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[plotting] restructuring more and more
This commit is contained in:
mzwiessele
parent
c07f3dbe98
commit
831e032ade
18 changed files with 330 additions and 272 deletions
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@ -1,166 +0,0 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import matplotlib as mpl
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from matplotlib import pyplot as pb
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import sys
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#sys.path.append('/home/james/mlprojects/sitran_cluster/')
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#from switch_pylab_backend import *
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#this stuff isn;t really Tango related: maybe it could be moved out? TODO
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def removeRightTicks(ax=None):
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ax = ax or pb.gca()
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for i, line in enumerate(ax.get_yticklines()):
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if i%2 == 1: # odd indices
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line.set_visible(False)
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def removeUpperTicks(ax=None):
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ax = ax or pb.gca()
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for i, line in enumerate(ax.get_xticklines()):
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if i%2 == 1: # odd indices
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line.set_visible(False)
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def fewerXticks(ax=None,divideby=2):
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ax = ax or pb.gca()
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ax.set_xticks(ax.get_xticks()[::divideby])
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colorsHex = {\
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"Aluminium6":"#2e3436",\
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"Aluminium5":"#555753",\
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"Aluminium4":"#888a85",\
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"Aluminium3":"#babdb6",\
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"Aluminium2":"#d3d7cf",\
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"Aluminium1":"#eeeeec",\
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"lightPurple":"#ad7fa8",\
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"mediumPurple":"#75507b",\
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"darkPurple":"#5c3566",\
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"lightBlue":"#729fcf",\
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"mediumBlue":"#3465a4",\
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"darkBlue": "#204a87",\
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"lightGreen":"#8ae234",\
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"mediumGreen":"#73d216",\
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"darkGreen":"#4e9a06",\
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"lightChocolate":"#e9b96e",\
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"mediumChocolate":"#c17d11",\
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"darkChocolate":"#8f5902",\
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"lightRed":"#ef2929",\
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"mediumRed":"#cc0000",\
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"darkRed":"#a40000",\
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"lightOrange":"#fcaf3e",\
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"mediumOrange":"#f57900",\
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"darkOrange":"#ce5c00",\
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"lightButter":"#fce94f",\
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"mediumButter":"#edd400",\
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"darkButter":"#c4a000"}
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darkList = [colorsHex['darkBlue'],colorsHex['darkRed'],colorsHex['darkGreen'], colorsHex['darkOrange'], colorsHex['darkButter'], colorsHex['darkPurple'], colorsHex['darkChocolate'], colorsHex['Aluminium6']]
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mediumList = [colorsHex['mediumBlue'], colorsHex['mediumRed'],colorsHex['mediumGreen'], colorsHex['mediumOrange'], colorsHex['mediumButter'], colorsHex['mediumPurple'], colorsHex['mediumChocolate'], colorsHex['Aluminium5']]
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lightList = [colorsHex['lightBlue'], colorsHex['lightRed'],colorsHex['lightGreen'], colorsHex['lightOrange'], colorsHex['lightButter'], colorsHex['lightPurple'], colorsHex['lightChocolate'], colorsHex['Aluminium4']]
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def currentDark():
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return darkList[-1]
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def currentMedium():
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return mediumList[-1]
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def currentLight():
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return lightList[-1]
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def nextDark():
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darkList.append(darkList.pop(0))
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return darkList[-1]
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def nextMedium():
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mediumList.append(mediumList.pop(0))
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return mediumList[-1]
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def nextLight():
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lightList.append(lightList.pop(0))
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return lightList[-1]
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def reset():
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while not darkList[0]==colorsHex['darkBlue']:
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darkList.append(darkList.pop(0))
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while not mediumList[0]==colorsHex['mediumBlue']:
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mediumList.append(mediumList.pop(0))
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while not lightList[0]==colorsHex['lightBlue']:
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lightList.append(lightList.pop(0))
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def setLightFigures():
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mpl.rcParams['axes.edgecolor']=colorsHex['Aluminium6']
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mpl.rcParams['axes.facecolor']=colorsHex['Aluminium2']
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mpl.rcParams['axes.labelcolor']=colorsHex['Aluminium6']
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mpl.rcParams['figure.edgecolor']=colorsHex['Aluminium6']
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mpl.rcParams['figure.facecolor']=colorsHex['Aluminium2']
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mpl.rcParams['grid.color']=colorsHex['Aluminium6']
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mpl.rcParams['savefig.edgecolor']=colorsHex['Aluminium2']
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mpl.rcParams['savefig.facecolor']=colorsHex['Aluminium2']
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mpl.rcParams['text.color']=colorsHex['Aluminium6']
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mpl.rcParams['xtick.color']=colorsHex['Aluminium6']
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mpl.rcParams['ytick.color']=colorsHex['Aluminium6']
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def setDarkFigures():
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mpl.rcParams['axes.edgecolor']=colorsHex['Aluminium2']
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mpl.rcParams['axes.facecolor']=colorsHex['Aluminium6']
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mpl.rcParams['axes.labelcolor']=colorsHex['Aluminium2']
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mpl.rcParams['figure.edgecolor']=colorsHex['Aluminium2']
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mpl.rcParams['figure.facecolor']=colorsHex['Aluminium6']
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mpl.rcParams['grid.color']=colorsHex['Aluminium2']
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mpl.rcParams['savefig.edgecolor']=colorsHex['Aluminium6']
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mpl.rcParams['savefig.facecolor']=colorsHex['Aluminium6']
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mpl.rcParams['text.color']=colorsHex['Aluminium2']
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mpl.rcParams['xtick.color']=colorsHex['Aluminium2']
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mpl.rcParams['ytick.color']=colorsHex['Aluminium2']
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def hex2rgb(hexcolor):
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hexcolor = [hexcolor[1+2*i:1+2*(i+1)] for i in range(3)]
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r,g,b = [int(n,16) for n in hexcolor]
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return (r,g,b)
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colorsRGB = dict([(k,hex2rgb(i)) for k,i in colorsHex.items()])
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cdict_RB = {'red' :((0.,colorsRGB['mediumRed'][0]/256.,colorsRGB['mediumRed'][0]/256.),
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(.5,colorsRGB['mediumPurple'][0]/256.,colorsRGB['mediumPurple'][0]/256.),
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(1.,colorsRGB['mediumBlue'][0]/256.,colorsRGB['mediumBlue'][0]/256.)),
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'green':((0.,colorsRGB['mediumRed'][1]/256.,colorsRGB['mediumRed'][1]/256.),
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(.5,colorsRGB['mediumPurple'][1]/256.,colorsRGB['mediumPurple'][1]/256.),
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(1.,colorsRGB['mediumBlue'][1]/256.,colorsRGB['mediumBlue'][1]/256.)),
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'blue':((0.,colorsRGB['mediumRed'][2]/256.,colorsRGB['mediumRed'][2]/256.),
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(.5,colorsRGB['mediumPurple'][2]/256.,colorsRGB['mediumPurple'][2]/256.),
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(1.,colorsRGB['mediumBlue'][2]/256.,colorsRGB['mediumBlue'][2]/256.))}
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cdict_BGR = {'red' :((0.,colorsRGB['mediumBlue'][0]/256.,colorsRGB['mediumBlue'][0]/256.),
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(.5,colorsRGB['mediumGreen'][0]/256.,colorsRGB['mediumGreen'][0]/256.),
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(1.,colorsRGB['mediumRed'][0]/256.,colorsRGB['mediumRed'][0]/256.)),
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'green':((0.,colorsRGB['mediumBlue'][1]/256.,colorsRGB['mediumBlue'][1]/256.),
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(.5,colorsRGB['mediumGreen'][1]/256.,colorsRGB['mediumGreen'][1]/256.),
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(1.,colorsRGB['mediumRed'][1]/256.,colorsRGB['mediumRed'][1]/256.)),
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'blue':((0.,colorsRGB['mediumBlue'][2]/256.,colorsRGB['mediumBlue'][2]/256.),
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(.5,colorsRGB['mediumGreen'][2]/256.,colorsRGB['mediumGreen'][2]/256.),
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(1.,colorsRGB['mediumRed'][2]/256.,colorsRGB['mediumRed'][2]/256.))}
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cdict_Alu = {'red' :((0./5,colorsRGB['Aluminium1'][0]/256.,colorsRGB['Aluminium1'][0]/256.),
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(1./5,colorsRGB['Aluminium2'][0]/256.,colorsRGB['Aluminium2'][0]/256.),
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(2./5,colorsRGB['Aluminium3'][0]/256.,colorsRGB['Aluminium3'][0]/256.),
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(3./5,colorsRGB['Aluminium4'][0]/256.,colorsRGB['Aluminium4'][0]/256.),
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(4./5,colorsRGB['Aluminium5'][0]/256.,colorsRGB['Aluminium5'][0]/256.),
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(5./5,colorsRGB['Aluminium6'][0]/256.,colorsRGB['Aluminium6'][0]/256.)),
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'green' :((0./5,colorsRGB['Aluminium1'][1]/256.,colorsRGB['Aluminium1'][1]/256.),
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(1./5,colorsRGB['Aluminium2'][1]/256.,colorsRGB['Aluminium2'][1]/256.),
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(2./5,colorsRGB['Aluminium3'][1]/256.,colorsRGB['Aluminium3'][1]/256.),
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(3./5,colorsRGB['Aluminium4'][1]/256.,colorsRGB['Aluminium4'][1]/256.),
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(4./5,colorsRGB['Aluminium5'][1]/256.,colorsRGB['Aluminium5'][1]/256.),
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(5./5,colorsRGB['Aluminium6'][1]/256.,colorsRGB['Aluminium6'][1]/256.)),
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'blue' :((0./5,colorsRGB['Aluminium1'][2]/256.,colorsRGB['Aluminium1'][2]/256.),
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(1./5,colorsRGB['Aluminium2'][2]/256.,colorsRGB['Aluminium2'][2]/256.),
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(2./5,colorsRGB['Aluminium3'][2]/256.,colorsRGB['Aluminium3'][2]/256.),
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(3./5,colorsRGB['Aluminium4'][2]/256.,colorsRGB['Aluminium4'][2]/256.),
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(4./5,colorsRGB['Aluminium5'][2]/256.,colorsRGB['Aluminium5'][2]/256.),
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(5./5,colorsRGB['Aluminium6'][2]/256.,colorsRGB['Aluminium6'][2]/256.))}
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# cmap_Alu = mpl.colors.LinearSegmentedColormap('TangoAluminium',cdict_Alu,256)
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# cmap_BGR = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_BGR,256)
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# cmap_RB = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_RB,256)
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if __name__=='__main__':
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from matplotlib import pyplot as pb
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pb.figure()
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pb.pcolor(pb.rand(10,10),cmap=cmap_RB)
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pb.colorbar()
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pb.show()
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@ -8,7 +8,7 @@ from . import variational_plots
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from . import kernel_plots
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from . import dim_reduction_plots
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from . import mapping_plots
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from . import Tango
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from GPy.plotting.gpy_plot import Tango
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from . import visualize
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from . import latent_space_visualizations
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from . import inference_plots
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@ -30,7 +30,7 @@
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from matplotlib.colors import LinearSegmentedColormap
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from matplotlib import cm
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from . import Tango
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from GPy.plotting.gpy_plot import Tango
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'''
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This file is for defaults for the gpy plot, specific to the plotting library.
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@ -43,17 +43,24 @@ In the code, always ise plotting.gpy_plots.defaults to get the defaults, as
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it gives back an empty default, when defaults are not defined.
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'''
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# Data:
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# Data plots:
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data_1d = dict(lw=1.5, marker='x', edgecolor='k')
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data_2d = dict(s=35, edgecolors='none', linewidth=0., cmap=cm.get_cmap('hot'), alpha=.5)
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inducing_1d = dict(lw=0, s=500, facecolors=Tango.colorsHex['darkRed'])
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inducing_2d = dict(s=14, edgecolors='k', linewidth=.4, facecolors='white', alpha=.5)
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xerrorbar = dict(ecolor='k', fmt='none', elinewidth=.5, alpha=.5)
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yerrorbar = dict(ecolor=Tango.colorsHex['darkRed'], fmt='none', elinewidth=.5, alpha=.5)
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xerrorbar = dict(color='k', fmt='none', elinewidth=.5, alpha=.5)
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yerrorbar = dict(color=Tango.colorsHex['darkRed'], fmt='none', elinewidth=.5, alpha=.5)
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# GP plots
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# GP plots:
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meanplot_1d = dict(color=Tango.colorsHex['mediumBlue'], linewidth=2)
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meanplot_2d = dict(cmap='hot', linewidth=.5)
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samples_1d = dict(color=Tango.colorsHex['mediumBlue'], linewidth=.3)
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confidence_interval = dict(edgecolor=Tango.colorsHex['darkBlue'],linewidth=.5,facecolor=Tango.colorsHex['lightBlue'],alpha=.2)
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density = dict(alpha=.5, facecolor=Tango.colorsHex['mediumBlue'], edgecolors='none')
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confidence_interval = dict(edgecolor=Tango.colorsHex['darkBlue'], linewidth=.5, color=Tango.colorsHex['lightBlue'],alpha=.2)
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density = dict(alpha=.5, color=Tango.colorsHex['mediumBlue'])
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# GPLVM plots:
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data_y_1d = dict(linewidth=0, cmap='RdBu', s=40)
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data_y_1d_plot = dict(color='k', linewidth=1.5)
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# Kernel plots:
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ard = dict(edgecolor='k', linewidth=1.2)
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@ -7,31 +7,13 @@ from ...core.parameterization.variational import VariationalPosterior
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from .base_plots import x_frame2D
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import itertools
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try:
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from . import Tango
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from GPy.plotting.gpy_plot import Tango
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from matplotlib.cm import get_cmap
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from matplotlib import pyplot as pb
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from matplotlib import cm
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except:
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pass
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def most_significant_input_dimensions(model, which_indices):
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"""
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Determine which dimensions should be plotted
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"""
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if which_indices is None:
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if model.input_dim == 1:
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input_1 = 0
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input_2 = None
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if model.input_dim == 2:
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input_1, input_2 = 0, 1
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else:
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try:
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input_1, input_2 = np.argsort(model.input_sensitivity())[::-1][:2]
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except:
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raise ValueError("cannot automatically determine which dimensions to plot, please pass 'which_indices'")
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else:
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input_1, input_2 = which_indices
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return input_1, input_2
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def plot_latent(model, labels=None, which_indices=None,
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resolution=50, ax=None, marker='o', s=40,
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@ -52,7 +34,7 @@ def plot_latent(model, labels=None, which_indices=None,
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if labels is None:
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labels = np.ones(model.num_data)
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input_1, input_2 = most_significant_input_dimensions(model, which_indices)
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input_1, input_2 = model.get_most_significant_input_dimensions(which_indices)
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#fethch the data points X that we'd like to plot
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X = model.X
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@ -219,7 +201,7 @@ def plot_magnification(model, labels=None, which_indices=None,
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if labels is None:
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labels = np.ones(model.num_data)
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input_1, input_2 = most_significant_input_dimensions(model, which_indices)
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input_1, input_2 = model.get_most_significant_input_dimensions(which_indices)
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#fethch the data points X that we'd like to plot
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X = model.X
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@ -366,7 +348,7 @@ def plot_magnification(model, labels=None, which_indices=None,
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def plot_steepest_gradient_map(model, fignum=None, ax=None, which_indices=None, labels=None, data_labels=None, data_marker='o', data_s=40, resolution=20, aspect='auto', updates=False, ** kwargs):
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input_1, input_2 = significant_dims = most_significant_input_dimensions(model, which_indices)
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input_1, input_2 = significant_dims = model.get_most_significant_input_dimensions(which_indices)
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X = np.zeros((resolution ** 2, model.input_dim))
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indices = np.r_[:X.shape[0]]
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@ -3,13 +3,10 @@
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import numpy as np
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from matplotlib import pyplot as pb
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from . import Tango
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from matplotlib.textpath import TextPath
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from matplotlib.transforms import offset_copy
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from .base_plots import ax_default
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def add_bar_labels(fig, ax, bars, bottom=0):
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transOffset = offset_copy(ax.transData, fig=fig,
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x=0., y= -2., units='points')
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@ -40,63 +37,6 @@ def plot_bars(fig, ax, x, ard_params, color, name, bottom=0):
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color=color, edgecolor='k', linewidth=1.2,
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label=name.replace("_"," "))
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def plot_ARD(kernel, fignum=None, ax=None, title='', legend=False, filtering=None):
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"""
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If an ARD kernel is present, plot a bar representation using matplotlib
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:param fignum: figure number of the plot
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:param ax: matplotlib axis to plot on
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:param title:
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title of the plot,
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pass '' to not print a title
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pass None for a generic title
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:param filtering: list of names, which to use for plotting ARD parameters.
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Only kernels which match names in the list of names in filtering
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will be used for plotting.
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:type filtering: list of names to use for ARD plot
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"""
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fig, ax = ax_default(fignum,ax)
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if title is None:
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ax.set_title('ARD parameters, %s kernel' % kernel.name)
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else:
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ax.set_title(title)
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Tango.reset()
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bars = []
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ard_params = np.atleast_2d(kernel.input_sensitivity(summarize=False))
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bottom = 0
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last_bottom = bottom
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x = np.arange(kernel.input_dim)
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if filtering is None:
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filtering = kernel.parameter_names(recursive=False)
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for i in range(ard_params.shape[0]):
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if kernel.parameters[i].name in filtering:
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c = Tango.nextMedium()
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bars.append(plot_bars(fig, ax, x, ard_params[i,:], c, kernel.parameters[i].name, bottom=bottom))
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last_bottom = ard_params[i,:]
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bottom += last_bottom
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else:
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print("filtering out {}".format(kernel.parameters[i].name))
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ax.set_xlim(-.5, kernel.input_dim - .5)
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add_bar_labels(fig, ax, [bars[-1]], bottom=bottom-last_bottom)
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if legend:
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if title is '':
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mode = 'expand'
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if len(bars) > 1:
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mode = 'expand'
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ax.legend(bbox_to_anchor=(0., 1.02, 1., 1.02), loc=3,
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ncol=len(bars), mode=mode, borderaxespad=0.)
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fig.tight_layout(rect=(0, 0, 1, .9))
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else:
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ax.legend()
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return ax
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@ -111,7 +51,7 @@ def plot(kernel,x=None, fignum=None, ax=None, title=None, plot_limits=None, reso
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:resolution: the resolution of the lines used in plotting
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:mpl_kwargs avalid keyword arguments to pass through to matplotlib (e.g. lw=7)
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"""
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fig, ax = ax_default(fignum,ax)
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_, ax = ax_default(fignum,ax)
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if title is None:
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ax.set_title('%s kernel' % kernel.name)
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|
|||
|
|
@ -3,7 +3,7 @@
|
|||
|
||||
import numpy as np
|
||||
try:
|
||||
from . import Tango
|
||||
from GPy.plotting.gpy_plot import Tango
|
||||
from matplotlib import pyplot as pb
|
||||
except:
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
|
||||
import numpy as np
|
||||
from . import Tango
|
||||
from .base_plots import gpplot, x_frame1D, x_frame2D,gperrors
|
||||
from ...models.gp_coregionalized_regression import GPCoregionalizedRegression
|
||||
from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
|
||||
|
|
@ -11,7 +10,7 @@ from ...core.parameterization.variational import VariationalPosterior
|
|||
from matplotlib import pyplot as plt
|
||||
from .base_plots import gradient_fill
|
||||
from functools import wraps
|
||||
|
||||
from .gpy_plot import Tango
|
||||
|
||||
def plot_data(self, which_data_rows='all',
|
||||
which_data_ycols='all', visible_dims=None,
|
||||
|
|
|
|||
|
|
@ -38,22 +38,36 @@ class MatplotlibPlots(AbstractPlottingLibrary):
|
|||
super(MatplotlibPlots, self).__init__()
|
||||
self._defaults = defaults.__dict__
|
||||
|
||||
def get_new_canvas(self, kwargs):
|
||||
def get_new_canvas(self, plot_3d=False, kwargs):
|
||||
if plot_3d:
|
||||
from matplotlib.mplot3d import Axis3D # @UnusedImport
|
||||
pr = '3d'
|
||||
else: pr=None
|
||||
if 'ax' in kwargs:
|
||||
ax = kwargs.pop('ax')
|
||||
elif 'num' in kwargs and 'figsize' in kwargs:
|
||||
ax = plt.figure(num=kwargs.pop('num'), figsize=kwargs.pop('figsize')).add_subplot(111)
|
||||
ax = plt.figure(num=kwargs.pop('num'), figsize=kwargs.pop('figsize')).add_subplot(111, projection=pr)
|
||||
elif 'num' in kwargs:
|
||||
ax = plt.figure(num=kwargs.pop('num')).add_subplot(111)
|
||||
ax = plt.figure(num=kwargs.pop('num')).add_subplot(111, projection=pr)
|
||||
elif 'figsize' in kwargs:
|
||||
ax = plt.figure(figsize=kwargs.pop('figsize')).add_subplot(111)
|
||||
ax = plt.figure(figsize=kwargs.pop('figsize')).add_subplot(111, projection=pr)
|
||||
else:
|
||||
ax = plt.figure().add_subplot(111)
|
||||
ax = plt.figure().add_subplot(111, projection=pr)
|
||||
# Add ax to kwargs to add all subsequent plots to this axis:
|
||||
#kwargs['ax'] = ax
|
||||
return ax, kwargs
|
||||
|
||||
def show_canvas(self, ax, plots):
|
||||
def show_canvas(self, ax, plots, xlabel=None, ylabel=None,
|
||||
zlabel=None, title=None, xlim=None, ylim=None,
|
||||
zlim=None, legend=True, **kwargs):
|
||||
ax.set_xlabel(xlabel)
|
||||
ax.set_ylabel(ylabel)
|
||||
|
||||
if zlabel is not None:
|
||||
ax.set_zlabel(zlabel)
|
||||
|
||||
ax.set_title(title)
|
||||
|
||||
try:
|
||||
ax.autoscale_view()
|
||||
ax.figure.canvas.draw()
|
||||
|
|
@ -62,13 +76,13 @@ class MatplotlibPlots(AbstractPlottingLibrary):
|
|||
pass
|
||||
return plots
|
||||
|
||||
def scatter(self, ax, X, Y, **kwargs):
|
||||
return ax.scatter(X, Y, **kwargs)
|
||||
def scatter(self, ax, X, Y, color=None, label=None, **kwargs):
|
||||
return ax.scatter(X, Y, c=color, label=label, **kwargs)
|
||||
|
||||
def plot(self, ax, X, Y, **kwargs):
|
||||
return ax.plot(X, Y, **kwargs)
|
||||
def plot(self, ax, X, Y, color=None, label=None, **kwargs):
|
||||
return ax.plot(X, Y, color=color, label=label, **kwargs)
|
||||
|
||||
def plot_axis_lines(self, ax, X, **kwargs):
|
||||
def plot_axis_lines(self, ax, X, color=None, label=None, **kwargs):
|
||||
from matplotlib import transforms
|
||||
from matplotlib.path import Path
|
||||
if 'transform' not in kwargs:
|
||||
|
|
@ -76,31 +90,44 @@ class MatplotlibPlots(AbstractPlottingLibrary):
|
|||
if 'marker' not in kwargs:
|
||||
kwargs['marker'] = Path([[-.2,0.], [-.2,.5], [0.,1.], [.2,.5], [.2,0.], [-.2,0.]],
|
||||
[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY])
|
||||
return ax.scatter(X, np.zeros_like(X), **kwargs)
|
||||
return ax.scatter(X, np.zeros_like(X), c=color, label=label, **kwargs)
|
||||
|
||||
def xerrorbar(self, ax, X, Y, error, **kwargs):
|
||||
def barplot(self, ax, x, height, width=0.8, bottom=0, color=None, label=None, **kwargs):
|
||||
if 'align' not in kwargs:
|
||||
kwargs['align'] = 'center'
|
||||
return ax.bar(left=x, height=height, width=width,
|
||||
bottom=bottom, label=label, color=color,
|
||||
**kwargs)
|
||||
|
||||
def xerrorbar(self, ax, X, Y, error, color=None, label=None, **kwargs):
|
||||
if not('linestyle' in kwargs or 'ls' in kwargs):
|
||||
kwargs['ls'] = 'none'
|
||||
return ax.errorbar(X, Y, xerr=error, **kwargs)
|
||||
return ax.errorbar(X, Y, xerr=error, ecolor=color, label=label, **kwargs)
|
||||
|
||||
def yerrorbar(self, ax, X, Y, error, **kwargs):
|
||||
def yerrorbar(self, ax, X, Y, error, color=None, label=None, **kwargs):
|
||||
if not('linestyle' in kwargs or 'ls' in kwargs):
|
||||
kwargs['ls'] = 'none'
|
||||
return ax.errorbar(X, Y, yerr=error, **kwargs)
|
||||
return ax.errorbar(X, Y, yerr=error, ecolor=color, label=label, **kwargs)
|
||||
|
||||
def imshow(self, ax, X, **kwargs):
|
||||
return ax.imshow(**kwargs)
|
||||
def imshow(self, ax, X, label=None, **kwargs):
|
||||
return ax.imshow(X, label=label, **kwargs)
|
||||
|
||||
def contour(self, ax, X, Y, C, levels=20, **kwargs):
|
||||
return ax.contour(X, Y, C, levels=np.linspace(C.min(), C.max(), levels), **kwargs)
|
||||
def contour(self, ax, X, Y, C, levels=20, label=None, **kwargs):
|
||||
return ax.contour(X, Y, C, levels=np.linspace(C.min(), C.max(), levels), label=label, **kwargs)
|
||||
|
||||
def fill_between(self, ax, X, lower, upper, **kwargs):
|
||||
return ax.fill_between(X, lower, upper, **kwargs)
|
||||
def fill_between(self, ax, X, lower, upper, color=None, label=None, **kwargs):
|
||||
return ax.fill_between(X, lower, upper, facecolor=color, label=label, **kwargs)
|
||||
|
||||
def fill_gradient(self, canvas, X, percentiles, **kwargs):
|
||||
def fill_gradient(self, canvas, X, percentiles, color=None, label=None, **kwargs):
|
||||
ax = canvas
|
||||
plots = []
|
||||
|
||||
if 'edgecolors' not in kwargs:
|
||||
kwargs['edgecolors'] = 'none'
|
||||
|
||||
if 'facecolors' not in kwargs:
|
||||
kwargs['facecolors'] = color
|
||||
|
||||
if 'facecolors' in kwargs:
|
||||
kwargs['facecolor'] = kwargs.pop('facecolors')
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue