Feat: Add generic notebook to plot calibration constants

@ahmedk

This generic notebook stays to plot calibration constants for small detectors without memory cells.

A constant for Jungfrau detector are stores with meta-data gain=0, however file contain all gain stages. Device name for Jungfrau detector Jungfrau_M039 is Jungfrau1.

Additional small feature to show symbols on plots in case of constant does not exist, or all pixels are bad or 0.

cal_tools/cal_tools/ana_tools.py

@@ -352,6 +352,13 @@ class HMType(Enum):
     INSET_AXIS = 2
 
 
+class IMType(Enum):
+    NO_CONST = -1e+4
+    ALL_NAN = -2e+4
+    STRANGE_VAL = -3e+4
+    ALL_BAD = -4e+4
+    NO_BPMAP = -5e+4
+
 def get_range(data, scale):
     """
     Return a range calculated by median absolute deviations
@@ -386,10 +393,24 @@ def hm_combine(data, fname=None, htype=None, **kwargs):
     fig = plt.figure(figsize=(10, 12))
     ax = fig.add_subplot(111)
 
+    data_txt = np.full(data.shape, '').astype(str)
+    data_txt[data == IMType.NO_CONST.value] = 'X'
+    data_txt[data == IMType.ALL_NAN.value] = 'n'
+    data_txt[data == IMType.ALL_BAD.value] = 'B'
+    data_txt[data == IMType.STRANGE_VAL.value] = '0'
+    data_txt[data == IMType.NO_BPMAP.value] = 'N'
+
+    data[data == IMType.NO_CONST.value] = np.nan
+    data[data == IMType.ALL_NAN.value] = np.nan
+    data[data == IMType.ALL_BAD.value] = np.nan
+    data[data == IMType.STRANGE_VAL.value] = np.nan
+    data[data == IMType.NO_BPMAP.value] = np.nan
+
     xana.heatmapPlot(data, add_panels=False, cmap='viridis',
                      cb_pad=0.6 if htype == HMType.INSET_AXIS else 0.1,
                      use_axis=ax,
                      **kwargs)
+
     plt.setp(ax.yaxis.get_majorticklabels(), rotation=90)
 
     pad = kwargs.get('pad', [0.125, 0.125, 0.1, 0.18])
@@ -406,6 +427,14 @@ def hm_combine(data, fname=None, htype=None, **kwargs):
     h_frame = 1 - pad_b - pad_t
     w_frame = 1 - pad_l - pad_r
 
+    # Loop over data dimensions and create text annotations.
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            _ = ax.text(j+0.5, i+0.5, data_txt[i, j],
+                        horizontalalignment="center",
+                        verticalalignment="center",
+                        color="black")
+
     if htype == HMType.INSET_1D:
         ax.tick_params(axis='y', which='major', pad=50)
         for y in range(data.shape[0]):