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/opt/imh-python/lib/python3.9/site-packages/numpy/lib/

/opt/imh-python/lib/python3.9/site-packages/numpy/lib

Name	Size	Mode	Actions
tests/	-	0755	rm
__pycache__/	-	0755	rm
arraypad.py	31803	0644	edit dl rm
arraypad.pyi	1728	0644	edit dl rm
arraysetops.py	33655	0644	edit dl rm
arraysetops.pyi	8372	0644	edit dl rm
arrayterator.py	7063	0644	edit dl rm
arrayterator.pyi	1537	0644	edit dl rm
format.py	34769	0644	edit dl rm
format.pyi	748	0644	edit dl rm
function_base.py	189103	0644	edit dl rm
function_base.pyi	16585	0644	edit dl rm
histograms.py	37778	0644	edit dl rm
histograms.pyi	995	0644	edit dl rm
index_tricks.py	31346	0644	edit dl rm
index_tricks.pyi	4251	0644	edit dl rm
mixins.py	7071	0644	edit dl rm
mixins.pyi	3117	0644	edit dl rm
nanfunctions.py	65775	0644	edit dl rm
nanfunctions.pyi	606	0644	edit dl rm
npyio.py	97316	0644	edit dl rm
npyio.pyi	9728	0644	edit dl rm
polynomial.py	44133	0644	edit dl rm
polynomial.pyi	6958	0644	edit dl rm
recfunctions.py	59423	0644	edit dl rm
scimath.py	15037	0644	edit dl rm
scimath.pyi	2883	0644	edit dl rm
setup.py	405	0644	edit dl rm
shape_base.py	38947	0644	edit dl rm
shape_base.pyi	5341	0644	edit dl rm
stride_tricks.py	17911	0644	edit dl rm
stride_tricks.pyi	1747	0644	edit dl rm
twodim_base.py	32947	0644	edit dl rm
twodim_base.pyi	5370	0644	edit dl rm
type_check.py	19954	0644	edit dl rm
type_check.pyi	5571	0644	edit dl rm
ufunclike.py	6325	0644	edit dl rm
ufunclike.pyi	1293	0644	edit dl rm
user_array.py	7721	0644	edit dl rm
utils.py	37804	0644	edit dl rm
utils.pyi	2360	0644	edit dl rm
_datasource.py	22631	0644	edit dl rm
_iotools.py	30868	0644	edit dl rm
_version.py	4855	0644	edit dl rm
_version.pyi	633	0644	edit dl rm
__init__.py	2713	0644	edit dl rm
__init__.pyi	5596	0644	edit dl rm

Edit: /opt/imh-python/lib/python3.9/site-packages/numpy/lib/arrayterator.py (7063B)

"""
A buffered iterator for big arrays.

This module solves the problem of iterating over a big file-based array
without having to read it into memory. The `Arrayterator` class wraps
an array object, and when iterated it will return sub-arrays with at most
a user-specified number of elements.

"""
from operator import mul
from functools import reduce

__all__ = ['Arrayterator']

class Arrayterator:
    """
    Buffered iterator for big arrays.

`Arrayterator` creates a buffered iterator for reading big arrays in small
    contiguous blocks. The class is useful for objects stored in the
    file system. It allows iteration over the object *without* reading
    everything in memory; instead, small blocks are read and iterated over.

`Arrayterator` can be used with any object that supports multidimensional
    slices. This includes NumPy arrays, but also variables from
    Scientific.IO.NetCDF or pynetcdf for example.

Parameters
    ----------
    var : array_like
        The object to iterate over.
    buf_size : int, optional
        The buffer size. If `buf_size` is supplied, the maximum amount of
        data that will be read into memory is `buf_size` elements.
        Default is None, which will read as many element as possible
        into memory.

Attributes
    ----------
    var
    buf_size
    start
    stop
    step
    shape
    flat

See Also
    --------
    ndenumerate : Multidimensional array iterator.
    flatiter : Flat array iterator.
    memmap : Create a memory-map to an array stored in a binary file on disk.

Notes
    -----
    The algorithm works by first finding a "running dimension", along which
    the blocks will be extracted. Given an array of dimensions
    ``(d1, d2, ..., dn)``, e.g. if `buf_size` is smaller than ``d1``, the
    first dimension will be used. If, on the other hand,
    ``d1 < buf_size < d1*d2`` the second dimension will be used, and so on.
    Blocks are extracted along this dimension, and when the last block is
    returned the process continues from the next dimension, until all
    elements have been read.

Examples
    --------
    >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
    >>> a_itor = np.lib.Arrayterator(a, 2)
    >>> a_itor.shape
    (3, 4, 5, 6)

Now we can iterate over ``a_itor``, and it will return arrays of size
    two. Since `buf_size` was smaller than any dimension, the first
    dimension will be iterated over first:

>>> for subarr in a_itor:
    ...     if not subarr.all():
    ...         print(subarr, subarr.shape) # doctest: +SKIP
    >>> # [[[[0 1]]]] (1, 1, 1, 2)

"""

def __init__(self, var, buf_size=None):
        self.var = var
        self.buf_size = buf_size

self.start = [0 for dim in var.shape]
        self.stop = [dim for dim in var.shape]
        self.step = [1 for dim in var.shape]

def __getattr__(self, attr):
        return getattr(self.var, attr)

def __getitem__(self, index):
        """
        Return a new arrayterator.

"""
        # Fix index, handling ellipsis and incomplete slices.
        if not isinstance(index, tuple):
            index = (index,)
        fixed = []
        length, dims = len(index), self.ndim
        for slice_ in index:
            if slice_ is Ellipsis:
                fixed.extend([slice(None)] * (dims-length+1))
                length = len(fixed)
            elif isinstance(slice_, int):
                fixed.append(slice(slice_, slice_+1, 1))
            else:
                fixed.append(slice_)
        index = tuple(fixed)
        if len(index) < dims:
            index += (slice(None),) * (dims-len(index))

# Return a new arrayterator object.
        out = self.__class__(self.var, self.buf_size)
        for i, (start, stop, step, slice_) in enumerate(
                zip(self.start, self.stop, self.step, index)):
            out.start[i] = start + (slice_.start or 0)
            out.step[i] = step * (slice_.step or 1)
            out.stop[i] = start + (slice_.stop or stop-start)
            out.stop[i] = min(stop, out.stop[i])
        return out

def __array__(self):
        """
        Return corresponding data.

"""
        slice_ = tuple(slice(*t) for t in zip(
                self.start, self.stop, self.step))
        return self.var[slice_]

@property
    def flat(self):
        """
        A 1-D flat iterator for Arrayterator objects.

This iterator returns elements of the array to be iterated over in
        `Arrayterator` one by one. It is similar to `flatiter`.

See Also
        --------
        Arrayterator
        flatiter

Examples
        --------
        >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
        >>> a_itor = np.lib.Arrayterator(a, 2)

>>> for subarr in a_itor.flat:
        ...     if not subarr:
        ...         print(subarr, type(subarr))
        ...
        0 <class 'numpy.int64'>

"""
        for block in self:
            yield from block.flat

@property
    def shape(self):
        """
        The shape of the array to be iterated over.

For an example, see `Arrayterator`.

"""
        return tuple(((stop-start-1)//step+1) for start, stop, step in
                zip(self.start, self.stop, self.step))

def __iter__(self):
        # Skip arrays with degenerate dimensions
        if [dim for dim in self.shape if dim <= 0]:
            return

start = self.start[:]
        stop = self.stop[:]
        step = self.step[:]
        ndims = self.var.ndim

while True:
            count = self.buf_size or reduce(mul, self.shape)

# iterate over each dimension, looking for the
            # running dimension (ie, the dimension along which
            # the blocks will be built from)
            rundim = 0
            for i in range(ndims-1, -1, -1):
                # if count is zero we ran out of elements to read
                # along higher dimensions, so we read only a single position
                if count == 0:
                    stop[i] = start[i]+1
                elif count <= self.shape[i]:
                    # limit along this dimension
                    stop[i] = start[i] + count*step[i]
                    rundim = i
                else:
                    # read everything along this dimension
                    stop[i] = self.stop[i]
                stop[i] = min(self.stop[i], stop[i])
                count = count//self.shape[i]

# yield a block
            slice_ = tuple(slice(*t) for t in zip(start, stop, step))
            yield self.var[slice_]

# Update start position, taking care of overflow to
            # other dimensions
            start[rundim] = stop[rundim]  # start where we stopped
            for i in range(ndims-1, 0, -1):
                if start[i] >= self.stop[i]:
                    start[i] = self.start[i]
                    start[i-1] += self.step[i-1]
            if start[0] >= self.stop[0]:
                return

cmd: