def main():
# Define the simulations to be validated
r_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5",
start_year=1990,
end_year=2010,
label="CRCM5-L1")
r_config_list = [r_config]
r_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-r.hdf5",
start_year=1990,
end_year=2010,
label="CRCM5-NL")
r_config_list.append(r_config)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=r_config.data_path)
bmp_info.should_draw_grey_map_background = True
bmp_info.should_draw_basin_boundaries = False
bmp_info.map_bg_color = "0.75"
station_ids = ["104001", "093806", "093801", "081002", "081007", "080718"]
# get river network information used in the model
flow_directions = analysis.get_array_from_file(
r_config.data_path, var_name=infovar.HDF_FLOW_DIRECTIONS_NAME)
accumulation_area_km2 = analysis.get_array_from_file(
path=r_config.data_path, var_name=infovar.HDF_ACCUMULATION_AREA_NAME)
cell_manager = CellManager(flow_dirs=flow_directions,
lons2d=bmp_info.lons,
lats2d=bmp_info.lats,
accumulation_area_km2=accumulation_area_km2)
# Get the list of stations to indicate on the bias map
stations = cehq_station.read_station_data(start_date=None,
end_date=None,
selected_ids=station_ids)
""":type : list[Station]"""
xx, yy = bmp_info.get_proj_xy()
station_to_modelpoint = cell_manager.get_model_points_for_stations(
station_list=stations)
upstream_edges = cell_manager.get_upstream_polygons_for_points(
model_point_list=station_to_modelpoint.values(), xx=xx, yy=yy)
bmp_info.draw_colorbar_for_each_subplot = True
# Validate temperature, precip and swe
obs_path_anusplin = "/home/huziy/skynet3_rech1/anusplin_links"
obs_path_swe = "data/swe_ross_brown/swe.nc"
model_var_to_obs_path = OrderedDict([("TT", obs_path_anusplin),
("I5", obs_path_swe)])
model_var_to_season = OrderedDict([
("TT", OrderedDict([("Spring", range(3, 6))])),
("I5", OrderedDict([("Winter", [1, 2, 12])]))
])
vname_to_obs_data = {}
# parameters that won't change in the loop over variable names
params_const = dict(rconfig=r_config, bmp_info=bmp_info)
for vname, obs_path in model_var_to_obs_path.items():
season_to_obs_data = get_seasonal_clim_obs_data(
vname=vname,
obs_path=obs_path,
season_to_months=model_var_to_season[vname],
**params_const)
# Comment swe over lakes, since I5 calculated only for land
if vname in [
"I5",
]:
for season in season_to_obs_data:
season_to_obs_data[season] = maskoceans(
bmp_info.lons,
bmp_info.lats,
season_to_obs_data[season],
inlands=True)
vname_to_obs_data[vname] = season_to_obs_data
# Plotting
plot_all_vars_in_one_fig = True
fig = None
gs = None
if plot_all_vars_in_one_fig:
plot_utils.apply_plot_params(font_size=12,
width_pt=None,
width_cm=25,
height_cm=20)
fig = plt.figure()
ncols = len(model_var_to_obs_path) + 1
gs = GridSpec(len(r_config_list),
ncols,
width_ratios=(ncols - 1) * [
1.,
] + [
0.05,
])
else:
plot_utils.apply_plot_params(font_size=12,
width_pt=None,
width_cm=25,
height_cm=25)
station_x_list = []
station_y_list = []
mvarname_to_cs = {}
for row, r_config in enumerate(r_config_list):
for col, mname in enumerate(model_var_to_obs_path):
row_axes = [
fig.add_subplot(gs[row, col]),
]
mvarname_to_cs[mname] = compare_vars(
vname_model=mname,
vname_to_obs=vname_to_obs_data,
r_config=r_config,
season_to_months=model_var_to_season[mname],
bmp_info_agg=bmp_info,
axes_list=row_axes)
# -1 in order to exclude colorbars
for the_ax in row_axes:
the_ax.set_title(the_ax.get_title() + ", {}".format(
infovar.get_long_display_label_for_var(mname)))
# Need titles only for the first row
if row > 0:
the_ax.set_title("")
if col == 0:
the_ax.set_ylabel(r_config.label)
else:
the_ax.set_ylabel("")
draw_upstream_area_bounds(the_ax, upstream_edges, color="g")
if len(station_x_list) == 0:
for the_station in stations:
xst, yst = bmp_info.basemap(the_station.longitude,
the_station.latitude)
station_x_list.append(xst)
station_y_list.append(yst)
bmp_info.basemap.scatter(station_x_list,
station_y_list,
c="g",
ax=the_ax,
s=20,
zorder=10,
alpha=0.5)
# Save the figure if necessary
if plot_all_vars_in_one_fig:
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
fig_path = img_folder.joinpath("{}.png".format(
"_".join(model_var_to_obs_path)))
with fig_path.open("wb") as figfile:
fig.savefig(figfile, format="png", bbox_inches="tight")
plt.close(fig)
def main():
season_to_months = DEFAULT_SEASON_TO_MONTHS
r_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5",
start_year=1990, end_year=2010, label="CRCM5-L"
)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=r_config.data_path)
bmp_info.should_draw_grey_map_background = True
bmp_info.should_draw_basin_boundaries = False
bmp_info.map_bg_color = "0.75"
station_ids = [
"104001", "093806", "093801", "081002", "081007", "080718"
]
# get river network information used in the model
flow_directions = analysis.get_array_from_file(r_config.data_path, var_name=infovar.HDF_FLOW_DIRECTIONS_NAME)
accumulation_area_km2 = analysis.get_array_from_file(path=r_config.data_path,
var_name=infovar.HDF_ACCUMULATION_AREA_NAME)
cell_manager = CellManager(flow_dirs=flow_directions,
lons2d=bmp_info.lons,
lats2d=bmp_info.lats, accumulation_area_km2=accumulation_area_km2)
# Get the list of stations to indicate on the bias map
stations = cehq_station.read_station_data(
start_date=None, end_date=None, selected_ids=station_ids
)
""":type : list[Station]"""
xx, yy = bmp_info.get_proj_xy()
station_to_modelpoint = cell_manager.get_model_points_for_stations(station_list=stations)
upstream_edges = cell_manager.get_upstream_polygons_for_points(
model_point_list=station_to_modelpoint.values(), xx=xx, yy=yy)
# Validate temperature, precip and swe
obs_path_anusplin = "/home/huziy/skynet3_rech1/anusplin_links"
obs_path_swe = "data/swe_ross_brown/swe.nc"
model_var_to_obs_path = OrderedDict([
("TT", obs_path_anusplin),
# ("PR", obs_path_anusplin),
("I5", obs_path_swe)
])
vname_to_obs_data = {}
# parameters that won't change in the loop over variable names
params_const = dict(rconfig=r_config, bmp_info=bmp_info, season_to_months=season_to_months)
for vname, obs_path in model_var_to_obs_path.items():
season_to_obs_data = get_seasonal_clim_obs_data(vname=vname, obs_path=obs_path, **params_const)
# Comment swe over lakes, since I5 calculated only for land
if vname in ["I5", ]:
for season in season_to_obs_data:
season_to_obs_data[season] = maskoceans(bmp_info.lons, bmp_info.lats,
season_to_obs_data[season],
inlands=True)
vname_to_obs_data[vname] = season_to_obs_data
# Plotting
plot_all_vars_in_one_fig = True
fig = None
gs = None
row_axes = []
ncols = None
if plot_all_vars_in_one_fig:
plot_utils.apply_plot_params(font_size=12, width_pt=None, width_cm=25, height_cm=20)
fig = plt.figure()
ncols = len(season_to_months) + 1
gs = GridSpec(len(model_var_to_obs_path), ncols, width_ratios=(ncols - 1) * [1., ] + [0.05, ])
else:
plot_utils.apply_plot_params(font_size=12, width_pt=None, width_cm=25, height_cm=25)
row = 0
station_x_list = []
station_y_list = []
for mname in model_var_to_obs_path:
if plot_all_vars_in_one_fig:
row_axes = [fig.add_subplot(gs[row, col]) for col in range(ncols)]
compare_vars(vname_model=mname, vname_to_obs=vname_to_obs_data,
r_config=r_config,
season_to_months=season_to_months,
bmp_info_agg=bmp_info,
axes_list=row_axes)
# -1 in order to exclude colorbars
for the_ax in row_axes[:-1]:
# Need titles only for the first row
if row > 0:
the_ax.set_title("")
draw_upstream_area_bounds(the_ax, upstream_edges)
if len(station_x_list) == 0:
for the_station in stations:
xst, yst = bmp_info.basemap(the_station.longitude, the_station.latitude)
station_x_list.append(xst)
station_y_list.append(yst)
bmp_info.basemap.scatter(station_x_list, station_y_list, c="g", ax=the_ax, s=5, zorder=10, alpha=0.5)
# Hide fall swe
if mname in ["I5"]:
row_axes[-2].set_visible(False)
row += 1
# Save the figure if necessary
if plot_all_vars_in_one_fig:
fig_path = img_folder.joinpath("{}.png".format("_".join(model_var_to_obs_path)))
with fig_path.open("wb") as figfile:
fig.savefig(figfile, format="png", bbox_inches="tight")
plt.close(fig)
def plot_basin_outlets(shape_file=BASIN_BOUNDARIES_FILE,
bmp_info=None,
directions=None,
accumulation_areas=None,
lake_fraction_field=None):
assert isinstance(bmp_info, BasemapInfo)
driver = ogr.GetDriverByName("ESRI Shapefile")
print(driver)
ds = driver.Open(shape_file, 0)
assert isinstance(ds, ogr.DataSource)
layer = ds.GetLayer()
assert isinstance(layer, ogr.Layer)
print(layer.GetFeatureCount())
latlong_proj = osr.SpatialReference()
latlong_proj.ImportFromEPSG(4326)
utm_proj = layer.GetSpatialRef()
# create Coordinate Transformation
coord_transform = osr.CoordinateTransformation(latlong_proj, utm_proj)
utm_coords = coord_transform.TransformPoints(
list(zip(bmp_info.lons.flatten(), bmp_info.lats.flatten())))
utm_coords = np.asarray(utm_coords)
x_utm = utm_coords[:, 0].reshape(bmp_info.lons.shape)
y_utm = utm_coords[:, 1].reshape(bmp_info.lons.shape)
basin_mask = np.zeros_like(bmp_info.lons)
cell_manager = CellManager(directions,
accumulation_area_km2=accumulation_areas,
lons2d=bmp_info.lons,
lats2d=bmp_info.lats)
index = 1
basins = []
basin_names = []
basin_name_to_mask = {}
for feature in layer:
assert isinstance(feature, ogr.Feature)
# print feature["FID"]
geom = feature.GetGeometryRef()
assert isinstance(geom, ogr.Geometry)
basins.append(ogr.CreateGeometryFromWkb(geom.ExportToWkb()))
basin_names.append(feature["abr"])
accumulation_areas_temp = accumulation_areas.copy()
lons_out, lats_out = [], []
basin_names_out = []
name_to_ij_out = OrderedDict()
min_basin_area = min(b.GetArea() * 1.0e-6 for b in basins)
while len(basins):
fm = np.max(accumulation_areas_temp)
i, j = np.where(fm == accumulation_areas_temp)
i, j = i[0], j[0]
p = ogr.CreateGeometryFromWkt("POINT ({} {})".format(
x_utm[i, j], y_utm[i, j]))
b_selected = None
name_selected = None
for name, b in zip(basin_names, basins):
assert isinstance(b, ogr.Geometry)
assert isinstance(p, ogr.Geometry)
if b.Contains(p.Buffer(2000 * 2**0.5)):
# Check if there is an upstream cell from the same basin
the_mask = cell_manager.get_mask_of_upstream_cells_connected_with_by_indices(
i, j)
# Save the mask of the basin for future use
basin_name_to_mask[name] = the_mask
# if is_part_of_points_in(b, x_utm[the_mask == 1], y_utm[the_mask == 1]):
# continue
b_selected = b
name_selected = name
# basin_names_out.append(name)
lons_out.append(bmp_info.lons[i, j])
lats_out.append(bmp_info.lats[i, j])
name_to_ij_out[name] = (i, j)
basin_mask[the_mask == 1] = index
index += 1
break
if b_selected is not None:
basins.remove(b_selected)
basin_names.remove(name_selected)
outlet_index_in_basin = 1
current_basin_name = name_selected
while current_basin_name in basin_names_out:
current_basin_name = name_selected + str(outlet_index_in_basin)
outlet_index_in_basin += 1
basin_names_out.append(current_basin_name)
print(len(basins), basin_names_out)
accumulation_areas_temp[i, j] = -1
plot_utils.apply_plot_params(font_size=12,
width_pt=None,
width_cm=20,
height_cm=20)
gs = GridSpec(2, 2, width_ratios=[1.0, 0.5], wspace=0.01)
fig = plt.figure()
ax = fig.add_subplot(gs[1, 0])
xx, yy = bmp_info.get_proj_xy()
bmp_info.basemap.drawcoastlines(linewidth=0.5, ax=ax)
bmp_info.basemap.drawrivers(zorder=5, color="0.5", ax=ax)
upstream_edges = cell_manager.get_upstream_polygons_for_points(
model_point_list=[
ModelPoint(ix=i, jy=j) for (i, j) in name_to_ij_out.values()
],
xx=xx,
yy=yy)
upstream_edges_latlon = cell_manager.get_upstream_polygons_for_points(
model_point_list=[
ModelPoint(ix=i, jy=j) for (i, j) in name_to_ij_out.values()
],
xx=bmp_info.lons,
yy=bmp_info.lats)
plot_utils.draw_upstream_area_bounds(ax,
upstream_edges=upstream_edges,
color="r",
linewidth=0.6)
plot_utils.save_to_shape_file(upstream_edges_latlon, in_proj=None)
xs, ys = bmp_info.basemap(lons_out, lats_out)
bmp_info.basemap.scatter(xs, ys, c="0.75", s=30, zorder=10)
bmp_info.basemap.drawparallels(np.arange(-90, 90, 5),
labels=[True, False, False, False],
linewidth=0.5)
bmp_info.basemap.drawmeridians(np.arange(-180, 180, 5),
labels=[False, False, False, True],
linewidth=0.5)
cmap = cm.get_cmap("rainbow", index - 1)
bn = BoundaryNorm(list(range(index + 1)), index - 1)
# basin_mask = np.ma.masked_where(basin_mask < 0.5, basin_mask)
# bmp_info.basemap.pcolormesh(xx, yy, basin_mask, norm=bn, cmap=cmap, ax=ax)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
print(xmin, xmax, ymin, ymax)
dx = xmax - xmin
dy = ymax - ymin
step_y = 0.1
step_x = 0.12
y0_frac = 0.75
y0_frac_bottom = 0.02
x0_frac = 0.35
bname_to_text_coords = {
"RDO": (xmin + x0_frac * dx, ymin + y0_frac_bottom * dy),
"STM": (xmin + (x0_frac + step_x) * dx, ymin + y0_frac_bottom * dy),
"SAG":
(xmin + (x0_frac + 2 * step_x) * dx, ymin + y0_frac_bottom * dy),
"BOM":
(xmin + (x0_frac + 3 * step_x) * dx, ymin + y0_frac_bottom * dy),
"MAN":
(xmin + (x0_frac + 4 * step_x) * dx, ymin + y0_frac_bottom * dy),
"MOI":
(xmin + (x0_frac + 5 * step_x) * dx, ymin + y0_frac_bottom * dy),
"ROM": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac_bottom + step_y) * dy),
"NAT": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac_bottom + 2 * step_y) * dy),
######
"CHU": (xmin + (x0_frac + 5 * step_x) * dx, ymin + y0_frac * dy),
"GEO": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac + step_y) * dy),
"BAL": (xmin + (x0_frac + 5 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"PYR": (xmin + (x0_frac + 4 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"MEL": (xmin + (x0_frac + 3 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"FEU": (xmin + (x0_frac + 2 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
"ARN": (xmin + (x0_frac + 1 * step_x) * dx,
ymin + (y0_frac + 2 * step_y) * dy),
######
"CAN": (xmin + 0.1 * dx, ymin + 0.80 * dy),
"GRB": (xmin + 0.1 * dx, ymin + (0.80 - step_y) * dy),
"LGR": (xmin + 0.1 * dx, ymin + (0.80 - 2 * step_y) * dy),
"RUP": (xmin + 0.1 * dx, ymin + (0.80 - 3 * step_y) * dy),
"WAS": (xmin + 0.1 * dx, ymin + (0.80 - 4 * step_y) * dy),
"BEL": (xmin + 0.1 * dx, ymin + (0.80 - 5 * step_y) * dy),
}
# bmp_info.basemap.readshapefile(".".join(BASIN_BOUNDARIES_FILE.split(".")[:-1]).replace("utm18", "latlon"), "basin",
# linewidth=1.2, ax=ax, zorder=9)
for name, xa, ya, lona, lata in zip(basin_names_out, xs, ys, lons_out,
lats_out):
ax.annotate(name,
xy=(xa, ya),
xytext=bname_to_text_coords[name],
textcoords='data',
ha='right',
va='bottom',
bbox=dict(boxstyle='round,pad=0.4', fc='white'),
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3,rad=0',
linewidth=0.25),
font_properties=FontProperties(size=8),
zorder=20)
print(r"{} & {:.0f} \\".format(
name, accumulation_areas[name_to_ij_out[name]]))
# Plot zonally averaged lake fraction
ax = fig.add_subplot(gs[1, 1])
ydata = range(lake_fraction_field.shape[1])
ax.plot(lake_fraction_field.mean(axis=0) * 100, ydata, lw=2)
ax.fill_betweenx(ydata, lake_fraction_field.mean(axis=0) * 100, alpha=0.5)
ax.set_xlabel("Lake fraction (%)")
ax.set_ylim(min(ydata), max(ydata))
ax.xaxis.set_tick_params(direction='out', width=1)
ax.yaxis.set_tick_params(direction='out', width=1)
ax.xaxis.set_ticks_position("bottom")
ax.yaxis.set_ticks_position("none")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for tl in ax.yaxis.get_ticklabels():
tl.set_visible(False)
# plot elevation, buffer zone, big lakes, grid cells
ax = fig.add_subplot(gs[0, :])
geophy_file = "/RESCUE/skynet3_rech1/huziy/from_guillimin/geophys_Quebec_0.1deg_260x260_with_dd_v6"
r = RPN(geophy_file)
elev = r.get_first_record_for_name("ME")
lkfr = r.get_first_record_for_name("LKFR")
fldr = r.get_first_record_for_name("FLDR")
params = r.get_proj_parameters_for_the_last_read_rec()
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**params)
bsmp = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
resolution="l")
xx, yy = bsmp(lons, lats)
dx = (xx[0, 0] - xx[-1, 0]) / xx.shape[0]
dy = (yy[0, 0] - yy[0, -1]) / yy.shape[1]
xx_ll_crnrs = xx - dx / 2
yy_ll_crnrs = yy - dy / 2
xx_ur_crnrs = xx + dx / 2
yy_ur_crnrs = yy + dy / 2
ll_lon, ll_lat = bsmp(xx_ll_crnrs[0, 0], yy_ll_crnrs[0, 0], inverse=True)
ur_lon, ur_lat = bsmp(xx_ur_crnrs[-1, -1],
yy_ur_crnrs[-1, -1],
inverse=True)
crnr_lons = np.array([[ll_lon, ll_lon], [ur_lon, ur_lon]])
crnr_lats = np.array([[ll_lat, ll_lat], [ur_lat, ur_lat]])
bsmp = rll.get_basemap_object_for_lons_lats(lons2d=crnr_lons,
lats2d=crnr_lats)
# plot elevation
levs = [0, 100, 200, 300, 500, 700, 1000, 1500, 2000, 2800]
norm = BoundaryNorm(levs, len(levs) - 1)
the_cmap = my_colormaps.get_cmap_from_ncl_spec_file(
path="colormap_files/OceanLakeLandSnow.rgb", ncolors=len(levs) - 1)
lons[lons > 180] -= 360
me_to_plot = maskoceans(lons, lats, elev, resolution="l")
im = bsmp.contourf(xx,
yy,
me_to_plot,
cmap=the_cmap,
levels=levs,
norm=norm,
ax=ax)
bsmp.colorbar(im)
bsmp.drawcoastlines(linewidth=0.5, ax=ax)
# show large lake points
gl_lakes = np.ma.masked_where((lkfr < 0.6) | (fldr <= 0) | (fldr > 128),
lkfr)
gl_lakes[~gl_lakes.mask] = 1.0
bsmp.pcolormesh(xx,
yy,
gl_lakes,
cmap=cm.get_cmap("Blues"),
ax=ax,
vmin=0,
vmax=1,
zorder=3)
# show free zone border
margin = 20
x1 = xx_ll_crnrs[margin, margin]
x2 = xx_ur_crnrs[-margin, margin]
y1 = yy_ll_crnrs[margin, margin]
y2 = yy_ur_crnrs[margin, -margin]
pol_corners = ((x1, y1), (x2, y1), (x2, y2), (x1, y2))
ax.add_patch(Polygon(xy=pol_corners, fc="none", ls="solid", lw=3,
zorder=5))
# show blending zone border (with halo zone)
margin = 10
x1 = xx_ll_crnrs[margin, margin]
x2 = xx_ur_crnrs[-margin, margin]
y1 = yy_ll_crnrs[margin, margin]
y2 = yy_ur_crnrs[margin, -margin]
pol_corners = ((x1, y1), (x2, y1), (x2, y2), (x1, y2))
ax.add_patch(
Polygon(xy=pol_corners, fc="none", ls="dashed", lw=3, zorder=5))
# show the grid
step = 20
xx_ll_crnrs_ext = np.zeros([n + 1 for n in xx_ll_crnrs.shape])
yy_ll_crnrs_ext = np.zeros([n + 1 for n in yy_ll_crnrs.shape])
xx_ll_crnrs_ext[:-1, :-1] = xx_ll_crnrs
yy_ll_crnrs_ext[:-1, :-1] = yy_ll_crnrs
xx_ll_crnrs_ext[:-1, -1] = xx_ll_crnrs[:, -1]
yy_ll_crnrs_ext[-1, :-1] = yy_ll_crnrs[-1, :]
xx_ll_crnrs_ext[-1, :] = xx_ur_crnrs[-1, -1]
yy_ll_crnrs_ext[:, -1] = yy_ur_crnrs[-1, -1]
bsmp.pcolormesh(xx_ll_crnrs_ext[::step, ::step],
yy_ll_crnrs_ext[::step, ::step],
np.ma.masked_all_like(xx_ll_crnrs_ext)[::step, ::step],
edgecolors="0.6",
ax=ax,
linewidth=0.05,
zorder=4,
alpha=0.5)
ax.set_title("Elevation (m)")
# plt.show()
fig.savefig("qc_basin_outlets_points.png", bbox_inches="tight")
# plt.show()
plt.close(fig)
return name_to_ij_out, basin_name_to_mask
def plot_station_positions(directions_file_path: Path, stations: list, ax: Axes, grid_config: GridConfig=default_domains.bc_mh_044,
save_upstream_boundaries_to_shp=False):
with Dataset(str(directions_file_path)) as ds:
flow_dirs = ds.variables["flow_direction_value"][:]
flow_acc_area = ds.variables["accumulation_area"][:]
lons_2d, lats_2d = [ds.variables[k][:] for k in ["lon", "lat"]]
basemap, reg_of_interest = grid_config.get_basemap_using_shape_with_polygons_of_interest(lons_2d, lats_2d,
shp_path=default_domains.MH_BASINS_PATH,
resolution="i")
cell_manager = CellManager(flow_dirs, lons2d=lons_2d, lats2d=lats_2d, accumulation_area_km2=flow_acc_area)
station_to_model_point = cell_manager.get_model_points_for_stations(station_list=stations, nneighbours=8)
#####
xx, yy = basemap(lons_2d, lats_2d)
upstream_edges = cell_manager.get_upstream_polygons_for_points(
model_point_list=list(station_to_model_point.values()), xx=xx, yy=yy)
upstream_edges_latlon = cell_manager.get_upstream_polygons_for_points(
model_point_list=list(station_to_model_point.values()), xx=lons_2d, yy=lats_2d)
plot_utils.draw_upstream_area_bounds(ax, upstream_edges=upstream_edges, color="r", linewidth=0.6)
if save_upstream_boundaries_to_shp:
plot_utils.save_to_shape_file(upstream_edges_latlon, folder_path="mh/engage_report/upstream_stations_areas/mh_{}".format(grid_config.dx), in_proj=None)
basemap.drawrivers(linewidth=0.2)
basemap.drawstates(linewidth=0.1)
basemap.drawcountries(linewidth=0.1)
basemap.drawcoastlines(linewidth=0.2)
pos_ids, lons_pos, lats_pos = [], [], []
pos_labels = []
legend_lines = []
for i, (s, mp) in enumerate(sorted(station_to_model_point.items(), key=lambda p: p[0].latitude, reverse=True), start=1):
pos_ids.append(s.id)
pos_labels.append(i)
lons_pos.append(mp.longitude)
lats_pos.append(mp.latitude)
legend_lines.append("{}: {}".format(i, s.id))
xm, ym = basemap(lons_pos, lats_pos)
ax.scatter(xm, ym, c="g", s=20)
for txt, x1, y1, pos_label in zip(pos_ids, xm, ym, pos_labels):
ax.annotate(pos_label, xy=(x1, y1))
at = AnchoredText("\n".join(legend_lines), prop=dict(size=8), frameon=True, loc=1)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax.add_artist(at)
