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Updated to cleared jupyter notebooks

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florez_j committed 2025-01-14 14:46:43 +01:00
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notebooks/demo_create_and_visualize_hdf5_file.ipynb
+4 -4
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@@ -20,8 +20,8 @@
"try:\n",
" import src.hdf5_writer as hdf5_writer\n",
" import src.hdf5_ops as hdf5_ops\n",
" import src.hdf5_vis as h5vis\n",
" import src.napp_plotlib as napp\n",
" import visualization.hdf5_vis as h5vis\n",
" import visualization.napp_plotlib as napp\n",
"\n",
" import utils.g5505_utils as utils\n",
" #import pipelines.metadata_revision as metadata_revision\n",
@@ -129,7 +129,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "multiphase_chemistry_env",
"language": "python",
"name": "python3"
},
@@ -143,7 +143,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.9"
}
},
"nbformat": 4,
notebooks/demo_data_integration.ipynb
+18 -3609
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notebooks/demo_hdf5_data_sharing_and_plotting.ipynb
+7 -344
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@@ -2,17 +2,9 @@
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Imports successful!\n"
]
}
],
"outputs": [],
"source": [
"import os\n",
"from nbutils import add_project_path_to_sys_path\n",
@@ -27,7 +19,7 @@
"\n",
"try:\n",
" import src.hdf5_ops as hdf5_ops\n",
" import src.napp_plotlib as napp\n",
" import visualization.napp_plotlib as napp\n",
" #import pipelines.metadata_revision as metadata_revision\n",
" print(\"Imports successful!\")\n",
"except ImportError as e:\n",
@@ -36,299 +28,9 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>xRayEkinRange_eV_1</th>\n",
" <th>xRayEkinRange_eV_2</th>\n",
" <th>scientaPassEnergy_eV</th>\n",
" <th>scientaDwellTime_ms</th>\n",
" <th>regionName</th>\n",
" <th>scientaAcquisitionMode</th>\n",
" <th>scientaEkinRange_eV_1</th>\n",
" <th>name</th>\n",
" <th>scientaEkinRange_eV_2</th>\n",
" <th>scientaEkinStep_eV</th>\n",
" <th>...</th>\n",
" <th>xRayEkin_eV</th>\n",
" <th>scientaEkin_eV</th>\n",
" <th>beamlineInt</th>\n",
" <th>imageRoh</th>\n",
" <th>scientaEkinRoh_eV</th>\n",
" <th>bindingEnergy_eV</th>\n",
" <th>spectrum_countsNorm</th>\n",
" <th>logScaleMode</th>\n",
" <th>logScaleDate</th>\n",
" <th>image</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MEAS_1</th>\n",
" <td>750.0</td>\n",
" <td>750.0</td>\n",
" <td>50.0</td>\n",
" <td>520.0</td>\n",
" <td>Cl2p_750eV</td>\n",
" <td>Swept</td>\n",
" <td>536.0</td>\n",
" <td>0041041_Cl2p_750eV.ibw</td>\n",
" <td>554.5</td>\n",
" <td>0.1</td>\n",
" <td>...</td>\n",
" <td>[[750.0]]</td>\n",
" <td>[[536.0], [536.1], [536.2], [536.3000000000001...</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[3015.9615384615386, 3158.653846153846, 3492....</td>\n",
" <td>[[536.0], [536.1], [536.2], [536.3000000000001...</td>\n",
" <td>[[208.5888042824772, 208.4888042824772, 208.38...</td>\n",
" <td>[[-0.002970900528877043, -0.002370528834745835...</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[3015.9615384615386, 3158.653846153846, 3492....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAS_10</th>\n",
" <td>750.0</td>\n",
" <td>750.0</td>\n",
" <td>20.0</td>\n",
" <td>520.0</td>\n",
" <td>Cl2p_750eV</td>\n",
" <td>Swept</td>\n",
" <td>539.0</td>\n",
" <td>0110110_Cl2p_750eV.ibw</td>\n",
" <td>553.0</td>\n",
" <td>0.1</td>\n",
" <td>...</td>\n",
" <td>[[750.0]]</td>\n",
" <td>[[539.0], [539.1], [539.2], [539.3000000000001...</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[1183.076923076923, 1641.1538461538462, 778.0...</td>\n",
" <td>[[539.0], [539.1], [539.2], [539.3000000000001...</td>\n",
" <td>[[208.09547634289822, 207.9954763428982, 207.8...</td>\n",
" <td>[[-0.002713117091577911, 0.0017431800916657154...</td>\n",
" <td>[[0.0], [0.0], [0.0], [0.0]]</td>\n",
" <td>[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0....</td>\n",
" <td>[[1183.076923076923, 1641.1538461538462, 778.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAS_11</th>\n",
" <td>750.0</td>\n",
" <td>750.0</td>\n",
" <td>20.0</td>\n",
" <td>520.0</td>\n",
" <td>Cl2p_750eV</td>\n",
" <td>Swept</td>\n",
" <td>539.0</td>\n",
" <td>0113113_Cl2p_750eV.ibw</td>\n",
" <td>553.0</td>\n",
" <td>0.1</td>\n",
" <td>...</td>\n",
" <td>[[750.0]]</td>\n",
" <td>[[539.0], [539.1], [539.2], [539.3000000000001...</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[1557.6923076923076, 1026.1538461538462, 1706...</td>\n",
" <td>[[539.0], [539.1], [539.2], [539.3000000000001...</td>\n",
" <td>[[208.28273417166213, 208.1827341716621, 208.0...</td>\n",
" <td>[[-0.002957306068744318, 0.005511387878246225,...</td>\n",
" <td>[[0.0], [0.0], [0.0], [0.0]]</td>\n",
" <td>[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0....</td>\n",
" <td>[[1557.6923076923076, 1026.1538461538462, 1706...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAS_12</th>\n",
" <td>750.0</td>\n",
" <td>750.0</td>\n",
" <td>20.0</td>\n",
" <td>520.0</td>\n",
" <td>Cl2p_750eV</td>\n",
" <td>Swept</td>\n",
" <td>539.0</td>\n",
" <td>0116116_Cl2p_750eV.ibw</td>\n",
" <td>553.0</td>\n",
" <td>0.1</td>\n",
" <td>...</td>\n",
" <td>[[750.0]]</td>\n",
" <td>[[539.0], [539.1], [539.2], [539.3000000000001...</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[1050.7692307692307, 1054.6153846153845, 1569...</td>\n",
" <td>[[539.0], [539.1], [539.2], [539.3000000000001...</td>\n",
" <td>[[208.33751693711383, 208.2375169371138, 208.1...</td>\n",
" <td>[[-0.0015217068251615732, 0.001336813298549339...</td>\n",
" <td>[[0.0], [0.0], [0.0], [0.0]]</td>\n",
" <td>[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0....</td>\n",
" <td>[[1050.7692307692307, 1054.6153846153845, 1569...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAS_13</th>\n",
" <td>750.0</td>\n",
" <td>750.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>merge_Cl2p_750eV.ibw</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>[[750.0]]</td>\n",
" <td>[[nan]]</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[nan]]</td>\n",
" <td>[[nan]]</td>\n",
" <td>[[208.38880428247717, 208.28880428247714, 208....</td>\n",
" <td>[[nan]]</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[0.0]]</td>\n",
" <td>[[nan]]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 69 columns</p>\n",
"</div>"
],
"text/plain": [
" xRayEkinRange_eV_1 xRayEkinRange_eV_2 scientaPassEnergy_eV \\\n",
"MEAS_1 750.0 750.0 50.0 \n",
"MEAS_10 750.0 750.0 20.0 \n",
"MEAS_11 750.0 750.0 20.0 \n",
"MEAS_12 750.0 750.0 20.0 \n",
"MEAS_13 750.0 750.0 NaN \n",
"\n",
" scientaDwellTime_ms regionName scientaAcquisitionMode \\\n",
"MEAS_1 520.0 Cl2p_750eV Swept \n",
"MEAS_10 520.0 Cl2p_750eV Swept \n",
"MEAS_11 520.0 Cl2p_750eV Swept \n",
"MEAS_12 520.0 Cl2p_750eV Swept \n",
"MEAS_13 NaN \n",
"\n",
" scientaEkinRange_eV_1 name scientaEkinRange_eV_2 \\\n",
"MEAS_1 536.0 0041041_Cl2p_750eV.ibw 554.5 \n",
"MEAS_10 539.0 0110110_Cl2p_750eV.ibw 553.0 \n",
"MEAS_11 539.0 0113113_Cl2p_750eV.ibw 553.0 \n",
"MEAS_12 539.0 0116116_Cl2p_750eV.ibw 553.0 \n",
"MEAS_13 NaN merge_Cl2p_750eV.ibw NaN \n",
"\n",
" scientaEkinStep_eV ... xRayEkin_eV \\\n",
"MEAS_1 0.1 ... [[750.0]] \n",
"MEAS_10 0.1 ... [[750.0]] \n",
"MEAS_11 0.1 ... [[750.0]] \n",
"MEAS_12 0.1 ... [[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": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"# Define h5 file name and make sure file is located at the current working dir\n",
"filename = '../input_files/FileList_v2.h5'\n",
@@ -345,48 +47,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\florez_j\\AppData\\Local\\Temp\\ipykernel_10944\\1771346545.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
" dataframe['image'][0].shape\n"
]
},
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"dataframe['image'][0].shape\n",
"\n",