def main(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 = "interflow_correlations_months={}_{}-{}.pdf".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, i1_clim, 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(params["varname1"], params["varname2"]))
# data_list.append(to_plot3)
# correlate evaporation and soil moisture
params.update(dict(varname2="AV", level2=0, varname1="I1", 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(
infovar.get_display_label_for_var(params["varname1"]),
infovar.get_display_label_for_var(params["varname2"])))
data_list.append(to_plot3)
# correlate interflow and evaporation
params.update(dict(varname2="AV", level2=0, varname1="INTF", level1=0))
corr4, intf_clim, av_clim = 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)
# TODO: Correlate infiltration and surface runoff
# 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)
# plot timeseries
the_mask = corr4 < -0.1
varname_to_ts = {
"INTF": get_mean_over(the_mask, intf_clim),
"LH": get_mean_over(the_mask, av_clim),
"SM": get_mean_over(the_mask, i1_clim)
}
from matplotlib import gridspec
fig = plt.figure()
fig.set_figheight(3 * fig.get_figheight())
gs = gridspec.GridSpec(len(varname_to_ts), 1)
d0 = datetime(2001, 1, 1)
dt = timedelta(days=1)
dates = [d0 + dt * i for i in range(365) if (d0 + dt * i).month in months]
sfmt = ScalarFormatter()
dfmt = DateFormatter("%d%b")
for i, (label, data) in enumerate(varname_to_ts.items()):
ax = fig.add_subplot(gs[i, 0])
ax.plot(dates, data, label=label, lw=2)
ax.grid()
ax.legend()
ax.yaxis.set_major_formatter(sfmt)
if i < len(varname_to_ts) - 1:
ax.xaxis.set_ticklabels([])
else:
ax.xaxis.set_major_formatter(dfmt)
fig.savefig(os.path.join(
img_folder,
"aa_ts_{}_{}.png".format(os.path.basename(default_path),
"-".join(str(m) for m in months))),
dpi=cpp.FIG_SAVE_DPI)
def main_compare_two_simulations():
"""
draw (params2 - params0) climatologic profiles, and a region of interest
"""
#folder for storing result images
img_folder = ""
start_date = datetime(1980, 1, 1)
end_date = datetime(2010, 12, 31)
path0 = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"
path2 = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
exp_label = "interflow_effect_soil"
img_folder = "images_for_lake-river_paper"
img_folder = os.path.join(img_folder, exp_label)
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
rectangle = IndexRectangle(lower_left_point=IndexPoint(40, 20),
width=100,
height=45)
params2 = InputParams(hdf_path=path2,
is_for_comparison=True,
start_date=start_date,
end_date=end_date,
rectangle=rectangle)
params0 = InputParams(hdf_path=path0,
is_for_comparison=True,
start_date=start_date,
end_date=end_date,
rectangle=rectangle)
imin, jmin, w, h = params0.get_start_end_indices_of_selected_region()
i_sel, j_sel = np.where(params0.get_land_mask_using_flow_dirs())
i_sel_1 = i_sel[(i_sel >= imin) & (i_sel < imin + w) & (j_sel >= jmin) &
(j_sel < jmin + h)]
j_sel_1 = j_sel[(i_sel >= imin) & (i_sel < imin + w) & (j_sel >= jmin) &
(j_sel < jmin + h)]
i_sel = i_sel_1
j_sel = j_sel_1
levs2d, dnums2d = None, None
#plot the profile
fig = plt.figure()
gs = gridspec.GridSpec(len(params0.var_list), 2)
#The number of levels of interest
n_select_level = 5
#calculate and plot differences
for vindex, var_name in enumerate(params0.var_list):
print("plotting {0} ...".format(var_name))
dates, levels, data2 = params2.calculate_mean_clim_for_3d_var(
var_name=var_name)
_, _, data0 = params0.calculate_mean_clim_for_3d_var(var_name=var_name)
data = data2 - data0
#calculate the profile
selected_diff = data[:, :n_select_level, i_sel, j_sel]
sel_data = (data0[:, :n_select_level, i_sel, j_sel] +
data2[:, :n_select_level, i_sel, j_sel]) * 0.5
selected_mean = np.zeros_like(sel_data)
where_to_compute = np.abs(selected_diff) > 0
selected_mean[where_to_compute] = selected_diff[
where_to_compute] / sel_data[where_to_compute] * 100.0
selected_mean = selected_mean.mean(axis=2)
#rectangle subplot
ax = fig.add_subplot(gs[:, 0])
params0.basemap.drawcoastlines(linewidth=cpp.COASTLINE_WIDTH, ax=ax)
ax.add_patch(params0.get_mpl_rectangle_for_selected_region())
#profile subplot
ax = fig.add_subplot(gs[vindex, 1])
assert isinstance(ax, Axes)
if levs2d is None:
ax.set_ylabel("Depth (m)")
levels_meters = np.cumsum([
0,
] + class_conf.level_width_list_26_default)[:-1][:n_select_level]
dnums = date2num(dates)
levs2d, dnums2d = np.meshgrid(levels_meters, dnums)
vmin, vmax = selected_mean.min(), selected_mean.max()
d = max(abs(vmin), abs(vmax))
ncolors = 11
cmap = cm.get_cmap("RdBu_r", ncolors)
color_levs = np.linspace(-d, d, ncolors + 1)
step = color_levs[1] - color_levs[0]
ndec = abs(floor(np.log10(step)))
color_levs = np.round(color_levs, decimals=int(ndec))
img = ax.contourf(dnums2d,
levs2d,
selected_mean,
cmap=cmap,
levels=color_levs)
cb = plt.colorbar(img, ticks=color_levs[::2])
cb.ax.set_aspect(10)
ax.xaxis.set_major_formatter(DateFormatter("%d\n%b"))
ax.xaxis.set_major_locator(MonthLocator(bymonth=list(range(1, 13, 2))))
if vindex < len(params0.var_list) - 1:
ax.xaxis.set_ticklabels([])
ax.invert_yaxis()
ax.yaxis.set_major_locator(MaxNLocator(nbins=5))
#ax.grid(b = True)
ax.annotate(infovar.get_display_label_for_var(var_name),
xy=(0.8, 0.2),
xycoords="axes fraction",
bbox=dict(facecolor="w"))
#construct the path to the output figure
impath = os.path.join(
img_folder,
params0.get_imfilename_for_var(var_name="_".join(params0.var_list)))
#save the figure
fig.savefig(impath, dpi=cpp.FIG_SAVE_DPI, bbox_inches="tight")
plt.close(fig)
def main_compare_two_simulations():
"""
draw (params2 - params0) climatologic profiles, and a region of interest
"""
#folder for storing result images
img_folder = ""
start_date = datetime(1980, 1, 1)
end_date = datetime(2010, 12, 31)
path0 = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"
path2 = "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
exp_label = "interflow_effect_soil"
img_folder = "images_for_lake-river_paper"
img_folder = os.path.join(img_folder, exp_label)
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
rectangle = IndexRectangle(
lower_left_point=IndexPoint(40, 20),
width=100, height=45
)
params2 = InputParams(hdf_path = path2,
is_for_comparison=True, start_date=start_date, end_date=end_date, rectangle=rectangle)
params0 = InputParams(hdf_path=path0,
is_for_comparison=True, start_date=start_date, end_date=end_date, rectangle=rectangle)
imin, jmin, w, h = params0.get_start_end_indices_of_selected_region()
i_sel, j_sel = np.where(params0.get_land_mask_using_flow_dirs())
i_sel_1 = i_sel[(i_sel >= imin) & (i_sel < imin + w) & (j_sel >= jmin) & (j_sel < jmin + h)]
j_sel_1 = j_sel[(i_sel >= imin) & (i_sel < imin + w) & (j_sel >= jmin) & (j_sel < jmin + h)]
i_sel = i_sel_1
j_sel = j_sel_1
levs2d, dnums2d = None, None
#plot the profile
fig = plt.figure()
gs = gridspec.GridSpec(len(params0.var_list), 2)
#The number of levels of interest
n_select_level = 5
#calculate and plot differences
for vindex, var_name in enumerate(params0.var_list):
print("plotting {0} ...".format(var_name))
dates, levels, data2 = params2.calculate_mean_clim_for_3d_var(var_name=var_name)
_, _, data0 = params0.calculate_mean_clim_for_3d_var(var_name=var_name)
data = data2 - data0
#calculate the profile
selected_diff = data[:, :n_select_level, i_sel, j_sel]
sel_data = (data0[:, :n_select_level, i_sel, j_sel] + data2[:, :n_select_level, i_sel, j_sel]) * 0.5
selected_mean = np.zeros_like(sel_data)
where_to_compute = np.abs(selected_diff) > 0
selected_mean[where_to_compute] = selected_diff[where_to_compute] / sel_data[where_to_compute] * 100.0
selected_mean = selected_mean.mean(axis=2)
#rectangle subplot
ax = fig.add_subplot(gs[:, 0])
params0.basemap.drawcoastlines(linewidth=cpp.COASTLINE_WIDTH, ax=ax)
ax.add_patch(params0.get_mpl_rectangle_for_selected_region())
#profile subplot
ax = fig.add_subplot(gs[vindex, 1])
assert isinstance(ax, Axes)
if levs2d is None:
ax.set_ylabel("Depth (m)")
levels_meters = np.cumsum([0, ] + class_conf.level_width_list_26_default)[:-1][:n_select_level]
dnums = date2num(dates)
levs2d, dnums2d = np.meshgrid(levels_meters, dnums)
vmin, vmax = selected_mean.min(), selected_mean.max()
d = max(abs(vmin), abs(vmax))
ncolors = 11
cmap = cm.get_cmap("RdBu_r", ncolors)
color_levs = np.linspace(-d, d, ncolors + 1)
step = color_levs[1] - color_levs[0]
ndec = abs(floor(np.log10(step)))
color_levs = np.round(color_levs, decimals=int(ndec))
img = ax.contourf(dnums2d, levs2d, selected_mean, cmap = cmap, levels = color_levs)
cb = plt.colorbar(img, ticks = color_levs[::2])
cb.ax.set_aspect(10)
ax.xaxis.set_major_formatter(DateFormatter("%d\n%b"))
ax.xaxis.set_major_locator(MonthLocator(bymonth=list(range(1, 13, 2))))
if vindex < len(params0.var_list) - 1:
ax.xaxis.set_ticklabels([])
ax.invert_yaxis()
ax.yaxis.set_major_locator(MaxNLocator(nbins=5))
#ax.grid(b = True)
ax.annotate(infovar.get_display_label_for_var(var_name),
xy = (0.8, 0.2), xycoords = "axes fraction",
bbox = dict(facecolor = "w"))
#construct the path to the output figure
impath = os.path.join(img_folder, params0.get_imfilename_for_var(var_name = "_".join(params0.var_list)))
#save the figure
fig.savefig(impath, dpi=cpp.FIG_SAVE_DPI, bbox_inches = "tight")
plt.close(fig)
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
