From 043301989c0aae67388999ef17faab763bf1d92b Mon Sep 17 00:00:00 2001 From: rettigl Date: Wed, 25 Oct 2023 10:00:40 +0200 Subject: [PATCH] working energy calibration --- sed/config/default.yaml | 2 +- tutorial/5 - hextof workflow.ipynb | 476 +------------------ tutorial/Flash energy calibration.ipynb | 590 ++++-------------------- tutorial/hextof_config.yaml | 4 +- 4 files changed, 104 insertions(+), 968 deletions(-) diff --git a/sed/config/default.yaml b/sed/config/default.yaml index 10c362e6..dead1293 100644 --- a/sed/config/default.yaml +++ b/sed/config/default.yaml @@ -27,7 +27,7 @@ dataframe: energy_column: "energy" # dataframe column containing delay data delay_column: "delay" - # time length of a base time-of-flight bin in ns + # time length of a base time-of-flight bin in s tof_binwidth: 4.125e-12 # Binning factor of the tof_column-data compared to tof_binwidth (2^(tof_binning-1)) tof_binning: 1 diff --git a/tutorial/5 - hextof workflow.ipynb b/tutorial/5 - hextof workflow.ipynb index e42a0594..ae5aa5b0 100644 --- a/tutorial/5 - hextof workflow.ipynb +++ b/tutorial/5 - hextof workflow.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -45,23 +45,9 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Folder config loaded from: [/mnt/pcshare/users/Laurenz/AreaB/sed/sed/tutorial/sed_config.yaml]\n", - "User config loaded from: [/mnt/pcshare/users/Laurenz/AreaB/sed/sed/tutorial/hextof_config.yaml]\n", - "Default config loaded from: [/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/config/default.yaml]\n", - "Reading files: 0 new files of 3 total.\n", - "All files converted successfully!\n", - "Filling nan values...\n", - "loading complete in 0.05 s\n" - ] - } - ], + "outputs": [], "source": [ "config={\"core\": {\"paths\": {\"data_raw_dir\": \"../../flash_test_data/fl1user3/\", \"data_parquet_dir\": \"../../flash_test_data/parquet/\"}}}\n", "sp = SedProcessor(runs=[44638], config=config, user_config=config_file, system_config={}, collect_metadata=False)" @@ -69,17 +55,9 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aligning 8s sectors of dataframe\n" - ] - } - ], + "outputs": [], "source": [ "sp.add_jitter()\n", "sp.align_dld_sectors()" @@ -101,24 +79,9 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9b14b49dafdf4d5faa103de6998833f4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00\\n\"+\n \"

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" - ], - "text/plain": [ - " trainId pulseId electronId timeStamp dldPosX dldPosY \\\n", - "0 1640388889 3 0 1.678800e+09 623.785094 649.785094 \n", - "1 1640388889 3 1 1.678800e+09 625.367363 647.367363 \n", - "2 1640388889 4 0 1.678800e+09 684.003223 663.003223 \n", - "3 1640388889 4 1 1.678800e+09 684.512967 679.512967 \n", - "4 1640388889 4 2 1.678800e+09 685.691489 661.691489 \n", - "\n", - " dldTimeSteps cryoTemperature crystalVoltage dldTimeBinSize ... \\\n", - "0 5710.785156 301.76001 -0.001524 0.020576 ... \n", - "1 5711.367188 301.76001 -0.001524 0.020576 ... \n", - "2 5726.003418 301.76001 -0.001524 0.020576 ... \n", - "3 5724.513184 301.76001 -0.001524 0.020576 ... \n", - "4 5721.691406 301.76001 -0.001524 0.020576 ... \n", - "\n", - " sampleBias sampleTemperature tofVoltage pulserSignAdc \\\n", - "0 0.001856 302.73999 9.9989 35014.0 \n", - "1 0.001856 302.73999 9.9989 35014.0 \n", - "2 0.001856 302.73999 9.9989 35023.0 \n", - "3 0.001856 302.73999 9.9989 35023.0 \n", - "4 0.001856 302.73999 9.9989 35023.0 \n", - "\n", - " monochromatorPhotonEnergy gmdBda bam delayStage dldSectorID \\\n", - "0 NaN NaN -846.53125 NaN 1 \n", - "1 NaN NaN -846.53125 NaN 4 \n", - "2 NaN NaN -846.09375 NaN 1 \n", - "3 NaN NaN -846.09375 NaN 0 \n", - "4 NaN NaN -846.09375 NaN 7 \n", - "\n", - " dldTime \n", - "0 3760.187814 \n", - "1 3760.571044 \n", - "2 3770.208068 \n", - "3 3769.226844 \n", - "4 3767.368884 \n", - "\n", - "[5 rows x 22 columns]" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sp.dataframe.head()" ] }, { "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 8\n", - " # data points = 6\n", - " # variables = 3\n", - " chi-square = 17.5000000\n", - " reduced chi-square = 5.83333333\n", - " Akaike info crit = 12.4226485\n", - " Bayesian info crit = 11.7979269\n", - "## Warning: uncertainties could not be estimated:\n", - " d: at initial value\n", - " t0: at initial value\n", - "[[Variables]]\n", - " d: 1.00000000 (init = 1)\n", - " t0: 1.0000e-06 (init = 1e-06)\n", - " E0: -2.50000000 (init = -5)\n", - "Quality of Calibration:\n" - ] - }, - { - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; 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embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "8618" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "E/TOF relationship:\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bf982a1ff057404fbf5296459cd04d52", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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[KeyError(['dldTimeSteps']), KeyError(['dldTimeSteps']), KeyError(['dldTimeSteps'])]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/tutorial/Flash energy calibration.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m sp \u001b[39m=\u001b[39m SedProcessor(runs\u001b[39m=\u001b[39;49m[\u001b[39m44638\u001b[39;49m], config\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mcore\u001b[39;49m\u001b[39m\"\u001b[39;49m: {\u001b[39m\"\u001b[39;49m\u001b[39mpaths\u001b[39;49m\u001b[39m\"\u001b[39;49m: {\u001b[39m\"\u001b[39;49m\u001b[39mdata_raw_dir\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m\"\u001b[39;49m\u001b[39m../../flash_test_data/fl1user3/\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mdata_parquet_dir\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m\"\u001b[39;49m\u001b[39m../../flash_test_data/parquet/\u001b[39;49m\u001b[39m\"\u001b[39;49m}}}, system_config\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mconfig_flash_energy_calib.yaml\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/core/processor.py:144\u001b[0m, in \u001b[0;36mSedProcessor.__init__\u001b[0;34m(self, metadata, config, dataframe, files, folder, runs, collect_metadata, **kwds)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[39m# Load data if provided:\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[39mif\u001b[39;00m dataframe \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m files \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m folder \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m runs \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 144\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mload(\n\u001b[1;32m 145\u001b[0m dataframe\u001b[39m=\u001b[39;49mdataframe,\n\u001b[1;32m 146\u001b[0m metadata\u001b[39m=\u001b[39;49mmetadata,\n\u001b[1;32m 147\u001b[0m files\u001b[39m=\u001b[39;49mfiles,\n\u001b[1;32m 148\u001b[0m folder\u001b[39m=\u001b[39;49mfolder,\n\u001b[1;32m 149\u001b[0m runs\u001b[39m=\u001b[39;49mruns,\n\u001b[1;32m 150\u001b[0m collect_metadata\u001b[39m=\u001b[39;49mcollect_metadata,\n\u001b[1;32m 151\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds,\n\u001b[1;32m 152\u001b[0m )\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/core/processor.py:300\u001b[0m, in \u001b[0;36mSedProcessor.load\u001b[0;34m(self, dataframe, metadata, files, folder, runs, collect_metadata, **kwds)\u001b[0m\n\u001b[1;32m 292\u001b[0m dataframe, metadata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader\u001b[39m.\u001b[39mread_dataframe(\n\u001b[1;32m 293\u001b[0m folders\u001b[39m=\u001b[39mcast(\u001b[39mstr\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcpy(folder)),\n\u001b[1;32m 294\u001b[0m runs\u001b[39m=\u001b[39mruns,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds,\n\u001b[1;32m 298\u001b[0m )\n\u001b[1;32m 299\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 300\u001b[0m dataframe, metadata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mloader\u001b[39m.\u001b[39;49mread_dataframe(\n\u001b[1;32m 301\u001b[0m runs\u001b[39m=\u001b[39;49mruns,\n\u001b[1;32m 302\u001b[0m metadata\u001b[39m=\u001b[39;49mmetadata,\n\u001b[1;32m 303\u001b[0m collect_metadata\u001b[39m=\u001b[39;49mcollect_metadata,\n\u001b[1;32m 304\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds,\n\u001b[1;32m 305\u001b[0m )\n\u001b[1;32m 307\u001b[0m \u001b[39melif\u001b[39;00m folder \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 308\u001b[0m dataframe, metadata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader\u001b[39m.\u001b[39mread_dataframe(\n\u001b[1;32m 309\u001b[0m folders\u001b[39m=\u001b[39mcast(\u001b[39mstr\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcpy(folder)),\n\u001b[1;32m 310\u001b[0m metadata\u001b[39m=\u001b[39mmetadata,\n\u001b[1;32m 311\u001b[0m collect_metadata\u001b[39m=\u001b[39mcollect_metadata,\n\u001b[1;32m 312\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds,\n\u001b[1;32m 313\u001b[0m )\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/loader/flash/loader.py:857\u001b[0m, in \u001b[0;36mFlashLoader.read_dataframe\u001b[0;34m(self, files, folders, runs, ftype, metadata, collect_metadata, **kwds)\u001b[0m\n\u001b[1;32m 847\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 848\u001b[0m \u001b[39m# This call takes care of files and folders. As we have converted runs into files\u001b[39;00m\n\u001b[1;32m 849\u001b[0m \u001b[39m# already, they are just stored in the class by this call.\u001b[39;00m\n\u001b[1;32m 850\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mread_dataframe(\n\u001b[1;32m 851\u001b[0m files\u001b[39m=\u001b[39mfiles,\n\u001b[1;32m 852\u001b[0m folders\u001b[39m=\u001b[39mfolders,\n\u001b[1;32m 853\u001b[0m ftype\u001b[39m=\u001b[39mftype,\n\u001b[1;32m 854\u001b[0m metadata\u001b[39m=\u001b[39mmetadata,\n\u001b[1;32m 855\u001b[0m )\n\u001b[0;32m--> 857\u001b[0m dataframe \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparquet_handler(data_parquet_dir, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m 859\u001b[0m metadata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mparse_metadata() \u001b[39mif\u001b[39;00m collect_metadata \u001b[39melse\u001b[39;00m {}\n\u001b[1;32m 860\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mloading complete in \u001b[39m\u001b[39m{\u001b[39;00mtime\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m t0\u001b[39m:\u001b[39;00m\u001b[39m.2f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m s\u001b[39m\u001b[39m\"\u001b[39m)\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/loader/flash/loader.py:741\u001b[0m, in \u001b[0;36mFlashLoader.parquet_handler\u001b[0;34m(self, data_parquet_dir, detector, parquet_path, converted, load_parquet, save_parquet)\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m 735\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mThe final parquet for this run(s) does not exist yet. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 736\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mIf it is in another location, please provide the path as parquet_path.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 737\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39mexc\u001b[39;00m\n\u001b[1;32m 739\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 740\u001b[0m \u001b[39m# Obtain the filenames from the method which handles buffer file creation/reading\u001b[39;00m\n\u001b[0;32m--> 741\u001b[0m _, parquet_filenames \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuffer_file_handler(\n\u001b[1;32m 742\u001b[0m data_parquet_dir,\n\u001b[1;32m 743\u001b[0m detector,\n\u001b[1;32m 744\u001b[0m )\n\u001b[1;32m 745\u001b[0m \u001b[39m# Read all parquet files into one dataframe using dask\u001b[39;00m\n\u001b[1;32m 746\u001b[0m dataframe \u001b[39m=\u001b[39m dd\u001b[39m.\u001b[39mread_parquet(parquet_filenames, calculate_divisions\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/loader/flash/loader.py:677\u001b[0m, in \u001b[0;36mFlashLoader.buffer_file_handler\u001b[0;34m(self, data_parquet_dir, detector)\u001b[0m\n\u001b[1;32m 672\u001b[0m error \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39m\u001b[39mlen\u001b[39m(files_to_read), verbose\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)(\n\u001b[1;32m 673\u001b[0m delayed(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcreate_buffer_file)(h5_path, parquet_path)\n\u001b[1;32m 674\u001b[0m \u001b[39mfor\u001b[39;00m h5_path, parquet_path \u001b[39min\u001b[39;00m files_to_read\n\u001b[1;32m 675\u001b[0m )\n\u001b[1;32m 676\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(error):\n\u001b[0;32m--> 677\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mConversion failed for some files. \u001b[39m\u001b[39m{\u001b[39;00merror\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 679\u001b[0m \u001b[39m# Raise an error if the conversion failed for any files\u001b[39;00m\n\u001b[1;32m 680\u001b[0m \u001b[39m# TODO: merge this and the previous error trackings\u001b[39;00m\n\u001b[1;32m 681\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfailed_files_error:\n", - "\u001b[0;31mRuntimeError\u001b[0m: Conversion failed for some files. [KeyError(['dldTimeSteps']), KeyError(['dldTimeSteps']), KeyError(['dldTimeSteps'])]" - ] - } - ], + "outputs": [], "source": [ "sp = SedProcessor(runs=[44638], config={\"core\": {\"paths\": {\"data_raw_dir\": \"../../flash_test_data/fl1user3/\", \"data_parquet_dir\": \"../../flash_test_data/parquet/\"}}}, system_config=\"config_flash_energy_calib.yaml\")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "248a41a7", "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Metadata inference failed in `apply_jitter`.\n\nYou have supplied a custom function and Dask is unable to \ndetermine the type of output that that function returns. \n\nTo resolve this please provide a meta= keyword.\nThe docstring of the Dask function you ran should have more information.\n\nOriginal error is below:\n------------------------\nKeyError('dldTimeSteps')\n\nTraceback:\n---------\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/utils.py\", line 192, in raise_on_meta_error\n yield\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/core.py\", line 6782, in _emulate\n return func(*_extract_meta(args, True), **_extract_meta(kwargs, True))\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/core/dfops.py\", line 68, in apply_jitter\n df[col_jittered] = df[col] + amp * jitter\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/core/frame.py\", line 3804, in __getitem__\n indexer = self.columns.get_loc(key)\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/core/indexes/base.py\", line 3805, in get_loc\n raise KeyError(key) from err\n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/core/indexes/base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'dldTimeSteps'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/utils.py:192\u001b[0m, in \u001b[0;36mraise_on_meta_error\u001b[0;34m(funcname, udf)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 192\u001b[0m \u001b[39myield\u001b[39;00m\n\u001b[1;32m 193\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/core.py:6782\u001b[0m, in \u001b[0;36m_emulate\u001b[0;34m(func, udf, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6781\u001b[0m \u001b[39mwith\u001b[39;00m raise_on_meta_error(funcname(func), udf\u001b[39m=\u001b[39mudf), check_numeric_only_deprecation():\n\u001b[0;32m-> 6782\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49m_extract_meta(args, \u001b[39mTrue\u001b[39;49;00m), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m_extract_meta(kwargs, \u001b[39mTrue\u001b[39;49;00m))\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/core/dfops.py:68\u001b[0m, in \u001b[0;36mapply_jitter\u001b[0;34m(df, cols, cols_jittered, amps, jitter_type)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[39mfor\u001b[39;00m (col, col_jittered, amp) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(cols, cols_jittered, amps):\n\u001b[0;32m---> 68\u001b[0m df[col_jittered] \u001b[39m=\u001b[39m df[col] \u001b[39m+\u001b[39m amp \u001b[39m*\u001b[39m jitter\n\u001b[1;32m 70\u001b[0m \u001b[39mreturn\u001b[39;00m df\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/core/frame.py:3804\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3803\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3804\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 3805\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3805\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3807\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3808\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3809\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n", - "\u001b[0;31mKeyError\u001b[0m: 'dldTimeSteps'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/tutorial/Flash energy calibration.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m sp\u001b[39m.\u001b[39;49madd_jitter()\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/core/processor.py:1331\u001b[0m, in \u001b[0;36mSedProcessor.add_jitter\u001b[0;34m(self, cols, amps, **kwds)\u001b[0m\n\u001b[1;32m 1328\u001b[0m \u001b[39mif\u001b[39;00m amps \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1329\u001b[0m amps \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_config[\u001b[39m\"\u001b[39m\u001b[39mdataframe\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mjitter_amps\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m-> 1331\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataframe \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataframe\u001b[39m.\u001b[39;49mmap_partitions(\n\u001b[1;32m 1332\u001b[0m apply_jitter,\n\u001b[1;32m 1333\u001b[0m cols\u001b[39m=\u001b[39;49mcols,\n\u001b[1;32m 1334\u001b[0m cols_jittered\u001b[39m=\u001b[39;49mcols,\n\u001b[1;32m 1335\u001b[0m amps\u001b[39m=\u001b[39;49mamps,\n\u001b[1;32m 1336\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds,\n\u001b[1;32m 1337\u001b[0m )\n\u001b[1;32m 1338\u001b[0m metadata \u001b[39m=\u001b[39m []\n\u001b[1;32m 1339\u001b[0m \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m cols:\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/core.py:999\u001b[0m, in \u001b[0;36m_Frame.map_partitions\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 871\u001b[0m \u001b[39m@insert_meta_param_description\u001b[39m(pad\u001b[39m=\u001b[39m\u001b[39m12\u001b[39m)\n\u001b[1;32m 872\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmap_partitions\u001b[39m(\u001b[39mself\u001b[39m, func, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 873\u001b[0m \u001b[39m\"\"\"Apply Python function on each DataFrame partition.\u001b[39;00m\n\u001b[1;32m 874\u001b[0m \n\u001b[1;32m 875\u001b[0m \u001b[39m Note that the index and divisions are assumed to remain unchanged.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 997\u001b[0m \u001b[39m None as the division.\u001b[39;00m\n\u001b[1;32m 998\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 999\u001b[0m \u001b[39mreturn\u001b[39;00m map_partitions(func, \u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/core.py:6852\u001b[0m, in \u001b[0;36mmap_partitions\u001b[0;34m(func, meta, enforce_metadata, transform_divisions, align_dataframes, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6845\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 6846\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m. If you don\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt want the partitions to be aligned, and are \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 6847\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcalling `map_partitions` directly, pass `align_dataframes=False`.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 6848\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n\u001b[1;32m 6850\u001b[0m dfs \u001b[39m=\u001b[39m [df \u001b[39mfor\u001b[39;00m df \u001b[39min\u001b[39;00m args \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(df, _Frame)]\n\u001b[0;32m-> 6852\u001b[0m meta \u001b[39m=\u001b[39m _get_meta_map_partitions(args, dfs, func, kwargs, meta, parent_meta)\n\u001b[1;32m 6853\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mall\u001b[39m(\u001b[39misinstance\u001b[39m(arg, Scalar) \u001b[39mfor\u001b[39;00m arg \u001b[39min\u001b[39;00m args):\n\u001b[1;32m 6854\u001b[0m layer \u001b[39m=\u001b[39m {\n\u001b[1;32m 6855\u001b[0m (name, \u001b[39m0\u001b[39m): (\n\u001b[1;32m 6856\u001b[0m apply,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6860\u001b[0m )\n\u001b[1;32m 6861\u001b[0m }\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/core.py:6963\u001b[0m, in \u001b[0;36m_get_meta_map_partitions\u001b[0;34m(args, dfs, func, kwargs, meta, parent_meta)\u001b[0m\n\u001b[1;32m 6959\u001b[0m parent_meta \u001b[39m=\u001b[39m dfs[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39m_meta\n\u001b[1;32m 6960\u001b[0m \u001b[39mif\u001b[39;00m meta \u001b[39mis\u001b[39;00m no_default:\n\u001b[1;32m 6961\u001b[0m \u001b[39m# Use non-normalized kwargs here, as we want the real values (not\u001b[39;00m\n\u001b[1;32m 6962\u001b[0m \u001b[39m# delayed values)\u001b[39;00m\n\u001b[0;32m-> 6963\u001b[0m meta \u001b[39m=\u001b[39m _emulate(func, \u001b[39m*\u001b[39;49margs, udf\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 6964\u001b[0m meta_is_emulated \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 6965\u001b[0m \u001b[39melse\u001b[39;00m:\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/core.py:6782\u001b[0m, in \u001b[0;36m_emulate\u001b[0;34m(func, udf, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6777\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 6778\u001b[0m \u001b[39mApply a function using args / kwargs. If arguments contain dd.DataFrame /\u001b[39;00m\n\u001b[1;32m 6779\u001b[0m \u001b[39mdd.Series, using internal cache (``_meta``) for calculation\u001b[39;00m\n\u001b[1;32m 6780\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 6781\u001b[0m \u001b[39mwith\u001b[39;00m raise_on_meta_error(funcname(func), udf\u001b[39m=\u001b[39mudf), check_numeric_only_deprecation():\n\u001b[0;32m-> 6782\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39m_extract_meta(args, \u001b[39mTrue\u001b[39;00m), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m_extract_meta(kwargs, \u001b[39mTrue\u001b[39;00m))\n", - "File \u001b[0;32m~/.conda/envs/.pyenv/lib/python3.8/contextlib.py:131\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m 129\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mtype\u001b[39m()\n\u001b[1;32m 130\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 131\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgen\u001b[39m.\u001b[39;49mthrow(\u001b[39mtype\u001b[39;49m, value, traceback)\n\u001b[1;32m 132\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m \u001b[39mas\u001b[39;00m exc:\n\u001b[1;32m 133\u001b[0m \u001b[39m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[1;32m 134\u001b[0m \u001b[39m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[1;32m 135\u001b[0m \u001b[39m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n\u001b[1;32m 136\u001b[0m \u001b[39mreturn\u001b[39;00m exc \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m value\n", - "File \u001b[0;32m/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/utils.py:213\u001b[0m, in \u001b[0;36mraise_on_meta_error\u001b[0;34m(funcname, udf)\u001b[0m\n\u001b[1;32m 204\u001b[0m msg \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[1;32m 205\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mOriginal error is below:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 206\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m------------------------\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m{2}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 211\u001b[0m )\n\u001b[1;32m 212\u001b[0m msg \u001b[39m=\u001b[39m msg\u001b[39m.\u001b[39mformat(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m in `\u001b[39m\u001b[39m{\u001b[39;00mfuncname\u001b[39m}\u001b[39;00m\u001b[39m`\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m funcname \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mrepr\u001b[39m(e), tb)\n\u001b[0;32m--> 213\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: Metadata inference failed in `apply_jitter`.\n\nYou have supplied a custom function and Dask is unable to \ndetermine the type of output that that function returns. \n\nTo resolve this please provide a meta= keyword.\nThe docstring of the Dask function you ran should have more information.\n\nOriginal error is below:\n------------------------\nKeyError('dldTimeSteps')\n\nTraceback:\n---------\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/utils.py\", line 192, in raise_on_meta_error\n yield\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/dask/dataframe/core.py\", line 6782, in _emulate\n return func(*_extract_meta(args, True), **_extract_meta(kwargs, True))\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/sed/sed/core/dfops.py\", line 68, in apply_jitter\n df[col_jittered] = df[col] + amp * jitter\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/core/frame.py\", line 3804, in __getitem__\n indexer = self.columns.get_loc(key)\n File \"/mnt/pcshare/users/Laurenz/AreaB/sed/.pyenv/lib/python3.8/site-packages/pandas/core/indexes/base.py\", line 3805, in get_loc\n raise KeyError(key) from err\n" - ] - } - ], + "outputs": [], "source": [ "sp.add_jitter()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, + "id": "90aea967", + "metadata": {}, + "outputs": [], + "source": [ + "sp.append_tof_ns_axis(tof_ns_column=\"tof_ns\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3c9d426", + "metadata": {}, + "outputs": [], + "source": [ + "sp.dataframe.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5629d262", + "metadata": {}, + "outputs": [], + "source": [ + "data = sp.dataframe['dldTime'].partitions[0].compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8225d879", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "2b867e40", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0a0eb241ce07418c9dede3123a9f2810", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

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Application\",\"version\":\"2.4.3\"}};\n const render_items = [{\"docid\":\"4f4e37c9-331b-4ffe-84f2-ea93ea0afeba\",\"root_ids\":[\"1290\"],\"roots\":{\"1290\":\"947fd486-053b-42fa-b676-6874499f7dab\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "1290" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "E/TOF relationship:\n" - ] - }, - { - "data": { - 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656.754474 672.754474 \n", - "7 1640388889 8 0 1.678800e+09 705.136128 812.136128 \n", - "8 1640388889 8 1 1.678800e+09 710.309368 814.309368 \n", - "9 1640388889 16 0 1.678800e+09 675.043822 689.043822 \n", - "\n", - " dldTime cryoTemperature crystalVoltage dldTimeBinSize ... \\\n", - "0 45689.282613 301.76001 -0.001524 0.020576 ... \n", - "1 45691.636437 301.76001 -0.001524 0.020576 ... \n", - "2 45809.198502 301.76001 -0.001524 0.020576 ... \n", - "3 45800.114657 301.76001 -0.001524 0.020576 ... \n", - "4 45783.442973 301.76001 -0.001524 0.020576 ... \n", - "5 45789.727714 301.76001 -0.001524 0.020576 ... \n", - "6 43120.754474 301.76001 -0.001524 0.020576 ... \n", - "7 44955.136128 301.76001 -0.001524 0.020576 ... \n", - "8 44962.309368 301.76001 -0.001524 0.020576 ... \n", - "9 45527.043822 301.76001 -0.001524 0.020576 ... \n", - "\n", - " extractorVoltage sampleBias sampleTemperature tofVoltage pulserSignAdc \\\n", - "0 6029.379883 0.001856 302.73999 9.9989 35014.0 \n", - "1 6029.379883 0.001856 302.73999 9.9989 35014.0 \n", - "2 6029.379883 0.001856 302.73999 9.9989 35023.0 \n", - "3 6029.379883 0.001856 302.73999 9.9989 35023.0 \n", - "4 6029.379883 0.001856 302.73999 9.9989 35023.0 \n", - "5 6029.379883 0.001856 302.73999 9.9989 35023.0 \n", - "6 6029.379883 0.001856 302.73999 9.9989 35018.0 \n", - "7 6029.379883 0.001856 302.73999 9.9989 35016.0 \n", - "8 6029.379883 0.001856 302.73999 9.9989 35016.0 \n", - "9 6029.379883 0.001856 302.73999 9.9989 35016.0 \n", - "\n", - " monochromatorPhotonEnergy gmdBda bam delayStage energy \n", - "0 NaN NaN -846.53125 NaN -1.385106 \n", - "1 NaN NaN -846.53125 NaN -1.387787 \n", - "2 NaN NaN -846.09375 NaN -1.520523 \n", - "3 NaN NaN -846.09375 NaN -1.510350 \n", - "4 NaN NaN -846.09375 NaN -1.491643 \n", - "5 NaN NaN -846.09375 NaN -1.498700 \n", - "6 NaN NaN -860.09375 NaN 2.220200 \n", - "7 NaN NaN -854.06250 NaN -0.500170 \n", - "8 NaN NaN -854.06250 NaN -0.509309 \n", - "9 NaN NaN -1124.37500 NaN -1.197971 \n", - "\n", - "[10 rows x 21 columns]\n" - ] - } - ], + "outputs": [], "source": [ "sp.append_energy_axis(preview=True)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "59c83544", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1789a2ebeaa14842b59d30fefb5e5490", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "db24113033ce4f95a4bb73f1e01f372f", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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