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using for loop to install conda package

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"""Module for RBF interpolation."""
import warnings
from itertools import combinations_with_replacement
import numpy as np
from numpy.linalg import LinAlgError
from scipy.spatial import KDTree
from scipy.special import comb
from scipy.linalg.lapack import dgesv # type: ignore[attr-defined]
from ._rbfinterp_pythran import (_build_system,
_build_evaluation_coefficients,
_polynomial_matrix)
__all__ = ["RBFInterpolator"]
# These RBFs are implemented.
_AVAILABLE = {
"linear",
"thin_plate_spline",
"cubic",
"quintic",
"multiquadric",
"inverse_multiquadric",
"inverse_quadratic",
"gaussian"
}
# The shape parameter does not need to be specified when using these RBFs.
_SCALE_INVARIANT = {"linear", "thin_plate_spline", "cubic", "quintic"}
# For RBFs that are conditionally positive definite of order m, the interpolant
# should include polynomial terms with degree >= m - 1. Define the minimum
# degrees here. These values are from Chapter 8 of Fasshauer's "Meshfree
# Approximation Methods with MATLAB". The RBFs that are not in this dictionary
# are positive definite and do not need polynomial terms.
_NAME_TO_MIN_DEGREE = {
"multiquadric": 0,
"linear": 0,
"thin_plate_spline": 1,
"cubic": 1,
"quintic": 2
}
def _monomial_powers(ndim, degree):
"""Return the powers for each monomial in a polynomial.
Parameters
----------
ndim : int
Number of variables in the polynomial.
degree : int
Degree of the polynomial.
Returns
-------
(nmonos, ndim) int ndarray
Array where each row contains the powers for each variable in a
monomial.
"""
nmonos = comb(degree + ndim, ndim, exact=True)
out = np.zeros((nmonos, ndim), dtype=int)
count = 0
for deg in range(degree + 1):
for mono in combinations_with_replacement(range(ndim), deg):
# `mono` is a tuple of variables in the current monomial with
# multiplicity indicating power (e.g., (0, 1, 1) represents x*y**2)
for var in mono:
out[count, var] += 1
count += 1
return out
def _build_and_solve_system(y, d, smoothing, kernel, epsilon, powers):
"""Build and solve the RBF interpolation system of equations.
Parameters
----------
y : (P, N) float ndarray
Data point coordinates.
d : (P, S) float ndarray
Data values at `y`.
smoothing : (P,) float ndarray
Smoothing parameter for each data point.
kernel : str
Name of the RBF.
epsilon : float
Shape parameter.
powers : (R, N) int ndarray
The exponents for each monomial in the polynomial.
Returns
-------
coeffs : (P + R, S) float ndarray
Coefficients for each RBF and monomial.
shift : (N,) float ndarray
Domain shift used to create the polynomial matrix.
scale : (N,) float ndarray
Domain scaling used to create the polynomial matrix.
"""
lhs, rhs, shift, scale = _build_system(
y, d, smoothing, kernel, epsilon, powers
)
_, _, coeffs, info = dgesv(lhs, rhs, overwrite_a=True, overwrite_b=True)
if info < 0:
raise ValueError(f"The {-info}-th argument had an illegal value.")
elif info > 0:
msg = "Singular matrix."
nmonos = powers.shape[0]
if nmonos > 0:
pmat = _polynomial_matrix((y - shift)/scale, powers)
rank = np.linalg.matrix_rank(pmat)
if rank < nmonos:
msg = (
"Singular matrix. The matrix of monomials evaluated at "
"the data point coordinates does not have full column "
f"rank ({rank}/{nmonos})."
)
raise LinAlgError(msg)
return shift, scale, coeffs
class RBFInterpolator:
"""Radial basis function (RBF) interpolation in N dimensions.
Parameters
----------
y : (P, N) array_like
Data point coordinates.
d : (P, ...) array_like
Data values at `y`.
neighbors : int, optional
If specified, the value of the interpolant at each evaluation point
will be computed using only this many nearest data points. All the data
points are used by default.
smoothing : float or (P,) array_like, optional
Smoothing parameter. The interpolant perfectly fits the data when this
is set to 0. For large values, the interpolant approaches a least
squares fit of a polynomial with the specified degree. Default is 0.
kernel : str, optional
Type of RBF. This should be one of
- 'linear' : ``-r``
- 'thin_plate_spline' : ``r**2 * log(r)``
- 'cubic' : ``r**3``
- 'quintic' : ``-r**5``
- 'multiquadric' : ``-sqrt(1 + r**2)``
- 'inverse_multiquadric' : ``1/sqrt(1 + r**2)``
- 'inverse_quadratic' : ``1/(1 + r**2)``
- 'gaussian' : ``exp(-r**2)``
Default is 'thin_plate_spline'.
epsilon : float, optional
Shape parameter that scales the input to the RBF. If `kernel` is
'linear', 'thin_plate_spline', 'cubic', or 'quintic', this defaults to
1 and can be ignored because it has the same effect as scaling the
smoothing parameter. Otherwise, this must be specified.
degree : int, optional
Degree of the added polynomial. For some RBFs the interpolant may not
be well-posed if the polynomial degree is too small. Those RBFs and
their corresponding minimum degrees are
- 'multiquadric' : 0
- 'linear' : 0
- 'thin_plate_spline' : 1
- 'cubic' : 1
- 'quintic' : 2
The default value is the minimum degree for `kernel` or 0 if there is
no minimum degree. Set this to -1 for no added polynomial.
Notes
-----
An RBF is a scalar valued function in N-dimensional space whose value at
:math:`x` can be expressed in terms of :math:`r=||x - c||`, where :math:`c`
is the center of the RBF.
An RBF interpolant for the vector of data values :math:`d`, which are from
locations :math:`y`, is a linear combination of RBFs centered at :math:`y`
plus a polynomial with a specified degree. The RBF interpolant is written
as
.. math::
f(x) = K(x, y) a + P(x) b,
where :math:`K(x, y)` is a matrix of RBFs with centers at :math:`y`
evaluated at the points :math:`x`, and :math:`P(x)` is a matrix of
monomials, which span polynomials with the specified degree, evaluated at
:math:`x`. The coefficients :math:`a` and :math:`b` are the solution to the
linear equations
.. math::
(K(y, y) + \\lambda I) a + P(y) b = d
and
.. math::
P(y)^T a = 0,
where :math:`\\lambda` is a non-negative smoothing parameter that controls
how well we want to fit the data. The data are fit exactly when the
smoothing parameter is 0.
The above system is uniquely solvable if the following requirements are
met:
- :math:`P(y)` must have full column rank. :math:`P(y)` always has full
column rank when `degree` is -1 or 0. When `degree` is 1,
:math:`P(y)` has full column rank if the data point locations are not
all collinear (N=2), coplanar (N=3), etc.
- If `kernel` is 'multiquadric', 'linear', 'thin_plate_spline',
'cubic', or 'quintic', then `degree` must not be lower than the
minimum value listed above.
- If `smoothing` is 0, then each data point location must be distinct.
When using an RBF that is not scale invariant ('multiquadric',
'inverse_multiquadric', 'inverse_quadratic', or 'gaussian'), an appropriate
shape parameter must be chosen (e.g., through cross validation). Smaller
values for the shape parameter correspond to wider RBFs. The problem can
become ill-conditioned or singular when the shape parameter is too small.
The memory required to solve for the RBF interpolation coefficients
increases quadratically with the number of data points, which can become
impractical when interpolating more than about a thousand data points.
To overcome memory limitations for large interpolation problems, the
`neighbors` argument can be specified to compute an RBF interpolant for
each evaluation point using only the nearest data points.
.. versionadded:: 1.7.0
See Also
--------
NearestNDInterpolator
LinearNDInterpolator
CloughTocher2DInterpolator
References
----------
.. [1] Fasshauer, G., 2007. Meshfree Approximation Methods with Matlab.
World Scientific Publishing Co.
.. [2] http://amadeus.math.iit.edu/~fass/603_ch3.pdf
.. [3] Wahba, G., 1990. Spline Models for Observational Data. SIAM.
.. [4] http://pages.stat.wisc.edu/~wahba/stat860public/lect/lect8/lect8.pdf
Examples
--------
Demonstrate interpolating scattered data to a grid in 2-D.
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import RBFInterpolator
>>> from scipy.stats.qmc import Halton
>>> rng = np.random.default_rng()
>>> xobs = 2*Halton(2, seed=rng).random(100) - 1
>>> yobs = np.sum(xobs, axis=1)*np.exp(-6*np.sum(xobs**2, axis=1))
>>> xgrid = np.mgrid[-1:1:50j, -1:1:50j]
>>> xflat = xgrid.reshape(2, -1).T
>>> yflat = RBFInterpolator(xobs, yobs)(xflat)
>>> ygrid = yflat.reshape(50, 50)
>>> fig, ax = plt.subplots()
>>> ax.pcolormesh(*xgrid, ygrid, vmin=-0.25, vmax=0.25, shading='gouraud')
>>> p = ax.scatter(*xobs.T, c=yobs, s=50, ec='k', vmin=-0.25, vmax=0.25)
>>> fig.colorbar(p)
>>> plt.show()
"""
def __init__(self, y, d,
neighbors=None,
smoothing=0.0,
kernel="thin_plate_spline",
epsilon=None,
degree=None):
y = np.asarray(y, dtype=float, order="C")
if y.ndim != 2:
raise ValueError("`y` must be a 2-dimensional array.")
ny, ndim = y.shape
d_dtype = complex if np.iscomplexobj(d) else float
d = np.asarray(d, dtype=d_dtype, order="C")
if d.shape[0] != ny:
raise ValueError(
f"Expected the first axis of `d` to have length {ny}."
)
d_shape = d.shape[1:]
d = d.reshape((ny, -1))
# If `d` is complex, convert it to a float array with twice as many
# columns. Otherwise, the LHS matrix would need to be converted to
# complex and take up 2x more memory than necessary.
d = d.view(float)
if np.isscalar(smoothing):
smoothing = np.full(ny, smoothing, dtype=float)
else:
smoothing = np.asarray(smoothing, dtype=float, order="C")
if smoothing.shape != (ny,):
raise ValueError(
"Expected `smoothing` to be a scalar or have shape "
f"({ny},)."
)
kernel = kernel.lower()
if kernel not in _AVAILABLE:
raise ValueError(f"`kernel` must be one of {_AVAILABLE}.")
if epsilon is None:
if kernel in _SCALE_INVARIANT:
epsilon = 1.0
else:
raise ValueError(
"`epsilon` must be specified if `kernel` is not one of "
f"{_SCALE_INVARIANT}."
)
else:
epsilon = float(epsilon)
min_degree = _NAME_TO_MIN_DEGREE.get(kernel, -1)
if degree is None:
degree = max(min_degree, 0)
else:
degree = int(degree)
if degree < -1:
raise ValueError("`degree` must be at least -1.")
elif degree < min_degree:
warnings.warn(
f"`degree` should not be below {min_degree} when `kernel` "
f"is '{kernel}'. The interpolant may not be uniquely "
"solvable, and the smoothing parameter may have an "
"unintuitive effect.",
UserWarning
)
if neighbors is None:
nobs = ny
else:
# Make sure the number of nearest neighbors used for interpolation
# does not exceed the number of observations.
neighbors = int(min(neighbors, ny))
nobs = neighbors
powers = _monomial_powers(ndim, degree)
# The polynomial matrix must have full column rank in order for the
# interpolant to be well-posed, which is not possible if there are
# fewer observations than monomials.
if powers.shape[0] > nobs:
raise ValueError(
f"At least {powers.shape[0]} data points are required when "
f"`degree` is {degree} and the number of dimensions is {ndim}."
)
if neighbors is None:
shift, scale, coeffs = _build_and_solve_system(
y, d, smoothing, kernel, epsilon, powers
)
# Make these attributes private since they do not always exist.
self._shift = shift
self._scale = scale
self._coeffs = coeffs
else:
self._tree = KDTree(y)
self.y = y
self.d = d
self.d_shape = d_shape
self.d_dtype = d_dtype
self.neighbors = neighbors
self.smoothing = smoothing
self.kernel = kernel
self.epsilon = epsilon
self.powers = powers
def _chunk_evaluator(
self,
x,
y,
shift,
scale,
coeffs,
memory_budget=1000000
):
"""
Evaluate the interpolation while controlling memory consumption.
We chunk the input if we need more memory than specified.
Parameters
----------
x : (Q, N) float ndarray
array of points on which to evaluate
y: (P, N) float ndarray
array of points on which we know function values
shift: (N, ) ndarray
Domain shift used to create the polynomial matrix.
scale : (N,) float ndarray
Domain scaling used to create the polynomial matrix.
coeffs: (P+R, S) float ndarray
Coefficients in front of basis functions
memory_budget: int
Total amount of memory (in units of sizeof(float)) we wish
to devote for storing the array of coefficients for
interpolated points. If we need more memory than that, we
chunk the input.
Returns
-------
(Q, S) float ndarray
Interpolated array
"""
nx, ndim = x.shape
if self.neighbors is None:
nnei = len(y)
else:
nnei = self.neighbors
# in each chunk we consume the same space we already occupy
chunksize = memory_budget // ((self.powers.shape[0] + nnei)) + 1
if chunksize <= nx:
out = np.empty((nx, self.d.shape[1]), dtype=float)
for i in range(0, nx, chunksize):
vec = _build_evaluation_coefficients(
x[i:i + chunksize, :],
y,
self.kernel,
self.epsilon,
self.powers,
shift,
scale)
out[i:i + chunksize, :] = np.dot(vec, coeffs)
else:
vec = _build_evaluation_coefficients(
x,
y,
self.kernel,
self.epsilon,
self.powers,
shift,
scale)
out = np.dot(vec, coeffs)
return out
def __call__(self, x):
"""Evaluate the interpolant at `x`.
Parameters
----------
x : (Q, N) array_like
Evaluation point coordinates.
Returns
-------
(Q, ...) ndarray
Values of the interpolant at `x`.
"""
x = np.asarray(x, dtype=float, order="C")
if x.ndim != 2:
raise ValueError("`x` must be a 2-dimensional array.")
nx, ndim = x.shape
if ndim != self.y.shape[1]:
raise ValueError("Expected the second axis of `x` to have length "
f"{self.y.shape[1]}.")
# Our memory budget for storing RBF coefficients is
# based on how many floats in memory we already occupy
# If this number is below 1e6 we just use 1e6
# This memory budget is used to decide how we chunk
# the inputs
memory_budget = max(x.size + self.y.size + self.d.size, 1000000)
if self.neighbors is None:
out = self._chunk_evaluator(
x,
self.y,
self._shift,
self._scale,
self._coeffs,
memory_budget=memory_budget)
else:
# Get the indices of the k nearest observation points to each
# evaluation point.
_, yindices = self._tree.query(x, self.neighbors)
if self.neighbors == 1:
# `KDTree` squeezes the output when neighbors=1.
yindices = yindices[:, None]
# Multiple evaluation points may have the same neighborhood of
# observation points. Make the neighborhoods unique so that we only
# compute the interpolation coefficients once for each
# neighborhood.
yindices = np.sort(yindices, axis=1)
yindices, inv = np.unique(yindices, return_inverse=True, axis=0)
# `inv` tells us which neighborhood will be used by each evaluation
# point. Now we find which evaluation points will be using each
# neighborhood.
xindices = [[] for _ in range(len(yindices))]
for i, j in enumerate(inv):
xindices[j].append(i)
out = np.empty((nx, self.d.shape[1]), dtype=float)
for xidx, yidx in zip(xindices, yindices):
# `yidx` are the indices of the observations in this
# neighborhood. `xidx` are the indices of the evaluation points
# that are using this neighborhood.
xnbr = x[xidx]
ynbr = self.y[yidx]
dnbr = self.d[yidx]
snbr = self.smoothing[yidx]
shift, scale, coeffs = _build_and_solve_system(
ynbr,
dnbr,
snbr,
self.kernel,
self.epsilon,
self.powers,
)
out[xidx] = self._chunk_evaluator(
xnbr,
ynbr,
shift,
scale,
coeffs,
memory_budget=memory_budget)
out = out.view(self.d_dtype)
out = out.reshape((nx, ) + self.d_shape)
return out