add supports for palettable cmap

Author: DingWB

PyComplexHeatmap/annotations.py

@@ -155,7 +155,7 @@ def _check_cmap(self, cmap):
 				elif self.df[col].nunique() <= 20:
 					self.cmap = "tab20"
 				else:
-					self.cmap = "cmap50"
+					self.cmap = "random50"
 			elif self.df.dtypes[col] == float or self.df.dtypes[col] == int:
 				self.cmap = "jet"
 			else:
@@ -1505,7 +1505,7 @@ def _check_cmap(self, cmap):
 					elif self.df[col].nunique() <= 20:
 						self.cmap[col] = "tab20"
 					else:
-						self.cmap[col] = "cmap50"
+						self.cmap[col] = "random50"
 				elif self.df.dtypes[col] == float or self.df.dtypes[col] == int:
 					self.cmap[col] = "jet"
 				else:

PyComplexHeatmap/colors.py

@@ -5,149 +5,218 @@
 from matplotlib.colors import LinearSegmentedColormap, ListedColormap, CSS4_COLORS
 import random
 from .utils import _calculate_luminance
+import pandas as pd
+default_cmaps = matplotlib.pyplot.colormaps()
 
 def register_cmap(c):
-    try:
-        plt.register_cmap(cmap=c)
-    except:
-        matplotlib.colormaps.register(c, force=True)
+	try:
+		plt.register_cmap(cmap=c)
+	except:
+		matplotlib.colormaps.register(c, force=True)
 
 def define_cmap():
-    all_cmaps = matplotlib.pyplot.colormaps()
-    # if "binarize" not in all_cmaps:
-    #     c = LinearSegmentedColormap.from_list(
-    #         "binarize", [(0, 'lightgray'), (1, "black")]
-    #     )
-    if "exp1" not in all_cmaps:
-        c = LinearSegmentedColormap.from_list(
-            "exp1", [(0, "blue"), (0.5, "yellow"), (1, "red")]
-        )
-        register_cmap(c)
-    if "exp2" not in all_cmaps:
-        c = LinearSegmentedColormap.from_list(
-            "exp2", [(0, "#4A5EA0"), (0.5, "#F9FFB2"), (1, "#A42A26")]
-        )
-        register_cmap(c)
-    if "meth1" not in all_cmaps:
-        c = LinearSegmentedColormap.from_list(
-            "meth1",
-            [
-                (0, "#282972"),
-                (0.2, "#36C4F2"),
-                (0.4, "#C1D62F"),
-                (0.6, "#F9AD17"),
-                (0.8, "#EC3024"),
-                (1, "#7D1416"),
-            ],
-        )
-        register_cmap(c)
-    if "meth2" not in all_cmaps:
-        c = LinearSegmentedColormap.from_list(
-            "meth2",
-            [
-                (0, "#4B5DC6"),
-                (0.1, "#439CFE"),
-                (0.3, "#55B849"),
-                (0.5, "gold"),
-                (0.7, "darkorange"),
-                (0.9, "tomato"),
-                (1, "red"),
-            ],
-        )
-        register_cmap(c)
-    if "diverging1" not in all_cmaps:
-        c = LinearSegmentedColormap.from_list(
-            "diverging1", [(0, "#67a9cf"), (0.5, "#f7f7f7"), (1, "#ef8a62")]
-        )
-        register_cmap(c)
+	all_cmaps = matplotlib.pyplot.colormaps()
+	# if "binarize" not in all_cmaps:
+	#     c = LinearSegmentedColormap.from_list(
+	#         "binarize", [(0, 'lightgray'), (1, "black")]
+	#     )
+	if "exp1" not in all_cmaps:
+		c = LinearSegmentedColormap.from_list(
+			"exp1", [(0, "blue"), (0.5, "yellow"), (1, "red")]
+		)
+		register_cmap(c)
+	if "exp2" not in all_cmaps:
+		c = LinearSegmentedColormap.from_list(
+			"exp2", [(0, "#4A5EA0"), (0.5, "#F9FFB2"), (1, "#A42A26")]
+		)
+		register_cmap(c)
+	if "meth1" not in all_cmaps:
+		c = LinearSegmentedColormap.from_list(
+			"meth1",
+			[
+				(0, "#282972"),
+				(0.2, "#36C4F2"),
+				(0.4, "#C1D62F"),
+				(0.6, "#F9AD17"),
+				(0.8, "#EC3024"),
+				(1, "#7D1416"),
+			],
+		)
+		register_cmap(c)
+	if "meth2" not in all_cmaps:
+		c = LinearSegmentedColormap.from_list(
+			"meth2",
+			[
+				(0, "#4B5DC6"),
+				(0.1, "#439CFE"),
+				(0.3, "#55B849"),
+				(0.5, "gold"),
+				(0.7, "darkorange"),
+				(0.9, "tomato"),
+				(1, "red"),
+			],
+		)
+		register_cmap(c)
+	if "diverging1" not in all_cmaps:
+		c = LinearSegmentedColormap.from_list(
+			"diverging1", [(0, "#67a9cf"), (0.5, "#f7f7f7"), (1, "#ef8a62")]
+		)
+		register_cmap(c)
 
-    if "cmap50" not in all_cmaps:
-        colors = []
-        for c in CSS4_COLORS:
-            l = _calculate_luminance(c)
-            if l > 0.25 and l < 0.8:
-                colors.append(c)
-        c = ListedColormap(
-            random.sample(
-                [
-                    c
-                    for c in CSS4_COLORS.keys()
-                    if c not in ["white", "snow"] and "gray" not in c
-                ],
-                100,
-            ),
-            "cmap50",
-        )
-        register_cmap(c)
+	if "random50" not in all_cmaps:
+		colors = []
+		for c in CSS4_COLORS:
+			l = _calculate_luminance(c)
+			if l > 0.25 and l < 0.8:
+				colors.append(c)
+		c = ListedColormap(
+			random.sample([c for c in CSS4_COLORS.keys()
+					if c not in ["white", "snow"] and "gray" not in c],
+				50),
+			"random50",
+		)
+		register_cmap(c)
 
-    if "parula" not in all_cmaps:
-        cm_data = [
-            [0.2081, 0.1663, 0.5292],
-            [0.2116238095, 0.1897809524, 0.5776761905],
-            [0.212252381, 0.2137714286, 0.6269714286],
-            [0.2081, 0.2386, 0.6770857143],
-            [0.1959047619, 0.2644571429, 0.7279],
-            [0.1707285714, 0.2919380952, 0.779247619],
-            [0.1252714286, 0.3242428571, 0.8302714286],
-            [0.0591333333, 0.3598333333, 0.8683333333],
-            [0.0116952381, 0.3875095238, 0.8819571429],
-            [0.0059571429, 0.4086142857, 0.8828428571],
-            [0.0165142857, 0.4266, 0.8786333333],
-            [0.032852381, 0.4430428571, 0.8719571429],
-            [0.0498142857, 0.4585714286, 0.8640571429],
-            [0.0629333333, 0.4736904762, 0.8554380952],
-            [0.0722666667, 0.4886666667, 0.8467],
-            [0.0779428571, 0.5039857143, 0.8383714286],
-            [0.079347619, 0.5200238095, 0.8311809524],
-            [0.0749428571, 0.5375428571, 0.8262714286],
-            [0.0640571429, 0.5569857143, 0.8239571429],
-            [0.0487714286, 0.5772238095, 0.8228285714],
-            [0.0343428571, 0.5965809524, 0.819852381],
-            [0.0265, 0.6137, 0.8135],
-            [0.0238904762, 0.6286619048, 0.8037619048],
-            [0.0230904762, 0.6417857143, 0.7912666667],
-            [0.0227714286, 0.6534857143, 0.7767571429],
-            [0.0266619048, 0.6641952381, 0.7607190476],
-            [0.0383714286, 0.6742714286, 0.743552381],
-            [0.0589714286, 0.6837571429, 0.7253857143],
-            [0.0843, 0.6928333333, 0.7061666667],
-            [0.1132952381, 0.7015, 0.6858571429],
-            [0.1452714286, 0.7097571429, 0.6646285714],
-            [0.1801333333, 0.7176571429, 0.6424333333],
-            [0.2178285714, 0.7250428571, 0.6192619048],
-            [0.2586428571, 0.7317142857, 0.5954285714],
-            [0.3021714286, 0.7376047619, 0.5711857143],
-            [0.3481666667, 0.7424333333, 0.5472666667],
-            [0.3952571429, 0.7459, 0.5244428571],
-            [0.4420095238, 0.7480809524, 0.5033142857],
-            [0.4871238095, 0.7490619048, 0.4839761905],
-            [0.5300285714, 0.7491142857, 0.4661142857],
-            [0.5708571429, 0.7485190476, 0.4493904762],
-            [0.609852381, 0.7473142857, 0.4336857143],
-            [0.6473, 0.7456, 0.4188],
-            [0.6834190476, 0.7434761905, 0.4044333333],
-            [0.7184095238, 0.7411333333, 0.3904761905],
-            [0.7524857143, 0.7384, 0.3768142857],
-            [0.7858428571, 0.7355666667, 0.3632714286],
-            [0.8185047619, 0.7327333333, 0.3497904762],
-            [0.8506571429, 0.7299, 0.3360285714],
-            [0.8824333333, 0.7274333333, 0.3217],
-            [0.9139333333, 0.7257857143, 0.3062761905],
-            [0.9449571429, 0.7261142857, 0.2886428571],
-            [0.9738952381, 0.7313952381, 0.266647619],
-            [0.9937714286, 0.7454571429, 0.240347619],
-            [0.9990428571, 0.7653142857, 0.2164142857],
-            [0.9955333333, 0.7860571429, 0.196652381],
-            [0.988, 0.8066, 0.1793666667],
-            [0.9788571429, 0.8271428571, 0.1633142857],
-            [0.9697, 0.8481380952, 0.147452381],
-            [0.9625857143, 0.8705142857, 0.1309],
-            [0.9588714286, 0.8949, 0.1132428571],
-            [0.9598238095, 0.9218333333, 0.0948380952],
-            [0.9661, 0.9514428571, 0.0755333333],
-            [0.9763, 0.9831, 0.0538],
-        ]
-        c = LinearSegmentedColormap.from_list("parula", cm_data)
-        register_cmap(c)
+	if "random100" not in all_cmaps:
+		colors = []
+		for c in CSS4_COLORS:
+			l = _calculate_luminance(c)
+			if l > 0.25 and l < 0.8:
+				colors.append(c)
+		c = ListedColormap(
+			random.sample([c for c in CSS4_COLORS.keys()
+					if c not in ["white", "snow"] and "gray" not in c],
+				100),
+			"random100",
+		)
+		register_cmap(c)
+
+	if "parula" not in all_cmaps:
+		cm_data = [
+			[0.2081, 0.1663, 0.5292],
+			[0.2116238095, 0.1897809524, 0.5776761905],
+			[0.212252381, 0.2137714286, 0.6269714286],
+			[0.2081, 0.2386, 0.6770857143],
+			[0.1959047619, 0.2644571429, 0.7279],
+			[0.1707285714, 0.2919380952, 0.779247619],
+			[0.1252714286, 0.3242428571, 0.8302714286],
+			[0.0591333333, 0.3598333333, 0.8683333333],
+			[0.0116952381, 0.3875095238, 0.8819571429],
+			[0.0059571429, 0.4086142857, 0.8828428571],
+			[0.0165142857, 0.4266, 0.8786333333],
+			[0.032852381, 0.4430428571, 0.8719571429],
+			[0.0498142857, 0.4585714286, 0.8640571429],
+			[0.0629333333, 0.4736904762, 0.8554380952],
+			[0.0722666667, 0.4886666667, 0.8467],
+			[0.0779428571, 0.5039857143, 0.8383714286],
+			[0.079347619, 0.5200238095, 0.8311809524],
+			[0.0749428571, 0.5375428571, 0.8262714286],
+			[0.0640571429, 0.5569857143, 0.8239571429],
+			[0.0487714286, 0.5772238095, 0.8228285714],
+			[0.0343428571, 0.5965809524, 0.819852381],
+			[0.0265, 0.6137, 0.8135],
+			[0.0238904762, 0.6286619048, 0.8037619048],
+			[0.0230904762, 0.6417857143, 0.7912666667],
+			[0.0227714286, 0.6534857143, 0.7767571429],
+			[0.0266619048, 0.6641952381, 0.7607190476],
+			[0.0383714286, 0.6742714286, 0.743552381],
+			[0.0589714286, 0.6837571429, 0.7253857143],
+			[0.0843, 0.6928333333, 0.7061666667],
+			[0.1132952381, 0.7015, 0.6858571429],
+			[0.1452714286, 0.7097571429, 0.6646285714],
+			[0.1801333333, 0.7176571429, 0.6424333333],
+			[0.2178285714, 0.7250428571, 0.6192619048],
+			[0.2586428571, 0.7317142857, 0.5954285714],
+			[0.3021714286, 0.7376047619, 0.5711857143],
+			[0.3481666667, 0.7424333333, 0.5472666667],
+			[0.3952571429, 0.7459, 0.5244428571],
+			[0.4420095238, 0.7480809524, 0.5033142857],
+			[0.4871238095, 0.7490619048, 0.4839761905],
+			[0.5300285714, 0.7491142857, 0.4661142857],
+			[0.5708571429, 0.7485190476, 0.4493904762],
+			[0.609852381, 0.7473142857, 0.4336857143],
+			[0.6473, 0.7456, 0.4188],
+			[0.6834190476, 0.7434761905, 0.4044333333],
+			[0.7184095238, 0.7411333333, 0.3904761905],
+			[0.7524857143, 0.7384, 0.3768142857],
+			[0.7858428571, 0.7355666667, 0.3632714286],
+			[0.8185047619, 0.7327333333, 0.3497904762],
+			[0.8506571429, 0.7299, 0.3360285714],
+			[0.8824333333, 0.7274333333, 0.3217],
+			[0.9139333333, 0.7257857143, 0.3062761905],
+			[0.9449571429, 0.7261142857, 0.2886428571],
+			[0.9738952381, 0.7313952381, 0.266647619],
+			[0.9937714286, 0.7454571429, 0.240347619],
+			[0.9990428571, 0.7653142857, 0.2164142857],
+			[0.9955333333, 0.7860571429, 0.196652381],
+			[0.988, 0.8066, 0.1793666667],
+			[0.9788571429, 0.8271428571, 0.1633142857],
+			[0.9697, 0.8481380952, 0.147452381],
+			[0.9625857143, 0.8705142857, 0.1309],
+			[0.9588714286, 0.8949, 0.1132428571],
+			[0.9598238095, 0.9218333333, 0.0948380952],
+			[0.9661, 0.9514428571, 0.0755333333],
+			[0.9763, 0.9831, 0.0538],
+		]
+		c = LinearSegmentedColormap.from_list("parula", cm_data)
+		register_cmap(c)
+
+def get_palettable_colors():
+	"""
+	category: sequential, diverging, qualitative
+	"""
+	R=[]
+	for category in ['sequential','diverging','qualitative']:
+		for source in dir(palettable):
+			color_source=getattr(palettable,source)
+			if not hasattr(color_source,category):
+				continue
+			color_category=getattr(color_source,category)
+			color_names=[]
+			for name in dir(color_category):
+				color=getattr(color_category,name)
+				if not hasattr(color,'mpl_colors'):
+					continue
+				R.append([category,color.name,color.type,color.number,color.colors,color.hex_colors,color.mpl_colors,color.mpl_colormap])
+	df=pd.DataFrame(R,columns=['category','name','type','number','colors','hex_colors','mpl_colors','mpl_colormap'])
+	df.insert(1,'prefix',df.name.apply(lambda x:x.split('_')[0]))
+	df.sort_values(['prefix','number'],ascending=[True,False],inplace=True)
+	max_n = df.groupby('prefix').number.max().to_dict()
+	df.insert(5, 'max_n', df.prefix.map(max_n))
+	df = df.loc[df.number == df.max_n]
+	df['Name'] = df.name.apply(lambda x: x.split('_')[0] if not x.endswith('_r') else x.split('_')[0] + '_r')
+	mpl_colormaps = []
+	for cmap, prefix in zip(df.mpl_colormap.tolist(), df.Name.tolist()):
+		cmap.name = prefix
+		mpl_colormaps.append(cmap)
+	df.mpl_colormap = mpl_colormaps
+	df = df.loc[~ df.Name.isin(default_cmaps)]
+	return df
+
+def register_palettable():
+	df=get_palettable_colors()
+	all_cmaps = matplotlib.pyplot.colormaps()
+
+	# register qualitative colormap
+	df1=df.loc[df.category == 'qualitative']
+	for colors,name in zip (df1.mpl_colors.tolist(),df1.Name.tolist()):
+		if name in all_cmaps:
+			continue
+		c = ListedColormap(colors,name)
+		register_cmap(c)
+
+	# register sequential and diverging colormap
+	df1 = df.loc[df.category != 'qualitative']
+	for colors,name in zip (df1.mpl_colors.tolist(),df1.Name.tolist()):
+		if name in all_cmaps:
+			continue
+		c = LinearSegmentedColormap.from_list(name, colors)
+		register_cmap(c)
 
 define_cmap()
+try:
+	import palettable
+	register_palettable()
+except:
+	print("Warning: Please install palettable to enable palettable colormaps")
+