Diagramme de Taylor avec Matplotlib
Exemple intéressant de diagramme de Taylor avec matplotlib réalisé par Yannick Copin.
#!/usr/bin/env python
# Copyright: This document has been placed in the public domain.
"""
Taylor diagram (Taylor, 2001) test implementation.
http://www-pcmdi.llnl.gov/about/staff/Taylor/CV/Taylor_diagram_primer.htm
"""
__version__ = "Time-stamp: <2012-02-17 20:59:35 ycopin>"
__author__ = "Yannick Copin <yannick.copin@laposte.net>"
import numpy as NP
import matplotlib.pyplot as PLT
class TaylorDiagram(object):
"""Taylor diagram: plot model standard deviation and correlation
to reference (data) sample in a single-quadrant polar plot, with
r=stddev and theta=arccos(correlation).
"""
def __init__(self, refstd, fig=None, rect=111, label='_'):
"""Set up Taylor diagram axes, i.e. single quadrant polar
plot, using mpl_toolkits.axisartist.floating_axes. refstd is
the reference standard deviation to be compared to.
"""
from matplotlib.projections import PolarAxes
import mpl_toolkits.axisartist.floating_axes as FA
import mpl_toolkits.axisartist.grid_finder as GF
self.refstd = refstd # Reference standard deviation
tr = PolarAxes.PolarTransform()
# Correlation labels
rlocs = NP.concatenate((NP.arange(10)/10.,[0.95,0.99]))
tlocs = NP.arccos(rlocs) # Conversion to polar angles
gl1 = GF.FixedLocator(tlocs) # Positions
tf1 = GF.DictFormatter(dict(zip(tlocs, map(str,rlocs))))
# Standard deviation axis extent
self.smin = 0
self.smax = 1.5*self.refstd
ghelper = FA.GridHelperCurveLinear(tr,
extremes=(0,NP.pi/2, # 1st quadrant
self.smin,self.smax),
grid_locator1=gl1,
tick_formatter1=tf1,
)
if fig is None:
fig = PLT.figure()
ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
fig.add_subplot(ax)
# Adjust axes
ax.axis["top"].set_axis_direction("bottom") # "Angle axis"
ax.axis["top"].toggle(ticklabels=True, label=True)
ax.axis["top"].major_ticklabels.set_axis_direction("top")
ax.axis["top"].label.set_axis_direction("top")
ax.axis["top"].label.set_text("Correlation")
ax.axis["left"].set_axis_direction("bottom") # "X axis"
ax.axis["left"].label.set_text("Standard deviation")
ax.axis["right"].set_axis_direction("top") # "Y axis"
ax.axis["right"].toggle(ticklabels=True)
ax.axis["right"].major_ticklabels.set_axis_direction("left")
ax.axis["bottom"].set_visible(False) # Useless
# Contours along standard deviations
ax.grid(False)
self._ax = ax # Graphical axes
self.ax = ax.get_aux_axes(tr) # Polar coordinates
# Add reference point and stddev contour
print "Reference std:", self.refstd
l, = self.ax.plot([0], self.refstd, 'k*',
ls='', ms=10, label=label)
t = NP.linspace(0, NP.pi/2)
r = NP.zeros_like(t) + self.refstd
self.ax.plot(t,r, 'k--', label='_')
# Collect sample points for latter use (e.g. legend)
self.samplePoints = [l]
def add_sample(self, stddev, corrcoef, *args, **kwargs):
"""Add sample (stddev,corrcoeff) to the Taylor diagram. args
and kwargs are directly propagated to the Figure.plot
command."""
l, = self.ax.plot(NP.arccos(corrcoef), stddev,
*args, **kwargs) # (theta,radius)
self.samplePoints.append(l)
return l
def add_contours(self, levels=5, **kwargs):
"""Add constant centered RMS difference contours."""
rs,ts = NP.meshgrid(NP.linspace(self.smin,self.smax),
NP.linspace(0,NP.pi/2))
# Compute centered RMS difference
rms = NP.sqrt(self.refstd**2 + rs**2 - 2*self.refstd*rs*NP.cos(ts))
contours = self.ax.contour(ts, rs, rms, levels, **kwargs)
return contours
if __name__=='__main__':
# Reference dataset
x = NP.linspace(0,4*NP.pi,100)
data = NP.sin(x)
refstd = data.std(ddof=1) # Reference standard deviation
# Models
m1 = data + 0.2*NP.random.randn(len(x)) # Model 1
m2 = 0.8*data + .1*NP.random.randn(len(x)) # Model 2
m3 = NP.sin(x-NP.pi/10) # Model 3
# Compute stddev and correlation coefficient of models
samples = NP.array([ [m.std(ddof=1), NP.corrcoef(data, m)[0,1]]
for m in (m1,m2,m3)])
fig = PLT.figure(figsize=(10,4))
ax1 = fig.add_subplot(1,2,1, xlabel='X', ylabel='Y')
# Taylor diagram
dia = TaylorDiagram(refstd, fig=fig, rect=122, label="Reference")
colors = PLT.matplotlib.cm.jet(NP.linspace(0,1,len(samples)))
ax1.plot(x,data,'ko', label='Data')
for i,m in enumerate([m1,m2,m3]):
ax1.plot(x,m, c=colors[i], label='Model %d' % (i+1))
ax1.legend(numpoints=1, prop=dict(size='small'), loc='best')
# Add samples to Taylor diagram
for i,(stddev,corrcoef) in enumerate(samples):
dia.add_sample(stddev, corrcoef, marker='s', ls='', c=colors[i],
label="Model %d" % (i+1))
# Add RMS contours, and label them
contours = dia.add_contours(colors='0.5')
PLT.clabel(contours, inline=1, fontsize=10)
# Add a figure legend
fig.legend(dia.samplePoints,
[ p.get_label() for p in dia.samplePoints ],
numpoints=1, prop=dict(size='small'), loc='upper right')
PLT.savefig('test_taylor.png')
PLT.show()
Exemple d'application
#!/usr/bin/env python
__version__ = "Time-stamp: <2012-08-13 16:52 ycopin@lyopc469>"
__author__ = "Yannick Copin <yannick.copin@laposte.net>"
"""
Example of use of TaylorDiagram. Illustration dataset courtesy of
Michael Rawlins.
Rawlins, M. A., R. S. Bradley, H. F. Diaz, 2012. Assessment of
regional climate model simulation estimates over the Northeast U.S.,
Journal of Geophysical Research, in review.
"""
from taylorDiagram import TaylorDiagram
import numpy as NP
import matplotlib.pyplot as PLT
# Reference std
stdrefs = dict(winter=48.491,
spring=44.927,
summer=37.664,
autumn=41.589)
# Sample std,rho: Be sure to check order and that correct numbers are placed!
samples = dict(winter=[[17.831, 0.360, "CCSM CRCM"],
[27.062, 0.360, "CCSM MM5"],
[33.125, 0.585, "CCSM WRFG"],
[25.939, 0.385, "CGCM3 CRCM"],
[29.593, 0.509, "CGCM3 RCM3"],
[35.807, 0.609, "CGCM3 WRFG"],
[38.449, 0.342, "GFDL ECP2"],
[29.593, 0.509, "GFDL RCM3"],
[71.215, 0.473, "HADCM3 HRM3"]],
spring=[[32.174, -0.262, "CCSM CRCM"],
[24.042, -0.055, "CCSM MM5"],
[29.647, -0.040, "CCSM WRFG"],
[22.820, 0.222, "CGCM3 CRCM"],
[20.505, 0.445, "CGCM3 RCM3"],
[26.917, 0.332, "CGCM3 WRFG"],
[25.776, 0.366, "GFDL ECP2"],
[18.018, 0.452, "GFDL RCM3"],
[79.875, 0.447, "HADCM3 HRM3"]],
summer=[[35.863, 0.096, "CCSM CRCM"],
[43.771, 0.367, "CCSM MM5"],
[35.890, 0.267, "CCSM WRFG"],
[49.658, 0.134, "CGCM3 CRCM"],
[28.972, 0.027, "CGCM3 RCM3"],
[60.396, 0.191, "CGCM3 WRFG"],
[46.529, 0.258, "GFDL ECP2"],
[35.230, -0.014, "GFDL RCM3"],
[87.562, 0.503, "HADCM3 HRM3"]],
autumn=[[27.374, 0.150, "CCSM CRCM"],
[20.270, 0.451, "CCSM MM5"],
[21.070, 0.505, "CCSM WRFG"],
[25.666, 0.517, "CGCM3 CRCM"],
[35.073, 0.205, "CGCM3 RCM3"],
[25.666, 0.517, "CGCM3 WRFG"],
[23.409, 0.353, "GFDL ECP2"],
[29.367, 0.235, "GFDL RCM3"],
[70.065, 0.444, "HADCM3 HRM3"]])
# Colormap (see http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps)
colors = PLT.matplotlib.cm.Set1(NP.linspace(0,1,len(samples['winter'])))
# Here set placement of the points marking 95th and 99th significance
# levels. For more than 102 samples (degrees freedom > 100), critical
# correlation levels are 0.195 and 0.254 for 95th and 99th
# significance levels respectively. Set these by eyeball using the
# standard deviation x and y axis.
#x95 = [0.01, 0.68] # For Tair, this is for 95th level (r = 0.195)
#y95 = [0.0, 3.45]
#x99 = [0.01, 0.95] # For Tair, this is for 99th level (r = 0.254)
#y99 = [0.0, 3.45]
x95 = [0.05, 13.9] # For Prcp, this is for 95th level (r = 0.195)
y95 = [0.0, 71.0]
x99 = [0.05, 19.0] # For Prcp, this is for 99th level (r = 0.254)
y99 = [0.0, 70.0]
rects = dict(winter=221,
spring=222,
summer=223,
autumn=224)
fig = PLT.figure(figsize=(11,8))
fig.suptitle("Precipitations", size='x-large')
for season in ['winter','spring','summer','autumn']:
dia = TaylorDiagram(stdrefs[season], fig=fig, rect=rects[season],
label='Reference')
dia.ax.plot(x95,y95,color='k')
dia.ax.plot(x99,y99,color='k')
# Add samples to Taylor diagram
for i,(stddev,corrcoef,name) in enumerate(samples[season]):
dia.add_sample(stddev, corrcoef,
marker='$%d$' % (i+1), ms=10, ls='',
#mfc='k', mec='k', # B&W
mfc=colors[i], mec=colors[i], # Colors
label=name)
# Add RMS contours, and label them
contours = dia.add_contours(levels=5, colors='0.5') # 5 levels
dia.ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
# Tricky: ax is the polar ax (used for plots), _ax is the
# container (used for layout)
dia._ax.set_title(season.capitalize())
# Add a figure legend and title. For loc option, place x,y tuple inside [ ].
# Can also use special options here:
# http://matplotlib.sourceforge.net/users/legend_guide.html
fig.legend(dia.samplePoints,
[ p.get_label() for p in dia.samplePoints ],
numpoints=1, prop=dict(size='small'), loc='center')
fig.tight_layout()
PLT.savefig('test_taylor_4panel.png')
PLT.show()