def main():
pf_mask_file = "pf_mask_qc0.1deg.220x220.nc"
if not os.path.isfile(pf_mask_file):
path_to_rpn_with_targ_grid = "/RESCUE/skynet3_rech1/huziy/from_guillimin/new_outputs/current_climate_30_yr_sims/quebec_0.1_crcm5-hcd-rl-intfl_ITFS/Samples/quebec_crcm5-hcd-rl-intfl_197901/pm1979010100_00008928p"
save_pf_mask_to_netcdf(pf_mask_file, path_to_rpn_with_target_grid=path_to_rpn_with_targ_grid)
with Dataset(pf_mask_file) as ds:
pf_mask = ds.variables["pf_type"][:]
print(ds.variables.keys())
lay_widths = qc01deg_sim_layer_widths
data_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
start_year = 1990
end_year = 1990
alt_field = do_calculations(data_path=data_path, lay_widths=lay_widths, start_year=start_year, end_year=end_year)
alt_field = np.ma.masked_where((alt_field < 0) | (alt_field > 8), alt_field)
# do the plotting
plot_utils.apply_plot_params()
fig = plt.figure()
bmp_info = analysis.get_basemap_info_from_hdf(file_path=data_path)
# Mask oceans
alt_field = maskoceans(bmp_info.lons, bmp_info.lats, alt_field)
alt_field = np.ma.masked_where((pf_mask > 2) | (pf_mask < 1), alt_field)
alt_field[(pf_mask == 2) & (~alt_field.mask)] = 5.5
xx, yy = bmp_info.get_proj_xy()
img = bmp_info.basemap.contourf(xx, yy, alt_field, extend="both")
bmp_info.basemap.colorbar(img)
bmp_info.basemap.drawcoastlines()
plt.contour(xx, yy, pf_mask, levels=[1, 2, 3], colors="k", linewidth=2)
fig_path = "{}_alt_{}-{}.png".format(data_path[:-5], start_year, end_year)
fig.savefig(fig_path, transparent=True, dpi=common_plot_params.FIG_SAVE_DPI, bbox_inches="tight")
print("Saving the plot to {}".format(fig_path))
plt.show()
def main():
plot_permafrost = False
plot_glaciers = False
if not os.path.isdir(img_folder):
print(os.mkdir(img_folder))
geophy_file = "/BIG1/skynet1_exec1/winger/Floods/Geophys/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"
# Save the permafrost mask to file
pfmask_file = os.path.join(img_folder, "pf_mask_na_0.11deg.nc")
if not os.path.isfile(pfmask_file):
save_pf_mask_to_netcdf(rpn_field_name_with_target_grid="VF", path_to_rpn_with_target_grid=geophy_file, path=pfmask_file)
with Dataset(pfmask_file) as ds:
pf_mask = ds.variables["pf_type"][:]
r = RPN(geophy_file)
lkfr = r.get_record_for_name_and_level(varname="VF", level=3)
glac = r.get_record_for_name_and_level(varname="VF", level=2)
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
proj_params = r.get_proj_parameters_for_the_last_read_rec()
rll = RotatedLatLon(**proj_params)
ill, jll = 0, 250
right_margin = 50
lkfr = lkfr[ill:-right_margin, jll:]
lons = lons[ill:-right_margin, jll:]
lats = lats[ill:-right_margin, jll:]
glac = glac[ill:-right_margin, jll:]
pf_mask = pf_mask[ill:-right_margin, jll:]
bmp = rll.get_basemap_object_for_lons_lats(lons2d=lons, lats2d=lats, resolution="l")
xx, yy = bmp(lons, lats)
plot_utils.apply_plot_params(font_size=8, width_cm=10, height_cm=10)
fig = plt.figure()
# Plot lakes
clevs = np.arange(0, 1.1, 0.1)
lkfr = np.ma.masked_where(lkfr <= 0, lkfr)
cs = bmp.contourf(xx, yy, lkfr, levels=clevs, cmap="Blues")
# bmp.colorbar(cs)
bmp.drawcoastlines(linewidth=0.1)
bmp.drawcountries(linewidth=0.1)
# plot glaciers
glval = 0.65
glac = (glac > 0.001) * glval
glac = np.ma.masked_where(glac < 0.5, glac)
cmap = cm.get_cmap("Greys")
if plot_glaciers:
cs = bmp.pcolormesh(xx, yy, glac, cmap="Greys", alpha=0.7, vmin=0, vmax=1)
#bmp.readshapefile(os.path.join(img_folder, "sasha_glaciers/sasha_glaciers"), "glacier_poly", color=cmap(glval))
plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(glval)), ], ["Glaciers", ], loc=3)
# Plot permafrost boundaries
with Dataset("permafrost_types_arctic_using_contains.nc") as ds:
# pf_mask = ds.variables["pf_type"][:]
# lons_s, lats_s = ds.variables["longitude"][:], ds.variables["latitude"][:]
#
# x, y, z = lat_lon.lon_lat_to_cartesian(lons_s.flatten(), lats_s.flatten())
# ktree = KDTree(list(zip(x, y, z)))
#
# lons_copy = lons.copy()
# lons_copy[lons > 180] -= 360
# tmp = np.zeros_like(lons)
# tmp = maskoceans(lons_copy, lats, tmp, inlands=False)
#
# xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons[~tmp.mask], lats[~tmp.mask])
# dists, inds = ktree.query(list(zip(xt, yt, zt)))
#
# pf_mask1[~tmp.mask] = pf_mask.flatten()[inds]
pf_mask1 = np.ma.masked_where((pf_mask > 2) | (pf_mask < 1), pf_mask)
pf_mask1[~pf_mask1.mask] = 1
# bmp.contourf(xx, yy, pf_mask, levels=[2.5, 3.5, 4.5], cmap=ListedColormap(["yellow", "orange", "red"]), alpha=0.7)
#bmp.contour(xx, yy, pf_mask, levels=[2.5, 3.5, 4.5], colors="k", linewidths=1)
# bmp.drawrivers(color=cm.get_cmap("Blues")(0.8))
ncolors = 2
norm = BoundaryNorm([0.5, 1.5, 2.5], ncolors=ncolors)
cmap = ListedColormap(["c", "violet"])
alpha = 0.7
if plot_permafrost:
bmp.pcolormesh(xx, yy, pf_mask1, norm=norm, cmap=cmap, alpha=alpha)
plt.legend([Rectangle((0, 0), 5, 5, fc=cmap(0), alpha=alpha), ], ["Permafrost",], loc=3)
# tree line
# bmp.readshapefile(os.path.join(img_folder, "Bernardo/treeline_current"), "treeline", linewidth=0.5, color="orange")
# bmp.readshapefile(os.path.join(img_folder, "Bernardo/treeline_future"), "treeline", linewidth=0.5, color="orange")
print(os.path.join(img_folder, "lakes_glaciers_pf_veg.png"))
fig.savefig(os.path.join(img_folder, "lakes_glaciers_pf_veg.png"), dpi=400, bbox_inches="tight", transparent=True)
