pencil.read.fortran_file
========================

.. py:module:: pencil.read.fortran_file


Classes
-------

.. autoapisummary::

   pencil.read.fortran_file.FortranFileExt


Module Contents
---------------

.. py:class:: FortranFileExt

   Bases: :py:obj:`scipy.io.FortranFile`


   .. py:method:: read_record(*dtypes, **kwargs)

      Reads a record of a given type from the file.

      :param \*dtypes: Data type(s) specifying the size and endianness of the data.
      :type \*dtypes: dtypes, optional

      :returns: **data** -- A 1-D array object.
      :rtype: ndarray

      :raises FortranEOFError: To signal that no further records are available
      :raises FortranFormattingError: To signal that the end of the file was encountered
          part-way through a record

      .. rubric:: Notes

      If the record contains a multidimensional array, you can specify
      the size in the dtype. For example::

          INTEGER var(5,4)

      can be read with::

          read_record('(4,5)i4').T

      Note that this function does **not** assume the file data is in Fortran
      column major order, so you need to (i) swap the order of dimensions
      when reading and (ii) transpose the resulting array.

      Alternatively, you can read the data as a 1-D array and handle the
      ordering yourself. For example::

          read_record('i4').reshape(5, 4, order='F')

      For records that contain several variables or mixed types (as opposed
      to single scalar or array types), give them as separate arguments::

          double precision :: a
          integer :: b
          write(1) a, b

          record = f.read_record('<f4', '<i4')
          a = record[0]  # first number
          b = record[1]  # second number

      and if any of the variables are arrays, the shape can be specified as
      the third item in the relevant dtype::

          double precision :: a
          integer :: b(3,4)
          write(1) a, b

          record = f.read_record('<f4', np.dtype(('<i4', (4, 3))))
          a = record[0]
          b = record[1].T

      NumPy also supports a short syntax for this kind of type::

          record = f.read_record('<f4', '(3,3)<i4')

      .. seealso:: :obj:`read_reals`, :obj:`read_ints`