3jane / numpy_ext Goto Github PK

   ...
    arr = np.empty(shape) # <- There is creates nans array with default (float64) dtype
    arr.fill(np.nan)
    return arr

def prepend_na(array: np.ndarray, n: int) -> np.ndarray:
    """
    Return a copy of array with nans inserted at the beginning.

    Parameters
    ----------
    array : np.ndarray
        Input array.
    n : int
        Number of elements to insert.

    Returns
    -------
    np.ndarray
        New array with nans added at the beginning.

    Examples
    --------
    >>> prepend_na(np.array([1, 2]), 2)
    array([nan, nan,  1.,  2.])
    """
    return np.hstack((nans(n, array[0].dtype), array)) #<-  here


def nans(shape: Union[int, Tuple[int]], dtype) -> np.ndarray: #<-  here
    """
    Return a new array of a given shape and type, filled with np.nan values.

    Parameters
    ----------
    shape : int or tuple of ints
        Shape of the new array, e.g., (2, 3) or 2.

    Returns
    -------
    np.ndarray
        Array of np.nans of the given shape.

    Examples
    --------
    >>> nans(3)
    array([nan, nan, nan])
    >>> nans((2, 2))
    array([[nan, nan],
           [nan, nan]])
    """
    arr = np.empty(shape, dtype = dtype) # <-  here
    arr.fill(np.nan)
    return arr

dt_arr = np.array(['2007-07-13', '2006-01-13', '2010-08-13', '2007-07-14', '2006-01-15', '2010-08-16', ], dtype='datetime64')
dt_2arr = np.arange(6)

winsize = 2

def delegate(index, data, external_counters, payload_func):
        
        it = external_counters[0]
        
        it+=1
        print ('>>>', index[0],'__', data[0], it,  end = '->')
        res = payload_func(index, data, external_counters)
        external_counters[0] = it
        
        print ('>>>', res, index.shape[0])
        #if it < index.shape[0]: return np.nan # this is ugly workaround
        return  res

df_sts_meta = pd.DataFrame()

payload_func = lambda index, data, external_counters: index[0]

rolling_apply(delegate, winsize, dt_arr, dt_2arr, external_counters=[0,0],payload_func = payload_func, n_jobs = 1)

import numpy as np
import numpy_ext as npext

def min_max(arr):
    return np.min(arr), np.max(arr)

a = np.arange(1000)

b = npext.rolling_apply(min_max, 5, a)

return np.hstack(
        (
            nans(n, array[0].dtype) if len(array) and hasattr(array[0], 'dtype') else nans(n),
            array
        )
    )

    return np.vstack(
        (
            nans((n, len(array[0])), array[0].dtype) if len(array) and hasattr(array[0], 'dtype') else nans((n, len(array[0]))),
            array
        )
    )

def nans(shape: Union[int, Tuple[int, ...]], dtype=np.float64) -> np.ndarray:

    if np.issubdtype(dtype, np.integer):
        dtype = np.float
    arr = np.empty(shape, dtype=dtype)
    arr.fill(np.nan)
    return arr

.rolling(3, center=True, min_periods=0)

import numpy as np
import numpy_ext as npext

a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3
npext.rolling_apply(np.sum, window, a, prepend_nans=True)
array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
npext.rolling_apply(np.sum, window, a, prepend_nans=False)
array([ 3,  6,  9, 12, 15, 18, 21, 24])

pd.DataFrame(a).rolling(3, center=True, min_periods=0).apply(np.sum).values.flatten()
array([ 1.,  3.,  6.,  9., 12., 15., 18., 21., 24., 17.])

numpy==1.20.1
joblib==1.0.1

numpy>=1.20.1,<1.21
joblib>=1.0.1,<1.1.0