Merge pull request #2931 from ericpre/fix_overlay_plot_image

Fix overlay plot image

conda_environment_dev.yml

@@ -5,6 +5,9 @@ dependencies:
 # We pin freetype and matplotlib for the image comparison
 - freetype=2.10
 - matplotlib-base=3.5.1
+# temporary pinning until the conda-forge pinning is fixed
+# tifffile 2022.2.2 does support for python 3.7
+- tifffile <2022.4.22
 - cython
 - pytest
 - pytest-mpl

hyperspy/drawing/utils.py

@@ -17,6 +17,7 @@
 # along with HyperSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
 
 import copy
+from functools import partial
 import itertools
 import logging
 from packaging.version import Version
@@ -25,14 +26,13 @@
 
 import dask.array as da
 import traits.api as t
-import matplotlib.pyplot as plt
+import numpy as np
+
 import matplotlib as mpl
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from matplotlib.backend_bases import key_press_handler
-import numpy as np
-from functools import partial
-from matplotlib.colors import LinearSegmentedColormap
-from matplotlib.colors import BASE_COLORS, to_rgba
+from matplotlib.colors import LinearSegmentedColormap, BASE_COLORS, to_rgba
+import matplotlib.pyplot as plt
 
 from hyperspy.defaults_parser import preferences
 from hyperspy.misc.utils import to_numpy
@@ -429,12 +429,11 @@ def _make_cascade_subplot(spectra, ax, color, linestyle, padding=1, **kwargs):
                            np.nanmin(spectrum.data))
         if spectrum_yrange > max_value:
             max_value = spectrum_yrange
-    for spectrum_index, (spectrum, color, linestyle) in enumerate(
+    for i, (spectrum, color, linestyle) in enumerate(
             zip(spectra, color, linestyle)):
         x_axis = spectrum.axes_manager.signal_axes[0]
-        spectrum = _transpose_if_required(spectrum, 1)
-        data_to_plot = to_numpy((spectrum.data - spectrum.data.min()) /
-                                float(max_value) + spectrum_index * padding)
+        data = _parse_array(_transpose_if_required(spectrum, 1))
+        data_to_plot = (data - data.min()) / float(max_value) + i * padding
         ax.plot(x_axis.axis, data_to_plot, color=color, ls=linestyle,
                 **kwargs)
         set_xaxis_lims(ax, x_axis)
@@ -482,7 +481,7 @@ def plot_images(images,
                 labelwrap=30,
                 suptitle=None,
                 suptitle_fontsize=18,
-                colorbar='multi',
+                colorbar='default',
                 centre_colormap='auto',
                 scalebar=None,
                 scalebar_color='white',
@@ -500,7 +499,7 @@ def plot_images(images,
                 alphas=1.0,
                 legend_picking=True,
                 legend_loc='upper right',
-                pixel_size_factor=1,
+                pixel_size_factor=None,
                 **kwargs):
     """Plot multiple images either as sub-images or overlayed in one figure.
 
@@ -551,13 +550,15 @@ def plot_images(images,
         this parameter will override the automatically determined title.
     suptitle_fontsize : int, optional
         Font size to use for super title at top of figure.
-    colorbar : 'multi', None, 'single', optional
-        Controls the type of colorbars that are plotted.
-        If None, no colorbar is plotted.
-        If 'multi' (default), individual colorbars are plotted for each
-        (non-RGB) image
+    colorbar : 'default', 'multi', 'single', None, optional
+        Controls the type of colorbars that are plotted, incompatible with
+        ``overlay=True``.
+        If 'default', same as 'multi' when ``overlay=False``, otherwise same
+        as ``None``.
+        If 'multi', individual colorbars are plotted for each (non-RGB) image.
         If 'single', all (non-RGB) images are plotted on the same scale,
-        and one colorbar is shown for all
+        and one colorbar is shown for all.
+        If None, no colorbar is plotted.
     centre_colormap : 'auto', True, False, optional
         If True, the centre of the color scheme is set to zero. This is
         particularly useful when using diverging color schemes. If 'auto'
@@ -603,7 +604,7 @@ def plot_images(images,
         values will require more overlap in titles before activing the
         auto-label code.
     fig : mpl figure, optional
-        If set, the images will be plotted to an existing MPL figure
+        If set, the images will be plotted to an existing matplotlib figure.
     vmin, vmax: scalar, str, None
         If str, formatted as 'xth', use this value to calculate the percentage
         of pixels that are left out of the lower and upper bounds.
@@ -625,15 +626,17 @@ def plot_images(images,
         Float value or a list of floats corresponding to the alpha value of
         each color.
     legend_picking: bool, optional
-        If True (default), a spectrum can be toggled on and off by clicking on
-        the legended line.
+        If True (default), an image can be toggled on and off by clicking on
+        the legended line. For ``overlay=True`` only.
     legend_loc : str, int, optional
         This parameter controls where the legend is placed on the figure
         see the :py:func:`matplotlib.pyplot.legend` docstring for valid values
-    pixel_size_factor : int or float, optional
-        Default value is 1. Sets the size of the figure when plotting an overlay image. The higher
-        the number the larger the figure and therefore a greater number of
-        pixels are used. This value will be ignored if a Figure is provided.
+    pixel_size_factor : None, int or float, optional
+        If ``None`` (default), the size of the figure is taken from the
+        matplotlib ``rcParams``. Otherwise sets the size of the figure when
+        plotting an overlay image. The higher the number the larger the figure
+        and therefore a greater number of pixels are used. This value will be
+        ignored if a Figure is provided.
     **kwargs, optional
         Additional keyword arguments passed to :py:func:`matplotlib.pyplot.imshow`.
 
@@ -704,6 +707,15 @@ def __check_single_colorbar(cbar):
               if sig.axes_manager.navigation_size > 0
               else 1)
 
+    # Check compatibility of colorbar and overlay arguments
+    if overlay and colorbar != 'default':
+        _logger.info(f"`colorbar='{colorbar}'` is incompatible with "
+                     "`overlay=True`. Colorbar is disable.")
+        colorbar = None
+    # Setting the default value
+    elif colorbar == 'default':
+        colorbar = 'multi'
+
     # If no cmap given, get default colormap from pyplot:
     if cmap is None:
         cmap = [preferences.Plot.cmap_signal]
@@ -841,22 +853,52 @@ def __check_single_colorbar(cbar):
     else:
         raise ValueError("Did not understand input of labels.")
 
-    # Start of non-overlay?
+    # Check if we need to add a scalebar for some of the images
+    if isinstance(scalebar, (list, tuple)) and all(isinstance(x, int)
+                                                   for x in scalebar):
+        scalelist = True
+    else:
+        scalelist = False
+
+    if scalebar not in [None, False, 'all'] and scalelist is False:
+        raise ValueError("Did not understand scalebar input. Must be None, "
+                         "'all', or list of ints.")
+
     # Determine appropriate number of images per row
-    rows = int(np.ceil(n / float(per_row)))
-    if n < per_row:
-        per_row = n
+    if overlay:
+        # only a single image
+        per_row = rows = 1
+    else:
+        rows = int(np.ceil(n / float(per_row)))
+        if n < per_row:
+            per_row = n
 
     # Set overall figure size and define figure (if not pre-existing)
     if fig is None:
-        k = max(plt.rcParams['figure.figsize']) / max(per_row, rows)
-        if overlay:
-            shape = images[0].data.shape
-            dpi = 100
-            f = plt.figure(figsize=[pixel_size_factor*v/dpi for v in shape],
-                           dpi=dpi)
+        w, h = plt.rcParams['figure.figsize']
+        dpi = plt.rcParams['figure.dpi']  
+        if overlay and axes_decor == 'off':
+            shape = images[0].axes_manager.signal_shape
+            if pixel_size_factor is None:
+                # Cap the maximum dimension of figure to
+                # plt.rcParams['figure.figsize']
+                aspect_ratio = shape[0] / shape[1]
+                if aspect_ratio >= w / h:
+                    if label is not None and w / aspect_ratio < 1.0:
+                        # Needs enough space for the labels
+                        w = 1.0 * aspect_ratio
+                    figsize = (w, w / aspect_ratio)
+                else:
+                    if scalebar is not None and h * aspect_ratio < 2.0:
+                        # Needs enough width for the scalebar
+                        h = 2.0 / aspect_ratio
+                    figsize = (h * aspect_ratio, h)
+            else:
+                figsize = [pixel_size_factor*v/dpi for v in shape]
         else:
-            f = plt.figure(figsize=(tuple(k * i for i in (per_row, rows))))
+            k = max(w, h) / max(per_row, rows)
+            figsize=[k * i for i in (per_row, rows)]
+        f = plt.figure(figsize=figsize, dpi=dpi)
     else:
         f = fig
 
@@ -878,12 +920,7 @@ def __check_single_colorbar(cbar):
         colorbar = None
         warnings.warn("Sorry, colorbar is not implemented for RGB images.")
 
-    # Check if we need to add a scalebar for some of the images
-    if isinstance(scalebar, list) and all(isinstance(x, int)
-                                          for x in scalebar):
-        scalelist = True
-    else:
-        scalelist = False
+
 
     def check_list_length(arg, arg_name):
         if isinstance(arg, (list, tuple)):
@@ -934,16 +971,16 @@ def transparent_single_color_cmap(color):
                 images[0].axes_manager[0].scale):
                 raise ValueError("Images are not the same scale and so should"
                                  "not be overlayed.")
-
-        _logger.warning('vmin is ignored when overlaying images.')
+
+        if vmin is not None:
+            _logger.warning('`vmin` is ignored when overlaying images.')
 
         import matplotlib.patches as mpatches
+        factor = plt.rcParams['font.size'] / 100
         if not suptitle and axes_decor == 'off':
             ax = f.add_axes([0, 0, 1, 1])
-        elif not suptitle:
-            ax = f.add_axes([0.1, 0.1, 1, 1])
         else:
-            ax = f.add_axes([0.1, 0.1, 0.9, 0.8])
+            ax = f.add_subplot()
         patches = []
 
         #If no colors are selected use BASE_COLORS
@@ -988,12 +1025,12 @@ def transparent_single_color_cmap(color):
 
         if label is not None:
             plt.legend(handles=patches, loc=legend_loc)
-            if legend_picking is True:
+            if legend_picking:
                 animate_legend(fig=f, ax=ax, plot_type='images')
 
         set_axes_decor(ax, axes_decor)
 
-        if scalebar=='all':
+        if scalebar == 'all':
             axes = im.axes_manager.signal_axes
             ax.scalebar = ScaleBar(
                         ax=ax,
@@ -1003,7 +1040,7 @@ def transparent_single_color_cmap(color):
         axes_list.append(ax)
 
     #Below is for non-overlayed images
-    if not overlay:
+    else:
         # Loop through each image, adding subplot for each one
         for i, ims in enumerate(images):
             # Get handles for the signal axes and axes_manager
@@ -1159,20 +1196,6 @@ def transparent_single_color_cmap(color):
         f.subplots_adjust(top=0.85)
         f.suptitle(suptitle, fontsize=suptitle_fontsize)
 
-    # If we want to plot scalebars, loop through the list of axes and add them
-    if scalebar is None or scalebar is False:
-        # Do nothing if no scalebars are called for
-        pass
-    elif scalebar == 'all':
-        # scalebars were taken care of in the plotting loop
-        pass
-    elif scalelist:
-        # scalebars were taken care of in the plotting loop
-        pass
-    else:
-        raise ValueError("Did not understand scalebar input. Must be None, "
-                         "'all', or list of ints.")
-
     # Adjust subplot spacing according to user's specification
     if padding is not None:
         plt.subplots_adjust(**padding)
@@ -1526,7 +1549,8 @@ def update_line(spectrum, line):
 def animate_legend(fig=None, ax=None, plot_type='spectra'):
     """Animate the legend of a figure.
 
-    A spectrum can be toggled on and off by clicking on the line in the legend.
+    A spectrum or image can be toggled on and off by clicking on the line in
+    the legend.
 
     Parameters
     ----------
@@ -1664,3 +1688,4 @@ def picker_kwargs(value, kwargs={}):
         kwargs['picker'] = value
 
     return kwargs
+

hyperspy/tests/drawing/test_plot_signal2d.py

@@ -15,6 +15,8 @@
 # You should have received a copy of the GNU General Public License
 # along with HyperSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
 
+import logging
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pytest
@@ -589,6 +591,7 @@ def test_plot_autoscale_data_changed(autoscale):
         np.testing.assert_allclose(imf._vmin, _vmin)
         np.testing.assert_allclose(imf._vmax, _vmax)
 
+
 @pytest.mark.parametrize("axes_decor", ['all', 'off'])
 @pytest.mark.parametrize("label", ['auto', ['b','g']])
 @pytest.mark.parametrize("colors", ['auto', ['b','g']])
@@ -616,3 +619,76 @@ def test_plot_scale_different_sign():
     s2.plot()
     assert s2._plot.signal_plot.pixel_units is not None
     assert s2._plot.signal_plot.scalebar is True
+
+
+def test_plot_images_overlay_colorbar():
+    s = hs.signals.Signal2D(np.arange(100).reshape(10, 10))
+    hs.plot.plot_images([s, s], overlay=True, colorbar='single',
+                            axes_decor='off')
+
+
+def test_plot_images_overlay_aspect_ratio():
+    s = hs.signals.Signal2D(np.arange(100).reshape(2, 50))
+    hs.plot.plot_images([s, s], overlay=True, axes_decor='off')
+    f = plt.gcf()
+    np.testing.assert_allclose((f.get_figwidth(), f.get_figheight()), (25.0, 1.0))
+
+    s = hs.signals.Signal2D(np.arange(100).reshape(20, 5))
+    hs.plot.plot_images([s, s], overlay=True, axes_decor='off', scalebar='all')
+    f = plt.gcf()
+    np.testing.assert_allclose((f.get_figwidth(), f.get_figheight()), (2.0, 8.0))
+
+
+def test_plot_images_overlay_figsize():
+    """Test figure size for different aspect ratio of image."""
+    # Set reference figure size
+    plt.rcParams['figure.figsize'] = [6.4, 4.8]
+
+    # aspect_ratio is 1
+    s = hs.signals.Signal2D(np.random.random((10, 10)))
+    hs.plot.plot_images([s, s], overlay=True, scalebar='all', axes_decor='off')
+    f = plt.gcf()
+    np.testing.assert_allclose((f.get_figwidth(), f.get_figheight()), (4.8, 4.8))
+
+    # aspect_ratio is 64 / 48
+    s = hs.signals.Signal2D(np.random.random((48, 64)))
+    hs.plot.plot_images([s, s], overlay=True, scalebar='all', axes_decor='off')
+    f = plt.gcf()
+    np.testing.assert_allclose((f.get_figwidth(), f.get_figheight()), (6.4, 4.8))
+
+    # aspect_ratio is 2
+    s = hs.signals.Signal2D(np.random.random((10, 20)))
+    hs.plot.plot_images([s, s], overlay=True, scalebar='all', axes_decor='off')
+    f = plt.gcf()
+    np.testing.assert_allclose((f.get_figwidth(), f.get_figheight()), (6.4, 3.2))
+
+    # aspect_ratio is 0.5
+    s = hs.signals.Signal2D(np.random.random((20, 10)))
+    hs.plot.plot_images([s, s], overlay=True, scalebar='all', axes_decor='off')
+    f = plt.gcf()
+    np.testing.assert_allclose((f.get_figwidth(), f.get_figheight()), (2.4, 4.8))
+
+
+def test_plot_images_overlay_vmin_warning(caplog):
+    s = hs.signals.Signal2D(np.arange(100).reshape(10, 10))
+    with caplog.at_level(logging.WARNING):
+        hs.plot.plot_images([s, s], overlay=True, vmin=0)  
+
+    assert "`vmin` is ignored when overlaying images." in caplog.text
+
+
+def test_plot_scalebar_error():
+    s = hs.signals.Signal2D(np.arange(100).reshape(10, 10))
+    with pytest.raises(ValueError):
+        hs.plot.plot_images([s, s], scalebar='unsupported_argument')
+
+
+def test_plot_scalebar_list():
+    s = hs.signals.Signal2D(np.arange(100).reshape(10, 10))
+    ax0, ax1 = hs.plot.plot_images([s, s], scalebar=[0, 1])
+    assert hasattr(ax0, 'scalebar')
+    assert hasattr(ax1, 'scalebar')
+
+    ax0, ax1 = hs.plot.plot_images([s, s], scalebar=[0])
+    assert hasattr(ax0, 'scalebar')
+    assert not hasattr(ax1, 'scalebar')