def plot_pr_runoff_correlations(start_year=1980, end_year=2010, months=None):
default_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
# default_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5"
if months is None:
months = list(range(1, 13))
img_folder = os.path.join("interflow_corr_images",
os.path.basename(default_path))
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
img_filename = "PR_TRAF_TDRA_interflow_correlations_months={}_{}-{}.jpg".format(
"-".join(str(m) for m in months), start_year, end_year)
lons, lats, basemap = analysis.get_basemap_from_hdf(file_path=default_path)
lons[lons > 180] -= 360
x, y = basemap(lons, lats)
# Correlate surface runoff and PR
params = dict(path1=default_path,
varname1="PR",
level1=0,
path2=default_path,
level2=0,
varname2="TRAF",
months=months)
params.update(dict(
start_year=start_year,
end_year=end_year,
))
title_list = []
data_list = []
corr1, intf_clim, i1_clim = calculate_correlation_field_for_climatology(
**params)
to_plot1 = maskoceans(lons, lats, corr1)
title_list.append("Corr({}, {})".format(params["varname1"],
params["varname2"]))
data_list.append(to_plot1)
# correlate drainage and PR
params.update(dict(varname2="TDRA", level2=0, varname1="PR", level1=0))
corr4, i1_clim, av_clim = calculate_correlation_field_for_climatology(
**params)
to_plot3 = np.ma.masked_where(to_plot1.mask, corr4)
title_list.append("Corr({}, {})".format(params["varname1"],
params["varname2"]))
data_list.append(to_plot3)
# Do plotting
clevels = np.arange(-1, 1.2, 0.2)
npanels = len(data_list)
gs = GridSpec(1, npanels + 1, width_ratios=[
1.0,
] * npanels + [
0.05,
])
fig = plt.figure()
assert isinstance(fig, Figure)
fig.set_figheight(1.5 * fig.get_figheight())
img = None
for col in range(npanels):
ax = fig.add_subplot(gs[0, col])
basemap.drawmapboundary(fill_color="0.75", ax=ax)
img = basemap.contourf(x,
y,
data_list[col],
levels=clevels,
cmap=cm.get_cmap("RdBu_r",
len(clevels) - 1))
plt.title(title_list[col])
basemap.drawcoastlines(linewidth=cpp.COASTLINE_WIDTH, ax=ax)
plt.colorbar(img, cax=fig.add_subplot(gs[0, npanels]))
fig.savefig(os.path.join(img_folder, img_filename), dpi=cpp.FIG_SAVE_DPI)
def main(start_year=1980, end_year=2010, months=None, ylabel="",
fig=None, current_row=0, gs=None):
default_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
# default_path = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5"
if months is None:
months = list(range(1, 13))
img_folder = os.path.join("interflow_corr_images", os.path.basename(default_path))
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
img_filename = "4x1_correlations_INTF-PR-I1-Infilt_months={}_{}-{}.png".format("-".join(str(m) for m in months),
start_year, end_year)
lons, lats, basemap = analysis.get_basemap_from_hdf(file_path=default_path)
lons[lons > 180] -= 360
x, y = basemap(lons, lats)
# Correlate interflow rate and soil moisture
params = dict(
path1=default_path,
varname1="INTF",
level1=0,
path2=default_path,
level2=0,
varname2="I1",
months=months
)
params.update(dict(
start_year=start_year, end_year=end_year,
))
title_list = []
data_list = []
corr1, intf_clim, i1_clim = calculate_correlation_field_for_climatology(**params)
to_plot1 = maskoceans(lons, lats, corr1)
# title_list.append("Corr({}, {})".format(
# infovar.get_display_label_for_var(params["varname1"]),
# infovar.get_display_label_for_var(params["varname2"])))
# data_list.append(to_plot1)
# correlate interflow and precip
params.update(dict(varname2="PR", level2=0))
corr2, _, pr_clim = calculate_correlation_field_for_climatology(**params)
to_plot2 = np.ma.masked_where(to_plot1.mask, corr2)
title_list.append("Corr({}, {})".format(
infovar.get_display_label_for_var(params["varname1"]),
infovar.get_display_label_for_var(params["varname2"])))
data_list.append(to_plot2)
# correlate precip and soil moisture
# params.update(dict(varname1="I1", level1=0))
# corr3, _, _ = calculate_correlation_field_for_climatology(**params)
# to_plot3 = np.ma.masked_where(to_plot2.mask, corr3)
# title_list.append("Corr({}, {})".format(
# infovar.get_display_label_for_var(params["varname1"]),
# infovar.get_display_label_for_var(params["varname2"])))
# data_list.append(to_plot3)
# correlate interflow and infiltration
# corr4 = calculate_correlation_of_infiltration_rate_with(start_year=start_year,
# end_year=end_year,
# path_for_infiltration_data=default_path,
# path2=default_path,
# varname2="INTF",
# level2=0, months=months)
# Correlate interflow rate with latent heat flux
params = dict(
path1=default_path,
varname1="INTF",
level1=0,
path2=default_path,
level2=0,
varname2="AV",
months=months,
start_year=start_year, end_year=end_year,
)
corr4, _, _ = calculate_correlation_field_for_climatology(**params)
to_plot4 = np.ma.masked_where(to_plot1.mask, corr4)
title_list.append("Corr({}, {})".format(
infovar.get_display_label_for_var(params["varname1"]),
infovar.get_display_label_for_var(params["varname2"])))
data_list.append(to_plot4)
# Correlate interflow rate with soil ice
params = dict(
path1=default_path,
varname1="INTF",
level1=0,
path2=default_path,
level2=0,
varname2="I2",
months=months,
start_year=start_year, end_year=end_year,
)
corr4, _, _ = calculate_correlation_field_for_climatology(**params)
to_plot4 = np.ma.masked_where(to_plot1.mask, corr4)
title_list.append("Corr({}, {})".format(
infovar.get_display_label_for_var(params["varname1"]),
infovar.get_display_label_for_var(params["varname2"])))
data_list.append(to_plot4)
# Correlate interflow rate with swe
params = dict(
path1=default_path,
varname1="INTF",
level1=0,
path2=default_path,
level2=0,
varname2="I5",
months=months,
start_year=start_year, end_year=end_year,
)
corr4, _, _ = calculate_correlation_field_for_climatology(**params)
to_plot4 = np.ma.masked_where(to_plot1.mask, corr4)
title_list.append("Corr({}, {})".format(
infovar.get_display_label_for_var(params["varname1"]),
infovar.get_display_label_for_var(params["varname2"])))
data_list.append(to_plot4)
# Correlate LHF rate with swe
params = dict(
path1=default_path,
varname1="AV",
level1=0,
path2=default_path,
level2=0,
varname2="I5",
months=months,
start_year=start_year, end_year=end_year,
)
corr4, _, _ = calculate_correlation_field_for_climatology(**params)
to_plot4 = np.ma.masked_where(to_plot1.mask, corr4)
title_list.append("Corr({}, {})".format(
infovar.get_display_label_for_var(params["varname1"]),
infovar.get_display_label_for_var(params["varname2"])))
data_list.append(to_plot4)
# Do plotting
clevels = np.arange(-1, 1.2, 0.2)
npanels = len(data_list)
if gs is None:
gs = GridSpec(1, npanels + 1, width_ratios=[1.0, ] * npanels + [0.05, ])
is_subplot = fig is not None
fig = plt.figure() if fig is None else fig
assert isinstance(fig, Figure)
# fig.set_figheight(1.5 * fig.get_figheight())
img = None
for col in range(npanels):
ax = fig.add_subplot(gs[current_row, col])
if not col:
ax.set_ylabel(ylabel)
basemap.drawmapboundary(fill_color="0.75", ax=ax)
img = basemap.contourf(x, y, data_list[col], levels=clevels, cmap=cm.get_cmap("RdBu_r", len(clevels) - 1))
if current_row == 0:
ax.set_title(title_list[col])
basemap.drawcoastlines(linewidth=cpp.COASTLINE_WIDTH, ax=ax)
if not is_subplot:
plt.colorbar(img, cax=fig.add_subplot(gs[0, npanels]))
fig.savefig(os.path.join(img_folder, img_filename), dpi=cpp.FIG_SAVE_DPI)
return img, img_folder
