def calculate_daily_mean_fields():
dates, clim_fields_hcd_rl = Crcm5ModelDataManager.hdf_get_daily_climatological_fields(
hdf_db_path="/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl_spinup.hdf",
var_name="STFL", level_index=None, use_grouping=True, start_year=1979, end_year=1988)
dates, clim_fields_hcd_rl_intfl = Crcm5ModelDataManager.hdf_get_daily_climatological_fields(
hdf_db_path="/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_spinup.hdf",
var_name="STFL", level_index=None, use_grouping=True, start_year=1979, end_year=1988)
# Calculate mean timeseries and take a difference
ts_hcd_rl = []
for field in clim_fields_hcd_rl:
field = np.asarray(field)
ts_hcd_rl.append(field[field >= 0].mean())
ts_hcd_rl = np.asarray(ts_hcd_rl)
ts_hcd_rl_intfl = []
for field in clim_fields_hcd_rl_intfl:
field = np.asarray(field)
ts_hcd_rl_intfl.append(field[field >= 0].mean())
ts_hcd_rl_intfl = np.asarray(ts_hcd_rl_intfl)
daily_diff_data = (ts_hcd_rl_intfl - ts_hcd_rl) / ts_hcd_rl * 100
daily_diff_ts = pd.TimeSeries(data=daily_diff_data, index=dates)
monthly_diff_ts = daily_diff_ts.resample("M", how="mean")
month_vals = np.asarray([d.month for d in dates])
month_mean_for_day = np.zeros(len(month_vals))
fig = plt.figure(figsize=(20, 6))
ax = plt.gca()
assert isinstance(ax, Axes)
ax.set_ylabel("$\left(Q_{\\rm hcd-rl-intfl} - Q_{\\rm hcd-rl}\\right)/Q_{\\rm hcd-rl} \\times 100\%$")
ax.plot(dates, daily_diff_data)
ax.plot(ax.get_xlim(), [0, 0], "k-")
# plot a mean for each month
for the_month in range(1, 13):
month_mean_for_day[month_vals == the_month] = monthly_diff_ts[the_month - 1]
month_dates = list(filter(lambda d: d.month == the_month, dates))
month_vals = np.ones((len(month_dates),)) * monthly_diff_ts[the_month - 1]
ax.plot(month_dates, month_vals, "r", lw=1.5)
ax.grid("on")
ax.xaxis.set_major_formatter(DateFormatter("%b/%d"))
ax.xaxis.set_major_locator(DayLocator(bymonthday=1))
plt.tight_layout()
plt.savefig("intfl_diff.png")
def get_daily_climatology(path_to_hdf_file="", var_name="STFL", level=None, start_year=None, end_year=None):
# if the difference of 2 variables is requested
opsign = "-" if "-" in var_name else "+" if "+" in var_name else None
if "-" in var_name or "+" in var_name:
v1name, v2name = var_name.replace(" ", "").split(opsign)
dates, v1data = get_daily_climatology(path_to_hdf_file=path_to_hdf_file, level=level,
start_year=start_year, end_year=end_year, var_name=v1name)
_, v2data = get_daily_climatology(path_to_hdf_file=path_to_hdf_file, level=level,
start_year=start_year, end_year=end_year, var_name=v2name)
return dates, v1data - v2data if opsign == "-" else v1data + v2data if opsign == "+" else None
if var_name.endswith("_min"):
return get_daily_min_climatology(path_to_hdf_file=path_to_hdf_file, var_name=var_name, level=level,
start_year=start_year, end_year=end_year)
elif var_name.endswith("_max"):
return get_daily_max_climatology(path_to_hdf_file=path_to_hdf_file, var_name=var_name, level=level,
start_year=start_year, end_year=end_year)
else:
return Crcm5ModelDataManager.hdf_get_daily_climatological_fields(hdf_db_path=path_to_hdf_file,
start_year=start_year,
end_year=end_year,
var_name=var_name,
level_index=level,
use_grouping=True)
