8000 Merge pull request #5368 from tacaswell/sundry_doc_changes · numpy/numpy@3ef77ee · GitHub
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Ralf Gommers
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Merge pull request #5368 from tacaswell/sundry_doc_changes
Cleanups in documentation formatting.
2 parents 2070ecf + 1b908fc commit 3ef77ee

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‎numpy/core/code_generators/ufunc_docstrings.py

Lines changed: 16 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -686,7 +686,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The ceiling of each element in `x`, with `float` dtype.
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See Also
@@ -716,7 +716,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The truncated value of each element in `x`.
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See Also
@@ -931,7 +931,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The quotient ``x1/x2``, element-wise. Returns a scalar if
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both ``x1`` and ``x2`` are scalars.
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@@ -999,7 +999,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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out : {ndarray, bool}
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out : ndarray or bool
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Output array of bools, or a single bool if x1 and x2 are scalars.
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See Also
@@ -1172,7 +1172,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The absolute values of `x`, the returned values are always floats.
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See Also
@@ -1202,7 +1202,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The floor of each element in `x`.
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See Also
@@ -1632,7 +1632,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, bool}
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y : ndarray or bool
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For scalar input, the result is a new boolean with value True if
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the input is NaN; otherwise the value is False.
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@@ -2046,7 +2046,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, bool}
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y : ndarray or bool
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Boolean result with the same shape as `x1` and `x2` of the logical
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AND operation on corresponding elements of `x1` and `x2`.
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@@ -2112,7 +2112,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, bool}
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y : ndarray or bool
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Boolean result with the same shape as `x1` and `x2` of the logical
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OR operation on elements of `x1` and `x2`.
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@@ -2193,7 +2193,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The maximum of `x1` and `x2`, element-wise. Returns scalar if
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both `x1` and `x2` are scalars.
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@@ -2251,7 +2251,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The minimum of `x1` and `x2`, element-wise. Returns scalar if
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both `x1` and `x2` are scalars.
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@@ -2309,7 +2309,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The minimum of `x1` and `x2`, element-wise. Returns scalar if
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both `x1` and `x2` are scalars.
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@@ -2366,7 +2366,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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y : {ndarray, scalar}
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y : ndarray or scalar
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The minimum of `x1` and `x2`, element-wise. Returns scalar if
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both `x1` and `x2` are scalars.
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@@ -2780,7 +2780,7 @@ def add_newdoc(place, name, doc):
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Returns
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-------
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out : {ndarray, scalar}
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out : ndarray or scalar
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Output array is same shape and type as `x`.
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See Also
@@ -3365,9 +3365,9 @@ def add_newdoc(place, name, doc):
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----------
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x : array_like
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Array of numbers to be decomposed.
3368-
out1: ndarray, optional
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out1 : ndarray, optional
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Output array for the mantissa. Must have the same shape as `x`.
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out2: ndarray, optional
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out2 : ndarray, optional
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Output array for the exponent. Must have the same shape as `x`.
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Returns

‎numpy/core/defchararray.py

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -109,7 +109,7 @@ def equal(x1, x2):
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Returns
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-------
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out : {ndarray, bool}
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out : ndarray or bool
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Output array of bools, or a single bool if x1 and x2 are scalars.
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See Also
@@ -133,7 +133,7 @@ def not_equal(x1, x2):
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Returns
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-------
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out : {ndarray, bool}
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out : ndarray or bool
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Output array of bools, or a single bool if x1 and x2 are scalars.
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See Also
@@ -158,7 +158,7 @@ def greater_equal(x1, x2):
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Returns
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-------
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out : {ndarray, bool}
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out : ndarray or bool
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Output array of bools, or a single bool if x1 and x2 are scalars.
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See Also
@@ -182,7 +182,7 @@ def less_equal(x1, x2):
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Returns
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-------
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out : {ndarray, bool}
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out : ndarray or bool
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Output array of bools, or a single bool if x1 and x2 are scalars.
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See Also
@@ -206,7 +206,7 @@ def greater(x1, x2):
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Returns
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-------
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out : {ndarray, bool}
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out : ndarray or bool
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Output array of bools, or a single bool if x1 and x2 are scalars.
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See Also
@@ -230,7 +230,7 @@ def less(x1, x2):
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Returns
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-------
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out : {ndarray, bool}
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out : ndarray or bool
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Output array of bools, or a single bool if x1 and x2 are scalars.
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See Also

‎numpy/core/fromnumeric.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -357,7 +357,7 @@ def repeat(a, repeats, axis=None):
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----------
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a : array_like
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Input array.
360-
repeats : {int, array of ints}
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repeats : int or array of ints
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The number of repetitions for each element. `repeats` is broadcasted
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to fit the shape of the given axis.
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axis : int, optional

‎numpy/core/numeric.py

Lines changed: 6 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1150,7 +1150,8 @@ def tensordot(a, b, axes=2):
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----------
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a, b : array_like, len(shape) >= 1
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Tensors to "dot".
1153-
axes : {integer_like, array_like}
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axes : int or (2,) array_like
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* integer_like
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If an int N, sum over the last N axes of `a` and the first N axes
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of `b` in order. The sizes of the corresponding axes must match.
@@ -1168,11 +1169,11 @@ def tensordot(a, b, axes=2):
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``axes = 0`` : tensor product $a\otimes b$
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``axes = 1`` : tensor dot product $a\cdot b$
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``axes = 2`` : (default) tensor double contraction $a:b$
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When `axes` is integer_like, the sequence for evaluation will be: first
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the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and
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When `axes` is integer_like, the sequence for evaluation will be: first
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the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and
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Nth axis in `b` last.
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When there is more than one axis to sum over - and they are not the last
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(first) axes of `a` (`b`) - the argument `axes` should consist of
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two sequences of the same length, with the first axis to sum over given

‎numpy/lib/arraypad.py

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1105,7 +1105,7 @@ def pad(array, pad_width, mode=None, **kwargs):
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((before, after),) yields same before and after pad for each axis.
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(pad,) or int is a shortcut for before = after = pad width for all
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axes.
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mode : {str, function}
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mode : str or function
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One of the following string values or a user supplied function.
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'constant'
@@ -1140,7 +1140,7 @@ def pad(array, pad_width, mode=None, **kwargs):
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end values are used to pad the beginning.
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<function>
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Padding function, see Notes.
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stat_length : {sequence, int}, optional
1143+
stat_length : sequence or int, optional
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Used in 'maximum', 'mean', 'median', and 'minimum'. Number of
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values at edge of each axis used to calculate the statistic value.
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@@ -1154,7 +1154,7 @@ def pad(array, pad_width, mode=None, **kwargs):
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length for all axes.
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Default is ``None``, to use the entire axis.
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constant_values : {sequence, int}, optional
1157+
constant_values : sequence or int, optional
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Used in 'constant'. The values to set the padded values for each
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axis.
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@@ -1168,7 +1168,7 @@ def pad(array, pad_width, mode=None, **kwargs):
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all axes.
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Default is 0.
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end_values : {sequence, int}, optional
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end_values : sequence or int, optional
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Used in 'linear_ramp'. The values used for the ending value of the
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linear_ramp and that will form the edge of the padded array.
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@@ -1182,7 +1182,7 @@ def pad(array, pad_width, mode=None, **kwargs):
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all axes.
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Default is 0.
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reflect_type : str {'even', 'odd'}, optional
1185+
reflect_type : {'even', 'odd'}, optional
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Used in 'reflect', and 'symmetric'. The 'even' style is the
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default with an unaltered reflection around the edge value. For
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the 'odd' style, the extented part of the array is created by

‎numpy/lib/financial.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -148,7 +148,7 @@ def pmt(rate, nper, pv, fv=0, when='end'):
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Number of compounding periods
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pv : array_like
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Present value
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fv : array_like (optional)
151+
fv : array_like, optional
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Future value (default = 0)
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when : {{'begin', 1}, {'end', 0}}, {string, int}
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When payments are due ('begin' (1) or 'end' (0))

‎numpy/lib/function_base.py

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -263,7 +263,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
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normed : bool, optional
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If False, returns the number of samples in each bin. If True,
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returns the bin density ``bin_count / sample_count / bin_volume``.
266-
weights : array_like (N,), optional
266+
weights : (N,) array_like, optional
267267
An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
268268
Weights are normalized to 1 if normed is True. If normed is False,
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the values of the returned histogram are equal to the sum of the
@@ -461,7 +461,7 @@ def average(a, axis=None, weights=None, returned=False):
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Returns
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-------
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average, [sum_of_weights] : {array_type, double}
464+
average, [sum_of_weights] : array_type or double
465465
Return the average along the specified axis. When returned is `True`,
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return a tuple with the average as the first element and the sum
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of the weights as the second element. The return type is `Float`
@@ -885,9 +885,9 @@ def copy(a, order='K'):
885885
def gradient(f, *varargs, **kwargs):
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"""
887887
Return the gradient of an N-dimensional array.
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The gradient is computed using second order accurate central differences
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in the interior and either first differences or second order accurate
890+
in the interior and either first differences or second order accurate
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one-sides (forward or backwards) differences at the boundaries. The
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returned gradient hence has the same shape as the input array.
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@@ -901,7 +901,7 @@ def gradient(f, *varargs, **kwargs):
901901
edge_order : {1, 2}, optional
902902
Gradient is calculated using N\ :sup:`th` order accurate differences
903903
at the boundaries. Default: 1.
904-
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.. versionadded:: 1.9.1
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Returns
@@ -1147,7 +1147,7 @@ def interp(x, xp, fp, left=None, right=None, period=None):
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Returns
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-------
1150-
y : {float, ndarray}
1150+
y : float or ndarray
11511151
The interpolated values, same shape as `x`.
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Raises
@@ -1250,7 +1250,7 @@ def angle(z, deg=0):
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Returns
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-------
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angle : {ndarray, scalar}
1253+
angle : ndarray or scalar
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The counterclockwise angle from the positive real axis on
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the complex plane, with dtype as numpy.float64.
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@@ -1980,7 +1980,7 @@ def corrcoef(x, y=None, rowvar=1, bias=0, ddof=None):
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observations (unbiased estimate). If `bias` is 1, then
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normalization is by ``N``. These values can be overridden by using
19821982
the keyword ``ddof`` in numpy versions >= 1.5.
1983-
ddof : {None, int}, optional
1983+
ddof : int, optional
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.. versionadded:: 1.5
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If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
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the number of observations; this overrides the value implied by

‎numpy/lib/polynomial.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -253,12 +253,12 @@ def polyint(p, m=1, k=None):
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Parameters
255255
----------
256-
p : {array_like, poly1d}
256+
p : array_like or poly1d
257257
Polynomial to differentiate.
258258
A sequence is interpreted as polynomial coefficients, see `poly1d`.
259259
m : int, optional
260260
Order of the antiderivative. (Default: 1)
261-
k : {None, list of `m` scalars, scalar}, optional
261+
k : list of `m` scalars or scalar, optional
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Integration constants. They are given in the order of integration:
263263
those corresponding to highest-order terms come first.
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‎numpy/ma/core.py

Lines changed: 1 addition & 1 deletion
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@@ -4978,7 +4978,7 @@ def argmin(self, axis=None, fill_value=None, out=None):
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Returns
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-------
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{ndarray, scalar}
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ndarray or scalar
49824982
If multi-dimension input, returns a new ndarray of indices to the
49834983
minimum values along the given axis. Otherwise, returns a scalar
49844984
of index to the minimum values along the given axis.

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