""" helper_funcs.py.
scavenged from enthought,interpolate
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from . import _interpolate # C extension. Does all the real work.
def atleast_1d_and_contiguous(ary, dtype=np.float64):
return np.atleast_1d(np.ascontiguousarray(ary, dtype))
@np.deprecate(message="'nearest' is deprecated in SciPy 1.0.0")
def nearest(x, y, new_x):
"""
Rounds each new x to nearest input x and returns corresponding input y.
Parameters
----------
x : array_like
Independent values.
y : array_like
Dependent values.
new_x : array_like
The x values to return the interpolate y values.
Returns
-------
nearest : ndarray
Rounds each `new_x` to nearest `x` and returns the corresponding `y`.
"""
shifted_x = np.concatenate((np.array([x[0]-1]), x[0:-1]))
midpoints_of_x = atleast_1d_and_contiguous(.5*(x + shifted_x))
new_x = atleast_1d_and_contiguous(new_x)
TINY = 1e-10
indices = np.searchsorted(midpoints_of_x, new_x+TINY)-1
indices = np.atleast_1d(np.clip(indices, 0, np.Inf).astype(int))
new_y = np.take(y, indices, axis=-1)
return new_y
@np.deprecate(message="'linear' is deprecated in SciPy 1.0.0")
def linear(x, y, new_x):
"""
Linearly interpolates values in new_x based on the values in x and y
Parameters
----------
x : array_like
Independent values
y : array_like
Dependent values
new_x : array_like
The x values to return the interpolated y values.
"""
x = atleast_1d_and_contiguous(x, np.float64)
y = atleast_1d_and_contiguous(y, np.float64)
new_x = atleast_1d_and_contiguous(new_x, np.float64)
if y.ndim > 2:
raise ValueError("`linear` only works with 1-D or 2-D arrays.")
if len(y.shape) == 2:
new_y = np.zeros((y.shape[0], len(new_x)), np.float64)
for i in range(len(new_y)): # for each row
_interpolate.linear_dddd(x, y[i], new_x, new_y[i])
else:
new_y = np.zeros(len(new_x), np.float64)
_interpolate.linear_dddd(x, y, new_x, new_y)
return new_y
@np.deprecate(message="'logarithmic' is deprecated in SciPy 1.0.0")
def logarithmic(x, y, new_x):
"""
Linearly interpolates values in new_x based in the log space of y.
Parameters
----------
x : array_like
Independent values.
y : array_like
Dependent values.
new_x : array_like
The x values to return interpolated y values at.
"""
x = atleast_1d_and_contiguous(x, np.float64)
y = atleast_1d_and_contiguous(y, np.float64)
new_x = atleast_1d_and_contiguous(new_x, np.float64)
if y.ndim > 2:
raise ValueError("`linear` only works with 1-D or 2-D arrays.")
if len(y.shape) == 2:
new_y = np.zeros((y.shape[0], len(new_x)), np.float64)
for i in range(len(new_y)):
_interpolate.loginterp_dddd(x, y[i], new_x, new_y[i])
else:
new_y = np.zeros(len(new_x), np.float64)
_interpolate.loginterp_dddd(x, y, new_x, new_y)
return new_y
@np.deprecate(message="'block_average_above' is deprecated in SciPy 1.0.0")
def block_average_above(x, y, new_x):
"""
Linearly interpolates values in new_x based on the values in x and y.
Parameters
----------
x : array_like
Independent values.
y : array_like
Dependent values.
new_x : array_like
The x values to interpolate y values.
"""
bad_index = None
x = atleast_1d_and_contiguous(x, np.float64)
y = atleast_1d_and_contiguous(y, np.float64)
new_x = atleast_1d_and_contiguous(new_x, np.float64)
if y.ndim > 2:
raise ValueError("`linear` only works with 1-D or 2-D arrays.")
if len(y.shape) == 2:
new_y = np.zeros((y.shape[0], len(new_x)), np.float64)
for i in range(len(new_y)):
bad_index = _interpolate.block_averave_above_dddd(x, y[i],
new_x, new_y[i])
if bad_index is not None:
break
else:
new_y = np.zeros(len(new_x), np.float64)
bad_index = _interpolate.block_average_above_dddd(x, y, new_x, new_y)
if bad_index is not None:
msg = "block_average_above cannot extrapolate and new_x[%d]=%f "\
"is out of the x range (%f, %f)" % \
(bad_index, new_x[bad_index], x[0], x[-1])
raise ValueError(msg)
return new_y
@np.deprecate(message="'block' is deprecated in SciPy 1.0.0")
def block(x, y, new_x):
"""
Essentially a step function.
For each `new_x`, finds largest j such that``x[j] < new_x[j]`` and
returns ``y[j]``.
Parameters
----------
x : array_like
Independent values.
y : array_like
Dependent values.
new_x : array_like
The x values used to calculate the interpolated y.
Returns
-------
block : ndarray
Return array, of same length as `x_new`.
"""
# find index of values in x that precede values in x
# This code is a little strange -- we really want a routine that
# returns the index of values where x[j] < x[index]
TINY = 1e-10
indices = np.searchsorted(x, new_x+TINY)-1
# If the value is at the front of the list, it'll have -1.
# In this case, we will use the first (0), element in the array.
# take requires the index array to be an Int
indices = np.atleast_1d(np.clip(indices, 0, np.Inf).astype(int))
new_y = np.take(y, indices, axis=-1)
return new_y
