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################################################################################################################################################################################################
#  Function                compiler       -O0             -O3             -O3 -Ofast             -O3 -funroll-loops             -O3 -Ofast -funroll-loops             -O3 -Ofast -funroll-all-loops             
################################################################################################################################################################################################
countthem                     gcc        1.000x        1.000x               1.000x                       1.000x                              1.000x                                  1.000x
countthem_ternary             gcc        1.669x        3.614x               4.397x                       5.271x                              6.009x                                  6.399x
countthem_simd                gcc        3.414x      

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## Timings with halotools v0.4 and Corrfunc v2.0.0. Updated on Sep 12 (random data, Corrfunc compiled with gcc6.0)
## Note this test is capped at 1 million because the first three codes take too long and halotools randomly subsamples 
## particles above 1 million. The algorithm in halotools is an *exact* copy of `Corrfunc` - so the timing comparison simply
## tests the capability of the cython compiler.

## Timings generated on Oct 17, 2016, with mostly ready version of Corrfunc v2.0. Non-periodic calculations with random points. 

## Npts  kdcount    cKDTree    KDTree     halotools  Corrfunc(AVX) Corrfunc(SSE42) Corrfunc(fallback)
  10000   0.039      0.057      0.087      0.026      0.240      0.053      0.052
  50000   0.381      0.399      0.936      0.121      0.226      0.224      0.214

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# encoding=utf-8
# para_async.py
import asyncio
import itertools

import aiohttp

@asyncio.coroutine
async def download(url, parts):

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import (division, print_function, absolute_import,
                        unicode_literals)
import numpy as np
from Corrfunc._countpairs import countpairs_wp as wp
from Corrfunc._countpairs import countpairs_xi as xi

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from __future__ import print_function
import Corrfunc
from Corrfunc._countpairs_mocks import countpairs_rp_pi_mocks as DDrppi_mocks
import numpy as np


def main():
    version = Corrfunc.__version__
    print("Using Corrfunc from file: {0} with version = {1}"
          .format(Corrfunc.__file__, version))

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from __future__ import print_function

import numpy as np
from Corrfunc.mocks import DDrppi_mocks
from Corrfunc.io import read_catalog
from Corrfunc.utils import convert_rp_pi_counts_to_wp
from Corrfunc.utils import convert_3d_counts_to_cf
import Corrfunc
from os.path import dirname, abspath, join as pjoin
from IPython.core.debugger import Tracer

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from __future__ import print_function

import numpy as np
from fast_histogram import histogram1d, histogram1d_wrapper

np.random.seed(42)
npoints=np.array([1, 10, 100])*1000000
for n in npoints:
    x = np.random.random(n)
    _ = histogram1d_wrapper(x, range=[-1, 2], bins=30)

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def check_quantity_has_only_one_element(list_of_quant):
    values = []
    for val, name in list_of_quant:
        try:
            if len(val) > 1:
                msg = "{0} must be a scalar quantity or an 1-element array.\n"\
                "Found = {1} instead".format(name, val)
            v = val[0]
        except TypeError:
            # scalar quantity has no len

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const int64_t bits_set_in_avx512_mask_float[] = { B16(0) };
const uint16_t masks_per_misalignment_value_float[] = {0b1111111111111111,
                                                         0b0000000000000001,
                                                         0b0000000000000011,
                                                         0b0000000000000111,
                                                         0b0000000000001111,
                                                         0b0000000000011111,
                                                         0b0000000000111111,
                                                         0b0000000001111111,
                                                         0b0000000011111111,

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import seaborn as sns
import pandas as pd
import warnings
import matplotlib.pyplot as plt;
import matplotlib;
matplotlib.style.use('ggplot');

txtfile='RSE-EOI-survey-list-anonymized-v2.csv'

rse_df = pd.read_csv(txtfile,