def main():
import application_properties
application_properties.set_current_directory()
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
rea_driven_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"
rea_driven_label = "ERAI-CRCM5-L"
gcm_driven_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
gcm_driven_label = "CanESM2-CRCM5-L"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
params = dict(
data_path=rea_driven_path, start_year=start_year_c, end_year=end_year_c, label=rea_driven_label)
geo_data_file = "/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
rea_driven_config = RunConfig(**params)
params.update(dict(data_path=gcm_driven_path, label=gcm_driven_label))
gcm_driven_config = RunConfig(**params)
r_obj = RPN(geo_data_file)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
lkfr = r_obj.get_first_record_for_name("ML")
bmp_info = analysis.get_basemap_info_from_hdf(file_path=rea_driven_path)
basin_name_to_out_indices_map, basin_name_to_basin_mask = get_basin_to_outlet_indices_map(bmp_info=bmp_info,
accumulation_areas=facc,
directions=fldr,
lake_fraction_field=lkfr)
# select lake rich basins
sel_basins = ["ARN", "PYR", "LGR", "RDO", "SAG", "WAS"]
basin_name_to_out_indices_map = {k: v for k, v in basin_name_to_out_indices_map.items() if k in sel_basins}
rea_driven_daily = analysis.get_daily_climatology_for_rconf(rea_driven_config, var_name=varname, level=0)
gcm_driven_daily = analysis.get_daily_climatology_for_rconf(gcm_driven_config, var_name=varname, level=0)
rea_driven_config.data_daily = rea_driven_daily
gcm_driven_config.data_daily = gcm_driven_daily
plot_comparison_hydrographs(basin_name_to_out_indices_map, rea_config=rea_driven_config, gcm_config=gcm_driven_config)
def main():
import application_properties
application_properties.set_current_directory()
plot_utils.apply_plot_params(font_size=12, width_cm=25, height_cm=25)
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
with_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
with_lakes_label = "CRCM5-L"
no_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
no_lakes_label = "CRCM5-NL"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
nyears_to_future = 75
with_lakes_config_c = RunConfig(data_path=with_lakes_c_path, start_year=start_year_c, end_year=end_year_c,
label=with_lakes_label)
with_lakes_config_f = with_lakes_config_c.get_shifted_config(nyears_to_future)
no_lakes_config_c = RunConfig(data_path=no_lakes_c_path, start_year=start_year_c, end_year=end_year_c,
label=no_lakes_label)
no_lakes_config_f = no_lakes_config_c.get_shifted_config(nyears_to_future)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=with_lakes_c_path)
# Calculate daily climatologic fields
with_lakes_c_daily = analysis.get_daily_climatology_for_rconf(with_lakes_config_c, var_name=varname, level=0)
with_lakes_f_daily = analysis.get_daily_climatology_for_rconf(with_lakes_config_f, var_name=varname, level=0)
no_lakes_c_daily = analysis.get_daily_climatology_for_rconf(no_lakes_config_c, var_name=varname, level=0)
no_lakes_f_daily = analysis.get_daily_climatology_for_rconf(no_lakes_config_f, var_name=varname, level=0)
configs = DataConfig(bmp_info, no_lakes_config_c, no_lakes_config_f, with_lakes_config_c, with_lakes_config_f)
args = (no_lakes_c_daily, no_lakes_f_daily, with_lakes_c_daily, with_lakes_f_daily)
data = Data(*[arg[1] for arg in args])
plot_comparison_std(configs, data, varname=varname)
def main():
import application_properties
application_properties.set_current_directory()
plot_utils.apply_plot_params(font_size=12, width_cm=25, height_cm=25)
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
with_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
with_lakes_label = "CRCM5-L"
no_lakes_c_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
no_lakes_label = "CRCM5-NL"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
nyears_to_future = 75
with_lakes_config_c = RunConfig(data_path=with_lakes_c_path,
start_year=start_year_c,
end_year=end_year_c,
label=with_lakes_label)
with_lakes_config_f = with_lakes_config_c.get_shifted_config(
nyears_to_future)
no_lakes_config_c = RunConfig(data_path=no_lakes_c_path,
start_year=start_year_c,
end_year=end_year_c,
label=no_lakes_label)
no_lakes_config_f = no_lakes_config_c.get_shifted_config(nyears_to_future)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=with_lakes_c_path)
# Calculate daily climatologic fields
with_lakes_c_daily = analysis.get_daily_climatology_for_rconf(
with_lakes_config_c, var_name=varname, level=0)
with_lakes_f_daily = analysis.get_daily_climatology_for_rconf(
with_lakes_config_f, var_name=varname, level=0)
no_lakes_c_daily = analysis.get_daily_climatology_for_rconf(
no_lakes_config_c, var_name=varname, level=0)
no_lakes_f_daily = analysis.get_daily_climatology_for_rconf(
no_lakes_config_f, var_name=varname, level=0)
configs = DataConfig(bmp_info, no_lakes_config_c, no_lakes_config_f,
with_lakes_config_c, with_lakes_config_f)
args = (no_lakes_c_daily, no_lakes_f_daily, with_lakes_c_daily,
with_lakes_f_daily)
data = Data(*[arg[1] for arg in args])
plot_comparison_std(configs, data, varname=varname)
def main():
start_year = 1980
end_year = 2010
season_to_months = OrderedDict([
("Winter", [12, 1, 2]),
("Spring", [3, 4, 5]),
("Summer", [6, 7, 8]),
("Fall", [9, 10, 11]),
])
model_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"
sim_config = RunConfig(data_path=model_path,
start_year=start_year,
end_year=end_year,
label="ERAI-CRCM5-L")
plot_corr_fields_and_std(vname="TT",
season_to_months=season_to_months,
sim_config=sim_config)
plot_corr_fields_and_std(vname="PR",
season_to_months=season_to_months,
sim_config=sim_config)
def main():
vname_model = "I5"
nx_agg = 2
ny_agg = 2
start_year = 1980
end_year = 2006
r_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=start_year, end_year=end_year, label="ERAI-CRCM5-L"
)
bmp_info = analysis.get_basemap_info(r_config=r_config)
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg, nagg_y=ny_agg)
season_to_months = OrderedDict([
("Winter", [12, 1, 2]),
("Spring", [3, 4, 5])
])
# Get the model data
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=r_config,
varname=vname_model, level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
season_to_clim_fields_model_agg[season] = aggregate_array(field, nagg_x=nx_agg, nagg_y=ny_agg)
# Get the EASE data
obs_manager = EaseSweManager()
season_to_clim_fields_obs = obs_manager.get_seasonal_clim_interpolated_to(target_lon2d=bmp_info_agg.lons, target_lat2d=bmp_info_agg.lats,
season_to_months=season_to_months, start_year=start_year, end_year=end_year)
# Do the plotting
plot_utils.apply_plot_params(font_size=10, width_cm=16, height_cm=24)
fig = plt.figure()
xx, yy = bmp_info_agg.get_proj_xy()
gs = GridSpec(3, len(season_to_clim_fields_model_agg) + 1, width_ratios=[1.0, ] * len(season_to_clim_fields_model_agg) + [0.05, ])
clevs = [0, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 500]
norm = BoundaryNorm(clevs, 256)
clevs_diff = np.arange(-100, 110, 10)
cs_val = None
cs_diff = None
col = 0
lons_agg_copy = bmp_info_agg.lons.copy()
lons_agg_copy[lons_agg_copy > 180] -= 360
lons_copy = bmp_info.lons.copy()
lons_copy[lons_copy > 180] -= 360
xx1, yy1 = bmp_info.get_proj_xy()
for season, mod_field in seasonal_clim_fields_model.items():
obs_field = season_to_clim_fields_obs[season]
row = 0
ax = fig.add_subplot(gs[row, col])
ax.set_title(season)
obs_field = maskoceans(lons_agg_copy, bmp_info_agg.lats, obs_field)
cs_val = bmp_info_agg.basemap.contourf(xx, yy, obs_field, levels=clevs, norm=norm, ax=ax, extend="max")
bmp_info_agg.basemap.drawcoastlines(linewidth=0.3, ax=ax)
if col == 0:
ax.set_ylabel("NSIDC")
row += 1
ax = fig.add_subplot(gs[row, col])
mod_field = maskoceans(lons_copy, bmp_info.lats, mod_field)
bmp_info.basemap.contourf(xx1, yy1, mod_field, levels=cs_val.levels, norm=cs_val.norm, ax=ax, extend="max")
bmp_info.basemap.drawcoastlines(linewidth=0.3, ax=ax)
if col == 0:
ax.set_ylabel(r_config.label)
row += 1
ax = fig.add_subplot(gs[row, col])
cs_diff = bmp_info_agg.basemap.contourf(xx, yy, season_to_clim_fields_model_agg[season] - obs_field, levels=clevs_diff, ax=ax, extend="both", cmap="seismic")
bmp_info_agg.basemap.drawcoastlines(linewidth=0.3, ax=ax)
if col == 0:
ax.set_ylabel("{} minus {}".format(r_config.label, "NSIDC"))
col += 1
# Add values colorbar
ax = fig.add_subplot(gs[0, -1])
plt.colorbar(cs_val, cax=ax)
ax.set_title("mm")
# Add differences colorbaar
ax = fig.add_subplot(gs[-1, -1])
plt.colorbar(cs_diff, cax=ax)
ax.set_title("mm")
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "NSIDC_vs_CRCM_swe.png"), dpi=common_plot_params.FIG_SAVE_DPI, bbox_inches="tight")
def main_interflow():
# Changes global plot properties mainly figure size and font size
plot_utils.apply_plot_params(font_size=12, width_cm=20)
# season_to_months = get_default_season_to_months_dict()
season_to_months = OrderedDict([("January", [1, ]), ("February", [2, ]), ("March", [3, ]), ])
var_names = ["TT", "HU", "PR", "AV", "TRAF", "I1", "STFL"]
levels = [0, ] * len(var_names)
plot_utils.apply_plot_params(font_size=10, width_pt=None, width_cm=20 * len(season_to_months) / 4.0,
height_cm=20 * len(var_names) / 5.0)
multipliers = {
"PR": 1.,
"TRAF": 1.,
"I1": infovar.soil_layer_widths_26_to_60[0] * 1000.0,
"TRAF+TDRA": 24 * 60 * 60
}
name_to_units = {
"TRAF": "mm/day", "I1": "mm", "PR": "mm/day", "TRAF+TDRA": "mm/day"
}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-L"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-LI"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
data_path=base_current_path, start_year=start_year_c, end_year=end_year_c, label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(
dict(data_path=modif_current_path, label=modif_label)
)
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([
("Current", OrderedDict([(base_label, base_config_c), (modif_label, modif_config_c)])),
("Future", OrderedDict([(base_label, base_config_f), (modif_label, modif_config_f)]))
])
# Plot the differences
fig = plt.figure()
gs = GridSpec(len(var_names), len(season_to_months) + 1, width_ratios=[1., ] * len(season_to_months) + [0.05, ])
config_dict.fig = fig
config_dict.gs = gs
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
config_dict.name_to_units = name_to_units
# Calculate and plot seasonal means
for vname, level, the_row in zip(var_names, levels, list(range(len(levels)))):
config_dict.the_row = the_row
_plot_row(vname=vname, level=level, config_dict=config_dict)
# Save the image to the file
img_path = get_image_path(base_config_c, base_config_f, modif_config_c, season_to_months=season_to_months)
fig.savefig(img_path, bbox_inches="tight")
def main():
obs_data_path = Path("/RESCUE/skynet3_rech1/huziy/obs_data_for_HLES/interploated_to_the_same_grid/GL_0.1_452x260/anusplin+_interpolated_tt_pr.nc")
start_year = 1980
end_year = 2010
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
vars_of_interest = [
LAKE_ICE_FRACTION,
]
sim_configs = {
HL_LABEL: RunConfig(data_path="/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected",
start_year=start_year, end_year=end_year, label=HL_LABEL),
NEMO_LABEL: RunConfig(data_path="/RECH2/huziy/coupling/coupled-GL-NEMO1h_30min/selected_fields",
start_year=start_year, end_year=end_year, label=NEMO_LABEL),
}
sim_labels = [HL_LABEL, NEMO_LABEL]
vname_to_level = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
}
# Calculations
# prepare params for interpolation
lons_t, lats_t, bsmap = get_target_lons_lats_basemap(sim_configs[HL_LABEL])
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons_t.flatten(), lats_t.flatten())
vname_map = {}
vname_map.update(default_varname_mappings.vname_map_CRCM5)
# Read and calculate observed seasonal means
store_config = {
"base_folder": obs_data_path.parent,
"data_source_type": data_source_types.ALL_VARS_IN_A_FOLDER_IN_NETCDF_FILES_OPEN_EACH_FILE_SEPARATELY,
"varname_mapping": vname_map,
"level_mapping": vname_to_level,
"offset_mapping": default_varname_mappings.vname_to_offset_CRCM5,
"multiplier_mapping": default_varname_mappings.vname_to_multiplier_CRCM5,
}
obs_dm = DataManager(store_config=store_config)
obs_data = {}
interp_indices = None
for vname in vars_of_interest:
# --
end_year_for_current_var = end_year
if vname == SWE:
end_year_for_current_var = min(1996, end_year)
# --
seas_to_year_to_max = obs_dm.get_seasonal_maxima(varname_internal=vname,
start_year=start_year,
end_year=end_year_for_current_var,
season_to_months=season_to_months)
seas_to_clim = {seas: np.array(list(y_to_means.values())).mean(axis=0) for seas, y_to_means in seas_to_year_to_max.items()}
obs_data[vname] = seas_to_clim
if interp_indices is None:
_, interp_indices = obs_dm.get_kdtree().query(list(zip(xt, yt, zt)))
for season in seas_to_clim:
seas_to_clim[season] = seas_to_clim[season].flatten()[interp_indices].reshape(lons_t.shape)
# Read and calculate simulated seasonal mean biases
sim_data = defaultdict(dict)
for label, r_config in sim_configs.items():
store_config = {
"base_folder": r_config.data_path,
"data_source_type": data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"varname_mapping": vname_map,
"level_mapping": vname_to_level,
"offset_mapping": default_varname_mappings.vname_to_offset_CRCM5,
"multiplier_mapping": default_varname_mappings.vname_to_multiplier_CRCM5,
}
dm = DataManager(store_config=store_config)
interp_indices = None
for vname in vars_of_interest:
# --
end_year_for_current_var = end_year
if vname == SWE:
end_year_for_current_var = min(1996, end_year)
# --
seas_to_year_to_max = dm.get_seasonal_maxima(varname_internal=vname,
start_year=start_year,
end_year=end_year_for_current_var,
season_to_months=season_to_months)
# get the climatology
seas_to_clim = {seas: np.array(list(y_to_means.values())).mean(axis=0) for seas, y_to_means in seas_to_year_to_max.items()}
sim_data[label][vname] = seas_to_clim
if interp_indices is None:
_, interp_indices = dm.get_kdtree().query(list(zip(xt, yt, zt)))
for season in seas_to_clim:
seas_to_clim[season] = seas_to_clim[season].flatten()[interp_indices].reshape(lons_t.shape) - obs_data[vname][season]
# Plotting: interpolate to the same grid and plot obs and biases
plot_utils.apply_plot_params(width_cm=32, height_cm=20, font_size=8)
xx, yy = bsmap(lons_t, lats_t)
lons_t[lons_t > 180] -= 360
field_mask = ~maskoceans(lons_t, lats_t, np.zeros_like(lons_t)).mask
for vname in vars_of_interest:
fig = plt.figure()
fig.suptitle(internal_name_to_title[vname] + "\n")
nrows = len(sim_configs) + 2
ncols = len(season_to_months)
gs = GridSpec(nrows=nrows, ncols=ncols)
# Plot the obs fields
current_row = 0
for col, season in enumerate(season_to_months):
field = obs_data[vname][season]
ax = fig.add_subplot(gs[current_row, col])
ax.set_title(season)
to_plot = np.ma.masked_where(field_mask, field) * internal_name_to_multiplier[vname]
clevs = get_clevs(vname)
if clevs is not None:
bnorm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("jet", len(clevs) - 1)
else:
cmap = "jet"
bnorm = None
cs = bsmap.contourf(xx, yy, to_plot, ax=ax, levels=get_clevs(vname), norm=bnorm, cmap=cmap)
bsmap.drawcoastlines()
bsmap.colorbar(cs, ax=ax)
if col == 0:
ax.set_ylabel("Obs")
# plot the biases
for sim_label in sim_labels:
current_row += 1
for col, season in enumerate(season_to_months):
field = sim_data[sim_label][vname][season]
ax = fig.add_subplot(gs[current_row, col])
clevs = get_clevs(vname + "bias")
if clevs is not None:
bnorm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("bwr", len(clevs) - 1)
else:
cmap = "bwr"
bnorm = None
to_plot = np.ma.masked_where(field_mask, field) * internal_name_to_multiplier[vname]
cs = bsmap.contourf(xx, yy, to_plot, ax=ax, extend="both", levels=get_clevs(vname + "bias"), cmap=cmap, norm=bnorm)
bsmap.drawcoastlines()
bsmap.colorbar(cs, ax=ax)
if col == 0:
ax.set_ylabel("{}\n-\nObs.".format(sim_label))
# plot differences between the biases
current_row += 1
for col, season in enumerate(season_to_months):
field = sim_data[NEMO_LABEL][vname][season] - sim_data[HL_LABEL][vname][season]
ax = fig.add_subplot(gs[current_row, col])
clevs = get_clevs(vname + "biasdiff")
if clevs is not None:
bnorm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("bwr", len(clevs) - 1)
else:
cmap = "bwr"
bnorm = None
to_plot = np.ma.masked_where(field_mask, field) * internal_name_to_multiplier[vname]
cs = bsmap.contourf(xx, yy, to_plot, ax=ax, extend="both", levels=get_clevs(vname + "biasdiff"), cmap=cmap, norm=bnorm)
bsmap.drawcoastlines()
bsmap.colorbar(cs, ax=ax)
if col == 0:
ax.set_ylabel("{}\n-\n{}".format(NEMO_LABEL, HL_LABEL))
fig.tight_layout()
# save a figure per variable
img_file = "seasonal_biases_{}_{}_{}-{}.png".format(vname,
"-".join([s for s in season_to_months]),
start_year, end_year)
img_file = img_folder.joinpath(img_file)
fig.savefig(str(img_file))
plt.close(fig)
def main(hdf_folder="/home/huziy/skynet3_rech1/hdf_store", start_year=1980, end_year=2010):
prepare()
all_markers = ["*", "s", "p", "+", "x", "d", "h"]
excluded = ["white", "w", "aliceblue", "azure"]
excluded.extend([ci for ci in colors.cnames if "yellow" in ci])
all_colors = ["k", "b", "r", "g", "m"] + sorted([ci for ci in colors.cnames if ci not in excluded])
# Station ids to get from the CEHQ database
ids_with_lakes_upstream = [
"104001", "093806", "093801", "081002", "081007", "080718"
]
selected_ids = ids_with_lakes_upstream
filedir = Path(hdf_folder)
sim_name_to_file_path = OrderedDict([
# ("CRCM5-LI", filedir.joinpath("quebec_0.1_crcm5-hcd-r.hdf5").as_posix()),
("ERAI-CRCM5-L", filedir.joinpath("quebec_0.1_crcm5-hcd-rl.hdf5").as_posix()),
# ("CanESM2-CRCM5-NL", filedir.joinpath("cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5").as_posix()),
("CanESM2-CRCM5-L",
filedir.joinpath("cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5").as_posix()),
# ("CanESM2-CRCM5-LI", filedir.joinpath("cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5").as_posix()),
])
obs_label = "Obs."
labels = [obs_label, ] + list(sim_name_to_file_path.keys())
label_to_marker = dict(zip(labels, all_markers))
label_to_color = dict(zip(labels, all_colors))
# Get the list of stations to do the comparison with
start_date = datetime(start_year, 1, 1)
end_date = datetime(end_year, 12, 31)
stations = cehq_station.read_station_data(
start_date=start_date, end_date=end_date, selected_ids=selected_ids
)
# Get geophysical fields from one of the model simulations
path0 = list(sim_name_to_file_path.values())[0]
lons2d, lats2d, basemap = analysis.get_basemap_from_hdf(file_path=path0)
flow_directions = analysis.get_array_from_file(path=path0, var_name=infovar.HDF_FLOW_DIRECTIONS_NAME)
lake_fraction = analysis.get_array_from_file(path=path0, var_name=infovar.HDF_LAKE_FRACTION_NAME)
accumulation_area_km2 = analysis.get_array_from_file(path=path0, var_name=infovar.HDF_ACCUMULATION_AREA_NAME)
area_m2 = analysis.get_array_from_file(path=path0, var_name=infovar.HDF_CELL_AREA_NAME_M2)
# Try to read cell areas im meters if it is not Ok then try in km2
if area_m2 is not None:
cell_area_km2 = area_m2 * 1.0e-6
else:
cell_area_km2 = analysis.get_array_from_file(path=path0, var_name=infovar.HDF_CELL_AREA_NAME_KM2)
# Create a cell manager if it is not provided
cell_manager = CellManager(flow_directions, accumulation_area_km2=accumulation_area_km2,
lons2d=lons2d, lats2d=lats2d)
# Get the list of the corresponding model points
station_to_modelpoint = cell_manager.get_model_points_for_stations(
station_list=stations,
lake_fraction=lake_fraction,
drainaige_area_reldiff_limit=0.1)
# plot_utils.apply_plot_params(font_size=10, width_cm=20, height_cm=18)
fig = plt.figure()
ncols = max([len(rp_list) for et, rp_list in ExtremeProperties.extreme_type_to_return_periods.items()])
nrows = len(ExtremeProperties.extreme_types)
gs = GridSpec(nrows, ncols)
ext_type_to_rp_to_ax = OrderedDict()
ax_with_legend = None
label_to_ax_to_xdata = {}
label_to_ax_to_ydata = {}
for row, ext_type in enumerate(ExtremeProperties.extreme_types):
ext_type_to_rp_to_ax[ext_type] = OrderedDict()
for col, rperiod in enumerate(ExtremeProperties.extreme_type_to_return_periods[ext_type]):
ax = fig.add_subplot(gs[row, col])
ext_type_to_rp_to_ax[ext_type][rperiod] = ax
if col == 0:
ax.set_ylabel(ext_type)
if row == nrows - 1 and col == ncols - 1:
ax_with_legend = ax
# Set axes labels
if row == nrows - 1:
ax.set_xlabel("Observations")
if col == 0:
ax.set_ylabel("Model")
for label in sim_name_to_file_path:
if label not in label_to_ax_to_xdata:
label_to_ax_to_xdata[label] = {ax: []}
label_to_ax_to_ydata[label] = {ax: []}
else:
label_to_ax_to_xdata[label][ax] = []
label_to_ax_to_ydata[label][ax] = []
ax.set_xscale("log")
ax.set_yscale("log")
print("Initial list of stations:")
sim_label_to_handle = {}
for s in stations:
print("{0}".format(s))
assert isinstance(s, Station)
print(len([y for y in s.get_list_of_complete_years() if start_year <= y <= end_year]))
df_ext_obs = extreme_commons.get_annual_extrema(ts_times=s.dates, ts_vals=s.values,
start_year=start_year, end_year=end_year)
mp = station_to_modelpoint[s]
assert isinstance(mp, ModelPoint)
years_of_interest = df_ext_obs.index
label_to_extrema_model = {}
# label -> ext_type -> [return period -> ret level, return period -> std]
label_to_return_levels = OrderedDict(
[(obs_label, OrderedDict())]
)
for sim_label, sim_path in sim_name_to_file_path.items():
label_to_return_levels[sim_label] = OrderedDict()
label_to_extrema_model[sim_label] = OrderedDict()
# Calculate the return levels and standard deviations
for ext_type in ExtremeProperties.extreme_types:
return_periods = ExtremeProperties.extreme_type_to_return_periods[ext_type]
# fit GEV distribution and apply non-parametric bootstrap to get std
label_to_return_levels[obs_label][ext_type] = gevfit.do_gevfit_for_a_point(df_ext_obs[ext_type].values,
extreme_type=ext_type,
return_periods=return_periods)
return_levels_obs, rl_stds_obs = label_to_return_levels[obs_label][ext_type]
# get annual extremas for the model output at the points colose to the stations
for sim_label, sim_path in sim_name_to_file_path.items():
label_to_return_levels[sim_label] = OrderedDict()
ext_field = analysis.get_annual_extrema(
rconfig=RunConfig(data_path=sim_path, start_year=start_year, end_year=end_year),
varname="STFL", months_of_interest=ExtremeProperties.extreme_type_to_month_of_interest[ext_type],
n_avg_days=ExtremeProperties.extreme_type_to_n_agv_days[ext_type],
high_flow=ext_type == ExtremeProperties.high)
# Select only those years when obs are available
ts_data = [v for y, v in zip(range(start_year, end_year + 1), ext_field[:, mp.ix, mp.jy]) if
y in years_of_interest]
ts_data = np.array(ts_data)
return_levels, rl_stds = gevfit.do_gevfit_for_a_point(ts_data, extreme_type=ext_type,
return_periods=return_periods)
# Do the plotting
for rp in return_periods:
ax = ext_type_to_rp_to_ax[ext_type][rp]
ax.set_title("T = {rp}-year".format(rp=rp))
# h = ax.errorbar(return_levels_obs[rp], return_levels[rp],
# marker=label_to_marker[sim_label], color=label_to_color[sim_label], label=sim_label,
# xerr=rl_stds_obs[rp] * 1.96, yerr=rl_stds[rp] * 1.96)
h = ax.scatter(return_levels_obs[rp], return_levels[rp],
marker=label_to_marker[sim_label], color=label_to_color[sim_label], label=sim_label)
# save the data for maybe further calculation of the correlation coefficients
label_to_ax_to_xdata[sim_label][ax].append(return_levels_obs[rp])
label_to_ax_to_ydata[sim_label][ax].append(return_levels[rp])
sim_label_to_handle[sim_label] = h
# Calculate the biases
for sim_label in sim_name_to_file_path:
for ext_type in ExtremeProperties.extreme_types:
ret_periods = ExtremeProperties.extreme_type_to_return_periods[ext_type]
for rp in ret_periods:
ax = ext_type_to_rp_to_ax[ext_type][rp]
mod = np.asarray(label_to_ax_to_ydata[sim_label][ax])
obs = np.asarray(label_to_ax_to_xdata[sim_label][ax])
bias = np.mean((mod - obs)/obs)
corr, pv = stats.pearsonr(mod, obs)
print("({sim_label}) Mean bias for {rp}-year {ext_type}-flow return level is: {bias}; corr={corr:.2f}; corr_pval={corr_pval:2g}".format(
sim_label=sim_label, rp=rp, bias=bias, corr=corr, corr_pval=pv,
ext_type=ext_type
))
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
for et, rp_to_ax in ext_type_to_rp_to_ax.items():
for rp, ax in rp_to_ax.items():
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
x1 = min(xmin, ymin)
x2 = min(xmax, ymax)
ax.plot([x1, x2], [x1, x2], "k--")
# ax.xaxis.set_major_locator(MaxNLocator(nbins=5))
# ax.yaxis.set_major_locator(MaxNLocator(nbins=5))
# ax.xaxis.set_major_formatter(sfmt)
# ax.yaxis.set_major_formatter(sfmt)
sim_labels = list(sim_name_to_file_path.keys())
ax_with_legend.legend([sim_label_to_handle[sl] for sl in sim_labels], sim_labels,
bbox_to_anchor=(1, -0.25), borderaxespad=0.0, loc="upper right",
ncol=2, scatterpoints=1, numpoints=1)
# Save the plot
img_file = "{}.eps".format("_".join(sorted(label_to_marker.keys())))
img_file = img_folder.joinpath(img_file)
fig.tight_layout()
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
def main():
start_year = 1980
end_year = 2003
months_of_obs = [12, 1, 2, 3, 4, 5]
r_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=start_year, end_year=end_year, label="ERAI-CRCM5-L"
)
var_name = "LC"
bmp_info = analysis.get_basemap_info(r_config=r_config)
lkid_to_mask = get_lake_masks(bmp_info.lons, bmp_info.lats)
cell_area_m2 = analysis.get_array_from_file(path=r_config.data_path, var_name="cell_area_m2")
# read the model data
lkid_to_ts_model = {}
for lkid, the_mask in lkid_to_mask.items():
lkid_to_ts_model[lkid] = analysis.get_area_mean_timeseries(r_config.data_path, var_name=var_name, the_mask=the_mask * cell_area_m2,
start_year=start_year, end_year=end_year)
df = lkid_to_ts_model[lkid]
# remove the last December
df = df.select(lambda d: not (d.year == end_year and d.month == 12))
# remove the first Jan and Feb
df = df.select(lambda d: not (d.year == start_year and d.month in [1, 2]))
# remove the Feb 29th
df = df.select(lambda d: not (d.month == 2 and d.day == 29))
# select months of interest
df = df.select(lambda d: d.month in months_of_obs)
# calculate the climatology
df = df.groupby(lambda d: datetime(2001 if d.month == 12 else 2002, d.month, d.day)).mean()
df.sort_index(inplace=True)
lkid_to_ts_model[lkid] = df * 100
# read obs data and calculate climatology
lkid_to_ts_obs = {}
for lkid in LAKE_IDS:
lkid_to_ts_obs[lkid] = GL_obs_timeseries.get_ts_from_file(path=os.path.join(OBS_DATA_FOLDER, "{}-30x.TXT".format(lkid)),
start_year=start_year, end_year=end_year - 1)
# get the climatology
dfm = lkid_to_ts_obs[lkid].mean(axis=1)
dfm.index = [datetime(2001, 1, 1) + timedelta(days=int(jd - 1)) for jd in dfm.index]
lkid_to_ts_obs[lkid] = dfm
# plotting
plot_utils.apply_plot_params(font_size=10)
fig = plt.figure()
gs = GridSpec(nrows=len(lkid_to_ts_model), ncols=2)
for row, lkid in enumerate(lkid_to_ts_model):
ax = fig.add_subplot(gs[row, 0])
mod = lkid_to_ts_model[lkid]
obs = lkid_to_ts_obs[lkid]
print(obs.index)
print(obs.values)
ax.plot(mod.index, mod.values, label=r_config.label, color="r", lw=2)
ax.plot(obs.index, obs.values, label="NOAA NIC/CIS", color="k", lw=2)
if row == 0:
ax.legend()
ax.set_title(lkid)
ax.xaxis.set_major_formatter(DateFormatter("%b"))
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "GL_ice-cover-validation.png"), bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
def plot_hydrographs():
plot_utils.apply_plot_params(font_size=14,
width_pt=None,
width_cm=20,
height_cm=20)
start_year = 1980
end_year = 2010
varname = "STFL"
base_config = RunConfig(
start_year=start_year,
end_year=end_year,
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
label="NI")
modif_config = RunConfig(
start_year=start_year,
end_year=end_year,
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5",
label="WI")
r_obj = RPN(GEO_DATA_FILE)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
lons, lats, bmp = analysis.get_basemap_from_hdf(
file_path=base_config.data_path)
basin_name_to_out_indices_map, basin_name_to_basin_mask = get_basin_to_outlet_indices_map(
lons=lons, lats=lats, accumulation_areas=facc, directions=fldr)
# Calculate the daily mean fields
dates, stf_base = analysis.get_daily_climatology_for_rconf(
base_config, var_name=varname, level=0)
_, stf_modif = analysis.get_daily_climatology_for_rconf(modif_config,
var_name=varname,
level=0)
for bname, (i_out, j_out) in basin_name_to_out_indices_map.items():
print(bname, i_out, j_out)
fig = plt.figure()
gs = GridSpec(2, 1, height_ratios=[1, 0.5], hspace=0.1)
ax = fig.add_subplot(gs[0, 0])
ax.plot(dates,
stf_base[:, i_out, j_out],
"b",
lw=2,
label=base_config.label)
ax.plot(dates,
stf_modif[:, i_out, j_out],
"r",
lw=2,
label=modif_config.label)
ax.set_title(bname)
format_axis(ax)
# Hide the tick labels from the x-axis of the upper plot
for tl in ax.xaxis.get_ticklabels():
tl.set_visible(False)
ax = fig.add_subplot(gs[1, 0])
ax.plot(dates,
stf_modif[:, i_out, j_out] - stf_base[:, i_out, j_out],
"k",
lw=2,
label="{}-{}".format(modif_config.label, base_config.label))
format_axis(ax)
fig.savefig(
str(
IMG_FOLDER.joinpath("{}_{}-{}.png".format(
bname, start_year, end_year))))
plt.close(fig)
def main():
start_year_c = 1980
end_year_c = 2010
img_folder = "cc_paper"
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
base_label = "CRCM5-L"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
future_shift_years = 75
params = dict(data_path=current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
data_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
params.update(dict(data_path=data_path, label="CRCM5-LI"))
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
varnames = ["I1+I2", "I0", "PR", "TRAF", "AV", "I0_max", "I0_min"]
levels = [0, 0, 0, 0, 0, 0, 0]
var_labels = ["SM", "ST", "PR", "TRAF", "LHF", "STmin", "STmax"]
# width of the first soil layer in mm
multipliers = [100, 1, 1000 * 24 * 3600, 24 * 3600, 1, 1, 1]
offsets = [
0,
] * len(varnames)
units = ["mm", "K", "mm/day", "mm/day", r"${\rm W/m^2}$", "K", "K"]
SimData = collections.namedtuple("SimData",
"base_c base_f modif_c modif_f")
mg_fields = None
fig = plt.figure()
gs = GridSpec(len(varnames), 5, width_ratios=2 * [
1,
] + [0.05, 1, 0.05])
lats_agg = lats.mean(axis=0)
diff_cmap = cm.get_cmap("RdBu_r", 10)
the_zip = zip(varnames, levels, var_labels, multipliers, offsets, units)
row = 0
for vname, level, var_label, multiplier, offset, unit_label in the_zip:
daily_dates, base_data_c = analysis.get_daily_climatology(
path_to_hdf_file=base_config_c.data_path,
var_name=vname,
level=level,
start_year=base_config_c.start_year,
end_year=base_config_c.end_year)
_, base_data_f = analysis.get_daily_climatology(
path_to_hdf_file=base_config_f.data_path,
var_name=vname,
level=level,
start_year=base_config_f.start_year,
end_year=base_config_f.end_year)
_, modif_data_c = analysis.get_daily_climatology(
path_to_hdf_file=modif_config_c.data_path,
var_name=vname,
level=level,
start_year=modif_config_c.start_year,
end_year=modif_config_c.end_year)
_, modif_data_f = analysis.get_daily_climatology(
path_to_hdf_file=modif_config_f.data_path,
var_name=vname,
level=level,
start_year=modif_config_f.start_year,
end_year=modif_config_f.end_year)
if mg_fields is None:
mg_fields = np.asarray([mg_field for d in daily_dates])
num_dates = date2num(daily_dates)
# create 2d dates and latitudes for the contour plots
lats_agg_2d, num_dates_2d = np.meshgrid(lats_agg, num_dates)
sim_data = SimData(_avg_along_lon(base_data_c, mg_fields),
_avg_along_lon(base_data_f, mg_fields),
_avg_along_lon(modif_data_c, mg_fields),
_avg_along_lon(modif_data_f, mg_fields))
# Unit conversion
sim_data = SimData(*[multiplier * si + offset for si in sim_data])
# Plot the row for the variable
all_axes = []
# Calculate nice color levels
delta = np.percentile(
np.abs([
sim_data.modif_c - sim_data.base_c,
sim_data.modif_f - sim_data.base_f
]), 99)
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=10, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
# Current
ax = fig.add_subplot(gs[row, 0])
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
sim_data.modif_c - sim_data.base_c,
extend="both",
levels=clevs,
cmap=diff_cmap)
if row == 0:
ax.set_title("Current ({}-{})".format(base_config_c.start_year,
base_config_c.end_year))
all_axes.append(ax)
# Future
ax = fig.add_subplot(gs[row, 1])
if row == 0:
ax.set_title("Future ({}-{})".format(base_config_f.start_year,
base_config_f.end_year))
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
sim_data.modif_f - sim_data.base_f,
levels=cs.levels,
extend="both",
cmap=diff_cmap)
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, 2])
plt.colorbar(cs, cax=cax)
cax.set_title("{} ({})\n".format(var_label, unit_label))
diff = (sim_data.modif_f - sim_data.base_f) - (sim_data.modif_c -
sim_data.base_c)
delta = np.percentile(np.abs(diff), 99)
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=10, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
ax = fig.add_subplot(gs[row, 3])
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
diff,
cmap=diff_cmap,
levels=clevs,
extend="both")
all_axes.append(ax)
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[row, 4]))
if row == 0:
ax.set_title("Future - Current")
cb.ax.set_title("{}\n".format(unit_label))
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_major_formatter(DateFormatter("%b"))
the_ax.xaxis.set_major_locator(MonthLocator(interval=2))
the_ax.grid()
if i == 0:
the_ax.set_ylabel("Latitude")
row += 1
fig.tight_layout()
img_path = Path(img_folder).joinpath(
"{}_long_avg_intf_impact_{}-{}_vs_{}-{}.png".format(
"_".join(varnames), base_config_f.start_year,
base_config_f.end_year, base_config_c.start_year,
base_config_c.end_year))
fig.savefig(str(img_path), bbox_inches="tight")
def main():
start_year_c = 1980
end_year_c = 2010
img_folder = "cc_paper"
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
base_label = "CRCM5-LI"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
future_shift_years = 90
params = dict(data_path=current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
varname = "INTF"
level = 0
daily_dates, intf_c = analysis.get_daily_climatology(
path_to_hdf_file=base_config_c.data_path,
var_name=varname,
level=level,
start_year=base_config_c.start_year,
end_year=base_config_c.end_year)
_, intf_f = analysis.get_daily_climatology(
path_to_hdf_file=base_config_f.data_path,
var_name=varname,
level=level,
start_year=base_config_f.start_year,
end_year=base_config_f.end_year)
mg_fields = np.asarray([mg_field for d in daily_dates])
mg_crit = 0.0001
the_mask = mg_fields <= mg_crit
# Convert to mm/day as well
intf_c = _avg_along_lon(intf_c, the_mask) * 24 * 3600
intf_f = _avg_along_lon(intf_f, the_mask) * 24 * 3600
lats_agg = lats.mean(axis=0)
num_dates = date2num(daily_dates)
lats_agg_2d, num_dates_2d = np.meshgrid(lats_agg, num_dates)
# Do the plotting
fig = plt.figure()
gs = GridSpec(2, 3, width_ratios=[1, 1, 0.05])
norm = SymLogNorm(5e-5)
all_axes = []
# Current
ax = fig.add_subplot(gs[0, 0])
cs = ax.contourf(num_dates_2d, lats_agg_2d, intf_c[:], 60, norm=norm)
ax.set_title("Current ({}-{})".format(base_config_c.start_year,
base_config_c.end_year))
all_axes.append(ax)
# Future
ax = fig.add_subplot(gs[0, 1])
ax.set_title("Future ({}-{})".format(base_config_f.start_year,
base_config_f.end_year))
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
intf_f[:],
levels=cs.levels,
norm=norm)
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[0, 2])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-1, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("mm/day")
cax.yaxis.get_offset_text().set_position((-2, 10))
# CC
diff_cmap = cm.get_cmap("RdBu_r", 20)
diff = (intf_f - intf_c) / (0.5 * (intf_c + intf_f)) * 100
diff[(intf_f == 0) & (intf_c == 0)] = 0
print(np.min(diff), np.max(diff))
print(np.any(diff.mask))
print(np.any(intf_c.mask))
print(np.any(intf_f.mask))
delta = 200
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=20, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
ax = fig.add_subplot(gs[1, 1])
cs = ax.contourf(num_dates_2d,
lats_agg_2d,
diff,
cmap=diff_cmap,
levels=clevs,
extend="both")
ax.set_title("Future - Current")
# ax.set_aspect("auto")
all_axes.append(ax)
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[1, -1]))
cb.ax.set_xlabel(r"%")
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_minor_formatter(
FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_formatter(FuncFormatter(lambda d, pos: ""))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_major_locator(MonthLocator())
the_ax.grid()
if i != 1:
the_ax.set_ylabel(r"Latitude ${\rm \left(^\circ N \right)}$")
# identify approximately the melting period and lower latitudes
march1 = date2num(datetime(daily_dates[0].year, 3, 1))
june1 = date2num(datetime(daily_dates[0].year, 6, 1))
sel_mask = (num_dates_2d >= march1) & (num_dates_2d < june1) & (lats_agg_2d
<= 50)
print("Mean interflow decrease in the southern regions: {}%".format(
diff[sel_mask].mean()))
# identify the regions of max interflow rates in current and future climates
lat_min = 55
lat_max = 57.5
may1 = date2num(datetime(daily_dates[0].year, 5, 1))
july1 = date2num(datetime(daily_dates[0].year, 7, 1))
mean_max_current = intf_c[(lats_agg_2d >= lat_min)
& (lats_agg_2d <= lat_max) &
(num_dates_2d <= july1) &
(num_dates_2d >= june1)].mean()
mean_max_future = intf_f[(lats_agg_2d >= lat_min)
& (lats_agg_2d <= lat_max) &
(num_dates_2d <= june1) &
(num_dates_2d >= may1)].mean()
print("Mean change in the maximum interflow rate: {} %".format(
(mean_max_future - mean_max_current) * 100 / mean_max_current))
img_file = Path(img_folder).joinpath("INTF_rate_longit_avg.png")
fig.tight_layout()
from crcm5.analyse_hdf import common_plot_params
fig.savefig(str(img_file),
bbox_inches="tight",
transparent=True,
dpi=common_plot_params.FIG_SAVE_DPI)
def main():
season_to_months = get_default_season_to_months_dict()
# var_names = ["TT", "HU", "PR", "AV", "STFL"]
# var_names = ["TRAF", "STFL", "TRAF+TDRA"]
# var_names = ["TT", "PR", "STFL"]
# var_names = ["TT", "PR", "I5", "STFL", "AV", "AH"]
var_names = ["TT", ]
levels = [0, ] * len(var_names)
multipliers = {
"PR": 1.0,
"TRAF+TDRA": 24 * 60 * 60
}
name_to_units = {
"TRAF": "mm/day", "I1": "mm", "PR": "mm/day", "TRAF+TDRA": "mm/day",
"I5": "mm", "AV": r"${\rm W/m^2}$"
}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
plot_cc_only_for = modif_label
# plot_cc_only_for = None
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
data_path=base_current_path, start_year=start_year_c, end_year=end_year_c, label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(
dict(data_path=modif_current_path, label=modif_label)
)
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([
("Current", OrderedDict([(base_label, base_config_c), (modif_label, modif_config_c)])),
("Future", OrderedDict([(base_label, base_config_f), (modif_label, modif_config_f)]))
])
# Changes global plot properties mainly figure size and font size
plot_utils.apply_plot_params(font_size=12, width_cm=25, height_cm=10)
# Plot the differences
fig = plt.figure()
gs = GridSpec(len(var_names), len(season_to_months) + 1, width_ratios=[1., ] * len(season_to_months) + [0.05, ])
config_dict.fig = fig
config_dict.gs = gs
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
config_dict.name_to_units = name_to_units
# Calculate and plot seasonal means
for vname, level, the_row in zip(var_names, levels, list(range(len(levels)))):
config_dict.the_row = the_row
_plot_row(vname=vname, level=level, config_dict=config_dict, plot_cc_only_for=plot_cc_only_for, mark_significance=False)
# Save the image to the file
if plot_cc_only_for is None:
img_path = get_image_path(base_config_c, base_config_f, modif_config_c, season_to_months=season_to_months)
else:
config_c = base_config_c if base_config_c.label == plot_cc_only_for else modif_config_c
config_f = base_config_f if base_config_f.label == plot_cc_only_for else modif_config_f
img_path = get_image_path(config_c, config_f, config_c, season_to_months=season_to_months)
fig.savefig(img_path, bbox_inches="tight", transparent=True, dpi=common_plot_params.FIG_SAVE_DPI)
print("saving the plot to: {}".format(img_path))
plt.close(fig)
def main():
# import seaborn as sns
# sns.set_context("paper", font_scale=2)
# sns.set_style("whitegrid")
level_widths_mm = MM_PER_METER * infovar.soil_layer_widths_26_to_60
avg_axis = "lon"
start_year_c = 1990
end_year_c = 2010
img_folder = Path("impact_of_interflow")
if not img_folder.exists():
img_folder.mkdir(parents=True)
# Configuration without interflow, to be compared with the one with intf.
base_config = RunConfig(
start_year=start_year_c,
end_year=end_year_c,
data_path=
"/home/huziy/skynet3_rech1/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
label="NI")
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
depth_to_bedrock_mm = r_obj.get_first_record_for_name("8L") * MM_PER_METER
# recompute layer widths to account for the depth to bedrock
layer_widths_3d = np.ones(depth_to_bedrock_mm.shape +
level_widths_mm.shape)
layer_widths_3d *= level_widths_mm[np.newaxis, np.newaxis, :]
layer_bottoms_3d = layer_widths_3d.cumsum(axis=2)
corrections = depth_to_bedrock_mm[:, :, np.newaxis] - layer_bottoms_3d
layer_widths_3d[corrections < 0] += corrections[corrections < 0]
layer_widths_3d[layer_widths_3d < 0] = 0
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
modif_config = RunConfig(start_year=start_year_c,
end_year=end_year_c,
data_path=current_path,
label="WI")
varname = "INTF"
level = 0
daily_dates, intf_c = analysis.get_daily_climatology_for_rconf(
modif_config, var_name=varname, level=level)
# Convert to mm/day as well
intf_c = _avg_along(intf_c, axis=avg_axis,
land_fraction=mg_field) * 24 * 3600
zagg = None
ztitle = ""
if avg_axis == "lon":
zagg = lats.mean(axis=0)
ztitle = "Latitude"
elif avg_axis == "lat":
zagg = lons.mean(axis=1)
ztitle = "Longitude"
num_dates = date2num(daily_dates)
z_agg_2d, num_dates_2d = np.meshgrid(zagg, num_dates)
# Do the plotting
plot_utils.apply_plot_params(font_size=14,
width_pt=None,
width_cm=20,
height_cm=10)
fig = plt.figure()
gs = GridSpec(1, 2, width_ratios=[1, 0.05])
all_axes = []
# ----------------------------------Interflow----------------------------------
row = 0
ax = fig.add_subplot(gs[row, 0])
cs = ax.contourf(num_dates_2d,
z_agg_2d,
intf_c[:],
60,
cmap="jet",
norm=SymLogNorm(5 * 1.0e-7))
ax.set_title("Interflow rate")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("mm/day")
cax.yaxis.get_offset_text().set_position((-0.5, 10))
# ----------------------------------Soil moisture----------------------------------
# layer_index = 0
# _, sm_mod = analysis.get_daily_climatology_for_rconf(modif_config, var_name="I1", level=layer_index)
# _, sm_base = analysis.get_daily_climatology_for_rconf(base_config, var_name="I1", level=layer_index)
#
# sm_mod = _avg_along(sm_mod, axis=avg_axis, land_fraction=mg_field, layer_depths=layer_widths_3d[:, :, layer_index])
# sm_base = _avg_along(sm_base, axis=avg_axis, land_fraction=mg_field, layer_depths=layer_widths_3d[:, :, layer_index])
#
# row += 1
# ax = fig.add_subplot(gs[row, 0])
# cs = ax.contourf(num_dates_2d, z_agg_2d, sm_mod - sm_base, cmap="jet")
# ax.set_title("Soil moisture (liq., level={})".format(layer_index + 1))
# all_axes.append(ax)
#
# # Colorbar for value plots
# cax = fig.add_subplot(gs[row, -1])
#
# sfmt = ScalarFormatter(useMathText=True)
# sfmt.set_powerlimits((-2, 2))
#
# plt.colorbar(cs, cax=cax, format=sfmt)
# cax.set_xlabel("mm")
# cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Soil moisture (level=2)----------------------------------
# layer_index = 1
# _, sm_mod = analysis.get_daily_climatology_for_rconf(modif_config, var_name="I1", level=layer_index)
# _, sm_base = analysis.get_daily_climatology_for_rconf(base_config, var_name="I1", level=layer_index)
#
# sm_mod = _avg_along(sm_mod, axis=avg_axis, land_fraction=mg_field, layer_depths=layer_widths_3d[:, :, layer_index])
# sm_base = _avg_along(sm_base, axis=avg_axis, land_fraction=mg_field, layer_depths=layer_widths_3d[:, :, layer_index])
#
# row += 1
# ax = fig.add_subplot(gs[row, 0])
# cs = ax.contourf(num_dates_2d, z_agg_2d, sm_mod - sm_base, cmap="jet")
# ax.set_title("Soil moisture (liq., level={})".format(layer_index + 1))
# all_axes.append(ax)
#
# # Colorbar for value plots
# cax = fig.add_subplot(gs[row, -1])
#
# sfmt = ScalarFormatter(useMathText=True)
# sfmt.set_powerlimits((-2, 2))
#
# plt.colorbar(cs, cax=cax, format=sfmt)
# cax.set_xlabel("mm")
# cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Soil moisture (level=3)----------------------------------
# layer_index = 2
# _, sm_mod = analysis.get_daily_climatology_for_rconf(modif_config, var_name="I1", level=layer_index)
# _, sm_base = analysis.get_daily_climatology_for_rconf(base_config, var_name="I1", level=layer_index)
#
# sm_mod = _avg_along(sm_mod, axis=avg_axis, land_fraction=mg_field, layer_depths=layer_widths_3d[:, :, layer_index])
# sm_base = _avg_along(sm_base, axis=avg_axis, land_fraction=mg_field, layer_depths=layer_widths_3d[:, :, layer_index])
#
# row += 1
# ax = fig.add_subplot(gs[row, 0])
# cs = ax.contourf(num_dates_2d, z_agg_2d, sm_mod - sm_base, cmap="jet")
# ax.set_title("Soil moisture (liq., level={})".format(layer_index + 1))
# all_axes.append(ax)
#
# # Colorbar for value plots
# cax = fig.add_subplot(gs[row, -1])
#
# sfmt = ScalarFormatter(useMathText=True)
# sfmt.set_powerlimits((-2, 2))
#
# plt.colorbar(cs, cax=cax, format=sfmt)
# cax.set_xlabel("mm")
# cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Evaporation----------------------------------
# _, av_mod = analysis.get_daily_climatology_for_rconf(modif_config, var_name="AV", level=0)
# _, av_base = analysis.get_daily_climatology_for_rconf(base_config, var_name="AV", level=0)
#
# av_mod = _avg_along(av_mod, land_fraction=mg_field, axis=avg_axis)
# av_base = _avg_along(av_base, land_fraction=mg_field, axis=avg_axis)
#
# row += 1
# ax = fig.add_subplot(gs[row, 0])
# cs = ax.contourf(num_dates_2d, z_agg_2d, av_mod - av_base, cmap="jet")
# ax.set_title("Evaporation (level=1)")
# all_axes.append(ax)
#
# # Colorbar for value plots
# cax = fig.add_subplot(gs[row, -1])
#
# sfmt = ScalarFormatter(useMathText=True)
# sfmt.set_powerlimits((-2, 2))
#
# plt.colorbar(cs, cax=cax, format=sfmt)
# cax.set_xlabel("W/m**2")
# cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Drainage----------------------------------
# _, dr_mod = analysis.get_daily_climatology_for_rconf(modif_config, var_name="TDRA", level=0)
# _, dr_base = analysis.get_daily_climatology_for_rconf(base_config, var_name="TDRA", level=0)
#
# dr_mod = _avg_along(dr_mod, the_mask, axis=avg_axis)
# dr_base = _avg_along(dr_base, the_mask, axis=avg_axis)
#
# row += 1
# ax = fig.add_subplot(gs[row, 0])
# cs = ax.contourf(num_dates_2d, z_agg_2d, (dr_mod - dr_base) * units.SECONDS_PER_DAY, cmap="jet")
# ax.set_title("Drainage (level=1)")
# all_axes.append(ax)
#
# # Colorbar for value plots
# cax = fig.add_subplot(gs[row, -1])
#
# sfmt = ScalarFormatter(useMathText=True)
# sfmt.set_powerlimits((-2, 2))
#
# plt.colorbar(cs, cax=cax, format=sfmt)
# cax.set_xlabel("mm/day")
# cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Soil temperature (level_index=0)----------------------------------
# layer_index = 0
# _, t_mod = analysis.get_daily_climatology_for_rconf(modif_config, var_name="I0", level=layer_index)
# _, t_base = analysis.get_daily_climatology_for_rconf(base_config, var_name="I0", level=layer_index)
#
# t_mod = _avg_along(t_mod, land_fraction=mg_field, axis=avg_axis)
# t_base = _avg_along(t_base, land_fraction=mg_field, axis=avg_axis)
#
# row += 1
# ax = fig.add_subplot(gs[row, 0])
# cs = ax.contourf(num_dates_2d, z_agg_2d, t_mod - t_base, cmap="jet")
# ax.set_title("Soil temperature (level={})".format(layer_index + 1))
# all_axes.append(ax)
#
# # Colorbar for value plots
# cax = fig.add_subplot(gs[row, -1])
#
# sfmt = ScalarFormatter(useMathText=True)
# sfmt.set_powerlimits((-2, 2))
#
# plt.colorbar(cs, cax=cax, format=sfmt)
# cax.set_xlabel("K")
# cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Soil moisture total (I1 + I2, level_index=0)----------------------------------
# _, sm_mod = analysis.get_daily_climatology_for_rconf(modif_config, var_name="I1+I2", level=0)
# _, sm_base = analysis.get_daily_climatology_for_rconf(base_config, var_name="I1+I2", level=0)
#
# sm_mod = _avg_along(sm_mod, the_mask, axis=avg_axis)
# sm_base = _avg_along(sm_base, the_mask, axis=avg_axis)
#
# row += 1
# ax = fig.add_subplot(gs[row, 0])
# cs = ax.contourf(num_dates_2d, z_agg_2d, (sm_mod - sm_base) * level_widths_mm[0], cmap="jet")
# ax.set_title("Soil moisture (liq.+ice, level=1)")
# all_axes.append(ax)
#
# # Colorbar for value plots
# cax = fig.add_subplot(gs[row, -1])
#
# sfmt = ScalarFormatter(useMathText=True)
# sfmt.set_powerlimits((-2, 2))
#
# plt.colorbar(cs, cax=cax, format=sfmt)
# cax.set_xlabel("mm")
# cax.yaxis.get_offset_text().set_position((-2, 10))
delta = 200
vmin = -delta
vmax = delta
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_major_formatter(
FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=1))
the_ax.grid(which="minor")
the_ax.set_ylabel(ztitle)
img_file = Path(img_folder).joinpath("INTF_rate_{}_avg_{}-{}.png".format(
avg_axis, start_year_c, end_year_c))
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
plt.show()
def main():
"""
Everything is aggregated to the CRU resolution before calculating biases
"""
season_to_months = DEFAULT_SEASON_TO_MONTHS
varnames = ["PR", "TT"]
plot_utils.apply_plot_params(font_size=5,
width_pt=None,
width_cm=15,
height_cm=4)
# reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
# start_year=1980, end_year=2010, label="ERAI-CRCM5-L")
reanalysis_driven_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5",
start_year=1980,
end_year=2010,
label="ERAI-CRCM5-RL_0.4deg")
bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config)
field_cmap = cm.get_cmap("jet", 10)
vname_to_clevels = {
"TT": np.arange(-30, 32, 2),
"PR": np.arange(0, 6.5, 0.5)
}
vname_to_anusplin_path = {
"TT": "/home/huziy/skynet3_rech1/anusplin_links",
"PR": "/home/huziy/skynet3_rech1/anusplin_links"
}
vname_to_cru_path = {
"TT":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"PR":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
}
xx_agg = None
yy_agg = None
for vname in varnames:
# get anusplin obs climatology
season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
vname=vname,
season_to_months=season_to_months,
bmp_info=bmp_info)
# get CRU obs values-------------------------
bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
bmp_info=bmp_info,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
vname=vname)
if xx_agg is None:
xx_agg, yy_agg = bmp_info_agg.get_proj_xy()
# get model data
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(
run_config=reanalysis_driven_config,
varname=vname,
level=0,
season_to_months=season_to_months)
###
biases_with_anusplin = OrderedDict()
biases_with_cru = OrderedDict()
nx_agg_anusplin = 4
ny_agg_anusplin = 4
nx_agg_model = 1
ny_agg_model = 1
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(
field, nagg_x=nx_agg_model, nagg_y=ny_agg_model)
if vname == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
biases_with_cru[season] = season_to_clim_fields_model_agg[
season] - season_to_obs_cru[season]
biases_with_anusplin[season] = season_to_clim_fields_model_agg[
season] - aggregate_array(season_to_obs_anusplin[season],
nagg_x=nx_agg_anusplin,
nagg_y=ny_agg_anusplin)
# Do the plotting
fig = plt.figure()
clevs = [c for c in np.arange(-0.5, 0.55, 0.05)
] if vname == "PR" else np.arange(-2, 2.2, 0.2)
gs = GridSpec(1,
len(biases_with_cru) + 1,
width_ratios=len(biases_with_cru) * [
1.,
] + [
0.05,
])
col = 0
cs = None
cmap = "seismic"
fig.suptitle(
r"$\left| \delta_{\rm Hopkinson} \right| - \left| \delta_{\rm CRU} \right|$"
)
for season, cru_err in biases_with_cru.items():
anu_err = biases_with_anusplin[season]
ax = fig.add_subplot(gs[0, col])
diff = np.abs(anu_err) - np.abs(cru_err)
cs = bmp_info_agg.basemap.contourf(xx_agg,
yy_agg,
diff,
levels=clevs,
ax=ax,
extend="both",
cmap=cmap)
bmp_info_agg.basemap.drawcoastlines(ax=ax, linewidth=0.3)
good = diff[~diff.mask & ~np.isnan(diff)]
n_neg = sum(good < 0) / sum(good > 0)
print("season: {}, n-/n+ = {}".format(season, n_neg))
ax.set_title(season)
ax.set_xlabel(r"$n_{-}/n_{+} = $" + "{:.1f}".format(n_neg) + "\n" +
r"$\overline{\varepsilon} = $" +
"{:.2f}".format(good.mean()))
col += 1
ax = fig.add_subplot(gs[0, -1])
plt.colorbar(cs, cax=ax)
ax.set_title("mm/day" if vname == "PR" else r"${\rm ^\circ C}$")
fig.savefig(os.path.join(
img_folder, "comp_anu_and_cru_biases_for_{}_{}.png".format(
vname, reanalysis_driven_config.label)),
bbox_inches="tight",
dpi=common_plot_params.FIG_SAVE_DPI)
def main():
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
season_to_months = OrderedDict([
("Winter (DJF)", (1, 2, 12)),
("Spring (MAM)", range(3, 6)),
("Summer (JJA)", range(6, 9)),
("Fall (SON)", range(9, 12)),
])
varnames = ["TT", "PR"]
plot_utils.apply_plot_params(font_size=10,
width_pt=None,
width_cm=20,
height_cm=17)
# reanalysis_driven_config = RunConfig(data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
# start_year=1980, end_year=2010, label="ERAI-CRCM5-L")
#
reanalysis_driven_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5",
start_year=1980,
end_year=2010,
label="ERAI-CRCM5-L(0.4)")
nx_agg_model = 1
ny_agg_model = 1
nx_agg_anusplin = 4
ny_agg_anusplin = 4
gcm_driven_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5",
start_year=1980,
end_year=2010,
label="CanESM2-CRCM5-L")
bmp_info = analysis.get_basemap_info(r_config=reanalysis_driven_config)
xx, yy = bmp_info.get_proj_xy()
field_cmap = cm.get_cmap("jet", 10)
vname_to_clevels = {
"TT": np.arange(-30, 32, 2),
"PR": np.arange(0, 6.5, 0.5)
}
vname_to_anusplin_path = {
"TT": "/home/huziy/skynet3_rech1/anusplin_links",
"PR": "/home/huziy/skynet3_rech1/anusplin_links"
}
vname_to_cru_path = {
"TT":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"PR":
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
}
for vname in varnames:
fig = plt.figure()
ncols = len(season_to_months)
gs = GridSpec(4, ncols + 1, width_ratios=ncols * [
1.,
] + [
0.09,
])
clevels = vname_to_clevels[vname]
# get anusplin obs climatology
season_to_obs_anusplin = plot_performance_err_with_anusplin.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
vname=vname,
season_to_months=season_to_months,
bmp_info=bmp_info,
n_agg_x=nx_agg_anusplin,
n_agg_y=ny_agg_anusplin)
row = 0
# Plot CRU values-------------------------
bmp_info_agg, season_to_obs_cru = plot_performance_err_with_cru.get_seasonal_clim_obs_data(
rconfig=reanalysis_driven_config,
bmp_info=bmp_info,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
vname=vname)
# Mask out the Great Lakes
cru_mask = get_mask(bmp_info_agg.lons,
bmp_info_agg.lats,
shp_path=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"))
for season in season_to_obs_cru:
season_to_obs_cru[season] = np.ma.masked_where(
cru_mask > 0.5, season_to_obs_cru[season])
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
xx_agg, yy_agg = bmp_info_agg.get_proj_xy()
for j, (season, obs_field) in enumerate(season_to_obs_cru.items()):
ax = ax_list[j]
cs = bmp_info_agg.basemap.contourf(xx_agg,
yy_agg,
obs_field.copy(),
levels=clevels,
ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("CRU")
# plt.colorbar(cs, caax=ax_list[-1])
row += 1
# Plot ANUSPLIN values-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = None
for j, (season,
obs_field) in enumerate(season_to_obs_anusplin.items()):
ax = ax_list[j]
cs = bmp_info.basemap.contourf(xx,
yy,
obs_field,
levels=clevels,
ax=ax)
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4],
"basin",
ax=ax)
ax.set_title(season)
ax_list[0].set_ylabel("Hopkinson")
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
_format_axes(ax_list, vname=vname)
row += 1
# Plot model (CRCM) values-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# cs = None
#
# season_to_field_crcm = analysis.get_seasonal_climatology_for_runconfig(run_config=reanalysis_driven_config,
# varname=vname, level=0,
# season_to_months=season_to_months)
#
# for j, (season, crcm_field) in enumerate(season_to_field_crcm.items()):
# ax = ax_list[j]
# cs = bmp_info.basemap.contourf(xx, yy, crcm_field * 1000 * 24 * 3600, levels=clevels, ax=ax)
# bmp_info.basemap.drawcoastlines(ax=ax)
# bmp_info.basemap.readshapefile(BASIN_BOUNDARIES_SHP[:-4], "basin", ax=ax)
# ax.set_title(season)
#
# ax_list[0].set_ylabel(reanalysis_driven_config.label)
# cb = plt.colorbar(cs, cax=fig.add_subplot(gs[:2, -1]))
# cb.ax.set_xlabel(infovar.get_units(vname))
# _format_axes(ax_list, vname=vname)
# row += 1
# Plot (Model - CRU) Performance biases-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
cs = plot_performance_err_with_cru.compare_vars(
vname_model=vname,
vname_obs=None,
r_config=reanalysis_driven_config,
season_to_months=season_to_months,
obs_path=vname_to_cru_path[vname],
bmp_info_agg=bmp_info_agg,
diff_axes_list=ax_list,
mask_shape_file=os.path.join(GL_SHP_FOLDER, "gl_cst.shp"),
nx_agg_model=nx_agg_model,
ny_agg_model=ny_agg_model)
ax_list[0].set_ylabel(
"{label}\n--\nCRU".format(label=reanalysis_driven_config.label))
_format_axes(ax_list, vname=vname)
row += 1
# Plot performance+BFE errors with respect to CRU (Model - CRU)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_cru.compare_vars(vname, vname_obs=None, obs_path=vname_to_cru_path[vname],
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info_agg, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nCRU".format(label=gcm_driven_config.label))
# row += 1
# Plot performance errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
plot_performance_err_with_anusplin.compare_vars(
vname, {vname: season_to_obs_anusplin},
r_config=reanalysis_driven_config,
bmp_info_agg=bmp_info,
season_to_months=season_to_months,
axes_list=ax_list)
_format_axes(ax_list, vname=vname)
ax_list[0].set_ylabel("{label}\n--\nHopkinson".format(
label=reanalysis_driven_config.label))
row += 1
# Plot performance+BFE errors with respect to ANUSPLIN (Model - ANUSPLIN)-------------------------
# ax_list = [fig.add_subplot(gs[row, j]) for j in range(ncols)]
# plot_performance_err_with_anusplin.compare_vars(vname, {vname: season_to_obs_anusplin},
# r_config=gcm_driven_config,
# bmp_info_agg=bmp_info, season_to_months=season_to_months,
# axes_list=ax_list)
# _format_axes(ax_list, vname=vname)
# ax_list[0].set_ylabel("{label}\nvs\nHopkinson".format(label=gcm_driven_config.label))
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[-2:, -1]))
cb.ax.set_xlabel(infovar.get_units(vname))
# Save the plot
img_file = "{vname}_{sy}-{ey}_{sim_label}.png".format(
vname=vname,
sy=reanalysis_driven_config.start_year,
ey=reanalysis_driven_config.end_year,
sim_label=reanalysis_driven_config.label)
img_file = img_folder.joinpath(img_file)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def main():
plot_utils.apply_plot_params(font_size=12,
width_pt=None,
width_cm=25,
height_cm=25)
season_to_months = DEFAULT_SEASON_TO_MONTHS
r_config = RunConfig(
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=1980,
end_year=2009,
label="CRCM5-L")
# Number of points for aggregation
nx_agg = 5
ny_agg = 5
bmp_info = analysis.get_basemap_info_from_hdf(file_path=r_config.data_path)
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg, nagg_y=ny_agg)
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["air", "precip"]
obs_paths = [
"/RECH/data/Validation/Willmott_Matsuura/NetCDF/air.mon.mean.v301.nc",
"/RECH/data/Validation/Willmott_Matsuura/NetCDF/precip.mon.total.v301.nc"
]
plot_all_vars_in_one_fig = True
fig = None
gs = None
row_axes = None
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=12)
fig = plt.figure()
ncols = len(season_to_months) + 1
gs = GridSpec(len(model_vars),
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
for mname, oname, opath in zip(model_vars, obs_vars, obs_paths):
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_obs=oname,
r_config=r_config,
season_to_months=season_to_months,
nx_agg=nx_agg,
ny_agg=ny_agg,
bmp_info_agg=bmp_info_agg,
obs_path=opath,
axes_list=row_axes)
row += 1
# Save the figure if necessary
if plot_all_vars_in_one_fig:
fig_path = img_folder.joinpath("{}.png".format("_".join(model_vars)))
with fig_path.open("wb") as figfile:
fig.savefig(figfile, format="png", bbox_inches="tight")
plt.close(fig)
def main():
# import seaborn as sns
# sns.set_context("paper", font_scale=2)
# sns.set_style("whitegrid")
level_widths_mm = MM_PER_METER * infovar.soil_layer_widths_26_to_60
avg_axis = "lon"
start_year_c = 1980
end_year_c = 2010
img_folder = Path("cc_paper/lake_props")
if not img_folder.exists():
img_folder.mkdir(parents=True)
# Configuration without interflow, to be compared with the one with intf.
base_config = RunConfig(
start_year=start_year_c,
end_year=end_year_c,
data_path=
"/home/huziy/skynet3_rech1/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
label="CRCM5-L")
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
depth_to_bedrock_mm = r_obj.get_first_record_for_name("8L") * MM_PER_METER
lake_fraction = r_obj.get_first_record_for_name("ML")
# recompute layer widths to account for the depth to bedrock
layer_widths_3d = np.ones(depth_to_bedrock_mm.shape +
level_widths_mm.shape)
layer_widths_3d *= level_widths_mm[np.newaxis, np.newaxis, :]
layer_bottoms_3d = layer_widths_3d.cumsum(axis=2)
corrections = depth_to_bedrock_mm[:, :, np.newaxis] - layer_bottoms_3d
layer_widths_3d[corrections < 0] += corrections[corrections < 0]
layer_widths_3d[layer_widths_3d < 0] = 0
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
# Current and future configurations
current_config = RunConfig(start_year=start_year_c,
end_year=end_year_c,
data_path=current_path,
label="CRCM5-L")
n_shift_years = 90
future_config = current_config.get_shifted_config(n_shift_years)
print(future_config)
varname = "L1"
level = 0
daily_dates, lake_temp_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_temp_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname,
level=level)
# average along a dim
lake_temp_c = _avg_along(lake_temp_c,
axis=avg_axis,
lake_fraction=lake_fraction)
lake_temp_f = _avg_along(lake_temp_f,
axis=avg_axis,
lake_fraction=lake_fraction)
zagg = None
ztitle = ""
if avg_axis == "lon":
zagg = lats.mean(axis=0)
ztitle = "Latitude"
elif avg_axis == "lat":
zagg = lons.mean(axis=1)
ztitle = "Longitude"
num_dates = date2num(daily_dates)
z_agg_2d, num_dates_2d = np.meshgrid(zagg, num_dates)
# Do the plotting
plot_utils.apply_plot_params(font_size=14,
width_pt=None,
width_cm=20,
height_cm=30)
fig = plt.figure()
gs = GridSpec(4, 2, width_ratios=[1, 0.02])
all_axes = []
# ----------------------------------Lake temperature----------------------------------
row = 0
ax = fig.add_subplot(gs[row, 0])
cmap = cm.get_cmap("bwr", 15)
to_plot = lake_temp_f - lake_temp_c[:, :]
vmax = max(to_plot.max(), 0)
vmin = min(to_plot.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
to_plot,
cmap=cmap,
levels=clevs,
extend="both")
ax.set_title("Lake surface water temperature")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 3))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel(r"${\rm ^\circ C}$")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice thickness----------------------------------
level = 0
varname = "LD"
_, lake_ice_th_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_ice_th_f = analysis.get_daily_climatology_for_rconf(
future_config, var_name=varname, level=level)
lake_ice_th = _avg_along(lake_ice_th_f - lake_ice_th_c,
axis=avg_axis,
lake_fraction=lake_fraction)
# get the points for which the average should be calculated
winter_points = np.asarray(
[i for i, d in enumerate(daily_dates) if d.month in [1, 2, 12]])
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_th.max(), 0)
vmin = min(lake_ice_th.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
lake_ice_th,
cmap=cmap,
levels=clevs,
extend="both")
ax.set_title("Lake ice thickness")
print("Mean change in ice thickness: {} m".format(
lake_ice_th.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice fraction----------------------------------
level = 0
varname = "LC"
_, lake_ice_fraction_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_ice_fraction_f = analysis.get_daily_climatology_for_rconf(
future_config, var_name=varname, level=level)
lake_ice_fraction = _avg_along(lake_ice_fraction_f - lake_ice_fraction_c,
axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_fraction.max(), 0)
vmin = min(lake_ice_fraction.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
lake_ice_fraction,
cmap=cmap,
levels=clevs,
extend="both")
ax.set_title("Lake ice fraction")
print("Mean change in ice fraction: {}".format(
lake_ice_fraction.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake depth----------------------------------
level = 0
varname = "CLDP"
_, lake_depth_c = analysis.get_daily_climatology_for_rconf(
current_config, var_name=varname, level=level)
_, lake_depth_f = analysis.get_daily_climatology_for_rconf(
future_config, var_name=varname, level=level)
lake_depth_cc = _avg_along(lake_depth_f - lake_depth_c,
axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
clevs = [0.01, 0.1, 0.5, 0.75, 1, 2, 3]
clevs = [-c for c in reversed(clevs)] + clevs
cmap = cm.get_cmap("bwr", len(clevs) - 1)
norm = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
cs = ax.contourf(num_dates_2d,
z_agg_2d,
lake_depth_cc,
cmap=cmap,
levels=clevs,
norm=norm,
extend="both")
ax.set_title("Lake level")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt, ticks=clevs)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_major_formatter(
FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=1))
the_ax.grid(which="minor")
the_ax.set_ylabel(ztitle)
img_file = Path(img_folder).joinpath(
"cc_{}_Lake_props_current_{}_avg_{}-{}_vs_{fsy}-{fey}.png".format(
base_config.label,
avg_axis,
start_year_c,
end_year_c,
fsy=future_config.start_year,
fey=future_config.end_year))
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
plt.show()
def main():
# import seaborn as sns
# sns.set_context("paper", font_scale=2)
# sns.set_style("whitegrid")
level_widths_mm = MM_PER_METER * infovar.soil_layer_widths_26_to_60
avg_axis = "lon"
start_year_c = 1980
end_year_c = 2010
img_folder = Path("cc_paper/lake_props")
if not img_folder.exists():
img_folder.mkdir(parents=True)
# Configuration without interflow, to be compared with the one with intf.
base_config = RunConfig(start_year=start_year_c, end_year=end_year_c,
data_path="/home/huziy/skynet3_rech1/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
label="CRCM5-L")
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
depth_to_bedrock_mm = r_obj.get_first_record_for_name("8L") * MM_PER_METER
lake_fraction = r_obj.get_first_record_for_name("ML")
# recompute layer widths to account for the depth to bedrock
layer_widths_3d = np.ones(depth_to_bedrock_mm.shape + level_widths_mm.shape)
layer_widths_3d *= level_widths_mm[np.newaxis, np.newaxis, :]
layer_bottoms_3d = layer_widths_3d.cumsum(axis=2)
corrections = depth_to_bedrock_mm[:, :, np.newaxis] - layer_bottoms_3d
layer_widths_3d[corrections < 0] += corrections[corrections < 0]
layer_widths_3d[layer_widths_3d < 0] = 0
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
# Current and future configurations
current_config = RunConfig(start_year=start_year_c, end_year=end_year_c,
data_path=current_path,
label="CRCM5-L")
n_shift_years = 90
future_config = current_config.get_shifted_config(n_shift_years)
print(future_config)
varname = "L1"
level = 0
daily_dates, lake_temp_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_temp_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
# average along a dim
lake_temp_c = _avg_along(lake_temp_c, axis=avg_axis, lake_fraction=lake_fraction)
lake_temp_f = _avg_along(lake_temp_f, axis=avg_axis, lake_fraction=lake_fraction)
zagg = None
ztitle = ""
if avg_axis == "lon":
zagg = lats.mean(axis=0)
ztitle = "Latitude"
elif avg_axis == "lat":
zagg = lons.mean(axis=1)
ztitle = "Longitude"
num_dates = date2num(daily_dates)
z_agg_2d, num_dates_2d = np.meshgrid(zagg, num_dates)
# Do the plotting
plot_utils.apply_plot_params(font_size=14, width_pt=None, width_cm=20, height_cm=30)
fig = plt.figure()
gs = GridSpec(4, 2, width_ratios=[1, 0.02])
all_axes = []
# ----------------------------------Lake temperature----------------------------------
row = 0
ax = fig.add_subplot(gs[row, 0])
cmap = cm.get_cmap("bwr", 15)
to_plot = lake_temp_f - lake_temp_c[:, :]
vmax = max(to_plot.max(), 0)
vmin = min(to_plot.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d, z_agg_2d, to_plot, cmap=cmap, levels=clevs, extend="both")
ax.set_title("Lake surface water temperature")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 3))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel(r"${\rm ^\circ C}$")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice thickness----------------------------------
level = 0
varname = "LD"
_, lake_ice_th_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_ice_th_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
lake_ice_th = _avg_along(lake_ice_th_f - lake_ice_th_c, axis=avg_axis, lake_fraction=lake_fraction)
# get the points for which the average should be calculated
winter_points = np.asarray([i for i, d in enumerate(daily_dates) if d.month in [1, 2, 12]])
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_th.max(), 0)
vmin = min(lake_ice_th.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d, z_agg_2d, lake_ice_th, cmap=cmap, levels=clevs, extend="both")
ax.set_title("Lake ice thickness")
print("Mean change in ice thickness: {} m".format(lake_ice_th.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake ice fraction----------------------------------
level = 0
varname = "LC"
_, lake_ice_fraction_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_ice_fraction_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
lake_ice_fraction = _avg_along(lake_ice_fraction_f - lake_ice_fraction_c, axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
vmax = max(lake_ice_fraction.max(), 0)
vmin = min(lake_ice_fraction.min(), 0)
vmax = max(abs(vmin), abs(vmax))
# norm = MidpointNormalize(vmin=vmin, vmax=vmax)
clevs = MaxNLocator(cmap.N + 1).tick_values(-vmax, vmax)
cs = ax.contourf(num_dates_2d, z_agg_2d, lake_ice_fraction, cmap=cmap, levels=clevs, extend="both")
ax.set_title("Lake ice fraction")
print("Mean change in ice fraction: {}".format(lake_ice_fraction.mean(axis=1)[winter_points].mean()))
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("")
cax.yaxis.get_offset_text().set_position((-2, 10))
# ----------------------------------Lake depth----------------------------------
level = 0
varname = "CLDP"
_, lake_depth_c = analysis.get_daily_climatology_for_rconf(current_config,
var_name=varname, level=level)
_, lake_depth_f = analysis.get_daily_climatology_for_rconf(future_config,
var_name=varname, level=level)
lake_depth_cc = _avg_along(lake_depth_f - lake_depth_c, axis=avg_axis,
lake_fraction=lake_fraction)
row += 1
ax = fig.add_subplot(gs[row, 0])
clevs = [0.01, 0.1, 0.5, 0.75, 1, 2, 3]
clevs = [-c for c in reversed(clevs)] + clevs
cmap = cm.get_cmap("bwr", len(clevs) - 1)
norm = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)
cs = ax.contourf(num_dates_2d, z_agg_2d, lake_depth_cc, cmap=cmap, levels=clevs, norm=norm, extend="both")
ax.set_title("Lake level")
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[row, -1])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
plt.colorbar(cs, cax=cax, format=sfmt, ticks=clevs)
cax.set_xlabel("m")
cax.yaxis.get_offset_text().set_position((-2, 10))
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_major_formatter(FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=1))
the_ax.grid(which="minor")
the_ax.set_ylabel(ztitle)
img_file = Path(img_folder).joinpath("cc_{}_Lake_props_current_{}_avg_{}-{}_vs_{fsy}-{fey}.png".format(
base_config.label, avg_axis, start_year_c, end_year_c,
fsy=future_config.start_year, fey=future_config.end_year))
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
plt.show()
def main():
season_to_months = OrderedDict([(s, months) for s, months in DEFAULT_SEASON_TO_MONTHS.items()
if s.lower() not in ["summer", "fall"]])
r_config_01 = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=1980, end_year=1996, label="CRCM5-L"
)
r_config_04 = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.4_crcm5-hcd-rl.hdf5",
start_year=1980, end_year=1996, label="CRCM5-L(0.4)"
)
r_config = r_config_04
# Number of points for aggregation
nx_agg_model = 1
ny_agg_model = 1
nx_agg_obs = 2
ny_agg_obs = 2
r_config_cc = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5",
start_year=1980, end_year=1996, label="CRCM5-L"
)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=r_config.data_path)
if nx_agg_model * ny_agg_model > 1:
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg_model, nagg_y=ny_agg_model)
else:
bmp_info_agg = bmp_info
# Validate temperature and precip
model_vars = ["I5", ]
obs_vars = ["SWE", ]
obs_paths = [
"/RESCUE/skynet3_rech1/huziy/swe_ross_brown/swe.nc4",
]
plot_all_vars_in_one_fig = True
fig = None
gs = None
row_axes = None
obs_row_axes = None
ncols = None
if plot_all_vars_in_one_fig:
plot_utils.apply_plot_params(font_size=12, width_pt=None, width_cm=12, height_cm=18)
fig = plt.figure()
ncols = len(season_to_months) + 1
gs = GridSpec(len(model_vars) * 3, 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
for mname, oname, opath in zip(model_vars, obs_vars, obs_paths):
if plot_all_vars_in_one_fig:
# Obs row is for observed values
obs_row_axes = [fig.add_subplot(gs[row, col]) for col in range(ncols)]
obs_row_axes[-1].set_title("mm")
row += 1
model_row_axes = [fig.add_subplot(gs[row, col]) for col in range(ncols - 1)]
row += 1
row_axes = [fig.add_subplot(gs[row, col]) for col in range(ncols)]
row += 1
compare_vars(vname_model=mname, vname_obs=oname, r_config=r_config,
season_to_months=season_to_months,
nx_agg_model=nx_agg_model, ny_agg_model=ny_agg_model,
nx_agg_obs=nx_agg_obs, ny_agg_obs=ny_agg_obs,
bmp_info_agg=bmp_info_agg, bmp_info_model=bmp_info,
obs_path=opath,
diff_axes_list=row_axes, obs_axes_list=obs_row_axes, model_axes_list=model_row_axes)
if plot_all_vars_in_one_fig:
obs_row_axes[0].set_ylabel("(a)", rotation="horizontal", labelpad=10)
model_row_axes[0].set_ylabel("(b)", rotation="horizontal", labelpad=10)
row_axes[0].set_ylabel("(c)", rotation="horizontal", labelpad=10)
for ax in row_axes[:-1]:
ax.set_title("")
# row_axes[0].annotate("(a)", (-0.2, 0.5), font_properties=FontProperties(weight="bold"), xycoords="axes fraction")
# Plot bfe errs
# row_axes = [fig.add_subplot(gs[row, col]) for col in range(ncols)]
# plot_swe_bfes(r_config, r_config_cc, vname_model="I5", season_to_months=season_to_months,
# bmp_info=bmp_info, axes_list=row_axes)
#
# row_axes[-1].set_visible(False)
# row_axes[0].annotate("(b)", (0, 1.05), font_properties=FontProperties(weight="bold"), xycoords="axes fraction")
# row_axes[0].set_ylabel("(b)", rotation="horizontal", labelpad=10)
# for ax in row_axes[:-1]:
# ax.set_title("")
# Save the figure if necessary
if plot_all_vars_in_one_fig:
from crcm5.analyse_hdf import common_plot_params
fig_path = img_folder.joinpath("{}_{}.png".format("_".join(model_vars), r_config.label))
with fig_path.open("wb") as figfile:
fig.savefig(figfile, format="png", bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
plt.close(fig)
def main():
lkfr_limit = 0.05
model_data_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
start_year=start_year_c, end_year=end_year_c
)
params.update(
dict(data_path=model_data_current_path, label=modif_label)
)
model_config_c = RunConfig(**params)
model_config_f = model_config_c.get_shifted_config(future_shift_years)
bmp_info = analysis.get_basemap_info(r_config=model_config_c)
specific_cond_heat = 0.250100e7 # J/kg
water_density = 1000.0 # kg/m**3
season_to_months = OrderedDict([
("Summer", [6, 7, 8]),
])
lkfr = analysis.get_array_from_file(path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5", var_name=infovar.HDF_LAKE_FRACTION_NAME)
assert lkfr is not None, "Could not find lake fraction in the file"
# Current climate
traf_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="TRAF", level=5, season_to_months=season_to_months)
pr_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="PR", level=0, season_to_months=season_to_months)
lktemp_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="L1", level=0, season_to_months=season_to_months)
airtemp_c = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="TT", level=0, season_to_months=season_to_months)
lhc = OrderedDict([
(s, specific_cond_heat * (pr_c[s] * water_density - traf_c[s])) for s, traf in traf_c.items()
])
avc = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_c, varname="AV", level=0, season_to_months=season_to_months)
plt.figure()
lhc["Summer"] = np.ma.masked_where(lkfr < lkfr_limit, lhc["Summer"])
print("min: {}, max: {}".format(lhc["Summer"].min(), lhc["Summer"].max()))
cs = plt.contourf(lhc["Summer"].T)
plt.title("lhc")
plt.colorbar()
plt.figure()
cs = plt.contourf(avc["Summer"].T, levels=cs.levels, norm=cs.norm, cmap=cs.cmap)
plt.title("avc")
plt.colorbar()
# Future climate
traf_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="TRAF", level=5, season_to_months=season_to_months)
pr_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="PR", level=0,
season_to_months=season_to_months)
lktemp_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="L1", level=0,
season_to_months=season_to_months)
airtemp_f = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="TT", level=0,
season_to_months=season_to_months)
lhf = OrderedDict([
(s, specific_cond_heat * (pr_f[s] * water_density - traf_f[s])) for s, traf in traf_f.items()
])
plt.figure()
plt.pcolormesh(traf_c["Summer"].T)
plt.title("TRAF over lakes current")
plt.colorbar()
avf = analysis.get_seasonal_climatology_for_runconfig(run_config=model_config_f, varname="AV", level=0,
season_to_months=season_to_months)
plt.figure()
cs = plt.contourf(avf["Summer"].T)
plt.title("avf")
plt.colorbar()
plt.figure()
cs = plt.contourf(avf["Summer"].T - avc["Summer"].T, levels=np.arange(-40, 45, 5))
plt.title("d(av)")
plt.colorbar()
plt.figure()
plt.contourf(lhf["Summer"].T - lhc["Summer"].T, levels=cs.levels, cmap=cs.cmap, norm=cs.norm)
plt.title("d(lh)")
plt.colorbar()
# plotting
plot_utils.apply_plot_params(width_cm=15, height_cm=15, font_size=10)
gs = GridSpec(2, 2)
# tair_c_ts = analysis.get_area_mean_timeseries(model_config_c.data_path, var_name="TT", level_index=0,
# start_year=model_config_c.start_year, end_year=model_config_c.end_year,
# the_mask=lkfr >= lkfr_limit)
#
# tair_f_ts = analysis.get_area_mean_timeseries(model_config_f.data_path, var_name="TT", level_index=0,
# start_year=model_config_f.start_year, end_year=model_config_f.end_year,
# the_mask=lkfr >= lkfr_limit)
#
#
# tlake_c_ts = analysis.get_area_mean_timeseries(model_config_c.data_path, var_name="TT", level_index=0,
# start_year=model_config_c.start_year, end_year=model_config_c.end_year,
# the_mask=lkfr >= lkfr_limit)
#
# tlake_f_ts = analysis.get_area_mean_timeseries(model_config_f.data_path, var_name="TT", level_index=0,
# start_year=model_config_f.start_year, end_year=model_config_f.end_year,
# the_mask=lkfr >= lkfr_limit)
for season in season_to_months:
fig = plt.figure()
lktemp_c[season] -= 273.15
dT_c = np.ma.masked_where(lkfr < lkfr_limit, lktemp_c[season] - airtemp_c[season])
lktemp_f[season] -= 273.15
dT_f = np.ma.masked_where(lkfr < lkfr_limit, lktemp_f[season] - airtemp_f[season])
d = np.round(max(np.ma.abs(dT_c).max(), np.ma.abs(dT_f).max()))
vmin = -d
vmax = d
clevs = np.arange(-12, 13, 1)
ncolors = len(clevs) - 1
bn = BoundaryNorm(clevs, ncolors=ncolors)
cmap = cm.get_cmap("seismic", ncolors)
ax_list = []
fig.suptitle(season)
xx, yy = bmp_info.get_proj_xy()
# Current gradient
ax = fig.add_subplot(gs[0, 0])
ax.set_title(r"current: $T_{\rm lake} - T_{\rm atm}$")
cs = bmp_info.basemap.pcolormesh(xx, yy, dT_c, ax=ax, norm=bn, cmap=cmap)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Future Gradient
ax = fig.add_subplot(gs[0, 1])
ax.set_title(r"future: $T_{\rm lake} - T_{\rm atm}$")
cs = bmp_info.basemap.pcolormesh(xx, yy, dT_f, ax=ax, norm=cs.norm, cmap=cs.cmap, vmin=vmin, vmax=vmax)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Change in the gradient
ax = fig.add_subplot(gs[1, 0])
ax.set_title(r"$\Delta T_{\rm future} - \Delta T_{\rm current}$")
ddT = dT_f - dT_c
d = np.round(np.ma.abs(ddT).max())
clevs = np.arange(-3, 3.1, 0.1)
ncolors = len(clevs) - 1
bn = BoundaryNorm(clevs, ncolors=ncolors)
cmap = cm.get_cmap("seismic", ncolors)
cs = bmp_info.basemap.pcolormesh(xx, yy, ddT, norm=bn, cmap=cmap)
bmp_info.basemap.colorbar(cs, ax=ax, extend="both")
ax_list.append(ax)
# Change in the latent heat flux
# ax = fig.add_subplot(gs[1, 1])
# ax.set_title(r"$LE_{\rm future} - LE_{\rm current}$")
# dlh = np.ma.masked_where(lkfr < lkfr_limit, lhf[season] - lhc[season])
#
# d = np.round(np.ma.abs(dlh).max() // 10) * 10
# clevs = np.arange(0, 105, 5)
# bn = BoundaryNorm(clevs, ncolors=ncolors)
# cmap = cm.get_cmap("jet", ncolors)
#
# cs = bmp_info.basemap.pcolormesh(xx, yy, dlh, norm=bn, cmap=cmap)
# bmp_info.basemap.colorbar(cs, ax=ax, extend="max") # Change in the latent heat flux
# ax_list.append(ax)
for the_ax in ax_list:
bmp_info.basemap.drawcoastlines(linewidth=0.3, ax=the_ax)
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "lake_atm_gradients_and_fluxes_{}-{}_{}-{}.png".format(model_config_f.start_year, model_config_f.end_year, start_year_c, end_year_c)),
dpi=800,
bbox_inches="tight")
def main():
base_current_path = \
"/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = \
"/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
future_shift_years = 90
params = dict(data_path=base_current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
geo_data_file = "/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
base_configs = [base_config_c, base_config_f]
params.update(dict(data_path=modif_current_path, label=modif_label))
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
modif_configs = [modif_config_c, modif_config_f]
r_obj = RPN(geo_data_file)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
lake_fraction = r_obj.get_first_record_for_name_and_level("ML")
lake_fraction = np.ma.masked_where((fldr <= 0) | (fldr > 128),
lake_fraction)
bmp_info = analysis.get_basemap_info_from_hdf(file_path=base_current_path)
# plots basin boundaries and names on the domain plot
basin_name_to_out_indices_map, basin_name_to_basin_mask = plot_basin_outlets(
bmp_info=bmp_info,
accumulation_areas=facc,
directions=fldr,
lake_fraction_field=lake_fraction)
# basin_name_to_out_indices_map, basin_name_to_basin_mask = get_basin_to_outlet_indices_map(bmp_info=bmp_info,
# accumulation_areas=facc,
# directions=fldr,
# lake_fraction_field=lake_fraction)
data_to_plot = read_cc_and_cc_diff(
base_configs,
modif_configs,
name_to_indices=basin_name_to_out_indices_map,
varname=varname)
basin_name_to_basin_mask = basin_name_to_basin_mask if varname not in [
"STFA", "STFL"
] else None
img_path = get_image_path(base_config_c, base_config_f, modif_config_c,
modif_config_f, varname)
# select lake rich basins
sel_basins = ["ARN", "PYR", "LGR", "RDO", "SAG", "WAS"]
basin_name_to_out_indices_map = {
k: v
for k, v in basin_name_to_out_indices_map.items() if k in sel_basins
}
calculate_and_plot_climate_change_hydrographs(
data_to_plot,
name_to_out_indices=basin_name_to_out_indices_map,
months=list(range(1, 13)),
varname=varname,
img_path=img_path)
def main():
"""
"""
season_to_months = get_default_season_to_months_dict()
# season_to_months = OrderedDict([("April", [4, ]), ("May", [5, ]), ("June", [6, ]), ("July", [7, ])])
var_names = ["TT", "HU", "PR", "AV", "AH", "STFL", "TRAF", "I5", "I0"]
# var_names = ["TT", "PR"]
levels = [0, ] * len(var_names)
multipliers = {
"PR": 1.0e3 * 24.0 * 3600.,
"TRAF": 24 * 3600.0
}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
# base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# base_label = "CRCM5-L"
#
# modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
# modif_label = "CRCM5-LI"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(
data_path=base_current_path, start_year=start_year_c, end_year=end_year_c, label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(
dict(data_path=modif_current_path, label=modif_label)
)
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([
("Current", OrderedDict([(base_label, base_config_c), (modif_label, modif_config_c)])),
("Future", OrderedDict([(base_label, base_config_f), (modif_label, modif_config_f)]))
])
# Plot the differences
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
# Calculate and plot seasonal means
for vname, level in zip(var_names, levels):
data = get_data(vname=vname, level=level, config_dict=config_dict)
_plot_var(vname=vname, level=level, config_dict=config_dict, data_dict=data)
def main():
import application_properties
application_properties.set_current_directory()
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
rea_driven_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"
rea_driven_label = "ERAI-CRCM5-L"
gcm_driven_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
gcm_driven_label = "CanESM2-CRCM5-L"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
params = dict(data_path=rea_driven_path,
start_year=start_year_c,
end_year=end_year_c,
label=rea_driven_label)
geo_data_file = "/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
rea_driven_config = RunConfig(**params)
params.update(dict(data_path=gcm_driven_path, label=gcm_driven_label))
gcm_driven_config = RunConfig(**params)
r_obj = RPN(geo_data_file)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
lkfr = r_obj.get_first_record_for_name("ML")
bmp_info = analysis.get_basemap_info_from_hdf(file_path=rea_driven_path)
basin_name_to_out_indices_map, basin_name_to_basin_mask = get_basin_to_outlet_indices_map(
bmp_info=bmp_info,
accumulation_areas=facc,
directions=fldr,
lake_fraction_field=lkfr)
# select lake rich basins
sel_basins = ["ARN", "PYR", "LGR", "RDO", "SAG", "WAS"]
basin_name_to_out_indices_map = {
k: v
for k, v in basin_name_to_out_indices_map.items() if k in sel_basins
}
rea_driven_daily = analysis.get_daily_climatology_for_rconf(
rea_driven_config, var_name=varname, level=0)
gcm_driven_daily = analysis.get_daily_climatology_for_rconf(
gcm_driven_config, var_name=varname, level=0)
rea_driven_config.data_daily = rea_driven_daily
gcm_driven_config.data_daily = gcm_driven_daily
plot_comparison_hydrographs(basin_name_to_out_indices_map,
rea_config=rea_driven_config,
gcm_config=gcm_driven_config)
def main(vars_of_interest=None):
# Validation with CRU (temp, precip) and CMC SWE
# obs_data_path = Path("/RESCUE/skynet3_rech1/huziy/obs_data_for_HLES/interploated_to_the_same_grid/GL_0.1_452x260/anusplin+_interpolated_tt_pr.nc")
obs_data_path = Path("/HOME/huziy/skynet3_rech1/obs_data/mh_churchill_nelson_obs_fields")
CRU_PRECIP = True
sim_id = "mh_0.44"
add_shp_files = [
default_domains.MH_BASINS_PATH,
constants.upstream_station_boundaries_shp_path[sim_id]
]
start_year = 1981
end_year = 2009
MODEL_LABEL = "CRCM5 (0.44)"
# critical p-value for the ttest aka significance level
# p_crit = 0.05
p_crit = 1
coastlines_width = 0.3
vars_of_interest_default = [
# T_AIR_2M,
TOTAL_PREC,
# SWE,
# LAKE_ICE_FRACTION
]
if vars_of_interest is None:
vars_of_interest = vars_of_interest_default
vname_to_seasonmonths_map = {
SWE: OrderedDict([("DJF", [12, 1, 2])]),
T_AIR_2M: season_to_months,
TOTAL_PREC: OrderedDict([("Annual", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])]) # season_to_months,
}
sim_configs = {
MODEL_LABEL: RunConfig(data_path="/RECH2/huziy/BC-MH/bc_mh_044deg/Samples",
start_year=start_year, end_year=end_year, label=MODEL_LABEL),
}
grid_config = default_domains.bc_mh_044
sim_labels = [MODEL_LABEL, ]
vname_to_level = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
SWE: VerticalLevel(-1, level_kinds.ARBITRARY)
}
vname_map = {
default_varname_mappings.TOTAL_PREC: "pre",
default_varname_mappings.T_AIR_2M: "tmp",
default_varname_mappings.SWE: "SWE"
}
filename_prefix_mapping = {
default_varname_mappings.SWE: "pm",
default_varname_mappings.TOTAL_PREC: "pm",
default_varname_mappings.T_AIR_2M: "dm"
}
# Try to get the land_fraction for masking if necessary
land_fraction = None
try:
land_fraction = get_land_fraction(sim_configs[MODEL_LABEL])
except Exception:
pass
# Calculations
# prepare params for interpolation
lons_t, lats_t, bsmap = get_target_lons_lats_basemap(sim_configs[MODEL_LABEL])
bsmap, reg_of_interest_mask = grid_config.get_basemap_using_shape_with_polygons_of_interest(lons=lons_t, lats=lats_t,
shp_path=default_domains.MH_BASINS_PATH,
mask_margin=2, resolution="i")
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons_t.flatten(), lats_t.flatten())
obs_multipliers = default_varname_mappings.vname_to_multiplier_CRCM5.copy()
# Read and calculate observed seasonal means
store_config = {
"base_folder": obs_data_path.parent if not obs_data_path.is_dir() else obs_data_path,
"data_source_type": data_source_types.ALL_VARS_IN_A_FOLDER_IN_NETCDF_FILES_OPEN_EACH_FILE_SEPARATELY,
"varname_mapping": vname_map,
"level_mapping": vname_to_level,
"offset_mapping": default_varname_mappings.vname_to_offset_CRCM5,
"multiplier_mapping": obs_multipliers,
}
obs_dm = DataManager(store_config=store_config)
obs_data = {}
# need to save it for ttesting
obs_vname_to_season_to_std = {}
obs_vname_to_season_to_nobs = {}
interp_indices = None
for vname in vars_of_interest:
# --
end_year_for_current_var = end_year
if vname == SWE:
end_year_for_current_var = min(1996, end_year)
# --
seas_to_year_to_mean = obs_dm.get_seasonal_means(varname_internal=vname,
start_year=start_year,
end_year=end_year_for_current_var,
season_to_months=vname_to_seasonmonths_map[vname])
seas_to_clim = {seas: np.array(list(y_to_means.values())).mean(axis=0) for seas, y_to_means in seas_to_year_to_mean.items()}
# convert precip from mm/month (CRU) to mm/day
if vname in [TOTAL_PREC] and CRU_PRECIP:
for seas in seas_to_clim:
seas_to_clim[seas] *= 1. / (365.25 / 12)
seas_to_clim[seas] = np.ma.masked_where(np.isnan(seas_to_clim[seas]), seas_to_clim[seas])
print("{}: min={}, max={}".format(seas, seas_to_clim[seas].min(), seas_to_clim[seas].max()))
obs_data[vname] = seas_to_clim
if interp_indices is None:
_, interp_indices = obs_dm.get_kdtree().query(list(zip(xt, yt, zt)))
# need for ttests
season_to_std = {}
obs_vname_to_season_to_std[vname] = season_to_std
season_to_nobs = {}
obs_vname_to_season_to_nobs[vname] = season_to_nobs
for season in seas_to_clim:
seas_to_clim[season] = seas_to_clim[season].flatten()[interp_indices].reshape(lons_t.shape)
# save the yearly means for ttesting
season_to_std[season] = np.asarray([field.flatten()[interp_indices].reshape(lons_t.shape)
for field in seas_to_year_to_mean[season].values()]).std(axis=0)
season_to_nobs[season] = np.ones_like(lons_t) * len(seas_to_year_to_mean[season])
plt.show()
# Read and calculate simulated seasonal mean biases
mod_label_to_vname_to_season_to_std = {}
mod_label_to_vname_to_season_to_nobs = {}
model_data_multipliers = defaultdict(lambda: 1)
model_data_multipliers[TOTAL_PREC] = 1000 * 24 * 3600
sim_data = defaultdict(dict)
for label, r_config in sim_configs.items():
store_config = {
"base_folder": r_config.data_path,
"data_source_type": data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT,
"varname_mapping": default_varname_mappings.vname_map_CRCM5,
"level_mapping": vname_to_level,
"offset_mapping": default_varname_mappings.vname_to_offset_CRCM5,
"multiplier_mapping": model_data_multipliers,
"filename_prefix_mapping": filename_prefix_mapping
}
dm = DataManager(store_config=store_config)
mod_label_to_vname_to_season_to_std[label] = {}
mod_label_to_vname_to_season_to_nobs[label] = {}
interp_indices = None
for vname in vars_of_interest:
# --
end_year_for_current_var = end_year
if vname == SWE:
end_year_for_current_var = min(1996, end_year)
# --
seas_to_year_to_mean = dm.get_seasonal_means(varname_internal=vname,
start_year=start_year,
end_year=end_year_for_current_var,
season_to_months=vname_to_seasonmonths_map[vname])
# get the climatology
seas_to_clim = {seas: np.array(list(y_to_means.values())).mean(axis=0) for seas, y_to_means in seas_to_year_to_mean.items()}
sim_data[label][vname] = seas_to_clim
if interp_indices is None:
_, interp_indices = dm.get_kdtree().query(list(zip(xt, yt, zt)))
season_to_std = {}
mod_label_to_vname_to_season_to_std[label][vname] = season_to_std
season_to_nobs = {}
mod_label_to_vname_to_season_to_nobs[label][vname] = season_to_nobs
for season in seas_to_clim:
interpolated_field = seas_to_clim[season].flatten()[interp_indices].reshape(lons_t.shape)
seas_to_clim[season] = interpolated_field - obs_data[vname][season]
# calculate standard deviations of the interpolated fields
season_to_std[season] = np.asarray([field.flatten()[interp_indices].reshape(lons_t.shape) for field in seas_to_year_to_mean[season].values()]).std(axis=0)
# calculate numobs for the ttest
season_to_nobs[season] = np.ones_like(lons_t) * len(seas_to_year_to_mean[season])
xx, yy = bsmap(lons_t, lats_t)
lons_t[lons_t > 180] -= 360
field_mask = maskoceans(lons_t, lats_t, np.zeros_like(lons_t)).mask
for vname in vars_of_interest:
if vname not in [SWE]:
field_mask = np.zeros_like(field_mask, dtype=bool)
# Plotting: interpolate to the same grid and plot obs and biases
plot_utils.apply_plot_params(width_cm=32 / 4 * (len(vname_to_seasonmonths_map[vname])),
height_cm=25 / 3.0 * (len(sim_configs) + 1), font_size=8 * len(vname_to_seasonmonths_map[vname]))
fig = plt.figure()
# fig.suptitle(internal_name_to_title[vname] + "\n")
nrows = len(sim_configs) + 2
ncols = len(vname_to_seasonmonths_map[vname])
gs = GridSpec(nrows=nrows, ncols=ncols)
# Plot the obs fields
current_row = 0
for col, season in enumerate(vname_to_seasonmonths_map[vname]):
field = obs_data[vname][season]
ax = fig.add_subplot(gs[current_row, col])
ax.set_title(season)
to_plot = np.ma.masked_where(field_mask, field) * internal_name_to_multiplier[vname]
clevs = get_clevs(vname)
to_plot = np.ma.masked_where(~reg_of_interest_mask, to_plot)
if clevs is not None:
bnorm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("Blues", len(clevs) - 1)
else:
cmap = "jet"
bnorm = None
bsmap.drawmapboundary(fill_color="0.75")
# cs = bsmap.contourf(xx, yy, to_plot, ax=ax, levels=get_clevs(vname), norm=bnorm, cmap=cmap)
cs = bsmap.pcolormesh(xx, yy, to_plot, ax=ax, norm=bnorm, cmap=internal_name_to_cmap[vname])
bsmap.drawcoastlines(linewidth=coastlines_width)
# bsmap.drawstates(linewidth=0.1)
# bsmap.drawcountries(linewidth=0.2)
bsmap.colorbar(cs, ax=ax)
i = 0
bsmap.readshapefile(str(add_shp_files[i])[:-4], "field_{}".format(i), linewidth=0.5, color="m")
if col == 0:
ax.set_ylabel("Obs")
# plot the biases
for sim_label in sim_labels:
current_row += 1
for col, season in enumerate(vname_to_seasonmonths_map[vname]):
field = sim_data[sim_label][vname][season]
ax = fig.add_subplot(gs[current_row, col])
clevs = get_clevs(vname + "bias")
if clevs is not None:
bnorm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("bwr", len(clevs) - 1)
else:
cmap = "bwr"
bnorm = None
to_plot = np.ma.masked_where(field_mask, field) * internal_name_to_multiplier[vname]
# ttest
a = sim_data[sim_label][vname][season] + obs_data[vname][season] # Calculate the simulation data back from biases
std_a = mod_label_to_vname_to_season_to_std[sim_label][vname][season]
nobs_a = mod_label_to_vname_to_season_to_nobs[sim_label][vname][season]
b = obs_data[vname][season]
std_b = obs_vname_to_season_to_std[vname][season]
nobs_b = obs_vname_to_season_to_nobs[vname][season]
t, p = ttest_ind_from_stats(mean1=a, std1=std_a, nobs1=nobs_a,
mean2=b, std2=std_b, nobs2=nobs_b, equal_var=False)
# Mask non-significant differences as given by the ttest
to_plot = np.ma.masked_where(p > p_crit, to_plot)
# only focus on the basins of interest
to_plot = np.ma.masked_where(~reg_of_interest_mask, to_plot)
# cs = bsmap.contourf(xx, yy, to_plot, ax=ax, extend="both", levels=get_clevs(vname + "bias"), cmap=cmap, norm=bnorm)
bsmap.drawmapboundary(fill_color="0.75")
cs = bsmap.pcolormesh(xx, yy, to_plot, ax=ax, cmap=cmap, norm=bnorm)
bsmap.drawcoastlines(linewidth=coastlines_width)
bsmap.colorbar(cs, ax=ax, extend="both")
for i, shp in enumerate(add_shp_files[1:], start=1):
bsmap.readshapefile(str(shp)[:-4], "field_{}".format(i), linewidth=0.5, color="k")
if col == 0:
ax.set_ylabel("{}\n-\nObs.".format(sim_label))
fig.tight_layout()
# save a figure per variable
img_file = "seasonal_biases_{}_{}_{}-{}.png".format(vname,
"-".join([s for s in vname_to_seasonmonths_map[vname]]),
start_year, end_year)
if not img_folder.exists():
img_folder.mkdir(parents=True)
img_file = img_folder / img_file
fig.savefig(str(img_file), bbox_inches="tight", dpi=300)
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-rl.hdf5",
start_year=1980,
end_year=2010,
label="CRCM5-L")
bmp_info = analysis.get_basemap_info_from_hdf(file_path=r_config.data_path)
# Validate temperature and precip
model_vars = ["TT", "PR"]
# Get Anusplin data managers
obs_path = "/home/huziy/skynet3_rech1/anusplin_links"
pcp_obs_manager = AnuSplinManager(variable="pcp", folder_path=obs_path)
tmax_obs_manager = AnuSplinManager(variable="stmx", folder_path=obs_path)
tmin_obs_manager = AnuSplinManager(variable="stmn", folder_path=obs_path)
vname_to_obs_data = {}
for vname in model_vars:
if vname == "TT":
dates, vals_max = tmax_obs_manager.get_daily_clim_fields_interpolated_to(
start_year=r_config.start_year,
end_year=r_config.end_year,
lons_target=bmp_info.lons,
lats_target=bmp_info.lats)
_, vals_min = tmin_obs_manager.get_daily_clim_fields_interpolated_to(
start_year=r_config.start_year,
end_year=r_config.end_year,
lons_target=bmp_info.lons,
lats_target=bmp_info.lats)
daily_obs = (dates, (vals_min + vals_max) * 0.5)
elif vname == "PR":
daily_obs = pcp_obs_manager.get_daily_clim_fields_interpolated_to(
start_year=r_config.start_year,
end_year=r_config.end_year,
lons_target=bmp_info.lons,
lats_target=bmp_info.lats)
else:
raise Exception("Unknown variable: {}".format(vname))
season_to_obs_data = OrderedDict()
for season, months in season_to_months.items():
season_to_obs_data[season] = np.mean(
[f for d, f in zip(*daily_obs) if d.month in months], axis=0)
vname_to_obs_data[vname] = season_to_obs_data
plot_all_vars_in_one_fig = True
fig = None
gs = None
row_axes = None
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=12)
fig = plt.figure()
ncols = len(season_to_months) + 1
gs = GridSpec(len(model_vars),
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
for mname in model_vars:
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)
row += 1
# Save the figure if necessary
if plot_all_vars_in_one_fig:
fig_path = img_folder.joinpath("{}.png".format("_".join(model_vars)))
with fig_path.open("wb") as figfile:
fig.savefig(figfile, format="png", bbox_inches="tight")
plt.close(fig)
def main():
"""
"""
season_to_months = get_default_season_to_months_dict()
# season_to_months = OrderedDict([("April", [4, ]), ("May", [5, ]), ("June", [6, ]), ("July", [7, ])])
var_names = ["TT", "HU", "PR", "AV", "AH", "STFL", "TRAF", "I5", "I0"]
# var_names = ["TT", "PR"]
levels = [
0,
] * len(var_names)
multipliers = {"PR": 1.0e3 * 24.0 * 3600., "TRAF": 24 * 3600.0}
base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
base_label = "CanESM2-CRCM5-NL"
modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
"quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
modif_label = "CanESM2-CRCM5-L"
# base_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
# base_label = "CRCM5-L"
#
# modif_current_path = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/" \
# "quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
# modif_label = "CRCM5-LI"
start_year_c = 1980
end_year_c = 2010
future_shift_years = 90
params = dict(data_path=base_current_path,
start_year=start_year_c,
end_year=end_year_c,
label=base_label)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
params.update(dict(data_path=modif_current_path, label=modif_label))
modif_config_c = RunConfig(**params)
modif_config_f = modif_config_c.get_shifted_config(future_shift_years)
config_dict = OrderedDict([("Current",
OrderedDict([(base_label, base_config_c),
(modif_label, modif_config_c)])),
("Future",
OrderedDict([(base_label, base_config_f),
(modif_label, modif_config_f)]))])
# Plot the differences
config_dict.label_modif = modif_config_c.label
config_dict.label_base = base_config_c.label
config_dict.season_to_months = season_to_months
config_dict.multipliers = multipliers
lons, lats, bmp = analysis.get_basemap_from_hdf(base_current_path)
config_dict.lons = lons
config_dict.lats = lats
config_dict.basemap = bmp
# Calculate and plot seasonal means
for vname, level in zip(var_names, levels):
data = get_data(vname=vname, level=level, config_dict=config_dict)
_plot_var(vname=vname,
level=level,
config_dict=config_dict,
data_dict=data)
def main():
import application_properties
application_properties.set_current_directory()
# Create folder for output images
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
rea_driven_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"
rea_driven_label = "CRCM5-L-ERAI"
# gcm_driven_path_c = "/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-r-cc-canesm2-1980-2010.hdf5"
gcm_driven_path_c = "/home/huziy/skynet3_rech1/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-1980-2010.hdf5"
gcm_driven_label_c = "CRCM5-L"
gcm_driven_path_f = "/home/huziy/skynet3_rech1/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-cc-canesm2-2070-2100.hdf5"
start_year_c = 1980
end_year_c = 2010
varname = "STFL"
future_shift_years = 90
params = dict(
data_path=rea_driven_path, start_year=start_year_c, end_year=end_year_c, label=rea_driven_label)
geo_data_file = "/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
rea_driven_config = RunConfig(**params)
params.update(dict(data_path=gcm_driven_path_c, label=gcm_driven_label_c))
gcm_driven_config_c = RunConfig(**params)
gcm_driven_config_f = gcm_driven_config_c.get_shifted_config(shift_years=future_shift_years, data_path=gcm_driven_path_f)
r_obj = RPN(geo_data_file)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
lkfr = r_obj.get_first_record_for_name("ML")
# get basemap information
bmp_info = analysis.get_basemap_info_from_hdf(file_path=rea_driven_path)
# get basin mask
_, bname_to_mask = plot_basin_outlets(bmp_info=bmp_info, directions=fldr, accumulation_areas=facc, lake_fraction_field=lkfr)
all_basins_mask = np.zeros(fldr.shape)
for bname, the_mask in bname_to_mask.items():
all_basins_mask += the_mask
rs_gcm_c = get_return_levels_and_unc_using_bootstrap(gcm_driven_config_c, varname=varname)
rs_gcm_f = get_return_levels_and_unc_using_bootstrap(gcm_driven_config_f, varname=varname)
plot_utils.apply_plot_params(font_size=10, width_cm=20, height_cm=18)
# Plot return level changes
fig = plt.figure()
nplots = 0
for the_type, rp_to_rl in rs_gcm_c.return_lev_dict.items():
nplots += len(rp_to_rl)
ncols = 2
nrows = nplots // ncols + int(nplots % ncols != 0)
gs = GridSpec(nrows, ncols + 1, width_ratios=[1.0, ] * ncols + [0.05, ])
xx, yy = bmp_info.get_proj_xy()
cmap = cm.get_cmap("bwr", 20)
limits = {
"high": (-50, 50),
"low": (-150, 150)
}
for row, (the_type, rp_to_rl) in enumerate(sorted(rs_gcm_c.return_lev_dict.items(), key=lambda itm: itm[0])):
for col, rp in enumerate(sorted(rp_to_rl)):
ax = fig.add_subplot(gs[row, col])
rl = rp_to_rl[rp]
# Ignore 0 return levels in the current climate for percentage calculations
# rl = np.ma.masked_where(rl <= 0, rl)
rl_future = rs_gcm_f.return_lev_dict[the_type][rp]
# Calculate climate change signal
diff = (rl_future - rl) / (np.abs(rl + rl_future) * 0.5) * 100
diff[(rl_future <= 0) & (rl <= 0)] = 0
# diff = rl
diff = maskoceans(bmp_info.lons, bmp_info.lats, diff)
print(diff.min(), diff.max())
std_c = rs_gcm_c.std_dict[the_type][rp]
std_f = rs_gcm_f.std_dict[the_type][rp]
significance = (np.ma.abs(diff) >= 1.96 * (std_c + std_f)) & (~diff.mask)
significance = significance.astype(int)
vmin, vmax = limits[the_type]
diff = np.ma.masked_where(all_basins_mask < 0.5, diff)
im = bmp_info.basemap.pcolormesh(xx, yy, diff, vmin=vmin, vmax=vmax, cmap=cmap)
significance = np.ma.masked_where(all_basins_mask < 0.5, significance)
cs = bmp_info.basemap.contourf(xx, yy, significance,
levels=[0, 0.5, 1],
hatches=["////", None, None],
colors="none")
if row == nrows - 1 and col == ncols - 1:
# create a legend for the contour set
artists, labels = cs.legend_elements()
ax.legend([artists[0], ], ["not significant", ], handleheight=0.5,
bbox_to_anchor=(1, -0.1), loc="upper right", borderaxespad=0.)
ax.set_title("T = {}-year".format(rp))
if col == 0:
ax.set_ylabel("{} flow".format(the_type))
bmp_info.basemap.drawcoastlines(ax=ax)
bmp_info.basemap.drawmapboundary(fill_color="0.75")
bmp_info.basemap.readshapefile(".".join(quebec_info.BASIN_BOUNDARIES_DERIVED_10km.split(".")[:-1]).replace("utm18", "latlon"),
"basin",
linewidth=1.2, ax=ax)
if col == ncols - 1:
cax = fig.add_subplot(gs[row, -1])
plt.colorbar(im, cax=cax, extend="both")
cax.set_title("%")
# Print basin average changes
print("--- start ----------------{rp}-year {ext_type} flow ----------------".format(rp=rp, ext_type=the_type))
bname_to_diff = [(bname, diff[the_mask > 0.5].mean()) for bname, the_mask in bname_to_mask.items()]
bname_to_diff = list(sorted(bname_to_diff, key=lambda item: item[1]))
for bname, the_diff in bname_to_diff:
print("{bname}: cc = {cc:.2f}%".format(bname=bname, cc=the_diff))
print("--- end ----------------{rp}-year {ext_type} flow ----------------".format(rp=rp, ext_type=the_type))
img_file = img_folder.joinpath("rl_cc_{}.png".format(gcm_driven_config_c.label))
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def main():
import application_properties
application_properties.set_current_directory()
if not IMG_FOLDER.exists():
IMG_FOLDER.mkdir(parents=True)
plot_utils.apply_plot_params(font_size=14,
width_pt=None,
width_cm=20,
height_cm=20)
start_year = 1980
end_year = 2010
varname = "TDRA"
base_config = RunConfig(
start_year=start_year,
end_year=end_year,
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
label="NI")
modif_config = RunConfig(
start_year=start_year,
end_year=end_year,
data_path=
"/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5",
label="WI")
r_obj = RPN(GEO_DATA_FILE)
facc = r_obj.get_first_record_for_name("FAA")
fldr = r_obj.get_first_record_for_name("FLDR")
mg = r_obj.get_first_record_for_name("MG")
lons, lats, bmp = analysis.get_basemap_from_hdf(
file_path=base_config.data_path)
# Calculate the daily mean fields
dates, daily_clim_base = analysis.get_daily_climatology_for_rconf(
base_config, var_name=varname, level=0)
_, daily_clim_modif = analysis.get_daily_climatology_for_rconf(
modif_config, var_name=varname, level=0)
_, pr_base = analysis.get_daily_climatology_for_rconf(base_config,
var_name="PR",
level=0)
_, pr_modif = analysis.get_daily_climatology_for_rconf(modif_config,
var_name="PR",
level=0)
_, av_base = analysis.get_daily_climatology_for_rconf(base_config,
var_name="AV",
level=0)
_, av_modif = analysis.get_daily_climatology_for_rconf(modif_config,
var_name="AV",
level=0)
_, intf_rates = analysis.get_daily_climatology_for_rconf(modif_config,
var_name="INTF",
level=0)
# Plot the difference
fig = plt.figure()
xx, yy = bmp(lons, lats)
# daily_clim_base = np.array([f for d, f in zip(dates, daily_clim_base) if d.month not in range(3, 12)])
# daily_clim_modif = np.array([f for d, f in zip(dates, daily_clim_modif) if d.month not in range(3, 12)])
mean_base = np.mean(daily_clim_base, axis=0)
diff = (np.mean(daily_clim_modif, axis=0) - mean_base) * 24 * 3600
dpr = (pr_modif.sum(axis=0) - pr_base.sum(axis=0)) / pr_base.sum(axis=0)
dav = (av_modif.sum(axis=0) - av_base.sum(axis=0)) / av_base.sum(axis=0)
diff = np.ma.masked_where(
(mg <= 1.0e-3) | (dpr < 0) | (dav > 0) | (diff > 0), diff)
print("{}-ranges: {}, {}".format(varname, daily_clim_base.min(),
daily_clim_base.max()))
print("{}-ranges: {}, {}".format(varname, daily_clim_modif.min(),
daily_clim_modif.max()))
limit_base = np.percentile(daily_clim_base, 90, axis=0)
limit_modif = np.percentile(daily_clim_modif, 50, axis=0)
limit_lower_intf = 1.0e-4 / (24.0 * 60.0 * 60.0)
ndays_base = daily_clim_base > limit_base[np.newaxis, :, :]
ndays_base = ndays_base.sum(axis=0)
ndays_modif = daily_clim_modif > limit_base[np.newaxis, :, :]
ndays_modif = ndays_modif.sum(axis=0)
diff_days = np.ma.masked_where(
(mg <= 1.0e-4) | (intf_rates.max() < limit_lower_intf),
ndays_modif - ndays_base)
diff_days = np.ma.masked_where(diff_days > 0, diff_days)
print("diff_days ranges: {} to {}".format(diff_days.min(),
diff_days.max()))
im = bmp.pcolormesh(xx, yy, diff)
bmp.colorbar(im)
bmp.drawcoastlines()
img_file = IMG_FOLDER.joinpath("{}_{}-{}.png".format(
varname, start_year, end_year))
with img_file.open("wb") as imf:
fig.savefig(imf, bbox_inches="tight")
plt.show()
def main():
start_year = 1980
end_year = 2009
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
# critical p-value for the ttest aka significance level
p_crit = 1
vars_of_interest = [
# T_AIR_2M,
# TOTAL_PREC,
# SWE,
default_varname_mappings.LATENT_HF,
default_varname_mappings.SENSIBLE_HF,
default_varname_mappings.LWRAD_DOWN,
default_varname_mappings.SWRAD_DOWN
# LAKE_ICE_FRACTION
]
coastline_width = 0.3
vname_to_seasonmonths_map = {
SWE: OrderedDict([("November", [11]),
("December", [12]),
("January", [1, ])]),
LAKE_ICE_FRACTION: OrderedDict([
("December", [12]),
("January", [1, ]),
("February", [2, ]),
("March", [3, ]),
("April", [4, ])]),
T_AIR_2M: season_to_months,
TOTAL_PREC: season_to_months,
}
# set season to months mappings
for vname in vars_of_interest:
if vname not in vname_to_seasonmonths_map:
vname_to_seasonmonths_map[vname] = season_to_months
sim_configs = {
HL_LABEL: RunConfig(data_path="/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected",
start_year=start_year, end_year=end_year, label=HL_LABEL),
NEMO_LABEL: RunConfig(data_path="/RECH2/huziy/coupling/coupled-GL-NEMO1h_30min/selected_fields",
start_year=start_year, end_year=end_year, label=NEMO_LABEL),
}
sim_labels = [HL_LABEL, NEMO_LABEL]
vname_to_level = {
T_AIR_2M: VerticalLevel(1, level_kinds.HYBRID),
U_WE: VerticalLevel(1, level_kinds.HYBRID),
V_SN: VerticalLevel(1, level_kinds.HYBRID),
default_varname_mappings.LATENT_HF: VerticalLevel(5, level_kinds.ARBITRARY),
default_varname_mappings.SENSIBLE_HF: VerticalLevel(5, level_kinds.ARBITRARY),
}
# Try to get the land_fraction for masking if necessary
land_fraction = None
try:
first_ts_file = Path(sim_configs[HL_LABEL].data_path).parent / "pm1979010100_00000000p"
land_fraction = get_land_fraction(first_timestep_file=first_ts_file)
except Exception as err:
raise err
pass
# Calculations
# prepare params for interpolation
lons_t, lats_t, bsmap = get_target_lons_lats_basemap(sim_configs[HL_LABEL])
# get a subdomain of the simulation domain
nx, ny = lons_t.shape
iss = IndexSubspace(i_start=20, j_start=10, i_end=nx // 1.5, j_end=ny / 1.8)
# just to change basemap limits
lons_t, lats_t, bsmap = get_target_lons_lats_basemap(sim_configs[HL_LABEL], sub_space=iss)
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons_t.flatten(), lats_t.flatten())
vname_map = {}
vname_map.update(default_varname_mappings.vname_map_CRCM5)
# Read and calculate simulated seasonal means
mod_label_to_vname_to_season_to_std = {}
mod_label_to_vname_to_season_to_nobs = {}
sim_data = defaultdict(dict)
for label, r_config in sim_configs.items():
store_config = {
"base_folder": r_config.data_path,
"data_source_type": data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"varname_mapping": vname_map,
"level_mapping": vname_to_level,
"offset_mapping": default_varname_mappings.vname_to_offset_CRCM5,
"multiplier_mapping": default_varname_mappings.vname_to_multiplier_CRCM5,
}
dm = DataManager(store_config=store_config)
mod_label_to_vname_to_season_to_std[label] = {}
mod_label_to_vname_to_season_to_nobs[label] = {}
interp_indices = None
for vname in vars_of_interest:
# --
end_year_for_current_var = end_year
if vname == SWE:
end_year_for_current_var = min(1996, end_year)
# --
seas_to_year_to_mean = dm.get_seasonal_means(varname_internal=vname,
start_year=start_year,
end_year=end_year_for_current_var,
season_to_months=vname_to_seasonmonths_map[vname])
# get the climatology
seas_to_clim = {seas: np.array(list(y_to_means.values())).mean(axis=0) for seas, y_to_means in
seas_to_year_to_mean.items()}
sim_data[label][vname] = seas_to_clim
if interp_indices is None:
_, interp_indices = dm.get_kdtree().query(list(zip(xt, yt, zt)))
season_to_std = {}
mod_label_to_vname_to_season_to_std[label][vname] = season_to_std
season_to_nobs = {}
mod_label_to_vname_to_season_to_nobs[label][vname] = season_to_nobs
for season in seas_to_clim:
interpolated_field = seas_to_clim[season].flatten()[interp_indices].reshape(lons_t.shape)
seas_to_clim[season] = interpolated_field
# calculate standard deviations of the interpolated fields
season_to_std[season] = np.asarray([field.flatten()[interp_indices].reshape(lons_t.shape) for field in
seas_to_year_to_mean[season].values()]).std(axis=0)
# calculate numobs for the ttest
season_to_nobs[season] = np.ones_like(lons_t) * len(seas_to_year_to_mean[season])
# Plotting: interpolate to the same grid and plot obs and biases
xx, yy = bsmap(lons_t, lats_t)
lons_t[lons_t > 180] -= 360
for vname in vars_of_interest:
field_mask = maskoceans(lons_t, lats_t, np.zeros_like(lons_t), inlands=vname in [SWE]).mask
field_mask_lakes = maskoceans(lons_t, lats_t, np.zeros_like(lons_t), inlands=True).mask
plot_utils.apply_plot_params(width_cm=11 * len(vname_to_seasonmonths_map[vname]), height_cm=20, font_size=8)
fig = plt.figure()
nrows = len(sim_configs) + 1
ncols = len(vname_to_seasonmonths_map[vname])
gs = GridSpec(nrows=nrows, ncols=ncols)
# plot the fields
for current_row, sim_label in enumerate(sim_labels):
for col, season in enumerate(vname_to_seasonmonths_map[vname]):
field = sim_data[sim_label][vname][season]
ax = fig.add_subplot(gs[current_row, col])
if current_row == 0:
ax.set_title(season)
clevs = get_clevs(vname)
if clevs is not None:
bnorm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("viridis", len(clevs) - 1)
else:
cmap = "viridis"
bnorm = None
the_mask = field_mask_lakes if vname in [T_AIR_2M, TOTAL_PREC, SWE] else field_mask
to_plot = np.ma.masked_where(the_mask, field) * internal_name_to_multiplier[vname]
# temporary plot the actual values
cs = bsmap.contourf(xx, yy, to_plot, ax=ax, levels=get_clevs(vname), cmap=cmap, norm=bnorm, extend="both")
bsmap.drawcoastlines(linewidth=coastline_width)
bsmap.colorbar(cs, ax=ax)
if col == 0:
ax.set_ylabel("{}".format(sim_label))
# plot differences between the fields
for col, season in enumerate(vname_to_seasonmonths_map[vname]):
field = sim_data[NEMO_LABEL][vname][season] - sim_data[HL_LABEL][vname][season]
ax = fig.add_subplot(gs[-1, col])
clevs = get_clevs(vname + "biasdiff")
if clevs is not None:
bnorm = BoundaryNorm(clevs, len(clevs) - 1)
cmap = cm.get_cmap("bwr", len(clevs) - 1)
else:
cmap = "bwr"
bnorm = None
to_plot = field * internal_name_to_multiplier[vname]
# to_plot = np.ma.masked_where(field_mask, field) * internal_name_to_multiplier[vname]
# ttest
a = sim_data[NEMO_LABEL][vname][season] # Calculate the simulation data back from biases
std_a = mod_label_to_vname_to_season_to_std[NEMO_LABEL][vname][season]
nobs_a = mod_label_to_vname_to_season_to_nobs[NEMO_LABEL][vname][season]
b = sim_data[HL_LABEL][vname][season] # Calculate the simulation data back from biases
std_b = mod_label_to_vname_to_season_to_std[HL_LABEL][vname][season]
nobs_b = mod_label_to_vname_to_season_to_nobs[HL_LABEL][vname][season]
t, p = ttest_ind_from_stats(mean1=a, std1=std_a, nobs1=nobs_a,
mean2=b, std2=std_b, nobs2=nobs_b, equal_var=False)
# Mask non-significant differences as given by the ttest
to_plot = np.ma.masked_where(p > p_crit, to_plot)
# mask the points with not sufficient land fraction
if land_fraction is not None and vname in [SWE, ]:
to_plot = np.ma.masked_where(land_fraction < 0.05, to_plot)
# print("land fractions for large differences ", land_fraction[to_plot > 30])
cs = bsmap.contourf(xx, yy, to_plot, ax=ax, extend="both", levels=get_clevs(vname + "biasdiff"), cmap=cmap, norm=bnorm)
bsmap.drawcoastlines(linewidth=coastline_width)
bsmap.colorbar(cs, ax=ax)
if col == 0:
ax.set_ylabel("{}\n-\n{}".format(NEMO_LABEL, HL_LABEL))
fig.tight_layout()
# save a figure per variable
img_file = "seasonal_differences_noobs_{}_{}_{}-{}.png".format(vname,
"-".join([s for s in vname_to_seasonmonths_map[vname]]),
start_year, end_year)
img_file = img_folder.joinpath(img_file)
fig.savefig(str(img_file), dpi=300)
plt.close(fig)
def get_seasonal_sst_from_crcm5_outputs(sim_label,
start_year=1980,
end_year=2010,
season_to_months=None,
lons_target=None,
lats_target=None):
from lake_effect_snow.default_varname_mappings import T_AIR_2M
from lake_effect_snow.default_varname_mappings import U_WE
from lake_effect_snow.default_varname_mappings import V_SN
from lake_effect_snow.base_utils import VerticalLevel
from rpn import level_kinds
from lake_effect_snow import default_varname_mappings
from data.robust import data_source_types
from data.robust.data_manager import DataManager
sim_configs = {
sim_label:
RunConfig(
data_path=
"/RECH2/huziy/coupling/GL_440x260_0.1deg_GL_with_Hostetler/Samples_selected",
start_year=start_year,
end_year=end_year,
label=sim_label),
}
r_config = sim_configs[sim_label]
vname_to_level = {
T_AIR_2M:
VerticalLevel(1, level_kinds.HYBRID),
U_WE:
VerticalLevel(1, level_kinds.HYBRID),
V_SN:
VerticalLevel(1, level_kinds.HYBRID),
default_varname_mappings.LAKE_WATER_TEMP:
VerticalLevel(1, level_kinds.ARBITRARY)
}
vname_map = {}
vname_map.update(default_varname_mappings.vname_map_CRCM5)
store_config = {
"base_folder": r_config.data_path,
"data_source_type":
data_source_types.SAMPLES_FOLDER_FROM_CRCM_OUTPUT_VNAME_IN_FNAME,
"varname_mapping": vname_map,
"level_mapping": vname_to_level,
"offset_mapping": default_varname_mappings.vname_to_offset_CRCM5,
"multiplier_mapping":
default_varname_mappings.vname_to_multiplier_CRCM5,
}
dm = DataManager(store_config=store_config)
season_to_year_to_mean = dm.get_seasonal_means(
start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
varname_internal=default_varname_mappings.LAKE_WATER_TEMP)
result = {}
# fill in the result dictionary with seasonal means
for season in season_to_months:
result[season] = np.array([
field for field in season_to_year_to_mean[season].values()
]).mean(axis=0)
# interpolate the data
if lons_target is not None:
xt, yt, zt = lat_lon.lon_lat_to_cartesian(lons_target.flatten(),
lats_target.flatten())
dists, inds = dm.get_kdtree().query(list(zip(xt, yt, zt)))
for season in season_to_months:
result[season] = result[season].flatten()[inds].reshape(
lons_target.shape)
return result
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():
start_year_c = 1980
end_year_c = 2010
img_folder = "cc_paper"
current_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/cc-canesm2-driven/quebec_0.1_crcm5-hcd-rl-intfl-cc-canesm2-1980-2010.hdf5"
base_label = "CRCM5-LI"
# Need to read land fraction
geo_file_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/pm1979010100_00000000p"
r_obj = RPN(geo_file_path)
mg_field = r_obj.get_first_record_for_name("MG")
lons, lats = r_obj.get_longitudes_and_latitudes_for_the_last_read_rec()
r_obj.close()
future_shift_years = 90
params = dict(
data_path=current_path,
start_year=start_year_c, end_year=end_year_c,
label=base_label
)
base_config_c = RunConfig(**params)
base_config_f = base_config_c.get_shifted_config(future_shift_years)
varname = "INTF"
level = 0
daily_dates, intf_c = analysis.get_daily_climatology(path_to_hdf_file=base_config_c.data_path,
var_name=varname, level=level,
start_year=base_config_c.start_year,
end_year=base_config_c.end_year)
_, intf_f = analysis.get_daily_climatology(path_to_hdf_file=base_config_f.data_path,
var_name=varname, level=level,
start_year=base_config_f.start_year,
end_year=base_config_f.end_year)
mg_fields = np.asarray([mg_field for d in daily_dates])
mg_crit = 0.0001
the_mask = mg_fields <= mg_crit
# Convert to mm/day as well
intf_c = _avg_along_lon(intf_c, the_mask) * 24 * 3600
intf_f = _avg_along_lon(intf_f, the_mask) * 24 * 3600
lats_agg = lats.mean(axis=0)
num_dates = date2num(daily_dates)
lats_agg_2d, num_dates_2d = np.meshgrid(lats_agg, num_dates)
# Do the plotting
fig = plt.figure()
gs = GridSpec(2, 3, width_ratios=[1, 1, 0.05])
norm = SymLogNorm(5e-5)
all_axes = []
# Current
ax = fig.add_subplot(gs[0, 0])
cs = ax.contourf(num_dates_2d, lats_agg_2d, intf_c[:], 60, norm=norm)
ax.set_title("Current ({}-{})".format(
base_config_c.start_year, base_config_c.end_year))
all_axes.append(ax)
# Future
ax = fig.add_subplot(gs[0, 1])
ax.set_title("Future ({}-{})".format(
base_config_f.start_year, base_config_f.end_year))
cs = ax.contourf(num_dates_2d, lats_agg_2d, intf_f[:], levels=cs.levels, norm=norm)
all_axes.append(ax)
# Colorbar for value plots
cax = fig.add_subplot(gs[0, 2])
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-1, 2))
plt.colorbar(cs, cax=cax, format=sfmt)
cax.set_xlabel("mm/day")
cax.yaxis.get_offset_text().set_position((-2, 10))
# CC
diff_cmap = cm.get_cmap("RdBu_r", 20)
diff = (intf_f - intf_c) / (0.5 * (intf_c + intf_f)) * 100
diff[(intf_f == 0) & (intf_c == 0)] = 0
print(np.min(diff), np.max(diff))
print(np.any(diff.mask))
print(np.any(intf_c.mask))
print(np.any(intf_f.mask))
delta = 200
vmin = -delta
vmax = delta
locator = MaxNLocator(nbins=20, symmetric=True)
clevs = locator.tick_values(vmin=vmin, vmax=vmax)
ax = fig.add_subplot(gs[1, 1])
cs = ax.contourf(num_dates_2d, lats_agg_2d, diff, cmap=diff_cmap,
levels=clevs, extend="both")
ax.set_title("Future - Current")
# ax.set_aspect("auto")
all_axes.append(ax)
cb = plt.colorbar(cs, cax=fig.add_subplot(gs[1, -1]))
cb.ax.set_xlabel(r"%")
for i, the_ax in enumerate(all_axes):
the_ax.xaxis.set_minor_formatter(FuncFormatter(lambda d, pos: num2date(d).strftime("%b")[0]))
the_ax.xaxis.set_major_formatter(FuncFormatter(lambda d, pos: ""))
the_ax.xaxis.set_minor_locator(MonthLocator(bymonthday=15))
the_ax.xaxis.set_major_locator(MonthLocator())
the_ax.grid()
if i != 1:
the_ax.set_ylabel(r"Latitude ${\rm \left(^\circ N \right)}$")
# identify approximately the melting period and lower latitudes
march1 = date2num(datetime(daily_dates[0].year, 3, 1))
june1 = date2num(datetime(daily_dates[0].year, 6, 1))
sel_mask = (num_dates_2d >= march1) & (num_dates_2d < june1) & (lats_agg_2d <= 50)
print("Mean interflow decrease in the southern regions: {}%".format(diff[sel_mask].mean()))
# identify the regions of max interflow rates in current and future climates
lat_min = 55
lat_max = 57.5
may1 = date2num(datetime(daily_dates[0].year, 5, 1))
july1 = date2num(datetime(daily_dates[0].year, 7, 1))
mean_max_current = intf_c[(lats_agg_2d >= lat_min) & (lats_agg_2d <= lat_max) & (num_dates_2d <= july1) & (num_dates_2d >= june1)].mean()
mean_max_future = intf_f[(lats_agg_2d >= lat_min) & (lats_agg_2d <= lat_max) & (num_dates_2d <= june1) & (num_dates_2d >= may1)].mean()
print("Mean change in the maximum interflow rate: {} %".format((mean_max_future - mean_max_current) * 100 / mean_max_current))
img_file = Path(img_folder).joinpath("INTF_rate_longit_avg.png")
fig.tight_layout()
from crcm5.analyse_hdf import common_plot_params
fig.savefig(str(img_file), bbox_inches="tight", transparent=True, dpi=common_plot_params.FIG_SAVE_DPI)
