def calculate_correlation_field_for_climatology(start_year=None,
end_year=None,
path1="",
varname1="",
level1=None,
path2="",
varname2="",
level2=None,
months=None):
dates, data1 = analysis.get_daily_climatology(path_to_hdf_file=path1,
var_name=varname1,
level=level1,
start_year=start_year,
end_year=end_year)
dates, data2 = analysis.get_daily_climatology(path_to_hdf_file=path2,
var_name=varname2,
level=level2,
start_year=start_year,
end_year=end_year)
if months is None:
months = list(range(1, 13))
selfields1 = [f for date, f in zip(dates, data1) if date.month in months]
selfields2 = [f for date, f in zip(dates, data2) if date.month in months]
return calculate_correlation(
selfields1, selfields2), np.array(selfields1), np.array(selfields2)
def calculate_correlation_of_infiltration_rate_with(
start_year=None,
end_year=None,
path_for_infiltration_data="",
path2="",
varname2="",
level2=None,
months=None):
dates, pr_data = analysis.get_daily_climatology(
path_to_hdf_file=path_for_infiltration_data,
var_name="PR",
level=0,
start_year=start_year,
end_year=end_year)
# Take interflow calculated for soil subareas
dates, srunoff_data = analysis.get_daily_climatology(
path_to_hdf_file=path_for_infiltration_data,
var_name="TRAF",
level=0,
start_year=start_year,
end_year=end_year)
dates, evap_data = analysis.get_daily_climatology(
path_to_hdf_file=path_for_infiltration_data,
var_name="AV",
level=0,
start_year=start_year,
end_year=end_year)
# Convert lists to numpy arrays
pr_data = np.array(pr_data)
srunoff_data = np.array(srunoff_data)
evap_data = np.array(evap_data)
# calculate infiltration from runoff precip and evap
infiltration = pr_data - srunoff_data / crcm_constants.rho_water - \
evap_data / (crcm_constants.Lv_J_per_kg * crcm_constants.rho_water)
dates, data2 = analysis.get_daily_climatology(path_to_hdf_file=path2,
var_name=varname2,
level=level2,
start_year=start_year,
end_year=end_year)
if months is None:
months = list(range(1, 13))
selfields1 = [
f for date, f in zip(dates, infiltration) if date.month in months
]
selfields2 = [f for date, f in zip(dates, data2) if date.month in months]
return calculate_correlation(selfields1, selfields2)
def demo_interolate_daily_clim():
import crcm5.analyse_hdf.do_analysis_using_pytables as analysis
model_data_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5"
start_year = 1980
end_year = 2010
vmin = -30
vmax = 30
vname = "TT_max"
coef_mod = 1.0e3 * 24 * 3600 if vname == "PR" else 1.0
# get target lons and lats for testing
lon, lat, basemap = analysis.get_basemap_from_hdf(
file_path=model_data_path)
ans = AnuSplinManager(variable="stmx")
dates, fields = ans.get_daily_clim_fields_interpolated_to(start_year=start_year,
end_year=end_year,
lons_target=lon, lats_target=lat)
import matplotlib.pyplot as plt
x, y = basemap(lon, lat)
margin = 20
topo = _get_topography()[margin:-margin, margin:-margin]
# Plot obs data
plt.figure()
mean_obs = np.ma.array([fields[i] for i, d in enumerate(dates) if d.month in range(1, 13)]).mean(axis=0)
im = basemap.pcolormesh(x, y, mean_obs, vmin=vmin, vmax=vmax)
basemap.colorbar(im)
basemap.drawcoastlines()
plt.title("Anusplin")
print("Obs stdev = {}".format(mean_obs[~mean_obs.mask].std()))
print("Obs correlations: ", np.corrcoef(mean_obs[~mean_obs.mask], topo[~mean_obs.mask]))
# Plot model data
plt.figure()
dates, fields = analysis.get_daily_climatology(path_to_hdf_file=model_data_path, var_name=vname,
level=0,
start_year=start_year, end_year=end_year)
mean_mod = np.array([fields[i] for i, d in enumerate(dates) if d.month in range(1, 13)]).mean(axis=0) * coef_mod
im = basemap.pcolormesh(x, y, mean_mod, vmin=vmin, vmax=vmax)
basemap.colorbar(im)
basemap.drawcoastlines()
plt.title("Model")
print("Model correlations: ", np.corrcoef(mean_mod[~mean_obs.mask], topo[~mean_obs.mask]))
print("Model stdev = {}".format(mean_mod[~mean_obs.mask].std()))
plt.show()
def demo_interolate_daily_clim():
import crcm5.analyse_hdf.do_analysis_using_pytables as analysis
model_data_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-r.hdf5"
start_year = 1980
end_year = 2010
vmin = -30
vmax = 30
vname = "TT_max"
coef_mod = 1.0e3 * 24 * 3600 if vname == "PR" else 1.0
# get target lons and lats for testing
lon, lat, basemap = analysis.get_basemap_from_hdf(
file_path=model_data_path)
ans = AnuSplinManager(variable="stmx")
dates, fields = ans.get_daily_clim_fields_interpolated_to(
start_year=start_year,
end_year=end_year,
lons_target=lon,
lats_target=lat)
import matplotlib.pyplot as plt
x, y = basemap(lon, lat)
margin = 20
topo = _get_topography()[margin:-margin, margin:-margin]
# Plot obs data
plt.figure()
mean_obs = np.ma.array([
fields[i] for i, d in enumerate(dates) if d.month in range(1, 13)
]).mean(axis=0)
im = basemap.pcolormesh(x, y, mean_obs, vmin=vmin, vmax=vmax)
basemap.colorbar(im)
basemap.drawcoastlines()
plt.title("Anusplin")
print("Obs stdev = {}".format(mean_obs[~mean_obs.mask].std()))
print("Obs correlations: ",
np.corrcoef(mean_obs[~mean_obs.mask], topo[~mean_obs.mask]))
# Plot model data
plt.figure()
dates, fields = analysis.get_daily_climatology(
path_to_hdf_file=model_data_path,
var_name=vname,
level=0,
start_year=start_year,
end_year=end_year)
mean_mod = np.array([
fields[i] for i, d in enumerate(dates) if d.month in range(1, 13)
]).mean(axis=0) * coef_mod
im = basemap.pcolormesh(x, y, mean_mod, vmin=vmin, vmax=vmax)
basemap.colorbar(im)
basemap.drawcoastlines()
plt.title("Model")
print("Model correlations: ",
np.corrcoef(mean_mod[~mean_obs.mask], topo[~mean_obs.mask]))
print("Model stdev = {}".format(mean_mod[~mean_obs.mask].std()))
plt.show()
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():
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 read_cc_and_cc_diff(base_configs,
modif_configs,
name_to_indices=None,
varname=None):
"""
get cc for 2 different configurations and the difference
work with the area means if basin_name_to_basin_mask is not None
:param base_configs:
:param modif_configs:
:param name_to_indices:
:param varname:
:return:
"""
base_c, base_f = base_configs
modif_c, modif_f = modif_configs
# Sasha: custom end year
end_year_future = base_f.end_year
end_year_current = base_c.end_year
level = 0
daily_dates, data_clim_base_c = analysis.get_daily_climatology(
path_to_hdf_file=base_c.data_path,
var_name=varname,
level=level,
start_year=base_c.start_year,
end_year=end_year_current)
_, data_clim_base_f = analysis.get_daily_climatology(
path_to_hdf_file=base_f.data_path,
var_name=varname,
level=level,
start_year=base_f.start_year,
end_year=end_year_future)
_, data_clim_modif_c = analysis.get_daily_climatology(
path_to_hdf_file=modif_c.data_path,
var_name=varname,
level=level,
start_year=modif_c.start_year,
end_year=end_year_current)
_, data_clim_modif_f = analysis.get_daily_climatology(
path_to_hdf_file=modif_f.data_path,
var_name=varname,
level=level,
start_year=modif_f.start_year,
end_year=end_year_future)
delta_modif = data_clim_modif_f - data_clim_modif_c
delta_base = data_clim_base_f - data_clim_base_c
sim_label_to_cc_fields = {
base_c.label: delta_base,
modif_c.label: delta_modif
}
data_to_plot = DataToPlot(daily_dates=daily_dates,
simlabel_to_cc_fields=sim_label_to_cc_fields,
basin_name_to_out_indices=name_to_indices)
data_to_plot.set_base_and_modif_labels(base_c.label, modif_c.label)
return data_to_plot
def draw_model_comparison(model_points=None, stations=None, sim_name_to_file_name=None, hdf_folder=None,
start_year=None, end_year=None, cell_manager=None, stfl_name="STFA",
drainage_area_reldiff_min=0.1, plot_upstream_area_averaged=True,
sim_name_to_color=None):
"""
:param model_points: list of model point objects
:param stations: list of stations corresponding to the list of model points
:param cell_manager: is a CellManager instance which can be provided for better performance if necessary
len(model_points) == len(stations) if stations is not None.
if stations is None - then no measured streamflow will be plotted
"""
assert model_points is None or stations is None or len(stations) == len(model_points)
label_list = list(sim_name_to_file_name.keys()) # Needed to keep the order the same for all subplots
path0 = os.path.join(hdf_folder, list(sim_name_to_file_name.items())[0][1])
flow_directions = analysis.get_array_from_file(path=path0, var_name="flow_direction")
lake_fraction = analysis.get_array_from_file(path=path0, var_name="lake_fraction")
# mask lake fraction in the ocean
lake_fraction = np.ma.masked_where((flow_directions <= 0) | (flow_directions > 128), lake_fraction)
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)
print("cell area ranges from {} to {}".format(cell_area_km2.min(), cell_area_km2.max()))
# print "plotting from {0}".format(path0)
# plt.pcolormesh(lake_fraction.transpose())
# plt.colorbar()
# plt.show()
# exit()
file_scores = open("scores_{0}_{1}-{2}.txt".format("_".join(label_list), start_year, end_year), "w")
file_correlations = open("corr_{0}_{1}-{2}.txt".format("_".join(label_list), start_year, end_year), "w")
file_annual_discharge = open("flow_{0}_{1}-{2}.txt".format("_".join(label_list), start_year, end_year), "w")
text_files = [file_scores, file_correlations, file_annual_discharge]
# write the following columns to the scores file
header_format = "{0:10s}\t{1:10s}\t{2:10s}\t" + "\t".join(["{" + str(i + 3) + ":10s}"
for i in range(len(sim_name_to_file_name))])
line_format = "{0:10s}\t{1:10.1f}\t{2:10.1f}\t" + "\t".join(["{" + str(i + 3) + ":10.1f}"
for i in range(len(sim_name_to_file_name))])
header_ns = ("ID", "DAo", "DAm",) + tuple(["NS({0})".format(key) for key in sim_name_to_file_name])
file_scores.write(header_format.format(*header_ns) + "\n")
header_qyear = ("ID", "DAo", "DAm",) + tuple(["Qyear({0})".format(key) for key in label_list]) + \
("Qyear(obs)",)
header_format_qyear = header_format + "\t{" + str(len(label_list) + 3) + ":10s}"
file_annual_discharge.write(header_format_qyear.format(*header_qyear) + "\n")
lons2d, lats2d, basemap = analysis.get_basemap_from_hdf(file_path=path0)
# Create a cell manager if it is not provided
if cell_manager is None:
cell_manager = CellManager(flow_directions, accumulation_area_km2=accumulation_area_km2,
lons2d=lons2d, lats2d=lats2d)
if stations is not None:
# 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=drainage_area_reldiff_min)
station_list = list(station_to_modelpoint.keys())
station_list.sort(key=lambda st1: st1.latitude, reverse=True)
mp_list = [station_to_modelpoint[st] for st in station_list]
else:
mp_list = model_points
station_list = None
# sort so that the northernmost stations appear uppermost
mp_list.sort(key=lambda mpt: mpt.latitude, reverse=True)
# set ids to the model points so they can be distinguished easier
model_point.set_model_point_ids(mp_list)
# ###Uncomment the lines below for the validation plot in paper 2
# brewer2mpl.get_map args: set name set type number of colors
# bmap = brewer2mpl.get_map("Set1", "qualitative", 9)
# Change the default colors
# mpl.rcParams["axes.color_cycle"] = bmap.mpl_colors
# For the streamflow only plot
ncols = 3
nrows = max(len(mp_list) // ncols, 1)
if ncols * nrows < len(mp_list):
nrows += 1
figure_stfl = plt.figure(figsize=(4 * ncols, 3 * nrows))
gs_stfl = gridspec.GridSpec(nrows=nrows, ncols=ncols)
# a flag which signifies if a legend should be added to the plot, it is needed so we ahve only one legend per plot
legend_added = False
ax_stfl = None
all_years = [y for y in range(start_year, end_year + 1)]
if station_list is not None:
processed_stations = station_list
else:
processed_stations = [None] * len(mp_list)
processed_model_points = mp_list
plot_point_positions_with_upstream_areas(processed_stations, processed_model_points, basemap,
cell_manager, lake_fraction_field=lake_fraction)
if plot_upstream_area_averaged:
# create obs data managers
anusplin_tmin = AnuSplinManager(variable="stmn")
anusplin_tmax = AnuSplinManager(variable="stmx")
anusplin_pcp = AnuSplinManager(variable="pcp")
daily_dates, obs_tmin_fields = anusplin_tmin.get_daily_clim_fields_interpolated_to(
start_year=start_year, end_year=end_year,
lons_target=lons2d, lats_target=lats2d)
_, obs_tmax_fields = anusplin_tmax.get_daily_clim_fields_interpolated_to(
start_year=start_year, end_year=end_year,
lons_target=lons2d, lats_target=lats2d)
_, obs_pcp_fields = anusplin_pcp.get_daily_clim_fields_interpolated_to(
start_year=start_year, end_year=end_year,
lons_target=lons2d, lats_target=lats2d)
swe_path = "/skynet3_rech1/huziy/swe_ross_brown/swe.nc4"
if not os.path.isfile(os.path.realpath(swe_path)):
raise IOError("SWE-obs file {} does not exist".format(swe_path))
swe_manager = SweDataManager(path=swe_path, var_name="SWE")
obs_swe_daily_clim = swe_manager.get_daily_climatology(start_year, end_year)
interpolated_obs_swe_clim = swe_manager.interpolate_daily_climatology_to(obs_swe_daily_clim,
lons2d_target=lons2d,
lats2d_target=lats2d)
values_obs = None
for i, the_model_point in enumerate(mp_list):
ax_stfl = figure_stfl.add_subplot(gs_stfl[i // ncols, i % ncols], sharex=ax_stfl)
assert isinstance(the_model_point, ModelPoint)
# Check the number of years accessible for the station if the list of stations is given
the_station = None if station_list is None else station_list[i]
if the_station is not None:
assert isinstance(the_station, Station)
year_list = the_station.get_list_of_complete_years()
year_list = list(filter(lambda yi: start_year <= yi <= end_year, year_list))
if len(year_list) < 1:
continue
else:
year_list = all_years
fig = plt.figure(figsize=(12, 15))
gs = gridspec.GridSpec(4, 4, wspace=1)
# plot station position
ax = fig.add_subplot(gs[3, 0:2])
upstream_mask = _plot_station_position(ax, the_station, basemap, cell_manager, the_model_point)
# plot streamflows
ax = fig.add_subplot(gs[0:2, 0:2])
dates = None
model_daily_temp_clim = {}
model_daily_precip_clim = {}
model_daily_clim_surf_runoff = {}
model_daily_clim_subsurf_runoff = {}
model_daily_clim_swe = {}
# get model data for the list of years
simlabel_to_vals = {}
for label in label_list:
fname = sim_name_to_file_name[label]
if hdf_folder is None:
fpath = fname
else:
fpath = os.path.join(hdf_folder, fname)
if plot_upstream_area_averaged:
# read temperature data and calculate daily climatologic fileds
_, model_daily_temp_clim[label] = analysis.get_daily_climatology(
path_to_hdf_file=fpath, var_name="TT", level=0, start_year=start_year, end_year=end_year)
# read modelled precip and calculate daily climatologic fields
_, model_daily_precip_clim[label] = analysis.get_daily_climatology(
path_to_hdf_file=fpath, var_name="PR", level=0, start_year=start_year, end_year=end_year)
# read modelled surface runoff and calculate daily climatologic fields
_, model_daily_clim_surf_runoff[label] = analysis.get_daily_climatology(
path_to_hdf_file=fpath, var_name="TRAF", level=0, start_year=start_year, end_year=end_year)
# read modelled subsurface runoff and calculate daily climatologic fields
_, model_daily_clim_subsurf_runoff[label] = analysis.get_daily_climatology(
path_to_hdf_file=fpath, var_name="TDRA", level=0, start_year=start_year, end_year=end_year)
# read modelled swe and calculate daily climatologic fields
_, model_daily_clim_swe[label] = analysis.get_daily_climatology(
path_to_hdf_file=fpath, var_name="I5", level=0, start_year=start_year, end_year=end_year)
dates, values_model = analysis.get_daily_climatology_for_a_point(path=fpath,
var_name=stfl_name,
years_of_interest=year_list,
i_index=the_model_point.ix,
j_index=the_model_point.jy)
ax.plot(dates, values_model, label=label, lw=2)
if sim_name_to_color is None:
ax_stfl.plot(dates, values_model, label=label, lw=2)
else:
ax_stfl.plot(dates, values_model, sim_name_to_color[label], label=label, lw=2)
print(20 * "!!!")
print("{} -> {}".format(label, sim_name_to_color[label]))
print(20 * "!!!")
simlabel_to_vals[label] = values_model
if the_station is not None:
assert isinstance(the_station, Station)
dates, values_obs = the_station.get_daily_climatology_for_complete_years_with_pandas(stamp_dates=dates,
years=year_list)
# To keep the colors consistent for all the variables, the obs Should be plotted last
ax.plot(dates, values_obs, label="Obs.", lw=2)
# no ticklabels for streamflow plot
plt.setp(ax.get_xticklabels(), visible=False)
if sim_name_to_color is None:
ax_stfl.plot(dates, values_obs, label="Obs.", lw=2)
else:
ax_stfl.plot(dates, values_obs, label="Obs.", lw=2, color=sim_name_to_color["Obs."])
# Print excesss from streamflow validation
for label, values_model in simlabel_to_vals.items():
calclulate_spring_peak_err(dates, values_obs, values_model,
st_id="{}: {}".format(label, the_station.id),
da_mod=the_model_point.accumulation_area,
da_obs=the_station.drainage_km2)
ax.set_ylabel(r"Streamflow: ${\rm m^3/s}$")
assert isinstance(ax, Axes)
assert isinstance(fig, Figure)
upstream_area_km2 = np.sum(cell_area_km2[upstream_mask == 1])
# Put some information about the point
if the_station is not None:
lf_upstream = lake_fraction[upstream_mask == 1]
point_info = "{0}".format(the_station.id)
write_annual_flows_to_txt(label_list, simlabel_to_vals, values_obs, file_annual_discharge,
station_id=the_station.id,
da_obs=the_station.drainage_km2, da_mod=the_model_point.accumulation_area)
else:
point_info = "{0}".format(the_model_point.point_id)
ax.annotate(point_info, (0.8, 0.8), xycoords="axes fraction",
bbox=dict(facecolor="white", alpha=0.5),
va="top", ha="right")
ax.legend(loc=(0.0, 1.05), borderaxespad=0, ncol=3)
ax.xaxis.set_minor_formatter(FuncFormatter(lambda x, pos: num2date(x).strftime("%b")[0]))
ax.xaxis.set_minor_locator(MonthLocator(bymonthday=15))
ax.xaxis.set_major_locator(MonthLocator())
ax.grid()
streamflow_axes = ax # save streamflow axes for later use
if not legend_added:
ax_stfl.legend(loc="lower left", bbox_to_anchor=(0, 1.15), borderaxespad=0, ncol=3)
ax_stfl.xaxis.set_minor_formatter(FuncFormatter(lambda x, pos: num2date(x).strftime("%b")[0]))
ax_stfl.xaxis.set_minor_locator(MonthLocator(bymonthday=15))
ax_stfl.xaxis.set_major_locator(MonthLocator())
ax_stfl.set_ylabel(r"Streamflow ${\rm m^3/s}$")
legend_added = True
plt.setp(ax_stfl.get_xmajorticklabels(), visible=False)
ax_stfl.yaxis.set_major_locator(MaxNLocator(nbins=5))
sfmt = ScalarFormatter(useMathText=True)
sfmt.set_powerlimits((-2, 2))
ax_stfl.yaxis.set_major_formatter(sfmt)
ax_stfl.grid()
# annotate streamflow-only panel plot
ax_stfl.annotate(point_info, (0.05, 0.95), xycoords="axes fraction",
bbox=dict(facecolor="white"),
va="top", ha="left")
if plot_upstream_area_averaged:
# plot temperature comparisons (tmod - daily with anusplin tmin and tmax)
ax = fig.add_subplot(gs[3, 2:], sharex=streamflow_axes)
_validate_temperature_with_anusplin(ax, the_model_point, cell_area_km2=cell_area_km2,
upstream_mask=upstream_mask,
daily_dates=daily_dates,
obs_tmin_clim_fields=obs_tmin_fields,
obs_tmax_clim_fields=obs_tmax_fields,
model_data_dict=model_daily_temp_clim,
simlabel_list=label_list)
# plot temperature comparisons (tmod - daily with anusplin tmin and tmax)
ax = fig.add_subplot(gs[2, 2:], sharex=streamflow_axes)
_validate_precip_with_anusplin(ax, the_model_point, cell_area_km2=cell_area_km2,
upstream_mask=upstream_mask,
daily_dates=daily_dates,
obs_precip_clim_fields=obs_pcp_fields,
model_data_dict=model_daily_precip_clim,
simlabel_list=label_list)
# plot mean upstream surface runoff
ax = fig.add_subplot(gs[0, 2:], sharex=streamflow_axes)
_plot_upstream_surface_runoff(ax, the_model_point, cell_area_km2=cell_area_km2,
upstream_mask=upstream_mask,
daily_dates=daily_dates,
model_data_dict=model_daily_clim_surf_runoff,
simlabel_list=label_list)
# plot mean upstream subsurface runoff
ax = fig.add_subplot(gs[1, 2:], sharex=streamflow_axes, sharey=ax)
_plot_upstream_subsurface_runoff(ax, the_model_point, cell_area_km2=cell_area_km2,
upstream_mask=upstream_mask,
daily_dates=daily_dates,
model_data_dict=model_daily_clim_subsurf_runoff,
simlabel_list=label_list)
# plot mean upstream swe comparison
ax = fig.add_subplot(gs[2, 0:2], sharex=streamflow_axes)
print("Validating SWE for ", the_station.id, "--" * 20)
_validate_swe_with_ross_brown(ax, the_model_point, cell_area_km2=cell_area_km2,
upstream_mask=upstream_mask,
daily_dates=daily_dates,
model_data_dict=model_daily_clim_swe,
obs_swe_clim_fields=interpolated_obs_swe_clim,
simlabel_list=label_list)
if the_station is not None:
im_name = "comp_point_with_obs_{0}_{1}_{2}.png".format(the_station.id,
the_station.source,
"_".join(label_list))
im_folder_path = os.path.join(images_folder, the_station.source)
else:
im_name = "comp_point_with_obs_{0}_{1}.png".format(the_model_point.point_id,
"_".join(label_list))
im_folder_path = os.path.join(images_folder, "outlets_point_comp")
# create a folder for a given source of observed streamflow if it does not exist yet
if not os.path.isdir(im_folder_path):
os.mkdir(im_folder_path)
im_path = os.path.join(im_folder_path, im_name)
if plot_upstream_area_averaged:
fig.savefig(im_path, dpi=cpp.FIG_SAVE_DPI, bbox_inches="tight", transparent=True)
plt.close(fig)
# return # temporary plot only one point
assert isinstance(figure_stfl, Figure)
figure_stfl.tight_layout()
figure_stfl.savefig(os.path.join(images_folder,
"comp_point_with_obs_{0}.png".format("_".join(label_list))),
bbox_inches="tight", transparent=True, dpi=cpp.FIG_SAVE_DPI)
plt.close(figure_stfl)
# close information text files
for f in text_files:
f.close()
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)
