mirror of
https://github.com/meteoinfo/MeteoInfo.git
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257 lines
6.7 KiB
Python
257 lines
6.7 KiB
Python
# coding=utf-8
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from .numeric import asarray, array, isscalar
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from ._ndarray import NDArray
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from .dimarray import DimArray
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from org.meteoinfo.ndarray.math import ArrayUtil, ArrayMath
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__all__ = ['cumprod', 'cumsum', 'ndim', 'nonzero', 'prod', 'ravel', 'searchsorted', 'sum',
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'where']
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def ndim(a):
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"""
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Return the number of dimensions of an array.
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Parameters
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----------
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a : array_like
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Input array. If it is not already a ndarray, a conversion is
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attempted.
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Returns
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-------
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number_of_dimensions : int
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The number of dimensions in `a`. Scalars are zero-dimensional.
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See Also
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--------
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ndarray.ndim : equivalent method
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shape : dimensions of array
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ndarray.shape : dimensions of array
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Examples
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--------
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>>> np.ndim([[1,2,3],[4,5,6]])
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2
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>>> np.ndim(np.array([[1,2,3],[4,5,6]]))
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2
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>>> np.ndim(1)
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0
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"""
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if isscalar(a):
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return 0
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try:
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return a.ndim
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except AttributeError:
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return asarray(a).ndim
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def ravel(a):
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"""
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Return a contiguous flattened array.
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:param a: (*array*) Input array.
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:return: A contiguous flattened array.
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"""
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if isinstance(a, (list, tuple)):
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a = array(a)
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return a.ravel()
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def nonzero(a):
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"""
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Return the indices of the elements that are non-zero.
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Returns a tuple of arrays, one for each dimension of a, containing the indices of the
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non-zero elements in that dimension.
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:param a: (*array_like*) Input array.
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:returns: (*tuple*) Indices of elements that are non-zero.
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"""
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if isinstance(a, list):
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a = array(a)
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ra = ArrayUtil.nonzero(a.asarray())
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if ra is None:
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return None
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r = []
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for aa in ra:
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r.append(NDArray(aa))
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return tuple(r)
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def where(condition):
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"""
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Return elements, either from x or y, depending on condition.
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If only condition is given, return condition.nonzero().
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:param condition: (*array_like*) Input array.
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:returns: (*tuple*) Indices of elements that are non-zero.
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"""
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return nonzero(condition)
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def searchsorted(a, v, side='left', sorter=None):
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"""
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Find indices where elements should be inserted to maintain order.
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:param a: (*array_like*) Input 1-D array. If sorter is None, then it must be sorted in ascending order,
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otherwise sorter must be an array of indices that sort it.
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:param v: (*array_like*) Values to insert into a.
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:param side: (*str*) [left | right], default is `left`. If `left`, the index of the first suitable location found is given.
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If `right`, return the last such index. If there is no suitable index, return either 0 or N (where N
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is the length of a).
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:param sorter: (*array_like*) Optional array of integer indices that sort array a into ascending order.
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They are typically the result of argsort.
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:return: (*array_like*) Array of insertion points with the same shape as v.
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"""
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if isinstance(a, (list, tuple)):
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a = array(a).asarray()
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elif isinstance(a, NDArray):
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a = a.asarray()
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if isinstance(v, (list, tuple)):
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v = array(v).asarray()
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elif isinstance(v, NDArray):
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v = v.asarray()
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left = True if side == 'left' else False
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r = ArrayUtil.searchSorted(a, v, left)
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if isinstance(r, int):
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return r
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else:
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return NDArray(r)
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def sum(x, axis=None):
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"""
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Sum of array elements over a given axis.
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:param x: (*array_like or list*) Input values.
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:param axis: (*int*) Axis along which the standard deviation is computed.
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The default is to compute the standard deviation of the flattened array.
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:returns: (*array_like*) Sum result
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"""
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if isinstance(x, (list, tuple)):
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if isinstance(x[0], NDArray):
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a = []
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for xx in x:
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a.append(xx.asarray())
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r = ArrayMath.sum(a)
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if type(x[0]) is NDArray:
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return NDArray(r)
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else:
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return DimArray(NDArray(r), x[0].dims, x[0].fill_value, x[0].proj)
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else:
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x = array(x)
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if axis is None:
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r = ArrayMath.sum(x.asarray())
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return r
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else:
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r = x.sum(axis)
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if isinstance(x, DimArray):
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dims = []
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for i in range(0, x.ndim):
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if i != axis:
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dims.append(x.dims[i])
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return DimArray(r, dims, x.fill_value, x.proj)
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else:
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return r
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def prod(x, axis=None):
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"""
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Product of array elements over a given axis.
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:param x: (*array_like or list*) Input values.
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:param axis: (*int*) Axis along which the standard deviation is computed.
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The default is to compute the standard deviation of the flattened array.
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:returns: (*array_like*) Product result
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"""
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if isinstance(x, (list, tuple)):
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x = array(x)
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if axis is None:
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r = ArrayMath.prod(x.asarray())
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return r
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else:
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r = ArrayMath.prod(x.asarray(), axis)
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if type(x) is NDArray:
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return NDArray(r)
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else:
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dims = []
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for i in range(0, x.ndim):
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if i != axis:
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dims.append(x.dims[i])
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return DimArray(NDArray(r), dims, x.fill_value, x.proj)
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def cumsum(a, axis=None):
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"""
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Return the cumulative summary of elements along a given axis.
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Parameters
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----------
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a : array_like
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Input array.
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axis : int, optional
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Axis along which the cumulative summary is computed. By default
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the input is flattened.
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Returns
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-------
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cumsum : ndarray
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A new array holding the result is returned.
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"""
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if axis is None:
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r = ArrayMath.cumsum(a._array)
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else:
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r = ArrayMath.cumsum(a._array, axis)
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if type(a) is NDArray:
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return NDArray(r)
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else:
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dims = []
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for i in range(0, a.ndim):
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dims.append(a.dims[i])
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return DimArray(r, dims, a.fill_value, a.proj)
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def cumprod(a, axis=None):
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"""
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Return the cumulative product of elements along a given axis.
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Parameters
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----------
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a : array_like
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Input array.
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axis : int, optional
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Axis along which the cumulative product is computed. By default
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the input is flattened.
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Returns
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-------
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cumprod : ndarray
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A new array holding the result is returned.
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"""
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if axis is None:
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r = ArrayMath.cumprod(a._array)
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else:
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r = ArrayMath.cumprod(a._array, axis)
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if type(a) is NDArray:
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return NDArray(r)
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else:
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dims = []
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for i in range(0, a.ndim):
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dims.append(a.dims[i])
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return DimArray(r, dims, a.fill_value, a.proj)
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