def log(self):
mean_download_rate = stats.avg(self.download_rates)
std_download_rate = stats.std(self.download_rates)
mean_upload_rate = stats.avg(self.upload_rates)
std_upload_rate = stats.std(self.upload_rates)
logger.log("--*--Torrent statistics--*--")
logger.log("Download rate (KiB/s) - mean: %f" % mean_download_rate)
logger.log("Download rate (KiB/s) - standard deviation: %f" % std_download_rate)
logger.log("Upload rate (KiB/s) - mean: %f" % mean_upload_rate)
logger.log("Upload rate (KiB/s) - standard deviation: %f" % std_upload_rate)
logger.log_to_file("download_rate_mean, %f\r\n" % mean_download_rate)
logger.log_to_file("download_rate_stdev, %f\r\n" % std_download_rate)
logger.log_to_file("upload_rate_mean, %f\r\n" % mean_upload_rate)
logger.log_to_file("upload_rate_stdev, %f\r\n" % std_upload_rate)
if self.download_finished:
logger.log("Download time (s): %d" % self.download_time)
logger.log_to_file("download_time, %d\r\n" % self.download_time)
else:
logger.log_to_file("download_time, %d\r\n" % -1)
self.buffer_manager.log()
def main():
opts = parse_args(sys.argv)
for (root, dirnames, filenames) in os.walk(opts['data_dir']):
filenames = filter(lambda name: name.endswith('.dat'), filenames)
break
data = [parse_file(opts['data_dir'] + '/' + filename) for filename in filenames]
delta_t1 = [float(datum['end'] - datum['start'])/(opts['comp_mhz']*1000000)
for datum in data]
delta_t2 = [float(datum['register'] - datum['start'])/(opts['comp_mhz']*1000000)
for datum in data]
result = {}
result['qemu_delta_t'] = avg(delta_t1)
result['qemu_delta_t_stddev'] = stddev(delta_t1)
result['total_delta_t'] = avg(delta_t2)
result['total_delta_t_sttdev'] = stddev(delta_t2)
result['hostname'] = socket.gethostname()
result['comp_mhz'] = opts['comp_mhz']
result['override_clean_check'] = True
result['git_rev'] = get_git_rev(result['override_clean_check'])
result_filename = opts['data_dir'] + '/' + 'summary'
f = open(result_filename,'w+')
json.dump(result, f, indent=4)
f.write("\n")
f.close()
print('Wrote summary to %s' % result_filename)
def main():
global sample_cutoff, gpu_sample_cutoff, sample_cutoff_last
opts = parse_args(sys.argv)
sample_cutoff = opts.get('sample_cutoff', sample_cutoff)
sample_cutoff_last = opts.get('sample_cutoff_last', sample_cutoff_last)
num_runs = 1
while os.path.exists(sample_filename(opts['data_dir'], num_runs)):
num_runs += 1
num_runs -= 1
runs = []
for i in range(1,num_runs+1):
samples_file = sample_filename(opts['data_dir'], i)
samples = parse_mpstat(samples_file)
gpu_samples_file = gpu_sample_filename(opts['data_dir'], i)
gpu_samples = parse_gpu_samples(gpu_samples_file)
print(samples)
run = {}
run['avg'] = avg(samples)
run['stddev'] = stddev(samples)
if gpu_samples:
run['avg_frames'] = avg(gpu_samples)
run['stddev_frames'] = stddev(gpu_samples)
runs.append(run)
result = {}
result['sample_cutoff'] = sample_cutoff
result['sample_cutoff_last'] = sample_cutoff_last
result['gpu_sample_cutoff'] = gpu_sample_cutoff
result['git_rev'] = get_git_rev(override_clean_check=True)
result['override_clean_check'] = True
result['runs'] = runs
result['enc'] = runs[0].has_key('avg_frames')
run_avgs = [run['avg'] for run in runs]
run_stddevs = [run['stddev'] for run in runs]
result['run_avg'] = avg(run_avgs)
result['run_stddev'] = stddev(run_avgs)
result['run_var'] = max(run_stddevs)
if result['enc']:
run_avg_frames = [run['avg_frames'] for run in runs]
run_stddev_frames = [run['stddev_frames'] for run in runs]
result['run_avg_frames'] = avg(run_avg_frames)
result['run_stddev_frames'] = stddev(run_avg_frames)
result['run_var_frames'] = max(run_stddev_frames)
result_filename = opts['data_dir'] + '/' + 'summary'
f = open(result_filename, 'w+')
json.dump(result, f, indent=4)
f.write('\n')
f.close()
print('Wrote summary to %s' % result_filename)
def centroid(*points):
"""Calculate the centroid point of a points set in a 2-dimensional space"""
cx = stats.avg()
cy = stats.avg()
cx.next()
cy.next()
for x, y in points:
x = float(x)
y = float(y)
cx.send(x)
cy.send(y)
return cx.next(), cy.next()
def centroid(*points):
"""Calculate the centroid point of a points set in a 2-dimensional space"""
cx = stats.avg()
cy = stats.avg()
cx.next()
cy.next()
for x, y in points:
x = float(x)
y = float(y)
cx.send(x)
cy.send(y)
return cx.next(), cy.next()
def log(self):
interruptions = len(self.buffering_time) - 1
# Checking if player is on initial buffering state
if interruptions > 0 or not self.is_buffering:
initial_wait = self.buffering_time[0]
else:
initial_wait = -1
# Removing invalid samples
buffering_time = self.buffering_time[1:]
if self.is_buffering:
buffering_time = buffering_time[:-1]
# Calculating statistics
mean_time = stats.avg(buffering_time)
std_time = stats.std(buffering_time)
# Logging
logger.log("--*--Buffer statistics--*--")
logger.log("Time to start playback (s): %d" % initial_wait)
logger.log("Number of interruptions: %d" % interruptions)
logger.log("Interruption time (s) - mean: %f" % mean_time)
logger.log("Interruption time (s) - standard deviation: %f" % std_time)
logger.log("Interruptions (s): %r" % buffering_time)
logger.log_to_file("playback_start_time, %d\r\n" % initial_wait)
logger.log_to_file("interruptions, %d\r\n" % interruptions)
logger.log_to_file("interruption_time_mean, %f\r\n" % mean_time)
logger.log_to_file("interruption_time_stdev, %f\r\n" % std_time)
def log(self):
interruptions = len(self.buffering_time) - 1
# Checking if player is on initial buffering state
if interruptions > 0 or not self.is_buffering:
initial_wait = self.buffering_time[0]
else:
initial_wait = -1
# Removing invalid samples
buffering_time = self.buffering_time[1:]
if self.is_buffering:
buffering_time = buffering_time[:-1]
# Calculating statistics
mean_time = stats.avg(buffering_time)
std_time = stats.std(buffering_time)
# Logging
logger.log("--*--Buffer statistics--*--")
logger.log("Time to start playback (s): %d" % initial_wait)
logger.log("Number of interruptions: %d" % interruptions)
logger.log("Interruption time (s) - mean: %f" % mean_time)
logger.log("Interruption time (s) - standard deviation: %f" % std_time)
logger.log("Interruptions (s): %r" % buffering_time)
logger.log_to_file("playback_start_time, %d\r\n" % initial_wait)
logger.log_to_file("interruptions, %d\r\n" % interruptions)
logger.log_to_file("interruption_time_mean, %f\r\n" % mean_time)
logger.log_to_file("interruption_time_stdev, %f\r\n" % std_time)
def write_travel_times(fileName="output.csv"):
outputFile = open(fileName, "w")
result = get_rides_fastest()
print "**** Writing results to file ****"
length = len(result.keys())
for i, key in enumerate(result.keys()):
outputFile.write(str(key[0]) + ";" + str(key[1]) + ",")
outputFile.write("%.02f" % avg(result[key]) + "," + "%.02f" % stddev(result[key]) + "," + "%.02f" % stderr(result[key]) + "," + str(len(result[key])))
# outputFile.write(",")
# outputFile.write(",".join([str(element) for element in result[key]]))
outputFile.write("\n")
if i % 400000 == 0:
progress = 100.0*i/length
if progress > 105:
break
if i > 0:
sys.stdout.write('\r')
sys.stdout.write("Progress: " + "%.01f" % progress + "%" + " completed.")
sys.stdout.flush()
sys.stdout.write("\rProgress: " + "%.01f" % 100.0 + "%" + " completed.\n")
sys.stdout.flush()
outputFile.close()
return None
short_list.append(short_corr) print ticker + " - Processed " + str((i / float(len(earnings_list))) * 100) + '%' else: print "Warning: could not get historical prices for ticker: " + ticker earnings_list.remove(earnings_info) i += 1 corr_change = stats.minus(short_list, long_list) # corr_change = [math.fabs(a) for a in corr_change] print len(corr_change) print len(earnings_list) change_avg = stats.avg(corr_change) change_sigma = stats.sigma(corr_change) price_changes = [] for i in range(0, len(earnings_list)): earnings_list[i].append(long_list[i]) earnings_list[i].append(short_list[i]) prev_date = yfDate(getPrevWeekday(earnings_date)) next_date = yfDate(getNextWeekday(earnings_date)) ticker = earnings_list[i][1] before_quote = yf.get_historical_prices(ticker, prev_date, prev_date) before_quote = yfutils.get_open_as_float(before_quote)
