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