fix histogram data plot for flexible subplot numbers

9f6a65a2 · Karim Ahmed · 1dca99c3 · 9f6a65a2
Commit 9f6a65a2 authored 1 year ago by Karim Ahmed
--- a/notebooks/Jungfrau/Jungfrau_gain_Spectra_Fit_Summary_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_gain_Spectra_Fit_Summary_NBC.ipynb
@@ -113,15 +113,27 @@
    "plt.show()"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Histogram data for all cells for each module"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
-    "fixed_cols = 4\n",
+    "if nmods > 4:\n",
-    "fig, axs = plt.subplots(\n",
+    "    fixed_cols = 4\n",
-    "    math.ceil(nmods / fixed_cols), 4, figsize=(20, 10))  # Adjust for spatial histograms\n",
+    "    row, col = math.ceil(nmods / fixed_cols), 4\n",
+    "else:\n",
+    "    row, col = 1, nmods\n",
+    "\n",
+    "fig, axs = plt.subplots(row, col, figsize=(20, 10))  # Adjust for spatial histograms\n",
+    "axs = axs.ravel()\n",
    "for i, da in enumerate (da_to_pdu.keys()):\n",
    "    with h5file(\n",
    "        Path(out_folder) / histo_temp.format(run, proposal.upper(), da),\n",
@@ -131,17 +143,16 @@
    "    row, col = divmod(i, 4)\n",
    "    for m in range(histos.shape[1]):\n",
    "        cell_hist = histos[m]\n",
-    "        axs[row, col].plot(cell_hist.ravel(), label=f'Cell {m}')\n",
+    "        axs[i].plot(cell_hist.ravel(), label=f'Cell {m}')\n",
-    "        axs[row, col].set_title(f\"{da} ({da_to_pdu[da]})\")\n",
+    "        axs[i].set_title(f\"{da} ({da_to_pdu[da]})\")\n",
    "\n",
-    "for i, ax in enumerate(axs.flat):\n",
+    "for i, ax in enumerate(axs):\n",
-    "    if i > nmods:\n",
+    "    if i > nmods-1:\n",
    "        ax.set_visible(False)  # Hide unused subplots\n",
    "# Create a legend for the whole figure\n",
-    "handles, labels = axs[0, 0].get_legend_handles_labels()\n",
+    "handles, labels = axs[0].get_legend_handles_labels()\n",
-    "fig.legend(handles, labels, loc=\"center right\")\n",
+    "fig.legend(handles, labels, loc=\"center right\" if nmods > 4 else \"upper right\")\n",
-    "plt.title(\"Histogram for all cell for each module.\")\n",
+    "plt.tight_layout(pad=3)\n",
-    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }

 %% Cell type:markdown id: tags:
 # Jungfrau Spectra Fit Summary
 Author: European XFEL Detector Department, Version: 1.0
 Summary for plotting Spectra Fit results for Jungfrau FF histogram and fitting notebook
 %% Cell type:code id: tags:
 ``` python
 in_folder = '/gpfs/exfel/exp/SPB/202330/p900343/raw'  # RAW data path, required
 out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/remove/JF4M_SPB_gain/r67"  # Output path for gain data, required
 metadata_folder = ""  # Directory containing calibration_metadata.yml when run by xfel-calibrate.
 runs = [67]
 # Parameters used to access raw data.
 karabo_da = []  # list of data aggregators, which corresponds to different JF modules. This is only needed for the detectors of one module.
 karabo_id = "SPB_IRDA_JF4M"  # detector identifier.
 creation_time = ""  # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. "2022-06-28 13:00:00"
 fit_func = 'CHARGE_SHARING'  # function used to fit the single photon peak\
 g0_fit_dataset = 'gainMap_fit'  # name of the data structure in the fit files
 spectra_fit_temp = 'R{:04d}_{}_Gain_Spectra_{}_{}_Fit.h5'
 histo_temp = 'R{:04d}_{}_Gain_Spectra_{}_Histo.h5'
 ```
 %% Cell type:code id: tags:
 ``` python
 import math
 import warnings
 from pathlib import Path
 warnings.filterwarnings('ignore')
 from h5py import File as h5file
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 from IPython.display import display
 matplotlib.use("agg")
 %matplotlib inline
 from cal_tools.plotting import init_jungfrau_geom
 from XFELDetAna.plotting.simpleplot import simplePlot
 from cal_tools.restful_config import calibration_client
 from cal_tools.calcat_interface import CalCatApi
 ```
 %% Cell type:code id: tags:
 ``` python
 expected_modules, geom = init_jungfrau_geom(karabo_id=karabo_id, karabo_da=karabo_da)
 nmods = len(expected_modules)
 ```
 %% Cell type:code id: tags:
 ``` python
 calcat_client = calibration_client()
 calcat = CalCatApi(client=calcat_client)
 detector_id = calcat.detector(karabo_id)['id']
 da_mapping = calcat.physical_detector_units(detector_id, pdu_snapshot_at=creation_time)
 da_to_pdu = {k: v["physical_name"] for k, v in da_mapping.items()}
 ```
 %% Cell type:code id: tags:
 ``` python
 proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
 run = runs[0]  # TODO this will need to be fixed when I start implementing multiple runs.
 stacked_constants = np.full(geom.expected_data_shape, np.nan)  # nmods, 512, 1024
 for i, da in enumerate (da_to_pdu.keys()):
   with h5file(
      Path(out_folder) / spectra_fit_temp.format(run, proposal.upper(), da, fit_func),
      'r'
   ) as f:
      # f[g0_fit_dataset] shape is 1024, 512, mem_cells
      stacked_constants[i] = np.moveaxis(
         np.mean(np.array(f[g0_fit_dataset]), axis=-1), 0, 1)
 fig, ax = plt.subplots(figsize=(18, 10))
 vmin, vmax = np.percentile(stacked_constants, [5, 95])
 geom.plot_data_fast(
   stacked_constants,
   ax=ax,
   vmin=vmin,
   vmax=vmax,
   colorbar={'shrink': 1, 'pad': 0.01},
 )
 ax.set_title(f'{karabo_id} - One photon peak position', size=18)
 plt.show()
 ```
+%% Cell type:markdown id: tags:
+## Histogram data for all cells for each module
 %% Cell type:code id: tags:
 ``` python
-fixed_cols = 4
+if nmods > 4:
-fig, axs = plt.subplots(
+    fixed_cols = 4
-    math.ceil(nmods / fixed_cols), 4, figsize=(20, 10))  # Adjust for spatial histograms
+    row, col = math.ceil(nmods / fixed_cols), 4
+else:
+    row, col = 1, nmods
+fig, axs = plt.subplots(row, col, figsize=(20, 10))  # Adjust for spatial histograms
+axs = axs.ravel()
 for i, da in enumerate (da_to_pdu.keys()):
    with h5file(
        Path(out_folder) / histo_temp.format(run, proposal.upper(), da),
        'r'
    ) as f:
        histos = f["histos"][:]
    row, col = divmod(i, 4)
    for m in range(histos.shape[1]):
        cell_hist = histos[m]
-        axs[row, col].plot(cell_hist.ravel(), label=f'Cell {m}')
+        axs[i].plot(cell_hist.ravel(), label=f'Cell {m}')
-        axs[row, col].set_title(f"{da} ({da_to_pdu[da]})")
+        axs[i].set_title(f"{da} ({da_to_pdu[da]})")
-for i, ax in enumerate(axs.flat):
+for i, ax in enumerate(axs):
-    if i > nmods:
+    if i > nmods-1:
        ax.set_visible(False)  # Hide unused subplots
 # Create a legend for the whole figure
-handles, labels = axs[0, 0].get_legend_handles_labels()
+handles, labels = axs[0].get_legend_handles_labels()
-fig.legend(handles, labels, loc="center right")
+fig.legend(handles, labels, loc="center right" if nmods > 4 else "upper right")
-plt.title("Histogram for all cell for each module.")
+plt.tight_layout(pad=3)
-plt.tight_layout()
 plt.show()
 ```