from __future__ import (absolute_import, division, print_function)
import matplotlib.pyplot as plt
import iris
import iris.quickplot as qplt
import iris.plot as iplt
fname = iris.sample_data_path('air_temp.pp')
temperature_cube = iris.load_cube(fname)
# Get the Purples "Brewer" palette.
brewer_cmap = plt.get_cmap('brewer_Purples_09')
# Draw the contours, with n-levels set for the map colours (9).
# NOTE: needed as the map is non-interpolated, but matplotlib does not provide
# any special behaviour for these.
qplt.contourf(temperature_cube, brewer_cmap.N, cmap=brewer_cmap)
# Add a citation to the plot.
iplt.citation(iris.plot.BREWER_CITE)
# Add coastlines to the map created by contourf.
plt.gca().coastlines()
plt.show()
import matplotlib.cm as mpl_cm
import matplotlib.pyplot as plt
import iris
import iris.quickplot as qplt
import iris.plot as iplt
fname = iris.sample_data_path('air_temp.pp')
temperature_cube = iris.load_cube(fname)
# Load a Cynthia Brewer palette.
brewer_cmap = mpl_cm.get_cmap('brewer_RdBu_11')
# Draw the contour with 25 levels
qplt.contourf(temperature_cube, 25, cmap=brewer_cmap)
# Add a citation to the plot.
iplt.citation(iris.plot.BREWER_CITE)
# Add coastlines to the map created by contourf
plt.gca().coastlines()
plt.show()
def compare_variable(varname, limits, scaling, units):
varname = varname[0]
def open_variable(veg):
annual_average, grid = loadFile(veg, varname, 2000, 1)
annual_average[:,:,:] = 0.0
TS = np.zeros([len(years)*12])
t = 0
for y in years:
for m in range(1,13):
dat, nn = loadFile(veg, varname, y, m, grid = grid)
annual_average = annual_average + dat
TS[t] = global_total(dat)
t = t + 1
annual_average = annual_average * scaling / t
fname = arr_output_dir + veg + '_' + varname + '.nc'
output_file(fname, varname, annual_average)
TS = TS * scaling /(12 * 1000000000000.0)
return(fname, TS)
aa1, ts1 = open_variable(veg_dir[0])
aa2, ts2 = open_variable(veg_dir[1])
#############
## Plot ##
#############
## setup plot
git = 'repo: ' + git_info.url + '\n' + 'rev: ' + git_info.rev
crs_latlon = ccrs.PlateCarree()
crs_proj = ccrs.Robinson()
nz = iris.load_cube(aa1).shape[0]
if nz== 5:
figsize = (28, 12)
fontsize = 16
px = 3
py = 5
labs = [['BL'],['NL'],['C3'],['C4'],['Shrub']]
ndiagnose_lims = 6
elif nz == 4:
figsize = (20, 12)
fontsize = 16
px = 3
py = 4
labs =[['DPM'], ['RPM'], ['BIO'], ['HUM']]
ndiagnose_lims = 6
elif nz == 1:
figsize = (12, 8)
fontsize = 20
px = 2
py = 2
labs = [['']]
ndiagnose_lims = 6
else:
browser()
cmap1 = mpl_cm.get_cmap('brewer_YlGn_09')
cmap2 = mpl_cm.get_cmap('brewer_RdYlBu_11') #revserse
#cmap2 = reverse_colourmap(cmap2)
dcmap = mpl_cm.get_cmap('brewer_PRGn_11')
## Plot maps
def plot_map(fname , limits, title, ssp, extend = 'max'):
if len(fname) == 2:
plotable = [iris.load_cube(i) for i in fname]
plotable = plotable[1] - plotable[0]
else:
plotable = iris.load_cube(fname)
nz = iris.load_cube(aa1).shape[0]
ssp = (ssp - 1) * nz
for i in range(0, nz):
ssp = ssp + 1
print(ssp)
ax = fig.add_subplot(px, py, ssp, projection = crs_proj)
ax.set_extent((-180, 170, -65, 90.0), crs = crs_latlon)
ax.coastlines(linewidth = 0.5, color = 'navy')
ax.gridlines(crs = crs_latlon, linestyle = '--')
if diagnose_lims: limits = hist_limits(plotable[i], ndiagnose_lims)
if limits[0] < 0.0:
if limits[-1] <= 0.0:
cmap = cmap2
#norm = colours.BoundaryNorm(boundaries = np.append(limits, 0.01), ncolors = 8)
norm = colours.BoundaryNorm(boundaries = limits, ncolors = 7)
else:
cmap = dcmap
norm = colours.BoundaryNorm(boundaries = limits, ncolors = 12)
#norm = MidpointNormalize(midpoint=0.)
else:
cmap = cmap1
norm = colours.BoundaryNorm(boundaries = limits, ncolors = 8)
plt.gca().coastlines()
cbi = qplt.contourf(plotable[i], limits, cmap = cmap, extend = extend, norm = norm)
#plt.colorbar(cbi)
titlei = title + labs[i][0]
plt.title(titlei)
fig = plt.figure(figsize = figsize)
fig.suptitle(varname, fontsize = fontsize)
plot_map(aa1, limits[0], 'Veg1', 1)
plot_map(aa2, limits[0], 'Veg2', 2)
plot_map([aa1, aa2], limits[1], 'Difference', 3, 'both')
iplt.citation(git)
## line plot
if nz == 1:
ax = fig.add_subplot(224)
t = np.arange(years[0], years[-1] + 1, 1.0/12.0)
ax.get_xaxis().get_major_formatter().set_scientific(False)
plt.plot(t, ts1, 'r', label="Veg 1")
plt.plot(t, ts2, 'b--', label="Veg 2")
#plt.plot(t, ts1, 'r', t, ts2, 'b--')
plt.ylabel(units)
plt.legend(bbox_to_anchor=(0., 0.898, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.)
fig_name = 'figs/' + varname + '_veg2-veg1_comparison.pdf'
plt.savefig(fig_name, bbox_inches = 'tight')
def plot_map_helper(cube, title_name, save_out=False):
"""
Plot (and save) maps given date and variable in cube
:param cube: data
:param title_name: title of graph
:param save_out: if set, then saves to png file
"""
# Plot the graph
fig, ax = plt.subplots(figsize=(11, 4))
# Get the Purples "Brewer" palette for plot
brewer_cmap = plt.get_cmap('brewer_Purples_09')
# Draw the contours, with n-levels set for the map colours
lats = cube.coord('latitude')
lons = cube.coord('longitude')
masked_array = np.ma.array(cube.data, mask=np.isnan(cube.data))
# Get max and min of lons and lats
lon_min, lon_max, lat_min, lat_max = min(lons.points), max(
lons.points), min(lats.points), max(lats.points)
# Only set if one lat/lon
frame, cf = False, None
try:
# Overlay world map
m = Basemap(projection='mill', lat_ts=10, resolution='c', ax=ax)
# Plot actual data
scat = False
if len(lons.points) < 30 or len(lats.points) < 30:
scat = True
if len(lons.points) == 1 and len(lats.points) == 1:
frame = True
label = "Point (" + str(lons.points[0]) + ", " + str(
lons.points[0]) + ")"
m.scatter(lons.points,
lats.points,
latlon=True,
s=20,
c='blue',
marker='o',
label=label)
else:
m.scatter(lons.points,
lats.points,
latlon=True,
s=20,
c='blue',
marker='o')
else:
# Overlay world map
m = Basemap(projection='mill', lat_ts=10, llcrnrlon=lon_min, \
urcrnrlon=lon_max, llcrnrlat=lat_min, urcrnrlat=lat_max, \
resolution='c', ax=ax)
x, y = m(*np.meshgrid(lons.points, lats.points))
cf = m.contourf(x,
y,
masked_array,
shading='flat',
cmap=brewer_cmap)
m.drawcoastlines()
m.drawparallels(np.arange(-90., 90., 30.), labels=[1, 0, 0, 0], ax=ax)
m.drawmeridians(np.arange(-180., 180., 60.),
labels=[0, 0, 0, 1],
ax=ax)
except Exception as err:
print("ERROR in function plot_map: " + str(err))
plt.close(fig)
return None
if not scat:
fig.colorbar(cf, ax=ax, label=cube.units)
# Add a citation to the plot.
iplt.citation(iris.plot.BREWER_CITE)
ax.set_title(title_name)
# Have legend if point
if frame:
plt.legend(frameon=True)
if save_out:
title_name = make_into_file_name(title_name)
plt.savefig(os.path.join(directories.ANALYSIS, title_name))
print("Map is saved in the " + directories.ANALYSIS +
" folder as a png file.")
def test_figure(self):
iplt.citation(self.text, figure=self.figure)
self.check_graphic()
def test_axes(self):
iplt.citation(self.text, axes=self.axes)
self.check_graphic()
def test(self):
iplt.citation(self.text)
self.check_graphic()
lonLat[1:, 0] = round2grid(lonLat[1:, 0], lon)
lonLat[1:, 1] = round2grid(lonLat[1:, 1], lat)
#################################################
## Output ##
#################################################
git = 'repo: ' + git_info.url() + '\n' + 'rev: ' + git_info.rev
## Write to file
np.savetxt(file_out, lonLat[:,[1,0]], fmt = '%.4f', delimiter = '\t')
## Plot
crs_latlon = ccrs.PlateCarree()
crs_proj = ccrs.Robinson()
fig = plt.figure()
ax = plt.axes(projection = crs_proj)
ax.set_extent((-180, 170, -65, 90.0), crs=crs_latlon)
ax.coastlines(linewidth=0.75, color='navy')
ax.gridlines(crs=crs_latlon, linestyle='--')
iplt.contourf(mask, [-0.5, 0.5, 1.5], colors=('#6666ff', '#acff88', 'b'))
lonLat = crs_proj.transform_points(crs_latlon,lonLat[1:,0],lonLat[1:,1])# lonLat[1:,]
plt.scatter(lonLat[:,0],lonLat[:,1], c = '#aa0000', marker = 'x')
plt.gca().coastlines()
iplt.citation(git)
plt.savefig(fig_name, bbox_inches='tight')
def test_axes(self):
iplt.citation(self.text, axes=self.axes)
self.check_graphic()
def test_figure(self):
iplt.citation(self.text, figure=self.figure)
self.check_graphic()
def test(self):
iplt.citation(self.text)
self.check_graphic()
