{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shimadzu HPVX2 Offline Correction\n",
"\n",
"Author: Egor Sobolev\n",
"\n",
"Offline dynamic flat-field correction for Shimadzu HPVX2 cameras"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"in_folder = \"/gpfs/exfel/exp/SPB/202121/p002919/raw/\" # input folder, required\n",
"out_folder = '/gpfs/exfel/data/scratch/esobolev/test/shimadzu' # output folder, required\n",
"metadata_folder = \"\" # Directory containing calibration_metadata.yml when run by xfel-calibrate\n",
"run = 30 # which run to read data from, required\n",
"\n",
"# Data files parameters.\n",
"karabo_da = ['HPVX01'] # data aggregators\n",
"karabo_id = \"SPB_EHD_HPVX2_2\" # karabo prefix of Shimadzu HPV-X2 devices\n",
"#receiver_id = \"PNCCD_FMT-0\" # inset for receiver devices\n",
"#path_template = 'RAW-R{:04d}-{}-S{{:05d}}.h5' # the template to use to access data\n",
"instrument_source_template = '{}/CAM/CAMERA:daqOutput' # data source path in h5file. Template filled with karabo_id\n",
"image_key = \"data.image.pixels\" # image data key in Karabo or exdf notation\n",
"\n",
"# Database access parameters.\n",
"use_dir_creation_date = True # use dir creation date as data production reference date\n",
"cal_db_interface = \"tcp://max-exfl-cal001:8021\" # calibration DB interface to use\n",
"cal_db_timeout = 300000 # timeout on caldb requests\n",
"db_output = False # if True, the notebook sends dark constants to the calibration database\n",
"local_output = True # if True, the notebook saves dark constants locally\n",
"creation_time = \"\" # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC.00 e.g. 2019-07-04 11:02:41.00\n",
"\n",
"n_components = 20 # number of principal components of flat-field to use in correction\n",
"downsample_factors = [1, 1] # list of downsample factors for each image dimention (y, x)\n",
"\n",
"constants_folder = \"/gpfs/exfel/data/scratch/esobolev/test/shimadzu\"\n",
"db_module = \"SHIMADZU_HPVX2_M001\"\n",
"\n",
"num_proc = 32 # number of processes running correction in parallel\n",
"\n",
"corrected_source_template = '{}/CORR/CAMERA:daqOutput' # data source path in h5file. Template filled with karabo_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import h5py\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from extra_data import RunDirectory\n",
"\n",
"%matplotlib inline\n",
"from cal_tools.step_timing import StepTimer\n",
"from cal_tools.files import sequence_trains, DataFile\n",
"\n",
"from dffc.correction import DynamicFlatFieldCorrectionCython as DynamicFlatFieldCorrection\n",
"from dffc.offline import FlatFieldCorrectionFileProcessor\n",
"from dffc.draw import plot_images, plot_camera_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"instrument = karabo_id.split(\"_\")[0]\n",
"source = instrument_source_template.format(karabo_id)\n",
"\n",
"print(f\"Detector in use is {karabo_id}\")\n",
"print(f\"Instrument {instrument}\")\n",
"\n",
"step_timer = StepTimer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calibration constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"step_timer.start()\n",
"\n",
"# Offsets\n",
"constant_name = \"Offset\"\n",
"const_file = f\"{constants_folder}/const_{constant_name}_{db_module}.h5\"\n",
"if not os.path.isfile(const_file):\n",
" raise FileNotFoundError(f\"{constant_name} constants are not found for {karabo_id}.\")\n",
"\n",
"with h5py.File(const_file, 'r') as f:\n",
" dark_conditions = dict(\n",
" num_frames=int(f[\"condition/Memory cells/value\"][()]),\n",
" nx=int(f[\"condition/Pixels X/value\"][()]),\n",
" ny=int(f[\"condition/Pixels Y/value\"][()]),\n",
" n_components=int(f[\"condition/FF components/value\"][()]),\n",
" )\n",
" dark = f[\"data\"][:]\n",
" dark_creation_time = f[\"creation_time\"][()].decode()\n",
"\n",
"print(f\"{constant_name}: {dark_creation_time}\")\n",
"\n",
"# Flat-field components\n",
"constant_name = \"ComponentsFF\"\n",
"const_file = f\"{constants_folder}/const_{constant_name}_{db_module}.h5\"\n",
"if not os.path.isfile(const_file):\n",
" raise FileNotFoundError(f\"{constant_name} constants are not found for {karabo_id}.\")\n",
"\n",
"with h5py.File(const_file, 'r') as f:\n",
" flat_conditions = dict(\n",
" num_frames=int(f[\"condition/Memory cells/value\"][()]),\n",
" nx=int(f[\"condition/Pixels X/value\"][()]),\n",
" ny=int(f[\"condition/Pixels Y/value\"][()]),\n",
" n_components=int(f[\"condition/FF components/value\"][()]),\n",
" )\n",
" flat = f[\"data\"][:]\n",
" components = flat[1:]\n",
" flat = flat[0]\n",
" flat_creation_time = f[\"creation_time\"][()].decode()\n",
"\n",
"print(f\"{constant_name}: {dark_creation_time}\")\n",
"\n",
"if not all(flat_conditions[key] == value for key, value in dark_conditions.items()):\n",
" raise ValueError(\"Conditions for offsets and flat-field components are different\")\n",
"\n",
"conditions = type(\"Conditions\", (), flat_conditions)\n",
"\n",
"print(f\"Image size: {conditions.nx} x {conditions.ny} px\")\n",
"print(f\"Number of flat-field components: {conditions.n_components}\")\n",
"\n",
"if conditions.n_components < n_components:\n",
" warnings.warn(\n",
" f\"The correction set to use {n_components} flat-field components, \"\n",
" f\"but constants contains only {conditions.n_components}.\"\n",
" \"The settings adjusted to the number of available components.\"\n",
" )\n",
"else:\n",
" components = components[:n_components]\n",
"\n",
"step_timer.done_step(\"Load calibration constants\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Correction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"step_timer.start()\n",
"dc = RunDirectory(f\"{in_folder}/r{run:04d}\")\n",
"\n",
"num_trains, num_cells = dc[source][image_key].shape[:2]\n",
"num_images = num_trains * num_cells\n",
"print(\"Number of trains:\", num_trains)\n",
"print(\"Number of images:\", num_images)\n",
"\n",
"dffc = DynamicFlatFieldCorrection.from_constants(\n",
" dark, flat, components, downsample_factors)\n",
"\n",
"proc = FlatFieldCorrectionFileProcessor(dffc, num_proc, source, image_key)\n",
"\n",
"proc.start_workers()\n",
"proc.run(dc)\n",
"proc.join_workers()\n",
"\n",
"train_ids = proc.rdr.trains\n",
"corrected_images = np.stack(proc.rdr.results, 0)\n",
"step_timer.done_step(\"Correct images\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"step_timer.start()\n",
"\n",
"corr_source = corrected_source_template.format(karabo_id)\n",
"channel = image_key.partition('.')[0]\n",
"data_source_id = corr_source + '/' + channel\n",
"\n",
"ts = dc.train_timestamps().astype(np.uint64)\n",
"ts = ts[np.in1d(dc.train_ids, train_ids)]\n",
"\n",
"for seq_id, train_mask in sequence_trains(train_ids):\n",
" seq_train_ids = train_ids[train_mask]\n",
" seq_timestamps = ts[train_mask]\n",
" ntrains = len(seq_train_ids)\n",
" \n",
" f = DataFile.from_details(out_folder, karabo_da[0], run, seq_id)\n",
" src = f.create_instrument_source(corr_source)\n",
" \n",
" f.create_metadata(like=dc, instrument_channels=(data_source_id,))\n",
" f.create_index(seq_train_ids, timestamps=seq_timestamps)\n",
" \n",
" channels = {\n",
" image_key.partition('.')[0]: np.ones(ntrains, int)\n",
" }\n",
" src.create_index(**channels)\n",
" src.create_key(image_key, corrected_images[train_mask])\n",
"\n",
" f.close()\n",
" \n",
"step_timer.done_step(\"Save corrected images\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The first raw image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"step_timer.start()\n",
"\n",
"counts = dc[source][image_key].data_counts()\n",
"i = np.flatnonzero(counts.values)\n",
"\n",
"raw_images = dc[source][image_key].select_trains(np.s_[i]).ndarray()\n",
"plot_camera_image(raw_images[0, 0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The first corrected image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_camera_image(corrected_images[0, 0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The first corrected images in the trains (up to 20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_images(corrected_images[:20, 0], figsize=(13, 8))\n",
"plt.show()\n",
"step_timer.done_step(\"Draw examples of corrected images\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"Total processing time {step_timer.timespan():.01f} s\")\n",
"step_timer.print_summary()"
]
}
],
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