# (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.
#
import numpy
import iris
import matplotlib
import matplotlib.colors
import Meteorographica.utils as utils
# Define a colour map appropriate for precip plots
# Dark green with varying transparency
precip_cmap = matplotlib.colors.LinearSegmentedColormap('p_cmap',
{'red' : ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'green': ((0.0, 0.3, 0.3),
(1.0, 0.3, 0.3)),
'blue' : ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'alpha': ((0.0, 0.0, 0.0),
(0.2, 0.0, 0.0),
(1.0, 0.95, 0.95)) })
# Plot precip as a colour map
[docs]def plot_cmesh(ax,pe,**kwargs):
"""Plots a variable as a colour map.
This is the same as :meth:`matplotlib.axes.Axes.pcolorfast`, except that it takes an :class:`iris.cube.Cube` instead of an array of colour values, and its colour defaults are chosen for plots of precipitation rate.
Args:
ax (:obj:`cartopy.mpl.geoaxes.GeoAxes`): Axes on which to draw.
pe (:obj:`iris.cube.Cube`): Variable to plot - must be 2d, with dimensions latitude and longitude.
Keyword Args:
raw (:obj:`bool`): If True, plot the colourmap on the native data resolution. If False (default), regrid the data to the given resolution before plotting.
resolution (:obj:`float`): What lat:lon resolution (in degrees) to interpolate pe.data to before plotting. Defaults to 0.25.
scale (:obj:`float`): This function is tuned for 20CR precip data - rates in Kg*m-2*s-1. For accumulated precip it will be necessary to scale it to an equivalent range. For CERA-20C, try 10. For ERA5, try 10 for enda and 3.6 for oper. Defaults to 1.
sqrt (:obj:`bool`): Heavy precip (tropical) is so much bigger than light precip that it helps a lot to flatten the distribution before plotting. Apply a square-root filter to the data before plotting? Defaults to True.
cmap (:obj:`matplotlib.colors.LinearSegmentedColormap`): Mapping of pe.data to plot colour. Defaults to green semi-transparent.
vmin (:obj:`float`): Data value that is shown as 'no precip' (after scaling and filtering). Increase this to show low-previp as zero instead. Defaults to 0.
vmax (:obj:`float`): Data value that is shown as 'heavy precip - darkest colour' (after scaling and filtering). Defaults to 0.025 Kg*m-2*s-1.
alpha (:obj:`float`): transparency to plot at, defaults to 1 (opaque).
zorder (:obj:`float`): Standard matplotlib parameter determining which things are plotted on top (high zorder), and which underneath (low zorder), Defaults to 40.
Returns:
See :meth:`matplotlib.axes.Axes.pcolorfast` - also adds the image to the plot.
|
"""
# Set keyword argument defaults
kwargs.setdefault('resolution',None)
kwargs.setdefault('scale' ,1.0)
kwargs.setdefault('sqrt' ,True)
kwargs.setdefault('cmap' ,precip_cmap)
kwargs.setdefault('vmin' ,0.0)
kwargs.setdefault('vmax' ,0.025)
kwargs.setdefault('alpha' ,1.0)
kwargs.setdefault('zorder' ,40)
if kwargs.get('resolution') is None:
cmesh_p=pe
else:
plot_cube=utils.dummy_cube(ax,kwargs.get('resolution'))
cmesh_p = pe.regrid(plot_cube,iris.analysis.Linear())
cmesh_p.data=cmesh_p.data*kwargs.get('scale')
if kwargs.get('sqrt'):
cmesh_p.data=numpy.sqrt(cmesh_p.data)
lats = cmesh_p.coord('latitude').points
lons = cmesh_p.coord('longitude').points
prate_img=ax.pcolorfast(lons, lats, cmesh_p.data,
cmap=kwargs.get('cmap'),
vmin=kwargs.get('vmin'),
vmax=kwargs.get('vmax'),
alpha=kwargs.get('alpha'),
zorder=kwargs.get('zorder'))
return prate_img
# Plot precip
[docs]def plot(ax,pe,**kwargs):
"""Plot precipitation.
Generic function for plotting precipitation. Use the 'type' argument to choose the plot style.
Args:
ax (:obj:`cartopy.mpl.geoaxes.GeoAxes`): Axes on which to draw.
pe (:obj:`iris.cube.Cube`): Variable to plot - must be 2d, with dimensions latitude and longitude.
Keyword Args:
type (:obj:`str`): Style to plot. Default is 'cmap', which delegates plotting to :meth:`plot_cmesh` and at the moment this is the only choice.
Other keyword arguments are passed to the style-specific plotting function.
|
"""
kwargs.setdefault('type','cmesh')
if kwargs.get('type')=='cmesh':
return plot_cmesh(ax,pe,**kwargs)
raise Exception('Unsupported precipitation plot type %s' %
kwargs.get('type'))
