def test_gmap_transformed():
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
dem.set_subset(margin=-100)
dem = mercator_grid(center_ll=(10.76, 46.798444), extent=(10000, 7000))
i, j = dem.ij_coordinates
g = GoogleVisibleMap(x=i, y=j, crs=dem, size_x=500, size_y=400)
img = g.get_vardata()
m = Map(dem, countries=False)
with pytest.raises(ValueError):
m.set_data(img)
m.set_lonlat_contours(interval=0.025)
m.set_shapefile(get_demo_file('Hintereisferner.shp'),
linewidths=2,
edgecolor='darkred')
m.set_rgb(img, g.grid)
fig, ax = plt.subplots(1, 1)
m.visualize(ax=ax, addcbar=False)
plt.tight_layout()
return fig
def test_plot_region_model():
gdirs = random_for_plot()
dfc = utils.compile_task_log(gdirs, task_names=['run_random_climate_plot'])
assert np.all(dfc['run_random_climate_plot'] == 'SUCCESS')
# We prepare for the plot, which needs our own map to proceed.
# Lets do a local mercator grid
g = salem.mercator_grid(center_ll=(10.86, 46.85), extent=(27000, 21000))
# And a map accordingly
sm = salem.Map(g, countries=False)
sm.set_topography(get_demo_file('srtm_oetztal.tif'))
# Give this to the plot function
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdirs,
smap=sm,
ax=ax,
filesuffix='_plot',
vmax=250,
modelyr=10,
linewidth=1.5)
fig.tight_layout()
return fig
def test_gmap_transformed():
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
dem.set_subset(margin=-100)
dem = mercator_grid(center_ll=(10.76, 46.798444),
extent=(10000, 7000))
i, j = dem.ij_coordinates
g = GoogleVisibleMap(x=i, y=j, crs=dem, size_x=500, size_y=400)
img = g.get_vardata()
m = Map(dem, countries=False)
with pytest.raises(ValueError):
m.set_data(img)
m.set_lonlat_contours(interval=0.025)
m.set_shapefile(get_demo_file('Hintereisferner.shp'),
linewidths=2, edgecolor='darkred')
m.set_rgb(img, g.grid)
fig, ax = plt.subplots(1, 1)
m.visualize(ax=ax, addcbar=False)
plt.tight_layout()
return fig
def test_ice_cap():
testdir = os.path.join(get_test_dir(), 'tmp_icecap')
utils.mkdir(testdir, reset=True)
cfg.initialize()
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['dem_file'] = get_demo_file('dem_RGI50-05.08389.tif')
cfg.PARAMS['border'] = 60
cfg.PATHS['working_dir'] = testdir
df = gpd.read_file(get_demo_file('divides_RGI50-05.08389.shp'))
df['Area'] = df.Area * 1e-6 # cause it was in m2
df['RGIId'] = ['RGI50-05.08389_d{:02d}'.format(d + 1) for d in df.index]
gdirs = workflow.init_glacier_regions(df)
workflow.gis_prepro_tasks(gdirs)
from salem import mercator_grid, Map
smap = mercator_grid((gdirs[0].cenlon, gdirs[0].cenlat),
extent=[20000, 23000])
smap = Map(smap)
fig, ax = plt.subplots()
graphics.plot_catchment_width(gdirs,
ax=ax,
add_intersects=True,
add_touches=True,
smap=smap)
fig.tight_layout()
shutil.rmtree(testdir)
return fig
def test_merca_map():
grid = mercator_grid(center_ll=(11.38, 47.26), extent=(2000000, 2000000))
m1 = Map(grid)
grid = mercator_grid(center_ll=(11.38, 47.26),
extent=(2000000, 2000000),
origin='upper-left')
m2 = Map(grid)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
m1.visualize(ax=ax1, addcbar=False)
m2.visualize(ax=ax2, addcbar=False)
plt.tight_layout()
return fig
def test_increase_coverage(self):
# Just for coverage -> empty shapes should not trigger an error
grid = mercator_grid(center_ll=(-20, 40), extent=(2000, 2000), nx=10)
c = graphics.Map(grid)
# Assigning wrongly shaped data should, however
self.assertRaises(ValueError, c.set_data, np.zeros((3, 8)))
def test_increase_coverage(self):
# Just for coverage -> empty shapes should not trigger an error
grid = mercator_grid(center_ll=(-20, 40), extent=(2000, 2000), nx=10)
c = graphics.Map(grid)
# Assigning wrongly shaped data should, however
self.assertRaises(ValueError, c.set_data, np.zeros((3, 8)))
def test_merca_map():
grid = mercator_grid(center_ll=(11.38, 47.26),
extent=(2000000, 2000000))
m1 = Map(grid)
grid = mercator_grid(center_ll=(11.38, 47.26),
extent=(2000000, 2000000),
order='ul')
m2 = Map(grid)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
m1.visualize(ax=ax1, addcbar=False)
m2.visualize(ax=ax2, addcbar=False)
plt.tight_layout()
return fig
def test_merca_map():
grid = mercator_grid(center_ll=(11.38, 47.26), extent=(2000000, 2000000))
m1 = Map(grid)
m1.set_scale_bar(color='red')
grid = mercator_grid(center_ll=(11.38, 47.26),
extent=(2000000, 2000000),
origin='upper-left')
m2 = Map(grid)
m2.set_scale_bar(length=700000, location=(0.3, 0.05))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
m1.visualize(ax=ax1, addcbar=False)
m2.visualize(ax=ax2, addcbar=False)
plt.tight_layout()
return fig
def test_visible(self):
import matplotlib as mpl
x = [91.176036, 92.05, 88.880927]
y = [29.649702, 31.483333, 29.264956]
g = GoogleVisibleMap(x=x,
y=y,
size_x=400,
size_y=400,
maptype='terrain')
img = g.get_vardata()[..., :3]
i, j = g.grid.transform(x, y, nearest=True)
for _i, _j in zip(i, j):
img[_j - 3:_j + 4, _i - 3:_i + 4, 0] = 1
img[_j - 3:_j + 4, _i - 3:_i + 4, 1:] = 0
# from PIL import Image
# Image.fromarray((img * 255).astype(np.uint8)).save(
# get_demo_file('hef_google_visible.png'))
ref = mpl.image.imread(get_demo_file('hef_google_visible.png'))
rmsd = np.sqrt(np.mean((ref - img)**2))
self.assertTrue(rmsd < 1e-1)
self.assertRaises(ValueError, GoogleVisibleMap, x=x, y=y, zoom=12)
fw = get_demo_file('wrf_tip_d1.nc')
d = GeoNetcdf(fw)
i, j = d.grid.ij_coordinates
g = GoogleVisibleMap(x=i, y=j, crs=d.grid, size_x=500, size_y=500)
img = g.get_vardata()[..., :3]
mask = g.grid.map_gridded_data(i * 0 + 1, d.grid)
img[np.nonzero(mask)] = np.clip(img[np.nonzero(mask)] + 0.3, 0, 1)
# from PIL import Image
# Image.fromarray((img * 255).astype(np.uint8)).save(
# get_demo_file('hef_google_visible_grid.png'))
ref = mpl.image.imread(get_demo_file('hef_google_visible_grid.png'))
rmsd = np.sqrt(np.mean((ref - img)**2))
self.assertTrue(rmsd < 5e-1)
gm = GoogleVisibleMap(x=i, y=j, crs=d.grid, size_x=500, size_y=500)
gm2 = GoogleVisibleMap(x=i,
y=j,
crs=d.grid,
scale=2,
size_x=500,
size_y=500)
assert (gm.grid.nx * 2) == gm2.grid.nx
assert gm.grid.extent == gm2.grid.extent
# Test regression for non array inputs
grid = mercator_grid(center_ll=(72.5, 30.), extent=(2.0e6, 2.0e6))
GoogleVisibleMap(x=[0, grid.nx - 1], y=[0, grid.ny - 1], crs=grid)
def test_merca_nolabels():
grid = mercator_grid(center_ll=(11.38, 47.26), extent=(2000000, 2000000))
m1 = Map(grid)
m1.set_lonlat_contours(add_tick_labels=False)
fig, ax = plt.subplots(1)
m1.visualize(ax=ax)
fig.tight_layout()
return fig
def test_hef_default_spline():
grid = mercator_grid(center_ll=(10.76, 46.798444), extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=0)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
c.set_topography(get_demo_file('hef_srtm.tif'))
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, addcbar=False, title='Default: spline deg 3')
plt.tight_layout()
return fig
def test_hef_default_spline():
grid = mercator_grid(center_ll=(10.76, 46.798444),
extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=0)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
c.set_topography(get_demo_file('hef_srtm.tif'))
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, addcbar=False, title='Default: spline deg 3')
plt.tight_layout()
return fig
def test_merca_nolabels():
grid = mercator_grid(center_ll=(11.38, 47.26),
extent=(2000000, 2000000))
m1 = Map(grid)
m1.set_lonlat_contours(add_tick_labels=False)
fig, ax = plt.subplots(1)
m1.visualize(ax=ax)
fig.tight_layout()
return fig
def test_hef_from_array():
grid = mercator_grid(center_ll=(10.76, 46.798444), extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=0)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
c.set_topography(mytopo, crs=dem.grid, interp='spline')
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, addcbar=False, title='From array')
plt.tight_layout()
return fig
def test_hef_from_array():
grid = mercator_grid(center_ll=(10.76, 46.798444),
extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=0)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
c.set_topography(mytopo, crs=dem.grid, interp='spline')
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, addcbar=False, title='From array')
plt.tight_layout()
return fig
def test_plot_region_inversion():
gdirs = up_to_inversion()
# We prepare for the plot, which needs our own map to proceed.
# Lets do a local mercator grid
g = salem.mercator_grid(center_ll=(10.86, 46.85), extent=(27000, 21000))
# And a map accordingly
sm = salem.Map(g, countries=False)
sm.set_topography(get_demo_file('srtm_oetztal.tif'))
# Give this to the plot function
fig, ax = plt.subplots()
graphics.plot_inversion(gdirs, smap=sm, ax=ax, linewidth=1.5, vmax=250)
fig.tight_layout()
return fig
def test_plot_region_inversion():
gdirs = up_to_inversion()
# We prepare for the plot, which needs our own map to proceed.
# Lets do a local mercator grid
g = salem.mercator_grid(center_ll=(10.86, 46.85),
extent=(27000, 21000))
# And a map accordingly
sm = salem.Map(g, countries=False)
sm.set_topography(get_demo_file('srtm_oetztal.tif'))
# Give this to the plot function
fig, ax = plt.subplots()
graphics.plot_inversion(gdirs, smap=sm, ax=ax, linewidth=1.5, vmax=250)
fig.tight_layout()
return fig
def test_hef_topo_withnan():
grid = mercator_grid(center_ll=(10.76, 46.798444), extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=10)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
h = c.set_topography(mytopo, crs=dem.grid, interp='spline')
c.set_lonlat_contours()
c.set_cmap(get_cmap('topo'))
c.set_plot_params(nlevels=256)
# Try with nan data
h[-100:, -100:] = np.NaN
c.set_data(h)
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, title='color with NaN')
plt.tight_layout()
return fig
def test_hef_topo_withnan():
grid = mercator_grid(center_ll=(10.76, 46.798444),
extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=10)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
h = c.set_topography(mytopo, crs=dem.grid, interp='spline')
c.set_lonlat_contours()
c.set_cmap(get_cmap('topo'))
c.set_plot_params(nlevels=256)
# Try with nan data
h[-100:, -100:] = np.NaN
c.set_data(h)
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, title='color with NaN')
plt.tight_layout()
return fig
def test_plot_region_model():
gdirs = random_for_plot()
dfc = utils.compile_task_log(gdirs,
task_names=['run_random_climate_plot'])
assert np.all(dfc['run_random_climate_plot'] == 'SUCCESS')
# We prepare for the plot, which needs our own map to proceed.
# Lets do a local mercator grid
g = salem.mercator_grid(center_ll=(10.86, 46.85),
extent=(27000, 21000))
# And a map accordingly
sm = salem.Map(g, countries=False)
sm.set_topography(get_demo_file('srtm_oetztal.tif'))
# Give this to the plot function
fig, ax = plt.subplots()
graphics.plot_modeloutput_map(gdirs, smap=sm, ax=ax,
filesuffix='_plot', vmax=250,
modelyr=10, linewidth=1.5)
fig.tight_layout()
return fig
===================
Topographic shading
===================
Add topographic shading to a plot
"""
from salem import mercator_grid, Map, get_demo_file
import matplotlib.pyplot as plt
# prepare the figure
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 7))
# map extent
grid = mercator_grid(center_ll=(10.76, 46.79), extent=(18000, 14000))
sm = Map(grid, countries=False)
sm.set_lonlat_contours(interval=0)
sm.set_scale_bar()
# add topography
fpath = get_demo_file('hef_srtm.tif')
sm.set_topography(fpath)
sm.visualize(ax=ax1, addcbar=False, title='relief_factor=0.7 (default)')
# stronger shading
sm.set_topography(fpath, relief_factor=1.4)
sm.visualize(ax=ax2, addcbar=False, title='relief_factor=1.4')
# add color shading
z = sm.set_topography(fpath)
# -*- coding: utf-8 -*-
"""
==============================
Add a Natural Earth background
==============================
An alternative to Google Static Maps
"""
import salem
import matplotlib.pyplot as plt
# get the map from a predefined grid
grid = salem.mercator_grid(transverse=False,
center_ll=(16., 0.),
extent=(8e6, 9e6))
smap = salem.Map(grid)
# Add the background (other resolutions include: 'mr', 'hr')
smap.set_rgb(natural_earth='lr')
# done!
smap.visualize()
plt.show()
filesuffix='_defaults')
execute_entity_task(tasks.random_glacier_evolution, gdirs,
nyears=500, bias=0, seed=seed, temperature_bias=-0.2,
filesuffix='_tbias')
utils.compile_run_output(gdirs, filesuffix='_defaults')
utils.compile_run_output(gdirs, filesuffix='_tbias')
# ds = xr.open_dataset(os.path.join(base_dir, 'run_output_defaults.nc'))
# (ds.volume.sum(dim='rgi_id') * 1e-9).plot()
# plt.show()
# exit()
# We prepare for the plot, which needs our own map to proceed.
# Lets do a local mercator grid
g = salem.mercator_grid(center_ll=(-19.61, 63.63),
extent=(18000, 14500))
# And a map accordingly
sm = salem.Map(g, countries=False)
sm.set_lonlat_contours(add_xtick_labels=False)
z = sm.set_topography('/home/mowglie/disk/OGGM_INPUT/tmp/ISL.tif')
sm.set_data(z)
# Figs
f = 0.9
f, axs = plt.subplots(2, 1, figsize=(7*f, 10*f))
graphics.plot_domain(gdirs, ax=axs[0], smap=sm)
sm.set_data()
sm.set_lonlat_contours()
print(f.read())
g, maps = geogrid_simulator(fpath)
print(g)
print('ss')
print(maps[0])
maps[0].set_rgb(natural_earth='lr')
maps[0].visualize(title='Anidamiento Qollpana Marzo 2018', addcbar=False)
plt.savefig('QollpanaMarzo2018.png', dpi=300)
plt.show()
plt.close()
#ALBBCK
ds = open_xr_dataset(
"/home/opti3040a/Documentos/WRF15-08-19/wrfout_d01_2018-03-05_00:00:00")
grid = mercator_grid(center_ll=(-60.0, -17.9), extent=(75e5, 75e5))
smap = Map(grid, nx=500)
# VAR_SSO
smap.set_data(ds.LANDMASK)
smap.visualize()
plt.show()
# # get the data at the latest time step
# ds = salem.open_wrf_dataset("/home/opti3040a/Documentos/WRF_1s/WRF_OUT/wrfout_d03_2018-06-01_09:00:00").isel(time=-1)
# print(ds.WS)
# # get the wind data at 10000 m a.s.l.
# u = ds.salem.wrf_zlevel('U', 2800.)
# v = ds.salem.wrf_zlevel('V', 2800.)
# ws = ds.salem.wrf_zlevel('WS', 2800.)
# # get the axes ready
===================
Topographic shading
===================
Add topographic shading to a plot
"""
from salem import mercator_grid, Map, get_demo_file
import matplotlib.pyplot as plt
# prepare the figure
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 7))
# map extent
grid = mercator_grid(center_ll=(10.76, 46.79), extent=(18000, 14000))
sm = Map(grid, countries=False)
sm.set_lonlat_contours(interval=0)
# add topography
fpath = get_demo_file('hef_srtm.tif')
sm.set_topography(fpath)
sm.visualize(ax=ax1, addcbar=False, title='relief_factor=0.7 (default)')
# stronger shading
sm.set_topography(fpath, relief_factor=1.4)
sm.visualize(ax=ax2, addcbar=False, title='relief_factor=1.4')
# add color shading
z = sm.set_topography(fpath)
sm.set_data(z)
