def _get_figure(map_region='default', map_par=None, fig_par=None,
fig=None, ax=None, m=None, image=None):
"""Returns a matplotlib figure based on the parameters.
The idea is that I create a :class:`Structure` that contains the figure,
ax, and m instance. I also add a field for "indices".
This is so as to be able to reuse figures. This saves a huge amount of
time, as creating then basemap instance can be time consuming.
USAGE::
>>> fig = _get_figure()
or
>>> fig = _get_figure(map_region='polarcat')
Returns
This will return the "FIG" object, which has attributes: `fig`, `ax`,
`m`, and `indices`. The indices are used for deleting lines, texts,
collections, etc. if and when we are reusing the figure instance. The
indices basically give us a reference to the *empty* map, so we can
delete lines without losing meridians or parallels for example.
============ ======================================
keys description
============ ======================================
fig A pyplot.fig instance, use
plt.figure(FIG.fig.number) to make the
fig active (for example to use
plt.savefig('filename.png')
m The basemap instance so you can do:
x,y = FIG.m(lon,lat)
ax The axes
indices with index for texts, images, lines,
and collections
============ ======================================
"""
fig_par = fig_par or {}
figure = Structure()
if m is None:
fig, m = mp.get_base_image(map_region, map_par, fig_par, image=image)
elif fig is None:
map_par, fig_par = mp.map_regions(map_region, map_par, fig_par)
fig = plt.figure(**fig_par)
figure.fig = fig
figure.m = m
figure.ax = ax if ax is not None else fig.gca()
figure.indices = Structure(
texts=len(figure.ax.texts),
images=len(figure.ax.images),
collections=len(figure.ax.collections),
lines=len(figure.ax.lines),
)
print("Using figure: %s" % figure.fig.number)
return figure
def plot_curtain(H, data,
nx=None,
ny=None,
data_range=None,
units='ppbv',
datainfo_str=None,
asl=True,
plottitle=None,
log=True,
figure=None,
cax_title=None,
method='contourf',
fig_par=None):
""" plot_sensitivity: core function for plotting FLEXPART output.
Usage::
> FIG = plot_sensitivity(H,data,*kwargs)
This returns the FIGURE object, and plots the sensitivity from the data contained in the "D"
array.
Inputs
H = a :class:`Header` instance for a FLEXPART run.
data = a 2d data array containing the sensitivity values to plot, this can be extracted from a
grid instance (see :func:`readgridV8` and :func:`get_slabs`)
Returns
A "mapping.py" ``FIGURE`` object.
Arguments
.. tabularcolumns:: |l|L|
============= ================================================
keyword Description [default]
============= ================================================
data_range range of data for scale bars, if None will
be taken from min/max of data
asl [True] plot the units in m.a.s.l
cax_title string to be passed as colorbar title (units will
be passed to the format argument)
units units for the scale bar
datainfo_str A string for labeling the scale bar.
plottitle Title for the plot.
rel_i Release index to plot from the data array
map_region A map_region specified in mapping.py
projection [deprecated] use pre-defined map_regions.
dropm Force creation of a new basemap instance
coords Used with autofit option. An array of lat,lon
values for the mapping module to autofit a
basemap instance to.
autofit Try to generate map_region automagically (flakey)
overlay Force removal of previous figure elements.
transform For use with imshow method, if your data is not
in same coordinates as projection, try to transform
the data to the basemap projection.
log Create a logarithmic color scale.
figure A FIGURE instance from mapping module get_FIGURE
MapPar A Structure of paramters to be passed to the
basemap class when creating an instance.
method The method to use for plotting array data. May be
one of: [pcolormesh], imshow, or contourf
lsmask set to True to draw a grey landseamask [False]
============= ================================================
.. todo::
A lot!! There are some problems here and it is sensitive to options.
lsmask = True seems to only work with certain projections (POLARCAT)
.. note::
This is the primary main function for creating plots of flexpart output.
Most the other routines are simply wrappers to this function, passing
arguments in with some predefined settings. For more information on the
mechanics of this function, see the mapping.py module and the matplotlib
basemap toolkit.
"""
methods = ['imshow', 'pcolormesh', 'contourf', 'contour', 'None']
assert method in methods, "method keyword must be one of: %s" % methods
if figure is None:
fig_par = fig_par or {}
fig = plt.figure(**fig_par)
ax = fig.add_subplot(111)
figure = Structure(fig=fig, ax=ax)
else:
fig = figure.fig
ax = figure.ax
# Make the figure current
plt.figure(fig.number)
plt.axes(ax)
# Get min/max range
#data = data.slabs[0]
if data_range is not None:
dat_min = data_range[0]
dat_max = data_range[1]
else:
dat_min, dat_max = [data.min(), data.max()]
if log:
clevs = _log_clevs(dat_min, dat_max)
else:
clevs = [i for i in np.arange(dat_min, dat_max, (dat_max - dat_min) / 100)]
# # Set up the IMAGE
# # cmapnames = ['jet', 'hsv', 'gist_ncar', 'gist_rainbow', 'cool', 'spectral']
colmap = _gen_flexpart_colormap()
colmap.set_over(color='k', alpha=0.8)
topodat = data
if method == 'imshow':
im = plt.imshow(np.flipud(topodat), cmap=colmap, zorder=-1,
norm=mpl.colors.LogNorm(vmin=clevs[0],
vmax=clevs[-1]))
if method == 'pcolormesh':
im = plt.pcolormesh(nx, ny, topodat, cmap=colmap,
norm=mpl.colors.LogNorm(vmin=clevs[0],
vmax=clevs[-1]))
if method == 'contourf':
print('*******')
print(nx)
print(ny)
print('*******')
print(topodat)
print('*******')
im = plt.contourf(topodat)
# im = plt.contourf(nx, ny, topodat, cmap=colmap, levels=clevs,
# norm=mpl.colors.LogNorm(vmin=clevs[0],
# vmax=clevs[-1]))
if method == 'contour':
im = plt.contour(nx, ny, topodat, cmap=colmap,
norm=mpl.colors.LogNorm(vmin=clevs[0],
vmax=clevs[-1]))
# # Get the current axes, and properties for use later
pos = ax.get_position()
l, b, w, h = pos.bounds
# # CREATE COLORBAR
# # Note, with upgrades to matplotlib and basemap had to make some
# # changes here... no more 'ghost' axes
# # does a colorbar already exist?
try:
cb = figure.cb
cax = figure.cax
cb.update_normal(im)
except:
# # make a copy of the image object, change
# # colormap to linear version of the precip colormap.
# # create new axis for colorbar.
h = 0.8 * h
l = l + w + .02
b = 0.5 - (h / 2)
w = 0.025
cax = plt.axes([l, b, w, h])
# # using im2, not im (hack to prevent colors from being
# # too compressed at the low end on the colorbar - results
# # from highly nonuniform colormap)
cb = fig.colorbar(im, cax=cax) # , format='%3.2g') # draw colorbar
figure.cax = cax
figure.cb = cb
# # set colorbar label and ticks
p_cax = mpl.font_manager.FontProperties(size='6')
clabels = list(clevs[::10]) # #clevs, by 10 steps
clabels.append(clevs[-1]) # # add the last label
# cax.set_yticks(np.linspace(clabels[0],clabels[-1],len(clabels)))
cax.set_yticks(np.linspace(0, 1, len(clabels)))
cax.set_yticklabels(['%3.2g' % cl for cl in clabels])
# fontproperties=p_cax)
if cax_title:
cax.set_title(cax_title.format(units), fontproperties=p_cax)
else:
cax.set_title('sensitivity\n({0})'.format(units),
fontproperties=p_cax)
# # make the original axes current again
plt.axes(ax)
plt.grid(True)
figure.ax = ax
figure.fig = fig
if plottitle != None:
figure.ax.set_title(plottitle, fontsize=10)
return figure
def map_regions(map_region='default', map_par=None, fig_par=None):
"""Given a `map_region`, return the associated parameters in mapping DB.
USAGE::
map_par, fig_par = map_regions(map_region="polarcat")
The list of regions know by reflexible by default is located in the
package file named 'mapping_db.yml'. If you want to create a new region,
or override an existing one in reflexible itself, you can create it in
your own YAML file. For example, suppose that you have the next
'myregions.yml' file::
northern_hemisphere:
descr: Northern Hemisphere
alias: my_own_nh
map_par:
projection: cyl
llcrnrlat: 0.5
urcrnrlat: 90
llcrnrlon: -180.7
urcrnrlon: 180
resolution: c
# anchor: W
fig_par:
figsize: [8, 3] # w,h tuple
axlocs: [0.1, 0.1, .7, .8]
For informing reflexible on where your mapping file is located, you just
create the REFLEXIBLE_MAPDB environment variable with its path::
$ export REFLEXIBLE_MAPDB = $HOME/my_analysis/myregions.yml
Since this moment on, the definitions in your file will be used (with
highest priority) for finding the regions specified in `map_region`.
Returns
Two dictionaries, first a map_par dictionary with keywords that are the
same as what is need to create a basemap instance of matplotlib:
============ ==========================
keys description
============ ==========================
llcrnrlat lower left latitude
llcrnrlon lower left longitude
urcrnrlat upper right latitude
urcrnrlon upper right longitude
area_thresh area threshold
resolution resolution
projection projection
lat_1 lat_1
lon_0 lon_0
rsphere (6378137.00,6356752.3142)
m you can pass an m object
which is needed for some
regions
============ ==========================
Second, a fig_par dictionary that contains options that may be passed to
the :mod:`matplotlib.pyplot` :func:`figure` function:
============ ==========================
keys description
============ ==========================
figsize size of the figure
axlocs locations of the axes
============ ==========================
.. note::
You can override the returned map_par and fig_par dicts by passing
dicts through the optional `map_par` and `fig_par` parameters.
"""
# Set some default values
map_par_ = Structure(anchor='C')
fig_par_ = Structure(
figsize=[8, 7], # w,h tuple
axlocs=[0.05, 0.01, .8, .9], # rect = l,b,w,h
)
# Get the database out of the system YAML file
mapdb_file = os.path.join(os.path.dirname(__file__), 'mapping_db.yml')
with open(mapdb_file) as mapdb:
mapping_db = yaml.safe_load(mapdb)
# and merge it with a possible one pointed by REFLEXIBLE_MAPDB env var
if 'REFLEXIBLE_MAPDB' in os.environ:
user_mapdb_file = os.environ['REFLEXIBLE_MAPDB']
with open(user_mapdb_file) as mapdb:
mapping_db.update(yaml.safe_load(mapdb))
# Lookup the region and its aliases
try:
region = mapping_db[map_region]
except KeyError:
# Lookup aliases
for key in mapping_db:
if 'alias' in mapping_db[key]:
alias = mapping_db[key]['alias']
if map_region in re.split(r',\s*', alias):
region = mapping_db[key]
break
else:
raise KeyError("region {} not found".format(map_region))
# Get the params
map_par_.update(region['map_par']) # map_par should be always there
fig_par_.update(region.get('fig_par', {})) # fig_par not always present
# Override params if `map_par` or `fig_par` are passed
map_par_.update(map_par or {})
fig_par_.update(fig_par or {})
# Just in case
map_par_.pop('m', None)
return map_par_, fig_par_
def _get_figure(map_region='default', map_par=None, fig_par=None,
fig=None, ax=None, m=None, image=None):
"""Returns a matplotlib figure based on the parameters.
The idea is that I create a :class:`Structure` that contains the figure,
ax, and m instance. I also add a field for "indices".
This is so as to be able to reuse figures. This saves a huge amount of
time, as creating then basemap instance can be time consuming.
USAGE::
>>> fig = _get_figure()
or
>>> fig = _get_figure(map_region='polarcat')
Returns
This will return the "FIG" object, which has attributes: `fig`, `ax`,
`m`, and `indices`. The indices are used for deleting lines, texts,
collections, etc. if and when we are reusing the figure instance. The
indices basically give us a reference to the *empty* map, so we can
delete lines without losing meridians or parallels for example.
============ ======================================
keys description
============ ======================================
fig A pyplot.fig instance, use
plt.figure(FIG.fig.number) to make the
fig active (for example to use
plt.savefig('filename.png')
m The basemap instance so you can do:
x,y = FIG.m(lon,lat)
ax The axes
indices with index for texts, images, lines,
and collections
============ ======================================
"""
fig_par = fig_par or {}
figure = Structure()
if m is None:
if image:
fig, m = mp.get_base_image(image, map_region, map_par, fig_par)
else:
fig, m = mp.get_base1(map_region, map_par, fig_par, fig=fig)
if fig is None:
map_par, fig_par = mp.map_regions(map_region, map_par, fig_par)
fig = plt.figure(**fig_par)
figure.fig = fig
figure.m = m
figure.ax = ax if ax is not None else fig.gca()
figure.indices = Structure(
texts=len(figure.ax.texts),
images=len(figure.ax.images),
collections=len(figure.ax.collections),
lines=len(figure.ax.lines),
)
print("Using figure: %s" % figure.fig.number)
return figure
def plot_curtain(H, data, data_range=None,
nx=None,
ny=None,
units='ppbv',
datainfo_str=None,
asl=True,
plottitle=None,
log=True,
figure=None,
cax_title=None,
method='contourf',
fig_par=None):
""" plot_sensitivity: core function for plotting FLEXPART output.
Usage::
> FIG = plot_sensitivity(H,data,*kwargs)
This returns the FIGURE object, and plots the sensitivity from the data contained in the "D"
array.
Inputs
H = a :class:`Header` instance for a FLEXPART run.
data = a 2d data array containing the sensitivity values to plot, this can be extracted from a
grid instance (see :func:`readgridV8` and :func:`get_slabs`)
Returns
A "mapping.py" ``FIGURE`` object.
Arguments
.. tabularcolumns:: |l|L|
============= ================================================
keyword Description [default]
============= ================================================
data_range range of data for scale bars, if None will
be taken from min/max of data
asl [True] plot the units in m.a.s.l
cax_title string to be passed as colorbar title (units will
be passed to the format argument)
units units for the scale bar
datainfo_str A string for labeling the scale bar.
plottitle Title for the plot.
rel_i Release index to plot from the data array
map_region A map_region specified in mapping.py
projection [deprecated] use pre-defined map_regions.
dropm Force creation of a new basemap instance
coords Used with autofit option. An array of lat,lon
values for the mapping module to autofit a
basemap instance to.
autofit Try to generate map_region automagically (flakey)
overlay Force removal of previous figure elements.
transform For use with imshow method, if your data is not
in same coordinates as projection, try to transform
the data to the basemap projection.
log Create a logarithmic color scale.
figure A FIGURE instance from mapping module get_FIGURE
MapPar A Structure of paramters to be passed to the
basemap class when creating an instance.
method The method to use for plotting array data. May be
one of: [pcolormesh], imshow, or contourf
lsmask set to True to draw a grey landseamask [False]
============= ================================================
.. todo::
A lot!! There are some problems here and it is sensitive to options.
lsmask = True seems to only work with certain projections (POLARCAT)
.. note::
This is the primary main function for creating plots of flexpart output.
Most the other routines are simply wrappers to this function, passing
arguments in with some predefined settings. For more information on the
mechanics of this function, see the mapping.py module and the matplotlib
basemap toolkit.
"""
methods = ['imshow', 'pcolormesh', 'contourf', 'contour', 'None']
assert method in methods, "method keyword must be one of: %s" % methods
if figure is None:
fig_par = fig_par or {}
fig = plt.figure(**fig_par)
ax = fig.add_subplot(111)
figure = Structure(fig=fig, ax=ax)
else:
fig = figure.fig
ax = figure.ax
# Make the figure current
plt.figure(fig.number)
plt.axes(ax)
# Get min/max range
#data = data.slabs[0]
if data_range is not None:
dat_min = data_range[0]
dat_max = data_range[1]
else:
dat_min, dat_max = [data.min(), data.max()]
if log:
clevs = _log_clevs(dat_min, dat_max)
else:
clevs = [i for i in np.arange(dat_min, dat_max, (dat_max - dat_min) / 100)]
# # Set up the IMAGE
# # cmapnames = ['jet', 'hsv', 'gist_ncar', 'gist_rainbow', 'cool', 'spectral']
colmap = _gen_flexpart_colormap()
colmap.set_over(color='k', alpha=0.8)
topodat = data
if method == 'imshow':
im = plt.imshow(np.flipud(topodat), cmap=colmap, zorder=-1,
norm=mpl.colors.LogNorm(vmin=clevs[0],
vmax=clevs[-1]))
if method == 'pcolormesh':
im = plt.pcolormesh(nx, ny, topodat, cmap=colmap,
norm=mpl.colors.LogNorm(vmin=clevs[0],
vmax=clevs[-1]))
if method == 'contourf':
im = plt.contourf(topodat)
# im = plt.contourf(nx, ny, topodat, cmap=colmap, levels=clevs,
# norm=mpl.colors.LogNorm(vmin=clevs[0],
# vmax=clevs[-1]))
if method == 'contour':
im = plt.contour(nx, ny, topodat, cmap=colmap,
norm=mpl.colors.LogNorm(vmin=clevs[0],
vmax=clevs[-1]))
# # Get the current axes, and properties for use later
pos = ax.get_position()
l, b, w, h = pos.bounds
# # CREATE COLORBAR
# # Note, with upgrades to matplotlib and basemap had to make some
# # changes here... no more 'ghost' axes
# # does a colorbar already exist?
try:
cb = figure.cb
cax = figure.cax
cb.update_normal(im)
except:
# # make a copy of the image object, change
# # colormap to linear version of the precip colormap.
# # create new axis for colorbar.
h = 0.8 * h
l = l + w + .02
b = 0.5 - (h / 2)
w = 0.025
cax = plt.axes([l, b, w, h])
# # using im2, not im (hack to prevent colors from being
# # too compressed at the low end on the colorbar - results
# # from highly nonuniform colormap)
cb = fig.colorbar(im, cax=cax) # , format='%3.2g') # draw colorbar
figure.cax = cax
figure.cb = cb
# # set colorbar label and ticks
p_cax = mpl.font_manager.FontProperties(size='6')
clabels = list(clevs[::10]) # #clevs, by 10 steps
clabels.append(clevs[-1]) # # add the last label
# cax.set_yticks(np.linspace(clabels[0],clabels[-1],len(clabels)))
cax.set_yticks(np.linspace(0, 1, len(clabels)))
cax.set_yticklabels(['%3.2g' % cl for cl in clabels])
# fontproperties=p_cax)
if cax_title:
cax.set_title(cax_title.format(units), fontproperties=p_cax)
else:
cax.set_title('sensitivity\n({0})'.format(units),
fontproperties=p_cax)
# # make the original axes current again
plt.axes(ax)
plt.grid(True)
figure.ax = ax
figure.fig = fig
if plottitle != None:
figure.ax.set_title(plottitle, fontsize=10)
return figure