# cython: profile=False

# This file is part of h5py, a Python interface to the HDF5 library.
#
# http://www.h5py.org
#
# Copyright 2008-2013 Andrew Collette and contributors
#
# License:  Standard 3-clause BSD; see "license.txt" for full license terms
#           and contributor agreement.

from numpy cimport ndarray, import_array, \
                    NPY_UINT16, NPY_UINT32, NPY_UINT64,  npy_intp, \
                    PyArray_SimpleNew, PyArray_ContiguousFromAny, \
                    PyArray_FROM_OTF, PyArray_DIM, \
                    NPY_CONTIGUOUS, NPY_NOTSWAPPED, NPY_FORCECAST, \
                    NPY_C_CONTIGUOUS, NPY_WRITEABLE


# Initialization
import_array()

# === Exception-aware memory allocation =======================================

cdef void* emalloc(size_t size) except? NULL:
    # Wrapper for malloc(size) with the following behavior:
    # 1. Always returns NULL for emalloc(0)
    # 2. Raises RuntimeError for emalloc(size<0) and returns NULL
    # 3. Raises RuntimeError if allocation fails and returns NULL

    cdef void *retval = NULL

    if size == 0:
        return NULL

    retval = malloc(size)
    if retval == NULL:
        errmsg = "Can't malloc %d bytes" % size
        PyErr_SetString(MemoryError, errmsg)
        return NULL

    return retval

cdef void efree(void* what):
    free(what)

def _test_emalloc(size_t size):
    """Stub to simplify unit tests"""
    cdef void* mem
    mem = emalloc(size)
    if size == 0:
        assert mem == NULL
    efree(mem)

# === Testing of NumPy arrays =================================================

cdef int check_numpy(ndarray arr, hid_t space_id, int write):
    # -1 if exception, NOT AUTOMATICALLY CHECKED

    cdef int required_flags
    cdef hsize_t arr_rank
    cdef hsize_t space_rank
    cdef int i

    if arr is None:
        PyErr_SetString(TypeError, "Array is None")
        return -1

    # Validate array flags

    if write:
        if not (arr.flags & NPY_C_CONTIGUOUS and arr.flags & NPY_WRITEABLE):
            PyErr_SetString(TypeError, "Array must be C-contiguous and writable")
            return -1
    else:
        if not (arr.flags & NPY_C_CONTIGUOUS):
            PyErr_SetString(TypeError, "Array must be C-contiguous")
            return -1

    return 1

cpdef int check_numpy_write(ndarray arr, hid_t space_id=-1) except -1:
    return check_numpy(arr, space_id, 1)

cpdef int check_numpy_read(ndarray arr, hid_t space_id=-1) except -1:
    return check_numpy(arr, space_id, 0)

# === Conversion between HDF5 buffers and tuples ==============================

cdef int convert_tuple(object tpl, hsize_t *dims, hsize_t rank) except -1:
    # Convert a Python tuple to an hsize_t array.  You must allocate
    # the array yourself and pass both it and the size to this function.
    # Returns 0 on success, -1 on failure and raises an exception.
    cdef int i

    if len(tpl) != rank:
        raise ValueError("Tuple length incompatible with array")
    
    try:
        for i from 0<=i<rank:
            dims[i] = tpl[i]
    except TypeError:
        raise TypeError("Can't convert element %d (%s) to hsize_t" % (i, tpl[i]))

    return 0
    
cdef object convert_dims(hsize_t* dims, hsize_t rank):
    # Convert an hsize_t array to a Python tuple of ints.

    cdef list dims_list
    cdef int i
    dims_list = []

    for i from 0<=i<rank:
        dims_list.append(int(dims[i]))

    return tuple(dims_list)
    

cdef object create_numpy_hsize(int rank, hsize_t* dims):
    # Create an empty Numpy array which can hold HDF5 hsize_t entries

    cdef int typecode
    cdef npy_intp* dims_npy
    cdef ndarray arr
    cdef int i

    if sizeof(hsize_t) == 2:
        typecode = NPY_UINT16
    elif sizeof(hsize_t) == 4:
        typecode = NPY_UINT32
    elif sizeof(hsize_t) == 8:
        typecode = NPY_UINT64
    else:
        raise RuntimeError("Can't map hsize_t %d to Numpy typecode" % sizeof(hsize_t))

    dims_npy = <npy_intp*>emalloc(sizeof(npy_intp)*rank)

    try:
        for i from 0<=i<rank:
            dims_npy[i] = dims[i]
        arr = PyArray_SimpleNew(rank, dims_npy, typecode)
    finally:
        efree(dims_npy)

    return arr

cdef object create_hsize_array(object arr):
    # Create a NumPy array of hsize_t uints initialized to an existing array

    cdef int typecode
    cdef ndarray outarr

    if sizeof(hsize_t) == 2:
        typecode = NPY_UINT16
    elif sizeof(hsize_t) == 4:
        typecode = NPY_UINT32
    elif sizeof(hsize_t) == 8:
        typecode = NPY_UINT64
    else:
        raise RuntimeError("Can't map hsize_t %d to Numpy typecode" % sizeof(hsize_t))

    return PyArray_FROM_OTF(arr, typecode, NPY_CONTIGUOUS | NPY_NOTSWAPPED | NPY_FORCECAST)


# === Argument testing ========================================================

cdef int require_tuple(object tpl, int none_allowed, int size, char* name) except -1:
    # Ensure that tpl is in fact a tuple, or None if none_allowed is nonzero.
    # If size >= 0, also ensure that the length matches.
    # Otherwise raises ValueError

    if (tpl is None and none_allowed) or \
      (isinstance(tpl, tuple) and (size < 0 or len(tpl) == size)):
        return 1

    nmsg = "" if size < 0 else " of size %d" % size
    smsg = "" if not none_allowed else " or None"

    msg = "%s must be a tuple%s%s." % (name, smsg, nmsg)
    PyErr_SetString(ValueError, msg)
    return -1