def daily_temperature_range(tasmax,
tasmin,
freq: str = "YS") -> xarray.DataArray:
r"""Mean of daily temperature range.
The mean difference between the daily maximum temperature and the daily minimum temperature.
Parameters
----------
tasmax : xarray.DataArray
Maximum daily temperature values [℃] or [K]
tasmin : xarray.DataArray
Minimum daily temperature values [℃] or [K]
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
The average variation in daily temperature range for the given time period.
Notes
-----
Let :math:`TX_{ij}` and :math:`TN_{ij}` be the daily maximum and minimum temperature at day :math:`i`
of period :math:`j`. Then the mean diurnal temperature range in period :math:`j` is:
.. math::
DTR_j = \frac{ \sum_{i=1}^I (TX_{ij} - TN_{ij}) }{I}
"""
q = 1 * utils.units2pint(tasmax) - 0 * utils.units2pint(tasmin)
dtr = tasmax - tasmin
out = dtr.resample(time=freq).mean(dim="time", keep_attrs=True)
out.attrs["units"] = f"{q.units}"
return out
def test_cfunits2pint(self, pr_series):
u = units2pint(pr_series([1, 2]))
assert (str(u)) == "kilogram / meter ** 2 / second"
assert pint2cfunits(u) == "kg m-2 s-1"
u = units2pint("m^3 s-1")
assert str(u) == "meter ** 3 / second"
assert pint2cfunits(u) == "m^3 s-1"
def test_cfunits2pint(self, pr_series):
u = units2pint(pr_series([1, 2]))
assert (str(u)) == 'kilogram / meter ** 2 / second'
assert pint2cfunits(u) == 'kg m-2 s-1'
u = units2pint('m^3 s-1')
assert str(u) == 'meter ** 3 / second'
assert pint2cfunits(u) == 'm^3 s-1'
def extreme_temperature_range(tasmax: xarray.DataArray,
tasmin: xarray.DataArray,
freq: str = "YS") -> xarray.DataArray:
r"""Extreme intra-period temperature range.
The maximum of max temperature (TXx) minus the minimum of min temperature (TNn) for the given time period.
Parameters
----------
tasmax : xarray.DataArray
Maximum daily temperature values [℃] or [K]
tasmin : xarray.DataArray
Minimum daily temperature values [℃] or [K]
freq : Optional[str[
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
Extreme intra-period temperature range for the given time period.
Notes
-----
Let :math:`TX_{ij}` and :math:`TN_{ij}` be the daily maximum and minimum temperature at day :math:`i`
of period :math:`j`. Then the extreme temperature range in period :math:`j` is:
.. math::
ETR_j = max(TX_{ij}) - min(TN_{ij})
"""
q = 1 * utils.units2pint(tasmax) - 0 * utils.units2pint(tasmin)
tx_max = tasmax.resample(time=freq).max(dim="time")
tn_min = tasmin.resample(time=freq).min(dim="time")
out = tx_max - tn_min
out.attrs["units"] = f"{q.units}"
return out
def daily_temperature_range_variability(tasmax: xarray.DataArray,
tasmin: xarray.DataArray,
freq: str = "YS") -> xarray.DataArray:
r"""Mean absolute day-to-day variation in daily temperature range.
Mean absolute day-to-day variation in daily temperature range.
Parameters
----------
tasmax : xarray.DataArray
Maximum daily temperature values [℃] or [K]
tasmin : xarray.DataArray
Minimum daily temperature values [℃] or [K]
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
The average day-to-day variation in daily temperature range for the given time period.
Notes
-----
Let :math:`TX_{ij}` and :math:`TN_{ij}` be the daily maximum and minimum temperature at
day :math:`i` of period :math:`j`. Then calculated is the absolute day-to-day differences in
period :math:`j` is:
.. math::
vDTR_j = \frac{ \sum_{i=2}^{I} |(TX_{ij}-TN_{ij})-(TX_{i-1,j}-TN_{i-1,j})| }{I}
"""
q = 1 * utils.units2pint(tasmax) - 0 * utils.units2pint(tasmin)
vdtr = abs((tasmax - tasmin).diff(dim="time"))
out = vdtr.resample(time=freq).mean(dim="time")
out.attrs["units"] = f"{q.units}"
return out
def ts_fit_graph(ts, params):
"""Create graphic showing an histogram of the data and the distribution fitted to it.
Parameters
----------
ts : str
Path to netCDF file storing the time series.
params : str
Path to netCDF file storing the distribution parameters.
Returns
-------
fig
"""
from xclim.generic import get_dist
n = ts.nbasins.size
dist = params.attrs['long_name'].split(' ')[0]
fig, axes = plt.subplots(n, figsize=(10, 6), squeeze=False)
for i in range(n):
ax = axes.flat[i]
ax2 = plt.twinx(ax)
p = params.isel(nbasins=i)
# Plot histogram of time series as density then as a normal count.
density, bins, patches = ax.hist(ts.isel(nbasins=i).dropna(dim='time'),
alpha=.5,
density=True,
bins='auto',
label="__nolabel__")
ax2.hist(
ts.isel(nbasins=i).dropna(dim='time'),
bins=bins,
facecolor=(1, 1, 1, 0.01),
edgecolor='gray',
linewidth=1,
)
# Plot pdf of distribution
dc = get_dist(dist)(*params.isel(nbasins=i))
mn = dc.ppf(.01)
mx = dc.ppf(.99)
q = np.linspace(mn, mx, 200)
pdf = dc.pdf(q)
ps = ', '.join(["{:.1f}".format(x) for x in p.values])
ax.plot(q,
pdf,
'-',
label="{}({})".format(params.attrs['scipy_dist'], ps))
# Labels
ax.set_xlabel("{} (${:~P}$)".format(ts.long_name,
units2pint(ts.units)))
ax.set_ylabel("Probability density")
ax2.set_ylabel("Histogram count")
ax.legend(frameon=False)
plt.tight_layout()
return fig
