update center for DotClustermapPlotter

Author: DingWB

PyComplexHeatmap/clustermap.py

@@ -18,7 +18,7 @@
 	_index_to_label,
 	_index_to_ticklabels,
 	plot_legend_list,
-	get_colormap,
+	get_colormap,adjust_cmap,
 	evaluate_bezier,getControlPoints
 )
 
@@ -615,34 +615,8 @@ def plot_heatmap(
 		else:
 			vmax = np.nanmax(calc_data)
 
-	# Choose default colormaps if not provided
-	if isinstance(cmap, str):
-		try:
-			cmap = get_colormap(cmap).copy()
-		except:
-			cmap = get_colormap(cmap)
-
-	cmap.set_bad(color=na_col)  # set the color for NaN values
-	# Recenter a divergent colormap
-	if center is not None:
-		# bad = cmap(np.ma.masked_invalid([np.nan]))[0]  # set the first color as the na_color
-		under = cmap(-np.inf)
-		over = cmap(np.inf)
-		under_set = under != cmap(0)
-		over_set = over != cmap(cmap.N - 1)
-
-		vrange = max(vmax - center, center - vmin)
-		normlize = matplotlib.colors.Normalize(center - vrange, center + vrange)
-		cmin, cmax = normlize([vmin, vmax])
-		cc = np.linspace(cmin, cmax, 256)
-		cmap = matplotlib.colors.ListedColormap(cmap(cc))
-		# self.cmap.set_bad(bad)
-		if under_set:
-			cmap.set_under(
-				under
-			)  # set the color of -np.inf as the color for low out-of-range values.
-		if over_set:
-			cmap.set_over(over)
+	cmap=adjust_cmap(cmap,vmin=vmin,vmax=vmax,center=center,
+					 na_col=na_col)
 
 	# Sort out the annotations
 	if annot is None or annot is False:
@@ -2326,12 +2300,6 @@ def collect_legends(self):
 				if annotation.label_max_width > self.label_max_width:
 					self.label_max_width = annotation.label_max_width
 		if self.legend:
-			# vmax = self.kwargs.get(
-			# 	"vmax", np.nanmax(self.data2d[self.data2d != np.inf])
-			# )
-			# vmin = self.kwargs.get(
-			# 	"vmin", np.nanmin(self.data2d[self.data2d != -np.inf])
-			# )
 			self.legend_kws.setdefault("vmin", self.kwargs.get('vmin')) #round(vmin, 2))
 			self.legend_kws.setdefault("vmax", self.kwargs.get('vmax')) #round(vmax, 2))
 			self.legend_kws.setdefault("center", self.kwargs.get('center',None))

PyComplexHeatmap/dotHeatmap.py

@@ -5,9 +5,8 @@
 import numpy as np
 import matplotlib
 import matplotlib.pylab as plt
-from .utils import mm2inch, plot_legend_list, despine, get_colormap
+from .utils import mm2inch, despine, get_colormap,adjust_cmap
 from .clustermap import ClusterMapPlotter
-from matplotlib.ticker import MultipleLocator, FormatStrFormatter
 
 
 # =============================================================================
@@ -21,13 +20,13 @@ def scale(values, vmin=None, vmax=None):
 	delta = vmax - vmin
 	return [(j - vmin) / delta for j in values]
 
-
 # =============================================================================
 def dotHeatmap2d(
 	data,
 	hue=None,
 	vmin=None,
 	vmax=None,
+	center=None,
 	ax=None,
 	colors=None,
 	cmap=None,
@@ -154,6 +153,7 @@ def dotHeatmap2d(
 	kwargs.setdefault(
 		"norm", matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)
 	)
+
 	kwargs["cmap"] = cmap
 	if hue is None:
 		#plot using c
@@ -163,6 +163,8 @@ def dotHeatmap2d(
 			if df1.shape[0] == 0:
 				continue
 			kwargs["marker"] = mk
+			if isinstance(kwargs["cmap"],str) and not center is None:
+				kwargs["cmap"]=adjust_cmap(kwargs["cmap"],vmin=vmin,vmax=vmax,center=center)
 			ax.scatter(
 				x=df1.X.values,
 				y=df1.Y.values,
@@ -177,6 +179,8 @@ def dotHeatmap2d(
 				df1 = df.loc[df.Hue == h].copy()
 				if df1.shape[0] == 0:
 					continue
+				if isinstance(cmap[h], str) and not center is None:
+					cmap[h] = adjust_cmap(cmap[h], vmin=vmin, vmax=vmax, center=center)
 				kwargs["cmap"] = cmap[h]
 				for mk in df1.Markers.unique():
 					# df2 = df1.query("Markers==@mk").copy()

PyComplexHeatmap/utils.py

@@ -395,7 +395,36 @@ def cluster_labels(labels=None, xticks=None, majority=True):
 	x = [np.mean(clusters_x[i]) for i in clusters_x]
 	return labels, x
 
+def adjust_cmap(cmap,vmin,vmax,center=None,na_col='white'):
+	# Choose default colormaps if not provided
+	if isinstance(cmap, str):
+		try:
+			cmap = get_colormap(cmap).copy()
+		except:
+			cmap = get_colormap(cmap)
+
+	cmap.set_bad(color=na_col)  # set the color for NaN values
+	# Recenter a divergent colormap
+	if center is not None:
+		# bad = cmap(np.ma.masked_invalid([np.nan]))[0]  # set the first color as the na_color
+		under = cmap(-np.inf)
+		over = cmap(np.inf)
+		under_set = under != cmap(0)
+		over_set = over != cmap(cmap.N - 1)
 
+		vrange = max(vmax - center, center - vmin)
+		normlize = matplotlib.colors.Normalize(center - vrange, center + vrange)
+		cmin, cmax = normlize([vmin, vmax])
+		cc = np.linspace(cmin, cmax, 256)
+		cmap = matplotlib.colors.ListedColormap(cmap(cc))
+		# self.cmap.set_bad(bad)
+		if under_set:
+			cmap.set_under(
+				under
+			)  # set the color of -np.inf as the color for low out-of-range values.
+		if over_set:
+			cmap.set_over(over)
+	return cmap
 # =============================================================================
 def plot_color_dict_legend(
 	D=None, ax=None, title=None, color_text=True, label_side="right", kws=None
@@ -518,17 +547,19 @@ def plot_cmap_legend(
 	vcenter= (vmax + vmin) / 2
 	center=cbar_kws.pop("center",None)
 	if center is None:
-		cbar_kws.setdefault("ticks", [vmin, vcenter, vmax])
+		center=vcenter
 		m = plt.cm.ScalarMappable(
 			norm=matplotlib.colors.Normalize(vmin=vmin, vmax=vmax), cmap=cmap
 		)
 	else:
 		m = plt.cm.ScalarMappable(
-			norm=matplotlib.colors.CenteredNorm(vcenter=center), cmap=cmap
+			norm=matplotlib.colors.TwoSlopeNorm(center,vmin=vmin, vmax=vmax), cmap=cmap
 		)
+	cbar_kws.setdefault("ticks", [vmin, center, vmax])
 	cax.yaxis.set_label_position(label_side)
 	cax.yaxis.set_ticks_position(label_side)
 	cbar = ax.figure.colorbar(m, cax=cax, **cbar_kws)  # use_gridspec=True
+	# cbar.set_ticks([vmin,center,vmax])
 	# cbar.outline.set_color('white')
 	# cbar.outline.set_linewidth(2)
 	# cbar.dividers.set_color('red')