diff --git a/docs/examples/histogram_artist_example.ipynb b/docs/examples/histogram_artist_example.ipynb index 2264b4c..84f4dee 100644 --- a/docs/examples/histogram_artist_example.ipynb +++ b/docs/examples/histogram_artist_example.ipynb @@ -139,7 +139,7 @@ "
cat10_modified_first_transparent
\"cat10_modified_first_transparent
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" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -333,7 +333,7 @@ "
magma
\"magma
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" ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -364,7 +364,7 @@ "
viridis
\"viridis
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bad
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" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -416,7 +416,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:452: UserWarning: Log normalization applied to color indices with min value 0.01. Values below 0.01 were set to 0.01.\n", + "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:462: UserWarning: Log normalization applied to color indices with min value 0.01. Values below 0.01 were set to 0.01.\n", " warnings.warn(\n" ] }, @@ -544,7 +544,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:452: UserWarning: Log normalization applied to color indices with min value 1.0. Values below 0.01 were set to 0.01.\n", + "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:462: UserWarning: Log normalization applied to color indices with min value 1.0. Values below 0.01 were set to 0.01.\n", " warnings.warn(\n" ] }, @@ -574,7 +574,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:452: UserWarning: Log normalization applied to color indices with min value 0.01. Values below 0.01 were set to 0.01.\n", + "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:462: UserWarning: Log normalization applied to color indices with min value 0.01. Values below 0.01 were set to 0.01.\n", " warnings.warn(\n" ] }, @@ -623,7 +623,7 @@ "
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" ], "text/plain": [ - "" + "" ] }, "execution_count": 19, @@ -817,7 +817,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:452: UserWarning: Log normalization applied to color indices with min value 0.01. Values below 0.01 were set to 0.01.\n", + "C:\\Users\\mazo260d\\Documents\\GitHub\\biaplotter\\src\\biaplotter\\artists_base.py:462: UserWarning: Log normalization applied to color indices with min value 0.01. Values below 0.01 were set to 0.01.\n", " warnings.warn(\n" ] }, @@ -861,7 +861,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/src/biaplotter/__init__.py b/src/biaplotter/__init__.py index ef186d1..a16c0bd 100644 --- a/src/biaplotter/__init__.py +++ b/src/biaplotter/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.4.0" +__version__ = "0.4.1" from .artists import Histogram2D, Scatter from .colormap import BiaColormap from .plotter import CanvasWidget diff --git a/src/biaplotter/artists.py b/src/biaplotter/artists.py index 40546fb..0301010 100644 --- a/src/biaplotter/artists.py +++ b/src/biaplotter/artists.py @@ -2,6 +2,7 @@ from typing import List, Tuple, Union import matplotlib.pyplot as plt +import matplotlib.patches as mpatches import numpy as np from matplotlib.colors import Colormap, Normalize from nap_plot_tools.cmap import (cat10_mod_cmap, @@ -383,6 +384,7 @@ def __init__( self._bin_alpha = None self._bins = bins self._highlighted = None # Initialize highlight mask + self._highlighted_bin_patches = [] self._histogram_colormap = BiaColormap(histogram_colormap) self._histogram_interpolation = "nearest" self._overlay_interpolation = "nearest" @@ -747,11 +749,6 @@ def _get_normalization( def _highlight_data(self, boolean_mask: np.ndarray): """Highlight data points based on the provided indices.""" if boolean_mask is None or len(boolean_mask) == 0: - # Remove previous highlighted patches if they exist - if hasattr(self, "_highlighted_bin_patches"): - for patch in self._highlighted_bin_patches: - patch.remove() - self._highlighted_bin_patches = [] # Reset all bins to fully opaque self.bin_alpha = np.ones_like(self._histogram[0]) self._highlighted = None @@ -777,15 +774,6 @@ def _highlight_data(self, boolean_mask: np.ndarray): self.bin_alpha = alphas # Draw rectangle patches around highlighted bins - import matplotlib.patches as mpatches - - # Remove previous rectangle patches if they exist - if hasattr(self, "_highlighted_bin_patches"): - for patch in self._highlighted_bin_patches: - patch.remove() - self._highlighted_bin_patches = [] - - # Add new rectangle patches for currently highlighted bins for bin_x in range(highlighted_bins.shape[0]): for bin_y in range(highlighted_bins.shape[1]): if highlighted_bins[bin_x, bin_y]: diff --git a/src/biaplotter/artists_base.py b/src/biaplotter/artists_base.py index 61291f9..ba5287e 100644 --- a/src/biaplotter/artists_base.py +++ b/src/biaplotter/artists_base.py @@ -141,6 +141,7 @@ def _remove_artists(self, keys: List[str] = None): if hasattr(self, "_highlighted_bin_patches"): for patch in self._highlighted_bin_patches: patch.remove() + self._highlighted_bin_patches = [] for artist in self._mpl_artists.values(): artist.remove() self._mpl_artists = {} @@ -152,6 +153,7 @@ def _remove_artists(self, keys: List[str] = None): if hasattr(self, "_highlighted_bin_patches"): for patch in self._highlighted_bin_patches: patch.remove() + self._highlighted_bin_patches = [] self._mpl_artists[key].remove() del self._mpl_artists[key] @@ -241,6 +243,9 @@ def visible(self, value: bool): """Sets the visibility of the artists.""" self._visible = value [a.set_visible(value) for a in self._mpl_artists.values()] + if hasattr(self, "_highlighted_bin_patches"): + for patch in self._highlighted_bin_patches: + patch.set_visible(value) self.draw() @property diff --git a/src/biaplotter/plotter.py b/src/biaplotter/plotter.py index a8dcef8..563d571 100644 --- a/src/biaplotter/plotter.py +++ b/src/biaplotter/plotter.py @@ -121,6 +121,14 @@ def hideEvent(self, event): self.napari_viewer.bind_key("Escape", None, overwrite=True) super().hideEvent(event) + def showEvent(self, event): + """Handles the show event of the widget. + + Re-binds Escape to clear selections and highlights. + """ + super().showEvent(event) + self.napari_viewer.bind_key("Escape", self._on_escape, overwrite=True) + def _initialize_mpl_toolbar(self): """ Replaces the default matplotlib toolbar with a custom one that emits signals.