profiling of elephant.spike_train_correlation.cross_correlation_histogram

README

To see simple statistics, run without arguments:

$ python profile_cch.py

To save results to file, provide a filename: 

$ python profile_cch.py my.csv

You can compare results from different version. First checkout a different branch of elephant, then run the benchmark again:

$ python profile_cch.py other_branch.csv

To display results:

$ python show_stats.py *.csv

btel.csv

pietro.csv

profile_cch.py

import timeit
import quantities as pq
import elephant.spike_train_correlation as corr
from elephant.conversion import BinnedSpikeTrain
from functools import partial
from elephant.spike_train_generation import homogeneous_poisson_process as poisson
from collections import namedtuple
import pandas as pd

import sys

results = []
runs = 5

Params = namedtuple("Params", ['t_stop', 'rate', 'window_width'])

param_list = [Params(100, 1000 , 50.),
              Params(100, 1, 50.),
              Params(100, 1, 100000),
              Params(100, 1000, 100000),
              Params(100, 1, None),
              Params(100, 1000, None)]

for p in param_list:
    benchmark = {}
    spikedata = [poisson(p.rate * pq.Hz, t_start=0 * pq.s, t_stop=p.t_stop * pq.s) for _ in range(2)]
    st1 = BinnedSpikeTrain(spikedata[0], binsize=1 * pq.ms)
    st2 = BinnedSpikeTrain(spikedata[1], binsize=1 * pq.ms)

    if p.window_width:
        window=[-p.window_width * pq.ms/ 2., p.window_width * pq.ms/ 2.]
    else:
        window = None
    for method in ['speed', 'memory']:
        func = partial(corr.cross_correlation_histogram, st1, st2, window=window, method=method)
        timer = timeit.Timer(func)
        benchmark[method] = timer.timeit(runs)
    benchmark.update(p._asdict())
    results.append(benchmark)

df = pd.DataFrame(results)
if len(sys.argv) > 1:
    df.to_csv(sys.argv[1], index=False)
else:
    print(df.set_index(['rate', 't_stop', 'window_width']))

profile_stats.txt

# benchmark results
# btel: git commit 59a9c57
# pietro: git commit 62e1db

                                btel                 pietro           
                              memory      speed      memory      speed
rate t_stop window_width                                              
1000 100    50             21.527427   0.031363   47.646526  35.572358
1    100    50              0.015904   0.029850    0.015704  34.367412
            100000          0.031851  31.207008    0.018264  34.483782
1000 100    100000        253.660432  31.388677  288.690367  35.324806
1    100    NaN             0.025822  44.642633    0.025641  45.243037
1000 100    NaN           333.329719  45.046806  435.867851  44.638054

show_stats.py

#!/usr/bin/env python
#coding=utf-8

import pandas as pd
import sys
import os

if __name__ == "__main__":
    data_frames = {}
    fullpaths = sys.argv[1:]
    
    for fullpath in fullpaths:
        path, fname = os.path.split(fullpath)
        core, ext = os.path.splitext(fname)
        df = pd.DataFrame.from_csv(fullpath)
        df = df.reset_index()
        df2 = df.set_index(['rate', 't_stop', 'window_width'])
        data_frames[core] = df2
    merged = pd.concat(data_frames, axis=1)
    print(merged)