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RE: How to turn 360 day calendars into 365 days in CDO? » main.py

ibrahim erkol, 2022-04-09 12:08

 
#!/usr/bin/env python
# Copyright: This document has been placed in the public domain.

"""
Taylor diagram (Taylor, 2001) implementation.
Note: If you have found these software useful for your research, I would
appreciate an acknowledgment.
"""

__version__ = "Time-stamp: <2018-12-06 11:43:41 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='_', srange=(0, 1.5), extend=False):
"""
Set up Taylor diagram axes, i.e. single quadrant polar
plot, using `mpl_toolkits.axisartist.floating_axes`.
Parameters:
* refstd: reference standard deviation to be compared to
* fig: input Figure or None
* rect: subplot definition
* label: reference label
* srange: stddev axis extension, in units of *refstd*
* extend: extend diagram to negative correlations
"""

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.array([0, 0.2, 0.4, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 1])
if extend:
# Diagram extended to negative correlations
self.tmax = NP.pi
rlocs = NP.concatenate((-rlocs[:0:-1], rlocs))
else:
# Diagram limited to positive correlations
self.tmax = NP.pi/2
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 (in units of reference stddev)
self.smin = srange[0] * self.refstd
self.smax = srange[1] * self.refstd

ghelper = FA.GridHelperCurveLinear(
tr,
extremes=(0, self.tmax, 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(
"bottom" if extend else "left")

if self.smin:
ax.axis["bottom"].toggle(ticklabels=False, label=False)
else:
ax.axis["bottom"].set_visible(False) # Unused

self._ax = ax # Graphical axes
self.ax = ax.get_aux_axes(tr) # Polar coordinates

# Add reference point and stddev contour
l, = self.ax.plot([0], self.refstd, 'k*',
ls='', ms=10, label=label)
t = NP.linspace(0, self.tmax)
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_grid(self, *args, **kwargs):
"""Add a grid."""

self._ax.grid(*args, **kwargs)

def add_contours(self, levels=5, **kwargs):
"""
Add constant centered RMS difference contours, defined by *levels*.
"""

rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),
NP.linspace(0, self.tmax))
# 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


def test1():
"""Display a Taylor diagram in a separate axis."""

# Reference dataset
x = NP.linspace(0, 4*NP.pi, 100)
data = NP.sin(x)
refstd = data.std(ddof=1) # Reference standard deviation

# Generate 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",
srange=(0.5, 1.5))

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 the models to Taylor diagram
for i, (stddev, corrcoef) in enumerate(samples):
dia.add_sample(stddev, corrcoef,
marker='$%d$' % (i+1), ms=10, ls='',
mfc=colors[i], mec=colors[i],
label="Model %d" % (i+1))

# Add grid
dia.add_grid()

# Add RMS contours, and label them
contours = dia.add_contours(colors='0.5')
PLT.clabel(contours, inline=1, fontsize=10, fmt='%.2f')

# 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')

return dia


def test2():
"""
Climatology-oriented example (after iteration w/ Michael A. Rawlins).
"""

# Reference std
stdref = 48.491

# Samples std,rho,name
samples = [[25.939, 0.385, "Model A"],
[29.593, 0.509, "Model B"],
[33.125, 0.585, "Model C"],
[29.593, 0.509, "Model D"],
[71.215, 0.473, "Model E"],
[27.062, 0.360, "Model F"],
[38.449, 0.342, "Model G"],
[35.807, 0.609, "Model H"],
[17.831, 0.360, "Model I"]]

fig = PLT.figure()

dia = TaylorDiagram(stdref, fig=fig, label='Reference', extend=True)
dia.samplePoints[0].set_color('r') # Mark reference point as a red star

# Add models to Taylor diagram
for i, (stddev, corrcoef, name) in enumerate(samples):
dia.add_sample(stddev, corrcoef,
marker='$%d$' % (i+1), ms=10, ls='',
mfc='k', mec='k',
label=name)

# Add RMS contours, and label them
contours = dia.add_contours(levels=5, colors='0.5') # 5 levels in grey
PLT.clabel(contours, inline=1, fontsize=10, fmt='%.0f')

dia.add_grid() # Add grid
dia._ax.axis[:].major_ticks.set_tick_out(True) # Put ticks outward

# Add a figure legend and title
fig.legend(dia.samplePoints,
[ p.get_label() for p in dia.samplePoints ],
numpoints=1, prop=dict(size='small'), loc='upper right')
fig.suptitle("Taylor diagram", size='x-large') # Figure title

return dia


if __name__ == '__main__':

dia = test1()
dia = test2()

PLT.show()
test_taylor_4panel.py
#!/usr/bin/env python

__version__ = "Time-stamp: <2018-12-06 11:55:22 ycopin>"
__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 United States, Journal of
Geophysical Research (2012JGRD..11723112R).
"""

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()
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