Pyarrayobject header file

Pointers to the data for the input arguments are first, followed by the pointers to the data for the output arguments. How many bytes must be skipped to get to the next element in the sequence is specified by the corresponding entry in the steps array. The last argument allows the loop to receive extra information. This is commonly used so that a single, generic vector loop can be used for multiple functions.

In this case, the actual scalar function to call is passed in as extradata. The size of this function pointer array is ntypes. Extra data to be passed to the 1-d vector loops or NULL if no extra-data is needed.

This C-array must be the same size i. Several C-API calls for UFuncs are just 1-d vector loops that make use of this extra data to receive a pointer to the actual function to call. The number of supported data types for the ufunc. This number specifies how many different 1-d loops of the builtin data types are available.

For each of the ntypes functions, the corresponding set of type numbers in this array shows how the args argument should be interpreted in the 1-d vector loop. These type numbers do not have to be the same type and mixed-type ufuncs are supported. Documentation for the ufunc.

Any dynamically allocated memory. Currently, this is used for dynamic ufuncs created from a python function to store room for the types, data, and name members. For ufuncs dynamically created from python functions, this member holds a reference to the underlying Python function. A dictionary of user-defined 1-d vector loops stored as CObject ptrs for user-defined types.

A loop may be registered by the user for any user-defined type. It is retrieved by type number. This is an iterator object that makes it easy to loop over an N-dimensional array. It is the object returned from the flat attribute of an ndarray. It is also used extensively throughout the implementation internals to loop over an N-dimensional array.

This object implements the next method and can be used anywhere an iterator can be used in Python. It contains associated information used to quickly march through the array. The members of the PyArrayIterObject structure are used in these calculations. Iterator objects keep their own dimension and strides information about an array. An -dimensional index into the array. How many bytes needed to jump from the end of a dimension back to its beginning.

This array is used in computing an N-d index from a 1-d index. It contains needed products of the dimensions. It is used to simplify calculations when possible. How to use an array iterator on a C-level is explained more fully in later sections. This type provides an iterator that encapsulates the concept of broadcasting.

It allows arrays to be broadcast together so that the loop progresses in C-style contiguous fashion over the broadcasted array. Broadcasting is performed by adjusting array iterators so that each iterator represents the broadcasted shape and size, but has its strides adjusted so that the correct element from the array is used at each iteration. The shape of the broadcasted result only nd slots are used. An array of iterator objects that holds the iterators for the arrays to be broadcast together.

On return, the iterators are adjusted for broadcasting. When the flags attribute is retrieved from Python, a special builtin object of this type is constructed.

This special type makes it easier to work with the different flags by accessing them as attributes or by accessing them as if the object were a dictionary with the flag names as entries. There is a Python type for each of the different built-in data types that can be present in the array Most of these are simple wrappers around the corresponding data type in C.

The structure of these scalar objects is not exposed to C-code. A few new C-structures were found to be useful in the development of NumPy. The structure is:. The length of the list of integers. It is assumed safe to access ptr [0] to ptr [len-1]. This is equivalent to the buffer object structure in Python up to the ptr member. On bit platforms i. It is useful to represent a generic single-segment chunk of memory. The Python object this chunk of memory comes from.

Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. PyArrayObject definition is missing Ask Question. Asked 8 years ago. Active 8 years ago. Viewed 1k times. I'm a beginner, how do I troubleshoot this? Improve this question. Now you can use the C arrays to manipulate the data in the NumPy arrays.

You can pass the arrays to other C functions that do calculations, etc. Just remember you are operating on the original NumPy matrices and vectors. After your calculation you have to free any memory allocated in the construction of your C data for the NumPy arrays. This is done again by Utility functions.

This step is only necessary if you allocated memory to handle the arrays e. Finally, you return to the Python calling function, by returning a Python value or NumPy array.

I have C extensions which show examples of both. It is best to call the C extensions by calling a Python function that then calls the extension.

This is called a Python wrapper function. It puts a more pythonic look to your code e. It may seem like an unnecessary extra step, but it's worth it. You should keep those files handy probably printed out so you can follow the explanations of the code below. This function takes a NumPy matrix A , integer i , and Python float y as input and outputs a return NumPy matrix B each of whose components is equal to the square of the input matrix component times the integer times the float.

Here is the relevant code in one place:. You must include the following headers with Python. The static keyword in front of a function declaration makes this function private to your extension module. The linker just won't see it. This way you can use the same intuitional function names i.

The arguments are always the same,. The first one self is never used, but necessary because of how Python passes arguments. The second args is a pointer to a Python tuple that contains all of the arguments B,i,x of the function. This sets up a table of function names that will be the interface from your Python code to your C extension. It is important to get the name right each time it is used because there are strict requirements on using the module name in the code.

The name appears first in the method definitions table as the first part of the table name:. There are ways to use Python keywords, but I have not tried them out. These functions tell the Python interpreter what to call when the module is loaded. Now here is the actual function that you will call from Python code. I will split it up and explain each section. The C arrays cin and cout are Cpointers that will point eventually to the actual data in the NumPy arrays and allow you to manipulate it.

The array dims will be used to access n and m from the NumPy array. All this happens below. First we have to extract the input variables A, i, y from the args tuple. This is done by the call,. Note you must pass all C variables by reference. This is true even if the C variable is a pointer to a string see code in vecfcn1 routine. The format string tells the parsing function what type of variable to use. The common variables for Python all have letter names e. For data types that are not in standard Python like the NumPy arrays you use the O!

Note these are also passed by reference. The order must be maintained and match the calling interface of the Python function you want. The format string defines the interface and if you do not call the function from Python so the number of arguments match the number in the format string, you will get an error.

This is good since it will point to where the problem is. Next we have a check that the input matrix really is a matrix of NumPy type double. This test is also done in the Python wrapper for this C extension. It is better to do it there, but I include the test here to show you that you can do testing in the C extension and you can "reach into" the NumPy structure to pick out it's parameters.

Here's an example of reaching into the NumPy structure to get the dimensions of the matrix matin and assign them to local variables as mentioned above. Now we use these matrix parameters to generate a new NumPy matrix matout our output right here in our C extension.

Viewed 7k times. Improve this question. Community Bot 1 1 1 silver badge. Add a comment. Active Oldest Votes. Improve this answer. Paul Weibert Paul Weibert 1 1 silver badge 4 4 bronze badges.

Just conincidentaly found that the solution to this issue is well documented here in the numpy documentation — Paul Weibert. Does the link you provided advocate using a different solution from yours?