#!python
#cython: embedsignature=True
#########################################################################
#
#       License: BSD
#       Created: August 05, 2010
#       Author:  Francesc Alted -  francesc@blosc.org
#
########################################################################

from __future__ import absolute_import

import sys
import os
import os.path
import struct
import shutil
import tempfile
import json
import datetime

import numpy as np
cimport numpy as np
from numpy cimport (ndarray,
                    dtype,
                    import_array,
                    PyArray_GETITEM,
                    PyArray_SETITEM,
                    npy_intp,
                    )
import cython

import bcolz
from bcolz import utils, attrs, array2string

from .utils import build_carray

if sys.version_info >= (3, 0):
    _MAXINT = 2 ** 31 - 1
    _inttypes = (int, np.integer)
else:
    _MAXINT = sys.maxint
    _inttypes = (int, long, np.integer)

_KB = 1024
_MB = 1024 * _KB

# Directories for saving the data and metadata for bcolz persistency
DATA_DIR = 'data'
META_DIR = 'meta'
SIZES_FILE = 'sizes'
STORAGE_FILE = 'storage'

# For the persistence layer
EXTENSION = '.blp'
MAGIC = b'blpk'
BLOSCPACK_HEADER_LENGTH = 16
BLOSC_HEADER_LENGTH = 16
FORMAT_VERSION = 1
MAX_FORMAT_VERSION = 255
MAX_CHUNKS = (2 ** 63) - 1

# The type used for size values: indexes, coordinates, dimension
# lengths, row numbers, shapes, chunk shapes, byte counts...
SizeType = np.int64

# The native int type for this platform
IntType = np.dtype(np.int_)

#-----------------------------------------------------------------

# numpy functions & objects
from definitions cimport (malloc,
                          realloc,
                          free,
                          memcpy,
                          memset,
                          strdup,
                          strcmp,
                          PyBytes_AsString,
                          PyBytes_GET_SIZE,
                          PyBytes_FromStringAndSize,
                          PyBytes_AS_STRING,
                          Py_BEGIN_ALLOW_THREADS,
                          Py_END_ALLOW_THREADS,
                          PyBuffer_FromMemory,
                          Py_uintptr_t,
                          )

#-----------------------------------------------------------------


# Check blosc version
cdef extern from "check_blosc_version.h":
    pass

# Blosc routines
cdef extern from "blosc.h":
    cdef enum:
        BLOSC_MAX_OVERHEAD,
        BLOSC_VERSION_STRING,
        BLOSC_VERSION_DATE,
        BLOSC_MAX_TYPESIZE

    void blosc_init()
    void blosc_destroy()
    void blosc_get_versions(char *version_str, char *version_date)
    int blosc_set_nthreads(int nthreads)
    int blosc_set_compressor(const char*compname)
    int blosc_compress(int clevel, int doshuffle, size_t typesize,
                       size_t nbytes, void *src, void *dest,
                       size_t destsize) nogil
    int blosc_decompress(void *src, void *dest, size_t destsize) nogil
    int blosc_getitem(void *src, int start, int nitems, void *dest) nogil
    void blosc_free_resources()
    void blosc_cbuffer_sizes(void *cbuffer, size_t *nbytes,
                             size_t *cbytes, size_t *blocksize)
    void blosc_cbuffer_metainfo(void *cbuffer, size_t *typesize, int *flags)
    void blosc_cbuffer_versions(void *cbuffer, int *version, int *versionlz)
    void blosc_set_blocksize(size_t blocksize)
    char*blosc_list_compressors()


#----------------------------------------------------------------------------

# Initialization code

# The numpy API requires this function to be called before
# using any numpy facilities in an extension module.
import_array()

#-------------------------------------------------------------

# Some utilities
def blosc_compressor_list():
    """
    blosc_compressor_list()

    Returns a list of compressors available in the Blosc build.

    Parameters
    ----------
    None

    Returns
    -------
    out : list
        The list of names.
    """
    list_compr = blosc_list_compressors()
    if sys.version_info >= (3, 0):
        # Convert compressor names into regular strings in Python 3 (unicode)
        list_compr = list_compr.decode()
    clist = list_compr.split(',')
    return clist

def _blosc_set_nthreads(nthreads):
    """
    _blosc_set_nthreads(nthreads)

    Sets the number of threads that Blosc can use.

    Parameters
    ----------
    nthreads : int
        The desired number of threads to use.

    Returns
    -------
    out : int
        The previous setting for the number of threads.
    """
    return blosc_set_nthreads(nthreads)

def _blosc_init():
    """
    _blosc_init()

    Initialize the Blosc library.

    """
    blosc_init()

def _blosc_destroy():
    """
    _blosc_destroy()

    Finalize the Blosc library.

    """
    blosc_destroy()

def blosc_version():
    """
    blosc_version()

    Return the version of the Blosc library.

    """
    # All the 'decode' contorsions are for Python 3 returning actual strings
    ver_str = <char *> BLOSC_VERSION_STRING
    if hasattr(ver_str, "decode"):
        ver_str = ver_str.decode()
    ver_date = <char *> BLOSC_VERSION_DATE
    if hasattr(ver_date, "decode"):
        ver_date = ver_date.decode()
    return (ver_str, ver_date)

def list_bytes_to_str(lst):
    """The Python 3 JSON encoder doesn't accept 'bytes' objects,
    this utility function converts all bytes to strings.
    """
    if isinstance(lst, bytes):
        return lst.decode('ascii')
    elif isinstance(lst, list):
        return [list_bytes_to_str(x) for x in lst]
    else:
        return lst

# This is the same than in utils.py, but works faster in extensions
cdef get_len_of_range(npy_intp start, npy_intp stop, npy_intp step):
    """Get the length of a (start, stop, step) range."""
    cdef npy_intp n

    n = 0
    if start < stop:
        # Do not use a cython.cdiv here (do not ask me why!)
        n = ((stop - start - 1) // step + 1)
    return n

cdef clip_chunk(npy_intp nchunk, npy_intp chunklen,
                npy_intp start, npy_intp stop, npy_intp step):
    """Get the limits of a certain chunk based on its length."""
    cdef npy_intp startb, stopb, blen, distance

    startb = start - nchunk * chunklen
    stopb = stop - nchunk * chunklen

    # Check limits
    if (startb >= chunklen) or (stopb <= 0):
        return startb, stopb, 0  # null size
    if startb < 0:
        startb = 0
    if stopb > chunklen:
        stopb = chunklen

    # step corrections
    if step > 1:
        # Just correcting startb is enough
        distance = (nchunk * chunklen + startb) - start
        if distance % step > 0:
            startb += (step - (distance % step))
            if startb > chunklen:
                return startb, stopb, 0  # null size

    # Compute size of the clipped block
    blen = get_len_of_range(startb, stopb, step)

    return startb, stopb, blen

cdef int check_zeros(char *data, int nbytes):
    """Check whether [data, data+nbytes] is zero or not."""
    cdef int i, iszero, chunklen, leftover
    cdef size_t *sdata

    iszero = 1
    sdata = <size_t *> data
    chunklen = cython.cdiv(nbytes, sizeof(size_t))
    leftover = nbytes % sizeof(size_t)
    with nogil:
        for i from 0 <= i < chunklen:
            if sdata[i] != 0:
                iszero = 0
                break
        else:
            data += nbytes - leftover
            for i from 0 <= i < leftover:
                if data[i] != 0:
                    iszero = 0
                    break
    return iszero

cdef int true_count(char *data, int nbytes):
    """Count the number of true values in data (boolean)."""
    cdef int i, count

    with nogil:
        count = 0
        for i from 0 <= i < nbytes:
            count += <int> (data[i])
    return count

#-------------------------------------------------------------


cdef class chunk:
    """
    chunk(array, atom, cparams)

    Compressed in-memory container for a data chunk.

    This class is meant to be used only by the `carray` class.

    """

    property dtype:
        "The NumPy dtype for this chunk."
        def __get__(self):
            return self.atom

    def __cinit__(self, object dobject, object atom, object cparams,
                  object _memory=True, object _compr=False):
        cdef int itemsize, footprint
        cdef size_t nbytes, cbytes, blocksize
        cdef dtype dtype_
        cdef char *data

        self.atom = atom
        self.atomsize = atom.itemsize
        dtype_ = atom.base
        self.typekind = dtype_.kind
        # Hack for allowing strings with len > BLOSC_MAX_TYPESIZE
        if self.typekind == 'S':
            itemsize = 1
        elif self.typekind == 'U':
            itemsize = 4
        elif self.typekind == 'V' and dtype_.itemsize > BLOSC_MAX_TYPESIZE:
            itemsize = 1
        else:
            itemsize = dtype_.itemsize
        if itemsize > BLOSC_MAX_TYPESIZE:
            raise TypeError(
                "typesize is %d and bcolz does not currently support data "
                "types larger than %d bytes" % (itemsize, BLOSC_MAX_TYPESIZE))
        self.itemsize = itemsize
        self.dobject = None
        footprint = 0

        if _compr:
            # Data comes in an already compressed state inside a Python String
            self.data = PyBytes_AsString(dobject)
            # Increment the reference so that data don't go away
            self.dobject = dobject
            # Set size info for the instance
            blosc_cbuffer_sizes(self.data, &nbytes, &cbytes, &blocksize)
        elif dtype_ == 'O':
            # The objects should arrive here already pickled
            data = PyBytes_AsString(dobject)
            nbytes = PyBytes_GET_SIZE(dobject)
            cbytes, blocksize = self.compress_data(data, 1, nbytes, cparams)
        else:
            # Compress the data object (a NumPy object)
            nbytes, cbytes, blocksize, footprint = self.compress_arrdata(
                dobject, itemsize, cparams, _memory)
        footprint += 128  # add the (aprox) footprint of this instance in bytes

        # Fill instance data
        self.nbytes = nbytes
        self.cbytes = cbytes + footprint
        self.cdbytes = cbytes
        self.blocksize = blocksize

    cdef compress_arrdata(self, ndarray array, int itemsize,
                          object cparams, object _memory):
        """Compress data in `array` and put it in ``self.data``"""
        cdef size_t nbytes, cbytes, blocksize, footprint

        # Compute the total number of bytes in this array
        nbytes = array.itemsize * array.size
        cbytes = 0
        footprint = 0

        # Check whether incoming data can be expressed as a constant or not.
        # Disk-based chunks are not allowed to do this.
        self.isconstant = 0
        self.constant = None
        if _memory and (array.strides[0] == 0
                        or check_zeros(array.data, nbytes)):

            self.isconstant = 1
            # Get the NumPy constant.  Avoid this NumPy quirk:
            # np.array(['1'], dtype='S3').dtype != s[0].dtype
            if array.dtype.kind != 'S':
                self.constant = array[0]
            else:
                self.constant = np.array(array[0], dtype=array.dtype)
            # Add overhead (64 bytes for the overhead of the numpy container)
            footprint += 64 + self.constant.size * self.constant.itemsize

        if self.isconstant:
            blocksize = 4 * 1024  # use 4 KB as a cache for blocks
            # Make blocksize a multiple of itemsize
            if blocksize % itemsize > 0:
                blocksize = cython.cdiv(blocksize, itemsize) * itemsize
            # Correct in case we have a large itemsize
            if blocksize == 0:
                blocksize = itemsize
        else:
            if self.typekind == 'b':
                self.true_count = true_count(array.data, nbytes)

            if array.strides[0] == 0:
                # The chunk is made of constants.  Regenerate the actual data.
                array = array.copy()

            # Compress data
            cbytes, blocksize = self.compress_data(
                array.data, itemsize, nbytes, cparams)

        return (nbytes, cbytes, blocksize, footprint)

    cdef compress_data(self, char *data, size_t itemsize, size_t nbytes,
                       object cparams):
        """Compress data with `cparams` and return metadata."""
        cdef size_t nbytes_, cbytes, blocksize
        cdef int clevel, shuffle, ret
        cdef char *dest

        clevel = cparams.clevel
        shuffle = cparams.shuffle
        cname = cparams.cname
        if type(cname) != bytes:
            cname = cname.encode()
        if blosc_set_compressor(cname) < 0:
            raise ValueError(
                "Compressor '%s' is not available in this build" % cname)
        dest = <char *> malloc(nbytes + BLOSC_MAX_OVERHEAD)
        ret = blosc_compress(clevel, shuffle, itemsize, nbytes,
                             data, dest, nbytes + BLOSC_MAX_OVERHEAD)
        if ret <= 0:
            raise RuntimeError(
                "fatal error during Blosc compression: %d" % ret)
        # Free the unused data
        cbytes = ret;
        self.data = <char *> realloc(dest, cbytes)
        # Set size info for the instance
        blosc_cbuffer_sizes(self.data, &nbytes_, &cbytes, &blocksize)
        assert nbytes_ == nbytes

        return (cbytes, blocksize)

    def getdata(self):
        """Get a compressed string object out of this chunk (for persistence)."""
        cdef object string

        assert (not self.isconstant,
                "This function can only be used for persistency")
        string = PyBytes_FromStringAndSize(self.data,
                                            <Py_ssize_t> self.cdbytes)
        return string

    def getudata(self):
        """Get an uncompressed string out of this chunk (for 'O'bject types)."""
        cdef int ret
        cdef char *dest

        result_str = PyBytes_FromStringAndSize(NULL, self.nbytes)
        dest = PyBytes_AS_STRING(result_str);

        ret = blosc_decompress(self.data, dest, self.nbytes)
        if ret < 0:
            raise RuntimeError(
                "fatal error during Blosc decompression: %d" % ret)
        return result_str

    cdef void _getitem(self, int start, int stop, char *dest):
        """Read data from `start` to `stop` and return it as a numpy array."""
        cdef int ret, bsize, blen, nitems, nstart
        cdef ndarray constants

        blen = stop - start
        bsize = blen * self.atomsize
        nitems = cython.cdiv(bsize, self.itemsize)
        nstart = cython.cdiv(start * self.atomsize, self.itemsize)

        if self.isconstant:
            # The chunk is made of constants
            constants = np.ndarray(shape=(blen,), dtype=self.dtype,
                                   buffer=self.constant, strides=(0,)).copy()
            memcpy(dest, constants.data, bsize)
            return

        # Fill dest with uncompressed data
        if bsize == self.nbytes:
            ret = blosc_decompress(self.data, dest, bsize)
        else:
            ret = blosc_getitem(self.data, nstart, nitems, dest)
        if ret < 0:
            raise RuntimeError(
                "fatal error during Blosc decompression: %d" % ret)

    def __getitem__(self, object key):
        """__getitem__(self, key) -> values."""
        cdef ndarray array
        cdef object start, stop, step, clen, idx

        if isinstance(key, _inttypes):
            # Quickly return a single element
            array = np.empty(shape=(1,), dtype=self.dtype)
            self._getitem(key, key + 1, array.data)
            return PyArray_GETITEM(array, array.data)
        elif isinstance(key, slice):
            (start, stop, step) = key.start, key.stop, key.step
        elif isinstance(key, tuple) and self.dtype.shape != ():
            # Build an array to guess indices
            clen = cython.cdiv(self.nbytes, self.itemsize)
            idx = np.arange(clen, dtype=np.int32).reshape(self.dtype.shape)
            idx2 = idx(key)
            if idx2.flags.contiguous:
                # The slice represents a contiguous slice.  Get start and stop.
                start, stop = idx2.flatten()[[0, -1]]
                step = 1
            else:
                (start, stop, step) = key[0].start, key[0].stop, key[0].step
        else:
            raise IndexError("key not suitable:", key)

        # Get the corrected values for start, stop, step
        clen = cython.cdiv(self.nbytes, self.atomsize)
        (start, stop, step) = slice(start, stop, step).indices(clen)

        # Build a numpy container
        array = np.empty(shape=(stop - start,), dtype=self.dtype)
        # Read actual data
        self._getitem(start, stop, array.data)

        # Return the value depending on the step
        if step > 1:
            return array[::step]
        return array

    @property
    def pointer(self):
        return <Py_uintptr_t> self.data + BLOSCPACK_HEADER_LENGTH

    @property
    def viewof(self):
        return PyBuffer_FromMemory(<void*>self.data, <Py_ssize_t>self.cdbytes)

    def __setitem__(self, object key, object value):
        """__setitem__(self, key, value) -> None."""
        raise NotImplementedError()

    def __str__(self):
        """Represent the chunk as an string."""
        return str(self[:])

    def __repr__(self):
        """Represent the chunk as an string, with additional info."""
        cratio = self.nbytes / float(self.cbytes)
        fullrepr = "chunk(%s)  nbytes: %d; cbytes: %d; ratio: %.2f\n%r" % \
                   (self.dtype, self.nbytes, self.cbytes, cratio, str(self))
        return fullrepr

    def __dealloc__(self):
        """Release C resources before destruction."""
        if self.dobject:
            self.dobject = None  # DECREF pointer to data object
        else:
            free(self.data)  # explictly free the data area

cdef create_bloscpack_header(nchunks=None, format_version=FORMAT_VERSION):
    """ Create the bloscpack header string.

    Parameters
    ----------
    nchunks : int
        the number of chunks, default: None
    format_version : int
        the version format for the compressed file

    Returns
    -------
    bloscpack_header : string
        the header as string

    Notes
    -----

    The bloscpack header is 16 bytes as follows:

    |-0-|-1-|-2-|-3-|-4-|-5-|-6-|-7-|-8-|-9-|-A-|-B-|-C-|-D-|-E-|-F-|
    | b   l   p   k | ^ | RESERVED  |           nchunks             |
                   version

    The first four are the magic string 'blpk'. The next one is an 8 bit
    unsigned little-endian integer that encodes the format version. The next
    three are reserved, and the last eight are a signed  64 bit little endian
    integer that encodes the number of chunks

    The value of '-1' for 'nchunks' designates an unknown size and can be
    inserted by setting 'nchunks' to None.

    Raises
    ------
    ValueError
        if the nchunks argument is too large or negative
    struct.error
        if the format_version is too large or negative

    """
    if not 0 <= nchunks <= MAX_CHUNKS and nchunks is not None:
        raise ValueError(
            "'nchunks' must be in the range 0 <= n <= %d, not '%s'" %
            (MAX_CHUNKS, str(nchunks)))
    return (MAGIC + struct.pack('<B', format_version) + b'\x00\x00\x00' +
            struct.pack('<q', nchunks if nchunks is not None else -1))

if sys.version_info >= (3, 0):
    def decode_byte(byte):
        return byte
else:
    def decode_byte(byte):
        return int(byte.encode('hex'), 16)
def decode_uint32(fourbyte):
    return struct.unpack('<I', fourbyte)[0]

cdef decode_blosc_header(buffer_):
    """ Read and decode header from compressed Blosc buffer.

    Parameters
    ----------
    buffer_ : string of bytes
        the compressed buffer

    Returns
    -------
    settings : dict
        a dict containing the settings from Blosc

    Notes
    -----

    The Blosc 1.1.3 header is 16 bytes as follows:

    |-0-|-1-|-2-|-3-|-4-|-5-|-6-|-7-|-8-|-9-|-A-|-B-|-C-|-D-|-E-|-F-|
      ^   ^   ^   ^ |     nbytes    |   blocksize   |    ctbytes    |
      |   |   |   |
      |   |   |   +--typesize
      |   |   +------flags
      |   +----------versionlz
      +--------------version

    The first four are simply bytes, the last three are are each unsigned ints
    (uint32) each occupying 4 bytes. The header is always little-endian.
    'ctbytes' is the length of the buffer including header and nbytes is the
    length of the data when uncompressed.

    """
    return {'version': decode_byte(buffer_[0]),
            'versionlz': decode_byte(buffer_[1]),
            'flags': decode_byte(buffer_[2]),
            'typesize': decode_byte(buffer_[3]),
            'nbytes': decode_uint32(buffer_[4:8]),
            'blocksize': decode_uint32(buffer_[8:12]),
            'ctbytes': decode_uint32(buffer_[12:16])}

cdef class chunks(object):
    """Store the different carray chunks in a directory on-disk."""
    property mode:
        "The mode used to create/open the `mode`."
        def __get__(self):
            return self._mode
        def __set__(self, value):
            self._mode = value

    property rootdir:
        "The on-disk directory used for persistency."
        def __get__(self):
            return self._rootdir
        def __set__(self, value):
            self._rootdir = value

    property datadir:
        """The directory for data files."""
        def __get__(self):
            return os.path.join(self.rootdir, DATA_DIR)

    def __cinit__(self, rootdir, metainfo=None, _new=False):
        cdef ndarray lastchunkarr
        cdef void *decompressed
        cdef void *compressed
        cdef int leftover
        cdef char *lastchunk
        cdef size_t chunksize
        cdef object scomp
        cdef int ret
        cdef int itemsize, atomsize

        self._rootdir = rootdir
        self.nchunks = 0
        self.nchunk_cached = -1  # no chunk cached initially
        self.dtype, self.cparams, self.len, lastchunkarr, self._mode = metainfo
        atomsize = self.dtype.itemsize
        itemsize = self.dtype.base.itemsize

        # For 'O'bject types, the number of chunks is equal to the number of
        # elements
        if self.dtype.char == 'O':
            self.nchunks = self.len

        # Initialize last chunk (not valid for 'O'bject dtypes)
        if not _new and self.dtype.char != 'O':
            self.nchunks = cython.cdiv(self.len, len(lastchunkarr))
            chunksize = len(lastchunkarr) * atomsize
            lastchunk = lastchunkarr.data
            leftover = (self.len % len(lastchunkarr)) * atomsize
            if leftover:
                # Fill lastchunk with data on disk
                scomp = self.read_chunk(self.nchunks)
                compressed = PyBytes_AsString(scomp)
                ret = blosc_decompress(compressed, lastchunk, chunksize)
                if ret < 0:
                    raise RuntimeError(
                        "error decompressing the last chunk (error code: "
                        "%d)" % ret)

    cdef _chunk_file_name(self, nchunk):
        """ Determine the name of a chunk file. """
        return os.path.join(self.datadir, "__%d%s" % (nchunk, EXTENSION))

    cdef read_chunk(self, nchunk):
        """Read a chunk and return it in compressed form."""
        schunkfile = self._chunk_file_name(nchunk)
        if not os.path.exists(schunkfile):
            raise ValueError("chunkfile %s not found" % schunkfile)
        with open(schunkfile, 'rb') as schunk:
            bloscpack_header = schunk.read(BLOSCPACK_HEADER_LENGTH)
            blosc_header_raw = schunk.read(BLOSC_HEADER_LENGTH)
            blosc_header = decode_blosc_header(blosc_header_raw)
            ctbytes = blosc_header['ctbytes']
            nbytes = blosc_header['nbytes']
            # seek back BLOSC_HEADER_LENGTH bytes in file relative to current
            # position
            schunk.seek(-BLOSC_HEADER_LENGTH, 1)
            scomp = schunk.read(ctbytes)
        return scomp

    def __iter__(self):
       self._iter_count = 0
       return self

    def __next__(self):
        cdef int i
        if self._iter_count < self.nchunks:
            i = self._iter_count
            self._iter_count += 1
            return self.__getitem__(i)
        else:
            raise StopIteration()

    def __getitem__(self, nchunk):
        cdef void *decompressed
        cdef void *compressed

        if nchunk == self.nchunk_cached:
            # Hit!
            return self.chunk_cached
        else:
            scomp = self.read_chunk(nchunk)
            # Data chunk should be compressed already
            chunk_ = chunk(scomp, self.dtype, self.cparams,
                           _memory=False, _compr=True)
            # Fill cache
            self.nchunk_cached = nchunk
            self.chunk_cached = chunk_
        return chunk_

    def __setitem__(self, nchunk, chunk_):
        self._save(nchunk, chunk_)

    def __len__(self):
        return self.nchunks

    def free_cachemem(self):
        self.nchunk_cached = -1
        self.chunk_cached = None

    def append(self, chunk_):
        """Append an new chunk to the carray."""
        self._save(self.nchunks, chunk_)
        self.nchunks += 1

    cdef _save(self, nchunk, chunk_):
        """Save the `chunk_` as chunk #`nchunk`. """

        if self.mode == "r":
            raise IOError(
                "cannot modify data because mode is '%s'" % self.mode)

        schunkfile = self._chunk_file_name(nchunk)
        bloscpack_header = create_bloscpack_header(1)
        with open(schunkfile, 'wb') as schunk:
            schunk.write(bloscpack_header)
            data = chunk_.getdata()
            schunk.write(data)
        # Mark the cache as dirty if needed
        if nchunk == self.nchunk_cached:
            self.nchunk_cached = -1

    def flush(self, chunk_):
        """Flush the leftover chunk."""
        self._save(self.nchunks, chunk_)

    def pop(self):
        """Remove the last chunk and return it."""
        nchunk = self.nchunks - 1
        chunk_ = self.__getitem__(nchunk)
        schunkfile = self._chunk_file_name(nchunk)
        if not os.path.exists(schunkfile):
            raise IOError("chunk filename %s does exist" % schunkfile)
        os.remove(schunkfile)

        # When poping a chunk, we must be sure that we don't leave anything
        # behind (i.e. the lastchunk)
        schunkfile = self._chunk_file_name(nchunk + 1)
        if os.path.exists(schunkfile):
            os.remove(schunkfile)

        self.nchunks -= 1
        return chunk_

cdef class carray:
    """
    carray(array, cparams=None, dtype=None, dflt=None, expectedlen=None,
    chunklen=None, rootdir=None, mode='a')

    A compressed and enlargeable in-memory data container.

    `carray` exposes a series of methods for dealing with the compressed
    container in a NumPy-like way.

    Parameters
    ----------
    array : a NumPy-like object
        This is taken as the input to create the carray.  It can be any Python
        object that can be converted into a NumPy object.  The data type of
        the resulting carray will be the same as this NumPy object.
    cparams : instance of the `cparams` class, optional
        Parameters to the internal Blosc compressor.
    dtype : NumPy dtype
        Force this `dtype` for the carray (rather than the `array` one).
    dflt : Python or NumPy scalar
        The value to be used when enlarging the carray.  If None,
        the default is
        filling with zeros.
    expectedlen : int, optional
        A guess on the expected length of this object.  This will serve to
        decide the best `chunklen` used for compression and memory I/O
        purposes.
    chunklen : int, optional
        The number of items that fits into a chunk.  By specifying it you can
        explicitely set the chunk size used for compression and memory I/O.
        Only use it if you know what are you doing.
    rootdir : str, optional
        The directory where all the data and metadata will be stored.  If
        specified, then the carray object will be disk-based (i.e. all chunks
        will live on-disk, not in memory) and persistent (i.e. it can be
        restored in other session, e.g. via the `open()` top-level function).
    safe : bool (defaults to True)
        Coerces inputs to array types.  Set to false if you always give
        correctly typed, strided, and shaped arrays and if you never use Object
        dtype
    mode : str, optional
        The mode that a *persistent* carray should be created/opened.  The
        values can be:

          * 'r' for read-only
          * 'w' for read/write.  During carray creation, the `rootdir` will be
            removed if it exists.  During carray opening, the carray will be
            resized to 0.
          * 'a' for append (possible data inside `rootdir` will not be
          removed).

    """

    property leftover_ptr:
        """Pointer referring to the leftover_array"""
        def __get__(self):
            # Pointer to the leftovers chunk
            return self.lastchunkarr.ctypes.data

    property leftover_array:
        """Array containing the leftovers chunk (uncompressed chunk)"""
        def __get__(self):
            return self.lastchunkarr

    property leftover_bytes:
        """Number of bytes in the leftover_array"""
        def __get__(self):
            return self.leftover

    property leftover_elements:
        """Number of elements in the leftover_array"""
        def __get__(self):
            return cython.cdiv(self.leftover, self.atomsize)

    property nchunks:
        """Number of chunks in the carray"""
        def __get__(self):
            # TODO: do we need to handle the last chunk specially?
            return <npy_intp> cython.cdiv(self._nbytes, self._chunksize)

    property partitions:
        """List of tuples indicating the bounds for each chunk"""
        def __get__(self):
            nchunks = <npy_intp> cython.cdiv(self._nbytes, self._chunksize)
            chunklen = cython.cdiv(self._chunksize, self.atomsize)
            return [(i * chunklen, (i + 1) * chunklen) for i in
                    xrange(nchunks)]

    property attrs:
        """The attribute accessor.

        See Also
        --------

        attrs.attrs

        """
        def __get__(self):
            return self._attrs

    property cbytes:
        "The compressed size of this object (in bytes)."
        def __get__(self):
            return self._cbytes

    property chunklen:
        "The chunklen of this object (in rows)."
        def __get__(self):
            return self._chunklen

    property cparams:
        "The compression parameters for this object."
        def __get__(self):
            return self._cparams

    property dflt:
        "The default value of this object."
        def __get__(self):
            return self._dflt

    property dtype:
        "The dtype of this object."
        def __get__(self):
            return self._dtype.base

    property len:
        "The length (leading dimension) of this object."
        def __get__(self):
            if self._dtype.char == 'O':
                return len(self.chunks)
            else:
                # Important to do the cast in order to get a npy_intp result
                return <npy_intp> cython.cdiv(self._nbytes, self.atomsize)

    property mode:
        "The mode used to create/open the `mode`."
        def __get__(self):
            return self._mode
        def __set__(self, value):
            self._mode = value
            if hasattr(self.chunks, 'mode'):
                self.chunks.mode = value

    property nbytes:
        "The original (uncompressed) size of this object (in bytes)."
        def __get__(self):
            return self._nbytes

    property nleftover:
        "The number of leftover elements."
        def __get__(self):
            return cython.cdiv(self.leftover, self.atomsize)

    property ndim:
        "The number of dimensions of this object."
        def __get__(self):
            return len(self.shape)

    property safe:
        "Whether or not to perform type/shape checks on every operation."
        def __get__(self):
            return self._safe

    property shape:
        "The shape of this object."
        def __get__(self):
            # note: the int cast is in order to get a consistent type
            # across windows and linux
            return tuple((int(self.len),) + self._dtype.shape)

    property size:
        "The size of this object."
        def __get__(self):
            return np.prod(self.shape)

    property rootdir:
        "The on-disk directory used for persistency."
        def __get__(self):
            return self._rootdir
        def __set__(self, value):
            if not self.rootdir:
                raise ValueError(
                    "cannot modify the rootdir value of an in-memory carray")
            self._rootdir = value
            self.chunks.rootdir = value

    def __cinit__(self, object array=None, object cparams=None,
                  object dtype=None, object dflt=None,
                  object expectedlen=None, object chunklen=None,
                  object rootdir=None, object safe=True, object mode="a"):

        self._rootdir = rootdir
        if mode not in ('r', 'w', 'a'):
            raise ValueError("mode should be 'r', 'w' or 'a'")
        self._mode = mode
        self._safe = safe

        if array is not None:
            self._create_carray(array, cparams, dtype, dflt,
                               expectedlen, chunklen, rootdir, mode)
            _new = True
        elif rootdir is not None:
            meta_info = self._read_meta()
            self._open_carray(*meta_info)
            _new = False
        else:
            raise ValueError(
                "You need at least to pass an array or/and a rootdir")

        # Attach the attrs to this object
        self._attrs = attrs.attrs(self._rootdir, self.mode, _new=_new)

        # Cache a len-1 array for accelerating self[int] case
        self.arr1 = np.empty(shape=(1,), dtype=self._dtype)

        # Sentinels
        self.sss_mode = False
        self.wheretrue_mode = False
        self.where_mode = False
        self.idxcache = -1  # cache not initialized

    cdef _adapt_dtype(self, dtype_, shape):
        """adapt the dtype to one supported in carray.
        returns the adapted type with the shape modified accordingly.
        """
        if dtype_.hasobject:
            if dtype_ != np.object_:
                raise TypeError(repr(dtype_) + " is not a supported dtype")
        else:
            dtype_ = np.dtype((dtype_, shape[1:]))

        return dtype_

    def _create_carray(self, array, cparams, dtype, dflt,
                      expectedlen, chunklen, rootdir, mode):
        """Create a new array.

        There are restrictions creating carray objects with dtypes that have
        objects (dtype.hasobject is True). The only case where this dtype is
        supported is on unidimensional arrays whose dtype is object (objects in
        composite dtypes are not supported).

        """
        cdef int itemsize, atomsize, chunksize
        cdef ndarray lastchunkarr
        cdef object array_, _dflt

        # Check defaults for cparams
        if cparams is None:
            cparams = bcolz.cparams()

        if not isinstance(cparams, bcolz.cparams):
            raise ValueError(
                "`cparams` param must be an instance of `cparams` class")

        # Convert input to an appropriate type
        if type(dtype) is str:
            dtype = np.dtype(dtype)

        # avoid bad performance with another carray, as in utils it would
        # construct the temp ndarray using a slow iterator.
        #
        # TODO: There should be a fast path creating carrays from other carrays
        # (especially when dtypes and compression params match)
        if isinstance(array, carray):
            array = array[:]

        array_ = utils.to_ndarray(array, dtype)

        # if no base dtype is provided, use the dtype from the array.
        if dtype is None:
            dtype = array_.dtype.base

        # Multidimensional array.  The atom will have array_.shape[1:] dims.
        # atom dimensions will be stored in `self._dtype`, which is different
        # than `self.dtype` in that `self._dtype` dimensions are borrowed
        # from `self.shape`.  `self.dtype` will always be scalar (NumPy
        # convention).
        #
        # Note that objects are a special case. Carray does not support object
        # arrays of more than one dimension.
        self._dtype = dtype = self._adapt_dtype(dtype, array_.shape)

        # Check that atom size is less than 2 GB
        if dtype.itemsize >= 2 ** 31:
            raise ValueError("atomic size is too large (>= 2 GB)")

        # Check that atom size is larger than 0
        if dtype.itemsize == 0:
            raise TypeError("atomic size cannot be zero")

        self.atomsize = atomsize = dtype.itemsize
        self.itemsize = itemsize = dtype.base.itemsize

        # Check defaults for dflt
        _dflt = np.zeros((), dtype=dtype)
        if dflt is not None:
            if dtype.shape == ():
                _dflt[()] = dflt
            else:
                _dflt[:] = dflt
        self._dflt = _dflt

        # Compute the chunklen/chunksize
        if expectedlen is None:
            # Try a guess
            try:
                expectedlen = len(array_)
            except TypeError:
                raise NotImplementedError(
                    "creating carrays from scalar objects is not supported")
        try:
            self.expectedlen = expectedlen
        except OverflowError:
            raise OverflowError(
                "The size cannot be larger than 2**31 on 32-bit platforms")
        if chunklen is None:
            # Try a guess
            chunksize = utils.calc_chunksize(
                (expectedlen * atomsize) / float(_MB))
            # Chunksize must be a multiple of atomsize
            chunksize = cython.cdiv(chunksize, atomsize) * atomsize
            # Protection against large itemsizes
            if chunksize < atomsize:
                chunksize = atomsize
        else:
            if not isinstance(chunklen, int) or chunklen < 1:
                raise ValueError("chunklen must be a positive integer")
            chunksize = chunklen * atomsize
        chunklen = cython.cdiv(chunksize, atomsize)
        self._chunksize = chunksize
        self._chunklen = chunklen

        # Book memory for last chunk (uncompressed)
        # Use np.zeros here because they compress better
        lastchunkarr = np.zeros(dtype=dtype, shape=(chunklen,))
        self.lastchunk = lastchunkarr.data
        self.lastchunkarr = lastchunkarr

        # Create layout for data and metadata
        self._cparams = cparams
        self.chunks = []
        if rootdir is not None:
            self._mkdirs(rootdir, mode)
            metainfo = (
            dtype, cparams, self.shape[0], lastchunkarr, self._mode)
            self.chunks = chunks(self._rootdir, metainfo=metainfo, _new=True)
            # We can write the metainfo already
            self._write_meta()

        # Finally, fill the chunks
        # Object dtype requires special storage
        if array_.dtype.char == 'O':
            for obj in array_:
                self._store_obj(obj)
        else:
            self._fill_chunks(array_)

        # and flush the data pending...
        self.flush()

    def _open_carray(self, shape, cparams, dtype, dflt,
                    expectedlen, cbytes, chunklen, xchunks=None):
        """Open an existing array."""
        cdef ndarray lastchunkarr
        cdef object array_, _dflt
        cdef npy_intp calen

        if len(shape) == 1:
            self._dtype = dtype
        else:
            # Multidimensional array.  The atom will have array_.shape[1:]
            # dims.
            # atom dimensions will be stored in `self._dtype`, which is
            # different
            # than `self.dtype` in that `self._dtype` dimensions are borrowed
            # from `self.shape`.  `self.dtype` will always be scalar (NumPy
            # convention).
            self._dtype = dtype = np.dtype((dtype.base, shape[1:]))

        self._cparams = cparams
        self.atomsize = dtype.itemsize
        self.itemsize = dtype.base.itemsize
        self._chunklen = chunklen
        self._chunksize = chunklen * self.atomsize
        self._dflt = dflt
        self.expectedlen = expectedlen

        # Book memory for last chunk (uncompressed)
        # Use np.zeros here because they compress better
        lastchunkarr = np.zeros(dtype=dtype, shape=(chunklen,))
        self.lastchunk = lastchunkarr.data
        self.lastchunkarr = lastchunkarr

        if xchunks is None:
            # No chunks container, so read meta info from disk
            # Check rootdir hierarchy
            if not os.path.isdir(self._rootdir):
                raise IOError("root directory does not exist")
            self.datadir = os.path.join(self._rootdir, DATA_DIR)
            if not os.path.isdir(self.datadir):
                raise IOError("data directory does not exist")
            self.metadir = os.path.join(self._rootdir, META_DIR)
            if not os.path.isdir(self.metadir):
                raise IOError("meta directory does not exist")

            # Finally, open data directory
            metainfo = (dtype, cparams, shape[0], lastchunkarr, self._mode)
            self.chunks = chunks(self._rootdir, metainfo=metainfo, _new=False)
        else:
            self.chunks, lastchunkarr[:] = xchunks

        # Update some counters
        calen = shape[0]  # the length ot the carray
        self.leftover = cython.cmod(calen, chunklen) * self.atomsize
        self._cbytes = cbytes
        self._nbytes = calen * self.atomsize

        if self._mode == "w":
            # Remove all entries when mode is 'w'
            self.resize(0)

    def _fill_chunks(self, object array_):
        """Fill chunks, either in-memory or on-disk."""
        cdef int leftover, chunklen
        cdef npy_intp i, nchunks
        cdef npy_intp nbytes, cbytes
        cdef chunk chunk_
        cdef ndarray remainder

        # The number of bytes in incoming array
        nbytes = self.itemsize * array_.size
        self._nbytes = nbytes

        # Compress data in chunks
        cbytes = 0
        chunklen = self._chunklen
        nchunks = <npy_intp> cython.cdiv(nbytes, self._chunksize)
        for i from 0 <= i < nchunks:
            assert i * chunklen < array_.size, "i, nchunks: %d, %d" % (
            i, nchunks)
            chunk_ = chunk(array_[i * chunklen:(i + 1) * chunklen],
                           self._dtype, self._cparams,
                           _memory=self._rootdir is None)
            self.chunks.append(chunk_)
            cbytes += chunk_.cbytes
        self.leftover = leftover = cython.cmod(nbytes, self._chunksize)
        if leftover:
            remainder = array_[nchunks * chunklen:]
            memcpy(self.lastchunk, remainder.data, leftover)
        cbytes += self._chunksize  # count the space in last chunk
        self._cbytes = cbytes

    def _mkdirs(self, object rootdir, object mode):
        """Create the basic directory layout for persistent storage."""
        if os.path.exists(rootdir):
            if self._mode != "w":
                raise IOError(
                    "specified rootdir path '%s' already exists "
                    "and creation mode is '%s'" % (rootdir, mode))
            if os.path.isdir(rootdir):
                shutil.rmtree(rootdir)
            else:
                os.remove(rootdir)
        os.mkdir(rootdir)
        self.datadir = os.path.join(rootdir, DATA_DIR)
        os.mkdir(self.datadir)
        self.metadir = os.path.join(rootdir, META_DIR)
        os.mkdir(self.metadir)

    def _write_meta(self):
        """Write metadata persistently."""
        storagef = os.path.join(self.metadir, STORAGE_FILE)
        with open(storagef, 'wb') as storagefh:
            dflt_list = self.dflt.tolist()
            if type(dflt_list) in (datetime.datetime,
                                   datetime.date, datetime.time):
                # The datetime cannot be serialized with JSON.  Use a 0 int.
                dflt_list = 0
            # In Python 3, the json encoder doesn't accept bytes objects
            if sys.version_info >= (3, 0):
                dflt_list = list_bytes_to_str(dflt_list)
            storagefh.write(json.dumps({
                # str(self.dtype) produces bytes by default in cython.py3.
                # Calling .__str__() is a workaround.
                "dtype": self.dtype.__str__(),
                "cparams": {
                    "clevel": self.cparams.clevel,
                    "shuffle": self.cparams.shuffle,
                },
                "chunklen": self._chunklen,
                "expectedlen": self.expectedlen,
                "dflt": dflt_list,
            }, ensure_ascii=True).encode('ascii'))
            storagefh.write(b"\n")

    def _read_meta(self):
        """Read persistent metadata."""

        # First read the size info
        metadir = os.path.join(self._rootdir, META_DIR)
        shapef = os.path.join(metadir, SIZES_FILE)
        with open(shapef, 'rb') as shapefh:
            sizes = json.loads(shapefh.read().decode('ascii'))
        shape = sizes['shape']
        if type(shape) == list:
            shape = tuple(shape)
        nbytes = sizes["nbytes"]
        cbytes = sizes["cbytes"]

        # Then the rest of metadata
        storagef = os.path.join(metadir, STORAGE_FILE)
        with open(storagef, 'rb') as storagefh:
            data = json.loads(storagefh.read().decode('ascii'))
        dtype_ = np.dtype(data["dtype"])
        chunklen = data["chunklen"]
        cparams = bcolz.cparams(
            clevel=data["cparams"]["clevel"],
            shuffle=data["cparams"]["shuffle"])
        expectedlen = data["expectedlen"]
        dflt = data["dflt"]
        return (shape, cparams, dtype_, dflt, expectedlen, cbytes, chunklen)

    def _store_obj(self, object arrobj):
        cdef chunk chunk_
        import pickle

        pick_obj = pickle.dumps(arrobj, pickle.HIGHEST_PROTOCOL)
        chunk_ = chunk(pick_obj, np.dtype('O'), self._cparams,
                       _memory=self._rootdir is None)

        self.chunks.append(chunk_)
        # Update some counters
        nbytes, cbytes = chunk_.nbytes, chunk_.cbytes
        self._cbytes += cbytes
        self._nbytes += nbytes

    def append(self, object array):
        """Append a numpy `array` to this instance.

        Parameters
        ----------
        array : NumPy-like object
            The array to be appended.  Must be compatible with shape and
            type of
            the carray.

        """
        cdef int atomsize, itemsize, chunksize, leftover
        cdef int nbytesfirst, chunklen, start, stop
        cdef npy_intp nbytes, cbytes, bsize, i, nchunks, j
        cdef ndarray remainder, arrcpy, dflts
        cdef chunk chunk_

        if self.safe:
            if self.mode == "r":
                raise IOError(
                    "cannot modify data because mode is '%s'" % self.mode)

            arrcpy = utils.to_ndarray(array, self._dtype, safe=self._safe)
            if arrcpy.dtype != self._dtype.base:
                raise TypeError("array dtype does not match with self")

            # Object dtype requires special storage
            if arrcpy.dtype.char == 'O':
                for j in range(len(arrcpy)):
                    self._store_obj(arrcpy[j])
                return

            # Appending a single row should be supported
            if np.shape(arrcpy) == self._dtype.shape:
                arrcpy = arrcpy.reshape((1,) + np.shape(arrcpy))
            if np.shape(arrcpy)[1:] != self._dtype.shape:
                raise ValueError(
                    "array trailing dimensions do not match with self")
        else:
            arrcpy = array

        atomsize = self.atomsize
        itemsize = self.itemsize
        chunksize = self._chunksize
        chunks = self.chunks
        leftover = self.leftover
        bsize = arrcpy.size * itemsize
        cbytes = 0

        # Check if array fits in existing buffer
        if (bsize + leftover) < chunksize:
            # Data fits in lastchunk buffer.  Just copy it
            if arrcpy.strides[0] > 0:
                memcpy(self.lastchunk + leftover, arrcpy.data, bsize)
            else:
                start = cython.cdiv(leftover, atomsize)
                stop = cython.cdiv((leftover + bsize), atomsize)
                self.lastchunkarr[start:stop] = arrcpy
            leftover += bsize
        else:
            # Data does not fit in buffer.  Break it in chunks.

            # First, fill the last buffer completely (if needed)
            if leftover:
                nbytesfirst = chunksize - leftover
                if arrcpy.strides[0] > 0:
                    memcpy(self.lastchunk + leftover, arrcpy.data, nbytesfirst)
                else:
                    start = cython.cdiv(leftover, atomsize)
                    stop = cython.cdiv((leftover + nbytesfirst), atomsize)
                    self.lastchunkarr[start:stop] = arrcpy[start:stop]
                # Compress the last chunk and add it to the list
                chunk_ = chunk(self.lastchunkarr, self._dtype, self._cparams,
                               _memory=self._rootdir is None)
                chunks.append(chunk_)
                cbytes = chunk_.cbytes
            else:
                nbytesfirst = 0

            # Then fill other possible chunks
            nbytes = bsize - nbytesfirst
            nchunks = <npy_intp> cython.cdiv(nbytes, chunksize)
            chunklen = self._chunklen
            # Get a new view skipping the elements that have been already
            # copied
            remainder = arrcpy[cython.cdiv(nbytesfirst, atomsize):]
            for i from 0 <= i < nchunks:
                chunk_ = chunk(
                    remainder[i * chunklen:(i + 1) * chunklen], self._dtype,
                    self._cparams,
                    _memory=self._rootdir is None)
                chunks.append(chunk_)
                cbytes += chunk_.cbytes

            # Finally, deal with the leftover
            leftover = cython.cmod(nbytes, chunksize)
            if leftover:
                remainder = remainder[nchunks * chunklen:]
                if arrcpy.strides[0] > 0:
                    memcpy(self.lastchunk, remainder.data, leftover)
                else:
                    self.lastchunkarr[:len(remainder)] = remainder

        # Update some counters
        self.leftover = leftover
        self._cbytes += cbytes
        self._nbytes += bsize
        return

    def trim(self, object nitems):
        """Remove the trailing `nitems` from this instance.

        Parameters
        ----------
        nitems : int
            The number of trailing items to be trimmed.  If negative,
            the object
            is enlarged instead.

        """
        cdef int atomsize, leftover, leftover2
        cdef npy_intp cbytes, bsize, nchunk2
        cdef chunk chunk_

        if not isinstance(nitems, _inttypes + (float,)):
            raise TypeError("`nitems` must be an integer")

        # Check that we don't run out of space
        if nitems > self.len:
            raise ValueError("`nitems` must be less than total length")
        # A negative number of items means that we want to grow the object
        if nitems <= 0:
            self.resize(self.len - nitems)
            return

        atomsize = self.atomsize
        chunks = self.chunks
        leftover = self.leftover
        bsize = nitems * atomsize
        cbytes = 0

        # Check if items belong to the last chunk
        if (leftover - bsize) > 0:
            # Just update leftover counter
            leftover -= bsize
        else:
            # nitems larger than last chunk
            nchunk = cython.cdiv((self.len - nitems), self._chunklen)
            leftover2 = cython.cmod((self.len - nitems), self._chunklen)
            leftover = leftover2 * atomsize

            # Remove complete chunks
            nchunk2 = lnchunk = <npy_intp> cython.cdiv(
                self._nbytes, self._chunksize)
            while nchunk2 > nchunk:
                chunk_ = chunks.pop()
                cbytes += chunk_.cbytes
                nchunk2 -= 1

            # Finally, deal with the leftover
            if leftover:
                self.lastchunkarr[:leftover2] = chunk_[:leftover2]
                if self._rootdir:
                    # Last chunk is removed automatically by the chunks.pop(
                    # ) call, and
                    # always is counted as if it is not compressed (although
                    #  it is in
                    # this state on-disk)
                    cbytes += chunk_.nbytes

        # Update some counters
        self.leftover = leftover
        self._cbytes -= cbytes
        self._nbytes -= bsize
        # Flush last chunk and update counters on-disk
        self.flush()

    def resize(self, object nitems):
        """Resize the instance to have `nitems`.

        Parameters
        ----------
        nitems : int
            The final length of the object.  If `nitems` is larger than the
            actual
            length, new items will appended using `self.dflt` as filling
            values.

        """
        cdef object chunk

        if not isinstance(nitems, _inttypes + (float,)):
            raise TypeError("`nitems` must be an integer")

        if nitems == self.len:
            return
        elif nitems < 0:
            raise ValueError("`nitems` cannot be negative")

        if nitems > self.len:
            # Create a 0-strided array and append it to self
            chunk = np.ndarray(nitems - self.len, dtype=self._dtype,
                               buffer=self._dflt, strides=(0,))
            self.append(chunk)
            self.flush()
        else:
            # Just trim the excess of items
            self.trim(self.len - nitems)

    def reshape(self, newshape):
        """Returns a new carray containing the same data with a new shape.

        Parameters
        ----------
        newshape : int or tuple of ints
            The new shape should be compatible with the original shape. If
            an integer, then the result will be a 1-D array of that length.
            One shape dimension can be -1. In this case, the value is inferred
            from the length of the array and remaining dimensions.

        Returns
        -------
        reshaped_array : carray
            A copy of the original carray.

        """
        cdef npy_intp newlen, ilen, isize, osize, newsize, rsize, i
        cdef object ishape, oshape, pos, newdtype, out

        # Enforce newshape as tuple
        if isinstance(newshape, _inttypes):
            newshape = (newshape,)
        newsize = np.prod(newshape)

        ishape = self.shape
        ilen = ishape[0]
        isize = np.prod(ishape)

        # Check for -1 in newshape
        if -1 in newshape:
            if newshape.count(-1) > 1:
                raise ValueError("only one shape dimension can be -1")
            pos = newshape.index(-1)
            osize = np.prod(newshape[:pos] + newshape[pos + 1:])
            if isize == 0:
                newshape = newshape[:pos] + (0,) + newshape[pos + 1:]
            else:
                newshape = newshape[:pos] + (isize / osize,) + newshape[
                                                               pos + 1:]
            newsize = np.prod(newshape)

        # Check shape compatibility
        if isize != newsize:
            raise ValueError(
                "`newshape` is not compatible with the current one")
        # Create the output container
        newdtype = np.dtype((self._dtype.base, newshape[1:]))
        newlen = newshape[0]

        # If shapes are both n-dimensional, convert first to 1-dim shape
        # and then convert again to the final newshape.
        if len(ishape) > 1 and len(newshape) > 1:
            out = self.reshape(-1)
            return out.reshape(newshape)

        if self._rootdir:
            # If persistent, do the copy to a temporary dir
            absdir = os.path.dirname(self._rootdir)
            rootdir = tempfile.mkdtemp(suffix='__temp__', dir=absdir)
        else:
            rootdir = None

        # Create the final container and fill it
        out = carray([], dtype=newdtype, cparams=self.cparams,
                       expectedlen=newlen,
                       rootdir=rootdir, mode='w')
        if newlen < ilen:
            rsize = isize / newlen
            for i from 0 <= i < newlen:
                out.append(
                    self[i * rsize:(i + 1) * rsize].reshape(newdtype.shape))
        else:
            for i from 0 <= i < ilen:
                out.append(self[i].reshape(-1))
        out.flush()

        # Finally, rename the temporary data directory to self._rootdir
        if self._rootdir:
            shutil.rmtree(self._rootdir)
            os.rename(rootdir, self._rootdir)
            # Restore the rootdir and mode
            out.rootdir = self._rootdir
            out.mode = self._mode

        return out

    def copy(self, **kwargs):
        """Return a copy of this object.

        Parameters
        ----------
        kwargs : list of parameters or dictionary
            Any parameter supported by the carray constructor.

        Returns
        -------
        out : carray object
            The copy of this object.

        """
        cdef object chunklen

        # Get defaults for some parameters
        expectedlen = kwargs.pop('expectedlen', self.len)

        # Create a new, empty carray
        ccopy = carray(np.empty(0, dtype=self._dtype),
                       expectedlen=expectedlen,
                       **kwargs)

        # Now copy the carray chunk by chunk
        chunklen = self._chunklen
        for i from 0 <= i < self.len by chunklen:
            ccopy.append(self[i:i + chunklen])
        ccopy.flush()

        return ccopy

    def view(self):
        """Create a light weight view of the data in the original carray.

        Returns
        -------
        out : carray object
            The view of this object.

        See Also
        --------
        copy

        """
        # Create a light weight data container
        cview = carray(np.empty(0, dtype=self._dtype))
        # And populate it with metainfo (including chunks)
        meta_info = (self.shape, self.cparams, self.dtype, self.dflt,
                     self.expectedlen, self.cbytes, self.chunklen)
        cview._open_carray(*meta_info, xchunks=(self.chunks, self.lastchunkarr))
        return cview

    def sum(self, dtype=None):
        """Return the sum of the array elements.

        Parameters
        ----------
        dtype : NumPy dtype
            The desired type of the output.  If ``None``, the dtype of
            `self` is
            used.  An exception is when `self` has an integer type with less
            precision than the default platform integer.  In that case, the
            default platform integer is used instead (NumPy convention).


        Returns
        -------
        out : NumPy scalar with `dtype`

        """
        cdef chunk chunk_
        cdef npy_intp nchunk, nchunks
        cdef object result

        if dtype is None:
            dtype = self._dtype.base
            # Check if we have less precision than required for ints
            # (mimick NumPy logic)
            if dtype.kind in ('b', 'i') and dtype.itemsize < IntType.itemsize:
                dtype = IntType
        else:
            dtype = np.dtype(dtype)
        if dtype.kind == 'S':
            raise TypeError("cannot perform reduce with flexible type")

        # Get a container for the result
        result = np.zeros(1, dtype=dtype)[0]

        nchunks = <npy_intp> cython.cdiv(self._nbytes, self._chunksize)
        for chunk_ in self.chunks:
            if chunk_.isconstant:
                result += chunk_.constant * self._chunklen
            elif self._dtype.type == np.bool_:
                result += chunk_.true_count
            else:
                result += chunk_[:].sum(dtype=dtype)
        if self.leftover:
            leftover = self.len - nchunks * self._chunklen
            result += self.lastchunkarr[:leftover].sum(dtype=dtype)

        return result

    def __len__(self):
        return self.len

    def __sizeof__(self):
        return self._cbytes

    cdef int getitem_cache(self, npy_intp pos, char *dest):
        """Get a single item and put it in `dest`.  It caches a complete block.

        It returns 1 if asked `pos` can be copied to `dest`.  Else,
        this returns
        0.

        NOTE: As Blosc supports decompressing just a block inside a chunk, the
        data that is cached is a *block*, as it is the least amount of data
        that
        can be decompressed.  This saves both time and memory.

        IMPORTANT: Any update operation (e.g. __setitem__) *must* disable this
        cache by setting self.idxcache = -2.
        """
        cdef int ret, atomsize, blocksize, offset
        cdef int idxcache, posinbytes, blocklen
        cdef npy_intp nchunk, nchunks, chunklen
        cdef chunk chunk_

        atomsize = self.atomsize
        nchunks = <npy_intp> cython.cdiv(self._nbytes, self._chunksize)
        chunklen = self._chunklen
        nchunk = <npy_intp> cython.cdiv(pos, chunklen)

        # Check whether pos is in the last chunk
        if nchunk == nchunks and self.leftover:
            posinbytes = (pos % chunklen) * atomsize
            memcpy(dest, self.lastchunk + posinbytes, atomsize)
            return 1

        # Locate the *block* inside the chunk
        chunk_ = self.chunks[nchunk]
        blocksize = chunk_.blocksize
        blocklen = <npy_intp> cython.cdiv(blocksize, atomsize)

        if (atomsize > blocksize):
            # This request cannot be resolved here
            return 0

        # Check whether the cache block has to be initialized
        if self.idxcache < 0:
            self.blockcache = np.empty(shape=(blocklen,), dtype=self._dtype)
            self.datacache = self.blockcache.data
            # We don't want this to contribute to cbytes counter!
            # if self.idxcache == -1:
            #   # Absolute first time.  Add the cache size to cbytes counter.
            #   self._cbytes += chunksize

        # Check if block is cached
        idxcache = <npy_intp> cython.cdiv(pos, blocklen) * blocklen
        if idxcache == self.idxcache:
            # Hit!
            posinbytes = (pos % blocklen) * atomsize
            memcpy(dest, self.datacache + posinbytes, atomsize)
            return 1

        # No luck. Read a complete block.
        offset = idxcache % chunklen
        chunk_._getitem(offset, offset + blocklen, self.datacache)
        # Copy the interesting bits to dest
        posinbytes = (pos % blocklen) * atomsize
        memcpy(dest, self.datacache + posinbytes, atomsize)
        # Update the cache index
        self.idxcache = idxcache
        return 1

    def free_cachemem(self):
        """Release in-memory cached chunk"""
        if type(self.chunks) is not list:
            self.chunks.free_cachemem()
        self.idxcache = -1
        self.blockcache = None

    def _getitem_object(self, start, stop=None, step=None):
        """Retrieve elements of type object."""
        import pickle

        if stop is None and step is None:
            # Integer
            cchunk = self.chunks[start]
            chunk = cchunk.getudata()
            return pickle.loads(chunk)

        # Range
        objs = [self._getitem_object(i) for i in xrange(start, stop, step)]
        return np.array(objs, dtype=self._dtype)

    def __getitem__(self, object key):
        """ x.__getitem__(key) <==> x[key]

        Returns values based on `key`.  All the functionality of
        ``ndarray.__getitem__()`` is supported (including fancy indexing),
        plus a
        special support for expressions:

        Parameters
        ----------
        key : string
            It will be interpret as a boolean expression (computed via
            `eval`) and
            the elements where these values are true will be returned as a
            NumPy
            array.

        See Also
        --------
        eval

        """

        cdef int chunklen
        cdef npy_intp startb, stopb
        cdef npy_intp nchunk, keychunk, nchunks
        cdef npy_intp nwrow, blen
        cdef ndarray arr1
        cdef object start, stop, step
        cdef object arr

        chunklen = self._chunklen

        # Check for integer
        if isinstance(key, _inttypes):
            if key < 0:
                # To support negative values
                key += self.len
            if key >= self.len:
                raise IndexError("index out of range")
            arr1 = self.arr1
            if self.dtype.char == 'O':
                return self._getitem_object(key)
            if self.getitem_cache(key, arr1.data):
                if self.itemsize == self.atomsize:
                    return PyArray_GETITEM(arr1, arr1.data)
                else:
                    return arr1[0].copy()
            # Fallback action: use the slice code
            return np.squeeze(self[slice(key, key + 1, 1)])
        # Slices
        elif isinstance(key, slice):
            (start, stop, step) = key.start, key.stop, key.step
            if step and step <= 0:
                raise NotImplementedError("step in slice can only be positive")
        # Multidimensional keys
        elif isinstance(key, tuple):
            if len(key) == 0:
                raise ValueError("empty tuple not supported")
            elif len(key) == 1:
                return self[key[0]]
            # An n-dimensional slice
            # First, retrieve elements in the leading dimension
            arr = self[key[0]]
            # Then, keep only the required elements in other dimensions
            if type(key[0]) == slice:
                arr = arr[(slice(None),) + key[1:]]
            else:
                arr = arr[key[1:]]
            # Force a copy in case returned array is not contiguous
            if not arr.flags.contiguous:
                arr = arr.copy()
            return arr
        # List of integers (case of fancy indexing)
        elif isinstance(key, list):
            # Try to convert to a integer array
            try:
                key = np.array(key, dtype=np.int_)
            except:
                raise IndexError(
                    "key cannot be converted to an array of indices")
            return self[key]
        # A boolean or integer array (case of fancy indexing)
        elif hasattr(key, "dtype"):
            if key.dtype.type == np.bool_:
                # A boolean array
                if len(key) != self.len:
                    raise IndexError(
                        "boolean array length must match len(self)")
                if isinstance(key, carray):
                    count = key.sum()
                else:
                    count = -1
                return np.fromiter(self.where(key), dtype=self._dtype,
                                   count=count)
            elif np.issubsctype(key, np.int_):
                # An integer array
                return np.array([self[i] for i in key], dtype=self._dtype.base)
            else:
                raise IndexError(
                    "arrays used as indices must be integer (or boolean)")
        # A boolean expression (case of fancy indexing)
        elif type(key) is str:
            # Evaluate
            result = bcolz.eval(key)
            if result.dtype.type != np.bool_:
                raise IndexError("only boolean expressions supported")
            if len(result) != self.len:
                raise IndexError(
                    "boolean expression outcome must match len(self)")
            # Call __getitem__ again
            return self[result]
        # All the rest not implemented
        else:
            raise NotImplementedError("key not supported: %s" % repr(key))

        # From now on, will only deal with [start:stop:step] slices

        # Get the corrected values for start, stop, step
        (start, stop, step) = slice(start, stop, step).indices(self.len)

        # Build a numpy container
        blen = get_len_of_range(start, stop, step)
        arr = np.empty(shape=(blen,), dtype=self._dtype)
        if blen == 0:
            # If empty, return immediately
            return arr

        if self.dtype.char == 'O':
            return self._getitem_object(start, stop, step)

        # Fill it from data in chunks
        nwrow = 0
        nchunks = <npy_intp> cython.cdiv(self._nbytes, self._chunksize)
        if self.leftover > 0:
            nchunks += 1
        for nchunk from 0 <= nchunk < nchunks:
            # Compute start & stop for each block
            startb, stopb, blen = clip_chunk(nchunk, chunklen, start, stop,
                                             step)
            if blen == 0:
                continue
            # Get the data chunk and assign it to result array
            if nchunk == nchunks - 1 and self.leftover:
                arr[nwrow:nwrow + blen] = self.lastchunkarr[startb:stopb:step]
            else:
                arr[nwrow:nwrow + blen] = self.chunks[nchunk][
                                          startb:stopb:step]
            nwrow += blen

        return arr

    def __setitem__(self, object key, object value):
        """ x.__setitem__(key, value) <==> x[key] = value

        Sets values based on `key`.  All the functionality of
        ``ndarray.__setitem__()`` is supported (including fancy indexing),
        plus a
        special support for expressions:

        Parameters
        ----------
        key : string
            It will be interpret as a boolean expression (computed via
            `eval`) and
            the elements where these values are true will be set to `value`.

        See Also
        --------
        eval

        """
        cdef int chunklen
        cdef npy_intp startb, stopb
        cdef npy_intp nchunk, keychunk, nchunks
        cdef npy_intp nwrow, blen, vlen
        cdef chunk chunk_
        cdef object start, stop, step
        cdef object cdata, arr

        if self.mode == "r":
            raise IOError(
                "cannot modify data because mode is '%s'" % self.mode)

        # We are going to modify data.  Mark block cache as dirty.
        if self.idxcache >= 0:
            # -2 means that cbytes counter has not to be changed
            self.idxcache = -2

        # Check for integer
        if isinstance(key, _inttypes):
            if key < 0:
                # To support negative values
                key += self.len
            if key >= self.len:
                raise IndexError("index out of range")
            (start, stop, step) = key, key + 1, 1
        # Slices
        elif isinstance(key, slice):
            (start, stop, step) = key.start, key.stop, key.step
            if step:
                if step <= 0:
                    raise NotImplementedError(
                        "step in slice can only be positive")
        # Multidimensional keys
        elif isinstance(key, tuple):
            if len(key) == 0:
                raise ValueError("empty tuple not supported")
            elif len(key) == 1:
                self[key[0]] = value
                return
            # An n-dimensional slice
            # First, retrieve elements in the leading dimension
            arr = self[key[0]]
            # Then, assing only the requested elements in other dimensions
            if type(key[0]) == slice:
                arr[(slice(None),) + key[1:]] = value
            else:
                arr[key[1:]] = value
            # Finally, update this superset of values in self
            self[key[0]] = arr
            return
        # List of integers (case of fancy indexing)
        elif isinstance(key, list):
            # Try to convert to a integer array
            try:
                key = np.array(key, dtype=np.int_)
            except:
                raise IndexError(
                    "key cannot be converted to an array of indices")
            self[key] = value
            return
        # A boolean or integer array (case of fancy indexing)
        elif hasattr(key, "dtype"):
            if key.dtype.type == np.bool_:
                # A boolean array
                if len(key) != self.len:
                    raise ValueError(
                        "boolean array length must match len(self)")
                self.bool_update(key, value)
                return
            elif np.issubsctype(key, np.int_):
                # An integer array
                value = utils.to_ndarray(value, self._dtype, arrlen=len(key),
                        safe=self._safe)
                # XXX This could be optimised, but it works like this
                for i, item in enumerate(key):
                    self[item] = value[i]
                return
            else:
                raise IndexError(
                    "arrays used as indices must be of integer (or boolean) "
                    "type")
        # An boolean expression (case of fancy indexing)
        elif type(key) is str:
            # Evaluate
            result = bcolz.eval(key)
            if result.dtype.type != np.bool_:
                raise IndexError("only boolean expressions supported")
            if len(result) != self.len:
                raise IndexError(
                    "boolean expression outcome must match len(self)")
            # Call __setitem__ again
            self[result] = value
            return
        # All the rest not implemented
        else:
            raise NotImplementedError("key not supported: %s" % repr(key))

        # Get the corrected values for start, stop, step
        (start, stop, step) = slice(start, stop, step).indices(self.len)

        # Build a numpy object out of value
        vlen = get_len_of_range(start, stop, step)
        if vlen == 0:
            # If range is empty, return immediately
            return
        value = utils.to_ndarray(value, self._dtype, arrlen=vlen,
                safe=self._safe)

        # Fill it from data in chunks
        nwrow = 0
        chunklen = self._chunklen
        nchunks = <npy_intp> cython.cdiv(self._nbytes, self._chunksize)
        if self.leftover > 0:
            nchunks += 1
        for nchunk from 0 <= nchunk < nchunks:
            # Compute start & stop for each block
            startb, stopb, blen = clip_chunk(nchunk, chunklen, start, stop,
                                             step)
            if blen == 0:
                continue
            # Modify the data in chunk
            if nchunk == nchunks - 1 and self.leftover:
                self.lastchunkarr[startb:stopb:step] = value[
                                                       nwrow:nwrow + blen]
            else:
                # Get the data chunk
                chunk_ = self.chunks[nchunk]
                self._cbytes -= chunk_.cbytes
                # Get all the values there
                cdata = chunk_[:]
                # Overwrite it with data from value
                cdata[startb:stopb:step] = value[nwrow:nwrow + blen]
                # Replace the chunk
                chunk_ = chunk(cdata, self._dtype, self._cparams,
                               _memory=self._rootdir is None)
                self.chunks[nchunk] = chunk_
                # Update cbytes counter
                self._cbytes += chunk_.cbytes
            nwrow += blen

        # Safety check
        assert (nwrow == vlen)

    # This is a private function that is specific for `eval`
    def _getrange(self, npy_intp start, npy_intp blen, ndarray out):
        cdef int chunklen
        cdef npy_intp startb, stopb
        cdef npy_intp nwrow, stop, cblen
        cdef npy_intp schunk, echunk, nchunk, nchunks
        cdef chunk chunk_

        # Check that we are inside limits
        nrows = <npy_intp> cython.cdiv(self._nbytes, self.atomsize)
        if (start + blen) > nrows:
            blen = nrows - start

        # Fill `out` from data in chunks
        nwrow = 0
        stop = start + blen
        nchunks = <npy_intp> cython.cdiv(self._nbytes, self._chunksize)
        chunklen = cython.cdiv(self._chunksize, self.atomsize)
        schunk = <npy_intp> cython.cdiv(start, chunklen)
        echunk = <npy_intp> cython.cdiv((start + blen), chunklen)
        for nchunk from schunk <= nchunk <= echunk:
            # Compute start & stop for each block
            startb = start % chunklen
            stopb = chunklen
            if (start + startb) + chunklen > stop:
                # XXX I still have to explain why this expression works
                # for chunklen > (start + blen)
                stopb = (stop - start) + startb
                # stopb can never be larger than chunklen
                if stopb > chunklen:
                    stopb = chunklen
            cblen = stopb - startb
            if cblen == 0:
                continue
            # Get the data chunk and assign it to result array
            if nchunk == nchunks and self.leftover:
                out[nwrow:nwrow + cblen] = self.lastchunkarr[startb:stopb]
            else:
                chunk_ = self.chunks[nchunk]
                chunk_._getitem(startb, stopb,
                                out.data + nwrow * self.atomsize)
            nwrow += cblen
            start += cblen

    cdef void bool_update(self, boolarr, value):
        """Update self in positions where `boolarr` is true with `value`
        array."""
        cdef int chunklen
        cdef npy_intp startb, stopb
        cdef npy_intp nchunk, nchunks, nrows
        cdef npy_intp nwrow, blen, vlen, n
        cdef chunk chunk_
        cdef object cdata, boolb

        vlen = boolarr.sum()  # number of true values in bool array
        value = utils.to_ndarray(value, self._dtype, arrlen=vlen,
                safe=self._safe)

        # Fill it from data in chunks
        nwrow = 0
        chunklen = self._chunklen
        nchunks = <npy_intp> cython.cdiv(self._nbytes, self._chunksize)
        if self.leftover > 0:
            nchunks += 1
        nrows = <npy_intp> cython.cdiv(self._nbytes, self.atomsize)
        for nchunk from 0 <= nchunk < nchunks:
            # Compute start & stop for each block
            startb, stopb, _ = clip_chunk(nchunk, chunklen, 0, nrows, 1)
            # Get boolean values for this chunk
            n = nchunk * chunklen
            boolb = boolarr[n + startb:n + stopb]
            blen = boolb.sum()
            if blen == 0:
                continue
            # Modify the data in chunk
            if nchunk == nchunks - 1 and self.leftover:
                self.lastchunkarr[boolb] = value[nwrow:nwrow + blen]
            else:
                # Get the data chunk
                chunk_ = self.chunks[nchunk]
                self._cbytes -= chunk_.cbytes
                # Get all the values there
                cdata = chunk_[:]
                # Overwrite it with data from value
                cdata[boolb] = value[nwrow:nwrow + blen]
                # Replace the chunk
                chunk_ = chunk(cdata, self._dtype, self._cparams,
                               _memory=self._rootdir is None)
                self.chunks[nchunk] = chunk_
                # Update cbytes counter
                self._cbytes += chunk_.cbytes
            nwrow += blen

        # Safety check
        assert (nwrow == vlen)

    cdef reset_iter_sentinels(self):
        """Reset sentinels for iterator."""
        self.sss_mode = False
        self.wheretrue_mode = False
        self.where_mode = False
        self.where_arr = None
        self.nhits = 0
        self.limit = _MAXINT
        self.skip = 0
        self.start = 0
        self.stop = <npy_intp> cython.cdiv(self._nbytes, self.atomsize)
        self.step = 1
        self.iter_exhausted = False

    def __iter__(self):

        if self.iter_exhausted:
            # Iterator is exhausted, so return immediately
            return self

        if not (self.sss_mode or
                self.wheretrue_mode or
                self.where_mode):
            # No mode.  Probably a direct iter() call.  Use sss_mode here.
            return self.iter()

        # Initialize some internal values
        self.startb = 0
        self.nrowsread = self.start
        self._nrow = self.start - self.step
        self._row = -1  # a sentinel
        if self.where_mode and isinstance(self.where_arr, carray):
            self.nrowsinbuf = self.where_arr.chunklen
        else:
            self.nrowsinbuf = self._chunklen

        return self

    def iter(self, start=0, stop=None, step=1, limit=None, skip=0, _next=False):
        """Iterator with `start`, `stop` and `step` bounds.

        Parameters
        ----------
        start : int
            The starting item.
        stop : int
            The item after which the iterator stops.
        step : int
            The number of items incremented during each iteration.  Cannot be
            negative.
        limit : int
            A maximum number of elements to return.  The default is return
            everything.
        skip : int
            An initial number of elements to skip.  The default is 0.

        Returns
        -------
        out : iterator

        See Also
        --------
        where, wheretrue

        """
        # Check limits
        if step <= 0:
            raise NotImplementedError("step param can only be positive")
        if _next:
            cview = self
        else:
            cview = self.view()
        return cview._init_iter(start, stop, step, limit, skip)

    def _init_iter(self, start, stop, step, limit, skip):
        self.reset_iter_sentinels()
        self.sss_mode = True
        self.start, self.stop, self.step = \
            slice(start, stop, step).indices(self.len)
        if limit is not None:
            self.limit = limit + skip
        self.skip = skip
        return iter(self)

    def wheretrue(self, limit=None, skip=0):
        """Iterator that returns indices where this object is true.

        This is currently only useful for boolean carrays that are unidimensional.

        Parameters
        ----------
        limit : int
            A maximum number of elements to return.  The default is return
            everything.
        skip : int
            An initial number of elements to skip.  The default is 0.

        Returns
        -------
        out : iterator

        See Also
        --------
        iter, where

        """
        # Check self
        if self._dtype.base.type != np.bool_:
            raise ValueError("`self` is not an array of booleans")
        if self.ndim > 1:
            raise NotImplementedError("`self` is not unidimensional")
        cview = self.view()
        return cview._init_wheretrue(limit, skip)

    def _init_wheretrue(self, limit, skip):
        self.reset_iter_sentinels()
        self.wheretrue_mode = True
        if limit is not None:
            self.limit = limit + skip
        self.skip = skip
        return iter(self)

    def where(self, boolarr, limit=None, skip=0):
        """Iterator that returns values of this object where `boolarr` is true.

        This is currently only useful for boolean carrays that are unidimensional.

        Parameters
        ----------
        boolarr : a carray or NumPy array of boolean type
            The boolean values.
        limit : int
            A maximum number of elements to return.  The default is return
            everything.
        skip : int
            An initial number of elements to skip.  The default is 0.

        Returns
        -------
        out : iterator

        See Also
        --------
        iter, wheretrue

        """
        # Check input
        if self.ndim > 1:
            raise NotImplementedError("`self` is not unidimensional")
        if not hasattr(boolarr, "dtype"):
            raise ValueError("`boolarr` is not an array")
        if boolarr.dtype.type != np.bool_:
            raise ValueError("`boolarr` is not an array of booleans")
        if len(boolarr) != self.len:
            raise ValueError(
                "`boolarr` must be of the same length than ``self``")
        cview = self.view()
        return cview._init_where(boolarr, limit, skip)

    def _init_where(self, boolarr, limit, skip):
        self.reset_iter_sentinels()
        self.where_mode = True
        self.where_arr = boolarr
        if limit is not None:
            self.limit = limit + skip
        self.skip = skip
        return iter(self)

    def __next__(self):
        cdef char *vbool
        cdef int nhits_buf

        if not (self.sss_mode or
                self.wheretrue_mode or
                self.where_mode):
            # No mode.  Probably a direct call to next().  Use sss_mode here.
            self.iter(_next=True)

        self.nextelement = self._nrow + self.step
        while (self.nextelement < self.stop) and (self.nhits < self.limit):
            if self.nextelement >= self.nrowsread:
                # Skip until there is interesting information
                while self.nextelement >= self.nrowsread + self.nrowsinbuf:
                    self.nrowsread += self.nrowsinbuf
                # Compute the end for this iteration
                self.stopb = self.stop - self.nrowsread
                if self.stopb > self.nrowsinbuf:
                    self.stopb = self.nrowsinbuf
                self._row = self.startb - self.step

                # Skip chunks with zeros if in wheretrue_mode
                if self.wheretrue_mode and self.check_zeros(self):
                    self.nrowsread += self.nrowsinbuf
                    self.nextelement += self.nrowsinbuf
                    continue

                if self.where_mode:
                    # Skip chunks with zeros in where_arr
                    if self.check_zeros(self.where_arr):
                        self.nrowsread += self.nrowsinbuf
                        self.nextelement += self.nrowsinbuf
                        continue
                    # Read a chunk of the boolean array
                    self.where_buf = self.where_arr[
                                     self.nrowsread:self.nrowsread + self.nrowsinbuf]

                # Read a data chunk
                self.iobuf = self[
                             self.nrowsread:self.nrowsread + self.nrowsinbuf]
                self.nrowsread += self.nrowsinbuf

                # Check if we can skip this buffer
                if (self.wheretrue_mode or self.where_mode) and self.skip > 0:
                    if self.wheretrue_mode:
                        nhits_buf = self.iobuf.sum()
                    else:
                        nhits_buf = self.where_buf.sum()
                    if (self.nhits + nhits_buf) < self.skip:
                        self.nhits += nhits_buf
                        self.nextelement += self.nrowsinbuf
                        continue

            self._row += self.step
            self._nrow = self.nextelement
            if self._row + self.step >= self.stopb:
                # Compute the start row for the next buffer
                self.startb = (self._row + self.step) % self.nrowsinbuf
            self.nextelement = self._nrow + self.step

            # Return a value depending on the mode we are
            if self.wheretrue_mode:
                vbool = <char *> (self.iobuf.data + self._row)
                if vbool[0]:
                    self.nhits += 1
                    if self.nhits <= self.skip:
                        continue
                    return self._nrow
                else:
                    continue
            if self.where_mode:
                vbool = <char *> (self.where_buf.data + self._row)
                if not vbool[0]:
                    continue
            self.nhits += 1
            if self.nhits <= self.skip:
                continue
            # Return the current value in I/O buffer
            if self.itemsize == self.atomsize:
                return PyArray_GETITEM(
                    self.iobuf, self.iobuf.data + self._row * self.atomsize)
            else:
                return self.iobuf[self._row]

        else:
            # Release buffers
            self.iobuf = np.empty(0, dtype=self._dtype)
            self.where_buf = np.empty(0, dtype=np.bool_)
            self.iter_exhausted = True
            raise StopIteration  # end of iteration

    cdef int check_zeros(self, object barr):
        """Check for zeros.  Return 1 if all zeros, else return 0."""
        cdef int bsize
        cdef npy_intp nchunk
        cdef carray carr
        cdef ndarray ndarr
        cdef chunk chunk_

        if isinstance(barr, carray):
            # Check for zero'ed chunks in carrays
            carr = barr
            nchunk = <npy_intp> cython.cdiv(self.nrowsread, self.nrowsinbuf)
            if nchunk < len(carr.chunks):
                chunk_ = carr.chunks[nchunk]
                if chunk_.isconstant and chunk_.constant in (0, ''):
                    return 1
        else:
            # Check for zero'ed chunks in ndarrays
            ndarr = barr
            bsize = self.nrowsinbuf
            if self.nrowsread + bsize > self.len:
                bsize = self.len - self.nrowsread
            if check_zeros(ndarr.data + self.nrowsread, bsize):
                return 1
        return 0

    def _update_disk_sizes(self):
        """Update the sizes on-disk."""
        sizes = dict()
        if self._rootdir:
            sizes['shape'] = self.shape
            sizes['nbytes'] = self.nbytes
            sizes['cbytes'] = self.cbytes
            rowsf = os.path.join(self.metadir, SIZES_FILE)
            with open(rowsf, 'wb') as rowsfh:
                rowsfh.write(
                    json.dumps(sizes, ensure_ascii=True).encode('ascii'))
                rowsfh.write(b'\n')

    def flush(self):
        """Flush data in internal buffers to disk.

        This call should typically be done after performing modifications
        (__settitem__(), append()) in persistence mode.  If you don't do this,
        you risk losing part of your modifications.

        """
        cdef chunk chunk_
        cdef npy_intp nchunks
        cdef int leftover_atoms

        if self._rootdir is None:
            return

        if self.leftover:
            leftover_atoms = cython.cdiv(self.leftover, self.atomsize)
            chunk_ = chunk(self.lastchunkarr[:leftover_atoms], self.dtype,
                           self.cparams,
                           _memory=self._rootdir is None)
            # Flush this chunk to disk
            self.chunks.flush(chunk_)

        # Finally, update the sizes metadata on-disk
        self._update_disk_sizes()

    # XXX This does not work.  Will have to realize how to properly
    # flush buffers before self going away...
    # def __del__(self):
    #   # Make a flush to disk if this object get disposed
    #   self.flush()

    def purge(self):
        """ Remove the underlying data for on-disk arrays. """
        if self.rootdir:
            shutil.rmtree(self.rootdir)

    def __str__(self):
        return array2string(self)

    def __repr__(self):
        snbytes = utils.human_readable_size(self._nbytes)
        scbytes = utils.human_readable_size(self._cbytes)
        if not self._cbytes:
            cratio = np.nan
        else:
            cratio = self._nbytes / float(self._cbytes)
        header = "carray(%s, %s)\n" % (self.shape, self.dtype)
        header += "  nbytes: %s; cbytes: %s; ratio: %.2f\n" % (
            snbytes, scbytes, cratio)
        header += "  cparams := %r\n" % self.cparams
        if self._rootdir:
            header += "  rootdir := '%s'\n" % self._rootdir
            header += "  mode    := '%s'\n" % self.mode
        fullrepr = header + str(self)
        return fullrepr

    def __reduce__(self):
        if self.rootdir:
            return (build_carray, (None,self.rootdir,))
        else:
            return (build_carray,(self[:],None,))


    def __enter__(self):
        return self

    def __exit__(self, type, value, tb):
        if self.mode != 'r':
            self.flush()


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