from __future__ import annotations

import numpy as np
from numba import guvectorize

from pygama.dsp.utils import numba_defaults_kwargs as nb_kwargs


@guvectorize(
    ["void(float32[:], float32[:])", "void(float64[:], float64[:])"],
    "(n)->(n)",
    **nb_kwargs,
)
def log_check(w_in: np.ndarray, w_log: np.ndarray) -> None:
    """
    Calculate the logarithm of the waveform if all its values
    are positive; otherwise, return NaN.

    Parameters
    ----------
    w_in
        the input waveform.
    w_log
        the output waveform with logged values.

    JSON Configuration Example
    --------------------------

    .. code-block :: json

        "wf_logged": {
            "function": "log_check",
            "module": "pygama.dsp.processors",
            "args": ["wf_blsub[2100:]", "wf_logged"],
            "unit": "ADC"
        }
    """
    w_log[:] = np.nan

    if np.isnan(w_in).any():
        return

    if np.any(w_in <= 0):
        return

    w_log[:] = np.log(w_in[:])

22227ffcf3eab4cf144eae042095c596227cdf30
<mask token>

<mask token>


@guvectorize(['void(float32[:], float32[:])',
    'void(float64[:], float64[:])'], '(n)->(n)', **nb_kwargs)
def log_check(w_in: np.ndarray, w_log: np.ndarray) ->None:
    """
    Calculate the logarithm of the waveform if all its values
    are positive; otherwise, return NaN.

    Parameters
    ----------
    w_in
        the input waveform.
    w_log
        the output waveform with logged values.

    JSON Configuration Example
    --------------------------

    .. code-block :: json

        "wf_logged": {
            "function": "log_check",
            "module": "pygama.dsp.processors",
            "args": ["wf_blsub[2100:]", "wf_logged"],
            "unit": "ADC"
        }
    """
    w_log[:] = np.nan
    if np.isnan(w_in).any():
        return
    if np.any(w_in <= 0):
        return
    w_log[:] = np.log(w_in[:])

from __future__ import annotations
import numpy as np
from numba import guvectorize
from pygama.dsp.utils import numba_defaults_kwargs as nb_kwargs


@guvectorize(['void(float32[:], float32[:])',
    'void(float64[:], float64[:])'], '(n)->(n)', **nb_kwargs)
def log_check(w_in: np.ndarray, w_log: np.ndarray) ->None:
    """
    Calculate the logarithm of the waveform if all its values
    are positive; otherwise, return NaN.

    Parameters
    ----------
    w_in
        the input waveform.
    w_log
        the output waveform with logged values.

    JSON Configuration Example
    --------------------------

    .. code-block :: json

        "wf_logged": {
            "function": "log_check",
            "module": "pygama.dsp.processors",
            "args": ["wf_blsub[2100:]", "wf_logged"],
            "unit": "ADC"
        }
    """
    w_log[:] = np.nan
    if np.isnan(w_in).any():
        return
    if np.any(w_in <= 0):
        return
    w_log[:] = np.log(w_in[:])

from __future__ import annotations

import numpy as np
from numba import guvectorize

from pygama.dsp.utils import numba_defaults_kwargs as nb_kwargs


@guvectorize(
    ["void(float32[:], float32[:])", "void(float64[:], float64[:])"],
    "(n)->(n)",
    **nb_kwargs,
)
def log_check(w_in: np.ndarray, w_log: np.ndarray) -> None:
    """
    Calculate the logarithm of the waveform if all its values
    are positive; otherwise, return NaN.

    Parameters
    ----------
    w_in
        the input waveform.
    w_log
        the output waveform with logged values.

    JSON Configuration Example
    --------------------------

    .. code-block :: json

        "wf_logged": {
            "function": "log_check",
            "module": "pygama.dsp.processors",
            "args": ["wf_blsub[2100:]", "wf_logged"],
            "unit": "ADC"
        }
    """
    w_log[:] = np.nan

    if np.isnan(w_in).any():
        return

    if np.any(w_in <= 0):
        return

    w_log[:] = np.log(w_in[:])
