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Coloring by features as well as cluster #257

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Coloring by features as well as cluster #257

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756bdab
Implementation of plot coloring
Cryaaa May 26, 2023
9c4c174
Update _plotter.py
Cryaaa May 26, 2023
eecd45b
small aesthetic changes
Cryaaa May 30, 2023
4ed4119
small changes so colormap can always be selected
Cryaaa Jun 8, 2023
070769a
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jun 8, 2023
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146 changes: 140 additions & 6 deletions napari_clusters_plotter/_plotter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import pandas as pd
from matplotlib.figure import Figure
from napari.utils.colormaps import ALL_COLORMAPS
from napari_skimage_regionprops._parametric_images import map_measurements_on_labels
from napari_tools_menu import register_dock_widget
from qtpy import QtWidgets
from qtpy.QtCore import Qt
Expand All @@ -22,11 +23,13 @@
QVBoxLayout,
QWidget,
)
from vispy.color import Color

from ._plotter_utilities import (
apply_cluster_colors_to_bars,
clustered_plot_parameters,
estimate_number_bins,
feature_plot_parameters,
make_cluster_overlay_img,
unclustered_plot_parameters,
)
Expand Down Expand Up @@ -85,6 +88,7 @@ def __init__(self, napari_viewer):

self.analysed_layer = None
self.visualized_labels_layer = None
self.visualized_feature_layer = None

def manual_clustering_method(inside):
inside = np.array(inside) # leads to errors sometimes otherwise
Expand Down Expand Up @@ -221,11 +225,11 @@ def plotting_type_changed():
self.bin_number_container.setVisible(True)
self.log_scale_container.setVisible(True)
self.plot_hide_non_selected.setChecked(True)
self.colormap_container.setVisible(True)
# self.colormap_container.setVisible(True)
else:
self.bin_number_container.setVisible(False)
self.log_scale_container.setVisible(False)
self.colormap_container.setVisible(False)
# self.colormap_container.setVisible(False)
replot()

def bin_number_set():
Expand Down Expand Up @@ -310,7 +314,7 @@ def bin_auto():
options={"choices": list(ALL_COLORMAPS.keys())},
label="Colormap",
)
self.colormap_container.setVisible(False)
self.colormap_container.setVisible(True)
self.colormap_dropdown.native.currentIndexChanged.connect(replot)
self.advanced_options_container.layout().addWidget(self.colormap_container)

Expand Down Expand Up @@ -484,6 +488,16 @@ def update_axes_and_clustering_id_lists(self):
if "CLUSTER" in feature
]
)
self.plot_cluster_id.addItems(
[
feature
for feature in list(features.keys())
if "frame" not in feature.lower()
and "label" not in feature.lower()
and "CLUSTER" not in feature
]
)

self.plot_x_axis.setCurrentIndex(former_x_axis)
self.plot_y_axis.setCurrentIndex(former_y_axis)
self.plot_cluster_id.setCurrentIndex(former_cluster_id)
Expand All @@ -501,6 +515,10 @@ def run(
This function that runs after the run button is clicked.
"""

###############
# INITIALISATION
###############

if not self.isVisible() and force_redraw is False:
# don't redraw in case the plot is invisible anyway
return
Expand Down Expand Up @@ -534,11 +552,17 @@ def run(
tracking_data = len(self.analysed_layer.data.shape) == 4 and "frame" not in [
key.lower() for key in features.keys()
]

##########################
# CLUSTERING VISUALISATION
##########################

colors = get_nice_colormap()
if (
plot_cluster_name is not None
and plot_cluster_name != "label"
and plot_cluster_name in list(features.keys())
and "CLUSTER" in plot_cluster_name
):
if self.plot_hide_non_selected.isChecked():
features.loc[
Expand All @@ -550,6 +574,7 @@ def run(
self.label_ids = features["label"]
self.cluster_ids = features[plot_cluster_name].fillna(-1)

# Determine Current Frame
if len(self.analysed_layer.data.shape) == 4 and not tracking_data:
frame_id = features[POINTER].tolist()
current_frame = self.frame
Expand All @@ -559,6 +584,8 @@ def run(
else:
warnings.warn("Image dimensions too high for processing!")

############################
# Scatter Plot Visualisation
if self.plotting_type.currentText() == PlottingType.SCATTER.name:
a, sizes, colors_plot = clustered_plot_parameters(
cluster_id=self.cluster_ids,
Expand All @@ -574,6 +601,9 @@ def run(

self.graphics_widget.axes.set_xlabel(plot_x_axis_name)
self.graphics_widget.axes.set_ylabel(plot_y_axis_name)

#########################
# Histogram Visualisation
else:
if self.bin_auto.isChecked():
if plot_x_axis_name == plot_y_axis_name:
Expand Down Expand Up @@ -644,8 +674,8 @@ def run(

self.graphics_widget.match_napari_layout()

from vispy.color import Color

##############################
# Generating the cluster image
cmap = [Color(hex_name).RGBA.astype("float") / 255 for hex_name in colors]

# generate dictionary mapping each prediction to its respective color
Expand All @@ -663,11 +693,11 @@ def run(

keep_selection = list(self.viewer.layers.selection)

# Generating the cluster image
if redraw_cluster_image:
# depending on the dimensionality of the data
# generate the cluster image
if len(self.analysed_layer.data.shape) == 4:
# Check which kind of timelapse and modify labels accordingly
if not tracking_data:
max_timepoint = features[POINTER].max() + 1
label_id_list_per_timepoint = [
Expand All @@ -690,12 +720,14 @@ def run(
for i in range(self.analysed_layer.data.shape[0])
]

# Generate the dask image
cluster_image = dask_cluster_image_timelapse(
self.analysed_layer.data,
label_id_list_per_timepoint,
prediction_lists_per_timepoint,
)

# 3d case -> no dask needed
elif len(self.analysed_layer.data.shape) <= 3:
cluster_image = generate_cluster_image(
self.analysed_layer.data, self.label_ids, self.cluster_ids
Expand All @@ -704,6 +736,11 @@ def run(
warnings.warn("Image dimensions too high for processing!")
return

# hiding the feature layer:
if self.visualized_feature_layer is not None:
self.visualized_feature_layer.opacity = 0

# Adding the cluster image
# if the cluster image layer doesn't yet exist make it
# otherwise just update it
if (
Expand All @@ -726,6 +763,103 @@ def run(
for s in keep_selection:
self.viewer.layers.selection.add(s)

#######################
# FEATURE VISUALISATION
#######################
elif (
plot_cluster_name is not None
and plot_cluster_name != "label"
and plot_cluster_name in list(features.keys())
and "CLUSTER" not in plot_cluster_name
):
if self.plotting_type.currentText() != PlottingType.SCATTER.name:
warnings.warn("Feature Visualisation Only Availible in Scatter Plot!")
return
feature_values = features[plot_cluster_name].fillna(0)

# Determine Current Frame
if len(self.analysed_layer.data.shape) == 4 and not tracking_data:
frame_id = features[POINTER].tolist()
current_frame = self.frame
elif len(self.analysed_layer.data.shape) <= 3 or tracking_data:
frame_id = None
current_frame = None
else:
warnings.warn("Image dimensions too high for processing!")

############################
# Scatter Plot Visualisation

a, sizes, colors_plot = feature_plot_parameters(
feature_values=feature_values,
frame_id=frame_id,
current_frame=current_frame,
n_datapoints=number_of_points,
colormap=self.colormap_dropdown.value,
)

self.graphics_widget.make_scatter_plot(
self.data_x, self.data_y, colors_plot, sizes, a
)

self.graphics_widget.axes.set_xlabel(plot_x_axis_name)
self.graphics_widget.axes.set_ylabel(plot_y_axis_name)

keep_selection = list(self.viewer.layers.selection)

self.graphics_widget.match_napari_layout()

if redraw_cluster_image:
# depending on the dimensionality of the data
# generate the feature image
if len(self.analysed_layer.data.shape) > 4:
warnings.warn("Image dimensions too high for processing!")
return
else:
feature_image = map_measurements_on_labels(
self.analysed_layer, plot_cluster_name, self.viewer
)
# Adding the feature image
# if the feature image layer doesn't yet exist make it
# otherwise just update it
if (
self.visualized_feature_layer is None
or self.visualized_feature_layer not in self.viewer.layers
):
self.visualized_feature_layer = self.viewer.add_image(
feature_image,
name="feature visualisation",
scale=self.analysed_layer.scale,
)
else:
# Making sure that the feature visualisation layer is on top
if self.viewer.layers[-1] != self.visualized_feature_layer:
for i, layer in enumerate(self.viewer.layers):
if layer == self.visualized_feature_layer:
feature_layer_index = i
self.viewer.layers.move(
feature_layer_index, len(self.viewer.layers)
)
# updating data
self.visualized_feature_layer.data = feature_image

# Adjusting the Layer
self.visualized_feature_layer.contrast_limits = np.percentile(
features[plot_cluster_name].to_numpy(), (1, 99)
)
self.visualized_feature_layer.colormap = (
self.colormap_dropdown.value
)
self.visualized_feature_layer.opacity = 1

self.viewer.layers.selection.clear()
for s in keep_selection:
self.viewer.layers.selection.add(s)

###########################
# Unclustered Visualisation
###########################

else:
if len(self.analysed_layer.data.shape) == 4 and not tracking_data:
frame_id = features[POINTER].tolist()
Expand Down
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