def taylor_diagram(*args, **kwargs):
'''
Plot a Taylor diagram from statistics of different series.
taylor_diagram(STDs,RMSs,CORs,keyword=value)
The first 3 arguments must be the inputs as described below followed by
keywords in the format OPTION = value. An example call to the function
would be:
taylor_diagram(STDs,RMSs,CORs,markerdisplayed='marker')
INPUTS:
STDs: Standard deviations
RMSs: Centered Root Mean Square Difference
CORs: Correlation
Each of these inputs are one-dimensional with the same length. First
index corresponds to the reference series for the diagram. For
example STDs[1] is the standard deviation of the reference series
and STDs[2:N] are the standard deviations of the other series. Note
that only the latter are plotted.
Note that by definition the following relation must be true for all
series i:
RMSs(i) = sqrt(STDs(i).^2 + STDs(1)^2 - 2*STDs(i)*STDs(1).*CORs(i))
This relation is checked if the checkStats option is used, and if not
verified an error message is sent. This relation is not checked by
default. Please see Taylor's JGR article for more informations about
this relation.
OUTPUTS:
None.
LIST OF OPTIONS:
For an exhaustive list of options to customize your diagram, call the
function without arguments at a Python command line:
% python
>>> import skill_metrics as sm
>>> sm.taylor_diagram()
Reference:
Taylor, K. E. (2001), Summarizing multiple aspects of model
performance in a single diagram, J. Geophys. Res., 106(D7),
7183-7192, doi:10.1029/2000JD900719.
Author: Peter A. Rochford
Symplectic, LLC
www.thesymplectic.com
[email protected]
Created on Dec 3, 2016
'''
# Check for number of arguments
nargin = len(args)
STDs, RMSs, CORs = _get_taylor_diagram_arguments(*args, **kwargs)
if nargin == 0: return
# Get options
option = get_taylor_diagram_options(CORs, **kwargs)
# Check the input statistics if requested.
if option['checkstats'] == 'on':
check_taylor_stats(STDs[1:], RMSs[1:], CORs[1:], 0.01)
# Express statistics in polar coordinates.
rho = STDs
theta = np.arccos(CORs)
# Get axis values for plot
axes, cax = get_taylor_diagram_axes(rho, option)
# Plot axes for target diagram
if option['overlay'] == 'off':
# Draw circles about origin
overlay_taylor_diagram_circles(axes, cax, option)
# Draw lines emanating from origin
overlay_taylor_diagram_lines(axes, cax, option)
# Plot axes for Taylor diagram
ax = plot_taylor_axes(axes, cax, option)
# Plot marker on axis indicating observation STD
plot_taylor_obs(ax, STDs[0], axes, option)
# Plot data points. Note that only rho[1:N] and theta[1:N] are
# plotted.
X = np.multiply(rho[1:], np.cos(theta[1:]))
Y = np.multiply(rho[1:], np.sin(theta[1:]))
lowcase = option['markerdisplayed'].lower()
if lowcase == 'marker':
plot_pattern_diagram_markers(X, Y, option)
elif lowcase == 'colorbar':
plot_pattern_diagram_colorbar(X, Y, RMSs[1:], option)
else:
raise ValueError('Unrecognized option: ' + option['markerdisplayed'])
# Calculate statistics for target diagram
target_stats1 = sm.target_statistics(data.pred1, data.ref, 'data')
# Write statistics to Excel file for a single data set
filename = 'target_stats.xlsx'
sm.write_target_stats(filename, target_stats1, overwrite='on')
# Calculate statistics for Taylor diagram
# The first array element corresponds to the reference series
# for the while the second is that for the predicted series.
taylor_stats1 = sm.taylor_statistics(data.pred1, data.ref, 'data')
taylor_stats2 = sm.taylor_statistics(data.pred2, data.ref, 'data')
taylor_stats3 = sm.taylor_statistics(data.pred3, data.ref, 'data')
# Write statistics to Excel file
filename = 'taylor_stats.xlsx'
data = [taylor_stats1, taylor_stats2, taylor_stats3]
title = ['Expt. 1', 'Expt. 2', 'Expt. 3']
label = ['Observed', 'M1', 'M2', 'M3']
sm.write_taylor_stats(filename,
data,
title=title,
label=label,
overwrite=True)
# Check statistics for Taylor diagram
diff = sm.check_taylor_stats(taylor_stats1['sdev'], taylor_stats1['crmsd'],
taylor_stats1['ccoef'])
print('Difference in Taylor statistics = ' + str(diff))
