def cmap_builder(name, name2=None, name3=None):
"""return a colormap object compatible with matplotlib
If only parameter **name** is provided, it should be a known matplotlib
colormap name (e.g., jet). If **name2** is provided, then a new colormap
is created going from the color **name** to the color **name2** with a
linear scale. Finally, if **name3** is provided, a linear scaled colormap
is built from color **name** to color **name3** with the intermediate color
being the **name2**
Matplotlib colormap map names
"""
c = Colormap()
# an R colormap
if name and name2 and name3:
return c.cmap_linear(name, name2, name3)
elif name and name2:
return c.cmap_bicolor(name, name2)
elif name == 'heat':
return c.get_cmap_heat()
elif name == 'heat_r':
return c.get_cmap_heat_r()
# matplotlic colormaps
elif name in c.colormaps:
return c.cmap(name)
# some custom diverging colormaps with black in the middle.
elif name in c.diverging_black:
return c.cmap(name)
else:
#valid = c.colormaps + c.diverging_black
txt = "name provided {0} is not recognised. ".format(name)
txt += "\n valid name can be found in colormap.colormap_names"
raise ValueError(txt)
def get_colors(self, sequencial):
c = Colormap()
if sequencial:
mycmap = c.cmap('YlGnBu')
else:
mycmap = c.cmap('tab10')
rgba = mycmap(np.linspace(0, 1, 256)) * 255
long = len(rgba)
return rgba, long
def _plot_HiC(matrix_data, vmax, colors=['white', 'red']):
#vmax=thr
red_list = list()
green_list = list()
blue_list = list()
#colors=['white','red']
#colors=['darkblue','green','gold','darkred']
for color in colors:
col = Color(color).rgb
red_list.append(col[0])
green_list.append(col[1])
blue_list.append(col[2])
c = Colormap()
d = {'blue': blue_list, 'green': green_list, 'red': red_list}
mycmap = c.cmap(d)
fig, ax = plt.subplots(figsize=(8, 8))
#new_data=np.triu(matrix_data)
#new_data=np.transpose(new_data[:,::-1])
#mask = np.zeros_like(new_data)
#mask[np.tril_indices_from(mask,-1)] = True
#mask=np.transpose(mask[:,::-1])
#with sns.axes_style("white"):
#sns.heatmap(new_data,xticklabels=100,yticklabels=100,mask=mask,cmap=mycmap,cbar=False)
#ax.set_facecolor('w')
#fig.patch.set_facecolor('w')
ax.set_facecolor('w')
ax.grid(b=None)
sns.heatmap(matrix_data.T,
vmax=vmax,
xticklabels=100,
yticklabels=100,
cmap=mycmap,
cbar=False)
def cmap_builder(name, name2=None, name3=None):
"""return a colormap object compatible with matplotlib
If only parameter **name** is provided, it should be a known matplotlib
colormap name (e.g., jet). If **name2** is provided, then a new colormap
is created going from the color **name** to the color **name2** with a
linear scale. Finally, if **name3** is provided, a linear scaled colormap
is built from color **name** to color **name3** with the intermediate color
being the **name2**
Matplotlib colormap map names
"""
c = Colormap()
# an R colormap
if name and name2 and name3:
return c.cmap_linear(name, name2, name3)
elif name and name2:
return c.cmap_bicolor(name, name2)
elif name == 'heat':
return c.get_cmap_heat()
elif name == 'heat_r':
return c.get_cmap_heat_r()
# matplotlic colormaps
elif name in c.colormaps:
return c.cmap(name)
# some custom diverging colormaps with black in the middle.
elif name in c.diverging_black:
return c.cmap(name)
elif name.count("_") == 2:
name1, name2, name3 = name.split("_")
return c.cmap_linear(name1, name2, name3)
else:
#valid = c.colormaps + c.diverging_black
txt = "name provided {0} is not recognised. ".format(name)
txt += "\n valid name can be found in colormap.colormap_names"
raise ValueError(txt)
def _show_chic_clusterresult3(Results,
matrix_data,
colors=['white', 'green', 'blue', 'red']):
if np.max(matrix_data.shape) < 3000:
thr = np.percentile(matrix_data, 99.5)
else:
thr = np.percentile(matrix_data, 99.9)
matrix_data[matrix_data > thr] = thr
print(thr)
red_list = list()
green_list = list()
blue_list = list()
#['darkblue','seagreen','yellow','gold','coral','hotpink','red']
for color in colors:
col = Color(color).rgb
red_list.append(col[0])
green_list.append(col[1])
blue_list.append(col[2])
c = Colormap()
d = {'blue': blue_list, 'green': green_list, 'red': red_list}
mycmap = c.cmap(d)
#plt.subplots(figsize=(8,8))
#sns.heatmap(np.transpose(matrix_data[:,::-1]),cmap=mycmap)
#sns.heatmap(np.transpose(matrix_data[:,::-1]),xticklabels=100,yticklabels=500,cmap=mycmap)
plt.subplots(figsize=(8, 8))
#sns.heatmap(np.transpose(matrix_data[:,::-1]),cmap=mycmap)
#sns.heatmap(np.transpose(matrix_data[:,::-1]),xticklabels=100,yticklabels=500,cmap=mycmap,cbar=False)
sns.heatmap(matrix_data.T,
xticklabels=100,
yticklabels=1000,
cmap=mycmap,
cbar=False)
for i in Results.index:
upper = Results.ix[i, 'upper']
bottom = Results.ix[i, 'bottom']
left = Results.ix[i, 'left']
right = Results.ix[i, 'right']
x_loc = [upper, upper, bottom, bottom, upper]
y_loc = [left, right, right, left, left]
plt.plot(x_loc, y_loc, '-', color='k', lw=2.5)
plt.grid(b=None)
plt.show()
