[AGIPD][DARK-NB] add summary info over module for dark report

1. Adding similar chapters for summary over modules in DARK notebook.
2. Using extra geometry for plotting the mean over pixels and memory cells for calibration constants.
3. Plotting multiple gains over same plot axis from constant overview function
4. appending extra-geom to the requirements.

@karnem

cal_tools/cal_tools/plotting.py

@@ -104,41 +104,56 @@ def plot_badpix_3d(data, definitions, title=None, rebin_fac=2, azim=22.5):
     if title:
         t = ax.set_title(title)
 
-
-from IPython.display import HTML, display, Markdown, Latex
-import tabulate
-
-
-def create_constant_overview(constant, name, cells, vmin=None, vmax=None, entries=3,
-                            out_folder=None, infix=None):
+def create_constant_overview(constant, name, cells, vmin=None, vmax=None,
+                             entries=3, out_folder=None, infix=None, badpixels=None):
+    """
+    Create a step plot for constant data across memory cells for requested
+    gain entries
+
+    :param constant:
+    :param name: Name to be used for the x-axis
+    :param cells: Number of memory cells
+    :param vmin: plot minumim value boundaries
+    :param vmax: plot maximum value boundaries
+    :param entries: number of gain entries
+    TODO: remove unused inputs from notebooks.
+    :param out_folder: out_folder for showing .md table statistics
+    :param infix: infix for the output png image
+    :return:
+    """
     gmap = {0: 'High', 1: 'Medium', 2: 'Low'}
+    fig = plt.figure(figsize=(10, 5))
+    ax = fig.add_subplot(111)
     for g in range(entries):
-        fig = plt.figure(figsize=(10, 5))
-        ax = fig.add_subplot(111)
         table = []
+        dbp = None
 
         for qm in constant.keys():
             if len(constant[qm].shape) == 4:
                 d = constant[qm][..., g]
+                if badpixels is not None:
+                    dbp = np.copy(d)
+                    dbp[badpixels[qm][..., g] > 0] = np.nan
             else:
-                # This case was introduced for DSSC dark
-                # it uses this function but have a constant
-                # of shape (x, y, z), unlike AGIPD.
+                # e.g. DSSC
                 d = constant[qm]
-            #print("{} {}, gain {:0.2f}: mean: {:0.2f}, median: {:0.2f}, std: {:0.2f}".format(name, qm, g,
-            #                                                          np.nanmean(d),
-            #                                                          np.nanmedian(d),
-            #                                                          np.nanstd(d)))
-            table.append([name, qm, gmap[g], np.nanmean(d), np.nanmedian(d), np.nanstd(d)])
-            ax.step(np.arange(cells), np.nanmean(d, axis=(0,1)))
-        ax.set_xlabel("Memory cell")
-        ax.set_ylabel(name)
-        ax.set_title("{} Gain Median per Cell".format(gmap[g]))
-        if vmin and vmax:
-            ax.set_ylim(vmin, vmax)
-        #if out_folder and infix:
-        #    fig.savefig("{}/dark_analysis_{}_{}_per_cell_gain{}.png".format(out_folder,
-        #                                                                    infix,
-        #                                                                   name, g))
-        # headers = ["Constant", "Module", "Gain", "Mean", "Median", "Standard dev."]
-        #md = display(Latex(tabulate.tabulate(table, tablefmt='latex', headers=headers)))
+                if badpixels is not None:
+                    dbp = np.copy(d)
+                    dbp[badpixels[qm] > 0] = np.nan
+            # TODO: check if not used to remove.
+            table.append([name, qm, gmap[g], np.nanmean(d), np.nanmedian(d),
+                          np.nanstd(d)])
+
+            ax.step(np.arange(cells), np.nanmean(d, axis=(0,1)),
+                    label=f'{gmap[g]}-Gain', color=f'C{g}')
+            # Plotting good pixels only if bad-pixels were given
+            if dbp is not None:
+                ax.step(np.arange(cells), np.nanmean(dbp, axis=(0, 1)),
+                        label=f'Good pixels {gmap[g]}-Gain', color=f'C{g}',
+                        linestyle='--')
+    ax.set_xlabel("Memory cell")
+    ax.set_ylabel(name)
+    ax.legend()
+    ax.set_title(f"{name} Median per Cell".format(gmap[g]))
+    if vmin and vmax:
+        ax.set_ylim(vmin, vmax)