From c09c786bcdd496f3bc21d0f179a725674ec10e95 Mon Sep 17 00:00:00 2001
From: Eddie Hebert <ehebert@quantopian.com>
Date: Wed, 22 Aug 2012 13:41:07 -0400
Subject: [PATCH] Removes unused profiler for multiprocessor architecture.

---
 zipline/profile/prof_yappi.py | 93 -----------------------------------
 1 file changed, 93 deletions(-)
 delete mode 100644 zipline/profile/prof_yappi.py

diff --git a/zipline/profile/prof_yappi.py b/zipline/profile/prof_yappi.py
deleted file mode 100644
index 58878fe8..00000000
--- a/zipline/profile/prof_yappi.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from __future__ import division
-
-import logging
-from zipline.core.devsimulator import AddressAllocator
-import zipline.finance
-from zipline.optimize.factory import create_predictable_zipline
-import pandas as pd
-import numpy as np
-import os.path
-
-def convert_ystats(ystats):
-    """Convert yappi.get_stats().func_stats object to pandas
-    DataFrame.
-
-    """
-    func_names = [os.path.split(item[0])[-1] for item in ystats]
-    ncall = [float(item[1]) for item in ystats]
-    ttot = [float(item[2]) for item in ystats]
-    tsub = [float(item[3]) for item in ystats]
-    tavg = [float(item[4]) for item in ystats]
-    stats = pd.DataFrame({'ncall': ncall, 'ttot': ttot, 'tsub': tsub, 'tavg': tavg}, index=func_names)
-
-    return stats
-
-
-allocator = AddressAllocator(1000)
-
-config = {  'allocator'   :allocator,
-            'sid'         :133,
-            'trade_count' :5000,
-            'amplitude'   :30,
-            'base_price'  :50
-         }
-
-LOGGER = logging.getLogger('ZiplineLogger')
-
-import yappi
-
-def gen_single_stats(func, *args, **kwargs):
-    """Profile func(*args, **kwargs) with yappi.
-
-    Returns DataFrame of statistics.
-    """
-    yappi.start()
-    func(*args, **kwargs)
-    yappi.stop()
-    return convert_ystats(yappi.get_stats().func_stats)
-
-def gen_avg_stats(func, runs=1, *args, **kwargs):
-    """Profile func(*args, **kwargs) with yappi. Runs multiple times at computes the average.
-
-    Returns DataFrame of average statistics.
-    """
-
-    avg_stats = pd.concat([gen_single_stats() for i in range(runs)], keys=range(runs))
-    grouped = avg_stats.groupby(level=1)
-
-    return grouped.aggregate(np.mean)
-
-def run_updown(fname='before_stats.csv'):
-    """Profile a zipline with the UpDown tradesource (does not require
-    DB access) and the buy/sell algorithm (requires no
-    computation).
-
-    Saves output statics under fname.
-
-    Returns Dataframe of statistics.
-    """
-    zp, _ = create_predictable_zipline(config, simulate=False)
-    stats = gen_single_stats(zp.simulate, blocking=True)
-    stats.to_csv(fname)
-
-    return stats
-
-def calc_speedup(before='before_stats.csv', after='after_stats.csv'):
-    """Calculate speed-up between two previously run and saved
-    statistics under filename before and after.
-
-    Prints DataFrame of top 30 speed-ups and top 30 slow-downs.
-
-    """
-    old = pd.DataFrame.from_csv(before)
-    new = pd.DataFrame.from_csv(after)
-    speed_up = old / new
-    speed_up = speed_up.fillna(1)
-    speed_up = speed_up.sort(column='ttot', ascending=False)
-    slow_down = speed_up.sort(column='ttot', ascending=True)
-    print speed_up[:30]
-    print slow_down[:30]
-
-if __name__ == '__main__':
-    run_updown()
-    yappi.print_stats(sort_type=yappi.SORTTYPE_TTOT)
\ No newline at end of file