def plot_grid2D(lons, lats, tec_grid2D, datetime, title_label = ''):
LATS, LONS = np.meshgrid(lats, lons)
m = Basemap(llcrnrlon=-180,
llcrnrlat=-55,
urcrnrlon=180,
urcrnrlat=75,
projection='merc',
area_thresh=1000,
resolution='i')
m.drawstates()
m.drawcountries()
m.drawcoastlines()
parallels = np.arange(-90,90,20)
m.drawparallels(parallels,labels=[True,False,False,True])
meridians = np.arange(0,360,40)
m.drawmeridians(meridians,labels=[True,False,False,True])
m.scatter(LONS, LATS, c=tec_grid2D, latlon = True, linewidths=0, s=5)
m.colorbar()
plt.title('%s\n%s' % (title_label, datetime.isoformat(' ')))
def do_subplot(field, title, lons, lats, plot_num, pole, diff=False):
"""
Make an individual subplot.
"""
if pole == 'north':
projection = 'nplaea'
boundinglat = 50
else:
projection = 'splaea'
boundinglat = -50
m_base = Basemap(projection=projection, boundinglat=boundinglat,
lon_0=0)
x, y = m_base(lons, lats)
ax = fig.add_subplot(plot_num)
max = get_max_within_area(field, lats, boundinglat, pole)
if diff:
cmap = plt.get_cmap('RdBu_r')
min = -max
else:
cmap = plt.get_cmap()
min = 0
# Set the land points to be gray.
cmap.set_bad('0.65')
m_base.pcolormesh(x, y, field, vmax=max, vmin=min, cmap=cmap)
m_base.colorbar()
m_base.drawparallels(np.arange(-80.,81.,20.))
m_base.drawmeridians(np.arange(-180.,181.,20.))
if title:
plt.title(title)
def main():
plot_utils.apply_plot_params(width_cm=20, height_cm=20, font_size=10)
high_hles_years = [1993, 1995, 1998]
low_hles_years = [1997, 2001, 2006]
data_path = "/BIG1/skynet1_rech1/diro/sample_obsdata/eraint/eraint_uvslp_years_198111_201102_NDJmean_ts.nc"
with xr.open_dataset(data_path) as ds:
print(ds)
u = get_composit_for_name(ds, "u10", high_years_list=high_hles_years, low_years_list=low_hles_years)
v = get_composit_for_name(ds, "v10", high_years_list=high_hles_years, low_years_list=low_hles_years)
msl = get_composit_for_name(ds, "msl", high_years_list=high_hles_years, low_years_list=low_hles_years)
lons = ds["longitude"].values
lats = ds["latitude"].values
print(lats)
print(msl.shape)
print(lons.shape, lats.shape)
lons2d, lats2d = np.meshgrid(lons, lats)
fig = plt.figure()
map = Basemap(llcrnrlon=-130, llcrnrlat=22, urcrnrlon=-28,
urcrnrlat=65, projection='lcc', lat_1=33, lat_2=45,
lon_0=-95, resolution='i', area_thresh=10000)
clevs = np.arange(-11.5, 12, 1)
cmap = cm.get_cmap("bwr", len(clevs) - 1)
bn = BoundaryNorm(clevs, len(clevs) - 1)
x, y = map(lons2d, lats2d)
im = map.contourf(x, y, msl / 100, levels=clevs, norm=bn, cmap=cmap) # convert to mb (i.e hpa)
map.colorbar(im)
stride = 2
ux, vy = map.rotate_vector(u, v, lons2d, lats2d)
qk = map.quiver(x[::stride, ::stride], y[::stride, ::stride], ux[::stride, ::stride], vy[::stride, ::stride],
scale=10, width=0.01, units="inches")
plt.quiverkey(qk, 0.5, -0.1, 2, "2 m/s", coordinates="axes")
map.drawcoastlines(linewidth=0.5)
map.drawcountries()
map.drawstates()
#plt.show()
fig.savefig("hles_wind_compoosits.png", bbox_inches="tight", dpi=300)
def visualiseUrl( fig , url , fig1 ): nc = url #netCDF4.Dataset(url,'r') title = clipc_combine_process.getTitleNC(nc) lats = nc.variables['lat'][:] # extract/copy the data lons = nc.variables['lon'][:] lat_0 = lats.mean() lon_0 = lons.mean() ax = fig.add_subplot(fig1) basemap = Basemap(resolution='l',projection='ortho', lat_0=lat_0,lon_0=lon_0) #lat_ts=40, if len(lons.shape) == 1: # 2D Array no time dimmension data = nc.variables[title][:][:] xi, yi = basemap(*np.meshgrid(lons, lats)) elif len(lons.shape) == 2: # 3D Array time dimmension data = nc.variables[title][0][:][:] xi, yi = basemap(lons, lats) else: print "DATA SIZE NOT HANDLED" data = None # Plot Data cs = basemap.pcolormesh(xi,yi,np.squeeze(data)) # Add Coastlines, States, and Country Boundaries basemap.drawcoastlines() basemap.drawcountries() basemap.colorbar()
def vec_plot_on_earth(lons, lats, x_data, y_data, vmin=-4, vmax=12, ax=None):
if ax == None:
ax = plt.gca()
plot_lons, plot_x_data = extend_data(lons, lats, x_data)
plot_lons, plot_y_data = extend_data(lons, lats, y_data)
lons, lats = np.meshgrid(plot_lons, lats)
m = Basemap(ax=ax, projection='cyl', resolution='c', llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)
x, y = m(lons, lats)
mag = np.sqrt(plot_x_data**2 + plot_y_data**2)
vmin, vmax = mag.min(), mag.max()
m.contourf(x[:-1,:], y[:-1,:], mag, ax=ax)
#m.pcolormesh(x, y, mag, vmin=vmin, vmax=vmax)
#m.quiver(x, y, plot_x_data, plot_y_data)
skip = 5
m.quiver(x[::skip, ::skip], y[::skip, ::skip], plot_x_data[::skip, ::skip], plot_y_data[::skip, ::skip], ax=ax)
m.drawcoastlines(ax=ax)
m.drawparallels(np.arange(-90.,90.,45.), labels=[1, 0, 0, 0], fontsize=10, ax=ax)
m.drawmeridians(np.arange(-180.,180.,60.), labels=[0, 0, 0, 1], fontsize=10, ax=ax)
m.colorbar(location='bottom', pad='7%', ax=ax)
plt.show()
def plot_on_earth(lons, lats, data, vmin=-4, vmax=12, cbar_loc='left', cbar_ticks=None):
#import ipdb; ipdb.set_trace()
#if ax == None:
#ax = plt.gca()
plot_lons, plot_data = extend_data(lons, lats, data)
lons, lats = np.meshgrid(plot_lons, lats)
m = Basemap(projection='cyl', resolution='c', llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)
x, y = m(lons, lats)
m.pcolormesh(x, y, plot_data, vmin=vmin, vmax=vmax)
#m.pcolormesh(x, y, plot_data)
m.drawcoastlines()
if cbar_loc == 'left':
p_labels = [0, 1, 0, 0]
else:
p_labels = [1, 0, 0, 0]
m.drawparallels(np.arange(-90.,90.1,45.), labels=p_labels, fontsize=10)
m.drawmeridians(np.arange(-180.,180.,60.), labels=[0, 0, 0, 1], fontsize=10)
#import ipdb; ipdb.set_trace()
if cbar_ticks == None:
cbar = m.colorbar(location=cbar_loc, pad='7%')
else:
cbar = m.colorbar(location=cbar_loc, pad='7%', ticks=cbar_ticks)
if cbar_loc == 'left':
cbar.ax.xaxis.get_offset_text().set_position((10,0))
def vec_plot_south_america(lons, lats, sa_mask, data_x, data_y, ax=None):
if ax == None:
ax = plt.gca()
#print(sa_mask.shape)
#print(data_x.shape)
#print(data_y.shape)
#data_x_masked = np.ma.array(data_x, mask=sa_mask)
#data_y_masked = np.ma.array(data_y, mask=sa_mask)
plot_lons, plot_x_data = extend_data(lons, lats, data_x)
plot_lons, plot_y_data = extend_data(lons, lats, data_y)
lons, lats = np.meshgrid(plot_lons, lats)
m = Basemap(ax=ax, projection='cyl', resolution='c', llcrnrlat=-60, urcrnrlat=15, llcrnrlon=-85, urcrnrlon=-32)
x, y = m(lons, lats)
mag = np.sqrt(plot_x_data**2 + plot_y_data**2)
vmin, vmax = mag.min(), mag.max()
m.contourf(x[:-1,:], y[:-1,:], mag, ax=ax)
#m.pcolormesh(x, y, mag, vmin=vmin, vmax=vmax, ax=ax)
m.quiver(x, y, plot_x_data, plot_y_data, scale=40, ax=ax)
m.drawcoastlines(ax=ax)
m.drawparallels(np.arange(-60.,15.,10.), labels=[1, 0, 0, 0], fontsize=10, ax=ax)
m.drawmeridians(np.arange(-90.,-30.,10.), labels=[0, 0, 0, 1], fontsize=10, ax=ax)
m.colorbar(location='right', pad='5%', ax=ax)
plt.show()
def nsolLots():
''' Compare nSol estimates to insolation. '''
# Read the data first:
nsolTable = readNsol()
lons = [p['LonW'] for p in nsolTable]
lats = [p['LatN'] for p in nsolTable]
sizes = [p['ArrayOutput'] for p in nsolTable]
insolSizes = [p['TotalAnnualInsolation'] for p in nsolTable]
bmOptions = {'projection':'merc', 'lon_0':-95, 'lat_0':35, 'llcrnrlat':20, 'urcrnrlat':50,
'llcrnrlon':-130, 'urcrnrlon':-60, 'rsphere':6371200., 'resolution':'l', 'area_thresh':10000}
# First Plot
plt.subplot(211)
m1 = Basemap(**bmOptions)
m1.drawcoastlines()
m1.drawstates()
m1.drawcountries()
plt.title('Panel Output')
mesh1 = m1.pcolor(lons, lats, sizes, latlon=True, tri=True)
cbar1 = m1.colorbar(mesh1,location='right',pad='5%')
cbar1.set_label('kWh')
# Second Plot
plt.subplot(212)
m2 = Basemap(**bmOptions)
m2.drawcoastlines()
m2.drawstates()
m2.drawcountries()
plt.title('Annual Insolation')
mesh2 = m2.pcolor(lons, lats, insolSizes, latlon=True, tri=True)
cbar2 = m2.colorbar(mesh2,location='right',pad='5%')
cbar2.set_label('kWh/m^2')
def plot(self, projection='lambert', geopolygons=None, vmin=None, vmax=None):
# fig=plt.figure(num=None, figsize=(12, 12), dpi=80, facecolor='w', edgecolor='k')
lat_centre = (self.maxlat+self.minlat)/2.0
lon_centre = (self.maxlon+self.minlon)/2.0
if projection=='merc':
m=Basemap(projection='merc', llcrnrlat=self.minlat-5., urcrnrlat=self.maxlat+5., llcrnrlon=self.minlon-5.,
urcrnrlon=self.maxlon+5., lat_ts=20, resolution=resolution)
# m.drawparallels(np.arange(self.minlat,self.maxlat,self.dlat), labels=[1,0,0,1])
# m.drawmeridians(np.arange(self.minlon,self.maxlon,self.dlon), labels=[1,0,0,1])
m.drawparallels(np.arange(-80.0,80.0,5.0), labels=[1,0,0,1])
m.drawmeridians(np.arange(-170.0,170.0,5.0), labels=[1,0,0,1])
m.drawstates(color='g', linewidth=2.)
elif projection=='global':
m=Basemap(projection='ortho',lon_0=lon_centre, lat_0=lat_centre, resolution=resolution)
m.drawparallels(np.arange(-80.0,80.0,10.0), labels=[1,0,0,1])
m.drawmeridians(np.arange(-170.0,170.0,10.0), labels=[1,0,0,1])
elif projection=='regional_ortho':
m1 = Basemap(projection='ortho', lon_0=self.minlon, lat_0=self.minlat, resolution='l')
m = Basemap(projection='ortho', lon_0=self.minlon, lat_0=self.minlat, resolution=resolution,\
llcrnrx=0., llcrnry=0., urcrnrx=m1.urcrnrx/mapfactor, urcrnry=m1.urcrnry/3.5)
m.drawparallels(np.arange(-80.0,80.0,10.0), labels=[1,0,0,0], linewidth=2, fontsize=20)
# m.drawparallels(np.arange(-90.0,90.0,30.0),labels=[1,0,0,0], dashes=[10, 5], linewidth=2, fontsize=20)
# m.drawmeridians(np.arange(10,180.0,30.0), dashes=[10, 5], linewidth=2)
m.drawmeridians(np.arange(-170.0,170.0,10.0), linewidth=2)
elif projection=='lambert':
distEW, az, baz=obspy.geodetics.gps2dist_azimuth(self.minlat, self.minlon,
self.minlat, self.maxlon) # distance is in m
distNS, az, baz=obspy.geodetics.gps2dist_azimuth(self.minlat, self.minlon,
self.maxlat+2., self.minlon) # distance is in m
m = Basemap(width=distEW, height=distNS, rsphere=(6378137.00,6356752.3142), resolution='l', projection='lcc',\
lat_1=self.minlat, lat_2=self.maxlat, lon_0=lon_centre, lat_0=lat_centre+1)
m.drawparallels(np.arange(-80.0,80.0,10.0), linewidth=1, dashes=[2,2], labels=[1,1,0,0], fontsize=15)
m.drawmeridians(np.arange(-170.0,170.0,10.0), linewidth=1, dashes=[2,2], labels=[0,0,1,0], fontsize=15)
m.drawcoastlines(linewidth=1.0)
m.drawcountries()
# m.drawmapboundary(fill_color=[1.0,1.0,1.0])
m.fillcontinents(lake_color='#99ffff',zorder=0.2)
# m.drawlsmask(land_color='0.8', ocean_color='#99ffff')
m.drawmapboundary(fill_color="white")
cmap = colors.get_colormap('tomo_80_perc_linear_lightness')
# #
# evx, evy=m(129., 41.306)
# plt.plot(evx, evy, 'y*', markersize=20)
# #
x, y=m(self.lonArr, self.latArr)
im=m.pcolormesh(x, y, self.vArr, cmap=cmap, shading='gouraud', vmin=vmin, vmax=vmax)
try:
geopolygons.PlotPolygon(inbasemap=m)
except:
pass
try:
vrange=vmin+np.arange((vmax-vmin)/0.1+1)*0.1
cb = m.colorbar(im, "bottom", size="3%", pad='2%', ticks=vrange)
except:
cb = m.colorbar(im, "bottom", size="3%", pad='2%')
cb.ax.tick_params(labelsize=15)
cb.set_label("Input Phase Velocity (km/sec)", fontsize=15, rotation=0)
plt.show()
return
class PoleMapper(object):
"""docstring for PoleMapper"""
palette = cm.jet
def __init__(self, blat, strings, gridpts=1000, round=True):
super(PoleMapper, self).__init__()
self.blat = blat
self.gridpts = gridpts
self.basemap = Basemap(lon_0=180, boundinglat=blat, projection="spstere", round=round)
self.pole = "Southpole" if blat < 0 else "Northpole"
def create_map(self, hdata, strings, ax=None, vmin=None, vmax=None):
mode, chid, dayside = strings
if not ax:
fig, ax = plt.subplots()
self.palette.set_bad(ax.get_axis_bgcolor(), 1.0)
CS = self.basemap.pcolormesh(
hdata.xedges, hdata.yedges, hdata.H.T, shading="flat", cmap=self.palette, ax=ax, vmin=vmin, vmax=vmax
)
self.basemap.drawparallels(np.arange(-90, -85), latmax=-90, ax=ax, labels=[1, 1, 1, 1])
self.basemap.drawmeridians(np.arange(0, 360, 30), latmax=-90, ax=ax, labels=[1, 1, 1, 1])
self.basemap.colorbar(CS, ax=ax)
ax.set_title(" ".join([self.pole, chid, mode, dayside]))
plt.savefig("_".join(["southpole", chid, mode, str(self.gridpts), dayside]) + ".png", dpi=300)
def create_multimap(self, data, strings):
n = len(data)
fig, ax = plt.subplots(1, n)
for i, d in enumerate(data):
self.create_map(d, strings[i], ax.flatten()[i])
def _vec_plot_on_earth(lons, lats, x_data, y_data, vmin=-4, vmax=12, loc=None, colorbar=False):
plot_lons, plot_x_data = _extend_data(lons, lats, x_data)
plot_lons, plot_y_data = _extend_data(lons, lats, y_data)
lons, lats = np.meshgrid(plot_lons, lats)
if not loc:
m = Basemap(projection='cyl', resolution='c',
llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)
else:
m = Basemap(projection='cyl', resolution='c', **loc)
x, y = m(lons, lats)
mag = np.sqrt(plot_x_data**2 + plot_y_data**2)
vmin, vmax = mag.min(), mag.max()
m.contourf(x, y, mag)
# m.pcolormesh(x, y, mag, vmin=vmin, vmax=vmax)
# m.quiver(x, y, plot_x_data, plot_y_data)
skip = 1
m.quiver(x[::skip, ::skip], y[::skip, ::skip],
plot_x_data[::skip, ::skip], plot_y_data[::skip, ::skip], scale=500)
m.drawcoastlines()
m.drawparallels(np.arange(-90., 90., 45.), labels=[1, 0, 0, 0], fontsize=10)
m.drawmeridians(np.arange(-180., 180., 60.), labels=[0, 0, 0, 1], fontsize=10)
if colorbar:
m.colorbar(location='right', pad='7%')
return m
def _raster_on_earth(lons, lats, data, vmin=None, vmax=None, loc=None, colorbar=True, labels=True):
if not loc:
m = Basemap(projection='cyl', resolution='c',
llcrnrlat=-90, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)
else:
m = Basemap(projection='cyl', resolution='c', **loc)
if data is not None:
plot_lons, plot_data = _extend_data(lons, lats, data)
lons, lats = np.meshgrid(plot_lons, lats)
x, y = m(lons, lats)
if vmin:
m.pcolormesh(x, y, plot_data, vmin=vmin, vmax=vmax)
else:
m.pcolormesh(x, y, plot_data)
m.drawcoastlines()
if labels:
p_labels = [0, 1, 0, 0]
m.drawparallels(np.arange(-90., 90.1, 45.), labels=p_labels, fontsize=10)
m.drawmeridians(np.arange(-180., 180., 60.), labels=[0, 0, 0, 1], fontsize=10)
if colorbar and data is not None:
m.colorbar(location='right', pad='7%')
return m
def _fractions_map(data, dom_x, dom_y, title, case_id, plot_dir):
"""
Plot diagnostic plots of fraction variables using Basemap
"""
# ---------------------------------------------------------------- #
# Plot Fractions
today = date.today().strftime('%Y%m%d')
file_name = "{}_{}_{}.png".format(title.lower().replace(" ", "_"),
case_id.lower().replace(" ", "_"),
today)
pfname = os.path.join(plot_dir, file_name)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
dom_x[dom_x < 0] += 360.0
mask = data <= 0.0
data = np.ma.array(data, mask=mask)
cmap = matplotlib.cm.cool
cmap.set_bad(color=u'w')
# define projection
midx = int(dom_x.shape[1]/2)
midy = int(dom_x.shape[0]/2)
print midx, midy
projection = {'projection': 'stere',
'lon_0': dom_x[-1, midx],
'lat_0': dom_y[midy, midx],
'llcrnrlat': dom_y[-1, 0],
'urcrnrlat': dom_y[0, -1],
'llcrnrlon': dom_x[-1, 0],
'urcrnrlon': dom_x[0, -1],
'resolution': 'l'}
log.debug('Projection: %s', projection)
m = Basemap(**projection)
m.drawcoastlines()
m.drawcountries()
# draw parallels.
parallels = np.arange(-90., 90, 10.)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
# draw meridians
meridians = np.arange(-180., 180., 10.)
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
x, y = m(dom_x, dom_y) # compute map proj coordinates.
print 'passed 1'
cs = m.pcolormesh(x, y, data, cmap=cmap)
m.colorbar(cs, location='right', pad="5%")
plt.title(title)
fig.savefig(pfname)
# ---------------------------------------------------------------- #
return pfname
def stere(data,latitude,longitude,filename):
"""
:param data: CO浓度数据矩阵
:param latitude: 纬度
:param longitude: 经度
:param filename:
:return:
"""
ax = plt.gca()
fig = plt.figure()
m = Basemap(width=10000000,height=7500000,
resolution='l',projection='stere',\
lat_ts=35,lat_0=35,lon_0=107.)
nx = int((m.xmax-m.xmin)/5000.)+1
ny = int((m.ymax-m.ymin)/5000.)+1
topodat = m.transform_scalar(data,longitude,latitude,nx,ny)
im = m.imshow(topodat,cm.GMT_haxby,vmin=0,vmax=4e18)
m.drawcoastlines()
m.drawcountries()
m.drawparallels(np.arange(-80.,81.,20.),labels=[1,0,0,0])
# 画平行的纬度,前一个参数是表示起点，终点，间距的序列，后一个参数是指在哪一个方向显示纬度、经度值
m.drawmeridians(np.arange(-180.,181.,20.),labels=[0,0,0,1])
# 画平行的经度
m.colorbar(im)
plt.title("CO"+filename[12:14])
outname = filename+'.png'
fig.savefig(outname, dpi=fig.dpi)
def vec_plot_polar(lons, lats, data_x, data_y, pole, ax=None):
if ax == None:
ax = plt.gca()
plot_lons, plot_x_data = extend_data(lons, lats, data_x)
plot_lons, plot_y_data = extend_data(lons, lats, data_y)
lons, lats = np.meshgrid(plot_lons, lats)
if pole == 'N':
m = Basemap(ax=ax, resolution='c',projection='stere',lat_0=90.,lon_0=0, width=12000000,height=8000000)
elif pole == 'S':
m = Basemap(ax=ax, resolution='c',projection='stere',lat_0=-90.,lon_0=0, width=12000000,height=8000000)
x, y = m(lons, lats)
mag = np.sqrt(plot_x_data**2 + plot_y_data**2)
vmin, vmax = mag.min(), mag.max()
m.contourf(x[:-1,:], y[:-1,:], mag, ax=ax)
#m.pcolormesh(x, y, mag, vmin=vmin, vmax=vmax, ax=ax)
skip = 5
m.quiver(x[::5, ::5], y[::5, ::5], plot_x_data[::5, ::5], plot_y_data[::5, ::5], scale=50, ax=ax)
m.drawcoastlines(ax=ax)
m.drawparallels(np.arange(-60.,15.,10.), labels=[1, 0, 0, 0], fontsize=10, ax=ax)
m.drawmeridians(np.arange(-180.,180.,10.), fontsize=10, ax=ax)
m.colorbar(location='right', pad='5%', ax=ax)
plt.show()
def plot_cedata(lats, lons, data, title):
"""
Plot coefficient of efficiency data. Lats, lons, and data should have the
same temporal dimensions. Lats and lons should denote gridbox edges.
e.g. data has spatial dimensions of M (lats) x N (lons) then lats and lons
should have dimensions of M+1 x N+1.
Parameters
----------
lats: ndarray
Latitude array (gridbox edges)
lons: ndarray
Longitude array (gridbox edges)
data: ndarray
CE data
title: str
Plot title
"""
plt.close('all')
m = Basemap(projection='gall', llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=0, urcrnrlon=360, resolution='c')
m.drawcoastlines()
if data.min() < 0:
color = cm.bwr
else:
color = cm.OrRd
m.pcolor(lons, lats, data, latlon=True, cmap=color, vmin=-1, vmax=1)
m.colorbar()
plt.title(title)
plt.show()
def demo_north_pole():
r = RPN(path = "/home/huziy/skynet3_rech1/classOff_Andrey/era2/temp_3d")
t = r.get_first_record_for_name("I0")
lon, lat = r.get_longitudes_and_latitudes_for_the_last_read_rec()
r.close()
nx, ny = lon.shape
lon_0, lat_0 = lon[nx//2, ny//2], lat[nx//2, ny//2]
basemap = Basemap(projection = "omerc", lon_1=60, lat_1 = 89.999, lon_2=-30, lat_2=0, no_rot=True,
lon_0 = lon_0, lat_0 = lat_0,
llcrnrlon=lon[0, 0], llcrnrlat=lat[0,0],
urcrnrlon=lon[-1, -1], urcrnrlat=lat[-1, -1]
)
x, y = basemap(lon, lat)
basemap.contourf(x, y, t)
basemap.drawcoastlines()
basemap.colorbar()
#basemap.shadedrelief()
plt.show()
def draw_map(lat, lng, sic):
# setup south polar stereographic basemap.
# The longitude lon_0 is at 6-o'clock, and the
# latitude circle boundinglat is tangent to the edge
# of the map at lon_0. Default value of lat_ts
# (latitude of true scale) is pole.
m = Basemap(projection='spstere',boundinglat=-50,lon_0=270,resolution='h', round=True)
# Basemap()
plt.figure()
for beam in range(8):
x1,y1 = m(lng[beam], lat[beam])
m.hexbin(x1,y1, C=sic[beam],gridsize=len(sic[beam]),cmap=plt.cm.jet)
m.drawcoastlines()
m.fillcontinents(color='coral',lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-80.,81.,20.))
m.drawmeridians(np.arange(-180.,181.,20.))
m.drawmapboundary(fill_color='aqua')
m.colorbar(location="bottom",label="SIC") # draw colorbar
plt.title("North Polar Stereographic Projection")
plt.gcf().set_size_inches(18,10)
plt.show()
def _fractions_map(data, dom_x, dom_y, title, case_id, plot_dir):
'''
Plot diagnostic plots of fraction variables using Basemap
'''
# ---------------------------------------------------------------- #
# Plot Fractions
pfname = _make_filename(title, case_id, plot_dir)
fig = plt.figure(figsize=(8, 8))
fig.add_axes([0.1, 0.1, 0.8, 0.8])
dom_x[dom_x < 0] += 360.0
mask = data <= 0.0
data = np.ma.array(data, mask=mask)
cmap = matplotlib.cm.cool
cmap.set_bad(color='w')
# define projection
midx = int(dom_x.shape[1] / 2)
midy = int(dom_x.shape[0] / 2)
projection = {'projection': 'stere',
'lon_0': dom_x[-1, midx],
'lat_0': dom_y[midy, midx],
'llcrnrlat': dom_y[-1, 0],
'urcrnrlat': dom_y[0, -1],
'llcrnrlon': dom_x[-1, 0],
'urcrnrlon': dom_x[0, -1],
'resolution': 'l'}
log.debug('Projection: %s', projection)
m = Basemap(**projection)
m.drawcoastlines()
m.drawcountries()
# draw parallels.
parallels = np.arange(-90., 90, 10.)
m.drawparallels(parallels, labels=[1, 0, 0, 0])
# draw meridians
meridians = np.arange(-180., 180., 10.)
m.drawmeridians(meridians, labels=[0, 0, 0, 1])
x, y = m(dom_x, dom_y) # compute map proj coordinates.
cs = m.pcolormesh(x, y, data, cmap=cmap)
m.colorbar(cs, location='right', pad='5%')
plt.title(title)
fig.savefig(pfname)
plt.close()
# ---------------------------------------------------------------- #
return pfname
def drawHk(self, filename="nodet"):
# print sic
# setup south polar stereographic basemap.
if (len(self)!=0):
m = Basemap(projection='spstere',boundinglat=-50,lon_0=180,resolution='h', round=True)
lat = []
lng = []
sic = []
for s in self:
lng.append(s.getLon())
lat.append(s.getLat())
sic.append(s.getSic())
lng = np.array(lng)
lat = np.array(lat)
sic = np.array(sic)
plt.figure()
x1,y1= m(lng, lat)
print "x, y, sic", x1, y1, sic
m.hexbin(x1,y1, C=sic, gridsize=len(sic), cmap=plt.cm.jet)
m.drawcoastlines()
m.fillcontinents(lake_color='white')
# draw parallels and meridians.
m.drawparallels(np.arange(-80.,81.,20.))
m.drawmeridians(np.arange(-180.,181.,20.))
m.drawmapboundary(fill_color='green')
m.colorbar(location="right",label="SIC") # draw colorbar
plt.title("Sea Ice Concentration - South Pole")
fig = plt.gcf()
plt.show()
f_name = "./img/"+filename + "_.png"
fig.savefig(f_name)
plt.close()
# Delete auxiliar variables.
del m
del lng
del lat
del sic
del x1
del y1
return f_name
def scatter_subplots(lons1, lats1, data1, s1,
lons2, lats2, data2, s2,
title1=None, title2=None):
fig = plt.figure()
# first subplot
ax1 = fig.add_subplot(121)
m = Basemap(projection='cyl', llcrnrlat=10, urcrnrlat=35,
llcrnrlon=65, urcrnrlon=85)
m.drawcoastlines()
m.drawcountries()
parallels = np.arange(-90,90,15.)
m.drawparallels(parallels,labels=[1,0,0,0])
meridians = np.arange(-180,180,15.)
m.drawmeridians(meridians,labels=[0,0,0,1])
m.readshapefile(os.path.join('C:\\', 'Users', 'i.pfeil', 'Documents',
'0_IWMI_DATASETS', 'shapefiles', 'IND_adm',
'IND_adm2'), 'IN.MH.JN')
if title1:
plt.title(title1)
sc = m.scatter(lons1, lats1, c=data1, edgecolor='None', marker=',',
s=s1, vmin=0, vmax=1, cmap='RdYlGn')
m.colorbar(sc, 'right', size='5%', pad='2%')
# second subplot
ax2 = fig.add_subplot(122, sharex=ax1, sharey=ax1)
m = Basemap(projection='cyl', llcrnrlat=10, urcrnrlat=35,
llcrnrlon=65, urcrnrlon=85)
m.drawcoastlines()
m.drawcountries()
parallels = np.arange(-90,90,15.)
m.drawparallels(parallels,labels=[1,0,0,0])
meridians = np.arange(-180,180,15.)
m.drawmeridians(meridians,labels=[0,0,0,1])
m.readshapefile(os.path.join('C:\\', 'Users', 'i.pfeil', 'Documents',
'0_IWMI_DATASETS', 'shapefiles', 'IND_adm',
'IND_adm2'), 'IN.MH.JN')
if title2:
plt.title(title2)
sc = m.scatter(lons2, lats2, c=data2, edgecolor='None', marker=',',
s=s2, vmin=0, vmax=1, cmap='RdYlGn')
m.colorbar(sc, 'right', size='5%', pad='2%')
plt.show()
def hexagon_map(coordinates, temperature, hex_grid_size=(50,50)):
m = Basemap(projection='tmerc', lat_0=51, lon_0=10, llcrnrlat=47, llcrnrlon=5, urcrnrlat=55, urcrnrlon=16, resolution='i')
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary()
lats = coordinates[:,0]
lons = coordinates[:,1]
x, y = m(lons, lats)
m.hexbin(x, y, C = temperature, gridsize=hex_grid_size, linewidth=0.5, edgecolor='k')
m.colorbar(location='bottom')
plt.show()
class GridPlot(Argument, Settings):
"""ROC绘图"""
parameters = dict()
# 命令行参数,参考 https://docs.python.org/2/howto/argparse.html
command_args = [
[('npy',), {'help': u'指定一个csv2npy后生成的.npy文件', 'type': str, 'nargs': 1}],
[('-n','--name',), {'help': u'输出结果文件名', 'type': str, 'nargs': 1}],
[('-t','--title',), {'help': 'plot image title', 'type': str, 'nargs': 1}],
[('-v','--verbose',), {'help': u'输出详细信息', 'action':"store_true"}]
]
def __init__(self):
"""init"""
Argument.__init__(self)
Settings.__init__(self)
self.parse_args()
def parse_args(self):
"""parse arguments"""
if self.args.npy and os.path.isfile(self.args.npy[0]):
self.parameters['npy'] = self.args.npy[0]
else:
print('{} not found!'.format(self.args.npy[0]))
exit(-1)
self.parameters['name'] = self.args.name[0] if self.args.name else tf.mktemp(suffix='.png',dir='.')
self.parameters['title'] = self.args.title[0] if self.args.title else 'Grid Plot'
if self.args.verbose:
print("============parameters===========")
for key in self.parameters:
print(key,self.parameters[key])
self.process()
def process(self):
""" process """
##load data
data = np.load(self.parameters['npy'])
##create basemap
self.bm = Basemap(projection='cyl',resolution='l',lon_0=120)
self.bm.drawcoastlines(linewidth=0.25)
self.bm.drawcountries(linewidth=0.25)
#self.bm.fillcontinents(color='grey')
lons,lats = self.bm.makegrid(360,181)
x,y = self.bm(lons,lats)
self.bm.contourf(x,y,data)
##add colorbar
self.bm.colorbar(location='bottom',size='5%',label="mm")
##add plot title
plt.title(self.parameters['title'])
##save plot
plt.savefig(self.parameters['name'])
def plot(self,key='AOD'):
"""
Create a plot of a variable over the ORACLES study area.
Parameters
----------
key : string
See names for available datasets to plot.
Modification history
--------------------
Written: Michael Diamond, 08/16/2016, Seattle, WA
Modified: Michael Diamond, 08/21/2016
-Added ORACLES routine flight plan, Walvis Bay (orange), and Ascension Island
Modified: Michael Diamond, 09/02/2016, Swakopmund, Namibia
-Updated flight track
"""
plt.clf()
size = 16
font = 'Arial'
m = Basemap(llcrnrlon=self.lon.min(),llcrnrlat=self.lat.min(),urcrnrlon=self.lon.max(),\
urcrnrlat=self.lat.max(),projection='merc',resolution='i')
m.drawparallels(np.arange(-180,180,5),labels=[1,0,0,0],fontsize=size,fontname=font)
m.drawmeridians(np.arange(0,360,5),labels=[1,1,0,1],fontsize=size,fontname=font)
m.drawmapboundary(linewidth=1.5)
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(fill_color='steelblue')
m.fillcontinents(color='floralwhite',lake_color='steelblue',zorder=0)
if key == 'AOD':
m.pcolormesh(self.lon,self.lat,self.ds['%s' % key],cmap=self.colors['%s' % key],\
latlon=True,vmin=self.v['%s' % key][0],vmax=self.v['%s' % key][1])
cbar = m.colorbar()
cbar.ax.tick_params(labelsize=size-2)
cbar.set_label('[%s]' % self.units['%s' % key],fontsize=size,fontname=font)
elif key == 'ATYP':
m.pcolormesh(self.lon,self.lat,self.ds['%s' % key],cmap=self.colors['%s' % key],\
latlon=True,vmin=self.v['%s' % key][0],vmax=self.v['%s' % key][1])
plt.contourf(np.array(([5,1],[3,2])),cmap=self.colors['%s' % key],levels=[0,1,2,3,4,5])
cbar = m.colorbar(ticks=[0,1,2,3,4,5])
cbar.ax.set_yticklabels(['Sea Salt','Sulphate','Organic C','Black C','Dust'])
cbar.ax.tick_params(labelsize=size-2)
else:
print('Error: Invalid key. Check names for available datasets.')
m.scatter(14.5247,-22.9390,s=250,c='orange',marker='D',latlon=True)
m.scatter(-14.3559,-7.9467,s=375,c='c',marker='*',latlon=True)
m.scatter(-5.7089,-15.9650,s=375,c='chartreuse',marker='*',latlon=True)
m.plot([14.5247,13,0],[-22.9390,-23,-10],c='w',linewidth=5,linestyle='dashed',latlon=True)
m.plot([14.5247,13,0],[-22.9390,-23,-10],c='k',linewidth=3,linestyle='dashed',latlon=True)
plt.title('%s from MSG SEVIRI on %s/%s/%s at %s UTC' % \
(self.names['%s' % key],self.month,self.day,self.year,self.time),fontsize=size+4,fontname=font)
plt.show()
def PlotTomoMap(fname, dlon=0.5, dlat=0.5, title='', datatype='ph', outfname='', browseflag=False, saveflag=True):
"""
Plot Tomography Map
longitude latidute ZValue
"""
if title=='':
title=fname;
if outfname=='':
outfname=fname;
Inarray=np.loadtxt(fname)
LonLst=Inarray[:,0]
LatLst=Inarray[:,1]
ZValue=Inarray[:,2]
llcrnrlon=LonLst.min()
llcrnrlat=LatLst.min()
urcrnrlon=LonLst.max()
urcrnrlat=LatLst.max()
Nlon=int((urcrnrlon-llcrnrlon)/dlon)+1
Nlat=int((urcrnrlat-llcrnrlat)/dlat)+1
fig=plt.figure(num=None, figsize=(8, 12), dpi=80, facecolor='w', edgecolor='k')
m = Basemap(llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat, \
rsphere=(6378137.00,6356752.3142), resolution='l', projection='merc')
lon = LonLst
lat = LatLst
x,y = m(lon, lat)
xi = np.linspace(x.min(), x.max(), Nlon)
yi = np.linspace(y.min(), y.max(), Nlat)
xi, yi = np.meshgrid(xi, yi)
#-- Interpolating at the points in xi, yi
zi = griddata(x, y, ZValue, xi, yi)
# m.pcolormesh(xi, yi, zi, cmap='seismic_r', shading='gouraud')
cmap=matplotlib.cm.seismic_r
cmap.set_bad('w',1.)
m.imshow(zi, cmap=cmap)
m.drawcoastlines()
m.colorbar(location='bottom',size='2%')
# m.fillcontinents()
# draw parallels
m.drawparallels(np.arange(-90,90,10),labels=[1,1,0,1])
# draw meridians
m.drawmeridians(np.arange(-180,180,10),labels=[1,1,1,0])
plt.suptitle(title,y=0.9, fontsize=22);
if browseflag==True:
plt.draw()
plt.pause(1) # <-------
raw_input("<Hit Enter To Close>")
plt.close('all')
if saveflag==True:
fig.savefig(outfname+'.ps', format='ps')
return
def plot_avg_snfall_maps(b: Basemap, xx, yy, hles_snfall, total_snfall,
cmap=None, bnorm: BoundaryNorm=None, label=""):
fig = plt.figure()
gs = GridSpec(1, 3, wspace=0.01)
h = hles_snfall.mean(axis=0)
t = total_snfall.mean(axis=0)
axes_list = []
# hles
ax = fig.add_subplot(gs[0, 0])
cs = b.contourf(xx, yy, h, bnorm.boundaries, cmap=cmap, norm=bnorm, extend="max")
ax.set_title("HLES (cm)")
cb = b.colorbar(cs, location="bottom")
cb.ax.set_xticklabels(cb.ax.get_xticklabels(), rotation=45)
axes_list.append(ax)
# total
ax = fig.add_subplot(gs[0, 1])
cs = b.contourf(xx, yy, t, bnorm.boundaries, cmap=cmap, norm=bnorm, extend="max")
ax.set_title("Snowfall (cm)")
cb = b.colorbar(cs, location="bottom")
cb.ax.set_visible(False)
axes_list.append(ax)
# hles percentage
ax = fig.add_subplot(gs[0, 2])
clevs = np.arange(0, 100, 10)
cs = b.contourf(xx, yy, h / t * 100,
levels=clevs,
cmap=get_with_white_added("gist_ncar_r", white_end=0.1,
ncolors_out=len(clevs) - 1))
ax.set_title("HLES fraction (%)")
cb = b.colorbar(cs, location="bottom")
axes_list.append(ax)
# plot coastlines
for ax in axes_list:
b.drawcoastlines(ax=ax, linewidth=0.5)
fig.savefig(str(img_dir / f"mean_hles_total_snfall_{label}.png"),
bbox_inches="tight", dpi=250)
plt.close(fig)
def station_map(coordinates, temperature):
# map = Basemap(projection='lcc', lat_0 = 51, lon_0 = 10, resolution = 'i', width = 850000, height = 1000000)
map = Basemap(projection='tmerc', lat_0=51, lon_0=10, llcrnrlat=47, llcrnrlon=5, urcrnrlat=55, urcrnrlon=16, resolution='i')
map.drawcoastlines()
map.drawcountries()
map.drawmapboundary()
lats = coordinates[:,0]
lons = coordinates[:,1]
x, y = map(lons, lats)
# map.hexbin(x,y)
map.hexbin(x, y, C = temperature, gridsize=(9,9), linewidth=0.5, edgecolor='k')
map.colorbar(location='bottom')
plt.show()
def plotmap(data, lat=None, lon=None, cmap='jet'):
# If data is a DataArray, we can omit the lat, lon arrays and extract
# from the DataArray's coordinates
if isinstance(data, xray.DataArray):
lat, lon = data['lat'], data['lon']
xi, yi = np.meshgrid(lon, lat)
plt.figure()
m = Basemap()
m.drawcoastlines()
m.pcolormesh(xi, yi, data, cmap=cmap, latlon=True)
m.colorbar()
plt.draw()
def plot(data,title=' ',projection='cyl',file_name=datetime.datetime.now().isoformat(),show=1,cblabel='$\mu g/ m^3$',cmap=plt.cm.CMRmap_r,clevs=np.zeros(1),return_fig=0,dpi=300,lon=0,lat=0,colorbar_format_sci=0,saving_format='svg',scatter_points=0,f_size=20):
fig=plt.figure(figsize=(20, 12))
m = fig.add_subplot(1,1,1)
if projection=='merc':
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,\
llcrnrlon=-180,urcrnrlon=180,lat_ts=20)
else:
m = Basemap(projection=projection,lon_0=0)
m.drawcoastlines()
if isinstance(lon, int):
lon=readsav('/nfs/a107/eejvt/IDL_CODE/glon.sav')
if isinstance(lat, int):
lat=readsav('/nfs/a107/eejvt/IDL_CODE/glat.sav')
X,Y=np.meshgrid(lon.glon,lat.glat)
else:
if lon.ndim==1:
X,Y=np.meshgrid(lon,lat)
else:
X=np.copy(lon)
Y=np.copy(lat)
if type(clevs) is list:
cs=m.contourf(X,Y,data,clevs,latlon=True,cmap=cmap,norm= colors.BoundaryNorm(clevs, 256))
if colorbar_format_sci:
def fmt(x, pos):
a, b = '{:.1e}'.format(x).split('e')
b = int(b)
return r'${} \times 10^{{{}}}$'.format(a, b)
cb = m.colorbar(cs,"right",format=ticker.FuncFormatter(fmt),ticks=clevs)
else:
cb = m.colorbar(cs,format='%.2e',ticks=clevs)
else:
cs=m.contourf(X,Y,data,15,latlon=True,cmap=cmap)
cb = m.colorbar(cs)
if not isinstance(scatter_points,int):
m.scatter(scatter_points[:,0],scatter_points[:,100])
cb.set_label(cblabel,fontsize=f_size)
cb.ax.tick_params(labelsize=f_size)
plt.title(title,fontsize=f_size)
if os.path.isdir("PLOTS/"):
plt.savefig('PLOTS/'+file_name+'.'+saving_format,format=saving_format,dpi=dpi, bbox_inches='tight')
plt.savefig('PLOTS/'+file_name+'.svg',format='svg', bbox_inches='tight')
else:
plt.savefig(file_name+'.'+saving_format,format=saving_format,dpi=dpi, bbox_inches='tight')
plt.savefig(file_name+'.svg',format='svg', bbox_inches='tight')
if show:
plt.show()
if return_fig:
return fig
def plot_file(filename):
# Open the file using the NetCDF4 library
nc = Dataset(filename)
# analyze_dataset(nc)
latlon = nc.variables['geospatial_lat_lon_extent']
# print('geospatial_lat_lon_extent:', latlon)
# Original example uses Brightness Temperature values, 'CMI'
# But that's not in our dataset.
whichvar = 'Rad'
data = nc.variables[whichvar][:]
prettyname = nc.variables[whichvar].long_name
# Show it without basemap:
# plt.imshow(data, cmap='Greys')
# plt.show()
# Create the basemap reference for the Satellite Projection
bmap = Basemap(projection='geos',
# Extents
llcrnrlon=latlon.geospatial_westbound_longitude,
llcrnrlat=latlon.geospatial_southbound_latitude,
urcrnrlon=latlon.geospatial_eastbound_longitude,
urcrnrlat=latlon.geospatial_northbound_latitude,
# Should these be nadir, or center?
lon_0=latlon.geospatial_lon_nadir,
lat_0=latlon.geospatial_lat_nadir,
satellite_height=35786023.0, ellps='GRS80')
# Plot GOES-16 Channel using 170 and 378 as the temperature thresholds
bmap.imshow(data, origin='upper', cmap='Greys')
# Draw the coastlines, countries, parallels and meridians
bmap.drawcoastlines(linewidth=0.3, linestyle='solid', color='blue')
bmap.drawcountries(linewidth=0.3, linestyle='solid', color='blue')
# These don't seem to do anything
bmap.drawparallels(np.arange(-90.0, 90.0, 10.0),
linewidth=0.1, color='yellow')
bmap.drawmeridians(np.arange(0.0, 360.0, 10.0),
linewidth=0.1, color='yellow')
# Insert the legend
bmap.colorbar(location='bottom', label=prettyname)
# Show the plot
plt.show()
### Adjust maximum limits
values = np.arange(-2, 2.1, .2)
### Plot filled contours
cs = m.contourf(lons[:, :],
lats[:, :],
np.squeeze(diffsit[:, :]),
values,
latlon=True,
extend='both')
### Set colormap
cs.set_cmap(plt.cm.get_cmap('RdBu'))
### Set colorbar
cbar = m.colorbar(cs, drawedges=True, location='right', pad=0.4)
cbar.set_ticks(np.arange(-2, 3, 1))
cbar.set_ticklabels(map(str, np.arange(-2, 3, 1)))
cbar.set_label(r'\textbf{Difference (m)}')
cbar.ax.tick_params(axis='y', size=.2)
monthtitle = datetime.date(plotyr2, plotmonth, 1).strftime('%B')
fig.subplots_adjust(top=0.89)
plt.annotate(r'\textbf{Sea Ice Thickness -- [July, %s-%s]}' \
% (plotyr2,plotyr1),xy=(1,1),
xytext=(0.54,1.06),textcoords='axes fraction',
fontsize=15,color='w',ha='center')
plt.annotate(r'\textbf{GRAPHIC}: Zachary Labe (@ZLabe)',
textcoords='axes fraction',
xy=(0, 0),
# projection using the limits of the lat/lon data itself:
# clear graph
plt.clf()
m=Basemap(projection='mill',lat_ts=10,llcrnrlon=lon.min(), \
urcrnrlon=lon.max(),llcrnrlat=lat.min(),urcrnrlat=lat.max(), \
resolution='c')
# convert the lat/lon values to x/y projections.
x, y = m(*np.meshgrid(lon, lat))
# plot the field using the fast pcolormesh routine
# set the colormap to jet.
m.pcolormesh(x, y, data, shading='flat', cmap=plt.cm.jet)
m.colorbar(location='right')
# Add a coastline and axis values.
m.drawcoastlines()
m.fillcontinents()
m.drawmapboundary()
m.drawparallels(np.arange(-90., 120., 30.), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180., 180., 60.), labels=[0, 0, 0, 1])
# Add a colorbar and title, and then show the plot.
plt.title('NWW3 Significant Wave Height from NOMADS on %s' %
target_date_key)
plt.savefig(output_file_path)
print_with_notice("wave map on %s has been generated" %
(target_date.strftime('%Y/%m/%d')))
def processing(path):
"""
Generates images from NetCDF files
pathDir: root direcotry with "Data", "Output" and "Scripts" folders
pathFile: path to NetCDF file to be manipulated
outFolder: path to directory where the generated images should be saved
Output: .png image file
"""
# Getting information from the file name ============================================================
# Search for the GOES-16 channel in the file name
INPE_Band_ID = (path[path.find("S10635") + 6:path.find("_")])
# print(INPE_Band_ID)
# Get the band number subtracting the value by 332
Band = int(INPE_Band_ID) - 332
# Create a GOES-16 Bands string array
Wavelenghts = [
'[]', '[0.47 μm]', '[0.64 μm]', '[0.865 μm]', '[1.378 μm]',
'[1.61 μm]', '[2.25 μm]', '[3.90 μm]', '[6.19 μm]', '[6.95 μm]',
'[7.34 μm]', '[8.50 μm]', '[9.61 μm]', '[10.35 μm]', '[11.20 μm]',
'[12.30 μm]', '[13.30 μm]'
]
Band_Wavelenght = Wavelenghts[int(Band)]
# Search for the Scan start in the file name
Start = (path[path.find(INPE_Band_ID + "_") + 4:path.find(".nc")])
# Getting the date from the file name
year = Start[0:4]
month = Start[4:6]
day = Start[6:8]
date = day + "-" + month + "-" + year
time = Start[8:10] + ":" + Start[
10:12] + " UTC" # Time of the Start of the Scan
# Get the unit based on the channel. If channels 1 trough 6 is Albedo. If channels 7 to 16 is BT.
Unit = "Albedo (%)"
# Choose a title for the plot
Title = " GOES-16 ABI CMI Band " + str(
Band) + " " + Band_Wavelenght + " " + Unit + " " + date + " " + time
# Insert the institution name
Institution = "CEPAGRI - UNICAMP"
# Required libraries ================================================================================
# Open the file using the NetCDF4 library
nc = Dataset(path)
# Choose the visualization extent (min lon, min lat, max lon, max lat)
extent = [-115.98, -55.98, -25.01, 34.98]
min_lon = extent[0]
max_lon = extent[2]
min_lat = extent[1]
max_lat = extent[3]
# Get the latitudes
lats = nc.variables['lat'][:]
# Get the longitudes
lons = nc.variables['lon'][:]
# print (lats)
# print (lons)
# latitude lower and upper index
latli = np.argmin(np.abs(lats - extent[1]))
latui = np.argmin(np.abs(lats - extent[3]))
# longitude lower and upper index
lonli = np.argmin(np.abs(lons - extent[0]))
lonui = np.argmin(np.abs(lons - extent[2]))
# Extract the Brightness Temperature / Reflectance values from the NetCDF
data = nc.variables['Band1'][latli:latui, lonli:lonui]
# Flip the y axis, divede by 100
data = (np.flipud(data) / 100)
# Define the size of the saved picture ==============================================================
DPI = 150
ax = plt.figure(figsize=(2000 / float(DPI), 2000 / float(DPI)),
frameon=True,
dpi=DPI)
#====================================================================================================
# Plot the Data =====================================================================================
# Create the basemap reference for the Rectangular Projection
bmap = Basemap(llcrnrlon=extent[0],
llcrnrlat=extent[1],
urcrnrlon=extent[2],
urcrnrlat=extent[3],
epsg=4326)
# Draw the countries and Brazilian states shapefiles
bmap.readshapefile('/Scripts/GEONETCast/Shapefiles/BRA_adm1',
'BRA_adm1',
linewidth=0.50,
color='cyan')
bmap.readshapefile(
'/Scripts/GEONETCast/Shapefiles/ne_10m_admin_0_countries',
'ne_10m_admin_0_countries',
linewidth=0.50,
color='cyan')
# Draw parallels and meridians
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[False, False, False, False],
fmt='%g',
labelstyle="+/-",
xoffset=-0.80,
yoffset=-1.00,
size=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.0),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[False, False, False, False],
fmt='%g',
labelstyle="+/-",
xoffset=-0.80,
yoffset=-1.00,
size=7)
# Converts a CPT file to be used in Python
cpt = loadCPT(
'/Scripts/GEONETCast/Colortables/Square Root Visible Enhancement.cpt')
# Makes a linear interpolation
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Plot the GOES-16 channel with the converted CPT colors (you may alter the min and max to match your preference)
bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=0, vmax=100)
# Insert the colorbar at the bottom
cb = bmap.colorbar(location='bottom',
size='2%',
pad='-3.5%',
ticks=[20, 40, 60, 80])
cb.ax.set_xticklabels(['20', '40', '60', '80'])
cb.outline.set_visible(False) # Remove the colorbar outline
cb.ax.tick_params(width=0) # Remove the colorbar ticks
cb.ax.xaxis.set_tick_params(
pad=-14.5) # Put the colobar labels inside the colorbar
cb.ax.tick_params(
axis='x', colors='black',
labelsize=8) # Change the color and size of the colorbar labels
# Add a black rectangle in the bottom to insert the image description
lon_difference = (extent[2] - extent[0]) # Max Lon - Min Lon
currentAxis = plt.gca()
currentAxis.add_patch(
Rectangle((extent[0], extent[1]),
lon_difference,
lon_difference * 0.015,
alpha=1,
zorder=3,
facecolor='black'))
# Add the image description inside the black rectangle
lat_difference = (extent[3] - extent[1]) # Max lat - Min lat
plt.text(extent[0],
extent[1] + lat_difference * 0.003,
Title,
horizontalalignment='left',
color='white',
size=10)
plt.text(extent[2],
extent[1] + lat_difference * 0.003,
Institution,
horizontalalignment='right',
color='yellow',
size=10)
# Add logos / images to the plot
logo_GNC = plt.imread('/Scripts/GEONETCast/Logos/GNC_Logo.png')
logo_INPE = plt.imread('/Scripts/GEONETCast/Logos/INPE_Logo.png')
logo_NOAA = plt.imread('/Scripts/GEONETCast/Logos/NOAA_Logo.png')
logo_GOES = plt.imread('/Scripts/GEONETCast/Logos/GOES_Logo.png')
logo_cepagri = plt.imread('/Scripts/GEONETCast/Logos/Logo_CEPAGRI.png')
ax.figimage(logo_GNC, 20, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_INPE, 90, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_NOAA, 165, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_GOES, 228, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_cepagri, 326, 70, zorder=3, alpha=1, origin='upper')
# Save the result
# print('/Scripts/GEONETCast/Output/' + path[12:18] + '/INPE_G16_CH' + str(Band) + '_' + Start + '.png')
plt.savefig('/Scripts/GEONETCast/Output/' + path[12:18] + '/INPE_G16_CH' +
str(Band) + '_' + Start + '.png',
dpi=DPI,
bbox_inches='tight',
pad_inches=0)
#-----------------CALLING FUNCTION 4-----------------
#ASL_highlight, nino_highlight = highlight();
#-----------------END CALLING FUNCTION 4-----------------
#-----------------BEGIN HIGHLIGHT-----------------
#m.contour(x,y,ASL_highlight,1,colors='b')
#m.contour(x,y,nino_highlight,1,colors='r')
m.drawcoastlines(linewidth=1.25)
#m.fillcontinents(color='0.8')
m.drawparallels(np.arange(-80, 81, 20), labels=[1, 1, 0, 0])
m.drawmeridians(np.arange(0, 360, 60), labels=[0, 0, 0, 1])
# add colorbar.
cbar = m.colorbar(cs, pad="15%")
cbar.set_label(cbarunits, rotation=0)
plt.title(str(plottitle) + ' gradient:') # + str(amundsen_low))
plt.savefig(filename + '_gradient.png')
#plt.show()
# create figure.
fig = plt.figure(figsize=(8, 4.5))
ax = fig.add_axes([0.05, 0.05, 0.9, 0.85])
levels = [
970, 975, 980, 985, 990, 995, 1000, 1005, 1010, 1015, 1020, 1025, 1030
]
orig_cmap = plt.cm.coolwarm
cs = m.contourf(x, y, clean_data_intercept, 20, cmap=orig_cmap)
#cs = m.contourf(x,y,clean_data_intercept,15)
b_map.drawcoastlines(linewidth=0.1)
b_map.fillcontinents(color='black')
#b_map.drawcountries()
jet = plt.get_cmap('jet')
lonx, laty = b_map(lon, lat)
csOWL = b_map.scatter(lonx,
laty,
c=z,
marker='o',
s=0.1,
cmap=jet,
label='Field sites')
#ax.legend(handles=[cs],bbox_to_anchor=(0.43,0.25), borderaxespad=0.,fontsize=8)
cbar = b_map.colorbar(csOWL, location='right', pad="5%")
#csbb700OWLbar=b_map.colorbar(csbb700OWL,location='right',pad="5%")
cbar.set_label('Coverage probaility density', **csfont)
ax2 = plt.axes([0.01, 0.1, 0.85, 0.37])
ax2.yaxis.tick_right()
weights = np.ones_like(lon) / len(lon)
ax2.hist(lon, bins=100, color='blue', weights=weights)
ax2.tick_params(axis='y',
direction='in',
length=3,
width=1,
colors='black',
labelrotation=90)
ax2.tick_params(axis='x',
direction='in',
# create Basemap instance.
m = Basemap(projection='cyl', lat_0=0, lon_0=0)
m.drawcoastlines(linewidth=1.25)
m.fillcontinents(color='0.95')
#m.drawmapboundary(fill_color='0.9')
# cacluate colorbar ranges
#bounds_max=float("{0:.0f}".format(np.nanpercentile(Plot_Var, 99.99))) # Upper bound of plotted values to be used for colorbar, which is 99.99th percentile of data, with 0 decimals
bounds_max=33
bounds = np.arange(0, bounds_max, bounds_max/33)
norm = matplotlib.colors.BoundaryNorm(boundaries=bounds, ncolors=256)
# create a pseudo-color plot over the map
im1 = m.pcolormesh(Lon_img, Lat_img, Plot_Var, norm=norm, shading='flat', cmap=plt.cm.jet, latlon=True) # Choose colormap: https://matplotlib.org/users/colormaps.html
m.drawparallels(np.arange(-90.,90.001,30.),labels=[True,False,False,False], linewidth=0.01) # labels = [left,right,top,bottom]
m.drawmeridians(np.arange(0.,360.,30.),labels=[False,False,False,True], linewidth=0.01) # labels = [left,right,top,bottom]
# add colorbar
cbar = m.colorbar(im1,"right", size="3%", pad="2%", extend='max') # extend='both' will extend the colorbar in both sides (upper side and down side)
cbar.set_label(Var_plot_unit)
#set title
ax.set_title(Var_plot_name+', at surface - hist - average of '+start_date_cal_hist+'-'+end_date_cal_hist + ' - ' +str(GCM))
mng = plt.get_current_fig_manager()
mng.window.showMaximized() # Maximizes the plot window to save figures in full
#plt.show()
fig.savefig(dir_figs+Var_name+'_'+ str(GCM)+'_surface_hist-' +start_date_cal_hist+'-'+end_date_cal_hist + '.png', format='png', dpi=300, transparent=True, bbox_inches='tight')
plt.close()
print('Model '+str(GCM)+' - hist - processed successfully')
#################################################
#### Ploting for all models (with pcolormesh) ###
n_r=4 # Number of rows for subplot
n_c=4 # Number of columns for subplot
def fr2ghi2png(url,
colormapPath,
colormapName,
dirDest,
lat_name,
lon_name,
data_name,
geos=False):
# Dataset is the class behavior to open the file
# and create an instance of the ncCDF4 class
nc_fid = netCDF4.Dataset(url, 'r')
t_coverage = repr(nc_fid.getncattr('time_coverage_start'))
# print t_coverage
ds_name = repr(nc_fid.getncattr('dataset_name'))
# print ds_name
date = re.search('\'(.*?)\'', t_coverage).group(1)
print(date)
channel = re.search('-M\d(.*?)_', ds_name).group(1)
print(channel)
yl = date[0:4]
yy = date[2:4]
mt = date[5:7]
dd = date[8:10]
hh = date[11:13]
mm = date[14:16]
ss = date[17:19]
str_date = str(dd) + '/' + str(mt) + '/' + str(yl) + " " + str(
hh) + ":" + str(mm)
date = datetime.datetime.strptime(
str_date, '%d/%m/%Y %H:%M') - datetime.timedelta(hours=3)
name = channel + " " + date.strftime('%d-%m-%Y %H:%M')
filename = channel + "_" + yy + mt + dd + "_" + hh + mm + ss
# print "name: " + name
# extract/copy the data
data = nc_fid.variables[data_name][:]
if data_name == 'CMI' or data_name == 'Rad':
# Satellite height
sat_h = nc_fid.variables[
'goes_imager_projection'].perspective_point_height
# Satellite longitude
sat_lon = nc_fid.variables[
'goes_imager_projection'].longitude_of_projection_origin
# Satellite sweep
sat_sweep = nc_fid.variables['goes_imager_projection'].sweep_angle_axis
X = nc_fid.variables[lon_name][:] # longitud, eje X
Y = nc_fid.variables[lat_name][:] # latitud, eje Y
scene_id_val = repr(nc_fid.getncattr('scene_id'))
scene_id = re.search('\'(.*?)\'', scene_id_val).group(1)
nc_fid.close()
print("Realizando pasaje a K en C13 y truncamiento en los otros")
for d in numpy.nditer(data, op_flags=['readwrite']):
d = truncUno(d)
data *= 100
#######################################
#######################################
name = "GHI " + date.strftime('%d-%m-%Y %H:%M')
nc_fid_ghi = netCDF4.Dataset(url, 'r')
data = nc_fid_ghi.variables[data_name][:]
X = nc_fid_ghi.variables[lon_name][:] # longitud, eje X
Y = nc_fid_ghi.variables[lat_name][:] # latitud, eje Y
nc_fid_ghi.close()
for d in numpy.nditer(data, op_flags=['readwrite']):
d = truncUno(d)
data *= 100
X *= sat_h
Y *= sat_h
vmin = 0.0
vmax = 1200.0
ax = Basemap(projection='merc',\
llcrnrlat=-35.00,urcrnrlat=-30.00,\
llcrnrlon=-58.77,urcrnrlon=-53.00,\
resolution='f')
projection = Proj(proj='geos',
h=sat_h,
lon_0=sat_lon,
sweep=sat_sweep,
ellps='WGS84')
x_mesh, y_mesh = numpy.meshgrid(X, Y)
lons, lats = projection(x_mesh, y_mesh, inverse=True)
x, y = ax(lons, lats)
ax.drawcoastlines(linewidth=0.40)
ax.drawcountries(linewidth=0.40)
ax.drawstates(linewidth=0.20)
rincon_de_artigas_poly(ax)
par = ax.drawparallels(numpy.arange(-40, -20, 2),
labels=[1, 0, 0, 0],
linewidth=0.0,
fontsize=7,
color='white')
mer = ax.drawmeridians(numpy.arange(-70, -40, 2),
labels=[0, 0, 1, 0],
linewidth=0.0,
fontsize=7,
color='white')
setcolor(par, 'white')
setcolor(mer, 'white')
print("Calculando GHI...")
Isc = 1367.0
# JPTv1
a = 0.602
b = 0.576
c = -0.341
d = -13.149
def FR2GHI(lat, lon, fr):
Cz, Gamma = cosSolarZenithAngle(lat, lon, date, 0)
ghi = 0.
if Cz > 0.:
Fn = FnFunc(Gamma)
ghi = Isc * Fn * Cz * (a + b * Cz + c * Cz) + d * fr
if Isc * Fn * Cz != 0:
Kt = ghi / (Isc * Fn * Cz)
Kt = numpy.clip(Kt, 0.09, 0.85)
ghi = Kt * Isc * Fn * Cz
return ghi
FR2GHI_vect = numpy.vectorize(FR2GHI, otypes=[float])
GHI = FR2GHI_vect(lats, lons, data)
ticks = [0, 200, 400, 600, 800, 1000, 1200]
ticksLabels = ticks
cmap = gmtColormap('irradiancia_v6', colormapPath, 2048)
cs = ax.pcolormesh(x, y, GHI, vmin=vmin, vmax=vmax, cmap=cmap)
plt.clim(vmin, vmax)
# agrego el colorbar
cbar = ax.colorbar(cs, location='bottom', ticks=ticks) # , pad='3%'
cbar.ax.set_xlabel(
"Irradiancia solar global en plano horizontal ($W/m^2$)",
fontsize=7,
color='white')
cbar.ax.set_xticklabels(ticksLabels, fontsize=7, color='white')
# agrego el logo en el documento
logo_bw = plt.imread('/sat/PRS/dev/PRS-sat/PRSgoes/logo_300_bw.png')
plt.figimage(logo_bw, xo=5, yo=5)
if not os.path.isdir(dirDest + channel + '/' + yl + '/ghi'):
os.mkdir(dirDest + channel + "/" + yl + '/ghi')
# if
outPath = dirDest + channel + "/" + yl + "/ghi/"
whitePath = outPath + filename + '_ghi_white.png' # determino el nombre del archivo a escribir
outPath = outPath + filename + '_ghi.png' # determino el nombre del archivo a escribir
x_coord = 109
y_coord = -53
Noche = numpy.sum(data) <= 0.
if Noche: # print "es noche"
plt.annotate("NOCHE", (0, 0), (204, 15),
color='white',
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=10,
family='monospace')
plt.annotate(name + " UY", (0, 0), (x_coord, y_coord),
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=11,
family='monospace',
color='white')
plt.savefig(outPath, bbox_inches='tight', dpi=400,
transparent=True) # , facecolor='#4F7293'
cbar.ax.set_xlabel(
"Irradiancia solar global en plano horizontal ($W/m^2$)",
fontsize=7,
color='black')
cbar.ax.set_xticklabels(ticksLabels, fontsize=7, color='black')
# WHITE
setcolor(par, 'black')
setcolor(mer, 'black')
logo_color = plt.imread('/sat/PRS/dev/PRS-sat/PRSgoes/logo_300_color.png')
plt.figimage(logo_color, xo=5, yo=5)
nota_ghi_w = plt.annotate(name + " UY", (0, 0), (x_coord, y_coord),
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=11,
family='monospace',
color='black')
plt.savefig(whitePath,
bbox_inches='tight',
dpi=400,
transparent=False,
facecolor='white') # , facecolor='#4F7293'
copyfile(outPath, dirDest + channel + '_ghi.png')
copyfile(whitePath, dirDest + channel + '_ghi_white.png')
print("NUBOSIDAD")
# NUBOSIDAD
plt.clf()
ax.drawcoastlines(linewidth=0.40)
ax.drawcountries(linewidth=0.40)
ax.drawstates(linewidth=0.20)
rincon_de_artigas_poly(ax)
par = ax.drawparallels(numpy.arange(-40, -20, 2),
labels=[1, 0, 0, 0],
linewidth=0.0,
fontsize=7,
color='white')
mer = ax.drawmeridians(numpy.arange(-70, -40, 2),
labels=[0, 0, 1, 0],
linewidth=0.0,
fontsize=7,
color='white')
setcolor(par, 'white')
setcolor(mer, 'white')
vmin = 0.0
vmax = 100.0
ticks = [0, 20, 40, 60, 80, 100]
ticksLabels = ticks
cmap = gmtColormap('nubosidad', colormapPath, 2048)
cs = ax.pcolormesh(x, y, data, vmin=vmin, vmax=vmax, cmap=cmap)
plt.clim(vmin, vmax)
# agrego el colorbar
cbar = ax.colorbar(cs, location='bottom', ticks=ticks) # , pad='3%'
cbar.ax.set_xlabel("Nubosidad (%)", fontsize=7, color='white')
cbar.ax.set_xticklabels(ticksLabels, fontsize=7, color='white')
plt.figimage(logo_bw, xo=5, yo=5)
outPath = dirDest + channel + "/" + yl + "/ghi/"
outPath = outPath + filename + '_cloud.png' # determino el nombre del archivo a escribir
whitePath = outPath + filename + '_cloud_white.png' # determino el nombre del archivo a escribir
x_coord = 109
y_coord = -53
plt.annotate("C02 " + date.strftime('%d-%m-%Y %H:%M') + " UY", (0, 0),
(x_coord, y_coord),
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=11,
family='monospace',
color='white')
plt.draw()
plt.savefig(outPath, bbox_inches='tight', dpi=400,
transparent=True) # , facecolor='#4F7293'
# CLOUD WHITE
setcolor(par, 'black')
setcolor(mer, 'black')
logo_color = plt.imread('/sat/PRS/dev/PRS-sat/PRSgoes/logo_300_color.png')
plt.figimage(logo_color, xo=5, yo=5)
plt.annotate("C02 " + date.strftime('%d-%m-%Y %H:%M') + " UY", (0, 0),
(x_coord, y_coord),
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=11,
family='monospace',
color='black')
plt.savefig(whitePath,
bbox_inches='tight',
dpi=400,
transparent=False,
facecolor='white') # , facecolor='#4F7293'
copyfile(outPath, dirDest + channel + '_cloud.png')
copyfile(whitePath, dirDest + channel + '_cloud_white.png')
plt.close()
def plot_station_bot_sal(df,
var="PSAL_ADJUSTED",
elev=[0],
lons=[0],
lats=[0],
title=' ',
colorunit='Cond.',
cmin=33,
cmax=35.5,
contour=False,
save=False,
savename="savedFig.png",
wd=7,
ht=7):
#not_null = ~df[var].isnull()
color_var = df.loc[df.groupby('PROFILE_NUMBER').tail(1).index, var].values
x = df.loc[df.groupby('PROFILE_NUMBER').tail(1).index, 'LONGITUDE'].values
y = df.loc[df.groupby('PROFILE_NUMBER').tail(1).index, 'LATITUDE'].values
plt.figure(1, figsize=(wd, ht))
#m = Basemap(projection='hammer',lon_0=270)
# plot just upper right quadrant (corners determined from global map).
# keywords llcrnrx,llcrnry,urcrnrx,urcrnry used to define the lower
# left and upper right corners in map projection coordinates.
# llcrnrlat,llcrnrlon,urcrnrlon,urcrnrlat could be used to define
# lat/lon values of corners - but this won't work in cases such as this
# where one of the corners does not lie on the earth.
#m1 = Basemap(projection='ortho',lon_0=-9,lat_0=60,resolution=None)
#m = Basemap(projection='cyl', llcrnrlon=-120, llcrnrlat=-80, urcrnrlon=55,
# urcrnrlat=-30, lat_0 = -74, lon_0 =-43);
lat0 = -89
lon0 = 0
m1 = Basemap(projection='ortho', lat_0=lat0, lon_0=lon0, resolution='i')
width = m1.urcrnrx - m1.llcrnrx
height = m1.urcrnry - m1.llcrnry
m = Basemap(projection='ortho', lat_0=lat0, lon_0=lon0, resolution='i', llcrnrx=-width*0.30, llcrnry=-0.30*height,\
urcrnrx=0.30*width, urcrnry=0.30*height)
#lat_0 = -60, lon_0 = -20,
xm, ym = m(x, y)
m.drawmapboundary()
m.drawcoastlines()
m.readshapefile(
"/media/data/Datasets/Shapefiles/AntarcticGroundingLine/GSHHS_f_L6",
"GSHHS_f_L6",
color='m')
#m.fillcontinents(color='#ddaa66');
sc = m.scatter(xm, ym, c=color_var, vmin=cmin, vmax=cmax, s=0.3)
#, cmap='viridis'
cbar = m.colorbar(sc, pad="10%", location='bottom')
cbar.set_label(colorunit)
parallels = np.arange(-80, -50 + 1, 5.)
if (contour == True):
lon2, lat2 = np.meshgrid(
lons, lats) # get lat/lons of ny by nx evenly space grid.
xx, yy = m(lon2, lat2) # compute map proj coordinates.
levs = np.array([-600, -900, -1500])
cont = m.contour(xx,
yy,
elev,
levels=levs[::-1],
colors='0.5',
linestyles='-',
linewidths=(0.5, )) #clevs[::-1], , cmap='rainbow'
plt.clabel(cont, fmt='%2.1d', colors='0.5', fontsize=8)
m.drawparallels(parallels, labels=[1, 1, 1, 0])
meridians = np.arange(-180, 180, 20.)
m.drawmeridians(meridians, labels=[1, 1, 0, 1])
plt.title(title, y=1.05)
plt.tight_layout()
if (save == True):
plt.savefig(savename, dpi=150)
plt.show()
m.drawcoastlines(color='0.0', linewidth=4.5)
#m.drawcountries(color='0.', linewidth=4.5)
m.drawparallels(np.arange(-90.,91.,30.), labels=[1,0,0,1], dashes=[1,1], linewidth=1.0, color='0.5',fontsize='x-large', fontname='Times')
m.drawmeridians(np.arange(0., 360., 60.), labels=[1,0,0,1], dashes=[1,1], linewidth=1.0, color='0.5',fontsize='x-large', fontname='Times')
#PLOT ABSOLUTE
#cs = m.contourf(X2,Y2,mdata,levels,cmap=cmap,extend='both')
cs = m.contourf(X2,Y2,mdata,levels,cmap=cmap)
vmin,vmax = (-0.01251*max_val,max_val)
plt.tight_layout()
#cbar = m.colorbar(cs,location='bottom',pad='10%',ticks=np.linspace(vmin,vmax,7),format='%.1f')
ticks = [0.,0.0251*max_val,.1*max_val,.3*max_val,.5*max_val,.7*max_val,.8*max_val,1.*max_val]
cbar = m.colorbar(cs,location='bottom',pad='10%',ticks=ticks,format='%.1f')
#cbar.ax.get_yaxis().labelpad = 60
cbar.ax.get_xaxis().labelpad = 45
#cbar.ax.set_ylabel('EM (%)', rotation=270)
cbar.ax.set_xlabel('g N m$^{-2}$ yr$^{-1}$', rotation=0,color='black', size=78, fontname='Times')
#cbar.ax.set_xlabel('ECM tree basal area (%)', rotation=0,color='black', size=78)
#no coloredge
#cbar.solids.set_edgecolor("face")
cbar.solids.set_edgecolor("black")
cbar.solids.set_linewidth(6)
#cbar.set_clim(0.0,100)
cbar.set_clim(vmin,vmax)
plt.title(r'Mycorrhizal uptake', fontname='Times', fontsize=92,pad=26)
cbar.ax.tick_params(labelsize='xx-large')
#plt.savefig('em_steindinger_tot.pdf',bbox_inches="tight",dpi=300)
plt.savefig('figures/Nitrogen_fun/NACTIVE.png',bbox_inches="tight")
def draw_map(station_etude,
latm,
latM,
lonm,
lonM,
path,
all_pos,
hw,
vn_ITRF,
ve_ITRF,
plot_vertical_ITRF,
incvn_ITRF,
incve_ITRF,
incplot_vertical_ITRF,
plot_GEBCO=False,
plot_vertical=False,
plot_topo=True,
plot_ellipses=True,
coarse_lines=False,
legend_arrow_length=("1 cm/yr", 0.01),
legend_ellipse_size=("2 mm/yr", 0.002),
legend_position=(0.5, 0.9),
scale_arrow=15000000,
scale_ellipse=10000000,
name_stats=True,
name_stats_font_size=8,
name_stats_offset=(0.005, 0.01),
shorten_oversized_arrows=True,
exclude_points_out_of_range=True,
adjust_text=False,
pixels_hires_backgrnd=2000,
draw_borders=True,
full_return=False,
draw_latlon_lines=True):
"""
"""
nstation = len(station_etude)
fig, ax = plt.subplots(figsize=(7, 8))
if incvn_ITRF is None:
incvn_ITRF = np.zeros(len(station_etude))
if incve_ITRF is None:
incve_ITRF = np.zeros(len(station_etude))
if incplot_vertical_ITRF is None:
incplot_vertical_ITRF = np.zeros(len(station_etude))
if coarse_lines:
resolution = "l"
else:
resolution = "h"
m = Basemap(projection='merc',
llcrnrlat=latm,
urcrnrlat=latM,
llcrnrlon=lonm,
urcrnrlon=lonM,
lat_ts=-20,
resolution=resolution,
area_thresh=1,
epsg=3395)
if draw_borders:
m.drawcoastlines(linewidth=0.25)
m.drawcountries()
m.drawstates()
if plot_topo:
m.arcgisimage(service='World_Shaded_Relief',
xpixels=pixels_hires_backgrnd,
verbose=True)
else:
m.drawmapboundary(fill_color='#97B6E1')
m.fillcontinents(color='#EFEFDB', lake_color='#97B6E1', zorder=1)
m.shadedrelief()
if not plot_GEBCO:
color1 = 'black'
else:
color1 = 'white'
levels = [-8000, -7000, -6000, -5500, -5000, -4500,
-4000] #Niveaux pour l'echelle de couleur
gebconc = Dataset(path + 'GEBCO\\GRIDONE_2D.nc')
#Vecteur longitudes/latitudes
long_ini = gebconc.variables['lon'][:]
lats_ini = gebconc.variables['lat'][:]
(lats_area, longs_area,
gebco_area) = area(long_ini, lats_ini, gebconc, latm, latM, lonm,
lonM, '0-180') #lat bas,lat haut, long
(longs, lats, gebco) = split_grid(longs_area, lats_area, gebco_area, 2)
(longs_gr, lats_gr) = np.meshgrid(longs, lats)
fond = m.pcolormesh(longs_gr,
lats_gr,
gebco,
shading='flat',
cmap=LevelColormap(list(np.asarray(levels) * (1)),
cmap=cm.deep_r),
latlon=True) #ice,deep
cbar = m.colorbar(location='top', pad=0.5)
cbar.set_label('Depth [m] ', rotation=0)
if draw_latlon_lines:
m.drawparallels(np.arange(latm, latM, 1.),
fontsize=10,
color=color1,
labels=[True, False, False, False],
linewidth=0.25,
dashes=[10000, 1]) #de -180 a 360 par pas de 5° ;
m.drawmeridians(np.arange(lonm, lonM, 1.),
fontsize=10,
color=color1,
labels=[False, False, False, True],
linewidth=0.25,
dashes=[10000, 1]) # Haut / G/D/BAS
### Labels
plt.xlabel('Longitude (°) ', labelpad=25, fontsize=10)
plt.ylabel('Latitude (°) ', labelpad=40, fontsize=10)
ax.yaxis.set_label_position("left")
all_posx_proj = [0] * nstation
all_posy_proj = [0] * nstation
for i in range(nstation):
all_posx_proj[i], all_posy_proj[i] = m(
all_pos[i][1], all_pos[i][0]
) #positions xy des stations converties a la projection de la carte [m]
print(i, all_posx_proj[i], all_posy_proj[i])
m.plot(
all_posx_proj[i],
all_posy_proj[i],
'xkcd:black',
marker='.',
linestyle='none',
markersize=4,
lw=4,
zorder=20
) #point rouge à la station, zorder indique l'ordre vertical de dessin sur la carte, les nb elevés sont dessinés les plus hauts
# plt.text(all_posx_proj[i], all_posy_proj[i], station_etude[i], size=10,ha="center", va="center",bbox=dict(boxstyle="round",
# ec=(1., 0.5, 0.5),
# fc=(1., 0.8, 0.8),
# )
# )
############### DICO POUR FLECHES DEFINITION
arrow_prop_dict = dict(arrowstyle='->,head_length=0.8,head_width=0.3',
ec='xkcd:black',
fc='xkcd:black',
linewidth=2)
arrow_prop_dict_V_up = dict(arrowstyle='->,head_length=0.8,head_width=0.3',
ec='xkcd:green',
fc='xkcd:green',
linewidth=2)
arrow_prop_dict_V_down = dict(
arrowstyle='->,head_length=0.8,head_width=0.3',
ec='xkcd:red orange',
fc='xkcd:red orange',
linewidth=2)
arrow_prop_dict_V_up_short = dict(
arrowstyle='->,head_length=0.8,head_width=0.3',
ec='xkcd:green',
fc='xkcd:green',
linewidth=2,
linestyle="--")
arrow_prop_dict_V_down_short = dict(
arrowstyle='->,head_length=0.8,head_width=0.3',
ec='xkcd:red orange',
fc='xkcd:red orange',
linewidth=2,
linestyle="--")
if not plot_vertical: #CHAMP VITESSES HORIZONTALES
for i in range(nstation):
######### Exclude point if not in range
#latm,latM,lonm,lonM
bool_good_lat = latm < all_pos[i][0] < latM
bool_good_lon = lonm < all_pos[i][1] < lonM
if exclude_points_out_of_range and not (bool_good_lat
and bool_good_lon):
print("INFO : exclude point because out of range")
continue
x_end_arrow_ok = all_posx_proj[i] + np.multiply(
ve_ITRF[i], scale_arrow)
y_end_arrow_ok = all_posy_proj[i] + np.multiply(
vn_ITRF[i], scale_arrow)
ax.annotate(
s='',
xy=(x_end_arrow_ok, y_end_arrow_ok),
xytext=(all_posx_proj[i], all_posy_proj[i]),
arrowprops=arrow_prop_dict,
annotation_clip=True
) #xy point arrivee de la fleche, xytext l'origine de la fleche
a = math.atan(
(all_posx_proj[i] + np.multiply(ve_ITRF[i], scale_arrow) -
all_posy_proj[i]) /
(all_posy_proj[i] + np.multiply(ve_ITRF[i], scale_arrow) -
all_posy_proj[i])
) # l'angle d'orientation de l'ellipsoide, avec 0 au nord et +90 a l'ouest
e = Ellipse(
xy=(x_end_arrow_ok, y_end_arrow_ok),
width=np.multiply(2, incve_ITRF[i]) * scale_ellipse,
height=np.multiply(2, incvn_ITRF[i]) * scale_ellipse,
angle=a
) #multiplication par 2 pour obtenir la largeur et la hauteur de l'ellipse
# STATION NAME
if name_stats:
offset_x_ok, offset_y_ok = utils.axis_data_coords_sys_transform(
ax, name_stats_offset[0], name_stats_offset[1])
plt.text(all_posx_proj[i] + offset_x_ok,
all_posy_proj[i] + offset_y_ok,
station_etude[i],
fontsize=name_stats_font_size)
# ELIPSES
if plot_ellipses:
ax.add_artist(e)
e.set_clip_box(ax.bbox)
e.set_edgecolor('none')
e.set_facecolor('black')
e.set_alpha(0.3)
e.set_zorder(1)
else: # Champ de vitesses verticales ITRF
############### PLOT FLECHES
Text = []
for i in range(nstation):
######### Exclude point if not in range
#latm,latM,lonm,lonM
bool_good_lat = latm < all_pos[i][0] < latM
bool_good_lon = lonm < all_pos[i][1] < lonM
if exclude_points_out_of_range and not (bool_good_lat
and bool_good_lon):
print("INFO : exclude point because out of range")
continue
######### AJUSTEMENT SI FLECHES TROP GRANDES
x_end_arrow, y_end_arrow = all_posx_proj[i], all_posy_proj[
i] + np.multiply(plot_vertical_ITRF[i], scale_arrow)
x_end_axis_ref, y_end_axis_ref = utils.axis_data_coords_sys_transform(
ax, x_end_arrow, y_end_arrow, inverse=True)
if (y_end_axis_ref < 0.) and shorten_oversized_arrows:
shortened_arrow = True
x_end_arrow_ok = x_end_arrow
_, y_end_arrow_ok = utils.axis_data_coords_sys_transform(
ax, x_end_axis_ref, 0, inverse=False)
elif (y_end_axis_ref > 1.) and shorten_oversized_arrows:
shortened_arrow = True
x_end_arrow_ok = x_end_arrow
_, y_end_arrow_ok = utils.axis_data_coords_sys_transform(
ax, x_end_axis_ref, 1., inverse=False)
else:
shortened_arrow = False
x_end_arrow_ok = x_end_arrow
y_end_arrow_ok = y_end_arrow
######### FIN AJUSTEMENT SI FLECHES TROP GRANDES
### SELECT ARROW PROPERTY DEPENDING ON THE TYPE
if plot_vertical_ITRF[i] > 0 and not shortened_arrow:
arrow_prop_dict_V = arrow_prop_dict_V_up
elif plot_vertical_ITRF[i] > 0 and shortened_arrow:
arrow_prop_dict_V = arrow_prop_dict_V_up_short
elif plot_vertical_ITRF[i] <= 0 and not shortened_arrow:
arrow_prop_dict_V = arrow_prop_dict_V_down
elif plot_vertical_ITRF[i] <= 0 and shortened_arrow:
arrow_prop_dict_V = arrow_prop_dict_V_down_short
### PLOT
ax.annotate(s='',
xy=(x_end_arrow_ok, y_end_arrow_ok),
xytext=(all_posx_proj[i], all_posy_proj[i]),
arrowprops=arrow_prop_dict_V,
annotation_clip=False)
#xy point arrivee de la fleche, xytext l'origine de la fleche
### STATION NAME
if name_stats:
offset_x_ok, offset_y_ok = utils.axis_data_coords_sys_transform(
ax, name_stats_offset[0], name_stats_offset[1])
Text.append(
plt.text(all_posx_proj[i] + offset_x_ok,
all_posy_proj[i] + offset_y_ok,
station_etude[i],
fontsize=name_stats_font_size))
############### PLOT ELLIPSES
# angle de l'ellipse dépend de la direction du vecteur vitesse
a = math.atan(
(all_posx_proj[i] + np.multiply(
plot_vertical_ITRF[i], scale_arrow) - all_posx_proj[i]) /
(all_posy_proj[i] + np.multiply(
plot_vertical_ITRF[i], scale_arrow) - all_posy_proj[i])
) # l'angle d'orientation de l'ellipsoide, avec 0 au nord et +90 a l'ouest
# weird previous xy definition
# [all_posx_proj[i]+np.multiply(plot_vertical_ITRF[i],0),all_posy_proj[i]+0.85*np.multiply(plot_vertical_ITRF[i],scale_arrow)]
e = Ellipse(
xy=(x_end_arrow_ok, y_end_arrow_ok),
width=np.multiply(2, incplot_vertical_ITRF[i]) * scale_ellipse,
height=np.multiply(2, incplot_vertical_ITRF[i]) *
scale_ellipse,
angle=a
) #multiplication par 2 pour obtenir la largeur et la hauteur de l'ellipse
# ax.plot(all_posx_proj[i]+np.multiply(plot_vertical_ITRF[i],0),all_posy_proj[i]+np.multiply(plot_vertical_ITRF[i],scale_arrow),'xkcd:green',marker='.',linestyle='none',markersize=4,lw=4,zorder=20) #point rouge à la station, zorder indique l'ordre vertical de dessin sur la carte, les nb elevés sont dessinés les plus hauts
ax.add_artist(e)
e.set_clip_box(ax.bbox)
e.set_edgecolor('none')
e.set_facecolor('black')
e.set_alpha(0.3)
e.set_zorder(1)
############### LEGENDE
legend_arrow_length_label = legend_arrow_length[0]
legend_ellipse_size_label = legend_ellipse_size[0]
legend_arrow_length_metric = legend_arrow_length[1]
legend_ellipse_size_metric = legend_ellipse_size[1]
legend_position_x = legend_position[0]
legend_position_y = legend_position[1]
######## Fleche de Legende
### origine de la fleche de legende
xl, yl = utils.axis_data_coords_sys_transform(ax,
legend_position_x,
legend_position_y + 0.02,
inverse=False)
### fin de la fleche de legende
xe, ye = xl + np.multiply(legend_arrow_length_metric, scale_arrow), yl
plt.annotate(
s='', xy=(xe, ye), xytext=(xl, yl), arrowprops=arrow_prop_dict
) #xy point arrivee de la fleche, xytext l'origine de la fleche
props = dict(boxstyle='round', facecolor='black', alpha=0)
ax.text(legend_position_x,
legend_position_y + 0.035,
'Velocity : ' + legend_arrow_length_label,
transform=ax.transAxes,
fontsize=11,
color='black',
verticalalignment='bottom',
bbox=props) # place a text box in upper left in axes coords
######## Légende de l'ellipse d'incertitude
ax.text(legend_position_x,
legend_position_y,
'+/- ' + legend_ellipse_size_label + ' (radius)',
transform=ax.transAxes,
fontsize=11,
color='black',
verticalalignment='top',
bbox=props)
ells_legend = Ellipse(xy=[xe, ye],
width=np.multiply(
2,
np.multiply(legend_ellipse_size_metric,
scale_ellipse)),
height=np.multiply(
2,
np.multiply(legend_ellipse_size_metric,
scale_ellipse)),
angle=a)
if plot_ellipses:
ax.add_artist(ells_legend)
ells_legend.set_clip_box(ax.bbox)
ells_legend.set_edgecolor('none')
ells_legend.set_facecolor('black')
ells_legend.set_alpha(0.3)
ells_legend.set_zorder(1)
############### FINITION
for T in Text: #Put label in front
T.set_zorder(100)
if adjust_text:
from adjustText import adjust_text
adjust_text(
Text,
on_basemap=True) #arrowprops=dict(arrowstyle='->', color='red')
plt.tight_layout()
if full_return:
return fig, ax, m, Text
else:
return fig, ax, m, Text
delat = lats[1] - lats[0]
lons = (lons - 0.5 * delon).tolist()
lons.append(lons[-1] + delon)
lons = np.array(lons, np.float64)
lats = (lats - 0.5 * delat).tolist()
lats.append(lats[-1] + delat)
lats = np.array(lats, np.float64)
# creat figure, axes instances.
fig = plt.figure()
ax = fig.add_axes([0.05, 0.05, 0.9, 0.9])
# create Basemap instance for Robinson projection.
# coastlines not used, so resolution set to None to skip
# continent processing (this speeds things up a bit)
m = Basemap(projection='robin', lon_0=lons.mean(), resolution=None)
# compute map projection coordinates of grid.
x, y = m(*np.meshgrid(lons, lats))
# draw line around map projection limb.
# color background of map projection region.
# missing values over land will show up this color.
m.drawmapboundary(fill_color='0.3')
# plot sst, then ice with pcolor
im1 = m.pcolor(x, y, sst, shading='flat', cmap=plt.cm.jet)
im2 = m.pcolor(x, y, ice, shading='flat', cmap=plt.cm.gist_gray)
# draw parallels and meridians, but don't bother labelling them.
m.drawparallels(np.arange(-90., 120., 30.))
m.drawmeridians(np.arange(0., 420., 60.))
# add colorbar
cb = m.colorbar(im1, "bottom", size="5%", pad="2%")
# add a title.
ax.set_title('SST and ICE analysis for %s' % date)
plt.savefig('plotsst.png')
### Plotting with Basemap ###
#############################
m = Basemap(projection='npstere',boundinglat=61.5,lat_0=90,lat_ts=70,lon_0=-35,\
resolution='l',round=False)
#m.drawcoastlines()
m.fillcontinents(color='grey') #,lake_color='aqua')
#m.drawparallels(np.arange(-80.,81.,5.))
#m.drawmeridians(np.arange(-180.,181.,20.))
#m.drawmapboundary(fill_color='aqua')
x, y = m(lon1, lat1, inverse=False) #,indexing='ij')
plt.figure(1)
c2 = m.contourf(x, y, RESr, np.arange(0.0, 0.11, 0.0001))
cb = m.colorbar(ticks=(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09,
0.10, 0.11))
cb.set_label('')
plt.set_cmap('jet')
#cb.set_clim(np.nanmin(RESr),np.nanmax(RESr))
plt.title('ResReal _ ' + str(b[0]) + '-' + str(b[-1] + 1))
m = Basemap(projection='npstere',boundinglat=61.5,lat_0=90,lat_ts=70,lon_0=-35,\
resolution='l',round=False)
#m.drawcoastlines()
m.fillcontinents(color='grey') #,lake_color='aqua')
plt.figure(2)
c2 = m.contourf(x, y, RESi, np.arange(0.0, 0.11, 0.0001))
cb = m.colorbar(ticks=(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09,
0.10, 0.11))
cb.set_label('')
lat = HEAT_data.variables['lat'][:]
temp = HEAT_data.variables['TEMP'][:]
HEAT_data.close()
lon, temp = PeriodicBoundaries(lon, lat, temp)
#-----------------------------------------------------------------------------------------
fig, ax = subplots()
m = Basemap(projection='spstere',boundinglat=-60,lon_0=180,resolution='i', area_thresh=0.01)
m.drawcoastlines(linewidth=0.5)
m.drawcountries()
m.fillcontinents(color='#cc9966',lake_color='#99ffff')
m.drawmapboundary(fill_color='azure')
m.drawparallels(np.arange(-80,81,20),labels=[0,0,0,0])
m.drawmeridians(np.arange(-180,180,30),labels=[1,0,0,1])
x, y = np.meshgrid(lon, lat)
x, y = m(x, y)
CS = m.contourf(x, y, temp, levels = np.arange(-3, 3.01, 0.2), extend = 'both', cmap = 'RdBu_r')
cbar = m.colorbar(CS, ticks = np.arange(-3, 3.01, 1))
cbar.set_label('Temperature difference ($^{\circ}$C)')
ax.set_title('e) HR-CESM Control minus Mercator')
show()
def uhi_kriging(shpFiles, lonlat, stationName, mLon, mLat, mTem, levels,
outfilename):
#绘图参数设置
isStationInfoOn = False #站点信息是否标注
isaxInfoOn = True # 坐标轴信息是否标注
cmap = cmaps.amwg256 #BlAqGrYeOrReVi200 #色标选择 http://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml 参考该网址 MPL_YlOrRd amwg256
levels = levels
fbl = 0.05 #克里金插值空间分辨率
picTitle = 'Temperature'
zuobiaoSetInter = 0.5 #坐标轴经纬度间隔设置
# 初始化
shpFile = shpFiles[0]
bjshp = shpFiles[1]
#天津经纬度范围设置
llat = 38.5
ulat = 40.3
llon = 116.7
ulon = 118.05
fig = plt.figure(figsize=(6, 6))
plt.rc('font', size=15, weight='bold')
ax = fig.add_subplot(111)
# 底图读取及地图设置
m=Basemap(llcrnrlon=llon,llcrnrlat=llat,urcrnrlon=ulon,urcrnrlat=ulat,\
projection='cyl',resolution='c') # 等距投影
m.readshapefile(shpFile, 'Name', linewidth=1, color='k')
# 坐标系经纬度转换
xllon, yllat = m(llon, llat)
xulon, yulat = m(ulon, ulat)
xmLon, ymLat = m(mLon, mLat)
# meshgrid set the grid range
gridx = np.arange(xllon, xulon, fbl)
gridy = np.arange(yllat, yulat, fbl)
Xlon, Ylat = np.meshgrid(gridx, gridy)
#插值处理
OK = OrdinaryKriging(xmLon,
ymLat,
mTem,
variogram_model='linear',
verbose=False,
enable_plotting=False)
z, ss = OK.execute('grid', gridx, gridy)
cf = m.contourf(Xlon, Ylat, z, levels=levels, cmap=cmap)
m.colorbar(cf,location='right',format='%.1f',size=0.3,\
ticks=levels) #,label='单位：℃')
# 添加站名信息
if isStationInfoOn == True:
m.scatter(xmLon, ymLat, c='k', s=10, marker='o')
for i in range(0, len(xmLon)):
# pass
plt.text(xmLon[i],
ymLat[i],
stationName[i],
va='bottom',
fontsize=12)
# plt.text(xmLon[i],ymLat[i],mTem[i],va='top',fontsize=12)
plt.title(picTitle)
# 坐标轴标注
if isaxInfoOn == True:
lon_label = []
lat_label = []
lon_num = np.arange(xllon, xulon, zuobiaoSetInter)
for lon in lon_num:
lon_label.append(str(lon) + '°E')
lat_num = np.arange(yllat, yulat, zuobiaoSetInter)
for lat in lat_num:
lat_label.append(str(lat) + '°N')
plt.yticks(lat_num, lat_label)
plt.xticks(lon_num, lon_label)
# 白化
maskout.shp2clip(cf, ax, bjshp, 0.005588) #0.005588为gis图周长信息
# picture introduction
# plt.show()
plt.savefig(r'./results/' + outfilename, dpi=600)
#图片输出
'''
urcrnrlon=360,
urcrnrlat=90,
projection='mill')
lons, lats = np.meshgrid(lon, lat)
x, y = m(lons, lats)
fig1 = plt.figure(figsize=(16, 20))
ax = fig1.add_axes([0.1, 0.1, 0.8, 0.8])
clevs = np.linspace(-240, 240, 21)
CS1 = m.contour(x, y, pre_1997, clevs, linewidths=0.5, colors='k')
CS2 = m.contourf(x, y, pre_1997, clevs, cmap=plt.cm.RdBu_r)
m.drawmapboundary(fill_color='#99ffff')
m.drawcoastlines(linewidth=1.5)
m.drawparallels(np.arange(-90, 90, 20), labels=[1, 1, 0, 0])
m.drawmeridians(np.arange(0., 360., 20.), labels=[0, 0, 0, 1])
cbar = m.colorbar(CS2, location='bottom', pad="5%")
cbar.set_label('mm')
plt.title('Regression Precipitation Anomaly in 1997')
plt.savefig("./1997.png", dpi=500)
fig2 = plt.figure(figsize=(16, 20))
ax = fig2.add_axes([0.1, 0.1, 0.8, 0.8])
clevs = np.linspace(-240, 240, 21)
CS1 = m.contour(x, y, pre_1998, clevs, linewidths=0.5, colors='k')
CS2 = m.contourf(x, y, pre_1998, clevs, cmap=plt.cm.RdBu_r)
m.drawmapboundary(fill_color='#99ffff')
m.drawcoastlines(linewidth=1.5)
m.drawparallels(np.arange(-90, 90, 20), labels=[1, 1, 0, 0])
m.drawmeridians(np.arange(0., 360., 20.), labels=[0, 0, 0, 1])
cbar = m.colorbar(CS2, location='bottom', pad="5%")
cbar.set_label('mm')
def metpyplot(shpFiles, lonlat, mLon, mLat, mTem):
# 初始化
#绘图参数设置
isStationInfoOn = False #站点信息是否标注
isaxInfoOn = False # 坐标轴信息是否标注
cmap = cmaps.BlAqGrYeOrReVi200 #色标选择 http://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml 参考该网址 MPL_YlOrRd amwg256
legendRange = [int(min(mTem)), math.ceil(max(mTem))]
fbl = 0.05 #克里金插值空间分辨率
sebiaoNums = 15 #色标个数
picTitle = 'Temperature'
zuobiaoSetInter = 0.5 #坐标轴经纬度间隔设置
# shpPath=r'./shp/TJ/'
# shpName='TJ_all'
## bjName='TJ_bj'
shpFile = shpFiles[0]
# bjshp=shpPath+bjName
#天津经纬度范围设置
# llat=38.5
# ulat=40.3
# llon=116.7
# ulon=118.05
llon = lonlat[0]
ulon = lonlat[1]
llat = lonlat[2]
ulat = lonlat[3]
# fig=plt.figure(figsize=(16,9))
plt.rc('font', size=15, weight='bold')
# ax=fig.add_subplot(111)
# 底图读取及地图设置
m=Basemap(llcrnrlon=llon,llcrnrlat=llat,urcrnrlon=ulon,urcrnrlat=ulat,\
projection='cyl',resolution='c') # 等距投影
m.readshapefile(shpFile, 'Name', linewidth=1, color='k')
# 绘制经纬线
parallels = np.arange(llat, ulat, 0.5)
m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10) # 绘制纬线
meridians = np.arange(llon, ulon, 0.5)
m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10) # 绘制经线
# 坐标系经纬度转换
xllon, yllat = m(llon, llat)
xulon, yulat = m(ulon, ulat)
xmLon, ymLat = m(mLon, mLat)
# meshgrid set the grid range
gridx = np.arange(xllon, xulon, fbl)
gridy = np.arange(yllat, yulat, fbl)
Xlon, Ylat = np.meshgrid(gridx, gridy)
#插值处理
# OK=OrdinaryKriging(xmLon,ymLat,mTem,variogram_model='linear',
# verbose=False,enable_plotting=False)
# z,ss=OK.execute('grid',gridx,gridy)
gx, gy, img = interpolate(xmLon, ymLat, mTem, \
interp_type='linear', hres=fbl)
levels = np.linspace(legendRange[0], legendRange[1], sebiaoNums)
cs = m.contourf(gx, gy, img, levels, cmap=cmap)
m.colorbar(cs)
# cf=m.contourf(Xlon,Ylat,z,levels=levels,cmap=cmap)
# m.colorbar(cf,location='right',format='%.1f',size=0.3,\
# ticks=np.linspace(legendRange[0],legendRange[1],sebiaoNums),label='单位：℃')
#
# 添加站名信息
if isStationInfoOn == True:
m.scatter(xmLon, ymLat, c='k', s=10, marker='o')
for i in range(0, len(xmLon)):
# pass
plt.text(xmLon[i],
ymLat[i],
stationName[i],
va='bottom',
fontsize=12)
# plt.text(xmLon[i],ymLat[i],mTem[i],va='top',fontsize=12)
plt.title(picTitle)
# 坐标轴标注
if isaxInfoOn == True:
lon_label = []
lat_label = []
lon_num = np.arange(xllon, xulon, zuobiaoSetInter)
for lon in lon_num:
lon_label.append(str(lon) + '°E')
lat_num = np.arange(yllat, yulat, zuobiaoSetInter)
for lat in lat_num:
lat_label.append(str(lat) + '°N')
plt.yticks(lat_num, lat_label)
plt.xticks(lon_num, lon_label)
# 白化
# maskout.shp2clip(cf,ax,bjshp,0.005588) #0.005588为gis图周长信息
# picture introduction
plt.show()
yy,
zz,
levels=levels,
colors=('k', ),
linewidths=(0.1, ))
plt.clabel(cs, fmt='%1.0f', colors='k', fontsize=3)
levels = np.concatenate((np.arange(-6, 0,
0.5), np.arange(0.5, 6.1, 0.5)))
cmap = plt.cm.get_cmap("jet_r")
cs = m.contourf(xx, yy, zz, cmap=cmap, levels=levels, extend="both")
cs.cmap.set_over('black')
cs.cmap.set_under('purple')
# colorbar for contourfill
cb = m.colorbar(cs,
location='right',
ticks=np.arange(-6, 6.1, 1),
pad="5%")
cb.ax.set_title('(%)', fontsize=10)
#cb.set_label('%')
ax.set_title("{:s}".format(model_tag))
elif model_tag in ['kappa']:
dz = (np.max(zz) - np.min(zz)) / 10
dz = round_to_1(dz)
levels = np.arange(np.min(zz), np.max(zz) + dz / 2, dz)
cmap = plt.cm.get_cmap("jet")
cs = m.contourf(xx, yy, zz, cmap=cmap, levels=levels, extend="both")
cs.cmap.set_over('purple')
cs.cmap.set_under('black')
# colorbar for contourfill
cb = m.colorbar(cs, location='right', pad="5%", format="%.2f")
#cb.set_label('% mean')
def plot_compare(plot_list, date_time):
formated_dt = date_time[0:4] + '-' + date_time[4:6] + '-' + date_time[
6:8] + '_' + date_time[8:10] + ':' + date_time[10:12] + ':00'
m8_df, m11_df = plot_list
fig = plt.figure()
latitude = m8_df['LATITUDE'].values
longitude = m8_df['LONGITUDE'].values
FRP = m8_df['FRP'].values
ax = fig.add_subplot(121)
m_m8 = Basemap(llcrnrlon=-30,
llcrnrlat=-35,
urcrnrlon=60,
urcrnrlat=40,
resolution='i',
projection='tmerc',
lon_0=15,
lat_0=0,
ax=ax)
#m_m8 = Basemap(llcrnrlon=28,llcrnrlat=8,urcrnrlon=32,urcrnrlat=10,
# resolution='i',projection='tmerc',lon_0=30,lat_0=9,ax=ax)
m_m8.drawcoastlines(linewidth=0.5)
m_m8.drawmapboundary()
#m.fillcontinents(lake_color='aqua',zorder=0)
m_m8.drawparallels(np.arange(-35, 60., 20), labels=[True], fontsize=5)
m_m8.drawmeridians(np.arange(-40., 100., 20))
#m_m8.drawcountries(linewidth=0.25)
x, y = m_m8(longitude, latitude)
sc = plt.scatter(x,
y,
c=FRP,
s=10,
alpha=0.8,
cmap='hot',
marker=',',
zorder=1,
norm=mpl.colors.SymLogNorm(linthresh=2, vmin=0,
vmax=1000))
plt.title('Meteosat-8', fontsize=9)
cb = m_m8.colorbar(sc, pad=0.001, ticks=[0, 1000]) #location='bottom')
cb.ax.tick_params(labelsize=8)
cb.ax.set_yticklabels([])
#cb.set_ticks([0,200,400,600,800]) # need editting to be more case adaptive range
#cb.ax.set_ylabel('FRP',fontsize=9)
latitude = m11_df['LATITUDE'].values
longitude = m11_df['LONGITUDE'].values
FRP = m11_df['FRP'].values
ax = fig.add_subplot(122)
m_m11 = Basemap(llcrnrlon=-30,
llcrnrlat=-35,
urcrnrlon=60,
urcrnrlat=40,
resolution='i',
projection='tmerc',
lon_0=15,
lat_0=0,
ax=ax)
#m_m11 = Basemap(llcrnrlon=28,llcrnrlat=8,urcrnrlon=32,urcrnrlat=10,
# resolution='i',projection='tmerc',lon_0=30,lat_0=9,ax=ax)
m_m11.drawcoastlines(linewidth=0.5)
m_m11.drawmapboundary()
#m.fillcontinents(lake_color='aqua',zorder=0)
m_m11.drawparallels(np.arange(-35, 60., 20), labels=[True], fontsize=5)
m_m11.drawmeridians(np.arange(-40., 100., 20), labels=[True])
#m_m11.drawcountries(linewidth=0.25)
# m_ice.shadedrelief()
x, y = m_m11(longitude, latitude)
sc = plt.scatter(x,
y,
c=FRP,
s=10,
alpha=0.8,
cmap='hot',
marker=',',
zorder=1,
norm=mpl.colors.SymLogNorm(linthresh=2, vmin=0,
vmax=1000))
plt.title('Meteosat-11', fontsize=9)
plt.suptitle('FRP-PIXEL on ' + formated_dt, fontsize=12, y=0.09)
#fig.subplots_adjust(right=0.8)
rcParams['figure.subplot.wspace'] = 0.2 # more width between subplots
#cb_ax = fig.add_axes([0, 800, 0,800])
cb = m_m11.colorbar(sc, pad=0.001) #location='bottom')
cb.ax.tick_params(labelsize=8)
#cb.ax.set_yticklabels([0,10,50,100,200,500,1000]) # need editting to be more case adaptive range
cb.ax.set_ylabel('FRP (MW)', fontsize=9)
#fig.tight_layout()
plt.savefig('FRP_cluster_' + date_time, dpi=500)
plt.close()
latt, lont, MCdivt = pp.grid_for_map(lat, lon, MCdiv)
latt, lont, Trdivt = pp.grid_for_map(lat, lon, Trdiv)
latt, lont, uvdht = pp.grid_for_map(lat, lon, uvdh)
latt, lont, wdht = pp.grid_for_map(lat, lon, wdh)
latt, lont, whbott = pp.grid_for_map(lat, lon, whbot)
latt, lont, whtopt = pp.grid_for_map(lat, lon, whtop)
latt, lont, vertdivt = pp.grid_for_map(lat, lon, vertdiv)
lons, lats = np.meshgrid(lont, latt)
x, y = m(lons, lats)
cmap = plt.cm.RdYlBu
#%%
plt.figure()
m.drawcoastlines()
m.drawcountries()
m.contourf(x, y, np.mean(nett, 0), clev, cmap=cmap, extend='both')
cb = m.colorbar(location='bottom', size='5%', pad='8%')
plt.tight_layout()
plt.figure()
m.drawcoastlines()
m.drawcountries()
m.contourf(x, y, np.mean(Totdivt + det, 0), clev, cmap=cmap, extend='both')
cb = m.colorbar(location='bottom', size='5%', pad='8%')
plt.tight_layout()
m.drawcoastlines()
m.drawcountries()
m.contourf(x,
y,
np.mean(Totdivt + vertdivt + det, 0),
clev,
cmap=cmap,
extend='both')
llcrnrlon=rlon_0,
urcrnrlon=rlon_1,
resolution='c')
m.drawcoastlines()
m.drawparallels(
np.arange(-10, 60, 20), labels=[1, 0, 0, 0],
fontsize=9) #labels=[left,right,top,bottom] #horizontal line
m.drawmeridians(
np.arange(80, 180, 20), labels=[0, 0, 0, 1],
fontsize=9) #labels=[left,right,top,bottom] #vertical line
m.imshow(array, cmap=cmap, vmin=0, vmax=100) #draw figure
plt.title(fn_name, fontsize=9)
cbar = m.colorbar(
size="5%", pad=0.05,
extend='max') #pad=distance, extend='colorbar shape,,'
cbar.ax.tick_params(labelsize=8) #labelsize=colorbar font size
cbar.set_label('Rainfall intensity(mm/hr)', fontsize=8,
ha='center') #ha='label text position'
#------------------
'''
n=n+1
#---PE FILE SUBPLOT---
plt.subplot(6,2,n) #subplot(y,x,position)
#---READ PE FILE---BINARY FILE!
fb_name = 'CMORPH_V1.0_ADJ_8km-30min_'+i+hh
pe = np.fromfile(pe_path+fb_name, dtype=np.float64).reshape(1649,4948)
#calculate session#
###################
NDVI = (B04 - B03) / (B04 + B03)
NDVI[NDVI < 0.2] = np.nan
B13[B13 < clearthreshold] = np.nan
#NDVI = np.ma.masked_invalid(np.atleast_2d(NDVI))
NDVI = np.ma.masked_where(np.isnan(NDVI), NDVI)
#NDVI = np.ma.masked_where(np.isnan(B13),NDVI)
print NDVI
###############
#plotting time#
###############
m = Basemap(resolution='l', projection='merc', \
llcrnrlon=90, llcrnrlat=-20, urcrnrlon=149, urcrnrlat=20) #for mercator
#draw map coastline, etc
m.drawcoastlines(linewidth=0.75)
m.drawcountries(linewidth=0.75)
#make our lon and lat 2D
lon1, lat1 = np.meshgrid(lon, lat)
#for NDVI
plt.title('NDVI')
cs = m.pcolormesh(lon1, lat1, np.squeeze(NDVI), cmap='coolwarm', latlon=True)
cbar = m.colorbar(cs, location='bottom', pad="10%") #add color bar
plt.show()
llcrnrlon=lon_min,
urcrnrlon=lon_max,
lat_ts=35,
resolution='c')
# Construct a heatmap
lons = df_age.index.values
lats = df_age.columns.values
x, y = np.meshgrid(lons, lats)
px, py = m5a(x, y)
data_values = df_age.values
masked_data = np.ma.masked_invalid(data_values.T)
cmap = plt.cm.viridis
cmap.set_bad(color="#191919")
# Plot the heatmap
m5a.pcolormesh(px, py, masked_data, cmap=cmap, zorder=5)
m5a.colorbar().set_label("average age")
plt.title("Average age per grid area in Beijing")
# Plot count per grid
plt.subplot(122)
m5b = Basemap(projection='merc',
llcrnrlat=lat_min,
urcrnrlat=lat_max,
llcrnrlon=lon_min,
urcrnrlon=lon_max,
lat_ts=35,
resolution='c')
# Construct a heatmap
data_values = df_cnt.values
masked_data = np.ma.masked_invalid(data_values.T)
cmap = plt.cm.viridis
START_YEAR = 1950
LAST_YEAR = 2013
n_cities = 500
random_cities = city_means.sample(n_cities).index
year_text = plt.text(-170, 80, str(START_YEAR), fontsize=15)
temp_markers = get_temp_markers(random_cities, START_YEAR)
xs, ys = map(temp_markers['lon'], temp_markers['lat'])
scat = map.scatter(xs,
ys,
s=temp_markers['size'],
c=temp_markers['color'],
cmap=cmap,
marker='o',
alpha=0.3,
zorder=10)
ani = animation.FuncAnimation(fig,
update,
interval=500,
frames=LAST_YEAR - START_YEAR + 1)
cbar = map.colorbar(scat, location='bottom')
cbar.set_label(
'0.0 -- min temperature record for the city 1.0 -- max temperature record for the city'
)
plt.title('Mean year temperatures for {} random cities'.format(n_cities))
plt.show()
ani.save('animation.gif', writer='imagemagick', fps=2)
m.drawcoastlines()
m.drawstates(), m.drawcountries()
cs = m.contourf(lon2,
lat2,
zhmean[3, :, :].T,
clevs,
cmap=zhcolor,
extend='both')
cs2 = m.contour(lonhou.T,
lathou.T,
sig_rotation[:, :, 3],
colors='k',
linewidths=1.5)
m.drawcounties()
cbar = m.colorbar(cs, size='2%')
cbar.ax.set_ylabel('dBZ', weight='bold', name='Calibri', size=14)
cticks = []
for i in clevs:
cticks.append(int(i)) if i.is_integer() else cticks.append(i)
cbar.set_ticks(clevs[::4])
cbar.set_ticklabels(cticks[::4])
for i in cbar.ax.yaxis.get_ticklabels():
i.set_family('Calibri')
i.set_size(14)
x2star, y2star = m(-95.3698, 29.7604)
m.plot(x2star, y2star, 'ro', markersize=7, color='k')
label = 'Houston'
plt.text(x2star + 0.05, y2star + 0.05, label)
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180,
resolution='c')
map.drawcoastlines()
map.drawcountries()
map.drawmapboundary()
x, y = map(lon, lat)
cs1 = map.pcolormesh(x, y, am3new1y, cmap=cmap, norm=norm)
#cs1 = map.pcolormesh(x,y,m3newp*10000/gridarea,cmap=plt.cm.Greens,norm=colors.PowerNorm(gamma=1./2.),vmin=0,vmax=9)
#cs1 = map.pcolormesh(x,y,m3newp/m3newa,cmap=plt.cm.gist_earth,vmin=0,vmax=10)
plt.axis('off')
cbar = map.colorbar(cs1,
location='bottom',
size="5%",
pad="2%",
ticks=bounds,
extend='max')
#cbar.ax.set_xticklabels(['0', '0.01', '0.05','0.1','0.5','1','2','3','4','5','6']) # horizontal colorbar
cbar.ax.tick_params(labelsize=16)
ax1 = fig.add_subplot(222)
ax1.set_title("Maize ISAM (t grains / ha croparea)", fontsize=20)
map = Basemap(projection='cyl',
llcrnrlat=-62,
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180,
resolution='c')
def test_ocean_plot():
dirs = "../result_h/test/test_iw/"
mkdir(dirs)
start_list_plus_1month = start_list + [20170901]
for i, start in enumerate(start_list):
print("******************* {}/{} *******************".format(
i + 1, M))
month_end = start_list_plus_1month[i + 1]
month_end = date(month_end // 10000, (month_end % 10000) // 100,
(month_end % 10000) % 100) - timedelta(days=1)
end = start + month_end.day - 1
try:
#csv_Helmert_90の当該月のcsvを読み込む
#hermert_file_name_90 = "../data/csv_Helmert_both_90/Helmert_both_90_" + str(start)[:6] + ".csv"
#helmert_90_data = pd.read_csv(hermert_file_name_90)
#csv_Helmert_30の当該月のcsvを読み込む
hermert_file_name_30 = "../data/csv_Helmert_30/Helmert_30_" + str(
start)[:6] + ".csv"
helmert_30_data = pd.read_csv(hermert_file_name_30)
#木村のcsvの読み込み(90)
#coeff_file_name_90 = "../data/csv_A_90/ssc_amsr_ads" + str(start)[2:6] + "_90_fin.csv"
#coeff_90_data = calc_data.get_1month_coeff_data(coeff_file_name_90)
#木村のcsvの読み込み(30)
coeff_file_name_30 = "../data/csv_A_30/ssc_amsr_ads" + str(
start)[2:6] + "_30_fin.csv"
coeff_30_data = calc_data.get_1month_coeff_data(coeff_file_name_30)
except:
continue
#ocean_90_h_u = helmert_90_data["ocean_u_90"].values
#ocean_90_h_v = helmert_90_data["ocean_v_90"].values
#ocean_90_h_speed = np.sqrt(ocean_90_h_u**2 + ocean_90_h_v**2)
ocean_30_h_u = helmert_30_data["ocean_u"].values
ocean_30_h_v = helmert_30_data["ocean_v"].values
ocean_30_h_speed = np.sqrt(ocean_30_h_u**2 + ocean_30_h_v**2)
#ocean_90_c_u = coeff_90_data["mean_ocean_u"].values
#ocean_90_c_v = coeff_90_data["mean_ocean_v"].values
#ocean_90_c_speed = np.sqrt(ocean_90_c_u**2 + ocean_90_c_v**2)
ocean_30_c_u = coeff_30_data["mean_ocean_u"].values
ocean_30_c_v = coeff_30_data["mean_ocean_v"].values
ocean_30_c_speed = np.sqrt(ocean_30_c_u**2 + ocean_30_c_v**2)
"""
fig, axes = plt.subplots(1, 2)
ratio_90 = ocean_90_c_speed/ocean_90_h_speed
ratio_30 = ocean_30_c_speed/ocean_30_h_speed
axes[0].hist(ratio_30[ocean_idx][~np.isnan(ratio_30[ocean_idx])], range=(0,3), bins=150)
axes[1].hist(ratio_90[ocean_idx][~np.isnan(ratio_90[ocean_idx])], range=(0,3), bins=150)
"""
ratio_w = ocean_30_c_u / ocean_30_h_u
N_c_h = helmert_30_data["mean_iw_v"].values
N_c_c = coeff_30_data["mean_ice_v"].values
N_c_diff = N_c_h - N_c_c
#plt.hist(N_c_diff[~np.isnan(N_c_diff)], range=(-5,5), bins=11, alpha=0.5)
#tmp = N_c_diff[~np.isnan(N_c_diff)]
#print(len(np.where(tmp>0)[0]))
m = Basemap(lon_0=180,
boundinglat=50,
resolution='l',
projection='npstere')
m.drawcoastlines(color='0.15')
m.fillcontinents(color='#555555')
lon = np.array(latlon_ex.Lon)
lat = np.array(latlon_ex.Lat)
x, y = m(lon, lat)
x1 = np.reshape(x, (145, 145), order='F')
y1 = np.reshape(y, (145, 145), order='F')
dx1 = (x1[1, 0] - x1[0, 0]) / 2
dy1 = (y1[0, 1] - y1[0, 0]) / 2
x2 = np.linspace(x1[0, 0], x1[144, 0], 145)
y2 = np.linspace(y1[0, 0], y1[0, 144], 145)
xx, yy = np.meshgrid(x2, y2)
xx, yy = xx.T, yy.T
xx = np.hstack([xx, xx[:, 0].reshape(145, 1)])
xx_ex = np.vstack([xx, (xx[144, :] + (xx[1, 0] - xx[0, 0]))])
yy = np.vstack([yy, yy[0, :]])
yy_ex = np.hstack([(yy[:, 0].reshape(146, 1) + (yy[0, 0] - yy[0, 1])),
yy])
data = np.ma.masked_invalid(N_c_diff)
data1 = np.reshape(data, (145, 145), order='F')
m.pcolormesh(xx_ex - dx1,
yy_ex + dy1,
data1,
cmap=plt.cm.jet,
vmax=0.5,
vmin=-0.5)
#fig.colorbar(im, ax=ax)
m.colorbar()
#plt.hist(N_c_c[~np.isnan(N_c_c)], range=(20,35), bins=16)
plt.savefig(dirs + "iw_v_" + str(start)[:6] + ".png", dpi=450)
plt.close()
urcrnrlat=latlon[3],
resolution='i')
x, y = map(lons_prism, lats_prism)
totalprecip = maskoceans(lons_prism, lats_prism, totalprecip)
#map.drawlsmask(land_color=(0, 0, 0, 0), ocean_color='deeppink', lakes=False)
csAVG = map.contourf(x,
y,
totalprecip,
levels,
cmap=cmap,
norm=matplotlib.colors.BoundaryNorm(levels, cmap.N))
map.drawcoastlines(linewidth=.5)
map.drawstates()
map.drawcountries()
cbar = map.colorbar(csAVG, location='bottom', pad="5%")
cbar.ax.tick_params(labelsize=12)
plt.title('NCAR Ensemble Control', fontsize=18)
#cbar.ax.set_xlabel('Mean Daily Precipitation from Oct. 2015 to Mar. 2016 (mm)', fontsize = 10)
######################## prism #############################################
ax = fig.add_subplot(232)
map = Basemap(projection='merc',
llcrnrlon=latlon[0],
llcrnrlat=latlon[1],
urcrnrlon=latlon[2],
urcrnrlat=latlon[3],
resolution='i')
x, y = map(lons_prism, lats_prism)
precip_tot = maskoceans(lons_prism, lats_prism, precip_tot)
def run(FILE_NAME):
GEO_FILE_NAME = 'MYD03.A2002226.0000.005.2009193071127.hdf'
GEO_FILE_NAME = os.path.join(os.environ['HDFEOS_ZOO_DIR'], GEO_FILE_NAME)
DATAFIELD_NAME = 'EV_1KM_Emissive'
if USE_NETCDF4:
from netCDF4 import Dataset
nc = Dataset(FILE_NAME)
# Just read the first level, Band 20.
# lat/lon resolution.
data = nc.variables[DATAFIELD_NAME][0, :, :].astype(np.float64)
# Retrieve the geolocation data from MYD03 product.
nc_geo = Dataset(GEO_FILE_NAME)
longitude = nc_geo.variables['Longitude'][:]
latitude = nc_geo.variables['Latitude'][:]
# Retrieve attributes.
units = nc.variables[DATAFIELD_NAME].radiance_units
long_name = nc.variables[DATAFIELD_NAME].long_name
# The scale and offset attributes do not have standard names in this
# case, so we have to apply the scaling equation ourselves.
scale_factor = nc.variables[DATAFIELD_NAME].radiance_scales[0]
add_offset = nc.variables[DATAFIELD_NAME].radiance_offsets[0]
valid_range = nc.variables[DATAFIELD_NAME].valid_range
_FillValue = nc.variables[DATAFIELD_NAME]._FillValue
valid_min = valid_range[0]
valid_max = valid_range[1]
# Retrieve dimension name.
dimname = nc.variables[DATAFIELD_NAME].dimensions[0]
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
data3D = hdf.select(DATAFIELD_NAME)
data = data3D[0, :, :].astype(np.double)
hdf_geo = SD(GEO_FILE_NAME, SDC.READ)
# Read geolocation dataset from MOD03 product.
lat = hdf_geo.select('Latitude')
latitude = lat[:, :]
lon = hdf_geo.select('Longitude')
longitude = lon[:, :]
# Retrieve attributes.
attrs = data3D.attributes(full=1)
lna = attrs["long_name"]
long_name = lna[0]
aoa = attrs["radiance_offsets"]
add_offset = aoa[0][0]
fva = attrs["_FillValue"]
_FillValue = fva[0]
sfa = attrs["radiance_scales"]
scale_factor = sfa[0][0]
vra = attrs["valid_range"]
valid_min = vra[0][0]
valid_max = vra[0][1]
ua = attrs["radiance_units"]
units = ua[0]
# Retrieve dimension name.
dim = data3D.dim(0)
dimname = dim.info()[0]
invalid = np.logical_or(data > valid_max, data < valid_min)
invalid = np.logical_or(invalid, data == _FillValue)
data[invalid] = np.nan
data = (data - add_offset) * scale_factor
data = np.ma.masked_array(data, np.isnan(data))
# The data is close to the equator in Africa, so a global projection is
# not needed.
m = Basemap(projection='cyl',
resolution='l',
llcrnrlat=-5,
urcrnrlat=30,
llcrnrlon=5,
urcrnrlon=45)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(0, 50, 10), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(0, 50., 10), labels=[0, 0, 0, 1])
m.pcolormesh(longitude, latitude, data, latlon=True)
cb = m.colorbar()
cb.set_label(units, fontsize=8)
basename = os.path.basename(FILE_NAME)
plt.title('{0}\n{1}\nat {2}=0'.format(basename,
'Radiance derived from ' + long_name,
dimname),
fontsize=11)
fig = plt.gcf()
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
def get_temp(lat, lon, elev):
day = datetime.date(2020, 2, 1)
df = spark.createDataFrame(
[('STATION', day, float(lat), float(lon), float(elev), 1.1)],
tmax_schema)
predictions = model.transform(df)
result = predictions.select("prediction").collect()
temp = result[0]['prediction']
return temp
def get_temps(latlonelevs):
return [get_temp(lat, lon, elev) for lat, lon, elev in latlonelevs]
fig = plt.figure(num=None, figsize=(16, 10))
m = Basemap(projection='cyl', resolution='c', lat_0=0, lon_0=0)
m.drawcoastlines()
lats, lons = np.meshgrid(np.arange(-90, 90, .5), np.arange(-180, 180, .5))
elevs = [
eg.get_elevations(np.array([lat, lon]).T) for lat, lon in zip(lats, lons)
]
temps = [
get_temps(np.array([lat, lon, elev]).T)
for lat, lon, elev in zip(lats, lons, elevs)
]
print(temps)
cs = m.pcolormesh(lons, lats, temps, cmap='jet')
m.colorbar(cs)
plt.title("Eckert IV Projection")
plt.savefig('2a.png')
ax1 = fig.add_subplot(221)
ax1.set_title("CLM yield difference 2010s (t/ha)",fontsize=18)
map = Basemap(projection ='cyl', llcrnrlat=-65, urcrnrlat=90,llcrnrlon=-180, urcrnrlon=180, resolution='c')
x,y = map(lon2,lat2)
map.drawcoastlines()
map.drawcountries()
map.drawmapboundary()
clmhis= ma.masked_where(clmhis<=0.,clmhis)
clmhis1= ma.masked_where(clmhis1<=0.,clmhis1)
cc=clmhis1-clmhis
cc= ma.masked_where(cc==0.,cc)
cs = map.pcolormesh(x,y,cc,cmap=plt.cm.bwr,vmin=-1,vmax=1)
cbar = map.colorbar(cs,location='bottom',size="4%",pad="2%")
cbar.ax.tick_params(labelsize=12)
plt.axis('off')
ax2 = fig.add_subplot(222)
ax2.set_title("ISAM CWP relative change 2010s (%)",fontsize=18)
map = Basemap(projection ='cyl', llcrnrlat=-65, urcrnrlat=90,llcrnrlon=-180, urcrnrlon=180, resolution='c')
map.drawcoastlines()
map.drawcountries()
map.drawmapboundary()
yield_new= ma.masked_where(yield_new<=0.,yield_new)
yield_new1= ma.masked_where(yield_new1<=0.,yield_new1)
cisa=(yield_new1/yield_new1et)-(yield_new/yield_newet)
cisa= ma.masked_where(cisa==0.,cisa)
