scipy sp1.5-0.3.1 (latest)

val get_py : string -> Py.Object.t

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

module BytesIO : sig ... end

module EmptyStructMarker : sig ... end

module MatFileReader : sig ... end

module MatReadError : sig ... end

module MatReadWarning : sig ... end

module MatWriteError : sig ... end

module MatlabFunction : sig ... end

module MatlabObject : sig ... end

module VarReader5 : sig ... end

module VarWriter5 : sig ... end

module ZlibInputStream : sig ... end

module Mat_struct : sig ... end

val arr_dtype_number : 
  arr:Py.Object.t ->
  num:Py.Object.t ->
  unit ->
  Py.Object.t

Return dtype for given number of items per element

val arr_to_chars : Py.Object.t -> Py.Object.t

Convert string array to char array

val asbytes : Py.Object.t -> Py.Object.t

None

val asstr : Py.Object.t -> Py.Object.t

None

val docfiller : Py.Object.t -> Py.Object.t

None

val matdims : 
  ?oned_as:[ `Column | `Row ] ->
  arr:[> `Ndarray ] Np.Obj.t ->
  unit ->
  Py.Object.t

Determine equivalent MATLAB dimensions for given array

Parameters ---------- arr : ndarray Input array oned_as : 'column', 'row', optional Whether 1-D arrays are returned as MATLAB row or column matrices. Default is 'column'.

Returns ------- dims : tuple Shape tuple, in the form MATLAB expects it.

Notes ----- We had to decide what shape a 1 dimensional array would be by default. ``np.atleast_2d`` thinks it is a row vector. The default for a vector in MATLAB (e.g., ``>> 1:12``) is a row vector.

Versions of scipy up to and including 0.11 resulted (accidentally) in 1-D arrays being read as column vectors. For the moment, we maintain the same tradition here.

Examples -------- >>> matdims(np.array(1)) # NumPy scalar (1, 1) >>> matdims(np.array(1)) # 1-D array, 1 element (1, 1) >>> matdims(np.array(1,2)) # 1-D array, 2 elements (2, 1) >>> matdims(np.array([2],[3])) # 2-D array, column vector (2, 1) >>> matdims(np.array([2,3])) # 2-D array, row vector (1, 2) >>> matdims(np.array([[2,3]])) # 3-D array, rowish vector (1, 1, 2) >>> matdims(np.array()) # empty 1-D array (0, 0) >>> matdims(np.array([])) # empty 2-D array (0, 0) >>> matdims(np.array([[]])) # empty 3-D array (0, 0, 0)

Optional argument flips 1-D shape behavior.

>>> matdims(np.array(1,2), 'row') # 1-D array, 2 elements (1, 2)

The argument has to make sense though

>>> matdims(np.array(1,2), 'bizarre') Traceback (most recent call last): ... ValueError: 1-D option 'bizarre' is strange

val read_dtype : 
  mat_stream:Py.Object.t ->
  a_dtype:Np.Dtype.t ->
  unit ->
  [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Generic get of byte stream data of known type

Parameters ---------- mat_stream : file_like object MATLAB (tm) mat file stream a_dtype : dtype dtype of array to read. `a_dtype` is assumed to be correct endianness.

Returns ------- arr : ndarray Array of dtype `a_dtype` read from stream.

val to_writeable : Py.Object.t -> Py.Object.t option

Convert input object ``source`` to something we can write

Parameters ---------- source : object

Returns ------- arr : None or ndarray or EmptyStructMarker If `source` cannot be converted to something we can write to a matfile, return None. If `source` is equivalent to an empty dictionary, return ``EmptyStructMarker``. Otherwise return `source` converted to an ndarray with contents for writing to matfile.

val varmats_from_mat : 
  Py.Object.t ->
  [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Pull variables out of mat 5 file as a sequence of mat file objects

This can be useful with a difficult mat file, containing unreadable variables. This routine pulls the variables out in raw form and puts them, unread, back into a file stream for saving or reading. Another use is the pathological case where there is more than one variable of the same name in the file; this routine returns the duplicates, whereas the standard reader will overwrite duplicates in the returned dictionary.

The file pointer in `file_obj` will be undefined. File pointers for the returned file-like objects are set at 0.

Parameters ---------- file_obj : file-like file object containing mat file

Returns ------- named_mats : list list contains tuples of (name, BytesIO) where BytesIO is a file-like object containing mat file contents as for a single variable. The BytesIO contains a string with the original header and a single var. If ``var_file_obj`` is an individual BytesIO instance, then save as a mat file with something like ``open('test.mat', 'wb').write(var_file_obj.read())``

Examples -------- >>> import scipy.io

BytesIO is from the ``io`` module in Python 3, and is ``cStringIO`` for Python < 3.

>>> mat_fileobj = BytesIO() >>> scipy.io.savemat(mat_fileobj, 'b': np.arange(10), 'a': 'a string') >>> varmats = varmats_from_mat(mat_fileobj) >>> sorted(name for name, str_obj in varmats) 'a', 'b'

package scipy