# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2020 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2020 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2020 UT-Battelle, LLC. All rights reserved.
#
# Additional copyright and license information can be found in the LICENSE file
# distributed with this code, or at
# https://raw.githubusercontent.com/MPAS-Dev/MPAS-Analysis/master/LICENSE
"""
Functions for plotting remapped horizontal fields (and comparing with reference
data sets)
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani, Luke Van Roekel, Greg Streletz
from __future__ import absolute_import, division, print_function, \
unicode_literals
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as cols
import matplotlib.ticker as mticker
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import cartopy
from cartopy.util import add_cyclic_point
from mpas_analysis.shared.plot.colormap import setup_colormap
from mpas_analysis.shared.plot.title import limit_title
from mpas_analysis.shared.plot.save import savefig
from mpas_analysis.shared.projection import get_cartopy_projection
[docs]def plot_polar_comparison(
config,
lon,
lat,
modelArray,
refArray,
diffArray,
colorMapSectionName,
fileout,
title=None,
plotProjection='npstere',
latmin=50.0,
lon0=0,
modelTitle='Model',
refTitle='Observations',
diffTitle='Model-Observations',
cbarlabel='units',
titleFontSize=None,
defaultFontSize=None,
figsize=None,
dpi=None,
vertical=False,
maxTitleLength=60):
"""
Plots a data set around either the north or south pole.
Parameters
----------
config : mpas_analysis.configuration.MpasAnalysisConfigParser
the configuration, containing a [plot] section with options that
control plotting
lon, lat : float arrays
longitude and latitude arrays
modelArray, refArray : numpy.ndarray
model and observational or control run data sets
diffArray : float array
difference between modelArray and refArray
colorMapSectionName : str
section name in ``config`` where color map info can be found.
fileout : str
the file name to be written
title : str, optional
the subtitle of the plot
plotProjection : {'npstere', 'spstere'}, optional
projection for the plot (north or south pole)
modelTitle : str, optional
title of the model panel
refTitle : str, optional
title of the observations or control run panel
diffTitle : str, optional
title of the difference (bias) panel
cbarlabel : str, optional
label on the colorbar
titleFontSize : int, optional
size of the title font
defaultFontSize : int, optional
the size of text other than the title
figsize : tuple of float, optional
the size of the figure in inches. If ``None``, the figure size is
``(8, 22)`` if ``vertical == True`` and ``(22, 8)`` otherwise.
dpi : int, optional
the number of dots per inch of the figure, taken from section ``plot``
option ``dpi`` in the config file by default
vertical : bool, optional
whether the subplots should be stacked vertically rather than
horizontally
maxTitleLength : int, optional
the maximum number of characters in the title, beyond which it is
truncated with a trailing ellipsis
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani
def do_subplot(ax, field, title, colormap, norm, levels, ticks, contours,
lineWidth, lineColor):
"""
Make a subplot within the figure.
"""
data_crs = cartopy.crs.PlateCarree()
ax.set_extent(extent, crs=data_crs)
title = limit_title(title, maxTitleLength)
ax.set_title(title, y=1.06, **plottitle_font)
gl = ax.gridlines(crs=data_crs, color='k', linestyle=':', zorder=5,
draw_labels=True)
gl.xlocator = mticker.FixedLocator(np.arange(-180., 181., 20.))
gl.ylocator = mticker.FixedLocator(np.arange(-80., 81., 10.))
gl.n_steps = 100
gl.right_labels = False
gl.xformatter = cartopy.mpl.gridliner.LONGITUDE_FORMATTER
gl.yformatter = cartopy.mpl.gridliner.LATITUDE_FORMATTER
gl.rotate_labels = False
fieldPeriodic, lonPeriodic = add_cyclic_point(field, lon)
LonsPeriodic, LatsPeriodic = np.meshgrid(lonPeriodic, lat)
if levels is None:
plotHandle = ax.pcolormesh(LonsPeriodic, LatsPeriodic,
fieldPeriodic, cmap=colormap,
norm=norm, transform=data_crs,
zorder=1, rasterized=True)
else:
plotHandle = ax.contourf(LonsPeriodic, LatsPeriodic,
fieldPeriodic, cmap=colormap,
norm=norm, levels=levels,
transform=data_crs,
zorder=1)
_add_land_lakes_coastline(ax)
if contours is not None:
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
ax.contour(LonsPeriodic, LatsPeriodic, fieldPeriodic,
levels=contours, colors=lineColor,
linewidths=lineWidth, transform=data_crs)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1,
axes_class=plt.Axes)
cbar = plt.colorbar(plotHandle, cax=cax)
cbar.set_label(cbarlabel)
if ticks is not None:
cbar.set_ticks(ticks)
cbar.set_ticklabels(['{}'.format(tick) for tick in ticks])
if defaultFontSize is None:
defaultFontSize = config.getint('plot', 'defaultFontSize')
matplotlib.rc('font', size=defaultFontSize)
if dpi is None:
dpi = config.getint('plot', 'dpi')
dictModelRef = setup_colormap(config, colorMapSectionName, suffix='Result')
dictDiff = setup_colormap(config, colorMapSectionName, suffix='Difference')
if refArray is None:
if figsize is None:
figsize = (8, 8.5)
subplots = [111]
elif vertical:
if figsize is None:
figsize = (8, 22)
subplots = [311, 312, 313]
else:
if figsize is None:
figsize = (22, 7.5)
subplots = [131, 132, 133]
fig = plt.figure(figsize=figsize, dpi=dpi)
if (title is not None):
if titleFontSize is None:
titleFontSize = config.get('plot', 'titleFontSize')
title_font = {'size': titleFontSize,
'color': config.get('plot', 'titleFontColor'),
'weight': config.get('plot', 'titleFontWeight')}
fig.suptitle(title, y=0.95, **title_font)
plottitle_font = {'size': config.get('plot',
'threePanelPlotTitleFontSize')}
if plotProjection == 'npstere':
projection = cartopy.crs.NorthPolarStereo()
extent = [-180, 180, latmin, 90]
elif plotProjection == 'spstere':
projection = cartopy.crs.SouthPolarStereo()
extent = [-180, 180, -90, latmin]
else:
raise ValueError('Unexpected plot projection {}'.format(
plotProjection))
ax = plt.subplot(subplots[0], projection=projection)
do_subplot(ax=ax, field=modelArray, title=modelTitle, **dictModelRef)
if refArray is not None:
ax = plt.subplot(subplots[1], projection=projection)
do_subplot(ax=ax, field=refArray, title=refTitle, **dictModelRef)
ax = plt.subplot(subplots[2], projection=projection)
do_subplot(ax=ax, field=diffArray, title=diffTitle, **dictDiff)
fig.canvas.draw()
plt.tight_layout(pad=4.)
if vertical:
plt.subplots_adjust(top=0.9)
if fileout is not None:
savefig(fileout, config)
plt.close()
[docs]def plot_global_comparison(
config,
Lons,
Lats,
modelArray,
refArray,
diffArray,
colorMapSectionName,
fileout,
title=None,
modelTitle='Model',
refTitle='Observations',
diffTitle='Model-Observations',
cbarlabel='units',
titleFontSize=None,
defaultFontSize=None,
figsize=None,
dpi=None,
lineWidth=1,
lineColor='black',
maxTitleLength=60):
"""
Plots a data set as a longitude/latitude map.
Parameters
----------
config : mpas_analysis.configuration.MpasAnalysisConfigParser
the configuration, containing a [plot] section with options that
control plotting
Lons, Lats : numpy.ndarray
longitude and latitude arrays
modelArray, refArray : numpy.ndarray
model and observational or control run data sets
diffArray : float array
difference between modelArray and refArray
colorMapSectionName : str
section name in ``config`` where color map info can be found.
fileout : str
the file name to be written
title : str, optional
the subtitle of the plot
modelTitle : str, optional
title of the model panel
refTitle : str, optional
title of the observations or control run panel
diffTitle : str, optional
title of the difference (bias) panel
cbarlabel : str, optional
label on the colorbar
titleFontSize : int, optional
size of the title font
defaultFontSize : int, optional
the size of text other than the title
figsize : tuple of float, optional
the size of the figure in inches
dpi : int, optional
the number of dots per inch of the figure, taken from section ``plot``
option ``dpi`` in the config file by default
lineWidth : int, optional
the line width of contour lines (if specified)
lineColor : str, optional
the color of contour lines (if specified)
maxTitleLength : int, optional
the maximum number of characters in the title, beyond which it is
truncated with a trailing ellipsis
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani
def plot_panel(ax, title, array, colormap, norm, levels, ticks, contours,
lineWidth, lineColor):
ax.set_extent(extent, crs=projection)
title = limit_title(title, maxTitleLength)
ax.set_title(title, y=1.02, **plottitle_font)
gl = ax.gridlines(crs=projection, color='k', linestyle=':', zorder=5,
draw_labels=True)
gl.right_labels = False
gl.top_labels = False
gl.xlocator = mticker.FixedLocator(np.arange(-180., 181., 60.))
gl.ylocator = mticker.FixedLocator(np.arange(-80., 81., 20.))
gl.xformatter = cartopy.mpl.gridliner.LONGITUDE_FORMATTER
gl.yformatter = cartopy.mpl.gridliner.LATITUDE_FORMATTER
if levels is None:
plotHandle = ax.pcolormesh(Lons, Lats, array, cmap=colormap,
norm=norm, transform=projection,
zorder=1, rasterized=True)
else:
plotHandle = ax.contourf(Lons, Lats, array, cmap=colormap,
norm=norm, levels=levels, extend='both',
transform=projection, zorder=1)
_add_land_lakes_coastline(ax)
if contours is not None:
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
ax.contour(Lons, Lats, array, levels=contours, colors=lineColor,
linewidths=lineWidth, transform=projection)
cax = inset_axes(ax, width='5%', height='60%', loc='center right',
bbox_to_anchor=(0.08, 0., 1, 1),
bbox_transform=ax.transAxes, borderpad=0)
cbar = plt.colorbar(plotHandle, cax=cax, ticks=ticks, boundaries=ticks)
cbar.set_label(cbarlabel)
if defaultFontSize is None:
defaultFontSize = config.getint('plot', 'defaultFontSize')
matplotlib.rc('font', size=defaultFontSize)
# set up figure
if dpi is None:
dpi = config.getint('plot', 'dpi')
if figsize is None:
# set the defaults, depending on if we have 1 or 3 panels
if refArray is None:
figsize = (8, 5)
else:
figsize = (8, 13)
fig = plt.figure(figsize=figsize, dpi=dpi)
if (title is not None):
if titleFontSize is None:
titleFontSize = config.get('plot', 'titleFontSize')
title_font = {'size': titleFontSize,
'color': config.get('plot', 'titleFontColor'),
'weight': config.get('plot', 'titleFontWeight')}
fig.suptitle(title, y=0.935, **title_font)
plottitle_font = {'size': config.get('plot',
'threePanelPlotTitleFontSize')}
if refArray is None:
subplots = [111]
else:
subplots = [311, 312, 313]
projection = cartopy.crs.PlateCarree()
extent = [-180, 180, -85, 85]
dictModelRef = setup_colormap(config, colorMapSectionName, suffix='Result')
dictDiff = setup_colormap(config, colorMapSectionName, suffix='Difference')
axes = []
ax = plt.subplot(subplots[0], projection=projection)
plot_panel(ax, modelTitle, modelArray, **dictModelRef)
axes.append(ax)
if refArray is not None:
ax = plt.subplot(subplots[1], projection=projection)
plot_panel(ax, refTitle, refArray, **dictModelRef)
axes.append(ax)
ax = plt.subplot(subplots[2], projection=projection)
plot_panel(ax, diffTitle, diffArray, **dictDiff)
axes.append(ax)
_add_stats(modelArray, refArray, diffArray, Lats, axes)
if fileout is not None:
savefig(fileout, config, pad_inches=0.2)
plt.close()
def plot_projection_comparison(
config,
x,
y,
landMask,
modelArray,
refArray,
diffArray,
fileout,
colorMapSectionName,
projectionName,
title=None,
modelTitle='Model',
refTitle='Observations',
diffTitle='Model-Observations',
cbarlabel='units',
titleFontSize=None,
cartopyGridFontSize=None,
defaultFontSize=None,
vertical=False,
maxTitleLength=55):
"""
Plots a data set as a projection map.
Parameters
----------
config : mpas_analysis.configuration.MpasAnalysisConfigParser
the configuration, containing a [plot] section with options that
control plotting
x, y : numpy.ndarrays
1D x and y arrays of the corners of grid cells on the projection grid
landMask : numpy.ndarrays
model and observational or control run data sets
modelArray, refArray : numpy.ndarrays
model and observational or control run data sets
diffArray : float array
difference between modelArray and refArray
fileout : str
the file name to be written
colorMapSectionName : str
section name in ``config`` where color map info can be found.
title : str, optional
the subtitle of the plot
modelTitle : str, optional
title of the model panel
refTitle : str, optional
title of the observations or control run panel
diffTitle : str, optional
title of the difference (bias) panel
cbarlabel : str, optional
label on the colorbar
titleFontSize : int, optional
size of the title font
cartopyGridFontSize : int, optional
the size of text used by cartopy to label lon and lat
defaultFontSize : int, optional
the size of text other than the title
vertical : bool, optional
whether the subplots should be stacked vertically rather than
horizontally
projectionName : str, optional
the name of projection that the data is on, one of the projections
available via
:py:func:`mpas_analysis.shared.projection.get_cartopy_projection()`.
maxTitleLength : int, optional
the maximum number of characters in the title, beyond which it is
truncated with a trailing ellipsis
"""
# Authors
# -------
# Xylar Asay-Davis
def plot_panel(ax, title, array, colormap, norm, levels, ticks, contours,
lineWidth, lineColor):
title = limit_title(title, maxTitleLength)
ax.set_title(title, y=1.06, **plottitle_font)
ax.set_extent(extent, crs=projection)
gl = ax.gridlines(crs=cartopy.crs.PlateCarree(), color='k',
linestyle=':', zorder=5, draw_labels=True)
gl.xlocator = mticker.FixedLocator(lonLines)
gl.ylocator = mticker.FixedLocator(latLines)
gl.n_steps = 100
gl.right_labels = False
gl.left_labels = left_labels
gl.xformatter = cartopy.mpl.gridliner.LONGITUDE_FORMATTER
gl.yformatter = cartopy.mpl.gridliner.LATITUDE_FORMATTER
gl.xlabel_style['size'] = cartopyGridFontSize
gl.ylabel_style['size'] = cartopyGridFontSize
gl.rotate_labels = False
if levels is None:
plotHandle = ax.pcolormesh(x, y, array, cmap=colormap, norm=norm,
rasterized=True)
else:
plotHandle = ax.contourf(xCenter, yCenter, array, cmap=colormap,
norm=norm, levels=levels, extend='both')
if useCartopyCoastline:
_add_land_lakes_coastline(ax, ice_shelves=False)
else:
# add the model coastline
plt.pcolormesh(x, y, landMask, cmap=landColorMap)
plt.contour(xCenter, yCenter, landMask.mask, (0.5,), colors='k',
linewidths=0.5)
if contours is not None:
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
x_center = 0.5*(x[0:-1] + x[1:])
y_center = 0.5*(y[0:-1] + y[1:])
ax.contour(x_center, y_center, array, levels=contours,
colors=lineColor, linewidths=lineWidth)
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05,
axes_class=plt.Axes)
cbar = plt.colorbar(plotHandle, cax=cax)
cbar.set_label(cbarlabel)
if ticks is not None:
cbar.set_ticks(ticks)
cbar.set_ticklabels(['{}'.format(tick) for tick in ticks])
if defaultFontSize is None:
defaultFontSize = config.getint('plot', 'defaultFontSize')
matplotlib.rc('font', size=defaultFontSize)
if cartopyGridFontSize is None:
cartopyGridFontSize = config.getint('plot', 'cartopyGridFontSize')
# set up figure
dpi = config.getint('plot', 'dpi')
section = f'plot_{projectionName}'
useCartopyCoastline = config.getboolean(section, 'useCartopyCoastline')
if refArray is None:
figsize = config.getExpression(section, 'onePanelFigSize')
subplots = [111]
elif vertical:
figsize = config.getExpression(section, 'threePanelVertFigSize')
subplots = [311, 312, 313]
else:
figsize = config.getExpression(section, 'threePanelHorizFigSize')
subplots = [131, 132, 133]
latLines = config.getExpression(section, 'latLines', usenumpyfunc=True)
lonLines = config.getExpression(section, 'lonLines', usenumpyfunc=True)
# put latitude labels on the left unless we're in a polar projection
left_labels = projectionName not in ['arctic', 'antarctic']
dictModelRef = setup_colormap(config, colorMapSectionName, suffix='Result')
dictDiff = setup_colormap(config, colorMapSectionName, suffix='Difference')
fig = plt.figure(figsize=figsize, dpi=dpi)
if title is not None:
if titleFontSize is None:
titleFontSize = config.get('plot', 'titleFontSize')
title_font = {'size': titleFontSize,
'color': config.get('plot', 'titleFontColor'),
'weight': config.get('plot', 'titleFontWeight')}
fig.suptitle(title, y=0.95, **title_font)
plottitle_font = {'size': config.get('plot',
'threePanelPlotTitleFontSize')}
# set up land colormap
if not useCartopyCoastline:
colorList = [(0.8, 0.8, 0.8), (0.8, 0.8, 0.8)]
landColorMap = cols.LinearSegmentedColormap.from_list('land', colorList)
# locations of centers for contour plots
xCenter = 0.5 * (x[1:] + x[0:-1])
yCenter = 0.5 * (y[1:] + y[0:-1])
projection = get_cartopy_projection(projectionName)
extent = [x[0], x[-1], y[0], y[-1]]
ax = plt.subplot(subplots[0], projection=projection)
plot_panel(ax, modelTitle, modelArray, **dictModelRef)
if refArray is not None:
ax = plt.subplot(subplots[1], projection=projection)
plot_panel(ax, refTitle, refArray, **dictModelRef)
ax = plt.subplot(subplots[2], projection=projection)
plot_panel(ax, diffTitle, diffArray, **dictDiff)
if fileout is not None:
savefig(fileout, config)
plt.close()
def _add_stats(modelArray, refArray, diffArray, Lats, axes):
""" compute the means, std devs. and Pearson correlation """
weights = np.cos(np.deg2rad(Lats))
modelMean = np.average(modelArray, weights=weights)
_add_stats_text(
names=['Min', 'Mean', 'Max'],
values=[np.amin(modelArray), modelMean, np.amax(modelArray)],
ax=axes[0], loc='upper')
if refArray is not None:
if isinstance(modelArray, np.ma.MaskedArray):
# make sure we're using the MPAS land mask for all 3 sets of stats
mask = modelArray.mask
if isinstance(refArray, np.ma.MaskedArray):
# mask invalid where either model or ref array is invalid
mask = np.logical_or(mask, refArray.mask)
refArray = np.ma.array(refArray, mask=mask)
modelAnom = modelArray - modelMean
modelVar = np.average(modelAnom ** 2, weights=weights)
refMean = np.average(refArray, weights=weights)
refAnom = refArray - refMean
refVar = np.average(refAnom**2, weights=weights)
_add_stats_text(
names=['Min', 'Mean', 'Max'],
values=[np.amin(refArray), refMean, np.amax(refArray)],
ax=axes[1], loc='upper')
diffMean = np.average(diffArray, weights=weights)
diffVar = np.average((diffArray - diffMean)**2, weights=weights)
diffRMSE = np.sqrt(diffVar)
_add_stats_text(
names=['Min', 'Mean', 'Max'],
values=[np.amin(diffArray), diffMean, np.amax(diffArray)],
ax=axes[2], loc='upper')
covar = np.average(modelAnom*refAnom, weights=weights)
corr = covar/np.sqrt(modelVar*refVar)
_add_stats_text(
names=['RMSE', 'Corr'],
values=[diffRMSE, corr],
ax=axes[2], loc='lower')
def _add_stats_text(names, values, ax, loc):
if loc == 'upper':
text_ax = inset_axes(ax, width='17%', height='20%', loc='upper right',
bbox_to_anchor=(0.2, 0.1, 1., 1.),
bbox_transform=ax.transAxes, borderpad=0)
else:
text_ax = inset_axes(ax, width='17%', height='20%', loc='lower right',
bbox_to_anchor=(0.2, 0.03, 1., 1.),
bbox_transform=ax.transAxes, borderpad=0)
text = '\n'.join(names)
text_ax.text(0., 0., text, fontsize=10, horizontalalignment='left')
text = '\n'.join(['{:6.4g}'.format(val) for val in values])
text_ax.text(1., 0., text, fontsize=10, horizontalalignment='right')
text_ax.axis('off')
def _add_land_lakes_coastline(ax, ice_shelves=True):
land_50m = cartopy.feature.NaturalEarthFeature(
'physical', 'land', '50m', edgecolor='k',
facecolor='#cccccc', linewidth=0.5)
lakes_50m = cartopy.feature.NaturalEarthFeature(
'physical', 'lakes', '50m', edgecolor='k',
facecolor='white',
linewidth=0.5)
ax.add_feature(land_50m, zorder=2)
if ice_shelves:
ice_50m = cartopy.feature.NaturalEarthFeature(
'physical', 'antarctic_ice_shelves_polys', '50m', edgecolor='k',
facecolor='lightgray', linewidth=0.5)
ax.add_feature(ice_50m, zorder=3)
ax.add_feature(lakes_50m, zorder=4)
# vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python