Jun 8, 2014 · Jul 7, 2014 · Jul 16, 2014 · Jul 8, 2014 · Jul 10, 2014 · Jul 15, 2014
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
 # Basic operations


 def gradient(f, *varargs):
 def gradient(f, *varargs, **kwargs):
    """
    Return the gradient of an N-dimensional array.


    The gradient is computed using second order accurate central differences
    in the interior andsecond order accurate one-sides (forward or backwards)
    differences at the boundaries. The returned gradient hence has the same
    shape as the input array.
    in the interior andeither first differences or second order accurate
 one-sides (forward or backwards)differences at the boundaries. The
 returned gradient hence has the sameshape as the input array.

    Parameters
    ----------
    f : array_like
      An N-dimensional array containing samples of a scalar function.
    `*varargs` : scalars
      0, 1, or N scalars specifying the sample distances in each direction,
      that is: `dx`, `dy`, `dz`, ... The default distance is 1.
        An N-dimensional array containing samples of a scalar function.
    varargs : list of scalar, optional
        N scalars specifying the sample distances for each dimension,
        i.e. `dx`, `dy`, `dz`, ... Default distance: 1.
    edge_order : {1, 2}, optional
        Gradient is calculated using N\ :sup:`th` order accurate differences
        at the boundaries. Default: 1.

        .. versionadded:: 1.9.1

    Returns
    -------
    gradient : ndarray
      N arrays of the same shape as `f` giving the derivative of `f` with
      respect to each dimension.
 N arrays of the same shape as `f` giving the derivative of `f` with
 respect to each dimension.

    Examples
    --------

    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
    [array([[ 2.,  2., -1.],
           [ 2.,  2., -1.]]),
    array([[ 1. ,  2.5,  4. ],
           [ 1. ,  1. ,  1. ]])]
            [ 2.,  2., -1.]]), array([[ 1. ,  2.5,  4. ],
            [ 1. ,  1. ,  1. ]])]

    >>> x = np.array([0,1,2,3,4])
    >>> dx = gradient(x)
    >>> x = np.array([0, 1, 2, 3,4])
    >>> dx =np.gradient(x)
    >>> y = x**2
    >>> gradient(y,dx)
    array([0.,  2.,  4.,  6.,  8.])
    >>>np.gradient(y, dx, edge_order=2)
    array([-0.,  2.,  4.,  6.,  8.])
    """
    f = np.asanyarray(f)
    N = len(f.shape)  # number of dimensions
        raise SyntaxError(
            "invalid number of arguments")

    edge_order = kwargs.pop('edge_order', 1)
    if kwargs:
        raise TypeError('"{}" are not valid keyword arguments.'.format(
                                                  '", "'.join(kwargs.keys())))
    if edge_order > 2:
        raise ValueError("'edge_order' greater than 2 not supported")

    # use central differences on interior and one-sided differences on the
    # endpoints. This preserves second order-accuracy over the full domain.

                "at least two elements are required.")

        # Numerical differentiation: 1st order edges, 2nd order interior
        if y.shape[axis] == 2:
        if y.shape[axis] == 2 or edge_order == 1:
            # Use first order differences for time data
            out = np.empty_like(y, dtype=otype)

diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py

    # Process the filling_values ...............................
    # Rename the input for convenience
    user_filling_values = filling_values or []
    user_filling_values = filling_values
    if user_filling_values is None:
        user_filling_values = []
    # Define the default
    filling_values = [None] * nbcols
    # We have a dictionary : update each entry individually
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
             '1910-10-12', '1910-12-12', '1912-12-12'],
            dtype='datetime64[D]')
        dx = np.array(
            [-7, -3, 0, 31, 61, 396,1066],
            [-5, -3, 0, 31, 61, 396,731],
            dtype='timedelta64[D]')
        assert_array_equal(gradient(x), dx)
        assert_(dx.dtype == np.dtype('timedelta64[D]'))
            [-5, -3, 10, 12, 61, 321, 300],
            dtype='timedelta64[D]')
        dx = np.array(
            [-3, 7, 7, 25, 154, 119, -161],
            [2, 7, 7, 25, 154, 119, -21],
            dtype='timedelta64[D]')
        assert_array_equal(gradient(x), dx)
        assert_(dx.dtype == np.dtype('timedelta64[D]'))
        dx = x[1] - x[0]
        y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
        analytical = 6 * x ** 2 + 8 * x + 2
        num_error = np.abs((np.gradient(y, dx) / analytical) - 1)
        num_error = np.abs((np.gradient(y, dx, edge_order=2) / analytical) - 1)
        assert_(np.all(num_error < 0.03) == True)


diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
        ctrl = np.array([(0, 3), (4, -999)], dtype=[(_, int) for _ in "ac"])
        assert_equal(test, ctrl)

        data2 = "1,2,*,4\n5,*,7,8\n"
        test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int,
                             missing_values="*", filling_values=0)
        ctrl = np.array([[1, 2, 0, 4], [5, 0, 7, 8]])
        assert_equal(test, ctrl)
        test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int,
                             missing_values="*", filling_values=-1)
        ctrl = np.array([[1, 2, -1, 4], [5, -1, 7, 8]])
        assert_equal(test, ctrl)

    def test_withmissing_float(self):
        data = TextIO('A,B\n0,1.5\n2,-999.00')
        test = np.mafromtxt(data, dtype=None, delimiter=',',
Original file line number	Diff line number	Diff line change
Expand Up		@@ -883,28 +883,33 @@ def copy(a, order='K'):
		# Basic operations


		def gradient(f, *varargs):
		def gradient(f, varargs, *kwargs):
		"""
		Return the gradient of an N-dimensional array.


		The gradient is computed using second order accurate central differences
		in the interior andsecond order accurate one-sides (forward or backwards)
		differences at the boundaries. The returned gradient hence has the same
		shape as the input array.
		in the interior andeither first differences or second order accurate
		one-sides (forward or backwards)differences at the boundaries. The
		returned gradient hence has the sameshape as the input array.

		Parameters
		----------
		f : array_like
		An N-dimensional array containing samples of a scalar function.
		`*varargs` : scalars
		0, 1, or N scalars specifying the sample distances in each direction,
		that is: `dx`, `dy`, `dz`, ... The default distance is 1.
		An N-dimensional array containing samples of a scalar function.
		varargs : list of scalar, optional
		N scalars specifying the sample distances for each dimension,
		i.e. `dx`, `dy`, `dz`, ... Default distance: 1.
		edge_order : {1, 2}, optional
		Gradient is calculated using N\ :sup:`th` order accurate differences
		at the boundaries. Default: 1.

		.. versionadded:: 1.9.1

		Returns
		-------
		gradient : ndarray
		N arrays of the same shape as `f` giving the derivative of `f` with
		respect to each dimension.
		N arrays of the same shape as `f` giving the derivative of `f` with
		respect to each dimension.

		Examples
		--------
Expand All		@@ -916,15 +921,14 @@ def gradient(f, *varargs):

		>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
		[array([[ 2., 2., -1.],
		[ 2., 2., -1.]]),
		array([[ 1. , 2.5, 4. ],
		[ 1. , 1. , 1. ]])]
		[ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ],
		[ 1. , 1. , 1. ]])]

		>>> x = np.array([0,1,2,3,4])
		>>> dx = gradient(x)
		>>> x = np.array([0, 1, 2, 3,4])
		>>> dx =np.gradient(x)
		>>> y = x**2
		>>> gradient(y,dx)
		array([0., 2., 4., 6., 8.])
		>>>np.gradient(y, dx, edge_order=2)
		array([-0., 2., 4., 6., 8.])
		"""
		f = np.asanyarray(f)
		N = len(f.shape) # number of dimensions
Expand All		@@ -939,6 +943,13 @@ def gradient(f, *varargs):
		raise SyntaxError(
		"invalid number of arguments")

		edge_order = kwargs.pop('edge_order', 1)
		if kwargs:
		raise TypeError('"{}" are not valid keyword arguments.'.format(
		'", "'.join(kwargs.keys())))
		if edge_order > 2:
		raise ValueError("'edge_order' greater than 2 not supported")

		# use central differences on interior and one-sided differences on the
		# endpoints. This preserves second order-accuracy over the full domain.

Expand DownExpand Up		@@ -978,7 +989,7 @@ def gradient(f, *varargs):
		"at least two elements are required.")

		# Numerical differentiation: 1st order edges, 2nd order interior
		if y.shape[axis] == 2:
		if y.shape[axis] == 2 or edge_order == 1:
		# Use first order differences for time data
		out = np.empty_like(y, dtype=otype)

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1518,7 +1518,9 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,

		# Process the filling_values ...............................
		# Rename the input for convenience
		user_filling_values = filling_values or []
		user_filling_values = filling_values
		if user_filling_values is None:
		user_filling_values = []
		# Define the default
		filling_values = [None] * nbcols
		# We have a dictionary : update each entry individually
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -536,7 +536,7 @@ def test_datetime64(self):
		'1910-10-12', '1910-12-12', '1912-12-12'],
		dtype='datetime64[D]')
		dx = np.array(
		[-7, -3, 0, 31, 61, 396,1066],
		[-5, -3, 0, 31, 61, 396,731],
		dtype='timedelta64[D]')
		assert_array_equal(gradient(x), dx)
		assert_(dx.dtype == np.dtype('timedelta64[D]'))
Expand All		@@ -547,7 +547,7 @@ def test_timedelta64(self):
		[-5, -3, 10, 12, 61, 321, 300],
		dtype='timedelta64[D]')
		dx = np.array(
		[-3, 7, 7, 25, 154, 119, -161],
		[2, 7, 7, 25, 154, 119, -21],
		dtype='timedelta64[D]')
		assert_array_equal(gradient(x), dx)
		assert_(dx.dtype == np.dtype('timedelta64[D]'))
Expand All		@@ -561,7 +561,7 @@ def test_second_order_accurate(self):
		dx = x[1] - x[0]
		y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
		analytical = 6 * x ** 2 + 8 * x + 2
		num_error = np.abs((np.gradient(y, dx) / analytical) - 1)
		num_error = np.abs((np.gradient(y, dx, edge_order=2) / analytical) - 1)
		assert_(np.all(num_error < 0.03) == True)


Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1315,6 +1315,16 @@ def test_user_filling_values(self):
		ctrl = np.array([(0, 3), (4, -999)], dtype=[(_, int) for _ in "ac"])
		assert_equal(test, ctrl)

		data2 = "1,2,,4\n5,,7,8\n"
		test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int,
		missing_values="*", filling_values=0)
		ctrl = np.array([[1, 2, 0, 4], [5, 0, 7, 8]])
		assert_equal(test, ctrl)
		test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int,
		missing_values="*", filling_values=-1)
		ctrl = np.array([[1, 2, -1, 4], [5, -1, 7, 8]])
		assert_equal(test, ctrl)

		def test_withmissing_float(self):
		data = TextIO('A,B\n0,1.5\n2,-999.00')
		test = np.mafromtxt(data, dtype=None, delimiter=',',
Expand Down