def main():
data_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
start_year = 1980
end_year = 2010
vname = "TRAF"
level_index = 0
fldr = analysis.get_array_from_file(data_path, var_name="flow_direction")
lkfr = analysis.get_array_from_file(data_path, var_name="lake_fraction")
the_mask = np.ma.masked_all_like(fldr)
the_mask[fldr > 0] = (1 - lkfr)[fldr > 0]
ser = analysis.get_area_mean_timeseries(hdf_path=data_path, var_name=vname, level_index=level_index,
start_year=start_year, end_year=end_year, the_mask=the_mask)
monthly_ser = ser.groupby(lambda d: datetime(d.year, d.month, 15)).mean()
# do the plotting
plot_utils.apply_plot_params()
fig = plt.figure()
monthly_ser = monthly_ser * 24 * 3600 # convert to mm/day
monthly_ser.groupby(lambda d: d.month).plot()
ax = plt.gca()
assert isinstance(ax, Axes)
ax.grid()
fig.savefig(data_path[:-5] + "_{}_level_index_{}_{}-{}_timeseries.png".format(vname, level_index, start_year, end_year),
transparent=True, dpi=common_plot_params.FIG_SAVE_DPI, bbox_inches="tight")
plt.show()
def main():
data_path = "/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"
start_year = 1980
end_year = 2010
vname = "TRAF"
level_index = 0
fldr = analysis.get_array_from_file(data_path, var_name="flow_direction")
lkfr = analysis.get_array_from_file(data_path, var_name="lake_fraction")
the_mask = np.ma.masked_all_like(fldr)
the_mask[fldr > 0] = (1 - lkfr)[fldr > 0]
ser = analysis.get_area_mean_timeseries(hdf_path=data_path,
var_name=vname,
level_index=level_index,
start_year=start_year,
end_year=end_year,
the_mask=the_mask)
monthly_ser = ser.groupby(lambda d: datetime(d.year, d.month, 15)).mean()
# do the plotting
plot_utils.apply_plot_params()
fig = plt.figure()
monthly_ser = monthly_ser * 24 * 3600 # convert to mm/day
monthly_ser.groupby(lambda d: d.month).plot()
ax = plt.gca()
assert isinstance(ax, Axes)
ax.grid()
fig.savefig(data_path[:-5] +
"_{}_level_index_{}_{}-{}_timeseries.png".format(
vname, level_index, start_year, end_year),
transparent=True,
dpi=common_plot_params.FIG_SAVE_DPI,
bbox_inches="tight")
plt.show()
def main():
start_year = 1980
end_year = 2003
months_of_obs = [12, 1, 2, 3, 4, 5]
r_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=start_year, end_year=end_year, label="ERAI-CRCM5-L"
)
var_name = "LC"
bmp_info = analysis.get_basemap_info(r_config=r_config)
lkid_to_mask = get_lake_masks(bmp_info.lons, bmp_info.lats)
cell_area_m2 = analysis.get_array_from_file(path=r_config.data_path, var_name="cell_area_m2")
# read the model data
lkid_to_ts_model = {}
for lkid, the_mask in lkid_to_mask.items():
lkid_to_ts_model[lkid] = analysis.get_area_mean_timeseries(r_config.data_path, var_name=var_name, the_mask=the_mask * cell_area_m2,
start_year=start_year, end_year=end_year)
df = lkid_to_ts_model[lkid]
# remove the last December
df = df.select(lambda d: not (d.year == end_year and d.month == 12))
# remove the first Jan and Feb
df = df.select(lambda d: not (d.year == start_year and d.month in [1, 2]))
# remove the Feb 29th
df = df.select(lambda d: not (d.month == 2 and d.day == 29))
# select months of interest
df = df.select(lambda d: d.month in months_of_obs)
# calculate the climatology
df = df.groupby(lambda d: datetime(2001 if d.month == 12 else 2002, d.month, d.day)).mean()
df.sort_index(inplace=True)
lkid_to_ts_model[lkid] = df * 100
# read obs data and calculate climatology
lkid_to_ts_obs = {}
for lkid in LAKE_IDS:
lkid_to_ts_obs[lkid] = GL_obs_timeseries.get_ts_from_file(path=os.path.join(OBS_DATA_FOLDER, "{}-30x.TXT".format(lkid)),
start_year=start_year, end_year=end_year - 1)
# get the climatology
dfm = lkid_to_ts_obs[lkid].mean(axis=1)
dfm.index = [datetime(2001, 1, 1) + timedelta(days=int(jd - 1)) for jd in dfm.index]
lkid_to_ts_obs[lkid] = dfm
# plotting
plot_utils.apply_plot_params(font_size=10)
fig = plt.figure()
gs = GridSpec(nrows=len(lkid_to_ts_model), ncols=2)
for row, lkid in enumerate(lkid_to_ts_model):
ax = fig.add_subplot(gs[row, 0])
mod = lkid_to_ts_model[lkid]
obs = lkid_to_ts_obs[lkid]
print(obs.index)
print(obs.values)
ax.plot(mod.index, mod.values, label=r_config.label, color="r", lw=2)
ax.plot(obs.index, obs.values, label="NOAA NIC/CIS", color="k", lw=2)
if row == 0:
ax.legend()
ax.set_title(lkid)
ax.xaxis.set_major_formatter(DateFormatter("%b"))
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "GL_ice-cover-validation.png"), bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
