Metadata-Version: 1.1
Name: eigency
Version: 1.77
Summary: Cython interface between the numpy arrays and the Matrix/Array classes of the Eigen C++ library
Home-page: https://github.com/wouterboomsma/eigency
Author: Wouter Boomsma
Author-email: wb@di.ku.dk
License: MIT
Description-Content-Type: UNKNOWN
Description: [![Build Status](https://travis-ci.org/wouterboomsma/eigency.svg?branch=master)](https://travis-ci.org/wouterboomsma/eigency)
        
        # Eigency
        Eigency is a Cython interface between Numpy arrays and Matrix/Array
        objects from the Eigen C++ library. It is intended to simplify the
        process of writing C++ extensions using the Eigen library. Eigency is
        designed to reuse the underlying storage of the arrays when passing
        data back and forth, and will thus avoid making unnecessary copies
        whenever possible. Only in cases where copies are explicitly requested
        by your C++ code will they be made.
        
        Below is a description of a range of common usage scenarios. A full working
        example of both setup and these different use cases is available in the
        `test` directory distributed with the this package.
        
        ## Setup
        To import eigency functionality, add the following to your `.pyx` file:
        ```
        from eigency.core cimport *
        ```
        In addition, in the `setup.py` file, the include directories must be
        set up to include the eigency includes. This can be done by calling
        the `get_includes` function in the `eigency` module:
        ```
        import eigency
        ...
        extensions = [
            Extension("module-dir-name/module-name", ["module-dir-name/module-name.pyx"],
                      include_dirs = [".", "module-dir-name"] + eigency.get_includes()
                      ),
        ]
        ```
        Eigency includes a version of the Eigen library, and the `get_includes` function will include the path to this directory. If you
        have your own version of Eigen, just set the `include_eigen` option to False, and add your own path instead:
        
        ```
            include_dirs = [".", "module-dir-name", 'path-to-own-eigen'] + eigency.get_includes(include_eigen=False)
        ```
        
        ## From Numpy to Eigen
        Assume we are writing a Cython interface to the following C++ function:
        
        ```c++
        void function_w_mat_arg(const Eigen::Map<Eigen::MatrixXd> &mat) {
            std::cout << mat << "\n";
        }
        ```
        
        Note that we use `Eigen::Map` to ensure that we can reuse the storage
        of the numpy array, thus avoiding making a copy. Assuming the C++ code
        is in a file called `functions.h`, the corresponding `.pyx` entry could look like this:
        
        ```
        cdef extern from "functions.h":
             cdef void _function_w_mat_arg "function_w_mat_arg"(Map[MatrixXd] &)
        
        # This will be exposed to Python
        def function_w_mat_arg(np.ndarray array):
            return _function_w_mat_arg(Map[MatrixXd](array))
        ```
        
        The last line contains the actual conversion. `Map` is an Eigency
        type that derives from the real Eigen map, and will take care of
        the conversion from the numpy array to the corresponding Eigen type. 
        
        We can now call the C++ function directly from Python:
        ```python
        >>> import numpy as np
        >>> import eigency_tests
        >>> x = np.array([[1.1, 2.2], [3.3, 4.4]])
        >>> eigency_tests.function_w_mat_arg(x)
        1.1 3.3
        2.2 4.4
        ```
        (if you are wondering about why the matrix is transposed, please 
        see the Storage layout section below).
        
        ## Types matter
        
        The basic idea behind eigency is to share the underlying representation of a 
        numpy array between Python and C++. This means that somewhere in the process, 
        we need to make explicit which numerical types we are dealing with. In the
        function above, we specify that we expect an Eigen MatrixXd, which means
        that the numpy array must also contain double (i.e. float64) values. If we instead provide
        a numpy array of ints, we will get strange results. 
        
        ```python
        >>> import numpy as np
        >>> import eigency_tests
        >>> x = np.array([[1, 2], [3, 4]])
        >>> eigency_tests.function_w_mat_arg(x)
        4.94066e-324  1.4822e-323
        9.88131e-324 1.97626e-323
        ```
        This is because we are explicitly asking C++ to interpret out python integer
        values as floats. 
        
        To avoid this type of error, you can force your cython function to
        accept only numpy arrays of a specific type:
        
        ```
        cdef extern from "functions.h":
             cdef void _function_w_mat_arg "function_w_mat_arg"(Map[MatrixXd] &)
        
        # This will be exposed to Python
        def function_w_mat_arg(np.ndarray[np.float64_t, ndim=2] array):
            return _function_w_mat_arg(Map[MatrixXd](array))
        ```
        
        (Note that when using this technique to select the type, you also need to specify 
        the dimensions of the array (this will default to 1)). Using this new definition, 
        users will get an error when passing arrays of the wrong type:
        
        ```python
        >>> import numpy as np
        >>> import eigency_tests
        >>> x = np.array([[1, 2], [3, 4]])
        >>> eigency_tests.function_w_mat_arg(x)
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
          File "eigency_tests/eigency_tests.pyx", line 87, in eigency_tests.eigency_tests.function_w_mat_arg
        ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'long'
        ```
        
        Since it avoids many surprises, it is strongly recommended to use this technique
        to specify the full types of numpy arrays in your cython code whenever 
        possible.
        
        
        ## Writing Eigen Map types in Cython
        
        Since Cython does not support nested fused types, you cannot write types like `Map[Matrix[double, 2, 2]]`. In most cases, you won't need to, since you can just use Eigens convenience typedefs, such as `Map[VectorXd]`. If you need the additional flexibility of the full specification, you can use the `FlattenedMap` type, where all type arguments can be specified at top level, for instance `FlattenedMap[Matrix, double, _2, _3]` or `FlattenedMap[Matrix, double, _2, Dynamic]`. Note that dimensions must be prefixed with an underscore.
        
        Using full specifications of the Eigen types, the previous example would look like this:
        ```
        cdef extern from "functions.h":
             cdef void _function_w_mat_arg "function_w_mat_arg" (FlattenedMap[Matrix, double, Dynamic, Dynamic] &)
        
        # This will be exposed to Python
        def function_w_mat_arg(np.ndarray[np.float64_t, ndim=2] array):
            return _function_w_mat_arg(FlattenedMap[Matrix, double, Dynamic, Dynamic](array))
        ```
        
        `FlattenedType` takes four template parameters: arraytype, scalartype,
        rows and cols.  Eigen supports a few other template arguments for
        setting the storage layout and Map strides. Since cython does not
        support default template arguments for fused types, we have instead
        defined separate types for this purpose. These are called
        `FlattenedMapWithOrder` and `FlattenedMapWithStride` with five and eight
        template arguments, respectively. For details on their use, see the section
        about storage layout below.
        
        ## From Numpy to Eigen (insisting on a copy)
        
        Eigency will not complain if the C++ function you interface with does
        not take a Eigen Map object, but instead a regular Eigen Matrix or
        Array. However, in such cases, a copy will be made. Actually, the
        procedure is exactly the same as above. In the `.pyx` file, you still
        define everything exactly the same way as for the Map case described above.
        
        For instance, given the following C++ function:
        ```c++
        void function_w_vec_arg_no_map(const Eigen::VectorXd &vec);
        ```
        
        The Cython definitions would still look like this:
        
        ```
        cdef extern from "functions.h":
             cdef void _function_w_vec_arg_no_map "function_w_vec_arg_no_map"(Map[VectorXd] &)
        
        # This will be exposed to Python
        def function_w_vec_arg_no_map(np.ndarray[np.float64_t] array):
            return _function_w_vec_arg_no_map(Map[VectorXd](array))
        ```
        
        Cython will not mind the fact that the argument type in the extern
        declaration (a Map type) differs from the actual one in the `.h` file,
        as long as one can be assigned to the other. Since Map objects can be
        assigned to their corresponding Matrix/Array types this works
        seemlessly. But keep in mind that this assignment will make a copy of
        the underlying data.
        
        ## Eigen to Numpy
        
        C++ functions returning a reference to an Eigen Matrix/Array can also
        be transferred to numpy arrays without copying their content.  Assume
        we have a class with a single getter function that returns an Eigen
        matrix member:
        
        ```c++
        class MyClass {
        public:
            MyClass():
                matrix(Eigen::Matrix3d::Constant(3.)) {
            }
            Eigen::MatrixXd &get_matrix() {
                return this->matrix;
            }
        private:
            Eigen::Matrix3d matrix;
        };
        ```
        
        The Cython C++ class interface is specified as usual:
        
        ```
             cdef cppclass _MyClass "MyClass":
                 _MyClass "MyClass"() except +
                 Matrix3d &get_matrix()
        ```
        
        And the corresponding Python wrapper:
        
        ```python
        cdef class MyClass:
            cdef _MyClass *thisptr;
        
            def __cinit__(self):
                self.thisptr = new _MyClass()
        
            def __dealloc__(self):
                del self.thisptr
        
            def get_matrix(self):
                return ndarray(self.thisptr.get_matrix())
        ```
        
        This last line contains the actual conversion. Again, eigency has its
        own version of `ndarray`, that will take care of the conversion for
        you.
        
        Due to limitations in Cython, Eigency cannot deal with full
        Matrix/Array template specifications as return types
        (e.g. `Matrix[double, 4, 2]`). However, as a workaround, you can use
        `PlainObjectBase` as a return type in such cases (or in all cases if
        you prefer):
        
        ```
                 PlainObjectBase &get_matrix()
        ```
        
        ## Overriding default behavior
        
        The `ndarray` conversion type specifier will attempt do guess whether you want a copy
        or a view, depending on the return type. Most of the time, this is
        probably what you want. However, there might be cases where you want
        to override this behavior. For instance, functions returning const
        references will result in a copy of the array, since the const-ness
        cannot be enforced in Python. However, you can always override the
        default behavior by using the `ndarray_copy` or `ndarray_view`
        functions.
        
        Expanding the `MyClass` example from before:
        
        ```c++
        class MyClass {
        public:
            ...
            const Eigen::MatrixXd &get_const_matrix() {
                return this->matrix;
            }
            ...
        };
        ```
        
        With the corresponding cython interface specification
        The Cython C++ class interface is specified as usual:
        
        ```
             cdef cppclass _MyClass "MyClass":
                 ...
                 const Matrix3d &get_const_matrix()
        ```
        
        The following would return a copy
        
        ```python
        cdef class MyClass:
            ...
            def get_const_matrix(self):
                return ndarray(self.thisptr.get_const_matrix())
        ```
        
        while the following would force it to return a view
        
        ```python
        cdef class MyClass:
            ...
            def get_const_matrix(self):
                return ndarray_view(self.thisptr.get_const_matrix())
        ```
        
        ## Eigen to Numpy (non-reference return values)
        
        Functions returning an Eigen object (not a reference), are specified
        in a similar way. For instance, given the following C++ function:
        
        ```c++
        Eigen::Matrix3d function_w_mat_retval();
        ```
        
        The Cython code could be written as:
        
        ```
        cdef extern from "functions.h":
             cdef Matrix3d _function_w_mat_retval "function_w_mat_retval" ()
        
        # This will be exposed to Python
        def function_w_mat_retval():
            return ndarray_copy(_function_w_mat_retval())
        ```
        
        As mentioned above, you can replace `Matrix3d` (or any other Eigen return type) with
        `PlainObjectBase`, which is especially relevant when working with
        Eigen object that do not have an associated convenience typedef.
        
        Note that we use `ndarray_copy` instead of `ndarray` to explicitly
        state that a copy should be made. In c++11 compliant compilers, it
        will detect the rvalue reference and automatically make a copy even if
        you just use `ndarray` (see next section), but to ensure that it works
        also with older compilers it is recommended to always use
        `ndarray_copy` when returning newly constructed eigen values.
        
        
        ## Corrupt data when returning non-map types
        The tendency of Eigency to avoid copies whenever possible can lead
        to corrupted data when returning non-map Eigen arrays. For instance,
        in the `function_w_mat_retval` from the previous section, a temporary
        value will be returned from C++, and we have to take care to make
        a copy of this data instead of letting the resulting numpy array
        refer directly to this memory. In C++11, this situation can be
        detected directly using rvalue references, and it will therefore
        automatically make a copy: 
        ```
        def function_w_mat_retval():
            # This works in C++11, because it detects the rvalue reference
            return ndarray(_function_w_mat_retval()) 
        ```
        
        However, to make sure it works with older compilers,
        it is recommended to use the `ndarray_copy` conversion:
        
        ```
        def function_w_mat_retval():
            # Explicit request for copy - this always works
            return ndarray_copy(_function_w_mat_retval()) 
        ```
        
        
        
        ## Storage layout - why arrays are sometimes transposed
        
        The default storage layout used in numpy and Eigen differ. Numpy uses
        a row-major layout (C-style) per default while Eigen uses a
        column-major layout (Fortran style) by default.  In Eigency, we prioritize to
        avoid copying of data whenever possible, which can have unexpected
        consequences in some cases: There is no problem when passing values
        from C++ to Python - we just adjust the storage layout of the returned
        numpy array to match that of Eigen. However, since the storage layout
        is encoded into the _type_ of the Eigen array (or the type of the
        Map), we cannot automatically change the layout in the Python to C++ direction. In
        Eigency, we have therefore opted to return the transposed array/matrix
        in such cases. This provides the user with the flexibility to deal
        with the problem either in Python (use order="F" when constructing
        your numpy array), or on the C++ side: (1) explicitly define your
        argument to have the row-major storage layout, 2) manually set the Map
        stride, or 3) just call `.transpose()` on the received
        array/matrix). 
        
        As an example, consider the case of a C++ function that both receives
        and returns a Eigen Map type, thus acting as a filter:
        
        ```c++
        Eigen::Map<Eigen::ArrayXXd> function_filter(Eigen::Map<Eigen::ArrayXXd> &mat) {
            return mat;
        }
        ```
        
        The Cython code could be:
        
        ```
        cdef extern from "functions.h":
            ...
            cdef Map[ArrayXXd] &_function_filter1 "function_filter1" (Map[ArrayXXd] &)
        
        def function_filter1(np.ndarray[np.float64_t, ndim=2] array):
            return ndarray(_function_filter1(Map[ArrayXXd](array)))
        
        ```
        
        If we call this function from Python in the standard way, we will
        see that the array is transposed on the way from Python to C++, and
        remains that way when it is again returned to Python:
        
        ```python
        >>> x = np.array([[1., 2., 3., 4.], [5., 6., 7., 8.]])
        >>> y = function_filter1(x)
        >>> print x
        [[ 1.  2.  3.  4.]
         [ 5.  6.  7.  8.]]
        >>> print y
        [[ 1.  5.]
         [ 2.  6.]
         [ 3.  7.]
         [ 4.  8.]]
        ```
        
        The simplest way to avoid this is to tell numpy to use a
        column-major array layout instead of the default row-major
        layout. This can be done using the order='F' option:
        
        ```python
        >>> x = np.array([[1., 2., 3., 4.], [5., 6., 7., 8.]], order='F')
        >>> y = function_filter1(x)
        >>> print x
        [[ 1.  2.  3.  4.]
         [ 5.  6.  7.  8.]]
        >>> print y
        [[ 1.  2.  3.  4.]
         [ 5.  6.  7.  8.]]
        ```
        
        The other alternative is to tell Eigen to use RowMajor layout. This
        requires changing the C++ function definition:
        
        ```c++
        typedef Eigen::Map<Eigen::Array<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> > RowMajorArrayMap;
        
        RowMajorArrayMap &function_filter2(RowMajorArrayMap &mat) {
            return mat;
        }
        ```
        
        To write the corresponding Cython definition, we need the expanded version of
        `FlattenedMap` called `FlattenedMapWithOrder`, which allows us to specify
        the storage order:
        
        ```
        cdef extern from "functions.h":
            ...
            cdef PlainObjectBase _function_filter2 "function_filter2" (FlattenedMapWithOrder[Array, double, Dynamic, Dynamic, RowMajor])
        
        def function_filter2(np.ndarray[np.float64_t, ndim=2] array):
            return ndarray(_function_filter2(FlattenedMapWithOrder[Array, double, Dynamic, Dynamic, RowMajor](array)))
        ```
        
        Another alternative is to keep the array itself in RowMajor format,
        but use different stride values for the Map type:
        
        ```c++
        typedef Eigen::Map<Eigen::ArrayXXd, Eigen::Unaligned, Eigen::Stride<1, Eigen::Dynamic> > CustomStrideMap;
        
        CustomStrideMap &function_filter3(CustomStrideMap &);
        ```
        
        In this case, in Cython, we need to use the even more extended
        `FlattenedMap` type called `FlattenedMapWithStride`, taking eight
        arguments:
        
        ```
        cdef extern from "functions.h":
            ...
            cdef PlainObjectBase _function_filter3 "function_filter3" (FlattenedMapWithStride[Array, double, Dynamic, Dynamic, ColMajor, Unaligned, _1, Dynamic])
        
        def function_filter3(np.ndarray[np.float64_t, ndim=2] array):
            return ndarray(_function_filter3(FlattenedMapWithStride[Array, double, Dynamic, Dynamic, ColMajor, Unaligned, _1, Dynamic](array)))
        ```
        
        In all three cases, the returned array will now be of the same shape
        as the original.
        
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
