def test_with_scalar_field(self):
# Transform and regrid vector (with no projection transform) with an
# additional scalar field.
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan],
[np.nan, 2.675, 2.15, 2.675, np.nan],
[5.5, 4.75, 4., 4.75, 5.5]])
expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
src_crs = target_crs = ccrs.PlateCarree()
x_grid, y_grid, u_grid, v_grid, s_grid = \
vec_trans.vector_scalar_to_grid(src_crs, target_crs, (5, 3),
self.x, self.y,
self.u, self.v, self.s)
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(u_grid, expected_u_grid)
assert_array_almost_equal(v_grid, expected_v_grid)
assert_array_almost_equal(s_grid, expected_s_grid)
def test_with_scalar_field(self):
# Transform and regrid vector (with no projection transform) with an
# additional scalar field.
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan],
[np.nan, 2.675, 2.15, 2.675, np.nan],
[5.5, 4.75, 4., 4.75, 5.5]])
expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
src_crs = target_crs = ccrs.PlateCarree()
x_grid, y_grid, u_grid, v_grid, s_grid = \
vec_trans.vector_scalar_to_grid(src_crs, target_crs, (5, 3),
self.x, self.y,
self.u, self.v, self.s)
assert_array_equal(x_grid, expected_x_grid)
assert_array_equal(y_grid, expected_y_grid)
assert_array_almost_equal(u_grid, expected_u_grid)
assert_array_almost_equal(v_grid, expected_v_grid)
assert_array_almost_equal(s_grid, expected_s_grid)
def test_with_transform(self):
# Transform and regrid vector.
target_crs = ccrs.PlateCarree()
src_crs = ccrs.NorthPolarStereo()
input_coords = [src_crs.transform_point(xp, yp, target_crs)
for xp, yp in zip(self.x, self.y)]
x_nps = np.array([ic[0] for ic in input_coords])
y_nps = np.array([ic[1] for ic in input_coords])
u_nps, v_nps = src_crs.transform_vectors(target_crs, self.x, self.y,
self.u, self.v)
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan],
[np.nan, 2.675, 2.15, 2.675, np.nan],
[5.5, 4.75, 4., 4.75, 5.5]])
x_grid, y_grid, u_grid, v_grid = vec_trans.vector_scalar_to_grid(
src_crs, target_crs, (5, 3), x_nps, y_nps, u_nps, v_nps)
assert_array_almost_equal(x_grid, expected_x_grid)
assert_array_almost_equal(y_grid, expected_y_grid)
# Vector transforms are somewhat approximate, so we are more lenient
# with the returned values since we have transformed twice.
assert_array_almost_equal(u_grid, expected_u_grid, decimal=4)
assert_array_almost_equal(v_grid, expected_v_grid, decimal=4)
def test_with_transform(self):
# Transform and regrid vector.
target_crs = ccrs.PlateCarree()
src_crs = ccrs.NorthPolarStereo()
input_coords = [src_crs.transform_point(xp, yp, target_crs)
for xp, yp in zip(self.x, self.y)]
x_nps = np.array([ic[0] for ic in input_coords])
y_nps = np.array([ic[1] for ic in input_coords])
u_nps, v_nps = src_crs.transform_vectors(target_crs, self.x, self.y,
self.u, self.v)
expected_x_grid = np.array([[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.],
[-10., -5., 0., 5., 10.]])
expected_y_grid = np.array([[5., 5., 5., 5., 5.],
[7.5, 7.5, 7.5, 7.5, 7.5],
[10., 10., 10., 10., 10]])
expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan],
[np.nan, 2.5, 3.5, 2.5, np.nan],
[2., 3., 4., 3., 2.]])
expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan],
[np.nan, 2.675, 2.15, 2.675, np.nan],
[5.5, 4.75, 4., 4.75, 5.5]])
x_grid, y_grid, u_grid, v_grid = vec_trans.vector_scalar_to_grid(
src_crs, target_crs, (5, 3), x_nps, y_nps, u_nps, v_nps)
assert_array_almost_equal(x_grid, expected_x_grid)
assert_array_almost_equal(y_grid, expected_y_grid)
# Vector transforms are somewhat approximate, so we are more lenient
# with the returned values since we have transformed twice.
assert_array_almost_equal(u_grid, expected_u_grid, decimal=4)
assert_array_almost_equal(v_grid, expected_v_grid, decimal=4)
def plot_base(data, itime, var_levels, oname):
#land,coastline,states,reader=features
#Size
#WXGA size
#plt.figure(figsize=(12.80,7.20),dpi=100)
t1 = dt.datetime.now()
#FHD size
fig = plt.figure(figsize=(19.20, 10.80), dpi=100)
#Adjusting margins figure
fig.subplots_adjust(left=0.05, right=0.95, top=0.97, bottom=0.03)
ax = plt.subplot(
1,
1,
1,
#ax=fig.add_subplot(1,1,1,
projection=proj,
)
ax.set_extent(ax_lim, proj)
#load features
#ax.add_feature(ocean,
# facecolor=feature.COLORS['water'],
# )
ax.add_feature(
land,
zorder=10,
)
ax.add_feature(
feature.BORDERS,
zorder=11,
)
ax.add_feature(
coastline,
edgecolor='black',
zorder=12,
)
ax.add_feature(
states,
edgecolor='gray',
zorder=12,
)
#print('features:',dt.datetime.now()-t1, end=' ')
#url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'
#layer = 'VIIRS_CityLights_2012'
#ax.add_wmts(url, layer)
#print('names:',dt.datetime.now()-t1, end=' ')
#logo plot
im = image.imread('logo_atmosfera.png')
ax.imshow(
im,
aspect='equal',
extent=(lon_min + 0.1, lon_min + 1.1, lat_max, lat_max - 0.27),
zorder=15,
)
#print('logo:',dt.datetime.now()-t1, end=' ')
#info text
txt_box = '\n'.join((
'>Distribución del sargazo + elevación +',
' Corriente en superficie',
'>Modelo: FVCOM',
'>Grupo Interacción Océano-Atmósfera',
))
#
#bbox_props = dict(boxstyle="pad=0.8", fc="white", ec="b", lw=2)
#info box plot
draw_info(ax, title, txt_box)
#print('box::',dt.datetime.now()-t1, end=' ')
#wind plot
#u=np.expand_dims(data['u'][itime],axis=0)
#v=np.expand_dims(data['v'][itime],axis=0)
#magn=np.expand_dims(data['magnitude'][itime],axis=0)
x, y, u, v = cvt.vector_scalar_to_grid(
proj,
proj,
25,
data['lonc'],
data['latc'],
data['u'][itime],
data['v'][itime],
)
#ind=np.arange(0,len(u),20)
ax.quiver(
x,
y,
u,
v,
scale=2,
scale_units='inches',
pivot='middle',
color='blue',
zorder=25,
)
#ax.barbs(
# x,y,u,v,
# #sizes=dict(emptybarb=0.1, spacing=2.2, height=0.5),
# #linewidth=0.95,
# #length=10,
# #pivot='middle',
# #transform=ccrs.PlateCarree(),
# zorder=25,
# )
#wplot=ax.streamplot(
# data['lonc'],
# data['latc'],
# u,v,
# #linewidth=1,
# #density=2,
# color='red',
# #cmap=plt.cm.tab10,
# #levels=np.arange(0,2,0.2),
# zorder=5,
# )
#city names
for name in city:
ax.text(
name[0],
name[1],
name[2],
#fontsize=2,
zorder=17,
#clip_on=True,
ha='center',
va='top',
transform=ccrs.PlateCarree(),
color='black',
)
#city points plot
ax.scatter(
xp,
yp,
100,
marker='*',
color='white',
transform=ccrs.PlateCarree(),
zorder=15,
)
#print('points:',dt.datetime.now()-t1, end=' ')
#apply limits
#plt.axis(ax_lim)
ax_leg = ax.gridlines(
draw_labels=True,
linestyle='--',
)
ax_leg.xlabels_top = False
ax_leg.ylabels_right = False
ax_leg.xlocator = mticker.FixedLocator(
range(int(lon_min),
int(lon_max) + 1, 1))
ax_leg.xformatter = LONGITUDE_FORMATTER
ax_leg.ylocator = mticker.FixedLocator(
range(int(lat_min),
int(lat_max) + 2, 1))
ax_leg.yformatter = LATITUDE_FORMATTER
#plot
l_map = ax.tricontour(
data['lon'],
data['lat'],
data['nv'],
data['var'][itime],
colors='gray',
levels=var_levels,
)
#ax.clabel(z_map,
# )
#print('lines:',dt.datetime.now()-t1, end=' ')
var_map = plt.tricontourf(
data['lon'],
data['lat'],
data['nv'],
data['var'][itime],
levels=var_levels,
#transform=ccrs.Mercator(),
#cmap=plt.cm.coolwarm,
cmap=my_cbar,
)
#print('fill:',dt.datetime.now()-t1, end=' ')
#print(data['time'][itime])
plt.title(data['time'][itime])
#color bar
cbar = plt.colorbar(
var_map,
#ax=ax,
#ticks=t_ticks,
ticks=var_levels,
#shrink=0.75,
#orientation='horizontal',
aspect=50,
pad=0.02,
fraction=0.03,
)
cbar.ax.set_ylabel('Elevación de la superficie [m]')
##mat plot
ax.plot(
data['par_lon'],
data['par_lat'],
'r,',
#color='red',
#marker='*',
#markersize=12,
transform=ccrs.PlateCarree(),
zorder=1,
)
#print('mat:',dt.datetime.now()-t1, )
plt.savefig('{}_{:03}.png'.format(oname, itime))
return 0
labelpad=4,
rotation=0.)
cbar.ax.tick_params(labelsize=12)
else:
# change the gridded vector data, don't update other plot elements;
# this speeds things up immensely, especially with high-res maps
if regrid:
# manually perform calculations normally done by ax.quiver()
target_extent = ax.get_extent(ax.projection)
_, _, Ys_trans, Xs_trans, Ms_trans = cvt.vector_scalar_to_grid(
proj_data,
projection,
regrid,
Lons,
Lats,
Ys[i] * Ms[i],
Xs[i] * Ms[i], (10**Ms[i]),
target_extent=target_extent)
else:
Ys_trans, Xs_trans = projection.transform_vectors(
proj_data, Lons, Lats, Ys[i] * Ms[i], Xs[i] * Ms[i])
Ms_trans = 10**Ms[i]
Q.set_UVC(Ys_trans, Xs_trans, Ms_trans)
# change scatter plot "offsets"
avail = ObsFits[i].astype(bool)
S_avail.set_offsets(
np.array(list(zip(ObsLons, ObsLats)))[avail])
S_miss.set_offsets(
