diff --git a/notebooks/LPD/LPD_Correct_Fast.ipynb b/notebooks/LPD/LPD_Correct_Fast.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..136a29bc66cadcbc1d88288b2eb7a0aad9c2e98b
--- /dev/null
+++ b/notebooks/LPD/LPD_Correct_Fast.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# LPD Offline Correction #\n",
+ "\n",
+ "Author: European XFEL Data Analysis Group"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-12-03T15:19:56.056417Z",
+ "start_time": "2018-12-03T15:19:56.003012Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Input parameters\n",
+ "in_folder = \"/gpfs/exfel/exp/FXE/202201/p003073/raw/\" # the folder to read data from, required\n",
+ "out_folder = \"/gpfs/exfel/data/scratch/schmidtp/random/LPD_test\" # the folder to output to, required\n",
+ "metadata_folder = '' # Directory containing calibration_metadata.yml when run by xfel-calibrate.\n",
+ "sequences = [0] # sequences to correct, set to empty for all, range allowed\n",
+ "modules = '' # modules to correct, set to -1 for all, range allowed, used only when karabo_da is empty\n",
+ "run = 10 # runs to process, required\n",
+ "\n",
+ "karabo_id = \"FXE_DET_LPD1M-1\" # karabo karabo_id\n",
+ "karabo_da = '' # a list of data aggregators names, Default empty string for selecting all data aggregators\n",
+ "input_source = '{karabo_id}/DET/{module_index}CH0:xtdf' # Input fast data source.\n",
+ "output_source = '' # Output fast data source, empty to use same as input\n",
+ "\n",
+ "# CalCat parameters\n",
+ "use_dir_creation_date = True # use the creation date of the directory for database time derivation\n",
+ "cal_db_root = '/gpfs/exfel/d/cal/caldb_store'\n",
+ "\n",
+ "# Operating conditions\n",
+ "mem_cells = 512 # Memory cells.\n",
+ "bias_voltage = 250.0 # Detector bias voltage.\n",
+ "capacitor = '5pF' # Capacitor setting: 5pF or 50pF\n",
+ "photon_energy = 9.2 # Photon energy in kEv.\n",
+ "category = 0 # Whom to blame.\n",
+ "\n",
+ "# Correction parameters.\n",
+ "offset_corr = True # Offset correction.\n",
+ "rel_gain = True # Gain correction based on RelativeGain constant.\n",
+ "ff_map = True # Gain correction based on FFMap constant.\n",
+ "gain_amp_map = True # Gain correction based on GainAmpMap constant.\n",
+ "\n",
+ "# Output options\n",
+ "overwrite = True # set to True if existing data should be overwritten\n",
+ "\n",
+ "sequences_per_node = 1 # sequence files to process per node\n",
+ "\n",
+ "def balance_sequences(in_folder, run, sequences, sequences_per_node, karabo_da):\n",
+ " from xfel_calibrate.calibrate import balance_sequences as bs\n",
+ " return bs(in_folder, run, sequences, sequences_per_node, karabo_da)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-12-03T15:19:56.990566Z",
+ "start_time": "2018-12-03T15:19:56.058378Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from collections import OrderedDict\n",
+ "from pathlib import Path\n",
+ "from time import perf_counter\n",
+ "import gc\n",
+ "import re\n",
+ "import warnings\n",
+ "\n",
+ "import numpy as np\n",
+ "import h5py\n",
+ "\n",
+ "import matplotlib\n",
+ "matplotlib.use('agg')\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "from calibration_client import CalibrationClient\n",
+ "from calibration_client.modules import CalibrationConstantVersion\n",
+ "import extra_data as xd\n",
+ "import extra_geom as xg\n",
+ "import pasha as psh\n",
+ "\n",
+ "from extra_data.components import LPD1M\n",
+ "\n",
+ "from cal_tools.enums import BadPixels\n",
+ "from cal_tools.lpdalgs import correct_lpd_frames\n",
+ "from cal_tools.tools import CalibrationMetadata, get_dir_creation_date, write_compressed_frames\n",
+ "from cal_tools.h5_copy_except import h5_copy_except_paths\n",
+ "from cal_tools.files import DataFile\n",
+ "from cal_tools.restful_config import restful_config"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Prepare environment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "file_re = re.compile(r'^RAW-R(\\d{4})-(\\w+\\d+)-S(\\d{5})$') # This should probably move to cal_tools\n",
+ "\n",
+ "out_folder = Path(out_folder)\n",
+ "out_folder.mkdir(exist_ok=True)\n",
+ "\n",
+ "output_source = output_source or input_source\n",
+ "\n",
+ "cal_db_root = Path(cal_db_root)\n",
+ "\n",
+ "metadata = CalibrationMetadata(metadata_folder or out_folder)\n",
+ "\n",
+ "if use_dir_creation_date:\n",
+ " creation_time = get_dir_creation_date(in_folder, run) \n",
+ "else:\n",
+ " from datetime import datetime\n",
+ " creation_time = datetime.now()\n",
+ " \n",
+ "print(f'Using {creation_time.isoformat()} as creation time')\n",
+ "\n",
+ "# Pick all modules/aggregators or those selected.\n",
+ "if not karabo_da:\n",
+ " if not modules:\n",
+ " modules = list(range(16))\n",
+ "\n",
+ " karabo_da = [f'LPD{i:02d}' for i in modules]\n",
+ " \n",
+ "# Pick all sequences or those selected.\n",
+ "if not sequences:\n",
+ " do_sequence = lambda seq: True\n",
+ "else:\n",
+ " do_sequence = [int(x) for x in sequences].__contains__ \n",
+ " \n",
+ "# List of detector sources.\n",
+ "det_inp_sources = [input_source.format(karabo_id=karabo_id, module_index=int(da[-2:])) for da in karabo_da]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Select data to process"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Open the entire input directory.\n",
+ "dc_all = xd.RunDirectory(Path(in_folder) / f'r{run:04d}') \\\n",
+ " .select([(src, 'image.*') for src in det_inp_sources])\n",
+ "\n",
+ "out_folder = Path(out_folder) \n",
+ "\n",
+ "data_to_process = []\n",
+ "\n",
+ "for file_access in dc_all.files:\n",
+ " match = file_re.match(Path(file_access.filename).stem)\n",
+ " \n",
+ " if match[2] not in karabo_da or not do_sequence(int(match[3])):\n",
+ " continue\n",
+ " \n",
+ " outp_path = out_folder / 'CORR-R{run:04d}-{aggregator}-S{seq:05d}.h5'.format(\n",
+ " run=int(match[1]), aggregator=match[2], seq=int(match[3]))\n",
+ "\n",
+ " data_to_process.append((file_access.filename, match[2], outp_path))\n",
+ "\n",
+ "print('Files to process:')\n",
+ "for data_descr in sorted(data_to_process, key=lambda x: f'{x[1]}{x[0]}'):\n",
+ " print('\\t'.join(data_descr[1::-1]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Obtain and prepare calibration constants"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Connect to CalCat.\n",
+ "calcat_config = restful_config['calcat']\n",
+ "client = CalibrationClient(\n",
+ " client_id=calcat_config['user-id'],\n",
+ " client_secret=calcat_config['user-secret'],\n",
+ " user_email=calcat_config['user-email'],\n",
+ " base_api_url=calcat_config['base-api-url'],\n",
+ " token_url=calcat_config['token-url'],\n",
+ " refresh_url=calcat_config['refresh-url'],\n",
+ " auth_url=calcat_config['auth-url'],\n",
+ " scope='')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dark_calibrations = {\n",
+ " 1: 'Offset', # np.float32\n",
+ " 14: 'BadPixelsDark' # should be np.uint32, but is np.float64\n",
+ "}\n",
+ "\n",
+ "dark_condition = [\n",
+ " dict(parameter_id=1, value=bias_voltage), # Sensor bias voltage\n",
+ " dict(parameter_id=7, value=mem_cells), # Memory cells\n",
+ " dict(parameter_id=15, value=capacitor), # Feedback capacitor\n",
+ " dict(parameter_id=13, value=256), # Pixels X\n",
+ " dict(parameter_id=14, value=256), # Pixels Y\n",
+ "]\n",
+ "\n",
+ "illuminated_calibrations = {\n",
+ " 20: 'BadPixelsFF', # np.uint32\n",
+ " 42: 'GainAmpMap', # np.float32\n",
+ " 43: 'FFMap', # np.float32\n",
+ " 44: 'RelativeGain' # np.float32\n",
+ "}\n",
+ "\n",
+ "illuminated_condition = dark_condition.copy()\n",
+ "illuminated_condition += [\n",
+ " dict(parameter_id=3, value=photon_energy), # Source energy\n",
+ " dict(parameter_id=25, value=category) # category\n",
+ "]\n",
+ "\n",
+ "const_data = {}\n",
+ "const_load_mp = psh.ProcessContext(num_workers=24)\n",
+ "\n",
+ "print('Querying calibration database', end='', flush=True)\n",
+ "start = perf_counter()\n",
+ "for calibrations, condition in [\n",
+ " (dark_calibrations, dark_condition),\n",
+ " (illuminated_calibrations, illuminated_condition)\n",
+ "]:\n",
+ " resp = CalibrationConstantVersion.get_closest_by_time_by_detector_conditions(\n",
+ " client, 'FXE_DET_LPD1M-1', list(calibrations.keys()),\n",
+ " {'parameters_conditions_attributes': condition},\n",
+ " karabo_da='', event_at=None, snapshot_at=None)\n",
+ "\n",
+ " if not resp['success']:\n",
+ " raise RuntimeError(resp)\n",
+ "\n",
+ " for ccv in resp['data']:\n",
+ " cc = ccv['calibration_constant']\n",
+ " da = ccv['physical_detector_unit']['karabo_da']\n",
+ " calibration_name = calibrations[cc['calibration_id']]\n",
+ " \n",
+ " dtype = np.uint32 if calibration_name.startswith('BadPixels') else np.float32\n",
+ " \n",
+ " const_data[(da, calibration_name)] = dict(\n",
+ " path=Path(ccv['path_to_file']) / ccv['file_name'],\n",
+ " dataset=ccv['data_set_name'],\n",
+ " data=const_load_mp.alloc(shape=(256, 256, mem_cells, 3), dtype=dtype)\n",
+ " )\n",
+ " print('.', end='', flush=True)\n",
+ " \n",
+ "total_time = perf_counter() - start\n",
+ "print(f'{total_time:.1f}s')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_constant_dataset(wid, index, const_descr):\n",
+ " ccv_entry = const_data[const_descr]\n",
+ " \n",
+ " with h5py.File(cal_db_root / ccv_entry['path'], 'r') as fp:\n",
+ " fp[ccv_entry['dataset'] + '/data'].read_direct(ccv_entry['data'])\n",
+ " \n",
+ " print('.', end='', flush=True)\n",
+ "\n",
+ "print('Loading calibration data', end='', flush=True)\n",
+ "start = perf_counter()\n",
+ "const_load_mp.map(load_constant_dataset, list(const_data.keys()))\n",
+ "total_time = perf_counter() - start\n",
+ "\n",
+ "print(f'{total_time:.1f}s')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccv_offsets = {}\n",
+ "ccv_gains = {}\n",
+ "ccv_masks = {}\n",
+ "\n",
+ "def prepare_constants(wid, index, aggregator):\n",
+ " consts = {calibration_name: entry['data']\n",
+ " for (aggregator_, calibration_name), entry\n",
+ " in const_data.items()\n",
+ " if aggregator == aggregator_}\n",
+ " \n",
+ " if offset_corr and 'Offset' in consts:\n",
+ " ccv_offsets[aggregator] = consts['Offset'].astype(np.float32)\n",
+ " else:\n",
+ " ccv_offsets[aggregator] = np.zeros((256, 256, mem_cells, 3), dtype=np.float32)\n",
+ " \n",
+ " ccv_gains[aggregator] = np.ones((256, 256, mem_cells, 3), dtype=np.float32)\n",
+ " \n",
+ " if 'BadPixelsDark' in consts:\n",
+ " ccv_masks[aggregator] = consts['BadPixelsDark'].astype(np.uint32)\n",
+ " else:\n",
+ " ccv_masks[aggregator] = np.zeros((256, 256, mem_cells, 3), dtype=np.uint32)\n",
+ " \n",
+ " if rel_gain and 'RelativeGain' in consts:\n",
+ " ccv_gains[aggregator] *= consts['RelativeGain']\n",
+ " \n",
+ " if ff_map and 'FFMap' in consts:\n",
+ " ccv_gains[aggregator] *= consts['FFMap']\n",
+ " \n",
+ " if 'BadPixelsFF' in consts:\n",
+ " np.bitwise_or(ccv_masks[aggregator], consts['BadPixelsFF'], out=ccv_masks[aggregator])\n",
+ " \n",
+ " if gain_amp_map and 'GainAmpMap' in consts:\n",
+ " ccv_gains[aggregator] *= consts['GainAmpMap']\n",
+ " \n",
+ " print('.', end='', flush=True)\n",
+ " \n",
+ "\n",
+ "print('Preparing constants', end='', flush=True)\n",
+ "start = perf_counter()\n",
+ "psh.ThreadContext(num_workers=len(karabo_da)).map(prepare_constants, karabo_da)\n",
+ "total_time = perf_counter() - start\n",
+ "print(f'{total_time:.1f}s')\n",
+ "\n",
+ "const_data.clear() # Clear raw constants data now to save memory.\n",
+ "gc.collect();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def correct_file(wid, index, work):\n",
+ " filename, aggregator, outp_path = work\n",
+ " module_index = int(aggregator[-2:])\n",
+ " \n",
+ " start = perf_counter()\n",
+ " dc = xd.H5File(filename).select('*', 'image.*', require_all=True)\n",
+ " inp_source = dc[input_source.format(karabo_id=karabo_id, module_index=module_index)]\n",
+ " open_time = perf_counter() - start\n",
+ " \n",
+ " # Load raw data for this file.\n",
+ " start = perf_counter()\n",
+ " in_data = inp_source['image.data'].ndarray().squeeze()\n",
+ " in_cell = inp_source['image.cellId'].ndarray().squeeze()\n",
+ " in_pulse = inp_source['image.pulseId'].ndarray().squeeze()\n",
+ " read_time = perf_counter() - start\n",
+ " \n",
+ " # Allocate output arrays.\n",
+ " out_pixels = np.zeros((in_data.shape[0], 256, 256), dtype=np.float32)\n",
+ " out_gain = np.zeros((in_data.shape[0], 256, 256), dtype=np.uint8)\n",
+ " out_mask = np.zeros((in_data.shape[0], 256, 256), dtype=np.uint32)\n",
+ " \n",
+ " start = perf_counter()\n",
+ " correct_lpd_frames(in_data, in_cell,\n",
+ " out_pixels, out_gain, out_mask,\n",
+ " ccv_offsets[aggregator], ccv_gains[aggregator], ccv_masks[aggregator],\n",
+ " num_threads=num_threads_per_worker)\n",
+ " correct_time = perf_counter() - start\n",
+ " \n",
+ " image_counts = inp_source['image.data'].data_counts(labelled=False)\n",
+ " \n",
+ " start = perf_counter()\n",
+ " if (not outp_path.exists() or overwrite) and image_counts.sum() > 0:\n",
+ " fa = dc.files[0]\n",
+ " sel_trains = np.isin(fa.train_ids, dc.train_ids)\n",
+ " \n",
+ " outp_source_name = output_source.format(karabo_id=karabo_id, module_index=module_index)\n",
+ " \n",
+ " DataFile.instrument_source_pattern = re.compile(r'^[\\w\\/-]+:\\w+$')\n",
+ " with DataFile(outp_path, 'w') as outp_file: \n",
+ " outp_file.create_index(\n",
+ " train_ids=dc.train_ids,\n",
+ " timestamp=fa.file['INDEX/timestamp'][sel_trains],\n",
+ " flag=fa.validity_flag[sel_trains])\n",
+ " \n",
+ " outp_source = outp_file.create_instrument_source(outp_source_name)\n",
+ " \n",
+ " outp_source.create_index(image=image_counts)\n",
+ " outp_source.create_key('image.data', data=out_pixels)\n",
+ " outp_source.create_key('image.cellId', data=in_cell)\n",
+ " outp_source.create_key('image.pulseId', data=in_pulse)\n",
+ " write_compressed_frames(\n",
+ " out_gain, outp_file, f'INSTRUMENT/{outp_source_name}/image/gain', comp_threads=8)\n",
+ " write_compressed_frames(\n",
+ " out_mask, outp_file, f'INSTRUMENT/{outp_source_name}/image/mask', comp_threads=8)\n",
+ " \n",
+ " outp_file.create_metadata(like=dc)\n",
+ " write_time = perf_counter() - start\n",
+ " \n",
+ " total_time = open_time + read_time + correct_time + write_time\n",
+ " frame_rate = in_data.shape[0] / total_time\n",
+ " \n",
+ " print('{}\\t{}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{}\\t{:.1f}'.format(\n",
+ " wid, aggregator, open_time, read_time, correct_time, write_time, total_time,\n",
+ " in_data.shape[0], frame_rate))\n",
+ " \n",
+ " in_data = None\n",
+ " in_cell = None\n",
+ " in_pulse = None\n",
+ " out_pixels = None\n",
+ " out_gain = None\n",
+ " out_mask = None\n",
+ " gc.collect()\n",
+ "\n",
+ "# 8/18, 479s\n",
+ "# 10/18, 464s\n",
+ " \n",
+ "num_workers = 5\n",
+ "num_threads_per_worker = 24\n",
+ "print('worker\\tDA\\topen\\tread\\tcorrect\\twrite\\ttotal\\tframes\\trate')\n",
+ " \n",
+ "start = perf_counter()\n",
+ "psh.ProcessContext(num_workers=num_workers).map(correct_file, data_to_process)\n",
+ "total_time = perf_counter() - start\n",
+ "\n",
+ "print(f'Total time: {total_time:.1f}s')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Data preview for first train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "geom = xg.LPD_1MGeometry.from_quad_positions(\n",
+ " [(11.4, 299), (-11.5, 8), (254.5, -16), (278.5, 275)])\n",
+ "\n",
+ "output_paths = [outp_path for _, _, outp_path in data_to_process if outp_path.exists()]\n",
+ "dc = xd.DataCollection.from_paths(output_paths).select_trains(np.s_[0])\n",
+ "\n",
+ "LPD1M._source_re = re.compile(r'(?P<detname>.+_LPD1M.*)\\/(?:DET|CORR)\\/(?P<modno>\\d+)CH')\n",
+ "det = LPD1M(dc, detector_name=karabo_id)\n",
+ "data = det.get_array('image.data')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Intensity histogram across all cells"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "left_edge_ratio = 0.01\n",
+ "right_edge_ratio = 0.99\n",
+ "\n",
+ "fig, ax = plt.subplots(num=1, clear=True, figsize=(15, 6))\n",
+ "values, bins, _ = ax.hist(np.ravel(data.data), bins=500, range=(-5000, 20000))\n",
+ "\n",
+ "def find_nearest_index(array, value):\n",
+ " return (np.abs(array - value)).argmin()\n",
+ "\n",
+ "cum_values = np.cumsum(values)\n",
+ "vmin = bins[find_nearest_index(cum_values, cum_values[-1]*left_edge_ratio)]\n",
+ "vmax = bins[find_nearest_index(cum_values, cum_values[-1]*right_edge_ratio)]\n",
+ "\n",
+ "max_value = values.max()\n",
+ "ax.vlines([vmin, vmax], 0, max_value, color='red', linewidth=5, alpha=0.2)\n",
+ "ax.text(vmin, max_value, f'{left_edge_ratio*100:.0f}%',\n",
+ " color='red', ha='center', va='bottom', size='large')\n",
+ "ax.text(vmax, max_value, f'{right_edge_ratio*100:.0f}%',\n",
+ " color='red', ha='center', va='bottom', size='large')\n",
+ "ax.text(vmax+(vmax-vmin)*0.01, max_value/2, 'Colormap interval',\n",
+ " color='red', rotation=90, ha='left', va='center', size='x-large')\n",
+ "\n",
+ "ax.set_xlim(-5000, 20000)\n",
+ "ax.set_ylim(0, max_value*1.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### First memory cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(num=2, figsize=(15, 15), clear=True, nrows=1, ncols=1)\n",
+ "geom.plot_data_fast(data[:, 0, 0], ax=ax, vmin=vmin, vmax=vmax);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Train average"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2018-11-13T18:24:57.547563Z",
+ "start_time": "2018-11-13T18:24:56.995005Z"
+ },
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(num=3, figsize=(15, 15), clear=True, nrows=1, ncols=1)\n",
+ "geom.plot_data_fast(data[:, 0].mean(axis=1), ax=ax, vmin=vmin, vmax=vmax);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Lowest gain stage per pixel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "highest_gain_stage = det.get_array('image.gain', pulses=np.s_[:]).max(axis=(1, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(num=4, figsize=(15, 15), clear=True, nrows=1, ncols=1)\n",
+ "p = geom.plot_data_fast(highest_gain_stage, ax=ax, vmin=0, vmax=2);\n",
+ "\n",
+ "cb = ax.images[0].colorbar\n",
+ "cb.set_ticks([0, 1, 2])\n",
+ "cb.set_ticklabels(['High gain', 'Medium gain', 'Low gain'])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "pycal",
+ "language": "python",
+ "name": "pycal"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/xfel_calibrate/notebooks.py b/src/xfel_calibrate/notebooks.py
index 299722f1da4069fc15933f42eeb1774fc58a413c..a26c4afdf8ea3da72a9de9300f0796b016ad517a 100644
--- a/src/xfel_calibrate/notebooks.py
+++ b/src/xfel_calibrate/notebooks.py
@@ -85,11 +85,11 @@ notebooks = {
"cluster cores": 8},
},
"CORRECT": {
- "notebook": "notebooks/LPD/LPD_Correct_and_Verify.ipynb",
+ "notebook": "notebooks/LPD/LPD_Correct_Fast.ipynb",
"concurrency": {"parameter": "sequences",
"default concurrency": [-1],
"use function": "balance_sequences",
- "cluster cores": 32},
+ "cluster cores": 16},
},
"XGM_MINE": {
"notebook": "notebooks/LPD/Mine_RadIntensity_vs_XGM_NBC.ipynb",