def entry_for_cc_canesm2_gl():
"""
for CanESM2 driven CRCM5_NEMO simulation
"""
data_root = common_params.data_root
label_to_datapath = OrderedDict([
(common_params.crcm_nemo_cur_label, data_root /
"lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_1989-2010_1989-2010/merged/"
),
(common_params.crcm_nemo_fut_label, data_root /
"lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_2079-2100_2079-2100/merged/"
),
])
cur_st_date = datetime(1989, 1, 1)
cur_en_date = datetime(2011, 1, 1) # end date not inclusive
fut_st_date = datetime(2079, 1, 1)
fut_en_date = datetime(2101, 1, 1) # end date not inclusive
cur_period = Period(cur_st_date, cur_en_date)
fut_period = Period(fut_st_date, fut_en_date)
periods_info = CcPeriodsInfo(cur_period=cur_period, fut_period=fut_period)
season_to_months = OrderedDict([("ND", [11, 12]), ("JF", [1, 2]),
("MA", [3, 4])])
var_pairs = [("hles_snow", "TT"), ("hles_snow", "PR"),
("hles_snow", "lake_ice_fraction")]
var_display_names = {
"hles_snow": "HLES",
"lake_ice_fraction": "Mean Lake ice \nfraction",
"TT": "2m air\n temperature",
"PR": "total\nprecipitation"
}
plot_utils.apply_plot_params(width_cm=25, height_cm=25, font_size=8)
gl_mask = get_gl_mask(label_to_datapath[common_params.crcm_nemo_cur_label])
hles_region_mask = get_mask_of_points_near_lakes(gl_mask,
npoints_radius=20)
main(label_to_data_path=label_to_datapath,
var_pairs=var_pairs,
periods_info=periods_info,
vname_display_names=var_display_names,
season_to_months=season_to_months,
hles_region_mask=hles_region_mask,
lakes_mask=gl_mask)
def entry_for_cc_canesm2_gl():
"""
for CanESM2 driven CRCM5_NEMO simulation
"""
data_root = common_params.data_root
label_to_datapath = OrderedDict([
(common_params.crcm_nemo_cur_label,
data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_1989-2010_1989-2010/merged/"),
(common_params.crcm_nemo_fut_label,
data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_2079-2100_2079-2100/merged/"),
])
cur_st_date = datetime(1989, 1, 1)
cur_en_date = datetime(2011, 1, 1) # end date not inclusive
fut_st_date = datetime(2079, 1, 1)
fut_en_date = datetime(2101, 1, 1) # end date not inclusive
cur_period = Period(cur_st_date, cur_en_date)
fut_period = Period(fut_st_date, fut_en_date)
periods_info = CcPeriodsInfo(cur_period=cur_period, fut_period=fut_period)
season_to_months = OrderedDict([
("ND", [11, 12]),
("JF", [1, 2]),
("MA", [3, 4])
])
var_pairs = [("hles_snow", "TT"), ("hles_snow", "PR"), ("hles_snow", "lake_ice_fraction")]
var_display_names = {
"hles_snow": "HLES",
"lake_ice_fraction": "Mean Lake ice \nfraction",
"TT": "2m air\n temperature",
"PR": "total\nprecipitation"
}
plot_utils.apply_plot_params(width_cm=25, height_cm=25, font_size=8)
gl_mask = get_gl_mask(label_to_datapath[common_params.crcm_nemo_cur_label])
hles_region_mask = get_mask_of_points_near_lakes(gl_mask, npoints_radius=20)
main(label_to_data_path=label_to_datapath, var_pairs=var_pairs, periods_info=periods_info,
vname_display_names=var_display_names, season_to_months=season_to_months,
hles_region_mask=hles_region_mask, lakes_mask=gl_mask)
def main(varname=""):
plot_utils.apply_plot_params(width_cm=22, height_cm=5, font_size=8)
# series = get_monthly_accumulations_area_avg(data_dir="/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_NEMO_1980-2009_monthly",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_HL_1980-2009_monthly",
# varname=varname)
selected_months = [9, 10, 11, 12, 1, 2, 3, 4, 5]
data_root = common_params.data_root
label_to_datapath = OrderedDict([
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009"),
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_daily_Obs_monthly_icefix_1980-2009"),
(common_params.crcm_nemo_cur_label, data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_1989-2010_1989-2010"),
(common_params.crcm_nemo_fut_label, data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_2079-2100_2079-2100"),
])
label_to_series = OrderedDict()
label_to_color = {
common_params.crcm_nemo_cur_label: "skyblue",
common_params.crcm_nemo_fut_label: "salmon"
}
gl_mask = get_gl_mask(label_to_datapath[common_params.crcm_nemo_cur_label] / "merged")
hles_region_mask = get_mask_of_points_near_lakes(gl_mask, npoints_radius=20)
# select a file from the directory
sel_file = None
for f in label_to_datapath[common_params.crcm_nemo_cur_label].iterdir():
sel_file = f
with xarray.open_dataset(sel_file) as ds:
hles_region_mask_lons, hles_region_mask_lats = [ds[k].values for k in ["lon", "lat"]]
for label, datapath in label_to_datapath.items():
series = get_monthly_accumulations_area_avg(data_dir=datapath, varname=varname,
fname_suffix="_daily.nc",
region_of_interest_mask=hles_region_mask)
label_to_series[label] = series
#
# print(series)
# assert isinstance(series, pd.Series)
# ax = series.plot(kind="bar", width=1)
#
# ax.set_ylabel("%")
# ax.set_xlabel("Month")
gs = GridSpec(1, 2, wspace=0.)
fig = plt.figure()
ax = fig.add_subplot(gs[0, 1])
start_date = datetime(2001, 10, 1)
dates = [start_date.replace(month=(start_date.month + i) % 13 + int((start_date.month + i) % 13 == 0),
year=start_date.year + (start_date.month + i) // 13) for i in range(13)]
def format_month_label(x, pos):
print(num2date(x))
return "{:%b}".format(num2date(x))
# calculate bar widths
dates_num = date2num(dates)
width = np.diff(dates_num) / (len(label_to_series) * 1.5)
width = np.array([width[0] for _ in width])
# select the months
width = np.array([w for w, d in zip(width, dates) if d.month in selected_months])
dates = [d for d in dates[:-1] if d.month in selected_months]
dates_num = date2num(dates)
label_to_handle = OrderedDict()
label_to_annual_hles = OrderedDict()
for i, (label, series) in enumerate(label_to_series.items()):
values = [series[d.month] for d in dates]
# convert to percentages
values_sum = sum(values)
# save the total annual hles for later reuse
label_to_annual_hles[label] = values_sum
values = [v / values_sum * 100 for v in values]
print(label, values)
print(f"sum(values) = {sum(values)}")
h = ax.bar(dates_num + i * width, values, width=width, align="edge", linewidth=0.5,
edgecolor="k", facecolor=label_to_color[label], label=label, zorder=10)
label_to_handle[label] = h
ax.set_ylabel("% of total HLES")
ax.set_title("(b) Monthly HLES distribution")
ax.xaxis.set_major_formatter(FuncFormatter(func=format_month_label))
ax.xaxis.set_major_locator(MonthLocator(bymonthday=int(sum(width[:len(label_to_series)]) / 2.) + 1))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# ax.set_title(common_params.varname_to_display_name[varname])
ax.yaxis.grid(True, linestyle="--", linewidth=0.5)
# ax.text(1, 1, "(a)", fontdict=dict(weight="bold"), transform=ax.transAxes, va="top", ha="right")
ax_with_legend = ax
# area average annual total HLES
text_align_props = dict(transform=ax.transAxes, va="bottom", ha="right")
cur_hles_annual = label_to_annual_hles[common_params.crcm_nemo_cur_label]
fut_hles_annual = label_to_annual_hles[common_params.crcm_nemo_fut_label]
ax.text(1, 0.2, r"$\Delta_{\rm total}$" + f"({(fut_hles_annual - cur_hles_annual) / cur_hles_annual * 100:.1f}%)",
**text_align_props, fontdict=dict(size=6))
# Plot the domain and the HLES region of interest
ax = fig.add_subplot(gs[0, 0])
topo_nc_file = data_root / "geophys_452x260_me.nc"
ax = plot_domain_and_interest_region(ax, topo_nc_file)
ax.set_title("(a) Experimental domain")
# Add a common legend
labels = list(label_to_handle)
handles = [label_to_handle[l] for l in labels]
ax_with_legend.legend(handles, labels, bbox_to_anchor=(0, -0.18), loc="upper left", borderaxespad=0., ncol=2)
# ax.grid()
sel_months_str = "_".join([str(m) for m in selected_months])
common_params.img_folder.mkdir(exist_ok=True)
img_file = common_params.img_folder / f"{varname}_histo_cc_m{sel_months_str}_domain.png"
print(f"Saving plot to {img_file}")
fig.savefig(img_file, **common_params.image_file_options)
def main(varname=""):
plot_utils.apply_plot_params(width_cm=18, height_cm=5.5, font_size=8)
# series = get_monthly_accumulations_area_avg(data_dir="/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_NEMO_1980-2009_monthly",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_HL_1980-2009_monthly",
# varname=varname)
selected_months = [9, 10, 11, 12, 1, 2, 3, 4, 5]
data_root = common_params.data_root
label_to_datapath = OrderedDict([
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009"),
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_daily_Obs_monthly_icefix_1980-2009"),
(common_params.crcm_nemo_cur_label, data_root /
"lake_effect_analysis_CRCM5_HL_CanESM2_RCP85_1989-2010_1989-2010"),
(common_params.crcm_nemo_fut_label, data_root /
"lake_effect_analysis_CRCM5_HL_CanESM2_RCP85_2079-2100_2079-2100"),
])
label_to_series = OrderedDict()
label_to_color = {
common_params.crcm_nemo_cur_label: "skyblue",
common_params.crcm_nemo_fut_label: "salmon"
}
gl_mask = get_gl_mask(
label_to_datapath[common_params.crcm_nemo_cur_label] / "merged")
hles_region_mask = get_mask_of_points_near_lakes(gl_mask,
npoints_radius=10)
for label, datapath in label_to_datapath.items():
series = get_monthly_accumulations_area_avg(
data_dir=datapath,
varname=varname,
fname_suffix="_daily.nc",
region_of_interest_mask=hles_region_mask)
label_to_series[label] = series
#
# print(series)
# assert isinstance(series, pd.Series)
# ax = series.plot(kind="bar", width=1)
#
# ax.set_ylabel("%")
# ax.set_xlabel("Month")
gs = GridSpec(1, 2, wspace=0.4)
fig = plt.figure()
ax = fig.add_subplot(gs[0, 0])
start_date = datetime(2001, 10, 1)
dates = [
start_date.replace(month=(start_date.month + i) % 13 +
int((start_date.month + i) % 13 == 0),
year=start_date.year + (start_date.month + i) // 13)
for i in range(13)
]
def format_month_label(x, pos):
print(num2date(x))
return "{:%b}".format(num2date(x))
# calculate bar widths
dates_num = date2num(dates)
width = np.diff(dates_num) / (len(label_to_series) * 1.5)
width = np.array([width[0] for _ in width])
# select the months
width = np.array(
[w for w, d in zip(width, dates) if d.month in selected_months])
dates = [d for d in dates[:-1] if d.month in selected_months]
dates_num = date2num(dates)
label_to_handle = OrderedDict()
label_to_annual_hles = OrderedDict()
for i, (label, series) in enumerate(label_to_series.items()):
values = [series[d.month] for d in dates]
# convert to percentages
values_sum = sum(values)
# save the total annual hles for later reuse
label_to_annual_hles[label] = values_sum
values = [v / values_sum * 100 for v in values]
print(label, values)
print(f"sum(values) = {sum(values)}")
h = ax.bar(dates_num + i * width,
values,
width=width,
align="edge",
linewidth=0.5,
edgecolor="k",
facecolor=label_to_color[label],
label=label,
zorder=10)
label_to_handle[label] = h
ax.set_ylabel("% of total HLES")
ax.xaxis.set_major_formatter(FuncFormatter(func=format_month_label))
ax.xaxis.set_major_locator(
MonthLocator(bymonthday=int(sum(width[:len(label_to_series)]) / 2.) +
1))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# ax.set_title(common_params.varname_to_display_name[varname])
ax.yaxis.grid(True, linestyle="--", linewidth=0.5)
ax.text(1,
1,
"(a)",
fontdict=dict(weight="bold"),
transform=ax.transAxes,
va="top",
ha="right")
ax_with_legend = ax
# area average annual total HLES
text_align_props = dict(transform=ax.transAxes, va="bottom", ha="right")
cur_hles_annual = label_to_annual_hles[common_params.crcm_nemo_cur_label]
fut_hles_annual = label_to_annual_hles[common_params.crcm_nemo_fut_label]
ax.text(
1,
0.2,
r"$\Delta_{\rm total}$" +
f"({(fut_hles_annual - cur_hles_annual) / cur_hles_annual * 100:.1f}%)",
**text_align_props,
fontdict=dict(size=6))
print(width[:len(label_to_series)])
# plot HLES amount changes for each month
ax = fig.add_subplot(gs[0, 1], sharex=ax)
cur_data = label_to_series[common_params.crcm_nemo_cur_label]
fut_data = label_to_series[common_params.crcm_nemo_fut_label]
perc_change = (fut_data - cur_data) / cur_data * 100.0
perc_change_sel = [perc_change[d.month] for d in dates]
h = ax.bar(dates_num + width,
perc_change_sel,
edgecolor="k",
linewidth=0.5,
facecolor="orange",
width=10,
align="center",
zorder=10)
label_to_handle[r"CRCM5_NEMO(f-c)"] = h
ax.set_ylabel("% of current HLES")
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.grid(True, linestyle="--", linewidth=0.5)
ax.text(1,
1,
"(b)",
fontdict=dict(weight="bold"),
transform=ax.transAxes,
va="top",
ha="right")
# Add a common legend
labels = list(label_to_handle)
handles = [label_to_handle[l] for l in labels]
ax_with_legend.legend(handles,
labels,
bbox_to_anchor=(0, -0.18),
loc="upper left",
borderaxespad=0.,
ncol=1)
# ax.grid()
sel_months_str = "_".join([str(m) for m in selected_months])
common_params.img_folder.mkdir(exist_ok=True)
img_file = common_params.img_folder / f"{varname}_histo_cc_m{sel_months_str}_HL.png"
print(f"Saving plot to {img_file}")
fig.savefig(img_file, **common_params.image_file_options)
def main(varname=""):
plot_utils.apply_plot_params(width_cm=22, height_cm=5, font_size=8)
# series = get_monthly_accumulations_area_avg(data_dir="/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_NEMO_1980-2009_monthly",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_HL_1980-2009_monthly",
# varname=varname)
hles_bin_edges = np.arange(0.1, 0.34, 0.02)
# selected_months = [10, 11, 12, 1, 2, 3, 4, 5]
selected_seasons = OrderedDict([("ND", [11, 12]), ("JF", [1, 2]),
("MA", [3, 4]),
("NDJFMA", [11, 12, 1, 2, 3, 4])])
data_root = common_params.data_root
label_to_datapath = OrderedDict([
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009"),
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_daily_Obs_monthly_icefix_1980-2009"),
# (common_params.crcm_nemo_cur_label, data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_1989-2010_1989-2010" / "merged"),
# (common_params.crcm_nemo_fut_label, data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_2079-2100_2079-2100" / "merged"),
(common_params.crcm_nemo_cur_label, data_root /
"lake_effect_analysis_CRCM5_NEMO_fix_CanESM2_RCP85_1989-2010_monthly_1989-2010"
/ "merged"),
(common_params.crcm_nemo_fut_label, data_root /
"lake_effect_analysis_CRCM5_NEMO_fix_CanESM2_RCP85_2079-2100_monthly_2079-2100"
/ "merged"),
])
# longutudes and latitudes of the focus region around the Great Lakes (we define it, mostly for performance
# issues and to eliminate regions with 0 hles that still are in the 200 km HLES zone)
focus_region_lonlat_nc_file = data_root / "lon_lat.nc"
label_to_series = OrderedDict()
label_to_color = {
common_params.crcm_nemo_cur_label: "skyblue",
common_params.crcm_nemo_fut_label: "salmon"
}
gl_mask = get_gl_mask(label_to_datapath[common_params.crcm_nemo_cur_label])
hles_region_mask = get_mask_of_points_near_lakes(gl_mask,
npoints_radius=20)
# select a file from the directory
sel_file = None
for f in label_to_datapath[common_params.crcm_nemo_cur_label].iterdir():
if f.is_file():
sel_file = f
break
assert sel_file is not None, f"Could not find any files in {label_to_datapath[common_params.crcm_nemo_cur_label]}"
# Take into account the focus region
with xarray.open_dataset(sel_file) as ds:
hles_region_mask_lons, hles_region_mask_lats = [
ds[k].values for k in ["lon", "lat"]
]
with xarray.open_dataset(focus_region_lonlat_nc_file) as ds_focus:
focus_lons, focus_lats = [
ds_focus[k].values for k in ["lon", "lat"]
]
coords_src = lat_lon.lon_lat_to_cartesian(
hles_region_mask_lons.flatten(), hles_region_mask_lats.flatten())
coords_dst = lat_lon.lon_lat_to_cartesian(focus_lons.flatten(),
focus_lats.flatten())
ktree = KDTree(list(zip(*coords_src)))
dists, inds = ktree.query(list(zip(*coords_dst)), k=1)
focus_mask = hles_region_mask.flatten()
focus_mask[...] = False
focus_mask[inds] = True
focus_mask.shape = hles_region_mask.shape
for seas_name, selected_months in selected_seasons.items():
# read and calculate
for label, datapath in label_to_datapath.items():
hles_file = None
# select hles file in the folder
for f in datapath.iterdir():
if f.name.endswith("_daily.nc"):
hles_file = f
break
assert hles_file is not None, f"Could not find any HLES files in {datapath}"
series = get_hles_amount_distribution_from_merged(
data_file=hles_file,
varname=varname,
region_of_interest_mask=hles_region_mask & focus_mask,
selected_months=selected_months,
bin_edges=hles_bin_edges)
label_to_series[label] = series
# plotting
gs = GridSpec(1, 1, wspace=0.05)
fig = plt.figure()
ax = fig.add_subplot(gs[0, 0])
# calculate bar widths
widths = np.diff(hles_bin_edges)
label_to_handle = OrderedDict()
for i, (label, series) in enumerate(label_to_series.items()):
values = series.values if hasattr(series, "values") else series
# values = values / values.sum() * 100
logger.debug([label, values])
logger.debug(f"sum(values) = {sum(values)}")
# h = ax.bar(hles_bin_edges[:-1] + i * widths / len(label_to_series), values, width=widths / len(label_to_series),
# align="edge", linewidth=0.5,
# edgecolor="k",
# facecolor=label_to_color[label], label=label, zorder=10)
h = ax.plot(hles_bin_edges[:-1],
values,
color=label_to_color[label],
marker="o",
label=label,
markersize=2.5)
# label_to_handle[label] = h
ax.set_xlabel("HLES (m)")
ax.set_title(f"HLES distribution, {seas_name}")
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# ax.set_title(common_params.varname_to_display_name[varname])
ax.yaxis.grid(True, linestyle="--", linewidth=0.5)
# ax.text(1, 1, "(a)", fontdict=dict(weight="bold"), transform=ax.transAxes, va="top", ha="right")
ax_with_legend = ax
ax.set_xlim((0.1, None))
# area average annual total HLES
text_align_props = dict(transform=ax.transAxes,
va="bottom",
ha="right")
# Plot the domain and the HLES region of interest
# ax = fig.add_subplot(gs[0, 0])
# topo_nc_file = data_root / "geophys_452x260_me.nc"
# ax = plot_domain_and_interest_region(ax, topo_nc_file, focus_region_lonlat_nc_file=focus_region_lonlat_nc_file)
# ax.set_title("(a) Experimental domain")
#
# # Add a common legend
labels = list(label_to_handle)
handles = [label_to_handle[l] for l in labels]
ax_with_legend.legend(bbox_to_anchor=(0, -0.18),
loc="upper left",
borderaxespad=0.,
ncol=2)
# ax.grid()
sel_months_str = "_".join([str(m) for m in selected_months])
common_params.img_folder.mkdir(exist_ok=True)
img_file = common_params.img_folder / f"{varname}_histo_amount_cc_m{sel_months_str}_domain.png"
print(f"Saving plot to {img_file}")
fig.savefig(img_file, **common_params.image_file_options)
def plot_domain_and_interest_region(ax: Axes,
topo_nc_file_path: Path,
focus_region_lonlat_nc_file: Path = None):
"""
:param focus_region_lonlat_nc_file: Path to the file containing focus region lons and lats
:param region_mask_lats: latitudes corresponding to the region mask
:param region_mask_lons:
:param ax:
:param topo_nc_file_path:
:param region_mask:
Note: below is the expected structure of the input netcdf file
$ ncdump -h geophys_452x260_me.nc
netcdf geophys_452x260_me {
dimensions:
x = 452 ;
y = 260 ;
variables:
float ME(x, y) ;
float lon(x, y) ;
float lat(x, y) ;
int proj_params ;
proj_params:grid_type = "E" ;
proj_params:lat1 = 0. ;
proj_params:lon1 = 180. ;
proj_params:lat2 = 1. ;
proj_params:lon2 = 276. ;
}
"""
# read the model topography from the file
with xarray.open_dataset(topo_nc_file_path) as topo_ds:
topo_lons, topo_lats, topo = [
topo_ds[k].values for k in ["lon", "lat", "ME"]
]
prj_params = topo_ds["proj_params"]
rll = RotatedLatLon(lon1=prj_params.lon1,
lat1=prj_params.lat1,
lon2=prj_params.lon2,
lat2=prj_params.lat2)
rot_pole_cpy = rll.get_cartopy_projection_obj()
ax.set_visible(False)
ax = ax.figure.add_axes(ax.get_position().bounds, projection=rot_pole_cpy)
# ax.coastlines()
gl_mask = get_gl_mask(topo_nc_file_path)
region_mask = get_mask_of_points_near_lakes(gl_mask, npoints_radius=20)
topo_lons[topo_lons > 180] -= 360
xll, yll = rot_pole_cpy.transform_point(topo_lons[0, 0], topo_lats[0, 0],
cartopy.crs.PlateCarree())
xur, yur = rot_pole_cpy.transform_point(topo_lons[-1, -1], topo_lats[-1,
-1],
cartopy.crs.PlateCarree())
map_extent = [xll, xur, yll, yur]
print("Map extent: ", map_extent)
topo_clevs = [0, 100, 200, 300, 400, 500, 600, 800, 1000, 1200]
# bn = BoundaryNorm(topo_clevs, len(topo_clevs) - 1)
cmap = cm.get_cmap("terrain")
ocean_color = cmap(0.18)
cmap, norm = colors.from_levels_and_colors(
topo_clevs, cmap(np.linspace(0.3, 1,
len(topo_clevs) - 1)))
xyz_coords = rot_pole_cpy.transform_points(cartopy.crs.PlateCarree(),
topo_lons, topo_lats)
xx = xyz_coords[:, :, 0]
yy = xyz_coords[:, :, 1]
add_rectangle(ax,
xx,
yy,
margin=20,
edge_style="solid",
zorder=10,
linewidth=0.5)
add_rectangle(ax,
xx,
yy,
margin=10,
edge_style="dashed",
zorder=10,
linewidth=0.5)
# plot a rectangle for the focus region
if focus_region_lonlat_nc_file is not None:
with xarray.open_dataset(focus_region_lonlat_nc_file) as fr:
focus_lons, focus_lats = fr["lon"].data, fr["lat"].data
xyz_coords = rot_pole_cpy.transform_points(
cartopy.crs.PlateCarree(), focus_lons, focus_lats)
xxf, yyf = xyz_coords[..., 0], xyz_coords[..., 1]
add_rectangle(ax,
xxf,
yyf,
edge_style="solid",
margin=0,
edgecolor="magenta",
zorder=10,
linewidth=1)
cs = ax.pcolormesh(topo_lons[:, :],
topo_lats[:, :],
topo[:, :],
transform=cartopy.crs.PlateCarree(),
cmap=cmap,
norm=norm)
to_plot = np.ma.masked_where(region_mask < 0.5, region_mask)
ax.scatter(topo_lons,
topo_lats,
to_plot * 0.01,
c="cyan",
transform=cartopy.crs.PlateCarree(),
alpha=0.5)
# Add geographic features
line_color = "k"
ax.add_feature(common_params.LAKES_50m,
facecolor=cartopy.feature.COLORS["water"],
edgecolor=line_color,
linewidth=0.5)
ax.add_feature(common_params.OCEAN_50m,
facecolor=cartopy.feature.COLORS["water"],
edgecolor=line_color,
linewidth=0.5)
ax.add_feature(common_params.COASTLINE_50m,
facecolor="none",
edgecolor=line_color,
linewidth=0.5)
ax.add_feature(common_params.RIVERS_50m,
facecolor="none",
edgecolor=line_color,
linewidth=0.5)
ax.set_extent(map_extent, crs=rot_pole_cpy)
# improve colorbar
divider = make_axes_locatable(ax)
ax_cb = divider.new_vertical(size="5%",
pad=0.1,
axes_class=plt.Axes,
pack_start=True)
ax.figure.add_axes(ax_cb)
cb = plt.colorbar(cs, cax=ax_cb, orientation="horizontal")
cb.ax.set_xticklabels(topo_clevs, rotation=45)
return ax
def main(varname=""):
plot_utils.apply_plot_params(width_cm=22, height_cm=5, font_size=8)
# series = get_monthly_accumulations_area_avg(data_dir="/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_NEMO_1980-2009_monthly",
# varname=varname)
# series = get_monthly_accumulations_area_avg(data_dir="/RESCUE/skynet3_rech1/huziy/Netbeans Projects/Python/RPN/lake_effect_analysis_CRCM5_HL_1980-2009_monthly",
# varname=varname)
selected_months = [10, 11, 12, 1, 2, 3, 4, 5]
data_root = common_params.data_root
label_to_datapath = OrderedDict([
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_Obs_monthly_1980-2009"),
# ("Obs", "/HOME/huziy/skynet3_rech1/Netbeans Projects/Python/RPN/lake_effect_analysis_daily_Obs_monthly_icefix_1980-2009"),
# (common_params.crcm_nemo_cur_label, data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_1989-2010_1989-2010" / "merged"),
# (common_params.crcm_nemo_fut_label, data_root / "lake_effect_analysis_CRCM5_NEMO_CanESM2_RCP85_2079-2100_2079-2100" / "merged"),
(common_params.crcm_nemo_cur_label, data_root /
"lake_effect_analysis_CRCM5_NEMO_fix_CanESM2_RCP85_1989-2010_monthly_1989-2010"
/ "merged"),
(common_params.crcm_nemo_fut_label, data_root /
"lake_effect_analysis_CRCM5_NEMO_fix_CanESM2_RCP85_2079-2100_monthly_2079-2100"
/ "merged"),
])
# longutudes and latitudes of the focus region around the Great Lakes (we define it, mostly for performance
# issues and to eliminate regions with 0 hles that still are in the 200 km HLES zone)
focus_region_lonlat_nc_file = data_root / "lon_lat.nc"
label_to_series = OrderedDict()
label_to_color = {
common_params.crcm_nemo_cur_label: "skyblue",
common_params.crcm_nemo_fut_label: "salmon"
}
gl_mask = get_gl_mask(label_to_datapath[common_params.crcm_nemo_cur_label])
hles_region_mask = get_mask_of_points_near_lakes(gl_mask,
npoints_radius=20)
# select a file from the directory
sel_file = None
for f in label_to_datapath[common_params.crcm_nemo_cur_label].iterdir():
if f.is_file():
sel_file = f
break
assert sel_file is not None, f"Could not find any files in {label_to_datapath[common_params.crcm_nemo_cur_label]}"
# Take into account the focus region
with xarray.open_dataset(sel_file) as ds:
hles_region_mask_lons, hles_region_mask_lats = [
ds[k].values for k in ["lon", "lat"]
]
with xarray.open_dataset(focus_region_lonlat_nc_file) as ds_focus:
focus_lons, focus_lats = [
ds_focus[k].values for k in ["lon", "lat"]
]
coords_src = lat_lon.lon_lat_to_cartesian(
hles_region_mask_lons.flatten(), hles_region_mask_lats.flatten())
coords_dst = lat_lon.lon_lat_to_cartesian(focus_lons.flatten(),
focus_lats.flatten())
ktree = KDTree(list(zip(*coords_src)))
dists, inds = ktree.query(list(zip(*coords_dst)), k=1)
focus_mask = hles_region_mask.flatten()
focus_mask[...] = False
focus_mask[inds] = True
focus_mask.shape = hles_region_mask.shape
for label, datapath in label_to_datapath.items():
hles_file = None
for f in datapath.iterdir():
if f.name.endswith("_daily.nc"):
hles_file = f
break
assert hles_file is not None, f"Could not find any HLES files in {datapath}"
series = get_monthly_accumulations_area_avg_from_merged(
data_file=hles_file,
varname=varname,
region_of_interest_mask=hles_region_mask & focus_mask)
label_to_series[label] = series
# plotting
gs = GridSpec(1, 2, wspace=0.05)
fig = plt.figure()
ax = fig.add_subplot(gs[0, 1])
start_date = datetime(2001, 10, 1)
dates = [
start_date.replace(month=(start_date.month + i) % 13 +
int((start_date.month + i) % 13 == 0),
year=start_date.year + (start_date.month + i) // 13)
for i in range(13)
]
def format_month_label(x, pos):
logging.debug(num2date(x))
return "{:%b}".format(num2date(x))
# calculate bar widths
dates_num = date2num(dates)
width = np.diff(dates_num) / (len(label_to_series) * 1.5)
width = np.array([width[0] for _ in width])
# select the months
width = np.array(
[w for w, d in zip(width, dates) if d.month in selected_months])
dates = [d for d in dates[:-1] if d.month in selected_months]
dates_num = date2num(dates)
label_to_handle = OrderedDict()
label_to_annual_hles = OrderedDict()
for i, (label, series) in enumerate(label_to_series.items()):
values = [series[d.month] * 100 for d in dates]
# convert to percentages
values_sum = sum(values)
# save the total annual hles for later reuse
label_to_annual_hles[label] = values_sum
# values = [v / values_sum * 100 for v in values]
logger.debug([label, values])
logger.debug(f"sum(values) = {sum(values)}")
h = ax.bar(dates_num + i * width,
values,
width=width,
align="edge",
linewidth=0.5,
edgecolor="k",
facecolor=label_to_color[label],
label=label,
zorder=10)
label_to_handle[label] = h
ax.set_ylabel("HLES (cm/day)")
ax.set_title("(b) Monthly HLES distribution")
ax.xaxis.set_major_formatter(FuncFormatter(func=format_month_label))
ax.xaxis.set_major_locator(
MonthLocator(bymonthday=int(sum(width[:len(label_to_series)]) / 2.) +
1))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# ax.set_title(common_params.varname_to_display_name[varname])
ax.yaxis.grid(True, linestyle="--", linewidth=0.5)
# ax.text(1, 1, "(a)", fontdict=dict(weight="bold"), transform=ax.transAxes, va="top", ha="right")
ax_with_legend = ax
# area average annual total HLES
text_align_props = dict(transform=ax.transAxes, va="bottom", ha="right")
cur_hles_annual = label_to_annual_hles[common_params.crcm_nemo_cur_label]
fut_hles_annual = label_to_annual_hles[common_params.crcm_nemo_fut_label]
ax.text(
1,
0.2,
r"$\Delta_{\rm total}$" +
f"({(fut_hles_annual - cur_hles_annual) / cur_hles_annual * 100:.1f}%)",
**text_align_props,
fontdict=dict(size=6))
# Plot the domain and the HLES region of interest
ax = fig.add_subplot(gs[0, 0])
topo_nc_file = data_root / "geophys_452x260_me.nc"
ax = plot_domain_and_interest_region(
ax,
topo_nc_file,
focus_region_lonlat_nc_file=focus_region_lonlat_nc_file)
ax.set_title("(a) Experimental domain")
# Add a common legend
labels = list(label_to_handle)
handles = [label_to_handle[l] for l in labels]
ax_with_legend.legend(handles,
labels,
bbox_to_anchor=(0, -0.18),
loc="upper left",
borderaxespad=0.,
ncol=2)
# ax.grid()
sel_months_str = "_".join([str(m) for m in selected_months])
common_params.img_folder.mkdir(exist_ok=True)
img_file = common_params.img_folder / f"{varname}_histo_cc_m{sel_months_str}_domain.png"
print(f"Saving plot to {img_file}")
fig.savefig(img_file, **common_params.image_file_options)