def test_gshhs():
ax = plt.axes(projection=ccrs.Mollweide())
ax.set_extent([138, 142, 32, 42], ccrs.Geodetic())
ax.stock_img()
# Draw coastlines.
ax.add_feature(cfeature.GSHHSFeature('coarse', edgecolor='red'))
# Draw higher resolution lakes (and test overriding of kwargs)
ax.add_feature(cfeature.GSHHSFeature('low', levels=[2], facecolor='green'),
facecolor='blue')
def plot_dp(storm, datafile1, datafile2=None):
#Single file netCDF reading
ncf=datafile1
nco=netCDF4.Dataset(ncf)
#Get fields to plot
lon=nco.variables['longitude'][:]
lat=nco.variables['latitude'][:]
timeindays=nco.variables['time'][:]
dp=nco.variables['dp'][:]
triangles=nco.variables['tri'][:,:]
reflon=np.linspace(lon.min(),lon.max(),1000)
reflat=np.linspace(lat.min(),lat.max(),1000)
#reflon=np.linspace(-80.40, -74.75, 1000)
#reflat=np.linspace(32.50, 36.60, 1000)
#reflon=np.linspace(-75.70, -71.05, 1000)
#reflat=np.linspace(38.50, 41.40, 1000)
reflon,reflat=np.meshgrid(reflon,reflat)
plt.figure(figsize = [6.4, 3.8])
flatness=0.10 # flatness is from 0-.5 .5 is equilateral triangle
triangles=triangles-1 # Correct indices for Python's zero-base
tri=Triangulation(lon,lat,triangles=triangles)
mask = TriAnalyzer(tri).get_flat_tri_mask(flatness)
tri.set_mask(mask)
# Loop through each time step and plot results
for ind in range(0, len(timeindays)):
plt.clf()
ax = plt.axes(projection=ccrs.Mercator())
dt = datetime.datetime.combine(datetime.date(1990, 1, 1), datetime.time(0, 0)) + datetime.timedelta(days=timeindays[ind])
dstr = datetime.date.strftime(dt,'%Y%m%d%H:%M:%S')
dstr = dstr[0:8]+' '+dstr[8:17]
print('Plotting '+dstr)
par=np.double(dp[ind,:])
tli=LinearTriInterpolator(tri,par)
par_interp=tli(reflon,reflat)
plt.pcolormesh(reflon,reflat,par_interp,vmin=0.0,vmax=360.0,shading='flat',cmap=plt.cm.jet, transform=ccrs.PlateCarree())
cb = plt.colorbar()
cb.ax.tick_params(labelsize=8)
coast = cfeature.GSHHSFeature(scale='high',edgecolor='black',facecolor='none',linewidth=0.25)
ax.add_feature(coast)
plt.xticks(fontsize=9)
plt.yticks(fontsize=9)
figtitle = storm.capitalize()+': Peak Dir (deg): '+dstr
plt.title(figtitle)
dtlabel = datetime.date.strftime(dt,'%Y%m%d%H%M%S')
dtlabel = dtlabel[0:8]+'_'+dtlabel[8:14]
filenm = 'nsem_'+storm+'_dp_'+dtlabel+'.png'
plt.savefig(filenm,dpi=150,bbox_inches='tight',pad_inches=0.1)
del(par)
del(par_interp)
def plot_world_map(lons, lats, data, metadata, plotpath):
# plot generic world map
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1,
1,
1,
projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
ax.set_extent([-180, 180, -90, 90])
if metadata['var'] == 'wind':
vmin = 0
vmax = 12
elif metadata['var'] == 'hs':
vmin = 0
vmax = 5.5
else:
vmin = np.nanmin(data)
vmax = np.nanmax(data)
#cmap = 'viridis' #blue to yellow matlab
cmap = 'jet'
cbarlabel = '%s' % (metadata['var'])
plttitle = 'WW3 Plot of variable %s' % (metadata['var'])
cs = ax.pcolormesh(lons, lats, data, vmin=vmin, vmax=vmax, cmap=cmap)
cb = plt.colorbar(cs, orientation='horizontal', shrink=0.5, pad=.04)
cb.set_label(cbarlabel, fontsize=12)
plt.title(plttitle)
plt.savefig(plotpath)
plt.close('all')
def grab_gshhg_features(scale, levels, extent):
"""Grabs lat/lon coordinates for GSHHG features.
Grab geographical feature data from the GSHHG database for a specified extent,
level of detail, and resolution. Outputs simple lat/lon coordinates -- useful
for plotting backends (like Bokeh) that don't have fancy geo feature integration.
Args:
scale: A string, can be either f(ull), h(igh), i(ntermediate), l(ow), or c(rap)
depending upon the desired resolution.
levels: Specify which level(s) of feature to plot; [1] is only coastlines
and [1, 2, 3, 4] is everything.
extent: Specify [lonmin, lonmax, latmin, latmax] in decimal degrees.
Returns:
features: A dictionary containing coordinates of geo features
(WGS84 lat/lon, decimal degrees).
"""
unformatted_features = list(cf.GSHHSFeature(scale=scale, levels=levels).intersecting_geometries(extent))
features = {'latitude':[], 'longitude':[]}
for feature in unformatted_features:
lons = feature.exterior.coords.xy[0].tolist()
lats = feature.exterior.coords.xy[1].tolist()
features['longitude'].append(lons)
features['latitude'].append(lats)
return features
def plot_world_map(lons, lats, data, metadata, plotpath):
# plot generic world map
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1,
1,
1,
projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
ax.set_extent([-180, 180, -90, 90])
#vmin = np.nanmin(data)
#vmax = np.nanmax(data)
vminmax = float(metadata['vminmaxvar'])
vmin = -1 * vminmax
vmax = vminmax
cmap = 'bwr'
cbarlabel = '%s' % (metadata['var'])
plttitle = '%s' % (metadata['tag'])
cs = ax.pcolormesh(lons, lats, data, vmin=vmin, vmax=vmax, cmap=cmap)
cb = plt.colorbar(cs,
extend='both',
orientation='horizontal',
shrink=0.5,
pad=.04)
cb.set_label(cbarlabel, fontsize=12)
plt.title(plttitle)
plt.savefig(plotpath)
plt.close('all')
def plot_world_map(lons, lats, data, metadata, plotpath, screen, lonr, latr, comment):
# plot generic world map
if screen.upper() == "NO":
matplotlib.use('agg')
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
listWE = lonr.split(',')
listSN = latr.split(',')
lonBeg = lonS2F(listWE[0])
lonEnd = lonS2F(listWE[1])
latBeg = latS2F(listSN[0])
latEnd = latS2F(listSN[1])
scaleWE = 360/(lonEnd-lonBeg)*10
scaleSN = 180/(latEnd-latBeg)*10
scaleMax = max(scaleWE,scaleSN)
ax.set_extent([lonBeg, lonEnd, latBeg, latEnd])
vmax = np.nanmean(data)+np.nanstd(data)*2
vmin = np.nanmean(data)-np.nanstd(data)*2
cmap = 'viridis'
cbarlabel = '%s' % (metadata['var'])
if comment == '':
plttitle = 'Variable: %s' % (metadata['var'])
else:
plttitle = 'Variable: %s; %s' % (metadata['var'], comment)
cs = plt.scatter(lons, lats, c=data, s=scaleMax,
cmap=cmap, transform=ccrs.PlateCarree(),vmin=vmin,vmax=vmax)
cb = plt.colorbar(cs, orientation='horizontal', shrink=0.5, pad=.04)
cb.set_label(cbarlabel, fontsize=12)
plt.title(plttitle)
if screen.upper() == "NO":
plt.savefig(plotpath)
plt.close('all')
else:
plt.show()
def plot_segments(segments=None,
mission_name='',
poly=None,
lat_min=-22.5,
lat_max=-20,
lon_min=165.5,
lon_max=168.5):
"""
Plot ADCP transects from regions of intres
"""
ax = add_map(lat_min=lat_min,
lat_max=lat_max,
lon_min=lon_min,
lon_max=lon_max)
for ds in segments:
ax.plot(ds['lon'],
ds['lat'],
'.',
markersize=2,
label='%s_s%s' %
(ds.attrs['mission'], ds.attrs['segment_number']))
land = cfeature.GSHHSFeature(scale='intermediate',
levels=[1],
facecolor=cfeature.COLORS['land'])
if poly is not None:
ax.add_geometries(poly, ccrs.PlateCarree(), alpha=0.2)
plt.title(mission_name)
#plt.legend()
plt.tight_layout()
def draw_map():
lat_sp = 20
lon_sp = 30 #60 #
title_font = 14
label_font = 10
ds = xr.open_dataset(fileout)
ilon = ds.lon
ilat = ds.lat
var = ds['preci'].resample(time="5D").mean()
var = var * 3600 * 24 * 1000 #mm/day
time = var.indexes['time'].to_datetimeindex()
ds = xr.open_dataset(
"/home/ys17-19/renql/project/TP_NUDG/analysis/mdata/gtopo30_0.9x1.25.nc"
)
phis = ds['PHIS'].sel(lon=ilon, lat=ilat, method="nearest").load()
phis = phis / 9.8 # transfer from m2/s2 to m
cnlevels = np.arange(1, 18, 1)
fcolors = cmaps.precip2_17lev
norm = colors.BoundaryNorm(boundaries=cnlevels,
ncolors=fcolors.N,
extend='both')
for nt in range(len(time)):
title = 'CESM AMIP %02d-%02d preci (mm/day)' % (time[nt].month,
time[nt].day)
fig = plt.figure(figsize=(12, 9), dpi=100)
axe = plt.axes(projection=ccrs.PlateCarree(central_longitude=180.0))
axe.add_feature(cfeat.GSHHSFeature(levels=[1, 2], edgecolor='k'),
linewidth=0.8,
zorder=1)
axe.set_title(title, fontsize=title_font)
cont = axe.contourf(var.lon,
var.lat,
var[nt, :, :],
cnlevels,
transform=ccrs.PlateCarree(),
cmap=fcolors,
extend='both',
norm=norm)
topo = axe.contour(phis.lon,
phis.lat,
phis, [1500, 3000],
transform=ccrs.PlateCarree(),
colors='black',
linewidths=1.5)
axe.set_yticks(np.arange(lats, latn, lat_sp), crs=ccrs.PlateCarree())
axe.yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))
axe.set_xticks(np.arange(lonl, lonr, lon_sp), crs=ccrs.PlateCarree())
axe.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))
cb = plt.colorbar(cont)
plt.savefig('%s%d' % (figdir, nt),
bbox_inches='tight',
pad_inches=0.01)
def get_map(region=None,
zoom=1,
projection=ccrs.Mercator(),
res='i',
draw_coastlines=True,
figsize=(8, 10),
shape=(1, 1)):
extents = []
if isinstance(region, str):
region = [region]
for r in region:
extents.append(extent_from_region(r))
fig, axes = plt.subplots(
shape[0],
shape[1],
figsize=figsize,
subplot_kw={
# kwargs passed to add_subplot()
'projection': projection
})
if not isinstance(axes, (list, np.ndarray)):
axes = [axes]
for ax, e in zip(axes, extents):
ax.set_extent(e)
ax.add_feature(cfeat.GSHHSFeature(
scale=res)) if draw_coastlines else False
if len(axes) == 1:
return fig, axes[0]
else:
return fig, axes
def plot_world_map(lons, lats, data, metadata, plotpath):
# plot generic world map
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1,
1,
1,
projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
ax.set_extent([-180, 180, -90, 90])
cmap = 'jet'
cbarlabel = '%s' % (metadata['var'])
plttitle = 'MOM6 Plot of variable %s' % (metadata['var'])
if metadata['var'] == 'MLD_003':
bounds = np.array([
10, 20, 30, 40, 50, 60, 80, 100, 150, 200, 250, 300, 400, 500, 1000
])
norm = mcolors.BoundaryNorm(boundaries=bounds, ncolors=256)
cs = ax.pcolormesh(lons, lats, data, norm=norm, cmap=cmap)
else:
vmin = np.nanmin(data)
vmax = np.nanmax(data)
cs = ax.pcolormesh(lons, lats, data, vmin=vmin, vmax=vmax, cmap=cmap)
cb = plt.colorbar(cs,
extend='both',
orientation='horizontal',
shrink=0.5,
pad=.04)
cb.set_label(cbarlabel, fontsize=12)
plt.title(plttitle)
plt.savefig(plotpath)
plt.close('all')
def nice_map(dataarray, ax=None, title='', transform=ccrs.PlateCarree(),
**kwargs):
"""
Make a nice map with gridlines and coordinates
Parameters
----------
ax: matplotlib axes object, optional
If None, uses the current axis
title: str, optional
Title of the plot
"""
if ax is None:
ax = plt.subplot(1, 1, 1, projection=transform)
dataarray.plot.pcolormesh(ax=ax, transform=transform, **kwargs)
land = cfeature.GSHHSFeature(scale='intermediate', levels=[1],
facecolor=cfeature.COLORS['land'])
ax.add_feature(land)
ax.set_title(title)
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
plt.tight_layout()
def plot_spatial(diag):
n = len(diag.diff)
mean, std, mx, mn = calculate_stats(diag.diff)
plt.figure(figsize=(15,12))
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
ax.set_extent([-180, 180, -90, 90])
norm = mcolors.BoundaryNorm(boundaries=diag.variables[diag.variable]['boundsize'], ncolors=256)
cs = plt.scatter(diag.lons, diag.lats, c=diag.diff, s=30,
norm=norm, cmap='bwr', #edgecolors='gray', linewidth=0.25,
transform=ccrs.PlateCarree())
if diag.dtype == 'conv' and diag.variable == 'q':
t = ('n: %s\nstd: %s\nmean: %s' % (n,np.round(std,6),np.round(mean,6)))
ax.text(-175, -70, t, fontsize=16, transform=ccrs.PlateCarree())
else:
t = ('n: %s\nstd: %s\nmean: %s\nmax: %s\nmin: %s' % (n,np.round(std,3),np.round(mean,3), np.round(mx,3), np.round(mn,3)))
ax.text(-175, -70, t, fontsize=16, transform=ccrs.PlateCarree())
labels = diag.get_labels()
cb = plt.colorbar(cs, shrink=0.5, pad=.04, extend='both')
cb.set_label(labels[diag.ftype]['xlabel'], fontsize=12)
title_split = labels[diag.ftype]['left_title'].split('\n')
plt.title("%s\n%s" % (title_split[0], '\n'.join(wrap(title_split[-1], 40))), loc='left', fontsize=14)
plt.title(labels[diag.ftype]['right_title'], loc='right', fontweight='semibold', fontsize=14)
plt.savefig(labels[diag.ftype]['save_title'], bbox_inches='tight', pad_inches=0.1)
return 0
def plt_ref_vertical(figname):
f = Dataset(figname, mode='r')
print(f)
nlon = f.dimensions['lon'].size
nlat = f.dimensions['lat'].size
lat = f.variables['lats'][:]
lon = f.variables['lons'][:]
sphum = f.variables['sphum'][:, :, :, :]
temp = f.variables['T'][:, :, :, :]
f.close()
x, y = np.meshgrid(lon, lat)
print(x[:, :])
print(y[:, :])
# create figure and axes instances
plt.figure(figsize=(15, 12))
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
# ax.coastlines()
ax.set_extent([-180, 180, -90, 90])
# cmap,bounds,norm=ncepy.create_ncep_radar_ref_color_table()
# cs=plt.scatter(glat,nlev,c=oref,s=58,cmap=cmap,norm=norm,marker="s",edgecolor='none')
# cmap=plt.get_cmap("Greys")
data = np.zeros((nlat, nlon))
data[:, :] = temp[0, 0, :, :]
print(data)
upperbound = np.max(data)
lowerbound = np.min(data)
bins = (upperbound - lowerbound) / 10.0
clevs = np.arange(lowerbound, upperbound + bins, bins)
norm = colors.BoundaryNorm(boundaries=clevs, ncolors=256)
#cs=plt.scatter(x,y,c=pres,s=40,cmap=cmap,norm=norm,marker=(verts_function(1,1,0.25),0),edgecolor='none')
#cs=plt.scatter(x,y,c=pres,s=40,cmap='bwr',norm=norm,marker="s",edgecolor='none')
#cs=plt.scatter(x,y,c=data,s=10,cmap='bwr',norm=norm,transform=ccrs.PlateCarree())
cs = plt.contourf(x,
y,
data,
clevs,
cmap='bwr',
transform=ccrs.PlateCarree())
cb = plt.colorbar(cs, shrink=0.5, pad=.04, extend='both')
cb.ax.tick_params(labelsize=5.0)
# plt.ylim([0,60])
# plt.xlim([24,52])
# plt.title(titlename+' latitude CREF',fontsize=25)
# clevs=bins
# cbar = plt.colorbar(cs,location='bottom',pad=0.05,ticks=clevs)
# titlename=
plt.savefig('./' + figname + '.png', bbox_inches='tight', dpi=100)
def plot_world_map(tiles, lons, lats, data, metadata, plotpath, screen, lonr,
latr, extreme, comment):
# plot generic world map
if screen.upper() == "NO":
matplotlib.use('agg')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1,
1,
1,
projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
listWE = lonr.split(',')
listSN = latr.split(',')
lonBeg = lonS2F(listWE[0])
lonEnd = lonS2F(listWE[1])
latBeg = latS2F(listSN[0])
latEnd = latS2F(listSN[1])
ax.set_extent([lonBeg, lonEnd, latBeg, latEnd])
# dealing with missing values (assuming absolute values > 1.E8)
data_new = data.copy()
data_new[abs(data) > 1.E8] = np.nan
if not extreme == '':
strMinMax = extreme.split(',')
vmin = float(strMinMax[0])
vmax = float(strMinMax[1])
invalid = np.logical_or(
data_new == np.nan, np.logical_or(data_new > vmax,
data_new < vmin))
data_new[invalid] = np.nan
data_new = np.ma.masked_where(np.isnan(data_new), data_new)
vmin = np.nanmin(data_new)
vmax = np.nanmax(data_new)
#vmax = np.nanmean(data_new)+np.nanstd(data_new)*2
#vmin = np.nanmean(data_new)-np.nanstd(data_new)*2
cmap = 'viridis'
cbarlabel = '%s' % (metadata['var'])
if comment == '':
plttitle = 'Variable: %s' % (metadata['var'])
else:
plttitle = 'Variable: %s; %s' % (metadata['var'], comment)
for t in range(tiles):
cs = ax.pcolormesh(lons[..., t],
lats[..., t],
data_new[..., t],
vmin=vmin,
vmax=vmax,
cmap=cmap)
cb = plt.colorbar(cs, orientation='horizontal', shrink=0.5, pad=.04)
cb.set_label(cbarlabel, fontsize=12)
plt.title(plttitle)
if screen.upper() == "NO" or screen.upper() == "ALL":
plt.savefig(plotpath)
if screen.upper() == "ALL":
plt.show()
plt.close('all')
else:
plt.show()
def pltallbuoytracks(loc, BD):
Buoys = ['02', '03', '09', '07', '12', '13', '14', '16']
fig = plt.figure(figsize=(12, 12), frameon=True)
ax = plt.axes(projection=ccrs.LambertAzimuthalEqualArea(
central_longitude=25.0, central_latitude=77.0))
ax.set_extent([16, 28, 74.5, 80])
colors = [
'red', 'green', 'magenta', 'darkblue', 'lime', 'orange', 'yellow',
'olive'
]
fig.canvas.draw()
xticks = [0, 4, 12, 16, 20, 24, 28, 32, 36]
yticks = [72, 74.5, 77, 79.5, 82]
ax.gridlines(xlocs=xticks, ylocs=yticks)
# Label the end-points of the gridlines using the custom tick makers:
ax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)
ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)
lambert_xticks(ax, xticks)
lambert_yticks(ax, yticks)
i = 0
pt = 96 * 1
for b in Buoys:
# Plotting b-uoy obs and buoy simulated locations
Xib = BD[b + '_x']
Yib = BD[b + '_y']
plt.scatter(Xib[pt],
Yib[pt],
color='black',
transform=ccrs.PlateCarree())
plt.text(Xib[pt],
Yib[pt],
b,
transform=ccrs.PlateCarree(),
fontsize=15,
fontweight='bold')
plt.plot(Xib[pt:],
Yib[pt:],
color=colors[i],
transform=ccrs.PlateCarree(),
label=b)
i += 1
# gebco wms background
service = 'https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?'
ax.add_wms(service,
layers=['GEBCO_LATEST'],
wms_kwargs={
'width': 900 * 2,
'height': 600 * 2
})
feature = cpf.GSHHSFeature(scale='i',
levels=[1],
facecolor='#e6e1e1',
alpha=1)
ax.add_feature(feature)
plt.savefig(loc + '/allbuoytracks.jpg', format='jpg', dpi=600)
plt.close(fig)
def add_map(lon_min=-180,
lon_max=180,
lat_min=-90,
lat_max=90,
central_longitude=0.,
scale='auto',
ax=None):
"""
Add the map to the existing plot using cartopy
Parameterss
----------
lon_min : float, optional
Western boundary, default is -180
lon_max : float, optional
Eastern boundary, default is 180
lat_min : float, optional
Southern boundary, default is -90
lat_max : float, optional
Northern boundary, default is 90
central_longitude : float, optional
Central longitude, default is 180
scale : {‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, ‘full’}, optional
The map scale, default is 'auto'
ax : GeoAxes, optional
A new GeoAxes will be created if None
Returns
-------
ax : GeoAxes
Return the current GeoAxes instance
"""
extent = (lon_min, lon_max, lat_min, lat_max)
if ax is None:
ax = plt.subplot(
1,
1,
1,
projection=ccrs.PlateCarree(central_longitude=central_longitude))
ax.set_extent(extent)
land = cfeature.GSHHSFeature(scale=scale,
levels=[1],
facecolor=cfeature.COLORS['land'])
ax.add_feature(land)
gl = ax.gridlines(draw_labels=True,
linestyle=':',
color='black',
alpha=0.5)
gl.xlabels_top = False
gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
return ax
def land(self, facecolor="white", **kwargs):
"""
Draw the land mask
Parameters
----------
facecolor: string, optional
color to draw the mask with
**kwargs: additional arguments to add_feature
"""
self.ax.add_feature(cft.GSHHSFeature(self.res, [1]),
facecolor=facecolor, **kwargs)
def plotMap():
#Set the projection information
proj = ccrs.PlateCarree()
#Create a figure with an axes object on which we will plot. Pass the projection to that axes.
fig, ax = plt.subplots(subplot_kw=dict(projection=proj))
#Zoom in
img_extent = [140, 162, 34, 50]
ax.set_extent(img_extent, crs=proj)
#Add map features
# ax.add_feature(cfeature.LAND, facecolor='0.9'
# ) #Grayscale colors can be set using 0 (black) to 1 (white)
land_50m = cfeature.NaturalEarthFeature('physical',
'land',
'50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
ax.add_feature(land_50m)
ax.add_feature(
cfeature.LAKES,
alpha=0.9) #Alpha sets transparency (0 is transparent, 1 is solid)
ax.add_feature(cfeature.BORDERS, zorder=10)
# ax.add_feature(cfeature.COASTLINE, zorder=10)
gshhs = cfeature.GSHHSFeature(scale='i', levels=None)
ax.add_feature(gshhs)
#We can use additional features from Natural Earth (http://www.naturalearthdata.com/features/)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
ax.add_feature(states_provinces, edgecolor='gray', zorder=10)
#Add lat/lon gridlines every 20° to the map
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=0.1,
alpha=1,
linestyle='-')
gl.xlabels_top = False
gl.ylabels_left = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
ax.text(142.2, 43.5, 'Hokkaido', transform=proj)
ax.text(140.4, 39.7, 'Honshu', transform=proj)
return fig, ax
def coast2wkb(lonmin, lonmax, latmin, latmax, GSHHSres, coastfile):
# Global cartopy feature from GSHHS
gfeat = cfeature.GSHHSFeature(scale=GSHHSres)
# As shapely collection generator
coll = gfeat.geometries()
# Polygon representation of the regional domain
frame = geom.box(lonmin, latmin, lonmax, latmax)
# The intersection
B = (frame.intersection(p) for p in coll if frame.intersects(p))
# Save to file
with open(coastfile, mode='wb') as fp:
wkb.dump(geom.MultiPolygon(flatten(B)), fp, output_dimension=2)
def plot_spatial(data, metadata, lats, lons):
stats = calculate_stats(data)
plt.figure(figsize=(15, 12))
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
ax.set_extent([-180, 180, -90, 90])
upperbound = (np.round(stats['Std'] * 2) / 2) * 5
lowerbound = 0 - upperbound
bins = (upperbound - lowerbound) / 10
norm = mcolors.BoundaryNorm(boundaries=np.arange(lowerbound,
upperbound + bins, bins),
ncolors=256)
cs = plt.scatter(
lons,
lats,
c=data,
s=30,
norm=norm,
cmap='bwr', #edgecolors='gray', linewidth=0.25,
transform=ccrs.PlateCarree())
labels = plot_labels(metadata, stats)
ax.text(-175,
-70,
labels['statText'],
fontsize=14,
transform=ccrs.PlateCarree())
cb = plt.colorbar(cs, shrink=0.5, pad=.04, extend='both')
cb.set_label(labels['xLabel'], fontsize=12)
plt.title(labels['leftTitle'], loc='left', fontsize=14)
plt.title(labels['dateTitle'],
loc='right',
fontweight='semibold',
fontsize=14)
plt.savefig('%s_spatial.png' % labels['saveFile'],
bbox_inches='tight',
pad_inches=0.1)
return
def _plot_geographic_context(ax, hires=False):
"""
Plot geographic basemap information on a map axis. Plots simple coastlines for
unprojected plots.
Args:
ax (:class:`~cartopy.mpl.geoaxes.GeoAxes`): Existing axis to plot into
hires (bool): If `True`, use higher-resolution coastlines (default: `False`)
"""
# Since unprojected grids have regional/global extent, just show the
# coastlines and borders
if hires:
gshhs_scale = 'intermediate'
lake_scale = '10m'
else:
gshhs_scale = 'low'
lake_scale = '50m'
ax.add_feature(
cfeature.GSHHSFeature(scale=gshhs_scale),
facecolor=cfeature.COLORS['land'],
zorder=0,
)
ax.background_patch.set_facecolor(cfeature.COLORS['water'])
ax.add_feature(
cfeature.LAKES.with_scale(lake_scale),
facecolor=cfeature.COLORS['water'],
edgecolor='black',
zorder=0,
)
# Add states and provinces borders
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
ax.add_feature(states_provinces, edgecolor='gray')
ax.add_feature(cfeature.BORDERS, edgecolor='gray')
# Add gridlines and labels
ax.gridlines(draw_labels=["x", "y", "left", "bottom"],
linewidth=1,
color='gray',
alpha=0.5,
linestyle='--')
def no_data_spatial(metadata, outDir='./'):
fig = plt.figure(figsize=(15,12))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree(central_longitude=0))
ax.add_feature(cfeature.GSHHSFeature(scale='auto'))
ax.set_extent([-180, 180, -90, 90])
stats = None
labels = plot_labels(metadata, stats)
ax.text(0,0, 'No Data', fontsize=32, alpha=0.6, ha='center')
plt.title(labels['leftTitle'], loc='left', fontsize=14)
plt.title(labels['dateTitle'], loc='right', fontweight='semibold', fontsize=14)
plt.savefig(outDir+'/%s_spatial.png' % labels['saveFile'], bbox_inches='tight', pad_inches=0.1)
plt.close('all')
return
def plticepos(Xib, Yib, Xis, Yis, path):
fig = plt.figure(figsize=(12, 12), frameon=True)
ax = plt.axes(projection=ccrs.LambertAzimuthalEqualArea(
central_longitude=25.0, central_latitude=77.0))
# ax.set_extent([15,33,74,81])
ax.set_extent([16, 28, 74, 78])
# Define gridline locations and draw the lines using cartopy's built-in gridliner:
# *must* call draw in order to get the axis boundary used to add ticks:
fig.canvas.draw()
xticks = [0, 4, 12, 16, 20, 24, 28, 32, 36]
yticks = [72, 74, 76, 78, 80, 82]
ax.gridlines(xlocs=xticks, ylocs=yticks)
# Label the end-points of the gridlines using the custom tick makers:
ax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)
ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)
lambert_xticks(ax, xticks)
lambert_yticks(ax, yticks)
# Plotting buoy obs and buoy simulated locations
plt.plot(Xib,
Yib,
color='red',
transform=ccrs.PlateCarree(),
label='buoy drift')
plt.plot(Xis,
Yis,
'--',
color='yellow',
transform=ccrs.PlateCarree(),
label='simulated ice drift')
# gebco wms background
service = 'https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?'
ax.add_wms(service,
layers=['GEBCO_LATEST'],
wms_kwargs={
'width': 900 * 2,
'height': 600 * 2
})
feature = cpf.GSHHSFeature(scale='i',
levels=[1],
facecolor='#e6e1e1',
alpha=1)
ax.add_feature(feature)
plt.legend(prop={"size": 16}, framealpha=1)
plt.savefig(path + '/ice_drift.jpg', dpi=400)
plt.close(fig)
def _plot_geographic_context(ax, utm, hires=False):
"""
Plot geographic basemap information on a map axis. Plots a background image
for UTM-projected plots and simple coastlines for unprojected plots.
Args:
ax (:class:`~cartopy.mpl.geoaxes.GeoAxes`): Existing axis to plot into
utm (bool): Flag specifying if the axis is projected to UTM or not
hires (bool): If `True`, use higher-resolution images/coastlines
(default: `False`)
"""
# Since projected grids cover less area and may not include coastlines,
# use a background image to provide geographical context (can be slow)
if utm:
if hires:
zoom_level = 12
else:
zoom_level = 8
ax.add_image(Stamen(style='terrain-background'), zoom_level)
# Since unprojected grids have regional/global extent, just show the
# coastlines
else:
if hires:
gshhs_scale = 'intermediate'
lake_scale = '10m'
else:
gshhs_scale = 'low'
lake_scale = '50m'
ax.add_feature(
cfeature.GSHHSFeature(scale=gshhs_scale),
facecolor=cfeature.COLORS['land'],
zorder=0,
)
ax.background_patch.set_facecolor(cfeature.COLORS['water'])
ax.add_feature(
cfeature.LAKES.with_scale(lake_scale),
facecolor=cfeature.COLORS['water'],
edgecolor='black',
zorder=0,
)
def plot(self, fscale=10):
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.mpl.ticker as cticker
from shapely.geometry import MultiPoint
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
# First set some plot parameters:
bbox = self.bbox(buffer=0.1)
minLon, minLat, maxLon, maxLat = bbox
extents = [minLon, maxLon, minLat, maxLat]
# create figure and plot/map
fig, ax = plt.subplots(1,
1,
figsize=(fscale, fscale * (maxLon - minLon) /
(maxLat - minLat)),
subplot_kw={'projection': ccrs.PlateCarree()})
ax.set_extent(extents, crs=ccrs.PlateCarree())
coastline = cfeature.GSHHSFeature(scale='auto',
edgecolor='black',
facecolor=cfeature.COLORS['land'])
ax.add_feature(coastline, zorder=0)
ax.add_feature(cfeature.BORDERS, linewidth=2)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
# Plot the model domain
bx, by = self.boundary_points()
poly = plt.Polygon(list(zip(bx, by)), facecolor='r', alpha=0.05)
ax.add_patch(poly)
ax.plot(bx, by, lw=2, color='k')
return fig, ax
def scatter(ds,
color,
minLon=None,
minLat=None,
maxLon=None,
maxLat=None,
fscale=10):
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from datetime import datetime
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.mpl.ticker as cticker
from shapely.geometry import MultiPoint
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
# First set some plot parameters:
if not minLon: minLon = ds.LONGITUDE.min().values.item()
if not minLat: minLat = ds.LATITUDE.min().values.item()
if not maxLon: maxLon = ds.LONGITUDE.max().values.item()
if not maxLat: maxLat = ds.LATITUDE.max().values.item()
extents = [minLon, maxLon, minLat, maxLat]
# create figure and plot/map
fig, ax = plt.subplots(1,
1,
figsize=(fscale, fscale * (maxLon - minLon) /
(maxLat - minLat)),
subplot_kw={'projection': ccrs.PlateCarree()})
ax.set_extent(extents, crs=ccrs.PlateCarree())
coastline = cfeature.GSHHSFeature(scale='auto',
edgecolor='black',
facecolor=cfeature.COLORS['land'])
ax.add_feature(coastline, zorder=0)
ax.add_feature(cfeature.BORDERS, linewidth=2)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='gray',
alpha=0.5,
linestyle='--')
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
if 'PARTITION' in ds:
sc = ax.scatter(
[ds.sel(PARTITION=part).LONGITUDE for part in ds.PARTITION],
[ds.sel(PARTITION=part).LATITUDE for part in ds.PARTITION],
c=[ds.sel(PARTITION=part)[color] for part in ds.PARTITION])
else:
sc = ax.scatter(ds.LONGITUDE, ds.LATITUDE, c=ds[color])
axins0 = inset_axes(ax,
width="5%",
height="100%",
loc='lower left',
bbox_to_anchor=(1.07, 0, 1, 1),
bbox_transform=ax.transAxes)
cb = fig.colorbar(sc,
orientation='vertical',
cax=axins0,
ticklocation='right')
if color == 'TIME':
cb.ax.set_yticklabels([
(datetime.fromtimestamp(i // 10**9)).strftime('%b %d %Y')
for i in cb.get_ticks()
])
if 'units' in ds[color].attrs.keys():
cb.set_label(f"{ds[color].standard_name}\n [{ds[color].units}]")
return fig, ax
def Z500_VEL(datetime, steps=0, model= "MEPS", domain_name = None, domain_lonlat = None, legend=False, info = False,grid=True):
for dt in datetime: #modelrun at time..
date = dt[0:-2]
hour = int(dt[-2:])
param_sfc = ["air_pressure_at_sea_level", "surface_geopotential"]
param_pl = ["air_temperature_pl", "geopotential_pl"] #"air_temperature_2m",
param_sfx = ["SST","SIC"] #add later
p_levels = [850,1000]
param = param_sfc + param_pl
split = False
print("\n######## Checking if your request is possible ############")
try:
check_all = check_data(date=dt, model=model, param=param, levtype="pl", p_level=p_levels, step=steps)
check_sfx = check_data(date=dt, model=model, param=param_sfx, step=steps)
except ValueError:
split = True
try:
print("--------> Splitting up your request to find match ############")
check_sfc = check_data(date=dt, model=model, param=param_sfc)
check_pl = check_data(date=dt, model=model, param=param_pl, levtype="pl", p_level=p_levels)
check_sfx = check_data(date=dt, model=model, param=param_sfx)
print(check_pl.file)
except ValueError:
print("!!!!! Sorry this plot is not availbale for this date. Try with another datetime !!!!!")
break
print("--------> Found match for your request ############")
if not split:
file_all = check_all.file.loc[0]
data_domain = domain_input_handler(dt, model,domain_name, domain_lonlat, file_all)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model, data_domain=data_domain, param=param, file=file_all, step=steps,
date=dt, p_level=p_levels)
dmap_mepsdfx = get_data(model=model, data_domain=data_domain, param=param_sfx, file=check_sfx.file.loc[0], step=steps,date=dt)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
tmap_meps = dmap_meps # two names for same value, no copying done.
dmap_mepsdfx.retrieve()
else:
# get sfc level data
file_sfc = check_sfc.file.loc[0]
data_domain = domain_input_handler(dt, model,domain_name, domain_lonlat, file_sfc)
#lonlat = np.array(data_domain.lonlat)
dmap_meps = get_data(model=model, param=param_sfc, file=file_sfc, step=steps, date=dt, data_domain=data_domain)
print("\n######## Retrieving data ############")
print(f"--------> from: {dmap_meps.url} ")
dmap_meps.retrieve()
# get pressure level data
file_pl = check_pl.file.loc[0]
tmap_meps = get_data(model=model, data_domain=data_domain, param=param_pl, file=file_pl, step=steps, date=dt, p_level=p_levels)
print("\n######## Retrieving data ############")
print(f"--------> from: {tmap_meps.url} ")
tmap_meps.retrieve()
dmap_mepsdfx = get_data(model=model, data_domain=data_domain, param=param_sfx, file=check_sfx.file.loc[0],
step=steps,
date=dt)
dmap_mepsdfx.retrieve()
#CALCULATE
pt = potential_temperatur(dmap_meps.air_temperature_pl, dmap_meps.pressure*100.)
pt_sst = potential_temperatur(dmap_mepsdfx.SST, dmap_meps.air_pressure_at_sea_level[:,0,:,:])
dpt = pt[:,np.where(dmap_meps.pressure==1000)[0],:,:]-pt[:,np.where(dmap_meps.pressure==850)[0],:,:]
dpt_sst =pt_sst[:,:,:] - pt[:,np.where(dmap_meps.pressure==850)[0],:,:].squeeze()
#dpt_sst =abs(pt_sst[:,:,:] - pt[:,np.where(dmap_meps.pressure==850)[0],:,:].squeeze())
# convert fields
dmap_meps.air_pressure_at_sea_level/=100
tmap_meps.geopotential_pl/=10.0
lon0 = dmap_meps.longitude_of_central_meridian_projection_lambert
lat0 = dmap_meps.latitude_of_projection_origin_projection_lambert
parallels = dmap_meps.standard_parallel_projection_lambert
# setting up projection
# setting up projection
globe = ccrs.Globe(ellipse='sphere', semimajor_axis=6371000., semiminor_axis=6371000.)
crs = ccrs.LambertConformal(central_longitude=lon0, central_latitude=lat0, standard_parallels=parallels,
globe=globe)
for tim in np.arange(np.min(steps), np.max(steps)+1, 1):
fig1, ax1 = plt.subplots(1, 1, figsize=(7, 9),
subplot_kw={'projection': crs})
ttt = tim #+ np.min(steps)
tidx = tim - np.min(steps)
print('Plotting {0} + {1:02d} UTC'.format(dt, ttt))
plev2 = 0
embr = 0
ZS = dmap_meps.surface_geopotential[tidx, 0, :, :]
MSLP = np.where(ZS < 3000, dmap_meps.air_pressure_at_sea_level[tidx, 0, :, :], np.NaN).squeeze()
Z = (tmap_meps.geopotential_pl[tidx, plev2, :, :]).squeeze()
DELTAPT=dpt[tidx, 0, :, :]
DELTAPT = dpt_sst[tidx,:,:]
ICE = dmap_mepsdfx.SIC[tidx, :, :]
DELTAPT = np.where( ICE <= 0.99,DELTAPT,0)
lvl = range(-1,13)
C = [[255,255,255 ], # grey #[255,255,255],#gre
[204,191,189 ], # grey
[155,132,127 ], # grey
[118,86,80], # lillac, 39 64 197 149,53,229
[138,109,81], # blue dark,7,67,194 [218,81,14],
[181,165,102], #
[229,226,124], ##
[213,250,128],
[125,231,111],
[55,212,95],
[25,184,111],
[17,138,234],
[21,82,198],
[37,34,137]]
C = np.array(C)
C = np.divide(C, 255.) # RGB has to be between 0 and 1 in python
CF_prec = plt.contourf(dmap_meps.x, dmap_meps.y, DELTAPT, zorder=0,
antialiased=True,extend = "max", levels=lvl, colors=C, vmin=0, vmax=12)#
CF_ice = plt.contour(dmap_meps.x, dmap_meps.y, ICE, zorder=1, linewidths=2.5, colors="black", levels=[0.1, 0.5]) #
# MSLP with contour labels every 10 hPa
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=1, alpha=1.0,
levels=np.arange(round(np.nanmin(MSLP), -1) - 10, round(np.nanmax(MSLP), -1) + 10, 1),
colors='grey', linewidths=0.5)
C_P = ax1.contour(dmap_meps.x, dmap_meps.y, MSLP, zorder=2, alpha=1.0,
levels=np.arange(round(np.nanmin(MSLP), -1) - 10, round(np.nanmax(MSLP), -1) + 10, 10),
colors='grey', linewidths=1.0)
ax1.clabel(C_P, C_P.levels, inline=True, fmt="%3.0f", fontsize=10)
#CS = ax1.contour(dmap_meps.x, dmap_meps.y, Z, zorder=3, alpha=1.0,
# levels=np.arange(4600, 5800, 20), colors="blue", linewidths=0.7)
#ax1.clabel(CS, CS.levels, inline=True, fmt="%4.0f", fontsize=10)
ax1.add_feature(cfeature.GSHHSFeature(scale='intermediate')) # ‘auto’, ‘coarse’, ‘low’, ‘intermediate’, ‘high, or ‘full’ (default is ‘auto’).
ax1.text(0, 1, "{0}_CAOi_{1}+{2:02d}".format(model, dt, ttt), ha='left', va='bottom', transform=ax1.transAxes, color='black')
##########################################################
legend=True
if legend:
proxy = [plt.axhline(y=0, xmin=1, xmax=1, color="grey"),
plt.axhline(y=0, xmin=1, xmax=1, color="black",linewidth=4)]
try:
ax_cb = adjustable_colorbar_cax(fig1, ax1)
plt.colorbar(CF_prec,cax = ax_cb, fraction=0.046, pad=0.01, aspect=25, label=r"$\theta_{SST}-\theta_{850}$", extend="both")
except:
pass
lg = ax1.legend(proxy, [f"MSLP [hPa]",
f"Sea ice at 10%, 80%, 99%"])
frame = lg.get_frame()
frame.set_facecolor('white')
frame.set_alpha(1)
make_modelrun_folder = setup_directory(OUTPUTPATH, "{0}".format(dt))
if grid:
nicegrid(ax=ax1)
if domain_name != model and data_domain != None: # weird bug.. cuts off when sees no data value
ax1.set_extent(data_domain.lonlat)
print("filename: "+make_modelrun_folder + "/{0}_{1}_CAOi_{2}+{3:02d}.png".format(model, domain_name, dt, ttt))
fig1.savefig(make_modelrun_folder + "/{0}_{1}_CAOi_{2}+{3:02d}.png".format(model, domain_name, dt, ttt), bbox_inches="tight", dpi=200)
ax1.cla()
plt.clf()
plt.close(fig1)
plt.close("all")
def plot2d_bias(cfg, plot_params):
"""Plot 2d maps of the bias relative to climatology.
Parameters
----------
model_filenames:OrderedDict
OrderedDict with model names as keys and input files as values.
cmor_var: str
name of the variable
depth: int
we will plot the data on the model level
that is closest to the `depth`.
diagworkdir: str
path to the working directory
diagplotdir: str
path to the plot directory
levels: tuple
values to be used for vmin and vmax in the form of (vmin, vmax)
dpi: int
the dpi values to save the figure
observations: str
name of the observations
projection: instance of cartopy projection (ccrs)
bbox: list
bounding box. It will be the input for cartopy `set_extent`.
ncols: int
number of columns.
Retuns
------
None
"""
# setupa a base figure
figure, axis = create_plot(plot_params['model_filenames'],
ncols=plot_params['ncols'],
projection=plot_params['projection'])
# get the filename of observations
ifilename_obs = genfilename(cfg['work_dir'],
plot_params['variable'],
plot_params['observations'],
data_type='timmean',
extension='.nc')
# get the metadata for observations (we just need a size)
metadata = load_meta(
datapath=plot_params['model_filenames'][plot_params['observations']],
fxpath=None)
lon2d = metadata['lon2d']
# Create an empty array to store the mean.
# One point larger along long to acount for cyclic point
model_mean = np.zeros((lon2d.shape[0], lon2d.shape[1] + 1))
print("MODEL MEAN SHAPE {}".format(model_mean.shape))
# delete observations from the model list
model_filenames = plot_params['model_filenames'].copy()
del model_filenames[plot_params['observations']]
# loop over models
index = None
for index, mmodel in enumerate(model_filenames):
logger.info("Plot plot2d_bias %s for %s", plot_params['variable'],
mmodel)
# get the filename with the mean generated by the `timemean`
ifilename = genfilename(cfg['work_dir'],
plot_params['variable'],
mmodel,
data_type='timmean',
extension='.nc')
# do the interpolation to the observation grid
# the output is
lonc, latc, target_depth, data_obs, interpolated = interpolate_esmf(
ifilename_obs, ifilename, plot_params['depth'],
plot_params['variable'])
# get the label and convert data if needed
cb_label, data_obs = label_and_conversion(plot_params['variable'],
data_obs)
cb_label, interpolated = label_and_conversion(plot_params['variable'],
interpolated)
# add to the mean model
model_mean = model_mean + interpolated
# set the map extent
left, right, down, upper = plot_params['bbox']
axis[index].set_extent([left, right, down, upper],
crs=ccrs.PlateCarree())
# Only pcolormesh is working for now with cartopy,
# contourf is failing to plot curvilinear meshes,
# let along the unstructures ones.
image = axis[index].contourf(
lonc,
latc,
interpolated - data_obs,
levels=plot_params['levels'],
extend='both',
# vmin=contours[0],
# vmax=contours[-1],
transform=ccrs.PlateCarree(),
cmap=plot_params['cmap'],
)
# fill continents
axis[index].add_feature(
cfeature.GSHHSFeature(levels=[1],
scale="low",
facecolor="lightgray"))
axis[index].set_title("{}, {} m".format(mmodel, int(target_depth)),
size=18)
axis[index].set_rasterization_zorder(-1)
# calculate the model mean and plot it
if index:
index = index
else:
index = 0
model_mean = model_mean / len(model_filenames)
axis[index + 1].set_extent([left, right, down, upper],
crs=ccrs.PlateCarree())
image = axis[index + 1].contourf(
lonc,
latc,
model_mean - data_obs,
levels=plot_params['levels'],
extend='both',
# vmin=contours[0],
# vmax=contours[-1],
transform=ccrs.PlateCarree(),
cmap=cmo.balance,
)
axis[index + 1].add_feature(
cfeature.GSHHSFeature(levels=[1], scale="low", facecolor="lightgray"))
axis[index + 1].set_title("Model mean bias, {} m".format(
int(target_depth)),
size=18)
axis[index + 1].set_rasterization_zorder(-1)
# delete the axis that are not needed
for delind in range(index + 2, len(axis)):
figure.delaxes(axis[delind])
# set common colorbar
colorbar = figure.colorbar(image,
orientation='horizontal',
ax=axis,
pad=0.01,
shrink=0.9)
colorbar.set_label(cb_label, rotation='horizontal', size=18)
colorbar.ax.tick_params(labelsize=18)
# save the picture
pltoutname = genfilename(cfg['plot_dir'],
plot_params['variable'],
"MULTIMODEL",
data_type='plot2d_bias_{}_level'.format(
str(int(target_depth))))
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = [ifilename]
plot_params['areacello'] = None
plot_params['mmodel'] = None
plot_params['region'] = "Global"
plt.savefig(pltoutname, dpi=plot_params['dpi'])
provenance_record = get_provenance_record(plot_params, 'plot2d_bias',
'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)
def main():
"""
lon0 = 35.25; lat0 = -23.60
lon1 = 35.25; lat1 = -24.0
lon2 = 35.65; lat2 = -24.0
lon3 = 35.65; lat3 = -23.60
https://stackoverflow.com/questions/52356926/how-to-set-offset-for-python-cartopy-geometry
"""
# Create the figure
fig = plt.figure(figsize=(5,6))
from cartopy.io.img_tiles import OSM
# Add OSM image as background for the selected extension
tiler = OSM()
ax = plt.axes(projection=tiler.crs)
# Openstreet layer zoom detail
zoom=12
ax.add_image(tiler, zoom, alpha=0.75)
extent = [35.25, 35.65, -24.05, -23.60]
ax.set_extent(extent)
# Set figure title
ax.set_title('Study area',fontsize=10)
# Plot coastline from GSHHSF
coast_line=cfeature.GSHHSFeature(scale='full')
ax.add_feature(coast_line, alpha=1.0, linewidths=0.5, edgecolor='black' )
plotWetlands(ax)
plotRivers(ax)
plotInterestPoint(ax)
# plot difters track
plotTrack(ax)
# Plot escale bar
scaleBar(ax, length=10, location=(0.8, 0.80), linewidth=1.5)
plotGridLines(ax)
# Criate a subfigure
#subfigure = [.58, .09, .30, .20]
ax2 = fig.add_axes([.58, .09, .30, .20])
plotMozambique(ax2)
# Adjust plot the the figure
plt.tight_layout()
plt.show()
saveFig(fig, file='estudyarea')
def plot2d_original_grid(cfg, plot_params):
"""Plot 2d maps on original grid using cartopy.
Parameters
----------
model_filenames:OrderedDict
OrderedDict with model names as keys and input files as values.
cmor_var: str
name of the variable
depth: int
we will plot the data on the model
level that is closest to the `depth`.
Ignored if explicit_depths is provided.
levels: tuple
values to be used for vmin and vmax in the form of (vmin, vmax)
diagworkdir: str
path to the working directory
diagplotdir: str
path to the plot directory
cmap: matplotlib colormap
colormap
dpi: int
the dpi values to save the figure
explicit_depths: dict
Output of the `aw_core` function.
It's a dictionary where for each model there is a maximum temperature,
depth level in the model, index of the depth level in the model.
If provided the `depth` parameter is excluded.
projection: instance of cartopy projection (ccrs)
bbox: list
bounding box. It will be the input for cartopy `set_extent`.
ncols: int
number of columns.
Retuns
------
None
"""
figure, axis = create_plot(plot_params['model_filenames'],
ncols=plot_params['ncols'],
projection=plot_params['projection'])
index = None
for index, mmodel in enumerate(plot_params['model_filenames']):
logger.info("Plot plot2d_original_grid %s for %s",
plot_params['variable'], mmodel)
ifilename = genfilename(cfg['work_dir'],
plot_params['variable'],
mmodel,
data_type='timmean',
extension='.nc')
metadata = load_meta(ifilename, fxpath=None)
datafile = metadata['datafile']
lon2d = metadata['lon2d']
lat2d = metadata['lat2d']
lev = metadata['lev']
if not plot_params['explicit_depths']:
depth_target, level_target = closest_depth(lev,
plot_params['depth'])
else:
level_target = plot_params['explicit_depths'][mmodel][
'maxvalue_index']
depth_target = lev[level_target]
if datafile.variables[plot_params['variable']].ndim < 4:
data = datafile.variables[plot_params['variable']][
level_target, :, :]
else:
data = datafile.variables[plot_params['variable']][
0, level_target, :, :]
cb_label, data = label_and_conversion(plot_params['variable'], data)
left, right, down, upper = plot_params['bbox']
axis[index].set_extent([left, right, down, upper],
crs=ccrs.PlateCarree())
# Only pcolormesh is working for now with cartopy,
# contourf is failing to plot curvilinear meshes,
# let along the unstructures ones.
image = axis[index].pcolormesh(
lon2d,
lat2d,
data,
vmin=plot_params['levels'][0],
vmax=plot_params['levels'][-1],
transform=ccrs.PlateCarree(),
cmap=plot_params['cmap'],
)
axis[index].add_feature(
cfeature.GSHHSFeature(levels=[1],
scale="low",
facecolor="lightgray"))
axis[index].set_title("{}, {} m".format(mmodel,
np.round(depth_target, 1)),
size=18)
axis[index].set_rasterization_zorder(-1)
# delete unused axis
for delind in range(index + 1, len(axis)):
figure.delaxes(axis[delind])
# set common colorbar
colorbar = figure.colorbar(image,
orientation='horizontal',
ax=axis,
pad=0.01,
shrink=0.9)
colorbar.set_label(cb_label, rotation='horizontal', size=18)
colorbar.ax.tick_params(labelsize=18)
if not plot_params['explicit_depths']:
plot_type = 'plot2d_{}_depth'.format(str(plot_params['depth']))
else:
plot_type = "plot2d_different_levels"
# save the figure
pltoutname = genfilename(cfg['plot_dir'],
plot_params['variable'],
"MULTIMODEL",
data_type=plot_type)
plot_params['basedir'] = cfg['plot_dir']
plot_params['ori_file'] = [ifilename]
plot_params['areacello'] = None
plot_params['mmodel'] = None
plot_params['region'] = "Global"
plt.savefig(pltoutname, dpi=plot_params['dpi'])
provenance_record = get_provenance_record(plot_params, 'plot2d', 'png')
with ProvenanceLogger(cfg) as provenance_logger:
provenance_logger.log(pltoutname + '.png', provenance_record)