fastplotlib
diff --git a/‎fastplotlib/widgets/histogram_lut.py
Copy file name to clipboardExpand all lines: fastplotlib/widgets/histogram_lut.py
+9-6Lines changed: 9 additions & 6 deletions b/‎fastplotlib/widgets/histogram_lut.py
Copy file name to clipboardExpand all lines: fastplotlib/widgets/histogram_lut.py
+9-6Lines changed: 9 additions & 6 deletions
diff --git a/‎fastplotlib/widgets/image.py
Copy file name to clipboardExpand all lines: fastplotlib/widgets/image.py
+16-5Lines changed: 16 additions & 5 deletions b/‎fastplotlib/widgets/image.py
Copy file name to clipboardExpand all lines: fastplotlib/widgets/image.py
+16-5Lines changed: 16 additions & 5 deletions
@@ -25,8 +25,8 @@ def __init__(
         ----------
         data
         image_graphic
-        nbins
-        flank_divisor: float, default 5.0
+        nbins: int, defaut 100. Total number of bins used in the histogram
+        flank_divisor: float, default 5.0. Fraction of empty histogram bins on the tails of the distribution
             set `np.inf` for no flanks
         kwargs
         """
@@ -161,9 +161,10 @@ def _calculate_histogram(self, data):
             hist, edges = np.histogram(data_ss, bins=self._nbins)
 
         # used if data ptp <= 10 because event things get weird
-        # with tiny world objects due to  floating point error
+        # with tiny world objects due to floating point error
         # so if ptp <= 10, scale up by a factor
-        self._scale_factor: int = max(1, 100 * int(10 / np.ptp(data_ss)))
+        data_interval = edges[-1] - edges[0]
+        self._scale_factor: int = max(1, 100 * int(10 / data_interval))
 
         edges = edges * self._scale_factor
 
@@ -178,15 +179,17 @@ def _calculate_histogram(self, data):
         )
 
         edges_flanked = np.concatenate((flank_left, edges, flank_right))
-        np.unique(np.diff(edges_flanked))
 
         hist_flanked = np.concatenate(
             (np.zeros(flank_nbins), hist, np.zeros(flank_nbins))
         )
 
         # scale 0-100 to make it easier to see
         # float32 data can produce unnecessarily high values
-        hist_scaled = hist_flanked / (hist_flanked.max() / 100)
+        hist_scale_value = hist_flanked.max()
+        if np.allclose(hist_scale_value, 0):
+            hist_scale_value = 1
+        hist_scaled = hist_flanked / (hist_scale_value / 100)
 
         if edges_flanked.size > hist_scaled.size:
             # we don't care about accuracy here so if it's off by 1-2 bins that's fine
 
@@ -368,15 +368,25 @@ def __init__(
             if all([_is_arraylike(d) for d in data]):
                 # Grid computations
                 if figure_shape is None:
-                    figure_shape = calculate_figure_shape(len(data))
+                    if figure_kwargs is not None:
+                        if 'shape' in figure_kwargs:
+                            figure_shape = figure_kwargs['shape']
+                    else:
+                        figure_shape = calculate_figure_shape(len(data))
 
-                # verify that user-specified figure shape is large enough for the number of image arrays passed
-                elif figure_shape[0] * figure_shape[1] < len(data):
+                # Regardless of how figure_shape is computed, below code
+                # verifies that figure shape is large enough for the number of image arrays passed
+                if figure_shape[0] * figure_shape[1] < len(data):
+                    original_shape = (figure_shape[0], figure_shape[1])
                     figure_shape = calculate_figure_shape(len(data))
                     warn(
-                        f"Invalid `figure_shape` passed, setting figure shape to: {figure_shape}"
+                        f"Original `figure_shape` was: {original_shape} "
+                        f" but data length is {len(data)}"
+                        f" Resetting figure shape to: {figure_shape}"
                     )
 
+
+
                 self._data: list[np.ndarray] = data
 
                 # Establish number of image dimensions and number of scrollable dimensions for each array
@@ -506,13 +516,14 @@ def __init__(
         # update the default kwargs with any user-specified kwargs
         # user specified kwargs will overwrite the defaults
         figure_kwargs_default.update(figure_kwargs)
+        figure_kwargs_default['shape'] = figure_shape
 
         if graphic_kwargs is None:
             graphic_kwargs = dict()
 
         graphic_kwargs.update({"cmap": cmap})
 
-        self._figure: Figure = Figure(shape=figure_shape, **figure_kwargs_default)
+        self._figure: Figure = Figure(**figure_kwargs_default)
 
         self._histogram_widget = histogram_widget
         for data_ix, (d, subplot) in enumerate(zip(self.data, self.figure)):