1159 lines
34 KiB
Python
1159 lines
34 KiB
Python
"""
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Routines for filling missing data.
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"""
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from __future__ import annotations
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from functools import wraps
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from typing import (
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TYPE_CHECKING,
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Any,
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Literal,
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cast,
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overload,
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)
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import numpy as np
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from pandas._libs import (
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NaT,
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algos,
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lib,
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)
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from pandas._typing import (
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ArrayLike,
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AxisInt,
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F,
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ReindexMethod,
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npt,
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)
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from pandas.compat._optional import import_optional_dependency
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from pandas.core.dtypes.cast import infer_dtype_from
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from pandas.core.dtypes.common import (
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is_array_like,
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is_bool_dtype,
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is_numeric_dtype,
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is_numeric_v_string_like,
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is_object_dtype,
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needs_i8_conversion,
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)
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from pandas.core.dtypes.dtypes import DatetimeTZDtype
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from pandas.core.dtypes.missing import (
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is_valid_na_for_dtype,
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isna,
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na_value_for_dtype,
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)
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if TYPE_CHECKING:
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from pandas import Index
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def check_value_size(value, mask: npt.NDArray[np.bool_], length: int):
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"""
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Validate the size of the values passed to ExtensionArray.fillna.
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"""
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if is_array_like(value):
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if len(value) != length:
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raise ValueError(
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f"Length of 'value' does not match. Got ({len(value)}) "
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f" expected {length}"
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)
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value = value[mask]
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return value
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def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
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"""
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Return a masking array of same size/shape as arr
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with entries equaling any member of values_to_mask set to True
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Parameters
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----------
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arr : ArrayLike
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values_to_mask: list, tuple, or scalar
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Returns
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-------
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np.ndarray[bool]
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"""
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# When called from Block.replace/replace_list, values_to_mask is a scalar
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# known to be holdable by arr.
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# When called from Series._single_replace, values_to_mask is tuple or list
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dtype, values_to_mask = infer_dtype_from(values_to_mask)
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if isinstance(dtype, np.dtype):
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values_to_mask = np.array(values_to_mask, dtype=dtype)
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else:
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cls = dtype.construct_array_type()
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if not lib.is_list_like(values_to_mask):
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values_to_mask = [values_to_mask]
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values_to_mask = cls._from_sequence(values_to_mask, dtype=dtype, copy=False)
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potential_na = False
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if is_object_dtype(arr.dtype):
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# pre-compute mask to avoid comparison to NA
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potential_na = True
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arr_mask = ~isna(arr)
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na_mask = isna(values_to_mask)
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nonna = values_to_mask[~na_mask]
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# GH 21977
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mask = np.zeros(arr.shape, dtype=bool)
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if (
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is_numeric_dtype(arr.dtype)
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and not is_bool_dtype(arr.dtype)
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and is_bool_dtype(nonna.dtype)
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):
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pass
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elif (
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is_bool_dtype(arr.dtype)
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and is_numeric_dtype(nonna.dtype)
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and not is_bool_dtype(nonna.dtype)
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):
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pass
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else:
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for x in nonna:
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if is_numeric_v_string_like(arr, x):
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# GH#29553 prevent numpy deprecation warnings
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pass
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else:
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if potential_na:
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new_mask = np.zeros(arr.shape, dtype=np.bool_)
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new_mask[arr_mask] = arr[arr_mask] == x
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else:
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new_mask = arr == x
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if not isinstance(new_mask, np.ndarray):
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# usually BooleanArray
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new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
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mask |= new_mask
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if na_mask.any():
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mask |= isna(arr)
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return mask
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@overload
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def clean_fill_method(
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method: Literal["ffill", "pad", "bfill", "backfill"],
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*,
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allow_nearest: Literal[False] = ...,
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) -> Literal["pad", "backfill"]:
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...
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@overload
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def clean_fill_method(
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method: Literal["ffill", "pad", "bfill", "backfill", "nearest"],
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*,
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allow_nearest: Literal[True],
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) -> Literal["pad", "backfill", "nearest"]:
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...
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def clean_fill_method(
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method: Literal["ffill", "pad", "bfill", "backfill", "nearest"],
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*,
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allow_nearest: bool = False,
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) -> Literal["pad", "backfill", "nearest"]:
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if isinstance(method, str):
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# error: Incompatible types in assignment (expression has type "str", variable
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# has type "Literal['ffill', 'pad', 'bfill', 'backfill', 'nearest']")
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method = method.lower() # type: ignore[assignment]
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if method == "ffill":
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method = "pad"
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elif method == "bfill":
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method = "backfill"
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valid_methods = ["pad", "backfill"]
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expecting = "pad (ffill) or backfill (bfill)"
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if allow_nearest:
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valid_methods.append("nearest")
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expecting = "pad (ffill), backfill (bfill) or nearest"
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if method not in valid_methods:
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raise ValueError(f"Invalid fill method. Expecting {expecting}. Got {method}")
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return method
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# interpolation methods that dispatch to np.interp
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NP_METHODS = ["linear", "time", "index", "values"]
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# interpolation methods that dispatch to _interpolate_scipy_wrapper
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SP_METHODS = [
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"nearest",
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"zero",
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"slinear",
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"quadratic",
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"cubic",
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"barycentric",
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"krogh",
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"spline",
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"polynomial",
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"from_derivatives",
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"piecewise_polynomial",
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"pchip",
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"akima",
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"cubicspline",
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]
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def clean_interp_method(method: str, index: Index, **kwargs) -> str:
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order = kwargs.get("order")
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if method in ("spline", "polynomial") and order is None:
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raise ValueError("You must specify the order of the spline or polynomial.")
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valid = NP_METHODS + SP_METHODS
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if method not in valid:
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raise ValueError(f"method must be one of {valid}. Got '{method}' instead.")
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if method in ("krogh", "piecewise_polynomial", "pchip"):
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if not index.is_monotonic_increasing:
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raise ValueError(
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f"{method} interpolation requires that the index be monotonic."
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)
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return method
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def find_valid_index(how: str, is_valid: npt.NDArray[np.bool_]) -> int | None:
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"""
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Retrieves the positional index of the first valid value.
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Parameters
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----------
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how : {'first', 'last'}
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Use this parameter to change between the first or last valid index.
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is_valid: np.ndarray
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Mask to find na_values.
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Returns
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-------
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int or None
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"""
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assert how in ["first", "last"]
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if len(is_valid) == 0: # early stop
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return None
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if is_valid.ndim == 2:
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is_valid = is_valid.any(axis=1) # reduce axis 1
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if how == "first":
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idxpos = is_valid[::].argmax()
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elif how == "last":
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idxpos = len(is_valid) - 1 - is_valid[::-1].argmax()
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chk_notna = is_valid[idxpos]
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if not chk_notna:
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return None
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# Incompatible return value type (got "signedinteger[Any]",
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# expected "Optional[int]")
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return idxpos # type: ignore[return-value]
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def validate_limit_direction(
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limit_direction: str,
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) -> Literal["forward", "backward", "both"]:
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valid_limit_directions = ["forward", "backward", "both"]
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limit_direction = limit_direction.lower()
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if limit_direction not in valid_limit_directions:
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raise ValueError(
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"Invalid limit_direction: expecting one of "
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f"{valid_limit_directions}, got '{limit_direction}'."
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)
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# error: Incompatible return value type (got "str", expected
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# "Literal['forward', 'backward', 'both']")
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return limit_direction # type: ignore[return-value]
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def validate_limit_area(limit_area: str | None) -> Literal["inside", "outside"] | None:
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if limit_area is not None:
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valid_limit_areas = ["inside", "outside"]
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limit_area = limit_area.lower()
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if limit_area not in valid_limit_areas:
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raise ValueError(
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f"Invalid limit_area: expecting one of {valid_limit_areas}, got "
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f"{limit_area}."
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)
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# error: Incompatible return value type (got "Optional[str]", expected
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# "Optional[Literal['inside', 'outside']]")
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return limit_area # type: ignore[return-value]
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def infer_limit_direction(
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limit_direction: Literal["backward", "forward", "both"] | None, method: str
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) -> Literal["backward", "forward", "both"]:
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# Set `limit_direction` depending on `method`
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if limit_direction is None:
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if method in ("backfill", "bfill"):
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limit_direction = "backward"
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else:
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limit_direction = "forward"
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else:
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if method in ("pad", "ffill") and limit_direction != "forward":
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raise ValueError(
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f"`limit_direction` must be 'forward' for method `{method}`"
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)
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if method in ("backfill", "bfill") and limit_direction != "backward":
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raise ValueError(
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f"`limit_direction` must be 'backward' for method `{method}`"
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)
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return limit_direction
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def get_interp_index(method, index: Index) -> Index:
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# create/use the index
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if method == "linear":
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# prior default
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from pandas import Index
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index = Index(np.arange(len(index)))
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else:
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methods = {"index", "values", "nearest", "time"}
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is_numeric_or_datetime = (
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is_numeric_dtype(index.dtype)
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or isinstance(index.dtype, DatetimeTZDtype)
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or lib.is_np_dtype(index.dtype, "mM")
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)
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if method not in methods and not is_numeric_or_datetime:
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raise ValueError(
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"Index column must be numeric or datetime type when "
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f"using {method} method other than linear. "
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"Try setting a numeric or datetime index column before "
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"interpolating."
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)
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if isna(index).any():
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raise NotImplementedError(
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"Interpolation with NaNs in the index "
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"has not been implemented. Try filling "
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"those NaNs before interpolating."
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)
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return index
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def interpolate_2d_inplace(
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data: np.ndarray, # floating dtype
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index: Index,
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axis: AxisInt,
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method: str = "linear",
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limit: int | None = None,
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limit_direction: str = "forward",
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limit_area: str | None = None,
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fill_value: Any | None = None,
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mask=None,
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**kwargs,
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) -> None:
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"""
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Column-wise application of _interpolate_1d.
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Notes
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-----
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Alters 'data' in-place.
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The signature does differ from _interpolate_1d because it only
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includes what is needed for Block.interpolate.
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"""
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# validate the interp method
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clean_interp_method(method, index, **kwargs)
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if is_valid_na_for_dtype(fill_value, data.dtype):
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fill_value = na_value_for_dtype(data.dtype, compat=False)
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if method == "time":
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if not needs_i8_conversion(index.dtype):
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raise ValueError(
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"time-weighted interpolation only works "
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"on Series or DataFrames with a "
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"DatetimeIndex"
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)
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method = "values"
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limit_direction = validate_limit_direction(limit_direction)
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limit_area_validated = validate_limit_area(limit_area)
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# default limit is unlimited GH #16282
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limit = algos.validate_limit(nobs=None, limit=limit)
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indices = _index_to_interp_indices(index, method)
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def func(yvalues: np.ndarray) -> None:
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# process 1-d slices in the axis direction
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_interpolate_1d(
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indices=indices,
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yvalues=yvalues,
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method=method,
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limit=limit,
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limit_direction=limit_direction,
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limit_area=limit_area_validated,
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fill_value=fill_value,
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bounds_error=False,
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mask=mask,
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**kwargs,
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)
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# error: Argument 1 to "apply_along_axis" has incompatible type
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# "Callable[[ndarray[Any, Any]], None]"; expected "Callable[...,
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# Union[_SupportsArray[dtype[<nothing>]], Sequence[_SupportsArray
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# [dtype[<nothing>]]], Sequence[Sequence[_SupportsArray[dtype[<nothing>]]]],
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# Sequence[Sequence[Sequence[_SupportsArray[dtype[<nothing>]]]]],
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# Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[<nothing>]]]]]]]]"
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np.apply_along_axis(func, axis, data) # type: ignore[arg-type]
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def _index_to_interp_indices(index: Index, method: str) -> np.ndarray:
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"""
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Convert Index to ndarray of indices to pass to NumPy/SciPy.
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"""
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xarr = index._values
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if needs_i8_conversion(xarr.dtype):
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# GH#1646 for dt64tz
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xarr = xarr.view("i8")
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if method == "linear":
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inds = xarr
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inds = cast(np.ndarray, inds)
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else:
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inds = np.asarray(xarr)
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if method in ("values", "index"):
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if inds.dtype == np.object_:
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inds = lib.maybe_convert_objects(inds)
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return inds
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|
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def _interpolate_1d(
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indices: np.ndarray,
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yvalues: np.ndarray,
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method: str = "linear",
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limit: int | None = None,
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limit_direction: str = "forward",
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limit_area: Literal["inside", "outside"] | None = None,
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fill_value: Any | None = None,
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bounds_error: bool = False,
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order: int | None = None,
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mask=None,
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**kwargs,
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) -> None:
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"""
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Logic for the 1-d interpolation. The input
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indices and yvalues will each be 1-d arrays of the same length.
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Bounds_error is currently hardcoded to False since non-scipy ones don't
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take it as an argument.
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|
Notes
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-----
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Fills 'yvalues' in-place.
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"""
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if mask is not None:
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invalid = mask
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else:
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invalid = isna(yvalues)
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valid = ~invalid
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if not valid.any():
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return
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if valid.all():
|
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return
|
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# These are sets of index pointers to invalid values... i.e. {0, 1, etc...
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all_nans = set(np.flatnonzero(invalid))
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first_valid_index = find_valid_index(how="first", is_valid=valid)
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if first_valid_index is None: # no nan found in start
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first_valid_index = 0
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start_nans = set(range(first_valid_index))
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last_valid_index = find_valid_index(how="last", is_valid=valid)
|
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if last_valid_index is None: # no nan found in end
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last_valid_index = len(yvalues)
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end_nans = set(range(1 + last_valid_index, len(valid)))
|
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# Like the sets above, preserve_nans contains indices of invalid values,
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# but in this case, it is the final set of indices that need to be
|
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# preserved as NaN after the interpolation.
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# For example if limit_direction='forward' then preserve_nans will
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# contain indices of NaNs at the beginning of the series, and NaNs that
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# are more than 'limit' away from the prior non-NaN.
|
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|
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# set preserve_nans based on direction using _interp_limit
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preserve_nans: list | set
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if limit_direction == "forward":
|
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preserve_nans = start_nans | set(_interp_limit(invalid, limit, 0))
|
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elif limit_direction == "backward":
|
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preserve_nans = end_nans | set(_interp_limit(invalid, 0, limit))
|
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else:
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# both directions... just use _interp_limit
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preserve_nans = set(_interp_limit(invalid, limit, limit))
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|
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# if limit_area is set, add either mid or outside indices
|
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# to preserve_nans GH #16284
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if limit_area == "inside":
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# preserve NaNs on the outside
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preserve_nans |= start_nans | end_nans
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elif limit_area == "outside":
|
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# preserve NaNs on the inside
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mid_nans = all_nans - start_nans - end_nans
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preserve_nans |= mid_nans
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|
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# sort preserve_nans and convert to list
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|
preserve_nans = sorted(preserve_nans)
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|
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is_datetimelike = yvalues.dtype.kind in "mM"
|
|
|
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if is_datetimelike:
|
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yvalues = yvalues.view("i8")
|
|
|
|
if method in NP_METHODS:
|
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# np.interp requires sorted X values, #21037
|
|
|
|
indexer = np.argsort(indices[valid])
|
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yvalues[invalid] = np.interp(
|
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indices[invalid], indices[valid][indexer], yvalues[valid][indexer]
|
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)
|
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else:
|
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yvalues[invalid] = _interpolate_scipy_wrapper(
|
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indices[valid],
|
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yvalues[valid],
|
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indices[invalid],
|
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method=method,
|
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fill_value=fill_value,
|
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bounds_error=bounds_error,
|
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order=order,
|
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**kwargs,
|
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)
|
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|
|
if mask is not None:
|
|
mask[:] = False
|
|
mask[preserve_nans] = True
|
|
elif is_datetimelike:
|
|
yvalues[preserve_nans] = NaT.value
|
|
else:
|
|
yvalues[preserve_nans] = np.nan
|
|
return
|
|
|
|
|
|
def _interpolate_scipy_wrapper(
|
|
x: np.ndarray,
|
|
y: np.ndarray,
|
|
new_x: np.ndarray,
|
|
method: str,
|
|
fill_value=None,
|
|
bounds_error: bool = False,
|
|
order=None,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Passed off to scipy.interpolate.interp1d. method is scipy's kind.
|
|
Returns an array interpolated at new_x. Add any new methods to
|
|
the list in _clean_interp_method.
|
|
"""
|
|
extra = f"{method} interpolation requires SciPy."
|
|
import_optional_dependency("scipy", extra=extra)
|
|
from scipy import interpolate
|
|
|
|
new_x = np.asarray(new_x)
|
|
|
|
# ignores some kwargs that could be passed along.
|
|
alt_methods = {
|
|
"barycentric": interpolate.barycentric_interpolate,
|
|
"krogh": interpolate.krogh_interpolate,
|
|
"from_derivatives": _from_derivatives,
|
|
"piecewise_polynomial": _from_derivatives,
|
|
"cubicspline": _cubicspline_interpolate,
|
|
"akima": _akima_interpolate,
|
|
"pchip": interpolate.pchip_interpolate,
|
|
}
|
|
|
|
interp1d_methods = [
|
|
"nearest",
|
|
"zero",
|
|
"slinear",
|
|
"quadratic",
|
|
"cubic",
|
|
"polynomial",
|
|
]
|
|
if method in interp1d_methods:
|
|
if method == "polynomial":
|
|
kind = order
|
|
else:
|
|
kind = method
|
|
terp = interpolate.interp1d(
|
|
x, y, kind=kind, fill_value=fill_value, bounds_error=bounds_error
|
|
)
|
|
new_y = terp(new_x)
|
|
elif method == "spline":
|
|
# GH #10633, #24014
|
|
if isna(order) or (order <= 0):
|
|
raise ValueError(
|
|
f"order needs to be specified and greater than 0; got order: {order}"
|
|
)
|
|
terp = interpolate.UnivariateSpline(x, y, k=order, **kwargs)
|
|
new_y = terp(new_x)
|
|
else:
|
|
# GH 7295: need to be able to write for some reason
|
|
# in some circumstances: check all three
|
|
if not x.flags.writeable:
|
|
x = x.copy()
|
|
if not y.flags.writeable:
|
|
y = y.copy()
|
|
if not new_x.flags.writeable:
|
|
new_x = new_x.copy()
|
|
terp = alt_methods[method]
|
|
new_y = terp(x, y, new_x, **kwargs)
|
|
return new_y
|
|
|
|
|
|
def _from_derivatives(
|
|
xi: np.ndarray,
|
|
yi: np.ndarray,
|
|
x: np.ndarray,
|
|
order=None,
|
|
der: int | list[int] | None = 0,
|
|
extrapolate: bool = False,
|
|
):
|
|
"""
|
|
Convenience function for interpolate.BPoly.from_derivatives.
|
|
|
|
Construct a piecewise polynomial in the Bernstein basis, compatible
|
|
with the specified values and derivatives at breakpoints.
|
|
|
|
Parameters
|
|
----------
|
|
xi : array-like
|
|
sorted 1D array of x-coordinates
|
|
yi : array-like or list of array-likes
|
|
yi[i][j] is the j-th derivative known at xi[i]
|
|
order: None or int or array-like of ints. Default: None.
|
|
Specifies the degree of local polynomials. If not None, some
|
|
derivatives are ignored.
|
|
der : int or list
|
|
How many derivatives to extract; None for all potentially nonzero
|
|
derivatives (that is a number equal to the number of points), or a
|
|
list of derivatives to extract. This number includes the function
|
|
value as 0th derivative.
|
|
extrapolate : bool, optional
|
|
Whether to extrapolate to ouf-of-bounds points based on first and last
|
|
intervals, or to return NaNs. Default: True.
|
|
|
|
See Also
|
|
--------
|
|
scipy.interpolate.BPoly.from_derivatives
|
|
|
|
Returns
|
|
-------
|
|
y : scalar or array-like
|
|
The result, of length R or length M or M by R.
|
|
"""
|
|
from scipy import interpolate
|
|
|
|
# return the method for compat with scipy version & backwards compat
|
|
method = interpolate.BPoly.from_derivatives
|
|
m = method(xi, yi.reshape(-1, 1), orders=order, extrapolate=extrapolate)
|
|
|
|
return m(x)
|
|
|
|
|
|
def _akima_interpolate(
|
|
xi: np.ndarray,
|
|
yi: np.ndarray,
|
|
x: np.ndarray,
|
|
der: int | list[int] | None = 0,
|
|
axis: AxisInt = 0,
|
|
):
|
|
"""
|
|
Convenience function for akima interpolation.
|
|
xi and yi are arrays of values used to approximate some function f,
|
|
with ``yi = f(xi)``.
|
|
|
|
See `Akima1DInterpolator` for details.
|
|
|
|
Parameters
|
|
----------
|
|
xi : np.ndarray
|
|
A sorted list of x-coordinates, of length N.
|
|
yi : np.ndarray
|
|
A 1-D array of real values. `yi`'s length along the interpolation
|
|
axis must be equal to the length of `xi`. If N-D array, use axis
|
|
parameter to select correct axis.
|
|
x : np.ndarray
|
|
Of length M.
|
|
der : int, optional
|
|
How many derivatives to extract; None for all potentially
|
|
nonzero derivatives (that is a number equal to the number
|
|
of points), or a list of derivatives to extract. This number
|
|
includes the function value as 0th derivative.
|
|
axis : int, optional
|
|
Axis in the yi array corresponding to the x-coordinate values.
|
|
|
|
See Also
|
|
--------
|
|
scipy.interpolate.Akima1DInterpolator
|
|
|
|
Returns
|
|
-------
|
|
y : scalar or array-like
|
|
The result, of length R or length M or M by R,
|
|
|
|
"""
|
|
from scipy import interpolate
|
|
|
|
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
|
|
|
|
return P(x, nu=der)
|
|
|
|
|
|
def _cubicspline_interpolate(
|
|
xi: np.ndarray,
|
|
yi: np.ndarray,
|
|
x: np.ndarray,
|
|
axis: AxisInt = 0,
|
|
bc_type: str | tuple[Any, Any] = "not-a-knot",
|
|
extrapolate=None,
|
|
):
|
|
"""
|
|
Convenience function for cubic spline data interpolator.
|
|
|
|
See `scipy.interpolate.CubicSpline` for details.
|
|
|
|
Parameters
|
|
----------
|
|
xi : np.ndarray, shape (n,)
|
|
1-d array containing values of the independent variable.
|
|
Values must be real, finite and in strictly increasing order.
|
|
yi : np.ndarray
|
|
Array containing values of the dependent variable. It can have
|
|
arbitrary number of dimensions, but the length along ``axis``
|
|
(see below) must match the length of ``x``. Values must be finite.
|
|
x : np.ndarray, shape (m,)
|
|
axis : int, optional
|
|
Axis along which `y` is assumed to be varying. Meaning that for
|
|
``x[i]`` the corresponding values are ``np.take(y, i, axis=axis)``.
|
|
Default is 0.
|
|
bc_type : string or 2-tuple, optional
|
|
Boundary condition type. Two additional equations, given by the
|
|
boundary conditions, are required to determine all coefficients of
|
|
polynomials on each segment [2]_.
|
|
If `bc_type` is a string, then the specified condition will be applied
|
|
at both ends of a spline. Available conditions are:
|
|
* 'not-a-knot' (default): The first and second segment at a curve end
|
|
are the same polynomial. It is a good default when there is no
|
|
information on boundary conditions.
|
|
* 'periodic': The interpolated functions is assumed to be periodic
|
|
of period ``x[-1] - x[0]``. The first and last value of `y` must be
|
|
identical: ``y[0] == y[-1]``. This boundary condition will result in
|
|
``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``.
|
|
* 'clamped': The first derivative at curves ends are zero. Assuming
|
|
a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition.
|
|
* 'natural': The second derivative at curve ends are zero. Assuming
|
|
a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition.
|
|
If `bc_type` is a 2-tuple, the first and the second value will be
|
|
applied at the curve start and end respectively. The tuple values can
|
|
be one of the previously mentioned strings (except 'periodic') or a
|
|
tuple `(order, deriv_values)` allowing to specify arbitrary
|
|
derivatives at curve ends:
|
|
* `order`: the derivative order, 1 or 2.
|
|
* `deriv_value`: array-like containing derivative values, shape must
|
|
be the same as `y`, excluding ``axis`` dimension. For example, if
|
|
`y` is 1D, then `deriv_value` must be a scalar. If `y` is 3D with
|
|
the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2D
|
|
and have the shape (n0, n1).
|
|
extrapolate : {bool, 'periodic', None}, optional
|
|
If bool, determines whether to extrapolate to out-of-bounds points
|
|
based on first and last intervals, or to return NaNs. If 'periodic',
|
|
periodic extrapolation is used. If None (default), ``extrapolate`` is
|
|
set to 'periodic' for ``bc_type='periodic'`` and to True otherwise.
|
|
|
|
See Also
|
|
--------
|
|
scipy.interpolate.CubicHermiteSpline
|
|
|
|
Returns
|
|
-------
|
|
y : scalar or array-like
|
|
The result, of shape (m,)
|
|
|
|
References
|
|
----------
|
|
.. [1] `Cubic Spline Interpolation
|
|
<https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation>`_
|
|
on Wikiversity.
|
|
.. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978.
|
|
"""
|
|
from scipy import interpolate
|
|
|
|
P = interpolate.CubicSpline(
|
|
xi, yi, axis=axis, bc_type=bc_type, extrapolate=extrapolate
|
|
)
|
|
|
|
return P(x)
|
|
|
|
|
|
def _interpolate_with_limit_area(
|
|
values: np.ndarray,
|
|
method: Literal["pad", "backfill"],
|
|
limit: int | None,
|
|
limit_area: Literal["inside", "outside"],
|
|
) -> None:
|
|
"""
|
|
Apply interpolation and limit_area logic to values along a to-be-specified axis.
|
|
|
|
Parameters
|
|
----------
|
|
values: np.ndarray
|
|
Input array.
|
|
method: str
|
|
Interpolation method. Could be "bfill" or "pad"
|
|
limit: int, optional
|
|
Index limit on interpolation.
|
|
limit_area: {'inside', 'outside'}
|
|
Limit area for interpolation.
|
|
|
|
Notes
|
|
-----
|
|
Modifies values in-place.
|
|
"""
|
|
|
|
invalid = isna(values)
|
|
is_valid = ~invalid
|
|
|
|
if not invalid.all():
|
|
first = find_valid_index(how="first", is_valid=is_valid)
|
|
if first is None:
|
|
first = 0
|
|
last = find_valid_index(how="last", is_valid=is_valid)
|
|
if last is None:
|
|
last = len(values)
|
|
|
|
pad_or_backfill_inplace(
|
|
values,
|
|
method=method,
|
|
limit=limit,
|
|
limit_area=limit_area,
|
|
)
|
|
|
|
if limit_area == "inside":
|
|
invalid[first : last + 1] = False
|
|
elif limit_area == "outside":
|
|
invalid[:first] = invalid[last + 1 :] = False
|
|
else:
|
|
raise ValueError("limit_area should be 'inside' or 'outside'")
|
|
|
|
values[invalid] = np.nan
|
|
|
|
|
|
def pad_or_backfill_inplace(
|
|
values: np.ndarray,
|
|
method: Literal["pad", "backfill"] = "pad",
|
|
axis: AxisInt = 0,
|
|
limit: int | None = None,
|
|
limit_area: Literal["inside", "outside"] | None = None,
|
|
) -> None:
|
|
"""
|
|
Perform an actual interpolation of values, values will be make 2-d if
|
|
needed fills inplace, returns the result.
|
|
|
|
Parameters
|
|
----------
|
|
values: np.ndarray
|
|
Input array.
|
|
method: str, default "pad"
|
|
Interpolation method. Could be "bfill" or "pad"
|
|
axis: 0 or 1
|
|
Interpolation axis
|
|
limit: int, optional
|
|
Index limit on interpolation.
|
|
limit_area: str, optional
|
|
Limit area for interpolation. Can be "inside" or "outside"
|
|
|
|
Notes
|
|
-----
|
|
Modifies values in-place.
|
|
"""
|
|
transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
|
|
|
|
# reshape a 1 dim if needed
|
|
if values.ndim == 1:
|
|
if axis != 0: # pragma: no cover
|
|
raise AssertionError("cannot interpolate on a ndim == 1 with axis != 0")
|
|
values = values.reshape(tuple((1,) + values.shape))
|
|
|
|
method = clean_fill_method(method)
|
|
tvalues = transf(values)
|
|
|
|
func = get_fill_func(method, ndim=2)
|
|
# _pad_2d and _backfill_2d both modify tvalues inplace
|
|
func(tvalues, limit=limit, limit_area=limit_area)
|
|
|
|
|
|
def _fillna_prep(
|
|
values, mask: npt.NDArray[np.bool_] | None = None
|
|
) -> npt.NDArray[np.bool_]:
|
|
# boilerplate for _pad_1d, _backfill_1d, _pad_2d, _backfill_2d
|
|
|
|
if mask is None:
|
|
mask = isna(values)
|
|
|
|
return mask
|
|
|
|
|
|
def _datetimelike_compat(func: F) -> F:
|
|
"""
|
|
Wrapper to handle datetime64 and timedelta64 dtypes.
|
|
"""
|
|
|
|
@wraps(func)
|
|
def new_func(
|
|
values,
|
|
limit: int | None = None,
|
|
limit_area: Literal["inside", "outside"] | None = None,
|
|
mask=None,
|
|
):
|
|
if needs_i8_conversion(values.dtype):
|
|
if mask is None:
|
|
# This needs to occur before casting to int64
|
|
mask = isna(values)
|
|
|
|
result, mask = func(
|
|
values.view("i8"), limit=limit, limit_area=limit_area, mask=mask
|
|
)
|
|
return result.view(values.dtype), mask
|
|
|
|
return func(values, limit=limit, limit_area=limit_area, mask=mask)
|
|
|
|
return cast(F, new_func)
|
|
|
|
|
|
@_datetimelike_compat
|
|
def _pad_1d(
|
|
values: np.ndarray,
|
|
limit: int | None = None,
|
|
limit_area: Literal["inside", "outside"] | None = None,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
|
|
mask = _fillna_prep(values, mask)
|
|
if limit_area is not None and not mask.all():
|
|
_fill_limit_area_1d(mask, limit_area)
|
|
algos.pad_inplace(values, mask, limit=limit)
|
|
return values, mask
|
|
|
|
|
|
@_datetimelike_compat
|
|
def _backfill_1d(
|
|
values: np.ndarray,
|
|
limit: int | None = None,
|
|
limit_area: Literal["inside", "outside"] | None = None,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
|
|
mask = _fillna_prep(values, mask)
|
|
if limit_area is not None and not mask.all():
|
|
_fill_limit_area_1d(mask, limit_area)
|
|
algos.backfill_inplace(values, mask, limit=limit)
|
|
return values, mask
|
|
|
|
|
|
@_datetimelike_compat
|
|
def _pad_2d(
|
|
values: np.ndarray,
|
|
limit: int | None = None,
|
|
limit_area: Literal["inside", "outside"] | None = None,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
):
|
|
mask = _fillna_prep(values, mask)
|
|
if limit_area is not None:
|
|
_fill_limit_area_2d(mask, limit_area)
|
|
|
|
if values.size:
|
|
algos.pad_2d_inplace(values, mask, limit=limit)
|
|
else:
|
|
# for test coverage
|
|
pass
|
|
return values, mask
|
|
|
|
|
|
@_datetimelike_compat
|
|
def _backfill_2d(
|
|
values,
|
|
limit: int | None = None,
|
|
limit_area: Literal["inside", "outside"] | None = None,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
):
|
|
mask = _fillna_prep(values, mask)
|
|
if limit_area is not None:
|
|
_fill_limit_area_2d(mask, limit_area)
|
|
|
|
if values.size:
|
|
algos.backfill_2d_inplace(values, mask, limit=limit)
|
|
else:
|
|
# for test coverage
|
|
pass
|
|
return values, mask
|
|
|
|
|
|
def _fill_limit_area_1d(
|
|
mask: npt.NDArray[np.bool_], limit_area: Literal["outside", "inside"]
|
|
) -> None:
|
|
"""Prepare 1d mask for ffill/bfill with limit_area.
|
|
|
|
Caller is responsible for checking at least one value of mask is False.
|
|
When called, mask will no longer faithfully represent when
|
|
the corresponding are NA or not.
|
|
|
|
Parameters
|
|
----------
|
|
mask : np.ndarray[bool, ndim=1]
|
|
Mask representing NA values when filling.
|
|
limit_area : { "outside", "inside" }
|
|
Whether to limit filling to outside or inside the outer most non-NA value.
|
|
"""
|
|
neg_mask = ~mask
|
|
first = neg_mask.argmax()
|
|
last = len(neg_mask) - neg_mask[::-1].argmax() - 1
|
|
if limit_area == "inside":
|
|
mask[:first] = False
|
|
mask[last + 1 :] = False
|
|
elif limit_area == "outside":
|
|
mask[first + 1 : last] = False
|
|
|
|
|
|
def _fill_limit_area_2d(
|
|
mask: npt.NDArray[np.bool_], limit_area: Literal["outside", "inside"]
|
|
) -> None:
|
|
"""Prepare 2d mask for ffill/bfill with limit_area.
|
|
|
|
When called, mask will no longer faithfully represent when
|
|
the corresponding are NA or not.
|
|
|
|
Parameters
|
|
----------
|
|
mask : np.ndarray[bool, ndim=1]
|
|
Mask representing NA values when filling.
|
|
limit_area : { "outside", "inside" }
|
|
Whether to limit filling to outside or inside the outer most non-NA value.
|
|
"""
|
|
neg_mask = ~mask.T
|
|
if limit_area == "outside":
|
|
# Identify inside
|
|
la_mask = (
|
|
np.maximum.accumulate(neg_mask, axis=0)
|
|
& np.maximum.accumulate(neg_mask[::-1], axis=0)[::-1]
|
|
)
|
|
else:
|
|
# Identify outside
|
|
la_mask = (
|
|
~np.maximum.accumulate(neg_mask, axis=0)
|
|
| ~np.maximum.accumulate(neg_mask[::-1], axis=0)[::-1]
|
|
)
|
|
mask[la_mask.T] = False
|
|
|
|
|
|
_fill_methods = {"pad": _pad_1d, "backfill": _backfill_1d}
|
|
|
|
|
|
def get_fill_func(method, ndim: int = 1):
|
|
method = clean_fill_method(method)
|
|
if ndim == 1:
|
|
return _fill_methods[method]
|
|
return {"pad": _pad_2d, "backfill": _backfill_2d}[method]
|
|
|
|
|
|
def clean_reindex_fill_method(method) -> ReindexMethod | None:
|
|
if method is None:
|
|
return None
|
|
return clean_fill_method(method, allow_nearest=True)
|
|
|
|
|
|
def _interp_limit(
|
|
invalid: npt.NDArray[np.bool_], fw_limit: int | None, bw_limit: int | None
|
|
):
|
|
"""
|
|
Get indexers of values that won't be filled
|
|
because they exceed the limits.
|
|
|
|
Parameters
|
|
----------
|
|
invalid : np.ndarray[bool]
|
|
fw_limit : int or None
|
|
forward limit to index
|
|
bw_limit : int or None
|
|
backward limit to index
|
|
|
|
Returns
|
|
-------
|
|
set of indexers
|
|
|
|
Notes
|
|
-----
|
|
This is equivalent to the more readable, but slower
|
|
|
|
.. code-block:: python
|
|
|
|
def _interp_limit(invalid, fw_limit, bw_limit):
|
|
for x in np.where(invalid)[0]:
|
|
if invalid[max(0, x - fw_limit):x + bw_limit + 1].all():
|
|
yield x
|
|
"""
|
|
# handle forward first; the backward direction is the same except
|
|
# 1. operate on the reversed array
|
|
# 2. subtract the returned indices from N - 1
|
|
N = len(invalid)
|
|
f_idx = set()
|
|
b_idx = set()
|
|
|
|
def inner(invalid, limit: int):
|
|
limit = min(limit, N)
|
|
windowed = _rolling_window(invalid, limit + 1).all(1)
|
|
idx = set(np.where(windowed)[0] + limit) | set(
|
|
np.where((~invalid[: limit + 1]).cumsum() == 0)[0]
|
|
)
|
|
return idx
|
|
|
|
if fw_limit is not None:
|
|
if fw_limit == 0:
|
|
f_idx = set(np.where(invalid)[0])
|
|
else:
|
|
f_idx = inner(invalid, fw_limit)
|
|
|
|
if bw_limit is not None:
|
|
if bw_limit == 0:
|
|
# then we don't even need to care about backwards
|
|
# just use forwards
|
|
return f_idx
|
|
else:
|
|
b_idx_inv = list(inner(invalid[::-1], bw_limit))
|
|
b_idx = set(N - 1 - np.asarray(b_idx_inv))
|
|
if fw_limit == 0:
|
|
return b_idx
|
|
|
|
return f_idx & b_idx
|
|
|
|
|
|
def _rolling_window(a: npt.NDArray[np.bool_], window: int) -> npt.NDArray[np.bool_]:
|
|
"""
|
|
[True, True, False, True, False], 2 ->
|
|
|
|
[
|
|
[True, True],
|
|
[True, False],
|
|
[False, True],
|
|
[True, False],
|
|
]
|
|
"""
|
|
# https://stackoverflow.com/a/6811241
|
|
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
|
|
strides = a.strides + (a.strides[-1],)
|
|
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
|