Python numpy.extract() 使用实例

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Example 1

def seperateDataX(self, breaks, x):
    #   a function that seperates the data based on the breaks for given x

        numberOfParameters = len(breaks)
        numberOfSegments = numberOfParameters - 1

        self.numberOfParameters = numberOfParameters
        self.numberOfSegments = numberOfSegments

        #   Seperate Data into Segments
        sepDataX = [[] for i in range(self.numberOfSegments)]

        for i in range(0, self.numberOfSegments):
            dataX = []
            if i == 0:
                # the first index should always be inclusive
                aTest = x >= breaks[i]
            else:
                # the rest of the indexies should be exclusive
                aTest = x > breaks[i]
            dataX = np.extract(aTest, x)
            bTest = dataX <= breaks[i+1]
            dataX = np.extract(bTest, dataX)
            sepDataX[i] = np.array(dataX)
        return(sepDataX) 

Example 2

def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N it will be repeated.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

Example 3

def seperateData(self, breaks):
    #   a function that seperates the data based on the breaks

        numberOfParameters = len(breaks)
        numberOfSegments = numberOfParameters - 1

        self.numberOfParameters = numberOfParameters
        self.numberOfSegments = numberOfSegments

        #   Seperate Data into Segments
        sepDataX = [[] for i in range(self.numberOfSegments)]
        sepDataY = [[] for i in range(self.numberOfSegments)]

        for i in range(0, self.numberOfSegments):
            dataX = []
            dataY = []
            if i == 0:
                # the first index should always be inclusive
                aTest = self.xData >= breaks[i]
            else:
                # the rest of the indexies should be exclusive
                aTest = self.xData > breaks[i]
            dataX = np.extract(aTest, self.xData)
            dataY = np.extract(aTest, self.yData)
            bTest = dataX <= breaks[i+1]
            dataX = np.extract(bTest, dataX)
            dataY = np.extract(bTest, dataY)
            sepDataX[i] = np.array(dataX)
            sepDataY[i] = np.array(dataY)
        return(sepDataX, sepDataY) 

Example 4

def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N it will be repeated.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

Example 5

def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : array_like
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N it will be repeated.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    return _insert(arr, mask, vals) 

Example 6

def extract_off_diag(mtx):
    """
    extract off diagonal entries in mtx.
    The output vector is order in a column major manner.
    :param mtx: input matrix to extract the off diagonal entries
    :return:
    """
    # we transpose the matrix because the function np.extract will first flatten the matrix
    # withe ordering convention 'C' instead of 'F'!!
    extract_cond = np.reshape((1 - np.eye(*mtx.shape)).T.astype(bool), (-1, 1), order='F')
    return np.reshape(np.extract(extract_cond, mtx.T), (-1, 1), order='F') 

Example 7

def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N it will be repeated.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

Example 8

def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N, it will be repeated, and if elements of `a` are to be masked,
        this sequence must be non-empty.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

Example 9

def computeSumWithThreshold( dataNumpyArray, threshold):

  # convert to a mesh grid
  grid = numpy.meshgrid(dataNumpyArray)

  # Logical comparison
  #  1)  compute a boolean array of values less than the threshold
  compareThreshold = numpy.less (grid , threshold)

  #  2) compare and extract # TODO Not elegant, but works.  found this at http://stackoverflow.com/a/26511354
  boolThreshold = numpy.logical_and(compareThreshold , grid)

  # Create new array
  lowPlank = numpy.extract(boolThreshold, grid)
  return numpy.sum(lowPlank) 

Example 10

def __call__(self, data):
        if data.domain != self.pca.pre_domain:
            data = data.from_table(self.pca.pre_domain, data)
        pca_space = self.pca.transform(data.X)
        if self.components is not None:
            #set unused components to zero
            remove = np.ones(pca_space.shape[1])
            remove[self.components] = 0
            remove = np.extract(remove, np.arange(pca_space.shape[1]))
            pca_space[:,remove] = 0
        return self.pca.proj.inverse_transform(pca_space) 

Example 11

def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N it will be repeated.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

Example 12

def extract(condition, arr):
    """
    Return the elements of an array that satisfy some condition.

    This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
    `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.

    Note that `place` does the exact opposite of `extract`.

    Parameters
    ----------
    condition : array_like
        An array whose nonzero or True entries indicate the elements of `arr`
        to extract.
    arr : array_like
        Input array of the same size as `condition`.

    Returns
    -------
    extract : ndarray
        Rank 1 array of values from `arr` where `condition` is True.

    See Also
    --------
    take, put, copyto, compress, place

    Examples
    --------
    >>> arr = np.arange(12).reshape((3, 4))
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]])
    >>> condition = np.mod(arr, 3)==0
    >>> condition
    array([[ True, False, False,  True],
           [False, False,  True, False],
           [False,  True, False, False]], dtype=bool)
    >>> np.extract(condition, arr)
    array([0, 3, 6, 9])


    If `condition` is boolean:

    >>> arr[condition]
    array([0, 3, 6, 9])

    """
    return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) 

Example 13

def single_metrics(gt, pred, num_cl):
	t_px = np.zeros(num_cl)
	n_px = np.zeros(num_cl)
	n1_px = np.zeros(num_cl)
	px_class = np.unique(gt) 
	error = np.subtract(gt, pred)

	for i in px_class:
		t_px[i] += (np.where(gt == i)[0]).shape[0]
		n_px[i] += (np.where((gt == i) & (error == 0))[0]).shape[0]
		n1_px[i] += (np.where(pred == i)[0]).shape[0]

	return t_px, n_px, n1_px


# if __name__ == "__main__":
# 	pic_start = int(sys.argv[1])
# 	pic_end = int(sys.argv[2])
# 	num_cl = 22
# 	t_px = np.zeros(num_cl)
# 	n_px = np.zeros(num_cl)
# 	n1_px = np.zeros(num_cl)
	
# 	for idx in range(pic_start, pic_end+1):
# 		tmp_t, tmp_n, tmp_n1 = save_result(str(idx), num_cl, True)
# 		t_px += tmp_t
# 		n_px += tmp_n
# 		n1_px += tmp_n1

# 	t_sum = np.sum(t_px)
# 	n_sum = np.sum(n_px)
# 	px_acc = n_sum/t_sum
# 	condition_1 = t_px != 0
# 	c_n1 = np.extract(condition_1, n_px)
# 	c_t1 = np.extract(condition_1, t_px)
# 	condition_2 = (np.subtract(np.add(t_px, n1_px), n_px)) != 0
# 	c_n2 = np.extract(condition_2, n_px)
# 	c_d2 = np.extract(condition_2, (np.subtract(np.add(t_px, n1_px), n_px)))
# 	mean_acc = np.sum(np.divide(c_n1, c_t1))/num_cl
# 	mean_IU = np.sum(np.divide(c_n2, c_d2))/num_cl
# 	fw_IU = np.sum(np.divide(np.extract(condition_2, np.multiply(t_px, n_px)), c_d2))/t_sum
	
# 	print("========= metrics =========")
# 	print("pixel accuracy: " + str(px_acc))
# 	print("mean accuracy: " + str(mean_acc))
# 	print("mean IU: " + str(mean_IU))
# 	print("frequency weighted IU: " + str(fw_IU))
# 	print("")

# if __name__ == "__main__":
# 	num_cl = 22
# 	pic_id = int(sys.argv[1])
# 	save_compare_results(pic_id, num_cl) 

Example 14

def extract(condition, arr):
    """
    Return the elements of an array that satisfy some condition.

    This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
    `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.

    Note that `place` does the exact opposite of `extract`.

    Parameters
    ----------
    condition : array_like
        An array whose nonzero or True entries indicate the elements of `arr`
        to extract.
    arr : array_like
        Input array of the same size as `condition`.

    Returns
    -------
    extract : ndarray
        Rank 1 array of values from `arr` where `condition` is True.

    See Also
    --------
    take, put, copyto, compress, place

    Examples
    --------
    >>> arr = np.arange(12).reshape((3, 4))
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]])
    >>> condition = np.mod(arr, 3)==0
    >>> condition
    array([[ True, False, False,  True],
           [False, False,  True, False],
           [False,  True, False, False]], dtype=bool)
    >>> np.extract(condition, arr)
    array([0, 3, 6, 9])


    If `condition` is boolean:

    >>> arr[condition]
    array([0, 3, 6, 9])

    """
    return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) 

Example 15

def extract(condition, arr):
    """
    Return the elements of an array that satisfy some condition.

    This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
    `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.

    Note that `place` does the exact opposite of `extract`.

    Parameters
    ----------
    condition : array_like
        An array whose nonzero or True entries indicate the elements of `arr`
        to extract.
    arr : array_like
        Input array of the same size as `condition`.

    Returns
    -------
    extract : ndarray
        Rank 1 array of values from `arr` where `condition` is True.

    See Also
    --------
    take, put, copyto, compress, place

    Examples
    --------
    >>> arr = np.arange(12).reshape((3, 4))
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]])
    >>> condition = np.mod(arr, 3)==0
    >>> condition
    array([[ True, False, False,  True],
           [False, False,  True, False],
           [False,  True, False, False]], dtype=bool)
    >>> np.extract(condition, arr)
    array([0, 3, 6, 9])


    If `condition` is boolean:

    >>> arr[condition]
    array([0, 3, 6, 9])

    """
    return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) 

Example 16

def extract(condition, arr):
    """
    Return the elements of an array that satisfy some condition.

    This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
    `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.

    Note that `place` does the exact opposite of `extract`.

    Parameters
    ----------
    condition : array_like
        An array whose nonzero or True entries indicate the elements of `arr`
        to extract.
    arr : array_like
        Input array of the same size as `condition`.

    Returns
    -------
    extract : ndarray
        Rank 1 array of values from `arr` where `condition` is True.

    See Also
    --------
    take, put, copyto, compress, place

    Examples
    --------
    >>> arr = np.arange(12).reshape((3, 4))
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]])
    >>> condition = np.mod(arr, 3)==0
    >>> condition
    array([[ True, False, False,  True],
           [False, False,  True, False],
           [False,  True, False, False]], dtype=bool)
    >>> np.extract(condition, arr)
    array([0, 3, 6, 9])


    If `condition` is boolean:

    >>> arr[condition]
    array([0, 3, 6, 9])

    """
    return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) 

Example 17

def compress(condition, a, axis=None, out=None):
    """
    Return selected slices of an array along given axis.

    When working along a given axis, a slice along that axis is returned in
    `output` for each index where `condition` evaluates to True. When
    working on a 1-D array, `compress` is equivalent to `extract`.

    Parameters
    ----------
    condition : 1-D array of bools
        Array that selects which entries to return. If len(condition)
        is less than the size of `a` along the given axis, then output is
        truncated to the length of the condition array.
    a : array_like
        Array from which to extract a part.
    axis : int, optional
        Axis along which to take slices. If None (default), work on the
        flattened array.
    out : ndarray, optional
        Output array.  Its type is preserved and it must be of the right
        shape to hold the output.

    Returns
    -------
    compressed_array : ndarray
        A copy of `a` without the slices along axis for which `condition`
        is false.

    See Also
    --------
    take, choose, diag, diagonal, select
    ndarray.compress : Equivalent method in ndarray
    np.extract: Equivalent method when working on 1-D arrays
    numpy.doc.ufuncs : Section "Output arguments"

    Examples
    --------
    >>> a = np.array([[1, 2], [3, 4], [5, 6]])
    >>> a
    array([[1, 2],
           [3, 4],
           [5, 6]])
    >>> np.compress([0, 1], a, axis=0)
    array([[3, 4]])
    >>> np.compress([False, True, True], a, axis=0)
    array([[3, 4],
           [5, 6]])
    >>> np.compress([False, True], a, axis=1)
    array([[2],
           [4],
           [6]])

    Working on the flattened array does not return slices along an axis but
    selects elements.

    >>> np.compress([False, True], a)
    array([2])

    """
    return _wrapfunc(a, 'compress', condition, axis=axis, out=out) 

Example 18

def extract(condition, arr):
    """
    Return the elements of an array that satisfy some condition.

    This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
    `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.

    Note that `place` does the exact opposite of `extract`.

    Parameters
    ----------
    condition : array_like
        An array whose nonzero or True entries indicate the elements of `arr`
        to extract.
    arr : array_like
        Input array of the same size as `condition`.

    Returns
    -------
    extract : ndarray
        Rank 1 array of values from `arr` where `condition` is True.

    See Also
    --------
    take, put, copyto, compress, place

    Examples
    --------
    >>> arr = np.arange(12).reshape((3, 4))
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]])
    >>> condition = np.mod(arr, 3)==0
    >>> condition
    array([[ True, False, False,  True],
           [False, False,  True, False],
           [False,  True, False, False]], dtype=bool)
    >>> np.extract(condition, arr)
    array([0, 3, 6, 9])


    If `condition` is boolean:

    >>> arr[condition]
    array([0, 3, 6, 9])

    """
    return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) 

Example 19

def extract(condition, arr):
    """
    Return the elements of an array that satisfy some condition.

    This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
    `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.

    Note that `place` does the exact opposite of `extract`.

    Parameters
    ----------
    condition : array_like
        An array whose nonzero or True entries indicate the elements of `arr`
        to extract.
    arr : array_like
        Input array of the same size as `condition`.

    Returns
    -------
    extract : ndarray
        Rank 1 array of values from `arr` where `condition` is True.

    See Also
    --------
    take, put, copyto, compress, place

    Examples
    --------
    >>> arr = np.arange(12).reshape((3, 4))
    >>> arr
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]])
    >>> condition = np.mod(arr, 3)==0
    >>> condition
    array([[ True, False, False,  True],
           [False, False,  True, False],
           [False,  True, False, False]], dtype=bool)
    >>> np.extract(condition, arr)
    array([0, 3, 6, 9])


    If `condition` is boolean:

    >>> arr[condition]
    array([0, 3, 6, 9])

    """
    return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) 
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