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from __future__ import annotations
import warnings
import numpy as np
import pandas as pd
from numpy.core.multiarray import normalize_axis_index # type: ignore[attr-defined]
from xarray.core.options import OPTIONS
try:
import bottleneck as bn
_USE_BOTTLENECK = True
except ImportError:
# use numpy methods instead
bn = np
_USE_BOTTLENECK = False
def _select_along_axis(values, idx, axis):
other_ind = np.ix_(*[np.arange(s) for s in idx.shape])
sl = other_ind[:axis] + (idx,) + other_ind[axis:]
return values[sl]
def nanfirst(values, axis):
axis = normalize_axis_index(axis, values.ndim)
idx_first = np.argmax(~pd.isnull(values), axis=axis)
return _select_along_axis(values, idx_first, axis)
def nanlast(values, axis):
axis = normalize_axis_index(axis, values.ndim)
rev = (slice(None),) * axis + (slice(None, None, -1),)
idx_last = -1 - np.argmax(~pd.isnull(values)[rev], axis=axis)
return _select_along_axis(values, idx_last, axis)
def inverse_permutation(indices):
"""Return indices for an inverse permutation.
Parameters
----------
indices : 1D np.ndarray with dtype=int
Integer positions to assign elements to.
Returns
-------
inverse_permutation : 1D np.ndarray with dtype=int
Integer indices to take from the original array to create the
permutation.
"""
# use intp instead of int64 because of windows :(
inverse_permutation = np.empty(len(indices), dtype=np.intp)
inverse_permutation[indices] = np.arange(len(indices), dtype=np.intp)
return inverse_permutation
def _ensure_bool_is_ndarray(result, *args):
# numpy will sometimes return a scalar value from binary comparisons if it
# can't handle the comparison instead of broadcasting, e.g.,
# In [10]: 1 == np.array(['a', 'b'])
# Out[10]: False
# This function ensures that the result is the appropriate shape in these
# cases
if isinstance(result, bool):
shape = np.broadcast(*args).shape
constructor = np.ones if result else np.zeros
result = constructor(shape, dtype=bool)
return result
def array_eq(self, other):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", r"elementwise comparison failed")
return _ensure_bool_is_ndarray(self == other, self, other)
def array_ne(self, other):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", r"elementwise comparison failed")
return _ensure_bool_is_ndarray(self != other, self, other)
def _is_contiguous(positions):
"""Given a non-empty list, does it consist of contiguous integers?"""
previous = positions[0]
for current in positions[1:]:
if current != previous + 1:
return False
previous = current
return True
def _advanced_indexer_subspaces(key):
"""Indices of the advanced indexes subspaces for mixed indexing and vindex."""
if not isinstance(key, tuple):
key = (key,)
advanced_index_positions = [
i for i, k in enumerate(key) if not isinstance(k, slice)
]
if not advanced_index_positions or not _is_contiguous(advanced_index_positions):
# Nothing to reorder: dimensions on the indexing result are already
# ordered like vindex. See NumPy's rule for "Combining advanced and
# basic indexing":
# https://numpy.org/doc/stable/reference/arrays.indexing.html#combining-advanced-and-basic-indexing
return (), ()
non_slices = [k for k in key if not isinstance(k, slice)]
ndim = len(np.broadcast(*non_slices).shape)
mixed_positions = advanced_index_positions[0] + np.arange(ndim)
vindex_positions = np.arange(ndim)
return mixed_positions, vindex_positions
class NumpyVIndexAdapter:
"""Object that implements indexing like vindex on a np.ndarray.
This is a pure Python implementation of (some of) the logic in this NumPy
proposal: https://github.com/numpy/numpy/pull/6256
"""
def __init__(self, array):
self._array = array
def __getitem__(self, key):
mixed_positions, vindex_positions = _advanced_indexer_subspaces(key)
return np.moveaxis(self._array[key], mixed_positions, vindex_positions)
def __setitem__(self, key, value):
"""Value must have dimensionality matching the key."""
mixed_positions, vindex_positions = _advanced_indexer_subspaces(key)
self._array[key] = np.moveaxis(value, vindex_positions, mixed_positions)
def _create_bottleneck_method(name, npmodule=np):
def f(values, axis=None, **kwargs):
dtype = kwargs.get("dtype", None)
bn_func = getattr(bn, name, None)
if (
_USE_BOTTLENECK
and OPTIONS["use_bottleneck"]
and isinstance(values, np.ndarray)
and bn_func is not None
and not isinstance(axis, tuple)
and values.dtype.kind in "uifc"
and values.dtype.isnative
and (dtype is None or np.dtype(dtype) == values.dtype)
):
# bottleneck does not take care dtype, min_count
kwargs.pop("dtype", None)
result = bn_func(values, axis=axis, **kwargs)
else:
result = getattr(npmodule, name)(values, axis=axis, **kwargs)
return result
f.__name__ = name
return f
def _nanpolyfit_1d(arr, x, rcond=None):
out = np.full((x.shape[1] + 1,), np.nan)
mask = np.isnan(arr)
if not np.all(mask):
out[:-1], resid, rank, _ = np.linalg.lstsq(x[~mask, :], arr[~mask], rcond=rcond)
out[-1] = resid if resid.size > 0 else np.nan
warn_on_deficient_rank(rank, x.shape[1])
return out
def warn_on_deficient_rank(rank, order):
if rank != order:
warnings.warn("Polyfit may be poorly conditioned", np.RankWarning, stacklevel=2)
def least_squares(lhs, rhs, rcond=None, skipna=False):
if skipna:
added_dim = rhs.ndim == 1
if added_dim:
rhs = rhs.reshape(rhs.shape[0], 1)
nan_cols = np.any(np.isnan(rhs), axis=0)
out = np.empty((lhs.shape[1] + 1, rhs.shape[1]))
if np.any(nan_cols):
out[:, nan_cols] = np.apply_along_axis(
_nanpolyfit_1d, 0, rhs[:, nan_cols], lhs
)
if np.any(~nan_cols):
out[:-1, ~nan_cols], resids, rank, _ = np.linalg.lstsq(
lhs, rhs[:, ~nan_cols], rcond=rcond
)
out[-1, ~nan_cols] = resids if resids.size > 0 else np.nan
warn_on_deficient_rank(rank, lhs.shape[1])
coeffs = out[:-1, :]
residuals = out[-1, :]
if added_dim:
coeffs = coeffs.reshape(coeffs.shape[0])
residuals = residuals.reshape(residuals.shape[0])
else:
coeffs, residuals, rank, _ = np.linalg.lstsq(lhs, rhs, rcond=rcond)
if residuals.size == 0:
residuals = coeffs[0] * np.nan
warn_on_deficient_rank(rank, lhs.shape[1])
return coeffs, residuals
nanmin = _create_bottleneck_method("nanmin")
nanmax = _create_bottleneck_method("nanmax")
nanmean = _create_bottleneck_method("nanmean")
nanmedian = _create_bottleneck_method("nanmedian")
nanvar = _create_bottleneck_method("nanvar")
nanstd = _create_bottleneck_method("nanstd")
nanprod = _create_bottleneck_method("nanprod")
nancumsum = _create_bottleneck_method("nancumsum")
nancumprod = _create_bottleneck_method("nancumprod")
nanargmin = _create_bottleneck_method("nanargmin")
nanargmax = _create_bottleneck_method("nanargmax")
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