def interp1d(x, y, xnew, kind='linear', fill_value=np.nan, **kws):
"""interpolate x, y array onto new x values, using one of
linear, quadratic, or cubic interpolation
> ynew = interp1d(x, y, xnew, kind='linear')
Arguments
---------
x original x values
y original y values
xnew new x values for values to be interpolated to
kind method to use: one of 'linear', 'quadratic', 'cubic'
fill_value value to use to fill values for out-of-range x values
Notes
-----
unlike interp, this version will not extrapolate for values of `xnew`
that are outside the range of `x` -- it will use NaN or `fill_value`.
this is a bare-bones wrapping of scipy.interpolate.interp1d.
see also: interp
"""
if '_larch' in kws:
kws.pop('_larch')
kwargs = {
'kind': kind.lower(),
'fill_value': fill_value,
'copy': False,
'bounds_error': False
}
kwargs.update(kws)
return scipy_interp1d(x, y, **kwargs)(xnew)
def interp1d(x, y, xnew, kind='linear', fill_value=np.nan, **kws):
"""interpolate x, y array onto new x values, using one of
linear, quadratic, or cubic interpolation
> ynew = interp1d(x, y, xnew, kind='linear')
Arguments
---------
x original x values
y original y values
xnew new x values for values to be interpolated to
kind method to use: one of 'linear', 'quadratic', 'cubic'
fill_value value to use to fill values for out-of-range x values
Notes
-----
unlike interp, this version will not extrapolate for values of `xnew`
that are outside the range of `x` -- it will use NaN or `fill_value`.
this is a bare-bones wrapping of scipy.interpolate.interp1d.
see also: interp
"""
if '_larch' in kws:
kws.pop('_larch')
kwargs = {'kind': kind.lower(), 'fill_value': fill_value,
'copy': False, 'bounds_error': False}
kwargs.update(kws)
return scipy_interp1d(x, y, **kwargs)(xnew)
def interp1d(y, n=101, dtype=None, kind='linear', axis=-1, copy=True, bounds_error=True, fill_value=np.nan): t0 = np.arange(y.shape[0]) t1 = np.linspace(0, y.shape[0]-1, n) f = scipy_interp1d(t0, y, kind, axis, copy, bounds_error, fill_value) y1 = f(t1) if dtype != None: y1 = np.asarray(y1, dtype=dtype) return y1
def interp1d(y,
n=101,
dtype=None,
kind='linear',
axis=-1,
copy=True,
bounds_error=True,
fill_value=np.nan):
t0 = np.arange(y.shape[0])
t1 = np.linspace(0, y.shape[0] - 1, n)
f = scipy_interp1d(t0, y, kind, axis, copy, bounds_error, fill_value)
y1 = f(t1)
if dtype != None:
y1 = np.asarray(y1, dtype=dtype)
return y1