"""Benchmarks for ArrayProxy slicing of gzipped and non-gzipped files

Run benchmarks with::

    import nibabel as nib
    nib.bench()

If you have doctests enabled by default in nose (with a noserc file or
environment variable), and you have a numpy version <= 1.6.1, this will also
run the doctests, let's hope they pass.

Run this benchmark with:

    nosetests -s --match '(?:^|[\\b_\\.//-])[Bb]ench' /path/to/bench_arrayproxy_slicing.py
"""

from timeit import timeit
import gc
import itertools as it
import numpy as np
from unittest import mock

import nibabel as nib
from nibabel.tmpdirs import InTemporaryDirectory
from nibabel.openers import HAVE_INDEXED_GZIP

from .butils import print_git_title
from ..rstutils import rst_table

# if memory_profiler is installed, we get memory usage results
try:
    from memory_profiler import memory_usage
except ImportError:
    memory_usage = None


# Each test involves loading an image of shape SHAPE, and then slicing it
# NITERS times
NITERS = 50
SHAPE = (100, 100, 100, 100)

# One test is run for each combination of SLICEOBJS, KEEP_OPENS, and HAVE_IGZIP

# ':' gets replaced with slice(None)
# '?' gets replaced with a random index into the relevant axis
# numbers (assumed to be between 0 and 1) get scaled to the axis shape
SLICEOBJS = [
    ('?', ':', ':', ':'),
    (':', ':', ':', '?'),
    ('?', '?', '?', ':'),
]

KEEP_OPENS = [False, True, 'auto']

if HAVE_INDEXED_GZIP:
    HAVE_IGZIP = [False, True]
else:
    HAVE_IGZIP = [False]


def bench_arrayproxy_slicing():

    print_git_title('\nArrayProxy gzip slicing')

    # each test is a tuple containing
    # (HAVE_INDEXED_GZIP, keep_file_open, sliceobj)
    tests = list(it.product(HAVE_IGZIP, KEEP_OPENS, SLICEOBJS))

    # remove tests where HAVE_INDEXED_GZIP is True and keep_file_open is False,
    # because if keep_file_open is False, HAVE_INDEXED_GZIP has no effect
    tests = [t for t in tests if not (t[0] and not t[1])]

    testfile = 'testfile.nii'
    testfilegz = 'test.nii.gz'

    def get_test_label(test):
        have_igzip = test[0]
        keep_open = test[1]

        if not (have_igzip and keep_open):
            return 'gzip'
        else:
            return 'indexed_gzip'

    def fix_sliceobj(sliceobj):
        new_sliceobj = []
        for i, s in enumerate(sliceobj):
            if s == ':':
                new_sliceobj.append(slice(None))
            elif s == '?':
                new_sliceobj.append(np.random.randint(0, SHAPE[i]))
            else:
                new_sliceobj.append(int(s * SHAPE[i]))
        return tuple(new_sliceobj)

    def fmt_sliceobj(sliceobj):
        slcstr = []
        for i, s in enumerate(sliceobj):
            if s in ':?':
                slcstr.append(s)
            else:
                slcstr.append(str(int(s * SHAPE[i])))
        return '[{}]'.format(', '.join(slcstr))

    with InTemporaryDirectory():

        print('Generating test data... ({} MB)'.format(
            int(round(np.prod(SHAPE) * 4 / 1048576.))))

        data = np.array(np.random.random(SHAPE), dtype=np.float32)

        # zero out 10% of voxels so gzip has something to compress
        mask = np.random.random(SHAPE[:3]) > 0.1
        if len(SHAPE) > 3:
            data[mask, :] = 0
        else:
            data[mask] = 0

        # save uncompressed and compressed versions of the image
        img = nib.nifti1.Nifti1Image(data, np.eye(4))
        nib.save(img, testfilegz)
        nib.save(img, testfile)

        # each result is a tuple containing
        # (label, keep_open, sliceobj, testtime, basetime, testmem, basemem)
        #
        # where "basetime" is the time taken to load and slice a memmapped
        # (uncompressed)image, and "basemem" is memory usage for the same
        results = []

        # We use the same random seed for each slice object,
        seeds = [np.random.randint(0, 2 ** 32) for s in SLICEOBJS]

        for ti, test in enumerate(tests):

            label = get_test_label(test)
            have_igzip, keep_open, sliceobj = test
            seed = seeds[SLICEOBJS.index(sliceobj)]

            print('Running test {} of {} ({})...'.format(
                ti + 1, len(tests), label))

            # load uncompressed and compressed versions of the image
            img = nib.load(testfile, keep_file_open=keep_open)

            with mock.patch('nibabel.openers.HAVE_INDEXED_GZIP', have_igzip):
                imggz = nib.load(testfilegz, keep_file_open=keep_open)

            def basefunc():
                img.dataobj[fix_sliceobj(sliceobj)]

            def testfunc():
                with mock.patch('nibabel.openers.HAVE_INDEXED_GZIP',
                                have_igzip):
                    imggz.dataobj[fix_sliceobj(sliceobj)]

            # make sure nothing is floating around from the previous test
            # iteration, so memory profiling is (hopefully) more accurate
            gc.collect()

            if memory_usage is not None:
                membaseline = max(memory_usage(lambda: None))
                testmem = max(memory_usage(testfunc)) - membaseline
                basemem = max(memory_usage(basefunc)) - membaseline
            else:
                testmem = np.nan
                basemem = np.nan

            # reset the random number generator, so test and baseline use the
            # same slices
            np.random.seed(seed)
            testtime = float(timeit(testfunc, number=NITERS)) / float(NITERS)
            np.random.seed(seed)
            basetime = float(timeit(basefunc, number=NITERS)) / float(NITERS)

            results.append((label, keep_open, sliceobj, testtime, basetime,
                            testmem, basemem))

    data = np.zeros((len(results), 4))
    data[:, 0] = [r[3] for r in results]
    data[:, 1] = [r[4] for r in results]
    try:
        data[:, 2] = [r[3] / r[4] for r in results]
    except ZeroDivisionError:
        data[:, 2] = np.nan
    data[:, 3] = [r[5] - r[6] for r in results]

    rowlbls = ['Type {}, keep_open {}, slice {}'.format(
        r[0], r[1], fmt_sliceobj(r[2])) for r in results]
    collbls = ['Time', 'Baseline time', 'Time ratio', 'Memory deviation']

    print(rst_table(data, rowlbls, collbls))
