From 86be738216e37a3417083932b3fd4849c734410c Mon Sep 17 00:00:00 2001 From: Florez Ospina Juan Felipe Date: Tue, 31 Oct 2023 14:33:25 +0100 Subject: [PATCH] Rerun jupyter notebook and updated FileList.h5 and napp_plotlib.py due to changes on code dependencies. --- demo_hdf5_data_sharing_and_plotting.ipynb | 321 ++++++++++++++++++++-- input_files/FileList.h5 | Bin 2516908 -> 2548052 bytes napp_plotlib.py | 6 +- 3 files changed, 297 insertions(+), 30 deletions(-) diff --git a/demo_hdf5_data_sharing_and_plotting.ipynb b/demo_hdf5_data_sharing_and_plotting.ipynb index c9f9367..43532bb 100644 --- a/demo_hdf5_data_sharing_and_plotting.ipynb +++ b/demo_hdf5_data_sharing_and_plotting.ipynb @@ -2,29 +2,297 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Index(['scientaDwellTime_ms', 'regionName', 'scientaAcquisitionMode',\n", - " 'scientaEkinRange_eV', 'scientaEkinStep_eV', 'scientaLensMode',\n", - " 'scientaRegionIterations', 'scientaSequenceIterations', 'name',\n", - " 'spectrum_countsPerSecondRoh', 'importDate', 'analysisDir',\n", - " 'sampleTemp_dC', 'cellPressure_mbar', 'iceTemp_dC', 'smplX_mm',\n", - " 'smplY_mm', 'smplZ_mm', 'folder', 'sealingTemp', 'lastModifiedDatestr',\n", - " 'lastModifiedDatenum', 'peakPosition', 'peakArea', 'peakFWHM', 'sample',\n", - " 'logGenerateRange', 'logGenerateDate', 'creationDate',\n", - " 'logGenerateMode', 'logBackgroundRange', 'logBackgroundMode',\n", - " 'logBackgroundDate', 'bindingEnergyShift', 'xRayEkinRange_eV',\n", - " 'scientaPassEnergy_eV', 'scientaEkin_eV', 'beamlineInt', 'imageRoh',\n", - " 'scientaEkinRoh_eV', 'image', 'bindingEnergy_eV', 'spectrum_countsNorm',\n", - " 'logScaleMode', 'logScaleDate', 'spectrum_counts',\n", - " 'spectrum_countsPerSecond', 'xRayEkin_eV'],\n", + "Index(['xRayEkinRange_eV_1', 'xRayEkinRange_eV_2', 'scientaPassEnergy_eV',\n", + " 'scientaDwellTime_ms', 'regionName', 'scientaAcquisitionMode',\n", + " 'scientaEkinRange_eV_1', 'name', 'scientaEkinRange_eV_2',\n", + " 'scientaEkinStep_eV', 'scientaLensMode', 'scientaRegionIterations',\n", + " 'scientaSequenceIterations', 'spectrum_countsPerSecondRoh',\n", + " 'importDate', 'folder', 'analysisDir', 'sampleTemp_dC',\n", + " 'cellPressure_mbar', 'iceTemp_dC', 'smplX_mm', 'smplY_mm', 'smplZ_mm',\n", + " 'sealingTemp', 'lastModifiedDatestr', 'lastModifiedDatenum',\n", + " 'creationDate_1', 'peakPosition_1', 'peakPosition_2', 'peakPosition_3',\n", + " 'peakPosition_4', 'peakPosition_5', 'peakArea_1', 'peakArea_2',\n", + " 'peakArea_3', 'peakArea_4', 'creationDate_2', 'peakArea_5',\n", + " 'peakFWHM_1', 'peakFWHM_2', 'peakFWHM_3', 'peakFWHM_4', 'peakFWHM_5',\n", + " 'sample', 'logGenerateRange_1', 'logGenerateRange_2', 'logGenerateDate',\n", + " 'creationDate_3', 'logGenerateMode', 'logBackgroundRange_1',\n", + " 'logBackgroundRange_2', 'logBackgroundMode', 'logBackgroundDate',\n", + " 'bindingEnergyShift', 'creationDate_4', 'creationDate_5',\n", + " 'creationDate_6', 'spectrum_counts', 'spectrum_countsPerSecond',\n", + " 'xRayEkin_eV', 'scientaEkin_eV', 'beamlineInt', 'imageRoh',\n", + " 'scientaEkinRoh_eV', 'bindingEnergy_eV', 'spectrum_countsNorm',\n", + " 'logScaleMode', 'logScaleDate', 'image'],\n", " dtype='object')\n" ] + }, + { + "data": { + "text/html": [ + "
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[[750.0]] \n", + "MEAS_13 NaN ... [[750.0]] \n", + "\n", + " scientaEkin_eV beamlineInt \\\n", + "MEAS_1 [[536.0], [536.1], [536.2], [536.3000000000001... [[0.0]] \n", + "MEAS_10 [[539.0], [539.1], [539.2], [539.3000000000001... [[0.0]] \n", + "MEAS_11 [[539.0], [539.1], [539.2], [539.3000000000001... [[0.0]] \n", + "MEAS_12 [[539.0], [539.1], [539.2], [539.3000000000001... [[0.0]] \n", + "MEAS_13 [[nan]] [[0.0]] \n", + "\n", + " imageRoh \\\n", + "MEAS_1 [[3015.9615384615386, 3158.653846153846, 3492.... \n", + "MEAS_10 [[1183.076923076923, 1641.1538461538462, 778.0... \n", + "MEAS_11 [[1557.6923076923076, 1026.1538461538462, 1706... \n", + "MEAS_12 [[1050.7692307692307, 1054.6153846153845, 1569... \n", + "MEAS_13 [[nan]] \n", + "\n", + " scientaEkinRoh_eV \\\n", + "MEAS_1 [[536.0], [536.1], [536.2], [536.3000000000001... \n", + "MEAS_10 [[539.0], [539.1], [539.2], [539.3000000000001... \n", + "MEAS_11 [[539.0], [539.1], [539.2], [539.3000000000001... \n", + "MEAS_12 [[539.0], [539.1], [539.2], [539.3000000000001... \n", + "MEAS_13 [[nan]] \n", + "\n", + " bindingEnergy_eV \\\n", + "MEAS_1 [[208.5888042824772, 208.4888042824772, 208.38... \n", + "MEAS_10 [[208.09547634289822, 207.9954763428982, 207.8... \n", + "MEAS_11 [[208.28273417166213, 208.1827341716621, 208.0... \n", + "MEAS_12 [[208.33751693711383, 208.2375169371138, 208.1... \n", + "MEAS_13 [[208.38880428247717, 208.28880428247714, 208.... \n", + "\n", + " spectrum_countsNorm \\\n", + "MEAS_1 [[-0.002970900528877043, -0.002370528834745835... \n", + "MEAS_10 [[-0.002713117091577911, 0.0017431800916657154... \n", + "MEAS_11 [[-0.002957306068744318, 0.005511387878246225,... \n", + "MEAS_12 [[-0.0015217068251615732, 0.001336813298549339... \n", + "MEAS_13 [[nan]] \n", + "\n", + " logScaleMode \\\n", + "MEAS_1 [[0.0]] \n", + "MEAS_10 [[0.0], [0.0], [0.0], [0.0]] \n", + "MEAS_11 [[0.0], [0.0], [0.0], [0.0]] \n", + "MEAS_12 [[0.0], [0.0], [0.0], [0.0]] \n", + "MEAS_13 [[0.0]] \n", + "\n", + " logScaleDate \\\n", + "MEAS_1 [[0.0]] \n", + "MEAS_10 [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.... \n", + "MEAS_11 [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.... \n", + "MEAS_12 [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.... \n", + "MEAS_13 [[0.0]] \n", + "\n", + " image \n", + "MEAS_1 [[3015.9615384615386, 3158.653846153846, 3492.... \n", + "MEAS_10 [[1183.076923076923, 1641.1538461538462, 778.0... \n", + "MEAS_11 [[1557.6923076923076, 1026.1538461538462, 1706... \n", + "MEAS_12 [[1050.7692307692307, 1054.6153846153845, 1569... \n", + "MEAS_13 [[nan]] \n", + "\n", + "[5 rows x 69 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -41,17 +309,19 @@ "\n", "\n", "dataframe['lastModifiedDatestr']\n", - "print(dataframe.columns)\n" + "print(dataframe.columns)\n", + "\n", + "dataframe.head()" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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"text/plain": [ "
" ] @@ -83,29 +353,24 @@ "source": [ "import napp_plotlib as napp\n", "\n", + "dataframe['image'][0].shape\n", + "\n", "name_filter = (dataframe['name'] == '0116116_Cl2p_750eV.ibw').to_numpy()\n", - "date_filter = np.array(['Jun-2023' in date[0] for date in dataframe['lastModifiedDatestr']])\n", + "date_filter = np.array(['Jun-2023' in date for date in dataframe['lastModifiedDatestr']])\n", "\n", "filter = np.logical_and(name_filter.flatten(),date_filter.flatten()) \n", "\n", "napp.plot_image(dataframe,filter)\n", "napp.plot_spectra(dataframe,filter)\n", "\n", - "name_filter = np.array(['merge' in name[0] for name in dataframe['name'] ])\n", - "date_filter = np.array(['Jun-2023' in date[0] for date in dataframe['lastModifiedDatestr']])\n", + "name_filter = np.array(['merge' in name for name in dataframe['name'] ])\n", + "date_filter = np.array(['Jun-2023' in date for date in dataframe['lastModifiedDatestr']])\n", "filter = np.logical_and(name_filter.flatten(),date_filter.flatten()) \n", "\n", "\n", "napp.plot_spectra(dataframe,filter)\n", "\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/input_files/FileList.h5 b/input_files/FileList.h5 index c6cb86b346fe835f3b7e3b349e3f312d58fa4d36..4bc3bf9d2ffc68194fbbefc8914c9179afd4280f 100644 GIT binary patch delta 45985 zcmeI5dt6l2+W7bE#W2G#!ptzhh=7BDnv%{8Fo1%B7cw+Novh5fC1^%!pyrF_Ewds| z2gTfFW_ZceJmxK@%wyj25XX|#JYC3J*6FAjTAj*_e$Tq>z1IfvocFx1zr*J}_>VmM zd7i!2UeDgMzx!FwUVHP==XECzyr8S-Ec(uF>+++hyHj%CZwcR#jt+>XCr{f1{4eTE zWk6tcK-u-|!phpfnW(&*PFmg7aI5J;8yzvIviRpsumoCm6fEBtTv=Z_o7hV8mIh^2 z>JxIplXSufY&hblHhnC_J$B>KspO(kzb)e(M(&J??m>neM1!4K?!294PHuX|U^4T=lC3|Jb9r zz0@6YdsS~fqf4snzI#f**e3^Xtz5G^NtYz89%=4g*|6I&S6Xs;ZXe&ETtR=&YtK4` zX>iOI;oNJvorM(g=UM3iuds~#`Rbb^MhWA|p8*E_7~v54bIj_t69pgnbN)+(mBKpm z=hP~PBqWeOYuEL9K?sLGLxr;YoPJ>;*;>E#Pp5=_LKtpNzoE@lVJF%Aobe~aj`?d# zbdn|7s;_MSNAuhR8wYRDS#+r1Fc?-af`1^w2zv1FaxxxKUiRRT<@GR{yzIfF%Ijeu 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meas['name']) ax.set_xlabel('bindingEnergy_eV') ax.set_ylabel('counts Per Second') - ax.set_title('\n'+meas['sample'][0]+ '\n' + 'PE spectra') + ax.set_title('\n'+meas['sample']+ '\n' + 'PE spectra') + #ax.set_title('\n'+meas['sample'][0]+ '\n' + 'PE spectra') #ax.set_title(meas['name'][0] + '\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0]) ax.legend()