def setup_axes(ax, plot_bbox):
ax.coastlines(linewidth=0.4, color='#222222')
for spine in ax.spines.values():
spine.set_edgecolor('#222222')
spine.set_linewidth(0.4)
region = maps.get_countries('/home/liam/Downloads').loc['United States of America'].geometry
maps.features.add_polygons(ax, region, outline=True, zorder=100, linewidth=1, edgecolor='k')
ax.set_extent(plot_bbox)
import matplotlib.pyplot as plt
grids = {
'C60-global':
xr.open_dataset('/extra-space/C60_global/comparison_grid.nc'),
'C180-global':
xr.open_dataset('/extra-space/C180_global/comparison_grid.nc'),
'C180e-US': xr.open_dataset('/extra-space/C180e_US/comparison_grid.nc'),
}
masks = {}
for grid_name, grid in grids.items():
xc = grid['grid_boxes_centers'].isel(XY=0).values
yc = grid['grid_boxes_centers'].isel(XY=1).values
region = maps.get_countries('/home/liam/Downloads').loc[
'United States of America'].geometry.simplify(0.1)
masks[grid_name] = maps.mask_outside(xc, yc, region)
simulations = {
'C60-global':
xr.open_dataset('/extra-space/C60_global/GCHP_NO2_July2018.nc'),
'C180-global':
xr.open_dataset('/extra-space/C180_global/GCHP_NO2_July2018.nc'),
'C180e-US': xr.open_dataset('/extra-space/C180e_US/GCHP_NO2_July2018.nc'),
}
tropomi = {
'C180-global':
xr.open_dataset('/extra-space/C180_global/TROPOMI_NO2_July2018.nc'),
'C180e-US':
xr.open_dataset('/extra-space/C180e_US/TROPOMI_NO2_July2018.nc'),
def compare_grid(grid):
grid_xe = grid['lon_b']
grid_ye = grid['lat_b']
grid_xc = grid['lon']
grid_yc = grid['lat']
resolution = np.ones_like(grid_xc) * np.nan
xx, yy = np.meshgrid(np.linspace(-180, 180, 1000),
np.linspace(-90, 90, 500),
indexing='ij')
pts = []
data = []
region = maps.get_countries(
'/home/liam/Downloads/').loc['United States of America'].geometry
in_region = []
face5 = []
for nf in tqdm(range(6)):
xe = grid_xe[nf, ...]
ye = grid_ye[nf, ...]
n = xe.shape[0]
logical_x = xe[n // 2, n // 2]
logical_y = ye[n // 2, n // 2]
laea = pyproj.Proj(f'+proj=laea +lon_0={logical_x} +lat_0={logical_y}')
ll = laea.to_latlong()
transform = pyproj.Transformer.from_proj(ll, laea,
always_xy=True).transform
xe_laea, ye_laea = transform(xe, ye)
xy_laea = get_minor_xy(xe_laea, ye_laea)
for i in range(n - 1):
for j in range(n - 1):
resolution[nf, i, j] = np.sqrt(
shapely.geometry.Polygon(xy_laea[i, j, ...]).area / 1e6)
if region.contains(
shapely.geometry.Point(grid_xc[nf, i, j],
grid_yc[nf, i, j])):
in_region.append(resolution[nf, i, j])
if nf == 5:
face5.append(resolution[nf, i, j])
pts.extend(
np.moveaxis(
(grid_xc[nf, ...].flatten(), grid_yc[nf, ...].flatten()), 0,
-1))
data.extend(resolution[nf, ...].flatten())
print(
f'region stats:\n-- min: {np.min(in_region)}\n-- max: {np.max(in_region)}\n-- avg: {np.mean(in_region)}\n-- count: {len(in_region)}'
)
print(
f'face5 stats:\n-- min: {np.min(face5)}\n-- max: {np.max(face5)}\n-- avg: {np.mean(face5)}\n-- count: {len(face5)}'
)
# pcm = ax.pcolormesh(xe, ye, resolution, transform=ccrs.PlateCarree(), norm=norm, cmap=cmap)
# ax.contourf(grid_xc[nf,...], grid_yc[nf,...], resolution, levels=[25, 50, 100, 200, 400, 800, 1600],transform=ccrs.PlateCarree(), norm=norm, cmap=cmap)
data = np.array(data)
pts = np.array(pts)
interp = scipy.interpolate.LinearNDInterpolator(pts, data)
xi = np.moveaxis((xx.flatten(), yy.flatten()), 0, -1)
d = interp(xi)
d = d.reshape(xx.shape)
return grid_xe, grid_ye, resolution, grid_xc, grid_yc, xx, yy, d
ds1 = xr.open_dataset(
'/home/liam/Downloads/foobar/C180e-US/GCHP_NO2_July2018.nc')
ds2 = xr.open_dataset(
'/home/liam/Downloads/foobar/C180-global/GCHP_NO2_July2018.nc')
grid = xr.open_dataset('/home/liam/Downloads/scratch/comparison_grid.nc')
project = pyproj.Transformer.from_crs('epsg:4326',
'epsg:2163',
always_xy=True).transform
xc = grid['grid_boxes_centers'].isel(XY=0).values
yc = grid['grid_boxes_centers'].isel(XY=1).values
xc, yc = project(xc, yc)
US = maps.get_countries('/home/liam/Downloads').loc[
'United States of America'].geometry.convex_hull
US = shapely.ops.transform(project, US)
mask = maps.mask_outside(xc, yc, US)
da1 = ds1.GCHP_NO2.values[~mask]
da2 = ds2.GCHP_NO2.values[~mask]
keep = np.isfinite(da1) & np.isfinite(da2)
da1 = da1[keep]
da2 = da2[keep]
def plot_lognorm(dist, **kwargs):
dist = np.log(dist)
np.random.choice(dist.size, 50000, replace=False)
grid_a = xr.open_dataset(
f'/extra-space/sg-stats/Sept/{A_GRID}/grid_box_outlines_and_centers.nc'
)
grid_b = xr.open_dataset(
f'/extra-space/sg-stats/Sept/{B_GRID}/grid_box_outlines_and_centers.nc'
)
stacked_grid_a = grid_a.drop(['xe', 'ye', 'XdimE', 'YdimE'])\
.stack(boxes=['nf', 'Ydim', 'Xdim'])\
.transpose('boxes', 'POLYGON_PTS', 'XY')
stacked_grid_b = grid_b.drop(['xe', 'ye', 'XdimE', 'YdimE']) \
.stack(boxes=['nf', 'Ydim', 'Xdim']) \
.transpose('boxes', 'POLYGON_PTS', 'XY')
US = maps.get_countries('/home/liam/Downloads').loc[
'United States of America'].geometry.buffer(1).simplify(0.5)
masked_stacked_indexes_a = mask(grid_a.grid_boxes_centers, US)
masked_stacked_indexes_b = mask(grid_b.grid_boxes_centers, US)
M = tessellate_a_to_b(
stacked_grid_a.grid_boxes.isel(boxes=masked_stacked_indexes_a),
stacked_grid_b.grid_boxes.isel(boxes=masked_stacked_indexes_b),
)
M = full_tessellation_matrix(
M,
masked_stacked_indexes_a,
masked_stacked_indexes_b,
stacked_grid_a.sizes['boxes'],
stacked_grid_b.sizes['boxes'],
# Open grid def file
grid = xr.open_dataset(args['grid_def'])
import maps
if args['region'] == 'California':
region = maps.get_provinces_and_states(args['shapefiles']).loc['California'].geometry
stats_text_pos = dict(
x=0.5,
y=0.75,
horizontalalignment='left',
verticalalignment='top',
)
road_params = dict(linewidth=0.25, edgecolor=matplotlib.colors.to_rgba('snow', 0.5))
width=1.575
elif args['region'] == 'US':
region = maps.get_countries(args['shapefiles']).loc['United States of America'].geometry
stats_text_pos = dict(
x=0.03,
y=0.05,
horizontalalignment='left',
verticalalignment='bottom',
)
road_params = dict(linewidth=0.25, edgecolor=matplotlib.colors.to_rgba('snow', 0.9), alpha=0.6)
width=3.27
# width=4.72441/2
crs = ccrs.epsg(2163)
plt.figure(figsize=maps.figsize_fitting_polygon(region, crs, width=width), dpi=300)
ax = plt.axes(projection=crs)
maps.set_extent(ax, region)
maps.features.format_page(ax, linewidth_axis_spines=0)