diff --git a/AGIPD/AGIPD_Characterize_Gain_Combine.ipynb b/AGIPD/AGIPD_Characterize_Gain_Combine.ipynb
deleted file mode 100644
index 0ad5a418fc00822df85cd0ea6cc354d7f3cf45b4..0000000000000000000000000000000000000000
--- a/AGIPD/AGIPD_Characterize_Gain_Combine.ipynb
+++ /dev/null
@@ -1,760 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Combine Constants and Evaluate Bad Pixels #\n",
- "\n",
- "This notebook combines constants from various evaluations\n",
- "\n",
- "* dark image analysis, yielding offset and noise\n",
- "* flat field analysis, yielding X-ray gain\n",
- "* pulse capacitor analysis, yielding medium gain stage slopes and thresholds\n",
- "* charge injection analysis, yielding low gain stage slopes and thresholds\n",
- "\n",
- "into a single set of calibration constants. These constants do not include offset and noise as they need to be reevaluated more frequently.\n",
- "\n",
- "Additionally, a bad pixel mask for all gain stages is deduced from the input. The mask contains dedicated entries for all pixels and memory cells as well as all three gains stages. Each mask entry is encoded as:\n",
- "\n",
- "\n",
- "| Bit | Meaning |\n",
- "|-----|-------------------------------------------------------------------------------------------------|\n",
- "| 0 | set if offset of pixel is unusually low or high, or could not be properly evaluated |\n",
- "| 1 | set if X-ray deduced gain of pixel is unusually low or high, or could not be properly evaluated |\n",
- "| 2 | set if PI deduced gain of pixel is unusually low or high, also affecting the low gain slope |\n",
- "| 3 | set if CI deduced gain of pixel is unusually low or high, or could not be properly evaluated |\n",
- "| 4 | set if noise of pixel is unusually low or high, or could not be properly evaluated |\n",
- "| 5 | set if PI deduced gain could not properly be evaluated |\n",
- "| 6 | set if deduced thresholds are non-finite or 0. This will affect gaibs the threshold refers to |\n",
- "| 7 | currently unused |\n",
- "\n",
- "Corrected data will include mask entries for each pixel and each gain stage later on such that the mask may be applied via::\n",
- "\n",
- " gmask = np.choose(gain, mask[...,0], mask[...,0], mask[...,0])\n",
- " data[gmask != 0] = np.nan\n",
- " \n",
- "This will set all pixels with a mask entry in their gain stage to `np.nan`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# std library imports\n",
- "from functools import partial\n",
- "import h5py\n",
- "import os\n",
- "\n",
- "# numpy and matplot lib specific\n",
- "import numpy as np\n",
- "import matplotlib\n",
- "matplotlib.use(\"agg\")\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "\n",
- "# pyDetLib imports\n",
- "import XFELDetAna.xfelpycaltools as xcal\n",
- "import XFELDetAna.xfelpyanatools as xana\n",
- "\n",
- "from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions\n",
- "\n",
- "# usually no need to change these lines\n",
- "sensor_size = [128, 512]\n",
- "block_size = [64, 64]\n",
- "QUADRANTS = 4\n",
- "MODULES_PER_QUAD = 4\n",
- "DET_FILE_INSET = \"AGIPD\"\n",
- "\n",
- "# the following lines should be adjusted depending on data\n",
- "\n",
- "out_folder = \"/gpfs/exfel/exp/SPB/201701/p002012/proc/calibration/\"\n",
- "gain_output = \"{}/agipd_ff_store.h5\".format(out_folder)\n",
- "\n",
- "# change this to the inputs that should be used\n",
- "offset_store = \"/gpfs/exfel/exp/SPB/201701/p002012/proc/calibration/dark/agipd_offset_store_r0483_r0484_r0485.h5\"\n",
- "\n",
- "gain_store = \"/gpfs/exfel/exp/SPB/201701/p002012/proc/calibration/FF/agipd_gain_store_r0494_r0495_modules_{}.h5\"\n",
- "g_runs = [s for s in gain_store.split(\"_\") if s[0] == \"r\"]\n",
- "n_gain_stores = 16\n",
- "pc_store_base = \"/gpfs/exfel/exp/SPB/201701/p002012/proc/calibration/PC/agipd_pc_store_459_460_463_464_465_466_467_468_{}_{}.h5\"\n",
- "n_pc_stores = 16\n",
- "ci_store = \"/gpfs/exfel/data/scratch/haufs/gain_Data/AGIPD_ci_data_74.h5\"\n",
- "\n",
- "bias_voltage = 500 # SLURMHINT: bias_voltage, float\n",
- "cal_db_interface = \"tcp://max-exfl015:5005\" # SLURMHINT: db_host, str\n",
- "\n",
- "max_dev_high_gain = 0.2\n",
- "max_dev_med_gain = 0.5\n",
- "threshold_bounds_high_med = [100, 8100]\n",
- "\n",
- "thresholds_offset_sigma = 5.\n",
- "thresholds_offset_hard = [4000, 9000]\n",
- "\n",
- "thresholds_noise_sigma = 10.\n",
- "thresholds_noise_hard = [1, 20]\n",
- "\n",
- "thresholds_xraygain_hard = ((0.75, 1.25), (0.001, 0.25), (0.0001, 0.025))\n",
- "\n",
- "# cells in raw data\n",
- "max_cells = 64\n",
- "# actual memory cells: max_cells//2 if AGIPD is in interleaved mode\n",
- "memory_cells = 64\n",
- "# sequences to take data from\n",
- "sequences = range(1,3)\n",
- "# modules to characterize\n",
- "modules = range(1)\n",
- "\n",
- "NO_FLAT_FIELD_MODE = False\n",
- "\n",
- "local_output = False # SLURMHINT: local_output, bool\n",
- "\n",
- "db_output = True # SLURMHINT: db_output, bool\n",
- "db_input = db_output\n",
- "\n",
- "# these lines can usually stay as is\n",
- "gains = np.arange(3)\n",
- "cells = np.arange(max_cells)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Offset, noise and thresholds have been read in from calibration database:\n",
- "Offset map was injected on: 2018-04-11 13:32:33.208121\n",
- "Noise map was injected on: 2018-04-11 13:32:34.398314\n",
- "Threshold map was injected on: 2018-04-11 13:32:35.575589\n"
- ]
- }
- ],
- "source": [
- "from dateutil import parser\n",
- "offset_g = {}\n",
- "noise_g = {}\n",
- "threshold_o = {}\n",
- "if not db_input:\n",
- " print(\"Offset, noise and thresholds have been read in from: {}\".format(offset_store))\n",
- " store_file = h5py.File(offset_store, \"r\")\n",
- " for i in modules:\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " offset_g[qm] = np.array(store_file[\"{}/Offset/0/data\".format(qm)])\n",
- " noise_g[qm] = np.array(store_file[\"{}/Noise/0/data\".format(qm)])\n",
- " threshold_o[qm] = np.array(store_file[\"{}/Threshold/0/data\".format(qm)])\n",
- " store_file.close()\n",
- "else:\n",
- " print(\"Offset, noise and thresholds have been read in from calibration database:\")\n",
- " first_ex = True\n",
- " for i in modules:\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " metadata = ConstantMetaData()\n",
- " offset = Constants.AGIPD.Offset()\n",
- " metadata.calibration_constant = offset\n",
- "\n",
- " # set the operating condition\n",
- " condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)\n",
- " metadata.detector_condition = condition\n",
- "\n",
- " # specify the a version for this constant\n",
- " metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))\n",
- " metadata.retrieve(cal_db_interface)\n",
- " offset_g[qm] = offset.data\n",
- " \n",
- " if first_ex:\n",
- " print(\"Offset map was injected on: {}\".format(metadata.calibration_constant_version.begin_at))\n",
- " \n",
- " metadata = ConstantMetaData()\n",
- " noise = Constants.AGIPD.Noise()\n",
- " metadata.calibration_constant = noise\n",
- "\n",
- " # set the operating condition\n",
- " condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)\n",
- " metadata.detector_condition = condition\n",
- "\n",
- " # specify the a version for this constant\n",
- " metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))\n",
- " metadata.retrieve(cal_db_interface)\n",
- " noise_g[qm] = noise.data\n",
- " \n",
- " if first_ex:\n",
- " print(\"Noise map was injected on: {}\".format(metadata.calibration_constant_version.begin_at))\n",
- " \n",
- " metadata = ConstantMetaData()\n",
- " thresholds = Constants.AGIPD.ThresholdsDark()\n",
- " metadata.calibration_constant = thresholds\n",
- "\n",
- " # set the operating condition\n",
- " condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)\n",
- " metadata.detector_condition = condition\n",
- "\n",
- " # specify the a version for this constant\n",
- " metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))\n",
- " metadata.retrieve(cal_db_interface)\n",
- " threshold_o[qm] = thresholds.data\n",
- " \n",
- " if first_ex:\n",
- " print(\"Threshold map was injected on: {}\".format(metadata.calibration_constant_version.begin_at))\n",
- " first_ex = False"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Reading gain data from calibration database:\n",
- "FF gain map was injected on: 2018-04-11 13:37:26.554241\n"
- ]
- }
- ],
- "source": [
- "xray_gain_m = {}\n",
- "xray_gain_b = {}\n",
- "xray_mask_gain = {}\n",
- "xray_entries = {}\n",
- "medians = []\n",
- "if not NO_FLAT_FIELD_MODE:\n",
- " \n",
- " if not db_input:\n",
- " \n",
- " print(\"Reading gain data from {} files:\".format(n_gain_stores))\n",
- " for j in range(n_gain_stores):\n",
- " store_file = h5py.File(gain_store.format(j), \"r\")\n",
- " qm = \"Q{}M{}\".format(j//4+1, j%4+1)\n",
- " print(\"{}: {}\".format(qm, store_file))\n",
- " xgm = store_file[\"/{}/Gain/0/data\".format(qm)][()]\n",
- " \n",
- " medians.append(np.nanmedian(xgm))\n",
- " else:\n",
- " print(\"Reading gain data from calibration database:\")\n",
- " first_ex = True\n",
- " for j in modules:\n",
- " qm = \"Q{}M{}\".format(j//4+1, j%4+1)\n",
- " device = getattr(Detectors.AGIPD1M1, qm)\n",
- " # gains related\n",
- " metadata = ConstantMetaData()\n",
- " gain = Constants.AGIPD.SlopesFF()\n",
- " metadata.calibration_constant = gain\n",
- "\n",
- " # set the operating condition\n",
- " condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,\n",
- " pixels_x=512, pixels_y=128, beam_energy=None)\n",
- "\n",
- " metadata.detector_condition = condition\n",
- "\n",
- " metadata.calibration_constant_version = Versions.Now(device=device)\n",
- " metadata.retrieve(cal_db_interface)\n",
- " \n",
- " if first_ex:\n",
- " print(\"FF gain map was injected on: {}\".format(metadata.calibration_constant_version.begin_at))\n",
- " first_ex = False\n",
- " \n",
- " medians.append(np.nanmedian(gain.data[...,0]))\n",
- " \n",
- " \n",
- " global_med = np.nanmedian(np.array(medians))\n",
- " \n",
- " \n",
- " if local_output:\n",
- " store_file_out = h5py.File(gain_output.format(\"_\".join(g_runs)), \"w\")\n",
- " print(\"Outputting to intermediate file {}:\".format(store_file_out))\n",
- " \n",
- " \n",
- " k = 0\n",
- "\n",
- " for j in (modules if db_input else range(n_gain_stores)):\n",
- "\n",
- " qm = \"Q{}M{}\".format(j//4+1, j%4+1)\n",
- " qmout = qm\n",
- "\n",
- " xgm = np.zeros(sensor_size+[memory_cells], np.float32)\n",
- " xgb = np.zeros(sensor_size+[memory_cells], np.float32)\n",
- " xge = np.zeros(sensor_size+[memory_cells, 5], np.float32)\n",
- " xgk = np.zeros(sensor_size+[memory_cells], np.float32)\n",
- "\n",
- " if not db_input:\n",
- " store_file = h5py.File(gain_store.format(j), \"r\")\n",
- "\n",
- " xgm = store_file[\"/{}/Gain/0/data\".format(qm)][()]/global_med\n",
- " xgb = store_file[\"/{}/GainOffset/0/data\".format(qm)][()]\n",
- " xge = store_file[\"/{}/Entries/0/data\".format(qm)][()]\n",
- " xgk = store_file[\"/{}/BadPixels/0/data\".format(qm)][()]\n",
- " store_file.close()\n",
- " \n",
- " else:\n",
- " device = getattr(Detectors.AGIPD1M1, qm)\n",
- " # gains related\n",
- " metadata = ConstantMetaData()\n",
- " gain = Constants.AGIPD.SlopesFF()\n",
- " metadata.calibration_constant = gain\n",
- "\n",
- " # set the operating condition\n",
- " condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,\n",
- " pixels_x=512, pixels_y=128, beam_energy=None)\n",
- "\n",
- " metadata.detector_condition = condition\n",
- "\n",
- " metadata.calibration_constant_version = Versions.Now(device=device)\n",
- " metadata.retrieve(cal_db_interface)\n",
- " \n",
- " xgm = gain.data[...,0]\n",
- " xgb = gain.data[...,1]\n",
- " xge = None\n",
- " \n",
- " metadata = ConstantMetaData()\n",
- " gainbp = Constants.AGIPD.BadPixelsFF()\n",
- " metadata.calibration_constant = gain\n",
- "\n",
- " # set the operating condition\n",
- " condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,\n",
- " pixels_x=512, pixels_y=128, beam_energy=None)\n",
- "\n",
- " metadata.detector_condition = condition\n",
- "\n",
- " metadata.calibration_constant_version = Versions.Now(device=device)\n",
- " metadata.retrieve(cal_db_interface)\n",
- " xgk = gainbp.data\n",
- "\n",
- " if local_output:\n",
- " store_file_out[\"/{}/Gain/0/data\".format(qmout)] = xgm\n",
- " store_file_out[\"/{}/GainOffset/0/data\".format(qmout)] = xgb\n",
- " store_file_out[\"/{}/Entries/0/data\".format(qmout)] = xge\n",
- " store_file_out[\"/{}/BadPixels/0/data\".format(qmout)] = xgk\n",
- "\n",
- " xray_gain_m[qmout] = xgm\n",
- " xray_gain_b[qmout] = xgb\n",
- " xray_entries[qmout] = xge\n",
- " xray_mask_gain[qmout] = xgk\n",
- " k += 1\n",
- " \n",
- " if local_output:\n",
- " store_file_out.close()\n",
- "else:\n",
- " for i in range(16):\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " xray_gain_m[qm] = np.ones(sensor_size + [memory_cells], np.float32)\n",
- " xray_gain_b[qm] = np.zeros(sensor_size + [memory_cells], np.float32)\n",
- " xray_entries[qm] = np.ones(sensor_size + [memory_cells], np.float32)\n",
- " xray_mask_gain[qm] = np.ones(sensor_size + [memory_cells], np.uint8)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": false,
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Reading PC data from calibration database:\n",
- "PC gain map was injected on: 2018-04-11 13:55:36.548098\n"
- ]
- }
- ],
- "source": [
- "pc_high_m = {}\n",
- "pc_high_b = {}\n",
- "pc_med_m = {}\n",
- "pc_med_b = {}\n",
- "pc_med_o = {}\n",
- "pc_med_c = {}\n",
- "pc_med_a = {}\n",
- "pc_thresh = {}\n",
- "pc_thresh_bounds = {}\n",
- "pc_mask = {}\n",
- "pc_high_dev = {}\n",
- "pc_med_dev = {}\n",
- "\n",
- "\n",
- "if not db_input:\n",
- " \n",
- " for i in range(n_pc_stores):\n",
- " ofile = pc_store_base.format(i, i)\n",
- " store_file = h5py.File(ofile, \"r\")\n",
- "\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " qmout = qm\n",
- " pc_high_m[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'ml')][()], 0, 2)[...,:memory_cells]\n",
- " pc_high_b[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'bl')][()], 0, 2)[...,:memory_cells]\n",
- " pc_high_dev[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'devl')][()], 0, 2)[...,:memory_cells]\n",
- " pc_med_dev[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'devh')][()], 0, 2)[...,:memory_cells]\n",
- " pc_med_m[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'mh')][()], 0, 2)[...,:memory_cells]\n",
- " pc_med_b[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'bh')][()], 0, 2)[...,:memory_cells]\n",
- "\n",
- " pc_med_o[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'oh')][()], 0, 2)[...,:memory_cells]\n",
- " pc_med_c[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'ch')][()], 0, 2)[...,:memory_cells]\n",
- " pc_med_a[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'ah')][()], 0, 2)[...,:memory_cells]\n",
- " pc_thresh[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'tresh')][()], 0, 2)[...,:memory_cells]\n",
- " pc_thresh_bounds[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'tresh_bounds')][()], 0, 2)[...,:memory_cells]\n",
- " mask = np.zeros(pc_high_m[qmout].shape, np.uint8)\n",
- " mask[(pc_thresh[qmout] < threshold_bounds_high_med[0]) | (pc_thresh[qmout] > threshold_bounds_high_med[1])] += 1\n",
- " mask[(pc_high_dev[qmout] == 0) | (pc_med_dev[qmout] == 0)] += 2\n",
- " mask[(pc_high_dev[qmout] > max_dev_high_gain) | (pc_med_dev[qmout] >= max_dev_med_gain)] += 4\n",
- " mask[(pc_high_dev[qmout] < 0) | (pc_med_dev[qmout] < 0)] += 8\n",
- " mask[(~np.isfinite(pc_high_dev[qmout])) | (~np.isfinite(pc_med_dev[qmout]))] += 16\n",
- " pc_mask[qmout] = mask\n",
- " store_file.close()\n",
- "else:\n",
- " print(\"Reading PC data from calibration database:\")\n",
- " first_ex = True\n",
- " for i in modules:\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " qmout = qm\n",
- " device = getattr(Detectors.AGIPD1M1, qm)\n",
- " metadata = ConstantMetaData()\n",
- " pcdata = Constants.AGIPD.SlopesPC()\n",
- " metadata.calibration_constant = pcdata\n",
- "\n",
- " # set the operating condition\n",
- " condition = Conditions.Dark.AGIPD(memory_cells=74, bias_voltage=bias_voltage)\n",
- "\n",
- " metadata.detector_condition = condition\n",
- "\n",
- " metadata.calibration_constant_version = Versions.Now(device=device)\n",
- " metadata.retrieve(cal_db_interface)\n",
- " \n",
- " pc_high_m[qmout] = np.moveaxis(pcdata.data[0,...], 0, 2)[...,:memory_cells]\n",
- " pc_high_b[qmout] = np.moveaxis(pcdata.data[1,...], 0, 2)[...,:memory_cells]\n",
- " pc_high_dev[qmout] = np.moveaxis(pcdata.data[2,...], 0, 2)[...,:memory_cells]\n",
- " pc_med_dev[qmout] = np.moveaxis(pcdata.data[8,...], 0, 2)[...,:memory_cells]\n",
- " pc_med_m[qmout] = np.moveaxis(pcdata.data[3,...], 0, 2)[...,:memory_cells]\n",
- " pc_med_b[qmout] = np.moveaxis(pcdata.data[4,...], 0, 2)[...,:memory_cells]\n",
- "\n",
- " pc_med_o[qmout] = np.moveaxis(pcdata.data[5,...], 0, 2)[...,:memory_cells]\n",
- " pc_med_c[qmout] = np.moveaxis(pcdata.data[6,...], 0, 2)[...,:memory_cells]\n",
- " pc_med_a[qmout] = np.moveaxis(pcdata.data[7,...], 0, 2)[...,:memory_cells]\n",
- " pc_thresh[qmout] = np.moveaxis(pcdata.data[9,...], 0, 2)[...,:memory_cells]\n",
- " #pc_thresh_bounds[qmout] = np.moveaxis(store_file[\"/{}/{}/0/data\".format(qm, 'tresh_bounds')][()], 0, 2)[...,:memory_cells]\n",
- " pc_thresh_bounds[qmout] = None\n",
- " mask = np.zeros(pc_high_m[qmout].shape, np.uint8)\n",
- " mask[(pc_thresh[qmout] < threshold_bounds_high_med[0]) | (pc_thresh[qmout] > threshold_bounds_high_med[1])] += 1\n",
- " mask[(pc_high_dev[qmout] == 0) | (pc_med_dev[qmout] == 0)] += 2\n",
- " mask[(pc_high_dev[qmout] > max_dev_high_gain) | (pc_med_dev[qmout] >= max_dev_med_gain)] += 4\n",
- " mask[(pc_high_dev[qmout] < 0) | (pc_med_dev[qmout] < 0)] += 8\n",
- " mask[(~np.isfinite(pc_high_dev[qmout])) | (~np.isfinite(pc_med_dev[qmout]))] += 16\n",
- " pc_mask[qmout] = mask\n",
- " \n",
- " if first_ex:\n",
- " print(\"PC gain map was injected on: {}\".format(metadata.calibration_constant_version.begin_at))\n",
- " first_ex = False\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "store_file = h5py.File(ci_store, \"r\")\n",
- "ci_gain_correlation = {}\n",
- "ci_gain_b = {}\n",
- "ci_threshold = {}\n",
- "ci_mask = {}\n",
- "for i in range(16):\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " ci_gain_correlation[qm] = store_file[\"/{}/SlopeCorrelation/0/data\".format(qm)][()][...,:memory_cells]\n",
- " ci_gain_b[qm] = store_file[\"/{}/BaseOffset/0/data\".format(qm)][()][...,:memory_cells,:]\n",
- " ci_threshold[qm] = store_file[\"/{}/Threshold/0/data\".format(qm)][()][...,:memory_cells,:]\n",
- " ci_mask[qm] = store_file[\"/{}/SlopeCorrelation/0/mask\".format(qm)][()][...,:memory_cells,:]\n",
- " \n",
- "store_file.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "constants = {}\n",
- "\n",
- "for i in range(16): \n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " constants[qm] = {}\n",
- " g_m = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.float32)\n",
- " g_b = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.float32)\n",
- " g_o = np.zeros((sensor_size[0], sensor_size[1], memory_cells), np.float32)\n",
- " g_c = np.zeros((sensor_size[0], sensor_size[1], memory_cells), np.float32)\n",
- " g_a = np.zeros((sensor_size[0], sensor_size[1], memory_cells), np.float32)\n",
- " fac_high_med = pc_med_m[qm]/pc_high_m[qm]\n",
- " pc_gain_rel = pc_med_m[qm]*(np.nanmedian(xray_gain_m[qm])/np.nanmedian(pc_med_m[qm]))\n",
- " g_m[...,0] = xray_gain_m[qm]\n",
- " g_m[...,1] = xray_gain_m[qm]*fac_high_med\n",
- " g_m[...,2] = g_m[...,1]*ci_gain_correlation[qm]\n",
- " g_b[...,0] = xray_gain_b[qm]\n",
- " g_b[...,1] = pc_med_b[qm] - offset_g[qm][...,1] + xray_gain_b[qm] \n",
- " g_b[...,2] = ci_gain_b[qm][...,2] - offset_g[qm][...,2] + xray_gain_b[qm]\n",
- " \n",
- " g_o = pc_med_o[qm] - (xray_gain_b[qm]-pc_high_b[qm])/(pc_high_m[qm]-xray_gain_m[qm])\n",
- " g_a = pc_med_a[qm]\n",
- " g_c = pc_med_c[qm] * xray_gain_m[qm]/pc_high_m[qm]\n",
- " \n",
- " constants[qm]['RelativeGain'] = g_m\n",
- " constants[qm]['BaseOffset'] = g_b\n",
- " constants[qm]['RelativeGainNonLinOffset'] = g_o\n",
- " constants[qm]['RelativeGainNonLinAmplitude'] = g_a - xray_gain_b[qm]\n",
- " constants[qm]['RelativeGainNonLinScale'] = g_c\n",
- " \n",
- " th = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 2), np.float32)\n",
- " th[...,0] = threshold_o[qm][...,0]\n",
- " th[...,1] = ci_threshold[qm][...,1]\n",
- " constants[qm]['Threshold'] = th\n",
- " constants[qm]['ThresholdBounds'] = pc_thresh_bounds[qm]\n",
- " \n",
- " # bad pixels\n",
- " bp = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.uint8)\n",
- " # offset related bad pixels\n",
- " offset = offset_g[qm]\n",
- " offset_mn = np.nanmedian(offset, axis=(0,1))\n",
- " offset_std = np.nanstd(offset, axis=(0,1)) \n",
- " \n",
- " bp[(offset < offset_mn-thresholds_offset_sigma*offset_std) |\n",
- " (offset > offset_mn+thresholds_offset_sigma*offset_std)] |= 2**0\n",
- " bp[(offset < thresholds_offset_hard[0]) | (offset > thresholds_offset_hard[1])] |= 2**0\n",
- " bp[~np.isfinite(offset)] |= 2**0\n",
- " \n",
- " # noise related bad pixels\n",
- " noise = noise_g[qm]\n",
- " noise_mn = np.nanmedian(noise, axis=(0,1))\n",
- " noise_std = np.nanstd(noise, axis=(0,1)) \n",
- " \n",
- " bp[(noise < noise_mn-thresholds_noise_sigma*noise_std) |\n",
- " (noise > noise_mn+thresholds_noise_sigma*noise_std)] |= 2**4\n",
- " bp[(noise < thresholds_noise_hard[0]) | (noise > thresholds_noise_hard[1])] |= 2**4\n",
- " bp[~np.isfinite(noise)] |= 2**4\n",
- " \n",
- " # bad pixels from X-ray gain data\n",
- " xm = xray_mask_gain[qm]\n",
- " for i in range(3):\n",
- " tbp = bp[...,i]\n",
- " #tbp[xm != 0] |= 2**1\n",
- " #tbp[(g_m[...,i] < thresholds_xraygain_hard[i][0]) |\n",
- " # (g_m[...,i] > thresholds_xraygain_hard[i][1])] |= 2**1\n",
- " #if i > 0:\n",
- " # tbp[(tbp[...,0] != 0) & (tbp[...,i] == 0)] |= 2**1\n",
- " \n",
- " bp[...,i] = tbp\n",
- " \n",
- " # bad pixels for PC data\n",
- " pcm = pc_mask[qm]\n",
- " tbp = bp[...,1]\n",
- " tbp[pcm == 1] |= 2**2\n",
- " tbp[pcm > 1] |= 2**5\n",
- " tbp[th[...,0] > 9800] |= 2**7\n",
- " g_m_w = g_m[...,0]\n",
- " g_m_w[th[...,0] > 9800] = pc_gain_rel[th[...,0] > 9800]\n",
- " g_m[...,0] = g_m_w\n",
- " bp[...,0] = tbp\n",
- " bp[...,1] = tbp\n",
- " bp[...,2] = tbp\n",
- " \n",
- " # bad pixels for ci data\n",
- " tbp = bp[...,2]\n",
- " tbp[ci_mask != 0] |= 2**3\n",
- " bp[...,2] = tbp\n",
- " \n",
- " # threshold related bad pixels\n",
- " \n",
- " bp[~np.isfinite(th[...,0]), 0] |= 2**6\n",
- " bp[th[...,0] == 0, 0] |= 2**6\n",
- " \n",
- " bp[~np.isfinite(th[...,0]), 1] |= 2**6\n",
- " bp[th[...,0] == 0, 1] |= 2**6\n",
- " \n",
- " bp[~np.isfinite(th[...,1]), 1] |= 2**6\n",
- " bp[th[...,1] == 0, 1] |= 2**6\n",
- " \n",
- " bp[~np.isfinite(th[...,1]), 2] |= 2**6\n",
- " bp[th[...,1] == 0, 2] |= 2**6\n",
- " \n",
- " constants[qm]['BadPixels'] = bp\n",
- " \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "ofile = \"{}/agipd_base_store_{}_{}.h5\".format(out_folder, memory_cells, \"_\".join(g_runs))\n",
- "store_file = h5py.File(ofile, \"w\")\n",
- "for qm, r in constants.items(): \n",
- " for key, item in r.items():\n",
- " \n",
- " store_file[\"/{}/{}/0/data\".format(qm, key)] = item \n",
- "store_file.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "from mpl_toolkits.axes_grid1 import AxesGrid\n",
- "\n",
- "def show_overview(cell_to_preview, gain_to_preview):\n",
- " \n",
- " for module, data in constants.items():\n",
- " fig = plt.figure(figsize=(40,40))\n",
- " grid = AxesGrid(fig, 111,\n",
- " nrows_ncols=(4, 2),\n",
- " axes_pad=(0.9, 0.15),\n",
- " label_mode=\"1\",\n",
- " share_all=True,\n",
- " cbar_location=\"right\",\n",
- " cbar_mode=\"each\",\n",
- " cbar_size=\"7%\",\n",
- " cbar_pad=\"2%\",\n",
- " )\n",
- " i = 0\n",
- " for key, item in data.items():\n",
- " try:\n",
- " cf = 0\n",
- " if \"Threshold\" in key:\n",
- " cf = -1\n",
- " if len(item.shape) == 4:\n",
- " med = np.nanmedian(item[...,cell_to_preview, gain_to_preview + cf])\n",
- " else:\n",
- " med = np.nanmedian(item[...,cell_to_preview])\n",
- "\n",
- " bound = 0.2\n",
- " while(np.count_nonzero((item[...,cell_to_preview, gain_to_preview + cf] < med-np.abs(bound*med)) |\n",
- " (item[...,cell_to_preview, gain_to_preview + cf] > med+np.abs(bound*med)))/item[...,cell_to_preview, gain_to_preview + cf].size > 0.01): \n",
- " bound *=2\n",
- "\n",
- " if \"BadPixels\" in key:\n",
- " im = grid[i].imshow(np.log2(item[...,cell_to_preview, gain_to_preview + cf]).astype(np.float32), interpolation=\"nearest\",\n",
- " vmin=0, vmax=8, aspect='auto')\n",
- " pass\n",
- " \n",
- " else:\n",
- " \n",
- " if len(item.shape) == 4:\n",
- " im = grid[i].imshow(item[...,cell_to_preview, gain_to_preview + cf], interpolation=\"nearest\",\n",
- " vmin=med-np.abs(bound*med), vmax=np.abs(med+bound*med), aspect='auto')\n",
- " else:\n",
- " im = grid[i].imshow(item[...,cell_to_preview], interpolation=\"nearest\",\n",
- " vmin=med-np.abs(bound*med), vmax=med+np.abs(bound*med), aspect='auto')\n",
- " cb = grid.cbar_axes[i].colorbar(im)\n",
- "\n",
- " grid[i].text(20, 50, key, color=\"w\" if key != \"BadPixels\" else \"k\", fontsize=50)\n",
- "\n",
- " i += 1\n",
- " except:\n",
- " pass\n",
- " #grid[0].text(5, 20, module, color=\"r\" if key != \"BadPixels\" else \"k\", fontsize=20)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "show_overview(4, 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "show_overview(12, 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "show_overview(12, 2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/AGIPD/Characterize_AGIPD_Gain_Darks.py b/AGIPD/Characterize_AGIPD_Gain_Darks.py
deleted file mode 100644
index 3955daca86821ef664179d32985f506c737e4c44..0000000000000000000000000000000000000000
--- a/AGIPD/Characterize_AGIPD_Gain_Darks.py
+++ /dev/null
@@ -1,485 +0,0 @@
-import sys
-# coding: utf-8
-
-# # Characterize Dark Images #
-#
-# The following code analyzes a set of dark images taken with the AGIPD detector to deduce detector offsets and noise. Data for the detector's three gain stages needs to be present, separated into separate runs.
-#
-# The notebook explicitely does what pyDetLib provides in its offset calculation method for streaming data.
-
-# In[23]:
-
-
-# imports and things that do not usually need to be changed
-from collections import OrderedDict
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-# get_ipython().magic('matplotlib inline')
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-profile = str(sys.argv[-1])
-view = Client(profile=profile)[:]
-view.use_dill()
-
-from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
-
-gains = np.arange(3)
-offset_runs = OrderedDict()
-
-# no need to change this
-
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-DET_FILE_INSET = "AGIPD"
-IL_MODE = False # or True for interlaced data
-
-# adapt this to the run being investigated
-in_folder = str(sys.argv[1])
-offset_runs["high"] = str(sys.argv[2])
-offset_runs["med"] = str(sys.argv[3])
-offset_runs["low"] = str(sys.argv[4])
-out_folder = str(sys.argv[5])
-sequences = [int(s) for s in sys.argv[6].split(',')]
-
-max_cells = int(sys.argv[7])
-mem_cells = max_cells
-cells = np.arange(max_cells)
-
-local_output = bool(sys.argv[8])
-db_output = bool(sys.argv[9])
-bias_voltage = int(sys.argv[10])
-cal_db_interface = str(sys.argv[11])
-
-thresholds_offset_sigma = 3.
-thresholds_offset_hard = [4000, 8500]
-
-thresholds_noise_sigma = 5.
-thresholds_noise_hard = [4, 20]
-
-
-
-if not IL_MODE:
- max_cells*=2
-
-
-# In[2]:
-
-
-def combine_stack(d, sdim):
- """ A function for allowing to preview an assembled AGIPD image
- """
- combined = np.zeros((2048,2048, sdim))
- combined[...] = np.nan
- dy = 0
- for i in range(16):
-
- if i < 8:
- dx = -512
- mx = 1
- my = i % 8
- combined[my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx, :] = d[i][:,::-1,:]
- dy += 30
- if i == 3:
- dy += 30
- elif i < 12:
- dx = 100
- if i == 8:
- dy = 4*30 + 30 +50
-
- mx = 1
- my = i % 8 +4
- combined[my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx, :] = d[i][::-1,:,:]
- dy += 30
- else:
- dx = 100
- if i == 11:
- dy = 50
-
- mx = 1
- my = i - 14
-
- combined[my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx, :] = d[i][::-1,:,:]
- dy += 30
-
- return combined
-
-
-# The following lines will create a queue of files which will the be executed module-parallel. Distiguishing between different gains.
-
-# In[3]:
-
-
-# set everything up filewise
-from queue import Queue
-if not os.path.exists(out_folder):
- os.makedirs(out_folder)
-
-def map_modules_from_files(filelist):
- module_files = {}
- mod_ids = {}
- for quadrant in range(0, QUADRANTS):
- for module in range(0, MODULES_PER_QUAD):
- name = "Q{}M{}".format(quadrant + 1, module + 1)
- module_files[name] = Queue()
- num = quadrant * 4 + module
- mod_ids[name] = num
- file_infix = "{}{:02d}".format(DET_FILE_INSET, num)
- for file in filelist:
- if file_infix in file:
- module_files[name].put(file)
- return module_files, mod_ids
-
-gain_mapped_files = OrderedDict()
-for gain, run in offset_runs.items():
- ginfolder = "{}/{}".format(in_folder, run)
- dirlist = os.listdir(ginfolder)
- file_list = []
- for entry in dirlist:
- #only h5 file
- abs_entry = "{}/{}".format(ginfolder, entry)
- if os.path.isfile(abs_entry) and os.path.splitext(abs_entry)[1] == ".h5":
-
- if sequences is None:
- file_list.append(abs_entry)
- else:
- for seq in sequences:
- if "{:05d}.h5".format(seq) in abs_entry:
- file_list.append(os.path.abspath(abs_entry))
-
- mapped_files, mod_ids = map_modules_from_files(file_list)
- gain_mapped_files[gain] = mapped_files
-
-
-# ## Calculate Offsets, Noise and Thresholds ##
-#
-# The calculation is performed per-pixel and per-memory-cell. Offsets are simply the median value for a set of dark data taken at a given gain, noise the standard deviation, and gain-bit values the medians of the gain array.
-
-# In[4]:
-
-
-import copy
-from functools import partial
-def characterize_module(il_mode, cells, bp_thresh, inp):
- import numpy as np
- import copy
- import h5py
-
- filename, filename_out, channel = inp
- thresholds_offset_hard, thresholds_offset_sigma, thresholds_noise_hard, thresholds_noise_sigma = bp_thresh
-
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- infile.close()
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
-
- offset = np.zeros((im.shape[0], im.shape[1], cells//2))
- gains = np.zeros((im.shape[0], im.shape[1], cells//2))
- noise = np.zeros((im.shape[0], im.shape[1], cells//2))
-
- for cc in range(cells//2):
-
- offset[...,cc] = np.median(im[..., cc::cells//2], axis=2)
- noise[...,cc] = np.std(im[..., cc::cells//2], axis=2)
- gains[...,cc] = np.median(ga[..., cc::cells//2], axis=2)
-
- # bad pixels
- bp = np.zeros(offset.shape, np.uint8)
- # offset related bad pixels
- offset_mn = np.nanmedian(offset, axis=(0,1))
- offset_std = np.nanstd(offset, axis=(0,1))
-
- bp[(offset < offset_mn-thresholds_offset_sigma*offset_std) |
- (offset > offset_mn+thresholds_offset_sigma*offset_std)] |= 2**0
- bp[(offset < thresholds_offset_hard[0]) | (offset > thresholds_offset_hard[1])] |= 2**0
- bp[~np.isfinite(offset)] |= 2**0
-
- # noise related bad pixels
- noise_mn = np.nanmedian(noise, axis=(0,1))
- noise_std = np.nanstd(noise, axis=(0,1))
-
- bp[(noise < noise_mn-thresholds_noise_sigma*noise_std) |
- (noise > noise_mn+thresholds_noise_sigma*noise_std)] |= 2**4
- bp[(noise < thresholds_noise_hard[0]) | (noise > thresholds_noise_hard[1])] |= 2**4
- bp[~np.isfinite(noise)] |= 2**4
-
- return offset, noise, gains, bp
-
-
-offset_g = {}
-noise_g = {}
-gain_g = {}
-badpix_g = {}
-gg = 0
-for gain, mapped_files in gain_mapped_files.items():
-
- inp = []
- dones = []
- for i in range(16):
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- if qm in mapped_files and not mapped_files[qm].empty():
- fname_in = mapped_files[qm].get()
- dones.append(mapped_files[qm].empty())
- else:
- continue
- fout = os.path.abspath("{}/{}".format(out_folder, (os.path.split(fname_in)[-1]).replace("RAW", "CORR")))
- inp.append((fname_in, fout, i))
- first = False
- p = partial(characterize_module, IL_MODE, max_cells,
- (thresholds_offset_hard, thresholds_offset_sigma,
- thresholds_noise_hard, thresholds_noise_sigma))
- #results = list(map(p, inp))
- results = view.map_sync(p, inp)
- for i, r in enumerate(results):
- offset, noise, gain, bp = r
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- if qm not in offset_g:
- offset_g[qm] = np.zeros((offset.shape[0], offset.shape[1], offset.shape[2], 3))
- noise_g[qm] = np.zeros_like(offset_g[qm])
- gain_g[qm] = np.zeros_like(offset_g[qm])
- badpix_g[qm] = np.zeros_like(offset_g[qm])
-
- offset_g[qm][...,gg] = offset
- noise_g[qm][...,gg] = noise
- gain_g[qm][...,gg] = gain
- badpix_g[qm][...,gg] = bp
- gg +=1
-
-
-# The thresholds for gain switching are then defined as the mean value between in individual gain bit levels. Note that these thresholds need to be refined with charge induced thresholds, as the two are not the same.
-
-# In[5]:
-
-
-thresholds_g = {}
-for qm in gain_g.keys():
- thresholds_g[qm] = np.zeros((gain_g[qm].shape[0], gain_g[qm].shape[1], gain_g[qm].shape[2], 2))
- thresholds_g[qm][...,0] = (gain_g[qm][...,1]+gain_g[qm][...,0])/2
- thresholds_g[qm][...,1] = (gain_g[qm][...,2]+gain_g[qm][...,1])/2
-
-
-# The following code is for inspection purposes, it will render the constants for each module and gain and memory cell.
-
-# In[6]:
-
-
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-res = {}
-for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- res[qm] = {'Offset': offset_g[qm],
- 'Noise': noise_g[qm],
- 'Threshold': thresholds_g[qm],
- 'BadPixels': badpix_g[qm]
- }
-
-def show_overview(cell_to_preview, gain_to_preview):
-
- for module, data in res.items():
- fig = plt.figure(figsize=(40,40))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(2, 2),
- axes_pad=(0.9, 0.15),
- label_mode="1",
- share_all=True,
- cbar_location="right",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
- i = 0
- for key, item in data.items():
- cf = 0
- if "Threshold" in key:
- cf = -1
- if len(item.shape) == 4:
- med = np.nanmedian(item[...,cell_to_preview, gain_to_preview + cf])
- else:
- med = np.nanmedian(item[...,cell_to_preview])
-
- bound = 0.2
- while(np.count_nonzero((item[...,cell_to_preview, gain_to_preview + cf] < med-np.abs(bound*med)) |
- (item[...,cell_to_preview, gain_to_preview + cf] > med+np.abs(bound*med)))/item[...,cell_to_preview, gain_to_preview + cf].size > 0.01):
- bound *=2
-
- if "BadPixels" in key:
- im = grid[i].imshow(np.log2(item[...,cell_to_preview, gain_to_preview + cf]), interpolation="nearest",
- vmin=0, vmax=8, aspect='auto')
- else:
- if len(item.shape) == 4:
- im = grid[i].imshow(item[...,cell_to_preview, gain_to_preview + cf], interpolation="nearest",
- vmin=med-np.abs(bound*med), vmax=np.abs(med+bound*med), aspect='auto')
- else:
- im = grid[i].imshow(item[...,cell_to_preview], interpolation="nearest",
- vmin=med-np.abs(bound*med), vmax=med+np.abs(bound*med), aspect='auto')
- cb = grid.cbar_axes[i].colorbar(im)
-
- grid[i].text(20, 50, key, color="w" if key != "BadPixels" else "k", fontsize=50)
-
- i += 1
- grid[0].text(5, 20, module, color="r" if key != "BadPixels" else "k", fontsize=20)
- fig.savefig("{}/dark_analysis_{}_module_{}.png".format(out_folder,
- "_".join(offset_runs.values()),
- module))
-
-
-# In[7]:
-
-
-show_overview(12, 0)
-
-
-# In[8]:
-
-
-show_overview(12, 1)
-
-
-# In[9]:
-
-
-show_overview(4, 2)
-
-
-# In[10]:
-
-
-channel_mapping = {}
-for i in range(16):
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- channel_mapping[qm] = i
-
-def create_constant_overview(constant, name, vmin, vmax, entries=3):
- for g in range(entries):
- fig = plt.figure(figsize=(10, 5))
- ax = fig.add_subplot(111)
- for qm in constant.keys():
- d = constant[qm][...,g]
- print("{} {}, gain {:0.2f}: mean: {:0.2f}, median: {:0.2f}, std: {:0.2f}".format(name, qm, g,
- np.mean(d),
- np.median(d),
- np.std(d)))
-
- ax.step(np.arange(max_cells//2), np.median(d, axis=(0,1)))
- fig.savefig("{}/dark_analysis_{}_{}_per_cell_gain{}.png".format(out_folder,
- "_".join(offset_runs.values()),
- name, g))
-
-
-# In[11]:
-
-
-create_constant_overview(offset_g, "Offset", 4000, 8000)
-
-
-# In[12]:
-
-
-create_constant_overview(noise_g, "Noise", 0, 100)
-
-
-# In[13]:
-
-
-create_constant_overview(thresholds_g, "Threshold", 3000, 8000, 2)
-
-
-# Finally, we persist the data.
-
-# In[14]:
-
-
-if local_output:
- ofile = "{}/agipd_offset_store_{}.h5".format(out_folder, "_".join(offset_runs.values()))
- store_file = h5py.File(ofile, "w")
- for qm in offset_g.keys():
- store_file["{}/Offset/0/data".format(qm)] = offset_g[qm]
- store_file["{}/Noise/0/data".format(qm)] = noise_g[qm]
- store_file["{}/Threshold/0/data".format(qm)] = thresholds_g[qm]
- store_file["{}/BadPixels/0/data".format(qm)] = badpix_g[qm]
- store_file.close()
-
-
-# In[30]:
-
-
-from time import sleep
-if db_output:
- for qm in offset_g.keys():
- metadata = ConstantMetaData()
- offset = Constants.AGIPD.Offset()
- offset.data = offset_g[qm]
- metadata.calibration_constant = offset
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=mem_cells, bias_voltage=bias_voltage)
- device = getattr(Detectors.AGIPD1M1, qm)
- uuid = device.uuid
- condition.name = "Default AGIPD Condition - Cells {} - UUID: {}".format(mem_cells, int(uuid))
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
-
- metadata.send(cal_db_interface)
-
- metadata = ConstantMetaData()
- noise = Constants.AGIPD.Noise()
- noise.data = noise_g[qm]
- metadata.calibration_constant = noise
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=mem_cells, bias_voltage=bias_voltage)
- condition.name = "Default AGIPD Condition - Cells {} - UUID: {}".format(mem_cells, int(uuid))
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
- metadata = ConstantMetaData()
- thresholds = Constants.AGIPD.ThresholdsDark()
- thresholds.data = thresholds_g[qm]
- metadata.calibration_constant = thresholds
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=mem_cells, bias_voltage=bias_voltage)
- condition.name = "Default AGIPD Condition - Cells {} - UUID: {}".format(mem_cells, int(uuid))
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
-
-
-# In[ ]:
-
-
-
-
diff --git a/AGIPD/Characterize_AGIPD_Gain_FlatFields.py b/AGIPD/Characterize_AGIPD_Gain_FlatFields.py
deleted file mode 100644
index 1e7793a03b3de3db81103e34c90a560c1abd62b4..0000000000000000000000000000000000000000
--- a/AGIPD/Characterize_AGIPD_Gain_FlatFields.py
+++ /dev/null
@@ -1,842 +0,0 @@
-import sys
-
-# coding: utf-8
-
-# # Gain Characterization (Flat Fields) #
-#
-# The following code characterizes the gain of the AGIPD detector from flat field data, i.e. data with X-rays of defined intensity. This data should fullfil the following requirements:
-#
-# * intensity should be such that single photon peaks are visible
-# * data for all modules should be present
-# * no shadowing should occur on any of the modules
-# * each pixel should have at minimum arround 100 events per photon peak per memory cell
-# * if central beam edges are visible, they should not be significantly more intense
-#
-# Characterization is done by a weighted average algorithm, which evaluates the peak locations for all pixels
-# and memory cells of a given module. These locations are then fitted to a linear function of the average peak
-# location in each module, such that it yield a relative gain correction.
-
-# In[1]:
-
-
-# std library imports
-from functools import partial
-import h5py
-import os
-
-# numpy and matplot lib specific
-import numpy as np
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-# get_ipython().magic('matplotlib inline')
-
-# parallel processing via ipcluster
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-profile = str(sys.argv[-1])
-client = Client(profile=profile)
-view = client[:]
-view.use_dill()
-
-# pyDetLib imports
-import XFELDetAna.xfelpycaltools as xcal
-import XFELDetAna.xfelpyanatools as xana
-from XFELDetAna.util import env
-env.iprofile = profile
-
-from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
-
-# usually no need to change these lines
-sensor_size = [128, 512]
-block_size = [128, 512]
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-DET_FILE_INSET = "AGIPD"
-IL_MODE = bool(sys.argv[1])
-IL_MODE = False
-# the following lines should be adjusted depending on data
-in_folder = str(sys.argv[2])
-out_folder = str(sys.argv[3])
-# the runs the flat field data is in
-runs = [int(s) for s in sys.argv[4].split(',')]
-
-runs = ["r{:04d}".format(r) for r in runs]
-
-local_output = bool(sys.argv[5])
-
-db_output = bool(sys.argv[6])
-db_input = db_output
-
-bias_voltage = float(sys.argv[7])
-cal_db_interface = str(sys.argv[8])
-
-# change this to the offsets that should be used
-offset_store = str(sys.argv[9])
-
-# cells in raw data
-max_cells = int(sys.argv[10])
-# actual memory cells: max_cells//2 if AGIPD is in interleaved mode
-memory_cells = int(sys.argv[11])
-# sequences to take data from
-sequences = [int(s) for s in sys.argv[12].split(',')]
-# modules to characterize
-modules = [int(s) for s in sys.argv[13].split(',')]
-
-photon_energy = float(sys.argv[14])
-
-limit_trains = 20
-limit_trains_eval = 200
-
-print("Parameters are:")
-print("Memory cells: {}/{}".format(memory_cells, max_cells))
-print("Runs: {}".format(runs))
-print("Modules: {}".format(modules))
-print("Sequences: {}".format(sequences))
-print("Interlaced mode: {}".format(IL_MODE))
-print("Using DB: {}".format(db_output))
-
-# these lines can usually stay as is
-fbase = "{}/{{}}/RAW-{{}}-AGIPD{{:02d}}-S{{:05d}}.h5".format(in_folder)
-gains = np.arange(3)
-cells = np.arange(max_cells)
-
-
-# For the characterization offset maps for each module are needed. In the following these are read in
-
-# In[2]:
-
-
-from dateutil import parser
-offset_g = {}
-noise_g = {}
-thresholds_g = {}
-if not db_input:
- store_file = h5py.File(offset_store, "r")
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- offset_g[qm] = np.array(store_file["{}/Offset/0/data".format(qm)])
- noise_g[qm] = np.array(store_file["{}/Noise/0/data".format(qm)])
- thresholds_g[qm] = np.array(store_file["{}/Threshold/0/data".format(qm)])
- store_file.close()
-else:
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- metadata = ConstantMetaData()
- offset = Constants.AGIPD.Offset()
- metadata.calibration_constant = offset
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- offset_g[qm] = offset.data
-
- metadata = ConstantMetaData()
- noise = Constants.AGIPD.Noise()
- metadata.calibration_constant = noise
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- noise_g[qm] = noise.data
-
- metadata = ConstantMetaData()
- thresholds = Constants.AGIPD.ThresholdsDark()
- metadata.calibration_constant = thresholds
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- thresholds_g[qm] = thresholds.data
-
-
-# ## Initial peak estimates ##
-#
-# The following parallel code will read in the flat field runs, offset correct them and then, bin data of each
-# module into histograms.
-#
-# These histograms should then be inspected for initial peak estimates for the single, double, ... photon peaks,
-# as well es estimates of the relative hights of these peaks to one and another.
-
-# In[3]:
-
-
-def hist_single_module(fbase, runs, sequences, sensor_size, memory_cells, block_size,
- il_mode, limit_trains, profile, inp):
- """ This function calculates a per-pixel histogram for a single module
-
- Runs and sequences give the data to calculate histogram from
- """
- channel, offset, thresholds = inp
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- from XFELDetAna.util import env
- env.iprofile = profile
-
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
-
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size,
- offset,
- nCells=memory_cells,
- blockSize=block_size,
- gains=[0,1,2])
- offset_cor.mapper = offset_cor._view.map_sync
- offset_cor.debug() # force non-parallel processing since outer function will run concurrently
- hist_calc = xcal.HistogramCalculator(sensor_size,
- bins=4000,
- range=(-4000, 8000),
- blockSize=block_size)
- hist_calc.mapper = hist_calc._view.map_sync
- hist_calc.debug() # force non-parallel processing since outer function will run concurrently
- for run in runs:
- for seq in sequences:
- fname = fbase.format(run, run.upper(), channel, seq)
- d, ga, c = read_fun(fname, channel)
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
- hist_calc.fill(d)
- h, e, c, _ = hist_calc.get()
- return h, e, c
-
-inp = []
-for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], thresholds_g[qm]))
-
-p = partial(hist_single_module, fbase, runs, sequences,
- sensor_size, memory_cells, block_size, IL_MODE, limit_trains, profile)
-res_uncorr = view.map_sync(p, inp)
-
-
-# We inspect the resulting histograms for the estimates. Modules should look roughly the same as no significant deviation is to be expected. Non-function modules and artifacts of single pixels may be visible in the histogram.
-
-# In[4]:
-
-
-d = []
-qms = []
-for i, r in enumerate(res_uncorr):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- qms.append(qm)
- h, e, c = r
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid'
- })
-
-fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(0, 500),
- x_label="Intensity (ADU)",
- y_label="Counts")
-
-fig.savefig("{}/FF_module_{}_peak_pos.png".format(out_folder, ",".join(qms)))
-
-
-# In[5]:
-
-
-# these should be quite stable
-peak_estimates = [0, 55, 110, 165, 220]
-peak_heights = [5e7, 5e6, 1e6, 5e5, 1e5]
-peak_sigma = [5., 5., 5., 5., 5.]
-
-
-# ## Calculate relative gain per module ##
-#
-# Using the so obtained estimates, we calculate the relative gain per module. For this we use the weighted average method implemented in pyDetLib.
-#
-# Since for current AGIPD data taking only every second memory cells sees X-rays we account for this. For technical reasons, the subsequent cell will have the same constants copied, which are irrelevant as not X-rays are to be expected in these cells.
-
-# In[6]:
-
-
-block_size = [64, 64]
-def relgain_single_module(fbase, runs, sequences, peak_estimates,
- peak_heights, peak_sigma, memory_cells, sensor_size,
- block_size, il_mode, profile, limit_trains_eval, inp):
- """ A function for calculated the relative gain of a single AGIPD module
- """
-
- # import needed inline for parallel processing
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- from XFELDetAna.util import env
- env.iprofile = profile
-
- channel, offset, thresholds, noise = inp
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size, offset, nCells=memory_cells,
- blockSize=block_size, gains=[0,1,2])
- offset_cor.mapper = offset_cor._view.map_sync
-
- rel_gain = xcal.GainMapCalculator(sensor_size,
- peak_estimates,
- peak_sigma,
- nCells=memory_cells,
- peakHeights = peak_heights,
- noiseMap=noise,
- deviationType="relative")
- rel_gain.mapper = rel_gain._view.map_sync
- for run in runs:
- for seq in sequences:
- fname = fbase.format(run, run.upper(), channel, seq)
- d, ga, c = read_fun(fname, channel)
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
- rel_gain.fill(d, cellTable=c)
-
- gain_map = rel_gain.get()
- gain_map_func = rel_gain.getUsingFunc(inverse=False)
-
- pks, stds = rel_gain.getRawPeaks()
- return gain_map, pks, stds, gain_map_func
-
-inp = []
-for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], thresholds_g[qm], noise_g[qm][...,0]))
-
-p = partial(relgain_single_module, fbase, runs, sequences,
- peak_estimates, peak_heights, peak_sigma, memory_cells,
- sensor_size, block_size, IL_MODE, profile, limit_trains_eval)
-res_gain = list(map(p, inp)) # don't run concurently as inner function are parelllized
-
-
-# Finally, we inspect the results, by plotting the number of entries, peak locations and resulting gain maps for each peak. In the course of doing so, we identify bad pixels by either having 0 entries for a peak, or having `nan` values for the peak location.
-
-# In[7]:
-
-
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-
-gain_m = {}
-flatsff = {}
-gainoff_g = {}
-entries_g = {}
-mask_g = {}
-cell_to_preview = 12
-masks_eval_peaks = [1, 2]
-global_eval_peaks = [1]
-global_meds = {}
-
-for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- print(qm)
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- _, entries, stds, sow = gain
- gain_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- gain_mdb = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- entries_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells, 5))
- gainoff_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- mask_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells), np.uint8)
-
- gainoff_g[qm] = gainoff_db
- gain_m[qm] = gain_mdb
- entries_g[qm] = entries_db
-
- # create a mask for unregular pixels
- # first bit set if first peak has nan entries
- for pk in masks_eval_peaks:
- mask_db[(~np.isfinite(pks[...,pk])) | (np.abs(1-pks[...,pk]/np.nanmedian(pks[...,pk]) > 0.8) )] += 1
- # second bit set if entries are 0 for first peak
- mask_db[entries[...,1] == 0] += 2
- # third bit set if entries of a given adc show significant noise
- stds = []
- for ii in range(8):
- for jj in range(8):
- stds.append(np.std(entries_db[ii*16:(ii+1)*16,jj*64+2:(jj+1)*64-2,:,1], axis=(0,1)))
- avg_stds = np.median(np.array(stds), axis=0)
-
- for ii in range(8):
- for jj in range(8):
- std = np.std(entries_db[ii*16:(ii+1)*16,jj*64+2:(jj+1)*64-2,:,1], axis=(0,1))
- if np.any(std > 10*avg_stds):
- mask_db[ii*16:(ii+1)*16,jj*64:(jj+1)*64,std > avg_stds] +=4
-
- mask_g[qm] = mask_db
-
- flat = np.zeros((gains.shape[0], gains.shape[1], memory_cells, 3))
- for j in range(2,5):
- flat[...,j-2] = np.mean(entries[...,j]/np.mean(entries[...,j]))
- flat = np.mean(flat, axis=3)
- #flat_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- #for j in range(2):
- # flat_db[...,j::2] = flat
- flatsff[qm] = flat
-
- global_meds[qm] = np.nanmedian(pks[...,global_eval_peaks][np.max(mask_db, axis=2) != 0])
-
-
- fig = plt.figure(figsize=(10,10))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(5, 2),
- axes_pad=0.0,
- share_all=True,
- label_mode="L",
- cbar_location="top",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
- for j in range(5):
- im = grid[2*j].imshow(entries[...,cell_to_preview,j],
- interpolation="nearest", vmin=0, vmax=500)
- grid.cbar_axes[2*j].colorbar(im)
- im = grid[2*j+1].imshow(pks[...,cell_to_preview,j], interpolation="nearest", vmin=0, vmax=400)
- grid.cbar_axes[2*j+1].colorbar(im)
- rep_fig_path = "{}/entries_peaks_mod{}.png".format(out_folder, qm)
- with open('/tmp/Characterize_AGIPD_Gain_FlatFields_fig_mapping', 'a') as mapfile:
- mapfile.write(rep_fig_path)
- fig.savefig(rep_fig_path)
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- print(gains.shape)
- im = ax.imshow(gains[...,cell_to_preview,0], interpolation="nearest", vmin=0.85, vmax=1.15)
- fig.colorbar(im)
- rep_fig_path = "{}/gain_m_mod{}.png".format(out_folder, qm)
- with open('/tmp/Characterize_AGIPD_Gain_FlatFields_fig_mapping', 'a') as mapfile:
- mapfile.write(rep_fig_path)
- fig.savefig(rep_fig_path)
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- im = ax.imshow(gains[...,cell_to_preview,1], interpolation="nearest", vmin=-2, vmax=2)
- fig.colorbar(im)
- rep_fig_path = "{}/gain_b_mod{}.png".format(out_folder, qm)
- with open('/tmp/Characterize_AGIPD_Gain_FlatFields_fig_mapping', 'a') as mapfile:
- mapfile.write(rep_fig_path)
- fig.savefig(rep_fig_path)
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- im = ax.imshow(mask_db[...,cell_to_preview], interpolation="nearest")
- fig.colorbar(im)
- rep_fig_path = "{}/mask_mod{}.png".format(out_folder, qm)
- with open('/tmp/Characterize_AGIPD_Gain_FlatFields_fig_mapping', 'a') as mapfile:
- mapfile.write(rep_fig_path)
- fig.savefig(rep_fig_path)
-
-# Here we save the relevant constants
-
-# In[8]:
-
-
-if local_output:
- ofile = "{}/agipd_gain_store_{}_modules_{}.h5".format(out_folder, "_".join(runs), "_".join([str(m) for m in modules]))
- store_file = h5py.File(ofile, "w")
- for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- gainmap, entires, stds, sow = gain
- store_file["/{}/Gain/0/data".format(qm)] = gains[...,0]
- store_file["/{}/GainOffset/0/data".format(qm)] = gains[...,1]
- store_file["/{}/Flat/0/data".format(qm)] = flatsff[qm]
- store_file["/{}/Entries/0/data".format(qm)] = entires
- store_file["/{}/BadPixels/0/data".format(qm)] = mask_g[qm]
- store_file.close()
-
-
-# In[12]:
-
-
-if db_output:
- for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
-
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- gainmap, entires, stds, sow = gain
-
- device = getattr(Detectors.AGIPD1M1, qm)
- # gains related
- metadata = ConstantMetaData()
- gain = Constants.AGIPD.SlopesFF()
- gain.data = gains
- metadata.calibration_constant = gain
-
- # set the operating condition
- condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,
- pixels_x=512, pixels_y=128, beam_energy=None)
-
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
-
- # bad pixels
- metadata = ConstantMetaData()
- bp = Constants.AGIPD.BadPixelsFF()
- bp.data = mask_g[qm]
- metadata.calibration_constant = bp
-
- # set the operating condition
- condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,
- pixels_x=512, pixels_y=128, beam_energy=None)
-
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
-
-# ## Sanity check ##
-#
-# Finally, we perform a correction of the data used to derive the gain constants with said constants. We expect that the histograms of all modules now align.
-
-# In[45]:
-
-
-def hist_single_module(fbase, runs, sequences, il_mode, profile, limit_trains, memory_cells, inp):
- channel, offset, thresholds, relgain = inp
- gain, pks, std, gfunc = relgain
- gains, errors, chisq, valid, max_dev, stats = gfunc
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- import copy
- from XFELDetAna.util import env
- env.iprofile = profile
- sensor_size = [128, 512]
-
- block_size = [64, 64]
- def read_fun(filename, channel):
-
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
-
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size, offset, nCells=memory_cells, blockSize=block_size, gains=[0,1,2])
- offset_cor.debug()
-
- hist_calc = xcal.HistogramCalculator(sensor_size, bins=2000, range=(0, 2000), blockSize=block_size)
- hist_calc.debug()
-
- hist_calc_uncorr = xcal.HistogramCalculator(sensor_size, bins=2000, range=(0, 2000), blockSize=block_size)
- hist_calc_uncorr.debug()
-
-
- for run in runs:
- for seq in sequences:
-
- fname = fbase.format(run, run.upper(), channel, seq)
-
- d, ga, c = read_fun(fname, channel)
-
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
-
- hist_calc_uncorr.fill(d)
- #for cc in range(g.shape[2]//memory_cells):
- # td = d[...,cc*memory_cells:(cc+1)*memory_cells]
- # td = (td - relgainoff)/relgain
- # d[...,cc*memory_cells:(cc+1)*memory_cells] = td
- d = (d-gains[..., c, 1])/gains[..., c, 0]
- hist_calc.fill(d)
-
- h, e, c, _ = hist_calc.get()
- hu = hist_calc_uncorr.get()
- return h, e, c, hu[0]
-
-inp = []
-for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
-
- inp.append((i, offset_g[qm], thresholds_g[qm], res_gain[i]))
-
-p = partial(hist_single_module, fbase, runs, sequences, IL_MODE, profile, limit_trains, memory_cells)
-res = view.map_sync(p, inp)
-
-
-# In[97]:
-
-
-from iminuit import Minuit
-from iminuit.util import make_func_code, describe
-from IPython.display import HTML, display
-import tabulate
-
-# fitting
-par_ests = {}
-par_ests["mu0"] = 0
-par_ests["mu1"] = 50
-par_ests["mu2"] = 100
-par_ests["limit_mu0"] = [-25, 25]
-par_ests["limit_mu1"] = [25, 75]
-par_ests["limit_mu2"] = [75, 125]
-par_ests["s0"] = 5
-par_ests["s1"] = 5
-par_ests["s2"] = 5
-
-par_ests["throw_nan"] = False
-par_ests["pedantic"] = False
-par_ests["print_level"] = 1
-
-def gaussian3(x, mu0, s0, A0, mu1, s1, A1, mu2, s2, A2):
- return (A0/np.sqrt(2*np.pi*s0**2)*np.exp(-0.5*((x-mu0)/s0)**2) +
- A1/np.sqrt(2*np.pi*s1**2)*np.exp(-0.5*((x-mu1)/s1)**2) +
- A2/np.sqrt(2*np.pi*s2**2)*np.exp(-0.5*((x-mu2)/s2)**2))
-
-
-f_sig = describe(gaussian)[1:]
-
-class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
-
-d = []
-y_range_max = 0
-table = []
-headers = ['Module',
- 'FWHM (cor.) [ADU]', 'Separation (cor.) [$\sigma$]',
- 'FWHM (uncor.) [ADU]', 'Separation (uncor.) [$\sigma$]',
- 'Improvement'
- ]
-for i, r in enumerate(res):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- row = []
- row.append(qm)
-
- h, e, c, hu = r
-
-
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': '{}: corrected'.format(qm)
- })
-
- idx = (h > 0) & np.isfinite(h)
- h_non_zero = h[idx]
- c_non_zero = c[idx]
- par_ests["A0"] = np.float(h[np.argmin(abs(c-0))])
- par_ests["A1"] = np.float(h[np.argmin(abs(c-50))])
- par_ests["A2"] = np.float(h[np.argmin(abs(c-100))])
- wrapped = _Chi2Functor(gaussian3, c_non_zero, h_non_zero,
- np.sqrt(h_non_zero))
-
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- xt = np.arange(0, 200)
-
- yt = gaussian3(xt, m.values['mu0'], m.values['s0'], m.values['A0'],
- m.values['mu1'], m.values['s1'], m.values['A1'],
- m.values['mu2'], m.values['s2'], m.values['A2'])
-
- d.append({
- 'x': xt,
- 'y': yt,
- 'label': '{}: corrected (fit)'.format(qm)
- })
-
-
- d.append({
- 'x': c,
- 'y': hu,
- 'drawstyle': 'steps-mid',
- 'label': '{}: uncorrected'.format(qm)
- })
-
- row += [m.values['s1']*2.35, (m.values['mu1']-m.values['mu0'])/m.values['s1']]
-
-
- idx = (hu > 0) & np.isfinite(hu)
- h_non_zero = hu[idx]
- c_non_zero = c[idx]
- wrapped = _Chi2Functor(gaussian3, c_non_zero, h_non_zero,
- np.sqrt(h_non_zero))
-
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- xt = np.arange(0, 200)
-
- yt = gaussian3(xt, m.values['mu0'], m.values['s0'], m.values['A0'],
- m.values['mu1'], m.values['s1'], m.values['A1'],
- m.values['mu2'], m.values['s2'], m.values['A2'])
-
- d.append({
- 'x': xt,
- 'y': yt,
- 'label': '{}: uncorrected (fit)'.format(qm)
- })
-
- row += [m.values['s1']*2.35, (m.values['mu1']-m.values['mu0'])/m.values['s1']]
-
- row.append("{:0.2f} %".format(100*(row[3]/row[1]-1)))
-
- y_range_max = max(y_range_max, np.max(h[c>25])*1.5)
-
- # output table
- table.append(row)
-
-fig = xana.simplePlot(d, y_log=False, figsize="2col",
- aspect=2,
- x_range=(0, 200),
- legend='top-right-frame',
- y_range=(0, y_range_max),
- x_label='Energy (ADU)',
- y_label='Counts')
-
-display(HTML(tabulate.tabulate(table, tablefmt='html', headers=headers,
- numalign="right", floatfmt="0.2f")))
-
-
-# In[ ]:
-
-
-
-
diff --git a/AGIPD/Chracterize_AGIPD_Gain_PC.py b/AGIPD/Chracterize_AGIPD_Gain_PC.py
deleted file mode 100644
index 92d31441838517a6fde660605dc4a139b2d4dc0d..0000000000000000000000000000000000000000
--- a/AGIPD/Chracterize_AGIPD_Gain_PC.py
+++ /dev/null
@@ -1,1007 +0,0 @@
-import sys
-# coding: utf-8
-
-# # Characterize AGIPD Pulse Capacitor Data #
-#
-# The following code characterizes AGIPD gain via data take with the pulse capacitor source (PCS). The PCS allows scanning through the high and medium gains of AGIPD, by subsequently intecreasing the number of charge pulses from a on-ASIC capicitor, thus increasing the charge a pixel sees in a given integration time.
-#
-# Because induced charge does not originate from X-rays on the sensor, the gains evaluated here will later need to be rescaled with gains deduced from X-ray data.
-#
-# PCS data is organized into multiple runs, as the on-ASIC current source cannot supply all pixels of a given module with charge at the same time. Hence, only certain pixel rows will have seen charge for a given image. These rows then first need to be combined into single module images again.
-#
-# We then use a K-means clustering algorithm to identify components in the resulting per-pixel data series, matching to three general regions:
-#
-# * a high gain slope
-# * a transition region, where gain switching occurs
-# * a medium gain slope.
-#
-# The same regions are present in the gain-bit data and are used to deduce the switching threshold.
-#
-# The resulting slopes are then fitted with a linear function and a combination of a linear and exponential decay function to determine the relative gains of the pixels with respect to the module. Additionally, we deduce masks for bad pixels form the data.
-
-# In[1]:
-
-
-# imports, usually no need to change anything here
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-# get_ipython().magic('matplotlib inline')
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-profile = str(sys.argv[-1])
-view = Client(profile=profile)[:]
-view.use_dill()
-
-from functools import partial
-import XFELDetAna.xfelpycaltools as xcal
-import XFELDetAna.xfelpyanatools as xana
-import warnings
-warnings.filterwarnings('ignore')
-
-from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
-
-IL_MODE = bool(sys.argv[1])
-IL_MODE = False
-
-# the following lines need to be adapted to the data used for analysis
-maxcells = int(sys.argv[2])
-cells = int(sys.argv[3])
-path_temp = str(sys.argv[4])
-image_name_temp = 'RAW-R{:04d}-AGIPD{:02d}-S{:05d}.h5'
-modules = [int(s) for s in sys.argv[5].split(',')]
-out_folder = str(sys.argv[6])
-runs = [int(s) for s in sys.argv[7].split(',')]
-seqs = int(sys.argv[8])
-
-local_output = bool(sys.argv[9])
-db_output = bool(sys.argv[10])
-bias_voltage = int(sys.argv[11])
-cal_db_interface = str(sys.argv[12])
-
-fit_hook = True
-local_output = True
-db_output = False
-
-print("Parameters are:")
-print("Memory cells: {}/{}".format(cells, maxcells))
-print("Runs: {}".format(runs))
-print("Modules: {}".format(modules))
-print("Sequences: {}".format(seqs))
-print("Interlaced mode: {}".format(IL_MODE))
-
-
-# ## Read in data an merge ##
-#
-# The folling function will read in the data and merge the rows which have charge injected for a given run into a single charge injection dataset per module. The injected charge will increase for each image.
-
-# In[2]:
-
-
-run = runs[0]
-bursts_per_file = []
-channel = 0
-for seq in range(seqs):
- fname = os.path.join(path_temp.format(run),
- image_name_temp.format(run, channel, seq))
- print('Reading ',fname)
- f = h5py.File(fname, 'r', driver='core')
-
- count = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- f.close()
- bursts_per_file.append(np.count_nonzero(count))
-bursts_per_file = np.array(bursts_per_file)
-print("Bursts per sequence file are: {}".format(bursts_per_file))
-
-
-# In[3]:
-
-
-def read_and_merge_module_data(cells, path_temp, image_name_temp,
- runs, seqs, bursts_per_file, il_mode, channel):
- import h5py
- import numpy as np
- import os
-
- #bursts_per_file = np.hstack([0, bursts_per_file])
- bursts_total = np.sum(bursts_per_file)
-
- cfac = 2 if il_mode else 1
- def read_raw_data_file(fname, channel, cells = cells, cells_tot = cells, bursts = 250,
- skip_first_burst = True, first_burst_length = cells):
- f = h5py.File(fname, 'r', driver='core')
- #print('Reading ',fname)
- image_path_temp = 'INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data'
- cellID_path_temp = 'INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId'
- count = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- print(first_index, last_index)
- data = f[image_path_temp.format(channel)][first_index:last_index,...][()]
-
- cellID_all = f[cellID_path_temp.format(channel)][first_index:last_index,...][()]
- f.close()
-
- #bursts = int(data.shape[0]/adcells)
- print('Bursts: ', bursts)
- analog = np.zeros((bursts - skip_first_burst, cells//cfac, 128, 512))
- digital = np.zeros((bursts - skip_first_burst, cells//cfac, 128, 512))
- cellID = np.zeros(( (bursts - skip_first_burst) * cells))
- offset = skip_first_burst * first_burst_length
-
- for b in range(min(bursts, data.shape[0]//cells-1) - skip_first_burst-1):
- try:
-
- analog[b, : cells//cfac, ...] = np.swapaxes(data[b * cells_tot + offset : b * cells_tot + cells + offset : cfac,
- 0, ...], -1, -2)
- digital[b, : cells//cfac, ...] = np.swapaxes(data[b * cells_tot + cfac - 1 + skip_first_burst * first_burst_length :
- b * cells_tot + cells + cfac - 1 + offset :cfac, cfac%2, ...], -1, -2)
-
- cellID[ b * cells : (b + 1) * cells] = cellID_all[b * cells_tot + offset : b * cells_tot + cells + offset].flatten()
- except:
- print(b * cells_tot + offset, b * cells_tot + cells + offset)
- print(b, offset, cells, data.shape[0]//cells)
- raise AttributeError("Foo")
- return {'analog': analog, 'digital': digital, 'cellID': cellID}
-
-
- pc_data = {'analog': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'digital': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'cellID': np.zeros(((bursts_total) * cells))
- }
- pc_data_merged = {'analog': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'digital': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'cellID': np.zeros(((bursts_total) * cells))
- }
-
- for run_idx, run in enumerate(runs):
- #Read files in
- last_burst = 0
- for seq in range(seqs):
- fname = os.path.join(path_temp.format(run),
- image_name_temp.format(run, channel, seq))
- if seq == 0:
- skip_first_burst = True
- else:
- skip_first_burst = False
- bursts = bursts_per_file[seq]
-
- try:
- aa = read_raw_data_file(fname, channel, bursts = bursts,
- skip_first_burst = skip_first_burst,
- first_burst_length = cells)
- pc_data['analog'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = aa['analog']
- pc_data['digital'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = aa['digital']
- pc_data['cellID'][last_burst * cells : (last_burst+bursts_per_file[seq]-skip_first_burst) * cells, ...] = aa['cellID']
-
- except Exception as e:
- print(e)
- pc_data['analog'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = 0
- pc_data['digital'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = 0
- pc_data['cellID'][last_burst * cells : (last_burst+bursts_per_file[seq]-skip_first_burst) * cells, ...] = 0
- finally:
- last_burst += bursts_per_file[seq]-skip_first_burst
- # Copy injected rows
- for row_i in range(8):
- try:
- pc_data_merged['analog'][:,:,row_i * 8 + (7 - run_idx),:] = pc_data['analog'][:bursts_total,:cells//cfac,row_i * 8 + (7 - run_idx),:]
- pc_data_merged['analog'][:,:,64 + row_i * 8 + run_idx ,:] = pc_data['analog'][:bursts_total,:cells//cfac, 64 + row_i * 8 + run_idx,:]
- pc_data_merged['digital'][:,:,row_i * 8 + (7 - run_idx),:] = pc_data['digital'][:bursts_total,:cells//cfac,row_i * 8 + (7 - run_idx),:]
- pc_data_merged['digital'][:,:,64 + row_i * 8 + run_idx ,:] = pc_data['digital'][:bursts_total,:cells//cfac, 64 + row_i * 8 + run_idx,:]
- except:
- pass
- #Check cellIDs
- #Copy cellIDs of first run
- if run_idx == 0:
- pc_data_merged['cellID'][...] = pc_data['cellID'][...]
- #Check cellIDs of all the other runs
- else:
- print('cellID difference:{}'.format(np.sum(pc_data_merged['cellID']-pc_data['cellID'])))
- return pc_data_merged['analog'], pc_data_merged['digital'], pc_data_merged['cellID']
-
-p = partial(read_and_merge_module_data, maxcells, path_temp, image_name_temp,
- runs, seqs, bursts_per_file, IL_MODE)
-# chunk this a bit, so that we don't overuse available memory
-res = list(map(p, modules))
-
-
-
-# ## Slope clustering and Fitting ##
-#
-# The following two cells contain the actual algorithm logic as well as a preview of a single pixel and memory cells visualizing the data and the concepts.
-#
-# We start out with calculating an estimate of the slope in proximity of a given data value. This is done by calculating the slopes of a given value with 15 neighbours and averaging the result. Values are then clustered by these slopes into three regions via a K-means algorithm.
-#
-# * for the first region a linear function is fitted to the data, determining the gain slope and offset for the high gain mode.
-#
-# $$y = mx + b$$
-#
-# * for the second and third region a composite function of the form:
-#
-# $$y = A*e^{-(x-O)/C}+mx+b$$
-#
-# is fitted, covering both the transition region and the medium gain slope.
-
-# In[85]:
-
-
-from sklearn.cluster import KMeans
-from iminuit import Minuit
-from iminuit.util import make_func_code, describe
-
-def calc_m_cluster(x, y):
- scan_range = 15
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
-
- k = KMeans(n_clusters=3, n_jobs=-2)
- k.fit(m.reshape(-1, 1))
- ms = []
- for lbl in np.unique(k.labels_):
- xl = x[k.labels_ == lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[k.labels_ == lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
- return ms, k.labels_, k.cluster_centers_
-
-def rolling_window(a, window):
- shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
- strides = a.strides + (a.strides[-1],)
- return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
-
-
-def calc_m_cluster2(x, y, r1=5, r2=0, r3=1.5):
- scan_range = 15
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
- mm = np.zeros_like(m)
- mm[...] = np.nan
- m[scan_range//2:-scan_range//2+1] = np.mean(rolling_window(m, scan_range),-1)
- reg1 = m > r1
- reg2 = m < r2
- reg3 = (m > r2) & (m < r3)
- reg4 = ~(reg1 | reg2 | reg3)
- labels = [reg1, reg2, reg3, reg4]
- regions = np.zeros_like(x, np.uint8)
- for r, lbl in enumerate(labels):
- regions[lbl] = r
- scan_range = 30
- mregions = np.round(np.mean(rolling_window(regions, scan_range),-1))
- regions[...] = np.nan
- regions[scan_range//2:-scan_range//2+1] = mregions
-
-
- labels = [regions == 0, regions == 1, regions == 2, regions == 3]
-
- idx = np.arange(x.size)
- maxlbl = x.size-1
- for i in range(0, len(labels)-1):
- nidx = labels[i+1]
- if np.any(nidx):
- maxlbl = np.max(idx[nidx])
- cidx = idx > maxlbl
- if np.any(cidx):
- labels[i][cidx] = False
-
- ms = []
- for lbl in labels:
- xl = x[lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
-
- return ms, labels, None
-
-def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
-def lin_fun(x, m, b):
- return m*x+b
-
-def hook_fun(x, a, c, o, m, b):
- return a*np.exp(-(x-o)/c)+m*x+b
-
-
-# In[108]:
-
-
-test_pixels = []
-for i in range(250,254):
- for j in range(60,64):
- test_pixels.append((j,i))
-test_cells = [4]
-
-for mod, r in enumerate(res):
- dig, ana, cellId = r
- d = []
- d2 = []
- d3 = []
- for pix in test_pixels:
- for cell in test_cells:
- color = np.random.rand(3,1)
-
- x = np.arange(dig.shape[0])
- y = dig[:,cell, pix[0], pix[1]]
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- ms, labels, centers = calc_m_cluster2(x, y)
- bound = None
- bound_m = None
- markers = ['o','.','x','v']
- colors = ['b', 'r', 'g', 'k']
- for i, lbl in enumerate(labels):
- d.append({'x': x[lbl],
- 'y': y[lbl],
- 'marker': markers[i],
- 'color': colors[i],
- 'linewidth': 0
- })
- #if ms[i] < 0: # slope separating two regions
- # bound = np.min(x[lbl])
- # bound_m = ms[i]
- bound = np.min(x[labels[1]])
- bound_m = ms[1]
- if bound is None or bound < 20 and False:
- ya = ana[:,cell, pix[0], pix[1]][vidx]
- msa, labels, centers = calc_m_cluster2(x, ya, 25, -10, 25)
- if np.count_nonzero(labels[0]) > 0:
- bound = np.min(x[labels[0]])
- bound_m = ms[3]
- else:
- avg_g = np.nanmean(ya)
- bound = np.max(x[y < avg_g])
- bound_m = ms[3]
-
- #print(bound)
- # fit linear slope
- xl = x[(x<bound)]
- yl = y[(x<bound)]
- parms = {'m': bound_m, 'b': np.min(yl)}
- fitted = fit_data(lin_fun, xl, yl, np.sqrt(yl), parms)
-
- yf = lin_fun(xl, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((yl-yf)/yl))
-
- d3.append({'x': xl,
- 'y': yf,
- 'color': 'k',
- 'linewidth': 1,
- 'y2': (yf-yl)/np.sqrt(yl)
- })
-
- # fit hook slope
- if fit_hook:
- idx = (x >= bound) & (y > 0) & np.isfinite(x) & np.isfinite(y)
- xh = x[idx]
- yh = y[idx]
- parms = {'m': bound_m/10, 'b': np.min(yh[yh > 0]), 'a': np.max(yh), 'c': 0.5, 'o': bound-1}
- fitted = fit_data(hook_fun, xh, yh, np.sqrt(yh), parms)
- yf = hook_fun(xh, fitted['a'], fitted['c'], fitted['o'], fitted['m'], fitted['b'])
- max_devh = np.max(np.abs((yh-yf)/yh))
- #print(fitted)
- d3.append({'x': xh,
- 'y': yf,
- 'color': 'red',
- 'linewidth': 1,
- 'y2': (yf-yh)/np.sqrt(yh)
- })
-
- x = np.arange(ana.shape[0])
- y = ana[:,cell, pix[0], pix[1]]
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- #ms, labels, centers = calc_m_cluster2(x, y, 25, -10, 25)
- threshold = (np.mean(y[labels[0]])+np.mean(y[labels[2]]))/2
-
- for i, lbl in enumerate(labels):
-
- d2.append({'x': x[lbl],
- 'y': y[lbl],
- 'marker': markers[i],
- 'color': colors[i],
- 'lw': None
-
- })
-
- d2.append({'x': np.array([x[0], x[-1]]),
- 'y': np.ones(2)*threshold,
-
- 'color': 'k',
- 'lw': 1
-
- })
-
- #threshold = (np.min(y[x<bound]) + np.max(y[x>=bound]))/2
-
-
- fig = xana.simplePlot(d, y_label="PC pixel signal (ADU)", figsize='2col', aspect=2,
- x_label="step #")
- fig.savefig("{}/module_{}_pixel_plot.png".format(out_folder, modules[mod]))
-
- fig = xana.simplePlot(d2, y_label="PC gain signal (ADU)", figsize='2col', aspect=2,
- x_label="step #")
- fig.savefig("{}/module_{}_pixel_plot_gain.png".format(out_folder, modules[mod]))
-
- fig = xana.simplePlot(d3, secondpanel=True, y_label="PC signal (ADU)", figsize='2col', aspect=2,
- x_label="step #", y2_label="Residuals ($\sigma$)", y2_range=(-5,5))
- fig.savefig("{}/module_{}_pixel_plot_fits.png".format(out_folder, modules[mod]))
-
-
-# In[115]:
-
-
-test_pixels = []
-for i in range(96,128):
- for j in range(32,64):
- test_pixels.append((j,i))
-test_cells = [4]
-
-for mod, r in enumerate(res):
- dig, ana, cellId = r
- d = []
- d2 = []
- d3 = []
- for pix in test_pixels:
- for cell in test_cells:
- color = np.random.rand(3,1)
-
- x = np.arange(dig.shape[0])
- y = dig[:,cell, pix[0], pix[1]]
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- ms, labels, centers = calc_m_cluster2(x, y)
- bound = None
- bound_m = None
- markers = ['o','.','x','v']
- colors = ['b', 'r', 'g', 'k']
- for i, lbl in enumerate(labels):
- d.append({'x': x[lbl],
- 'y': y[lbl],
- 'marker': markers[i],
- 'color': colors[i],
- 'linewidth': 0
- })
- #if ms[i] < 0: # slope separating two regions
- # bound = np.min(x[lbl])
- # bound_m = ms[i]
- bound = np.min(x[labels[1]])
- bound_m = ms[1]
-
- # fit linear slope
- xl = x[(x<bound)]
- yl = y[(x<bound)]
- parms = {'m': bound_m, 'b': np.min(yl)}
- fitted = fit_data(lin_fun, xl, yl, np.sqrt(yl), parms)
-
- yf = lin_fun(xl, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((yl-yf)/yl))
-
- xtt = np.arange(ana.shape[0])
- ytt = ana[:,cell, pix[0], pix[1]]
-
- vidx = (ytt > 1000) & np.isfinite(ytt)
- xtt = xtt[vidx]
- ytt = ytt[vidx]
-
- #ms, labels, centers = calc_m_cluster2(x, y, 25, -10, 25)
- threshold = (np.mean(ytt[labels[0]])+np.mean(ytt[labels[2]]))/2
-
- if threshold > 10000 or threshold < 4000:
- d3.append({'x': xl,
- 'y': yf,
- 'color': 'k',
- 'linewidth': 1,
- 'y2': (yf-yl)/np.sqrt(yl)
- })
-
- # fit hook slope
- if fit_hook:
- idx = (x >= bound) & (y > 0) & np.isfinite(x) & np.isfinite(y)
- xh = x[idx]
- yh = y[idx]
- parms = {'m': bound_m/10, 'b': np.min(yh[yh > 0]), 'a': np.max(yh), 'c': 0.5, 'o': bound-1}
- fitted = fit_data(hook_fun, xh, yh, np.sqrt(yh), parms)
- yf = hook_fun(xh, fitted['a'], fitted['c'], fitted['o'], fitted['m'], fitted['b'])
- max_devh = np.max(np.abs((yh-yf)/yh))
- #print(fitted)
- if threshold > 10000 or threshold < 4000:
- d3.append({'x': xh,
- 'y': yf,
- 'color': 'red',
- 'linewidth': 1,
- 'y2': (yf-yh)/np.sqrt(yh)
- })
-
-
- if threshold > 10000 or threshold < 4000:
- for i, lbl in enumerate(labels):
-
- d2.append({'x': xtt[lbl],
- 'y': ytt[lbl],
- 'marker': markers[i],
- 'color': colors[i],
- 'lw': None
-
- })
-
- d2.append({'x': np.array([xtt[0], xtt[-1]]),
- 'y': np.ones(2)*threshold,
-
- 'color': 'k',
- 'lw': 1
-
- })
-
- #threshold = (np.min(y[x<bound]) + np.max(y[x>=bound]))/2
-
-
- fig = xana.simplePlot(d, y_label="PC pixel signal (ADU)", figsize='2col', aspect=2,
- x_label="step #")
- fig.savefig("{}/module_{}_pixel_plot_fail.png".format(out_folder, modules[mod]))
-
- fig = xana.simplePlot(d2, y_label="PC gain signal (ADU)", figsize='2col', aspect=2,
- x_label="step #")
- fig.savefig("{}/module_{}_pixel_plot_gain_fail.png".format(out_folder, modules[mod]))
-
- fig = xana.simplePlot(d3, secondpanel=True, y_label="PC signal (ADU)", figsize='2col', aspect=2,
- x_label="step #", y2_label="Residuals ($\sigma$)", y2_range=(-5,5))
- fig.savefig("{}/module_{}_pixel_plot_fits_fail.png".format(out_folder, modules[mod]))
-
-
-# Here we perform the calculations in column-parallel for all modules
-
-# In[110]:
-
-
-def calibrate_single_row(cells, fit_hook, inp):
-
- from sklearn.cluster import KMeans
- from iminuit import Minuit
- from iminuit.util import make_func_code, describe
- import numpy as np
-
- yrd, yra = inp
-
- def rolling_window(a, window):
- shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
- strides = a.strides + (a.strides[-1],)
- return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
-
-
- def calc_m_cluster2(x, y, r1=5, r2=0, r3=1.5):
- scan_range = 15
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
- mm = np.zeros_like(m)
- mm[...] = np.nan
- m[scan_range//2:-scan_range//2+1] = np.mean(rolling_window(m, scan_range),-1)
- reg1 = m > r1
- reg2 = m < r2
- reg3 = (m > r2) & (m < r3)
- reg4 = ~(reg1 | reg2 | reg3)
- labels = [reg1, reg2, reg3, reg4]
- regions = np.zeros_like(x, np.uint8)
- for r, lbl in enumerate(labels):
- regions[lbl] = r
- scan_range = 30
- mregions = np.round(np.mean(rolling_window(regions, scan_range),-1))
- regions[...] = np.nan
- regions[scan_range//2:-scan_range//2+1] = mregions
-
-
- labels = [regions == 0, regions == 1, regions == 2, regions == 3]
-
- idx = np.arange(x.size)
- maxlbl = x.size-1
- for i in range(0, len(labels)-1):
- nidx = labels[i+1]
- if np.any(nidx):
- maxlbl = np.max(idx[nidx])
- cidx = idx > maxlbl
- if np.any(cidx):
- labels[i][cidx] = False
-
- ms = []
- for lbl in labels:
- xl = x[lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
-
- return ms, labels, None
-
- def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
- def lin_fun(x, m, b):
- return m*x+b
-
- def hook_fun(x, a, c, o, m, b):
- return a*np.exp(-(x-o)/c)+m*x+b
-
- # linear slope
- ml = np.zeros(yrd.shape[1:])
- bl = np.zeros(yrd.shape[1:])
- devl = np.zeros(yrd.shape[1:])
- ml[...] = np.nan
- bl[...] = np.nan
- devl[...] = np.nan
-
- #hook function
- mh = np.zeros(yrd.shape[1:])
- bh = np.zeros(yrd.shape[1:])
- ch = np.zeros(yrd.shape[1:])
- oh = np.zeros(yrd.shape[1:])
- ah = np.zeros(yrd.shape[1:])
- devh = np.zeros(yrd.shape[1:])
- mh[...] = np.nan
- bh[...] = np.nan
- ch[...] = np.nan
- oh[...] = np.nan
- ah[...] = np.nan
- devh[...] = np.nan
-
- # threshold
- thresh = np.zeros(list(yrd.shape[1:])+[3,])
- thresh[...] = np.nan
- failures = []
- for cell in range(cells):
- for col in range(yrd.shape[-1]):
- try:
- y = yrd[:,cell, col]
- x = np.arange(y.shape[0])
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- ms, labels, centers = calc_m_cluster2(x, y)
-
- bound = np.min(x[labels[1]])
- bound_m = ms[1]
-
- # fit linear slope
- xl = x[x<bound]
- yl = y[x<bound]
- parms = {'m': bound_m, 'b': np.min(yl)}
- fitted = fit_data(lin_fun, xl, yl, np.sqrt(yl), parms)
- yf = lin_fun(xl, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((yl-yf)/yl))
- ml[cell,col] = fitted['m']
- bl[cell,col] = fitted['b']
- devl[cell,col] = max_devl
-
- # fit hook slope
- if fit_hook:
- idx = (x >= bound) & (y > 0) & np.isfinite(x) & np.isfinite(y)
- xh = x[idx]
- yh = y[idx]
- parms = {'m': bound_m/10, 'b': np.min(yh[yh > 0]), 'a': np.max(yh), 'c': 0.5, 'o': bound-1}
- fitted = fit_data(hook_fun, xh, yh, np.sqrt(yh), parms)
- yf = hook_fun(xh, fitted['a'], fitted['c'], fitted['o'], fitted['m'], fitted['b'])
- max_devh = np.max(np.abs((yh-yf)/yh))
-
- mh[cell,col] = fitted['m']
- bh[cell,col] = fitted['b']
- ah[cell,col] = fitted['a']
- oh[cell,col] = fitted['o']
- ch[cell,col] = fitted['c']
- devh[cell,col] = max_devh
-
- y = yra[:,cell, col]
- x = np.arange(y.shape[0])
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- threshold = (np.mean(y[labels[0]])+np.mean(y[labels[2]]))/2
- thresh[cell,col,0] = threshold
- thresh[cell,col,1] = np.mean(y[labels[0]])
- thresh[cell,col,2] = np.mean(y[labels[2]])
- except Exception as e:
- failures.append((cell, col, str(e)))
- return thresh, (ml, bl, devl), (mh, bh, ah, oh, ch, devh), failures
-
-fres = {}
-failures = []
-for i, r in enumerate(res):
- ii = modules[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- dig, ana, cellId = r
- inp = []
- for j in range(dig.shape[2]):
- inp.append((dig[:,:,j,:], ana[:,:,j,:]))
-
- p = partial(calibrate_single_row, cells, fit_hook)
- frs = view.map_sync(p, inp)
-
- # linear slope
- ml = np.zeros(dig.shape[1:])
- bl = np.zeros(dig.shape[1:])
- devl = np.zeros(dig.shape[1:])
-
- #hook function
- mh = np.zeros(dig.shape[1:])
- bh = np.zeros(dig.shape[1:])
- ch = np.zeros(dig.shape[1:])
- oh = np.zeros(dig.shape[1:])
- ah = np.zeros(dig.shape[1:])
- devh = np.zeros(dig.shape[1:])
-
- # threshold
- thresh = np.zeros(list(dig.shape[1:]))
- thresh_bounds = np.zeros(list(dig.shape[1:])+[2,])
-
- for j, fr in enumerate(frs):
- threshr, lin, hook, fails = fr
- mlr, blr, devlr = lin
- mhr, bhr, ahr, ohr, chro, devhr = hook
- failures.append(fails)
-
- ml[:,j,:] = mlr
- bl[:,j,:] = blr
- devl[:,j,:] = devlr
-
- mh[:,j,:] = mhr
- bh[:,j,:] = bhr
- oh[:,j,:] = ohr
- ch[:,j,:] = chro
- ah[:,j,:] = ahr
- devh[:,j,:] = devhr
-
- thresh[:,j,...] = threshr[...,0]
- thresh_bounds[:,j,...] = threshr[...,1:]
-
- fres[qm] = {'ml': ml,
- 'bl': bl,
- 'devl': devl,
- 'tresh': thresh,
- 'tresh_bounds': thresh_bounds}
- if fit_hook:
- fres[qm].update({
- 'mh': mh,
- 'bh': bh,
- 'oh': oh,
- 'ch': ch,
- 'ah': ah,
- 'devh': devh,
- })
-
-
-
-# Results of slope fitting from PC runs values are
-# distinguished on axis 0 by index:
-#
-# 0: linear slope - m value
-# 1: linear slope - b value
-# 2: linear slope - deviation
-# 3: hook function - m value
-# 4: hook function - b value
-# 5: hook function - o value
-# 6: hook function - c value
-# 7: hook function - a value
-# 8: hook function - deviation
-
-# In[111]:
-
-
-def slope_dict_to_arr(d):
- key_to_index = {
- "ml": 0,
- "bl": 1,
- "devl": 2,
- "mh": 3,
- "bh": 4,
- "oh": 5,
- "ch": 6,
- "ah": 7,
- "devh": 8,
- "thresh": 9
- }
- arr = np.zeros([9]+list(d["ml"].shape), np.float32)
- for key, item in d.items():
- arr[key_to_index[key],...] = item
- return arr
-
-
-# In[8]:
-
-
-if local_output:
- ofile = "{}/agipd_pc_store_{}_{}_{}.h5".format(out_folder, "_".join([str(run) for run in runs]), modules[0], modules[-1])
- store_file = h5py.File(ofile, "w")
- for qm, r in fres.items():
- for key, item in r.items():
- store_file["/{}/{}/0/data".format(qm, key)] = item
- #arr = slope_dict_to_arr(r)
- #store_file["/{}/SlopesPC/0/data".format(qm)] = arr
- store_file.close()
-
-
-# In[ ]:
-
-
-
-
-
-# In[9]:
-
-
-if db_output:
- for qm, r in fres.items():
- metadata = ConstantMetaData()
- slopespc = Constants.AGIPD.SlopesPC()
- slopespc.data = slope_dict_to_arr(r)
- metadata.calibration_constant = slopespc
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.send(cal_db_interface)
-
-
-# In[117]:
-
-
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-cell_to_preview = 4
-for module, data in fres.items():
- fig = plt.figure(figsize=(40,40))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(6 if fit_hook else 3, 2),
- axes_pad=(0.9, 0.15),
- label_mode="1",
- share_all=True,
- cbar_location="right",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
-
- mask = np.zeros(data['ml'].shape, np.uint8)
- mask[(data['tresh'][...,0] < 100) | (data['tresh'][...,0] > 8100)] += 1
- mask[(data['devl'] == 0)] += 2
- mask[(data['devl'] > 0.5)] += 4
- mask[(data['devl'] < 0) ] += 8
- mask[(~np.isfinite(data['devl']))] += 16
-
- i = 0
- for key, item in data.items():
- med = np.nanmedian(item)
- bound = 0.1
- while(np.count_nonzero((item < med-bound*med) | (item > med+bound*med))/item.size > 0.01):
- bound *=2
-
- if "bounds" in key:
- d = item[cell_to_preview,...,0]
- im = grid[i].imshow(d, interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med)
- else:
- d = item[cell_to_preview,...]
- im = grid[i].imshow(d, interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med)
- cb = grid.cbar_axes[i].colorbar(im)
-
- grid[i].text(20, 50, key, color="w", fontsize=50)
-
- i += 1
-
- im = grid[-1].imshow(mask[cell_to_preview,...], interpolation="nearest",
- vmin=0, vmax=1)
- cb = grid.cbar_axes[-1].colorbar(im)
-
- grid[-1].text(20, 50, "mask", color="w", fontsize=50)
- fig.savefig("{}/module_{}_PC.png".format(out_folder, module))
-
-
-# In[ ]:
-
-
-
-
diff --git a/AGIPD/conv_tmp.py b/AGIPD/conv_tmp.py
deleted file mode 100644
index 50e2c3d2e46aeb2b5c8573341820dd3a3c0df5e4..0000000000000000000000000000000000000000
--- a/AGIPD/conv_tmp.py
+++ /dev/null
@@ -1,851 +0,0 @@
-
-# coding: utf-8
-
-# # Gain Characterization (Flat Fields) #
-#
-# The following code characterizes the gain of the AGIPD detector from flat field data, i.e. data with X-rays of defined intensity. This data should fullfil the following requirements:
-#
-# * intensity should be such that single photon peaks are visible
-# * data for all modules should be present
-# * no shadowing should occur on any of the modules
-# * each pixel should have at minimum arround 100 events per photon peak per memory cell
-# * if central beam edges are visible, they should not be significantly more intense
-#
-# Characterization is done by a weighted average algorithm, which evaluates the peak locations for all pixels
-# and memory cells of a given module. These locations are then fitted to a linear function of the average peak
-# location in each module, such that it yield a relative gain correction.
-
-# In[1]:
-
-
-# std library imports
-from functools import partial
-import h5py
-import os
-
-# numpy and matplot lib specific
-import numpy as np
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-get_ipython().magic('matplotlib inline')
-
-# parallel processing via ipcluster
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-profile = "noDB" # SLURMHINT: profile, str
-client = Client(profile=profile)
-view = client[:]
-view.use_dill()
-
-# pyDetLib imports
-import XFELDetAna.xfelpycaltools as xcal
-import XFELDetAna.xfelpyanatools as xana
-from XFELDetAna.util import env
-env.iprofile = profile
-
-from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
-
-# usually no need to change these lines
-sensor_size = [128, 512]
-block_size = [128, 512]
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-DET_FILE_INSET = "AGIPD"
-IL_MODE = False # SLURMHINT: il_mode, bool
-IL_MODE = False
-# the following lines should be adjusted depending on data
-in_folder = "/gpfs/exfel/exp/SPB/201830/p900019/raw/" # SLURMHINT: rawpath, str
-out_folder = "/gpfs/exfel/exp/SPB/201830/p900019/proc/calibration/FF/" # SLURMHINT: out_folder, str
-# the runs the flat field data is in
-runs = [494,495] # SLURMHINT: runs, list
-
-runs = ["r{:04d}".format(r) for r in runs]
-
-local_output = False # SLURMHINT: local_output, bool
-
-db_output = True # SLURMHINT: db_output, bool
-db_input = db_output
-
-bias_voltage = 500 # SLURMHINT: bias_voltage, float
-cal_db_interface = "tcp://max-exfl015:5005" # SLURMHINT: db_host, str
-
-# change this to the offsets that should be used
-offset_store = "/gpfs/exfel/data/scratch/haufs/calibration/280218/AGIPD/agipd_offset_store_r0386_r0387_r0388.h5" # SLURMHINT: offset_store, str
-
-# cells in raw data
-max_cells = 64 # SLURMHINT: maxcells, int
-# actual memory cells: max_cells//2 if AGIPD is in interleaved mode
-memory_cells = 64 # SLURMHINT: cells, int
-# sequences to take data from
-sequences = range(1,2) # SLURMHINT: sequences, list
-# modules to characterize
-modules = range(1) # SLURMHINT: modules, list
-
-photon_energy = 9.4 # SLURMHINT: photon_energy, float
-
-limit_trains = 20
-limit_trains_eval = 200
-
-print("Parameters are:")
-print("Memory cells: {}/{}".format(memory_cells, max_cells))
-print("Runs: {}".format(runs))
-print("Modules: {}".format(modules))
-print("Sequences: {}".format(sequences))
-print("Interlaced mode: {}".format(IL_MODE))
-print("Using DB: {}".format(db_output))
-
-# these lines can usually stay as is
-fbase = "{}/{{}}/RAW-{{}}-AGIPD{{:02d}}-S{{:05d}}.h5".format(in_folder)
-gains = np.arange(3)
-cells = np.arange(max_cells)
-
-
-# For the characterization offset maps for each module are needed. In the following these are read in
-
-# In[2]:
-
-
-from dateutil import parser
-offset_g = {}
-noise_g = {}
-thresholds_g = {}
-if not db_input:
- store_file = h5py.File(offset_store, "r")
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- offset_g[qm] = np.array(store_file["{}/Offset/0/data".format(qm)])
- noise_g[qm] = np.array(store_file["{}/Noise/0/data".format(qm)])
- thresholds_g[qm] = np.array(store_file["{}/Threshold/0/data".format(qm)])
- store_file.close()
-else:
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- metadata = ConstantMetaData()
- offset = Constants.AGIPD.Offset()
- metadata.calibration_constant = offset
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- offset_g[qm] = offset.data
-
- metadata = ConstantMetaData()
- noise = Constants.AGIPD.Noise()
- metadata.calibration_constant = noise
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- noise_g[qm] = noise.data
-
- metadata = ConstantMetaData()
- thresholds = Constants.AGIPD.ThresholdsDark()
- metadata.calibration_constant = thresholds
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- thresholds_g[qm] = thresholds.data
-
-
-# ## Initial peak estimates ##
-#
-# The following parallel code will read in the flat field runs, offset correct them and then, bin data of each
-# module into histograms.
-#
-# These histograms should then be inspected for initial peak estimates for the single, double, ... photon peaks,
-# as well es estimates of the relative hights of these peaks to one and another.
-
-# In[3]:
-
-
-def hist_single_module(fbase, runs, sequences, sensor_size, memory_cells, block_size,
- il_mode, limit_trains, profile, inp):
- """ This function calculates a per-pixel histogram for a single module
-
- Runs and sequences give the data to calculate histogram from
- """
- channel, offset, thresholds = inp
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- from XFELDetAna.util import env
- env.iprofile = profile
-
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
-
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size,
- offset,
- nCells=memory_cells,
- blockSize=block_size,
- gains=[0,1,2])
- offset_cor.mapper = offset_cor._view.map_sync
- offset_cor.debug() # force non-parallel processing since outer function will run concurrently
- hist_calc = xcal.HistogramCalculator(sensor_size,
- bins=4000,
- range=(-4000, 8000),
- blockSize=block_size)
- hist_calc.mapper = hist_calc._view.map_sync
- hist_calc.debug() # force non-parallel processing since outer function will run concurrently
- for run in runs:
- for seq in sequences:
- fname = fbase.format(run, run.upper(), channel, seq)
- d, ga, c = read_fun(fname, channel)
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
- hist_calc.fill(d)
- h, e, c, _ = hist_calc.get()
- return h, e, c
-
-inp = []
-for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], thresholds_g[qm]))
-
-p = partial(hist_single_module, fbase, runs, sequences,
- sensor_size, memory_cells, block_size, IL_MODE, limit_trains, profile)
-res_uncorr = view.map_sync(p, inp)
-
-
-# We inspect the resulting histograms for the estimates. Modules should look roughly the same as no significant deviation is to be expected. Non-function modules and artifacts of single pixels may be visible in the histogram.
-
-# In[4]:
-
-
-d = []
-qms = []
-for i, r in enumerate(res_uncorr):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- qms.append(qm)
- h, e, c = r
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid'
- })
-
-fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(0, 500),
- x_label="Intensity (ADU)",
- y_label="Counts")
-
-fig.savefig("{}/FF_module_{}_peak_pos.png".format(out_folder, ",".join(qms)))
-
-
-# In[5]:
-
-
-# these should be quite stable
-peak_estimates = [0, 55, 110, 165, 220]
-peak_heights = [5e7, 5e6, 1e6, 5e5, 1e5]
-peak_sigma = [5., 5., 5., 5., 5.]
-
-
-# ## Calculate relative gain per module ##
-#
-# Using the so obtained estimates, we calculate the relative gain per module. For this we use the weighted average method implemented in pyDetLib.
-#
-# Since for current AGIPD data taking only every second memory cells sees X-rays we account for this. For technical reasons, the subsequent cell will have the same constants copied, which are irrelevant as not X-rays are to be expected in these cells.
-
-# In[6]:
-
-
-block_size = [64, 64]
-def relgain_single_module(fbase, runs, sequences, peak_estimates,
- peak_heights, peak_sigma, memory_cells, sensor_size,
- block_size, il_mode, profile, limit_trains_eval, inp):
- """ A function for calculated the relative gain of a single AGIPD module
- """
-
- # import needed inline for parallel processing
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- from XFELDetAna.util import env
- env.iprofile = profile
-
- channel, offset, thresholds, noise = inp
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size, offset, nCells=memory_cells,
- blockSize=block_size, gains=[0,1,2])
- offset_cor.mapper = offset_cor._view.map_sync
-
- rel_gain = xcal.GainMapCalculator(sensor_size,
- peak_estimates,
- peak_sigma,
- nCells=memory_cells,
- peakHeights = peak_heights,
- noiseMap=noise,
- deviationType="relative")
- rel_gain.mapper = rel_gain._view.map_sync
- for run in runs:
- for seq in sequences:
- fname = fbase.format(run, run.upper(), channel, seq)
- d, ga, c = read_fun(fname, channel)
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
- rel_gain.fill(d, cellTable=c)
-
- gain_map = rel_gain.get()
- gain_map_func = rel_gain.getUsingFunc(inverse=False)
-
- pks, stds = rel_gain.getRawPeaks()
- return gain_map, pks, stds, gain_map_func
-
-inp = []
-for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], thresholds_g[qm], noise_g[qm][...,0]))
-
-p = partial(relgain_single_module, fbase, runs, sequences,
- peak_estimates, peak_heights, peak_sigma, memory_cells,
- sensor_size, block_size, IL_MODE, profile, limit_trains_eval)
-res_gain = list(map(p, inp)) # don't run concurently as inner function are parelllized
-
-
-# Finally, we inspect the results, by plotting the number of entries, peak locations and resulting gain maps for each peak. In the course of doing so, we identify bad pixels by either having 0 entries for a peak, or having `nan` values for the peak location.
-
-# In[7]:
-
-
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-
-gain_m = {}
-flatsff = {}
-gainoff_g = {}
-entries_g = {}
-mask_g = {}
-cell_to_preview = 12
-masks_eval_peaks = [1, 2]
-global_eval_peaks = [1]
-global_meds = {}
-
-for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- print(qm)
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- _, entries, stds, sow = gain
- gain_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- gain_mdb = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- entries_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells, 5))
- gainoff_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- mask_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells), np.uint8)
-
- gainoff_g[qm] = gainoff_db
- gain_m[qm] = gain_mdb
- entries_g[qm] = entries_db
-
- # create a mask for unregular pixels
- # first bit set if first peak has nan entries
- for pk in masks_eval_peaks:
- mask_db[(~np.isfinite(pks[...,pk])) | (np.abs(1-pks[...,pk]/np.nanmedian(pks[...,pk]) > 0.8) )] += 1
- # second bit set if entries are 0 for first peak
- mask_db[entries[...,1] == 0] += 2
- # third bit set if entries of a given adc show significant noise
- stds = []
- for ii in range(8):
- for jj in range(8):
- stds.append(np.std(entries_db[ii*16:(ii+1)*16,jj*64+2:(jj+1)*64-2,:,1], axis=(0,1)))
- avg_stds = np.median(np.array(stds), axis=0)
-
- for ii in range(8):
- for jj in range(8):
- std = np.std(entries_db[ii*16:(ii+1)*16,jj*64+2:(jj+1)*64-2,:,1], axis=(0,1))
- if np.any(std > 10*avg_stds):
- mask_db[ii*16:(ii+1)*16,jj*64:(jj+1)*64,std > avg_stds] +=4
-
- mask_g[qm] = mask_db
-
- flat = np.zeros((gains.shape[0], gains.shape[1], memory_cells, 3))
- for j in range(2,5):
- flat[...,j-2] = np.mean(entries[...,j]/np.mean(entries[...,j]))
- flat = np.mean(flat, axis=3)
- #flat_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- #for j in range(2):
- # flat_db[...,j::2] = flat
- flatsff[qm] = flat
-
- global_meds[qm] = np.nanmedian(pks[...,global_eval_peaks][np.max(mask_db, axis=2) != 0])
-
-
- fig = plt.figure(figsize=(10,10))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(5, 2),
- axes_pad=0.0,
- share_all=True,
- label_mode="L",
- cbar_location="top",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
- for j in range(5):
- im = grid[2*j].imshow(entries[...,cell_to_preview,j],
- interpolation="nearest", vmin=0, vmax=500)
- grid.cbar_axes[2*j].colorbar(im)
- im = grid[2*j+1].imshow(pks[...,cell_to_preview,j], interpolation="nearest", vmin=0, vmax=400)
- grid.cbar_axes[2*j+1].colorbar(im)
- fig.savefig("{}/entries_peaks_mod{}.png".format(out_folder, qm)) # REPORT_FIG: "{}/entries_peaks_mod{}.png".format(out_folder, qm)
-
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- print(gains.shape)
- im = ax.imshow(gains[...,cell_to_preview,0], interpolation="nearest", vmin=0.85, vmax=1.15)
- fig.colorbar(im)
- fig.savefig("{}/gain_m_mod{}.png".format(out_folder, qm)) # REPORT_FIG: "{}/gain_m_mod{}.png".format(out_folder, qm)
-
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- im = ax.imshow(gains[...,cell_to_preview,1], interpolation="nearest", vmin=-2, vmax=2)
- fig.colorbar(im)
- fig.savefig("{}/gain_b_mod{}.png".format(out_folder, qm)) # REPORT_FIG: "{}/gain_b_mod{}.png".format(out_folder, qm)
-
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- im = ax.imshow(mask_db[...,cell_to_preview], interpolation="nearest")
- fig.colorbar(im)
- fig.savefig("{}/mask_mod{}.png".format(out_folder, qm)) # REPORT_FIG: "{}/mask_mod{}.png".format(out_folder, qm)
-
-
-# Here we save the relevant constants
-
-# In[8]:
-
-
-if local_output:
- ofile = "{}/agipd_gain_store_{}_modules_{}.h5".format(out_folder, "_".join(runs), "_".join([str(m) for m in modules]))
- store_file = h5py.File(ofile, "w")
- for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- gainmap, entires, stds, sow = gain
- store_file["/{}/Gain/0/data".format(qm)] = gains[...,0]
- store_file["/{}/GainOffset/0/data".format(qm)] = gains[...,1]
- store_file["/{}/Flat/0/data".format(qm)] = flatsff[qm]
- store_file["/{}/Entries/0/data".format(qm)] = entires
- store_file["/{}/BadPixels/0/data".format(qm)] = mask_g[qm]
- store_file.close()
-
-
-# In[12]:
-
-
-if db_output:
- for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
-
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- gainmap, entires, stds, sow = gain
-
- device = getattr(Detectors.AGIPD1M1, qm)
- # gains related
- metadata = ConstantMetaData()
- gain = Constants.AGIPD.SlopesFF()
- gain.data = gains
- metadata.calibration_constant = gain
-
- # set the operating condition
- condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,
- pixels_x=512, pixels_y=128, beam_energy=None)
-
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
-
- # bad pixels
- metadata = ConstantMetaData()
- bp = Constants.AGIPD.BadPixelsFF()
- bp.data = mask_g[qm]
- metadata.calibration_constant = bp
-
- # set the operating condition
- condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,
- pixels_x=512, pixels_y=128, beam_energy=None)
-
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
-
-# ## Sanity check ##
-#
-# Finally, we perform a correction of the data used to derive the gain constants with said constants. We expect that the histograms of all modules now align.
-
-# In[101]:
-
-
-sequences = [0]
-def hist_single_module(fbase, runs, sequences, il_mode, profile, limit_trains, memory_cells, inp):
- channel, offset, thresholds, relgain = inp
- gain, pks, std, gfunc = relgain
- gains, errors, chisq, valid, max_dev, stats = gfunc
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- import copy
- from XFELDetAna.util import env
- env.iprofile = profile
- sensor_size = [128, 512]
-
- block_size = [64, 64]
- def read_fun(filename, channel):
-
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- print(filename, first)
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
-
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size, offset, nCells=memory_cells, blockSize=block_size, gains=[0,1,2])
- offset_cor.debug()
-
- hist_calc = xcal.HistogramCalculator(sensor_size, bins=2000, range=(0, 2000), blockSize=block_size)
- hist_calc.debug()
-
- hist_calc_uncorr = xcal.HistogramCalculator(sensor_size, bins=2000, range=(0, 2000), blockSize=block_size)
- hist_calc_uncorr.debug()
-
-
- for run in runs:
- for seq in sequences:
-
- fname = fbase.format(run, run.upper(), channel, seq)
-
- d, ga, c = read_fun(fname, channel)
-
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
-
- hist_calc_uncorr.fill(d)
- #for cc in range(g.shape[2]//memory_cells):
- # td = d[...,cc*memory_cells:(cc+1)*memory_cells]
- # td = (td - relgainoff)/relgain
- # d[...,cc*memory_cells:(cc+1)*memory_cells] = td
- d = (d-gains[..., c, 1])/gains[..., c, 0]
- hist_calc.fill(d)
-
- h, e, c, _ = hist_calc.get()
- hu = hist_calc_uncorr.get()
- return h, e, c, hu[0]
-
-inp = []
-for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
-
- inp.append((i, offset_g[qm], thresholds_g[qm], res_gain[i]))
-
-p = partial(hist_single_module, fbase, runs, sequences, IL_MODE, profile, limit_trains, memory_cells)
-res = list(map(p, inp))
-#res = view.map_sync(p, inp)
-
-
-# In[97]:
-
-
-from iminuit import Minuit
-from iminuit.util import make_func_code, describe
-from IPython.display import HTML, display
-import tabulate
-
-# fitting
-par_ests = {}
-par_ests["mu0"] = 0
-par_ests["mu1"] = 50
-par_ests["mu2"] = 100
-par_ests["limit_mu0"] = [-25, 25]
-par_ests["limit_mu1"] = [25, 75]
-par_ests["limit_mu2"] = [75, 125]
-par_ests["s0"] = 5
-par_ests["s1"] = 5
-par_ests["s2"] = 5
-
-par_ests["throw_nan"] = False
-par_ests["pedantic"] = False
-par_ests["print_level"] = 1
-
-def gaussian3(x, mu0, s0, A0, mu1, s1, A1, mu2, s2, A2):
- return (A0/np.sqrt(2*np.pi*s0**2)*np.exp(-0.5*((x-mu0)/s0)**2) +
- A1/np.sqrt(2*np.pi*s1**2)*np.exp(-0.5*((x-mu1)/s1)**2) +
- A2/np.sqrt(2*np.pi*s2**2)*np.exp(-0.5*((x-mu2)/s2)**2))
-
-
-f_sig = describe(gaussian)[1:]
-
-class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
-
-d = []
-y_range_max = 0
-table = []
-headers = ['Module',
- 'FWHM (cor.) [ADU]', 'Separation (cor.) [$\sigma$]',
- 'FWHM (uncor.) [ADU]', 'Separation (uncor.) [$\sigma$]',
- 'Improvement'
- ]
-for i, r in enumerate(res):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- row = []
- row.append(qm)
-
- h, e, c, hu = r
-
-
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': '{}: corrected'.format(qm)
- })
-
- idx = (h > 0) & np.isfinite(h)
- h_non_zero = h[idx]
- c_non_zero = c[idx]
- par_ests["A0"] = np.float(h[np.argmin(abs(c-0))])
- par_ests["A1"] = np.float(h[np.argmin(abs(c-50))])
- par_ests["A2"] = np.float(h[np.argmin(abs(c-100))])
- wrapped = _Chi2Functor(gaussian3, c_non_zero, h_non_zero,
- np.sqrt(h_non_zero))
-
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- xt = np.arange(0, 200)
-
- yt = gaussian3(xt, m.values['mu0'], m.values['s0'], m.values['A0'],
- m.values['mu1'], m.values['s1'], m.values['A1'],
- m.values['mu2'], m.values['s2'], m.values['A2'])
-
- d.append({
- 'x': xt,
- 'y': yt,
- 'label': '{}: corrected (fit)'.format(qm)
- })
-
-
- d.append({
- 'x': c,
- 'y': hu,
- 'drawstyle': 'steps-mid',
- 'label': '{}: uncorrected'.format(qm)
- })
-
- row += [m.values['s1']*2.35, (m.values['mu1']-m.values['mu0'])/m.values['s1']]
-
-
- idx = (hu > 0) & np.isfinite(hu)
- h_non_zero = hu[idx]
- c_non_zero = c[idx]
- wrapped = _Chi2Functor(gaussian3, c_non_zero, h_non_zero,
- np.sqrt(h_non_zero))
-
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- xt = np.arange(0, 200)
-
- yt = gaussian3(xt, m.values['mu0'], m.values['s0'], m.values['A0'],
- m.values['mu1'], m.values['s1'], m.values['A1'],
- m.values['mu2'], m.values['s2'], m.values['A2'])
-
- d.append({
- 'x': xt,
- 'y': yt,
- 'label': '{}: uncorrected (fit)'.format(qm)
- })
-
- row += [m.values['s1']*2.35, (m.values['mu1']-m.values['mu0'])/m.values['s1']]
-
- row.append("{:0.2f} %".format(100*(row[3]/row[1]-1)))
-
- y_range_max = max(y_range_max, np.max(h[c>25])*1.5)
-
- # output table
- table.append(row)
-
-fig = xana.simplePlot(d, y_log=False, figsize="2col",
- aspect=2,
- x_range=(0, 200),
- legend='top-right-frame',
- y_range=(0, y_range_max),
- x_label='Energy (ADU)',
- y_label='Counts')
-
-display(HTML(tabulate.tabulate(table, tablefmt='html', headers=headers,
- numalign="right", floatfmt="0.2f")))
-
-
-# In[124]:
-
-
-filename = "/gpfs/exfel/exp/SPB/201830/p900019/raw/r0494/RAW-R0494-AGIPD00-S00000.h5"
-infile = h5py.File(filename, "r", driver="core")
-first = infile["/INDEX/trainId"][()].astype(np.uint64)
-
-
-# In[125]:
-
-
-for i in range(first.size):
- print(i, first[i])
-
-
-# In[ ]:
-
-
-
-
diff --git a/AGIPD/conv_tmp.py.py b/AGIPD/conv_tmp.py.py
deleted file mode 100644
index e003cf9b185c18debf0ef568ed867f3fb8549f0e..0000000000000000000000000000000000000000
--- a/AGIPD/conv_tmp.py.py
+++ /dev/null
@@ -1,701 +0,0 @@
-
-# coding: utf-8
-
-# # Characterize AGIPD Pulse Capacitor Data #
-#
-# The following code characterizes AGIPD gain via data take with the pulse capacitor source (PCS). The PCS allows scanning through the high and medium gains of AGIPD, by subsequently intecreasing the number of charge pulses from a on-ASIC capicitor, thus increasing the charge a pixel sees in a given integration time.
-#
-# Because induced charge does not originate from X-rays on the sensor, the gains evaluated here will later need to be rescaled with gains deduced from X-ray data.
-#
-# PCS data is organized into multiple runs, as the on-ASIC current source cannot supply all pixels of a given module with charge at the same time. Hence, only certain pixel rows will have seen charge for a given image. These rows then first need to be combined into single module images again.
-#
-# We then use a K-means clustering algorithm to identify components in the resulting per-pixel data series, matching to three general regions:
-#
-# * a high gain slope
-# * a transition region, where gain switching occurs
-# * a medium gain slope.
-#
-# The same regions are present in the gain-bit data and are used to deduce the switching threshold.
-#
-# The resulting slopes are then fitted with a linear function and a combination of a linear and exponential decay function to determine the relative gains of the pixels with respect to the module. Additionally, we deduce masks for bad pixels form the data.
-
-# In[1]:
-
-
-# imports, usually no need to change anything here
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-get_ipython().magic('matplotlib inline')
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-view = Client(profile="noDB")[:]
-view.use_dill()
-
-from functools import partial
-import XFELDetAna.xfelpycaltools as xcal
-import XFELDetAna.xfelpyanatools as xana
-import warnings
-warnings.filterwarnings('ignore')
-
-IL_MODE = False # SLURMHINT: IL_MODE
-
-# the following lines need to be adapted to the data used for analysis
-maxcells = 74 # SLURMHINT: maxcells, int
-cells = 74 # SLURMHINT: cells, int
-path_temp = '/gpfs/exfel/exp/SPB/201830/p900019/raw/r{:04d}/' # SLURMHINT: path_temp, str
-image_name_temp = 'RAW-R{:04d}-AGIPD{:02d}-S{:05d}.h5'
-modules = range(0,4) # SLURMHINT: modules, list
-out_folder = "/gpfs/exfel/data/scratch/haufs/calibration/280218/AGIPD/" # SLURMHINT: out_folder, str
-runs = list(range(94, 101))+[102] # SLURMHINT: runs, list
-seqs = 2 # SLURMHINT: seq, int
-
-
-
-# ## Read in data an merge ##
-#
-# The folling function will read in the data and merge the rows which have charge injected for a given run into a single charge injection dataset per module. The injected charge will increase for each image.
-
-# In[2]:
-
-
-run = runs[0]
-bursts_per_file = []
-channel = 0
-for seq in range(seqs):
- fname = os.path.join(path_temp.format(run),
- image_name_temp.format(run, channel, seq))
- f = h5py.File(fname, 'r', driver='core')
- #print('Reading ',fname)
- count = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- f.close()
- bursts_per_file.append(np.count_nonzero(count))
-bursts_per_file = np.array(bursts_per_file)
-print("Bursts per sequence file are: {}".format(bursts_per_file))
-
-
-# In[3]:
-
-
-def read_and_merge_module_data(cells, path_temp, image_name_temp,
- runs, seqs, bursts_per_file, il_mode, channel):
- import h5py
- import numpy as np
- import os
-
- #bursts_per_file = np.hstack([0, bursts_per_file])
- bursts_total = np.sum(bursts_per_file)
-
- cfac = 2 if il_mode else 1
- def read_raw_data_file(fname, channel, cells = cells, cells_tot = cells, bursts = 250,
- skip_first_burst = True, first_burst_length = cells):
- f = h5py.File(fname, 'r', driver='core')
- #print('Reading ',fname)
- image_path_temp = 'INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data'
- cellID_path_temp = 'INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId'
- count = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- print(first_index, last_index)
- data = f[image_path_temp.format(channel)][first_index:last_index,...][()]
-
- cellID_all = f[cellID_path_temp.format(channel)][first_index:last_index,...][()]
- f.close()
-
- #bursts = int(data.shape[0]/adcells)
- print('Bursts: ', bursts)
- analog = np.zeros((bursts - skip_first_burst, cells//cfac, 128, 512))
- digital = np.zeros((bursts - skip_first_burst, cells//cfac, 128, 512))
- cellID = np.zeros(( (bursts - skip_first_burst) * cells))
- offset = skip_first_burst * first_burst_length
-
- for b in range(min(bursts, data.shape[0]//cells-1) - skip_first_burst-1):
- try:
-
- analog[b, : cells//cfac, ...] = np.swapaxes(data[b * cells_tot + offset : b * cells_tot + cells + offset : cfac,
- 0, ...], -1, -2)
- digital[b, : cells//cfac, ...] = np.swapaxes(data[b * cells_tot + cfac - 1 + skip_first_burst * first_burst_length :
- b * cells_tot + cells + cfac - 1 + offset :cfac, cfac%2, ...], -1, -2)
-
- cellID[ b * cells : (b + 1) * cells] = cellID_all[b * cells_tot + offset : b * cells_tot + cells + offset].flatten()
- except:
- print(b * cells_tot + offset, b * cells_tot + cells + offset)
- print(b, offset, cells, data.shape[0]//cells)
- raise AttributeError("Foo")
- return {'analog': analog, 'digital': digital, 'cellID': cellID}
-
-
- pc_data = {'analog': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'digital': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'cellID': np.zeros(((bursts_total) * cells))
- }
- pc_data_merged = {'analog': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'digital': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'cellID': np.zeros(((bursts_total) * cells))
- }
-
- for run_idx, run in enumerate(runs):
- #Read files in
- last_burst = 0
- for seq in range(seqs):
- fname = os.path.join(path_temp.format(run),
- image_name_temp.format(run, channel, seq))
- if seq == 0:
- skip_first_burst = True
- else:
- skip_first_burst = False
- bursts = bursts_per_file[seq]
-
- if True:
- aa = read_raw_data_file(fname, channel, bursts = bursts,
- skip_first_burst = skip_first_burst,
- first_burst_length = cells)
- pc_data['analog'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = aa['analog']
- pc_data['digital'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = aa['digital']
- pc_data['cellID'][last_burst * cells : (last_burst+bursts_per_file[seq]-skip_first_burst) * cells, ...] = aa['cellID']
-
- #except Exception as e:
- # print(e)
- # pc_data['analog'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = 0
- # pc_data['digital'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = 0
- # pc_data['cellID'][last_burst * cells : (last_burst+bursts_per_file[seq]-skip_first_burst) * cells, ...] = 0
- #finally:
- last_burst += bursts_per_file[seq]-skip_first_burst
- # Copy injected rows
- for row_i in range(8):
- pc_data_merged['analog'][:,:,row_i * 8 + (7 - run_idx),:] = pc_data['analog'][:,:,row_i * 8 + (7 - run_idx),:]
- pc_data_merged['analog'][:,:,64 + row_i * 8 + run_idx ,:] = pc_data['analog'][:,:, 64 + row_i * 8 + run_idx,:]
- pc_data_merged['digital'][:,:,row_i * 8 + (7 - run_idx),:] = pc_data['digital'][:,:,row_i * 8 + (7 - run_idx),:]
- pc_data_merged['digital'][:,:,64 + row_i * 8 + run_idx ,:] = pc_data['digital'][:,:, 64 + row_i * 8 + run_idx,:]
- #Check cellIDs
- #Copy cellIDs of first run
- if run_idx == 0:
- pc_data_merged['cellID'][...] = pc_data['cellID'][...]
- #Check cellIDs of all the other runs
- else:
- print('cellID difference:{}'.format(np.sum(pc_data_merged['cellID']-pc_data['cellID'])))
- return pc_data_merged['analog'], pc_data_merged['digital'], pc_data_merged['cellID']
-
-p = partial(read_and_merge_module_data, maxcells, path_temp, image_name_temp,
- runs, seqs, bursts_per_file, IL_MODE)
-# chunk this a bit, so that we don't overuse available memory
-res = list(map(p, modules))
-
-
-
-# ## Slope clustering and Fitting ##
-#
-# The following two cells contain the actual algorithm logic as well as a preview of a single pixel and memory cells visualizing the data and the concepts.
-#
-# We start out with calculating an estimate of the slope in proximity of a given data value. This is done by calculating the slopes of a given value with 15 neighbours and averaging the result. Values are then clustered by these slopes into three regions via a K-means algorithm.
-#
-# * for the first region a linear function is fitted to the data, determining the gain slope and offset for the high gain mode.
-#
-# $$y = mx + b$$
-#
-# * for the second and third region a composite function of the form:
-#
-# $$y = A*e^{-(x-O)/C}+mx+b$$
-#
-# is fitted, covering both the transition region and the medium gain slope.
-
-# In[4]:
-
-
-for r in res:
- dig, ana, cellId = r
- plt.imshow(dig[0,4, ...])
-
-
-# In[5]:
-
-
-for r in res:
- dig, ana, cellId = r
- plt.imshow(ana[0,4, ...])
-
-
-
-# In[6]:
-
-
-from sklearn.cluster import KMeans
-from iminuit import Minuit
-from iminuit.util import make_func_code, describe
-
-def calc_m_cluster(x, y):
- scan_range = 15
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
-
- k = KMeans(n_clusters=3, n_jobs=-2)
- k.fit(m.reshape(-1, 1))
- ms = []
- for lbl in np.unique(k.labels_):
- xl = x[k.labels_ == lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[k.labels_ == lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
- return ms, k.labels_, k.cluster_centers_
-
-def calc_m_cluster2(x, y, r1=5, r2=0, r3=1.5):
- scan_range = 15
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
- reg1 = m > r1
- reg2 = m < r2
- reg3 = (m > r2) & (m < r3)
- reg4 = ~(reg1 | reg2 | reg3)
- labels = [reg1, reg2, reg3, reg4]
- ms = []
- for lbl in labels:
- xl = x[lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
-
- return ms, labels, None
-
-def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
-def lin_fun(x, m, b):
- return m*x+b
-
-def hook_fun(x, a, c, o, m, b):
- return a*np.exp(-(x-o)/c)+m*x+b
-
-
-# In[7]:
-
-
-test_pixels = [(127,r) for r in range(40, 41)]
-test_cells = [1]
-
-for r in res:
- dig, ana, cellId = r
- d = []
- for pix in test_pixels:
- for cell in test_cells:
- color = np.random.rand(3,1)
-
- x = np.arange(dig.shape[0])
- y = dig[:,cell, pix[0], pix[1]]
-
- ms, labels, centers = calc_m_cluster2(x, y)
- bound = None
- bound_m = None
-
- for i, lbl in enumerate(labels):
- d.append({'x': x[lbl],
- 'y': y[lbl],
- 'marker': 'o',
- 'color': 'green',
- 'linewidth': 0
- })
- if ms[i] < 0: # slope separating two regions
- bound = np.min(x[lbl])
- bound_m = ms[i]
-
- if bound is None or bound < 20:
- ya = ana[:,cell, pix[0], pix[1]]
- msa, labels, centers = calc_m_cluster2(x, ya, 25, -10, 25)
- if np.count_nonzero(labels[0]) > 0:
- bound = np.min(x[labels[0]])
- bound_m = ms[3]
- else:
- avg_g = np.nanmean(ya)
- bound = np.max(x[y < avg_g])
- bound_m = ms[3]
-
-
- # fit linear slope
- xl = x[(x<bound)]
- yl = y[(x<bound)]
- parms = {'m': bound_m, 'b': np.min(yl)}
- fitted = fit_data(lin_fun, xl, yl, np.sqrt(yl), parms)
-
- yf = lin_fun(xl, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((yl-yf)/yl))
-
- d.append({'x': xl,
- 'y': yf,
- 'color': 'red',
- 'linewidth': 3
- })
-
- # fit hook slope
- xh = x[x>=bound]
- yh = y[x>=bound]
- parms = {'m': bound_m/10, 'b': np.min(yh), 'a': np.max(yh), 'c': 0.5, 'o': bound-1}
- fitted = fit_data(hook_fun, xh, yh, np.sqrt(yh), parms)
- yf = hook_fun(xh, fitted['a'], fitted['c'], fitted['o'], fitted['m'], fitted['b'])
- max_devh = np.max(np.abs((yh-yf)/yh))
-
- d.append({'x': xh,
- 'y': yf,
- 'color': 'red',
- 'linewidth': 3
- })
-
- x = np.arange(ana.shape[0])
- y = ana[:,cell, pix[0], pix[1]]
- ms, labels, centers = calc_m_cluster2(x, y, 25, -10, 25)
- for i, lbl in enumerate(labels):
-
- d.append({'x': x[lbl],
- 'y': y[lbl],
- 'marker': 'o',
- 'color': 'green',
- 'lw': None
-
- })
-
- threshold = np.max(y[x<bound])
- xana.simplePlot(d)#, y_range=[9000, 10000])
-
-
-# Here we perform the calculations in column-parallel for all modules
-
-# In[8]:
-
-
-def calibrate_single_row(cells, inp):
-
- from sklearn.cluster import KMeans
- from iminuit import Minuit
- from iminuit.util import make_func_code, describe
- import numpy as np
-
- yrd, yra = inp
-
- def calc_m_cluster2(x, y, r1=5, r2=0, r3=1.5):
- scan_range = 25
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
- reg1 = m > r1
- reg2 = m < r2
- reg3 = (m > r2) & (m < r3)
- reg4 = ~(reg1 | reg2 | reg3)
- labels = [reg1, reg2, reg3, reg4]
- ms = []
- for lbl in labels:
- xl = x[lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
-
- return ms, labels, None
-
- def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
- def lin_fun(x, m, b):
- return m*x+b
-
- def hook_fun(x, a, c, o, m, b):
- return a*np.exp(-(x-o)/c)+m*x+b
-
- # linear slope
- ml = np.zeros(yrd.shape[1:])
- bl = np.zeros(yrd.shape[1:])
- devl = np.zeros(yrd.shape[1:])
- ml[...] = np.nan
- bl[...] = np.nan
- devl[...] = np.nan
-
- #hook function
- mh = np.zeros(yrd.shape[1:])
- bh = np.zeros(yrd.shape[1:])
- ch = np.zeros(yrd.shape[1:])
- oh = np.zeros(yrd.shape[1:])
- ah = np.zeros(yrd.shape[1:])
- devh = np.zeros(yrd.shape[1:])
- mh[...] = np.nan
- bh[...] = np.nan
- ch[...] = np.nan
- oh[...] = np.nan
- ah[...] = np.nan
- devh[...] = np.nan
-
- # threshold
- thresh = np.zeros(list(yrd.shape[1:])+[3,])
- thresh[...] = np.nan
- failures = []
- for cell in range(cells):
- for col in range(yrd.shape[-1]):
- try:
- y = yrd[:,cell, col]
- x = np.arange(y.shape[0])
- ms, labels, centers = calc_m_cluster2(x, y)
- bound = None
- bound_m = None
- for i, lbl in enumerate(labels):
- if ms[i] < 0: # slope separating two regions
- bound = np.min(x[lbl])
- bound_m = ms[i]
-
- lblhook = labels[2]
- if bound is None or bound < 20:
- ya = yra[:,cell, col]
- msa, labels, centers = calc_m_cluster2(x, ya, 15, -10, 25)
- if np.count_nonzero(labels[0]) > 0:
- bound = np.min(x[labels[0]])
- bound_m = ms[3]
- else:
- continue
-
- # fit linear slope
- xl = x[x<bound]
- yl = y[x<bound]
- parms = {'m': bound_m, 'b': np.min(yl)}
- fitted = fit_data(lin_fun, xl, yl, np.sqrt(yl), parms)
- yf = lin_fun(xl, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((yl-yf)/yl))
- ml[cell,col] = fitted['m']
- bl[cell,col] = fitted['b']
- devl[cell,col] = max_devl
-
- # fit hook slope
- xh = x[x>=bound]
- yh = y[x>=bound]
- parms = {'m': bound_m/10, 'b': np.min(yh), 'a': np.max(yh), 'c': 0.5, 'o': bound-1}
- fitted = fit_data(hook_fun, xh, yh, np.sqrt(yh), parms)
- yf = hook_fun(xh, fitted['a'], fitted['c'], fitted['o'], fitted['m'], fitted['b'])
- max_devh = np.max(np.abs((yh-yf)/yh))
-
- mh[cell,col] = fitted['m']
- bh[cell,col] = fitted['b']
- ah[cell,col] = fitted['a']
- oh[cell,col] = fitted['o']
- ch[cell,col] = fitted['c']
- devh[cell,col] = max_devh
-
- y = yra[:,cell, col]
- threshold = (np.max(y[x<bound]) + np.min(y[x>=bound]))/2
- thresh[cell,col,0] = threshold
- thresh[cell,col,1] = np.max(y[x<bound])
- thresh[cell,col,2] = np.min(y[x>=bound])
- except Exception as e:
- pass
- #failures.append((cell, col, str(e)))
- return thresh, (ml, bl, devl), (mh, bh, ah, oh, ch, devh), failures
-
-fres = {}
-failures = []
-for i, r in enumerate(res):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- dig, ana, cellId = r
- inp = []
- for j in range(dig.shape[2]):
- inp.append((dig[:,:,j,:], ana[:,:,j,:]))
-
- p = partial(calibrate_single_row, cells)
- frs = view.map_sync(p, inp)
-
- # linear slope
- ml = np.zeros(dig.shape[1:])
- bl = np.zeros(dig.shape[1:])
- devl = np.zeros(dig.shape[1:])
-
- #hook function
- mh = np.zeros(dig.shape[1:])
- bh = np.zeros(dig.shape[1:])
- ch = np.zeros(dig.shape[1:])
- oh = np.zeros(dig.shape[1:])
- ah = np.zeros(dig.shape[1:])
- devh = np.zeros(dig.shape[1:])
-
- # threshold
- thresh = np.zeros(list(dig.shape[1:]))
- thresh_bounds = np.zeros(list(dig.shape[1:])+[2,])
-
- for j, fr in enumerate(frs):
- threshr, lin, hook, fails = fr
- mlr, blr, devlr = lin
- mhr, bhr, ahr, ohr, chro, devhr = hook
- failures.append(fails)
-
- ml[:,j,:] = mlr
- bl[:,j,:] = blr
- devl[:,j,:] = devlr
-
- mh[:,j,:] = mhr
- bh[:,j,:] = bhr
- oh[:,j,:] = ohr
- ch[:,j,:] = chro
- ah[:,j,:] = ahr
- devh[:,j,:] = devhr
-
- thresh[:,j,...] = threshr[...,0]
- thresh_bounds[:,j,...] = threshr[...,1:]
-
- fres[qm] = {'ml': ml,
- 'bl': bl,
- 'devl': devl,
- 'mh': mh,
- 'bh': bh,
- 'oh': oh,
- 'ch': ch,
- 'ah': ah,
- 'devh': devh,
- 'tresh': thresh,
- 'tresh_bounds': thresh_bounds
- }
-
-
-
-# In[9]:
-
-
-ofile = "{}/agipd_pc_store_{}_4_{}_{}.h5".format(out_folder, "_".join([str(run) for run in runs]), modules[0], modules[-1])
-store_file = h5py.File(ofile, "w")
-for qm, r in fres.items():
- for key, item in r.items():
- store_file["/{}/{}/0/data".format(qm, key)] = item
-store_file.close()
-
-
-# In[11]:
-
-
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-cell_to_preview = 4
-for module, data in fres.items():
- fig = plt.figure(figsize=(40,40))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(6, 2),
- axes_pad=(0.9, 0.15),
- label_mode="1",
- share_all=True,
- cbar_location="right",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
-
- mask = np.zeros(data['ml'].shape, np.uint8)
- mask[(data['tresh'][...,0] < 100) | (data['tresh'][...,0] > 8100)] += 1
- mask[(data['devl'] == 0) | (data['devh'] == 0)] += 2
- mask[(data['devl'] > 0.5) | (data['devh'] >= 0.5)] += 4
- mask[(data['devl'] < 0) | (data['devh'] < 0)] += 8
- mask[(~np.isfinite(data['devl'])) | (~np.isfinite(data['devh']))] += 16
-
- i = 0
- for key, item in data.items():
- med = np.nanmedian(item)
- bound = 0.1
- while(np.count_nonzero((item < med-bound*med) | (item > med+bound*med))/item.size > 0.01):
- bound *=2
-
- if "bounds" in key:
- im = grid[i].imshow(item[cell_to_preview,...,0], interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med)
- else:
- im = grid[i].imshow(item[cell_to_preview,...], interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med)
- #cb = grid.cbar_axes[i].colorbar(im)
-
- grid[i].text(20, 50, key, color="w", fontsize=50)
-
- i += 1
-
- im = grid[-1].imshow(mask[cell_to_preview,...], interpolation="nearest",
- vmin=0, vmax=1)
- cb = grid.cbar_axes[-1].colorbar(im)
-
- grid[-1].text(20, 50, "mask", color="w", fontsize=50)
-
diff --git a/AGIPD/conv_tmp.txt b/AGIPD/conv_tmp.txt
deleted file mode 100644
index f5fa39a104961b32f9385f885cbbd81ee6c93524..0000000000000000000000000000000000000000
--- a/AGIPD/conv_tmp.txt
+++ /dev/null
@@ -1,660 +0,0 @@
-
-# imports, usually no need to change anything here
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-%matplotlib inline
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-profile = "noDB2" # SLURMHINT: profile, str
-view = Client(profile=profile)[:]
-view.use_dill()
-
-from functools import partial
-import XFELDetAna.xfelpycaltools as xcal
-import XFELDetAna.xfelpyanatools as xana
-import warnings
-warnings.filterwarnings('ignore')
-
-from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
-
-IL_MODE = False # SLURMHINT: il_mode, bool
-IL_MODE = False
-
-# the following lines need to be adapted to the data used for analysis
-maxcells = 74 # SLURMHINT: maxcells, int
-cells = 74 # SLURMHINT: cells, int
-path_temp = '/gpfs/exfel/exp/SPB/201830/p900019/raw/r{:04d}/' # SLURMHINT: path_temp, str
-image_name_temp = 'RAW-R{:04d}-AGIPD{:02d}-S{:05d}.h5'
-modules = range(0,1) # SLURMHINT: modules, list
-out_folder = "/gpfs/exfel/data/scratch/haufs/calibration/280218/AGIPD/" # SLURMHINT: out_folder, str
-runs = list(range(94, 101))+[102] # SLURMHINT: runs, list
-seqs = 2 # SLURMHINT: seq, int
-
-fit_hook = True
-
-print("Parameters are:")
-print("Memory cells: {}/{}".format(cells, maxcells))
-print("Runs: {}".format(runs))
-print("Modules: {}".format(modules))
-print("Sequences: {}".format(seqs))
-print("Interlaced mode: {}".format(IL_MODE))
-
-run = runs[0]
-bursts_per_file = []
-channel = 0
-for seq in range(seqs):
- fname = os.path.join(path_temp.format(run),
- image_name_temp.format(run, channel, seq))
- print('Reading ',fname)
- f = h5py.File(fname, 'r', driver='core')
-
- count = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- f.close()
- bursts_per_file.append(np.count_nonzero(count))
-bursts_per_file = np.array(bursts_per_file)
-print("Bursts per sequence file are: {}".format(bursts_per_file))
-
-def read_and_merge_module_data(cells, path_temp, image_name_temp,
- runs, seqs, bursts_per_file, il_mode, channel):
- import h5py
- import numpy as np
- import os
-
- #bursts_per_file = np.hstack([0, bursts_per_file])
- bursts_total = np.sum(bursts_per_file)
-
- cfac = 2 if il_mode else 1
- def read_raw_data_file(fname, channel, cells = cells, cells_tot = cells, bursts = 250,
- skip_first_burst = True, first_burst_length = cells):
- f = h5py.File(fname, 'r', driver='core')
- #print('Reading ',fname)
- image_path_temp = 'INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data'
- cellID_path_temp = 'INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId'
- count = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(f["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- print(first_index, last_index)
- data = f[image_path_temp.format(channel)][first_index:last_index,...][()]
-
- cellID_all = f[cellID_path_temp.format(channel)][first_index:last_index,...][()]
- f.close()
-
- #bursts = int(data.shape[0]/adcells)
- print('Bursts: ', bursts)
- analog = np.zeros((bursts - skip_first_burst, cells//cfac, 128, 512))
- digital = np.zeros((bursts - skip_first_burst, cells//cfac, 128, 512))
- cellID = np.zeros(( (bursts - skip_first_burst) * cells))
- offset = skip_first_burst * first_burst_length
-
- for b in range(min(bursts, data.shape[0]//cells-1) - skip_first_burst-1):
- try:
-
- analog[b, : cells//cfac, ...] = np.swapaxes(data[b * cells_tot + offset : b * cells_tot + cells + offset : cfac,
- 0, ...], -1, -2)
- digital[b, : cells//cfac, ...] = np.swapaxes(data[b * cells_tot + cfac - 1 + skip_first_burst * first_burst_length :
- b * cells_tot + cells + cfac - 1 + offset :cfac, cfac%2, ...], -1, -2)
-
- cellID[ b * cells : (b + 1) * cells] = cellID_all[b * cells_tot + offset : b * cells_tot + cells + offset].flatten()
- except:
- print(b * cells_tot + offset, b * cells_tot + cells + offset)
- print(b, offset, cells, data.shape[0]//cells)
- raise AttributeError("Foo")
- return {'analog': analog, 'digital': digital, 'cellID': cellID}
-
-
- pc_data = {'analog': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'digital': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'cellID': np.zeros(((bursts_total) * cells))
- }
- pc_data_merged = {'analog': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'digital': np.zeros((bursts_total, cells//cfac, 128, 512)),
- 'cellID': np.zeros(((bursts_total) * cells))
- }
-
- for run_idx, run in enumerate(runs):
- #Read files in
- last_burst = 0
- for seq in range(seqs):
- fname = os.path.join(path_temp.format(run),
- image_name_temp.format(run, channel, seq))
- if seq == 0:
- skip_first_burst = True
- else:
- skip_first_burst = False
- bursts = bursts_per_file[seq]
-
- try:
- aa = read_raw_data_file(fname, channel, bursts = bursts,
- skip_first_burst = skip_first_burst,
- first_burst_length = cells)
- pc_data['analog'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = aa['analog']
- pc_data['digital'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = aa['digital']
- pc_data['cellID'][last_burst * cells : (last_burst+bursts_per_file[seq]-skip_first_burst) * cells, ...] = aa['cellID']
-
- except Exception as e:
- print(e)
- pc_data['analog'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = 0
- pc_data['digital'][last_burst : last_burst+bursts_per_file[seq]-skip_first_burst, ...] = 0
- pc_data['cellID'][last_burst * cells : (last_burst+bursts_per_file[seq]-skip_first_burst) * cells, ...] = 0
- finally:
- last_burst += bursts_per_file[seq]-skip_first_burst
- # Copy injected rows
- for row_i in range(8):
- try:
- pc_data_merged['analog'][:,:,row_i * 8 + (7 - run_idx),:] = pc_data['analog'][:bursts_total,:cells//cfac,row_i * 8 + (7 - run_idx),:]
- pc_data_merged['analog'][:,:,64 + row_i * 8 + run_idx ,:] = pc_data['analog'][:bursts_total,:cells//cfac, 64 + row_i * 8 + run_idx,:]
- pc_data_merged['digital'][:,:,row_i * 8 + (7 - run_idx),:] = pc_data['digital'][:bursts_total,:cells//cfac,row_i * 8 + (7 - run_idx),:]
- pc_data_merged['digital'][:,:,64 + row_i * 8 + run_idx ,:] = pc_data['digital'][:bursts_total,:cells//cfac, 64 + row_i * 8 + run_idx,:]
- except:
- pass
- #Check cellIDs
- #Copy cellIDs of first run
- if run_idx == 0:
- pc_data_merged['cellID'][...] = pc_data['cellID'][...]
- #Check cellIDs of all the other runs
- else:
- print('cellID difference:{}'.format(np.sum(pc_data_merged['cellID']-pc_data['cellID'])))
- return pc_data_merged['analog'], pc_data_merged['digital'], pc_data_merged['cellID']
-
-p = partial(read_and_merge_module_data, maxcells, path_temp, image_name_temp,
- runs, seqs, bursts_per_file, IL_MODE)
-# chunk this a bit, so that we don't overuse available memory
-res = list(map(p, modules))
-
-
-from sklearn.cluster import KMeans
-from iminuit import Minuit
-from iminuit.util import make_func_code, describe
-
-def calc_m_cluster(x, y):
- scan_range = 15
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
-
- k = KMeans(n_clusters=3, n_jobs=-2)
- k.fit(m.reshape(-1, 1))
- ms = []
- for lbl in np.unique(k.labels_):
- xl = x[k.labels_ == lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[k.labels_ == lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
- return ms, k.labels_, k.cluster_centers_
-
-def calc_m_cluster2(x, y, r1=5, r2=0, r3=1.5):
- scan_range = 15
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
- reg1 = m > r1
- reg2 = m < r2
- reg3 = (m > r2) & (m < r3)
- reg4 = ~(reg1 | reg2 | reg3)
- labels = [reg1, reg2, reg3, reg4]
- ms = []
- for lbl in labels:
- xl = x[lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
-
- return ms, labels, None
-
-def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
-def lin_fun(x, m, b):
- return m*x+b
-
-def hook_fun(x, a, c, o, m, b):
- return a*np.exp(-(x-o)/c)+m*x+b
-
-test_pixels = [(127,r) for r in range(40, 41)]
-test_cells = [1]
-
-for mod, r in enumerate(res):
- dig, ana, cellId = r
- d = []
- for pix in test_pixels:
- for cell in test_cells:
- color = np.random.rand(3,1)
-
- x = np.arange(dig.shape[0])
- y = dig[:,cell, pix[0], pix[1]]
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- ms, labels, centers = calc_m_cluster2(x, y)
- bound = None
- bound_m = None
-
- for i, lbl in enumerate(labels):
- d.append({'x': x[lbl],
- 'y': y[lbl],
- 'marker': 'o',
- 'color': 'green',
- 'linewidth': 0
- })
- if ms[i] < 0: # slope separating two regions
- bound = np.min(x[lbl])
- bound_m = ms[i]
-
- if bound is None or bound < 20:
- ya = ana[:,cell, pix[0], pix[1]]
- msa, labels, centers = calc_m_cluster2(x, ya, 25, -10, 25)
- if np.count_nonzero(labels[0]) > 0:
- bound = np.min(x[labels[0]])
- bound_m = ms[3]
- else:
- avg_g = np.nanmean(ya)
- bound = np.max(x[y < avg_g])
- bound_m = ms[3]
-
- print(bound)
- # fit linear slope
- xl = x[(x<bound)]
- yl = y[(x<bound)]
- parms = {'m': bound_m, 'b': np.min(yl)}
- fitted = fit_data(lin_fun, xl, yl, np.sqrt(yl), parms)
-
- yf = lin_fun(xl, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((yl-yf)/yl))
-
- d.append({'x': xl,
- 'y': yf,
- 'color': 'red',
- 'linewidth': 3
- })
-
- # fit hook slope
- if fit_hook:
- idx = (x >= bound) & (y > 0) & np.isfinite(x) & np.isfinite(y)
- xh = x[idx]
- yh = y[idx]
- parms = {'m': bound_m/10, 'b': np.min(yh[yh > 0]), 'a': np.max(yh), 'c': 0.5, 'o': bound-1}
- fitted = fit_data(hook_fun, xh, yh, np.sqrt(yh), parms)
- yf = hook_fun(xh, fitted['a'], fitted['c'], fitted['o'], fitted['m'], fitted['b'])
- max_devh = np.max(np.abs((yh-yf)/yh))
- print(fitted)
- d.append({'x': xh,
- 'y': yf,
- 'color': 'red',
- 'linewidth': 3
- })
-
- x = np.arange(ana.shape[0])
- y = ana[:,cell, pix[0], pix[1]]
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- ms, labels, centers = calc_m_cluster2(x, y, 25, -10, 25)
- for i, lbl in enumerate(labels):
-
- d.append({'x': x[lbl],
- 'y': y[lbl],
- 'marker': 'o',
- 'color': 'green',
- 'lw': None
-
- })
-
- threshold = (np.min(y[x<bound]) + np.max(y[x>=bound]))/2
- print(threshold)
- fig = xana.simplePlot(d)
- fig.savefig("{}/module_{}_pixel_plot.png".format(out_folder, modules[mod]))
-
-def calibrate_single_row(cells, fit_hook, inp):
-
- from sklearn.cluster import KMeans
- from iminuit import Minuit
- from iminuit.util import make_func_code, describe
- import numpy as np
-
- yrd, yra = inp
-
- def calc_m_cluster2(x, y, r1=5, r2=0, r3=1.5):
- scan_range = 25
- ms = np.zeros((x.shape[0], scan_range))
- for i in range(scan_range):
- xdiffs = x - np.roll(x, i+1)
- ydiffs = y - np.roll(y, i+1)
- m = ydiffs/xdiffs
- ms[:,i] = m
- m = np.mean(ms, axis=1)
- reg1 = m > r1
- reg2 = m < r2
- reg3 = (m > r2) & (m < r3)
- reg4 = ~(reg1 | reg2 | reg3)
- labels = [reg1, reg2, reg3, reg4]
- ms = []
- for lbl in labels:
- xl = x[lbl]
- xd = np.reshape(xl, (len(xl), 1))
- xdiff = xd - xd.transpose()
-
- yl = y[lbl]
- yd = np.reshape(yl, (len(yl), 1))
- ydiff = yd - yd.transpose()
- ms.append(np.mean(np.nanmean(ydiff/xdiff, axis=0)))
-
- return ms, labels, None
-
- def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
- def lin_fun(x, m, b):
- return m*x+b
-
- def hook_fun(x, a, c, o, m, b):
- return a*np.exp(-(x-o)/c)+m*x+b
-
- # linear slope
- ml = np.zeros(yrd.shape[1:])
- bl = np.zeros(yrd.shape[1:])
- devl = np.zeros(yrd.shape[1:])
- ml[...] = np.nan
- bl[...] = np.nan
- devl[...] = np.nan
-
- #hook function
- mh = np.zeros(yrd.shape[1:])
- bh = np.zeros(yrd.shape[1:])
- ch = np.zeros(yrd.shape[1:])
- oh = np.zeros(yrd.shape[1:])
- ah = np.zeros(yrd.shape[1:])
- devh = np.zeros(yrd.shape[1:])
- mh[...] = np.nan
- bh[...] = np.nan
- ch[...] = np.nan
- oh[...] = np.nan
- ah[...] = np.nan
- devh[...] = np.nan
-
- # threshold
- thresh = np.zeros(list(yrd.shape[1:])+[3,])
- thresh[...] = np.nan
- failures = []
- for cell in range(cells):
- for col in range(yrd.shape[-1]):
- try:
- y = yrd[:,cell, col]
- x = np.arange(y.shape[0])
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- ms, labels, centers = calc_m_cluster2(x, y)
- bound = None
- bound_m = None
- for i, lbl in enumerate(labels):
- if ms[i] < 0: # slope separating two regions
- bound = np.min(x[lbl])
- bound_m = ms[i]
-
- lblhook = labels[2]
- if bound is None or bound < 20:
- ya = yra[:,cell, col]
- msa, labels, centers = calc_m_cluster2(x, ya, 15, -10, 25)
- if np.count_nonzero(labels[0]) > 0:
- bound = np.min(x[labels[0]])
- bound_m = ms[3]
- else:
- continue
-
- # fit linear slope
- xl = x[x<bound]
- yl = y[x<bound]
- parms = {'m': bound_m, 'b': np.min(yl)}
- fitted = fit_data(lin_fun, xl, yl, np.sqrt(yl), parms)
- yf = lin_fun(xl, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((yl-yf)/yl))
- ml[cell,col] = fitted['m']
- bl[cell,col] = fitted['b']
- devl[cell,col] = max_devl
-
- # fit hook slope
- if fit_hook:
- idx = (x >= bound) & (y > 0) & np.isfinite(x) & np.isfinite(y)
- xh = x[idx]
- yh = y[idx]
- parms = {'m': bound_m/10, 'b': np.min(yh[yh > 0]), 'a': np.max(yh), 'c': 0.5, 'o': bound-1}
- fitted = fit_data(hook_fun, xh, yh, np.sqrt(yh), parms)
- yf = hook_fun(xh, fitted['a'], fitted['c'], fitted['o'], fitted['m'], fitted['b'])
- max_devh = np.max(np.abs((yh-yf)/yh))
-
- mh[cell,col] = fitted['m']
- bh[cell,col] = fitted['b']
- ah[cell,col] = fitted['a']
- oh[cell,col] = fitted['o']
- ch[cell,col] = fitted['c']
- devh[cell,col] = max_devh
-
- y = yra[:,cell, col]
- x = np.arange(y.shape[0])
-
- vidx = (y > 1000) & np.isfinite(y)
- x = x[vidx]
- y = y[vidx]
-
- threshold = (np.min(y[x<bound]) + np.max(y[x>=bound]))/2
- thresh[cell,col,0] = threshold
- thresh[cell,col,1] = np.min(y[x<bound])
- thresh[cell,col,2] = np.max(y[x>=bound])
- except Exception as e:
- pass
- #failures.append((cell, col, str(e)))
- return thresh, (ml, bl, devl), (mh, bh, ah, oh, ch, devh), failures
-
-fres = {}
-failures = []
-for i, r in enumerate(res):
- ii = modules[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- dig, ana, cellId = r
- inp = []
- for j in range(dig.shape[2]):
- inp.append((dig[:,:,j,:], ana[:,:,j,:]))
-
- p = partial(calibrate_single_row, cells, fit_hook)
- frs = view.map_sync(p, inp)
-
- # linear slope
- ml = np.zeros(dig.shape[1:])
- bl = np.zeros(dig.shape[1:])
- devl = np.zeros(dig.shape[1:])
-
- #hook function
- mh = np.zeros(dig.shape[1:])
- bh = np.zeros(dig.shape[1:])
- ch = np.zeros(dig.shape[1:])
- oh = np.zeros(dig.shape[1:])
- ah = np.zeros(dig.shape[1:])
- devh = np.zeros(dig.shape[1:])
-
- # threshold
- thresh = np.zeros(list(dig.shape[1:]))
- thresh_bounds = np.zeros(list(dig.shape[1:])+[2,])
-
- for j, fr in enumerate(frs):
- threshr, lin, hook, fails = fr
- mlr, blr, devlr = lin
- mhr, bhr, ahr, ohr, chro, devhr = hook
- failures.append(fails)
-
- ml[:,j,:] = mlr
- bl[:,j,:] = blr
- devl[:,j,:] = devlr
-
- mh[:,j,:] = mhr
- bh[:,j,:] = bhr
- oh[:,j,:] = ohr
- ch[:,j,:] = chro
- ah[:,j,:] = ahr
- devh[:,j,:] = devhr
-
- thresh[:,j,...] = threshr[...,0]
- thresh_bounds[:,j,...] = threshr[...,1:]
-
- fres[qm] = {'ml': ml,
- 'bl': bl,
- 'devl': devl,
- 'tresh': thresh,
- 'tresh_bounds': thresh_bounds}
- if fit_hook:
- fres[qm].update({
- 'mh': mh,
- 'bh': bh,
- 'oh': oh,
- 'ch': ch,
- 'ah': ah,
- 'devh': devh,
- })
-
-
-ofile = "{}/agipd_pc_store_{}_{}_{}.h5".format(out_folder, "_".join([str(run) for run in runs]), modules[0], modules[-1])
-store_file = h5py.File(ofile, "w")
-for qm, r in fres.items():
- for key, item in r.items():
- store_file["/{}/{}/0/data".format(qm, key)] = item
-store_file.close()
-
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-cell_to_preview = 4
-for module, data in fres.items():
- fig = plt.figure(figsize=(40,40))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(6 if fit_hook else 3, 2),
- axes_pad=(0.9, 0.15),
- label_mode="1",
- share_all=True,
- cbar_location="right",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
-
- mask = np.zeros(data['ml'].shape, np.uint8)
- mask[(data['tresh'][...,0] < 100) | (data['tresh'][...,0] > 8100)] += 1
- mask[(data['devl'] == 0)] += 2
- mask[(data['devl'] > 0.5)] += 4
- mask[(data['devl'] < 0) ] += 8
- mask[(~np.isfinite(data['devl']))] += 16
-
- i = 0
- for key, item in data.items():
- med = np.nanmedian(item)
- bound = 0.1
- while(np.count_nonzero((item < med-bound*med) | (item > med+bound*med))/item.size > 0.01):
- bound *=2
-
- if "bounds" in key:
- im = grid[i].imshow(item[cell_to_preview,...,0], interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med)
- else:
- im = grid[i].imshow(item[cell_to_preview,...], interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med)
- #cb = grid.cbar_axes[i].colorbar(im)
-
- grid[i].text(20, 50, key, color="w", fontsize=50)
-
- i += 1
-
- im = grid[-1].imshow(mask[cell_to_preview,...], interpolation="nearest",
- vmin=0, vmax=1)
- cb = grid.cbar_axes[-1].colorbar(im)
-
- grid[-1].text(20, 50, "mask", color="w", fontsize=50)
- fig.savefig("{}/module_{}_PC.png".format(out_folder, module))
-
-
-
-
diff --git a/AGIPD/correct_agipd_batch.py b/AGIPD/correct_agipd_batch.py
deleted file mode 100644
index ae67acd1d68e89bd95aa5a40b5fc2990f2a96ce8..0000000000000000000000000000000000000000
--- a/AGIPD/correct_agipd_batch.py
+++ /dev/null
@@ -1,449 +0,0 @@
-
-# coding: utf-8
-
-# In[29]:
-
-import sys
-#in_folder, out_folder, base_store, offset_store, mem_cells, sequences
-in_folder = sys.argv[1] # "/gpfs/exfel/exp/SPB/201701/p002012/raw/r0100"
-out_folder = sys.argv[2]# "./corrected_test"
-base_store = sys.argv[3]
-offset_store = sys.argv[4]
-sequences = sys.argv[6] #[0]
-mem_cells = sys.argv[5]
-il_mode = False
-if "IL" in mem_cells.upper():
- il_mode = True
- mem_cells = mem_cells.upper().replace("IL", "")
-mem_cells = int(mem_cells) # 30
-max_cells = mem_cells//2 if il_mode else mem_cells
-
-print("Working in IL Mode: {}. Actual cells in use are: {}".format(il_mode, max_cells))
-
-overwrite = True if sys.argv[7] == "True" else False
-if sequences.upper() != "ALL":
- sequences = [int(s) for s in sequences.split(",")]
-else:
- sequences = None
-do_rel_gain = not(True if sys.argv[8] == "True" else False)
-print("correction relative gain: {}".format(do_rel_gain))
-uuid = sys.argv[9]
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("agg")
-import matplotlib.pyplot as plt
-from ipyparallel import Client
-print("Connecting to profile {}".format(uuid))
-view = Client(profile=uuid)[:]
-view.use_dill()
-gains = np.arange(3)
-cells = np.arange(max_cells)
-
-
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-DET_FILE_INSET = "AGIPD"
-
-if in_folder[-1] == "/":
- in_folder = in_folder[:-1]
-out_folder = "{}/{}".format(out_folder, os.path.split(in_folder)[-1])
-print("Outputting to {}".format(out_folder))
-
-if not os.path.exists(out_folder):
- os.makedirs(out_folder)
-elif not overwrite:
- raise AttributeError("Output path exists! Exiting")
-
-# In[2]:
-
-def combine_stack(d, sdim):
- combined = np.zeros((sdim, 2048,2048))
- combined[...] = np.nan
-
- dy = 0
- for i in range(16):
-
-
- if i < 8:
- dx = -512
- mx = 1
- my = i % 8
- combined[:, my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx] = np.rollaxis(d[i],2,1)[:,:,::-1]
- dy += 30
- if i == 3:
- dy += 30
- elif i < 12:
- dx = 100
- if i == 8:
- dy = 4*30 + 30 +50
-
- mx = 1
- my = i % 8 +4
- combined[:, my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx] = np.rollaxis(d[i],2,1)[:,::-1,:]
- dy += 30
- else:
- dx = 100
- if i == 11:
- dy = 50
-
- mx = 1
- my = i - 14
-
- combined[:, my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx] = np.rollaxis(d[i],2,1)[:,::-1,:]
- dy += 30
-
-
-
- return combined
-
-
-# In[3]:
-
-rel_gains = []
-offsets = []
-noise = []
-base_offsets = []
-thresholds = []
-masks = []
-tbounds = []
-med_gain_hook = []
-saveFile = h5py.File(base_store, "r")
-for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- data = np.array(saveFile["{}/RelativeGain/0/data".format(qm)])
- rel_gains.append(data)
- data = np.array(saveFile["{}/BaseOffset/0/data".format(qm)])
- base_offsets.append(data)
- data = np.array(saveFile["{}/Threshold/0/data".format(qm)])
- mask = np.array(saveFile["{}/BadPixels/0/data".format(qm)])
- masks.append(mask)
- thresholds.append(data)
- data = np.array(saveFile["{}/ThresholdBounds/0/data".format(qm)])
- tbounds.append(data)
- o = np.array(saveFile["{}/RelativeGainNonLinOffset/0/data".format(qm)])
- c = np.array(saveFile["{}/RelativeGainNonLinScale/0/data".format(qm)])
- a = np.array(saveFile["{}/RelativeGainNonLinAmplitude/0/data".format(qm)])
- med_gain_hook.append((o[...,:max_cells], c[...,:max_cells], a[...,:max_cells]))
-saveFile.close()
-
-
-saveFile = h5py.File(offset_store, "r")
-for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- data = np.array(saveFile["{}/Offset/0/data".format(qm)])
- offsets.append(data)
- data = np.array(saveFile["{}/Noise/0/data".format(qm)])
- noise.append(data)
-saveFile.close()
-
-
-# In[4]:
-
-# set everything up filewise
-from queue import Queue
-#if not os.path.exists(out_folder):
-# os.makedirs(out_folder)
-#elif not overwrite:
-# raise AttributeError("Output path exists! Exiting")
-
-def map_modules_from_files(filelist):
- module_files = {}
- mod_ids = {}
- for quadrant in range(0, QUADRANTS):
- for module in range(0, MODULES_PER_QUAD):
- name = "Q{}M{}".format(quadrant + 1, module + 1)
- module_files[name] = Queue()
- num = quadrant * 4 + module
- mod_ids[name] = num
- file_infix = "{}{:02d}".format(DET_FILE_INSET, num)
- for file in filelist:
- if file_infix in file:
- module_files[name].put(file)
- return module_files, mod_ids
-
-dirlist = os.listdir(in_folder)
-file_list = []
-for entry in dirlist:
- #only h5 file
- abs_entry = "{}/{}".format(in_folder, entry)
- if os.path.isfile(abs_entry) and os.path.splitext(abs_entry)[1] == ".h5":
-
- if sequences is None:
- file_list.append(abs_entry)
- else:
- for seq in sequences:
- if "{:05d}.h5".format(seq) in abs_entry:
- file_list.append(os.path.abspath(abs_entry))
-
-mapped_files, mod_ids = map_modules_from_files(file_list)
-
-
-# In[5]:
-
-import copy
-from functools import partial
-def correct_module(cells, il_mode, inp):
- import numpy as np
- import copy
- import h5py
- outfile = None
- if True: #try:
-
- filename, filename_out, channel, offset, base_offset, rel_gain, threshold, mask, do_rel_gain, noise, tbounds, mgh = inp
-
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- if np.count_nonzero(count != 0) == 0:
- infile.close()
- return
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
-
- cellid = np.squeeze(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index,...]))
- pulses = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)][first_index:last_index,...])
- trains = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)][first_index:last_index,...])
- statii = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)][first_index:last_index,...])
-
-
- dont_copy = ["data", "pulseId", "cellId", "trainId", "status"]
- dont_copy = ["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/{}".format(channel, do)
- for do in dont_copy]
-
- outfile = h5py.File(filename_out, "w", driver="core")
- def visitor(k, item):
- if k not in dont_copy:
- if isinstance(item, h5py.Group):
- outfile.create_group(k)
- elif isinstance(item, h5py.Dataset):
- group = str(k).split("/")
- group = "/".join(group[:-1])
- infile.copy(k, outfile[group])
-
- infile.visititems(visitor)
- outfile.flush()
- if il_mode:
- fixedCellIds = np.zeros(im.shape[2], cellid.dtype)
- fixedPulseIds = np.zeros(im.shape[2], pulses.dtype)
- fixedTrainIds = np.zeros(im.shape[2], trains.dtype)
- fixedStatii = np.zeros(im.shape[2], statii.dtype)
- for c in range(im.shape[2]//cells):
- fixedCellIds[c*cells:(c+1)*cells] = np.squeeze(cellid[c*2*cells:(c+1)*2*cells-cells])
- fixedPulseIds[c*cells:(c+1)*cells] = np.squeeze(pulses[c*2*cells:(c+1)*2*cells-cells])
- fixedTrainIds[c*cells:(c+1)*cells] = np.squeeze(trains[c*2*cells:(c+1)*2*cells-cells])
- fixedStatii[c*cells:(c+1)*cells] = np.squeeze(statii[c*2*cells:(c+1)*2*cells-cells])
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)] = fixedCellIds
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)] = fixedPulseIds
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)] = fixedTrainIds
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)] = fixedStatii
- else:
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)] = cellid
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)] = pulses
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)] = trains
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)] = statii
- outfile.flush()
- infile.close()
- if not il_mode:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
- else:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
-
- tmap = threshold
-
- gain = np.zeros(ga.shape, np.uint8)
-
- for cc in range(im.shape[2]//cells):
- tg = gain[...,cc*cells:(cc+1)*cells]
- tga = ga[...,cc*cells:(cc+1)*cells]
- tg[...] = 255
- tg[tga < tmap[..., 0]] = 0
- tg[tga > tmap[..., 1]] = 2
- tg[tg == 255] = 1
- #tg[tmap[..., 0] > 10000] = 0
- gain[...,cc*cells:(cc+1)*cells] = tg
-
-
-
- rc = rel_gain
- offsetb = offset + base_offset
-# o, c, a = mgh
-# a += offset[...,0]
- msk = np.zeros(list(im.shape), np.uint8)
- for cc in range(im.shape[2]//cells):
- tg = gain[...,cc*cells:(cc+1)*cells]
- offset = np.choose(tg, (offsetb[...,0], offsetb[...,1], offsetb[...,2]))
- rel_cor = np.choose(tg, (rc[...,0], rc[...,1], rc[...,2]))
- tim = im[...,cc*cells:(cc+1)*cells]
- tim = tim - offset
- #tim2 = copy.copy(tim[tg == 1])
- # correction using linear slopes
- if do_rel_gain:
- tim /= rel_cor
- # correction using inverse hook function
- #cm = c[tg == 1]*rc[...,1][tg == 1]
- #mo = rc[...,1][tg == 1]*o[tg == 1]
- #tim2 = (cm*lambertw(-a[tg == 1]*np.exp((mo-tim2)/cm)/cm)+tim2)/rc[...,1][tg == 1]
- #tim[tg == 1] = tim2
-
- im[...,cc*cells:(cc+1)*cells] = tim
-# mask[(tg > tbounds[...,0]) & (tg < tbounds[...,1]),1] |= 2**7
-# mask[tim <= 5*noise[...,0], 0] |= 2**7
- tmask = np.choose(tg, (mask[...,0], mask[...,1], mask[...,2]))
- #tmask[tmap[..., 0] > 10000] |= 2**7
- msk[...,cc*cells:(cc+1)*cells] = tmask
-
-
- #msk[im <= 5.*noise, 0] |= 2**7
-
- outfile["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)] = np.rollaxis(np.rollaxis(im,1), 2)
- outfile["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/gain".format(channel)] = np.rollaxis(np.rollaxis(gain,1), 2)
- outfile["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/mask".format(channel)] = np.rollaxis(np.rollaxis(msk,1), 2)
- #except Exception as e:
- # print(e)
- #finally:
- if outfile is not None:
- outfile.close()
-
-
-done = False
-first_files = []
-while not done:
- inp = []
- dones = []
- first = True
- for i in range(16):
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- if qm in mapped_files and not mapped_files[qm].empty():
- fname_in = mapped_files[qm].get()
- dones.append(mapped_files[qm].empty())
- else:
- first_files.append((None, None))
- continue
- fout = os.path.abspath("{}/{}".format(out_folder, (os.path.split(fname_in)[-1]).replace("RAW", "CORR")))
- if first:
- first_files.append((fname_in, fout))
- inp.append((fname_in, fout, i, offsets[i][...,:max_cells,:].astype(np.float32), base_offsets[i][...,:max_cells,:].astype(np.float32),
- rel_gains[i][...,:max_cells,:], thresholds[i][...,:max_cells,:], masks[i][...,:max_cells,:], do_rel_gain, noise[i][...,:max_cells,:],
- tbounds[i][...,:max_cells,:], med_gain_hook[i]))
- first = False
- p = partial(correct_module, max_cells, il_mode)
- r = view.map_sync(p, inp)
- #r = list(map(p, inp))
- done = all(dones)
-
-
-
-
-# In[6]:
-
-corrected = []
-raw = []
-gains = []
-for i, ff in enumerate(first_files):
- try:
- rf, cf = ff
- if rf is None:
- raise Exception("File not present")
- infile = h5py.File(rf, "r")
- raw.append(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(i)][:max_cells:2,0,...]))
- infile.close()
-
- infile = h5py.File(cf, "r")
- corrected.append(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(i)][:max_cells,...]))
- gains.append(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/gain".format(i)][:max_cells,...]))
- infile.close()
-
- except Exception as e:
- print(e)
- corrected.append(np.zeros((max_cells, 512, 128)))
-
-
-# In[16]:
-
-combined = combine_stack(corrected, corrected[0].shape[0])
-combined_raw = combine_stack(raw, raw[0].shape[0])
-combined_g = combine_stack(gains, gains[0].shape[0])
-
-
-# In[24]:
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined_raw[...][:1300,400:1600],axis=0), vmin=0, vmax=8000, cmap="jet")
-fig.colorbar(im, ax=ax)
-fig.savefig("{}/mean_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined[...][:1300,400:1600], axis=0), vmin=0, vmax=1000, cmap="jet")
-fig.colorbar(im, ax=ax)
-fig.savefig("{}/mean_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined_g[...][:1300,400:1600], axis=0), vmin=0, vmax=3, cmap="jet")
-fig.colorbar(im, ax=ax)
-fig.savefig("{}/mean_first_train_GAIN.png".format(out_folder))
-
-
-# In[26]:
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined_raw[...][:1300,400:1600],axis=0), vmin=0, vmax=8000, cmap="jet")
-plt.colorbar(im, ax=ax)
-fig.savefig("{}/std_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined[...][:1300,400:1600], axis=0), vmin=0, vmax=20000, cmap="jet")
-plt.colorbar(im, ax=ax)
-fig.savefig("{}/std_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined_g[...][:1300,400:1600], axis=0), vmin=0, vmax=3, cmap="jet")
-plt.colorbar(im, ax=ax)
-fig.savefig("{}/std_first_train_GAIN.png".format(out_folder))
-
-
-# In[25]:
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined_raw[...][:1300,400:1600],axis=0), vmin=0, vmax=8000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined[...][:1300,400:1600], axis=0), vmin=0, vmax=20000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined_g[...][:1300,400:1600], axis=0), vmin=0, vmax=3, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_GAIN.png".format(out_folder))
-
-
-
diff --git a/AGIPD/correct_batch_no_loop.py b/AGIPD/correct_batch_no_loop.py
deleted file mode 100644
index 5e62ab2d73a81de3e2bc0bf4533f7c2207ad4f31..0000000000000000000000000000000000000000
--- a/AGIPD/correct_batch_no_loop.py
+++ /dev/null
@@ -1,464 +0,0 @@
-
-# coding: utf-8
-
-# In[29]:
-
-import sys
-#in_folder, out_folder, base_store, offset_store, mem_cells, sequences
-in_folder = sys.argv[1] # "/gpfs/exfel/exp/SPB/201701/p002012/raw/r0100"
-out_folder = sys.argv[2]# "./corrected_test"
-base_store = sys.argv[3]
-offset_store = sys.argv[4]
-sequences = sys.argv[6] #[0]
-mem_cells = sys.argv[5]
-il_mode = False
-if "IL" in mem_cells.upper():
- il_mode = True
- mem_cells = mem_cells.upper().replace("IL", "")
-mem_cells = int(mem_cells) # 30
-max_cells = mem_cells//2 if il_mode else mem_cells
-
-print("Working in IL Mode: {}. Actual cells in use are: {}".format(il_mode, max_cells))
-
-overwrite = True if sys.argv[7] == "True" else False
-if sequences.upper() != "ALL":
- sequences = [int(s) for s in sequences.split(",")]
-else:
- sequences = None
-do_rel_gain = not(True if sys.argv[8] == "True" else False)
-print("correction relative gain: {}".format(do_rel_gain))
-uuid = sys.argv[9]
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("agg")
-import matplotlib.pyplot as plt
-from ipyparallel import Client
-print("Connecting to profile {}".format(uuid))
-view = Client(profile=uuid)[:]
-view.use_dill()
-gains = np.arange(3)
-cells = np.arange(max_cells)
-
-
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-DET_FILE_INSET = "AGIPD"
-
-if in_folder[-1] == "/":
- in_folder = in_folder[:-1]
-out_folder = "{}/{}".format(out_folder, os.path.split(in_folder)[-1])
-print("Outputting to {}".format(out_folder))
-
-if not os.path.exists(out_folder):
- os.makedirs(out_folder)
-elif not overwrite:
- raise AttributeError("Output path exists! Exiting")
-
-# In[2]:
-
-def combine_stack(d, sdim):
- combined = np.zeros((sdim, 2048,2048))
- combined[...] = np.nan
-
- dy = 0
- for i in range(16):
-
-
- if i < 8:
- dx = -512
- mx = 1
- my = i % 8
- combined[:, my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx] = np.rollaxis(d[i],2,1)[:,:,::-1]
- dy += 30
- if i == 3:
- dy += 30
- elif i < 12:
- dx = 100
- if i == 8:
- dy = 4*30 + 30 +50
-
- mx = 1
- my = i % 8 +4
- combined[:, my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx] = np.rollaxis(d[i],2,1)[:,::-1,:]
- dy += 30
- else:
- dx = 100
- if i == 11:
- dy = 50
-
- mx = 1
- my = i - 14
-
- combined[:, my*128+dy:(my+1)*128+dy,
- mx*512-dx:(mx+1)*512-dx] = np.rollaxis(d[i],2,1)[:,::-1,:]
- dy += 30
-
-
-
- return combined
-
-
-# In[3]:
-
-rel_gains = []
-offsets = []
-noise = []
-base_offsets = []
-thresholds = []
-masks = []
-tbounds = []
-med_gain_hook = []
-saveFile = h5py.File(base_store, "r")
-for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- data = np.array(saveFile["{}/RelativeGain/0/data".format(qm)])
- rel_gains.append(data)
- data = np.array(saveFile["{}/BaseOffset/0/data".format(qm)])
- base_offsets.append(data)
- data = np.array(saveFile["{}/Threshold/0/data".format(qm)])
- mask = np.array(saveFile["{}/BadPixels/0/data".format(qm)])
- masks.append(mask)
- thresholds.append(data)
- data = np.array(saveFile["{}/ThresholdBounds/0/data".format(qm)])
- tbounds.append(data)
- o = np.array(saveFile["{}/RelativeGainNonLinOffset/0/data".format(qm)])
- c = np.array(saveFile["{}/RelativeGainNonLinScale/0/data".format(qm)])
- a = np.array(saveFile["{}/RelativeGainNonLinAmplitude/0/data".format(qm)])
- med_gain_hook.append((o[...,:max_cells], c[...,:max_cells], a[...,:max_cells]))
-saveFile.close()
-
-
-saveFile = h5py.File(offset_store, "r")
-for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- data = np.array(saveFile["{}/Offset/0/data".format(qm)])
- offsets.append(data)
- data = np.array(saveFile["{}/Noise/0/data".format(qm)])
- noise.append(data)
-saveFile.close()
-
-
-# In[4]:
-
-# set everything up filewise
-from queue import Queue
-#if not os.path.exists(out_folder):
-# os.makedirs(out_folder)
-#elif not overwrite:
-# raise AttributeError("Output path exists! Exiting")
-
-def map_modules_from_files(filelist):
- module_files = {}
- mod_ids = {}
- for quadrant in range(0, QUADRANTS):
- for module in range(0, MODULES_PER_QUAD):
- name = "Q{}M{}".format(quadrant + 1, module + 1)
- module_files[name] = Queue()
- num = quadrant * 4 + module
- mod_ids[name] = num
- file_infix = "{}{:02d}".format(DET_FILE_INSET, num)
- for file in filelist:
- if file_infix in file:
- module_files[name].put(file)
- return module_files, mod_ids
-
-dirlist = os.listdir(in_folder)
-file_list = []
-for entry in dirlist:
- #only h5 file
- abs_entry = "{}/{}".format(in_folder, entry)
- if os.path.isfile(abs_entry) and os.path.splitext(abs_entry)[1] == ".h5":
-
- if sequences is None:
- file_list.append(abs_entry)
- else:
- for seq in sequences:
- if "{:05d}.h5".format(seq) in abs_entry:
- file_list.append(os.path.abspath(abs_entry))
-
-mapped_files, mod_ids = map_modules_from_files(file_list)
-
-
-# In[5]:
-
-import copy
-from functools import partial
-def correct_module(cells, il_mode, inp):
- import numpy as np
- import copy
- import h5py
- outfile = None
- try:
-
- filename, filename_out, channel, offset, base_offset, rel_gain, threshold, mask, do_rel_gain, noise, tbounds, mgh = inp
-
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- if np.count_nonzero(count != 0) == 0:
- infile.close()
- return
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
-
- cellid = np.squeeze(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index,...]))
- pulses = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)][first_index:last_index,...])
- trains = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)][first_index:last_index,...])
- statii = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)][first_index:last_index,...])
-
-
- dont_copy = ["data", "pulseId", "cellId", "trainId", "status"]
- dont_copy = ["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/{}".format(channel, do)
- for do in dont_copy]
-
- outfile = h5py.File(filename_out, "w", driver="core")
- def visitor(k, item):
- if k not in dont_copy:
- if isinstance(item, h5py.Group):
- outfile.create_group(k)
- elif isinstance(item, h5py.Dataset):
- group = str(k).split("/")
- group = "/".join(group[:-1])
- infile.copy(k, outfile[group])
-
- infile.visititems(visitor)
- outfile.flush()
-
- if il_mode:
- fixedCellIds = np.zeros(im.shape[2], cellid.dtype)
- fixedPulseIds = np.zeros(im.shape[2], pulses.dtype)
- fixedTrainIds = np.zeros(im.shape[2], trains.dtype)
- fixedStatii = np.zeros(im.shape[2], statii.dtype)
- for c in range(im.shape[2]//cells):
- fixedCellIds[c*cells:(c+1)*cells] = np.squeeze(cellid[c*2*cells:(c+1)*2*cells-cells])
- fixedPulseIds[c*cells:(c+1)*cells] = np.squeeze(pulses[c*2*cells:(c+1)*2*cells-cells])
- fixedTrainIds[c*cells:(c+1)*cells] = np.squeeze(trains[c*2*cells:(c+1)*2*cells-cells])
- fixedStatii[c*cells:(c+1)*cells] = np.squeeze(statii[c*2*cells:(c+1)*2*cells-cells])
- cellId = fixedCellIds
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)] = fixedCellIds
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)] = fixedPulseIds
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)] = fixedTrainIds
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)] = fixedStatii
- else:
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)] = cellid
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)] = pulses
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)] = trains
- outfile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)] = statii
- outfile.flush()
- infile.close()
- if not il_mode:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
- else:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
-
- tmap = threshold
-
- gain = np.zeros(ga.shape, np.uint8)
-
- tmap = threshold[...,cellid,:]
- gain[(ga >= tmap[...,0]) & (ga <= tmap[...,1])] = 1
- gain[(ga > tmap[...,1])] = 2
-
- """
- for cc in range(im.shape[2]//cells):
- tg = gain[...,cc*cells:(cc+1)*cells]
- tga = ga[...,cc*cells:(cc+1)*cells]
- tg[...] = 255
- tg[tga < tmap[..., 0]] = 0
- tg[tga > tmap[..., 1]] = 2
- tg[tg == 255] = 1
- gain[...,cc*cells:(cc+1)*cells] = tg
- """
-
-
- rc = rel_gain[...,cellId,:]
- offsetb = offset + base_offset
- offset = offsetb[...,cellId,:]
- offset = np.choose(gain, (offset[...,0], offset[...,1], offset[...,2]))
- rel_cor = np.choose(gain, (rc[...,0], rc[...,1], rc[...,2]))
- im -= offset
- if do_rel_gain:
- im /= rel_cor
-
- msk = mask[...,cellId,:]
- msk = np.choose(gain, (msk[...,0], msk[...,1], msk[...,2]))
-# o, c, a = mgh
-# a += offset[...,0]
- """
- msk = np.zeros(list(im.shape), np.uint8)
- for cc in range(im.shape[2]//cells):
- tg = gain[...,cc*cells:(cc+1)*cells]
- offset = np.choose(tg, (offsetb[...,0], offsetb[...,1], offsetb[...,2]))
- rel_cor = np.choose(tg, (rc[...,0], rc[...,1], rc[...,2]))
- tim = im[...,cc*cells:(cc+1)*cells]
- tim = tim - offset
- #tim2 = copy.copy(tim[tg == 1])
- # correction using linear slopes
- if do_rel_gain:
- tim /= rel_cor
- # correction using inverse hook function
- #cm = c[tg == 1]*rc[...,1][tg == 1]
- #mo = rc[...,1][tg == 1]*o[tg == 1]
- #tim2 = (cm*lambertw(-a[tg == 1]*np.exp((mo-tim2)/cm)/cm)+tim2)/rc[...,1][tg == 1]
- #tim[tg == 1] = tim2
-
- im[...,cc*cells:(cc+1)*cells] = tim
-# mask[(tg > tbounds[...,0]) & (tg < tbounds[...,1]),1] |= 2**7
-# mask[tim <= 5*noise[...,0], 0] |= 2**7
- msk[...,cc*cells:(cc+1)*cells,:] = mask
- """
-
- #msk[im <= 5.*noise, 0] |= 2**7
-
- outfile["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)] = np.rollaxis(np.rollaxis(im,1), 2)
- outfile["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/gain".format(channel)] = np.rollaxis(np.rollaxis(gain,1), 2)
- outfile["INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/mask".format(channel)] = np.rollaxis(np.rollaxis(msk,1), 2)
- except Exception as e:
- print(e)
- finally:
- if outfile is not None:
- outfile.close()
-
-
-done = False
-first_files = []
-while not done:
- inp = []
- dones = []
- first = True
- for i in range(16):
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- if qm in mapped_files and not mapped_files[qm].empty():
- fname_in = mapped_files[qm].get()
- dones.append(mapped_files[qm].empty())
- else:
- first_files.append((None, None))
- continue
- fout = os.path.abspath("{}/{}".format(out_folder, (os.path.split(fname_in)[-1]).replace("RAW", "CORR")))
- if first:
- first_files.append((fname_in, fout))
- inp.append((fname_in, fout, i, offsets[i][...,:max_cells,:].astype(np.float32), base_offsets[i][...,:max_cells,:].astype(np.float32),
- rel_gains[i][...,:max_cells,:], thresholds[i][...,:max_cells,:], masks[i][...,:max_cells,:], do_rel_gain, noise[i][...,:max_cells,:],
- tbounds[i][...,:max_cells,:], med_gain_hook[i]))
- first = False
- p = partial(correct_module, max_cells, il_mode)
- r = view.map_sync(p, inp)
-# r = list(map(p, inp))
- done = all(dones)
-
-
-
-
-# In[6]:
-
-corrected = []
-raw = []
-gains = []
-for i, ff in enumerate(first_files):
- try:
- rf, cf = ff
- if rf is None:
- raise Exception("File not present")
- infile = h5py.File(rf, "r")
- raw.append(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(i)][:max_cells:2,0,...]))
- infile.close()
-
- infile = h5py.File(cf, "r")
- corrected.append(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(i)][:max_cells,...]))
- gains.append(np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/gain".format(i)][:max_cells,...]))
- infile.close()
-
- except Exception as e:
- print(e)
- corrected.append(np.zeros((max_cells, 512, 128)))
-
-
-# In[16]:
-
-combined = combine_stack(corrected, corrected[0].shape[0])
-combined_raw = combine_stack(raw, raw[0].shape[0])
-combined_g = combine_stack(gains, gains[0].shape[0])
-
-
-# In[24]:
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined_raw[...][:1300,400:1600],axis=0), vmin=0, vmax=8000, cmap="jet")
-fig.colorbar(im, ax=ax)
-fig.savefig("{}/mean_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined[...][:1300,400:1600], axis=0), vmin=0, vmax=1000, cmap="jet")
-fig.colorbar(im, ax=ax)
-fig.savefig("{}/mean_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined_g[...][:1300,400:1600], axis=0), vmin=0, vmax=3, cmap="jet")
-fig.colorbar(im, ax=ax)
-fig.savefig("{}/mean_first_train_GAIN.png".format(out_folder))
-
-
-# In[26]:
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined_raw[...][:1300,400:1600],axis=0), vmin=0, vmax=8000, cmap="jet")
-plt.colorbar(im, ax=ax)
-fig.savefig("{}/std_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined[...][:1300,400:1600], axis=0), vmin=0, vmax=20000, cmap="jet")
-plt.colorbar(im, ax=ax)
-fig.savefig("{}/std_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined_g[...][:1300,400:1600], axis=0), vmin=0, vmax=3, cmap="jet")
-plt.colorbar(im, ax=ax)
-fig.savefig("{}/std_first_train_GAIN.png".format(out_folder))
-
-
-# In[25]:
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined_raw[...][:1300,400:1600],axis=0), vmin=0, vmax=8000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined[...][:1300,400:1600], axis=0), vmin=0, vmax=20000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(10,5))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined_g[...][:1300,400:1600], axis=0), vmin=0, vmax=3, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_GAIN.png".format(out_folder))
-
-
-
-
diff --git a/AGIPD/finalize.sh b/AGIPD/finalize.sh
deleted file mode 100644
index 2a5aa764237c68e31e565624f86613b509084947..0000000000000000000000000000000000000000
--- a/AGIPD/finalize.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/bin/bash
-echo 'Running finalize script'
-python3 -c "from slurm_tools import finalize; finalize(['974134', '974135', '974136', '974137', '974138', '974139', '974140', '974141', '974142', '974143', '974144', '974145', '974146', '974147', '974148', '974149'], '/gpfs/exfel/data/user/haufs/scripted_offline_cal/pycalibration/AGIPD/slurm_tmp_e3f3a6c7-8aea-44fd-956f-572ae71b4812', 'AGIPD Calibration', 'AGIPD Flat Fields', 'S. Hauf', '0.1')"
diff --git a/AGIPD/outtest.rst b/AGIPD/outtest.rst
deleted file mode 100644
index 420d96aaa92c3f40e6054ae6781a47721bef1447..0000000000000000000000000000000000000000
--- a/AGIPD/outtest.rst
+++ /dev/null
@@ -1,1336 +0,0 @@
-
-Gain Characterization (Flat Fields)
-===================================
-
-The following code characterizes the gain of the AGIPD detector from
-flat field data, i.e. data with X-rays of defined intensity. This data
-should fullfil the following requirements:
-
-- intensity should be such that single photon peaks are visible
-- data for all modules should be present
-- no shadowing should occur on any of the modules
-- each pixel should have at minimum arround 100 events per photon peak
- per memory cell
-- if central beam edges are visible, they should not be significantly
- more intense
-
-Characterization is done by a weighted average algorithm, which
-evaluates the peak locations for all pixels and memory cells of a given
-module. These locations are then fitted to a linear function of the
-average peak location in each module, such that it yield a relative gain
-correction.
-
-.. code:: ipython3
-
- # std library imports
- from functools import partial
- import h5py
- import os
-
- # numpy and matplot lib specific
- import numpy as np
- import matplotlib
- matplotlib.use("Agg")
- import matplotlib.pyplot as plt
- %matplotlib inline
-
- # parallel processing via ipcluster
- # make sure a cluster is running with ipcluster start --n=32, give it a while to start
- from ipyparallel import Client
- profile = "noDB" # SLURMHINT: profile, str
- client = Client(profile=profile)
- view = client[:]
- view.use_dill()
-
- # pyDetLib imports
- import XFELDetAna.xfelpycaltools as xcal
- import XFELDetAna.xfelpyanatools as xana
- from XFELDetAna.util import env
- env.iprofile = profile
-
- from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
-
- # usually no need to change these lines
- sensor_size = [128, 512]
- block_size = [128, 512]
- QUADRANTS = 4
- MODULES_PER_QUAD = 4
- DET_FILE_INSET = "AGIPD"
- IL_MODE = False # SLURMHINT: il_mode, bool
- IL_MODE = False
- # the following lines should be adjusted depending on data
- in_folder = "/gpfs/exfel/exp/SPB/201830/p900019/raw/" # SLURMHINT: rawpath, str
- out_folder = "/gpfs/exfel/exp/SPB/201830/p900019/proc/calibration/FF/" # SLURMHINT: out_folder, str
- # the runs the flat field data is in
- runs = [494,495] # SLURMHINT: runs, list
-
- runs = ["r{:04d}".format(r) for r in runs]
-
- local_output = False # SLURMHINT: local_output, bool
-
- db_output = True # SLURMHINT: db_output, bool
- db_input = db_output
-
- bias_voltage = 500 # SLURMHINT: bias_voltage, float
- cal_db_interface = "tcp://max-exfl015:5005" # SLURMHINT: db_host, str
-
- # change this to the offsets that should be used
- offset_store = "/gpfs/exfel/data/scratch/haufs/calibration/280218/AGIPD/agipd_offset_store_r0386_r0387_r0388.h5" # SLURMHINT: offset_store, str
-
- # cells in raw data
- max_cells = 64 # SLURMHINT: maxcells, int
- # actual memory cells: max_cells//2 if AGIPD is in interleaved mode
- memory_cells = 64 # SLURMHINT: cells, int
- # sequences to take data from
- sequences = range(1,2) # SLURMHINT: sequences, list
- # modules to characterize
- modules = range(1) # SLURMHINT: modules, list
-
- photon_energy = 9.4 # SLURMHINT: photon_energy, float
-
- limit_trains = 20
- limit_trains_eval = 200
-
- print("Parameters are:")
- print("Memory cells: {}/{}".format(memory_cells, max_cells))
- print("Runs: {}".format(runs))
- print("Modules: {}".format(modules))
- print("Sequences: {}".format(sequences))
- print("Interlaced mode: {}".format(IL_MODE))
- print("Using DB: {}".format(db_output))
-
- # these lines can usually stay as is
- fbase = "{}/{{}}/RAW-{{}}-AGIPD{{:02d}}-S{{:05d}}.h5".format(in_folder)
- gains = np.arange(3)
- cells = np.arange(max_cells)
-
-
-.. parsed-literal::
-
- Disabled GPU usage after pyCuda import failed!: libcuda.so.1: cannot open shared object file: No such file or directory
- Using Cython were available
- Parameters are:
- Memory cells: 64/64
- Runs: ['r0494', 'r0495']
- Modules: range(0, 1)
- Sequences: range(1, 2)
- Interlaced mode: False
- Using DB: True
-
-
-For the characterization offset maps for each module are needed. In the
-following these are read in
-
-.. code:: ipython3
-
- from dateutil import parser
- offset_g = {}
- noise_g = {}
- thresholds_g = {}
- if not db_input:
- store_file = h5py.File(offset_store, "r")
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- offset_g[qm] = np.array(store_file["{}/Offset/0/data".format(qm)])
- noise_g[qm] = np.array(store_file["{}/Noise/0/data".format(qm)])
- thresholds_g[qm] = np.array(store_file["{}/Threshold/0/data".format(qm)])
- store_file.close()
- else:
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- metadata = ConstantMetaData()
- offset = Constants.AGIPD.Offset()
- metadata.calibration_constant = offset
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- offset_g[qm] = offset.data
-
- metadata = ConstantMetaData()
- noise = Constants.AGIPD.Noise()
- metadata.calibration_constant = noise
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- noise_g[qm] = noise.data
-
- metadata = ConstantMetaData()
- thresholds = Constants.AGIPD.ThresholdsDark()
- metadata.calibration_constant = thresholds
-
- # set the operating condition
- condition = Conditions.Dark.AGIPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.AGIPD1M1, qm))
- metadata.retrieve(cal_db_interface)
- thresholds_g[qm] = thresholds.data
-
-Initial peak estimates
-----------------------
-
-The following parallel code will read in the flat field runs, offset
-correct them and then, bin data of each module into histograms.
-
-These histograms should then be inspected for initial peak estimates for
-the single, double, ... photon peaks, as well es estimates of the
-relative hights of these peaks to one and another.
-
-.. code:: ipython3
-
- def hist_single_module(fbase, runs, sequences, sensor_size, memory_cells, block_size,
- il_mode, limit_trains, profile, inp):
- """ This function calculates a per-pixel histogram for a single module
-
- Runs and sequences give the data to calculate histogram from
- """
- channel, offset, thresholds = inp
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- from XFELDetAna.util import env
- env.iprofile = profile
-
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
-
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size,
- offset,
- nCells=memory_cells,
- blockSize=block_size,
- gains=[0,1,2])
- offset_cor.mapper = offset_cor._view.map_sync
- offset_cor.debug() # force non-parallel processing since outer function will run concurrently
- hist_calc = xcal.HistogramCalculator(sensor_size,
- bins=4000,
- range=(-4000, 8000),
- blockSize=block_size)
- hist_calc.mapper = hist_calc._view.map_sync
- hist_calc.debug() # force non-parallel processing since outer function will run concurrently
- for run in runs:
- for seq in sequences:
- fname = fbase.format(run, run.upper(), channel, seq)
- d, ga, c = read_fun(fname, channel)
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
- hist_calc.fill(d)
- h, e, c, _ = hist_calc.get()
- return h, e, c
-
- inp = []
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], thresholds_g[qm]))
-
- p = partial(hist_single_module, fbase, runs, sequences,
- sensor_size, memory_cells, block_size, IL_MODE, limit_trains, profile)
- res_uncorr = view.map_sync(p, inp)
-
-We inspect the resulting histograms for the estimates. Modules should
-look roughly the same as no significant deviation is to be expected.
-Non-function modules and artifacts of single pixels may be visible in
-the histogram.
-
-.. code:: ipython3
-
- d = []
- qms = []
- for i, r in enumerate(res_uncorr):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- qms.append(qm)
- h, e, c = r
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid'
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(0, 500),
- x_label="Intensity (ADU)",
- y_label="Counts")
-
- fig.savefig("{}/FF_module_{}_peak_pos.png".format(out_folder, ",".join(qms)))
-
-
-
-.. image:: outtest_files/outtest_7_0.png
-
-
-.. code:: ipython3
-
- # these should be quite stable
- peak_estimates = [0, 55, 110, 165, 220]
- peak_heights = [5e7, 5e6, 1e6, 5e5, 1e5]
- peak_sigma = [5., 5., 5., 5., 5.]
-
-Calculate relative gain per module
-----------------------------------
-
-Using the so obtained estimates, we calculate the relative gain per
-module. For this we use the weighted average method implemented in
-pyDetLib.
-
-Since for current AGIPD data taking only every second memory cells sees
-X-rays we account for this. For technical reasons, the subsequent cell
-will have the same constants copied, which are irrelevant as not X-rays
-are to be expected in these cells.
-
-.. code:: ipython3
-
- block_size = [64, 64]
- def relgain_single_module(fbase, runs, sequences, peak_estimates,
- peak_heights, peak_sigma, memory_cells, sensor_size,
- block_size, il_mode, profile, limit_trains_eval, inp):
- """ A function for calculated the relative gain of a single AGIPD module
- """
-
- # import needed inline for parallel processing
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- from XFELDetAna.util import env
- env.iprofile = profile
-
- channel, offset, thresholds, noise = inp
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size, offset, nCells=memory_cells,
- blockSize=block_size, gains=[0,1,2])
- offset_cor.mapper = offset_cor._view.map_sync
-
- rel_gain = xcal.GainMapCalculator(sensor_size,
- peak_estimates,
- peak_sigma,
- nCells=memory_cells,
- peakHeights = peak_heights,
- noiseMap=noise,
- deviationType="relative")
- rel_gain.mapper = rel_gain._view.map_sync
- for run in runs:
- for seq in sequences:
- fname = fbase.format(run, run.upper(), channel, seq)
- d, ga, c = read_fun(fname, channel)
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
- rel_gain.fill(d, cellTable=c)
-
- gain_map = rel_gain.get()
- gain_map_func = rel_gain.getUsingFunc(inverse=False)
-
- pks, stds = rel_gain.getRawPeaks()
- return gain_map, pks, stds, gain_map_func
-
- inp = []
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], thresholds_g[qm], noise_g[qm][...,0]))
-
- p = partial(relgain_single_module, fbase, runs, sequences,
- peak_estimates, peak_heights, peak_sigma, memory_cells,
- sensor_size, block_size, IL_MODE, profile, limit_trains_eval)
- res_gain = list(map(p, inp)) # don't run concurently as inner function are parelllized
-
-Finally, we inspect the results, by plotting the number of entries, peak
-locations and resulting gain maps for each peak. In the course of doing
-so, we identify bad pixels by either having 0 entries for a peak, or
-having ``nan`` values for the peak location.
-
-.. code:: ipython3
-
- from mpl_toolkits.axes_grid1 import AxesGrid
-
-
- gain_m = {}
- flatsff = {}
- gainoff_g = {}
- entries_g = {}
- mask_g = {}
- cell_to_preview = 12
- masks_eval_peaks = [1, 2]
- global_eval_peaks = [1]
- global_meds = {}
-
- for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- print(qm)
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- _, entries, stds, sow = gain
- gain_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- gain_mdb = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- entries_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells, 5))
- gainoff_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- mask_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells), np.uint8)
-
- gainoff_g[qm] = gainoff_db
- gain_m[qm] = gain_mdb
- entries_g[qm] = entries_db
-
- # create a mask for unregular pixels
- # first bit set if first peak has nan entries
- for pk in masks_eval_peaks:
- mask_db[(~np.isfinite(pks[...,pk])) | (np.abs(1-pks[...,pk]/np.nanmedian(pks[...,pk]) > 0.8) )] += 1
- # second bit set if entries are 0 for first peak
- mask_db[entries[...,1] == 0] += 2
- # third bit set if entries of a given adc show significant noise
- stds = []
- for ii in range(8):
- for jj in range(8):
- stds.append(np.std(entries_db[ii*16:(ii+1)*16,jj*64+2:(jj+1)*64-2,:,1], axis=(0,1)))
- avg_stds = np.median(np.array(stds), axis=0)
-
- for ii in range(8):
- for jj in range(8):
- std = np.std(entries_db[ii*16:(ii+1)*16,jj*64+2:(jj+1)*64-2,:,1], axis=(0,1))
- if np.any(std > 10*avg_stds):
- mask_db[ii*16:(ii+1)*16,jj*64:(jj+1)*64,std > avg_stds] +=4
-
- mask_g[qm] = mask_db
-
- flat = np.zeros((gains.shape[0], gains.shape[1], memory_cells, 3))
- for j in range(2,5):
- flat[...,j-2] = np.mean(entries[...,j]/np.mean(entries[...,j]))
- flat = np.mean(flat, axis=3)
- #flat_db = np.zeros((gains.shape[0], gains.shape[1], memory_cells))
- #for j in range(2):
- # flat_db[...,j::2] = flat
- flatsff[qm] = flat
-
- global_meds[qm] = np.nanmedian(pks[...,global_eval_peaks][np.max(mask_db, axis=2) != 0])
-
-
- fig = plt.figure(figsize=(10,10))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(5, 2),
- axes_pad=0.0,
- share_all=True,
- label_mode="L",
- cbar_location="top",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
- for j in range(5):
- im = grid[2*j].imshow(entries[...,cell_to_preview,j],
- interpolation="nearest", vmin=0, vmax=500)
- grid.cbar_axes[2*j].colorbar(im)
- im = grid[2*j+1].imshow(pks[...,cell_to_preview,j], interpolation="nearest", vmin=0, vmax=400)
- grid.cbar_axes[2*j+1].colorbar(im)
- fig.savefig("{}/entries_peaks_mod{}.png".format(out_folder, qm)) # SLURMHINT fig_output
-
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- print(gains.shape)
- im = ax.imshow(gains[...,cell_to_preview,0], interpolation="nearest", vmin=0.85, vmax=1.15)
- fig.colorbar(im)
- fig.savefig("{}/gain_m_mod{}.png".format(out_folder, qm)) # SLURMHINT fig_output
-
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- im = ax.imshow(gains[...,cell_to_preview,1], interpolation="nearest", vmin=-2, vmax=2)
- fig.colorbar(im)
- fig.savefig("{}/gain_b_mod{}.png".format(out_folder, qm)) # SLURMHINT fig_output
-
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(111)
- im = ax.imshow(mask_db[...,cell_to_preview], interpolation="nearest")
- fig.colorbar(im)
- fig.savefig("{}/mask_mod{}.png".format(out_folder, qm)) # SLURMHINT fig_output
-
-
-.. parsed-literal::
-
- Q1M1
-
-
-.. parsed-literal::
-
- /gpfs/exfel/data/user/haufs/karabo/extern/lib/python3.4/site-packages/numpy/core/fromnumeric.py:639: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
- a.partition(kth, axis=axis, kind=kind, order=order)
-
-
-.. parsed-literal::
-
- (128, 512, 64, 2)
-
-
-
-.. image:: outtest_files/outtest_12_3.png
-
-
-
-.. image:: outtest_files/outtest_12_4.png
-
-
-
-.. image:: outtest_files/outtest_12_5.png
-
-
-
-.. image:: outtest_files/outtest_12_6.png
-
-
-Here we save the relevant constants
-
-.. code:: ipython3
-
- if local_output:
- ofile = "{}/agipd_gain_store_{}_modules_{}.h5".format(out_folder, "_".join(runs), "_".join([str(m) for m in modules]))
- store_file = h5py.File(ofile, "w")
- for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- gainmap, entires, stds, sow = gain
- store_file["/{}/Gain/0/data".format(qm)] = gains[...,0]
- store_file["/{}/GainOffset/0/data".format(qm)] = gains[...,1]
- store_file["/{}/Flat/0/data".format(qm)] = flatsff[qm]
- store_file["/{}/Entries/0/data".format(qm)] = entires
- store_file["/{}/BadPixels/0/data".format(qm)] = mask_g[qm]
- store_file.close()
-
-.. code:: ipython3
-
- if db_output:
- for i, r in enumerate(res_gain):
- ii = list(modules)[i]
- qm = "Q{}M{}".format(ii//4+1, ii%4+1)
-
- gain, pks, std, gfunc = r
- gains, errors, chisq, valid, max_dev, stats = gfunc
- gainmap, entires, stds, sow = gain
-
- device = getattr(Detectors.AGIPD1M1, qm)
- # gains related
- metadata = ConstantMetaData()
- gain = Constants.AGIPD.SlopesFF()
- gain.data = gains
- metadata.calibration_constant = gain
-
- # set the operating condition
- condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,
- pixels_x=512, pixels_y=128, beam_energy=None)
-
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
-
- # bad pixels
- metadata = ConstantMetaData()
- bp = Constants.AGIPD.BadPixelsFF()
- bp.data = mask_g[qm]
- metadata.calibration_constant = bp
-
- # set the operating condition
- condition = Conditions.Illuminated.AGIPD(memory_cells, bias_voltage, 9.2,
- pixels_x=512, pixels_y=128, beam_energy=None)
-
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=device)
- metadata.send(cal_db_interface)
-
-Sanity check
-------------
-
-Finally, we perform a correction of the data used to derive the gain
-constants with said constants. We expect that the histograms of all
-modules now align.
-
-.. code:: ipython3
-
- def hist_single_module(fbase, runs, sequences, il_mode, profile, limit_trains, memory_cells, inp):
- channel, offset, thresholds, relgain = inp
- gain, pks, std, gfunc = relgain
- gains, errors, chisq, valid, max_dev, stats = gfunc
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
- import copy
- from XFELDetAna.util import env
- env.iprofile = profile
- sensor_size = [128, 512]
-
- block_size = [64, 64]
- def read_fun(filename, channel):
-
- infile = h5py.File(filename, "r", driver="core")
- count = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- if limit_trains is not None:
- last_index = min(limit_trains*memory_cells+first_index, last_index)
-
- im = np.array(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index:last_index,...])
- carr = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index]
- cells = np.squeeze(np.array(carr))
- infile.close()
-
-
- if il_mode:
- ga = im[1::2, 0, ...]
- im = im[0::2, 0, ...].astype(np.float32)
- else:
- ga = im[:, 1, ...]
- im = im[:, 0, ...].astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- ga = np.rollaxis(ga, 2)
- ga = np.rollaxis(ga, 2, 1)
- return im, ga, cells
-
- offset_cor = xcal.OffsetCorrection(sensor_size, offset, nCells=memory_cells, blockSize=block_size, gains=[0,1,2])
- offset_cor.debug()
-
- hist_calc = xcal.HistogramCalculator(sensor_size, bins=2000, range=(0, 2000), blockSize=block_size)
- hist_calc.debug()
-
- hist_calc_uncorr = xcal.HistogramCalculator(sensor_size, bins=2000, range=(0, 2000), blockSize=block_size)
- hist_calc_uncorr.debug()
-
-
- for run in runs:
- for seq in sequences:
-
- fname = fbase.format(run, run.upper(), channel, seq)
-
- d, ga, c = read_fun(fname, channel)
-
- # we need to do proper gain thresholding
- g = np.zeros(ga.shape, np.uint8)
- g[...] = 2
- for cc in range(g.shape[2]//memory_cells):
- tga = ga[...,cc*memory_cells:(cc+1)*memory_cells]
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
- tg[tga < thresholds[...,1]] = 1
- tg[tga < thresholds[...,0]] = 0
- g[...,cc*memory_cells:(cc+1)*memory_cells] = tg
- d = offset_cor.correct(d, cellTable=c, gainMap=g)
-
- hist_calc_uncorr.fill(d)
- #for cc in range(g.shape[2]//memory_cells):
- # td = d[...,cc*memory_cells:(cc+1)*memory_cells]
- # td = (td - relgainoff)/relgain
- # d[...,cc*memory_cells:(cc+1)*memory_cells] = td
- d = (d-gains[..., c, 1])/gains[..., c, 0]
- hist_calc.fill(d)
-
- h, e, c, _ = hist_calc.get()
- hu = hist_calc_uncorr.get()
- return h, e, c, hu[0]
-
- inp = []
- for i in modules:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
-
- inp.append((i, offset_g[qm], thresholds_g[qm], res_gain[i]))
-
- p = partial(hist_single_module, fbase, runs, sequences, IL_MODE, profile, limit_trains, memory_cells)
- res = view.map_sync(p, inp)
-
-
-.. code:: ipython3
-
- from iminuit import Minuit
- from iminuit.util import make_func_code, describe
- from IPython.display import HTML, display
- import tabulate
-
- # fitting
- par_ests = {}
- par_ests["mu0"] = 0
- par_ests["mu1"] = 50
- par_ests["mu2"] = 100
- par_ests["limit_mu0"] = [-25, 25]
- par_ests["limit_mu1"] = [25, 75]
- par_ests["limit_mu2"] = [75, 125]
- par_ests["s0"] = 5
- par_ests["s1"] = 5
- par_ests["s2"] = 5
-
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 1
-
- def gaussian3(x, mu0, s0, A0, mu1, s1, A1, mu2, s2, A2):
- return (A0/np.sqrt(2*np.pi*s0**2)*np.exp(-0.5*((x-mu0)/s0)**2) +
- A1/np.sqrt(2*np.pi*s1**2)*np.exp(-0.5*((x-mu1)/s1)**2) +
- A2/np.sqrt(2*np.pi*s2**2)*np.exp(-0.5*((x-mu2)/s2)**2))
-
-
- f_sig = describe(gaussian)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- # chi2 = sum((y-self.f(x,*arg))**2 for x,y in zip(self.x,self.y))
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
-
- d = []
- y_range_max = 0
- table = []
- headers = ['Module',
- 'FWHM (cor.) [ADU]', 'Separation (cor.) [$\sigma$]',
- 'FWHM (uncor.) [ADU]', 'Separation (uncor.) [$\sigma$]',
- 'Improvement'
- ]
- for i, r in enumerate(res):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- row = []
- row.append(qm)
-
- h, e, c, hu = r
-
-
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': '{}: corrected'.format(qm)
- })
-
- idx = (h > 0) & np.isfinite(h)
- h_non_zero = h[idx]
- c_non_zero = c[idx]
- par_ests["A0"] = np.float(h[np.argmin(abs(c-0))])
- par_ests["A1"] = np.float(h[np.argmin(abs(c-50))])
- par_ests["A2"] = np.float(h[np.argmin(abs(c-100))])
- wrapped = _Chi2Functor(gaussian3, c_non_zero, h_non_zero,
- np.sqrt(h_non_zero))
-
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- xt = np.arange(0, 200)
-
- yt = gaussian3(xt, m.values['mu0'], m.values['s0'], m.values['A0'],
- m.values['mu1'], m.values['s1'], m.values['A1'],
- m.values['mu2'], m.values['s2'], m.values['A2'])
-
- d.append({
- 'x': xt,
- 'y': yt,
- 'label': '{}: corrected (fit)'.format(qm)
- })
-
-
- d.append({
- 'x': c,
- 'y': hu,
- 'drawstyle': 'steps-mid',
- 'label': '{}: uncorrected'.format(qm)
- })
-
- row += [m.values['s1']*2.35, (m.values['mu1']-m.values['mu0'])/m.values['s1']]
-
-
- idx = (hu > 0) & np.isfinite(hu)
- h_non_zero = hu[idx]
- c_non_zero = c[idx]
- wrapped = _Chi2Functor(gaussian3, c_non_zero, h_non_zero,
- np.sqrt(h_non_zero))
-
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- xt = np.arange(0, 200)
-
- yt = gaussian3(xt, m.values['mu0'], m.values['s0'], m.values['A0'],
- m.values['mu1'], m.values['s1'], m.values['A1'],
- m.values['mu2'], m.values['s2'], m.values['A2'])
-
- d.append({
- 'x': xt,
- 'y': yt,
- 'label': '{}: uncorrected (fit)'.format(qm)
- })
-
- row += [m.values['s1']*2.35, (m.values['mu1']-m.values['mu0'])/m.values['s1']]
-
- row.append("{:0.2f} %".format(100*(row[3]/row[1]-1)))
-
- y_range_max = max(y_range_max, np.max(h[c>25])*1.5)
-
- # output table
- table.append(row)
-
- fig = xana.simplePlot(d, y_log=False, figsize="2col",
- aspect=2,
- x_range=(0, 200),
- legend='top-right-frame',
- y_range=(0, y_range_max),
- x_label='Energy (ADU)',
- y_label='Counts')
-
- display(HTML(tabulate.tabulate(table, tablefmt='html', headers=headers,
- numalign="right", floatfmt="0.2f")))
-
-
-
-.. raw:: html
-
- <hr>
-
-
-
-.. raw:: html
-
-
- <table>
- <tr>
- <td title="Minimum value of function">FCN = 133881082.05771542</td>
- <td title="Total number of call to FCN so far">TOTAL NCALL = 960</td>
- <td title="Number of call in last migrad">NCALLS = 960</td>
- </tr>
- <tr>
- <td title="Estimated distance to minimum">EDM = 1.2477106491270762e-06</td>
- <td title="Maximum EDM definition of convergence">GOAL EDM = 1e-05</td>
- <td title="Error def. Amount of increase in FCN to be defined as 1 standard deviation">
- UP = 1.0</td>
- </tr>
- </table>
-
- <table>
- <tr>
- <td align="center" title="Validity of the migrad call">Valid</td>
- <td align="center" title="Validity of parameters">Valid Param</td>
- <td align="center" title="Is Covariance matrix accurate?">Accurate Covar</td>
- <td align="center" title="Positive definiteness of covariance matrix">PosDef</td>
- <td align="center" title="Was covariance matrix made posdef by adding diagonal element">Made PosDef</td>
- </tr>
- <tr>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">False</td>
- </tr>
- <tr>
- <td align="center" title="Was last hesse call fail?">Hesse Fail</td>
- <td align="center" title="Validity of covariance">HasCov</td>
- <td align="center" title="Is EDM above goal EDM?">Above EDM</td>
- <td align="center"></td>
- <td align="center" title="Did last migrad call reach max call limit?">Reach calllim</td>
- </tr>
- <tr>
- <td align="center" style="background-color:#92CCA6">False</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">False</td>
- <td align="center"></td>
- <td align="center" style="background-color:#92CCA6">False</td>
- </tr>
- </table>
-
-
-
-
-.. raw:: html
-
-
- <table>
- <tr>
- <td><a href="#" onclick="$('#DQdhYIELXA').toggle()">+</a></td>
- <td title="Variable name">Name</td>
- <td title="Value of parameter">Value</td>
- <td title="Parabolic error">Parab Error</td>
- <td title="Minos lower error">Minos Error-</td>
- <td title="Minos upper error">Minos Error+</td>
- <td title="Lower limit of the parameter">Limit-</td>
- <td title="Upper limit of the parameter">Limit+</td>
- <td title="Is the parameter fixed in the fit">FIXED</td>
- </tr>
-
- <tr>
- <td>1</td>
- <td>mu0</td>
- <td>-15.3044</td>
- <td>0.00259452</td>
- <td>0</td>
- <td>0</td>
- <td>-25.0</td>
- <td>25.0</td>
- <td></td>
- </tr>
-
- <tr>
- <td>2</td>
- <td>s0</td>
- <td>14.0036</td>
- <td>0.000802586</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>3</td>
- <td>A0</td>
- <td>1.54495e+08</td>
- <td>31110.8</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>4</td>
- <td>mu1</td>
- <td>46.6531</td>
- <td>0.00022721</td>
- <td>0</td>
- <td>0</td>
- <td>25.0</td>
- <td>75.0</td>
- <td></td>
- </tr>
-
- <tr>
- <td>5</td>
- <td>s1</td>
- <td>12.3036</td>
- <td>0.00026397</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>6</td>
- <td>A1</td>
- <td>1.25953e+07</td>
- <td>214.933</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>7</td>
- <td>mu2</td>
- <td>102.944</td>
- <td>0.000573639</td>
- <td>0</td>
- <td>0</td>
- <td>75.0</td>
- <td>125.0</td>
- <td></td>
- </tr>
-
- <tr>
- <td>8</td>
- <td>s2</td>
- <td>13.8301</td>
- <td>0.000768015</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>9</td>
- <td>A2</td>
- <td>3.62659e+06</td>
- <td>138.521</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- </table>
-
- <pre id="DQdhYIELXA" style="display:none;">
- <textarea rows="24" cols="50" onclick="this.select()" readonly>\begin{tabular}{|c|r|r|r|r|r|r|r|c|}
- \hline
- & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\
- \hline
- 1 & mu0 & -1.530e+01 & 2.595e-03 & & & -2.500e+01 & 2.500e+01 & \\
- \hline
- 2 & s0 & 1.400e+01 & 8.026e-04 & & & & & \\
- \hline
- 3 & A0 & 1.545e+08 & 3.111e+04 & & & & & \\
- \hline
- 4 & mu1 & 4.665e+01 & 2.272e-04 & & & 2.500e+01 & 7.500e+01 & \\
- \hline
- 5 & s1 & 1.230e+01 & 2.640e-04 & & & & & \\
- \hline
- 6 & A1 & 1.260e+07 & 2.149e+02 & & & & & \\
- \hline
- 7 & mu2 & 1.029e+02 & 5.736e-04 & & & 7.500e+01 & 1.250e+02 & \\
- \hline
- 8 & s2 & 1.383e+01 & 7.680e-04 & & & & & \\
- \hline
- 9 & A2 & 3.627e+06 & 1.385e+02 & & & & & \\
- \hline
- \end{tabular}</textarea>
- </pre>
-
-
-
-
-.. raw:: html
-
- <hr>
-
-
-
-.. raw:: html
-
- <hr>
-
-
-
-.. raw:: html
-
-
- <table>
- <tr>
- <td title="Minimum value of function">FCN = 125923523.06719978</td>
- <td title="Total number of call to FCN so far">TOTAL NCALL = 1427</td>
- <td title="Number of call in last migrad">NCALLS = 1427</td>
- </tr>
- <tr>
- <td title="Estimated distance to minimum">EDM = 1.8729638413100447e-05</td>
- <td title="Maximum EDM definition of convergence">GOAL EDM = 1e-05</td>
- <td title="Error def. Amount of increase in FCN to be defined as 1 standard deviation">
- UP = 1.0</td>
- </tr>
- </table>
-
- <table>
- <tr>
- <td align="center" title="Validity of the migrad call">Valid</td>
- <td align="center" title="Validity of parameters">Valid Param</td>
- <td align="center" title="Is Covariance matrix accurate?">Accurate Covar</td>
- <td align="center" title="Positive definiteness of covariance matrix">PosDef</td>
- <td align="center" title="Was covariance matrix made posdef by adding diagonal element">Made PosDef</td>
- </tr>
- <tr>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">False</td>
- </tr>
- <tr>
- <td align="center" title="Was last hesse call fail?">Hesse Fail</td>
- <td align="center" title="Validity of covariance">HasCov</td>
- <td align="center" title="Is EDM above goal EDM?">Above EDM</td>
- <td align="center"></td>
- <td align="center" title="Did last migrad call reach max call limit?">Reach calllim</td>
- </tr>
- <tr>
- <td align="center" style="background-color:#92CCA6">False</td>
- <td align="center" style="background-color:#92CCA6">True</td>
- <td align="center" style="background-color:#92CCA6">False</td>
- <td align="center"></td>
- <td align="center" style="background-color:#92CCA6">False</td>
- </tr>
- </table>
-
-
-
-
-.. raw:: html
-
-
- <table>
- <tr>
- <td><a href="#" onclick="$('#FPgsBavqdj').toggle()">+</a></td>
- <td title="Variable name">Name</td>
- <td title="Value of parameter">Value</td>
- <td title="Parabolic error">Parab Error</td>
- <td title="Minos lower error">Minos Error-</td>
- <td title="Minos upper error">Minos Error+</td>
- <td title="Lower limit of the parameter">Limit-</td>
- <td title="Upper limit of the parameter">Limit+</td>
- <td title="Is the parameter fixed in the fit">FIXED</td>
- </tr>
-
- <tr>
- <td>1</td>
- <td>mu0</td>
- <td>-5.95935</td>
- <td>0.00138655</td>
- <td>0</td>
- <td>0</td>
- <td>-25.0</td>
- <td>25.0</td>
- <td></td>
- </tr>
-
- <tr>
- <td>2</td>
- <td>s0</td>
- <td>13.8924</td>
- <td>0.000711989</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>3</td>
- <td>A0</td>
- <td>7.43719e+07</td>
- <td>7143.32</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>4</td>
- <td>mu1</td>
- <td>45.1214</td>
- <td>0.00059322</td>
- <td>0</td>
- <td>0</td>
- <td>25.0</td>
- <td>75.0</td>
- <td></td>
- </tr>
-
- <tr>
- <td>5</td>
- <td>s1</td>
- <td>14.7315</td>
- <td>0.000657353</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>6</td>
- <td>A1</td>
- <td>1.20825e+07</td>
- <td>685.796</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>7</td>
- <td>mu2</td>
- <td>99.9679</td>
- <td>0.00280685</td>
- <td>0</td>
- <td>0</td>
- <td>75.0</td>
- <td>125.0</td>
- <td></td>
- </tr>
-
- <tr>
- <td>8</td>
- <td>s2</td>
- <td>-25.7588</td>
- <td>0.00354927</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- <tr>
- <td>9</td>
- <td>A2</td>
- <td>5.05777e+06</td>
- <td>547.509</td>
- <td>0</td>
- <td>0</td>
- <td></td>
- <td></td>
- <td></td>
- </tr>
-
- </table>
-
- <pre id="FPgsBavqdj" style="display:none;">
- <textarea rows="24" cols="50" onclick="this.select()" readonly>\begin{tabular}{|c|r|r|r|r|r|r|r|c|}
- \hline
- & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\
- \hline
- 1 & mu0 & -5.959e+00 & 1.387e-03 & & & -2.500e+01 & 2.500e+01 & \\
- \hline
- 2 & s0 & 1.389e+01 & 7.120e-04 & & & & & \\
- \hline
- 3 & A0 & 7.437e+07 & 7.143e+03 & & & & & \\
- \hline
- 4 & mu1 & 4.512e+01 & 5.932e-04 & & & 2.500e+01 & 7.500e+01 & \\
- \hline
- 5 & s1 & 1.473e+01 & 6.574e-04 & & & & & \\
- \hline
- 6 & A1 & 1.208e+07 & 6.858e+02 & & & & & \\
- \hline
- 7 & mu2 & 9.997e+01 & 2.807e-03 & & & 7.500e+01 & 1.250e+02 & \\
- \hline
- 8 & s2 & -2.576e+01 & 3.549e-03 & & & & & \\
- \hline
- 9 & A2 & 5.058e+06 & 5.475e+02 & & & & & \\
- \hline
- \end{tabular}</textarea>
- </pre>
-
-
-
-
-.. raw:: html
-
- <hr>
-
-
-
-.. raw:: html
-
- <table>
- <thead>
- <tr><th>Module </th><th style="text-align: right;"> FWHM (cor.) [ADU]</th><th style="text-align: right;"> Separation (cor.) [$\sigma$]</th><th style="text-align: right;"> FWHM (uncor.) [ADU]</th><th style="text-align: right;"> Separation (uncor.) [$\sigma$]</th><th>Improvement </th></tr>
- </thead>
- <tbody>
- <tr><td>Q1M1 </td><td style="text-align: right;"> 28.91</td><td style="text-align: right;"> 5.04</td><td style="text-align: right;"> 34.62</td><td style="text-align: right;"> 3.47</td><td>19.73 % </td></tr>
- </tbody>
- </table>
-
-
-
-.. image:: outtest_files/outtest_18_9.png
-
-
diff --git a/AGIPD/parallel_hist.py b/AGIPD/parallel_hist.py
deleted file mode 100644
index 89c03130055dd6cd47e10e5b36d56ea55cf8b394..0000000000000000000000000000000000000000
--- a/AGIPD/parallel_hist.py
+++ /dev/null
@@ -1,73 +0,0 @@
-in_folder = "/gpfs/exfel/exp/SPB/201701/"
-proposals = ["p002013"]
-detector = "AGIPD"
-
-import warnings
-warnings.filterwarnings('ignore')
-
-import copy
-import os
-import h5py
-import numpy as np
-import sys
-from functools import partial
-
-files = sys.argv[2].split(",")
-profile = sys.argv[3]
-outpath = sys.argv[4]
-modules = sys.argv[5].split(",")
-
-
-# parallel processing via ipcluster
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-client = Client(profile=profile)
-view = client[:]
-view.use_dill()
-
-
-
-mhists = np.zeros((74, 64, 500, 500))
-
-
-for i, file in enumerate(files):
- module = int(modules[i])
- print("Processing {} to {} on {} for module {}".format(file, outpath, profile, module))
- try:
-
- infile = h5py.File(file, "r")
- cells = np.squeeze(infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/cellId".format(module)][()])
- infile.close()
-
- def do_cellhist(file, module, cells, cell):
- import numpy as np
- import h5py
- cidx = cells == cell
- infile = h5py.File(file, "r")
- icell = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(module)][cidx,0,:,:64]
- gcell = infile["/INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/image/data".format(module)][cidx,1,:,:64]
- infile.close()
- rowh = []
- for row in range(64):
-
- h, _, _ = np.histogram2d(gcell[...,row].flatten(),
- icell[...,row].flatten(), bins=(500, 500),
- range=((0,12000),(0,12000)))
- rowh.append(h)
- return rowh
-
-
- mapped_data = [c for c in np.unique(cells)]
- res = view.map_sync(partial(do_cellhist, file, module, cells), mapped_data)
-
- for cell, cellh in enumerate(res):
- for row, h in enumerate(cellh):
- mhists[cell, row, ...] += h
-
-
- except Exception as e:
- print(e)
- pass
-
-
-np.savez_compressed("{}/mod{}_{}".format(outpath, module, profile), mhists)
\ No newline at end of file
diff --git a/AGIPD/run_parallel_hist.py b/AGIPD/run_parallel_hist.py
deleted file mode 100644
index f2e5ac9198594915ef260be1d7ad2bd15cda7932..0000000000000000000000000000000000000000
--- a/AGIPD/run_parallel_hist.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import sys
-in_folder = sys.argv[1] #"/gpfs/exfel/exp/SPB/201701/"
-proposals = sys.argv[2].split(",") #["p002013"]
-out_path = sys.argv[3]
-exclude = sys.argv[4]
-detector = "AGIPD"
-files_per_node = 50
-
-import warnings
-warnings.filterwarnings('ignore')
-
-import copy
-import os
-import os
-from subprocess import Popen, PIPE
-from time import sleep
-import time
-from uuid import uuid4
-
-modules = range(3,4)
-
-excludes = []
-for ex in exclude.split(","):
- if "-" in ex:
- start, end = ex.split("-")
-
- excludes += ["r{:04d}".format(r) for r in range(int(start), int(end))]
- else:
- excludes.append("r{:04d}".format(int(ex)))
-
-print("Excluding: {}".format(", ".join(excludes)))
-
-if proposals == "all":
- proposals = os.listdir(in_folder)
-
-files = []
-for proposal in proposals:
- pfolder = "{}/{}/raw".format(in_folder, proposal)
- runs = os.listdir(pfolder)
- for run in runs:
- if run in excludes:
- continue
- rfolder = "{}/{}".format(pfolder, run)
- for file in os.listdir(rfolder):
- for module in modules:
- fcheck = "RAW-{}-{}{:02d}".format(run.upper(), detector, module)
- if fcheck in file:
- files.append((module, "{}/{}".format(rfolder, file)))
-
-print("Working on {} files".format(len(files)))
-
-srun_base = ["sbatch", "-p", "exfel", "-t", "04:00:00"]
-
-srun_base += [os.path.abspath("{}/run_parallel_hist.sh".format(os.getcwd())),
- os.path.abspath("{}/parallel_hist.py".format(os.getcwd())),
- in_folder
- ]
-for i in range(len(files)//files_per_node):
- fls = []
- modules = []
- for fm in files[i*files_per_node:(i+1)*files_per_node]:
- module, file = fm
- fls.append(file)
- modules.append(str(module))
- s_run = copy.copy(srun_base)
- s_run += [",".join(fls), "parallel_hist_{}".format(uuid4()), out_path, ",".join(modules), exclude]
- Popen(s_run)
\ No newline at end of file
diff --git a/AGIPD/run_parallel_hist.sh b/AGIPD/run_parallel_hist.sh
deleted file mode 100755
index 4357fcae08bff1b6d2a8c045a4aa41067b1d597d..0000000000000000000000000000000000000000
--- a/AGIPD/run_parallel_hist.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-#SBATCH -N 1 # number of nodes
-#SBATCH -n 32 # number of cores
-#module load python3
-#~/.local/bin/ipcluster start --n=32 --daemon --ip="*"
-source /gpfs/exfel/data/user/haufs/karabo/activate
-ipython profile create ${4} --parallel
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/ipcluster start --n=32 --profile=${4} --daemon &
-sleep 30
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/python "$@"
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/ipcluster stop --profile=${4}
-rm -rf "/home/haufs/.ipython/profile_${4}"
-#echo ${11}
\ No newline at end of file
diff --git a/AGIPD/slurm_CI.py b/AGIPD/slurm_CI.py
deleted file mode 100644
index 0b245074da82eae31f87163fa543735770bcc67a..0000000000000000000000000000000000000000
--- a/AGIPD/slurm_CI.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import argparse
-import copy
-import glob
-import os
-from subprocess import Popen, PIPE
-from time import sleep
-import time
-from uuid import uuid4
-
-parser = argparse.ArgumentParser(description="Main entry point "
- "for offline calibration")
-parser.add_argument("--rawpath", type=str)
-parser.add_argument("--output", type=str)
-parser.add_argument("--mem-cells", type=str)
-parser.add_argument("--runs", type=str)
-parser.add_argument("--sequences", type=str)
-parser.add_argument("--interlaced", action="store_true", default=False)
-parser.add_argument("--modules", type=str, default="all")
-parser.add_argument("--nodb", action="store_true", default=False)
-parser.add_argument("--bias", type=str, default=500)
-parser.add_argument("--dbhost", type=str, default="tcp://max-exfl015:5005")
-
-
-def notebook_to_python():
- nb_name = "Chracterize_AGIPD_Gain_PC.ipynb"
- conv = ["jupyter", "nbconvert", "--to", "python",
- nb_name, "--output", "conv_tmp"]
-
- Popen(conv).wait()
-
- mapping = {}
- arg_cnt = 1
- has_profile = False
- with open("./conv_tmp.py", "r") as infile:
- with open(nb_name.replace("ipynb", "py"), "w") as outfile:
- outfile.write("import sys")
- for line in infile.readlines():
- if "SLURMHINT" in line:
-
- line, hint = line.split("#")
- field, assign = line.split("=")
- parm = hint.split("SLURMHINT:")[1]
- parm, typ = parm.split(",")
- parm = parm.strip()
- if parm == "profile":
- mapping[-1] = parm
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=-1)
- else:
- mapping[arg_cnt] = parm
- if typ.strip() != "list":
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=arg_cnt)
- else:
- line = "{field} = [int(s) for s in sys.argv[{arg_cnt}].split(',')]\n".format(field=field, arg_cnt=arg_cnt)
- arg_cnt += 1
- outfile.write(line)
- else:
- if "get_ipython()" in line:
- line = "# "+line
- outfile.write(line)
-
- return mapping
-
-
-def run():
- args = vars(parser.parse_args())
- path_temp = "{}/r{{:04d}}".format(args["rawpath"])
- out_folder= args["output"]
- cells = args["mem_cells"]
- il_mode = bool(args["interlaced"])
- mods = args["modules"]
- runs_in = args["runs"]
- sequences = args["sequences"]
- modules = range(16) if mods.upper()=="ALL" else [int(m) for m in mods.split(",")]
- nodb = bool(args["nodb"])
- bias = args["bias"]
- dbhost = args["dbhost"]
-
- runs = []
- for rcomp in runs_in.split(","):
- if "-" in rcomp:
- start, end = rcomp.split("-")
- runs += list(range(int(start), int(end)))
- else:
- runs += [int(rcomp)]
-
-
- out_mapping = notebook_to_python()
-
- for module in modules:
- in_mapping = {"maxcells": cells,
- "cells": cells,
- "path_temp": path_temp,
- "il_mode": il_mode,
- "modules": module,
- "runs": ",".join([str(r) for r in runs]),
- "out_folder": out_folder,
- "seq": sequences,
- "local_output": True if nodb else False,
- "db_output": False if nodb else True,
- "bias_voltage": bias,
- "db_host": dbhost
- }
-
- srun_base = ["sbatch", "-p", "exfel", "-t", "24:00:00"]
-
- srun_base += [os.path.abspath("{}/slurm_CI.sh".format(os.getcwd())),
- os.path.abspath("{}/Chracterize_AGIPD_Gain_PC.py".format(os.getcwd()))]
-
- for key in sorted(out_mapping.keys()):
- if out_mapping[key] in in_mapping:
- srun_base.append(str(in_mapping[out_mapping[key]]))
- Popen(srun_base).wait()
-
-if __name__ == "__main__":
- run()
-
-
-
diff --git a/AGIPD/slurm_CI.sh b/AGIPD/slurm_CI.sh
deleted file mode 100644
index 57dc6a7f850952c60a9fb9c67013ada5fbfff84c..0000000000000000000000000000000000000000
--- a/AGIPD/slurm_CI.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-source /gpfs/exfel/data/user/haufs/karabo/activate
-uuid=$(uuidgen)
-echo "File is: ${2}"
-echo ${uuid} >> $2
-export MPLBACKEND=AGG
-ipython profile create ${uuid} --parallel
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/ipcluster start --n=32 --profile=${uuid} --daemon &
-sleep 30
-echo "Running script"
-jupyter nbconvert --to rst --ExecutePreprocessor.timeout=36000 --ExecutePreprocessor.allow_errors=True --TemplateExporter.exclude_input=True --execute $1
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/ipcluster stop --profile=${uuid}
-rm -rf "/home/haufs/.ipython/profile_${uuid}"
-
diff --git a/AGIPD/slurm_DARK.py b/AGIPD/slurm_DARK.py
deleted file mode 100644
index 154fa0fcd0e48759b72e7a9cde508fb797b82a68..0000000000000000000000000000000000000000
--- a/AGIPD/slurm_DARK.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import argparse
-import copy
-import glob
-import os
-from subprocess import Popen, PIPE
-from time import sleep
-import time
-from uuid import uuid4
-
-parser = argparse.ArgumentParser(description="Main entry point "
- "for offline calibration")
-parser.add_argument("--rawpath", type=str, default="/gpfs/exfel/exp/SPB/201830/p900019/raw/")
-parser.add_argument("--output", type=str, default="/gpfs/exfel/data/scratch/haufs/calibration/280218/AGIPD/dark")
-parser.add_argument("--mem-cells", type=str, default=64)
-parser.add_argument("--run-high", type=str)
-parser.add_argument("--run-med", type=str)
-parser.add_argument("--run-low", type=str)
-parser.add_argument("--sequences", type=str, default=1)
-parser.add_argument("--nodb", action="store_true", default=False)
-parser.add_argument("--bias", type=str, default=500)
-parser.add_argument("--dbhost", type=str, default="tcp://max-exfl015:5005")
-
-
-def notebook_to_python():
- nb_name = "Characterize_AGIPD_Gain_Darks.ipynb"
- conv = ["jupyter", "nbconvert", "--to", "python",
- nb_name, "--output", "conv_tmp"]
-
- Popen(conv).wait()
-
- mapping = {}
- arg_cnt = 1
- has_profile = False
- with open("./conv_tmp.py", "r") as infile:
- with open(nb_name.replace("ipynb", "py"), "w") as outfile:
- outfile.write("import sys")
- for line in infile.readlines():
- if "SLURMHINT" in line:
-
- line, hint = line.split("#")
- field, assign = line.split("=")
- parm = hint.split("SLURMHINT:")[1]
- parm, typ = parm.split(",")
- parm = parm.strip()
- if parm == "profile":
- mapping[-1] = parm
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=-1)
- else:
- mapping[arg_cnt] = parm
- if typ.strip() != "list":
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=arg_cnt)
- else:
- line = "{field} = [int(s) for s in sys.argv[{arg_cnt}].split(',')]\n".format(field=field, arg_cnt=arg_cnt)
- arg_cnt += 1
- outfile.write(line)
- else:
- if "get_ipython()" in line:
- line = "# "+line
- outfile.write(line)
-
- return mapping
-
-
-def check_run(run):
- if run.upper()[0] != "R":
- run = "r{:04d}".format(int(run))
- return run.lower()
-
-def run():
- args = vars(parser.parse_args())
-
- raw_path = args["rawpath"]
- out_folder= args["output"]
- cells = args["mem_cells"]
- run_high = check_run(args["run_high"])
- run_med = check_run(args["run_med"])
- run_low = check_run(args["run_low"])
- nodb = bool(args["nodb"])
- bias = args["bias"]
- dbhost = args["dbhost"]
-
- sequences = args["sequences"]
-
-
- out_mapping = notebook_to_python()
-
-
- in_mapping = {"mem_cells": cells,
- "run_high": run_high,
- "run_med": run_med,
- "run_low": run_low,
- "out_folder": out_folder,
- "seq": sequences,
- "rawpath": raw_path,
- "local_output": True if nodb else False,
- "db_output": False if nodb else True,
- "bias_voltage": bias,
- "db_host": dbhost
- }
-
- srun_base = ["sbatch", "-p", "exfel", "-t", "24:00:00"]
-
- srun_base += [os.path.abspath("{}/slurm_CI.sh".format(os.getcwd())),
- os.path.abspath("{}/Characterize_AGIPD_Gain_Darks.py".format(os.getcwd()))]
-
- for key in sorted(out_mapping.keys()):
- if out_mapping[key] in in_mapping:
- srun_base.append(str(in_mapping[out_mapping[key]]))
- Popen(srun_base).wait()
-
-if __name__ == "__main__":
- run()
-
-
-
diff --git a/AGIPD/slurm_FF.py b/AGIPD/slurm_FF.py
deleted file mode 100644
index 8358c131f86bf099cdb8b49c02cf80d856dd846f..0000000000000000000000000000000000000000
--- a/AGIPD/slurm_FF.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import argparse
-import copy
-import glob
-import os
-from subprocess import Popen, PIPE, check_output
-from time import sleep
-import time
-from uuid import uuid4
-
-from slurm_tools import notebook_to_python, combine_report, make_report
-
-parser = argparse.ArgumentParser(description="Main entry point "
- "for offline calibration")
-parser.add_argument("--rawpath", type=str)
-parser.add_argument("--output", type=str)
-parser.add_argument("--mem-cells", type=str)
-parser.add_argument("--runs", type=str)
-parser.add_argument("--sequences", type=str)
-parser.add_argument("--offset-store", type=str)
-parser.add_argument("--interlaced", action="store_true", default=False)
-parser.add_argument("--modules", type=str, default="all")
-parser.add_argument("--nodb", action="store_true", default=False)
-parser.add_argument("--bias", type=float, default=500)
-parser.add_argument("--dbhost", type=str, default="tcp://max-exfl015:5005")
-parser.add_argument("--photon-energy", type=float)
-
-
-def run():
- nb_name = "Characterize_AGIPD_Gain_FlatFields.ipynb"
- run_uuid = uuid4()
- run_tmp_path = "{}/slurm_tmp_{}".format(os.getcwd(), run_uuid)
-
- os.makedirs(run_tmp_path)
-
- args = vars(parser.parse_args())
- rawpath = args["rawpath"]
- out_folder= args["output"]
- cells = args["mem_cells"]
- il_mode = bool(args["interlaced"])
- mods = args["modules"]
- runs_in = args["runs"]
- seq_in = args["sequences"]
- offset_store = args["offset_store"]
- modules = range(16) if mods.upper()=="ALL" else [int(m) for m in mods.split(",")]
- nodb = bool(args["nodb"])
- bias = args["bias"]
- dbhost = args["dbhost"]
- photon_energy = args["photon_energy"]
-
- runs = []
- for rcomp in runs_in.split(","):
- if "-" in rcomp:
- start, end = rcomp.split("-")
- runs += list(range(int(start), int(end)))
- else:
- runs += [int(rcomp)]
-
- seqs = []
- for rcomp in seq_in.split(","):
- if "-" in rcomp:
- start, end = rcomp.split("-")
- seqs += list(range(int(start), int(end)))
- else:
- seqs += [int(rcomp)]
-
- joblist = []
- for module in modules:
- out_mapping = notebook_to_python(nb_name, run_tmp_path, module)
- in_mapping = {"maxcells": cells,
- "cells": cells,
- "rawpath": rawpath,
- "il_mode": il_mode,
- "modules": module,
- "runs": ",".join([str(r) for r in runs]),
- "out_folder": out_folder,
- "offset_store": offset_store,
- "sequences": ",".join([str(s) for s in seqs]),
- "local_output": True if nodb else False,
- "db_output": False if nodb else True,
- "bias_voltage": bias,
- "db_host": dbhost,
- "photon_energy": photon_energy
- }
-
- srun_base = ["sbatch", "-p", "exfel", "-t", "24:00:00"]
-
- srun_base += [os.path.abspath("{}/slurm_CI.sh".format(os.getcwd())),
- os.path.abspath("{}/{}_{}.ipynb".format(run_tmp_path,
- nb_name.replace("ipynb", ""),
- module))]
- srun_base += ["{}/argfile_{}".format(run_tmp_path, module)]
- with open("{}/argfile_{}".format(run_tmp_path, module), "w") as argfile:
- for key in sorted(out_mapping.keys()):
- if out_mapping[key] in in_mapping:
- #srun_base.append(str(in_mapping[out_mapping[key]]))
- argfile.write(str(in_mapping[out_mapping[key]])+"\n")
-
- print(srun_base)
- output = check_output(srun_base).decode('utf8')
- print(output)
- for line in output.split("\n"):
- if "Submitted batch job " in line:
- joblist.append(line.split(" ")[3])
- print("Submitted jobs: {}".format(joblist))
-
- cmd = '"from slurm_tools import finalize; finalize({joblist}, \'{run_path}\', \'{project}\', \'{calibration}\', \'{author}\', \'{version}\')"'
- with open("{}/finalize.sh".format(run_tmp_path), "w") as finfile:
- finfile.write("#!/bin/bash\n")
- finfile.write("echo 'Running finalize script'\n")
- finfile.write("python3 -c {}\n".format(cmd.format(joblist=joblist, run_path=run_tmp_path, project="AGIPD Calibration",
- calibration="AGIPD Flat Fields", author="S. Hauf", version="0.1")))
-
- srun_base = ["sbatch", "-p", "exfel", "-t", "24:00:00", "--chdir", os.getcwd(), "{}/finalize.sh".format(run_tmp_path)]
-
- Popen(srun_base).wait()
- #sphinx_path = combine_report(run_tmp_path, "AGIPD Flat Field Cal.")
- #make_report(sphinx_path, "AGIPD Calibration", "S. Hauf", "0.1")
-
-if __name__ == "__main__":
- run()
-
-
diff --git a/AGIPD/slurm_tools.py b/AGIPD/slurm_tools.py
deleted file mode 100644
index b28ca3510fabde142e25c0e2df87e70475d6e454..0000000000000000000000000000000000000000
--- a/AGIPD/slurm_tools.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import argparse
-import copy
-import glob
-from jinja2 import Template
-import os
-from os.path import isfile, isdir, splitext
-import shutil
-from subprocess import Popen, PIPE, check_output
-import textwrap
-from time import sleep
-import time
-from uuid import uuid4
-
-
-argcode = """
-argv = []
-with open('{}/argfile_{}', 'r') as argfile:
- for line in argfile.readlines():
- argv.append(line.strip())
-"""
-
-
-def notebook_to_python(nb_name, run_path, module):
-
- conv = ["jupyter", "nbconvert", "--to", "notebook",
- nb_name, "--output", "{}/conv_tmp".format(run_path)]
-
- Popen(conv).wait()
-
- mapping = {}
- arg_cnt = 0
- has_profile = False
- first_source = True
- with open("{}/conv_tmp.ipynb".format(run_path), "r") as infile:
- with open("{}/{}_{}.ipynb".format(run_path, nb_name.replace("ipynb", ""), module), "w") as outfile:
- celltype = None
- for line in infile.readlines():
- if "cell_type" in line:
- _, typ = line.split(":")
- if "code" in typ:
- celltype = "code"
- else:
- celltype = None
- if '"source": [' in line and first_source and celltype == "code":
-
- first_source = False
- for aline in argcode.format(run_path, module).split("\n"):
- line += '"{}\\n",\n'.format(aline)
-
- if "SLURMHINT" in line and celltype == "code":
-
- line, hint = line.split("#")
- field, assign = line.split("=")
- field = field.strip()[1:]
- parm = hint.split("SLURMHINT:")[1]
- parm, typ, _ = parm.split(",")
- typ = typ.strip().replace("\\n", "")
- parm = parm.strip()
- if parm == "profile":
- mapping[-1] = parm
- line = '"{field} = {typ}(argv[{arg_cnt}])\\n",\n'.format(field=field,
- typ=typ[:-1],
- arg_cnt=-1)
- else:
- mapping[arg_cnt] = parm
- if "list" not in typ:
- line = '"{field} = {typ}(argv[{arg_cnt}])\\n",\n'.format(field=field,
- typ=typ[:-1],
- arg_cnt=arg_cnt)
- else:
- ltmp = '"{field} = [int(s) for s in argv[{arg_cnt}].split(\',\')]\\n",\n'
- line = ltmp.format(field=field, arg_cnt=arg_cnt)
- arg_cnt += 1
- outfile.write(line)
-
- return mapping
-
-
-def combine_report(run_path, calibration):
- sphinx_path = "{}/sphinx_rep".format(os.path.abspath(run_path))
- os.makedirs(sphinx_path)
- direntries = os.listdir(run_path)
-
- for entry in direntries:
-
- if isfile("{}/{}".format(run_path, entry)):
- name, ext = splitext("{}".format(entry))
-
- if ext == ".rst":
- group, module = name.split(".")
- with open("{}/{}.rst".format(sphinx_path, group), "a") as gfile:
- title = "{} - {}".format(calibration, module)
- gfile.write(title + "\n")
- gfile.write( "=" *len (title) + "\n")
- gfile.write("\n")
- with open("{}/{}".format(run_path, entry), "r") as ifile:
- for line in ifile.readlines():
- gfile.write(line)
- gfile.write("\n\n")
- if isdir("{}/{}".format(run_path, entry)):
- shutil.copytree("{}/{}".format(run_path, entry), "{}/{}".format(sphinx_path, entry))
- return sphinx_path
-
-def make_report(run_path, project, author, version):
- run_path = os.path.abspath(run_path)
- try:
- import subprocess
- subprocess.check_call(["sphinx-quickstart",
- "--quiet",
- "--project={}".format(project),
- "--author={}".format(author),
- "-v", str(version),
- "--suffix=.rst",
- "--master=index",
- "--ext-intersphinx",
- "--ext-mathjax",
- "--makefile",
- "--no-batchfile", run_path])
-
- except subprocess.CalledProcessError:
- raise Exception("Failed to run sphinx-quickbuild. Is sphinx installed?"
- "Generated simple index.rst instead")
-
- # quickbuild went well we need to edit the index file
- from shutil import move
-
- direntries = os.listdir(run_path)
- files_to_handle = []
- for entry in direntries:
- if isfile("{}/{}".format(run_path, entry)):
- name, ext = splitext("{}".format(entry))
- if ext == ".rst" and "index" not in name:
- files_to_handle.append(name)
-
- with open("{}/index.rst.tmp".format(run_path), "w") as mf:
- with open("{}/index.rst".format(run_path), "r") as mfr:
- indexTmp = Template('''
- .. toctree::
- :maxdepth: 2
- {% for k in keys %}
- {{ k }}
- {%- endfor %}
- ''')
- for line in mfr:
- line = line.replace(".. toctree::", textwrap.dedent(
- indexTmp.render(keys=files_to_handle)))
- line = line.replace(":maxdepth: 2", "")
- line = line.replace("Documentation", "Calibration")
- mf.write(line)
- cdir = os.getcwd()
-
- os.remove("{}/index.rst".format(run_path))
- move("{}/index.rst.tmp".format(run_path), "{}/index.rst".format(run_path))
-
- # finally call the make scripts
-
- os.chdir(run_path)
- try:
- import subprocess
- subprocess.check_call(["make", "latexpdf"])
-
- except subprocess.CalledProcessError:
- self.log.ERROR("Failed to make html documentation")
-
-def finalize(joblist, run_path, project, calibration, author, version):
-
-
- print("Waiting on jobs to finish: {}".format(joblist))
- while True:
- found_jobs = set()
- output = check_output(['squeue']).decode('utf8')
- for line in output.split("\n"):
- for job in joblist:
- if str(job) in line:
- found_jobs.add(job)
- if len(found_jobs) == 0:
- break
- sleep(10)
- sphinx_path = combine_report(run_path, calibration)
- make_report(sphinx_path, project, author, version)
-
diff --git a/LPD/Characterize_LPD_GAIN_CI.py b/LPD/Characterize_LPD_GAIN_CI.py
deleted file mode 100644
index efac995f3a872e99f0708518806c8b0ddfa7c64c..0000000000000000000000000000000000000000
--- a/LPD/Characterize_LPD_GAIN_CI.py
+++ /dev/null
@@ -1,917 +0,0 @@
-import sys
-# coding: utf-8
-
-# # LPD Gain Characterization (Charge Injection) #
-#
-# The following code characterizes the gain of the LPD detector from charge injection data, i.e. data with charge injected into the amplifiers, bypassing the sensor. The data needs to fulfil the following requirements:
-#
-# * each file should represent one scan point for one mudle, defined by detector gain setting
-# and charge injections setting
-# * settings need to overlap at at least one point for two neighboring gain ranges
-# * 100 samples or more per pixel and memory cell should be present for each setting.
-#
-# The data is then analyzed by calcualting the per-pixel, per memory cell mean of the samples for each setting. These means are then normalized to the median peak position of a all means of the first module. Overlapping settings in neighboring gain ranges are used to deduce the slopes of the different gains with respect to the high gain setting.
-
-# In[37]:
-
-
-# std library imports
-from functools import partial
-import h5py
-import os
-
-# numpy and matplot lib specific
-import numpy as np
-import matplotlib
-matplotlib.use("agg")
-import matplotlib.pyplot as plt
-# get_ipython().magic('matplotlib inline')
-
-# parallel processing via ipcluster
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-profile = str(sys.argv[-1])
-view = Client(profile=profile)[:]
-view.use_dill()
-
-
-import warnings
-warnings.filterwarnings('ignore')
-
-# pyDetLib imports
-import XFELDetAna.xfelpycaltools as xcal
-import XFELDetAna.xfelpyanatools as xana
-
-# usually no need to change these lines
-sensor_size = [256, 256]
-block_size = [64, 64]
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-
-# the following lines should be adjusted depending on data
-in_folder = str(sys.argv[1])
-out_folder = str(sys.argv[2])
-
-mod_corrs = [0]*16
-# mod_corrs[0] = 1
-
-# change this to the offsets that shoudl be used
-capacitance = str(sys.argv[3])
-offset_store = str(sys.argv[4])
-
-# actual memory cells
-memory_cells = int(sys.argv[5])
-cells = np.arange(memory_cells)
-
-# modules to characterize
-modules = [int(s) for s in sys.argv[6].split(',')]
-
-# these lines can usually stay as is
-fbase = "{}/data_q{{}}m{{}}_{{}}.h5".format(in_folder)
-h5path = "/data"
-
-
-# For the characterization offset maps for each module are needed. In the following these are read in
-
-# In[38]:
-
-
-store_file = h5py.File(offset_store, "r")
-offset_g = {}
-noise_g = {}
-
-for i in modules:
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- offset_g[qm] = np.array(store_file["{}/Offset/0/data".format(qm)])
- noise_g[qm] = np.array(store_file["{}/Noise/0/data".format(qm)])
- except:
- pass
-store_file.close()
-
-
-# The CI runs are organized into files. Each file is generated for a scan point in a charge injection scan. The following code is used for generating the scan points and identifies each file with a setting. Additionally, overlaps of neigboring gains are identified for later use.
-
-# In[39]:
-
-
-# sort out which runs are which:
-myList = []
-updated = True
-if updated:
- for b in [0, 6, 12, 18, 24, 30]:
- myList.append(['5pF', 100, 1, b])
- for b in [0, 6]:
- myList.append(['5pF', 10, 1, b])
- myList.append(['50pF', 100, 1, b])
- for b in [0, 6, 12, 18, 24, 30]:
- myList.append(['5pF', 10, 2, b])
- myList.append(['50pF', 100, 2, b])
- for b in [0, 6]:
- myList.append(['5pF', 1, 2, b])
- myList.append(['50pF', 10, 2, b])
- for b in [0, 4, 8]:
- myList.append(['5pF', 1, 4, b])
- myList.append(['50pF', 10, 4, b])
- myList.append(['50pF', 1, 4, b])
-
-else:
- for b in [0, 1, 5, 10, 15]:
- myList.append(['5pF', 100, 1, b])
- for b in [10, 15]:
- myList.append(['5pF', 10, 1, b])
- myList.append(['5pF', 1, 2, b])
- myList.append(['50pF', 100, 1, b])
- myList.append(['50pF', 10, 2, b])
- for b in [5, 10, 15]:
- myList.append(['5pF', 10, 2, b])
- myList.append(['50pF', 100, 2, b])
- for b in [0, 2, 4, 6]:
- myList.append(['5pF', 1, 4, b])
- myList.append(['50pF', 10, 4, b])
- for b in [4, 6, 10, 15]:
- myList.append(['50pF', 1, 4, b])
-
-# filter into 5pf and 50pf settings
-setting_5f = [i for i in range(len(myList)) if "5pF" in myList[i]]
-gains_5f = [int(2-np.log10(myList[i][1])) for i in range(len(myList)) if "5pF" in myList[i]]
-setting_50f = [i for i in range(len(myList)) if "50pF" in myList[i]]
-gains_50f = [int(2-np.log10(myList[i][1])) for i in range(len(myList)) if "50pF" in myList[i]]
-
-# find overlaps in settings to scale gains between ranges
-def find_overlaps(settings):
- gain_1 = [s for s in settings if myList[s][1] == 1]
- gain_10 = [s for s in settings if myList[s][1] == 10]
- gain_100 = [s for s in settings if myList[s][1] == 100]
- overlaps_100_10 = []
- for s100 in gain_100:
- for s10 in gain_10:
- if myList[s100][3] == myList[s10][3] and myList[s100][2] == myList[s10][2]:
- overlaps_100_10.append((s100, s10))
-
- overlaps_10_1 = []
- for s10 in gain_10:
- for s1 in gain_1:
- if myList[s10][3] == myList[s1][3] and myList[s10][2] == myList[s1][2]:
- overlaps_10_1.append((s10, s1))
-
- return overlaps_100_10, overlaps_10_1
-
-def seq_in_gain(settings):
- seq = []
- gains = {1: 0, 10: 0, 100: 0}
- for s in settings:
- seq.append(gains[myList[s][1]])
- gains[myList[s][1]] += 1
- return seq
-
-overlaps_100_10_5f, overlaps_10_1_5f = find_overlaps(setting_5f)
-overlaps_100_10_50f, overlaps_10_1_50f = find_overlaps(setting_50f)
-
-seq_5pf = seq_in_gain(setting_5f)
-seq_50pf = seq_in_gain(setting_50f)
-
-if capacitance == "5pf":
- overlaps_100_10 = overlaps_100_10_5f
- overlaps_10_1 = overlaps_10_1_5f
- seq = seq_5pf
- setting = setting_5f
- gains = gains_5f
-else:
- overlaps_100_10 = overlaps_100_10_50f
- overlaps_10_1 = overlaps_10_1_50f
- seq = seq_50pf
- setting = setting_50f
- gains = gains_50f
-
-
-# ## Scan point mean values ##
-#
-# The following code will read in the data relevant for a given setting in a module-parallel fashion, offset correct the data according to the gain setting and then calculate the per-pixel, per-memory-cell mean value.
-
-# In[40]:
-
-
-def get_means_single_module(fbase, settings, gains, sensor_size, memory_cells, block_size,
- inp):
- """ This function calculates a per-pixel histogram for a single module
-
- Runs and sequences give the data to calculate histogram from
- """
- channel, offset, mod_corr = inp
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
-
- def splitOffGainLPD(d):
- msk = np.zeros(d.shape, np.uint16)
- msk[...] = 0b0000111111111111
- data = np.bitwise_and(d, msk)
- gain = np.zeros(data.shape, np.uint8)
- gain[data >= 2**12] = 1
- gain[data >= 2**13] = 2
- return data, gain
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- imarr = infile["/data".format(channel)]
- im = np.array(imarr)
- infile.close()
-
- im, ga = splitOffGainLPD(im.astype(np.uint16))
- return im.astype(np.float32), ga
-
-
- means_low, means_med, means_high = [], [], []
- if offset is None:
- return means_low, means_med, means_high
-
- om = offset
- for i, setting in enumerate(settings):
- gain = gains[i]
- try:
- if i < mod_corr:
- means_high.append(np.zeros((sensor_size[0], sensor_size[1], memory_cells)))
- continue
- fname = fbase.format(channel//4+1, channel%4+1, setting)
- print(fname)
-
- d, g = read_fun(fname, channel)
-
- for cc in range(d.shape[2]//memory_cells):
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
-
- offset = np.choose(gain, (om[...,0], om[...,1], om[...,2]))
- tim = d[...,cc*memory_cells:(cc+1)*memory_cells]
-
- tim = tim - offset
- d[...,cc*memory_cells:(cc+1)*memory_cells] = tim
- print(np.mean(tim), np.mean(offset))
- mn = np.zeros((d.shape[0], d.shape[1], memory_cells))
- for cell in range(memory_cells):
- mn[...,cell] = np.nanmean(d[...,cell::memory_cells], axis=2)
- if gain == 0:
- means_high.append((setting, mn))
- elif gain == 1:
- means_med.append((setting, mn))
- else:
- means_low.append((setting, mn))
- except Exception as e:
- pass
-
- return means_low, means_med, means_high
-
-inp = []
-
-for i in modules:
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], mod_corrs[i]))
- except:
- inp.append((i, None, None))
-
-p = partial(get_means_single_module, fbase, setting, gains,
- sensor_size, memory_cells, block_size)
-res_uncorr_int = view.map_sync(p, inp)
-
-
-# We reformat data and create an array index to peak mapping
-
-# In[41]:
-
-
-res_uncorr = []
-indices_in_settings = {}
-
-for ii, r in enumerate(res_uncorr_int):
- i = list(modules)[ii]
- means_low, means_med, means_high = r
- res_uncorr.append(([m[1] for m in means_low],
- [m[1] for m in means_med],
- [m[1] for m in means_high]))
- indices_in_settings["low"] = [m[0] for m in means_low]
- indices_in_settings["med"] = [m[0] for m in means_med]
- indices_in_settings["high"] = [m[0] for m in means_high]
-
-
-# Create plots of the mean values we've evaluated for each gain and each modules
-
-# In[42]:
-
-
-cell = 1
-for ii, r in enumerate(res_uncorr):
- i = list(modules)[ii]
- means_low, means_med, means_high = r
-
- d = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- for kk, mn in enumerate(means_high):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(-1000, 4000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_high_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
- d = []
- for kk, mn in enumerate(means_med):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(-1000, 4000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_med_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
- d = []
- for kk, mn in enumerate(means_low):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(-1000, 4000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_low_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
-
-# In[47]:
-
-
-ref_pk_pos_low = np.median(np.array(res_uncorr[0][0]), axis=(1,2,3))
-ref_pk_pos_med = np.median(np.array(res_uncorr[0][1]), axis=(1,2,3))
-ref_pk_pos_high = np.median(np.array(res_uncorr[0][2]), axis=(1,2,3))
-sort_low = np.argsort(ref_pk_pos_low)
-sort_med = np.argsort(ref_pk_pos_med)
-sort_high = np.argsort(ref_pk_pos_high)
-ref_pos = ref_pk_pos_low[sort_low], ref_pk_pos_med[sort_med], ref_pk_pos_high[sort_high]
-
-
-# In[48]:
-
-
-slopes = []
-for overlap in overlaps_100_10:
- idx1, idx2 = overlap[0], overlap[1]
- idx1 = indices_in_settings["high"].index(idx1)
- idx2 = indices_in_settings["med"].index(idx2)
- #idx1 = sort_high[indices_in_settings["high"].index(idx1)]
- #idx2 = sort_med[indices_in_settings["med"].index(idx2)]
- slope = np.mean(ref_pk_pos_high[idx1]/ref_pk_pos_med[idx2])
-
- slopes.append(slope)
-slope_100_10 = np.mean(slopes)
-
-slopes = []
-for overlap in overlaps_10_1:
- idx1, idx2 = overlap[0], overlap[1]
- idx1 = indices_in_settings["med"].index(idx1)
- idx2 = indices_in_settings["low"].index(idx2)
-
- #idx1 = sort_med[indices_in_settings["med"].index(idx1)]
- #idx2 = sort_low[indices_in_settings["low"].index(idx2)]
- slope = np.mean(ref_pk_pos_med[idx1]/ref_pk_pos_low[idx2])
- slopes.append(slope)
-slope_10_1 = np.mean(slopes)
-
-
-# In[49]:
-
-
-
-cell = 1
-for ii, r in enumerate(res_uncorr):
- i = list(modules)[ii]
- means_low, means_med, means_high = r
-
- d = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- for kk, mn in enumerate(means_high):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_med):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c*slope_100_10,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_low):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c*slope_10_1*slope_100_10,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(100, 30000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_all_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
-
-# In[51]:
-
-
-for m,_ in enumerate(modules):
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(131)
- mns = np.array(res_uncorr[m][2])
- mn_im = np.zeros((mns.shape[0], 500))
- for j in range(mns.shape[0]):
- h, _ = np.histogram(mns[sort_high[j],...].flatten(), bins=500, range=(0,4096))
- mn_im[j,:] = h
- ax.imshow(np.rot90(mn_im), interpolation="nearest", aspect="auto",
- extent=[0,ref_pk_pos_high.shape[0],0,4096], cmap='jet')
-
- ax.scatter(np.arange(ref_pk_pos_high.shape[0])+0.5, ref_pk_pos_high[sort_high])
- ax.set_ylim(0, 4096)
- ax.set_xlabel("Peak index")
- ax.set_ylabel("Peak position (ADU)")
-
- ax = fig.add_subplot(132)
- mns = np.array(res_uncorr[m][1])
- mn_im = np.zeros((mns.shape[0], 500))
- for j in range(mns.shape[0]):
- h, _ = np.histogram(mns[sort_med[j],...].flatten(), bins=500, range=(0,4096))
- mn_im[j,:] = h
- ax.imshow(np.rot90(mn_im), interpolation="nearest", aspect="auto",
- extent=[0,ref_pk_pos_med.shape[0],0,4096], cmap='jet')
-
- ax.scatter(np.arange(ref_pk_pos_med.shape[0])+0.5, ref_pk_pos_med[sort_med])
- ax.set_xlabel("Peak index")
- ax.set_ylim(0, 4096)
-
-
- ax = fig.add_subplot(133)
- mns = np.array(res_uncorr[m][0])
- mn_im = np.zeros((mns.shape[0], 500))
- for j in range(mns.shape[0]):
- h, _ = np.histogram(mns[sort_low[j],...].flatten(), bins=500, range=(0,4096))
- mn_im[j,:] = h
- ax.imshow(np.rot90(mn_im), interpolation="nearest", aspect="auto",
- extent=[0,ref_pk_pos_low.shape[0],0,4096], cmap='jet')
-
- ax.scatter(np.arange(ref_pk_pos_low.shape[0]), ref_pk_pos_low[sort_low])
- ax.set_xlabel("Peak index")
- ax.set_ylim(0, 4096)
-
- fig.savefig("{}/peaks_centroid_vs_dist_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
-
-# In[52]:
-
-
-def calib_gain(gidx, sort_idx, limit_peaks=False):
- def calibrate_single_row(cells, xrd, inp):
-
- from sklearn.cluster import KMeans
- from iminuit import Minuit
- from iminuit.util import make_func_code, describe
- import numpy as np
-
- yrd = inp
-
- def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
- def lin_fun(x, m, b):
- return m*x + b
-
- # linear slope
- ml = np.zeros(yrd.shape[1:])
- bl = np.zeros(yrd.shape[1:])
- devl = np.zeros(yrd.shape[1:])
- ml[...] = np.nan
- bl[...] = np.nan
- devl[...] = np.nan
-
- failures = []
- outliers = []
- for cell in range(cells):
- for col in range(yrd.shape[-2]):
- try:
-
- y = yrd[:,col, cell]
- x = xrd
-
- parms = {'m': 1, 'b': 0}
- fitted = fit_data(lin_fun, x, y, np.sqrt(y), parms)
- yf = lin_fun(x, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((y-yf)/y))
- ml[col, cell] = fitted['m']
- bl[col, cell] = fitted['b']
- devl[col, cell] = max_devl
- if max_devl > 0.1:
- outliers.append((cell, col, y))
- except Exception as e:
- failures.append((cell, col, str(e)))
- return (ml, bl, devl), failures, outliers
-
- fres = {}
- failures = []
- outliers = []
- for i, r in enumerate(res_uncorr):
- if len(r[gidx]) == 0:
- continue
- means = np.array(r[gidx])
- inp = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- if not limit_peaks:
- for j in range(means.shape[2]):
- inp.append(means[sort_idx,:,j,:])
-
- p = partial(calibrate_single_row, memory_cells, ref_pos[gidx])
- else:
- for j in range(means.shape[2]):
- inp.append(means[limit_peaks[0]:limit_peaks[1],:,j,:])
- p = partial(calibrate_single_row, memory_cells, ref_pos[gidx][limit_peaks[0]:limit_peaks[1]])
- frs = view.map_sync(p, inp)
-
- # linear slope
- ml = np.zeros(means.shape[1:])
- bl = np.zeros(means.shape[1:])
- devl = np.zeros(means.shape[1:])
-
- for j, fr in enumerate(frs):
- lin, fails, outs = fr
- mlr, blr, devlr = lin
- failures.append(fails)
- outliers.append(outs)
-
- ml[:,j,:] = mlr
- bl[:,j,:] = blr
- devl[:,j,:] = devlr
-
- fres[qm] = {'ml': ml,
- 'bl': bl,
- 'devl': devl,
- }
- return fres, failures, outliers
-
-
-# In[53]:
-
-
-fres_high, failures, _ = calib_gain(2, sort_high)
-fres_med, failures, _ = calib_gain(1, sort_med)
-fres_low, failures, outliers = calib_gain(0, sort_low)
-
-
-# In[54]:
-
-
-print(failures[0])
-
-
-# In[55]:
-
-
-def plot_for_gain(fres):
- masks = {}
- import matplotlib.pyplot as plt
- from mpl_toolkits.axes_grid1 import AxesGrid
-
- cell_to_preview = 4
- for module, data in fres.items():
- fig = plt.figure(figsize=(20,20))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(2, 2),
- axes_pad=(0.9, 0.15),
- label_mode="1",
- share_all=True,
- cbar_location="right",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
-
- mask = np.zeros(data['ml'].shape, np.uint8)
- mask[(data['devl'] == 0)] += 2
- mask[(data['devl'] > 0.5)] += 4
- mask[(data['devl'] < 0)] += 8
- mask[(~np.isfinite(data['devl']))] += 16
-
- i = 0
- for key, item in data.items():
- med = np.abs(np.nanmedian(item))
- bound = 0.1
- max_cnt = 0
- while (np.count_nonzero((item < med-bound*med) |
- (item > med+bound*med))/item.size > 0.01):
- bound *=2
- max_cnt += 1
-
- im = grid[i].imshow(item[...,cell_to_preview], interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med, aspect=1)
- cb = grid.cbar_axes[i].colorbar(im)
-
- grid[i].text(20, 50, key, color="w", fontsize=50)
-
- i += 1
-
- im = grid[-1].imshow(mask[..., cell_to_preview], interpolation="nearest",
- vmin=0, vmax=1, aspect=1)
- cb = grid.cbar_axes[-1].colorbar(im)
-
- grid[-1].text(20, 50, "mask", color="w", fontsize=50)
-
- masks[module] = mask
- return masks
-
-
-# In[56]:
-
-
-mask_low = plot_for_gain(fres_low)
-mask_med = plot_for_gain(fres_med)
-mask_high = plot_for_gain(fres_high)
-
-
-# In[57]:
-
-
-fres = {}
-for module in fres_low.keys():
- gain_m = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.float32)
- gain_b = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.float32)
- mask = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.uint8)
-
- gain_m[...,0] = fres_low[module]['ml']
- gain_m[...,1] = np.array(fres_med[module]['ml'])/slope_100_10
- gain_m[...,2] = np.array(fres_high[module]['ml'])/(slope_10_1*slope_100_10)
-
- gain_b[...,0] = fres_low[module]['bl']
- gain_b[...,1] = fres_med[module]['bl']
- gain_b[...,2] = fres_high[module]['bl']
-
- mask[...,0] = mask_low[module]
- mask[...,1] = mask_med[module]
- mask[...,2] = mask_high[module]
-
- fres[module] = {}
- fres[module]['RelativeGain'] = gain_m
- fres[module]['RelativeGainOffset'] = gain_b
- fres[module]['BadPixels'] = mask
-
-
-# In[58]:
-
-
-
-ofile = "{}/lpd_ci_store_{}_16_{}.h5".format(out_folder,
- "_".join([str(m) for m in modules]),
- capacitance)
-store_file = h5py.File(ofile, "w")
-for qm, r in fres.items():
- for key, item in r.items():
- store_file["/{}/{}/0/data".format(qm, key)] = item
-store_file.close()
-
-
-# In[59]:
-
-
-def correct_single_module(fbase, settings, gains, sensor_size, memory_cells, block_size,
- inp):
- """ This function calculates a per-pixel histogram for a single module
-
- Runs and sequences give the data to calculate histogram from
- """
- if inp is None:
- return [], [], []
- channel, om, mod_corr, rg, rbg = inp
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
-
- def splitOffGainLPD(d):
- msk = np.zeros(d.shape, np.uint16)
- msk[...] = 0b0000111111111111
- data = np.bitwise_and(d, msk)
- gain = np.zeros(data.shape, np.uint8)
- gain[data >= 2**12] = 1
- gain[data >= 2**13] = 2
- return data, gain
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- imarr = infile["/data".format(channel)]
- im = np.array(imarr)
- infile.close()
-
- im, ga = splitOffGainLPD(im.astype(np.uint16))
- return im.astype(np.float32), ga
-
-
- means_low, means_med, means_high = [], [], []
-
- for i, setting in enumerate(settings):
- gain = gains[i]
- try:
- if i < mod_corr:
- means_high.append(np.zeros((sensor_size[0],
- sensor_size[1],
- memory_cells)))
- continue
- fname = fbase.format(channel//4+1, channel%4+1, setting)
- print(fname)
-
- d, g = read_fun(fname, channel)
- for cc in range(d.shape[2]//memory_cells):
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
-
- offset = np.choose(gain, (om[...,0], om[...,1], om[...,2]))
- rgain = np.choose(gain, (rg[...,0], rg[...,1], rg[...,2]))
- #rgainb = np.choose(gain, (rbg[...,0], rbg[...,1], rbg[...,2]))
- tim = d[...,cc*memory_cells:(cc+1)*memory_cells]
-
- tim = tim - offset
- tim = (tim)/rgain
- d[...,cc*memory_cells:(cc+1)*memory_cells] = tim
-
- mn = np.zeros((d.shape[0], d.shape[1], memory_cells))
- for cell in range(memory_cells):
- mn[...,cell] = np.nanmean(d[...,cell::memory_cells], axis=2)
- if gain == 0:
- means_high.append(mn)
- elif gain == 1:
- means_med.append(mn)
- else:
- means_low.append(mn)
- except Exception as e:
- print(e)
-
- return means_low, means_med, means_high
-
-inp = []
-for i in modules:
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g_50pf[qm], mod_corrs[i], fres[qm]['RelativeGain'], fres[qm]['RelativeGainOffset']))
- except:
- inp.append(None)
-
-p = partial(correct_single_module, fbase, setting_50f, gains_50f,
- sensor_size, memory_cells, block_size)
-res_corr = view.map_sync(p, inp)
-
-
-# In[60]:
-
-
-
-cell = 1
-for i, r in enumerate(res_corr):
- means_low, means_med, means_high = r
-
- d = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- for kk, mn in enumerate(means_high):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(0, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_med):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(0, 40000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_low):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(0, 400000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(0, 300000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
-
-# In[ ]:
-
-
-
-
-
-# In[ ]:
-
-
-
-
diff --git a/LPD/LPDChar_Darks.py b/LPD/LPDChar_Darks.py
deleted file mode 100644
index 2d1992d0be54afecf579505da5d4dc5be6387d4e..0000000000000000000000000000000000000000
--- a/LPD/LPDChar_Darks.py
+++ /dev/null
@@ -1,601 +0,0 @@
-import sys
-# coding: utf-8
-
-# # Offset Characterization #
-#
-# This notebook allows you to recharacterize dark images to get a new offset map. It will correctly handle veto settings, but note that if you veto cells you will not be able to use these offsets for runs with different veto settings - vetoed cells will have zero offset.
-#
-# Usually you will only need to alter the cell directly below this comment.
-
-# In[2]:
-
-
-from collections import OrderedDict
-in_folder = str(sys.argv[1])
-gain_runs = OrderedDict()
-gain_runs["high_5pf"] = str(sys.argv[2])
-gain_runs["med_5pf"] = str(sys.argv[3])
-gain_runs["low_5pf"] = str(sys.argv[4])
-gain_runs["high_50pf"] = str(sys.argv[5])
-gain_runs["med_50pf"] = str(sys.argv[6])
-gain_runs["low_50pf"] = str(sys.argv[7])
-
-capacitor_settings = [int(s) for s in sys.argv[8].split(',')]
-capacitor_settings = ['{}pf'.format(c) for c in capacitor_settings]
-
-out_folder = str(sys.argv[9])
-sequences = [int(s) for s in sys.argv[10].split(',')]
-max_cells = int(sys.argv[11])
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("agg")
-import matplotlib.pyplot as plt
-# get_ipython().magic('matplotlib inline')
-from ipyparallel import Client
-profile = str(sys.argv[-1])
-client = Client(profile=profile)
-view = client[:]
-view.use_dill()
-
-gains = np.arange(3)
-
-cells = np.arange(max_cells)
-
-
-thresholds_offset_sigma = 3.
-thresholds_offset_hard = [400, 1500]
-
-thresholds_noise_sigma = 5.
-thresholds_noise_hard = [1, 25]
-
-skip_first_ntrains = 10
-
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-DET_FILE_INSET = "LPD"
-
-
-# In[3]:
-
-
-def combine_stack(d, sdim):
- """
- A function for assembling LPD images (at least coarsely)
- """
- combined = np.zeros((2048,2048, sdim))
- combined[...] = np.nan
-
- map_x = [1,0,0,1]
- map_y = [1,1,0,0]
- to_map = d
- dx = 64
- dy = 0
- for q in range(4):
- if q == 1 or q == 2:
- dy = 40
- else:
- dy = 0
- if q == 3 or q == 2:
- dx = 0
- else:
- dx = 40
- qcomb = np.zeros((512,512,sdim))
- for m in range(4):
- mx = map_x[m]
- my = map_y[m]
- qcomb [mx*256:(mx+1)*256, my*256:(my+1)*256,:] = np.rollaxis(to_map[q*4+m][::-1,:, :], 1)
- mx = map_x[q]
- my = map_y[q]
- combined [mx*512+dx:(mx+1)*512+dx, my*512+dy:(my+1)*512+dy, :] = qcomb
- return combined
-
-def combine_stack_slast_rev(combined, sdim):
- """
- Revert assembled images back to tiles.
- """
- tiles = [None]*16
-
- map_x = [1,0,0,1]
- map_y = [1,1,0,0]
-
- dx = 64
- dy = 0
- for q in range(4):
- mx = map_x[q]
- my = map_y[q]
- qcomb = combined[mx*512+dx:(mx+1)*512+dx, my*512+dy:(my+1)*512+dy]
-
- if q == 1 or q == 2:
- dy = 40
- else:
- dy = 0
- if q == 3 or q == 2:
- dx = 0
- else:
- dx = 40
- qtiles = []
- for m in range(4):
- mx = map_x[m]
- my = map_y[m]
- tiles[q*4+m] = np.rollaxis(qcomb[mx*256:(mx+1)*256, my*256:(my+1)*256][::-1,:], 1)
-
- return tiles
-
-def plot_per_cell_with_hist(d, cells, figsize=None, vmin=0, vmax=8000):
- """
- Plot assembled images alongside a per super-module histogram.
- """
- if figsize is None:
- figsize = (20, 4*len(cells))
- fig = plt.figure(figsize=figsize)
- combined = combine_stack(d, d[0].shape[2])
-
- for a, i in enumerate(cells):
- ax1 = fig.add_subplot(len(cells), 2, 2*a+1)
- ax2 = fig.add_subplot(len(cells), 2, 2*a+2)
- im = ax1.imshow(combined[...,i], interpolation="nearest", vmin=vmin, vmax=vmax)
- cb = fig.colorbar(im, ax=ax1)
- cb.set_label("Cell {}".format(i))
- for ii, dd in enumerate(d):
- h, e = np.histogram(dd[..., i].flatten(), bins=1000, range=(vmin, vmax))
- c = (e[1:]+e[:-1])/2
- ax2.plot(c, h, label="module {}".format(ii))
- ax2.legend(prop={'size': 6})
-
-
-# In[4]:
-
-
-# the following will map the files for concurrent processing
-
-from queue import Queue
-if not os.path.exists(out_folder):
- os.makedirs(out_folder)
-
-def map_modules_from_files(filelist):
- module_files = {}
- mod_ids = {}
- for i in range(16):
-
- name = "Q{}M{}".format(i // 4 + 1, i % 4 +1)
- module_files[name] = Queue()
- mod_ids[name] = i
- file_infix = "{}{:02d}".format(DET_FILE_INSET, i)
- for file in filelist:
- if file_infix in file:
- module_files[name].put(file)
- return module_files, mod_ids
-
-gain_mapped_files = OrderedDict()
-for gain, run in gain_runs.items():
- ginfolder = "{}/{}".format(in_folder, run)
- dirlist = os.listdir(ginfolder)
- file_list = []
- for entry in dirlist:
- #only h5 file
- abs_entry = "{}/{}".format(ginfolder, entry)
- if os.path.isfile(abs_entry) and os.path.splitext(abs_entry)[1] == ".h5":
-
- if sequences is None:
- file_list.append(abs_entry)
- else:
- for seq in sequences:
- if "{:05d}.h5".format(seq) in abs_entry:
- file_list.append(os.path.abspath(abs_entry))
-
- mapped_files, mod_ids = map_modules_from_files(file_list)
- gain_mapped_files[gain] = mapped_files
-
-
-# In[5]:
-
-
-# the actual characterization - to not eded this without consultation
-import copy
-from functools import partial
-def characterize_module(cells, bp_thresh, skip_first_ntrains, inp):
- import numpy as np
- import copy
- import h5py
-
- def splitOffGainLPD(d):
- msk = np.zeros(d.shape, np.uint16)
- msk[...] = 0b0000111111111111
- data = np.bitwise_and(d, msk)
- msk[...] = 0b0011000000000000
- gain = np.bitwise_and(d, msk)//4096
- gain[gain > 2] = 2
- return data, gain
-
- filename, filename_out, channel = inp
- thresholds_offset_hard, thresholds_offset_sigma, thresholds_noise_hard, thresholds_noise_sigma = bp_thresh
-
- infile = h5py.File(filename, "r", driver="core")
- im = np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][skip_first_ntrains*cells:,...])
- cellid = np.squeeze(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][skip_first_ntrains*cells:,...]))
- infile.close()
-
- im, g = splitOffGainLPD(im[:, 0, ...])
- im = im.astype(np.float32)
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
-
- offset = np.zeros((im.shape[0], im.shape[1], cells))
- noise = np.zeros((im.shape[0], im.shape[1], cells))
- for cc in range(cells):
- idx = cellid == cc
- if np.any(idx):
-
- offset[...,cc] = np.median(im[:,:, idx], axis=2)
- noise[...,cc] = np.std(im[:,:,idx], axis=2)
-
- # bad pixels
- bp = np.zeros(offset.shape, np.uint8)
- # offset related bad pixels
- offset_mn = np.nanmedian(offset, axis=(0,1))
- offset_std = np.nanstd(offset, axis=(0,1))
-
- bp[(offset < offset_mn-thresholds_offset_sigma*offset_std) |
- (offset > offset_mn+thresholds_offset_sigma*offset_std)] |= 2**0
- bp[(offset < thresholds_offset_hard[0]) | (offset > thresholds_offset_hard[1])] |= 2**0
- bp[~np.isfinite(offset)] |= 2**0
-
- # noise related bad pixels
- noise_mn = np.nanmedian(noise, axis=(0,1))
- noise_std = np.nanstd(noise, axis=(0,1))
-
- bp[(noise < noise_mn-thresholds_noise_sigma*noise_std) |
- (noise > noise_mn+thresholds_noise_sigma*noise_std)] |= 2**4
- bp[(noise < thresholds_noise_hard[0]) | (noise > thresholds_noise_hard[1])] |= 2**4
- bp[~np.isfinite(noise)] |= 2**4
-
- return offset, noise, channel, bp
-
-offset_g = {}
-noise_g = {}
-badpix_g = {}
-
-gg = 0
-old_cap = None
-for gain, mapped_files in gain_mapped_files.items():
- cap = gain.split("_")[1]
- if cap != old_cap:
- gg = 0
- old_cap = cap
- offset_g[cap] = {}
- noise_g[cap] = {}
- badpix_g[cap] = {}
-
- dones = []
- inp = []
-
- for i in range(16):
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- if qm in mapped_files and not mapped_files[qm].empty():
- fname_in = mapped_files[qm].get()
- dones.append(mapped_files[qm].empty())
-
- else:
- continue
- fout = os.path.abspath("{}/{}".format(out_folder, (os.path.split(fname_in)[-1]).replace("RAW", "CORR")))
- inp.append((fname_in, fout, i))
- first = False
- p = partial(characterize_module, max_cells,
- (thresholds_offset_hard, thresholds_offset_sigma,
- thresholds_noise_hard, thresholds_noise_sigma),
- skip_first_ntrains)
- results = view.map_sync(p, inp)
- for r in results:
- offset, noise, i, bp= r
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- if qm not in offset_g[cap]:
- offset_g[cap][qm] = np.zeros((offset.shape[0], offset.shape[1], offset.shape[2], 3))
- noise_g[cap][qm] = np.zeros_like(offset_g[cap][qm])
- badpix_g[cap][qm] = np.zeros_like(offset_g[cap][qm])
- offset_g[cap][qm][...,gg] = offset
- noise_g[cap][qm][...,gg] = noise
- badpix_g[cap][qm][...,gg] = bp
- gg +=1
-
-
-# In[6]:
-
-
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-
-def show_overview(cell_to_preview, gain_to_preview, cap):
- res = {}
- for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- try:
- res[qm] = {'Offset': offset_g[cap][qm],
- 'Noise': noise_g[cap][qm],
- 'BadPixels': badpix_g[cap][qm]
- }
- except:
- res[qm] = None
-
-
- for module, data in res.items():
- if data is None:
- continue
- fig = plt.figure(figsize=(15,15))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(2, 2),
- axes_pad=(0.9, 0.15),
- label_mode="1",
- share_all=True,
- cbar_location="right",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
- i = 0
- for key, item in data.items():
- cf = 0
- if "Threshold" in key:
- cf = -1
- if len(item.shape) == 4:
- med = np.nanmedian(item[...,cell_to_preview, gain_to_preview + cf])
- else:
- med = np.nanmedian(item[...,cell_to_preview])
-
- bound = 0.2
- while(np.count_nonzero((item[...,cell_to_preview, gain_to_preview + cf] < med-np.abs(bound*med)) |
- (item[...,cell_to_preview, gain_to_preview + cf] > med+np.abs(bound*med)))/item[...,cell_to_preview, gain_to_preview + cf].size > 0.01):
- bound *=2
-
- if "BadPixels" in key:
- im = grid[i].imshow(np.log2(item[...,cell_to_preview, gain_to_preview + cf]), interpolation="nearest",
- vmin=0, vmax=8, aspect='auto')
- else:
- if len(item.shape) == 4:
- im = grid[i].imshow(item[...,cell_to_preview, gain_to_preview + cf], interpolation="nearest",
- vmin=med-np.abs(bound*med), vmax=np.abs(med+bound*med), aspect='auto')
- else:
- im = grid[i].imshow(item[...,cell_to_preview], interpolation="nearest",
- vmin=med-np.abs(bound*med), vmax=med+np.abs(bound*med), aspect='auto')
- cb = grid.cbar_axes[i].colorbar(im)
-
- grid[i].text(20, 50, key, color="w" if key != "BadPixels" else "k", fontsize=50)
-
- i += 1
- grid[0].text(5, 20, module, color="r" if key != "BadPixels" else "k", fontsize=20)
- rns = []
- for cap in capacitor_settings:
- for k, v in gain_runs.items():
- if cap in k:
- rns.append(v)
- fig.savefig("{}/dark_analysis_{}_gain_{}_{}.png".format(out_folder,
- "_".join(rns),
- gain_to_preview, cap))
-
-
-# In[7]:
-
-
-for cap in capacitor_settings:
- show_overview(4, 0, cap)
-
-
-# In[8]:
-
-
-for cap in capacitor_settings:
- show_overview(4, 1, cap)
-
-
-# In[9]:
-
-
-for cap in capacitor_settings:
- show_overview(4, 2, cap)
-
-
-# In[10]:
-
-
-channel_mapping = {}
-for i in range(16):
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- channel_mapping[qm] = i
-
-def create_constant_overview(constant, name, vmin, vmax, cap, entries=3):
- """
- Creates a few plots and statistics for characterization constants.
- """
- fig = plt.figure(figsize=(12, 7))
- ax = None
- for g in range(entries):
- ax = fig.add_subplot(3,1,g+1,sharex=ax)
- for qm in constant.keys():
- d = constant[qm][...,g]
- print("{} {}, gain {:0.2f}: mean: {:0.2f}, median: {:0.2f}, std: {:0.2f}".format(name, qm, g,
- np.mean(d),
- np.median(d),
- np.std(d)))
-
- ax.step(np.arange(max_cells), np.median(d, axis=(0,1)))
- if g == entries - 1:
- ax.set_xlabel("Memory cell")
- else:
- plt.setp(ax.get_xticklabels(), visible=False)
-
- ax.set_ylabel("{} (ADU)".format(name))
- ax.text(0.1, 0.9, "{:d}x gain".format(10**(2-g)), transform=ax.transAxes)
- rns = []
- for cap in capacitor_settings:
- for k, v in gain_runs.items():
- if cap in k:
- rns.append(v)
- fig.savefig("{}/dark_analysis_{}_{}_{}.png".format(out_folder,
- "_".join(rns),
- name, cap))
-
-
-
-# ### Inspect the Offset Constants ###
-#
-# The following will give you an overview over the deduced offset constants. Note that vetoed cells will show zero offset and that only the first 32 memory cells are displayed. Make sure what you see matches your veto settings and the veto settings of the data you intend to correct
-
-# In[11]:
-
-
-for cap in capacitor_settings:
- create_constant_overview(offset_g[cap], "Offset", 700, 1300, cap)
-
-
-# In[12]:
-
-
-for cap in capacitor_settings:
- create_constant_overview(noise_g[cap], "Noise", 0, 50, cap)
-
-
-# In[13]:
-
-
-# save everything to file.
-for cap in capacitor_settings:
- runs = [v for k, v in gain_runs.items() if cap in k]
- ofile = "{}/lpd_offset_store_{}_{}.h5".format(out_folder, "_".join(runs), cap)
- store_file = h5py.File(ofile, "w")
- for qm in offset_g[cap].keys():
- store_file["{}/Offset/0/data".format(qm)] = offset_g[cap][qm]
- store_file["{}/Noise/0/data".format(qm)] = noise_g[cap][qm]
- store_file["{}/BadPixelsDark/0/data".format(qm)] = noise_g[cap][qm]
- store_file.close()
-
-
-# In[14]:
-
-
-def show_hists(gain_to_preview, cap, ranges):
- res = OrderedDict()
- for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- try:
- res[qm] = OrderedDict()
- res[qm]['Offset'] = offset_g[cap][qm]
- res[qm]['Noise'] = noise_g[cap][qm]
- res[qm]['BadPixels'] = copy.copy(badpix_g[cap][qm])
- res[qm]['BadPixels'][res[qm]['BadPixels'] == 0] = np.nan
- except:
- res[qm] = None
- from mpl_toolkits.axes_grid1 import AxesGrid
- i = 0
- fig = plt.figure(figsize=(10, 16))
-
- for module, item in res.items():
- if item is None:
- i += 3
- continue
-
- for constant, data in item.items():
- ax = fig.add_subplot(16, 3, i+1)
-
- h, e = np.histogram(data[...,gain_to_preview], bins=ranges[constant][-1],
- range=ranges[constant][:2])
- c = (e[1:]+e[:-1])/2
- ax.step(c, h)
- if i < (len(list(res.values()))-1)*len(list(item.values())):
- plt.setp(ax.get_xticklabels(), visible=False)
- else:
- ax.set_xlabel(constant)
- if i % 3 == 0:
- ax.set_ylabel(module)
- plt.setp(ax.get_yticklabels(), visible=False)
- i += 1
- rns = []
-
- for k, v in gain_runs.items():
- if cap in k:
- rns.append(v)
- fig.savefig("{}/dark_analysis_hist_{}_gain_{}_{}.png".format(out_folder,
- "_".join(rns),
- gain_to_preview, cap))
-
-
-# In[15]:
-
-
-for cap in capacitor_settings:
- show_hists(0, cap, {'Offset': (1000, 1500, 50), 'Noise': (0, 50, 50), 'BadPixels': (0, 32, 32)})
-
-
-# In[16]:
-
-
-for cap in capacitor_settings:
- show_hists(1, cap, {'Offset': (600, 950, 50), 'Noise': (2, 5, 50), 'BadPixels': (0, 32, 32)})
-
-
-# In[17]:
-
-
-for cap in capacitor_settings:
- show_hists(2, cap, {'Offset': (600, 950, 50), 'Noise': (1, 5, 50), 'BadPixels': (0, 32, 32)})
-
-
-# In[18]:
-
-
-colors = ['yellowgreen', 'red', 'gold', 'lightskyblue',
- 'white','lightcoral','blue','pink', 'darkgreen',
- 'yellow','grey','violet','magenta','cyan']
-
-import matplotlib.patches as ptchs
-
-
-lbls = ["good", "offset", "noise", "both"]
-def show_bp(gain_to_preview, cap):
- res = OrderedDict()
- fig = plt.figure(figsize=(10,10))
- for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- try:
- bp = badpix_g[cap][qm]
- ax = fig.add_subplot(4,4,i+1)
- labels = np.unique(bp).tolist()
-
- fracs = [np.count_nonzero(bp[...,gain_to_preview] == c) for c in labels]
- explode = [0.25*int(bool(label)) for label in labels]
- patches, texts = plt.pie(fracs, explode=explode,
- shadow=True,
- startangle=90, colors=colors[:len(labels)])
- ax.text(0.35, 0.3, qm, transform=ax.transAxes, fontsize=12)
-
- for k in range(3):
- ax.add_patch(
- ptchs.Rectangle(
- (0.8, 0.9-0.1*k), # (x,y)
- 0.05, # width
- 0.05, # height
- color = colors[k+1],
-
- transform=ax.transAxes
- )
-
- )
- ax.text(0.9, 0.9-0.1*k,
- "{:0.2f}%".format(fracs[k+1]/np.sum(fracs)*100),
- transform=ax.transAxes, fontsize=10)
-
- except Exception as e:
- pass
- ax.legend(patches, lbls, loc=8, bbox_to_anchor=(1.5, 0.5))
-
-
-# In[ ]:
-
-
-
-
diff --git a/LPD/SLURM_Dark.py b/LPD/SLURM_Dark.py
deleted file mode 100644
index 8691b50cb36dad52eb5a45262b38669a30d26601..0000000000000000000000000000000000000000
--- a/LPD/SLURM_Dark.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import argparse
-import copy
-import glob
-import os
-from subprocess import Popen, PIPE
-from time import sleep
-import time
-from uuid import uuid4
-
-parser = argparse.ArgumentParser(description="Main entry point "
- "for offline calibration")
-parser.add_argument("--rawpath", type=str)
-parser.add_argument("--output", type=str)
-parser.add_argument("--mem-cells", type=str, default=128)
-parser.add_argument("--run-high", type=str)
-parser.add_argument("--run-med", type=str)
-parser.add_argument("--run-low", type=str)
-parser.add_argument("--5pF", action="store_true", default=False)
-parser.add_argument("--50pF", action="store_true", default=False)
-
-parser.add_argument("--sequences", type=str, default=1)
-
-def notebook_to_python():
- nb_name = "LPDChar_Darks.ipynb"
- conv = ["jupyter", "nbconvert", "--to", "script",
- nb_name, "--output", "conv_tmp"]
-
- Popen(conv).wait()
-
- mapping = {}
- arg_cnt = 1
- has_profile = False
- with open("./conv_tmp.py", "r") as infile:
- with open(nb_name.replace("ipynb", "py"), "w") as outfile:
- outfile.write("import sys")
- for line in infile.readlines():
- if "SLURMHINT" in line:
-
- line, hint = line.split("#")
- field, assign = line.split("=")
- parm = hint.split("SLURMHINT:")[1]
- parm, typ = parm.split(",")
- parm = parm.strip()
- if parm == "profile":
- mapping[-1] = parm
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=-1)
- else:
- mapping[arg_cnt] = parm
- if typ.strip() != "list":
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=arg_cnt)
- else:
- line = "{field} = [int(s) for s in sys.argv[{arg_cnt}].split(',')]\n".format(field=field, arg_cnt=arg_cnt)
- arg_cnt += 1
- outfile.write(line)
- else:
- if "get_ipython()" in line:
- line = "# "+line
- outfile.write(line)
-
- return mapping
-
-
-def check_run(run):
- if run.upper()[0] != "R":
- run = "r{:04d}".format(int(run))
- return run.lower()
-
-def run():
- args = vars(parser.parse_args())
-
- raw_path = args["rawpath"]
- out_folder= args["output"]
- cells = args["mem_cells"]
- run_high = check_run(args["run_high"])
- run_med = check_run(args["run_med"])
- run_low = check_run(args["run_low"])
- c5pf = bool(args["5pF"])
- c50pf = bool(args["50pF"])
- if c5pf and c50pf:
- raise AttributeError("Can only give one capacitor setting")
- if not (c5pf or c50pf):
- raise AttributeError("Need to give one capacitor setting")
-
-
- sequences = args["sequences"]
-
-
- out_mapping = notebook_to_python()
-
- if c5pf:
- in_mapping = {"mem_cells": cells,
- "high_5pf": run_high,
- "med_5pf": run_med,
- "low_5pf": run_low,
- "high_50pf": "",
- "med_50pf": "",
- "low_50pf": "",
- "out_folder": out_folder,
- "seq": sequences,
- "rawpath": raw_path,
- "capsettings" : "5"
- }
- else:
- in_mapping = {"mem_cells": cells,
- "high_50pf": run_high,
- "med_50pf": run_med,
- "low_50pf": run_low,
- "high_5pf": "",
- "med_5pf": "",
- "low_5pf": "",
- "out_folder": out_folder,
- "seq": sequences,
- "rawpath": raw_path,
- "capsettings" : "50"
- }
-
-
- srun_base = ["sbatch", "-p", "exfel", "-t", "24:00:00"]
-
- srun_base += [os.path.abspath("{}/slurm_CI.sh".format(os.getcwd())),
- os.path.abspath("{}/LPDChar_Darks.py".format(os.getcwd()))]
-
- for key in sorted(out_mapping.keys()):
- if out_mapping[key] in in_mapping:
- srun_base.append(str(in_mapping[out_mapping[key]]))
- Popen(srun_base).wait()
-
-if __name__ == "__main__":
- run()
diff --git a/LPD/Untitled.ipynb b/LPD/Untitled.ipynb
deleted file mode 100644
index b4ce940ae9204fa582ef4684749db32e6c5f18a4..0000000000000000000000000000000000000000
--- a/LPD/Untitled.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import h5py\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "from XFELDetAna import xfelpyanatools as xana\n",
- "\n",
- "max_cells = 32\n",
- "run = 216\n",
- "proposal = 2016\n",
- "campaign = 201701\n",
- "instrument = \"FXE\"\n",
- "modules = range(16)\n",
- "sequence = 0\n",
- "files = []\n",
- "fbase = \"/gpfs/exfel/exp/{}/{}/p{:06d}/proc/r{:04d}/CORR-R{:04d}-LPD{:02d}-S{:05d}.h5\"\n",
- "for m in modules:\n",
- " fname = fbase.format(instrument, campaign, proposal, run, run, m, sequence)\n",
- " files.append(fname)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 143,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def combine_stack(d, sdim, adjust=False):\n",
- " \"\"\"\n",
- " A function for assembling LPD images (at least coarsely)\n",
- " \"\"\"\n",
- " combined = np.zeros((sdim, 2048,2048))\n",
- " combined[...] = np.nan\n",
- " \n",
- " map_x = [1,0,0,1]\n",
- " map_y = [1,1,0,0]\n",
- " to_map = d\n",
- " dx = 64\n",
- " dy = 0\n",
- " for q in range(4):\n",
- " if q == 1 or q == 2:\n",
- " dy = 50\n",
- " else:\n",
- " dy = 0\n",
- " if q == 3 or q == 2:\n",
- " dx = 0\n",
- " else:\n",
- " dx = 40\n",
- " qcomb = np.zeros((sdim, 512,512))\n",
- " for m in range(4):\n",
- " mx = map_x[m]\n",
- " my = map_y[m]\n",
- " tm = to_map[q*4+m][:,:,::-1] .astype(np.float32)\n",
- " if adjust:\n",
- " for c in range(3):\n",
- " cc1 = c*128\n",
- " cc2 = c*128-1\n",
- " if cc1 >= 0 and cc1 < tm.shape[2]:\n",
- " tm[:,:,cc1] /= np.sqrt(2)\n",
- " if cc2 >= 0 and cc2 < tm.shape[2]:\n",
- " tm[:,:,cc2] /= np.sqrt(2)\n",
- " if q == 3 and m == 1:\n",
- " tm *= 1.5\n",
- " for c in range(8):\n",
- " cc1 = c*32\n",
- " cc2 = c*32-1\n",
- " if cc1 >= 0 and cc1 < tm.shape[1]:\n",
- " tm[:,cc1,:] /= np.sqrt(2)\n",
- " if cc2 >= 0 and cc2 < tm.shape[1]:\n",
- " tm[:,cc2,:] /= np.sqrt(2)\n",
- " qcomb [:,mx*256:(mx+1)*256, my*256:(my+1)*256] = tm\n",
- " mx = map_x[q]\n",
- " my = map_y[q]\n",
- " combined [:,mx*512+dx:(mx+1)*512+dx, my*512+dy:(my+1)*512+dy] = qcomb\n",
- " return combined"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 100,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD00-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD01-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD02-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD03-S00000.h5\n",
- "unable to open file (File accessibilty: Unable to open file)\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD04-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD05-S00000.h5\n",
- "unable to open file (File accessibilty: Unable to open file)\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD06-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD07-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD08-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD09-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD10-S00000.h5\n",
- "unable to open file (File accessibilty: Unable to open file)\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD11-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD12-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD13-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD14-S00000.h5\n",
- "/gpfs/exfel/exp/FXE/201701/p002016/proc/r0216/CORR-R0216-LPD15-S00000.h5\n"
- ]
- }
- ],
- "source": [
- "corrected = []\n",
- "gains = []\n",
- "\n",
- "for i, ff in enumerate(files):\n",
- " try:\n",
- " print(ff)\n",
- " infile = h5py.File(ff, \"r\")\n",
- " corrected.append(np.array(infile[\"/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data\".format(i)][5*max_cells:6*max_cells,...]))\n",
- " gains.append(np.array(infile[\"/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/gain\".format(i)][5*max_cells:6*max_cells,...]))\n",
- " infile.close()\n",
- " \n",
- " except Exception as e:\n",
- " print(e)\n",
- " corrected.append(np.zeros((max_cells, 256, 256)))\n",
- " gains.append(np.zeros((max_cells, 256, 256)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 148,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "combined = combine_stack(corrected, corrected[0].shape[0], adjust=True)\n",
- "combined_g = combine_stack(gains, gains[0].shape[0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 149,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/haufs/.local/lib/python3.4/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in greater_equal\n",
- " This is separate from the ipykernel package so we can avoid doing imports until\n",
- "/home/haufs/.local/lib/python3.4/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in greater_equal\n",
- " after removing the cwd from sys.path.\n",
- "/gpfs/exfel/data/user/haufs/karabo/extern/lib/python3.4/site-packages/matplotlib-1.5.2-py3.4-linux-x86_64.egg/matplotlib/colors.py:581: RuntimeWarning: invalid value encountered in less\n",
- " cbook._putmask(xa, xa < 0.0, -1)\n"
- ]
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAA50AAANJCAYAAACPg1hzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b6a835fef28>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "d = combined[4,:1100,:1100]\n",
- "dg = combined_g[4,:1100,:1100]\n",
- "d[dg >= 1] *= 9.85\n",
- "d[dg >= 2] *= 7.44\n",
- "y,x = np.indices((d.shape)) # first determine radii of all pixels\n",
- "center = [610, 620]\n",
- "r = np.sqrt((x-center[0])**2+(y-center[1])**2)\n",
- "#d[(r>=360) & (r < 363)] = 10000\n",
- "fig = xana.heatmapPlot(d, vmin=0, vmax=8000,\n",
- " cmap=\"jet\",\n",
- " x_range=[0,1100],\n",
- " y_range=[0,1100],\n",
- " aspect=1,\n",
- " add_panels=False,\n",
- " lut_label=\"Intensity (ADU)\")\n",
- "fig.set_size_inches(15,15)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/LPD/Untitled1.ipynb b/LPD/Untitled1.ipynb
deleted file mode 100644
index c4051f3d5a8a93e90ae74712cb263b3e600a72d5..0000000000000000000000000000000000000000
--- a/LPD/Untitled1.ipynb
+++ /dev/null
@@ -1,34 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/LPD/Untitled2.ipynb b/LPD/Untitled2.ipynb
deleted file mode 100644
index 2a65943e1c41930f1ff72fa680e1187c99d3ef92..0000000000000000000000000000000000000000
--- a/LPD/Untitled2.ipynb
+++ /dev/null
@@ -1,191 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import h5py\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "f = h5py.File(\"/gpfs/exfel/exp/FXE/201701/p002045/proc/r0002/CORR-R0002-LPD01-S00000.h5\", \"r\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "d = f[\"/INSTRUMENT/FXE_DET_LPD1M-1/DET/1CH0:xtdf/image/data\"][()]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x2b6d45ad47b8>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAJGCAYAAABMYR/JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYTBdp4mzAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b6d459d7da0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(10, 10))\n",
- "ax = fig.add_subplot(111)\n",
- "im = ax.imshow(np.mean(d[256*3:256*5,...], axis=0), interpolation=\"nearest\", vmin=-10, vmax=10)\n",
- "fig.colorbar(im)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "f = h5py.File(\"/gpfs/exfel/exp/FXE/201701/p002045/raw/r0003/RAW-R0003-LPD01-S00000.h5\", \"r\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "d = f[\"/INSTRUMENT/FXE_DET_LPD1M-1/DET/1CH0:xtdf/image/data\"][()]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def splitOffGainLPD(d):\n",
- " msk = np.zeros(d.shape, np.uint16)\n",
- " msk[...] = 0b0000111111111111\n",
- " data = np.bitwise_and(d, msk)\n",
- " msk[...] = 0b0011000000000000\n",
- " gain = np.bitwise_and(d, msk)//4096\n",
- " gain[gain > 2] = 2\n",
- " return data, gain"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "dd, g = splitOffGainLPD(d)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 0., 0., 6553600.]),\n",
- " array([ 0., 1., 2., 3.]),\n",
- " <a list of 3 Patch objects>)"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEACAYAAAB78OvLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGT5JREFUeJzt3X+sX/V93/HnCwiQDLAutJhhoHgCE6i6BihmFZvynTtj\nyBR+SMN1tMx262nTmimWJk3DyQRGjdqChupME0hV02BbCxYBNXgKq+8Y9qZKEAhxRhSocbfBsAmX\n1b+qNFNl4L0/vp/Q44vt+732MZd7+3xIV5z7Pp/P53s+Pvj78jmf8703VYUkSX04baYPQJI0dxgq\nkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN5MGSpJFiXZmeR77b+HknwxyViS8SS7kmxLMq/TZ12S3Ule\nSXJzp35dkpeSvJpkQ6d+ZpItrc+zSS7r7FvV2u9KsrJTvzzJc23fo0nO6OePRJJ0oqYMlap6taqu\nrarrgOuBvwD+ELgbeLqqrgKeAdYBJLkGWA5cDdwKPJQkbbiHgTVVtQhYlGRZq68B9lfVlcAG4IE2\n1hhwD3ADcCNwbye87gcebGMdbGNIkmbQdG9//QPgf1bVG8DtwMZW3wjc0bZvA7ZU1TtV9RqwG1ic\n5CLg3Kp6obXb1OnTHetxYEnbXgaMV9WhqjoIjAO3tH1LgCc6r3/nNOciSerZdEPlV4FvtO35VTUB\nUFVvARe2+gLgjU6fva22ANjTqe9ptSP6VNW7wKEk5x9rrCQXAAeq6r3OWBdPcy6SpJ6NHCpJPsbw\nKuSbrTT557v0+fNeMnWTkdpIkj5E01ncvhV4sar+rH0/kWR+VU20W1tvt/pe4NJOv0ta7Vj1bp83\nk5wOnFdV+5PsBQaT+myvqn1J5iU5rV2tdMc6QhJ/uJkknYCqmvY/3qdz++tzwKOd77cCq9v2KuDJ\nTn1Fe6JrIXAF8Hy7RXYoyeK2cL9yUp9Vbfsuhgv/ANuApS1AxoClrQawvbWd/PofUFVz9uvee++d\n8WNwbs7P+c29rxM10pVKkk8wXKT/Z53y/cBjSX4deJ3hE19U1ctJHgNeBg4Dv1F/dYRfAB4Bzgae\nqqo/avWvAZuT7Ab2ASvaWAeS/CbwXYa31+6r4YI9DJ8+29L272xjSJJm0EihUlU/AX52Um0/w6A5\nWvvfBn77KPUXgV84Sv0vaaF0lH2PMAyiyfX/zfAxY0nSR4SfqJ/lBoPBTB/CKTOX5wbOb7ab6/M7\nUTmZe2ezQZKa63OUpL4loU7xQr0kScdlqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hIknpjqEiS\neuOv4JV0Slx00eVMTLw+04ehD5mfqJd0Sgx/GLl/92YvP1EvSZphhookqTeGiiSpN4aKJKk3hook\nqTeGiiSpN4aKJKk3hookqTeGiiSpN4aKJKk3hookqTcjhUqSeUm+meSVJD9McmOSsSTjSXYl2ZZk\nXqf9uiS7W/ubO/XrkryU5NUkGzr1M5NsaX2eTXJZZ9+q1n5XkpWd+uVJnmv7Hk3iD8eUpBk26pXK\nV4Gnqupq4BeBPwHuBp6uqquAZ4B1AEmuAZYDVwO3Ag9l+JPlAB4G1lTVImBRkmWtvgbYX1VXAhuA\nB9pYY8A9wA3AjcC9nfC6H3iwjXWwjSFJmkFThkqS84C/V1VfB6iqd6rqEHA7sLE12wjc0bZvA7a0\ndq8Bu4HFSS4Czq2qF1q7TZ0+3bEeB5a07WXAeFUdqqqDwDhwS9u3BHii8/p3jjxrSdIpMcqVykLg\nz5J8Pcn3kvxekk8A86tqAqCq3gIubO0XAG90+u9ttQXAnk59T6sd0aeq3gUOJTn/WGMluQA4UFXv\ndca6eJQJS5JOnVHWIc4ArgO+UFXfTfK7DG99Tf5FCX3+4oRRfob/yD/nf/369e9vDwYDBoPB9I9I\nkua0He3r5IwSKnuAN6rqu+37JxiGykSS+VU10W5tvd327wUu7fS/pNWOVe/2eTPJ6cB5VbU/yV5g\nMKnP9qra1x4eOK1drXTH+oBuqEiSjmbAkW+3953QKFPe/mq3uN5IsqiVfgX4IbAVWN1qq4An2/ZW\nYEV7omshcAXwfLtFdijJ4rZwv3JSn1Vt+y6GC/8A24ClLUDGgKWtBrC9tZ38+pKkGTLSrxNO8ovA\n7wMfA/4X8GvA6cBjDK8wXgeWt8V0kqxj+DTWYWBtVY23+vXAI8DZDJ8mW9vqZwGbgWuBfcCKtshP\nktXAlxneXvtKVW1q9YXAFmAM2Al8vqoOH+XY/XXC0gzw1wnPdif264T9HfWSTglDZbbzd9RLkmaY\noSJJ6o2hIknqjaEiSeqNoSJJ6o2hIknqjaEiSeqNoSJJ6o2hIknqjaEiSeqNoSJJ6o2hIknqjaEi\nSeqNoSJJ6o2hIknqjaEiSeqNoSJJ6o2hIknqjaEiSeqNoSJJ6o2hIknqjaEiSeqNoSJJ6s1IoZLk\ntST/I8nOJM+32liS8SS7kmxLMq/Tfl2S3UleSXJzp35dkpeSvJpkQ6d+ZpItrc+zSS7r7FvV2u9K\nsrJTvzzJc23fo0nOONk/DEnSyRn1SuU9YFBV11bV4la7G3i6qq4CngHWASS5BlgOXA3cCjyUJK3P\nw8CaqloELEqyrNXXAPur6kpgA/BAG2sMuAe4AbgRuLcTXvcDD7axDrYxJEkzaNRQyVHa3g5sbNsb\ngTva9m3Alqp6p6peA3YDi5NcBJxbVS+0dps6fbpjPQ4sadvLgPGqOlRVB4Fx4Ja2bwnwROf17xxx\nLpKkU2TUUCngvyR5Ick/bbX5VTUBUFVvARe2+gLgjU7fva22ANjTqe9ptSP6VNW7wKEk5x9rrCQX\nAAeq6r3OWBePOBdJ0iky6jrETVX1oyQ/C4wn2cUwaLomf38yMnWTkdoAsH79+ve3B4MBg8Fg+kck\nSXPajvZ1ckYKlar6Ufvv/03yLWAxMJFkflVNtFtbb7fme4FLO90vabVj1bt93kxyOnBeVe1PshcY\nTOqzvar2JZmX5LR2tdId6wO6oSJJOpoBR77d3ndCo0x5+yvJJ5Kc07b/BnAz8ANgK7C6NVsFPNm2\ntwIr2hNdC4ErgOfbLbJDSRa3hfuVk/qsatt3MVz4B9gGLG0BMgYsbTWA7a3t5NeXJM2QVB3/rlUL\nhj9keHvrDOA/VtXvtDWPxxheYbwOLG+L6SRZx/BprMPA2qoab/XrgUeAs4Gnqmptq58FbAauBfYB\nK9oiP0lWA19ur/+VqtrUOa4twBiwE/h8VR0+yvHXVHOU1L/hvx39uzd7haoaeZnh/V5z/Q3XUJFm\nhqEy251YqPiJeklSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlS\nbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8MFUlSbwwVSVJvDBVJUm8M\nFUlSbwwVSVJvRg6VJKcl+V6Sre37sSTjSXYl2ZZkXqftuiS7k7yS5OZO/bokLyV5NcmGTv3MJFta\nn2eTXNbZt6q135VkZad+eZLn2r5Hk5xxMn8QkqSTN50rlbXAy53v7waerqqrgGeAdQBJrgGWA1cD\ntwIPJUnr8zCwpqoWAYuSLGv1NcD+qroS2AA80MYaA+4BbgBuBO7thNf9wINtrINtDEnSDBopVJJc\nAnwG+P1O+XZgY9veCNzRtm8DtlTVO1X1GrAbWJzkIuDcqnqhtdvU6dMd63FgSdteBoxX1aGqOgiM\nA7e0fUuAJzqvf+coc5EknTqjXqn8LvCvgerU5lfVBEBVvQVc2OoLgDc67fa22gJgT6e+p9WO6FNV\n7wKHkpx/rLGSXAAcqKr3OmNdPOJcJEmnyJTrEEn+ITBRVd9PMjhO0zrOvunK1E1GagPA+vXr398e\nDAYMBoPpH5EkzWk72tfJGWVx+ybgtiSfAT4OnJtkM/BWkvlVNdFubb3d2u8FLu30v6TVjlXv9nkz\nyenAeVW1P8leYDCpz/aq2pdkXpLT2tVKd6wP6IaKJOloBhz5dnvfCY0y5e2vqvpSVV1WVX8LWAE8\nU1X/BPhPwOrWbBXwZNveCqxoT3QtBK4Anm+3yA4lWdwW7ldO6rOqbd/FcOEfYBuwtAXIGLC01QC2\nt7aTX1+SNENO5jHc3wEeS/LrwOsMn/iiql5O8hjDJ8UOA79RVT+9NfYF4BHgbOCpqvqjVv8asDnJ\nbmAfw/Ciqg4k+U3guwxvr93XFuxh+PTZlrZ/ZxtDkjSD8lfv93NTkprrc5Q+ioY3JPy7N3uFqhp5\n7fqn/ES9JKk3hookqTeGiiSpN4aKJKk3hookqTeGiiSpN4aKJKk3hookqTeGiiSpN4aKJKk3hook\nqTeGiiSpN4aKJKk3hookqTeGiiSpN4aKJKk3hookqTeGiiSpN4aKJKk3hookqTeGiiSpN4aKJKk3\nhookqTdThkqSs5J8J8nOJD9M8lutPpZkPMmuJNuSzOv0WZdkd5JXktzcqV+X5KUkrybZ0KmfmWRL\n6/Nskss6+1a19ruSrOzUL0/yXNv3aJIz+vgDkSSduClDpar+Evj7VXUt8LeBJUluAu4Gnq6qq4Bn\ngHUASa4BlgNXA7cCDyVJG+5hYE1VLQIWJVnW6muA/VV1JbABeKCNNQbcA9wA3Ajc2wmv+4EH21gH\n2xiSpBk00u2vqvpJ2zyr9TkA3A5sbPWNwB1t+zZgS1W9U1WvAbuBxUkuAs6tqhdau02dPt2xHgeW\ntO1lwHhVHaqqg8A4cEvbtwR4ovP6d44yF0nSqTNSqCQ5LclO4C1gR1W9DMyvqgmAqnoLuLA1XwC8\n0em+t9UWAHs69T2tdkSfqnoXOJTk/GONleQC4EBVvdcZ6+JR5iJJOnVGWodob97XJjkP2JZkANTk\nZj0eV6ZuMlIbANavX//+9mAwYDAYTP+IJGlO29G+Ts60Frer6s+TPAX8EjCRZH5VTbRbW2+3ZnuB\nSzvdLmm1Y9W7fd5McjpwXlXtT7IXGEzqs72q9iWZl+S0FnjdsT6gGyqSpKMZcOTb7X0nNMooT3/9\nzE8Xx5N8HFgK7AS2Aqtbs1XAk217K7CiPdG1ELgCeL7dIjuUZHFbuF85qc+qtn0Xw4V/gG3A0hYg\nY+21t7V921vbya8vSZohqTr+Xaskv8BwITwMQ2hzVf27tubxGMMrjNeB5W0xnSTrGD6NdRhYW1Xj\nrX498AhwNvBUVa1t9bOAzcC1wD5gRVvkJ8lq4MsMb699pao2tfpCYAswxjDkPl9Vh49y/DXVHCX1\nb/hvR//uzV6hqkZeZni/11x/wzVUpJlhqMx2JxYqfqJektQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtD\nRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS\n1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1JspQyXJJUmeSfLDJD9I8sVWH0synmRXkm1J\n5nX6rEuyO8krSW7u1K9L8lKSV5Ns6NTPTLKl9Xk2yWWdfata+11JVnbqlyd5ru17NMkZffyBSJJO\n3ChXKu8A/6qqfh74ZeALST4J3A08XVVXAc8A6wCSXAMsB64GbgUeSpI21sPAmqpaBCxKsqzV1wD7\nq+pKYAPwQBtrDLgHuAG4Ebi3E173Aw+2sQ62MSRJM2jKUKmqt6rq+237x8ArwCXA7cDG1mwjcEfb\nvg3YUlXvVNVrwG5gcZKLgHOr6oXWblOnT3esx4ElbXsZMF5Vh6rqIDAO3NL2LQGe6Lz+naNOWpJ0\nakxrTSXJ5cCngOeA+VU1AcPgAS5szRYAb3S67W21BcCeTn1Pqx3Rp6reBQ4lOf9YYyW5ADhQVe91\nxrp4OnORJPVv5HWIJOcwvIpYW1U/TlKTmkz+/mRk6iYjtQFg/fr1728PBgMGg8H0j0iS5rQd7evk\njBQqbRH8cWBzVT3ZyhNJ5lfVRLu19Xar7wUu7XS/pNWOVe/2eTPJ6cB5VbU/yV5gMKnP9qral2Re\nktPa1Up3rA/ohook6WgGHPl2e98JjTLq7a8/AF6uqq92aluB1W17FfBkp76iPdG1ELgCeL7dIjuU\nZHFbuF85qc+qtn0Xw4V/gG3A0hYgY8DSVgPY3tpOfn1J0gxJ1fHvWiW5CfjvwA8Y3uIq4EvA88Bj\nDK8wXgeWt8V0kqxj+DTWYYa3y8Zb/XrgEeBs4KmqWtvqZwGbgWuBfcCKtshPktXAl9vrfqWqNrX6\nQmALMAbsBD5fVYePcvw11Rwl9W/4b0f/7s1eoapGXmZ4v9dcf8M1VKSZYajMdicWKn6iXpLUG0NF\nktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLU\nG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktSbKUMlydeS\nTCR5qVMbSzKeZFeSbUnmdfatS7I7yStJbu7Ur0vyUpJXk2zo1M9MsqX1eTbJZZ19q1r7XUlWduqX\nJ3mu7Xs0yRkn+wchSTp5o1ypfB1YNql2N/B0VV0FPAOsA0hyDbAcuBq4FXgoSVqfh4E1VbUIWJTk\np2OuAfZX1ZXABuCBNtYYcA9wA3AjcG8nvO4HHmxjHWxjSJJm2JShUlV/DByYVL4d2Ni2NwJ3tO3b\ngC1V9U5VvQbsBhYnuQg4t6peaO02dfp0x3ocWNK2lwHjVXWoqg4C48Atbd8S4InO69851TwkSafe\nia6pXFhVEwBV9RZwYasvAN7otNvbaguAPZ36nlY7ok9VvQscSnL+scZKcgFwoKre64x18QnOQ5LU\no77WIqqncQAydZOR2rxv/fr1728PBgMGg8H0jkiS5rwd7evknGioTCSZX1UT7dbW262+F7i00+6S\nVjtWvdvnzSSnA+dV1f4ke4HBpD7bq2pfknlJTmtXK92xjqobKpKkoxlw5FvufSc0yqi3v8KRVwdb\ngdVtexXwZKe+oj3RtRC4Ani+3SI7lGRxW7hfOanPqrZ9F8OFf4BtwNIWIGPA0lYD2N7aTn59SdIM\nStXx71wl+QbD+LoAmADuBb4FfJPhFcbrwPK2mE6SdQyfxjoMrK2q8Va/HngEOBt4qqrWtvpZwGbg\nWmAfsKIt8pNkNfBlhrfXvlJVm1p9IbAFGAN2Ap+vqsPHOP6aao6S+jf896N/92avUFXTWmqAEUJl\ntjNUpJlhqMx2JxYqfqJektQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLU\nG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtDRZLUG0NFktQbQ0WS1BtD\nRZLUG0NFktSbWR0qSW5J8idJXk3yb2b6eCTpr7tZGypJTgP+A7AM+Hngc0k+ObNH9eHbsWPHTB/C\nKTOX5wZzf35z346ZPoCPpFkbKsBiYHdVvV5Vh4EtwO0zfEwfurn8xjSX5wZzf35z346ZPoCPpNkc\nKguANzrf72k1SdIMOWOmD+DD8NnPfnamD+GU2bVrFy+++OJMH8Ypcc4558z0IUiaplTVTB/DCUny\nd4D1VXVL+/5uoKrq/kntZucEJWmGVVWm22c2h8rpwC7gV4AfAc8Dn6uqV2b0wCTpr7FZe/urqt5N\n8i+BcYZrQ18zUCRpZs3aKxVJ0kfPbH76632jfAgyyb9PsjvJ95N86sM+xpMx1fySfDrJwSTfa1//\ndiaO80Ql+VqSiSQvHafNrDx/U81tDpy7S5I8k+SHSX6Q5IvHaDdbz9+U85ut5zDJWUm+k2Rnm99v\nHaPd9M5dVc3qL4bB+KfAzwEfA74PfHJSm1uBb7ftG4HnZvq4e57fp4GtM32sJzHHvwt8CnjpGPtn\n8/mbam6z/dxdBHyqbZ/DcJ1zLv39G2V+s/YcAp9o/z0deA646WTP3Vy4UhnlQ5C3A5sAquo7wLwk\n8z/cwzxho37Ic9pPaXxUVNUfAweO02TWnr8R5gaz+9y9VVXfb9s/Bl7hg58Xm83nb5T5wSw9h1X1\nk7Z5FsN/wE7+f3Xa524uhMooH4Kc3GbvUdp8VI36Ic9fbpen305yzYdzaB+a2Xz+RjEnzl2Syxle\nlX1n0q45cf6OMz+YpecwyWlJdgJvATuq6uVJTaZ97mbt0186wovAZVX1kyS3At8CFs3wMWk0c+Lc\nJTkHeBxY2/5FP6dMMb9Zew6r6j3g2iTnAeNJPl1V/+1kxpwLVyp7gcs631/SapPbXDpFm4+qKedX\nVT/+6WVsVf1n4GNJzv/wDvGUm83n77jmwrlLcgbDN9zNVfXkUZrM6vM31fzmwjmsqj8Hvg380qRd\n0z53cyFUXgCuSPJzSc4EVgBbJ7XZCqyE9z+Jf7CqJj7cwzxhU86ve48zyWKGj4rv/3AP86SFY9+X\nns3nD44ztzly7v4AeLmqvnqM/bP9/B13frP1HCb5mSTz2vbHgaUMHwTqmva5m/W3v+oYH4JM8s+H\nu+v3quqpJJ9J8qfAXwC/NpPHPB2jzA/4R0n+BXAY+H/Ar87cEU9fkm8AA+CCJP8HuBc4kzlw/qaa\nG7P/3N0E/GPgB+3efAFfYvi04lw4f1POj9l7Dv8msDFJGL63bK6q/3qy751++FGS1Ju5cPtLkvQR\nYahIknpjqEiSemOoSJJ6Y6hIknpjqEiSemOoSJJ6Y6hIknrz/wEAO4RelRaNvQAAAABJRU5ErkJg\ngg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b0a8a21b2b0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.hist(g[:100,...].flatten(), bins=3, range=(0, 3))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/LPD/Untitled3.ipynb b/LPD/Untitled3.ipynb
deleted file mode 100644
index f42a7d2c1ea8ff4ef3f56c41efb576e1ad36250a..0000000000000000000000000000000000000000
--- a/LPD/Untitled3.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from collections import OrderedDict\n",
- "\n",
- "import h5py\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "\n",
- "gain_store = \"/gpfs/exfel/data/scratch/haufs/gain_data_lpd/lpd_ci_store_ci001_16_5f.h5\"\n",
- "max_cells = 128"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\"unable to open object (Symbol table: Can't open object)\"\n"
- ]
- }
- ],
- "source": [
- "gains = {}\n",
- "bad_pixels = {}\n",
- "f = h5py.File(gain_store, \"r\")\n",
- "for i in range(16):\n",
- " try:\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " gains[qm] = f[\"{}/RelativeGain/0/data\".format(qm)][()]\n",
- " bad_pixels[qm] = f[\"{}/BadPixels/0/data\".format(qm)][()]\n",
- " except Exception as e: \n",
- " pass\n",
- "f.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "colors = ['yellowgreen', 'red', 'gold', 'lightskyblue', \n",
- " 'white','lightcoral','blue','pink', 'darkgreen', \n",
- " 'yellow','grey','violet','magenta','cyan']\n",
- "\n",
- "import matplotlib.patches as ptchs\n",
- "\n",
- "\n",
- "lbls = [\"good\", \"high dev.\", \"fit failed\"]\n",
- "def show_bp(gain_to_preview):\n",
- " res = OrderedDict()\n",
- " fig = plt.figure(figsize=(10,10))\n",
- " for i in range(16):\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " try:\n",
- " bp = bad_pixels[qm]\n",
- " ax = fig.add_subplot(4,4,i+1)\n",
- " labels = np.unique(bp).tolist()\n",
- " fracs = [np.count_nonzero(bp[...,gain_to_preview] == c) for c in labels]\n",
- " explode = [0.25*int(bool(label)) for label in labels]\n",
- " patches, texts = plt.pie(fracs, explode=explode,\n",
- " shadow=True,\n",
- " startangle=90, colors=colors[:len(labels)])\n",
- " ax.text(0.35, 0.3, qm, transform=ax.transAxes, fontsize=12)\n",
- "\n",
- " for k in range(2):\n",
- " ax.add_patch(\n",
- " ptchs.Rectangle(\n",
- " (0.8, 0.9-0.1*k), # (x,y)\n",
- " 0.05, # width\n",
- " 0.05, # height\n",
- " color = colors[k+1],\n",
- "\n",
- " transform=ax.transAxes\n",
- " )\n",
- " \n",
- " )\n",
- " ax.text(0.9, 0.9-0.1*k,\n",
- " \"{:0.2f}%\".format(fracs[k+1]/np.sum(fracs)*100),\n",
- " transform=ax.transAxes, fontsize=10)\n",
- "\n",
- " except Exception as e:\n",
- " pass\n",
- " ax.legend(patches, lbls, loc=8, bbox_to_anchor=(1.5, 0.5))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAI8CAYAAAA5uok0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b6bfdc33e80>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "show_bp(0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAI8CAYAAAA5uok0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b6bfdbd6a20>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "show_bp(1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAI8CAYAAAA5uok0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6f8Dy8CcetoFO6oAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b6b8c18b438>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "show_bp(2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "channel_mapping = {}\n",
- "for i in range(16):\n",
- " qm = \"Q{}M{}\".format(i//4 +1, i % 4 + 1)\n",
- " channel_mapping[qm] = i \n",
- " \n",
- "def create_constant_overview(constant, name, limits, entries=3):\n",
- " \"\"\"\n",
- " Creates a few plots and statistics for characterization constants.\n",
- " \"\"\"\n",
- " fig = plt.figure(figsize=(12, 7))\n",
- " ax = None\n",
- " for g in range(entries):\n",
- " ax = fig.add_subplot(3,1,g+1,sharex=ax)\n",
- " for qm in constant.keys():\n",
- " d = constant[qm][...,g]\n",
- " print(\"{} {}, gain {:0.2f}: mean: {:0.2f}, median: {:0.2f}, std: {:0.2f}\".format(name, qm, g,\n",
- " np.nanmean(d),\n",
- " np.nanmedian(d),\n",
- " np.nanstd(d)))\n",
- " \n",
- " ax.step(np.arange(max_cells), np.nanmedian(d, axis=(0,1))) \n",
- " if g == entries - 1:\n",
- " ax.set_xlabel(\"Memory cell\")\n",
- " else:\n",
- " plt.setp(ax.get_xticklabels(), visible=False)\n",
- " \n",
- " ax.set_ylabel(\"{} (ADU)\".format(name))\n",
- " ax.set_ylim(limits[g])\n",
- " ax.text(0.1, 0.9, \"{:d}x gain\".format(10**(2-g)), transform=ax.transAxes)\n",
- " \n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Relative gain Q4M1, gain 0.00: mean: 0.97, median: 0.98, std: 0.11\n",
- "Relative gain Q3M4, gain 0.00: mean: 0.98, median: 0.98, std: 0.04\n",
- "Relative gain Q1M1, gain 0.00: mean: 1.00, median: 1.00, std: 0.05\n",
- "Relative gain Q3M2, gain 0.00: mean: 0.99, median: 0.99, std: 0.04\n",
- "Relative gain Q3M1, gain 0.00: mean: 0.99, median: 0.99, std: 0.04\n",
- "Relative gain Q4M4, gain 0.00: mean: 0.99, median: 1.00, std: 0.04\n",
- "Relative gain Q4M2, gain 0.00: mean: 0.97, median: 0.97, std: 0.04\n",
- "Relative gain Q1M3, gain 0.00: mean: 0.98, median: 0.98, std: 0.04\n",
- "Relative gain Q2M4, gain 0.00: mean: 0.99, median: 0.99, std: 0.04\n",
- "Relative gain Q3M3, gain 0.00: mean: 0.99, median: 0.99, std: 0.04\n",
- "Relative gain Q4M3, gain 0.00: mean: 0.98, median: 0.98, std: 0.04\n",
- "Relative gain Q2M1, gain 0.00: mean: 0.99, median: 1.00, std: 0.06\n",
- "Relative gain Q2M3, gain 0.00: mean: 1.00, median: 1.00, std: 0.04\n",
- "Relative gain Q1M2, gain 0.00: mean: 0.99, median: 0.99, std: 0.05\n",
- "Relative gain Q1M4, gain 0.00: mean: 1.93, median: 1.93, std: 0.08\n",
- "Relative gain Q4M1, gain 1.00: mean: 0.09, median: 0.09, std: 0.01\n",
- "Relative gain Q3M4, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q1M1, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q3M2, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q3M1, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q4M4, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q4M2, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q1M3, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q2M4, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q3M3, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q4M3, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q2M1, gain 1.00: mean: 0.09, median: 0.09, std: 0.01\n",
- "Relative gain Q2M3, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q1M2, gain 1.00: mean: 0.09, median: 0.09, std: 0.00\n",
- "Relative gain Q1M4, gain 1.00: mean: 0.12, median: 0.12, std: 0.00\n",
- "Relative gain Q4M1, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q3M4, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q1M1, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q3M2, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q3M1, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q4M4, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q4M2, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q1M3, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q2M4, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q3M3, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q4M3, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q2M1, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q2M3, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q1M2, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n",
- "Relative gain Q1M4, gain 2.00: mean: 0.01, median: 0.01, std: 0.00\n"
- ]
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAGyCAYAAABdkIqfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGKjMSrbt7QAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b6b8c8fe0f0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "create_constant_overview(gains, \"Relative gain\", [(0.95, 1.05), (0.085,0.088), (0.0051, 0.006)])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 100,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "\n",
- "def show_hists(ranges):\n",
- " res = OrderedDict()\n",
- " for i in range(16):\n",
- " qm = \"Q{}M{}\".format(i//4+1, i%4+1)\n",
- " try:\n",
- " res[qm] = OrderedDict()\n",
- " res[qm]['Gain 100x'] = gains[qm][...,0]\n",
- " res[qm]['Gain 10x'] = gains[qm][...,1]\n",
- " res[qm]['Gain 1x'] = gains[qm][...,2]\n",
- " \n",
- " except:\n",
- " res[qm] = None\n",
- " from mpl_toolkits.axes_grid1 import AxesGrid\n",
- " i = 0\n",
- " fig = plt.figure(figsize=(10, 16))\n",
- " \n",
- " for module, item in res.items():\n",
- " if item is None: \n",
- " i += 3\n",
- " continue\n",
- " \n",
- " for constant, data in item.items():\n",
- " ax = fig.add_subplot(16, 3, i+1)\n",
- " h, e = np.histogram(data[...], bins=ranges[constant][-1],\n",
- " range=ranges[constant][:2])\n",
- " c = (e[1:]+e[:-1])/2\n",
- " ax.step(c, h) \n",
- " if i < (len(list(res.values()))-1)*len(list(item.values())):\n",
- " plt.setp(ax.get_xticklabels(), visible=False)\n",
- " else:\n",
- " ax.set_xlabel(constant)\n",
- " if i % 3 == 0:\n",
- " ax.set_ylabel(module)\n",
- " plt.setp(ax.get_yticklabels(), visible=False)\n",
- " plt.locator_params(nbins=4)\n",
- " i += 1\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 101,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfs/exfel/data/user/haufs/karabo/extern/lib/python3.4/site-packages/numpy/lib/function_base.py:583: RuntimeWarning: invalid value encountered in greater_equal\n",
- " keep = (tmp_a >= mn)\n",
- "/gpfs/exfel/data/user/haufs/karabo/extern/lib/python3.4/site-packages/numpy/lib/function_base.py:584: RuntimeWarning: invalid value encountered in less_equal\n",
- " keep &= (tmp_a <= mx)\n"
- ]
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAOoCAYAAADiQeFPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHkTJBgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x2b6bfe685128>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "show_hists({'Gain 100x': (0.8, 1.2, 50), 'Gain 10x': (0.08, 0.095, 50), 'Gain 1x': (0.001, 0.01, 50)})\n"
- ]
- }
- ],
- "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": "3.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/LPD/conv_tmp.py b/LPD/conv_tmp.py
deleted file mode 100644
index 7f2070717331803499a1abbea6217da03ea50968..0000000000000000000000000000000000000000
--- a/LPD/conv_tmp.py
+++ /dev/null
@@ -1,917 +0,0 @@
-
-# coding: utf-8
-
-# # LPD Gain Characterization (Charge Injection) #
-#
-# The following code characterizes the gain of the LPD detector from charge injection data, i.e. data with charge injected into the amplifiers, bypassing the sensor. The data needs to fulfil the following requirements:
-#
-# * each file should represent one scan point for one mudle, defined by detector gain setting
-# and charge injections setting
-# * settings need to overlap at at least one point for two neighboring gain ranges
-# * 100 samples or more per pixel and memory cell should be present for each setting.
-#
-# The data is then analyzed by calcualting the per-pixel, per memory cell mean of the samples for each setting. These means are then normalized to the median peak position of a all means of the first module. Overlapping settings in neighboring gain ranges are used to deduce the slopes of the different gains with respect to the high gain setting.
-
-# In[37]:
-
-
-# std library imports
-from functools import partial
-import h5py
-import os
-
-# numpy and matplot lib specific
-import numpy as np
-import matplotlib
-matplotlib.use("agg")
-import matplotlib.pyplot as plt
-get_ipython().magic('matplotlib inline')
-
-# parallel processing via ipcluster
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-from ipyparallel import Client
-profile = "noDB2" # SLURMHINT: profile, str
-view = Client(profile=profile)[:]
-view.use_dill()
-
-
-import warnings
-warnings.filterwarnings('ignore')
-
-# pyDetLib imports
-import XFELDetAna.xfelpycaltools as xcal
-import XFELDetAna.xfelpyanatools as xana
-
-# usually no need to change these lines
-sensor_size = [256, 256]
-block_size = [64, 64]
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-
-# the following lines should be adjusted depending on data
-in_folder = "/gpfs/exfel/exp/FXE/201830/p900020/proc/CI/" # SLURMHINT: in_folder, str
-out_folder = "/gpfs/exfel/exp/FXE/201830/p900020/proc/calibration/" # SLURMHINT: out_folder, str
-
-mod_corrs = [0]*16
-# mod_corrs[0] = 1
-
-# change this to the offsets that shoudl be used
-capacitance = '5pf' # SLURMHINT: capacitance, str
-offset_store = "/gpfs/exfel/exp/FXE/201830/p900020/proc/calibration/dark/lpd_offset_store_r0021_r0022_r0023_5pf.h5" # SLURMHINT: offset_store, str
-
-# actual memory cells
-memory_cells = 128 # SLURMHINT: memory_cells, int
-cells = np.arange(memory_cells)
-
-# modules to characterize
-modules = range(3,4) # SLURMHINT: modules, list
-
-# these lines can usually stay as is
-fbase = "{}/data_q{{}}m{{}}_{{}}.h5".format(in_folder)
-h5path = "/data"
-
-
-# For the characterization offset maps for each module are needed. In the following these are read in
-
-# In[38]:
-
-
-store_file = h5py.File(offset_store, "r")
-offset_g = {}
-noise_g = {}
-
-for i in modules:
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- offset_g[qm] = np.array(store_file["{}/Offset/0/data".format(qm)])
- noise_g[qm] = np.array(store_file["{}/Noise/0/data".format(qm)])
- except:
- pass
-store_file.close()
-
-
-# The CI runs are organized into files. Each file is generated for a scan point in a charge injection scan. The following code is used for generating the scan points and identifies each file with a setting. Additionally, overlaps of neigboring gains are identified for later use.
-
-# In[39]:
-
-
-# sort out which runs are which:
-myList = []
-updated = True
-if updated:
- for b in [0, 6, 12, 18, 24, 30]:
- myList.append(['5pF', 100, 1, b])
- for b in [0, 6]:
- myList.append(['5pF', 10, 1, b])
- myList.append(['50pF', 100, 1, b])
- for b in [0, 6, 12, 18, 24, 30]:
- myList.append(['5pF', 10, 2, b])
- myList.append(['50pF', 100, 2, b])
- for b in [0, 6]:
- myList.append(['5pF', 1, 2, b])
- myList.append(['50pF', 10, 2, b])
- for b in [0, 4, 8]:
- myList.append(['5pF', 1, 4, b])
- myList.append(['50pF', 10, 4, b])
- myList.append(['50pF', 1, 4, b])
-
-else:
- for b in [0, 1, 5, 10, 15]:
- myList.append(['5pF', 100, 1, b])
- for b in [10, 15]:
- myList.append(['5pF', 10, 1, b])
- myList.append(['5pF', 1, 2, b])
- myList.append(['50pF', 100, 1, b])
- myList.append(['50pF', 10, 2, b])
- for b in [5, 10, 15]:
- myList.append(['5pF', 10, 2, b])
- myList.append(['50pF', 100, 2, b])
- for b in [0, 2, 4, 6]:
- myList.append(['5pF', 1, 4, b])
- myList.append(['50pF', 10, 4, b])
- for b in [4, 6, 10, 15]:
- myList.append(['50pF', 1, 4, b])
-
-# filter into 5pf and 50pf settings
-setting_5f = [i for i in range(len(myList)) if "5pF" in myList[i]]
-gains_5f = [int(2-np.log10(myList[i][1])) for i in range(len(myList)) if "5pF" in myList[i]]
-setting_50f = [i for i in range(len(myList)) if "50pF" in myList[i]]
-gains_50f = [int(2-np.log10(myList[i][1])) for i in range(len(myList)) if "50pF" in myList[i]]
-
-# find overlaps in settings to scale gains between ranges
-def find_overlaps(settings):
- gain_1 = [s for s in settings if myList[s][1] == 1]
- gain_10 = [s for s in settings if myList[s][1] == 10]
- gain_100 = [s for s in settings if myList[s][1] == 100]
- overlaps_100_10 = []
- for s100 in gain_100:
- for s10 in gain_10:
- if myList[s100][3] == myList[s10][3] and myList[s100][2] == myList[s10][2]:
- overlaps_100_10.append((s100, s10))
-
- overlaps_10_1 = []
- for s10 in gain_10:
- for s1 in gain_1:
- if myList[s10][3] == myList[s1][3] and myList[s10][2] == myList[s1][2]:
- overlaps_10_1.append((s10, s1))
-
- return overlaps_100_10, overlaps_10_1
-
-def seq_in_gain(settings):
- seq = []
- gains = {1: 0, 10: 0, 100: 0}
- for s in settings:
- seq.append(gains[myList[s][1]])
- gains[myList[s][1]] += 1
- return seq
-
-overlaps_100_10_5f, overlaps_10_1_5f = find_overlaps(setting_5f)
-overlaps_100_10_50f, overlaps_10_1_50f = find_overlaps(setting_50f)
-
-seq_5pf = seq_in_gain(setting_5f)
-seq_50pf = seq_in_gain(setting_50f)
-
-if capacitance == "5pf":
- overlaps_100_10 = overlaps_100_10_5f
- overlaps_10_1 = overlaps_10_1_5f
- seq = seq_5pf
- setting = setting_5f
- gains = gains_5f
-else:
- overlaps_100_10 = overlaps_100_10_50f
- overlaps_10_1 = overlaps_10_1_50f
- seq = seq_50pf
- setting = setting_50f
- gains = gains_50f
-
-
-# ## Scan point mean values ##
-#
-# The following code will read in the data relevant for a given setting in a module-parallel fashion, offset correct the data according to the gain setting and then calculate the per-pixel, per-memory-cell mean value.
-
-# In[40]:
-
-
-def get_means_single_module(fbase, settings, gains, sensor_size, memory_cells, block_size,
- inp):
- """ This function calculates a per-pixel histogram for a single module
-
- Runs and sequences give the data to calculate histogram from
- """
- channel, offset, mod_corr = inp
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
-
- def splitOffGainLPD(d):
- msk = np.zeros(d.shape, np.uint16)
- msk[...] = 0b0000111111111111
- data = np.bitwise_and(d, msk)
- gain = np.zeros(data.shape, np.uint8)
- gain[data >= 2**12] = 1
- gain[data >= 2**13] = 2
- return data, gain
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- imarr = infile["/data".format(channel)]
- im = np.array(imarr)
- infile.close()
-
- im, ga = splitOffGainLPD(im.astype(np.uint16))
- return im.astype(np.float32), ga
-
-
- means_low, means_med, means_high = [], [], []
- if offset is None:
- return means_low, means_med, means_high
-
- om = offset
- for i, setting in enumerate(settings):
- gain = gains[i]
- try:
- if i < mod_corr:
- means_high.append(np.zeros((sensor_size[0], sensor_size[1], memory_cells)))
- continue
- fname = fbase.format(channel//4+1, channel%4+1, setting)
- print(fname)
-
- d, g = read_fun(fname, channel)
-
- for cc in range(d.shape[2]//memory_cells):
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
-
- offset = np.choose(gain, (om[...,0], om[...,1], om[...,2]))
- tim = d[...,cc*memory_cells:(cc+1)*memory_cells]
-
- tim = tim - offset
- d[...,cc*memory_cells:(cc+1)*memory_cells] = tim
- print(np.mean(tim), np.mean(offset))
- mn = np.zeros((d.shape[0], d.shape[1], memory_cells))
- for cell in range(memory_cells):
- mn[...,cell] = np.nanmean(d[...,cell::memory_cells], axis=2)
- if gain == 0:
- means_high.append((setting, mn))
- elif gain == 1:
- means_med.append((setting, mn))
- else:
- means_low.append((setting, mn))
- except Exception as e:
- pass
-
- return means_low, means_med, means_high
-
-inp = []
-
-for i in modules:
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g[qm], mod_corrs[i]))
- except:
- inp.append((i, None, None))
-
-p = partial(get_means_single_module, fbase, setting, gains,
- sensor_size, memory_cells, block_size)
-res_uncorr_int = view.map_sync(p, inp)
-
-
-# We reformat data and create an array index to peak mapping
-
-# In[41]:
-
-
-res_uncorr = []
-indices_in_settings = {}
-
-for ii, r in enumerate(res_uncorr_int):
- i = list(modules)[ii]
- means_low, means_med, means_high = r
- res_uncorr.append(([m[1] for m in means_low],
- [m[1] for m in means_med],
- [m[1] for m in means_high]))
- indices_in_settings["low"] = [m[0] for m in means_low]
- indices_in_settings["med"] = [m[0] for m in means_med]
- indices_in_settings["high"] = [m[0] for m in means_high]
-
-
-# Create plots of the mean values we've evaluated for each gain and each modules
-
-# In[42]:
-
-
-cell = 1
-for ii, r in enumerate(res_uncorr):
- i = list(modules)[ii]
- means_low, means_med, means_high = r
-
- d = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- for kk, mn in enumerate(means_high):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(-1000, 4000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_high_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
- d = []
- for kk, mn in enumerate(means_med):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(-1000, 4000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_med_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
- d = []
- for kk, mn in enumerate(means_low):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(-1000, 4000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_low_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
-
-# In[47]:
-
-
-ref_pk_pos_low = np.median(np.array(res_uncorr[0][0]), axis=(1,2,3))
-ref_pk_pos_med = np.median(np.array(res_uncorr[0][1]), axis=(1,2,3))
-ref_pk_pos_high = np.median(np.array(res_uncorr[0][2]), axis=(1,2,3))
-sort_low = np.argsort(ref_pk_pos_low)
-sort_med = np.argsort(ref_pk_pos_med)
-sort_high = np.argsort(ref_pk_pos_high)
-ref_pos = ref_pk_pos_low[sort_low], ref_pk_pos_med[sort_med], ref_pk_pos_high[sort_high]
-
-
-# In[48]:
-
-
-slopes = []
-for overlap in overlaps_100_10:
- idx1, idx2 = overlap[0], overlap[1]
- idx1 = indices_in_settings["high"].index(idx1)
- idx2 = indices_in_settings["med"].index(idx2)
- #idx1 = sort_high[indices_in_settings["high"].index(idx1)]
- #idx2 = sort_med[indices_in_settings["med"].index(idx2)]
- slope = np.mean(ref_pk_pos_high[idx1]/ref_pk_pos_med[idx2])
-
- slopes.append(slope)
-slope_100_10 = np.mean(slopes)
-
-slopes = []
-for overlap in overlaps_10_1:
- idx1, idx2 = overlap[0], overlap[1]
- idx1 = indices_in_settings["med"].index(idx1)
- idx2 = indices_in_settings["low"].index(idx2)
-
- #idx1 = sort_med[indices_in_settings["med"].index(idx1)]
- #idx2 = sort_low[indices_in_settings["low"].index(idx2)]
- slope = np.mean(ref_pk_pos_med[idx1]/ref_pk_pos_low[idx2])
- slopes.append(slope)
-slope_10_1 = np.mean(slopes)
-
-
-# In[49]:
-
-
-
-cell = 1
-for ii, r in enumerate(res_uncorr):
- i = list(modules)[ii]
- means_low, means_med, means_high = r
-
- d = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- for kk, mn in enumerate(means_high):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_med):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c*slope_100_10,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_low):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(-1000, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c*slope_10_1*slope_100_10,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(100, 30000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
- fig.savefig("{}/peaks_gain_all_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
-
-# In[51]:
-
-
-for m,_ in enumerate(modules):
- fig = plt.figure(figsize=(10,5))
- ax = fig.add_subplot(131)
- mns = np.array(res_uncorr[m][2])
- mn_im = np.zeros((mns.shape[0], 500))
- for j in range(mns.shape[0]):
- h, _ = np.histogram(mns[sort_high[j],...].flatten(), bins=500, range=(0,4096))
- mn_im[j,:] = h
- ax.imshow(np.rot90(mn_im), interpolation="nearest", aspect="auto",
- extent=[0,ref_pk_pos_high.shape[0],0,4096], cmap='jet')
-
- ax.scatter(np.arange(ref_pk_pos_high.shape[0])+0.5, ref_pk_pos_high[sort_high])
- ax.set_ylim(0, 4096)
- ax.set_xlabel("Peak index")
- ax.set_ylabel("Peak position (ADU)")
-
- ax = fig.add_subplot(132)
- mns = np.array(res_uncorr[m][1])
- mn_im = np.zeros((mns.shape[0], 500))
- for j in range(mns.shape[0]):
- h, _ = np.histogram(mns[sort_med[j],...].flatten(), bins=500, range=(0,4096))
- mn_im[j,:] = h
- ax.imshow(np.rot90(mn_im), interpolation="nearest", aspect="auto",
- extent=[0,ref_pk_pos_med.shape[0],0,4096], cmap='jet')
-
- ax.scatter(np.arange(ref_pk_pos_med.shape[0])+0.5, ref_pk_pos_med[sort_med])
- ax.set_xlabel("Peak index")
- ax.set_ylim(0, 4096)
-
-
- ax = fig.add_subplot(133)
- mns = np.array(res_uncorr[m][0])
- mn_im = np.zeros((mns.shape[0], 500))
- for j in range(mns.shape[0]):
- h, _ = np.histogram(mns[sort_low[j],...].flatten(), bins=500, range=(0,4096))
- mn_im[j,:] = h
- ax.imshow(np.rot90(mn_im), interpolation="nearest", aspect="auto",
- extent=[0,ref_pk_pos_low.shape[0],0,4096], cmap='jet')
-
- ax.scatter(np.arange(ref_pk_pos_low.shape[0]), ref_pk_pos_low[sort_low])
- ax.set_xlabel("Peak index")
- ax.set_ylim(0, 4096)
-
- fig.savefig("{}/peaks_centroid_vs_dist_{}_module_{}.png".format(out_folder,
- capacitance,
- "_".join([str(m) for m in modules])))
-
-
-# In[52]:
-
-
-def calib_gain(gidx, sort_idx, limit_peaks=False):
- def calibrate_single_row(cells, xrd, inp):
-
- from sklearn.cluster import KMeans
- from iminuit import Minuit
- from iminuit.util import make_func_code, describe
- import numpy as np
-
- yrd = inp
-
- def fit_data(fun, x, y, yerr, par_ests):
- par_ests["throw_nan"] = False
- par_ests["pedantic"] = False
- par_ests["print_level"] = 0
-
- f_sig = describe(fun)[1:]
-
- class _Chi2Functor:
- def __init__(self, f, x, y, err):
- self.f = f
- self.x = x
- self.y = y
- self.err = err
- f_sig = describe(f)
- # this is how you fake function
- # signature dynamically
- self.func_code = make_func_code(
- f_sig[1:]) # docking off independent variable
- self.func_defaults = None # this keeps numpy.vectorize happy
-
- def __call__(self, *arg):
- # notice that it accept variable length
- # positional arguments
- return np.sum(((self.f(self.x, *arg) - self.y) ** 2) / self.err)
-
- wrapped = _Chi2Functor(fun, x, y, yerr)
- m = Minuit(wrapped, **par_ests)
- fmin = m.migrad()
-
- return m.values
-
- def lin_fun(x, m, b):
- return m*x + b
-
- # linear slope
- ml = np.zeros(yrd.shape[1:])
- bl = np.zeros(yrd.shape[1:])
- devl = np.zeros(yrd.shape[1:])
- ml[...] = np.nan
- bl[...] = np.nan
- devl[...] = np.nan
-
- failures = []
- outliers = []
- for cell in range(cells):
- for col in range(yrd.shape[-2]):
- try:
-
- y = yrd[:,col, cell]
- x = xrd
-
- parms = {'m': 1, 'b': 0}
- fitted = fit_data(lin_fun, x, y, np.sqrt(y), parms)
- yf = lin_fun(x, fitted['m'], fitted['b'])
- max_devl = np.max(np.abs((y-yf)/y))
- ml[col, cell] = fitted['m']
- bl[col, cell] = fitted['b']
- devl[col, cell] = max_devl
- if max_devl > 0.1:
- outliers.append((cell, col, y))
- except Exception as e:
- failures.append((cell, col, str(e)))
- return (ml, bl, devl), failures, outliers
-
- fres = {}
- failures = []
- outliers = []
- for i, r in enumerate(res_uncorr):
- if len(r[gidx]) == 0:
- continue
- means = np.array(r[gidx])
- inp = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- if not limit_peaks:
- for j in range(means.shape[2]):
- inp.append(means[sort_idx,:,j,:])
-
- p = partial(calibrate_single_row, memory_cells, ref_pos[gidx])
- else:
- for j in range(means.shape[2]):
- inp.append(means[limit_peaks[0]:limit_peaks[1],:,j,:])
- p = partial(calibrate_single_row, memory_cells, ref_pos[gidx][limit_peaks[0]:limit_peaks[1]])
- frs = view.map_sync(p, inp)
-
- # linear slope
- ml = np.zeros(means.shape[1:])
- bl = np.zeros(means.shape[1:])
- devl = np.zeros(means.shape[1:])
-
- for j, fr in enumerate(frs):
- lin, fails, outs = fr
- mlr, blr, devlr = lin
- failures.append(fails)
- outliers.append(outs)
-
- ml[:,j,:] = mlr
- bl[:,j,:] = blr
- devl[:,j,:] = devlr
-
- fres[qm] = {'ml': ml,
- 'bl': bl,
- 'devl': devl,
- }
- return fres, failures, outliers
-
-
-# In[53]:
-
-
-fres_high, failures, _ = calib_gain(2, sort_high)
-fres_med, failures, _ = calib_gain(1, sort_med)
-fres_low, failures, outliers = calib_gain(0, sort_low)
-
-
-# In[54]:
-
-
-print(failures[0])
-
-
-# In[55]:
-
-
-def plot_for_gain(fres):
- masks = {}
- import matplotlib.pyplot as plt
- from mpl_toolkits.axes_grid1 import AxesGrid
-
- cell_to_preview = 4
- for module, data in fres.items():
- fig = plt.figure(figsize=(20,20))
- grid = AxesGrid(fig, 111,
- nrows_ncols=(2, 2),
- axes_pad=(0.9, 0.15),
- label_mode="1",
- share_all=True,
- cbar_location="right",
- cbar_mode="each",
- cbar_size="7%",
- cbar_pad="2%",
- )
-
-
- mask = np.zeros(data['ml'].shape, np.uint8)
- mask[(data['devl'] == 0)] += 2
- mask[(data['devl'] > 0.5)] += 4
- mask[(data['devl'] < 0)] += 8
- mask[(~np.isfinite(data['devl']))] += 16
-
- i = 0
- for key, item in data.items():
- med = np.abs(np.nanmedian(item))
- bound = 0.1
- max_cnt = 0
- while (np.count_nonzero((item < med-bound*med) |
- (item > med+bound*med))/item.size > 0.01):
- bound *=2
- max_cnt += 1
-
- im = grid[i].imshow(item[...,cell_to_preview], interpolation="nearest",
- vmin=med-bound*med, vmax=med+bound*med, aspect=1)
- cb = grid.cbar_axes[i].colorbar(im)
-
- grid[i].text(20, 50, key, color="w", fontsize=50)
-
- i += 1
-
- im = grid[-1].imshow(mask[..., cell_to_preview], interpolation="nearest",
- vmin=0, vmax=1, aspect=1)
- cb = grid.cbar_axes[-1].colorbar(im)
-
- grid[-1].text(20, 50, "mask", color="w", fontsize=50)
-
- masks[module] = mask
- return masks
-
-
-# In[56]:
-
-
-mask_low = plot_for_gain(fres_low)
-mask_med = plot_for_gain(fres_med)
-mask_high = plot_for_gain(fres_high)
-
-
-# In[57]:
-
-
-fres = {}
-for module in fres_low.keys():
- gain_m = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.float32)
- gain_b = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.float32)
- mask = np.zeros((sensor_size[0], sensor_size[1], memory_cells, 3), np.uint8)
-
- gain_m[...,0] = fres_low[module]['ml']
- gain_m[...,1] = np.array(fres_med[module]['ml'])/slope_100_10
- gain_m[...,2] = np.array(fres_high[module]['ml'])/(slope_10_1*slope_100_10)
-
- gain_b[...,0] = fres_low[module]['bl']
- gain_b[...,1] = fres_med[module]['bl']
- gain_b[...,2] = fres_high[module]['bl']
-
- mask[...,0] = mask_low[module]
- mask[...,1] = mask_med[module]
- mask[...,2] = mask_high[module]
-
- fres[module] = {}
- fres[module]['RelativeGain'] = gain_m
- fres[module]['RelativeGainOffset'] = gain_b
- fres[module]['BadPixels'] = mask
-
-
-# In[58]:
-
-
-
-ofile = "{}/lpd_ci_store_{}_16_{}.h5".format(out_folder,
- "_".join([str(m) for m in modules]),
- capacitance)
-store_file = h5py.File(ofile, "w")
-for qm, r in fres.items():
- for key, item in r.items():
- store_file["/{}/{}/0/data".format(qm, key)] = item
-store_file.close()
-
-
-# In[59]:
-
-
-def correct_single_module(fbase, settings, gains, sensor_size, memory_cells, block_size,
- inp):
- """ This function calculates a per-pixel histogram for a single module
-
- Runs and sequences give the data to calculate histogram from
- """
- if inp is None:
- return [], [], []
- channel, om, mod_corr, rg, rbg = inp
-
- import XFELDetAna.xfelpycaltools as xcal
- import numpy as np
- import h5py
-
- def splitOffGainLPD(d):
- msk = np.zeros(d.shape, np.uint16)
- msk[...] = 0b0000111111111111
- data = np.bitwise_and(d, msk)
- gain = np.zeros(data.shape, np.uint8)
- gain[data >= 2**12] = 1
- gain[data >= 2**13] = 2
- return data, gain
-
- # function needs to be inline for parallell processing
- def read_fun(filename, channel):
- """ A reader function used by pyDetLib
- """
- infile = h5py.File(filename, "r", driver="core")
- imarr = infile["/data".format(channel)]
- im = np.array(imarr)
- infile.close()
-
- im, ga = splitOffGainLPD(im.astype(np.uint16))
- return im.astype(np.float32), ga
-
-
- means_low, means_med, means_high = [], [], []
-
- for i, setting in enumerate(settings):
- gain = gains[i]
- try:
- if i < mod_corr:
- means_high.append(np.zeros((sensor_size[0],
- sensor_size[1],
- memory_cells)))
- continue
- fname = fbase.format(channel//4+1, channel%4+1, setting)
- print(fname)
-
- d, g = read_fun(fname, channel)
- for cc in range(d.shape[2]//memory_cells):
- tg = g[...,cc*memory_cells:(cc+1)*memory_cells]
-
- offset = np.choose(gain, (om[...,0], om[...,1], om[...,2]))
- rgain = np.choose(gain, (rg[...,0], rg[...,1], rg[...,2]))
- #rgainb = np.choose(gain, (rbg[...,0], rbg[...,1], rbg[...,2]))
- tim = d[...,cc*memory_cells:(cc+1)*memory_cells]
-
- tim = tim - offset
- tim = (tim)/rgain
- d[...,cc*memory_cells:(cc+1)*memory_cells] = tim
-
- mn = np.zeros((d.shape[0], d.shape[1], memory_cells))
- for cell in range(memory_cells):
- mn[...,cell] = np.nanmean(d[...,cell::memory_cells], axis=2)
- if gain == 0:
- means_high.append(mn)
- elif gain == 1:
- means_med.append(mn)
- else:
- means_low.append(mn)
- except Exception as e:
- print(e)
-
- return means_low, means_med, means_high
-
-inp = []
-for i in modules:
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- inp.append((i, offset_g_50pf[qm], mod_corrs[i], fres[qm]['RelativeGain'], fres[qm]['RelativeGainOffset']))
- except:
- inp.append(None)
-
-p = partial(correct_single_module, fbase, setting_50f, gains_50f,
- sensor_size, memory_cells, block_size)
-res_corr = view.map_sync(p, inp)
-
-
-# In[60]:
-
-
-
-cell = 1
-for i, r in enumerate(res_corr):
- means_low, means_med, means_high = r
-
- d = []
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- for kk, mn in enumerate(means_high):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(0, 4000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_med):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(0, 40000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- for kk, mn in enumerate(means_low):
- h, e = np.histogram(mn[...,cell].flatten(), bins=1000, range=(0, 400000))
- c = (e[1:]+e[:-1])/2
- d.append({
- 'x': c,
- 'y': h,
- 'drawstyle': 'steps-mid',
- 'label': str(kk)
- })
-
- fig = xana.simplePlot(d, y_log=True,
- figsize="2col",
- aspect=2,
- x_range=(0, 300000),
- x_label="Intensity (ADU)",
- y_label="Counts",
- legend="top-right")
-
-
-# In[ ]:
-
-
-
-
-
-# In[ ]:
-
-
-
-
diff --git a/LPD/correct_lpd_batch.py b/LPD/correct_lpd_batch.py
deleted file mode 100644
index 2ee1dfaa89cc9954c69ce2431101561e38e93919..0000000000000000000000000000000000000000
--- a/LPD/correct_lpd_batch.py
+++ /dev/null
@@ -1,679 +0,0 @@
-
-# coding: utf-8
-
-# In[41]:
-
-
-import sys
-#in_folder, out_folder, base_store, offset_store, mem_cells, sequences
-in_folder = sys.argv[1] # "/gpfs/exfel/exp/SPB/201701/p002012/raw/r0100"
-out_folder = sys.argv[2]# "./corrected_test"
-relgain_store = sys.argv[3]
-relgain_store = relgain_store.replace("CHANID", "{}")
-offset_store = sys.argv[4]
-ff_store = sys.argv[11]
-sequences = sys.argv[6] #[0]
-mem_cells = int(sys.argv[5]) # 30
-max_cells = mem_cells
-overwrite = True if sys.argv[7] == "True" else False
-if sequences.upper() != "ALL":
- sequences = [int(s) for s in sequences.split(",")]
-else:
- sequences = None
-do_rel_gain = not(True if sys.argv[8] == "True" else False)
-do_ff = (True if sys.argv[10] == "True" else False)
-#do_ff = False
-#relgain_store = "/gpfs/exfel/d/proc/FXE/201830/p900020/calibration/lpd_ci_store_{}_16_5pf.h5"
-print("Applying FF corrections: {}".format(do_ff))
-print("Offset store: {}".format(offset_store))
-print("Rel gain store: {}".format(relgain_store))
-uuid = sys.argv[9]
-
-index_v = sys.argv[12]
-
-db_input = True
-bias_voltage = 500
-cal_db_interface = "tcp://max-exfl015:5005"
-
-use_dir_creation_date = True
-
-creation_time = None
-if use_dir_creation_date:
- import os
- import datetime
- creation_time = os.stat(in_folder).st_ctime
- creation_time = datetime.datetime.fromtimestamp(creation_time)
- print("Using {} as creation time".format(creation_time))
-
-
-# make sure a cluster is running with ipcluster start --n=32, give it a while to start
-import os
-import h5py
-import numpy as np
-import matplotlib
-matplotlib.use("agg")
-import matplotlib.pyplot as plt
-from ipyparallel import Client
-from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
-from collections import OrderedDict
-cap = 5
-print("Connecting to profile {}".format(uuid))
-view = Client(profile=uuid)[:]
-view.use_dill()
-gains = np.arange(3)
-cells = np.arange(max_cells)
-
-
-
-QUADRANTS = 4
-MODULES_PER_QUAD = 4
-DET_FILE_INSET = "LPD"
-CHUNK_SIZE = 512
-MAX_PAR = 32
-
-if in_folder[-1] == "/":
- in_folder = in_folder[:-1]
-out_folder = "{}/{}".format(out_folder, os.path.split(in_folder)[-1])
-print("Outputting to {}".format(out_folder))
-
-if not os.path.exists(out_folder):
- os.makedirs(out_folder)
-elif not overwrite:
- raise AttributeError("Output path exists! Exiting")
-
-max_cells_db = 128
-
-# In[42]:
-
-
-def combine_stack(d, sdim):
- combined = np.zeros((sdim, 2048,2048))
- combined[...] = np.nan
-
- map_x = [1,0,0,1]
- map_y = [1,1,0,0]
- to_map = d
- dx = 64
- dy = 0
- for q in range(4):
-
- if q == 1 or q == 2:
- dy = 40
- else:
- dy = 0
- if q == 3 or q == 2:
- dx = 0
- else:
- dx = 40
- qcomb = np.zeros((sdim, 512,512))
- for m in range(4):
- mx = map_x[m]
- my = map_y[m]
- try:
- qcomb [:, mx*256:(mx+1)*256, my*256:(my+1)*256] = to_map[q*4+m][:,:,::-1]
- except:
- pass
- mx = map_x[q]
- my = map_y[q]
- combined[:, mx*512+dx:(mx+1)*512+dx, my*512+dy:(my+1)*512+dy] = qcomb
-
- return combined
-
-
-# In[43]:
-
-
-rel_gains = []
-offsets = []
-bad_pixels = []
-flat_fields = []
-rel_gains_b = []
-if True:
- if "{}" in relgain_store:
- for i in range(16):
- try:
- saveFile = h5py.File(relgain_store.format(i), "r")
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- if qm in saveFile:
- data = np.array(saveFile["{}/RelativeGain/0/data".format(qm)])
- datab = np.array(saveFile["{}/RelativeGainOffset/0/data".format(qm)])
- mask = np.array(saveFile["{}/BadPixels/0/data".format(qm)])
- else: # older files always have a Q1M1 entry
- data = np.array(saveFile["Q1M1/RelativeGain/0/data".format(qm)])
- datab = np.array(saveFile["Q1M1/RelativeGainOffset/0/data".format(qm)])
- mask = np.array(saveFile["Q1M1/BadPixels/0/data".format(qm)])
- rel_gains.append(data)
- rel_gains_b.append(datab)
- bad_pixels.append(mask)
- except Exception as e:
- print(e)
- rel_gains.append(np.ones((256,256,max_cells,3)))
- rel_gains_b.append(np.ones((256,256,max_cells,3)))
- bad_pixels.append(np.zeros((256,256,max_cells,3), np.uint8))
- saveFile.close()
-
-
- else:
- saveFile = h5py.File(relgain_store, "r")
- for i in range(16):
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- data = np.array(saveFile["{}/RelativeGain/0/data".format(qm)])
- mask = np.array(saveFile["{}/BadPixels/0/data".format(qm)])
- rel_gains.append(data)
- bad_pixels.append(mask)
- except:
- rel_gains.append(np.ones((256,256,max_cells,3)))
- bad_pixels.append(np.zeros((256,256,max_cells,3), np.uint8))
- saveFile.close()
-
- """
- saveFile = h5py.File(offset_store, "r")
- for i in range(16):
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- data = np.array(saveFile["{}/Offset/0/data".format(qm)])
- offsets.append(data)
- bpdata = np.array(saveFile["{}/BadPixelsDark/0/data".format(qm)]).astype(np.uint8)
- try:
- bad_pixels[i] |= bpdata
- except:
- pass
- except Exception as e:
- print(e)
- offsets.append(np.zeros((256,256,max_cells,3)))
- saveFile.close()
- """
- saveFile = h5py.File(ff_store, "r")
- for i in range(16):
- try:
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- data = np.array(saveFile["{}/FlatField/0/data".format(qm)])
- flat_fields.append(np.rollaxis(data, 1)[::-1,::-1])
-
- except Exception as e:
- print(e)
- flat_fields.append(np.zeros((256,256)))
- saveFile.close()
-if True:
- import copy
- for i in range(16):
- qm = "Q{}M{}".format(i//4+1, i%4+1)
- metadata = ConstantMetaData()
- offset = Constants.LPD.Offset()
- metadata.calibration_constant = offset
-
- # set the operating condition
- condition = Conditions.Dark.LPD(memory_cells=max_cells_db, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- device=getattr(Detectors.LPD1M1, qm)
- if device:
- if creation_time is None:
- metadata.calibration_constant_version = Versions.Now(device=device)
- else:
- metadata.calibration_constant_version = Versions.Timespan(device=device,
- start=creation_time)
- try:
- metadata.retrieve(cal_db_interface)
- offsets.append(copy.copy(offset.data))
- except Exception as e:
- print("Could not retrieve offset from db for {}: {}".format(qm, e))
- offsets.append(np.zeros((256,256,max_cells,3)))
- else:
- print("Could not retrieve offset from db for {}".format(qm))
- offsets.append(np.zeros((256,256,max_cells,3)))
- """
- metadata = ConstantMetaData()
- slopes = Constants.LPD.SlopesCI()
- metadata.calibration_constant = slopes
-
- # set the operating condition
- condition = Conditions.Dark.LPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
- device=getattr(Detectors.LPD1M1, qm)
- if device:
- metadata.calibration_constant_version = Versions.Now(device)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.LPD1M1, qm))
- try:
- metadata.retrieve(cal_db_interface)
- rel_gains.append(copy.copy(slopes.data[...,0]))
- rel_gains_b.append(copy.copy(slopes.data[...,0]))
- except:
- rel_gains.append(np.ones((256,256,max_cells,3)))
- rel_gains_b.append(np.ones((256,256,max_cells,3)))
- else:
- rel_gains.append(np.ones((256,256,max_cells,3)))
- rel_gains_b.append(np.ones((256,256,max_cells,3)))
-
- # FF
- metadata = ConstantMetaData()
- slopes = Constants.LPD.SlopesFF()
- metadata.calibration_constant = slopes
-
- # set the operating condition
- condition = Conditions.Illuminated.LPD(1., bias_voltage, 9.2,
- capacitor=cap, beam_energy=None)
- metadata.detector_condition = condition
- device=getattr(Detectors.LPD1M1, qm)
- if device:
- metadata.calibration_constant_version = Versions.Now(device)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.LPD1M1, qm))
- try:
- metadata.retrieve(cal_db_interface)
- flat_fields.append(copy.copy(slopes.data))
- except:
- flat_fields.append(np.ones((256,256,max_cells,3)))
- else:
- flat_fields.append(np.ones((256,256,max_cells,3)))
-
- metadata = ConstantMetaData()
- bpixdark = Constants.LPD.BadPixelsDark()
- metadata.calibration_constant = bpixdark
-
- # set the operating condition
- condition = Conditions.Dark.LPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
- device=getattr(Detectors.LPD1M1, qm)
- if device:
- metadata.calibration_constant_version = Versions.Now(device)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.LPD1M1, qm))
- try:
- metadata.retrieve(cal_db_interface)
- except:
- bpixdark = None
-
- metadata = ConstantMetaData()
- bpixFF = Constants.LPD.BadPixelsFF()
- metadata.calibration_constant = bpixFF
-
- # set the operating condition
- condition = Conditions.Illuminated.LPD(1., bias_voltage, 9.2,
- capacitor=cap, beam_energy=None)
- metadata.detector_condition = condition
- device=getattr(Detectors.LPD1M1, qm)
- if device:
- metadata.calibration_constant_version = Versions.Now(device)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.LPD1M1, qm))
- try:
- metadata.retrieve(cal_db_interface)
- except:
- bpixFF = None
-
- metadata = ConstantMetaData()
- bpixslopes = Constants.LPD.BadPixelsCI()
- metadata.calibration_constant = bpixslopes
-
- # set the operating condition
- condition = Conditions.Dark.LPD(memory_cells=max_cells, bias_voltage=bias_voltage)
- metadata.detector_condition = condition
- device=getattr(Detectors.LPD1M1, qm)
- if device:
- metadata.calibration_constant_version = Versions.Now(device)
- metadata.detector_condition = condition
-
- # specify the a version for this constant
- metadata.calibration_constant_version = Versions.Now(device=getattr(Detectors.LPD1M1, qm))
- try:
- metadata.retrieve(cal_db_interface)
-
- bad_pixels.append(bpixdark.data.astype(np.uint32)
- | bpixslopes.data.astype(np.uint32)
- | bpixFF.data.astype(np.uint32))
- except:
- if bpixdark and bpixFF:
- bad_pixels.append(bpixdark.data.astype(np.uint32)| bpixFF.data.astype(np.uint32))
- elif bpixdark:
- bad_pixels.append(bpixdark.data.astype(np.uint32))
- else:
- bad_pixels.append(np.zeros((256,256,max_cells,3), np.uint8))
- else:
- bad_pixels.append(np.zeros((256,256,max_cells,3), np.uint8))
- """
-print("Retrieved calibration data")
-
-# set everything up filewise
-from queue import Queue
-if not os.path.exists(out_folder):
- os.makedirs(out_folder)
-
-def map_modules_from_files(filelist):
- module_files = OrderedDict()
- mod_ids = OrderedDict()
- for quadrant in range(0, QUADRANTS):
- for module in range(0, MODULES_PER_QUAD):
- name = "Q{}M{}".format(quadrant + 1, module + 1)
- module_files[name] = Queue()
- num = quadrant * 4 + module
- mod_ids[name] = num
- file_infix = "{}{:02d}".format(DET_FILE_INSET, num)
- for file in filelist:
- if file_infix in file:
- module_files[name].put(file)
-
- return module_files, mod_ids
-
-dirlist = sorted(os.listdir(in_folder))
-file_list = []
-
-for entry in dirlist:
- #only h5 file
- abs_entry = "{}/{}".format(in_folder, entry)
- if os.path.isfile(abs_entry) and os.path.splitext(abs_entry)[1] == ".h5":
-
- if sequences is None:
- file_list.append(abs_entry)
- else:
- for seq in sequences:
- if "{:05d}.h5".format(seq) in abs_entry:
- file_list.append(os.path.abspath(abs_entry))
-
-mapped_files, mod_ids = map_modules_from_files(file_list)
-
-print(file_list)
-# In[45]:
-
-
-import copy
-from functools import partial
-def correct_module(max_cells, do_ff, index_v, CHUNK_SIZE, inp):
- import numpy as np
- import copy
- import h5py
-
- def splitOffGainLPD(d):
- msk = np.zeros(d.shape, np.uint16)
- msk[...] = 0b0000111111111111
- data = np.bitwise_and(d, msk)
-
- gain = np.right_shift(d, 12)
- msk[...] = 0b0000000000000011
- gain = np.bitwise_and(gain, msk)
- return data, gain
-
- try:
-
- filename, filename_out, channel, offset, rel_gain, mask, flatfield, rel_gain_b = inp
-
- infile = h5py.File(filename, "r", driver="core")
- if index_v == "2":
- count = np.squeeze(infile["/INDEX/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/count".format(channel)])
- first = np.squeeze(infile["/INDEX/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/first".format(channel)])
- if np.count_nonzero(count != 0) == 0:
- print("File {} has no valid counts".format(infile))
- return
- last_index = int(first[count != 0][-1]+count[count != 0][-1])
- first_index = int(first[count != 0][0])
- elif index_v == "1":
- status = np.squeeze(infile["/INDEX/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)])
- if np.count_nonzero(status != 0) == 0:
- return
- last = np.squeeze(infile["/INDEX/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/last".format(channel)])
- last_index = int(last[status != 0][-1])
- first_index = int(last[status != 0][0])
- allcells = np.squeeze(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][first_index:last_index, ...]))
- single_image = np.array(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][first_index, ...]))
- can_calibrate = allcells < max_cells
- if np.count_nonzero(can_calibrate) == 0:
- return
- allcells = allcells[can_calibrate]
- firange = np.arange(first_index, last_index)
- firange = firange[can_calibrate]
-
- dont_copy = ["data", "cellId", "trainId", "pulseId", "status", "length"]
- dont_copy = ["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/{}".format(channel, do)
- for do in dont_copy]
-
- outfile = h5py.File(filename_out, "w", driver="core")
- def visitor(k, item):
- if k not in dont_copy:
- if isinstance(item, h5py.Group):
- outfile.create_group(k)
- elif isinstance(item, h5py.Dataset):
- group = str(k).split("/")
- group = "/".join(group[:-1])
- infile.copy(k, outfile[group])
-
- infile.visititems(visitor)
- outfile.flush()
-
- oshape = (firange.size, single_image.shape[2], single_image.shape[1])
-
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)] = np.zeros(oshape, np.float32)
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/gain".format(channel)] = np.zeros(oshape, np.uint8)
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/mask".format(channel)] = np.zeros(oshape, np.uint32)
-
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)] = np.zeros(firange.size, np.uint16)
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)] = np.zeros(firange.size, np.uint64)
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)] = np.zeros(firange.size, np.uint64)
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)] = np.zeros(firange.size, np.uint16)
-
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/length".format(channel)] = np.zeros(firange.size, np.uint32)
-
- #
- cidx = 0
- for irange in np.array_split(firange, firange.size//CHUNK_SIZE):
-
- im = np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][irange, ...])
- trainId = np.squeeze(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)][irange, ...])
- pulseId = np.squeeze(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)][irange, ...])
- status = np.squeeze(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)][irange, ...])
-
- cells = np.squeeze(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][irange, ...]))
-
-
- length = np.squeeze(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/length".format(channel)][irange, ...]))
-
-
-
- im, gain = splitOffGainLPD(im[:,0,...])
-
- im = im.astype(np.float32)
- im[gain > 2] = np.nan
- gain[gain > 2] = 0
-
- im = np.rollaxis(im, 2)
- im = np.rollaxis(im, 2, 1)
-
- gain = np.rollaxis(gain, 2)
- gain = np.rollaxis(gain, 2, 1)
-
- om = offset[...,cells,:]
- rc = rel_gain[...,cells,:]
- rbc = rel_gain_b[...,cells,:]
- og = np.choose(gain, (om[...,0], om[...,1], om[...,2]))
- rg = np.choose(gain, (rc[...,0], rc[...,1], rc[...,2]))
- rgb = np.choose(gain, (rbc[...,0], rbc[...,1], rbc[...,2]))
-
- mskg = mask[...,cells,:]
- msk = np.choose(gain, (mskg[...,0], mskg[...,1], mskg[...,2]))
- im -= og
-
- im = (im-rgb)/rg
- if do_ff:
- im /= flatfield[:,:,None]
- nidx = int(cidx+irange.size)
-
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data".format(channel)][cidx:nidx,...] = np.rollaxis(np.rollaxis(im,1), 2)
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/gain".format(channel)][cidx:nidx,...] = np.rollaxis(np.rollaxis(gain,1), 2)
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/mask".format(channel)][cidx:nidx,...] = np.rollaxis(np.rollaxis(msk,1), 2)
-
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/cellId".format(channel)][cidx:nidx] = cells
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/trainId".format(channel)][cidx:nidx] = trainId
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/pulseId".format(channel)][cidx:nidx] = pulseId
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/status".format(channel)][cidx:nidx] = status
- outfile["INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/length".format(channel)][cidx:nidx] = length
- cidx = nidx
-
- outfile.close()
- infile.close()
- except Exception as e:
- print(e)
- pass
-
-done = False
-first_files = []
-inp = []
-while not done:
-
- dones = []
- first = True
- for i in range(16):
- qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
- if qm in mapped_files and not mapped_files[qm].empty():
- fname_in = str(mapped_files[qm].get())
- dones.append(mapped_files[qm].empty())
- else:
- print("Skipping {}".format(qm))
- first_files.append((None, None))
- continue
- fout = os.path.abspath("{}/{}".format(out_folder, (os.path.split(fname_in)[-1]).replace("RAW", "CORR")))
- if first:
- first_files.append((fname_in, fout))
- inp.append((fname_in, fout, i, offsets[i][...,:max_cells,:].astype(np.float32),
- rel_gains[i][...,:max_cells,:], bad_pixels[i][...,:max_cells,:],
- flat_fields[i], rel_gains_b[i][...,:max_cells,:]))
- first = False
- if len(inp) > MAX_PAR:
- print("Running {} tasks parallel".format(len(inp)))
- p = partial(correct_module, max_cells, do_ff, index_v, CHUNK_SIZE)
- r = view.map_sync(p, inp)
- inp = []
-
-
- #r = list(map(p, inp))
- done = all(dones)
-#r.wait()
-
-# In[46]:
-
-
-corrected = []
-raw = []
-gains = []
-for i, ff in enumerate(first_files):
- try:
- rf, cf = ff
- if rf is not None:
-
- infile = h5py.File(rf, "r")
- raw.append(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data".format(i)][max_cells*0:1*max_cells,0,...]))
- infile.close()
-
- infile = h5py.File(cf, "r")
- corrected.append(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/data".format(i)][max_cells*0:1*max_cells,...]))
- gains.append(np.array(infile["/INSTRUMENT/FXE_DET_LPD1M-1/DET/{}CH0:xtdf/image/gain".format(i)][max_cells*0:1*max_cells,...]))
- infile.close()
- else:
- raise
-
- except Exception as e:
- print(e)
- corrected.append(np.zeros((max_cells, 256, 256)))
- raw.append(np.zeros((max_cells, 256, 256)))
- gains.append(np.zeros((max_cells, 256, 256)))
-
-
-# In[47]:
-
-
-
-combined = combine_stack(corrected, corrected[0].shape[0])
-combined_raw = combine_stack(raw, raw[0].shape[0])
-combined_g = combine_stack(gains, gains[0].shape[0])
-
-
-# In[48]:
-
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined_raw,axis=0)[:1200,:1200], vmin=0, vmax=8000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/mean_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined, axis=0)[:1200,:1200], vmin=-50, vmax=5000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/mean_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.mean(combined_g, axis=0)[:1200,:1200], vmin=0, vmax=3, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/mean_first_train_GAIN.png".format(out_folder))
-
-
-# In[49]:
-
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined_raw[:1200,:1200],axis=0), vmin=0, vmax=8000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/std_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined[:1200,:1200], axis=0), vmin=0, vmax=20000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/std_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.std(combined_g[:1200,:1200], axis=0), vmin=0, vmax=3, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/std_first_train_GAIN.png".format(out_folder))
-
-
-# In[50]:
-
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined_raw[:1200,:1200],axis=0), vmin=0, vmax=8000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_RAW.png".format(out_folder))
-
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined[:1200,:1200], axis=0), vmin=0, vmax=20000, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_CORR.png".format(out_folder))
-
-fig = plt.figure(figsize=(20,20))
-ax = fig.add_subplot(111)
-im = ax.imshow(np.max(combined_g[:1200,:1200], axis=0), vmin=0, vmax=3, cmap="jet")
-plt.colorbar(im)
-fig.savefig("{}/max_first_train_GAIN.png".format(out_folder))
-
-
-# In[ ]:
-
-
-
-
-
-# In[ ]:
-
-
-
-
-
diff --git a/LPD/slurm_CI.py b/LPD/slurm_CI.py
deleted file mode 100644
index b4a83f66608d0129758c02078d0c6123c1f99afe..0000000000000000000000000000000000000000
--- a/LPD/slurm_CI.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import argparse
-import copy
-import glob
-import os
-from subprocess import Popen, PIPE
-from time import sleep
-import time
-from uuid import uuid4
-
-parser = argparse.ArgumentParser(description="Main entry point "
- "for offline calibration")
-parser.add_argument("--input", type=str)
-parser.add_argument("--output", type=str)
-parser.add_argument("--mem-cells", type=str)
-parser.add_argument("--offset-store-5pf", type=str)
-parser.add_argument("--offset-store-50pf", type=str)
-parser.add_argument("--modules", type=str, default="all")
-
-
-def notebook_to_python():
- nb_name = "Characterize_LPD_GAIN_CI.ipynb"
- conv = ["jupyter", "nbconvert", "--to", "python",
- nb_name, "--output", "conv_tmp"]
-
- Popen(conv).wait()
-
- mapping = {}
- arg_cnt = 1
- has_profile = False
- with open("./conv_tmp.py", "r") as infile:
- with open(nb_name.replace("ipynb", "py"), "w") as outfile:
- outfile.write("import sys")
- for line in infile.readlines():
- if "SLURMHINT" in line:
-
- line, hint = line.split("#")
- field, assign = line.split("=")
- parm = hint.split("SLURMHINT:")[1]
- parm, typ = parm.split(",")
- parm = parm.strip()
- if parm == "profile":
- mapping[-1] = parm
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=-1)
- else:
- mapping[arg_cnt] = parm
- if typ.strip() != "list":
- line = "{field} = {typ}(sys.argv[{arg_cnt}])\n".format(field=field,
- typ=typ.strip(),
- arg_cnt=arg_cnt)
- else:
- line = "{field} = [int(s) for s in sys.argv[{arg_cnt}].split(',')]\n".format(field=field, arg_cnt=arg_cnt)
- arg_cnt += 1
- outfile.write(line)
- else:
- if "get_ipython()" in line:
- line = "# "+line
- outfile.write(line)
-
- return mapping
-
-
-def run():
- args = vars(parser.parse_args())
- rawpath = args["input"]
- out_folder= args["output"]
- cells = args["mem_cells"]
- mods = args["modules"]
- offset_store_5pf = args["offset_store_5pf"]
- offset_store_50pf = args["offset_store_50pf"]
- capacitances = []
- if offset_store_5pf is not None:
- capacitances.append(("5pf", offset_store_5pf))
- if offset_store_50pf is not None:
- capacitances.append(("50pf", offset_store_50pf))
- modules = range(16) if mods.upper()=="ALL" else [int(m) for m in mods.split(",")]
-
- out_mapping = notebook_to_python()
-
- for cof in capacitances:
- capacitance, offset_store = cof
- for module in modules:
- in_mapping = {"memory_cells": cells,
- "in_folder": rawpath,
- "modules": module,
- "out_folder": out_folder,
- "offset_store": offset_store,
- "capacitance": capacitance,
- }
-
- srun_base = ["sbatch", "-p", "exfel", "-t", "24:00:00"]
-
- srun_base += [os.path.abspath("{}/slurm_CI.sh".format(os.getcwd())),
- os.path.abspath("{}/Characterize_LPD_GAIN_CI.py".format(os.getcwd()))]
-
- for key in sorted(out_mapping.keys()):
- if out_mapping[key] in in_mapping:
- srun_base.append(str(in_mapping[out_mapping[key]]))
-
- print(srun_base)
- Popen(srun_base).wait()
-
-if __name__ == "__main__":
- run()
-
-
-
diff --git a/LPD/slurm_CI.sh b/LPD/slurm_CI.sh
deleted file mode 100644
index 0ef93b76c373e033fb1ebab87bc456b936619113..0000000000000000000000000000000000000000
--- a/LPD/slurm_CI.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-source /gpfs/exfel/data/user/haufs/karabo/activate
-uuid=$(uuidgen)
-export MPLBACKEND=AGG
-ipython profile create ${uuid} --parallel
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/ipcluster start --n=32 --profile=${uuid} --daemon &
-sleep 30
-echo "Running script"
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/python "$@" ${uuid}
-/gpfs/exfel/data/user/haufs/karabo/extern/bin/ipcluster stop --profile=${uuid}
-rm -rf "/home/haufs/.ipython/profile_${uuid}"
-
diff --git a/automode.py b/automode.py
deleted file mode 100644
index f1d48197f3ae7b9df3ec213c04b2ac74319db4ee..0000000000000000000000000000000000000000
--- a/automode.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import argparse
-import copy
-from dateutil import parser as dtparser
-import glob
-import os
-from subprocess import Popen, PIPE
-from time import sleep
-import datetime
-import time
-from uuid import uuid4
-
-parser = argparse.ArgumentParser(description="Main entry point "
- "for offline calibration")
-parser.add_argument("--input", type=str)
-parser.add_argument("--output", type=str)
-parser.add_argument("--base-cal-store", type=str)
-parser.add_argument("--offset-cal-store", type=str)
-parser.add_argument("--mem-cells", type=str)
-parser.add_argument("--detector", type=str)
-parser.add_argument("--type", type=str)
-parser.add_argument("--partition", type=str, default="upex")
-parser.add_argument("--no-relgain", action="store_true", default=False)
-parser.add_argument("--overwrite", action="store_true", default=False)
-parser.add_argument("--ff-cal-store", type=str, default="None")
-parser.add_argument("--no-ff", action="store_true", default=False)
-parser.add_argument("--runs", type=str, default="all")
-parser.add_argument("--only-new", action="store_true", default=False)
-parser.add_argument("--raw-version", type=str, default="2")
-parser.add_argument("--seqs", type=str, default="ALL")
-parser.add_argument("--start-time", type=str, default="now")
-
-LAST_MOD_DELAY = 120 # seconds
-
-def run():
- args = vars(parser.parse_args())
-
- inpath = os.path.abspath(args["input"])
- base_cal_store = os.path.abspath(args["base_cal_store"]) if "{}" not in args["base_cal_store"] else args["base_cal_store"]
- offset_cal_store = os.path.abspath(args["offset_cal_store"])
- output = os.path.abspath(args["output"])
- if not os.path.exists(output):
- os.makedirs(output)
- mem_cells = str(args["mem_cells"])
- partition = args["partition"]
- no_relgain = bool(args["no_relgain"])
- det = args["detector"].upper()
- overwrite = bool(args["overwrite"])
- ff_store = args["ff_cal_store"]
- raw_version = args["raw_version"]
- seqs = args["seqs"]
- start_time = args["start_time"]
- no_ff = True
- if ff_store and ff_store != "None":
- ff_store = os.path.abspath(ff_store)
- no_ff = False
-
- srun_base = ["sbatch", "-p", partition, "-t", "08:00:00"]
-
- srun_base += [os.path.abspath("{}/automode.sh".format(os.getcwd())),
- os.path.abspath("{}/calibrate.py".format(os.getcwd())),
- "--base-cal-store", base_cal_store,
- "--offset-cal-store", offset_cal_store,
- "--mem-cells", mem_cells,
- ]
-
- runs = args["runs"]
- if runs.upper() != "ALL":
- runs = runs.split(",")
- else:
- runs = runs.upper()
- only_new = bool(args["only_new"])
- print("Checking only new runs: {}".format(only_new))
- srun_base += ["--detector", det]
- pending = []
- first_loop = True
- if start_time.upper() == "NOW":
- start_time = time.time()
- else:
- start_time = dtparser.parse(start_time)
- if only_new:
- print("Only treating runs newer than: {}".format(start_time.isoformat()))
- while True:
- idirs = set(os.listdir(inpath))
- odirs = set(os.listdir(output))
- to_process = (idirs - odirs) if not overwrite else idirs
- for dir in to_process:
- if dir in pending or not dir:
- continue
- if runs != "ALL" and dir not in runs:
- continue
- # check if the folder is older than
- # a given delay, if not data might still
- # be transferred into it
- now = time.time()
- stat = os.stat("{}/{}".format(inpath, dir))
- last_mod = stat.st_mtime
- if now-last_mod < LAST_MOD_DELAY:
- continue
- if only_new and last_mod < start_time.timestamp():
- print("Skipping {} -> {}".format(dir, datetime.date.fromtimestamp(last_mod).isoformat()))
- pending.append(dir)
- continue
-
- s_run = copy.copy(srun_base)
- s_run += ["--uuid", "automode_{}".format(uuid4())]
- if no_relgain:
- s_run.append("--no-relgain")
- if overwrite:
- s_run.append("--overwrite")
- s_run += ["--input", "{}/{}".format(inpath, dir)]
- s_run += ["--output", output]
- s_run += ["--ff-cal-store", ff_store]
- s_run += ["--raw-version", raw_version]
- s_run += ["--sequences", seqs]
- if no_ff:
- s_run += ["--no-ff",]
- if not first_loop or not only_new:
- print(" ".join(s_run))
- Popen(s_run)
- pending.append(dir)
- first_loop = False
- sleep(10)
-
-if __name__ == "__main__":
- run()
-
-
diff --git a/cal_styles.css b/cal_styles.css
deleted file mode 100644
index ec7f83fb9b3dfa7a3a5423ea3703b469be19fd7e..0000000000000000000000000000000000000000
--- a/cal_styles.css
+++ /dev/null
@@ -1,144 +0,0 @@
-ul.mainmenu {
- list-style-type: none;
- margin: 0;
- padding: 0;
- width: 10%;
- background-color: #f1f1f1;
- height: 100%; /* Full height */
- position: fixed; /* Make it stick, even on scroll */
- overflow: auto; /* Enable scrolling if the sidenav has too much content */
- font-size: medium;
-}
-
-li.mainmenu {
- display: block;
- color: #000;
- padding: 8px 16px;
- text-decoration: none;
-}
-
-/* Change the link color on hover */
-li.mainmenu:hover {
- background-color: #555;
- color: white;
-}
-
-li.runtypemenu {
- font-size: smaller;
- padding-left: 16px;
-}
-
-li.runtypemenu:before {
- content: "\25BA" " ";
-}
-
-li.notebookmenuitem {
- font-size: smaller;
- padding-left: 24px;
-}
-
-li.notebookmenuitem:before {
- content: "+" " ";
-}
-
-div.pipelinearea {
- margin: 0;
- left: 12%;
- width: 80%;
- height: 100%; /* Full height */
- position: fixed; /* Make it stick, even on scroll */
- overflow: scroll; /* Enable scrolling if the sidenav has too much content */
- font-size: medium;
-}
-
-div.hidden_details {
- display: none;
-}
-
-div.serexecutionblock {
- width: 99%;
- position: relative;
- float: left;
- padding-bottom: 15px;
- border-bottom: 10px solid red;
-}
-
-div.parexecutionblock {
- width: 90%;
- min-height: 400px;
- background-color: #f2f6fc;
- position: relative;
- float: left;
- margin: 20px;
- padding: 15px;
- border: 1px solid grey;
-
-}
-
-div.form_container {
- position: relative;
- float: left;
-}
-
-
-div.form_notch_left {
- border-top: 20px solid #234172;
- border-bottom: 20px solid #234172;
- border-left: 10px solid transparent;
- float: left;
-}
-
-div.form_notch_right {
- border-top: 20px solid transparent;
- border-bottom: 20px solid transparent;
- border-left: 10px solid #234172;
- float: right;
-}
-
-
-div.form_content {
- left: 10px;
- line-height: 100%;
- padding: 15px;
- font-size: smaller;
- background-color: #234172;
- width: 500px;
- height: auto;
- display: inline-block;
- color: white;
-}
-
-.field_name {
- font-family: Aldrich;
- color: white;
- padding-top: 10px;
-}
-
-.field_name_bool {
- float: left;
- padding-top: 10px;
-}
-
-.form_field input {
- width: 90%;
- background: transparent;
- border: none;
- border-bottom: 1px dashed white;
- padding-top: 5px;
- outline: none;
- color: #ff530a;
-}
-
-.form_field input:hover{
- background: #7fafef;
- color: black;
-}
-
-.form_field input[type=checkbox] {
- width: auto;
-}
-
-.hidden {
- display: None;
-}
-
diff --git a/cal_web_interface.py b/cal_web_interface.py
deleted file mode 100644
index 8dac0792d7b3f93cc6a1f03c5e53e087894feb86..0000000000000000000000000000000000000000
--- a/cal_web_interface.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import argparse
-import base64
-import copy
-import glob
-from jinja2 import Template
-import json
-import os
-from os.path import isfile, isdir, splitext
-import shutil
-from subprocess import Popen, PIPE, check_output
-import sys
-import textwrap
-from time import sleep
-import time
-from uuid import uuid4
-
-import nbformat
-from nbconvert.preprocessors import ExecutePreprocessor
-from nbconvert.exporters import HTMLExporter
-import tornado.ioloop
-import tornado.web
-
-from nbparameterise import extract_parameters, replace_definitions
-from htmlform import build_form, build_page
-
-
-argcode = """
-argv = []
-with open('{}/argfile_{}', 'r') as argfile:
- for line in argfile.readlines():
- argv.append(line.strip())
-"""
-
-
-def notebook_to_python(nb_name, run_path, module):
-
- conv = ["jupyter", "nbconvert", "--to", "notebook",
- nb_name, "--output", "{}/conv_tmp".format(run_path)]
-
- Popen(conv).wait()
-
- mapping = {}
- arg_cnt = 0
- has_profile = False
- first_source = True
- with open("{}/conv_tmp.ipynb".format(run_path), "r") as infile:
- with open("{}/{}_{}.ipynb".format(run_path, nb_name.replace("ipynb", ""), module), "w") as outfile:
- celltype = None
- for line in infile.readlines():
- if "cell_type" in line:
- _, typ = line.split(":")
- if "code" in typ:
- celltype = "code"
- else:
- celltype = None
- if '"source": [' in line and first_source and celltype == "code":
-
- first_source = False
- for aline in argcode.format(run_path, module).split("\n"):
- line += '"{}\\n",\n'.format(aline)
-
- if "SLURMHINT" in line and celltype == "code":
-
- line, hint = line.split("#")
- field, assign = line.split("=")
- field = field.strip()[1:]
- parm = hint.split("SLURMHINT:")[1]
- parm, typ, _ = parm.split(",")
- typ = typ.strip().replace("\\n", "")
- parm = parm.strip()
- if parm == "profile":
- mapping[-1] = parm
- line = '"{field} = {typ}(argv[{arg_cnt}])\\n",\n'.format(field=field,
- typ=typ[:-1],
- arg_cnt=-1)
- else:
- mapping[arg_cnt] = parm
- if "list" not in typ:
- line = '"{field} = {typ}(argv[{arg_cnt}])\\n",\n'.format(field=field,
- typ=typ[:-1],
- arg_cnt=arg_cnt)
- else:
- ltmp = '"{field} = [int(s) for s in argv[{arg_cnt}].split(\',\')]\\n",\n'
- line = ltmp.format(field=field, arg_cnt=arg_cnt)
- arg_cnt += 1
- outfile.write(line)
-
- return mapping
-
-
-def combine_report(run_path, calibration):
- sphinx_path = "{}/sphinx_rep".format(os.path.abspath(run_path))
- os.makedirs(sphinx_path)
- direntries = os.listdir(run_path)
-
- for entry in direntries:
-
- if isfile("{}/{}".format(run_path, entry)):
- name, ext = splitext("{}".format(entry))
-
- if ext == ".rst":
- group, module = name.split(".")
- with open("{}/{}.rst".format(sphinx_path, group), "a") as gfile:
- title = "{} - {}".format(calibration, module)
- gfile.write(title + "\n")
- gfile.write( "=" *len (title) + "\n")
- gfile.write("\n")
- with open("{}/{}".format(run_path, entry), "r") as ifile:
- for line in ifile.readlines():
- gfile.write(line)
- gfile.write("\n\n")
- if isdir("{}/{}".format(run_path, entry)):
- shutil.copytree("{}/{}".format(run_path, entry), "{}/{}".format(sphinx_path, entry))
- return sphinx_path
-
-def make_report(run_path, project, author, version):
- run_path = os.path.abspath(run_path)
- try:
- import subprocess
- subprocess.check_call(["sphinx-quickstart",
- "--quiet",
- "--project={}".format(project),
- "--author={}".format(author),
- "-v", str(version),
- "--suffix=.rst",
- "--master=index",
- "--ext-intersphinx",
- "--ext-mathjax",
- "--makefile",
- "--no-batchfile", run_path])
-
- except subprocess.CalledProcessError:
- raise Exception("Failed to run sphinx-quickbuild. Is sphinx installed?"
- "Generated simple index.rst instead")
-
- # quickbuild went well we need to edit the index file
- from shutil import move
-
- direntries = os.listdir(run_path)
- files_to_handle = []
- for entry in direntries:
- if isfile("{}/{}".format(run_path, entry)):
- name, ext = splitext("{}".format(entry))
- if ext == ".rst" and "index" not in name:
- files_to_handle.append(name)
-
- with open("{}/index.rst.tmp".format(run_path), "w") as mf:
- with open("{}/index.rst".format(run_path), "r") as mfr:
- indexTmp = Template('''
- .. toctree::
- :maxdepth: 2
- {% for k in keys %}
- {{ k }}
- {%- endfor %}
- ''')
- for line in mfr:
- line = line.replace(".. toctree::", textwrap.dedent(
- indexTmp.render(keys=files_to_handle)))
- line = line.replace(":maxdepth: 2", "")
- line = line.replace("Documentation", "Calibration")
- mf.write(line)
- cdir = os.getcwd()
-
- os.remove("{}/index.rst".format(run_path))
- move("{}/index.rst.tmp".format(run_path), "{}/index.rst".format(run_path))
-
- # finally call the make scripts
-
- os.chdir(run_path)
- try:
- import subprocess
- subprocess.check_call(["make", "latexpdf"])
-
- except subprocess.CalledProcessError:
- self.log.ERROR("Failed to make html documentation")
-
-def finalize(joblist, run_path, project, calibration, author, version):
-
-
- print("Waiting on jobs to finish: {}".format(joblist))
- while True:
- found_jobs = set()
- output = check_output(['squeue']).decode('utf8')
- for line in output.split("\n"):
- for job in joblist:
- if str(job) in line:
- found_jobs.add(job)
- if len(found_jobs) == 0:
- break
- sleep(10)
- sphinx_path = combine_report(run_path, calibration)
- make_report(sphinx_path, project, author, version)
-
-
-static_path = os.path.join(os.path.dirname(__file__), 'static')
-
-
-class MainHandler(tornado.web.RequestHandler):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- config = self._parse_config()
- self.config = {}
- for detector, run_types in config.items():
- self.config[detector] = {}
- for run_type, characterizations in run_types.items():
- self.config[detector][run_type] = {}
- for characterization, notebook in characterizations.items():
- with open(notebook) as f:
- nb = nbformat.read(f, as_version=4)
- basename = os.path.basename(notebook)
- assert basename.endswith('.ipynb')
- nbname = basename[:-6]
- parameters = extract_parameters(nb)
- notebook = {"path": notebook,
- "name": nbname,
- "parms": parameters}
- self.config[detector][run_type][characterization] = notebook
-
- def _parse_config(self):
- with open("config.json", "r") as cfile:
- return json.load(cfile)
-
-
- def get(self):
- self.write(str(build_page(self.config)))
-
- #self.write(str(build_form(self.application.parameters,
- # self.application.nbname)))
-"""
-
-class JobListHander():
-
- def __init__(self, run_uuid, job_list):
- self.uuid = run_uuid
- self.job_list = job_list
-
- def list_runner(job_list):
- for job in job_list:
- self.path = path
- with open(path) as f:
- self.nb = nbformat.read(f, as_version=4)
-
- basename = os.path.basename(path)
- assert basename.endswith('.ipynb')
- self.nbname = basename[:-6]
- self.parameters = extract_parameters(self.nb)
-
- def run_list():
- for seq_jobs in self.job_list:
- for par_jobs in seq_jobs:
-
-"""
-
-class SubmissionHandler(tornado.web.RequestHandler):
- def post(self):
- print("Received post")
- job_list = tornado.escape.json_decode(self.request.body)
-
- run_uuid = uuid4()
- run_tmp_path = "{}/slurm_tmp_{}".format(os.getcwd(), run_uuid)
- #for outer_seq in job_list:
- print(job_list)
-
-
- return
-
- defined = []
- for v in self.application.parameters:
- if v.type is bool:
- inp = v.with_value(self.get_argument(v.name, default='off') == 'on')
- elif v.type is list:
- inp = v.with_value()
- else:
- inp = v.with_value(v.type(self.get_argument(v.name)))
- defined.append(inp)
-
- res = {'path': os.path.dirname(self.application.path)}
- nb = replace_definitions(self.application.nb, defined, execute_resources=res)
- output, _ = HTMLExporter().from_notebook_node(nb, res)
- self.write(output)
-
-
-class NbparameteriseApplication(tornado.web.Application):
- def __init__(self, path):
- self.path = path
- with open(path) as f:
- self.nb = nbformat.read(f, as_version=4)
-
- basename = os.path.basename(path)
- assert basename.endswith('.ipynb')
- self.nbname = basename[:-6]
- self.parameters = extract_parameters(self.nb)
- super().__init__([
- (r"/", MainHandler),
- (r"/submit", SubmissionHandler)
- ], static_path=static_path)
-
-
-def main():
- application = NbparameteriseApplication(sys.argv[1])
- application.listen(3131)
- print("Visit http://localhost:3131/")
- tornado.ioloop.IOLoop.instance().start()
-
-if __name__ == "__main__":
- main()
-
diff --git a/calibrate.py b/calibrate.py
deleted file mode 100644
index e384284d0d601498d1352411f7e7702d0091dde6..0000000000000000000000000000000000000000
--- a/calibrate.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import argparse
-from subprocess import Popen
-
-parser = argparse.ArgumentParser(description="Main entry point "
- "for offline calibration")
-parser.add_argument("--input", type=str)
-parser.add_argument("--output", type=str)
-parser.add_argument("--base-cal-store", type=str, default="None")
-parser.add_argument("--offset-cal-store", type=str)
-parser.add_argument("--mem-cells", type=str)
-parser.add_argument("--detector", type=str)
-parser.add_argument("--sequences", type=str, default="All")
-parser.add_argument("--overwrite", action="store_true")
-parser.add_argument("--no-relgain", action="store_true")
-parser.add_argument("--uuid", type=str, default="default")
-parser.add_argument("--ff-cal-store", type=str, default="None")
-parser.add_argument("--no-ff", action="store_true")
-parser.add_argument("--raw-version", type=str, default="2")
-
-args = vars(parser.parse_args())
-
-in_folder = args["input"]
-out_folder = args["output"]
-base_store = args["base_cal_store"]
-offset_store = args["offset_cal_store"]
-mem_cells = args["mem_cells"]
-detector = args["detector"]
-sequences = args["sequences"]
-overwrite = str(args["overwrite"])
-relgain = str(args["no_relgain"])
-doff = str(not bool(args["no_ff"]))
-ff_store = args["ff_cal_store"]
-raw_version = args["raw_version"]
-if not ff_store or ff_store == "None":
- doff = "False"
-
-if detector.upper() not in ["AGIPD", "LPD"]:
- raise AttributeError("Unknown detector: {}".format(detector))
-
-if detector.upper() == "AGIPD":
- script = "AGIPD/correct_agipd_batch.py"
-
-elif detector.upper() == "LPD":
- script = "LPD/correct_lpd_batch.py"
-
-uuid = args["uuid"]
-
-# get newest calibration files upon launch
-import glob
-import os
-
-def get_newest(search_dir_in):
- search_dir = search_dir_in
- if "{}" in search_dir:
- search_dir = search_dir.replace("{}", "*")
- files = list(filter(os.path.isfile, glob.glob(search_dir + "*")))
- files.sort(key=lambda x: os.path.getmtime(x))
- return str(files[-1])
-
-base_store = get_newest(base_store)
-offset_store = get_newest(offset_store)
-
-print("Using offset store: {}".format(offset_store))
-print("Using base store: {}".format(base_store))
-print("Using flat field store: {}".format(ff_store))
-print("Using RAW format version: {}".format(raw_version))
-
-cmd = ["/gpfs/exfel/data/user/haufs/karabo/extern/bin/python", script, in_folder, out_folder, base_store, offset_store, str(mem_cells), sequences, overwrite, relgain, uuid, doff, ff_store, raw_version]
-Popen(cmd).wait()
diff --git a/config.json b/config.json
deleted file mode 100644
index 112264b5a133e6f5a0a5a61558b533813090a7cf..0000000000000000000000000000000000000000
--- a/config.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
- "AGIPD": {
- "characterize": {
- "Flat field": "AGIPD/Characterize_AGIPD_Gain_FlatFields.ipynb",
- "Dark": "AGIPD/Characterize_AGIPD_Gain_Darks.ipynb"
- }
- },
- "LPD": {
- "characterize": {
- "Dark": "LPD/LPDChar_Darks.ipynb"
- }
- },
- "Test": {
- "characterize": {
- "SleeptTest": "Test/SleepTest.ipynb"
- }
- }
-}
diff --git a/setup_env_maxwell.sh b/setup_env_maxwell.sh
deleted file mode 100644
index c68f2d13080e37437239994a707b509a90f24831..0000000000000000000000000000000000000000
--- a/setup_env_maxwell.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#/bin/bash
-export MPLBACKEND=Agg
-source /gpfs/exfel/data/scratch/haufs/karabo/activate
-ipcluster start --n=16 --daemon
diff --git a/static/cal_styles.css b/static/cal_styles.css
deleted file mode 120000
index 7bd92d8cd6694023f01eaea0567280d035c2a759..0000000000000000000000000000000000000000
--- a/static/cal_styles.css
+++ /dev/null
@@ -1 +0,0 @@
-../cal_styles.css
\ No newline at end of file
diff --git a/static/web_cal.js b/static/web_cal.js
deleted file mode 120000
index 899592e3094f1b410f186a507f43e0e3967c0dc5..0000000000000000000000000000000000000000
--- a/static/web_cal.js
+++ /dev/null
@@ -1 +0,0 @@
-../web_cal.js
\ No newline at end of file
diff --git a/web_cal.js b/web_cal.js
deleted file mode 100644
index d48a78bfab30cfb545dc3a5a9e6112bc5adc40c1..0000000000000000000000000000000000000000
--- a/web_cal.js
+++ /dev/null
@@ -1,75 +0,0 @@
-function allowDrop(ev) {
- ev.preventDefault();
-}
-
-function drag(ev) {
- ev.dataTransfer.setData("text", ev.target.id);
-}
-
-function uuidv4() {
- return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {
- var r = Math.random() * 16 | 0, v = c == 'x' ? r : (r & 0x3 | 0x8);
- return v.toString(16);
- });
-}
-
-function drop(ev) {
- ev.preventDefault();
- var element_id = ev.dataTransfer.getData("text");
- var clone = document.getElementById(element_id+"_detail").cloneNode(true);
- clone.uuid = uuidv4()
- clone.id=clone.uuid
-
- if (ev.target.parentElement.childElementCount == 1) {
- var ser_execution_block = document.createElement("div");
- ser_execution_block.classList.add('serexecutionblock');
- var par_execution_block = document.createElement("div");
- par_execution_block.classList.add('parexecutionblock');
- par_execution_block.ondrop = drop;
- par_execution_block.ondragover = allowDrop;
- ser_execution_block.appendChild(par_execution_block);
- ev.target.parentElement.parentElement.appendChild(ser_execution_block);
- }
-
- if (ev.target.childElementCount == 0) {
- var par_execution_block = document.createElement("div");
- par_execution_block.classList.add('parexecutionblock');
- par_execution_block.ondrop = drop;
- par_execution_block.ondragover = allowDrop;
- ev.target.parentElement.appendChild(par_execution_block);
- }
-
-
- ev.target.appendChild(clone);
-
-}
-
-
-function run_jobs() {
- var job_list = [];
- var ser_blocks = document.getElementsByClassName("serexecutionblock");
-
- for (var i = 0; i < ser_blocks.length; ++i) {
- var ser_block = ser_blocks[i];
- var par_jobs = [];
- var par_blocks = ser_block.getElementsByClassName("parexecutionblock");
-
- for (var j = 0; j < par_blocks.length; ++j) {
- var inner_seq_jobs = [];
- var inner_ser = par_blocks[j].children;
- for (var k = 0; k < inner_ser.length; ++k) {
- job = {}
- job[inner_ser[k].uuid] = $(inner_ser[k]).find("form").first().serializeArray();
- inner_seq_jobs.push(job);
- }
- if (inner_seq_jobs.length) {
- par_jobs.push(inner_seq_jobs);
- }
- }
- if (par_jobs.length) {
- job_list.push(par_jobs);
- }
- }
-
- $.post("/submit", JSON.stringify({'job_list': job_list}));
-}
\ No newline at end of file