def main(nc_path_to_directions=""):
ds = Dataset(nc_path_to_directions)
margin = 20
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][:][margin:-margin, margin:-margin]
lkfr = ds.variables["lake_fraction"][:][margin:-margin, margin:-margin]
lkouts = ds.variables["lake_outlet"][:][margin:-margin, margin:-margin]
lkids = calculate_lake_ids(fldr, lkfr, lkouts)
# plotting
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
lons, lats = [ds.variables[key][margin:-margin, margin:-margin] for key in ["lon", "lat"]]
bsmap = gc.get_basemap(lons=lons, lats=lats)
x, y = bsmap(lons, lats)
fig = plt.figure(figsize=(15, 15))
img = bsmap.pcolormesh(x, y, lkids)
bsmap.colorbar(img)
bsmap.pcolormesh(x, y, lkouts, cmap="gray_r")
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]
bsmap.quiver(x, y, u, v,
pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1)
bsmap.drawcoastlines(linewidth=0.5)
bsmap.drawrivers(color="b")
# plt.savefig(nc_path_to_directions[:-3] + "png", bbox_inches="tight")
plt.show()
def plot_acc_area_with_glaciers():
gmask_vname = "VF"
gmask_level = 2
# gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85"
gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"
# stab reading of the glacier mask
# r = RPN(gmask_path)
# gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
# level=gmask_level)
r = RPN("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p")
r.get_first_record_for_name("PR") # Because I almost sure that PR is there
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="i")
# gmask = np.ma.masked_where(gmask < 0.01, gmask)
gmask = np.ma.masked_all(lons.shape)
mask_value = 0.25
gmask[~gmask.mask] = mask_value
path = "/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Java/DDM/directions_WestCaUs_dx0.11deg.nc"
# path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/infocell_West_NA_0.25deg_104x75_GLNM_PRSF_CanHR85_104x75.nc"
ds = Dataset(path)
margin = 20
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][:][margin:-margin, margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
x, y = basemap(lons, lats)
fig = plt.figure(figsize=(15, 15))
dx = (x[-1, -1] - x[0, 0]) / float(x.shape[0])
dy = (y[-1, -1] - y[0, 0]) / float(y.shape[1])
x1 = x - dx / 2.0
y1 = y - dy / 2.0
# im = basemap.pcolormesh(x1, y1, data, norm=LogNorm(vmin=1e3, vmax=1e7), cmap=cm.get_cmap("jet", 12))
# cb = basemap.colorbar(im)
# cb.ax.tick_params(labelsize=25)
cmap = cm.get_cmap("gray_r", 10)
basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]
basemap.quiver(x, y, u, v,
pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1)
basemap.drawcoastlines(linewidth=0.5)
basemap.drawrivers(color="b")
# plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(mask_value)), ], ["Glaciers", ], loc=3)
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
# "upsas",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
# zorder=2, linewidth=0.5, color="b")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
# zorder=2, linewidth=0.5, color="b")
basemap.readshapefile("/skynet3_exec2/aganji/NE_can/bow_river/bow_projected", "basin", color="m", linewidth=2)
# plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions.png", bbox_inches="tight")
plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png", bbox_inches="tight")
plt.show()
def plot_positions_of_station_list(ax, stations, model_points, basemap, cell_manager, fill_upstream_areas=True):
assert isinstance(ax, Axes)
from util import direction_and_value
delta = 1.0 / float(len(model_points))
darkness = 0
cmap = cm.get_cmap(name="jet", lut = len(model_points))
artists = []
labels = []
if None not in stations:
st_to_mp = dict(list(zip(stations, model_points)))
stlist_sorted = list(sorted(stations, key=lambda st: st.drainage_km2, reverse=True))
mplist_sorted = [st_to_mp[s] for s in stlist_sorted]
else:
mp_to_st = dict(list(zip(model_points, stations)))
mplist_sorted = list(sorted(model_points, key=lambda mp: mp.latitude, reverse=True))
stlist_sorted = [mp_to_st[mp] for mp in mplist_sorted]
for the_station, the_model_point in zip(stlist_sorted, mplist_sorted):
assert the_station is None or isinstance(the_station, Station)
assert isinstance(the_model_point, ModelPoint)
assert isinstance(cell_manager, CellManager)
x, y = basemap(cell_manager.lons2d, cell_manager.lats2d)
if the_station is not None:
x_station, y_station = basemap(the_station.longitude, the_station.latitude)
point_info = "{0}".format(the_station.id)
else:
x_station, y_station = basemap(the_model_point.longitude, the_model_point.latitude)
point_info = "({0}, {1})".format(the_model_point.ix, the_model_point.jy)
basemap.scatter(x_station, y_station, c="r", s=20, ax=ax, linewidths=0.5, zorder=2)
# show upstream cells
if fill_upstream_areas:
ups_mask = cell_manager.get_mask_of_upstream_cells_connected_with_by_indices(the_model_point.ix, the_model_point.jy)
x1d_start = x[ups_mask == 1]
y1d_start = y[ups_mask == 1]
fld1d = cell_manager.flow_directions[ups_mask == 1]
i_upstream, j_upstream = np.where(ups_mask == 1)
labels.append(point_info)
ishift, jshift = direction_and_value.flowdir_values_to_shift(fld1d)
print(type(i_upstream[0]), type(ishift[0]))
sub_i_upstream_next = i_upstream + ishift
sub_j_upstream_next = j_upstream + jshift
u = x[sub_i_upstream_next, sub_j_upstream_next] - x1d_start
v = y[sub_i_upstream_next, sub_j_upstream_next] - y1d_start
u2d = np.ma.masked_all_like(x)
v2d = np.ma.masked_all_like(y)
u2d[i_upstream, j_upstream] = u
v2d[i_upstream, j_upstream] = v
# basemap.quiver(x, y, u2d, v2d, angles="xy", scale_units="xy", scale=1, ax=ax)
img = basemap.pcolormesh(x, y, np.ma.masked_where(ups_mask < 0.5, ups_mask) * darkness,
cmap= cmap,
ax=ax, vmax=1, vmin=0)
p = Rectangle((0, 0), 1, 1, fc=cmap(darkness))
artists.append(p)
va = "top"
ha = "left" if the_station.id in ["093806", "081007"] else "right"
yshift = -2 if the_station.id in ["093806", "081007"] else -2
xshift = -2
ax.annotate("{}".format(the_station.id), (x_station, y_station),
ha=ha, va=va, textcoords="offset points", xytext=(xshift, yshift),
font_properties=FontProperties(size=12))
# ax.text(x_station, y_station, point_info, bbox=dict(facecolor="white"))
darkness += delta
# ax.legend(artists, labels, handleheight = 1, ncol = 3, loc=2)
basemap.drawcoastlines(linewidth=cpp.COASTLINE_WIDTH)
basemap.drawrivers(cpp.COASTLINE_WIDTH)
def plot_acc_area_with_glaciers(
gmask_vname: str = "VF",
gmask_level=2,
gmask_path="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85",
route_data_path="/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions_north_america_0.25deg_glaciers.nc",
lons_target=None,
lats_target=None,
basin_shape_files=None):
plot_scatter = False
# stab reading of the glacier mask
with RPN(gmask_path) as r:
gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
level=gmask_level)
# r = RPN("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p")
r.get_first_record_for_name(
"VF") # Because I almost sure that PR is there
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
lons_gmask, lats_gmask = r.get_longitudes_and_latitudes_for_the_last_read_rec(
)
gl_fraction_limit = 0.01
gmask = np.ma.masked_where(gmask < gl_fraction_limit, gmask)
mask_value = 0.25
gmask[~gmask.mask] = mask_value
if str(route_data_path).endswith(".nc"):
print("route_data_path ends with .nc => assuming netcdf format: {}".
format(route_data_path))
with Dataset(route_data_path) as ds:
var_name = "accumulation_area"
data = ds.variables[var_name][:]
# flow directions
fldr = ds.variables["flow_direction_value"][:]
coord_names = ["lon", "lat"] if "lon" in ds.variables else [
"longitudes", "latitudes"
]
lons_route, lats_route = [ds.variables[k] for k in coord_names]
else:
print(
"route_data_path does not end with .nc => assuming rpn format: {}".
format(route_data_path))
with RPN(route_data_path) as r:
data = r.get_first_record_for_name("FACC")
fldr = r.get_first_record_for_name("FLDR")
lons_route, lats_route = r.get_longitudes_and_latitudes_for_the_last_read_rec(
)
# do the spatial interpolation if required
xg, yg, zg = lat_lon.lon_lat_to_cartesian(lons_gmask.flatten(),
lats_gmask.flatten())
xr, yr, zr = lat_lon.lon_lat_to_cartesian(lons_route.flatten(),
lats_route.flatten())
if lons_target is None or lats_target is None:
lons_target, lats_target = lons_route, lats_route
xt, yt, zt = xr, yr, zr
else:
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons_target.flatten(),
lats_target.flatten())
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons_target,
lats2d=lats_target,
resolution="i")
ktree_route = KDTree(list(zip(xr, yr, zr)))
dists_route, inds_route = ktree_route.query(list(zip(xt, yt, zt)))
data = data.flatten()[inds_route].reshape(lons_target.shape)
fldr = fldr.flatten()[inds_route].reshape(lons_target.shape)
ktree_gmask = KDTree(list(zip(xg, yg, zg)))
dists_gmask, inds_gmask = ktree_gmask.query(list(zip(xt, yt, zt)))
gmask = gmask.flatten()[inds_gmask].reshape(lons_target.shape)
data = np.ma.masked_where(data <= 0, data)
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
xx, yy = basemap(lons_target, lats_target)
fig = plt.figure(figsize=(15, 15))
dx = (xx[-1, -1] - xx[0, 0]) / float(xx.shape[0])
dy = (yy[-1, -1] - yy[0, 0]) / float(yy.shape[1])
x1 = xx - dx / 2.0
y1 = yy - dy / 2.0
# Uncomment to plot the accumulation areas
im = basemap.pcolormesh(x1,
y1,
data,
norm=LogNorm(vmin=1e3, vmax=1e7),
cmap=cm.get_cmap("jet", 12))
cb = basemap.colorbar(im)
cmap = cm.get_cmap("gray_r", 10)
basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)
nx, ny = xx.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1),
inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1),
inds_j_next)
u = np.ma.masked_all_like(xx)
v = np.ma.masked_all_like(xx)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = xx[inds_i_next[good], inds_j_next[good]] - xx[inds_i[good],
inds_j[good]]
v[good] = yy[inds_i_next[good], inds_j_next[good]] - yy[inds_i[good],
inds_j[good]]
basemap.quiver(xx,
yy,
u,
v,
pivot="tail",
width=0.0005,
scale_units="xy",
headlength=20,
headwidth=15,
scale=1)
basemap.drawcoastlines(linewidth=0.5, zorder=5)
basemap.drawrivers(color="lightcoral", zorder=5, linewidth=3)
plt.legend([
Rectangle((0, 0), 5, 5, fc=cmap(mask_value)),
], [
r"Glacier ($\geq {}\%$)".format(gl_fraction_limit * 100),
],
loc=3)
watershed_bndry_width = 4
if basin_shape_files is not None:
for i, the_shp in enumerate(basin_shape_files):
basemap.readshapefile(the_shp[:-4],
"basin_{}".format(i),
zorder=2,
color="m",
linewidth=watershed_bndry_width)
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
# zorder=2, linewidth=watershed_bndry_width, color="m")
#
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
# zorder=2, linewidth=watershed_bndry_width, color="m")
#
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
# zorder=2, linewidth=watershed_bndry_width, color="m")
#
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
# zorder=2, linewidth=watershed_bndry_width, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
# "upsas",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
# zorder=2, linewidth=0.5, color="b")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
# zorder=2, linewidth=0.5, color="b")
if plot_scatter:
points_lat = [51.54, 49.2476]
points_lon = [-122.277, -122.784]
point_x, point_y = basemap(points_lon, points_lat)
basemap.scatter(point_x, point_y, c="g", s=20, zorder=3)
plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions_only.png",
bbox_inches="tight",
dpi=300)
# plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png", bbox_inches="tight")
plt.show()
def plot_directions(nc_path_to_directions="", grid_config=gc, margin=20, shape_path_to_focus_polygons=None):
"""
:param margin: margin=20 corresponds to the usual free zone
:param grid_config:
:param nc_path_to_directions:
:param shape_path_to_focus_polygons: if Not None, the path to the polygons
Everything outside the specified polygons is masked
"""
fig = plt.figure(figsize=(15, 15))
ds = Dataset(nc_path_to_directions)
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][margin:-margin, margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
lons, lats = [ds.variables[key][margin:-margin, margin:-margin] for key in ["lon", "lat"]]
reg_of_interest = None
if shape_path_to_focus_polygons is not None:
reg_of_interest = get_mask(lons, lats, shp_path=shape_path_to_focus_polygons) > 0
i_list, j_list = np.where(reg_of_interest)
mask_margin = 5 # margin of the mask in grid points
i_min = min(i_list) - mask_margin
i_max = max(i_list) + mask_margin
j_min = min(j_list) - mask_margin
j_max = max(j_list) + mask_margin
bsmap = grid_config.get_basemap(
lons=lons[i_min : i_max + 1, j_min : j_max + 1], lats=lats[i_min : i_max + 1, j_min : j_max + 1]
)
assert isinstance(bsmap, Basemap)
bsmap.readshapefile(shapefile=shape_path_to_focus_polygons[:-4], name="basin", linewidth=1, color="m")
else:
bsmap = grid_config.get_basemap(lons=lons, lats=lats)
x, y = bsmap(lons, lats)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]
bsmap.fillcontinents(color="0.2", lake_color="aqua")
bsmap.drawmapboundary(fill_color="aqua")
bsmap.quiver(
x, y, u, v, pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1, zorder=5
)
bsmap.drawcoastlines(linewidth=0.5)
bsmap.drawrivers(color="b")
plt.savefig(nc_path_to_directions[:-3] + ".png", bbox_inches="tight")
def get_model_points_for_stations(self, station_list, lake_fraction=None,
drainaige_area_reldiff_limit=None, nneighbours=4):
"""
returns a map {station => modelpoint} for comparison modeled streamflows with observed
:rtype dict
"""
# if drainaige_area_reldiff_limit is None:
# drainaige_area_reldiff_limit = self.DEFAULT_DRAINAGE_AREA_RELDIFF_MIN
# if nneighbours == 1:
# raise Exception("Searching over 1 neighbor is not very secure and not implemented yet")
station_to_model_point = {}
model_acc_area = self.accumulation_area_km2
model_acc_area_1d = model_acc_area.flatten()
grid = np.indices(model_acc_area.shape)
for s in station_list:
x, y, z = lat_lon.lon_lat_to_cartesian(s.longitude, s.latitude)
if s.drainage_km2 is None or nneighbours == 1:
# return the closest grid point
dists, inds = self.kdtree.query((x, y, z), k=1)
ix, jy = [g1.flatten()[inds] for g1 in grid]
imin = 0
dists = [dists]
if s.drainage_km2 is None:
print("Using the closest grid point, since the station does not report its drainage area: {}".format(s))
else:
if s.drainage_km2 < self.characteristic_distance ** 2 * 1e-12:
print("skipping {0}, because drainage area is too small: {1} km**2".format(s.id, s.drainage_km2))
continue
assert isinstance(s, Station)
dists, inds = self.kdtree.query((x, y, z), k=nneighbours)
deltaDaMin = np.min(np.abs(model_acc_area_1d[inds] - s.drainage_km2))
# this returns a list of numpy arrays
imin = np.where(np.abs(model_acc_area_1d[inds] - s.drainage_km2) == deltaDaMin)[0][0]
# deltaDa2D = np.abs(self.accumulation_area_km2 - s.drainage_km2)
# ij = np.where(deltaDa2D == deltaDaMin)
ix, jy = grid[0].flatten()[inds][imin], grid[1].flatten()[inds][imin]
# check if it is not global lake cell (move downstream if it is)
if lake_fraction is not None:
while lake_fraction[ix, jy] >= infovar.GLOBAL_LAKE_FRACTION:
di, dj = direction_and_value.flowdir_values_to_shift(self.flow_directions[ix, jy])
ix, jy = ix + di, jy + dj
# check if the gridcell is not too far from the station
# if dists[imin] > 2 * self.characteristic_distance:
# continue
# check if difference in drainage areas is not too big less than 10 %
if drainaige_area_reldiff_limit is not None and deltaDaMin / s.drainage_km2 > drainaige_area_reldiff_limit:
print("Drainage area relative difference is too high, skipping {}.".format(s.id))
print(deltaDaMin / s.drainage_km2, deltaDaMin, s.drainage_km2)
continue
mp = ModelPoint()
mp.ix = ix
mp.jy = jy
mp.longitude = self.lons2d[ix, jy]
mp.latitude = self.lats2d[ix, jy]
mp.accumulation_area = self.accumulation_area_km2[ix, jy]
try:
mp.distance_to_station = dists[imin]
except TypeError:
mp.distance_to_station = float(dists)
station_to_model_point[s] = mp
print("mp.accumulation_area_km2={}; s.drainage_km2={}".format(mp.accumulation_area, s.drainage_km2))
print("Found model point for the station {0}".format(s))
return station_to_model_point
def plot_flow_directions_and_basin_boundaries(ax, s, sim_name_to_station_to_model_point,
sim_name_to_manager=None):
assert isinstance(ax, Axes)
assert isinstance(s, Station)
assert isinstance(sim_name_to_station_to_model_point, dict)
mp_list = list(sim_name_to_station_to_model_point.items())[0][1][s]
#selecting only one (the first model point)
mp = mp_list[0]
manager = list(sim_name_to_manager.items())[0][1]
assert isinstance(manager, Crcm5ModelDataManager)
flow_in_mask = manager.get_mask_for_cells_upstream(mp.ix, mp.jy)
lons2d, lats2d = manager.lons2D, manager.lats2D
i_upstream, j_upstream = np.where(flow_in_mask == 1)
nx_total, ny_total = lons2d.shape
imin, imax = np.min(i_upstream), np.max(i_upstream)
jmin, jmax = np.min(j_upstream), np.max(j_upstream)
margin = 8
imin = max(0, imin - margin)
jmin = max(0, jmin - margin)
imax = min(nx_total - 1, imax + margin)
jmax = min(ny_total - 1, jmax + margin)
sub_lons2d = lons2d[imin:imax + 1, jmin:jmax + 1]
sub_lats2d = lats2d[imin:imax + 1, jmin:jmax + 1]
sub_flow_directions = manager.flow_directions[imin:imax + 1, jmin:jmax + 1]
sub_flow_in_mask = flow_in_mask[imin:imax + 1, jmin:jmax + 1]
sub_i_upstream, sub_j_upstream = np.where(sub_flow_in_mask == 1)
basemap = Crcm5ModelDataManager.get_rotpole_basemap_using_lons_lats(
lons2d=sub_lons2d, lats2d=sub_lats2d, resolution="h"
)
#plot all stations
#stations = sim_name_to_station_to_model_point.items()[0][1].keys()
x_list = [the_station.longitude for the_station in (s,)]
y_list = [the_station.latitude for the_station in (s,)]
basemap_big = Crcm5ModelDataManager.get_rotpole_basemap_using_lons_lats(
lons2d=lons2d, lats2d=lats2d
)
x_list, y_list = basemap_big(x_list, y_list)
basemap_big.scatter(x_list, y_list, c="r", s=40, linewidths=0, ax=ax)
basemap_big.drawcoastlines(ax=ax)
basemap_big.drawrivers(ax=ax)
ax.annotate(s.id, xy=basemap(s.longitude, s.latitude), xytext=(3, 3), textcoords='offset points',
font_properties=FontProperties(weight="bold"), bbox=dict(facecolor='w', alpha=1),
ha="left", va="bottom", zorder=2)
x_big, y_big = basemap_big(manager.lons2D, manager.lats2D)
####zoom to the area of interest
#axins = zoomed_inset_axes(ax, 3, loc=2)
displayCoords = ax.transData.transform((x_big[imin, jmin], y_big[imin, jmin]))
x_shift_fig, y_shift_fig = ax.figure.transFigure.inverted().transform(displayCoords)
print("After transData", displayCoords)
print("xshift and yshift", x_shift_fig, y_shift_fig)
#ax.annotate("yuyu", xy = ( 0.733264985153, 0.477182994408), xycoords = "figure fraction" )
rect = [0.1, y_shift_fig + 0.1, 0.4, 0.4]
axins = ax.figure.add_axes(rect)
#assert isinstance(axins, Axes)
x, y = basemap(sub_lons2d, sub_lats2d)
x1d_start = x[sub_flow_in_mask == 1]
y1d_start = y[sub_flow_in_mask == 1]
fld1d = sub_flow_directions[sub_flow_in_mask == 1]
from util import direction_and_value
ishift, jshift = direction_and_value.flowdir_values_to_shift(fld1d)
sub_i_upstream_next = sub_i_upstream + ishift
sub_j_upstream_next = sub_j_upstream + jshift
u = x[sub_i_upstream_next, sub_j_upstream_next] - x1d_start
v = y[sub_i_upstream_next, sub_j_upstream_next] - y1d_start
u2d = np.ma.masked_all_like(x)
v2d = np.ma.masked_all_like(y)
u2d[sub_i_upstream, sub_j_upstream] = u
v2d[sub_i_upstream, sub_j_upstream] = v
basemap.quiver(x, y, u2d, v2d, angles="xy", scale_units="xy", scale=1, ax=axins)
x_list = [the_station.longitude for the_station in (s,)]
y_list = [the_station.latitude for the_station in (s,)]
x_list, y_list = basemap(x_list, y_list)
basemap.scatter(x_list, y_list, c="r", s=40, linewidths=0)
basemap.drawcoastlines(ax=axins)
basemap.drawrivers(ax=axins)
#read waterbase file, and plot only the polygons which contain at least one upstream point
shp_path = "/skynet3_exec1/huziy/Netbeans Projects/Java/DDM/data/shape/waterbase/na_bas_ll_r500m/na_bas_ll_r500m.shp"
c = fiona.open(shp_path)
hits = c.filter(bbox=(sub_lons2d[0, 0], sub_lats2d[0, 0], sub_lons2d[-1, -1], sub_lats2d[-1, -1]))
points = [Point(the_x, the_y) for the_x, the_y in zip(x1d_start, y1d_start)]
selected_polygons = []
for feature in hits:
new_coords = []
old_coords = feature["geometry"]["coordinates"]
#transform to the map coordinates
for ring in old_coords:
x1 = [tup[0] for tup in ring]
y1 = [tup[1] for tup in ring]
x2, y2 = basemap(x1, y1)
new_coords.append(list(zip(x2, y2)))
feature["geometry"]["coordinates"] = new_coords
poly = shape(feature["geometry"])
#print poly, type(poly)
#print feature.keys()
#print feature["properties"]
prep_poly = prep.prep(poly)
hits = list(filter(prep_poly.contains, points))
if len(hits) > 2:
selected_polygons.append(feature["geometry"])
for p in selected_polygons:
axins.add_patch(PolygonPatch(p, fc="none", ec="b", lw=1.5))
zoom_lines_color = "#6600FF"
#set color of the frame
for child in axins.get_children():
if isinstance(child, Spine):
child.set_color(zoom_lines_color)
child.set_linewidth(3)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=1, loc2=3, fc="none", ec=zoom_lines_color, lw=3)
#basemap.readshapefile(, "basin")
pass
def _plot_station_position(ax, the_station, basemap, cell_manager, the_model_point):
assert the_station is None or isinstance(the_station, Station)
assert isinstance(the_model_point, ModelPoint)
assert isinstance(cell_manager, CellManager)
assert isinstance(ax, Axes)
x, y = basemap(cell_manager.lons2d, cell_manager.lats2d)
# plot the arrows for upstream cells
ups_mask = cell_manager.get_mask_of_upstream_cells_connected_with_by_indices(the_model_point.ix, the_model_point.jy)
x1d_start = x[ups_mask == 1]
y1d_start = y[ups_mask == 1]
fld1d = cell_manager.flow_directions[ups_mask == 1]
i_upstream, j_upstream = np.where(ups_mask == 1)
imin, jmin = i_upstream.min() - 60, j_upstream.min() - 60
imax, jmax = i_upstream.max() + 40, j_upstream.max() + 40
nx, ny = x.shape
imax, jmax = min(imax, nx - 1), min(jmax, ny - 1)
imin, jmin = max(imin, 0), max(jmin, 0)
basemap_initial_corners = [
basemap.llcrnrx, basemap.llcrnry,
basemap.urcrnrx, basemap.urcrnry
]
basemap.llcrnrx = x[imin, jmin]
basemap.llcrnry = y[imin, jmin]
basemap.urcrnrx = x[imax, jmax]
basemap.urcrnry = y[imax, jmax]
if the_station is not None:
x_station, y_station = basemap(the_station.longitude, the_station.latitude)
else:
x_station, y_station = basemap(the_model_point.longitude, the_model_point.latitude)
basemap.scatter(x_station, y_station, c="b", s=100, ax=ax, linewidths=1, zorder=2)
ax.annotate("{}".format(the_station.id), (x_station, y_station), va="top", ha="right")
from util import direction_and_value
ishift, jshift = direction_and_value.flowdir_values_to_shift(fld1d)
sub_i_upstream_next = i_upstream + ishift
sub_j_upstream_next = j_upstream + jshift
u = x[sub_i_upstream_next, sub_j_upstream_next] - x1d_start
v = y[sub_i_upstream_next, sub_j_upstream_next] - y1d_start
u2d = np.ma.masked_all_like(x)
v2d = np.ma.masked_all_like(y)
u2d[i_upstream, j_upstream] = u
v2d[i_upstream, j_upstream] = v
# basemap.quiver(x, y, u2d, v2d, angles="xy", scale_units="xy", scale=1, ax=ax)
basemap.pcolormesh(x, y, np.ma.masked_where(ups_mask < 0.5, ups_mask) * 0.5, cmap=cm.get_cmap(name="gray"),
ax=ax, vmax=1, vmin=0, zorder=1)
basemap.drawcoastlines(linewidth=cpp.COASTLINE_WIDTH)
# put back the initial corners of the basemap
basemap.llcrnrx, basemap.llcrnry, basemap.urcrnrx, basemap.urcrnry = basemap_initial_corners
return ups_mask
def plot_directions(nc_path_to_directions="", grid_config=gc, margin=20, shape_path_to_focus_polygons=None):
"""
:param margin: margin=20 corresponds to the usual free zone
:param grid_config:
:param nc_path_to_directions:
:param shape_path_to_focus_polygons: if Not None, the path to the polygons
Everything outside the specified polygons is masked
"""
fig = plt.figure(figsize=(15, 15))
ds = Dataset(nc_path_to_directions)
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][margin:-margin, margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
lons, lats = [ds.variables[key][margin:-margin, margin:-margin] for key in ["lon", "lat"]]
reg_of_interest = None
if shape_path_to_focus_polygons is not None:
reg_of_interest = get_mask(lons, lats, shp_path=shape_path_to_focus_polygons) > 0
i_list, j_list = np.where(reg_of_interest)
mask_margin = 5 # margin of the mask in grid points
i_min = min(i_list) - mask_margin
i_max = max(i_list) + mask_margin
j_min = min(j_list) - mask_margin
j_max = max(j_list) + mask_margin
bsmap = grid_config.get_basemap(lons=lons[i_min:i_max + 1, j_min:j_max + 1], lats=lats[i_min:i_max + 1, j_min:j_max + 1])
assert isinstance(bsmap, Basemap)
bsmap.readshapefile(shapefile=shape_path_to_focus_polygons[:-4], name="basin", linewidth=1, color="m")
else:
bsmap = grid_config.get_basemap(lons=lons, lats=lats)
x, y = bsmap(lons, lats)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1), inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1), inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good], inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good], inds_j[good]]
bsmap.fillcontinents(color='0.2', lake_color='aqua')
bsmap.drawmapboundary(fill_color="aqua")
bsmap.quiver(x, y, u, v,
pivot="tail", width=0.0005, scale_units="xy", headlength=20, headwidth=15, scale=1, zorder=5)
bsmap.drawcoastlines(linewidth=0.5)
bsmap.drawrivers(color="b")
plt.savefig(nc_path_to_directions[:-3] + ".png", bbox_inches="tight")
def main(nc_path_to_directions=""):
ds = Dataset(nc_path_to_directions)
margin = 20
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][:][margin:-margin,
margin:-margin]
lkfr = ds.variables["lake_fraction"][:][margin:-margin, margin:-margin]
lkouts = ds.variables["lake_outlet"][:][margin:-margin, margin:-margin]
lkids = calculate_lake_ids(fldr, lkfr, lkouts)
# plotting
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
lons, lats = [
ds.variables[key][margin:-margin, margin:-margin]
for key in ["lon", "lat"]
]
bsmap = gc.get_basemap(lons=lons, lats=lats)
x, y = bsmap(lons, lats)
fig = plt.figure(figsize=(15, 15))
img = bsmap.pcolormesh(x, y, lkids)
bsmap.colorbar(img)
bsmap.pcolormesh(x, y, lkouts, cmap="gray_r")
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1),
inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1),
inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good],
inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good],
inds_j[good]]
bsmap.quiver(x,
y,
u,
v,
pivot="tail",
width=0.0005,
scale_units="xy",
headlength=20,
headwidth=15,
scale=1)
bsmap.drawcoastlines(linewidth=0.5)
bsmap.drawrivers(color="b")
# plt.savefig(nc_path_to_directions[:-3] + "png", bbox_inches="tight")
plt.show()
def plot_acc_area_with_glaciers():
gmask_vname = "VF"
gmask_level = 2
# gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/geophys_West_NA_0.25deg_144x115_GLNM_PRSF_CanHR85"
gmask_path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"
# stab reading of the glacier mask
# r = RPN(gmask_path)
# gmask = r.get_first_record_for_name_and_level(varname=gmask_vname,
# level=gmask_level)
r = RPN(
"/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/pm2013061400_00000000p"
)
r.get_first_record_for_name("PR") # Because I almost sure that PR is there
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
basemap = rll.get_basemap_object_for_lons_lats(lons2d=lons,
lats2d=lats,
resolution="i")
# gmask = np.ma.masked_where(gmask < 0.01, gmask)
gmask = np.ma.masked_all(lons.shape)
mask_value = 0.25
gmask[~gmask.mask] = mask_value
path = "/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Java/DDM/directions_WestCaUs_dx0.11deg.nc"
# path = "/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/infocell_West_NA_0.25deg_104x75_GLNM_PRSF_CanHR85_104x75.nc"
ds = Dataset(path)
margin = 20
var_name = "accumulation_area"
data = ds.variables[var_name][margin:-margin, margin:-margin]
data = np.ma.masked_where(data <= 0, data)
# flow directions
fldr = ds.variables["flow_direction_value"][:][margin:-margin,
margin:-margin]
i_shifts, j_shifts = direction_and_value.flowdir_values_to_shift(fldr)
x, y = basemap(lons, lats)
fig = plt.figure(figsize=(15, 15))
dx = (x[-1, -1] - x[0, 0]) / float(x.shape[0])
dy = (y[-1, -1] - y[0, 0]) / float(y.shape[1])
x1 = x - dx / 2.0
y1 = y - dy / 2.0
# im = basemap.pcolormesh(x1, y1, data, norm=LogNorm(vmin=1e3, vmax=1e7), cmap=cm.get_cmap("jet", 12))
# cb = basemap.colorbar(im)
# cb.ax.tick_params(labelsize=25)
cmap = cm.get_cmap("gray_r", 10)
basemap.pcolormesh(x1, y1, gmask, cmap=cmap, vmin=0., vmax=1.)
nx, ny = x.shape
inds_j, inds_i = np.meshgrid(range(ny), range(nx))
inds_i_next = inds_i + i_shifts
inds_j_next = inds_j + j_shifts
inds_i_next = np.ma.masked_where((inds_i_next == nx) | (inds_i_next == -1),
inds_i_next)
inds_j_next = np.ma.masked_where((inds_j_next == ny) | (inds_j_next == -1),
inds_j_next)
u = np.ma.masked_all_like(x)
v = np.ma.masked_all_like(x)
good = (~inds_i_next.mask) & (~inds_j_next.mask)
u[good] = x[inds_i_next[good], inds_j_next[good]] - x[inds_i[good],
inds_j[good]]
v[good] = y[inds_i_next[good], inds_j_next[good]] - y[inds_i[good],
inds_j[good]]
basemap.quiver(x,
y,
u,
v,
pivot="tail",
width=0.0005,
scale_units="xy",
headlength=20,
headwidth=15,
scale=1)
basemap.drawcoastlines(linewidth=0.5)
basemap.drawrivers(color="b")
# plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(mask_value)), ], ["Glaciers", ], loc=3)
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/athabasca/athabasca_dissolved", "atabaska",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/fraizer/fraizer", "frazier",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/South_sas/South_sas_dissolved", "south_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/north_sas/north_sas", "north_sask",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/watersheds_up_sas/watershed_up_sas_proj",
# "upsas",
# zorder=2, linewidth=3, color="m")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network/network", "rivers",
# zorder=2, linewidth=0.5, color="b")
# basemap.readshapefile("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/lat_lon/network_up_sas/network_up_sout_sas_proj", "network",
# zorder=2, linewidth=0.5, color="b")
basemap.readshapefile(
"/skynet3_exec2/aganji/NE_can/bow_river/bow_projected",
"basin",
color="m",
linewidth=2)
# plt.savefig("/RESCUE/skynet3_rech1/huziy/CNRCWP/C3/directions.png", bbox_inches="tight")
plt.savefig(
"/RESCUE/skynet3_rech1/huziy/CNRCWP/Calgary_flood/directions.png",
bbox_inches="tight")
plt.show()
