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def reshape_for_fitting(arr, kernel_half_width=0, kernel_lag=0, pad_kwargs=None):
    """Reshape 1D array into a shape suitable for easy convolution/AR model fitting."""
    assert len(arr.shape) == 1, "Array must be 1D"
    n = len(arr)
    pad_kwargs = pad_kwargs or {}
    pad_left = max(kernel_half_width - kernel_lag, 0)
    pad_right = max(kernel_half_width + kernel_lag, 0)
    arr_padded = np.pad(arr, [(pad_left, pad_right)], **pad_kwargs)
    return stride_tricks.as_strided(
        arr_padded,

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kubectl get pods --sort-by=.status.conditions[0].lastTransitionTime

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def cartesian(arrays):
    """
    Generate a cartesian product of input arrays.

    Parameters
    ----------
    arrays : list of array-like
        1-D arrays to form the cartesian product of.
    out : ndarray
        Array to place the cartesian product in.

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def repeat(df, count_column):
    expanded_df = df.loc[
        df.index.repeat(repeats=df[count_column])
    ].reset_index(drop=True)
    expanded_df[count_column] = 1
    return expanded_df

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from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

get_available_gpus()

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 -- Code based on https://github.com/cmderdev/cmder/issues/1056
 -- with modifications to make it work with conda/virtual envs (https://github.com/cmderdev/cmder/issues/1056#issuecomment-237403714)

local clink_path_lua_file = clink.get_env('CMDER_ROOT')..'\\vendor\\clink-completions\\modules\\path.lua'
dofile(clink_path_lua_file)

---
 -- Find out current conda/virtual envs
 -- @return {false|conda/virtual env name}
---