diff --git a/notebooks/ePix100/Correction_ePix100_NBC.ipynb b/notebooks/ePix100/Correction_ePix100_NBC.ipynb
index 5158373979d84e14ac643d0c5f61dbd6ccaab81c..2f6e0d90b5dc28443fc38b9bf14a361904810ef1 100644
--- a/notebooks/ePix100/Correction_ePix100_NBC.ipynb
+++ b/notebooks/ePix100/Correction_ePix100_NBC.ipynb
@@ -24,15 +24,15 @@
"metadata": {},
"outputs": [],
"source": [
- "in_folder = \"/gpfs/exfel/exp/HED/202202/p003121/raw\" # input folder, required\n",
+ "in_folder = \"/gpfs/exfel/exp/MID/202301/p003346/raw\" # input folder, required\n",
"out_folder = \"\" # output folder, required\n",
"metadata_folder = \"\" # Directory containing calibration_metadata.yml when run by xfel-calibrate\n",
"sequences = [-1] # sequences to correct, set to -1 for all, range allowed\n",
"sequences_per_node = 1 # number of sequence files per cluster node if run as slurm job, set to 0 to not run SLURM parallel\n",
- "run = 156 # which run to read data from, required\n",
+ "run = 55 # which run to read data from, required\n",
"\n",
"# Parameters for accessing the raw data.\n",
- "karabo_id = \"HED_IA1_EPX100-1\" # karabo karabo_id\n",
+ "karabo_id = \"MID_EXP_EPIX-1\" # karabo karabo_id\n",
"karabo_da = \"EPIX01\" # data aggregators\n",
"db_module = \"\" # module id in the database\n",
"receiver_template = \"RECEIVER\" # detector receiver template for accessing raw data files\n",
@@ -92,14 +92,16 @@
"import matplotlib.pyplot as plt\n",
"from IPython.display import Latex, Markdown, display\n",
"from extra_data import RunDirectory, H5File\n",
+ "from extra_geom import Epix100Geometry\n",
+ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"from pathlib import Path\n",
"\n",
"import cal_tools.restful_config as rest_cfg\n",
- "from XFELDetAna import xfelpyanatools as xana\n",
"from XFELDetAna import xfelpycaltools as xcal\n",
"from cal_tools.calcat_interface import EPIX100_CalibrationData\n",
"from cal_tools.epix100 import epix100lib\n",
"from cal_tools.files import DataFile\n",
+ "from cal_tools.restful_config import restful_config\n",
"from cal_tools.tools import (\n",
" calcat_creation_time,\n",
" write_constants_fragment,\n",
@@ -110,6 +112,7 @@
"\n",
"prettyPlotting = True\n",
"\n",
+ "\n",
"%matplotlib inline"
]
},
@@ -134,6 +137,8 @@
"metadata": {},
"outputs": [],
"source": [
+ "prop_str = in_folder[in_folder.find('/p')+1:in_folder.find('/p')+8]\n",
+ "\n",
"in_folder = Path(in_folder)\n",
"out_folder = Path(out_folder)\n",
"\n",
@@ -250,13 +255,12 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieving calibration constants\n",
"\n",
- "As a first step, calibration constants have to be loaded."
+ "As a first step, dark maps have to be loaded."
]
},
{
@@ -321,35 +325,8 @@
"# Initializing some parameters.\n",
"hscale = 1\n",
"stats = True\n",
- "hrange = np.array([-50, 1000])\n",
- "nbins = hrange[1] - hrange[0]\n",
- "commonModeBlockSize = [x//2, y//2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "histCalOffsetCor = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=nbins,\n",
- " range=hrange,\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize\n",
- ")\n",
- "\n",
- "# *****************Histogram Calculators****************** #\n",
- "histCalCor = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=1050,\n",
- " range=[-50, 1000],\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize\n",
- ")"
+ "bins = np.arange(-50,1000)\n",
+ "hist = {'O': 0} # dictionary to store histograms"
]
},
{
@@ -359,14 +336,9 @@
"outputs": [],
"source": [
"if common_mode:\n",
- " histCalCMCor = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=nbins,\n",
- " range=hrange,\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize,\n",
- " )\n",
+ " \n",
+ " commonModeBlockSize = [x//2, y//8]\n",
+ " \n",
" cmCorrectionB = xcal.CommonModeCorrection(\n",
" shape=sensorSize,\n",
" blockSize=commonModeBlockSize, \n",
@@ -402,7 +374,9 @@
" stats=stats,\n",
" minFrac=cm_min_frac,\n",
" noiseSigma=cm_noise_sigma,\n",
- " )"
+ " )\n",
+ " \n",
+ " hist['CM'] = 0"
]
},
{
@@ -412,12 +386,11 @@
"outputs": [],
"source": [
"if relative_gain:\n",
- " gain_cnst = np.median(const_data[\"RelativeGainEPix100\"])\n",
- " hscale = gain_cnst\n",
- " plot_unit = 'keV'\n",
- " if photon_energy > 0:\n",
- " plot_unit = '$\\gamma$'\n",
- " hscale /= photon_energy\n",
+ " \n",
+ " # gain constant is given by the mode of the gain map \n",
+ " # because all bad pixels are masked using this value\n",
+ " _vals,_counts = np.unique(const_data[\"RelativeGainEPix100\"], return_counts=True)\n",
+ " gain_cnst = _vals[np.argmax(_counts)] \n",
" \n",
" gainCorrection = xcal.RelativeGainCorrection(\n",
" sensorSize,\n",
@@ -427,25 +400,16 @@
" blockSize=blockSize,\n",
" gains=None,\n",
" )\n",
- "\n",
- " histCalRelGainCor = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=nbins,\n",
- " range=hrange,\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize\n",
- " )\n",
+ " \n",
+ " hist['RG'] = 0\n",
"\n",
" if absolute_gain:\n",
- " histCalAbsGainCor = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=nbins,\n",
- " range=hrange*hscale,\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize\n",
- " )"
+ " hscale = gain_cnst\n",
+ " plot_unit = 'keV'\n",
+ " if photon_energy > 0:\n",
+ " plot_unit = '$\\gamma$'\n",
+ " hscale /= photon_energy\n",
+ " hist['AG'] = 0"
]
},
{
@@ -467,30 +431,8 @@
" blockSize=[x, y],\n",
" parallel=run_parallel,\n",
" )\n",
- " histCalCSCor = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=nbins,\n",
- " range=hrange,\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize,\n",
- " )\n",
- " histCalGainCorClusters = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=nbins,\n",
- " range=hrange*hscale,\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize\n",
- " )\n",
- " histCalGainCorSingles = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=nbins,\n",
- " range=hrange*hscale,\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " blockSize=blockSize\n",
- " )"
+ " hist['CS'] = 0\n",
+ " hist['S'] = 0"
]
},
{
@@ -511,11 +453,11 @@
" d = d[..., np.newaxis].astype(np.float32)\n",
" d = np.compress(\n",
" np.any(d > 0, axis=(0, 1)), d, axis=2)\n",
- " \n",
+ "\n",
" # Offset correction.\n",
" d -= const_data[\"OffsetEPix100\"]\n",
+ " hist['O'] += np.histogram(d,bins=bins)[0]\n",
"\n",
- " histCalOffsetCor.fill(d)\n",
" # Common Mode correction.\n",
" if common_mode:\n",
" # Block CM\n",
@@ -524,12 +466,15 @@
" d = cmCorrectionR.correct(d)\n",
" # COL CM\n",
" d = cmCorrectionC.correct(d)\n",
- " histCalCMCor.fill(d)\n",
"\n",
- " # relative gain correction.\n",
+ " hist['CM'] += np.histogram(d,bins=bins)[0]\n",
+ "\n",
+ "\n",
+ " # Relative gain correction.\n",
" if relative_gain:\n",
" d = gainCorrection.correct(d)\n",
- " histCalRelGainCor.fill(d)\n",
+ "\n",
+ " hist['RG'] += np.histogram(d,bins=bins)[0]\n",
"\n",
" \"\"\"The gain correction is currently applying\n",
" an absolute correction (not a relative correction\n",
@@ -552,34 +497,31 @@
" data_clu[index, ...] = np.squeeze(d_clu)\n",
" data_patterns[index, ...] = np.squeeze(patterns)\n",
"\n",
- " histCalCSCor.fill(d_clu)\n",
+ " hist['CS'] += np.histogram(d_clu,bins=bins)[0]\n",
"\n",
- " # absolute gain correction\n",
+ " d_sing = d_clu[patterns==100] # pattern 100 corresponds to single photons events\n",
+ " if len(d_sing):\n",
+ " hist['S'] += np.histogram(d_sing,bins=bins)[0]\n",
+ "\n",
+ " # Absolute gain correction\n",
" # changes data from ADU to keV (or n. of photons)\n",
" if absolute_gain:\n",
"\n",
" d = d * gain_cnst\n",
" if photon_energy > 0:\n",
" d /= photon_energy\n",
- " histCalAbsGainCor.fill(d)\n",
+ "\n",
+ " hist['AG'] += np.histogram(d,bins=bins)[0]\n",
"\n",
" if pattern_classification:\n",
" # Modify pattern classification.\n",
" d_clu = d_clu * gain_cnst\n",
- " \n",
" if photon_energy > 0:\n",
" d_clu /= photon_energy\n",
"\n",
" data_clu[index, ...] = np.squeeze(d_clu)\n",
"\n",
- " histCalGainCorClusters.fill(d_clu)\n",
- " \n",
- " d_sing = d_clu[patterns==100] # pattern 100 corresponds to single photons events\n",
- " if len(d_sing):\n",
- " histCalGainCorSingles.fill(d_sing)\n",
- "\n",
- " data[index, ...] = np.squeeze(d)\n",
- " histCalCor.fill(d)"
+ " data[index, ...] = np.squeeze(d)"
]
},
{
@@ -712,80 +654,52 @@
" exit(0)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot Histograms"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bins_ADU = bins[:-1]+np.diff(bins)[0]/2\n",
+ "bins_keV = bins_ADU*hscale"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "ho, eo, co, so = histCalCor.get()\n",
- "\n",
- "d = [{\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'Total corr.'\n",
- "}]\n",
- "\n",
- "ho, eo, co, so = histCalOffsetCor.get()\n",
- "\n",
- "d.append({\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'Offset corr.'\n",
- "})\n",
+ "# Histogram in ADU\n",
"\n",
- "if common_mode:\n",
- " ho, eo, co, so = histCalCMCor.get()\n",
- " d.append({\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'CM corr.'\n",
- " })\n",
- " \n",
- "if relative_gain :\n",
- " ho, eo, co, so = histCalRelGainCor.get()\n",
- " d.append({\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'Relative gain corr.'\n",
- " })\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(bins_ADU,hist['O'], label='Offset corr')\n",
"\n",
+ "if common_mode:\n",
+ " plt.plot(bins_ADU,hist['CM'], label='CM corr')\n",
+ "if relative_gain:\n",
+ " plt.plot(bins_ADU,hist['RG'], label='Relative Gain corr')\n",
"if pattern_classification:\n",
- " ho, eo, co, so = histCalCSCor.get()\n",
- " d.append({\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'Charge sharing corr.'\n",
- " })\n",
- "\n",
- "fig = xana.simplePlot(\n",
- " d, aspect=1, x_label=f'Energy (ADU)',\n",
- " y_label='Number of occurrences', figsize='2col',\n",
- " y_log=True, x_range=(-50, 500),\n",
- " legend='top-center-frame-2col',\n",
- ")\n",
- "plt.title(f'run {run} - {karabo_da}')\n",
- "plt.grid()"
+ " plt.plot(bins_ADU[bins_ADU>10],hist['CS'][bins_ADU>10], label='Charge Sharing corr')\n",
+ " if np.any(hist['S']):\n",
+ " plt.plot(bins_ADU,hist['S'], label='Singles')\n",
+ "\n",
+ "xtick_step = 50\n",
+ "plt.xlim(bins[0], bins[-1]+1)\n",
+ "plt.xticks(np.arange(bins[0],bins[-1]+2,xtick_step))\n",
+ "plt.xlabel('ADU',fontsize=12)\n",
+ "\n",
+ "plt.yscale('log')\n",
+ "plt.title(f'{karabo_id} | {prop_str}, r{run}', fontsize=14, fontweight='bold')\n",
+ "plt.legend(fontsize=12)\n",
+ "plt.grid(ls=':')"
]
},
{
@@ -794,54 +708,30 @@
"metadata": {},
"outputs": [],
"source": [
- "if absolute_gain :\n",
- " d = []\n",
- " ho, eo, co, so = histCalAbsGainCor.get()\n",
- " if co is not None: # avoid adding None array, if calculator is empty.\n",
- " d.append({\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'Absolute gain corr.'\n",
- " })\n",
+ "# Histogram in keV/number of photons\n",
+ "\n",
+ "if absolute_gain:\n",
+ " colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
+ " plt.figure(figsize=(12,8))\n",
"\n",
+ " if relative_gain:\n",
+ " plt.plot(bins_keV,hist['RG'], label='Absolute Gain corr', c=colors[2])\n",
+ " \n",
" if pattern_classification:\n",
- " ho, eo, co, so = histCalGainCorClusters.get()\n",
- " if co is not None: # avoid adding None array, if calculator is empty.\n",
- " d.append({\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'Charge sharing corr.'\n",
- " })\n",
- " \n",
- " ho, eo, co, so = histCalGainCorSingles.get()\n",
- " if co is not None: # avoid adding None array, if calculator is empty.\n",
- " d.append({\n",
- " 'x': co,\n",
- " 'y': ho,\n",
- " 'y_err': np.sqrt(ho[:]),\n",
- " 'drawstyle': 'steps-mid',\n",
- " 'errorstyle': 'bars',\n",
- " 'errorcoarsing': 2,\n",
- " 'label': 'Isolated photons (singles)'\n",
- " })\n",
- " \n",
- " fig = xana.simplePlot(\n",
- " d, aspect=1, x_label=f'Energy ({plot_unit})',\n",
- " y_label='Number of occurrences', figsize='2col',\n",
- " y_log=True, \n",
- " x_range=np.array((-50, 500))*hscale,\n",
- " legend='top-center-frame-2col',\n",
- " )\n",
- " plt.grid()\n",
- " plt.title(f'run {run} - {karabo_da}')"
+ " plt.plot(bins_keV[bins_keV>.5],hist['CS'][bins_keV>.5], label='Charge Sharing corr', c=colors[3])\n",
+ " if np.any(hist['S']):\n",
+ " plt.plot(bins_keV[bins_keV>.5],hist['S'][bins_keV>.5], label='Singles', c=colors[4])\n",
+ " \n",
+ " if photon_energy==0: # if keV instead of #photons\n",
+ " xtick_step = 5\n",
+ " plt.xlim(left=-2)\n",
+ " plt.xticks(np.arange(0,plt.gca().get_xlim()[1],xtick_step))\n",
+ " plt.xlabel(plot_unit,fontsize=12)\n",
+ "\n",
+ " plt.yscale('log')\n",
+ " plt.title(f'{karabo_id} | {prop_str}, r{run}', fontsize=14, fontweight='bold')\n",
+ " plt.legend(fontsize=12)\n",
+ " plt.grid(ls=':')"
]
},
{
@@ -857,15 +747,48 @@
"metadata": {},
"outputs": [],
"source": [
- "step_timer.start()\n",
- "fig = xana.heatmapPlot(\n",
- " np.nanmedian(data, axis=0),\n",
- " x_label='Columns', y_label='Rows',\n",
- " lut_label=f'Signal ({plot_unit})',\n",
- " x_range=(0, y),\n",
- " y_range=(0, x),\n",
- " vmin=-50, vmax=50)\n",
- "step_timer.done_step(f'Plotting mean image of {data.shape[0]} trains.')"
+ "geom = Epix100Geometry.from_relative_positions(top=[386.5, 364.5, 0.], bottom=[386.5, -12.5, 0.])\n",
+ "\n",
+ "if pattern_classification:\n",
+ " plt.subplots(1,2,figsize=(18,18)) if pattern_classification else plt.subplots(1,1,figsize=(9,9))\n",
+ " ax = plt.subplot(1,2,1)\n",
+ " ax.set_title(f'Before CS correction',fontsize=12,fontweight='bold');\n",
+ "else:\n",
+ " plt.subplots(1,1,figsize=(9,9))\n",
+ " ax = plt.subplot(1,1,1)\n",
+ " ax.set_title(f'{karabo_id} | {prop_str}, r{run} | Average of {data.shape[0]} trains',fontsize=12,fontweight='bold');\n",
+ " \n",
+ "# Average image before charge sharing corrcetion\n",
+ "divider = make_axes_locatable(ax)\n",
+ "cax = divider.append_axes('bottom', size='5%', pad=0.5)\n",
+ "\n",
+ "image = data.mean(axis=0)\n",
+ "vmin = max(image.mean()-2*image.std(),0)\n",
+ "vmax = image.mean()+3*image.std()\n",
+ "geom.plot_data(image,\n",
+ " ax=ax,\n",
+ " colorbar={'cax': cax, 'label': plot_unit, 'orientation': 'horizontal'},\n",
+ " origin='upper',\n",
+ " vmin=vmin,\n",
+ " vmax=vmax)\n",
+ "\n",
+ "# Average image after charge sharing corrcetion\n",
+ "if pattern_classification:\n",
+ "\n",
+ " ax = plt.subplot(1,2,2)\n",
+ " divider = make_axes_locatable(ax)\n",
+ " cax = divider.append_axes('bottom', size='5%', pad=0.5)\n",
+ "\n",
+ " image = data_clu.mean(axis=0)\n",
+ " geom.plot_data(image,\n",
+ " ax=ax,\n",
+ " colorbar={'cax': cax, 'label': plot_unit, 'orientation': 'horizontal'},\n",
+ " origin='upper',\n",
+ " vmin=vmin,\n",
+ " vmax=vmax)\n",
+ " ax.set_title(f'After CS correction',fontsize=12,fontweight='bold');\n",
+ "\n",
+ " plt.suptitle(f'{karabo_id} | {prop_str}, r{run} | Average of {data.shape[0]} trains',fontsize=14,fontweight='bold',y=.72);"
]
},
{
@@ -881,23 +804,56 @@
"metadata": {},
"outputs": [],
"source": [
- "step_timer.start()\n",
- "fig = xana.heatmapPlot(\n",
- " data[0, ...],\n",
- " x_label='Columns', y_label='Rows',\n",
- " lut_label=f'Signal ({plot_unit})',\n",
- " x_range=(0, y),\n",
- " y_range=(0, x),\n",
- " vmin=-50, vmax=50)\n",
- "step_timer.done_step(f'Plotting single shot of corrected data.')"
+ "train_idx = -1\n",
+ "\n",
+ "if pattern_classification:\n",
+ " plt.subplots(1,2,figsize=(18,18)) if pattern_classification else plt.subplots(1,1,figsize=(9,9))\n",
+ " ax = plt.subplot(1,2,1)\n",
+ " ax.set_title(f'Before CS correction',fontsize=12,fontweight='bold');\n",
+ "else:\n",
+ " plt.subplots(1,1,figsize=(9,9))\n",
+ " ax = plt.subplot(1,1,1)\n",
+ " ax.set_title(f'{karabo_id} | {prop_str}, r{run} | Single frame',fontsize=12,fontweight='bold');\n",
+ " \n",
+ "# Average image before charge sharing corrcetion\n",
+ "divider = make_axes_locatable(ax)\n",
+ "cax = divider.append_axes('bottom', size='5%', pad=0.5)\n",
+ "\n",
+ "image = data[train_idx]\n",
+ "vmin = max(image.mean()-2*image.std(),0)\n",
+ "vmax = image.mean()+3*image.std()\n",
+ "geom.plot_data(image,\n",
+ " ax=ax,\n",
+ " colorbar={'cax': cax, 'label': plot_unit, 'orientation': 'horizontal'},\n",
+ " origin='upper',\n",
+ " vmin=vmin,\n",
+ " vmax=vmax)\n",
+ "\n",
+ "# Average image after charge sharing corrcetion\n",
+ "if pattern_classification:\n",
+ "\n",
+ " ax = plt.subplot(1,2,2)\n",
+ " divider = make_axes_locatable(ax)\n",
+ " cax = divider.append_axes('bottom', size='5%', pad=0.5)\n",
+ "\n",
+ " image = data_clu[train_idx]\n",
+ " geom.plot_data(image,\n",
+ " ax=ax,\n",
+ " colorbar={'cax': cax, 'label': plot_unit, 'orientation': 'horizontal'},\n",
+ " origin='upper',\n",
+ " vmin=vmin,\n",
+ " vmax=vmax)\n",
+ " ax.set_title(f'After CS correction',fontsize=12,fontweight='bold');\n",
+ "\n",
+ " plt.suptitle(f'{karabo_id} | {prop_str}, r{run} | Single frame',fontsize=14,fontweight='bold',y=.72);"
]
}
],
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"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "cal_venv",
"language": "python",
- "name": "python3"
+ "name": "cal_venv"
},
"language_info": {
"codemirror_mode": {
@@ -909,7 +865,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.12"
+ "version": "3.8.11"
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