# (C) British Crown Copyright 2017, Met Office
#
# This code is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This code is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# Plot reanalysis pressure and DWR comparisons - error v. error plots.
import numpy
import matplotlib
[docs]def plot_eve(ax,dmonth,**kwargs):
"""Plot obs-reanalysis against reanalysis sd
Args:
ax (:obj:`cartopy.axes`): Axes to hold the plot
dmonth (:obj:`dict`): Components 'ensembles' and 'observations' each an array with the ensemble values and observation value at the time and place of each observation.
Kwargs:
range (:obj:`list`): data range in scatterplot, default is (0,6).
x_label (:obj:`str`): x label, default is 'Ensemble SD (hPa)'.
y_label (:obj:`str`): y label, default is 'Ob-Ensemble mean RMS (hPa)'.
point_size (:obj:`float`): Plot plot size, default 10.0.
point_size (:obj:`str`): Plot plot color, default 'blue'.
obs_errors (:obj:`list`): Assumed obs errors to show, default is (0,1,2)
nbins (:obj:`int`): Resolution of plot - number of points.
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"""
# Set keyword argument defaults
kwargs.setdefault('range',(0.0,6.0))
kwargs.setdefault('x_label','Ensemble SD (hPa)')
kwargs.setdefault('y_label','Ob-Ensemble mean RMS (hPa)')
kwargs.setdefault('point_size',100.0)
kwargs.setdefault('point_color','blue')
kwargs.setdefault('obs_errors',(0,1,2))
kwargs.setdefault('nbins',50)
# x & y-axis
ax.set_xlim(kwargs.get('range'))
ax.set_xlabel(kwargs.get('x_label'))
ax.set_ylim(kwargs.get('range'))
ax.set_ylabel(kwargs.get('y_label'))
# Background line
if kwargs.get('obs_errors') is not None:
for o_error in kwargs.get('obs_errors'):
x=numpy.linspace(kwargs.get('range')[0],
kwargs.get('range')[1])
y=numpy.sqrt(numpy.square(x)+o_error**2)
ax.add_line(matplotlib.lines.Line2D(
xdata=x,
ydata=y,
linestyle='solid',
linewidth=1,
color=(0.5,0.5,0.5,0.5),
zorder=1))
# Calculate std of each ensemble
ens_sd=[numpy.std(dmonth['ensembles'][idx][0],ddof=1) for idx in range(len(dmonth['ensembles']))]
resolution=1/float(kwargs.get('nbins'))*100
for idx in range(kwargs.get('nbins')):
sd_min=numpy.percentile(ens_sd,idx*resolution)
sd_max=numpy.percentile(ens_sd,(idx+1)*resolution)
selected=[]
x_select=[]
for sdx in range(len(ens_sd)):
if ens_sd[sdx]>=sd_min and ens_sd[sdx]<sd_max:
selected.append(numpy.mean(dmonth['ensembles'][sdx][0])-
dmonth['observations'][sdx])
x_select.append(ens_sd[sdx])
if len(selected)<10: continue
ax.scatter(numpy.mean(x_select),
numpy.sqrt(numpy.mean(numpy.square(selected))),
s=kwargs.get('point_size'),
marker='.',
alpha=1,
linewidths=0.01,
c=kwargs.get('point_color'))
[docs]def plot_vvv(ax,dmonth,**kwargs):
"""Plot obs-reanalysis against reanalysis variance
Args:
ax (:obj:`cartopy.axes`): Axes to hold the plot
dmonth (:obj:`dict`): Components 'ensembles' and 'observations' each an array with the ensemble valuues and observation value at the time and place of each observation.
Kwargs:
range (:obj:`list`): data range in scatterplot, default is (0,30).
x_label (:obj:`str`): x label, default is 'Ensemble Variance (hPa**2)'.
y_label (:obj:`str`): y label, default is 'Ob-Ensemble mean MS (hPa**2)'.
point_size (:obj:`float`): Plot plot size, default 100.0.
point_size (:obj:`str`): Plot plot color, default 'blue'.
obs_errors (:obj:`list`): Assumed obs errors to show, default is (0,1,2)
nbins (:obj:`int`): Resolution of plot - number of points.
|
"""
# Set keyword argument defaults
kwargs.setdefault('range',(0.0,30.0))
kwargs.setdefault('x_label','Ensemble Variance (hPa**2)')
kwargs.setdefault('y_label','Ob-Ensemble mean MS (hPa**2)')
kwargs.setdefault('point_size',100.0)
kwargs.setdefault('point_color','blue')
kwargs.setdefault('obs_errors',(0,1,2))
kwargs.setdefault('nbins',50)
# x & y-axis
ax.set_xlim(kwargs.get('range'))
ax.set_xlabel(kwargs.get('x_label'))
ax.set_ylim(kwargs.get('range'))
ax.set_ylabel(kwargs.get('y_label'))
# Background line
if kwargs.get('obs_errors') is not None:
for o_error in kwargs.get('obs_errors'):
x=numpy.linspace(kwargs.get('range')[0],
kwargs.get('range')[1])
y=x+o_error**2
ax.add_line(matplotlib.lines.Line2D(
xdata=x,
ydata=y,
linestyle='solid',
linewidth=1,
color=(0.5,0.5,0.5,0.5),
zorder=1))
# Calculate variance of each ensemble
ens_sd=[numpy.var(dmonth['ensembles'][idx][0],ddof=1) for idx in range(len(dmonth['ensembles']))]
resolution=1/float(kwargs.get('nbins'))*100
for idx in range(kwargs.get('nbins')):
sd_min=numpy.percentile(ens_sd,idx*resolution)
sd_max=numpy.percentile(ens_sd,(idx+1)*resolution)
selected=[]
x_select=[]
for sdx in range(len(ens_sd)):
if ens_sd[sdx]>=sd_min and ens_sd[sdx]<sd_max:
selected.append(numpy.mean(dmonth['ensembles'][sdx][0])-
dmonth['observations'][sdx])
x_select.append(ens_sd[sdx])
if len(selected)<10: continue
ax.scatter(numpy.mean(x_select),
numpy.mean(numpy.square(selected)),
s=kwargs.get('point_size'),
marker='.',
alpha=1,
linewidths=0.01,
c=kwargs.get('point_color'),
edgecolors=kwargs.get('point_color'))
