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 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():
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 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():
# 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():
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():
# 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():
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()
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():
# 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()
