{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Author: European XFEL Detector Group, Version: 1.0\n",
"\n",
"# Summary for processed of dark calibration constants and a comparison with previous injected constants.\n",
"\n",
"out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/fixed_gain/SPB_summary_fix2\" # path to output to, required\n",
"karabo_id = \"SPB_DET_AGIPD1M-1\" # detector instance\n",
"gain_names = ['High gain', 'Medium gain', 'Low gain'] # a list of gain names to be used in plotting\n",
"threshold_names = ['HG-MG threshold', 'MG_LG threshold'] # a list of gain names to be used in plotting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"import os\n",
"import warnings\n",
"from collections import OrderedDict\n",
"from pathlib import Path\n",
"\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import glob\n",
"\n",
"import h5py\n",
"import matplotlib\n",
"import numpy as np\n",
"import yaml\n",
"from IPython.display import Latex, Markdown, display\n",
"\n",
"matplotlib.use(\"agg\")\n",
"import matplotlib.gridspec as gridspec\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"import extra_geom\n",
"import tabulate\n",
"from cal_tools.ana_tools import get_range\n",
"from cal_tools.plotting import show_processed_modules\n",
"from cal_tools.tools import CalibrationMetadata, module_index_to_qm\n",
"from XFELDetAna.plotting.simpleplot import simplePlot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Note: this notebook assumes that local_output was set to True in the preceding characterization notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if \"AGIPD\" in karabo_id:\n",
" if \"SPB\" in karabo_id:\n",
" dinstance = \"AGIPD1M1\"\n",
" nmods = 16\n",
" elif \"MID\" in karabo_id:\n",
" dinstance = \"AGIPD1M2\"\n",
" nmods = 16\n",
" elif \"HED\" in karabo_id:\n",
" dinstance = \"AGIPD500K\"\n",
" nmods = 8\n",
" display(Markdown(\"\"\"\n",
" \n",
"# Summary of AGIPD dark characterization #\n",
"\n",
"The following report shows a set of dark images taken with the AGIPD detector to deduce detector offsets, noise, bad-pixel maps and thresholding. All four types of constants are evaluated per-pixel and per-memory cell.\n",
"\n",
"\n",
"**The offset** ($O$) is defined as the median ($M$) of the dark signal ($Ds$) over trains ($t$) for a given pixel ($x,y$) and memory cell ($c$). \n",
"\n",
"**The noise** $N$ is the standard deviation $\\sigma$ of the dark signal.\n",
"\n",
"$$ O_{x,y,c} = M(Ds)_{t} ,\\,\\,\\,\\,\\,\\, N_{x,y,c} = \\sigma(Ds)_{t}$$\n",
"\n",
"**The bad pixel** mask is encoded as a bit mask.\n",
"\n",
"**\"OFFSET_OUT_OF_THRESHOLD\":**\n",
"\n",
"Offset outside of bounds:\n",
"\n",
"$$M(O)_{x,y} - \\sigma(O)_{x,y} * \\mathrm{thresholds\\_offset\\_sigma} < O < M(O)_{x,y} + \\sigma(O)_{x,y} * \\mathrm{thresholds\\_offset\\_sigma} $$\n",
"\n",
"or offset outside of hard limits\n",
"\n",
"$$ \\mathrm{thresholds\\_offset\\_hard}_\\mathrm{low} < O < \\mathrm{thresholds\\_offset\\_hard}_\\mathrm{high} $$\n",
"\n",
"**\"NOISE_OUT_OF_THRESHOLD\":**\n",
"\n",
"Noise outside of bounds:\n",
"\n",
"$$M(N)_{x,y} - \\sigma(N)_{x,y} * \\mathrm{thresholds\\_noise\\_sigma} < N < M(N)_{x,y} + \\sigma(N)_{x,y} * \\mathrm{thresholds\\_noise\\_sigma} $$\n",
"\n",
"or noise outside of hard limits\n",
"\n",
"$$\\mathrm{thresholds\\_noise\\_hard}_\\mathrm{low} < N < \\mathrm{thresholds\\_noise\\_hard}_\\mathrm{high} $$\n",
"\n",
"**\"OFFSET_NOISE_EVAL_ERROR\":**\n",
"\n",
"Offset and Noise both not $nan$ values\n",
"\n",
"Values: $\\mathrm{thresholds\\_offset\\_sigma}$, $\\mathrm{thresholds\\_offset\\_hard}$, $\\mathrm{thresholds\\_noise\\_sigma}$, $\\mathrm{thresholds\\_noise\\_hard}$ are given as parameters.\n",
"\n",
"\"**\\\"GAIN_THRESHOLDING_ERROR\\\":**\n",
"\n",
"Bad gain separated pixels with sigma separation less than gain_separation_sigma_threshold\n",
"\n",
"$$ sigma\\_separation = \\\\frac{\\mathrm{gain\\_offset} - \\mathrm{previous\\_gain\\_offset}}{\\sqrt{\\mathrm{gain\\_offset_{std}}^\\mathrm{2} + \\mathrm{previuos\\_gain\\_offset_{std}}^\\mathrm{2}}}$$ \n",
"$$ Bad\\_separation = sigma\\_separation < \\mathrm{gain\\_separation\\_sigma\\_threshold} $$\n",
"\n",
"\"\"\"))\n",
" \n",
"elif \"LPD\" in karabo_id:\n",
" dinstance = \"LPD1M1\"\n",
" nmods = 16\n",
" display(Markdown(\"\"\"\n",
" \n",
"# Summary of LPD dark characterization #\n",
"\n",
"The following report shows a set of dark images taken with the LPD detector to deduce detector offsets, noise, bad-pixel maps. All three types of constants are evaluated per-pixel and per-memory cell.\n",
"\n",
"**The offset** ($O$) is defined as the median ($M$) of the dark signal ($Ds$) over trains ($t$) for a given pixel ($x,y$) and memory cell ($c$). \n",
"\n",
"**The noise** $N$ is the standard deviation $\\sigma$ of the dark signal.\n",
"\n",
"$$ O_{x,y,c} = M(Ds)_{t} ,\\,\\,\\,\\,\\,\\, N_{x,y,c} = \\sigma(Ds)_{t}$$\n",
"\n",
"**The bad pixel** mask is encoded as a bit mask.\n",
"\n",
"**\"OFFSET_OUT_OF_THRESHOLD\":**\n",
"\n",
"Offset outside of bounds:\n",
"\n",
"$$M(O)_{x,y} - \\sigma(O)_{x,y} * \\mathrm{thresholds\\_offset\\_sigma} < O < M(O)_{x,y} + \\sigma(O)_{x,y} * \\mathrm{thresholds\\_offset\\_sigma} $$\n",
"\n",
"or offset outside of hard limits\n",
"\n",
"$$ \\mathrm{thresholds\\_offset\\_hard}_\\mathrm{low} < O < \\mathrm{thresholds\\_offset\\_hard}_\\mathrm{high} $$\n",
"\n",
"**\"NOISE_OUT_OF_THRESHOLD\":**\n",
"\n",
"Noise outside of bounds:\n",
"\n",
"$$M(N)_{x,y} - \\sigma(N)_{x,y} * \\mathrm{thresholds\\_noise\\_sigma} < N < M(N)_{x,y} + \\sigma(N)_{x,y} * \\mathrm{thresholds\\_noise\\_sigma} $$\n",
"\n",
"or noise outside of hard limits\n",
"\n",
"$$\\mathrm{thresholds\\_noise\\_hard}_\\mathrm{low} < N < \\mathrm{thresholds\\_noise\\_hard}_\\mathrm{high} $$\n",
"\n",
"**\"OFFSET_NOISE_EVAL_ERROR\":**\n",
"\n",
"Offset and Noise both not $nan$ values \n",
"\n",
"\"Values: $\\\\mathrm{thresholds\\\\_offset\\\\_sigma}$, $\\\\mathrm{thresholds\\\\_offset\\\\_hard}$, $\\\\mathrm{thresholds\\\\_noise\\\\_sigma}$, $\\\\mathrm{thresholds\\\\_noise\\\\_hard}$ are given as parameters.\\n\",\n",
"\"\"\"))\n",
"elif \"DSSC\" in karabo_id:\n",
" dinstance = \"DSSC1M1\"\n",
" nmods = 16\n",
" display(Markdown(\"\"\"\n",
" \n",
"# Summary of DSSC dark characterization #\n",
" \n",
" \"\"\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"out_folder = Path(out_folder)\n",
"metadata = CalibrationMetadata(out_folder)\n",
"mod_mapping = metadata.setdefault(\"modules-mapping\", {})\n",
"old_constant_metadata = {}\n",
"for fn in out_folder.glob(\"module_metadata_*.yml\"):\n",
" with fn.open(\"r\") as fd:\n",
" fdict = yaml.safe_load(fd)\n",
" module = fdict[\"module\"]\n",
" mod_mapping[module] = fdict[\"pdu\"]\n",
" old_constant_metadata[module] = fdict[\"old-constants\"]\n",
" fn.unlink()\n",
"\n",
"metadata.save()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Preparing newly injected and previous constants from produced local folder in out_folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: After changes in the Cal DB interface read files from cal repository\n",
"# Load constants from local files\n",
"data = OrderedDict()\n",
"old_cons = OrderedDict()\n",
"mod_names = []\n",
"\n",
"# Loop over modules\n",
"for i in range(nmods):\n",
" qm = module_index_to_qm(i)\n",
" if not mod_mapping.get(qm):\n",
" continue\n",
" mod_pdu = mod_mapping[qm]\n",
" # loop over constants\n",
" for const in ['Offset', 'Noise', 'ThresholdsDark', 'BadPixelsDark']:\n",
" # first load new constant\n",
" fpath = out_folder / f\"const_{const}_{mod_pdu}.h5\"\n",
" \n",
" if not fpath.exists():\n",
" print(f\"No local output file {fpath} found\")\n",
" continue\n",
"\n",
" with h5py.File(fpath, 'r') as f:\n",
" if qm not in data:\n",
" mod_names.append(qm)\n",
" data[qm] = OrderedDict()\n",
"\n",
" data[qm][const] = f[\"data\"][()]\n",
"\n",
" # try finding old constants using paths from CalCat store\n",
" qm_mdata = old_constant_metadata[qm]\n",
"\n",
" if const not in qm_mdata:\n",
" continue\n",
"\n",
" filepath = qm_mdata[const][\"filepath\"]\n",
" h5path = qm_mdata[const][\"h5path\"]\n",
"\n",
" if not filepath or not h5path:\n",
" continue\n",
"\n",
" with h5py.File(filepath, \"r\") as fd:\n",
" old_cons.setdefault(qm, OrderedDict())[const] = fd[f\"{h5path}/data\"][:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cons_shape = {}\n",
"# extracting constant shape.\n",
"for qm, constant in data.items():\n",
" for cname, cons in constant.items():\n",
" shape = data[qm][cname].shape\n",
" if cname not in cons_shape:\n",
" cons_shape[cname] = shape\n",
" break\n",
"\n",
"constants = {}\n",
"prev_const = {}\n",
"\n",
"for cname, sh in cons_shape.items():\n",
" constants[cname]= np.zeros((len(mod_names),) + sh[:])\n",
" prev_const[cname]= np.zeros((len(mod_names),) + sh[:])\n",
"for i in range(len(mod_names)):\n",
" for cname, cval in constants.items():\n",
" cval[i] = data[mod_names[i]][cname]\n",
" if mod_names[i] in old_cons.keys():\n",
" prev_const[cname][i] = old_cons[mod_names[i]][cname]\n",
" else:\n",
" print(f\"No previous {cname} found for {mod_names[i]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(Markdown('## Processed modules'))\n",
"show_processed_modules(dinstance, constants, mod_names, mode=\"processed\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary figures across Modules ##\n",
"\n",
"The following plots give an overview of calibration constants averaged across pixels and memory cells. A bad pixel mask is applied."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if \"LPD\" in dinstance:\n",
" geom = extra_geom.LPD_1MGeometry.from_quad_positions(quad_pos=[(11.4, 299),\n",
" (-11.5, 8),\n",
" (254.5, -16),\n",
" (278.5, 275)])\n",
" pixels_y = 256\n",
" pixels_x = 256\n",
"\n",
"elif dinstance in ('AGIPD1M1', 'AGIPD1M2'):\n",
" geom = extra_geom.AGIPD_1MGeometry.from_quad_positions(quad_pos=[(-525, 625),\n",
" (-550, -10),\n",
" (520, -160),\n",
" (542.5, 475)])\n",
" pixels_y = 128\n",
" pixels_x = 512\n",
"\n",
"elif dinstance == \"AGIPD500K\":\n",
" geom = extra_geom.AGIPD_500K2GGeometry.from_origin()\n",
" pixels_y = 128\n",
" pixels_x = 512\n",
" \n",
"elif \"DSSC\" in dinstance:\n",
" pixels_y = 512\n",
" pixels_x = 128\n",
" quadpos = [(-130, 5), (-130, -125), (5, -125), (5, 5)]\n",
"\n",
" extrageom_pth = os.path.dirname(extra_geom.__file__)\n",
" geom = extra_geom.DSSC_1MGeometry.from_h5_file_and_quad_positions(\n",
" f\"{extrageom_pth}/tests/dssc_geo_june19.h5\", positions=quadpos)\n",
"\n",
"Mod_data=OrderedDict()\n",
"gainstages = 1\n",
"\n",
"for const_name, const in constants.items():\n",
" if const_name == 'BadPixelsDark':\n",
" continue\n",
" # Check if constant gain available in constant e.g. AGIPD, LPD\n",
" if len(const.shape) == 5:\n",
" gainstages = 3\n",
" else:\n",
" gainstages = 1\n",
"\n",
" for dname in ['{}', 'd-{}', 'dpct-{}']:\n",
" Mod_data[dname.format(const_name)] = OrderedDict()\n",
" \n",
" display(Markdown(f'##### {const_name}'))\n",
" print_once = True\n",
" for gain in range(gainstages):\n",
" if const_name == 'ThresholdsDark':\n",
" if gain > 1:\n",
" continue\n",
" glabel = threshold_names\n",
" else:\n",
" glabel = gain_names\n",
" for i in range(nmods):\n",
" qm = module_index_to_qm(i)\n",
" if qm in mod_names:\n",
" m_idx = mod_names.index(qm)\n",
" # Check if constant shape of 5 indices e.g. AGIPD, LPD\n",
" if len(const.shape) == 5:\n",
" values = np.nanmean(const[m_idx, :, :, :, gain], axis=2)\n",
" dval = np.nanmean(prev_const[const_name][m_idx, :, :, :, gain], axis=2)\n",
" else:\n",
" values = np.nanmean(const[m_idx, :, :, :], axis=2)\n",
" dval = np.nanmean(prev_const[const_name][m_idx, :, :, :], axis=2)\n",
" values[values == 0] = np.nan\n",
" dval[dval == 0] = np.nan\n",
" dval = values - dval\n",
" dval_pct = dval/values * 100\n",
"\n",
" values = np.moveaxis(values, 0, -1).reshape(1, values.shape[1], values.shape[0])\n",
" dval = np.moveaxis(dval, 0, -1).reshape(1, dval.shape[1], dval.shape[0])\n",
" dval_pct = np.moveaxis(dval_pct, 0, -1).reshape(1, dval_pct.shape[1], dval_pct.shape[0])\n",
" else:\n",
" # if module not available fill arrays with nan\n",
" values = np.zeros((1, pixels_x, pixels_y),dtype=np.float64)\n",
" values[values == 0] = np.nan\n",
" dval = values \n",
" dval_pct = dval\n",
" for k, v in {'{}': values, 'd-{}': dval , 'dpct-{}': dval_pct}.items():\n",
" try:\n",
" Mod_data[k.format(const_name)][gain_names[gain]] = \\\n",
" np.concatenate((Mod_data[k.format(const_name)][gain_names[gain]],\n",
" v), axis=0)\n",
" except:\n",
" Mod_data[k.format(const_name)][gain_names[gain]] = v\n",
" if np.count_nonzero(dval) == 0 and print_once:\n",
" display(Markdown(f'New and previous {const_name} are the same, hence there is no difference.'))\n",
" print_once = False\n",
" # Plotting constant overall modules.\n",
" display(Markdown(f'###### {glabel[gain]} ######'))\n",
"\n",
" gs = gridspec.GridSpec(2, 2)\n",
" fig = plt.figure(figsize=(24, 32))\n",
"\n",
" axis = OrderedDict()\n",
" axis = {\"ax0\": {\"cname\": \"{}\" ,\"gs\": gs[0, :], \"shrink\": 0.9, \"pad\": 0.05, \"label\": \"ADUs\", \"title\": '{}'},\n",
" \"ax1\": {\"cname\": \"d-{}\",\"gs\": gs[1, 0], \"shrink\": 0.6, \"pad\": 0.1, \"label\": \"ADUs\", \"title\": 'Difference with previous {}'},\n",
" \"ax2\": {\"cname\": \"dpct-{}\", \"gs\": gs[1, 1], \"shrink\": 0.6, \"pad\": 0.1, \"label\": \"%\", \"title\": 'Difference with previous {} %'}}\n",
"\n",
" for ax, axv in axis.items():\n",
" # Add the min and max plot values for each axis.\n",
" vmin, vmax = get_range(Mod_data[axv[\"cname\"].format(const_name)][gain_names[gain]], 2)\n",
" ax = fig.add_subplot(axv[\"gs\"])\n",
" geom.plot_data_fast(Mod_data[axv[\"cname\"].format(const_name)][gain_names[gain]],\n",
" vmin=vmin, vmax=vmax, ax=ax, \n",
" colorbar={'shrink': axv[\"shrink\"],\n",
" 'pad': axv[\"pad\"]\n",
" }\n",
" )\n",
"\n",
" colorbar = ax.images[0].colorbar\n",
" colorbar.set_label(axv[\"label\"])\n",
"\n",
" ax.set_title(axv[\"title\"].format(f\"{const_name} {glabel[gain]}\"), fontsize=15)\n",
" ax.set_xlabel('Columns', fontsize=15)\n",
" ax.set_ylabel('Rows', fontsize=15)\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Loop over modules and constants\n",
"for const_name, const in constants.items():\n",
" display(Markdown(f'### Summary across Modules - {const_name}'))\n",
"\n",
" for gain in range(gainstages):\n",
" if const_name == 'ThresholdsDark':\n",
" if gain == 2:\n",
" continue\n",
" glabel = threshold_names\n",
" else:\n",
" glabel = gain_names\n",
"\n",
" if len(const.shape) == 5:\n",
" data = np.copy(const[:, :, :, :, gain])\n",
" else:\n",
" data = np.copy(const[:, :, :, :])\n",
" if const_name != 'BadPixelsDark':\n",
" if \"BadPixelsDark\" in constants.keys():\n",
" label = f'{const_name} value [ADU], good pixels only'\n",
" if len(const.shape) == 5:\n",
" data[constants['BadPixelsDark'][:, :, :, :, gain] > 0] = np.nan\n",
" else:\n",
" data[constants['BadPixelsDark'][:, :, :, :] > 0] = np.nan\n",
" else:\n",
" label = f'{const_name} value [ADU], good and bad pixels'\n",
" datamean = np.nanmean(data, axis=(1, 2))\n",
"\n",
" fig = plt.figure(figsize=(15, 6), tight_layout={\n",
" 'pad': 0.2, 'w_pad': 1.3, 'h_pad': 1.3})\n",
" ax = fig.add_subplot(121)\n",
" else:\n",
" label = 'Fraction of bad pixels'\n",
" data[data > 0] = 1.0\n",
" datamean = np.nanmean(data, axis=(1, 2))\n",
" datamean[datamean == 1.0] = np.nan\n",
"\n",
" fig = plt.figure(figsize=(15, 6), tight_layout={\n",
" 'pad': 0.2, 'w_pad': 1.3, 'h_pad': 1.3})\n",
" ax = fig.add_subplot(111)\n",
"\n",
" d = []\n",
" for im, mod in enumerate(datamean):\n",
" d.append({'x': np.arange(mod.shape[0]),\n",
" 'y': mod,\n",
" 'drawstyle': 'steps-pre',\n",
" 'label': mod_names[im],\n",
" })\n",
"\n",
" _ = simplePlot(d, figsize=(10, 10), xrange=(-12, 510),\n",
" x_label='Memory Cell ID',\n",
" y_label=label,\n",
" use_axis=ax,\n",
" title='{}'.format(glabel[gain]),\n",
" title_position=[0.5, 1.18],\n",
" legend='outside-top-ncol6-frame', legend_size='18%',\n",
" legend_pad=0.00)\n",
" if const_name != 'BadPixelsDark':\n",
" ax = fig.add_subplot(122)\n",
" if \"BadPixelsDark\" in constants.keys():\n",
" label = f'$\\sigma$ {const_name} [ADU], good pixels only'\n",
" else:\n",
" label = f'$\\sigma$ {const_name} [ADU], good and bad pixels'\n",
" d = []\n",
" for im, mod in enumerate(np.nanstd(data, axis=(1, 2))):\n",
" d.append({'x': np.arange(mod.shape[0]),\n",
" 'y': mod,\n",
" 'drawstyle': 'steps-pre',\n",
" 'label': mod_names[im],\n",
" })\n",
"\n",
" _ = simplePlot(d, figsize=(10, 10), xrange=(-12, 510),\n",
" x_label='Memory Cell ID',\n",
" y_label=label,\n",
" use_axis=ax,\n",
" title='{} $\\sigma$'.format(glabel[gain]),\n",
" title_position=[0.5, 1.18],\n",
" legend='outside-top-ncol6-frame', legend_size='18%',\n",
" legend_pad=0.00)\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary tables across Modules ##\n",
"\n",
"Tables show values averaged across all pixels and memory cells of a given detector module."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if u'$' in tabulate.LATEX_ESCAPE_RULES:\n",
" del(tabulate.LATEX_ESCAPE_RULES[u'$'])\n",
" \n",
"if u'\\\\' in tabulate.LATEX_ESCAPE_RULES:\n",
" del(tabulate.LATEX_ESCAPE_RULES[u'\\\\'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"head = ['Module', 'High gain', 'Medium gain', 'Low gain']\n",
"head_th = ['Module', 'HG_MG threshold', 'MG_LG threshold']\n",
"for const_name, const in constants.items():\n",
" table = []\n",
"\n",
" for i_mod, mod in enumerate(mod_names):\n",
"\n",
" t_line = [mod]\n",
" for gain in range(gainstages):\n",
" \n",
" if const_name == 'ThresholdsDark': \n",
" if gain == 2:\n",
" continue\n",
" header = head_th\n",
" else:\n",
" header = head\n",
" if len(const.shape) == 5: \n",
" data = np.copy(const[i_mod, :, :, :, gain])\n",
" else:\n",
" data = np.copy(const[i_mod, :, :, :])\n",
" if const_name == 'BadPixelsDark':\n",
" data[data > 0] = 1.0\n",
"\n",
" datasum = np.nansum(data)\n",
" datamean = np.nanmean(data)\n",
" if datamean == 1.0:\n",
" datamean = np.nan\n",
" datasum = np.nan\n",
"\n",
" t_line.append(f'{datasum:6.0f} ({datamean:6.3f}) ')\n",
" label = '## Number(fraction) of bad pixels'\n",
" else:\n",
" if \"BadPixelsDark\" in constants.keys():\n",
" data[constants['BadPixelsDark']\n",
" [i_mod, :, :, :, gain] > 0] = np.nan\n",
" label = f'### Average {const_name} [ADU], good pixels only'\n",
" else:\n",
" label = f'### Average {const_name} [ADU], good and bad pixels'\n",
"\n",
" t_line.append(f'{np.nanmean(data):6.1f} $\\\\pm$ {np.nanstd(data):6.1f}')\n",
" label = f'## Average {const_name} [ADU], good pixels only'\n",
"\n",
" table.append(t_line)\n",
"\n",
" display(Markdown(label))\n",
" md = display(Latex(tabulate.tabulate(\n",
" table, tablefmt='latex', headers=header)))"
]
}
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