVe4hXGev13+RoGhZpRtdP1ysevEkV4p99eOh/yrUJxfQdLpLFLis0k2bQ1zrF7+rnTo3ppOvdrSsWdbvM3sNf4WhQUVbFkXx5b1cRTmV+Du6cjou7ow+q4uZs2VGgyCPfsSWbDwINkXSwgN8eahB/rSu2dbWb+7ZRU1zF9+iLU74rG1sWLGxJ5MHhktS6Q+LuUiHy3dTUp2IdGh/syZMpDI4JvHTdZrdSzeF8e3O45Sr9UxuXcHHh/eE3eZQ7PJeYV8sfsIOxIv4GRjzfSenXmgVzSu9ub17i6Wl7M8/iwrzyaQX1WFm50t4yMjGR8ZQQcf7xb726/TaTldkMfxvBzOFORxpjCPS1WVl6/72jsS4e5BmJsHIa7utHFxI8TV7aaOVXGgMnG29BR9XCbd7mYocH0HfJXjla4IKlwRUWhUrvH1q8QTVI3sJJOYQqMyKgmVJCGpVJfPq9QqVJJprzZeUzecN23qhr2FCrVJXEFlOrawNIktmAQV1JZqLC0tTHs1ltYWWFhaYGGlxtLK0hgTat0QC2qMA7WytsTK1hJrG0usba2wtrXCysbyjn750Ov0pCXlknAig7PH0zlzNJ2KUmMqNe9WrkT3CaVrvzA69WrbrAVKer2BY4cvsG7FMY4fSUWllujTP4IJ93SnQ3SQ7O9QrzewY9c5flp8kJzcMiLCfJk5oy/du8qb40y/WMwXC/dyOC4dH08nnpjWl6F9Im46LKw3GFh34Cxfrj5IaWUtY3q346m7Y/B2u/lLQHFlDV9uPcyqI2ewsrDgwYFdmDGwKw4ye5Tncgv4ak8sOxIvYGtpweSuHXioTxf8XMxLHac3GNifkcEv8WfZlZqG1mAgyMWFcZHhjI4IJ9S95fRrGyiureFsYT5JxYUklRSRWFxIamnx5Z4qgLuNLUHOrgQ6ORPo5EKgkzP+jk74OzrhY++ItYWF4kDl0DYwTHz48he3uxlA8wUBzDa/gcHNhBR+Xf4awYRrRB8um19HSOFmggtXiSoYP1wtonD5+jUiCwZT3KcQV50zGBpfN8V/ClO8qMEY42kwGIwxoY1iQA2X40JNsZ8GgUGnR68XGPR6YyyoVo9er0ev1aPXmz43xIJq9C0i/GBtZ4WtvTU2dtbY2FtjZ2+NrYMNtg422DnaYO9ke2VriAd1scfR1Q5HF3uc3BywaIY4QUtiMBjIPJ9PfGwap4+kcvrIBWqq6lGpJEI7tKL7gAh6DoqkbTu/Jv+zzblYwsbVJ9iy/hSVFbW0buvFXff0YNCIKNl6vHq9ga07zvLT4oPk51fQPtKPGQ/0pVsXeatPj8Vn8sXCvZzPLCSijTdPTe9Htw43n6usqqnnh01HWbLjJCoJpg7pwkOju+Nod/PhyYyCUr7YfJBtp8/jYm/DQ4O6MTWmM3Yy56JTC4uZv/84G+KTEAiGtQvlwV7RdA7wNftnUV5Xx9aU82xITOZIdjYGIQh2dWFYSAjDQkPo7OfbIsLzv4XOYCC7opy0shJSy0pIKyshs7yMrMpycqsqjf8fTEhAxlN/UxyoHJQ5UIXfG73egE6jMworNIgq1OuMogr1DSILWuprtUaBhTotmlqTqEKthrqaeuprjPva6nrqGuJBK2uprTLuqyvqqKupv2E7HJztcHKzx8ndAVdPJ1w8nXDxdMTN2xl3H2fcfFzw8HXBxdOp2bJ65qDT6kk6nUXcwfOcOJBCSvxFhBC4eznRrX84vYa0IzomtEmrfOvqtOzedpa1vxwl7Xw+Do42jBwfzV2Tu+Pt6yKrDq1Wz+ZtZ1i89BAFhZVEtffnoQf60qXzzXu1eoOBbQeSmLfsAPlFlfToFMQT0/oR0ebmw7M5ReV8teYQW2ITcbKz4aHRPbhnUCdsrG7+PSRk5/HF5sMcTMrA1cGWGQO7MjWmE3bW8l4ecsoqWBx7il9OnKWyrp4oP2+m9+zEyKhwbGTmPW1MYVU12y9cYPv5CxzJykZrMOBqa0vf4ED6BQfTNzgILwd5MbXNRaPXc7GynJyqSnKqKsitquT57neQEtHtRHGgCn9W9Do9NZV1VJXXUFVeQ2VpNZWlNVSWVlFeXEVFSTUVJVWUFVVSVlhBaWElFcVVv+ohq9QqPPxc8PJ3w7OVG16t3PAN8sQ7yB3fIA88/N1uqYMtK67i2N5kju1N5MT+FGqq6rGxs6JrvzB6D2lPj0ERZg/1CiFIOJ3Nml+OcmBPIgjo0z+c8fd0p5PMHqVWq2fTlngWLztMYZHRkT44PUZWj7Reo2PVtlP8tDqW8so6BvcKY9a9fQiWsWI3KauAL1cd4NDZDDxd7HlkTC/u6hclK2PL6Ywcvtp6hEPJmbjY23B//y5M6dMJZ3t5i22q6zWsOXWOJbGnSSsqwdnWmvGd2jG5axRh3vLE7q+lsr6evWnp7E5L50BGJsU1NQCEebjTKzCAHq1a0T2gFe52v5+YhzIHKhPFgSooXEGvN1BeWElxfhnFuWUU5ZZRdKmUwpxSCrKLKbhUQlFOGQb9lfkkCysLfIM9aNXWG/+2XgSE+hIY7ktAmA/2ji2beUOr0XHmWBqHtidwZMc5igsqsLBU07l3CP1GdaDX4HY4uZoXcF+QX876lcfZtOYklRW1tAn1ZuKUngwcFoWV9c17VxqNjs1bz1x2pO0i/ZgxPUZWYu/qmnqWbjjO0vUnqNfoGNEvkpmTe9NKxurkE8nZ/G/VQeJTc/Bxc2TW2F6M6dNOpiPNZd72WPYnpmNrZcnk3h14oH8XfFzlLbISQnA0/SI/H49nR2IqWr2ejv4+3BXdjlFRYbjaNe3nbhCCxIJC9qVncCQri5M5OdRqjZq1bdxcifbzI9rPl86+voR4uGNxiyQoFQcqE8WBKiiYh16np/BSKbmZReRlFpKTXsil1Hxy0gq5lFaATnNFpNvDz5XgCD9at29Fm6hWtG7vT6sQnxbpsRoMBlLOXOTAljMc2HqG/IulqNQquvQJYcDYzvQe1h57B/lhDPV1WnZtO8uqZUfITCvE2dWOMRO6Mn5yd9zcbz6cqNHo2LL9LEuWHSa/oIKwUB/un9abmN6hN10wVFpRw+K1x1ix5RR6vZ5RA9ozY2JP/L1dbmgnhOBIQiZfrz1EQnoePm6OPDSqB+P6tpelk5uSU8gPu46z5VQyAMM6hXF//2g6BsnXuS2trmXt6URWxyWQkl+EhUpF35AgxnaMYGB4G+xlDhP/Fhq9nrN5+Ry9eJGTl3KIy8mltLYWAGsLNRGenrT38qKdtxcRnp6Eerhjb9X8HLOKA5WJ4kAVFFoOvd5AXkYh2efzyErOJTM5l4xzl8hKyb0shm9tZ0Xb9q0I6RRESKdAwrsE0yrEu1mC9kIIzp+9xIEtZ9i76TQFl0qxtLKgW78wBo2PpsegSNlzpkII4o6ls2b5UWIPpqBWqxg4LIoJU3oQFnFz3VetVs+2nWdZ+nMsl3JKaR3swX1TejNoQMRNXxyKSqtYtPYYa7afRm8QjOgXyYy7exLge+PwHiEEh85m8N2GWOJTc/Bwtmf68K5MHNBRlsZuTkkFS/bHsSr2LFV1GqICfZjWtxPDO4XJcsQNJOUVsv50IhvPJJNfUYW1hZq+ocEMiwxhUHgbnMwQaPgthBBklpVxOjePhPwCEvLzScgvoEpzRXqylbMTIe7utHFzo7WrK63djJuXvb3shU+KA5WJ4kAVFG49Wo2Oi+fzSD17kQvxWVw4nUXqmezLC50cXOx44/vH6Nwvotn3EkKQdCqLvRtPs39LPCUFldg5WDN/299w9TBPGedSdglrfo5l26bT1NZomPX0EO59IEaWrV5vYNeeRBYvO0xmVjF9eoXw7tvyQuYKS6pYvO4Ya7bHo9Pp+fLtKXSM8L+pnRCC40nZ/LDpKEcTs3B3smPdh7NkO8HqOg3rjp1j6cFTZBSU4uXswOY3HpY1LNwYg0FwMusSWxPOsz3xAvkVVVioVLw1fgiTukSZVddN7yUE2WXlpBQVcb6omPPFxn16aSn1uisZjGwsLGjl7MzCeyfj6XDjYX7FgcpEkqRKIPl2t+MW4gEU3e5G3CL+zM8GyvPd6SjPd+cSLoS46dtW0xV//zwky3nTuFORJOn4n/X5/szPBsrz3ekoz3fnIkmSrGHJP0YWXQUFBQUFhTsMxYEqKCgoKCg0AcWBwrzb3YBbzJ/5+f7MzwbK893pKM935yLr2f7yi4gUFBQUFBSagtIDVVBQUFBQaAKKA1VQUFBQUGgCf1kHKknS95IkFUiSdPZ2t6WlkSQpQJKk3ZIkJUqSlCBJ0uzb3aaWRJIkG0mSjkqSdNr0fG/f7jbdCiRJUkuSFCdJ0obb3ZaWRpKkDEmSzkiSdEpuyMCdgiRJLpIkrZAkKcn0N9j7dreppZAkKdz0M2vYKiRJev52t6slkSTpBdP/lbOSJC2VJOm68kl/2TlQSZL6A1XAT0KIlpXGuM1IkuQL+AohTkqS5AicACYIIc7d5qa1CJJRj8teCFElSZIlcACYLYQ4cpub1qJIkjQH6AY4CSHG3u72tCSSJGUA3YQQf7pAfEmSfgT2CyHmS5JkBdgJIcpuc7NaHEmS1MAloKcQIvN2t6clkCTJH+P/k3ZCiFpJkpYDm4QQC36r/F+2ByqE2AeU3O523AqEELlCiJOm40ogEbi5DtgdgjBSZfpoadr+VG+CkiS1AsYA8293WxTkI0mSE9Af+A5ACKH5MzpPE0OA1D+L82yEBWArSZIFYAfkXK/gX9aB/lWQJCkYiAZib3NTWhTT8OYpoADYLoT4Uz0f8CnwMmC4Sbk7FQFskyTphCRJj93uxrQgbYBC4AfT8Pt8SZLMy6925zAVWHq7G9GSCCEuAR8DWUAuUC6E2Ha98ooD/RMjSZIDsBJ4XghRcbvb05IIIfRCiM5AK6CHJEl/mmF4SZLGAgVCiBO3uy23kBghRBdgFPC0aUrlz4AF0AX4SggRDVQDr97eJrU8pqHp8cAvt7stLYkkSa7AXUBrwA+wlyTp/uuVVxzonxTT3OBKYLEQYtXtbs+twjQ8tgcYeXtb0qLEAONN84TLgMGSJC26vU1qWYQQOaZ9AbAa6HF7W9RiXAQuNhoRWYHRof7ZGAWcFELk3+6GtDBDgXQhRKEQQgusAvpcr7DiQP+EmBbZfAckCiE+ud3taWkkSfKUJMnFdGyL8Zc+6bY2qgURQrwmhGglhAjGOEy2Swhx3bfgOw1JkuxNi9swDW8OB/4Uq+GFEHlAtiRJ4aZTQ4A/xeK9a5jGn2z41kQW0EuSJDvT/9EhGNeQ/CZ/WQcqSdJS4DAQLknSRUmSHrndbWpBYoAHMPZcGpabj77djWpBfIHdkiTFA8cwzoH+6UI9/sR4AwckSToNHAU2CiG23OY2tSTPAotNv5+dgfdub3NaFkmS7IBhGHtnfypMIwcrgJPAGYw+8rqyfn/ZMBYFBQUFBYXm8JftgSooKCgoKDQHxYEqKCgoKCg0AcWBKigoKCgoNAHFgSooKCgoKDQBxYEqKCgoKCg0AcWBKigoKCgoNAHFgSooKCgoKDQBxYEqKCj87kiSFClJ0temvJlP3u72KCg0BcWBKijcZiRJqrp5qeva2kqStNeUm9EcOytJkvaZUjZdey1YkqRaU7abW4IQIlEI8QRwL8acpw3PckqSJI0kSR636t4KCi2F4kAVFO5sHgZWCSH05hgJITTATmDKdYqkmrLdNAtJkjpIkrThms3LdG08xuTFO01tqjXd87r5FxUU/kgoDlRB4Q+CJElzJEk6a9qeb3T+TUmSkiRJ2i5J0lJJkl5qZDYdWGsqFyxJUqIkSd9KkpQgSdI2k9j+9VhjspfTtgclSYqXJOm0JEkLTfdKMuW7PCtJ0mJJkoZKknRQkqTzkiT1ABBCnBFCjL1mKzBdWyeE6CO3DQoKfzR+NXyjoKDw+yNJUldgJtATkIBYSZL2AmpgEsak6BYYRa5PmGysgDZCiIxGVYUC04QQj0qStNxke71UaGeB7jLa1h54A2MOzyJJktwAJyAEuAd4DKOo/31AX4x5Il8HJtygzoHARMAa2HSzNigo/BFRHKiCwh+DvsBqIUQ1gCRJq4B+GEeJ1gohak3n1zey8QDKrqknXQhxynR8Agi+3g2FEHrTfKOjEKLyBm0bDKwQQhSZ7EokSXIy3euMqV0JwE4hhJAk6cyN7muqYw/GPK4KCncsyhCugsIfA8nM8wC1gM015+obHeu5+UuyNVB3kzIS8Ftpmxrfy9Dos0HGfRUU7ngUB6qg8MdgHzDBlMjXHrgb2I9xkc04SZJsJElyAMY0GAghSgG1JEnXOtGrkCQpSJKk/0qS9Pk1c6vuQKEQQnuTtu0E7jWVxzSEq6Dwl0d5S1RQ+AMghDgpSdICjAmmAeYLIeIAJElaB5wGMoHjQHkj020Yh3933KD6pzD2VmuBDo3OD0LG/KMQIkGSpHeBvZIk6YE44K2bP5WCwp8bJaG2gsIfHEmSHIQQVZIk2WHsqT4mhDhpuhYNzBFCPHAD+4+AhUKI+GvOrwJeE0IkX3M+GNgghIhq4UeRhSRJGUC3hjlXBYU/KkoPVEHhj888SZLaYZzv/LHBeQIIIeIkSdotSZL6BrGgXwDvSZKUC1QKId42reBdc63zNKEHnCVJOtUSsaByMYXcHAYsMc6jKij8oVF6oAoKCgoKCk1AWUSkoKCgoKDQBBQHqqCgoKCg0AQUB6qgoKCgoNAEFAeqoKCgoKDQBBQHqqCgoKCg0AQUB6qgoKCgoNAEFAeqoKCgoKDQBBQHqqCgoKCg0AT+H8IIQ2zh6Y1uAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -218,9 +226,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "ERROR EmisGrid: You are using o_ii_atom_Z82-WFD96.dat, but restoring a file made with o_2.wgfa\n", "warng getEmisGridDict: Wrong emission map: ./pypics//emis_O2.pypic, creating it\n", - "warng getEmisGridDict: Wrong emission map: ./pypics//emis_O3.pypic, creating it\n", - "warng getEmisGridDict: Wrong emission map: ./pypics//emis_N2.pypic, creating it\n" + "ERROR EmisGrid: You are using o_iii_atom_FFT04-SZ00.dat, but restoring a file made with o_3.wgfa\n", + "warng getEmisGridDict: Wrong emission map: ./pypics//emis_O3.pypic, creating it\n" ] } ], @@ -241,9 +250,9 @@ { "data": { "text/plain": [ - "{'O2': ,\n", - " 'O3': ,\n", - " 'N2': }" + "{'O2': ,\n", + " 'O3': ,\n", + " 'N2': }" ] }, "execution_count": 10, @@ -379,7 +388,7 @@ { "data": { "text/plain": [ - "(7612.554292918343, 444.7122084104995)" + "(7602.6382828191445, 637.1108702214491)" ] }, "execution_count": 15, @@ -622,6 +631,26 @@ "diags.diags" ] }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7.48217073])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diags.eval_diag('[OII] 3727+/7325+')" + ] + }, { "cell_type": "code", "execution_count": 19, @@ -859,7 +888,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.9" } }, "nbformat": 4, diff --git a/docs/Notebooks/PyNeb_manual_7b.ipynb b/docs/Notebooks/PyNeb_manual_7b.ipynb index a31267e7..b98b1d25 100644 --- a/docs/Notebooks/PyNeb_manual_7b.ipynb +++ b/docs/Notebooks/PyNeb_manual_7b.ipynb @@ -257,7 +257,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.9" } }, "nbformat": 4, diff --git a/docs/Notebooks/PyNeb_manual_8.ipynb b/docs/Notebooks/PyNeb_manual_8.ipynb index c9114202..4cc5dc4b 100644 --- a/docs/Notebooks/PyNeb_manual_8.ipynb +++ b/docs/Notebooks/PyNeb_manual_8.ipynb @@ -221,7 +221,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.9" } }, "nbformat": 4, diff --git a/docs/Notebooks/PyNeb_manual_9.ipynb b/docs/Notebooks/PyNeb_manual_9.ipynb index 9b5256cd..01164661 100644 --- a/docs/Notebooks/PyNeb_manual_9.ipynb +++ b/docs/Notebooks/PyNeb_manual_9.ipynb @@ -65,10 +65,25 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "28.317514896392822\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mresb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mO3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetTemDen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mden\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mden\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwave1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5007\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwave2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4363\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m resc = N2.getTemDen(ratio, den=den,\n\u001b[0;32m----> 6\u001b[0;31m to_eval = '(L(6584) + L(6548)) / L(5755)')\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mresd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mS2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetTemDen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratio_S2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m11000\u001b[0m\u001b[0;34m,\u001b[0m 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-104,7 +119,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.834969282150269\n" + "12.611578941345215\n" ] } ], @@ -139,15 +154,9 @@ "Training set size = 30, Test set size = 0\n", "Regression Model SK_ANN\n", "Training 2 inputs for 1 outputs with 30 data\n", - "RM trained, with 145 iterations. Score = 1.000\n", - "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", - " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(10, 10), learning_rate='constant',\n", - " learning_rate_init=0.001, max_fun=15000, max_iter=20000,\n", - " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", - " power_t=0.5, random_state=None, shuffle=True, solver='lbfgs',\n", - " tol=1e-06, validation_fraction=0.1, verbose=False,\n", - " warm_start=False)\n", + "RM trained, with 301 iterations. Score = 1.000\n", + "MLPRegressor(activation='tanh', hidden_layer_sizes=(10, 10), max_iter=20000,\n", + " solver='lbfgs', tol=1e-06)\n", "Training time 0.1 s.\n", "Test data scaled. Log10 applied. \n", "Training set size = 30, Test set size = 1000\n", @@ -160,15 +169,9 @@ "Training set size = 900, Test set size = 0\n", "Regression Model SK_ANN\n", "Training 2 inputs for 1 outputs with 900 data\n", - "RM trained, with 399 iterations. Score = 1.000\n", - "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", - " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(10, 10), learning_rate='constant',\n", - " learning_rate_init=0.001, max_fun=15000, max_iter=20000,\n", - " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", - " power_t=0.5, random_state=None, shuffle=True, solver='lbfgs',\n", - " tol=1e-06, validation_fraction=0.1, verbose=False,\n", - " warm_start=False)\n", + "RM trained, with 440 iterations. Score = 1.000\n", + "MLPRegressor(activation='tanh', hidden_layer_sizes=(10, 10), max_iter=20000,\n", + " solver='lbfgs', tol=1e-06)\n", "Training time 0.3 s.\n", "Test data scaled. Log10 applied. \n", "Training set size = 900, Test set size = 1000\n", @@ -181,15 +184,9 @@ "Training set size = 900, Test set size = 0\n", "Regression Model SK_ANN\n", "Training 2 inputs for 1 outputs with 900 data\n", - "RM trained, with 251 iterations. Score = 1.000\n", - "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", - " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(10, 10), learning_rate='constant',\n", - " learning_rate_init=0.001, max_fun=15000, max_iter=20000,\n", - " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", - " power_t=0.5, random_state=None, shuffle=True, solver='lbfgs',\n", - " tol=1e-06, validation_fraction=0.1, verbose=False,\n", - " warm_start=False)\n", + "RM trained, with 300 iterations. Score = 1.000\n", + "MLPRegressor(activation='tanh', hidden_layer_sizes=(10, 10), max_iter=20000,\n", + " solver='lbfgs', tol=1e-06)\n", "Training time 0.2 s.\n", "Test data scaled. Log10 applied. \n", "Training set size = 900, Test set size = 1000\n", @@ -202,16 +199,10 @@ "Training set size = 30, Test set size = 0\n", "Regression Model SK_ANN\n", "Training 2 inputs for 1 outputs with 30 data\n", - "RM trained, with 1198 iterations. Score = 1.000\n", - "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", - " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(10, 10), learning_rate='constant',\n", - " learning_rate_init=0.001, max_fun=15000, max_iter=20000,\n", - " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", - " power_t=0.5, random_state=None, shuffle=True, solver='lbfgs',\n", - " tol=1e-06, validation_fraction=0.1, verbose=False,\n", - " warm_start=False)\n", - "Training time 0.3 s.\n", + "RM trained, with 1379 iterations. Score = 1.000\n", + "MLPRegressor(activation='tanh', hidden_layer_sizes=(10, 10), max_iter=20000,\n", + " solver='lbfgs', tol=1e-06)\n", + "Training time 0.4 s.\n", "Test data scaled. Log10 applied. \n", "Training set size = 30, Test set size = 1000\n", "Predicting from 2 inputs to 1 outputs using 1000 data in 0.00 secs.\n", @@ -223,20 +214,14 @@ "Training set size = 900, Test set size = 0\n", "Regression Model SK_ANN\n", "Training 2 inputs for 1 outputs with 900 data\n", - "RM trained, with 1271 iterations. Score = 1.000\n", - "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", - " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(10, 10), learning_rate='constant',\n", - " learning_rate_init=0.001, max_fun=15000, max_iter=20000,\n", - " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", - " power_t=0.5, random_state=None, shuffle=True, solver='lbfgs',\n", - " tol=1e-06, validation_fraction=0.1, verbose=False,\n", - " warm_start=False)\n", + "RM trained, with 1242 iterations. Score = 1.000\n", + "MLPRegressor(activation='tanh', hidden_layer_sizes=(10, 10), max_iter=20000,\n", + " solver='lbfgs', tol=1e-06)\n", "Training time 0.9 s.\n", "Test data scaled. Log10 applied. \n", "Training set size = 900, Test set size = 1000\n", "Predicting from 2 inputs to 1 outputs using 1000 data in 0.00 secs.\n", - "1.8168518543243408\n" + "2.1021597385406494\n" ] } ], @@ -270,11 +255,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9760922757550656 0.0014651252094208156\n", - "1.0023892610184786 0.0011300408975968939\n", - "1.0004085945791563 0.0017517642003213054\n", - "1.0052743290930866 0.007755331856418873\n", - "1.0047559158671264 0.008719083835010443\n" + "0.9855241000183226 0.001666799236078673\n", + "1.002702581349611 0.0016169579076851374\n", + "0.9995256689866661 0.001498990471096166\n", + "0.9971243421551492 0.008909597124836483\n", + "0.995962542945527 0.007307916627793927\n" ] } ], @@ -296,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -308,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -317,22 +302,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 42, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/core/diags.py:837: RuntimeWarning: invalid value encountered in greater\n", - " no_conv = ((abs(den_old - den) / den * 100) > tol_den) | ((abs(tem_old - tem) / tem * 100) > tol_tem)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "time for classical method: 94.363916 s.\n" + "time for classical method: 182.447404 s.\n" ] } ], @@ -347,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -370,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -409,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -425,18 +402,12 @@ "Regression Model SK_ANN\n", "Training 2 inputs for 2 outputs with 900 data\n", "RM trained, with 10064 iterations. Score = 0.999\n", - "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", - " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(10, 30, 10), learning_rate='constant',\n", - " learning_rate_init=0.001, max_fun=15000, max_iter=20000,\n", - " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", - " power_t=0.5, random_state=43, shuffle=True, solver='lbfgs',\n", - " tol=0.0001, validation_fraction=0.1, verbose=False,\n", - " warm_start=False)\n", - "Training time 12.5 s.\n", + "MLPRegressor(activation='tanh', hidden_layer_sizes=(10, 30, 10), max_iter=20000,\n", + " random_state=43, solver='lbfgs')\n", + "Training time 13.8 s.\n", "Test data scaled. Log10 applied. \n", - "Training set size = 900, Test set size = 1763\n", - "Predicting from 2 inputs to 2 outputs using 1763 data in 0.00 secs.\n", + "Training set size = 900, Test set size = 1765\n", + "Predicting from 2 inputs to 2 outputs using 1765 data in 0.00 secs.\n", "[0.99851671037496]\n", "Instantiation. V 0.17\n", "Training set size = 900, Test set size = 0\n", @@ -452,7 +423,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/christophemorisset/Google Drive/Pro/AI4neb/ai4neb/Regressor/RegressionModel.py:531: RuntimeWarning: invalid value encountered in log10\n", + "/Users/christophemorisset/Google Drive/Pro/AI4neb/ai4neb/Regressor/RegressionModel.py:529: RuntimeWarning: invalid value encountered in log10\n", " X = np.log10(X)\n" ] }, @@ -461,27 +432,21 @@ "output_type": "stream", "text": [ "RM trained, with 9423 iterations. Score = 0.999\n", - "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", - " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(10, 30, 10), learning_rate='constant',\n", - " learning_rate_init=0.001, max_fun=15000, max_iter=20000,\n", - " momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n", - " power_t=0.5, random_state=43, shuffle=True, solver='lbfgs',\n", - " tol=0.0001, validation_fraction=0.1, verbose=False,\n", - " warm_start=False)\n", - "Training time 11.6 s.\n", + "MLPRegressor(activation='tanh', hidden_layer_sizes=(10, 30, 10), max_iter=20000,\n", + " random_state=43, solver='lbfgs')\n", + "Training time 12.7 s.\n", "Test data scaled. Log10 applied. \n", - "Training set size = 900, Test set size = 1763\n", - "Predicting from 2 inputs to 2 outputs using 1763 data in 0.00 secs.\n", + "Training set size = 900, Test set size = 1768\n", + "Predicting from 2 inputs to 2 outputs using 1768 data in 0.00 secs.\n", "[0.998907315818436]\n", - "time for ANN method: 24.275235 s.\n" + "time for ANN method: 26.640556 s.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/christophemorisset/Google Drive/Pro/AI4neb/ai4neb/Regressor/RegressionModel.py:531: RuntimeWarning: invalid value encountered in log10\n", + "/Users/christophemorisset/Google Drive/Pro/AI4neb/ai4neb/Regressor/RegressionModel.py:529: RuntimeWarning: invalid value encountered in log10\n", " X = np.log10(X)\n" ] } @@ -503,12 +468,12 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -534,12 +499,12 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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fVduB/d2yJA3NESj5aU8Trao+kWT2hNVXABd1z/cCdwBv3LyqJI07R6AkTaOzquooQPe4ZcT1SBozjkBJ0jKSzAPzANu2bRtxNZPFke92Cz8z5zPtB0egJE2jh5JsBegejy21UVXtqaq5qpqbmZnZ1AIl9ZsjUFPIT34StwI7gd3d4y2jLUfSuHEEStJES/J+4FPA85I8mORqBsHpkiT3A5d0y5I0NEegJE20qnrNMi9dvKmFSJoojkBJkiQ1MkBJkiQ1MkBJkiQ18hyoCecVd5IkrT9HoCRJkho5AiVJ2jSOig/Hn1P/rTgCleTdSY4luWfRujOT7Etyf/d4xsaWKUmS1B/DHML7A+DSE9btAvZX1XZgf7csSZI0FVYMUFX1CeAvT1h9BbC3e74XuHJ9y5IkSeqv1Z4DdVZVHQWoqqNJtqxjTZIkTaXWc59O3P7I7svWsxydxIZfhZdkPsmBJAeOHz++0W8nSZK04VYboB5KshWgezy23IZVtaeq5qpqbmZmZpVvJ0mS1B+rDVC3Aju75zuBW9anHEmSpP4b5jYG7wc+BTwvyYNJrgZ2A5ckuR+4pFuWJEmaCiueRF5Vr1nmpYvXuRZJkqSx4FQukiRJjQxQE2B2123e9l+SpE1kgJIkSWpkgJIkSWq02juRS5KkEVjPUzYWvpd3MG/nCJQkSVIjA5QkSVIjA5QkSVIjz4GaIN7KQJKm2+L/B048r+lkr6mdI1CSJEmNDFCSJEmNPITXI0tdTnriYTmHXSVJGj1HoCRJkho5AiVJWleOnPfDyW6S2XLR0TAnn0/jyeuOQEmSJDUyQEmSJDUyQEmSJDXyHKgeGvbYtDfOXLtJPTYvSdpYBihJ0t/aiA8Vftjrv6V+70v93k52Yvpm6sOHXw/hSZIkNTJASZIkNfIQniZeH4Z6+zLsLUlaH45ASZIkNVrTCFSSS4HfAE4B3lVVu9elKknaBOPQw0Y5gjrMyKmjq/3XehL/au9Svtb3GObv0DDvt1n/ZlYdoJKcAvwWcAnwIPDZJLdW1Z+tV3GTbD3/0ml99fE/hD7WNO7sYZLWYi2H8C4AvlhVX6qq7wAfAK5Yn7IkacPZwySt2loC1NnAVxYtP9itk6RxYA+TtGqpqtXtmFwF/HBV/Xi3/Drggqr66RO2mwfmu8XnAX8+5Fs8B/j6qorbeH2tra91QX9r62td0N/aWuv6R1U1s1HFrNYwPWwN/Qsm5/e3mfpaW1/rgv7WNil1Ldu/1nIS+YPAuYuWzwG+duJGVbUH2NP6zZMcqKq51Ze3cfpaW1/rgv7W1te6oL+19bWuVVixh622f0F/f059rQv6W1tf64L+1jYNda3lEN5nge1JvifJdwGvBm5dj6IkaRPYwySt2qpHoKrqsSTXAB9lcAnwu6vq3nWrTJI2kD1M0lqs6T5QVfVh4MPrVMuJVjVsvkn6Wltf64L+1tbXuqC/tfW1rmZT2sP6Whf0t7a+1gX9rW3i61r1SeSSJEnTyqlcJEmSGvU6QCX5lSR3JzmU5PYkzx11TQuS/Nck93X13Zzk2aOuCQaXZie5N8kTSUZ+BUSSS5P8eZIvJtk16noWJHl3kmNJ7hl1LYslOTfJx5Mc7n6P1466pgVJnp7kM0k+39X2tlHX1Hd97WH2r+H1sYf1tX9Bf3vYRvSvXh/CS/LdVfVI9/xngBdU1U+OuCwAkrwM+Fh3Iup/BqiqN464LJI8H3gCeCfwH6rqwAhrOQX4XyyaKgN4TR+mykjyYuBR4D1V9cJR17MgyVZga1XdleRZwEHgyp78zAI8s6oeTXIq8Eng2qr69IhL662+9jD719D19LKH9bV/QX972Eb0r16PQC00ns4zgd6kvaq6vaoe6xY/zeAeMiNXVYerquVmfxupt1NlVNUngL8cdR0nqqqjVXVX9/xbwGF6cnfsGni0Wzy1++rNv8k+6msPs38NrZc9rK/9C/rbwzaif/U6QAEk+dUkXwF+FHjLqOtZxo8BfzLqInrIqTLWIMkscD5w54hL+VtJTklyCDgG7Kuq3tTWV2PQw+xfy7OHrUHfeth696+RB6gk/zPJPUt8XQFQVb9YVecCNwDX9Km2bptfBB7r6utNXT2RJdb14hN43yU5HbgJeMMJoxgjVVWPV9UOBiMWFyTp1eGDUehrD7N/rQt72Cr1sYetd/9a032g1kNVvXTITd8H3Aa8dQPLeZKVakuyE3gFcHFt4slkDT+zURtquh89WXd8/ibghqr64KjrWUpVPZzkDuBSoHcnsm6mvvYw+9e6sIetQt972Hr1r5GPQJ1Mku2LFi8H7htVLSdKcinwRuDyqvrrUdfTU06V0ag70fF64HBVXTfqehZLMrNwtVaS04CX0qN/k33U1x5m/xqaPaxRX3vYRvSvvl+FdxODGdCfAL4M/GRVfXW0VQ0k+SLwNOAb3apP9+TqmlcC/x2YAR4GDlXVD4+wnpcD7+Dvpsr41VHVsliS9wMXMZiZ+yHgrVV1/UiLApL8C+BPgS8w+HsP8ObujtkjleSfAnsZ/C6fAtxYVb882qr6ra89zP7VVFPvelhf+xf0t4dtRP/qdYCSJEnqo14fwpMkSeojA5QkSVIjA5QkSVIjA5QkSVIjA5QkSVIjA5QkSVIjA9QES/J4kkNJ7k3y+ST/PslTutfmkvy37vnTuukVDiX5kST/stvnUHfDsd5Z9Gd7blf/R7rpIH5q0TZ7kpy/aPlnkzyQ5DdHU7WkYdm/7F99N/KpXLSh/k837w9JtjCYSuIfMLjp2gHgQLfd+cCpi7b9XeDXqur3h3mT7s6zqaonVtx4/Sz+s10OHAReDtwF/HaS84CnVNXnFnaoqrcn+SYwt4l1Slod+5f9q9ccgZoSVXUMmAeuycBFST7UNab3Aju6T0Q/AbwKeEuSGwCS/HySzya5O8nbunWzSQ4n+W0G/+jPXWG73+s+Fd6+8Kkwyfd3nxw/n+SuJN+33Put4G+A03jyB4JfgV7OfC+pkf1LfWSAmiJV9SUGv/Mti9YdA34c+NOq2lFV72Qw19PPV9WPJnkZsB24ANgB/PMkL+52fx7wnqo6v3u+3Hbbgd+qqn/CYHqGf92tv6Fbfx7wg8DRFd5vOfuAfwjcCfyXhU90VeWkn9KEsH+pbzyEN33SuP3Luq+FoeTTGTSIB4AvV9Wnh9juL6rqULf+IDCb5FnA2VV1M0BV/V+ArgEt9X0+sVyBVfUY8G+6/U8FPgpcnuQ6YBuDJukEoNL4s3+pNwxQUyTJ9wKPA8eA5w+7G/Cfuk92i7/XLPBXQ2737UWrHmcwXL1cI1zy+zT4KQYTRv4A8B3gR4BP4Qzq0lizf6lvPIQ3JZLMAL8L/Ga1zSD9UeDHkpzefZ+zu/MOVrsdAFX1CPBgkiu77Z+W5Bmt32exJGcArwDeAzyDwUzgBTx9qD+ppF6yf6mPHIGabKclOQScCjwG/CFwXcs3qKrbkzwf+FQSgEeB1zL4JNa83QleB7wzyS8zOJHyqpN8n2NDlPsW4D9WVSX5KPB64AsMGq+k8WL/sn/1WtrCvNQPSR6tqtNXsd+/Beaq6pr1r0qSVmb/mgwewtO4eqS7bPm5w+6Q5GeBNwGPbFxZkrQi+9cEcARKkiSpkSNQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjQxQkiRJjZ66mW/2nOc8p2ZnZzfzLSWN2MGDB79eVTOjrmOt7F/S9DlZ/9rUADU7O8uBAwc28y0ljViSL4+6hvVg/5Kmz8n6l4fwJEmSGhmgJEmSGhmgJEmSGhmgJEmSGhmgJEmSGhmgJE28JKck+VySD3XLZybZl+T+7vGMUdcoabwYoCRNg2uBw4uWdwH7q2o7sL9blqShGaAkTbQk5wCXAe9atPoKYG/3fC9w5SaXJWnMGaAkTbp3AL8APLFo3VlVdRSge9wygrokjbFNvRO5ptvsrtsAOLL7shFXommR5BXAsao6mOSiVew/D8wDbNu2bX2L08RY6G1gf5smjkBJmmQXApcnOQJ8AHhJkvcCDyXZCtA9Hltq56raU1VzVTU3MzP20/lJWkcGKEkTq6reVFXnVNUs8GrgY1X1WuBWYGe32U7glhGVKGlMGaAkTaPdwCVJ7gcu6ZYlaWieAyVpKlTVHcAd3fNvABePsh5J480RKEmSpEYGKEmSpEYGKEmSpEYGKEmSpEYGKEmSpEYGKEmSpEYGKEmSpEYGKEmSpEYGKEmSpEbeiVybzpnLJUnjzhEoSZKkRgYoSZKkRgYoSZKkRgYoSZKkRgYoSZKkRkMFqCQ/m+TeJPckeX+Spyc5M8m+JPd3j2dsdLGSJEl9sGKASnI28DPAXFW9EDgFeDWwC9hfVduB/d2yJEnSxBv2EN5TgdOSPBV4BvA14Apgb/f6XuDKda9OkiSph1YMUFX1VeDXgAeAo8D/V1W3A2dV1dFum6PAlo0sVJIkqS9WvBN5d27TFcD3AA8D/yPJa4d9gyTzwDzAtm3bVlelJEnrYLUzITiDgk40zCG8lwJ/UVXHq+pvgA8CPwg8lGQrQPd4bKmdq2pPVc1V1dzMzMx61S1JkjQywwSoB4AXJXlGkgAXA4eBW4Gd3TY7gVs2pkRJWp3uiuHPJPl8dyXx27r1v5Tkq0kOdV8vH3WtksbLiofwqurOJH8M3AU8BnwO2AOcDtyY5GoGIeuqjSxUklbh28BLqurRJKcCn0zyJ91rb6+qXxthbZLG2IoBCqCq3gq89YTV32YwGiVJvVRVBTzaLZ7afdXoKpI0KbwTuaSJluSUJIcYnKe5r6ru7F66JsndSd7tjYAltTJASZpoVfV4Ve0AzgEuSPJC4HeA7wN2MLg9y68vtW+S+SQHkhw4fvz4JlUsaRwYoCRNhap6GLgDuLSqHuqC1RPA7wEXLLOPVxFLWpIBStLESjKT5Nnd89MY3JblvoVbsHReCdwzgvIkjbGhTiKXpDG1Fdib5BQGHxhvrKoPJfnDJDsYnFB+BPiJ0ZUoaRwZoCRNrKq6Gzh/ifWvG0E5kiaIh/AkSZIaOQIlSeqlk80/tx5z0y18j4X9ne9OLRyBkiRJamSAkiRJamSAkiRJamSAkiRJamSAkiRJauRVeJIkLWPxlXnSYo5ASZIkNTJASZIkNTJASZIkNTJASZIkNTJASZIkNfIqPEnS2FjqqrgT57STNoMjUJIkSY0MUJIkSY0MUJIkSY0MUJIkSY08iVySNBE2+2Ryp3mZbo5ASZIkNTJASZpYSZ6e5DNJPp/k3iRv69afmWRfkvu7xzNGXauk8WKAkjTJvg28pKrOA3YAlyZ5EbAL2F9V24H93bIkDc0AJWli1cCj3eKp3VcBVwB7u/V7gSs3vzpJ48wAJWmiJTklySHgGLCvqu4EzqqqowDd45YRlihpDHkVnqSJVlWPAzuSPBu4OckLh903yTwwD7Bt27aNKVBjx6vvBI5ASZoSVfUwcAdwKfBQkq0A3eOxZfbZU1VzVTU3MzOzWaVKGgMGKEkTK8lMN/JEktOAlwL3AbcCO7vNdgK3jKRASWPLQ3iSJtlWYG+SUxh8YLyxqj6U5FPAjUmuBh4ArhplkZLGjwFK0sSqqruB85dY/w3g4s2vSNKk8BCeJElSI0egJEk6gVfaaSWOQEmSJDUyQEmSJDUyQEmSJDUyQEmSJDUyQEmSJDXyKjxJUq9sxBVwm3VV3cL7HNl92aa8n0bHESiN1Oyu27xcWJI0doYKUEmeneSPk9yX5HCSH0hyZpJ9Se7vHs/Y6GIlSZL6YNgRqN8APlJV/xg4DzgM7AL2V9V2YH+3LEmSNPFWDFBJvht4MXA9QFV9p6oeBq4A9nab7QWu3JgSJUmS+mWYEajvBY4Dv5/kc0neleSZwFlVdRSge9yygXVKkiT1xjBX4T0V+GfAT1fVnUl+g4bDdUnmgXmAbdu2rapIjTdPEpe0Vi19xJ6jzTDMCNSDwINVdWe3/McMAtVDSbYCdI/Hltq5qvZU1VxVzc3MzKxHzZIkSSO1YoCqqv8NfCXJ87pVFwN/BtwK7OzW7QRu2ZAKJUmSembYG2n+NHBDku8CvgT8Owbh68YkVwMPAFdtTImSJEn9MlSAqqpDwNwSL128rtVIkiSNAe9ELkmS1MgAJUmS1MgAJUmS1MgAJWliJTk3yce7OTzvTXJtt/6Xknw1yaHu6+WjrlXSeBn2KjxJGkePAT9XVXcleRZwMMm+7rW3V9WvjbA2SWPMACVpYnXTTC1MOfWtJIeBs0dblaRJ4CE8SVMhySxwPrAwq8I1Se5O8u4kZ4yuMknjyAAlaeIlOR24CXhDVT0C/A7wfcAOBiNUv77MfvNJDiQ5cPz48c0qVxNgdtdtzsk34TyEpw1h41BfJDmVQXi6oao+CFBVDy16/feADy21b1XtAfYAzM3N1cZXK2lcOAIlaWIlCXA9cLiqrlu0fuuizV4J3LPZtUkab45ASZpkFwKvA76Q5FC37s3Aa5LsAAo4AvzEKIqTNL4MUFpXHrpTn1TVJ4Es8dKHN7sWSZPFQ3iSJEmNHIGSJE01R861Go5ASZIkNTJASZIkNTJASZIkNTJASZIkNTJASZIkNTJAaaw535QkaRQMUJIkSY0MUJIkSY28kaZ6ZfHhuCO7Lxv6NUmSNpMjUJIkSY0cgVIveCK4JGmcOAIlSZLUyAAlSZLUyAAlSZLUyAAlSZLUyAAlSZLUyKvw1FsLV+Z5zydp8ix15e0k/lv3/nWTyxEoSZKkRgYoSRMryblJPp7kcJJ7k1zbrT8zyb4k93ePZ4y6VknjxQAlaZI9BvxcVT0feBHw+iQvAHYB+6tqO7C/W5akoRmgJE2sqjpaVXd1z78FHAbOBq4A9nab7QWuHEmBksaWAUrSVEgyC5wP3AmcVVVHYRCygC0jLE3SGDJASZp4SU4HbgLeUFWPNOw3n+RAkgPHjx/fuAIljR0DlKSJluRUBuHphqr6YLf6oSRbu9e3AseW2req9lTVXFXNzczMbE7BksaCAUrSxEoS4HrgcFVdt+ilW4Gd3fOdwC2bXZuk8eaNNCVNsguB1wFfSHKoW/dmYDdwY5KrgQeAq0ZTnqRxZYCSNLGq6pNAlnn54s2sRdJk8RCeJElSI0egJEkaAefJG2+OQEmSJDUaOkAlOSXJ55J8qFt2LilJkjSVWkagrmUwDcIC55LSyMzuuu1Jw9+SJG2moQJUknOAy4B3LVrtXFKSJGkqDTsC9Q7gF4AnFq1zLilJkjSVVrwKL8krgGNVdTDJRa1vkGQemAfYtm1b6+6SpCnhYXmNk2FGoC4ELk9yBPgA8JIk78W5pCRJ0pRaMUBV1Zuq6pyqmgVeDXysql6Lc0lJkqQptZb7QO0GLklyP3BJtyxJkjTxmu5EXlV3AHd0z7+Bc0lJkqQp5J3IJUmSGhmgJEmSGhmgJEmSGhmgJEmSGhmgJEmSGhmgJEmSGjXdxkAaBad3kCT1jSNQkqR1NbvrNj/4LMGfy2QxQEmSJDUyQEmaaEneneRYknsWrfulJF9Ncqj7evkoa5Q0fgxQkibdHwCXLrH+7VW1o/v68CbXJGnMeRK51oXH9dVXVfWJJLOjrkPSZHEEStK0uibJ3d0hvjNGXYyk8WKAkjSNfgf4PmAHcBT49aU2SjKf5ECSA8ePH9/E8jTJvBpvMhigJE2dqnqoqh6vqieA3wMuWGa7PVU1V1VzMzMzm1ukpF4zQEmaOkm2Llp8JXDPcttK0lI8iVzSREvyfuAi4DlJHgTeClyUZAdQwBHgJ0ZVn6TxZICSNNGq6jVLrL5+0wuRNFE8hCdJktTIACVJktTIACVJktTIACVJktTIACVJktTIACVJktTIACVJ2hBOWaJJZoCSJElqZICSJElqZICSJElqZICSJElqZICSJElqZICSJElqZICSJElq9NRRF6Dx5f1dJEnTyhEoSZKkRgYoSZKkRgYoSZKkRgYoSZKkRgYoSZKkRgYoSRMtybuTHEtyz6J1ZybZl+T+7vGMUdYoafwYoCRNuj8ALj1h3S5gf1VtB/Z3y5I0NO8DpWbe/0njpKo+kWT2hNVXABd1z/cCdwBv3LyqJI07R6AkTaOzquooQPe4ZcT1SBozjkBJ0jKSzAPzANu2bRtxNZPBEeylLfxcjuy+bMSVaFiOQEmaRg8l2QrQPR5baqOq2lNVc1U1NzMzs6kFSuq3FQNUknOTfDzJ4ST3Jrm2W+9VLJLG1a3Azu75TuCWEdYiaQwNMwL1GPBzVfV84EXA65O8AK9ikTQGkrwf+BTwvCQPJrka2A1ckuR+4JJuWZKGtuI5UN0JlgsnW34ryWHgbLyKRdIYqKrXLPPSxZtaiKSJ0nQOVHcp8PnAnXgViyRJmlJDX4WX5HTgJuANVfVIkmH38yoWSZpwXl2naTPUCFSSUxmEpxuq6oPdaq9ikSRJU2mYq/ACXA8crqrrFr3kVSySJGkqDXMI70LgdcAXkhzq1r2ZwVUrN3ZXtDwAXLUhFWqkFg/Le4M3SZIGhrkK75PAcic8eRWLJEmaOt6JXJIkqZFz4UmS1DNLXdV4stMonEtv8zkCJUmS1MgAJUmS1MgAJUmS1MgAJUmS1MgAJUmS1Mir8CRJG8p58jSJHIGSJElqZICSJElqZICSJElqZICSJElqZICSJElqZICSJGmCze66zSshN4C3MZA0tZIcAb4FPA48VlVzo61I0rgwQEmadj9UVV8fdRGSxouH8CRJkhoZoCRNswJuT3Iwyfyoi5E0PjyEJ2maXVhVX0uyBdiX5L6q+sTCi12omgfYtm3bqGpcd4tPKD6y+7KhtltuW09O1rRyBErS1Kqqr3WPx4CbgQtOeH1PVc1V1dzMzMwoSpTUUwYoSVMpyTOTPGvhOfAy4J7RViVpXHgIT0NzqF4T5izg5iQw6IXvq6qPjLYkSePCACVpKlXVl4DzRl2HpPHkITxJkqRGjkDpSRYO053sypw+WuqqonH9s0jjwEP6/bfa35G9cziOQEmSJDUyQEmSJDUyQE0xZ+iWJGl1DFCSJEmNPIlcEz8KNey0FZtRgydlStJkMEBJkoYy6R+2+m6Yn3/r7+hkH+7W+sFv0j84eghPkiSpkSNQWtI0ftJc7aelSf+UJUn6+xyBkiRJamSAkiRJauQhvE2yXod5ljq0drKT/062jSRJWh0DlCSN2LBzOa71PL1htvHD1mht5Pmn63lLF/++GKCkv2eYy3olSdPNc6AkSZIaGaAkSZIaeQhvHa3XfYROdj7EMN9nvbYbV+v153MKGEnSchyBkiRJauQIlCRtgGGuolvP0eDV3rrkxP0mfYRaAy2/942Yg2+zbOQovgFqk23EFV7j/Jd7s63XfzIn2+Zk33PY+3hJkvptTYfwklya5M+TfDHJrvUqSpI2gz1M0mqtegQqySnAbwGXAA8Cn01ya1X92XoVt17WcwivZZShdSRiLdtpdTbi57vWkcRh/944crU249TDJPXPWkagLgC+WFVfqqrvAB8ArlifsiRpw9nDJK3aWgLU2cBXFi0/2K2TpHFgD5O0aqmq1e2YXAX8cFX9eLf8OuCCqvrpE7abB+a7xecBfz7kWzwH+Pqqitt4fa2tr3VBf2vra13Q39pa6/pHVTWzUcWs1jA9bA39Cybn97eZ+lpbX+uC/tY2KXUt27/WchXeg8C5i5bPAb524kZVtQfY0/rNkxyoqrnVl7dx+lpbX+uC/tbW17qgv7X1ta5VWLGHrbZ/QX9/Tn2tC/pbW1/rgv7WNg11reUQ3meB7Um+J8l3Aa8Gbl2PoiRpE9jDJK3aqkegquqxJNcAHwVOAd5dVfeuW2WStIHsYZLWYk030qyqDwMfXqdaTrSqYfNN0tfa+loX9Le2vtYF/a2tr3U1m9Ie1te6oL+19bUu6G9tE1/Xqk8ilyRJmlZOJixJktSo1wEqya8kuTvJoSS3J3nuqGtakOS/Jrmvq+/mJM8edU0wuDQ7yb1Jnkgy8isg+jpVRpJ3JzmW5J5R17JYknOTfDzJ4e73eO2oa1qQ5OlJPpPk811tbxt1TX3X1x5m/xpeH3tYX/sX9LeHbUT/6vUhvCTfXVWPdM9/BnhBVf3kiMsCIMnLgI91J6L+Z4CqeuOIyyLJ84EngHcC/6GqDoywllOA/8WiqTKA1/RhqowkLwYeBd5TVS8cdT0LkmwFtlbVXUmeBRwEruzJzyzAM6vq0SSnAp8Erq2qT4+4tN7qaw+zfw1dTy97WF/7F/S3h21E/+r1CNRC4+k8E+hN2quq26vqsW7x0wzuITNyVXW4qlpu9reRejtVRlV9AvjLUddxoqo6WlV3dc+/BRymJ3fHroFHu8VTu6/e/Jvso772MPvX0HrZw/rav6C/PWwj+levAxRAkl9N8hXgR4G3jLqeZfwY8CejLqKHnCpjDZLMAucDd464lL+V5JQkh4BjwL6q6k1tfTUGPcz+tTx72Br0rYetd/8aeYBK8j+T3LPE1xUAVfWLVXUucANwTZ9q67b5ReCxrr7e1NUTWWJdLz6B912S04GbgDecMIoxUlX1eFXtYDBicUGSXh0+GIW+9jD717qwh61SH3vYevevNd0Haj1U1UuH3PR9wG3AWzewnCdZqbYkO4FXABfXJp5M1vAzG7WhpvvRk3XH528CbqiqD466nqVU1cNJ7gAuBXp3Iutm6msPs3+tC3vYKvS9h61X/xr5CNTJJNm+aPFy4L5R1XKiJJcCbwQur6q/HnU9PeVUGY26Ex2vBw5X1XWjrmexJDMLV2slOQ14KT36N9lHfe1h9q+h2cMa9bWHbUT/6vtVeDcxmAH9CeDLwE9W1VdHW9VAki8CTwO+0a36dE+urnkl8N+BGeBh4FBV/fAI63k58A7+bqqMXx1VLYsleT9wEYOZuR8C3lpV14+0KCDJvwD+FPgCg7/3AG/u7pg9Ukn+KbCXwe/yKcCNVfXLo62q3/raw+xfTTX1rof1tX9Bf3vYRvSvXgcoSZKkPur1ITxJkqQ+MkBJkiQ1MkBJkiQ1MkBJkiQ1MkBJkiQ1MkBJkiQ1MkBNsCSPJzmU5N4kn0/y75M8pXttLsl/654/rZte4VCSH0nyL7t9DnU3HOudRX+253b1f6SbDuKnFm2zJ8n5i5Z/NskDSX5zNFVLGpb9y/7VdyOfykUb6v908/6QZAuDqST+AYObrh0ADnTbnQ+cumjb3wV+rap+f5g36e48m6p6YsWN18/iP9vlwEHg5cBdwG8nOQ94SlV9bmGHqnp7km8Cc5tYp6TVsX/Zv3rNEagpUVXHgHngmgxclORDXWN6L7Cj+0T0E8CrgLckuQEgyc8n+WySu5O8rVs3m+Rwkt9m8I/+3BW2+73uU+HtC58Kk3x/98nx80nuSvJ9y73fCv4GOI0nfyD4FejlzPeSGtm/1EcGqClSVV9i8DvfsmjdMeDHgT+tqh1V9U4Gcz39fFX9aJKXAduBC4AdwD9P8uJu9+cB76mq87vny223HfitqvonDKZn+Nfd+hu69ecBPwgcXeH9lrMP+IfAncB/WfhEV1VO+ilNCPuX+sZDeNMnjdu/rPtaGEo+nUGDeAD4clV9eojt/qKqDnXrDwKzSZ4FnF1VNwNU1f8F6BrQUt/nE8sVWFWPAf+m2/9U4KPA5UmuA7YxaJJOACqNP/uXesMANUWSfC/wOHAMeP6wuwH/qftkt/h7zQJ/NeR231606nEGw9XLNcIlv0+Dn2IwYeQPAN8BfgT4FM6gLo01+5f6xkN4UyLJDPC7wG9W2wzSHwV+LMnp3fc5uzvvYLXbAVBVjwAPJrmy2/5pSZ7R+n0WS3IG8ArgPcAzGMwEXsDTh/qTSuol+5f6yBGoyXZakkPAqcBjwB8C17V8g6q6PcnzgU8lAXgUeC2DT2LN253gdcA7k/wygxMprzrJ9zk2RLlvAf5jVVWSjwKvB77AoPFKGi/2L/tXr6UtzEv9kOTRqjp9Ffv9W2Cuqq5Z/6okaWX2r8ngITyNq0e6y5afO+wOSX4WeBPwyMaVJUkrsn9NAEegJEmSGjkCJUmS1MgAJUmS1MgAJUmS1MgAJUmS1MgAJUmS1Oj/B7IhDbxtZPN0AAAAAElFTkSuQmCC\n", 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" ] @@ -930,7 +895,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.9" } }, "nbformat": 4, From faa49b6067fd17572bcc9f78e538fdc9fc5ea998 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 5 Apr 2021 17:56:19 +0100 Subject: [PATCH 08/36] 1.1.15b3 --- pyneb/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/version.py b/pyneb/version.py index 606e2ff3..b7fe559f 100644 --- a/pyneb/version.py +++ b/pyneb/version.py @@ -1,2 +1,2 @@ # PyNeb version -__version__ = '1.1.15b2' +__version__ = '1.1.15b3' From 7e9afbd5a1ce7827568f6d48e282eae8cf1beeb9 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 5 Apr 2021 17:56:30 +0100 Subject: [PATCH 09/36] Add pipeline --- pyneb/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pyneb/__init__.py b/pyneb/__init__.py index 01a0ae65..25856661 100644 --- a/pyneb/__init__.py +++ b/pyneb/__init__.py @@ -32,6 +32,7 @@ from .core.pynebcore import Atom, RecAtom, getAtomDict, getRecEmissivity, EmissionLine, Observation, \ parseLineLabel, isValid +from .core.pipeline import PipeLine from .core.icf import ICF from .core.diags import Diagnostics, diags_dict from .core.emisGrid import EmisGrid, getEmisGridDict From 49303123311f25b44e57df2573aa70f80ee63a16 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 6 Apr 2021 22:11:47 +0100 Subject: [PATCH 10/36] adding ravel --- pyneb/core/diags.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/pyneb/core/diags.py b/pyneb/core/diags.py index e0e28378..f56abe22 100644 --- a/pyneb/core/diags.py +++ b/pyneb/core/diags.py @@ -788,17 +788,20 @@ def B(label, I=I, L=L): else: self.ANN = ANN # set the test values to the one we are looking for - self.ANN.set_test(np.array((value_tem, value_den)).T) + shape = value_tem.shape + self.ANN.set_test(np.array((value_tem.ravel(), value_den.ravel())).T) # predict the result and denormalize them self.ANN.predict() if self.ANN.isfin is None: tem = self.ANN.pred[:,0]*1e4 den = 10**self.ANN.pred[:,1] else: - tem = np.zeros_like(value_tem) * -10 + tem = np.zeros_like(value_tem.ravel()) * -10 tem[self.ANN.isfin] = self.ANN.pred[:,0]*1e4 - den = np.zeros_like(value_tem) * -10 + den = np.zeros_like(value_tem.ravel()) * -10 den[self.ANN.isfin] = 10**self.ANN.pred[:,1] + tem = np.reshape(tem, shape) + den = np.reshape(den, shape) if limit_res: mask = (temtem_max) tem[mask] = np.nan From 6b41af0fc487b1ed151740340e19d998c1bf9f19 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 6 Apr 2021 22:12:35 +0100 Subject: [PATCH 11/36] optimize MC for multi obs --- pyneb/core/pynebcore.py | 49 +++++++++++++++++++++++++++++++++++------ 1 file changed, 42 insertions(+), 7 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index ce98cec2..8b47c2ad 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -3950,10 +3950,13 @@ class EmissionLine(object): - label line label in the standard PyNeb format - obsIntens observed intensity - obsError uncertainty on the observed intensity + - errIsRelative Boolean. True if the errors are relative to the intensities, False if they + are in the same unit as the intensity (default: True) + """ def __init__(self, elem=None, spec=None, wave=None, blend=False, to_eval=None, label=None, - obsIntens=None, obsError=None, corrected=False, _unit=None): + obsIntens=None, obsError=None, corrected=False, _unit=None, errIsRelative=True): self.log_ = log_ self.calling = 'EmissionLine' @@ -4007,10 +4010,15 @@ def __init__(self, elem=None, spec=None, wave=None, blend=False, to_eval=None, l if obsError is None: self.obsError = np.zeros_like(self.obsIntens) else: - self.obsError = np.asarray(obsError, dtype=float) - + if errIsRelative: + self.obsError = np.asarray(obsError, dtype=float) + else: + self.obsError = np.asarray(obsError, dtype=float) / self.obsIntens if self.corrected: - self.corrError = np.asarray(self.obsError, dtype=float) + if errIsRelative: + self.corrError = np.asarray(self.obsError, dtype=float) + else: + self.corrError = np.asarray(self.obsError, dtype=float) / self.corrIntens else: self.corrError = np.zeros_like(self.obsError) ## @@ -4125,6 +4133,7 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err self.names = [] self.extinction = RedCorr(law=correcLaw) self.corrected = corrected + self.MC_added = False if self.corrected: self.extinction.law = 'No correction' if obsFile is not None: @@ -4148,7 +4157,7 @@ def addLine(self, line): if not isinstance(line, EmissionLine): self.log_.error('Trying to add an inappropriate record to observations', calling=self.calling) return None - if self.corrected: + if self.corrected and not line.corrected: line.corrected = True self.correctData(line) self.lines.append(line) @@ -4670,13 +4679,38 @@ def addMonteCarloObs(self, N=0, i_obs=None): N: number of new observations to be added for each original observation. i_obs: used in case only a given observations needs to be treated """ + if self.MC_added: + self.log_.error('Monte Carlo already applied to this observation', calling='addMonteCarloObs') n_lines = self.n_lines n_obs = self.n_obs if i_obs is None: self.log_.message('Entering', calling='addMonteCarloObs') - for i in range(n_obs): - self.addMonteCarloObs(i_obs=i, N=N) + + for l in self.lines: + l_ori = l.obsIntens + e_ori = l.obsError + l_new = np.repeat(l_ori[:, np.newaxis], N+1, axis=1) + e_new = np.repeat(e_ori[:, np.newaxis], N+1, axis=1) + norm = np.random.standard_normal(l_new.shape) + norm[:,0] = 0. + l_new *= (1 + e_new * norm) + l.obsIntens = l_new.ravel() + l.obsError = e_new.ravel() + if self.corrected: + l.corrIntens = l_new.ravel() + l.corrError = e_new.ravel() + self.log_.debug('Adding MC to {}. {} {} {} {}'.format(l, l_new.shape, + l_new.ravel().shape, + l.obsIntens.shape, + l.corrIntens.shape), + calling='addMonteCarloObs') + new_names = np.repeat(np.asarray(self.names)[:, np.newaxis], N+1, axis=1) + MC_names = np.asarray(['-MC-{}'.format(i) for i in np.arange(N+1)]) + MC_names[0] = '' + self.names = np.core.defchararray.add(new_names , MC_names) self.log_.message('Leaving', calling='addMonteCarloObs') + self.MC_added = True + else: if self.corrected: returnObs=False @@ -4690,4 +4724,5 @@ def addMonteCarloObs(self, N=0, i_obs=None): new_obs = intens * (all_new_obs[i,:] * error + 1) new_obs[new_obs < 0.] = 0. self.addObs('{0}-MC-{1}'.format(self.names[i_obs], i), new_obs, error) + self.MC_added = True From fade4b237f0fec88bc7a5e9ce6773e4b260afc21 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 8 Apr 2021 16:08:05 +0100 Subject: [PATCH 12/36] comment on error --- pyneb/core/pynebcore.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 8b47c2ad..008d6887 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -4045,7 +4045,7 @@ def correctIntens(self, RC, normWave=None): self.corrIntens = self.obsIntens * RC.getCorr(self.wave, normWave) else: self.corrIntens = self.obsIntens - self.corrError = self.obsError + self.corrError = self.obsError # error is supposed to be relative. def addObs(self, newObsIntens, newObsError=None): From 2fcf925432d3eb9cf5a60f6e6a31fff44e9be921 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 8 Apr 2021 16:08:29 +0100 Subject: [PATCH 13/36] avoid log(0) message --- pyneb/extinction/red_corr.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyneb/extinction/red_corr.py b/pyneb/extinction/red_corr.py index 135a3395..d39f36c8 100644 --- a/pyneb/extinction/red_corr.py +++ b/pyneb/extinction/red_corr.py @@ -296,7 +296,8 @@ def setCorr(self, obs_over_theo, wave1, wave2): f1 = np.log10(COR.getCorr(wave1)) f2 = np.log10(COR.getCorr(wave2)) if f1 != f2: - self.E_BV = 2.5 * np.log10(obs_over_theo) / (f1 - f2) + with np.errstate(invalid='ignore'): + self.E_BV = 2.5 * np.log10(obs_over_theo) / (f1 - f2) else: self.E_BV = 0. del COR From 58b828921749fa50bc343765d2353913a029cfe5 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 8 Apr 2021 16:10:02 +0100 Subject: [PATCH 14/36] removing pipeline --- pyneb/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/__init__.py b/pyneb/__init__.py index 25856661..5c1e4a9b 100644 --- a/pyneb/__init__.py +++ b/pyneb/__init__.py @@ -32,7 +32,7 @@ from .core.pynebcore import Atom, RecAtom, getAtomDict, getRecEmissivity, EmissionLine, Observation, \ parseLineLabel, isValid -from .core.pipeline import PipeLine +#from .core.pipeline import PipeLine from .core.icf import ICF from .core.diags import Diagnostics, diags_dict from .core.emisGrid import EmisGrid, getEmisGridDict From 04be209f46624270856330dc6dd0f77b1fab4cab Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Fri, 9 Apr 2021 13:00:06 +0100 Subject: [PATCH 15/36] conserve original frame --- pyneb/core/pynebcore.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 008d6887..e7c7538b 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -4692,8 +4692,8 @@ def addMonteCarloObs(self, N=0, i_obs=None): l_new = np.repeat(l_ori[:, np.newaxis], N+1, axis=1) e_new = np.repeat(e_ori[:, np.newaxis], N+1, axis=1) norm = np.random.standard_normal(l_new.shape) - norm[:,0] = 0. l_new *= (1 + e_new * norm) + l_new[:,0] = l_ori l.obsIntens = l_new.ravel() l.obsError = e_new.ravel() if self.corrected: From a271dda8f19c7eb85bb1e414466db3016ea051fc Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 13 Apr 2021 16:58:02 +0100 Subject: [PATCH 16/36] removing undefined diag --- pyneb/core/diags.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyneb/core/diags.py b/pyneb/core/diags.py index f56abe22..29c7e638 100644 --- a/pyneb/core/diags.py +++ b/pyneb/core/diags.py @@ -25,8 +25,8 @@ diags_dict['[OI] 5577/6300'] = ('O1', 'L(5577)/L(6300)', 'RMS([E(6300), E(5577)])') diags_dict['[OI] 5577/6300+'] = ('O1', 'L(5577)/(L(6300)+L(6364))', 'RMS([E(6300)*L(6300)/(L(6300)+L(6364)), E(6364)*L(6364)/(L(6300)+L(6364)), E(5577)])') diags_dict['[OII] 3726/3729'] = ('O2', 'L(3726)/L(3729)', 'RMS([E(3729), E(3726)])') -diags_dict['[OII] 3727+/7325+c'] = ('O2', '(B("3727A+"))/(B("7319A+")+B("7330A+"))', - 'RMS([BE("7319A+")*B("7319A+")/(B("7319A+")+B("7330A+")), BE("7330A+")*B("7330A+")/(B("7319A+")+B("7330A+")), BE("3727A+")])') +#diags_dict['[OII] 3727+/7325+c'] = ('O2', '(B("3727A+"))/(B("7319A+")+B("7330A+"))', +# 'RMS([BE("7319A+")*B("7319A+")/(B("7319A+")+B("7330A+")), BE("7330A+")*B("7330A+")/(B("7319A+")+B("7330A+")), BE("3727A+")])') diags_dict['[OII] 3727+/7325+'] = ('O2', '(L(3726)+L(3729))/(B("7319A+")+B("7330A+"))', 'RMS([E(3726)*L(3726)/(L(3726)+L(3729)), E(3729)*L(3729)/(L(3726)+L(3729)),BE("7319A+")*B("7319A+")/(B("7319A+")+B("7330A+")),BE("7330A+")*B("7330A+")/(B("7319A+")+B("7330A+"))])') #diags_dict['[OII] 3727+/7325+'] = ('O2', '(L(3726)+L(3729))/(B("7325A+"))', 'RMS([E(3726)*L(3726)/(L(3726)+L(3729)), E(3729)*L(3729)/(L(3726)+L(3729)),BE("7325A+")])') From 5d431250dc11236819853529b31d40c2e478d267 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 13 Apr 2021 16:58:17 +0100 Subject: [PATCH 17/36] Add recomb line labels --- pyneb/utils/init.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/pyneb/utils/init.py b/pyneb/utils/init.py index b9958b27..96f8a175 100644 --- a/pyneb/utils/init.py +++ b/pyneb/utils/init.py @@ -36,7 +36,7 @@ def _check_line_label_list(maxErrorA = 5.e-3, maxErrorm = 5.e-2): LINE_LABEL_LIST = {} LINE_LABEL_LIST['H1r'] = ['1216A', '1026A', '973A', '6563A', '4861A', '4341A', '4102A', '3970A', '3889A', - '3835A', '3798A', '1.87m', '1.28m', '1.09m', '9546A', '9229A'] + '3835A', '3798A', '1.87m', '1.28m', '1.09m', '9546A', '9229A', '8750A'] LINE_LABEL_LIST['He1r'] = ['5876A', '2945A', '3188A', '3614A', '3889A', '3965A', '4026A', '4121A', '4388A', '4438A', '4471A', '4713A', '4922A', '5016A', '5048A', '5876A', '6678A', '7065A', '7281A', '9464A', '10830A', '11013A', '11969A', '12527A', '12756A', '12785A', @@ -61,6 +61,7 @@ def _check_line_label_list(maxErrorA = 5.e-3, maxErrorm = 5.e-2): LINE_LABEL_LIST['C2'] = ['2325A', '2328A', '2323A', '2327A', '2322A', '2325A', '1335A', '1336A', '3131A', '3133A', '3136A', '1036A', '1037A', '1869A', '1870A', '1871A', '4636A', '4637A', '157.6m', '454.4m', '198.8m', '353.3m', '3967.2m'] +LINE_LABEL_LIST['C2r'] = ['9903+', '4267+', '7231+', '6580+', '2837+', '1761+', '1335+'] LINE_LABEL_LIST['C3'] = ['1910A', '1909A', '1907A', '977A', '2000A', '2001A', '2003A', '422.0m', '124.9m', '177.4m'] LINE_LABEL_LIST['C4'] = ['1551A', '1548A', '92.8m'] LINE_LABEL_LIST['Ca2'] = ['7292A', '7324A'] @@ -90,6 +91,16 @@ def _check_line_label_list(maxErrorA = 5.e-3, maxErrorm = 5.e-2): LINE_LABEL_LIST['N1'] = ['5200A', '5198A', '3467A', '3466A'] LINE_LABEL_LIST['N2'] = ['6527A', '6548A', '6584A', '3058A', '3063A', '3071A', '5755A', '2137A', '2139A', '2143A', '3177A', '7092A', '205.3m', '76.4m', '121.8m'] +LINE_LABEL_LIST['N2r'] = ['4026.08A', '4035.08A', '4039.35A', '4041.31A', '4043.53A', '4044.78A', + '4056.90A', '4058.16A', '4073.04A', '4076.91A', '4077.40A', '4082.27A', + '4082.89A', '4086.83A', '4087.30A', '4095.90A', '4096.58A', '4100.97A', + '4601.48A', '4607.16A', '4613.87A', '4621.39A', '4630.54A', '4643.09A', + '4774.24A', '4779.72A', '4781.19A', '4788.13A', '4793.65A', '4803.29A', + '4810.31A', '5001.14A', '5001.48A', '5005.15A', '5016.39A', '5025.66A', + '5040.72A', '5452.08A', '5454.22A', '5462.59A', '5478.10A', '5480.06A', + '5495.67A', '5666.63A', '5676.02A', '5679.56A', '5686.21A', '5710.77A', + '5730.65A', '5927.81A', '5931.78A', '5940.24A', '5941.65A', '5952.39A', + '5960.90A'] LINE_LABEL_LIST['N3'] = ['1749A', '1754A', '1747A', '1752A', '1744A', '1750A', '990A', '992A', '2280A', '2284A', '2288A', '2280A', '2283A', '2287A', '763A', '764A', '1356A', '1357A', '3334A', '3335A', '57.4m', '167.5m', '71.0m', '123.3m', '1492.1m'] @@ -108,6 +119,7 @@ def _check_line_label_list(maxErrorA = 5.e-3, maxErrorm = 5.e-2): '1929A', '7.65m', '22.7m', '9.2m', '15.5m', '334.4m'] LINE_LABEL_LIST['Ni3'] = ['7890A', '8500A', '6000A', '6401A', '6534A', '6682A', '6797A', '7125A', '6946A'] LINE_LABEL_LIST['O1'] = ['6300A', '6364A', '6392A', '2959A', '2973A', '2979A', '5577A', '63.2m', '44.1m', '145.5m'] +LINE_LABEL_LIST['O1r'] = ['8447+', '7773+', '9264+', '3947+', '1357+'] LINE_LABEL_LIST['O2'] = ['3729A', '3726A', '2470A', '7319A','7320A', '7330A', '7331A', '2470A', '834A', '1075A', '1260A', '833A', '1073A', '1258A', '833A', '1072A', '1256A', '499.3m', '5023.7m', '61.3m', '40.7m', '121.3m'] From 7010eb82a87757a30a73cf4ab04cca9361b8f1cf Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 13 Apr 2021 16:58:53 +0100 Subject: [PATCH 18/36] Add facility to read fits IFUs files. --- pyneb/core/pynebcore.py | 115 ++++++++++++++++++++++++++++++++++------ 1 file changed, 99 insertions(+), 16 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index e7c7538b..9136d7bc 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -12,6 +12,7 @@ import warnings import os import sys +from pathlib import Path from pyneb import config, log_, atomicData from ..utils.misc import int_to_roman, strExtract, parseAtom, quiet_divide, _returnNone, solve, bs @@ -35,6 +36,7 @@ from ..utils.multiprocs import getTemDen_helper if config.INSTALLED['pyfits from astropy']: import astropy.io.fits as pyfits + from astropy.wcs import WCS elif config.INSTALLED['pyfits']: import pyfits if config.INSTALLED['astropy Table']: @@ -4042,7 +4044,8 @@ def correctIntens(self, RC, normWave=None): calling=self.calling) return None if self.wave > 0.0: - self.corrIntens = self.obsIntens * RC.getCorr(self.wave, normWave) + with np.errstate(invalid='ignore'): + self.corrIntens = self.obsIntens * RC.getCorr(self.wave, normWave) else: self.corrIntens = self.obsIntens self.corrError = self.obsError # error is supposed to be relative. @@ -4094,7 +4097,7 @@ def __repr__(self): class Observation(object): def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err_default=0.10, - corrected=False, errIsRelative=True, correcLaw='F99'): + corrected=False, errIsRelative=True, correcLaw='F99', errStr='err'): """ Define the observation object, which is a collection of observated intensities of one or more emission lines for one or more objects, with the corresponding errors. @@ -4103,6 +4106,10 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err Parameters: - obsFile name of the file containing the observations. May be a file object or a file name + If the fileFormat is 'fits_IFU', the obsFile keyword is of the form e.g.: + 'dir/ngc6778_MUSE_*.fits' where * is of the form O3_5007A. + The associated error file must be named the same way, using errStr keyword value + e.g. dir/ngc6778_MUSE_O3_5007A_err.fits - fileFormat format of the data file, depending on how the wavelengths are ordered. Available formats are : - 'lines_in_cols' : Each object is on a different row. Each column corresponds to a given emission line. @@ -4112,6 +4119,7 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err - 'lines_in_rows' : Each object is on a different column. Each row corresponds to a given emission line. - 'lines_in_rows_err_cols' : Each object is on a different column. Each row corresponds to a given emission line. For each object (eg. "IC418"), an additional column (named eg "errIC418") contains the errors on the line intensities. + - 'fits_IFU': each emission line is stored into a fits file - delimiter character separating entries - err_default default uncertainty assumed on intensities. Will overwrite the error from the file. - corrected Boolean. True if the observed intensities are already corrected from extinction @@ -4119,6 +4127,7 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err - errIsRelative Boolean. True if the errors are relative to the intensities, False if they are in the same unit as the intensity (default: True) - correcLaw ['F99'] extinction law used to correct the observed lines. + - errStr - string used to identify error file when fileFormat is fits_IFU Example: Read a file containing corrected intensities: @@ -4134,11 +4143,15 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err self.extinction = RedCorr(law=correcLaw) self.corrected = corrected self.MC_added = False + self.N_MC = 0 + self.fits_shape = None + self.data_shape = None if self.corrected: self.extinction.law = 'No correction' if obsFile is not None: self.readData(obsFile=obsFile, fileFormat=fileFormat, delimiter=delimiter, - err_default=err_default, corrected=corrected, errIsRelative=errIsRelative) + err_default=err_default, corrected=corrected, errIsRelative=errIsRelative, + errStr=errStr) ## # @var log_ # myloggin object @@ -4299,7 +4312,7 @@ def getUniqueAtoms(self): def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_default=0.10, corrected=False, - errIsRelative=True): + errIsRelative=True, errStr='err'): """ Read observational data from an ascii file. The lines can be listed either in columns or in rows and the observed objects vary in the other direction. The uncertainty on the line intensities @@ -4311,23 +4324,33 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa Parameters: - - obsFile file containing the observations. May be a file object or a string + - obsFile file containing the observations. May be a file object or a string. + If the fileFormat is 'fits_IFU', the obsFile keyword is of the form e.g.: + 'dir/ngc6778_MUSE_*.fits' where * is of the form O3_5007A. + The associated error file must be named the same way, using errStr keyword value + e.g. dir/ngc6778_MUSE_O3_5007A_err.fits - fileFormat emission lines vary across columns ('lines_in_cols', default) or across rows ('lines_in_rows'), or across rows with errors in columns - ('lines_in_rows_err_cols')m in which case the column label must start with "err" + ('lines_in_rows_err_cols') in which case the column label must start with "err" + + The format may also be 'fits_IFU', in which case each emission line comes + from a different fits file + - delimiter field delimiter (default: None) - err_default default uncertainty on the line intensities - corrected Boolean. True if the observed intensities are already corrected from extinction (default: False) - errIsRelative Boolean. True if the errors are relative to the intensities, False if they are in the same unit as the intensity (default: False) + - errStr string to identify the error file in case the fileFormat is fits_IFU. """ - format_list = ['lines_in_cols', 'lines_in_cols2', 'lines_in_rows', 'lines_in_rows_err_cols'] + format_list = ['lines_in_cols', 'lines_in_cols2', 'lines_in_rows', + 'lines_in_rows_err_cols', 'fits_IFU'] if fileFormat not in format_list: self.log_.error('unknown format {0}'.format(fileFormat), calling='Observation.readData') - if type(obsFile) is str: + if type(obsFile) is str and fileFormat not in ('fits_IFU',): f = open(obsFile, 'r') closeAfterUse = True else: @@ -4370,7 +4393,6 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa self.log_.message('adding line {0}'.format(label), calling=self.calling) except: self.log_.warn('Impossible to add line'.format(label), calling=self.calling) - elif fileFormat == 'lines_in_cols2': if closeAfterUse: f.close() @@ -4490,7 +4512,60 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa self.log_.warn('Impossible to add line'.format(label), calling=self.calling) print(label, intens, error) - + elif fileFormat == 'fits_IFU': + path = Path(obsFile) + dir_ = path.parent + pattern = path.name + files = dir_.glob(pattern) + self.log_.debug('path: {}, dir_: {}, pattern: {}'.format(path, dir_, pattern), + calling='Observation.readData') + str1, str2 = pattern.split('*') + for f in files: + obs_file = f.name + if f.suffix == '.fits' and errStr not in obs_file: + self.log_.debug('analysing {}'.format(f), calling='Observation.readData') + lineID = strExtract(obs_file, str1, str2) + spl = lineID.split('_') + if len(spl) == 2: + atom = spl[0] + line = spl[1] + if atom in LINE_LABEL_LIST: + self.log_.message('Reading {}_{} from {}'.format(atom, line, f.name), + calling='Observation.readData') + fits_hdu = pyfits.open(f)[0] + fits_data = fits_hdu.data + if self.fits_shape is None: + self.fits_shape = fits_data.shape + else: + if fits_data.shape != self.fits_shape: + self.log_.error('data shape in file {} is {}. Previous shape was {}.'.format( + f.name, fits_data.shape, self.fits_shape)) + fits_data = fits_data.ravel() + err_file = dir_ / Path(f.stem+'_' + errStr + '.fits') + if err_file.exists(): + self.log_.message('Reading error {}_{} from {}'.format(atom, line, err_file.name), + calling='Observation.readData') + err_fits_hdu = pyfits.open(err_file)[0] + if err_fits_hdu.data.shape != self.fits_shape: + self.log_.error('error shape in file {} is {}. data shape is {}.'.format( + err_file.name, err_fits_hdu.data.shape, self.fits_shape)) + + err_fits_data = err_fits_hdu.data.ravel() + else: + err_fits_data = None + self.addLine(EmissionLine(label=lineID, + obsIntens=fits_data, + obsError=err_fits_data, + corrected=corrected, errIsRelative=errIsRelative)) + self.fits_header = fits_hdu.header + self.wcs = WCS(fits_hdu.header).celestial + else: + self.log_.debug('atom {} not in LINE_LABEL_LIST'.format(atom), + calling='Observation.readData') + + self.names = ['{}_{}'.format(str1, i) for i in range(self.n_obs)] + self.data_shape = self.fits_shape + if corrected: self.correctData() @@ -4603,8 +4678,8 @@ def def_EBV(self, label1="H1r_6563A", label2="H1r_4861A", r_theo=2.85): if line2 is None: self.log_.error('{0} is not a valid label or is not observed'.format(line2), calling=self.calling) return None - - obs_over_theo = (line1.obsIntens / line2.obsIntens) / r_theo + with np.errstate(divide='ignore'): + obs_over_theo = (line1.obsIntens / line2.obsIntens) / r_theo self.extinction.setCorr(obs_over_theo, line1.wave, line2.wave) @@ -4708,9 +4783,7 @@ def addMonteCarloObs(self, N=0, i_obs=None): MC_names = np.asarray(['-MC-{}'.format(i) for i in np.arange(N+1)]) MC_names[0] = '' self.names = np.core.defchararray.add(new_names , MC_names) - self.log_.message('Leaving', calling='addMonteCarloObs') - self.MC_added = True - + self.log_.message('Leaving', calling='addMonteCarloObs') else: if self.corrected: returnObs=False @@ -4724,5 +4797,15 @@ def addMonteCarloObs(self, N=0, i_obs=None): new_obs = intens * (all_new_obs[i,:] * error + 1) new_obs[new_obs < 0.] = 0. self.addObs('{0}-MC-{1}'.format(self.names[i_obs], i), new_obs, error) - self.MC_added = True + self.MC_added = True + self.N_MC = N + if self.fits_shape is not None: + self.data_shape = (self.fits_shape[0], self.fits_shape[1], self.N_MC+1) + else: + self.data_shape = (self.N_MC+1) + def reshape(self, data): + """ + Return data in shape of the original data (use with fits IFUs and/or MC) + """ + return np.reshape(data, self.data_shape) \ No newline at end of file From 57353abc540573b960790adcaf2674a63a78ab35 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 13 Apr 2021 18:22:55 +0100 Subject: [PATCH 19/36] Update pynebcore.py --- pyneb/core/pynebcore.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 9136d7bc..e46c9ebb 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -4552,6 +4552,8 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa err_fits_data = err_fits_hdu.data.ravel() else: + self.log_.message('No error file found for {}'.format(f.name), + calling='Observation.readData') err_fits_data = None self.addLine(EmissionLine(label=lineID, obsIntens=fits_data, From b06a4a9c753d11fb6f61f6cd8c483e032d620e95 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 15 Apr 2021 00:42:17 +0100 Subject: [PATCH 20/36] New default set of atomic data PYNEB_21_01, change in CII recomb --- pyneb/__init__.py | 2 +- pyneb/utils/physics.py | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/pyneb/__init__.py b/pyneb/__init__.py index 5c1e4a9b..e2ff3ef3 100644 --- a/pyneb/__init__.py +++ b/pyneb/__init__.py @@ -27,7 +27,7 @@ from .utils.manage_atomic_data import _ManageAtomicData atomicData = _ManageAtomicData() -atomicData.defaultDict = 'PYNEB_20_01' +atomicData.defaultDict = 'PYNEB_21_01' atomicData.resetDataFileDict() from .core.pynebcore import Atom, RecAtom, getAtomDict, getRecEmissivity, EmissionLine, Observation, \ diff --git a/pyneb/utils/physics.py b/pyneb/utils/physics.py index 5d96e2fa..ab16151d 100644 --- a/pyneb/utils/physics.py +++ b/pyneb/utils/physics.py @@ -817,7 +817,10 @@ def make_gsconf_file(outfile='gsconfs.dat'): _predefinedDataFileDict['PYNEB_20_01']['Cl3']['atom'] = 'cl_iii_atom_RGJ19.dat' _predefinedDataFileDict['PYNEB_20_01']['Ar4']['atom'] = 'ar_iv_atom_RGJ19.dat' - +_predefinedDataFileDict['PYNEB_21_01'] = deepcopy(_predefinedDataFileDict['PYNEB_20_01']) +_predefinedDataFileDict['PYNEB_21_01']['C2']['rec'] = 'c_ii_rec_D00.func' + + def airtovac(wave): """ Convert air wavelengths to vacuum wavelengths From 022d35ebd028895c6c5859fa00218b0549d71b6c Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 15 Apr 2021 00:42:35 +0100 Subject: [PATCH 21/36] add test_label_ionabundance --- pyneb/test/test_pyneb.py | 41 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 40 insertions(+), 1 deletion(-) diff --git a/pyneb/test/test_pyneb.py b/pyneb/test/test_pyneb.py index a1645ac9..1907745f 100644 --- a/pyneb/test/test_pyneb.py +++ b/pyneb/test/test_pyneb.py @@ -93,7 +93,7 @@ def test_diag_list(): def test_line_labels(): """ This tool scans all the lines from pn.LINE_LABEL_LIST, extract the wavelength and compare with the - wavelength given by the corresponding atom for the corresponding transition. Is the difference is greater + wavelength given by the corresponding atom for the corresponding transition. If the difference is greater than 1AA for optical or 0.1mu for IR, it prints the information """ for atom_str in pn.LINE_LABEL_LIST: @@ -122,6 +122,45 @@ def test_line_labels(): if dif > 1.0: print(atom, line) assert dif < 1.0 + +def test_label_ionabundance(): + """ + This tool test the different types of labels, wave and to_eval for Atom and RecAtom getIonabundance method + """ + atom = pn.Atom('O', 3) + try: + res = atom.getIonAbundance(1., 1e4, 1e2, wave=5007) + except: + print('Problem with O3.getIonAbundance(1., 1e4, 1e2, wave=5007)') + try: + res = atom.getIonAbundance(1., 1e4, 1e2, lev_i= 4, lev_j= 2) + except: + print('Problem with O3.getIonAbundance(1., 1e4, 1e2, lev_i= 4, lev_j= 2)') + try: + res = atom.getIonAbundance(1., 1e4, 1e2, to_eval='L(5007)') + except: + print('Problem with O3.getIonAbundance(1., 1e4, 1e2, to_eval="L(5007)"') + try: + res = atom.getIonAbundance(1., 1e4, 1e2, to_eval='I(4, 2)') + except: + print('Problem with O3.getIonAbundance(1., 1e4, 1e2, to_eval="I(4, 2)"') + + atom = pn.RecAtom('O', 2) + try: + res = atom.getIonAbundance(1., 1e4, 1e2, wave=4649.13) + except: + print('Problem with O2.getIonAbundance(1., 1e4, 1e2, wave=4649.13)') + try: + res = atom.getIonAbundance(1., 1e4, 1e2, to_eval='L(4649.13)') + except: + print('Problem with O3.getIonAbundance(1., 1e4, 1e2, to_eval=L(4649.13)') + try: + res = atom.getIonAbundance(1., 1e4, 1e2, to_eval='S("4649.13")') + except: + print('Problem with O3.getIonAbundance(1., 1e4, 1e2, to_eval=S("4649.13")') + + + def test_atom_instanciation(): """ From 636f846ec2be2e574ea998353acdf84a7bf31e03 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 15 Apr 2021 00:43:04 +0100 Subject: [PATCH 22/36] add new CII recomb line in LINE_LABEL_LIST --- pyneb/utils/init.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/pyneb/utils/init.py b/pyneb/utils/init.py index 96f8a175..092c5298 100644 --- a/pyneb/utils/init.py +++ b/pyneb/utils/init.py @@ -61,7 +61,22 @@ def _check_line_label_list(maxErrorA = 5.e-3, maxErrorm = 5.e-2): LINE_LABEL_LIST['C2'] = ['2325A', '2328A', '2323A', '2327A', '2322A', '2325A', '1335A', '1336A', '3131A', '3133A', '3136A', '1036A', '1037A', '1869A', '1870A', '1871A', '4636A', '4637A', '157.6m', '454.4m', '198.8m', '353.3m', '3967.2m'] -LINE_LABEL_LIST['C2r'] = ['9903+', '4267+', '7231+', '6580+', '2837+', '1761+', '1335+'] +LINE_LABEL_LIST['C2r'] = ['9903+', '4267+', '7231+', '6580+', '2837+', '1761+', '1335+', + '11641.0A', '4156.0A', '1632.0A', '40292.0A', '21809.0A', '12774.0A', + '7572.0A', '7508.0A', '4292.0A', '2140.0A', '1653.0A', '68870.0A', + '28127.0A', '14709.0A', '8213.0A', '14565.0A', '8138.0A', '4491.0A', + '4329.0A', '2189.0A', '1682.0A', '4463.0A', '47268.0A', '18662.0A', + '9314.0A', '46443.0A', '18451.0A', '9224.0A', '4803.0A', '8869.0A', + '4619.0A', '2261.0A', '8268.0A', '4752.0A', '30688.0A', '11579.0A', + '30154.0A', '11444.0A', '5342.0A', '10931.0A', '5122.0A', '2375.0A', + '4019.0A', '1796.0A', '18516.0A', '18199.0A', '6462.0A', '17000.0A', + '6151.0A', '2575.0A', '914.0A', '4639.0A', '1910.0A', '1505.0A', + '1157.0A', '9903.0A', '9230.0A', '2993.0A', '962.0A', '31211.0A', + '6259.0A', '2138.0A', '10301.0A', '997.0A', '1548.0A', '1270.0A', + '4267.0A', '1064.0A', '17847.0A', '2747.0A', '5890.0A', '2174.0A', + '1142.0A', '1930.0A', '3920.0A', '7235.0A', '6580.0A', '1762.0A', + '2841.0A', '1760.0A', '5060.0A', '3176.0A', '1232.0A', '1335.0A', + '1037.0A', '1324.0A', '1066.0A', '1721.0A'] LINE_LABEL_LIST['C3'] = ['1910A', '1909A', '1907A', '977A', '2000A', '2001A', '2003A', '422.0m', '124.9m', '177.4m'] LINE_LABEL_LIST['C4'] = ['1551A', '1548A', '92.8m'] LINE_LABEL_LIST['Ca2'] = ['7292A', '7324A'] From 98574e93780a34c36ef8fb933f17f95dcdcf32ae Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 15 Apr 2021 00:44:22 +0100 Subject: [PATCH 23/36] add addSum and removeLine. add systematic error option. --- pyneb/core/pynebcore.py | 65 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 60 insertions(+), 5 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index e46c9ebb..42cd95e6 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -3558,8 +3558,10 @@ def getEmissivity(self, tem, den, lev_i=None, lev_j=None, wave=None, label=None, if (lev_i is not None) and (lev_j is not None): label = '{0}_{1}'.format(lev_i, lev_j) if wave is not None: - label = '{0:.1f}'.format(wave) - label_str = self._getLabelStr(label, warn=False) + #label = '{0:.1f}'.format(wave) + #label_str = self._getLabelStr(label, warn=False) + label_str = self._getLabelStr(wave, warn=False) + label = label_str if label_str is None: ij = self.getTransition(wave) label = '{}_{}'.format(ij[0], ij[1]) @@ -3684,6 +3686,8 @@ def getIonAbundance(self, int_ratio, tem, den, lev_i= -1, lev_j= -1, wave= -1, l I = lambda lev_i, lev_j: self.getEmissivity(tem, den, lev_i, lev_j, product=False) L = lambda wave: self.getEmissivity(tem, den, wave=wave, product=False) S = lambda label: self.getEmissivity(tem, den, label=label, product=False) + + emis = eval(to_eval) try: emis = eval(to_eval) except: @@ -4097,7 +4101,8 @@ def __repr__(self): class Observation(object): def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err_default=0.10, - corrected=False, errIsRelative=True, correcLaw='F99', errStr='err'): + corrected=False, errIsRelative=True, correcLaw='F99', errStr='err', + addErrDefault = False): """ Define the observation object, which is a collection of observated intensities of one or more emission lines for one or more objects, with the corresponding errors. @@ -4128,6 +4133,7 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err are in the same unit as the intensity (default: True) - correcLaw ['F99'] extinction law used to correct the observed lines. - errStr - string used to identify error file when fileFormat is fits_IFU + - addErrDefault - if True, the default error is always quadratically added to the read error. Example: Read a file containing corrected intensities: @@ -4142,6 +4148,7 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err self.names = [] self.extinction = RedCorr(law=correcLaw) self.corrected = corrected + self.addErrDefault = addErrDefault self.MC_added = False self.N_MC = 0 self.fits_shape = None @@ -4174,6 +4181,15 @@ def addLine(self, line): line.corrected = True self.correctData(line) self.lines.append(line) + + def removeLine(self, lineLabel): + """ + + """ + + for l in self.lines: + if l.label == lineLabel: + self.lines.remove(l) def fillObs(self, lineLabel, default=np.nan): """ @@ -4215,6 +4231,33 @@ def addObs(self, name, newObsIntens, newObsError=None): line.addObs(newObsIntens[i], newObsError[i]) self.names.append(name) + def addSum(self, labelsToAdd, newLabel): + """ + Add a new observation. The intensity is the sum of the intensities of + the lines defined by the tupple labelsToAdd. The rror is the quadratic sum. + Example: + addSum(('O1r_7771A', 'O1r_7773A', 'O1r_7775A'), 'O1r_7773+') + """ + + intenses = self.getIntens(returnObs=True) + errors = self.getError(returnObs=True) + I = intenses[labelsToAdd[0]] + E = errors[labelsToAdd[0]] + atom = labelsToAdd[0].split('_')[0] + + for label in labelsToAdd[1:]: + if label.split('_')[0] != atom: + self.log_.error('Can not add lines from different atoms {} and {}.'.format( + label.split('_')[0],atom)) + I += intenses[label] + E = np.sqrt(E**2 + errors[label]**2) + to_eval = 'S("{}")'.format(newLabel.split('_')[1]) + newLine = EmissionLine(label=newLabel, obsIntens=I, obsError=E, + corrected=False, errIsRelative=False, + to_eval=to_eval) + self.addLine(newLine) + + @property def lineLabels(self): """ @@ -4381,6 +4424,8 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa error = data_tab[i_error].astype(np.float32) if not errIsRelative: error = quiet_divide(error, intens) + if self.addErrDefault: + error = np.sqrt(error**2 + err_default**2) except: self.log_.message('No error found for line {0}'.format(label), calling=self.calling) error = data_tab[1].astype(np.float32) * 0. + err_default @@ -4417,6 +4462,8 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa error = data_tab[label + 'e'] if not errIsRelative: error = error / intens + if self.addErrDefault: + error = np.sqrt(error**2 + err_default**2) except: self.log_.message('No error found for line {0}'.format(label), calling=self.calling) error = np.ones_like(data_tab[label]) * err_default @@ -4457,6 +4504,8 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa error = data_tab[:, i_error] if not errIsRelative: error = error / intens + if self.addErrDefault: + error = np.sqrt(error**2 + err_default**2) except: self.log_.message('No error found for line {0}'.format(label), calling=self.calling) error = np.ones_like(data_tab[:, 1]) * err_default @@ -4500,6 +4549,8 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa error = np.array([data_tab[i][name] for name in error_names]) if not errIsRelative: error = error / intens + if self.addErrDefault: + error = np.sqrt(error**2 + err_default**2) try: line2add = EmissionLine(label=label, obsIntens=intens, obsError=error) except: @@ -4551,14 +4602,18 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa err_file.name, err_fits_hdu.data.shape, self.fits_shape)) err_fits_data = err_fits_hdu.data.ravel() + if not errIsRelative: + err_fits_data = err_fits_data / fits_data + if self.addErrDefault: + err_fits_data = np.sqrt(err_fits_data**2 + err_default**2) else: self.log_.message('No error file found for {}'.format(f.name), calling='Observation.readData') - err_fits_data = None + err_fits_data = np.ones_like(fits_data) * err_default self.addLine(EmissionLine(label=lineID, obsIntens=fits_data, obsError=err_fits_data, - corrected=corrected, errIsRelative=errIsRelative)) + corrected=corrected, errIsRelative=True)) self.fits_header = fits_hdu.header self.wcs = WCS(fits_hdu.header).celestial else: From 124c24bc583c9c884f15e11044df6d377016bc01 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 15 Apr 2021 12:57:33 +0100 Subject: [PATCH 24/36] add recombination for OII 3727+ and 7325+ --- pyneb/atomic_data/o_ii_rec_P91.func | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/pyneb/atomic_data/o_ii_rec_P91.func b/pyneb/atomic_data/o_ii_rec_P91.func index c1bcb685..90a7267e 100644 --- a/pyneb/atomic_data/o_ii_rec_P91.func +++ b/pyneb/atomic_data/o_ii_rec_P91.func @@ -18,4 +18,9 @@ SOURCE Pequignot, Petitjean & Boisson, 1991, AandA, 251, 680 4341+ 434.1 A 0.633 -0.609 1.683 0.667 1.000 C 1.766 4341+ 434.1 B 0.792 -0.620 1.683 0.667 1.000 C 2.244 3736+ 373.6 A 0.047 -0.506 0.624 0.681 1.000 C 0.153 -3736+ 373.6 B 0.255 -0.565 1.683 0.667 1.000 C 0.669 \ No newline at end of file +3736+ 373.6 B 0.255 -0.565 1.683 0.667 1.000 C 0.669 +3727+ 372.7 A 1.175 -0.6182 0.0097 -0.2717 1.000 B 0.000 +3727+ 372.7 B 1.175 -0.6182 0.0097 -0.2717 1.000 B 0.000 +7325+ 732.5 A 0.657 -0.6166 0.00 1.000 0.80 B 0.000 +7325+ 732.5 B 0.657 -0.6166 0.00 1.000 0.80 B 0.000 + From 9af41b7b1c511b9741758a56a25da6dc0dbc8a2f Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 15 Apr 2021 12:58:17 +0100 Subject: [PATCH 25/36] bug corrected in addSum --- pyneb/core/pynebcore.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 42cd95e6..898ed367 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -4126,14 +4126,14 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err For each object (eg. "IC418"), an additional column (named eg "errIC418") contains the errors on the line intensities. - 'fits_IFU': each emission line is stored into a fits file - delimiter character separating entries - - err_default default uncertainty assumed on intensities. Will overwrite the error from the file. + - err_default [0.10] default uncertainty assumed on intensities. Will overwrite the error from the file. - corrected Boolean. True if the observed intensities are already corrected from extinction (default: False) - errIsRelative Boolean. True if the errors are relative to the intensities, False if they are in the same unit as the intensity (default: True) - correcLaw ['F99'] extinction law used to correct the observed lines. - errStr - string used to identify error file when fileFormat is fits_IFU - - addErrDefault - if True, the default error is always quadratically added to the read error. + - addErrDefault - [False] if True, the default error is always quadratically added to the read error. Example: Read a file containing corrected intensities: @@ -4234,7 +4234,7 @@ def addObs(self, name, newObsIntens, newObsError=None): def addSum(self, labelsToAdd, newLabel): """ Add a new observation. The intensity is the sum of the intensities of - the lines defined by the tupple labelsToAdd. The rror is the quadratic sum. + the lines defined by the tupple labelsToAdd. The error is the quadratic sum of the absolute errors. Example: addSum(('O1r_7771A', 'O1r_7773A', 'O1r_7775A'), 'O1r_7773+') """ @@ -4242,7 +4242,9 @@ def addSum(self, labelsToAdd, newLabel): intenses = self.getIntens(returnObs=True) errors = self.getError(returnObs=True) I = intenses[labelsToAdd[0]] - E = errors[labelsToAdd[0]] + E = errors[labelsToAdd[0]] * I + + atom = labelsToAdd[0].split('_')[0] for label in labelsToAdd[1:]: @@ -4250,10 +4252,11 @@ def addSum(self, labelsToAdd, newLabel): self.log_.error('Can not add lines from different atoms {} and {}.'.format( label.split('_')[0],atom)) I += intenses[label] - E = np.sqrt(E**2 + errors[label]**2) + E = np.sqrt(E**2 + (errors[label]*I)**2) to_eval = 'S("{}")'.format(newLabel.split('_')[1]) + E = E / I newLine = EmissionLine(label=newLabel, obsIntens=I, obsError=E, - corrected=False, errIsRelative=False, + corrected=False, errIsRelative=True, to_eval=to_eval) self.addLine(newLine) From f59ee1fbb1219c6eb241014ba15ec42755533d62 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Fri, 16 Apr 2021 13:44:05 +0100 Subject: [PATCH 26/36] Update pynebcore.py --- pyneb/core/pynebcore.py | 47 ++++++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 22 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 898ed367..2dfe7681 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -3865,7 +3865,7 @@ def getLineLabel(elem, spec, wave, blend=False): return atom_label, wave_label, line_label - +#%% def parseLineLabel(lineLabel): """ Parse the line label to extract the substrings referring to the atom (elem, spec and atom) @@ -3882,11 +3882,8 @@ def parseLineLabel(lineLabel): ## # @todo maybe rearrange the order so 1) it is compatible with getLineLabel, or 2) it lists all the strings first - # determine if the line is a blend or not - if lineLabel[-1] == '+' or lineLabel[-2] == '+': - blend = True - else: - blend = False + blend = False + wave_unit = 'A' # extract information on the atom elem_spec = strExtract(lineLabel, ' ', '_') elem, spec = parseAtom(elem_spec) @@ -3895,22 +3892,25 @@ def parseLineLabel(lineLabel): if elem_spec[-1] == 'r': atom_label += 'r' # extract information on the wave - if blend: - try: - wave = float(strExtract(lineLabel[:-2], '_', ' ')) - except: - wave = 0. - else: - try: - wave = float(strExtract(lineLabel[:-1], '_', ' ')) - except: - wave = 0. - wave_label = strExtract(lineLabel, '_', ' ') - if lineLabel[-1] == 'm': + wave_label = lineLabel.split('_')[1] + wave = '' + for s in wave_label: + if s.isdigit() or s == '.': + wave += s + elif s == '+': + blend = True + elif s in ('A', 'm'): + wave_unit = s + try: + wave = float(wave) + except: + wave = 0. + if wave_unit == 'm': wave = wave * 1.e4 return elem, spec, atom_label, wave, wave_label, blend +#%% def isValid(line_label): """ @@ -4049,7 +4049,9 @@ def correctIntens(self, RC, normWave=None): return None if self.wave > 0.0: with np.errstate(invalid='ignore'): - self.corrIntens = self.obsIntens * RC.getCorr(self.wave, normWave) + self.corrIntens = self.obsIntens * RC.getCorr(self.wave, normWave) + self.log_.debug('Correcting {} with wave = {}'.format(self.label, self.wave), + calling='EmissionLine.correctIntens') else: self.corrIntens = self.obsIntens self.corrError = self.obsError # error is supposed to be relative. @@ -4231,12 +4233,12 @@ def addObs(self, name, newObsIntens, newObsError=None): line.addObs(newObsIntens[i], newObsError[i]) self.names.append(name) - def addSum(self, labelsToAdd, newLabel): + def addSum(self, labelsToAdd, newLabel, to_eval=None): """ Add a new observation. The intensity is the sum of the intensities of the lines defined by the tupple labelsToAdd. The error is the quadratic sum of the absolute errors. Example: - addSum(('O1r_7771A', 'O1r_7773A', 'O1r_7775A'), 'O1r_7773+') + addSum(('O1r_7771A', 'O1r_7773A', 'O1r_7775A'), 'O1r_7773A+', to_eval = 'S("7773+")') """ intenses = self.getIntens(returnObs=True) @@ -4253,7 +4255,8 @@ def addSum(self, labelsToAdd, newLabel): label.split('_')[0],atom)) I += intenses[label] E = np.sqrt(E**2 + (errors[label]*I)**2) - to_eval = 'S("{}")'.format(newLabel.split('_')[1]) + if to_eval is None: + to_eval = 'S("{}")'.format(newLabel.split('_')[1]) E = E / I newLine = EmissionLine(label=newLabel, obsIntens=I, obsError=E, corrected=False, errIsRelative=True, From feff9447eb8f91c4ae81ced210c7474ec4d653ce Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 19 Apr 2021 11:54:43 +0100 Subject: [PATCH 27/36] Update continuum.py --- pyneb/core/continuum.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyneb/core/continuum.py b/pyneb/core/continuum.py index 8a91836c..fe755781 100644 --- a/pyneb/core/continuum.py +++ b/pyneb/core/continuum.py @@ -77,11 +77,11 @@ def make_cont_Ercolano(self, tem, case, wl): 4.6, 4.7, 4.8, 4.9, 5. ]) D = np.loadtxt(ROOT_DIR + '/' + 'atomic_data/coeff_ercolano_He2.txt') else: - print('Invalid case {0}'.format(case)) - return None + self.log_.warn('Invalid case {0}'.format(case), calling='Continuum.make_cont_Ercolano') + return np.nan if (tem < np.min(tab_T)).any() or (tem > np.max(tab_T)).any(): - print('Invalid temperature {0}'.format(tem)) - return None + self.log_.warn('Invalid temperature {0}'.format(tem), calling='Continuum.make_cont_Ercolano') + return np.nan BE_E_Ry = D[:,1] BE_E_erg = BE_E_Ry * CST.RYD_erg From 409552bac8610c2f777941754c45a4275348e32a Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 20 Apr 2021 14:19:41 +0100 Subject: [PATCH 28/36] add "mass" as a way to sort lines --- pyneb/core/pynebcore.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 2dfe7681..fdb6e672 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -3734,10 +3734,10 @@ def getRecEmissivity(tem, den, lev_i=None, lev_j=None, atom='H1', method='linear method=method, wave=wave, product=product) else: if (atom == 'H1') and (lev_i == 4) and (lev_j == 2): - self.log_.warn('Scipy is missing, {0} returning Hbeta'.format(calling), calling) + log_.warn('Scipy is missing, {0} returning Hbeta'.format(calling), calling) return getHbEmissivity(tem, den) else: - self.log_.error('Only Hbeta emissivity available, as scipy not installed', calling) + log_.error('Only Hbeta emissivity available, as scipy not installed', calling) def getHbEmissivity(tem= -1, den=1.): @@ -3865,7 +3865,7 @@ def getLineLabel(elem, spec, wave, blend=False): return atom_label, wave_label, line_label -#%% +#%% parseLineLabel def parseLineLabel(lineLabel): """ Parse the line label to extract the substrings referring to the atom (elem, spec and atom) @@ -3910,7 +3910,7 @@ def parseLineLabel(lineLabel): return elem, spec, atom_label, wave, wave_label, blend -#%% +#%% Observations def isValid(line_label): """ @@ -4346,6 +4346,8 @@ def getSortedLines(self, crit='atom'): return sorted(self.lines, key=lambda line: line.atom + str(line.wave)) elif crit == 'wave': return sorted(self.lines, key=lambda line: line.wave) + elif crit == 'mass': + return sorted(self.lines, key=lambda line: Z[line.elem]) else: self.log_.error('crit = {0} is not valid'.format(crit), calling=self.calling + '.getSortedLines') From 13514f20685dff167845a89ac71255535a667908 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 20 Apr 2021 17:17:41 +0100 Subject: [PATCH 29/36] Update pynebcore.py --- pyneb/core/pynebcore.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index fdb6e672..7b6b8d0a 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -4347,7 +4347,7 @@ def getSortedLines(self, crit='atom'): elif crit == 'wave': return sorted(self.lines, key=lambda line: line.wave) elif crit == 'mass': - return sorted(self.lines, key=lambda line: Z[line.elem]) + return sorted(self.lines, key=lambda line: (Z[line.elem], line.spec, line.wave)) else: self.log_.error('crit = {0} is not valid'.format(crit), calling=self.calling + '.getSortedLines') From 26ead6ec66ed1930e38f755ea0679dfd9099d83b Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 27 Apr 2021 20:00:11 +0100 Subject: [PATCH 30/36] resolving print a la python 2 --- pyneb/sample_scripts/FirstSteps.ipynb | 2835 ++++++++++--------- pyneb/sample_scripts/multi_comp.py | 28 +- pyneb/sample_scripts/ngc650_R1.py | 29 +- pyneb/sample_scripts/plot_all_diags.py | 2 +- pyneb/sample_scripts/startup.py | 6 +- pyneb/sample_scripts/two_components_map.py | 2 +- pyneb/sample_scripts/two_components_plot.py | 2 +- pyneb/test/profiling.py | 2 +- pyneb/utils/fits.py | 4 +- 9 files changed, 1498 insertions(+), 1412 deletions(-) diff --git a/pyneb/sample_scripts/FirstSteps.ipynb b/pyneb/sample_scripts/FirstSteps.ipynb index 213d9573..80d232ff 100644 --- a/pyneb/sample_scripts/FirstSteps.ipynb +++ b/pyneb/sample_scripts/FirstSteps.ipynb @@ -1,1437 +1,1524 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "GETTING STARTED\n", + "To get started, move into directory where PyNeb resides and enter python" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "# import code and modules\n", + "import pyneb as pn\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, { - "cells": [ + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "#######################################################################\n", + "# DEFINING ATOMS\n", + "\n", + "# define an OII atom\n", + "O2 = pn.Atom(\"O\", 2)\n", + "\n", + "# alternate syntax to define an atom (spec is a string)\n", + "N2 = pn.Atom(\"N\", \"2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GETTING STARTED\n", - "To get started, move into directory where PyNeb resides and enter python" + "name": "stdout", + "output_type": "stream", + "text": [ + "O\n", + "2\n", + "O2\n", + "oxygen\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "# import code and modules\n", - "import pyneb as pn\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#######################################################################\n", - "# DEFINING ATOMS\n", - "\n", - "# define an OII atom\n", - "O2 = pn.Atom(\"O\", 2)\n", - "\n", - "# alternate syntax to define an atom (spec is a string)\n", - "N2 = pn.Atom(\"N\", \"2\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# check atom definition\n", - "print O2.elem\n", - "print O2.spec\n", - "print O2.atom\n", - "print O2.name" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "O\n", - "2\n", - "O2\n", - "oxygen\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# explore the atom: builtin data\n", - "print O2.gs # ground-state configuration" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "p3\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# array of stat weights\n", - "print O2.getStatWeight()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 4. 6. 4. 4. 2. 6. 4. 2.]\n" - ] - } - ], - "prompt_number": 5 - }, + } + ], + "source": [ + "# check atom definition\n", + "print(O2.elem)\n", + "print(O2.spec)\n", + "print(O2.atom)\n", + "print(O2.name)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# stat weight of a given level\n", - "lev_i = 2\n", - "print O2.getStatWeight(lev_i)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "6.0\n" - ] - } - ], - "prompt_number": 6 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "p3\n" + ] + } + ], + "source": [ + "# explore the atom: builtin data\n", + "print(O2.gs) # ground-state configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# explore the atom: adopted atomic data\n", - "pn.atomicData.getPredefinedDataFileDict() # we suggest using the tab for this command...\n", - "pn.atomicData.getDirForFile('o_ii_atom_WFD96.fits') # wanna know where the file lies?\n", - "O2.printSources() # print bibliographic references of data used to build O2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "O2: Energy levels : NIST 2014\n", - "O2: A-values from level 6 : Wiese, Fuhr & Deters, 1996, JPCRD, Monograph 7, 369\n", - "O2: A-values from levels up to 5 : Zeippen 1982, MNRAS, 198, 111\n", - "O2: CS up to level 5 : Pradhan et al 2006, MNRAS 366, L6\n", - "O2: CS for levels from 6, interpolated to adapt to Pal07 Tem grid : Tayal 2007, ApJS, 171, 331\n" - ] - } - ], - "prompt_number": 7 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[4. 6. 4. 4. 2. 6. 4. 2.]\n" + ] + } + ], + "source": [ + "# array of stat weights\n", + "print(O2.getStatWeight())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print O2.NLevels # number of levels in the selected data\n", - "print O2.getEnergy(2) # energy of first excited level (ground = 1) in Angstrom^-1\n", - "print O2.getA(2,1) # transition probability of 2->1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "8\n", - "0.0002681055\n", - "3.82e-05\n" - ] - } - ], - "prompt_number": 8 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "6.0\n" + ] + } + ], + "source": [ + "# stat weight of a given level\n", + "lev_i = 2\n", + "print(O2.getStatWeight(lev_i))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "N2.plotGrotrian()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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RX3/9taxjEcJtrBrkYScPO1YDk4wZx4001xavEUz5cy84HpJ19VTqpHvu3DlW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\nmQ8+/6DYuVabFYvZM/7pEg4lEOgXSMc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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "O2: Energy levels : NIST 2014\n", + "O2: A-values from level 6 : Wiese, Fuhr & Deters, 1996, JPCRD, Monograph 7, 369\n", + "O2: A-values from levels up to 5 : Zeippen 1982, MNRAS, 198, 111\n", + "O2: CS up to level 5 : Pradhan et al 2006, MNRAS 366, L6\n", + "O2: CS for levels from 6, interpolated to adapt to Pal07 Tem grid : Tayal 2007, ApJS, 171, 331\n" + ] + } + ], + "source": [ + "# explore the atom: adopted atomic data\n", + "pn.atomicData.getPredefinedDataFileDict() # we suggest using the tab for this command...\n", + "pn.atomicData.getDirForFile('o_ii_atom_WFD96.fits') # wanna know where the file lies?\n", + "O2.printSources() # print bibliographic references of data used to build O2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# set temperature and density\n", - "tem = 15000.\n", - "den = 1000.\n", - "print O2.getPopulations(tem, den) # compute populations" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 9.73452672e-01 2.06878141e-02 5.85819178e-03 8.23867455e-07\n", - " 4.97864417e-07 2.92747483e-19 1.86769357e-19 9.15097829e-20]\n" - ] - } - ], - "prompt_number": 10 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "0.0002681055\n", + "3.82e-05\n" + ] + } + ], + "source": [ + "print(O2.NLevels) # number of levels in the selected data\n", + "print(O2.getEnergy(2)) # energy of first excited level (ground = 1) in Angstrom^-1\n", + "print(O2.getA(2,1)) # transition probability of 2->1" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print O2.getCritDensity(tem, level=2) # critical density of level 2 at tem" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1241.56280771\n" - ] - } - ], - "prompt_number": 11 - }, + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N2.plotGrotrian()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print O2.getOmega(tem, 2, 1) # effective collision strength of transition 2->1 at T=10000K" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.884\n" - ] - } - ], - "prompt_number": 12 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[9.74468368e-01 2.00608882e-02 5.46963327e-03 6.87357279e-07\n", + " 4.22917029e-07]\n" + ] + } + ], + "source": [ + "# set temperature and density\n", + "tem = 15000.\n", + "den = 1000.\n", + "print(O2.getPopulations(tem, den)) # compute populations" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print O2.getOmegaArray(2, 1) # array of effective collision strengths for 2->1 as a function of T" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.864 0.885 0.883 0.884 0.885 0.888]\n" - ] - } - ], - "prompt_number": 13 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "1380.6604606545584\n" + ] + } + ], + "source": [ + "print(O2.getCritDensity(tem, level=2)) # critical density of level 2 at tem" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print O2.getTemArray() # print array of temperatures of tabulated Omegas" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.1 0.5 1. 1.5 2. 2.5]\n" - ] - } - ], - "prompt_number": 14 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8388914238626578\n" + ] + } + ], + "source": [ + "print(O2.getOmega(tem, 2, 1)) # effective collision strength of transition 2->1 at T=10000K" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print O2.getCollRates(tem) # print collisional Rates at T=tem" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[[ 0.00000000e+00 1.18979040e-09 7.85850784e-10 1.15483097e-10\n", - " 5.59151024e-11 2.43623651e-13 1.58597662e-13 7.86743224e-14]\n", - " [ 1.03806015e-08 0.00000000e+00 1.59981194e-08 3.07654017e-09\n", - " 1.31147738e-09 6.66778113e-13 2.23121317e-13 4.58627385e-14]\n", - " [ 1.03042735e-08 2.40433049e-08 0.00000000e+00 2.97134567e-09\n", - " 1.94719300e-09 1.88185845e-13 4.35367075e-13 3.07870982e-13]\n", - " [ 5.60129741e-09 1.71033327e-08 1.09912251e-08 0.00000000e+00\n", - " 5.28323429e-09 5.74293400e-13 8.41205580e-13 5.60919861e-13]\n", - " [ 5.42515598e-09 1.45845102e-08 1.44083688e-08 1.05684857e-08\n", - " 0.00000000e+00 4.83020730e-12 2.26192125e-12 8.50040815e-13]\n", - " [ 1.59466706e-08 5.00241655e-09 9.39420949e-10 7.75022283e-10\n", - " 3.25861642e-09 0.00000000e+00 9.44365074e-09 3.75619863e-09]\n", - " [ 1.58175002e-08 2.55052788e-09 3.31145885e-09 1.72970882e-09\n", - " 2.32506685e-09 1.43889932e-08 0.00000000e+00 6.26493974e-09]\n", - " [ 1.58175002e-08 1.05684857e-09 4.72059027e-09 2.32506685e-09\n", - " 1.76141428e-09 1.15372635e-08 1.26293404e-08 0.00000000e+00]]\n" - ] - } - ], - "prompt_number": 15 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.796 0.797 0.798 0.801 0.808 0.817 0.823 0.83 0.832 0.832 0.831 0.833\n", + " 0.834 0.839 0.844 0.856 0.881 0.905 0.919]\n" + ] + } + ], + "source": [ + "print(O2.getOmegaArray(2, 1)) # array of effective collision strengths for 2->1 as a function of T" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "pn.atomicData.getAllAvailableFiles('O3')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "['o_iii_atom.chianti',\n", - " 'o_iii_atom_FFT04.dat',\n", - " 'o_iii_atom_GMZ97-WFD96.dat',\n", - " 'o_iii_atom_SZ00-WFD96.dat',\n", - " 'o_iii_coll.chianti',\n", - " 'o_iii_coll_AK99.dat',\n", - " 'o_iii_coll_LB94.dat',\n", - " 'o_iii_coll_Pal12-AK99.dat',\n", - " 'o_iii_coll_SSB13.dat']" - ] - } - ], - "prompt_number": 16 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[2. 2.18 2.3 2.48 2.7 2.88 3. 3.18 3.3 3.48 3.7 3.88 4. 4.18\n", + " 4.3 4.48 4.7 4.88 5. ]\n" + ] + } + ], + "source": [ + "print(O2.getTemArray()) # print array of temperatures of tabulated Omegas" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# This bit calls the script DataPlot.py to plot atomic data. \n", - "dataplot = pn.DataPlot('O', 3)\n", - "dataplot.plotAllA(figsize=(14, 10)) # transition probabilities plot " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "warng _ManageAtomicData: rec data not available for O3\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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zUp7ly0vsjv/E0zMtLY2PP/6YVatWUbFiRa5du8b9+/c5e/YsCxcuxNjYGDc3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\nk5eXFxER5efnk4GBgYQuIqK3336bXF1diYiorqaOxvDH0JIlSyR0ERG99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vI0Ol2YKygo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\n", - "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.00000000e+00 1.12907801e-09 7.48149900e-10 9.43885980e-11\n", + " 4.86376318e-11]\n", + " [9.85090222e-09 0.00000000e+00 1.38701270e-08 2.82031409e-09\n", + " 1.12657440e-09]\n", + " [9.80993005e-09 2.08451808e-08 0.00000000e+00 2.35004046e-09\n", + " 1.64105040e-09]\n", + " [4.57814715e-09 1.56789014e-08 8.69297165e-09 0.00000000e+00\n", + " 5.17412780e-09]\n", + " [4.71906029e-09 1.25282648e-08 1.21430487e-08 1.03502311e-08\n", + " 0.00000000e+00]]\n" + ] + } + ], + "source": [ + "print(O2.getCollRates(tem)) # print collisional Rates at T=tem" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "pn.config.setNlevels(6, 'O3', 'coll')\n", - "dataplot.plotOmega() # collision strength plot " - ], - "language": "python", + "data": { + "text/plain": [ + "['* o_iii_atom_FFT04-SZ00.dat',\n", + " '* o_iii_coll_SSB14.dat',\n", + " '* o_iii_rec_P91.func',\n", + " 'o_iii_atom.chianti',\n", + " 'o_iii_atom_FFT04.dat',\n", + " 'o_iii_atom_GMZ97-WFD96.dat',\n", + " 'o_iii_atom_SZ00-WFD96.dat',\n", + " 'o_iii_atom_TFF01.dat',\n", + " 'o_iii_atom_TZ17.dat',\n", + " 'o_iii_coll.chianti',\n", + " 'o_iii_coll_AK99.dat',\n", + " 'o_iii_coll_LB94.dat',\n", + " 'o_iii_coll_MBZ20.dat',\n", + " 'o_iii_coll_Pal12-AK99.dat',\n", + " 'o_iii_coll_TZ17.dat']" + ] + }, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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YZ2dnXFxcCAsTc58IrydPT0+aNGnCxx9/TGZmJtnZ2Zw8eZJhw4axbNkyYmNj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/cNERGhHDuWjo9PC44dS+fBBx/EycmJ/Px8XF1dq136bc5eOQDnz59j0+Z47vS4TBFw/IGyJ9yB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\nS/qBAwcSHx9PSEgIb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- "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, + "output_type": "execute_result" + } + ], + "source": [ + "pn.atomicData.getAllAvailableFiles('O3')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# customize atomic data \n", - "# First step: check which directories are searched for atomic data files\n", - "pn.atomicData.getAllDataFilePaths()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "['/home/morisset/Ureka/variants/common/lib/python2.7/site-packages/pyneb/utils/../atomic_data_fits/',\n", - " '/home/morisset/Ureka/variants/common/lib/python2.7/site-packages/pyneb/utils/../atomic_data/',\n", - " '/home/morisset/pyneb/trunk/pyneb/sample_scripts']" - ] - } - ], - "prompt_number": 19 + "name": "stdout", + "output_type": "stream", + "text": [ + "warng _ManageAtomicData: trc data not available for O3\n" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Add your selected directory to the list\n", - "pn.atomicData.addDataFilePath('/tmp')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# This bit calls the script DataPlot.py to plot atomic data. \n", + "dataplot = pn.DataPlot('O', 3, NLevels=5)\n", + "dataplot.plotAllA(figsize=(14, 10)) # transition probabilities plot " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Check if it's been added\n", - "pn.atomicData.getAllDataFilePaths()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "['/home/morisset/Ureka/variants/common/lib/python2.7/site-packages/pyneb/utils/../atomic_data_fits/',\n", - " '/home/morisset/Ureka/variants/common/lib/python2.7/site-packages/pyneb/utils/../atomic_data/',\n", - " '/home/morisset/pyneb/trunk/pyneb/sample_scripts',\n", - " '/tmp']" - ] - } - ], - "prompt_number": 21 + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/plot/plotAtomicData.py:473: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Remove it if you gave the wrong dir\n", - "pn.atomicData.removeDataFilePath('/tmp')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "warng _ManageAtomicData: {0} could not be removed from the path list\n" - ] - } - ], - "prompt_number": 22 - }, + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataplot.plotOmega() # collision strength plot " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Set 'o_iii_.fits' to be the OIII atom file\n", - "pn.atomicData.setDataFile('o_iii_coll_Pal12-AK99.dat')" - ], - "language": "python", + "data": { + "text/plain": [ + "['/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/atomic_data_fits/',\n", + " '/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/atomic_data/',\n", + " '/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/sample_scripts']" + ] + }, + "execution_count": 23, "metadata": {}, - "outputs": [], - "prompt_number": 23 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# customize atomic data \n", + "# First step: check which directories are searched for atomic data files\n", + "pn.atomicData.getAllDataFilePaths()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Add your selected directory to the list\n", + "pn.atomicData.addDataFilePath('/tmp')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# define an atom with the new data\n", - "O2test = pn.Atom(\"O\", 2)" - ], - "language": "python", + "data": { + "text/plain": [ + "['/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/atomic_data_fits/',\n", + " '/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/atomic_data/',\n", + " '/Users/christophemorisset/Google Drive/Pro/PyNeb_devel/pyneb/sample_scripts',\n", + " '/tmp']" + ] + }, + "execution_count": 25, "metadata": {}, - "outputs": [], - "prompt_number": 24 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# Check if it's been added\n", + "pn.atomicData.getAllDataFilePaths()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Remove it if you gave the wrong dir\n", + "pn.atomicData.removeDataFilePath('/tmp')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Set 'o_iii_.fits' to be the OIII atom file\n", + "pn.atomicData.setDataFile('o_iii_coll_Pal12-AK99.dat')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# define an atom with the new data\n", + "O2test = pn.Atom(\"O\", 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# define all atoms at once and put them in a dictionary\n", - "# (all of them defined with the latest dataset selected)\n", - "atoms = pn.getAtomDict() #this generates a lot of warnings as not all element-spectrum combination exist" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 25 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "warng _ManageAtomicData: rec data not available for Al2\n", + "warng _ManageAtomicData: rec data not available for Ar2\n", + "warng _ManageAtomicData: rec data not available for Ar3\n", + "warng _ManageAtomicData: rec data not available for Ar4\n", + "warng _ManageAtomicData: rec data not available for Ar5\n", + "warng _ManageAtomicData: rec data not available for Ba2\n", + "warng _ManageAtomicData: rec data not available for Ba4\n", + "ERROR None: No data for this case B\n", + "ERROR None: No data for this case B\n", + "warng _ManageAtomicData: rec data not available for Ca5\n", + "warng _ManageAtomicData: rec data not available for Cl2\n", + "warng _ManageAtomicData: rec data not available for Cl3\n", + "warng _ManageAtomicData: rec data not available for Cl4\n", + "warng _ManageAtomicData: rec data not available for Fe3\n", + "warng _ManageAtomicData: rec data not available for K4\n", + "warng _ManageAtomicData: rec data not available for K5\n", + "warng _ManageAtomicData: rec data not available for Mg5\n", + "warng _ManageAtomicData: rec data not available for Mg7\n", + "ERROR None: No data for this case B\n", + "warng _ManageAtomicData: rec data not available for Na4\n", + "warng _ManageAtomicData: rec data not available for Na6\n", + "warng _ManageAtomicData: rec data not available for Ne3\n", + "warng _ManageAtomicData: rec data not available for Ne4\n", + "warng _ManageAtomicData: rec data not available for Ne5\n", + "warng _ManageAtomicData: rec data not available for Ne6\n", + "warng _ManageAtomicData: rec data not available for Ni3\n", + "ERROR None: No data for this case B\n", + "warng _ManageAtomicData: rec data not available for S2\n", + "warng _ManageAtomicData: rec data not available for S3\n", + "warng _ManageAtomicData: rec data not available for S4\n", + "warng _ManageAtomicData: rec data not available for Si2\n", + "warng _ManageAtomicData: rec data not available for Si3\n", + "warng _ManageAtomicData: rec data not available for Xe3\n", + "warng _ManageAtomicData: rec data not available for Xe4\n", + "warng _ManageAtomicData: rec data not available for Xe6\n", + "warng _ManageAtomicData: rec data not available for Kr3\n", + "warng _ManageAtomicData: rec data not available for Kr4\n", + "warng _ManageAtomicData: rec data not available for Kr5\n", + "warng _ManageAtomicData: rec data not available for Se3\n", + "warng _ManageAtomicData: rec data not available for Se4\n", + "warng _ManageAtomicData: rec data not available for Br3\n", + "warng _ManageAtomicData: rec data not available for Br4\n", + "warng _ManageAtomicData: rec data not available for Rb4\n", + "warng _ManageAtomicData: rec data not available for Rb5\n", + "warng _ManageAtomicData: rec data not available for Rb6\n", + "warng _ManageAtomicData: rec data not available for Fe4\n", + "warng _ManageAtomicData: rec data not available for Fe5\n", + "warng _ManageAtomicData: rec data not available for Fe6\n", + "warng _ManageAtomicData: rec data not available for Fe7\n", + "warng _ManageAtomicData: rec data not available for Fe2\n", + "warng _ManageAtomicData: rec data not available for P2\n", + "warng _ManageAtomicData: rec data not available for Te3\n", + "warng _ManageAtomicData: rec data not available for Kr6\n", + "warng _ManageAtomicData: rec data not available for Br5\n" + ] + } + ], + "source": [ + "# define all atoms at once and put them in a dictionary\n", + "# (all of them defined with the latest dataset selected)\n", + "atoms = pn.getAtomDict() #this generates a lot of warnings as not all element-spectrum combination exist" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# see what atoms have been built\n", - "atoms" - ], - "language": "python", + "data": { + "text/plain": [ + "{'Al2': Atom Al2 from al_ii_atom_JSP86-HK87-VVF96-KS86.dat and al_ii_coll_KHAF92-TBK85-TBK84.dat,\n", + " 'Ar2': Atom Ar2 from ar_ii_atom_Bal06.dat and ar_ii_coll_PB95.dat,\n", + " 'Ar3': Atom Ar3 from ar_iii_atom_MB09.dat and ar_iii_coll_MB09.dat,\n", + " 'Ar4': Atom Ar4 from ar_iv_atom_RGJ19.dat and ar_iv_coll_RB97.dat,\n", + " 'Ar5': Atom Ar5 from ar_v_atom_LL93-MZ82-KS86.dat and ar_v_coll_GMZ95.dat,\n", + " 'Ba2': Atom Ba2 from ba_ii_atom_C04.dat and ba_ii_coll_SB98.dat,\n", + " 'Ba4': Atom Ba4 from ba_iv_atom_BHQZ95.dat and ba_iv_coll_SB98.dat,\n", + " 'C1': Atom C1 from c_i_atom_FFS85.dat and c_i_coll_JBK87-PA76.dat,\n", + " 'C1r': Atom C1 from c_i_rec_P91.func,\n", + " 'C2': Atom C2 from c_ii_atom_GMZ98.dat and c_ii_coll_BP92.dat,\n", + " 'C2r': Atom C2 from c_ii_rec_D00.func,\n", + " 'C3': Atom C3 from c_iii_atom_G83-NS78-WFD96.dat and c_iii_coll_Bal85.dat,\n", + " 'C4': Atom C4 from c_iv_atom_WFD96.dat and c_iv_coll_AK04.dat,\n", + " 'Ca5': Atom Ca5 from ca_v_atom_M83-KS86.dat and ca_v_coll_GMZ95.dat,\n", + " 'Cl2': Atom Cl2 from cl_ii_atom_MZ83.dat and cl_ii_coll_T04.dat,\n", + " 'Cl3': Atom Cl3 from cl_iii_atom_RGJ19.dat and cl_iii_coll_BZ89.dat,\n", + " 'Cl4': Atom Cl4 from cl_iv_atom_KS86-MZ82-EM84.dat and cl_iv_coll_GMZ95.dat,\n", + " 'Fe3': Atom Fe3 from fe_iii_atom_Q96_J00.dat and fe_iii_coll_Z96.dat,\n", + " 'K4': Atom K4 from k_iv_atom_M83-KS86.dat and k_iv_coll_GMZ95.dat,\n", + " 'K5': Atom K5 from k_v_atom_M83-KS86.dat and k_v_coll_BZL88.dat,\n", + " 'Mg5': Atom Mg5 from mg_v_atom_GMZ97.dat and mg_v_coll_BZ94.dat,\n", + " 'Mg7': Atom Mg7 from mg_vii_atom_GMZ97.dat and mg_vii_coll_LB94-U.dat,\n", + " 'N1': Atom N1 from n_i_atom_KS86-WFD96.dat and n_i_coll_PA76-DMR76.dat,\n", + " 'N1r': Atom N1 from n_i_rec_P91.func,\n", + " 'N2': Atom N2 from n_ii_atom_FFT04.dat and n_ii_coll_T11.dat,\n", + " 'N2r': Atom N2 from n_ii_rec_FSL11.func,\n", + " 'N3': Atom N3 from n_iii_atom_GMZ98.dat and n_iii_coll_BP92.dat,\n", + " 'N3r': Atom N3 from n_iii_rec_P91.func,\n", + " 'N4': Atom N4 from n_iv_atom_WFD96.dat and n_iv_coll_RBHB94.dat,\n", + " 'Na4': Atom Na4 from na_iv_atom_GMZ97.dat and na_iv_coll_BZ94.dat,\n", + " 'Na6': Atom Na6 from na_vi_atom_GMZ97.dat and na_vi_coll_LB94.dat,\n", + " 'Ne2': Atom Ne2 from ne_ii_atom_Bal06.dat and ne_ii_coll_GMB01.dat,\n", + " 'Ne2r': Atom Ne2 from ne_ii_rec_KSDN98.func,\n", + " 'Ne3': Atom Ne3 from ne_iii_atom_GMZ97.dat and ne_iii_coll_McLB00.dat,\n", + " 'Ne4': Atom Ne4 from ne_iv_atom_GFF84.dat and ne_iv_coll_G81.dat,\n", + " 'Ne5': Atom Ne5 from ne_v_atom_GMZ97-U-BD93.dat and ne_v_coll_DPNP13.dat,\n", + " 'Ne6': Atom Ne6 from ne_vi_atom_GMZ98.dat and ne_vi_coll_ZGP94.dat,\n", + " 'Ni3': Atom Ni3 from ni_iii_atom_B01.dat and ni_iii_coll_B01.dat,\n", + " 'O1': Atom O1 from o_i_atom_WFD96.dat and o_i_coll_BK95.dat,\n", + " 'O1r': Atom O1 from o_i_rec_P91.func,\n", + " 'O2': Atom O2 from o_ii_atom_Z82-WFD96.dat and o_ii_coll_Kal09.dat,\n", + " 'O2r': Atom O2 from o_ii_rec_SSB17-B-opt.hdf5,\n", + " 'O3': Atom O3 from o_iii_atom_FFT04-SZ00.dat and o_iii_coll_Pal12-AK99.dat,\n", + " 'O3r': Atom O3 from o_iii_rec_P91.func,\n", + " 'O4': Atom O4 from o_iv_atom_GMZ98.dat and o_iv_coll_BP92.dat,\n", + " 'O4r': Atom O4 from o_iv_rec_P91.func,\n", + " 'O5': Atom O5 from o_v_atom_H80-NS79.dat and o_v_coll_BBDK85.dat,\n", + " 'S2': Atom S2 from s_ii_atom_RGJ19.dat and s_ii_coll_TZ10.dat,\n", + " 'S3': Atom S3 from s_iii_atom_FFTI06.dat and s_iii_coll_TG99.dat,\n", + " 'S4': Atom S4 from s_iv_atom_JKD86-DHKD82.dat and s_iv_coll_DHKD82.dat,\n", + " 'Si2': Atom Si2 from si_ii_atom_BL93-CSB93-N77.dat and si_ii_coll_DK91.dat,\n", + " 'Si3': Atom Si3 from si_iii_atom_M83-OKH88-FW90-KS86.dat and si_iii_coll_DK94.dat,\n", + " 'Xe3': Atom Xe3 from xe_iii_atom_BHQZ95.dat and xe_iii_coll_SB98.dat,\n", + " 'Xe4': Atom Xe4 from xe_iv_atom_BHQZ95.dat and xe_iv_coll_SB98.dat,\n", + " 'Xe6': Atom Xe6 from xe_vi_atom_BHQZ95.dat and xe_vi_coll_SB98.dat,\n", + " 'Kr3': Atom Kr3 from kr_iii_atom_BH86.dat and kr_iii_coll_S97.dat,\n", + " 'Kr4': Atom Kr4 from kr_iv_atom_BH86.dat and kr_iv_coll_S97.dat,\n", + " 'Kr5': Atom Kr5 from kr_v_atom_BH86.dat and kr_v_coll_S97.dat,\n", + " 'Se3': Atom Se3 from se_iii_atom_S17.dat and se_iii_coll_S17.dat,\n", + " 'Se4': Atom Se4 from se_iv_atom_B05.dat and se_iv_coll_B05.dat,\n", + " 'Br3': Atom Br3 from br_iii_atom_BH86.dat and br_iii_coll_S97.dat,\n", + " 'Br4': Atom Br4 from br_iv_atom_BH86.dat and br_iv_coll_S97.dat,\n", + " 'Rb4': Atom Rb4 from rb_iv_atom_S16.dat and rb_iv_coll_S16.dat,\n", + " 'Rb5': Atom Rb5 from rb_v_atom_BH86.dat and rb_v_coll_S97.dat,\n", + " 'Rb6': Atom Rb6 from rb_vi_atom_BH86.dat and rb_vi_coll_S97.dat,\n", + " 'Fe4': Atom Fe4 from fe_iv_atom_FFRR08.dat and fe_iv_coll_ZP97.dat,\n", + " 'Fe5': Atom Fe5 from fe_v_atom_Nal00.dat and fe_v_coll_BGMcL07.dat,\n", + " 'Fe6': Atom Fe6 from fe_vi_atom_CP00.dat and fe_vi_coll_CP99.dat,\n", + " 'Fe7': Atom Fe7 from fe_vii_atom_WB08.dat and fe_vii_coll_WB08.dat,\n", + " '3He2': Atom 3He2 from 3he_ii_atom_cloudy.dat and 3he_ii_coll_cloudy.dat,\n", + " 'Fe2': Atom Fe2 from fe_ii_atom_B15.dat and fe_ii_coll_B15.dat,\n", + " 'P2': Atom P2 from p_ii_atom_MZ82.dat and p_ii_coll_T04.dat,\n", + " 'Te3': Atom Te3 from te_iii_atom_M18.dat and te_iii_coll_M18.dat,\n", + " 'Kr6': Atom Kr6 from kr_vi_atom_S17.dat and kr_vi_coll_S17.dat}" + ] + }, + "execution_count": 30, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 26, - "text": [ - "{'Al2': Atom Al2 from al_ii_atom_JSP86-HK87-VVF96-KS86.dat and al_ii_coll_KHAF92-TBK85-TBK84.dat,\n", - " 'Ar2': Atom Ar2 from ar_ii_atom_Bal06.dat and ar_ii_coll_PB95.dat,\n", - " 'Ar3': Atom Ar3 from ar_iii_atom_M83-KS86.dat and ar_iii_coll_GMZ95.dat,\n", - " 'Ar4': Atom Ar4 from ar_iv_atom_MZ82.dat and ar_iv_coll_RB97.dat,\n", - " 'Ar5': Atom Ar5 from ar_v_atom_LL93-MZ82-KS86.dat and ar_v_coll_GMZ95.dat,\n", - " 'Ba2': Atom Ba2 from ba_ii_atom_C04.dat and ba_ii_coll_SB98.dat,\n", - " 'Ba4': Atom Ba4 from ba_iv_atom_BHQZ95.dat and ba_iv_coll_SB98.dat,\n", - " 'Br3': Atom Br3 from br_iii_atom_BH86.dat and br_iii_coll_S97.dat,\n", - " 'Br4': Atom Br4 from br_iv_atom_BH86.dat and br_iv_coll_S97.dat,\n", - " 'C1': Atom C1 from c_i_atom_FFS85.dat and c_i_coll_JBK87-PA76.dat,\n", - " 'C2': Atom C2 from c_ii_atom_GMZ98.dat and c_ii_coll_BP92.dat,\n", - " 'C3': Atom C3 from c_iii_atom_G83-NS78-WFD96.dat and c_iii_coll_Bal85.dat,\n", - " 'C4': Atom C4 from c_iv_atom_WFD96.dat and c_iv_coll_AK04.dat,\n", - " 'Ca5': Atom Ca5 from ca_v_atom_M83-KS86.dat and ca_v_coll_GMZ95.dat,\n", - " 'Cl2': Atom Cl2 from cl_ii_atom_MZ83.dat and cl_ii_coll_T04.dat,\n", - " 'Cl3': Atom Cl3 from cl_iii_atom_M83-KS86.dat and cl_iii_coll_BZ89.dat,\n", - " 'Cl4': Atom Cl4 from cl_iv_atom_KS86-MZ82-EM84.dat and cl_iv_coll_GMZ95.dat,\n", - " 'Fe3': Atom Fe3 from fe_iii_atom_Q96_J00.dat and fe_iii_coll_Z96.dat,\n", - " 'K4': Atom K4 from k_iv_atom_M83-KS86.dat and k_iv_coll_GMZ95.dat,\n", - " 'K5': Atom K5 from k_v_atom_M83-KS86.dat and k_v_coll_BZL88.dat,\n", - " 'Kr3': Atom Kr3 from kr_iii_atom_BH86.dat and kr_iii_coll_S97.dat,\n", - " 'Kr4': Atom Kr4 from kr_iv_atom_BH86.dat and kr_iv_coll_S97.dat,\n", - " 'Kr5': Atom Kr5 from kr_v_atom_BH86.dat and kr_v_coll_S97.dat,\n", - " 'Mg5': Atom Mg5 from mg_v_atom_GMZ97.dat and mg_v_coll_BZ94.dat,\n", - " 'Mg7': Atom Mg7 from mg_vii_atom_GMZ97.dat and mg_vii_coll_LB94-U.dat,\n", - " 'N1': Atom N1 from n_i_atom_KS86-WFD96.dat and n_i_coll_PA76-DMR76.dat,\n", - " 'N2': Atom N2 from n_ii_atom_GMZ97-WFD96.dat and n_ii_coll_T11.dat,\n", - " 'N3': Atom N3 from n_iii_atom_GMZ98.dat and n_iii_coll_BP92.dat,\n", - " 'N4': Atom N4 from n_iv_atom_WFD96.dat and n_iv_coll_RBHB94.dat,\n", - " 'Na4': Atom Na4 from na_iv_atom_GMZ97.dat and na_iv_coll_BZ94.dat,\n", - " 'Na6': Atom Na6 from na_vi_atom_GMZ97.dat and na_vi_coll_LB94.dat,\n", - " 'Ne2': Atom Ne2 from ne_ii_atom_Bal06.dat and ne_ii_coll_GMB01.dat,\n", - " 'Ne3': Atom Ne3 from ne_iii_atom_GMZ97.dat and ne_iii_coll_McLB00.dat,\n", - " 'Ne4': Atom Ne4 from ne_iv_atom_BBZ89-BK88.dat and ne_iv_coll_G81.dat,\n", - " 'Ne5': Atom Ne5 from ne_v_atom_GMZ97-U-BD93.dat and ne_v_coll_LB94.dat,\n", - " 'Ne6': Atom Ne6 from ne_vi_atom_GMZ98.dat and ne_vi_coll_ZGP94.dat,\n", - " 'Ni3': Atom Ni3 from ni_iii_atom_B01.dat and ni_iii_coll_B01.dat,\n", - " 'O1': Atom O1 from o_i_atom_WFD96.dat and o_i_coll_BK95.dat,\n", - " 'O2': Atom O2 from o_ii_atom_Z82-WFD96.dat and o_ii_coll_P06-T07.dat,\n", - " 'O3': Atom O3 from o_iii_atom_SZ00-WFD96.dat and o_iii_coll_Pal12-AK99.dat,\n", - " 'O4': Atom O4 from o_iv_atom_GMZ98.dat and o_iv_coll_BP92.dat,\n", - " 'O5': Atom O5 from o_v_atom_H80-NS79.dat and o_v_coll_BBDK85.dat,\n", - " 'Rb4': Atom Rb4 from rb_iv_atom_BH86.dat and rb_iv_coll_S97.dat,\n", - " 'Rb5': Atom Rb5 from rb_v_atom_BH86.dat and rb_v_coll_S97.dat,\n", - " 'Rb6': Atom Rb6 from rb_vi_atom_BH86.dat and rb_vi_coll_S97.dat,\n", - " 'S2': Atom S2 from s_ii_atom_PKW09.dat and s_ii_coll_TZ10.dat,\n", - " 'S3': Atom S3 from s_iii_atom_PKW09.dat and s_iii_coll_GMZ95.dat,\n", - " 'S4': Atom S4 from s_iv_atom_JKD86-DHKD82.dat and s_iv_coll_DHKD82.dat,\n", - " 'Se3': Atom Se3 from se_iii_atom_BH86.dat and se_iii_coll_S97.dat,\n", - " 'Se4': Atom Se4 from se_iv_atom_B05.dat and se_iv_coll_B05.dat,\n", - " 'Si2': Atom Si2 from si_ii_atom_BL93-CSB93-N77.dat and si_ii_coll_DK91.dat,\n", - " 'Si3': Atom Si3 from si_iii_atom_M83-OKH88-FW90-KS86.dat and si_iii_coll_DK94.dat,\n", - " 'Xe3': Atom Xe3 from xe_iii_atom_BHQZ95.dat and xe_iii_coll_SB98.dat,\n", - " 'Xe4': Atom Xe4 from xe_iv_atom_BHQZ95.dat and xe_iv_coll_SB98.dat,\n", - " 'Xe6': Atom Xe6 from xe_vi_atom_BHQZ95.dat and xe_vi_coll_SB98.dat}" - ] - } - ], - "prompt_number": 26 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# see what atoms have been built\n", + "atoms" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# build only some atoms \n", + "atoms = pn.getAtomDict(atom_list=['O1', 'O2', 'O3', 'N2', 'N3'])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# build only some atoms \n", - "atoms = pn.getAtomDict(atom_list=['O1', 'O2', 'O3', 'N2', 'N3'])" - ], - "language": "python", + "data": { + "text/plain": [ + "'N'" + ] + }, + "execution_count": 32, "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# explore some specific atom in the atoms collection\n", + "atoms['N2'].elem" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# if you want to be able to access them directly rather than through a dictionary:\n", + "for key in atoms.keys():\n", + " vars()[key]=atoms[key]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# explore some specific atom in the atoms collection\n", - "atoms['N2'].elem" - ], - "language": "python", + "data": { + "text/plain": [ + "'O'" + ] + }, + "execution_count": 34, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "'N'" - ] - } - ], - "prompt_number": 28 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# for example\n", + "O2.elem" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# if you want to be able to access them directly rather than through a dictionary:\n", - "for key in atoms.keys():\n", - " vars()[key]=atoms[key]" - ], - "language": "python", + "data": { + "text/plain": [ + "['ANN_init_kwargs',\n", + " 'ANN_inst_kwargs',\n", + " 'ANN_n_temden',\n", + " 'AtomData',\n", + " 'AtomHeader',\n", + " 'CollData',\n", + " 'EnergyNLevels',\n", + " 'IP',\n", + " 'IP_up',\n", + " 'NIST',\n", + " 'NLevels',\n", + " 'Z',\n", + " '_A',\n", + " '_Energy',\n", + " '_StatWeight',\n", + " '_Transition',\n", + " '__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " '_getTemDen_1',\n", + " '_getTemDen_ANN',\n", + " '_getTemDen_MP',\n", + " '_test_lev',\n", + " 'atom',\n", + " 'atomFile',\n", + " 'atomFileType',\n", + " 'atomFitsFile',\n", + " 'atomFitsPath',\n", + " 'atomNLevels',\n", + " 'atomPath',\n", + " 'calling',\n", + " 'collFile',\n", + " 'collFileType',\n", + " 'collFitsFile',\n", + " 'collFitsPath',\n", + " 'collNLevels',\n", + " 'collPath',\n", + " 'elem',\n", + " 'energy_Ryd',\n", + " 'energy_eV',\n", + " 'getA',\n", + " 'getCollRates',\n", + " 'getCritDensity',\n", + " 'getDensityRange',\n", + " 'getEmissivity',\n", + " 'getEnergy',\n", + " 'getHighDensRatio',\n", + " 'getIonAbundance',\n", + " 'getLowDensRatio',\n", + " 'getOmega',\n", + " 'getOmegaArray',\n", + " 'getPopulations',\n", + " 'getSources',\n", + " 'getStatWeight',\n", + " 'getTemArray',\n", + " 'getTemDen',\n", + " 'getTransition',\n", + " 'gs',\n", + " 'is_valid',\n", + " 'lineList',\n", + " 'log_',\n", + " 'name',\n", + " 'plotEmiss',\n", + " 'plotGrotrian',\n", + " 'printIonic',\n", + " 'printSources',\n", + " 'printTemDen',\n", + " 'printTransition',\n", + " 'source',\n", + " 'spec',\n", + " 'tem_units',\n", + " 'type',\n", + " 'wave_Ang']" + ] + }, + "execution_count": 35, "metadata": {}, - "outputs": [], - "prompt_number": 29 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# list all atom features\n", + "dir(O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# for example\n", - "O2.elem" - ], - "language": "python", + "data": { + "text/plain": [ + "array([9.74468368e-01, 2.00608882e-02, 5.46963327e-03, 6.87357279e-07,\n", + " 4.22917029e-07])" + ] + }, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 30, - "text": [ - "'O'" - ] - } - ], - "prompt_number": 30 - }, + "output_type": "execute_result" + } + ], + "source": [ + "#######################################################################\n", + "# MAKING CALCULATIONS\n", + "\n", + "# set temperature and density\n", + "tem = 15000.\n", + "den = 1000.\n", + "\n", + "# compute populations\n", + "O2.getPopulations(tem, den)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# list all atom features\n", - "dir(O2)" - ], - "language": "python", + "data": { + "text/plain": [ + "array(2.61023869e-27)" + ] + }, + "execution_count": 37, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 31, - "text": [ - "['AtomData',\n", - " 'AtomHeader',\n", - " 'CollData',\n", - " 'EnergyNLevels',\n", - " 'NIST',\n", - " 'NLevels',\n", - " '_A',\n", - " '_Energy',\n", - " '_StatWeight',\n", - " '_Transition',\n", - " '__class__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__doc__',\n", - " '__format__',\n", - " '__getattribute__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__module__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '__weakref__',\n", - " '_getTemDen_1',\n", - " '_getTemDen_MP',\n", - " '_test_lev',\n", - " 'atom',\n", - " 'atomFile',\n", - " 'atomFileType',\n", - " 'atomFitsFile',\n", - " 'atomFitsPath',\n", - " 'atomNLevels',\n", - " 'atomPath',\n", - " 'calling',\n", - " 'collFile',\n", - " 'collFileType',\n", - " 'collFitsFile',\n", - " 'collFitsPath',\n", - " 'collNLevels',\n", - " 'collPath',\n", - " 'elem',\n", - " 'energy_Ryd',\n", - " 'energy_eV',\n", - " 'getA',\n", - " 'getCollRates',\n", - " 'getCritDensity',\n", - " 'getDensityRange',\n", - " 'getEmissivity',\n", - " 'getEnergy',\n", - " 'getHighDensRatio',\n", - " 'getIonAbundance',\n", - " 'getLowDensRatio',\n", - " 'getOmega',\n", - " 'getOmegaArray',\n", - " 'getPopulations',\n", - " 'getSources',\n", - " 'getStatWeight',\n", - " 'getTemArray',\n", - " 'getTemDen',\n", - " 'getTransition',\n", - " 'gs',\n", - " 'lineList',\n", - " 'log_',\n", - " 'name',\n", - " 'plotEmiss',\n", - " 'plotGrotrian',\n", - " 'plotGrotrian_bkp',\n", - " 'printIonic',\n", - " 'printSources',\n", - " 'printTemDen',\n", - " 'printTransition',\n", - " 'spec',\n", - " 'tem_units',\n", - " 'wave_Ang']" - ] - } - ], - "prompt_number": 31 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# compute emissivity of transition (lev_i, lev_j)\n", + "O2.getEmissivity(tem, den, 3, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "#######################################################################\n", - "# MAKING CALCULATIONS\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.834 0.84406867 0.85580808]\n", + "[[[0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", + " [3.80021542e-10 1.87127422e-09 2.94646469e-09]\n", + " [2.51710293e-10 1.24203112e-09 1.95669201e-09]\n", + " [1.63490224e-11 2.19985852e-10 4.89760515e-10]\n", + " [8.42755140e-12 1.12884048e-10 2.51995452e-10]]\n", "\n", - "# set temperature and density\n", - "tem = 15000.\n", - "den = 1000.\n", + " [[1.19944941e-08 8.58378169e-09 7.10610532e-09]\n", + " [0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", + " [1.72516467e-08 1.19734339e-08 9.89457622e-09]\n", + " [1.71535930e-09 3.54215582e-09 4.29556164e-09]\n", + " [6.83126125e-10 1.41281364e-09 1.70610924e-09]]\n", "\n", - "# compute populations\n", - "O2.getPopulations(tem, den)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 32, - "text": [ - "array([ 9.73452672e-01, 2.06878141e-02, 5.85819178e-03,\n", - " 8.23867455e-07, 4.97864417e-07, 2.92747483e-19,\n", - " 1.86769357e-19, 9.15097829e-20])" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# compute emissivity of transition (lev_i, lev_j)\n", - "O2.getEmissivity(tem, den, 3, 2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "array(2.79566801999487e-27)" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# also works if tem is an array\n", - "tem = np.array([10000, 20000, 30000])\n", - "print O2.getOmega(tem, 2, 1)\n", - "print O2.getCollRates(tem)\n", - "print O2.getPopulations(tem, den)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.883 0.885 0.888]\n", - "[[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 4.02348947e-10 1.96201773e-09 3.05729835e-09]\n", - " [ 2.65795165e-10 1.29506046e-09 2.02248448e-09]\n", - " [ 1.99892345e-11 2.67251627e-10 5.84802421e-10]\n", - " [ 9.70445312e-12 1.29457409e-10 2.82538118e-10]\n", - " [ 8.96839926e-16 3.94650511e-12 5.52112835e-11]\n", - " [ 5.78080057e-16 2.57945263e-12 3.36414737e-11]\n", - " [ 2.85632308e-16 1.28345197e-12 1.97988347e-11]]\n", - "\n", - " [[ 1.26992065e-08 9.00003412e-09 7.37340725e-09]\n", - " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 2.04495828e-08 1.34450801e-08 1.09499517e-08]\n", - " [ 1.90685065e-09 3.80720020e-09 4.44251634e-09]\n", - " [ 8.10078768e-10 1.62924843e-09 1.90190557e-09]\n", - " [ 9.42817655e-15 5.37417243e-12 4.08381064e-11]\n", - " [ 3.13031340e-15 1.80539043e-12 1.37728857e-11]\n", - " [ 6.40899379e-16 3.71833485e-13 2.84224001e-12]]\n", - "\n", - " [[ 1.26201062e-08 8.92376265e-09 7.32358693e-09]\n", - " [ 3.07628571e-08 2.01966867e-08 1.64407054e-08]\n", - " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 1.83452287e-09 3.68305597e-09 4.29999680e-09]\n", - " [ 1.20346626e-09 2.41880382e-09 2.82969770e-09]\n", - " [ 2.66348533e-15 1.51603323e-12 1.15147424e-11]\n", - " [ 6.11391395e-15 3.52109027e-12 2.68486513e-11]\n", - " [ 4.30641232e-15 2.49487492e-12 1.90613009e-11]]\n", - "\n", - " [[ 6.75229612e-09 4.91188303e-09 4.07281110e-09]\n", - " [ 2.04078982e-08 1.52542951e-08 1.28287322e-08]\n", - " [ 1.30515628e-08 9.82376606e-09 8.27017300e-09]\n", - " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 6.27589668e-09 4.72815457e-09 3.99769907e-09]\n", - " [ 1.56330473e-14 3.33604855e-12 1.82706770e-11]\n", - " [ 2.27202103e-14 4.90569440e-12 2.69725853e-11]\n", - " [ 1.50901700e-14 3.27761250e-12 1.80566897e-11]]\n", - "\n", - " [[ 6.55814064e-09 4.75934008e-09 3.93580522e-09]\n", - " [ 1.73445562e-08 1.30576766e-08 1.09853804e-08]\n", - " [ 1.71288279e-08 1.29051337e-08 1.08857398e-08]\n", - " [ 1.25553877e-08 9.45766298e-09 7.99616124e-09]\n", - " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 1.31497361e-13 2.80571519e-11 1.53654447e-10]\n", - " [ 6.10983006e-14 1.31903130e-11 7.25197774e-11]\n", - " [ 2.28704412e-14 4.96679053e-12 2.73612320e-11]]\n", - "\n", - " [[ 1.83944339e-08 1.45932757e-08 1.15334039e-08]\n", - " [ 6.12668402e-09 4.33221982e-09 3.53724267e-09]\n", - " [ 1.15055099e-09 8.13562407e-10 6.64270923e-10]\n", - " [ 9.49204566e-10 6.71188985e-10 5.48023512e-10]\n", - " [ 3.99097374e-09 2.82204460e-09 2.30418977e-09]\n", - " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 1.14758782e-08 8.21051421e-09 6.73014677e-09]\n", - " [ 4.54650503e-09 3.27218187e-09 2.68750980e-09]]\n", - "\n", - " [[ 1.82074694e-08 1.44763261e-08 1.06241831e-08]\n", - " [ 3.12374594e-09 2.20882193e-09 1.80349556e-09]\n", - " [ 4.05569224e-09 2.86780748e-09 2.34155500e-09]\n", - " [ 2.11845201e-09 1.49797178e-09 1.22308884e-09]\n", - " [ 2.84761370e-09 2.01356696e-09 1.64407054e-09]\n", - " [ 1.76228457e-08 1.24612337e-08 1.01745547e-08]\n", - " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 7.64267936e-09 5.43633202e-09 4.44752899e-09]]\n", - "\n", - " [[ 1.82074694e-08 1.44915804e-08 1.25547205e-08]\n", - " [ 1.29436986e-09 9.15257707e-10 7.47304789e-10]\n", - " [ 5.78151872e-09 4.08815109e-09 3.33796139e-09]\n", - " [ 2.84761370e-09 2.01356696e-09 1.64407054e-09]\n", - " [ 2.15728310e-09 1.52542951e-09 1.24550798e-09]\n", - " [ 1.41302043e-08 9.99156331e-09 8.15807728e-09]\n", - " [ 1.54677199e-08 1.09373296e-08 8.93029223e-09]\n", - " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00]]]\n", - "[[ 9.91510528e-01 9.54771880e-01 9.26853031e-01]\n", - " [ 6.52382507e-03 3.55526924e-02 5.80300307e-02]\n", - " [ 1.96540209e-03 9.67251526e-03 1.51110326e-02]\n", - " [ 1.52722031e-07 1.81470325e-06 3.68276302e-06]\n", - " [ 9.24915727e-08 1.09741196e-06 2.22248330e-06]\n", - " [ 1.11031751e-21 4.61530674e-18 6.23890714e-17]\n", - " [ 7.00138150e-22 2.96075896e-18 3.74403632e-17]\n", - " [ 3.41241993e-22 1.45647826e-18 2.16882451e-17]]\n" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# tem and den can be arrays as well as single numbers\n", - "tem = np.array([10000, 12000, 13000]) \n", - "den = np.array([100, 200, 300])\n", - "print O2.getPopulations(tem, den) # returns the n_tem x n_den x n_levels array of populations\n", - "print O2.getPopulations(tem, den, product=False) # element-by-element multiplication of tem and den (no scalar product: returns [pop(tem_1, den_1), pop(tem_2, den_2), ... pop(tem_n, den_n)] " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[[[ 9.98814476e-01 9.97747175e-01 9.96772963e-01]\n", - " [ 9.97907387e-01 9.96013650e-01 9.94278945e-01]\n", - " [ 9.97405907e-01 9.95053836e-01 9.92896515e-01]]\n", - "\n", - " [[ 1.01008493e-03 1.88979798e-03 2.66876864e-03]\n", - " [ 1.78472117e-03 3.35104230e-03 4.74572152e-03]\n", - " [ 2.21330225e-03 4.16149208e-03 5.89989095e-03]]\n", - "\n", - " [[ 1.75423488e-04 3.62994400e-04 5.58214848e-04]\n", - " [ 3.07855374e-04 6.35228166e-04 9.75206697e-04]\n", - " [ 3.80739831e-04 7.84561820e-04 1.20341751e-03]]\n", - "\n", - " [[ 9.46042099e-09 2.06242624e-08 3.32663143e-08]\n", - " [ 2.27603334e-08 4.93538457e-08 7.92928392e-08]\n", - " [ 3.17567799e-08 6.87002228e-08 1.10179853e-07]]\n", - "\n", - " [[ 5.74293754e-09 1.24796872e-08 2.00952981e-08]\n", - " [ 1.38088802e-08 2.98524440e-08 4.78814254e-08]\n", - " [ 1.92610165e-08 4.15448885e-08 6.65188695e-08]]\n", - "\n", - " [[ 1.05199547e-22 2.12219662e-22 3.20765635e-22]\n", - " [ 1.73857483e-21 3.50148439e-21 5.28486107e-21]\n", - " [ 5.10054414e-21 1.02642145e-20 1.54809732e-20]]\n", - "\n", - " [[ 6.72404280e-23 1.35240220e-22 2.03924919e-22]\n", - " [ 1.11693946e-21 2.24328436e-21 3.37820409e-21]\n", - " [ 3.28358334e-21 6.59011724e-21 9.91772235e-21]]\n", - "\n", - " [[ 3.30676779e-23 6.63814992e-23 9.99397367e-23]\n", - " [ 5.50498227e-22 1.10367440e-21 1.65962590e-21]\n", - " [ 1.61973590e-21 3.24523850e-21 4.87698469e-21]]]\n", - "[[ 9.98814476e-01 9.96013650e-01 9.92896515e-01]\n", - " [ 1.01008493e-03 3.35104230e-03 5.89989095e-03]\n", - " [ 1.75423488e-04 6.35228166e-04 1.20341751e-03]\n", - " [ 9.46042099e-09 4.93538457e-08 1.10179853e-07]\n", - " [ 5.74293754e-09 2.98524440e-08 6.65188695e-08]\n", - " [ 1.05199547e-22 3.50148439e-21 1.54809732e-20]\n", - " [ 6.72404280e-23 2.24328436e-21 9.91772235e-21]\n", - " [ 3.30676779e-23 1.10367440e-21 4.87698469e-21]]\n" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# find transition corresponding to given wavelength\n", - "N2.printTransition(6584)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Input wave: 6584.0\n", - "Closest wave found: 6583.5\n", - "Relative error: 8E-05 \n", - "Transition: 4 -> 3\n" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# temperature determination from an intensity ratio\n", - "N2.getTemDen(0.01, den=1000., wave1=5755, wave2=6584)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 37, - "text": [ - "8588.005788908411" - ] - } - ], - "prompt_number": 37 - }, + " [[1.19513484e-08 8.55835787e-09 7.08534686e-09]\n", + " [2.59521158e-08 1.79860360e-08 1.48561215e-08]\n", + " [0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", + " [1.43123107e-09 2.95216539e-09 3.56459531e-09]\n", + " [1.00339378e-09 2.05940718e-09 2.49676583e-09]]\n", + "\n", + " [[5.52264475e-09 4.04317380e-09 3.41089912e-09]\n", + " [1.83584792e-08 1.41923428e-08 1.24043685e-08]\n", + " [1.01823763e-08 7.87427137e-09 6.85577717e-09]\n", + " [0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", + " [6.14649675e-09 4.65381407e-09 4.06804055e-09]]\n", + "\n", + " [[5.69522740e-09 4.15004115e-09 3.51034056e-09]\n", + " [1.46263795e-08 1.13230514e-08 9.85446349e-09]\n", + " [1.42812142e-08 1.09876315e-08 9.60496350e-09]\n", + " [1.22965137e-08 9.30896070e-09 8.13685764e-09]\n", + " [0.00000000e+00 0.00000000e+00 0.00000000e+00]]]\n", + "[[9.91815755e-01 9.56504794e-01 9.29330631e-01]\n", + " [6.35855134e-03 3.43940081e-02 5.63143917e-02]\n", + " [1.82548920e-03 9.09873002e-03 1.43498309e-02]\n", + " [1.26622575e-07 1.52925514e-06 3.18625844e-06]\n", + " [7.78311901e-08 9.38821536e-07 1.96060918e-06]]\n" + ] + } + ], + "source": [ + "# also works if tem is an array\n", + "tem = np.array([10000, 20000, 30000])\n", + "print(O2.getOmega(tem, 2, 1))\n", + "print(O2.getCollRates(tem))\n", + "print(O2.getPopulations(tem, den))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# same as above, by specifying the levels involved\n", - "N2.getTemDen(0.01, den=1000., lev_i1=5, lev_j1=4, lev_i2=4, lev_j2=3)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 38, - "text": [ - "8588.005788908411" - ] - } - ], - "prompt_number": 38 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[9.98878029e-01 9.97858872e-01 9.96922776e-01]\n", + " [9.98019410e-01 9.96212446e-01 9.94547508e-01]\n", + " [9.97544938e-01 9.95301601e-01 9.93232313e-01]]\n", + "\n", + " [[9.57753866e-04 1.80282355e-03 2.55833017e-03]\n", + " [1.69187931e-03 3.19417248e-03 4.54370632e-03]\n", + " [2.09774000e-03 3.96480758e-03 5.64500125e-03]]\n", + "\n", + " [[1.64204847e-04 3.38277103e-04 5.18849618e-04]\n", + " [2.88680113e-04 5.93315430e-04 9.08678934e-04]\n", + " [3.57279095e-04 7.33499218e-04 1.12253769e-03]]\n", + "\n", + " [[7.75482765e-09 1.69421721e-08 2.73793081e-08]\n", + " [1.86812148e-08 4.06183659e-08 6.54110546e-08]\n", + " [2.60683216e-08 5.65585724e-08 9.09340968e-08]]\n", + "\n", + " [[4.94747738e-09 1.06910451e-08 1.71439551e-08]\n", + " [1.19270981e-08 2.56596463e-08 4.10074468e-08]\n", + " [1.66484824e-08 3.57461699e-08 5.70380805e-08]]]\n", + "[[9.98878029e-01 9.96212446e-01 9.93232313e-01]\n", + " [9.57753866e-04 3.19417248e-03 5.64500125e-03]\n", + " [1.64204847e-04 5.93315430e-04 1.12253769e-03]\n", + " [7.75482765e-09 4.06183659e-08 9.09340968e-08]\n", + " [4.94747738e-09 2.56596463e-08 5.70380805e-08]]\n" + ] + } + ], + "source": [ + "# tem and den can be arrays as well as single numbers\n", + "tem = np.array([10000, 12000, 13000]) \n", + "den = np.array([100, 200, 300])\n", + "print(O2.getPopulations(tem, den)) # returns the n_tem x n_den x n_levels array of populations\n", + "print(O2.getPopulations(tem, den, product=False)) # element-by-element multiplication of tem and den (no scalar product: returns [pop(tem_1, den_1), pop(tem_2, den_2), ... pop(tem_n, den_n)] " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# same as above, by specifying the levels involved\n", - "N2.getTemDen(0.01, den=1000., to_eval = 'L(5755) / L(6584)')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 39, - "text": [ - "8588.005788908411" - ] - } - ], - "prompt_number": 39 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Input wave: 6584.0\n", + "Closest wave found: 6583.5\n", + "Relative error: 8E-05 \n", + "Transition: 4 -> 3\n" + ] + } + ], + "source": [ + "# find transition corresponding to given wavelength\n", + "N2.printTransition(6584)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# no formal difference between temperature and density diagnostics, so beware of what you do\n", - "print N2.getTemDen(0.01, tem=8782., wave1=5755, wave2=6584)\n", - "print N2.getTemDen(0.01, tem=8882., wave1=5755, wave2=6584)" - ], - "language": "python", + "data": { + "text/plain": [ + "8573.093534627478" + ] + }, + "execution_count": 41, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "nan\n", - "nan" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 40 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# temperature determination from an intensity ratio\n", + "N2.getTemDen(0.01, den=1000., wave1=5755, wave2=6584)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# ionic abundance (intensity, temperature, density, transition)\n", - "O2.getIonAbundance(100, 1.5e4, 100., wave=3727)" - ], - "language": "python", + "data": { + "text/plain": [ + "8573.093534627478" + ] + }, + "execution_count": 42, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 41, - "text": [ - "1.8390892170722003e-05" - ] - } - ], - "prompt_number": 41 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# same as above, by specifying the levels involved\n", + "N2.getTemDen(0.01, den=1000., lev_i1=5, lev_j1=4, lev_i2=4, lev_j2=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# printout as in old nebular\n", - "O2.printIonic() # only prints transitions and corresponding wavelengths. Useful for a quick glance at the atom." - ], - "language": "python", + "data": { + "text/plain": [ + "8573.093534627478" + ] + }, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "elem = O\n", - "spec = 2\n", - "\n", - "warng Atom O2: Cannot print populations as tem or den is missing\n", - "warng Atom O2: Cannot print critical densities as tem is missing\n", - " 3728.81A \n", - " (2-->1) \n", - "\n", - "\n", - " 3726.03A 499.36m \n", - " (3-->1) (3-->2) \n", - "\n", - "\n", - " 2470.34A 7319.98A 7330.73A \n", - " (4-->1) (4-->2) (4-->3) \n", - "\n", - "\n", - " 2470.22A 7318.92A 7329.66A 5023.75m \n", - " (5-->1) (5-->2) (5-->3) (5-->4) \n", - "\n", - "\n", - " 834.47A 1074.96A 1075.19A 1259.93A 1259.97A \n", - " (6-->1) (6-->2) (6-->3) (6-->4) (6-->5) \n", - "\n", - "\n", - " 833.33A 1073.08A 1073.31A 1257.35A 1257.38A 61.25m \n", - " (7-->1) (7-->2) (7-->3) (7-->4) (7-->5) (7-->6) \n", - "\n", - "\n", - " 832.76A 1072.13A 1072.36A 1256.05A 1256.08A 40.70m 121.28m \n", - " (8-->1) (8-->2) (8-->3) (8-->4) (8-->5) (8-->6) (8-->7) \n", - "\n", - "\n" - ] - } - ], - "prompt_number": 42 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# same as above, by specifying the levels involved\n", + "N2.getTemDen(0.01, den=1000., to_eval = 'L(5755) / L(6584)')" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "O2.printIonic(tem=10000, den=100) # also prints line emissivities" - ], - "language": "python", + "data": { + "text/plain": [ + "1.957562060137208e-05" + ] + }, + "execution_count": 48, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "elem = O\n", - "spec = 2\n", - "temperature = 10000.0 K\n", - "density = 100.0 cm-3\n", - "\n", - "Level Populations Critical densities\n", - "Level 1: 9.988E-01 0.000E+00\n", - "Level 2: 1.010E-03 1.065E+03\n", - "Level 3: 1.754E-04 3.557E+03\n", - "Level 4: 9.460E-09 5.051E+06\n", - "Level 5: 5.743E-09 3.484E+06\n", - "Level 6: 1.052E-22 1.846E+16" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Level 7: 6.724E-23 1.555E+16\n", - "Level 8: 3.307E-23 1.448E+16\n", - "\n", - " 3728.81A \n", - " (2-->1) \n", - " 2.055E-21 \n", - "\n", - " 3726.03A 499.36m \n", - " (3-->1) (3-->2) \n", - " 1.543E-21 8.372E-28 \n", - "\n", - " 2470.34A 7319.98A 7330.73A \n", - " (4-->1) (4-->2) (4-->3) \n", - " 4.289E-23 3.003E-23 1.574E-23 \n", - "\n", - " 2470.22A 7318.92A 7329.66A 5023.75m \n", - " (5-->1) (5-->2) (5-->3) (5-->4) \n", - " 1.071E-23 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 9.583E-24 1.587E-23 4.722E-37 \n", - "\n", - " 834.47A 1074.96A 1075.19A 1259.93A 1259.97A \n", - " (6-->1) (6-->2) (6-->3) (6-->4) (6-->5) \n", - " 2.156E-26 0.000E+00 0.000E+00 0.000E+00 0.000E+00 \n", - "\n", - " 833.33A 1073.08A 1073.31A 1257.35A 1257.38A 61.25m \n", - " (7-->1) (7-->2) (7-->3) (7-->4) (7-->5) (7-->6) \n", - " 1.386E-26 0.000E+00 0.000E+00 0.000E+00 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 0.000E+00 0.000E+00 \n", - "\n", - " 832.76A 1072.13A 1072.36A 1256.05A 1256.08A 40.70m 121.28m \n", - " (8-->1) (8-->2) (8-->3) (8-->4) (8-->5) (8-->6) (8-->7) \n", - " 6.839E-27 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 \n", - "\n", - "# H-beta volume emissivity:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "1.235E-25 N(H+) * N(e-) (erg/s)\n" - ] - } - ], - "prompt_number": 43 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# ionic abundance (intensity, temperature, density, transition)\n", + "O2.getIonAbundance(100, 1.5e4, 100., wave=3727)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "O2.printIonic(tem=10000, den=100, printA=True, printPop=True, printCrit=True) # also prints populations and critical densities" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "elem = O\n", - "spec = 2\n", - "temperature = 10000.0 K\n", - "density = 100.0 cm-3\n", - "\n", - "Level Populations Critical densities\n", - "Level 1: 9.988E-01 0.000E+00\n", - "Level 2: 1.010E-03 1.065E+03\n", - "Level 3: 1.754E-04 3.557E+03\n", - "Level 4: 9.460E-09 5.051E+06\n", - "Level 5: 5.743E-09 3.484E+06\n", - "Level 6: 1.052E-22 1.846E+16" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Level 7: 6.724E-23 1.555E+16\n", - "Level 8: 3.307E-23 1.448E+16\n", - "\n", - "3.820E-05 \n", - " 3728.81A \n", - " (2-->1) \n", - " 2.055E-21 \n", - "\n", - "1.650E-04 1.200E-07 \n", - " 3726.03A 499.36m \n", - " (3-->1) (3-->2) \n", - " 1.543E-21 8.372E-28 \n", - "\n", - "5.640E-02 1.170E-01 6.140E-02 \n", - " 2470.34A 7319.98A 7330.73A \n", - " (4-->1) (4-->2) (4-->3) \n", - " 4.289E-23 3.003E-23 1.574E-23 \n", - "\n", - "2.320E-02 6.150E-02 1.020E-01 2.080E-11 \n", - " 2470.22A 7318.92A 7329.66A 5023.75m \n", - " (5-->1) (5-->2) (5-->3) (5-->4) \n", - " 1.071E-23 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 9.583E-24 1.587E-23 4.722E-37 \n", - "\n", - "8.610E+08 0.000E+00 0.000E+00 0.000E+00 0.000E+00 \n", - " 834.47A 1074.96A 1075.19A 1259.93A 1259.97A \n", - " (6-->1) (6-->2) (6-->3) (6-->4) (6-->5) \n", - " 2.156E-26 0.000E+00 0.000E+00 0.000E+00 0.000E+00 \n", - "\n", - "8.650E+08 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 \n", - " 833.33A 1073.08A 1073.31A 1257.35A 1257.38A 61.25m \n", - " (7-->1) (7-->2) (7-->3) (7-->4) (7-->5) (7-->6) \n", - " 1.386E-26 0.000E+00 0.000E+00 0.000E+00 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 0.000E+00 0.000E+00 \n", - "\n", - "8.670E+08 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 \n", - " 832.76A 1072.13A 1072.36A 1256.05A 1256.08A 40.70m 121.28m \n", - " (8-->1) (8-->2) (8-->3) (8-->4) (8-->5) (8-->6) (8-->7) \n", - " 6.839E-27 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 \n", - "\n", - "# H-beta volume emissivity:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "1.235E-25 N(H+) * N(e-) (erg/s)\n" - ] - } - ], - "prompt_number": 44 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "elem = O\n", + "spec = 2\n", + "\n", + "warng Atom O2: Cannot print populations as tem or den is missing\n", + "warng Atom O2: Cannot print critical densities as tem is missing\n", + " 3728.81A \n", + " (2-->1) \n", + "\n", + "\n", + " 3726.03A 499.36m \n", + " (3-->1) (3-->2) \n", + "\n", + "\n", + " 2470.34A 7319.98A 7330.73A \n", + " (4-->1) (4-->2) (4-->3) \n", + "\n", + "\n", + " 2470.22A 7318.92A 7329.66A 5023.75m \n", + " (5-->1) (5-->2) (5-->3) (5-->4) \n", + "\n", + "\n" + ] + } + ], + "source": [ + "# printout as in old nebular\n", + "O2.printIonic() # only prints transitions and corresponding wavelengths. Useful for a quick glance at the atom." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Compute Hb emissivity at T=10000K\n", - "H1 = pn.RecAtom('H', 1)\n", - "H1.getEmissivity(10000, 1e2, 4, 2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 45, - "text": [ - "array(1.235e-25)" - ] - } - ], - "prompt_number": 45 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "elem = O\n", + "spec = 2\n", + "temperature = 10000.0 K\n", + "density = 100.0 cm-3\n", + "\n", + "Level Populations Critical densities\n", + "Level 1: 9.989E-01 0.000E+00\n", + "Level 2: 9.578E-04 1.207E+03\n", + "Level 3: 1.642E-04 4.093E+03\n", + "Level 4: 7.755E-09 5.839E+06\n", + "Level 5: 4.947E-09 3.981E+06\n", + "\n", + " 3728.81A \n", + " (2-->1) \n", + " 1.948E-21 \n", + "\n", + " 3726.03A 499.36m \n", + " (3-->1) (3-->2) \n", + " 1.444E-21 7.836E-28 \n", + "\n", + " 2470.34A 7319.98A 7330.73A \n", + " (4-->1) (4-->2) (4-->3) \n", + " 3.516E-23 2.462E-23 1.290E-23 \n", + "\n", + " 2470.22A 7318.92A 7329.66A 5023.75m \n", + " (5-->1) (5-->2) (5-->3) (5-->4) \n", + " 9.227E-24 8.256E-24 1.367E-23 4.068E-37 \n", + "\n", + "# H-beta volume emissivity:\n", + "1.235E-25 N(H+) * N(e-) (erg/s)\n" + ] + } + ], + "source": [ + "O2.printIonic(tem=10000, den=100) # also prints line emissivities" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# simultaneously compute temperature and density from pairs of line ratios\n", - "# First of all, a Diagnostics object must be created and initialized with the relevant diagnostics.\n", - "diags = pn.Diagnostics() # this creates the object\n", - "diags.getAllDiags() # see what Diagnostics exist\n", - "tem, den = diags.getCrossTemDen('[NII] 5755/6548', '[SII] 6731/6716', 0.050, 1.0)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 46 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "elem = O\n", + "spec = 2\n", + "temperature = 10000.0 K\n", + "density = 100.0 cm-3\n", + "\n", + "Level Populations Critical densities\n", + "Level 1: 9.989E-01 0.000E+00\n", + "Level 2: 9.578E-04 1.207E+03\n", + "Level 3: 1.642E-04 4.093E+03\n", + "Level 4: 7.755E-09 5.839E+06\n", + "Level 5: 4.947E-09 3.981E+06\n", + "\n", + "3.820E-05 \n", + " 3728.81A \n", + " (2-->1) \n", + " 1.948E-21 \n", + "\n", + "1.650E-04 1.200E-07 \n", + " 3726.03A 499.36m \n", + " (3-->1) (3-->2) \n", + " 1.444E-21 7.836E-28 \n", + "\n", + "5.640E-02 1.170E-01 6.140E-02 \n", + " 2470.34A 7319.98A 7330.73A \n", + " (4-->1) (4-->2) (4-->3) \n", + " 3.516E-23 2.462E-23 1.290E-23 \n", + "\n", + "2.320E-02 6.150E-02 1.020E-01 2.080E-11 \n", + " 2470.22A 7318.92A 7329.66A 5023.75m \n", + " (5-->1) (5-->2) (5-->3) (5-->4) \n", + " 9.227E-24 8.256E-24 1.367E-23 4.068E-37 \n", + "\n", + "# H-beta volume emissivity:\n", + "1.235E-25 N(H+) * N(e-) (erg/s)\n" + ] + } + ], + "source": [ + "O2.printIonic(tem=10000, den=100, printA=True, printPop=True, printCrit=True) # also prints populations and critical densities" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "#TO BE CONTINUED FROM HERE\n", - "print tem, den" - ], - "language": "python", + "data": { + "text/plain": [ + "array(1.235e-25)" + ] + }, + "execution_count": 52, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "10526.7510913 507.170474716\n" - ] - } - ], - "prompt_number": 47 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute Hb emissivity at T=10000K\n", + "H1 = pn.RecAtom('H', 1)\n", + "H1.getEmissivity(10000, 1e2, 4, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# simultaneously compute temperature and density from pairs of line ratios\n", + "# First of all, a Diagnostics object must be created and initialized with the relevant diagnostics.\n", + "diags = pn.Diagnostics() # this creates the object\n", + "diags.getAllDiags() # see what Diagnostics exist\n", + "tem, den = diags.getCrossTemDen('[NII] 5755/6548', '[SII] 6731/6716', 0.050, 1.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 47 + "name": "stdout", + "output_type": "stream", + "text": [ + "10490.225428751679 724.3990279425481\n" + ] } ], - "metadata": {} + "source": [ + "#TO BE CONTINUED FROM HERE\n", + "print(tem, den)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pyneb/sample_scripts/multi_comp.py b/pyneb/sample_scripts/multi_comp.py index 24563663..aa2c4c23 100644 --- a/pyneb/sample_scripts/multi_comp.py +++ b/pyneb/sample_scripts/multi_comp.py @@ -98,28 +98,28 @@ def ab_ion5(atom, line, wave): ab_5 = np.log10(atom.getIonAbundance(emis3[line]/emis3['Hbeta']*100, temp_5, dens_S2_5, wave= wave)) return ab_1, ab_2, ab_3, ab_4, ab_5 -print ' Dens1 = %.0f, Dens2 = %.0f, Dens3 = %.0f, Dens4 = %.0f, Dens5 = %.0f' % \ - (dens_S2_1, dens_S2_2, dens_S2_3, dens_S2_4, dens_S2_5) -print ' Temp1 = %.0f, Temp2 = %.0f, Temp3 = %.0f, Temp4 = %.0f, Temp5 = %.0f' % \ - (temp_1, temp_2, temp_3, temp_4, temp_5) +print(' Dens1 = %.0f, Dens2 = %.0f, Dens3 = %.0f, Dens4 = %.0f, Dens5 = %.0f' % \ + (dens_S2_1, dens_S2_2, dens_S2_3, dens_S2_4, dens_S2_5)) +print(' Temp1 = %.0f, Temp2 = %.0f, Temp3 = %.0f, Temp4 = %.0f, Temp5 = %.0f' % \ + (temp_1, temp_2, temp_3, temp_4, temp_5)) for line in S2_lambda: ab_Sp1, ab_Sp2, ab_Sp3, ab_Sp4, ab_Sp5 = ab_ion5(S2, 'SII_'+str(line), line) - print ' S+%i : %.2f %.2f %.2f %.2f %.2f' % \ - (line,ab_Sp1-Sp, ab_Sp2-Sp, ab_Sp3-Sp, ab_Sp4-Sp, ab_Sp5-Sp) + print(' S+%i : %.2f %.2f %.2f %.2f %.2f' % \ + (line,ab_Sp1-Sp, ab_Sp2-Sp, ab_Sp3-Sp, ab_Sp4-Sp, ab_Sp5-Sp)) ab_Np1, ab_Np2, ab_Np3, ab_Np4, ab_Np5 = ab_ion5(N2, 'NII_6583', 6583) -print ' N+%i : %.2f %.2f %.2f %.2f %.2f' % \ - (6583,ab_Np1-Np, ab_Np2-Np, ab_Np3-Np, ab_Np4-Np, ab_Np5-Np) +print(' N+%i : %.2f %.2f %.2f %.2f %.2f' % \ + (6583,ab_Np1-Np, ab_Np2-Np, ab_Np3-Np, ab_Np4-Np, ab_Np5-Np)) ab_Op1, ab_Op2, ab_Op3, ab_Op4, ab_Op5 = ab_ion5(O2, 'OII_3726', 3726) -print ' O+%i : %.2f %.2f %.2f %.2f %.2f' % \ - (3726,ab_Op1-Op, ab_Op2-Op, ab_Op3-Op, ab_Op4-Op, ab_Op5-Op) +print(' O+%i : %.2f %.2f %.2f %.2f %.2f' % \ + (3726,ab_Op1-Op, ab_Op2-Op, ab_Op3-Op, ab_Op4-Op, ab_Op5-Op)) ab_Op1, ab_Op2, ab_Op3, ab_Op4, ab_Op5 = ab_ion5(O2, 'OII_3729', 3729) -print ' O+%i : %.2f %.2f %.2f %.2f %.2f' % \ - (3729,ab_Op1-Op, ab_Op2-Op, ab_Op3-Op, ab_Op4-Op, ab_Op5-Op) +print(' O+%i : %.2f %.2f %.2f %.2f %.2f' % \ + (3729,ab_Op1-Op, ab_Op2-Op, ab_Op3-Op, ab_Op4-Op, ab_Op5-Op)) ab_Opp1, ab_Opp2, ab_Opp3, ab_Opp4, ab_Opp5 = ab_ion5(O3, 'OIII_5007', 5007) -print 'O++%i : %.2f %.2f %.2f %.2f %.2f' % \ - (5007,ab_Opp1-Opp, ab_Opp2-Opp, ab_Opp3-Opp, ab_Opp4-Opp, ab_Opp5-Opp) +print('O++%i : %.2f %.2f %.2f %.2f %.2f' % \ + (5007,ab_Opp1-Opp, ab_Opp2-Opp, ab_Opp3-Opp, ab_Opp4-Opp, ab_Opp5-Opp)) diff --git a/pyneb/sample_scripts/ngc650_R1.py b/pyneb/sample_scripts/ngc650_R1.py index d3f90060..6180c286 100644 --- a/pyneb/sample_scripts/ngc650_R1.py +++ b/pyneb/sample_scripts/ngc650_R1.py @@ -5,7 +5,6 @@ import matplotlib.pyplot as plt import pyneb as pn -import sys import os @@ -140,16 +139,16 @@ tem_O3 = all_atoms['O3'].getTemDen(i5007/i4363, den=100., wave1=5007, wave2=4363) # Printout of physical conditions - print 'tem_O3: ', tem_O3 - print 'tem_N2: ', tem_N2 - print 'den_S2: ', den_S2 - print 'den_Ar4: ', den_Ar4 - print 'den_S3:', den_S3 - print 'den_Ne3: ', den_Ne3 - print 'i4686: ', i4686 - print 'i5876: ', i5876 - print 'i6678: ', i6678 - print 'i4861: ', i4861 + print('tem_O3: ', tem_O3) + print('tem_N2: ', tem_N2) + print('den_S2: ', den_S2) + print('den_Ar4: ', den_Ar4) + print('den_S3:', den_S3) + print('den_Ne3: ', den_Ne3) + print('i4686: ', i4686) + print('i5876: ', i5876) + print('i6678: ', i6678) + print('i4861: ', i4861) # Calculation and printout of abundances try: @@ -159,10 +158,10 @@ ab2 = all_atoms[line.atom].getIonAbundance(line.corrIntens, tem_N2, den_S2, to_eval=line.to_eval) ab3 = all_atoms[line.atom].getIonAbundance(line.corrIntens, tem_O3, den_Ar4, to_eval=line.to_eval) ab4 = all_atoms[line.atom].getIonAbundance(line.corrIntens, tem_O3, den_Ne3, to_eval=line.to_eval) - print '{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab)]) - print '{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab2)]) - print '{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab3)]) - print '{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab4)]) + print('{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab)])) + print('{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab2)])) + print('{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab3)])) + print('{0:9s}'.format(line.label) + ' '.join(['{0:>20.10e}'.format(t) for t in (ab4)])) else: pn.log_.warn('line from %s not used because ion not found' % line.atom, calling='ngc605_R1.py') pn.log_.timer('Ending ngc605_R1.py', calling='ngc605_R1.py') diff --git a/pyneb/sample_scripts/plot_all_diags.py b/pyneb/sample_scripts/plot_all_diags.py index 292f28dc..28742041 100644 --- a/pyneb/sample_scripts/plot_all_diags.py +++ b/pyneb/sample_scripts/plot_all_diags.py @@ -13,7 +13,7 @@ def plot_all(save=False): atom, diag_eval, err = diags_dict[diag] # Skip Fe III as they are so many if (atom in AA) and (atom != 'Fe3'): - print atom + print(atom) plt.figure() grid = pn.EmisGrid(atomObj=AA[atom]) grid.plotContours(to_eval=diag_eval) diff --git a/pyneb/sample_scripts/startup.py b/pyneb/sample_scripts/startup.py index 3caa915c..74655320 100644 --- a/pyneb/sample_scripts/startup.py +++ b/pyneb/sample_scripts/startup.py @@ -23,11 +23,11 @@ # inspect the header of atomic data file # print 'Atomic data header:' -print O2.AtomData.AtomHeader +print(O2.AtomData.AtomHeader) # inspect the header of collisional data file # print 'Collisional header:' -print O2.CollData.CollHeader +print(O2.CollData.CollHeader) # explore the atom: builtin data O2.gs # ground-state configuration @@ -164,7 +164,7 @@ guess_tem=10000, tol_tem = 1., tol_den = 1., max_iter = 5) #TO BE CONTINUED FROM HERE -print tem, den +print(tem, den) ####################################################################### # HANDLING OBSERVATIONS diff --git a/pyneb/sample_scripts/two_components_map.py b/pyneb/sample_scripts/two_components_map.py index 370a5e55..cef88d7c 100644 --- a/pyneb/sample_scripts/two_components_map.py +++ b/pyneb/sample_scripts/two_components_map.py @@ -110,7 +110,7 @@ def TOandTS(tem1=1.e4, tem2=1e4, den1=1e2, den2=5e5, vol1=5e8, denDiag='S2', ver # can be changed for i, vol1 in enumerate(vol1grid): T_Opp[i], T_Spp[i] = TOandTS(den1=1e2, den2=den2grid, vol1=vol1, denDiag='O2') - print 'Computing row n.', i, 'of', n_vol_points + print('Computing row n.', i, 'of', n_vol_points) plt.figure(1) plt.subplot(2, 2, 1) diff --git a/pyneb/sample_scripts/two_components_plot.py b/pyneb/sample_scripts/two_components_plot.py index 75910314..5e0a733a 100644 --- a/pyneb/sample_scripts/two_components_plot.py +++ b/pyneb/sample_scripts/two_components_plot.py @@ -33,7 +33,7 @@ def plot_2comp(tem1=1e4, tem2=1e4, dens1=3e2, dens2=5e5, mass1=1, mass2=5e-4): for diag in pn.diags_dict: if diag[0:7] != '[FeIII]': diags.addDiag(diag) - print 'Adding', diag + print('Adding', diag) diags.addClabel('[SIII] 6312/9069', '[SIII]A') diags.addClabel('[OIII] 4363/5007', '[OIII]A') """ diff --git a/pyneb/test/profiling.py b/pyneb/test/profiling.py index b032d403..f26b4d51 100644 --- a/pyneb/test/profiling.py +++ b/pyneb/test/profiling.py @@ -10,4 +10,4 @@ tem = np.linspace(5000, 15000, 50) den = np.logspace(1, 5, 50) em = o3.getEmissivity(tem, den, wave=5007, product=True) -print em +print(em) diff --git a/pyneb/utils/fits.py b/pyneb/utils/fits.py index b1cc6868..032a88b3 100644 --- a/pyneb/utils/fits.py +++ b/pyneb/utils/fits.py @@ -68,7 +68,7 @@ def writeAtom(input_file): # Read Es energy = all_data[0, :] NLevels = len(energy) - if units[0] is 'eV': + if units[0] == 'eV': energy = energy / (pn.CST.RYD_EV * pn.CST.RYD_ANG) # Read statistical weights stat_weight = all_data[1, :] @@ -213,7 +213,7 @@ def __init__(self, fitsFile, ext): def show(self): - print self.hdu[self.ext].header + print(self.hdu[self.ext].header) def up(self, key, value): From 6e7fbce40ec344274301f2c5053dc9e8adf1893d Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 27 Apr 2021 20:00:38 +0100 Subject: [PATCH 31/36] removing warning from /0 --- pyneb/core/pynebcore.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 7b6b8d0a..276122b6 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -4611,7 +4611,8 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa err_fits_data = err_fits_hdu.data.ravel() if not errIsRelative: - err_fits_data = err_fits_data / fits_data + with np.errstate(divide='ignore', invalid='ignore'): + err_fits_data = err_fits_data / fits_data if self.addErrDefault: err_fits_data = np.sqrt(err_fits_data**2 + err_default**2) else: @@ -4630,7 +4631,7 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa self.names = ['{}_{}'.format(str1, i) for i in range(self.n_obs)] self.data_shape = self.fits_shape - + if corrected: self.correctData() From 520f8a3ab97c170d4910c822e52e5c7a80325386 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Mon, 3 May 2021 17:08:50 +0100 Subject: [PATCH 32/36] adding Cutout option in Observation fits file --- pyneb/core/pynebcore.py | 39 +++++++++++++++++++++++++++++---------- 1 file changed, 29 insertions(+), 10 deletions(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 276122b6..093386a5 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -37,6 +37,7 @@ if config.INSTALLED['pyfits from astropy']: import astropy.io.fits as pyfits from astropy.wcs import WCS + from astropy.nddata.utils import Cutout2D elif config.INSTALLED['pyfits']: import pyfits if config.INSTALLED['astropy Table']: @@ -4104,7 +4105,7 @@ def __repr__(self): class Observation(object): def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err_default=0.10, corrected=False, errIsRelative=True, correcLaw='F99', errStr='err', - addErrDefault = False): + addErrDefault = False, Cutout2D_position=None, Cutout2D_size=None): """ Define the observation object, which is a collection of observated intensities of one or more emission lines for one or more objects, with the corresponding errors. @@ -4136,6 +4137,7 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err - correcLaw ['F99'] extinction law used to correct the observed lines. - errStr - string used to identify error file when fileFormat is fits_IFU - addErrDefault - [False] if True, the default error is always quadratically added to the read error. + - Cutout2D_position, Cutout2D_size: In case of reading fits images, crop the image to those pixel limits Example: Read a file containing corrected intensities: @@ -4160,7 +4162,8 @@ def __init__(self, obsFile=None, fileFormat='lines_in_cols', delimiter=None, err if obsFile is not None: self.readData(obsFile=obsFile, fileFormat=fileFormat, delimiter=delimiter, err_default=err_default, corrected=corrected, errIsRelative=errIsRelative, - errStr=errStr) + errStr=errStr, + Cutout2D_position=Cutout2D_position, Cutout2D_size=Cutout2D_size) ## # @var log_ # myloggin object @@ -4363,7 +4366,7 @@ def getUniqueAtoms(self): def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_default=0.10, corrected=False, - errIsRelative=True, errStr='err'): + errIsRelative=True, errStr='err', Cutout2D_position=None, Cutout2D_size=None): """ Read observational data from an ascii file. The lines can be listed either in columns or in rows and the observed objects vary in the other direction. The uncertainty on the line intensities @@ -4394,6 +4397,7 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa - errIsRelative Boolean. True if the errors are relative to the intensities, False if they are in the same unit as the intensity (default: False) - errStr string to identify the error file in case the fileFormat is fits_IFU. + - Cutout2D_position, Cutout2D_size: In case of reading fits images, crop the image to those pixel limits """ format_list = ['lines_in_cols', 'lines_in_cols2', 'lines_in_rows', @@ -4593,6 +4597,17 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa calling='Observation.readData') fits_hdu = pyfits.open(f)[0] fits_data = fits_hdu.data + self.origin_fits_shape = fits_data.shape + self.fits_header = fits_hdu.header + self.wcs = WCS(fits_hdu.header).celestial + if Cutout2D_position is not None: + self.log_.debug('Cutout2D applied to data shape {}.'.format(fits_data.shape), + calling='Observation.readData') + C2D = Cutout2D(data=fits_data, position=Cutout2D_position, + size=Cutout2D_size, wcs=WCS(fits_hdu.header), mode='trim', + copy=True) + fits_data = C2D.data + self.wcs = C2D.wcs if self.fits_shape is None: self.fits_shape = fits_data.shape else: @@ -4605,11 +4620,16 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa self.log_.message('Reading error {}_{} from {}'.format(atom, line, err_file.name), calling='Observation.readData') err_fits_hdu = pyfits.open(err_file)[0] - if err_fits_hdu.data.shape != self.fits_shape: + if err_fits_hdu.data.shape != self.origin_fits_shape: self.log_.error('error shape in file {} is {}. data shape is {}.'.format( err_file.name, err_fits_hdu.data.shape, self.fits_shape)) - - err_fits_data = err_fits_hdu.data.ravel() + err_fits_data = err_fits_hdu.data + if Cutout2D_position is not None: + C2D = Cutout2D(data=err_fits_data, position=Cutout2D_position, + size=Cutout2D_size, mode='trim', + copy=True) + err_fits_data = C2D.data + err_fits_data = err_fits_data.ravel() if not errIsRelative: with np.errstate(divide='ignore', invalid='ignore'): err_fits_data = err_fits_data / fits_data @@ -4623,8 +4643,6 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa obsIntens=fits_data, obsError=err_fits_data, corrected=corrected, errIsRelative=True)) - self.fits_header = fits_hdu.header - self.wcs = WCS(fits_hdu.header).celestial else: self.log_.debug('atom {} not in LINE_LABEL_LIST'.format(atom), calling='Observation.readData') @@ -4811,7 +4829,7 @@ def setAllErrors(self, err_default): - def addMonteCarloObs(self, N=0, i_obs=None): + def addMonteCarloObs(self, N=0, i_obs=None, random_seed=None): """ Adding MonteCarlo random-gauss values of fake observations to an obs object. The names of the fake observations will be OriginalName-MC-n, n ranging from 0 to N-1 @@ -4819,6 +4837,7 @@ def addMonteCarloObs(self, N=0, i_obs=None): Parameters: N: number of new observations to be added for each original observation. i_obs: used in case only a given observations needs to be treated + random_seed: [default] used to initialize the numpy random generator """ if self.MC_added: self.log_.error('Monte Carlo already applied to this observation', calling='addMonteCarloObs') @@ -4826,7 +4845,7 @@ def addMonteCarloObs(self, N=0, i_obs=None): n_obs = self.n_obs if i_obs is None: self.log_.message('Entering', calling='addMonteCarloObs') - + np.random.seed(random_seed) for l in self.lines: l_ori = l.obsIntens e_ori = l.obsError From 52695142b2df16bd4c00690fb63071ac550c33f8 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 6 May 2021 09:20:12 +0100 Subject: [PATCH 33/36] add dot to label --- pyneb/atomic_data/he_i_rec_P91.func | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/pyneb/atomic_data/he_i_rec_P91.func b/pyneb/atomic_data/he_i_rec_P91.func index 928a091c..bda12224 100644 --- a/pyneb/atomic_data/he_i_rec_P91.func +++ b/pyneb/atomic_data/he_i_rec_P91.func @@ -1,10 +1,10 @@ PEQ1991 SOURCE Pequignot, Petitjean & Boisson, 1991, AandA, 251, 680 -4471 447.1 A 0.429 -0.530 1.505 0.779 0.790 A 0.135 -4713 471.3 A 0.019 -0.518 -0.045 0.951 0.626 A 0.013 -5876 587.6 A 1.323 -0.696 1.683 0.667 1.000 A 0.493 -3889 388.9 A 0.511 -0.489 0.719 0.530 0.898 A 0.267 -7065 706.5 A 0.070 -0.509 -0.010 -0.263 1.000 A 0.071 -10830 1083.0 A 0.796 -0.525 0.148 0.719 1.000 A 0.693 -4922 492.2 A 0.135 -0.540 1.505 0.779 0.740 A 0.040 -7281 728.1 A 0.036 -0.503 0.165 0.614 1.000 A 0.031 +4471. 447.1 A 0.429 -0.530 1.505 0.779 0.790 A 0.135 +4713. 471.3 A 0.019 -0.518 -0.045 0.951 0.626 A 0.013 +5876. 587.6 A 1.323 -0.696 1.683 0.667 1.000 A 0.493 +3889. 388.9 A 0.511 -0.489 0.719 0.530 0.898 A 0.267 +7065. 706.5 A 0.070 -0.509 -0.010 -0.263 1.000 A 0.071 +10830. 1083.0 A 0.796 -0.525 0.148 0.719 1.000 A 0.693 +4922. 492.2 A 0.135 -0.540 1.505 0.779 0.740 A 0.040 +7281. 728.1 A 0.036 -0.503 0.165 0.614 1.000 A 0.031 From 712ffbb8cf6f00d9cf8c9c8795fb510cbc3d3184 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 6 May 2021 09:20:17 +0100 Subject: [PATCH 34/36] Update pynebcore.py --- pyneb/core/pynebcore.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/core/pynebcore.py b/pyneb/core/pynebcore.py index 093386a5..b833d315 100755 --- a/pyneb/core/pynebcore.py +++ b/pyneb/core/pynebcore.py @@ -4599,7 +4599,7 @@ def readData(self, obsFile, fileFormat='lines_in_cols', delimiter=None, err_defa fits_data = fits_hdu.data self.origin_fits_shape = fits_data.shape self.fits_header = fits_hdu.header - self.wcs = WCS(fits_hdu.header).celestial + self.wcs = WCS(self.fits_header).celestial if Cutout2D_position is not None: self.log_.debug('Cutout2D applied to data shape {}.'.format(fits_data.shape), calling='Observation.readData') From 0f6c245e75b64ec0317bec277b086142d7237723 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Thu, 6 May 2021 09:20:38 +0100 Subject: [PATCH 35/36] add 10_3 and 11_3 to HI lines --- pyneb/utils/init.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyneb/utils/init.py b/pyneb/utils/init.py index 092c5298..848e1f9f 100644 --- a/pyneb/utils/init.py +++ b/pyneb/utils/init.py @@ -36,7 +36,8 @@ def _check_line_label_list(maxErrorA = 5.e-3, maxErrorm = 5.e-2): LINE_LABEL_LIST = {} LINE_LABEL_LIST['H1r'] = ['1216A', '1026A', '973A', '6563A', '4861A', '4341A', '4102A', '3970A', '3889A', - '3835A', '3798A', '1.87m', '1.28m', '1.09m', '9546A', '9229A', '8750A'] + '3835A', '3798A', '1.87m', '1.28m', '1.09m', '9546A', '9229A', '8750A','8863A', + '9015A'] LINE_LABEL_LIST['He1r'] = ['5876A', '2945A', '3188A', '3614A', '3889A', '3965A', '4026A', '4121A', '4388A', '4438A', '4471A', '4713A', '4922A', '5016A', '5048A', '5876A', '6678A', '7065A', '7281A', '9464A', '10830A', '11013A', '11969A', '12527A', '12756A', '12785A', From 71c6d8dca696331c6e918a08314c6ab3ce87bfa8 Mon Sep 17 00:00:00 2001 From: Christophe Morisset Date: Tue, 11 May 2021 11:39:04 +0100 Subject: [PATCH 36/36] 1.1.15 --- pyneb/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyneb/version.py b/pyneb/version.py index b7fe559f..6ed92108 100644 --- a/pyneb/version.py +++ b/pyneb/version.py @@ -1,2 +1,2 @@ # PyNeb version -__version__ = '1.1.15b3' +__version__ = '1.1.15'