def show_reduction_pct(self, locations=None, site_labels=None):
""" locations: list of Location objects
site_labels ("fraction" | "names"): switch to turn on labeling of the markers of the sites specified by argument locations. If "fraction", sites are labeled with their fractional precipitation reduction; if "names", they are labeled with teh location name.
"""
frac = self.get_IDE_reduction()
cmap, norm = colormap_nlevs.setup_colormap(0.0,
1.0,
nlevs=11,
cmap=plt.get_cmap('YlGnBu'),
extend='neither')
wcm = WorldCalMap()
wcm.plot(frac,
self.dlon,
self.dlat,
vmin=0.0,
vmax=1.0,
midpoint=0.5,
bands_above=6,
bands_below=6,
extend='neither',
locations=locations,
cbar_tstr=((u'drought simulation precipitation as fraction '
u'of 1948\u20132004 annual mean precipitation')),
site_labels="names")
wcm.crop_save(
os.path.join(
os.getenv('HOME'), 'plots', 'maptest',
'IDE_pct_map_interp{}.png'.format(
self.lat.size != self.dlat.size)))
plt.close(wcm.fig)
def map_nearest_noaa_site(self):
"""Plot the top boundary using
stem_pytools.STEM_mapper.Mapper124x124, and save the map to
./top_bnd.pdf
"""
stem_lon = self.d.get_lon()
stem_lat = self.d.get_lat()
top_cmap, top_norm = colormap_nlevs.setup_colormap(
np.floor(self.top_bnd.min()),
np.ceil(self.top_bnd.max()),
nlevs=6,
cmap=plt.get_cmap('Oranges'),
extend='neither')
m = STEM_mapper.Mapper124x124(self.top_bnd).draw_map(
fast_or_pretty='pretty',
cmap=top_cmap,
norm=top_norm,
t_str='',
cbar_fmt_str='%d')
m.map.fig.set_figheight(8)
m.map.fig.set_figwidth(8)
sites_col = "#1b9e77" # http://colorbrewer2.org dark2[1]
fontsz = 14
for this_site in np.unique(self.nearest_site_array):
idx = np.where(self.sites_summary.site_code == this_site)[0][0]
this_x, this_y = m.map.map(self.sites_summary.longitude[idx],
self.sites_summary.latitude[idx])
m.map.ax_map.text(this_x,
this_y,
this_site,
fontsize=fontsz,
color=sites_col,
horizontalalignment='center',
verticalalignment='center')
# draw bounds between NOAA sites' regions of top bound
vals = np.unique(self.top_bnd)
levs = vals[0:-1] + (np.diff(vals) / 2.0)
m.map.map.contour(stem_lon, stem_lat, self.top_bnd,
levels=levs, latlon=True, colors=sites_col)
# plot lateral bounds cell indices on map
lat_bounds_col = "#7570b3" # http://colorbrewer2.org dark2[2]
lat = domain.get_2d_perimeter(d.get_lat())
lon = domain.get_2d_perimeter(d.get_lon())
x, y = m.map.map(lon, lat)
for i in (0, 62, 186, 330, 434):
m.map.ax_map.text(x[i], y[i], str(i),
color="black",
size=fontsz,
bbox={"color": "black", "facecolor": "white"})
# label the color bar
m.map.ax_cmap.set_title('[COS] (ppt)\n',
fontdict={'fontsize': fontsz})
m.map.ax_cmap.tick_params(labelsize=fontsz)
m.map.fig.savefig('top_bnd.pdf')
def __set_cmap_norm(self):
"""get colormap, normalizer
"""
self.cmap, self.norm = colormap_nlevs.setup_colormap(
vmin=self.vmin,
vmax=self.vmax,
nlevs=self.ncolorlevs,
cmap=self.base_colormap,
extend='neither')
def plot_landuse(ax, lon, lat, data):
cmap, norm = setup_colormap(
0,
21,
nlevs=21,
cmap=get_IGBP_modMODIS_21Category_PFTs_cmap())
cm = ax.pcolormesh(lon,
lat,
data,
cmap=cmap,
norm=norm)
ax.set_extent((lon.min(), lon.max(),
lat.min(), lat.max()))
ax.coastlines(resolution='10m', color='black')
return(cm)
nha_x = 33
nha_y = 70
cma_x = 29
cma_y = 60
sca_x = 23
sca_y = 44
molecules_m3_to_ppt = 1e12
cos_sca = cos_clim['data'][:, :, sca_x, sca_y] * molecules_m3_to_ppt
cmap, norm = colormap_nlevs.setup_colormap(vmin=cos_sca.min() - 1,
vmax=cos_sca.max() + 1,
nlevs=20,
cmap=plt.get_cmap('Blues'),
extend='neither')
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 8))
cm = ax.pcolormesh(np.transpose(cos_sca), cmap=cmap, norm=norm,
linewidth=0, rasterized=True)
ax.set_xlim([0, 1488])
ax.set_ylim([0, 22])
ax.set_ylabel('STEM Z level')
ax.set_xlabel('hours since 1 July 2008 00:00')
ax.set_title('SCA [COS], climatological bounds STEM run')
ax_cb = plt.colorbar(cm, cmap=cmap, norm=norm, ax=ax)
ax_cb.set_label('[COS] (ppt)')
ax_cb.solids.set_rasterized(True)
fig.savefig('/global/homes/t/twhilton/plots/SCA_COS.png')
def add_ocs_contour_plot(self,
lons,
lats,
data,
t_str=None,
vmax=None,
vmin=None,
n_levs=20,
cmap=cm.get_cmap('Blues'),
extend='neither',
cbar_t_str=None,
colorbar_args={},
plotfunc=None):
"""Draw filled contours of the specified OCS data over the
map.
INPUT PARMATERS:
lons: MxN numpy array of longitudes
lats: MxN numpy array of latitudes
data: MxN numpy array of data to be contoured
t_str: title string for the map axes
vmax: maximum value for color scale. Default is data.max()
vmin: minimum value for color scale. Default is data.min()
n_levs: number of contour levels. Default is 20.
cmap: color map to use for contours. e.g. cm.get_cmap('Blues'). See
http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps.
extend: one of [ 'neither' | 'both' | 'min' | 'max' ], and is
passed to matplotlib.pyplot.contourf via the 'extend' keyword.
cbar_t_str: title string for colorbar
colorbar_args: dict; additional keyword arguments to be passed
to matplotlib.pypolt.colorbar
plotfunc: {basemap.Basemap.pcolor} |
basemap.Basemap.pcolormesh | basemap.Basemap.contourf:
function to produce the contour plot.
"""
if vmin is None:
vmin = data.min()
if vmax is None:
vmax = data.max()
cmap, norm = colormap_nlevs.setup_colormap(vmin=vmin,
vmax=vmax,
nlevs=n_levs,
cmap=cmap,
extend=extend)
if plotfunc is None:
plotfunc = self.map.pcolor
cs = plotfunc(lons,
lats,
data,
ax=self.ax_map,
latlon=True,
cmap=cmap,
vmin=vmin,
vmax=vmax,
norm=norm)
if self.ax_cmap is not None:
# plot a color legend
plt.colorbar(mappable=cs,
cax=self.ax_cmap,
cmap=cmap,
norm=norm,
**colorbar_args)
if cbar_t_str is not None:
self.ax_cmap.set_title(cbar_t_str)
if t_str is not None:
# place a time label at (140, 20N) (out in the Pacific
# Ocean west of Mexico)
t_lab_lon = -140
t_lab_lat = 20
# t_str = datetime.strftime( t_str, '%d %B %Y %H:%M' )
self.ax_map.text(*self.map(t_lab_lon, t_lab_lat),
s=t_str,
bbox=dict(facecolor='white', alpha=0.5))
# self.draw_mw_box()
return(cs)
def draw_all_panels(cos, gpp, fCOS, models=None, models_str=None):
if models is None:
models = ['MPI_161',
'canibis_161',
'kettle_161',
'casa_m15_161',
'casa_gfed_161',
'casa_gfed_135',
'casa_gfed_187']
if models_str is None:
models_str = ['MPI',
'Can-IBIS',
'Kettle',
'CASA-m15',
'CASA-GFED3',
'CASA-GFED3',
'CASA-GFED3']
gpp_vmin = 0.0
gpp_vmax = np.percentile(np.dstack([gpp[k] for k in models]).flatten(), 99)
#gpp_vmax = 0.45 # np.dstack([GPP[k] for k in models]).flatten().max()
fcos_vmin = 0.0 # np.dstack([fCOS[k] for k in models]).flatten().min()
# fcos_vmax = np.percentile(np.dstack([fCOS[k] for k in models]).flatten(), 99)
fcos_vmax = np.dstack([fCOS[k] for k in models]).flatten().max()
cos_vmin = 0.0
cos_vmax = np.dstack([cos[k] for k in models]).flatten().max()
# cos_vmax = np.percentile(np.dstack([cos[k] for k in models]).flatten(), 99)
# cos_vmax = 80
print('ceil(max): {}'.format(
np.ceil(np.dstack([cos[k] for k in models]).flatten().max())))
fig, ax, cbar_ax = setup_panel_array(nrows=3, ncols=len(models))
map_objs = np.empty(ax.shape, dtype='object')
gpp_cmap, gpp_norm = colormap_nlevs.setup_colormap(
gpp_vmin, gpp_vmax,
nlevs=20,
cmap=plt.get_cmap('Greens'),
extend='max')
color_band_edges = [0.0, 3.5, 6.5, 12.0, 13.0]
color_band_edges = [0.0, 3.25, 6.5, 9.75, 13.0]
gpp_base_cmap = plt.cm.Greens(np.linspace(0.05,
0.95,
len(color_band_edges * 10)))
gpp_base_cmap_small = plt.cm.Greens(np.linspace(0.05,
0.95,
len(color_band_edges)))
gpp_base_cmap = gpp_base_cmap[[0, 10, 40, 45, 49], :]
print 'gpp_base_cmap ', gpp_base_cmap
print 'gpp_base_cmap_small ', gpp_base_cmap_small
gpp_cmap, gpp_norm = from_levels_and_colors(color_band_edges,
gpp_base_cmap_small,
extend='max')
mod_objs = ndp.get_runs()
for i, this_mod in enumerate(models):
if np.mod(i, 2) == 0:
# the GPP maps are duplicates within each GPP model, so
# only plot every other one
# plot GPP drawdown maps
print("plotting {model}({k}) GPP".format(model=models_str[i],
k=models[i]))
map_objs[0, i], cm = draw_map(
t_str='{}, LRU={}'.format(models_str[i],
mod_objs[this_mod].LRU),
ax=ax[0, i], # axis 0 is left-most on row 3
data=gpp[this_mod],
vmin=gpp_vmin,
vmax=gpp_vmax,
cmap=gpp_cmap,
norm=gpp_norm)
else:
fig.delaxes(ax[0, i])
all_gpp = np.dstack([v for v in gpp.values()]).flatten()
cb = colorbar_from_cmap_norm(gpp_cmap,
gpp_norm,
cbar_ax[0, 0],
'%0.2f',
all_gpp)
t = cbar_ax[0, 0].set_title('GPP ($\mu$mol C m$^{-2}$ s$^{-1}$)\n')
t.set_y(1.09)
t.set_fontsize(20)
fcos_cmap, fcos_norm = colormap_nlevs.setup_colormap(
fcos_vmin, fcos_vmax,
nlevs=6,
cmap=plt.get_cmap('Blues'),
extend='neither')
for i, this_mod in enumerate(models):
# plot fCOS drawdown maps
print("plotting {model}({k}) fCOS".format(model=models_str[i],
k=models[i]))
map_objs[1, i], cm = draw_map(t_str=None,
ax=ax[1, i],
data=fCOS[this_mod],
vmin=fcos_vmin,
vmax=fcos_vmax,
cmap=fcos_cmap,
norm=fcos_norm)
all_fcos = np.dstack([v for v in fCOS.values()]).flatten()
cb = colorbar_from_cmap_norm(fcos_cmap,
fcos_norm,
cbar_ax[1, 0],
'%d',
all_fcos)
t = cbar_ax[1, 0].set_title('$F_{plant}$ (pmol COS m$^{-2}$ s$^{-1})$')
t.set_y(1.09)
t.set_fontsize(20)
cos_cmap, cos_norm = colormap_nlevs.setup_colormap(
cos_vmin,
cos_vmax,
nlevs=7,
cmap=plt.get_cmap('Oranges'),
extend='max')
for i, this_mod in enumerate(models):
# plot [COS] drawdown maps
print("plotting {model}({k}) COS DD".format(model=models_str[i],
k=models[i]))
this_cos = cos[this_mod]
if any(this_cos.flatten() < 0):
warnings.warn('COS drawdown values < 0.0 set to 0.0')
this_cos[this_cos < 0] = 0
map_objs[2, i], cm = draw_map(t_str=None,
ax=ax[2, i],
data=this_cos,
vmin=cos_vmin,
vmax=cos_vmax,
cmap=cos_cmap,
norm=cos_norm)
all_dd = np.dstack([v for v in cos.values()]).flatten()
cb = colorbar_from_cmap_norm(cos_cmap,
cos_norm,
cbar_ax[2, 0],
'%d',
all_dd)
t = cbar_ax[2, 0].set_title('STEM [COS] drawdown (ppt)')
t.set_y(1.09)
t.set_fontsize(20)
# fig.tight_layout()
return(fig, map_objs, cos_cmap, cos_norm)
nha_x = 33
nha_y = 70
cma_x = 29
cma_y = 60
sca_x = 23
sca_y = 44
molecules_m3_to_ppt = 1e12
cos_sca = cos_clim['data'][:, :, sca_x, sca_y] * molecules_m3_to_ppt
cmap, norm = colormap_nlevs.setup_colormap(vmin=cos_sca.min() - 1,
vmax=cos_sca.max() + 1,
nlevs=20,
cmap=plt.get_cmap('Blues'),
extend='neither')
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 8))
cm = ax.pcolormesh(np.transpose(cos_sca),
cmap=cmap,
norm=norm,
linewidth=0,
rasterized=True)
ax.set_xlim([0, 1488])
ax.set_ylim([0, 22])
ax.set_ylabel('STEM Z level')
ax.set_xlabel('hours since 1 July 2008 00:00')
ax.set_title('SCA [COS], climatological bounds STEM run')
ax_cb = plt.colorbar(cm, cmap=cmap, norm=norm, ax=ax)
years = [1972, 1972]
months = [1, 2]
fnames = [
os.path.join(qian_dir,
'clmforc.Qian.c2006.T62.Prec.{}-{:02d}.nc'.format(y, m))
for y in years for m in months
]
pcp = [qpm.QianMonthlyPCPData(f, 'PRECTmms') for f in fnames]
for this_pcp in pcp:
print "parsing {}".format(os.path.basename(this_pcp.fname))
this_pcp.read_nc()
fig, ax = plt.subplots(nrows=len(years), ncols=1)
for y, m, this_pcp, this_ax in zip(itertools.cycle(years), months, pcp,
list(ax)):
this_m = qpm.setup_worldmap(this_ax)
cmap, norm = colormap_nlevs.setup_colormap(0.0,
0.0053803,
nlevs=11,
cmap=plt.get_cmap('Blues'),
extend='neither')
this_m.pcolormesh(this_pcp.lon,
this_pcp.lat,
this_pcp.data_all[0, ...],
latlon=True)
this_ax.annotate("{} {} pcp".format(y, m),
xy=(0, 1),
xycoords='axes fraction')
fig.savefig(os.path.join(os.getenv('CSCRATCH'), 'qian_pcp.png'))
