def linear_trend(x, eps=0.001, alpha=0.01):
#pf = np.polyfit(np.arange(len(x)), x, 1)
pf, slope, int, p, ind = mk.test(np.arange(len(x)),x.squeeze().values, eps=eps, alpha=alpha, Ha='upordown')
# we need to return a dataarray or else xarray's groupby won't be happy
if ind == 1:
issig = slope
else:
issig = np.nan
return issig
def linear_trend(x, eps=0.001, alpha=0.01):
#pf = np.polyfit(np.arange(len(x)), x, 1)
pf, slope, int, p, ind = mk.test(np.arange(len(x)),
x.squeeze().values,
eps=eps,
alpha=alpha,
Ha='upordown')
# we need to return a dataarray or else xarray's groupby won't be happy
if ind == 1:
issig = slope
else:
issig = np.nan
return issig
def linear_trend_mk(x, eps=0.001, alpha=0.01, nb_missing=None):
#pf = np.polyfit(np.arange(len(x)), x, 1)
pf, slope, int, p, ind = mk.test(np.arange(len(x)),x.squeeze().values, eps=eps, alpha=alpha, Ha='upordown')
# we need to return a dataarray or else xarray's groupby won't be happy
if nb_missing is not None:
if np.nansum(x.values==0)>=nb_missing:
p = np.nan
slope = np.nan
ind = 0
ds = xr.Dataset()
ds['slope'] = xr.DataArray(slope,)
ds['pval'] = xr.DataArray(p, )
ds['ind'] = xr.DataArray(ind)
return ds
def linear_trend_mk(x, eps=0.001, alpha=0.01):
#pf = np.polyfit(np.arange(len(x)), x, 1)
pf, slope, int, p, ind = mk.test(np.arange(len(x)),
x.squeeze().values,
eps=eps,
alpha=alpha,
Ha='upordown')
# we need to return a dataarray or else xarray's groupby won't be happy
if ind == 1:
issig = slope
else:
issig = np.nan
if np.nansum(x.values == 0) >= 10:
p = np.nan
ds = xr.Dataset()
ds['slope'] = xr.DataArray(issig, )
ds['pval'] = xr.DataArray(p, )
return ds