diff options
| -rw-r--r-- | doc/source/reference/arrays.classes.rst | 2 | ||||
| -rw-r--r-- | doc/source/reference/random/generator.rst | 84 | ||||
| -rw-r--r-- | doc/source/user/c-info.how-to-extend.rst | 8 | ||||
| -rw-r--r-- | doc/source/user/c-info.python-as-glue.rst | 8 | ||||
| -rw-r--r-- | doc/source/user/quickstart.rst | 6 |
5 files changed, 54 insertions, 54 deletions
diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst index 3b13530c7..83b419306 100644 --- a/doc/source/reference/arrays.classes.rst +++ b/doc/source/reference/arrays.classes.rst @@ -75,7 +75,7 @@ NumPy provides several hooks that classes can customize: :func:`~numpy.matmul`, which currently is not a Ufunc, but could be relatively easily be rewritten as a (set of) generalized Ufuncs. The same may happen with functions such as :func:`~numpy.median`, - :func:`~numpy.min`, and :func:`~numpy.argsort`. + :func:`~numpy.amin`, and :func:`~numpy.argsort`. Like with some other special methods in python, such as ``__hash__`` and ``__iter__``, it is possible to indicate that your class does *not* diff --git a/doc/source/reference/random/generator.rst b/doc/source/reference/random/generator.rst index c3803bcab..068143270 100644 --- a/doc/source/reference/random/generator.rst +++ b/doc/source/reference/random/generator.rst @@ -22,63 +22,63 @@ Accessing the BitGenerator .. autosummary:: :toctree: generated/ - ~Generator.bit_generator + ~numpy.random.Generator.bit_generator Simple random data ================== .. autosummary:: :toctree: generated/ - ~Generator.integers - ~Generator.random - ~Generator.choice - ~Generator.bytes + ~numpy.random.Generator.integers + ~numpy.random.Generator.random + ~numpy.random.Generator.choice + ~numpy.random.Generator.bytes Permutations ============ .. autosummary:: :toctree: generated/ - ~Generator.shuffle - ~Generator.permutation + ~numpy.random.Generator.shuffle + ~numpy.random.Generator.permutation Distributions ============= .. autosummary:: :toctree: generated/ - ~Generator.beta - ~Generator.binomial - ~Generator.chisquare - ~Generator.dirichlet - ~Generator.exponential - ~Generator.f - ~Generator.gamma - ~Generator.geometric - ~Generator.gumbel - ~Generator.hypergeometric - ~Generator.laplace - ~Generator.logistic - ~Generator.lognormal - ~Generator.logseries - ~Generator.multinomial - ~Generator.multivariate_normal - ~Generator.negative_binomial - ~Generator.noncentral_chisquare - ~Generator.noncentral_f - ~Generator.normal - ~Generator.pareto - ~Generator.poisson - ~Generator.power - ~Generator.rayleigh - ~Generator.standard_cauchy - ~Generator.standard_exponential - ~Generator.standard_gamma - ~Generator.standard_normal - ~Generator.standard_t - ~Generator.triangular - ~Generator.uniform - ~Generator.vonmises - ~Generator.wald - ~Generator.weibull - ~Generator.zipf + ~numpy.random.Generator.beta + ~numpy.random.Generator.binomial + ~numpy.random.Generator.chisquare + ~numpy.random.Generator.dirichlet + ~numpy.random.Generator.exponential + ~numpy.random.Generator.f + ~numpy.random.Generator.gamma + ~numpy.random.Generator.geometric + ~numpy.random.Generator.gumbel + ~numpy.random.Generator.hypergeometric + ~numpy.random.Generator.laplace + ~numpy.random.Generator.logistic + ~numpy.random.Generator.lognormal + ~numpy.random.Generator.logseries + ~numpy.random.Generator.multinomial + ~numpy.random.Generator.multivariate_normal + ~numpy.random.Generator.negative_binomial + ~numpy.random.Generator.noncentral_chisquare + ~numpy.random.Generator.noncentral_f + ~numpy.random.Generator.normal + ~numpy.random.Generator.pareto + ~numpy.random.Generator.poisson + ~numpy.random.Generator.power + ~numpy.random.Generator.rayleigh + ~numpy.random.Generator.standard_cauchy + ~numpy.random.Generator.standard_exponential + ~numpy.random.Generator.standard_gamma + ~numpy.random.Generator.standard_normal + ~numpy.random.Generator.standard_t + ~numpy.random.Generator.triangular + ~numpy.random.Generator.uniform + ~numpy.random.Generator.vonmises + ~numpy.random.Generator.wald + ~numpy.random.Generator.weibull + ~numpy.random.Generator.zipf diff --git a/doc/source/user/c-info.how-to-extend.rst b/doc/source/user/c-info.how-to-extend.rst index 3961325fb..00ef8ab74 100644 --- a/doc/source/user/c-info.how-to-extend.rst +++ b/doc/source/user/c-info.how-to-extend.rst @@ -342,7 +342,7 @@ The method is to 4. If you are writing the algorithm, then I recommend that you use the stride information contained in the array to access the elements of - the array (the :c:func:`PyArray_GETPTR` macros make this painless). Then, + the array (the :c:func:`PyArray_GetPtr` macros make this painless). Then, you can relax your requirements so as not to force a single-segment array and the data-copying that might result. @@ -463,7 +463,7 @@ writeable). The syntax is This flag is useful to specify an array that will be used for both input and output. :c:func:`PyArray_ResolveWritebackIfCopy` - must be called before :func:`Py_DECREF` at + must be called before :c:func:`Py_DECREF` at the end of the interface routine to write back the temporary data into the original array passed in. Use of the :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` or @@ -530,7 +530,7 @@ specific element of the array is determined only by the array of npy_intp variables, :c:func:`PyArray_STRIDES` (obj). In particular, this c-array of integers shows how many **bytes** must be added to the current element pointer to get to the next element in each dimension. -For arrays less than 4-dimensions there are :c:func:`PyArray_GETPTR{k}` +For arrays less than 4-dimensions there are ``PyArray_GETPTR{k}`` (obj, ...) macros where {k} is the integer 1, 2, 3, or 4 that make using the array strides easier. The arguments .... represent {k} non- negative integer indices into the array. For example, suppose ``E`` is @@ -543,7 +543,7 @@ contiguous arrays have particular striding patterns. Two array flags whether or not the striding pattern of a particular array matches the C-style contiguous or Fortran-style contiguous or neither. Whether or not the striding pattern matches a standard C or Fortran one can be -tested Using :c:func:`PyArray_ISCONTIGUOUS` (obj) and +tested Using :c:func:`PyArray_IS_C_CONTIGUOUS` (obj) and :c:func:`PyArray_ISFORTRAN` (obj) respectively. Most third-party libraries expect contiguous arrays. But, often it is not difficult to support general-purpose striding. I encourage you to use the striding diff --git a/doc/source/user/c-info.python-as-glue.rst b/doc/source/user/c-info.python-as-glue.rst index 01d2a64d1..8b1bc9a98 100644 --- a/doc/source/user/c-info.python-as-glue.rst +++ b/doc/source/user/c-info.python-as-glue.rst @@ -744,14 +744,14 @@ around this restriction that allow ctypes to integrate with other objects. 1. Don't set the argtypes attribute of the function object and define an - :obj:`_as_parameter_` method for the object you want to pass in. The - :obj:`_as_parameter_` method must return a Python int which will be passed + ``_as_parameter_`` method for the object you want to pass in. The + ``_as_parameter_`` method must return a Python int which will be passed directly to the function. 2. Set the argtypes attribute to a list whose entries contain objects with a classmethod named from_param that knows how to convert your object to an object that ctypes can understand (an int/long, string, - unicode, or object with the :obj:`_as_parameter_` attribute). + unicode, or object with the ``_as_parameter_`` attribute). NumPy uses both methods with a preference for the second method because it can be safer. The ctypes attribute of the ndarray returns @@ -764,7 +764,7 @@ correct type, shape, and has the correct flags set or risk nasty crashes if the data-pointer to inappropriate arrays are passed in. To implement the second method, NumPy provides the class-factory -function :func:`ndpointer` in the :mod:`ctypeslib` module. This +function :func:`ndpointer` in the :mod:`numpy.ctypeslib` module. This class-factory function produces an appropriate class that can be placed in an argtypes attribute entry of a ctypes function. The class will contain a from_param method which ctypes will use to convert any diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst index c8d964599..772625372 100644 --- a/doc/source/user/quickstart.rst +++ b/doc/source/user/quickstart.rst @@ -206,8 +206,8 @@ of elements that we want, instead of the step:: `empty_like`, `arange`, `linspace`, - `numpy.random.rand`, - `numpy.random.randn`, + `numpy.random.mtrand.RandomState.rand`, + `numpy.random.mtrand.RandomState.randn`, `fromfunction`, `fromfile` @@ -732,7 +732,7 @@ stacks 1D arrays as columns into a 2D array. It is equivalent to array([[ 4., 3.], [ 2., 8.]]) -On the other hand, the function `row_stack` is equivalent to `vstack` +On the other hand, the function `ma.row_stack` is equivalent to `vstack` for any input arrays. In general, for arrays of with more than two dimensions, `hstack` stacks along their second |