from __future__ import print_function
import numpy as np
import pandas as pd
from collections import OrderedDict #sorting participant df dict before pd.concat()
import matplotlib.pylab as plt
from matplotlib import pylab
import matplotlib as mpl
%matplotlib inline
mpl.rcParams['figure.figsize'] = (14,8)
pd.options.display.mpl_style = 'default'
#pd.set_option('display.multi_sparse', True)
from pprint import pprint
from pprint import pformat
#pickled_dbase = "c:/db_pickles/pickle - dbase - 2014-07-28a.pickle"
#dbase = pd.read_pickle(pickled_dbase)
sms_tasknames = ['T1_SMS_5', 'T1_SMS_8',
'Ticks_ISO_T2_5', 'Ticks_ISO_T2_8',
'Ticks_Linear_5', 'Ticks_Linear_8',
'Ticks_Phase_5', 'Ticks_Phase_8',
'Jits_ISO_5', 'Jits_ISO_8',
'Jits_Linear_5', 'Jits_Linear_8',
'Jits_Phase_5', 'Jits_Phase_8', ]
# Participants that are excluded from all performance analysis
pilot_data = ['010', '011', '012', '013', '014',]
non_english_fluent = ['023', '031', '045', '050', '070', '106',]
left_handed = ['042', '088',]
pro_inst_skill = ['026', '037']
excluded_all_tasks = pilot_data + non_english_fluent + left_handed + pro_inst_skill
param_all_tasks = lambda v: {task: v for task in sms_tasknames}
sms_params_entry = {
'stimulus_timing': {
'T1_SMS_5': 'iso',
'Ticks_ISO_T2_5': 'iso',
'Ticks_Linear_5': 'linear',
'Ticks_Phase_5': 'phase',
'Jits_ISO_5': 'iso',
'Jits_Linear_5': 'linear',
'Jits_Phase_5': 'phase',
'T1_SMS_8': 'iso',
'Ticks_ISO_T2_8': 'iso',
'Ticks_Linear_8': 'linear',
'Ticks_Phase_8': 'phase',
'Jits_ISO_8': 'iso',
'Jits_Linear_8': 'linear',
'Jits_Phase_8': 'phase',
},
'stimulus_style': {
'T1_SMS_5': 'tick',
'Ticks_ISO_T2_5': 'tick',
'Ticks_Linear_5': 'tick',
'Ticks_Phase_5': 'tick',
'Jits_ISO_5': 'jitter',
'Jits_Linear_5': 'jitter',
'Jits_Phase_5': 'jitter',
'T1_SMS_8': 'tick',
'Ticks_ISO_T2_8': 'tick',
'Ticks_Linear_8': 'tick',
'Ticks_Phase_8': 'tick',
'Jits_ISO_8': 'jitter',
'Jits_Linear_8': 'jitter',
'Jits_Phase_8': 'jitter',
},
'ISI': {
'T1_SMS_5': 500,
'Ticks_ISO_T2_5': 500,
'Ticks_Linear_5': '(varies)',
'Ticks_Phase_5': 500,
'Jits_ISO_5': 500,
'Jits_Linear_5': '(varies)',
'Jits_Phase_5': 500,
'T1_SMS_8': 800,
'Ticks_ISO_T2_8': 800,
'Ticks_Linear_8': '(varies)',
'Ticks_Phase_8': 800,
'Jits_ISO_8': 800,
'Jits_Linear_8': '(varies)',
'Jits_Phase_8': 800,
},
#used in filtering step
'wait_beats_after_subj_start': param_all_tasks(12),
#used in assigning outlier status / "outlier metric"
'minimum_percent_deviation_to_keep': param_all_tasks(-35),
'maximum_percent_deviation_to_keep': param_all_tasks(+20),
}
#reshape to task>param so parameter lists can be selected by task
sms_params = {task: {param_type: taskparams[task]
for (param_type, taskparams)
in sms_params_entry.items()}
for task in sms_tasknames}
phase_shift_beats = {800: OrderedDict([
(30, -20),
(48, +20),
(64, +40),
(81, -40),
(97, -80),
(114, +80),
(131, +160),
(150, -160)]),
500: OrderedDict([
(64, +20),
(81, -20),
(97, -50),
(114, +50),
(131, +100),
(150, -100)])
}
short_name = {'T1_SMS_5': 'iso5t1',
'T1_SMS_8': 'iso8t1',
'Ticks_ISO_T2_5': 'iso5t2',
'Ticks_ISO_T2_8': 'iso8t2',
'Ticks_Linear_5': 'lin5t',
'Ticks_Linear_8': 'lin8t',
'Ticks_Phase_5': 'phase5t',
'Ticks_Phase_8': 'phase8t',
'Jits_ISO_5': 'iso5j',
'Jits_ISO_8': 'iso8j',
'Jits_Linear_5': 'lin5j',
'Jits_Linear_8': 'lin8j',
'Jits_Phase_5': 'phase5j',
'Jits_Phase_8': 'phase8j',
}
sms_shortnames = short_name.values()
long_name = {v: k for (k, v) in short_name.items()}
print(sms_shortnames)
['iso5t1', 'phase5j', 'phase8j', 'iso8t1', 'lin8j', 'iso5t2', 'lin5j', 'iso8t2', 'phase5t', 'phase8t', 'lin8t', 'iso5j', 'iso8j', 'lin5t']
def general_task_pid_iterator(label_tasks=True,
label_pids=True,
concise_labels=False,
skip_to_task=None,
skip_to_pid=None):
for t in sms_tasknames:
if skip_to_task:
if t != skip_to_task:
continue
else:
skip_to_task = None
if label_tasks:
if concise_labels:
print('\n' + t)
else:
print('='*80 + '\n' + t + '\n' + '='*80)
for pid in task_pids[t]:
if skip_to_pid:
if pid != skip_to_pid:
continue
else:
skip_to_pid = None
if label_pids:
if concise_labels:
print(pid, end=',')
else:
print('-'*60)
print('P. ' + pid)
yield (t, pid)
import cPickle as pickle
pfile = "c:/db_pickles/pickle - smsbeats - 2014-10-03b.pickle"
with open(pfile) as f:
task_frames = pickle.load(f)
task_pids = {}
for (k, v) in task_frames.items():
pids = sorted(set(v.index.get_level_values('pid')))
task_pids[k] = [p for p in pids if p not in excluded_all_tasks]
for t in task_frames.keys():
task_frames[t] = task_frames[t].drop(excluded_all_tasks, level='pid')
for k, v in task_pids.items():
print(k, '\t', len(v))
T1_SMS_5 97 Jits_Phase_5 97 Ticks_Phase_5 97 Jits_Phase_8 97 Ticks_Phase_8 97 T1_SMS_8 97 Jits_Linear_8 97 Ticks_Linear_8 97 Ticks_ISO_T2_5 97 Jits_ISO_5 97 Jits_ISO_8 97 Ticks_Linear_5 97 Jits_Linear_5 96 Ticks_ISO_T2_8 97
# This function was pulled out from the earlier processing step-- instead
# of deciding what's an outlier while doing the initial data processing,
# we can look at it here, and perhaps experiment with different settings.
# (don't just mindlessly maximize reliability, though-- there could certainly
# be reliable aspects of the data that we still want to remove-- e.g.,
# how many beats a P waits to start, how often they skip a tap...)
def filter_taps(df,
task_params,
print_results=False):
'''
Input: a DataFrame consisting of the unfiltered list of taps (without targets).
Output: the dataframe with outlying taps tagged and startup beats removed.
'''
# drop initial beats from task recording: [n] beats from start of task
# or [n] beats from the participant's first tap, whichever comes later
nonfail_beats = df[df.is_failure == False].index.tolist()
first_played_beat = min(nonfail_beats)
#beatdrop1 = task_params['wait_beats_after_task_start']
beatdrop2 = first_played_beat + task_params['wait_beats_after_subj_start']
beats_to_drop_from_start = beatdrop2 #max([beatdrop1, beatdrop2])
df = df[beats_to_drop_from_start:] #slice by index name (zero-indexed beats)
# temporarily remove outliers to form a distribution of typical
# values, which we'll use to form upper and lower limits for
# filtering in the following step.
# "from left" = the largest negative deviations,
# "from right" = the largest positive deviations
#nworst_left = task_params['stdev_calcs_exclude_n_from_left']
#nworst_right = task_params['stdev_calcs_exclude_n_from_right']
#df_adj = df[(df.dev_perc > max(df.dev_perc.nsmallest(nworst_left))) &
# (df.dev_perc < min(df.dev_perc.nlargest(nworst_right)))]
# Actual filtering of the values based on the temporary distribution
# created above. (This way, we retain the biggest deviations, as long
# as they aren't actually outliers.)
#rem_beyond_stds = task_params['filter_outliers_beyond_x_stdevs']
#trimmed_mean = df_adj.dev_perc.mean()
#trimmed_std = df_adj.dev_perc.std()
#upper_limit = trimmed_mean + (trimmed_std * rem_beyond_stds)
#lower_limit = trimmed_mean - (trimmed_std * rem_beyond_stds)
lower_limit = task_params['minimum_percent_deviation_to_keep'] # -35
upper_limit = task_params['maximum_percent_deviation_to_keep'] # +20
#df_filt = df[(df.dev_perc <= upper_limit) &
# (df.dev_perc >= lower_limit)]
df['is_outlier'] = False
df.is_outlier = ( (df.dev_perc > upper_limit)
| (df.dev_perc < lower_limit))
#devperc_failure = task_params['min_percentISI_deviation_counted_as_failure']
#return df_filt
return df
def label_shift_ranges(task_taps_df):
'''input: a taps-only df for a single task (all participants).
output: the same df with ranges labeled ('is_range_1a' etc.)
'''
#label slicing is end-inclusive, so don't overlap beat numbers
# one beat removed from the start of each "a" range (a player isn't
# expected to be in synch with the actual perturbed tap, but we start
# measuring when the next one comes.)
# (the '0' range is before all the large shifts. Just placeholders...)
# shift_ranges = {0: {'a': ( 0, 96),
# 'b': ( 0, 96),},
# 1: {'a': ( 98, 104),
# 'b': (105, 113),},
# 2: {'a': (115, 121),
# 'b': (122, 130),},
# 3: {'a': (132, 139),
# 'b': (140, 149),},
# 4: {'a': (151, 159),
# 'b': (160, 169),},
# }
#shifts: ... 97, 114, 131, 150
#get the next four beats
shift_ranges = {0: {'a': ( 0, 96),
'b': ( 0, 96),},
1: {'a': ( 99, 101),
'b': (102, 113),},
2: {'a': (116, 118),
'b': (119, 130),},
3: {'a': (133, 135),
'b': (136, 149),},
4: {'a': (152, 154),
'b': (155, 169),},
}
def getbeatxs(df):
# groupby/apply doesn't seem to be set up well for selecting
# particular rows from each value of a multiindex... here, we'll
# have to remove the 'pid' index explicitly I guess.
df = df.reset_index('pid').drop('pid', axis=1)
for i in [0,1,2,3,4]:
for j in ['a','b']:
srx = shift_ranges[i][j]
label = 'is_range_' + str(i) + j
df[label] = False
df.loc[srx[0]:srx[1], label] = True
label = 'is_shiftedarea'
df[label] = False
df.loc[shift_ranges[1]['a'][0]:shift_ranges[1]['b'][1], label] = True
df.loc[shift_ranges[2]['a'][0]:shift_ranges[2]['b'][1], label] = True
df.loc[shift_ranges[3]['a'][0]:shift_ranges[3]['b'][1], label] = True
df.loc[shift_ranges[4]['a'][0]:shift_ranges[4]['b'][1], label] = True
return df
g = task_taps_df.groupby(level='pid')
return g.apply(getbeatxs)
#taps only (remove "target" data)
db_taps = {t: df.xs('tap', level='stamp')
for (t, df) in task_frames.items()}
phase_tasks = ['Ticks_Phase_5', 'Ticks_Phase_8',
'Jits_Phase_5', 'Jits_Phase_8', ]
for t in phase_tasks:
db_taps[t] = label_shift_ranges(db_taps[t])
print(t + ": shift range labels")
taps_filtered = OrderedDict()
outlier_rem_record = {}
for t in sms_tasknames:
print('\n' + t)
tdata = db_taps[t]
tparams = sms_params[t]
outlier_rem_record[t] = {}
tdata_filt = {}
for pid in task_pids[t]:
print(pid, end=",")
pdata = tdata.xs(pid)
#print(max(pdata.index))
#filters out certain intervals and adds "is_outlier" field
filtered_a = filter_taps(pdata, tparams)
#remove beats based on "is_outlier_ field, as added by filter_taps()
#but don't remove outliers in phase tasks
if t in phase_tasks:
filtered_b = filtered_a
else:
filtered_b = filtered_a[filtered_a.is_outlier != True]
#Need to worry about how to filter the phase tasks later...
tdata_filt[pid] = filtered_b
outlier_rem_record[t][pid] = len(filtered_a) - len(filtered_b)
taps_filtered[t] = pd.concat(tdata_filt, names=['pid'])
mean_rem = round(np.mean(outlier_rem_record[t].values()),1)
std_rem = round(np.std(outlier_rem_record[t].values()),1)
max_rem = max(outlier_rem_record[t].values())
print('\n outlier beats removed per P.: mean={}, sd={}, max={}'
.format(mean_rem, std_rem, max_rem))
print('\n' + '=' * 70)
Ticks_Phase_5: shift range labels Ticks_Phase_8: shift range labels Jits_Phase_5: shift range labels Jits_Phase_8: shift range labels T1_SMS_5 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=1.4, sd=5.6, max=34 ====================================================================== T1_SMS_8 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=1.5, sd=6.2, max=54 ====================================================================== Ticks_ISO_T2_5 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=1.6, sd=6.8, max=53 ====================================================================== Ticks_ISO_T2_8 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=2.8, sd=11.9, max=87 ====================================================================== Ticks_Linear_5 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=5.2, sd=14.2, max=75 ====================================================================== Ticks_Linear_8 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=4.1, sd=11.1, max=62 ====================================================================== Ticks_Phase_5 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=0.0, sd=0.0, max=0 ====================================================================== Ticks_Phase_8 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=0.0, sd=0.0, max=0 ====================================================================== Jits_ISO_5 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=1.2, sd=4.8, max=34 ====================================================================== Jits_ISO_8 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=1.9, sd=7.2, max=49 ====================================================================== Jits_Linear_5 015,016,017,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=9.2, sd=21.3, max=103 ====================================================================== Jits_Linear_8 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=7.8, sd=12.3, max=66 ====================================================================== Jits_Phase_5 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=0.0, sd=0.0, max=0 ====================================================================== Jits_Phase_8 015,016,017,018,019,020,021,022,024,025,027,028,029,030,032,033,034,035,036,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, outlier beats removed per P.: mean=0.0, sd=0.0, max=0 ======================================================================
taps_filtered['Ticks_Phase_5'].tail().T
pid | 121 | ||||
---|---|---|---|---|---|
beat | 165 | 166 | 167 | 168 | 169 |
beat_end | 82779.99 | 83279.83 | 83779.43 | 84279.52 | 84780.25 |
beat_start | 82275.13 | 82779.99 | 83279.83 | 83779.43 | 84279.52 |
beat_target | 82530.03 | 83029.94 | 83529.71 | 84029.15 | 84529.88 |
channel | 1 | 1 | 1 | 1 | 1 |
dev | -53.132 | -78.584 | -55.668 | -56.948 | -51.272 |
dev_perc | -10.42221 | -15.71957 | -11.13886 | -11.40228 | -10.23941 |
i | 321 | 323 | 325 | 327 | 329 |
ints | 473.172 | 474.46 | 522.68 | 498.164 | 506.408 |
is_failure | False | False | False | False | False |
micros | 6.837662e+08 | 6.842407e+08 | 6.847633e+08 | 6.852615e+08 | 6.857679e+08 |
multiple_taskms | |||||
pitch | 48 | 48 | 48 | 48 | 48 |
selection_case | 1 | 1 | 1 | 1 | 1 |
target_spiked | NaN | NaN | NaN | NaN | NaN |
task_ms | 82476.9 | 82951.36 | 83474.04 | 83972.2 | 84478.61 |
tinterval | NaN | NaN | NaN | NaN | NaN |
velocity | 17 | 21 | 18 | 18 | 18 |
shifted_ms_before_target | 7509.984 | 8009.896 | 8509.66 | 9009.104 | 9509.836 |
last_shift_val | -100 | -100 | -100 | -100 | -100 |
is_range_0a | False | False | False | False | False |
is_range_0b | False | False | False | False | False |
is_range_1a | False | False | False | False | False |
is_range_1b | False | False | False | False | False |
is_range_2a | False | False | False | False | False |
is_range_2b | False | False | False | False | False |
is_range_3a | False | False | False | False | False |
is_range_3b | False | False | False | False | False |
is_range_4a | False | False | False | False | False |
is_range_4b | True | True | True | True | True |
is_shiftedarea | True | True | True | True | True |
is_outlier | False | False | False | False | False |
def fig_dims(width, factor):
#WIDTH = 350.0 # the number latex spits out
#FACTOR = 0.45 # the fraction of the width you'd like the figure to occupy
fig_width_pt = width * factor
inches_per_pt = 1.0 / 72.27
golden_ratio = (np.sqrt(5) - 1.0) / 2.0 # because it looks good
fig_width_in = fig_width_pt * inches_per_pt # figure width in inches
fig_height_in = fig_width_in * golden_ratio # figure height in inches
fig_dims = [fig_width_in, fig_height_in] # fig dims as a list
return fig_dims
#don't adjust MPL defaults to pandas's preferred defaults
pd.options.display.mpl_style = None
mpl.rcdefaults()
from matplotlib import rcParams
#rcParams['axes.titlesize'] = 22
rcParams['font.size'] = 14
rcParams['xtick.labelsize'] = 12
rcParams['ytick.labelsize'] = 12
rcParams['legend.fontsize'] = 12
rcParams['font.family'] = 'serif'
rcParams['figure.facecolor'] = '1.0' # 0 black --> 1 white; grays
def task_hists(tdata):
figsize = fig_dims(2000, 0.45)
ax = avgtargs.plot(y = 'tinterval', linewidth=2, color='black', figsize=figsize)
avg_tap = avgtargs.tinterval + avgdevs.dev
upper_sd = avg_tap + SD_devs.dev
lower_sd = avg_tap - SD_devs.dev
#upper_sd.plot(y = 'dev', linewidth=3, color='black', linestyle="--")
#lower_sd.plot(y = 'dev', linewidth=3, color='black', linestyle="--")
#ax.plot(upper_sd.dev, linewidth=3, color='black', linestyle="--")
#ax.plot(lower_sd.dev, linewidth=3, color='black', linestyle="--")
avg_tap.plot(linewidth=1, color='black', linestyle="--", dashes=(5,3))
upper_sd.plot(linewidth=1, color='black', linestyle="-", marker="o", markersize=4)
lower_sd.plot(linewidth=1, color='black', linestyle="-", marker="o", markersize=4)
ax.set_ylabel("Milliseconds")
ax.set_xlabel("Interval number")
ax.grid(b=False, which='major', axis='both')
# set number of labeled "ticks" on each axis (overriding auto setting)
#ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(15))
#ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(10))
# (it will sometimes decide to show fewer than this, hence "max")
# Or to be precise:
ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(15))
#ax.xaxis.tick_bottom()
#ax.yaxis.tick_left()
#ax.spines["right"].set_color("none")
#ax.spines["top"].set_color("none")
ax.legend(["Target stimulus interval (TSI)",
"TSI + mean of absolute performance asynchronies",
u"Between-participants variability in absolute performance asynchronies (TSI ± 1 SD)"], loc="best")
ax.get_legend().set_title("")
ax.get_legend().draw_frame(False)
plt.show()
from matplotlib import rcParams
#rcParams['axes.titlesize'] = 22
rcParams['font.size'] = 14
rcParams['xtick.labelsize'] = 12
rcParams['ytick.labelsize'] = 12
rcParams['legend.fontsize'] = 12
rcParams['font.family'] = 'serif'
rcParams['figure.facecolor'] = '1.0' # 0 black --> 1 white; grays
# iso5t1 and iso8t1: Need to remove the extra intervals at the
# end of the task from the first few subs! (after beat 130-ish?)
# (Probably easiest and less confusing for future readers if they're just
# chopped out of the CSV file at the start.)
#don't adjust MPL defaults to pandas's preferred defaults
pd.options.display.mpl_style = None
mpl.rcdefaults()
from matplotlib import rcParams
#rcParams['axes.titlesize'] = 22
rcParams['font.size'] = 35
rcParams['xtick.labelsize'] = 16
rcParams['ytick.labelsize'] = 16
rcParams['legend.fontsize'] = 16
rcParams['font.family'] = 'serif'
rcParams['figure.facecolor'] = '1.0' # 0 black --> 1 white; grays
testdf = db_taps[long_name['phase5t']]
testdf[testdf.is_shiftedarea == True]
beat_end | beat_start | beat_target | channel | dev | dev_perc | i | ints | is_failure | micros | ... | is_range_0b | is_range_1a | is_range_1b | is_range_2a | is_range_2b | is_range_3a | is_range_3b | is_range_4a | is_range_4b | is_shiftedarea | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pid | beat | |||||||||||||||||||||
015 | 99 | 49709.410 | 49209.300 | 49459.060 | 1 | -94.080 | -18.834081 | 192 | 467.904 | False | 225832492 | ... | False | True | False | False | False | False | False | False | False | True |
100 | 50209.680 | 49709.410 | 49959.760 | 1 | -139.388 | -27.838626 | 194 | 455.392 | False | 226287884 | ... | False | True | False | False | False | False | False | False | False | True | |
101 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | ... | False | True | False | False | False | False | False | False | False | True | |
102 | 51209.464 | 50709.334 | 50959.068 | 1 | -91.160 | -18.251420 | 197 | NaN | False | 227335420 | ... | False | False | True | False | False | False | False | False | False | True | |
103 | 51709.724 | 51209.464 | 51459.860 | 1 | -77.856 | -15.546574 | 199 | 514.096 | False | 227849516 | ... | False | False | True | False | False | False | False | False | False | True | |
104 | 52210.096 | 51709.724 | 51959.588 | 1 | -115.624 | -23.137387 | 201 | 461.960 | False | 228311476 | ... | False | False | True | False | False | False | False | False | False | True | |
105 | 52709.802 | 52210.096 | 52460.604 | 1 | -97.380 | -19.436505 | 203 | 519.260 | False | 228830736 | ... | False | False | True | False | False | False | False | False | False | True | |
106 | 53209.358 | 52709.802 | 52959.000 | 1 | -98.572 | -19.777847 | 205 | 497.204 | False | 229327940 | ... | False | False | True | False | False | False | False | False | False | True | |
107 | 53709.588 | 53209.358 | 53459.716 | 1 | -130.644 | -26.091437 | 207 | 468.644 | False | 229796584 | ... | False | False | True | False | False | False | False | False | False | True | |
108 | 54209.378 | 53709.588 | 53959.460 | 1 | -115.620 | -23.135846 | 209 | 514.768 | False | 230311352 | ... | False | False | True | False | False | False | False | False | False | True | |
109 | 54709.094 | 54209.378 | 54459.296 | 1 | -69.056 | -13.815732 | 211 | 546.400 | False | 230857752 | ... | False | False | True | False | False | False | False | False | False | True | |
110 | 55209.202 | 54709.094 | 54958.892 | 1 | -54.332 | -10.875187 | 213 | 514.320 | False | 231372072 | ... | False | False | True | False | False | False | False | False | False | True | |
111 | 55709.422 | 55209.202 | 55459.512 | 1 | -16.108 | -3.217610 | 215 | 538.844 | False | 231910916 | ... | False | False | True | False | False | False | False | False | False | True | |
112 | 56209.318 | 55709.422 | 55959.332 | 1 | -1.664 | -0.332920 | 217 | 514.264 | False | 232425180 | ... | False | False | True | False | False | False | False | False | False | True | |
113 | 56734.158 | 56209.318 | 56459.304 | 1 | 1.340 | 0.268015 | 220 | 502.976 | False | 232928156 | ... | False | False | True | False | False | False | False | False | False | True | |
116 | 58259.248 | 57759.504 | 58009.380 | 1 | 8.500 | 1.700844 | 226 | 530.256 | False | 234485392 | ... | False | False | False | True | False | False | False | False | False | True | |
117 | 58759.478 | 58259.248 | 58509.116 | 1 | -12.220 | -2.445291 | 227 | 479.016 | False | 234964408 | ... | False | False | False | True | False | False | False | False | False | True | |
118 | 59259.672 | 58759.478 | 59009.840 | 1 | 7.308 | 1.459487 | 230 | 520.252 | False | 235484660 | ... | False | False | False | True | False | False | False | False | False | True | |
119 | 59759.342 | 59259.672 | 59509.504 | 1 | -20.436 | -4.089948 | 231 | 471.920 | False | 235956580 | ... | False | False | False | False | True | False | False | False | False | True | |
120 | 60259.128 | 59759.342 | 60009.180 | 1 | 1.412 | 0.282583 | 234 | 521.524 | False | 236478104 | ... | False | False | False | False | True | False | False | False | False | True | |
121 | 60759.434 | 60259.128 | 60509.076 | 1 | 4.836 | 0.967401 | 236 | 503.320 | False | 236981424 | ... | False | False | False | False | True | False | False | False | False | True | |
122 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | ... | False | False | False | False | True | False | False | False | False | True | |
123 | 61759.204 | 61259.562 | 61509.332 | 1 | -16.256 | -3.254194 | 238 | NaN | False | 237960588 | ... | False | False | False | False | True | False | False | False | False | True | |
124 | 62259.476 | 61759.204 | 62009.076 | 1 | -59.092 | -11.824454 | 240 | 456.908 | False | 238417496 | ... | False | False | False | False | True | False | False | False | False | True | |
125 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | ... | False | False | False | False | True | False | False | False | False | True | |
126 | 63259.534 | 62759.792 | 63009.708 | 1 | -68.880 | -13.780630 | 243 | NaN | False | 239408340 | ... | False | False | False | False | True | False | False | False | False | True | |
127 | 63759.244 | 63259.534 | 63509.360 | 1 | -96.904 | -19.394298 | 245 | 471.628 | False | 239879968 | ... | False | False | False | False | True | False | False | False | False | True | |
128 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | ... | False | False | False | False | True | False | False | False | False | True | |
129 | 64759.792 | 64259.560 | 64509.992 | 1 | 212.476 | 42.421895 | 250 | NaN | False | 241189980 | ... | False | False | False | False | True | False | False | False | False | True | |
130 | 65309.592 | 64759.792 | 65009.592 | 1 | 257.712 | 51.583667 | 252 | 544.836 | False | 241734816 | ... | False | False | False | False | True | False | False | False | False | True | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
121 | 138 | 69369.712 | 68869.998 | 69119.868 | 1 | -23.852 | -4.772882 | 268 | 498.788 | False | 670385312 | ... | False | False | False | False | False | False | True | False | False | True |
139 | 69869.436 | 69369.712 | 69619.556 | 1 | -24.896 | -4.982309 | 270 | 498.644 | False | 670883956 | ... | False | False | False | False | False | False | True | False | False | True | |
140 | 70369.610 | 69869.436 | 70119.316 | 1 | -44.216 | -8.847447 | 272 | 480.440 | False | 671364396 | ... | False | False | False | False | False | False | True | False | False | True | |
141 | 70869.822 | 70369.610 | 70619.904 | 1 | -39.496 | -7.889921 | 274 | 505.308 | False | 671869704 | ... | False | False | False | False | False | False | True | False | False | True | |
142 | 71369.622 | 70869.822 | 71119.740 | 1 | -42.652 | -8.533199 | 276 | 496.680 | False | 672366384 | ... | False | False | False | False | False | False | True | False | False | True | |
143 | 71869.760 | 71369.622 | 71619.504 | 1 | -35.460 | -7.095349 | 278 | 506.956 | False | 672873340 | ... | False | False | False | False | False | False | True | False | False | True | |
144 | 72369.970 | 71869.760 | 72120.016 | 1 | -25.792 | -5.153123 | 280 | 510.180 | False | 673383520 | ... | False | False | False | False | False | False | True | False | False | True | |
145 | 72869.694 | 72369.970 | 72619.924 | 1 | -16.728 | -3.346216 | 282 | 508.972 | False | 673892492 | ... | False | False | False | False | False | False | True | False | False | True | |
146 | 73369.384 | 72869.694 | 73119.464 | 1 | -17.780 | -3.559275 | 284 | 498.488 | False | 674390980 | ... | False | False | False | False | False | False | True | False | False | True | |
147 | 73869.708 | 73369.384 | 73619.304 | 1 | -17.108 | -3.422695 | 286 | 500.512 | False | 674891492 | ... | False | False | False | False | False | False | True | False | False | True | |
148 | 74369.962 | 73869.708 | 74120.112 | 1 | -17.700 | -3.534289 | 288 | 500.216 | False | 675391708 | ... | False | False | False | False | False | False | True | False | False | True | |
149 | 74819.930 | 74369.962 | 74619.812 | 1 | 6.108 | 1.222333 | 291 | 523.508 | False | 675915216 | ... | False | False | False | False | False | False | True | False | False | True | |
152 | 76269.356 | 75769.742 | 76019.476 | 1 | -174.136 | -34.864296 | 295 | NaN | False | 677134636 | ... | False | False | False | False | False | False | False | True | False | True | |
153 | 76769.566 | 76269.356 | 76519.236 | 1 | -144.032 | -28.820234 | 297 | 529.864 | False | 677664500 | ... | False | False | False | False | False | False | False | True | False | True | |
154 | 77269.934 | 76769.566 | 77019.896 | 1 | -143.732 | -28.708505 | 299 | 500.960 | False | 678165460 | ... | False | False | False | False | False | False | False | True | False | True | |
155 | 77769.704 | 77269.934 | 77519.972 | 1 | -71.088 | -14.215439 | 301 | 572.720 | False | 678738180 | ... | False | False | False | False | False | False | False | False | True | True | |
156 | 78269.768 | 77769.704 | 78019.436 | 1 | -75.668 | -15.149841 | 303 | 494.884 | False | 679233064 | ... | False | False | False | False | False | False | False | False | True | True | |
157 | 78770.018 | 78269.768 | 78520.100 | 1 | -70.496 | -14.080501 | 305 | 505.836 | False | 679738900 | ... | False | False | False | False | False | False | False | False | True | True | |
158 | 79269.706 | 78770.018 | 79019.936 | 1 | -72.664 | -14.537568 | 307 | 497.668 | False | 680236568 | ... | False | False | False | False | False | False | False | False | True | True | |
159 | 79769.356 | 79269.706 | 79519.476 | 1 | -46.540 | -9.316571 | 309 | 525.664 | False | 680762232 | ... | False | False | False | False | False | False | False | False | True | True | |
160 | 80269.568 | 79769.356 | 80019.236 | 1 | -29.976 | -5.998079 | 311 | 516.324 | False | 681278556 | ... | False | False | False | False | False | False | False | False | True | True | |
161 | 80769.780 | 80269.568 | 80519.900 | 1 | -32.304 | -6.452231 | 313 | 498.336 | False | 681776892 | ... | False | False | False | False | False | False | False | False | True | True | |
162 | 81269.468 | 80769.780 | 81019.660 | 1 | -41.136 | -8.231151 | 315 | 490.928 | False | 682267820 | ... | False | False | False | False | False | False | False | False | True | True | |
163 | 81769.756 | 81269.468 | 81519.276 | 1 | -53.356 | -10.679402 | 317 | 487.396 | False | 682755216 | ... | False | False | False | False | False | False | False | False | True | True | |
164 | 82275.134 | 81769.756 | 82020.236 | 1 | -16.508 | -3.295273 | 319 | 537.808 | False | 683293024 | ... | False | False | False | False | False | False | False | False | True | True | |
165 | 82779.988 | 82275.134 | 82530.032 | 1 | -53.132 | -10.422208 | 321 | 473.172 | False | 683766196 | ... | False | False | False | False | False | False | False | False | True | True | |
166 | 83279.826 | 82779.988 | 83029.944 | 1 | -78.584 | -15.719567 | 323 | 474.460 | False | 684240656 | ... | False | False | False | False | False | False | False | False | True | True | |
167 | 83779.430 | 83279.826 | 83529.708 | 1 | -55.668 | -11.138858 | 325 | 522.680 | False | 684763336 | ... | False | False | False | False | False | False | False | False | True | True | |
168 | 84279.518 | 83779.430 | 84029.152 | 1 | -56.948 | -11.402279 | 327 | 498.164 | False | 685261500 | ... | False | False | False | False | False | False | False | False | True | True | |
169 | 84780.250 | 84279.518 | 84529.884 | 1 | -51.272 | -10.239410 | 329 | 506.408 | False | 685767908 | ... | False | False | False | False | False | False | False | False | True | True |
6305 rows × 30 columns
tasks_using = sorted([k for k in db_taps.keys() if 'T1_SMS' not in k])
#print(tasks_using)
stack_dev_data = {t: db_taps[t] for t in tasks_using}
for t in tasks_using:
tdatadf = stack_dev_data[t]
if t in phase_tasks:
tdatadf = tdatadf[tdatadf.is_shiftedarea == True]
tdata = tdatadf.dev_perc
len_all = tdata.count()
num_pids = len(tdata.index.get_level_values('pid').unique())
print(short_name[t])
print("i = ", len_all)
print("N = ", num_pids)
plt.figure()
tdata.hist(figsize=(5,5), bins=60, color='white', grid=False, range=(-50,50))
#plt.show()
#plt.tight_layout()
plt.savefig("c:/_Sync/1020_histograms_raw_" + short_name[t] + '2.png',
format='png',
)
#n, bins, patches = plt.hist(db_taps[ 30, stacked=True, normed = True)
#plt.figure()
#plt.hist(stack_dev_data, stacked=True)
#plt.show()
iso5j i = 11326 N = 97 iso8j i = 11354 N = 97 lin5j i = 15941 N = 96 lin8j i = 16062 N = 97 phase5j i = 6244 N = 97 phase8j i = 6256 N = 97 iso5t2 i = 12242 N = 97 iso8t2 i = 11319 N = 97 lin5t i = 16045 N = 97 lin8t i = 16031 N = 97 phase5t i = 6199 N = 97 phase8t i = 6180 N = 97
#Phase tasks: portions NOT in a post-phase-shift period
phase_tasks = ['Ticks_Phase_5', 'Ticks_Phase_8', 'Jits_Phase_5', 'Jits_Phase_8']
phase_normal_sections = {}
phase_post_shift_sections = {}
phase_post_shift_all = {}
for t in phase_tasks:
# The only filtering done so far was to remove the
# first 12 beats after participant began tapping:
pdf = taps_filtered[t]
normal_period = pdf[ (pdf.is_range_0a==True)
| (pdf.is_range_1b==True)
| (pdf.is_range_2b==True)
| (pdf.is_range_3b==True)
| (pdf.is_range_4b==True)]
post_shift = pdf[ (pdf.is_range_1a==True)
| (pdf.is_range_2a==True)
| (pdf.is_range_3a==True)
| (pdf.is_range_4a==True)]
post_shift_all = pdf[pdf.is_shiftedarea==True]
phase_normal_sections[t] = normal_period
phase_post_shift_sections[t] = post_shift
phase_post_shift_all[t] = post_shift_all
responses_possible = 65. #intervals possible for each phase task (post-shift regions)
response_count = {}
for t in phase_tasks:
tdata = phase_post_shift_all[t]
for p in task_pids[t]:
responsecount = tdata.dev_perc.xs(p).count()
responsep = responsecount / responses_possible
response_p_dist.append(responsep)
if responsep < 0.9:
print(p, round(responsep,2))
015 0.88 020 0.89 066 0.88 068 0.86 077 0.86 080 0.86 015 0.85 025 0.66 109 0.89 114 0.75 089 0.88
measurement_region_overall_distributions = {'mean': {},
'sd': {},}
print("Before adjustment: \n\n")
for t, df in phase_post_shift_all.items():
print(t)
subs = df.groupby(level='pid').dev_perc
mean_of_means = subs.mean().mean()
mean_of_sds = subs.std().mean()
count = subs.mean().count()
measurement_region_overall_distributions['mean'][t] = mean_of_means
measurement_region_overall_distributions['sd'][t] = mean_of_sds
print('mean: %s' % mean_of_means)
print('sd: %s' % mean_of_sds)
print('n: %s' % count)
print("\n\nAfter adjustment: \n\n")
for t, df in phase_post_shift_all.items():
print(t)
dist_mean = measurement_region_overall_distributions['mean'][t]
dist_sd = measurement_region_overall_distributions['sd'][t]
subs = phase_post_shift_all[t].copy()
for p in task_pids[t]:
devp = subs.xs(p).dev_perc
upper_limit = 20
lower_limit = -35
devp[devp > upper_limit] = upper_limit
devp[devp < lower_limit] = lower_limit
#missing = len(devp) - devp.count()
#if missing > 18:
# print(p, missing)
#subs_replacena = subs.copy()
#subs_replacena[subs_replacena.dev_perc.isnull()] = dist_mean + (1 * dist_sd)
taps_filtered[t + "_mperiod_noreplace"] = subs
taps_filtered[t + "_mperiod_replaced"] = subs
task_pids[t + "_mperiod_noreplace"] = task_pids[t]
task_pids[t + "_mperiod_replaced"] = task_pids[t]
#recalc after adjustments
print(t)
devs = subs_replacena.groupby(level='pid').dev_perc
mean_of_means = devs.mean().mean()
mean_of_sds = devs.std().mean()
count = devs.mean().count()
print('mean: %s' % mean_of_means)
print('sd: %s' % mean_of_sds)
print('n: %s' % count)
Before adjustment: Jits_Phase_8 mean: -5.96495666546 sd: 8.23188608557 n: 97 Ticks_Phase_8 mean: -2.21579488663 sd: 7.22294888744 n: 97 Jits_Phase_5 mean: -3.54655307335 sd: 8.15543033207 n: 97 Ticks_Phase_5 mean: -2.85313620359 sd: 6.73483236926 n: 97 After adjustment: Jits_Phase_8 Jits_Phase_8 mean: -2.35410456371 sd: 6.47972834294 n: 97 Ticks_Phase_8 Ticks_Phase_8 mean: -2.35410456371 sd: 6.47972834294 n: 97 Jits_Phase_5 Jits_Phase_5 mean: -2.35410456371 sd: 6.47972834294 n: 97 Ticks_Phase_5 Ticks_Phase_5 mean: -2.35410456371 sd: 6.47972834294 n: 97
post_shift_overall_distributions = {'mean': {},
'sd': {},}
print("Before adjustment: \n\n")
for t, df in phase_post_shift_sections.items():
print(t)
subs = df.groupby(level='pid').dev_perc
mean_of_means = subs.mean().mean()
mean_of_sds = subs.std().mean()
count = subs.mean().count()
post_shift_overall_distributions['mean'][t] = mean_of_means
post_shift_overall_distributions['sd'][t] = mean_of_sds
print('mean: %s' % mean_of_means)
print('sd: %s' % mean_of_sds)
print('n: %s' % count)
print("\n\nAfter adjustment: \n\n")
for t, df in phase_post_shift_sections.items():
dist_mean = post_shift_overall_distributions['mean'][t]
dist_sd = post_shift_overall_distributions['sd'][t]
subs = phase_post_shift_sections[t].copy()
upper_limit = dist_mean + (2 * dist_sd)
lower_limit = dist_mean - (2 * dist_sd)
subs[subs.dev_perc > upper_limit] = upper_limit
subs[subs.dev_perc < lower_limit] = lower_limit
subs_replacena = subs.copy()
subs_replacena[subs_replacena.dev_perc.isnull()] = dist_mean + (1 * dist_sd)
taps_filtered[t + "_postshift_noreplace"] = subs
taps_filtered[t + "_postshift_replaced"] = subs
task_pids[t + "_postshift_noreplace"] = task_pids[t]
task_pids[t + "_postshift_replaced"] = task_pids[t]
#recalc after adjustments
print(t)
devs = subs_replacena.groupby(level='pid').dev_perc
mean_of_means = devs.mean().mean()
mean_of_sds = devs.std().mean()
count = devs.mean().count()
print('mean: %s' % mean_of_means)
print('sd: %s' % mean_of_sds)
print('n: %s' % count)
Before adjustment: Jits_Phase_8 mean: -4.43349283862 sd: 8.00491469163 n: 97 Ticks_Phase_8 mean: -2.32322083877 sd: 8.46003821128 n: 97 Jits_Phase_5 mean: -2.9143554828 sd: 9.001105935 n: 97 Ticks_Phase_5 mean: -1.49184382718 sd: 8.78255315072 n: 97 After adjustment: Jits_Phase_8 mean: -4.16182324728 sd: 6.56274308287 n: 97 Ticks_Phase_8 mean: -2.11984931529 sd: 6.80430966983 n: 97 Jits_Phase_5 mean: -2.91788161657 sd: 7.78822306467 n: 97 Ticks_Phase_5 mean: -1.41394797119 sd: 6.93054267767 n: 97
#treat the sections not just after a phase shift like the other tasks...
#t = 'Ticks_Phase_8'
for t in phase_tasks:
alt_taskname = t + "_normal"
print(alt_taskname)
tparams = sms_params[t]
tdata = phase_normal_sections[t]
tdata_filt = {}
outlier_rem_record[alt_taskname] = {}
for pid in task_pids[t]:
pdata = tdata.xs(pid)
filtered_a = filter_taps(pdata, tparams)
filtered_b = filtered_a[filtered_a.is_outlier != True]
tdata_filt[pid] = filtered_b
outlier_rem_record[t][pid] = len(filtered_a) - len(filtered_b)
taps_filtered[alt_taskname] = pd.concat(tdata_filt, names=['pid'])
task_pids[alt_taskname] = task_pids[t]
mean_rem = round(np.mean(outlier_rem_record[t].values()),1)
std_rem = round(np.std(outlier_rem_record[t].values()),1)
max_rem = max(outlier_rem_record[t].values())
print('\n outlier beats removed per P.: mean={}, sd={}, max={}'
.format(mean_rem, std_rem, max_rem))
print('\n' + '=' * 70)
Ticks_Phase_5_normal outlier beats removed per P.: mean=1.6, sd=7.2, max=68 ====================================================================== Ticks_Phase_8_normal outlier beats removed per P.: mean=3.3, sd=11.6, max=73 ====================================================================== Jits_Phase_5_normal outlier beats removed per P.: mean=2.6, sd=7.8, max=55 ====================================================================== Jits_Phase_8_normal outlier beats removed per P.: mean=3.6, sd=13.2, max=95 ======================================================================
taps_filtered.keys()
['T1_SMS_5', 'T1_SMS_8', 'Ticks_ISO_T2_5', 'Ticks_ISO_T2_8', 'Ticks_Linear_5', 'Ticks_Linear_8', 'Ticks_Phase_5', 'Ticks_Phase_8', 'Jits_ISO_5', 'Jits_ISO_8', 'Jits_Linear_5', 'Jits_Linear_8', 'Jits_Phase_5', 'Jits_Phase_8', 'Jits_Phase_8_mperiod_noreplace', 'Jits_Phase_8_mperiod_replaced', 'Ticks_Phase_8_mperiod_noreplace', 'Ticks_Phase_8_mperiod_replaced', 'Jits_Phase_5_mperiod_noreplace', 'Jits_Phase_5_mperiod_replaced', 'Ticks_Phase_5_mperiod_noreplace', 'Ticks_Phase_5_mperiod_replaced', 'Jits_Phase_8_postshift_noreplace', 'Jits_Phase_8_postshift_replaced', 'Ticks_Phase_8_postshift_noreplace', 'Ticks_Phase_8_postshift_replaced', 'Jits_Phase_5_postshift_noreplace', 'Jits_Phase_5_postshift_replaced', 'Ticks_Phase_5_postshift_noreplace', 'Ticks_Phase_5_postshift_replaced', 'Ticks_Phase_5_normal', 'Ticks_Phase_8_normal', 'Jits_Phase_5_normal', 'Jits_Phase_8_normal']
def sideplots(title, serieslist, namelist, **kwargs):
from matplotlib import pyplot as plt
assert len(serieslist) == len(namelist)
count = len(serieslist)
fig, axes = plt.subplots(nrows=count, ncols=3, **kwargs)
colors=['blue', 'green', 'red', 'white', 'orange']
plots = [(namelist[i], serieslist[i]) for i in range(count)]
for (i, (n, s)) in enumerate(plots):
try:
plot_color = colors[i]
except IndexError:
plot_color = 'blue'
ax_hist = plt.subplot2grid((count, 3), (i, 0), colspan=2)
ax_hist.set_title(n, fontsize=12)
ax_line = plt.subplot2grid((count, 3), (i, 2), colspan=1)
ax_line.set_title(n, fontsize=12)
s.plot(ax=ax_line, linewidth=3, color=plot_color)
s.hist(ax=ax_hist, bins=20, color=plot_color)
fig.suptitle(title, fontsize=22)
plt.show()
#fig.tight_layout()
short_name
{'Jits_ISO_5': 'iso5j', 'Jits_ISO_8': 'iso8j', 'Jits_Linear_5': 'lin5j', 'Jits_Linear_8': 'lin8j', 'Jits_Phase_5': 'phase5j', 'Jits_Phase_8': 'phase8j', 'T1_SMS_5': 'iso5t1', 'T1_SMS_8': 'iso8t1', 'Ticks_ISO_T2_5': 'iso5t2', 'Ticks_ISO_T2_8': 'iso8t2', 'Ticks_Linear_5': 'lin5t', 'Ticks_Linear_8': 'lin8t', 'Ticks_Phase_5': 'phase5t', 'Ticks_Phase_8': 'phase8t'}
t = long_name['phase5t']
for pid in task_pids[t]:
tdf = phase_post_shift_sections[t].xs(pid) #(unfiltered)
first_non_miss = tdf[tdf.is_failure==False]
first_beat_tapped = min(first_non_miss.index)
n = 12
after_first_n = tdf.ix[first_beat_tapped + n:]
missed_beats_count = len(after_first_n[after_first_n.is_failure==True])
sdf = after_first_n.dev_perc
full_count = len(after_first_n)
filt_df = sdf[(sdf >= -35) & (sdf <= 20)]
filt_count = len(sdf) - len(filt_df)
good_count = len(filt_df)
pct_retained = 100 * good_count / full_count
print(str(pct_retained) + '%')
if pct_retained < 75: print("\n\n\n!!!!\n")
sideplots(title = "P. {} - misses: {} - filtered out: {} - OK: {}".format(pid,
missed_beats_count,
filt_count,
good_count),
serieslist=[sdf, filt_df],
namelist=['raw', 'filtered'],
figsize=(19,6))
#if pid == '020': break
41% !!!!
100%
100%
100%
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-174-fc0fe5f876cd> in <module>() 11 for pid in task_pids[t]: 12 ---> 13 tdf = phase_post_shift_sections[t].xs(pid) #(unfiltered) 14 15 first_non_miss = tdf[tdf.is_failure==False] C:\Applications\_Data analysis\Anaconda\lib\site-packages\pandas\core\generic.pyc in xs(self, key, axis, level, copy, drop_level) 1345 if isinstance(index, MultiIndex): 1346 loc, new_index = self.index.get_loc_level(key, -> 1347 drop_level=drop_level) 1348 else: 1349 loc = self.index.get_loc(key) C:\Applications\_Data analysis\Anaconda\lib\site-packages\pandas\core\index.pyc in get_loc_level(self, key, level, drop_level) 3394 return new_index 3395 -> 3396 if isinstance(level, (tuple, list)): 3397 if len(key) != len(level): 3398 raise AssertionError('Key for location must have same ' KeyboardInterrupt:
#iso5t2: -30 to 30 looks good
#iso8t2:
#p. 49 mostly halfway between beats sometimes...
#p. 55 consistently tapping halfway between beats
#lin5t:
#p. 089 switches to half-beat tapping for the last portion of the trial
#switching to +/- 25%
#lin8t:
#p. 073 used half-taps
#iso5j (jitters):
#switching to -35 to +20
#pid='015'
t = long_name['iso8j']
for pid in task_pids[t]:
tdf = db_taps[t].xs(pid) #(unfiltered)
first_non_miss = tdf[tdf.is_failure==False]
first_beat_tapped = min(first_non_miss.index)
n = 12
after_first_n = tdf.ix[first_beat_tapped + n:]
missed_beats_count = len(after_first_n[after_first_n.is_failure==True])
sdf = after_first_n.dev_perc
full_count = len(after_first_n)
#filt_df = sdf[(sdf >= -35) & (sdf <= 20)]
filt_df = taps_filtered[t].xs(pid).dev_perc
filt_count = len(sdf) - len(filt_df)
good_count = len(filt_df)
pct_retained = 100 * good_count / full_count
print(str(pct_retained) + '%')
if pct_retained < 75: print("\n\n\n!!!!\n")
sideplots(title = "P. {} - misses: {} - filtered out: {} - OK: {}".format(pid,
missed_beats_count,
filt_count,
good_count),
serieslist=[sdf, filt_df],
namelist=['raw', 'filtered'],
figsize=(19,6))
#if pid == '020': break
66% !!!!
100%
100%
100%
100%
100%
100%
100%
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-151-756ac43b83e2> in <module>() 59 serieslist=[sdf, filt_df], 60 namelist=['raw', 'filtered'], ---> 61 figsize=(19,6)) 62 #if pid == '020': break <ipython-input-20-61523c910c2f> in sideplots(title, serieslist, namelist, **kwargs) 6 count = len(serieslist) 7 ----> 8 fig, axes = plt.subplots(nrows=count, ncols=3, **kwargs) 9 10 colors=['blue', 'green', 'red', 'white', 'orange'] C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\pyplot.pyc in subplots(nrows, ncols, sharex, sharey, squeeze, subplot_kw, **fig_kw) 1052 1053 # Create first subplot separately, so we can share it if requested -> 1054 ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw) 1055 #if sharex: 1056 # subplot_kw['sharex'] = ax0 C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\figure.pyc in add_subplot(self, *args, **kwargs) 912 self._axstack.remove(ax) 913 --> 914 a = subplot_class_factory(projection_class)(self, *args, **kwargs) 915 916 self._axstack.add(key, a) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axes.pyc in __init__(self, fig, *args, **kwargs) 9257 9258 # _axes_class is set in the subplot_class_factory -> 9259 self._axes_class.__init__(self, fig, self.figbox, **kwargs) 9260 9261 def __reduce__(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axes.pyc in __init__(self, fig, rect, axisbg, frameon, sharex, sharey, label, xscale, yscale, **kwargs) 447 448 # this call may differ for non-sep axes, eg polar --> 449 self._init_axis() 450 451 if axisbg is None: C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axes.pyc in _init_axis(self) 506 self.xaxis = maxis.XAxis(self) 507 self.spines['bottom'].register_axis(self.xaxis) --> 508 self.spines['top'].register_axis(self.xaxis) 509 self.yaxis = maxis.YAxis(self) 510 self.spines['left'].register_axis(self.yaxis) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\spines.pyc in register_axis(self, axis) 151 self.axis = axis 152 if self.axis is not None: --> 153 self.axis.cla() 154 155 def cla(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in cla(self) 740 self._set_artist_props(self.label) 741 --> 742 self.reset_ticks() 743 744 self.converter = None C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in reset_ticks(self) 754 755 self.majorTicks.extend([self._get_tick(major=True)]) --> 756 self.minorTicks.extend([self._get_tick(major=False)]) 757 self._lastNumMajorTicks = 1 758 self._lastNumMinorTicks = 1 C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in _get_tick(self, major) 1607 else: 1608 tick_kw = self._minor_tick_kw -> 1609 return XTick(self.axes, 0, '', major=major, **tick_kw) 1610 1611 def _get_label(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in __init__(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major) 138 self.apply_tickdir(tickdir) 139 --> 140 self.tick1line = self._get_tick1line() 141 self.tick2line = self._get_tick2line() 142 self.gridline = self._get_gridline() C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in _get_tick1line(self) 384 markersize=self._size, 385 markeredgewidth=self._width, --> 386 zorder=self._zorder, 387 ) 388 l.set_transform(self.axes.get_xaxis_transform(which='tick1')) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\lines.pyc in __init__(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs) 219 self.set_markeredgecolor(markeredgecolor) 220 self.set_markeredgewidth(markeredgewidth) --> 221 self.set_fillstyle(fillstyle) 222 223 self.verticalOffset = None C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\lines.pyc in set_fillstyle(self, fs) 335 ACCEPTS: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] 336 """ --> 337 self._marker.set_fillstyle(fs) 338 339 def set_markevery(self, every): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\markers.pyc in set_fillstyle(self, fillstyle) 205 % ' '.join(self.fillstyles)) 206 self._fillstyle = fillstyle --> 207 self._recache() 208 209 def get_joinstyle(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\markers.pyc in _recache(self) 182 self._capstyle = 'butt' 183 self._filled = True --> 184 self._marker_function() 185 186 def __nonzero__(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\markers.pyc in _set_tickup(self) 681 682 def _set_tickup(self): --> 683 self._transform = Affine2D().scale(1.0, 1.0) 684 self._snap_threshold = 1.0 685 self._filled = False C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\transforms.pyc in scale(self, sx, sy) 1866 [[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]], 1867 np.float_) -> 1868 self._mtx = np.dot(scale_mtx, self._mtx) 1869 self.invalidate() 1870 return self KeyboardInterrupt:
<matplotlib.figure.Figure at 0x17873b38>
insufficient_data_pids = {}
minimum_prop = 0.70 # must have at least 70% of the the data non-missed
# and in the keepable range (-35 to 20 percent deviation)
data_proportion = {}
# Three participants (49, 55, 73) have already been removed due to an earlier
# interation of this analysis that showed that they each had fewer than half
# of the tasks meeting the 70% qualifying data criterion.
for t in sms_tasknames:
print(t)
devs = taps_filtered[t].groupby(level='pid').dev_perc
task_beats_max = devs.count().max()
print('Max beats: %s' % task_beats_max)
proportion = devs.count() / task_beats_max
below_cutoff = proportion[proportion < minimum_prop]
print(below_cutoff)
T1_SMS_5 Max beats: 117 pid 055 0.692308 073 0.692308 Name: dev_perc, dtype: float64 T1_SMS_8 Max beats: 107 pid 073 0.439252 Name: dev_perc, dtype: float64 Ticks_ISO_T2_5 Max beats: 117 pid 049 0.521368 Name: dev_perc, dtype: float64 Ticks_ISO_T2_8 Max beats: 108 pid 015 0.583333 049 0.453704 055 0.166667 104 0.546296 Name: dev_perc, dtype: float64 Ticks_Linear_5 Max beats: 157 pid 015 0.560510 049 0.490446 055 0.605096 068 0.630573 073 0.662420 089 0.535032 Name: dev_perc, dtype: float64 Ticks_Linear_8 Max beats: 158 pid 029 0.613924 055 0.550633 073 0.563291 086 0.531646 Name: dev_perc, dtype: float64 Ticks_Phase_5 Max beats: 158 Series([], name: dev_perc, dtype: float64) Ticks_Phase_8 Max beats: 158 Series([], name: dev_perc, dtype: float64) Jits_ISO_5 Max beats: 107 pid 104 0.588785 Name: dev_perc, dtype: float64 Jits_ISO_8 Max beats: 108 pid 015 0.592593 049 0.694444 055 0.518519 Name: dev_perc, dtype: float64 Jits_Linear_5 Max beats: 157 pid 019 0.636943 035 0.687898 068 0.535032 073 0.585987 077 0.579618 089 0.312102 104 0.433121 112 0.292994 Name: dev_perc, dtype: float64 Jits_Linear_8 Max beats: 157 pid 068 0.630573 073 0.605096 089 0.528662 Name: dev_perc, dtype: float64 Jits_Phase_5 Max beats: 157 Series([], name: dev_perc, dtype: float64) Jits_Phase_8 Max beats: 157 Series([], name: dev_perc, dtype: float64)
short_name
{'Jits_ISO_5': 'iso5j', 'Jits_ISO_8': 'iso8j', 'Jits_Linear_5': 'lin5j', 'Jits_Linear_8': 'lin8j', 'Jits_Phase_5': 'phase5j', 'Jits_Phase_8': 'phase8j', 'T1_SMS_5': 'iso5t1', 'T1_SMS_8': 'iso8t1', 'Ticks_ISO_T2_5': 'iso5t2', 'Ticks_ISO_T2_8': 'iso8t2', 'Ticks_Linear_5': 'lin5t', 'Ticks_Linear_8': 'lin8t', 'Ticks_Phase_5': 'phase5t', 'Ticks_Phase_8': 'phase8t'}
#From arduino apparatus code:
#define LINEAR_800_STARTING_ISI 820 //
#define LINEAR_800_PCHANGE_EVERY 5 // decrease by 10 ms every five intervals (avg. -2ms per interval)
#define LINEAR_800_PCHANGE_AMOUNT -10
#define LINEAR_500_PCHANGE_EVERY 5 // increase by 10 ms every five intervals (avg. +2ms per interval)
#define LINEAR_500_PCHANGE_AMOUNT 10
#define LINEAR_500_STARTING_ISI 480 //
#tasks go from beat 0 to beat 169: start at 820, end at [820 - (165 * 10 / 5)] = 490
# start at 480, end at [480 + (165 * 10 / 5)] = 810
#Splitting into thirds (by interval count, not time duration!):
linear_tasks = ['Ticks_Linear_5', 'Ticks_Linear_8',
'Jits_Linear_5', 'Jits_Linear_8']
linear_part_taps = OrderedDict()
linear_part_dfs = OrderedDict()
for t in linear_tasks:
subtask_name_A = t + "ptA"
subtask_name_B = t + "ptB"
subtask_name_C = t + "ptC"
task_pids[subtask_name_A] = task_pids[t]
task_pids[subtask_name_B] = task_pids[t]
task_pids[subtask_name_C] = task_pids[t]
linear_part_taps[subtask_name_A] = {}
linear_part_taps[subtask_name_B] = {}
linear_part_taps[subtask_name_C] = {}
for p in task_pids[t]:
#ex_task_5 = db_taps[long_name['lin5t']].xs(p)
#ex_task_8 = db_taps[long_name['lin8t']].xs(p)
#first_part_800 = ex_task_8[10:65] # (10 to 64) --> 800ms to 700ms
#second_part_800 = ex_task_8[65:110] # (65 to 109) --> 690ms to 610ms
#third_part_800 = ex_task_8[110:165] # (110 to 164) --> 600ms to 500ms
#first_part_500 = ex_task_5[10:65] # (10 to 64) --> 500ms to 600ms
#second_part_500 = ex_task_5[65:110] # (65 to 109) --> 610ms to 690ms
#third_part_500 = ex_task_5[110:165] # (110 to 164) --> 590ms to 500ms
#but it comes out the same for all tasks:
taps = taps_filtered[t].xs(p)
first_part = taps[10:65]
second_part = taps[65:110]
third_part = taps[110:165]
linear_part_taps[subtask_name_A][p] = first_part
linear_part_taps[subtask_name_B][p] = second_part
linear_part_taps[subtask_name_C][p] = third_part
linear_part_names = linear_part_taps.keys()
# If we want to add this to the DFO, we'll need to translate the dict to a pd.concat set of pids.
#for (far, df) in linear_part_taps.items():
# taps_filtered[var] = df
#linear_part_taps[t + "ptB"]['110'].dev_perc.plot(figsize=(5,2))
#plt.show()
#---------------------
#sub_tasks = linear_part_taps.keys()
reshaped = {'Tick_Lin_500600': OrderedDict(),
'Tick_Lin_610690': OrderedDict(),
'Tick_Lin_700800': OrderedDict(),
'Jit_Lin_500600': OrderedDict(),
'Jit_Lin_610690': OrderedDict(),
'Jit_Lin_700800': OrderedDict(),
}
lin_tick_both = sorted(set(task_pids['Ticks_Linear_5'])
.intersection(task_pids['Ticks_Linear_8']))
lin_jits_both = sorted(set(task_pids['Jits_Linear_5'])
.intersection(task_pids['Jits_Linear_8']))
for p in lin_tick_both:
t500600 = pd.concat(axis=0, objs = [linear_part_taps['Ticks_Linear_5ptA'][p],
linear_part_taps['Ticks_Linear_8ptC'][p]])
t610690 = pd.concat(axis=0, objs = [linear_part_taps['Ticks_Linear_5ptB'][p],
linear_part_taps['Ticks_Linear_8ptB'][p]])
t700800 = pd.concat(axis=0, objs = [linear_part_taps['Ticks_Linear_5ptC'][p],
linear_part_taps['Ticks_Linear_8ptA'][p]])
reshaped['Tick_Lin_500600'][p] = t500600
reshaped['Tick_Lin_610690'][p] = t610690
reshaped['Tick_Lin_700800'][p] = t700800
for p in lin_jits_both:
j500600 = pd.concat(axis=0, objs = [linear_part_taps['Jits_Linear_5ptA'][p],
linear_part_taps['Jits_Linear_8ptC'][p]])
j610690 = pd.concat(axis=0, objs = [linear_part_taps['Jits_Linear_5ptB'][p],
linear_part_taps['Jits_Linear_8ptB'][p]])
j700800 = pd.concat(axis=0, objs = [linear_part_taps['Jits_Linear_5ptC'][p],
linear_part_taps['Jits_Linear_8ptA'][p]])
reshaped['Jit_Lin_500600'][p] = j500600
reshaped['Jit_Lin_610690'][p] = j610690
reshaped['Jit_Lin_700800'][p] = j700800
for t in ['Tick_Lin_500600', 'Tick_Lin_610690', 'Tick_Lin_700800']:
task_pids[t] = lin_tick_both
for t in ['Jit_Lin_500600', 'Jit_Lin_610690', 'Jit_Lin_700800']:
task_pids[t] = lin_jits_both
reshaped_dfs = OrderedDict()
for (taskname, task_p_dict) in reshaped.items():
print(taskname)
pids_df = pd.concat(axis=0, objs=task_p_dict.values(), keys=task_p_dict.keys(), names=['pid'])
reshaped_dfs[taskname] = pids_df
for (var, df) in reshaped_dfs.items():
taps_filtered[var] = df
#####
reshaped_dfs['Tick_Lin_700800'][::1000]
Tick_Lin_610690 Jit_Lin_610690 Tick_Lin_700800 Tick_Lin_500600 Jit_Lin_700800 Jit_Lin_500600
beat_end | beat_start | beat_target | channel | dev | dev_perc | i | ints | is_failure | micros | multiple_taskms | pitch | selection_case | target_spiked | task_ms | tinterval | velocity | is_outlier | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pid | beat | ||||||||||||||||||
015 | 24 | 19564.626 | 18789.464 | 19179.604 | 1 | -16.344 | -2.094633 | 46 | 745.800 | False | 474034564 | 48 | 1 | NaN | 19163.260 | NaN | 21 | False | |
027 | 28 | 22644.788 | 21874.758 | 22259.788 | 1 | -24.632 | -3.198712 | 52 | 740.512 | False | 566818248 | 48 | 1 | NaN | 22235.156 | NaN | 48 | False | |
039 | 51 | 39749.726 | 39029.210 | 39389.184 | 1 | 29.660 | 4.119742 | 102 | 770.692 | False | 2569465104 | 48 | 1 | NaN | 39418.844 | NaN | 41 | False | |
053 | 137 | 84584.722 | 83834.682 | 84209.424 | 1 | 5.732 | 0.764793 | 273 | 770.312 | False | 154049460 | 48 | 1 | NaN | 84215.156 | NaN | 30 | False | |
063 | 42 | 33179.636 | 32440.008 | 32810.024 | 1 | -8.352 | -1.128600 | 82 | 715.164 | False | 904649928 | 48 | 1 | NaN | 32801.672 | NaN | 52 | False | |
075 | 63 | 48260.200 | 47560.276 | 47909.988 | 1 | 2.748 | 0.392895 | 123 | 689.020 | False | 798640160 | 48 | 1 | NaN | 47912.736 | NaN | 73 | False | |
085 | 76 | 57114.462 | 56444.754 | 56779.652 | 1 | -36.364 | -5.429116 | 150 | 671.592 | False | 782278492 | 48 | 1 | NaN | 56743.288 | NaN | 19 | False | |
097 | 26 | 21104.554 | 20334.668 | 20719.592 | 1 | -15.172 | -1.970779 | 51 | 780.856 | False | 525228584 | 48 | 1 | NaN | 20704.420 | NaN | 29 | False | |
108 | 70 | 53059.276 | 52379.350 | 52719.252 | 1 | 9.936 | 1.461598 | 139 | 659.136 | False | 2258720748 | 48 | 1 | NaN | 52729.188 | NaN | 56 | False | |
119 | 164 | 105564.800 | 104759.910 | 105159.812 | 1 | 16.888 | 2.111517 | 326 | 818.732 | False | 876396388 | 48 | 1 | NaN | 105176.700 | NaN | 42 | False |
taps_filtered['Tick_Lin_610690']
beat_end | beat_start | beat_target | channel | dev | dev_perc | i | ints | is_failure | micros | multiple_taskms | pitch | selection_case | target_spiked | task_ms | tinterval | velocity | is_outlier | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pid | beat | ||||||||||||||||||
015 | 94 | 54075.302 | 53410.100 | 53740.360 | 1 | -118.024 | -17.868346 | 176 | 773.028 | False | 629975088 | 48 | 1 | NaN | 53622.336 | NaN | 22 | False | |
95 | 54745.014 | 54075.302 | 54410.244 | 1 | 117.272 | 17.506315 | 179 | 905.180 | False | 630880268 | 48 | 1 | NaN | 54527.516 | NaN | 26 | False | ||
99 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
101 | 58789.888 | 58109.982 | 58449.912 | 1 | -218.204 | -32.095431 | 189 | 774.016 | False | 634584460 | 48 | 1 | NaN | 58231.708 | NaN | 22 | False | ||
102 | 59469.768 | 58789.888 | 59129.864 | 1 | -200.104 | -29.429136 | 191 | 698.052 | False | 635282512 | 48 | 1 | NaN | 58929.760 | NaN | 24 | False | ||
103 | 60150.162 | 59469.768 | 59809.672 | 1 | -176.264 | -25.928497 | 193 | 703.648 | False | 635986160 | 48 | 1 | NaN | 59633.408 | NaN | 21 | False | ||
104 | 60835.310 | 60150.162 | 60490.652 | 1 | -102.000 | -14.978413 | 195 | 755.244 | False | 636741404 | 48 | 1 | NaN | 60388.652 | NaN | 14 | False | ||
105 | 61524.918 | 60835.310 | 61179.968 | 1 | -25.012 | -3.628525 | 197 | 766.304 | False | 637507708 | 48 | 1 | NaN | 61154.956 | NaN | 20 | False | ||
106 | 62214.958 | 61524.918 | 61869.868 | 1 | -42.124 | -6.105812 | 199 | 672.788 | False | 638180496 | 48 | 1 | NaN | 61827.744 | NaN | 24 | False | ||
107 | 62905.148 | 62214.958 | 62560.048 | 1 | 19.788 | 2.867078 | 202 | 752.092 | False | 638932588 | 48 | 1 | NaN | 62579.836 | NaN | 20 | False | ||
108 | 63595.308 | 62905.148 | 63250.248 | 1 | 52.824 | 7.653434 | 204 | 723.236 | False | 639655824 | 48 | 1 | NaN | 63303.072 | NaN | 19 | False | ||
109 | 64291.022 | 63595.308 | 63940.368 | 1 | 33.092 | 4.795108 | 206 | 670.388 | False | 640326212 | 48 | 1 | NaN | 63973.460 | NaN | 24 | False | ||
110 | 64990.966 | 64291.022 | 64641.676 | 1 | -1.492 | -0.212745 | 207 | 666.724 | False | 640992936 | 48 | 1 | NaN | 64640.184 | NaN | 28 | False | ||
111 | 65689.916 | 64990.966 | 65340.256 | 1 | 3.112 | 0.445475 | 210 | 703.184 | False | 641696120 | 48 | 1 | NaN | 65343.368 | NaN | 27 | False | ||
112 | 66389.834 | 65689.916 | 66039.576 | 1 | 25.312 | 3.619516 | 212 | 721.520 | False | 642417640 | 48 | 1 | NaN | 66064.888 | NaN | 27 | False | ||
113 | 67090.300 | 66389.834 | 66740.092 | 1 | 50.364 | 7.189557 | 214 | 725.568 | False | 643143208 | 48 | 1 | NaN | 66790.456 | NaN | 25 | False | ||
122 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
125 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
128 | 77875.580 | 77145.410 | 77510.404 | 1 | -12.536 | -1.717289 | 242 | 1020.964 | False | 653850620 | 48 | 1 | NaN | 77497.868 | NaN | 26 | False | ||
129 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
130 | 79350.172 | 78610.416 | 78980.076 | 1 | -183.668 | -24.842829 | 245 | NaN | False | 655149160 | 48 | 1 | NaN | 78796.408 | NaN | 21 | False | ||
132 | 80835.520 | 80090.560 | 80460.852 | 1 | -102.516 | -13.842589 | 249 | 969.084 | False | 656711088 | 48 | 1 | NaN | 80358.336 | NaN | 26 | False | ||
133 | 81580.418 | 80835.520 | 81210.188 | 1 | -137.360 | -18.330896 | 251 | 714.492 | False | 657425580 | 48 | 1 | NaN | 81072.828 | NaN | 24 | False | ||
134 | 82325.392 | 81580.418 | 81950.648 | 1 | 18.448 | 2.491424 | 254 | 896.268 | False | 658321848 | 48 | 1 | NaN | 81969.096 | NaN | 21 | False | ||
137 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
139 | 86080.556 | 85325.240 | 85700.616 | 1 | -257.304 | -34.272836 | 262 | 808.924 | False | 661796064 | 48 | 1 | NaN | 85443.312 | NaN | 19 | False | ||
140 | 86840.322 | 86080.556 | 86460.496 | 1 | -219.180 | -28.844028 | 264 | 798.004 | False | 662594068 | 48 | 1 | NaN | 86241.316 | NaN | 20 | False | ||
142 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
143 | 89120.058 | 88359.796 | 88739.696 | 1 | -213.648 | -28.118979 | 269 | NaN | False | 664878800 | 48 | 1 | NaN | 88526.048 | NaN | 18 | False | ||
145 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
121 | 95 | 69524.258 | 68893.980 | 69208.844 | 1 | -19.656 | -3.121348 | 184 | 613.864 | False | 1030425724 | 48 | 1 | NaN | 69189.188 | NaN | 21 | False | |
96 | 70154.540 | 69524.258 | 69839.672 | 1 | -45.348 | -7.188647 | 186 | 605.136 | False | 1031030860 | 48 | 1 | NaN | 69794.324 | NaN | 20 | False | ||
97 | 70784.294 | 70154.540 | 70469.408 | 1 | 21.092 | 3.349340 | 189 | 696.176 | False | 1031727036 | 48 | 1 | NaN | 70490.500 | NaN | 20 | False | ||
98 | 71414.012 | 70784.294 | 71099.180 | 1 | -5.884 | -0.934306 | 190 | 602.796 | False | 1032329832 | 48 | 1 | NaN | 71093.296 | NaN | 25 | False | ||
99 | 72039.202 | 71414.012 | 71728.844 | 1 | 26.928 | 4.276567 | 193 | 662.476 | False | 1032992308 | 48 | 1 | NaN | 71755.772 | NaN | 19 | False | ||
100 | 72659.332 | 72039.202 | 72349.560 | 1 | 50.080 | 8.068102 | 195 | 643.868 | False | 1033636176 | 48 | 1 | NaN | 72399.640 | NaN | 25 | False | ||
101 | 73279.314 | 72659.332 | 72969.104 | 1 | 59.144 | 9.546376 | 197 | 628.608 | False | 1034264784 | 48 | 1 | NaN | 73028.248 | NaN | 23 | False | ||
102 | 73899.246 | 73279.314 | 73589.524 | 1 | 20.500 | 3.304213 | 199 | 581.776 | False | 1034846560 | 48 | 1 | NaN | 73610.024 | NaN | 20 | False | ||
103 | 74519.372 | 73899.246 | 74208.968 | 1 | 9.144 | 1.476162 | 201 | 608.088 | False | 1035454648 | 48 | 1 | NaN | 74218.112 | NaN | 20 | False | ||
104 | 75134.410 | 74519.372 | 74829.776 | 1 | -20.408 | -3.287329 | 202 | 591.256 | False | 1036045904 | 48 | 1 | NaN | 74809.368 | NaN | 19 | False | ||
105 | 75744.060 | 75134.410 | 75439.044 | 1 | 28.444 | 4.668553 | 205 | 658.120 | False | 1036704024 | 48 | 1 | NaN | 75467.488 | NaN | 19 | False | ||
106 | 76354.266 | 75744.060 | 76049.076 | 1 | -12.388 | -2.030713 | 206 | 569.200 | False | 1037273224 | 48 | 1 | NaN | 76036.688 | NaN | 21 | False | ||
107 | 76964.610 | 76354.266 | 76659.456 | 1 | -42.884 | -7.025787 | 208 | 579.884 | False | 1037853108 | 48 | 1 | NaN | 76616.572 | NaN | 24 | False | ||
108 | 77574.370 | 76964.610 | 77269.764 | 1 | -37.336 | -6.117567 | 210 | 615.856 | False | 1038468964 | 48 | 1 | NaN | 77232.428 | NaN | 19 | False | ||
109 | 78179.112 | 77574.370 | 77878.976 | 1 | -22.068 | -3.622384 | 212 | 624.480 | False | 1039093444 | 48 | 1 | NaN | 77856.908 | NaN | 16 | False | ||
110 | 78779.474 | 78179.112 | 78479.248 | 1 | -20.556 | -3.424448 | 214 | 601.784 | False | 1039695228 | 48 | 1 | NaN | 78458.692 | NaN | 19 | False | ||
111 | 79379.500 | 78779.474 | 79079.700 | 1 | 1.644 | 0.273794 | 217 | 622.652 | False | 1040317880 | 48 | 1 | NaN | 79081.344 | NaN | 18 | False | ||
112 | 79979.472 | 79379.500 | 79679.300 | 1 | -12.628 | -2.106071 | 218 | 585.328 | False | 1040903208 | 48 | 1 | NaN | 79666.672 | NaN | 20 | False | ||
113 | 80579.574 | 79979.472 | 80279.644 | 1 | -35.764 | -5.957251 | 220 | 577.208 | False | 1041480416 | 48 | 1 | NaN | 80243.880 | NaN | 20 | False | ||
114 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | |
115 | 81764.240 | 81174.480 | 81469.456 | 1 | -31.564 | -5.350266 | 223 | NaN | False | 1042674428 | 48 | 1 | NaN | 81437.892 | NaN | 25 | False | ||
116 | 82353.922 | 81764.240 | 82059.024 | 1 | -15.024 | -2.548307 | 225 | 606.108 | False | 1043280536 | 48 | 1 | NaN | 82044.000 | NaN | 26 | False | ||
117 | 82944.212 | 82353.922 | 82648.820 | 1 | 0.968 | 0.164125 | 228 | 605.788 | False | 1043886324 | 48 | 1 | NaN | 82649.788 | NaN | 21 | False | ||
118 | 83534.610 | 82944.212 | 83239.604 | 1 | -22.056 | -3.733344 | 229 | 567.760 | False | 1044454084 | 48 | 1 | NaN | 83217.548 | NaN | 24 | False | ||
119 | 84119.530 | 83534.610 | 83829.616 | 1 | -8.652 | -1.466411 | 231 | 603.416 | False | 1045057500 | 48 | 1 | NaN | 83820.964 | NaN | 19 | False | ||
120 | 84699.558 | 84119.530 | 84409.444 | 1 | -17.784 | -3.067116 | 233 | 570.696 | False | 1045628196 | 48 | 1 | NaN | 84391.660 | NaN | 20 | False | ||
121 | 85279.550 | 84699.558 | 84989.672 | 1 | -31.068 | -5.354447 | 235 | 566.944 | False | 1046195140 | 48 | 1 | NaN | 84958.604 | NaN | 16 | False | ||
122 | 85859.156 | 85279.550 | 85569.428 | 1 | -52.232 | -9.009307 | 237 | 558.592 | False | 1046753732 | 48 | 1 | NaN | 85517.196 | NaN | 21 | False | ||
123 | 86439.248 | 85859.156 | 86148.884 | 1 | -85.568 | -14.766954 | 239 | 546.120 | False | 1047299852 | 48 | 1 | NaN | 86063.316 | NaN | 19 | False | ||
124 | 87014.710 | 86439.248 | 86729.612 | 1 | -29.844 | -5.139067 | 241 | 636.452 | False | 1047936304 | 48 | 1 | NaN | 86699.768 | NaN | 18 | False |
8601 rows × 18 columns
#taps_filtered.keys()
short_name['Jits_Phase_8_postshift_noreplace'] = 'phase8j_psk' #post shift, kept nans
short_name['Jits_Phase_8_postshift_replaced'] = 'phase8j_psr' #post shift, replaced nans
short_name['Ticks_Phase_8_postshift_noreplace'] = 'phase8tp_psk'
short_name['Ticks_Phase_8_postshift_replaced'] = 'phase8t_psr'
short_name['Jits_Phase_5_postshift_noreplace'] = 'phase5j_psk'
short_name['Jits_Phase_5_postshift_replaced'] = 'phase5j_psr'
short_name['Ticks_Phase_5_postshift_noreplace'] = 'phase5t_psk'
short_name['Ticks_Phase_5_postshift_replaced'] = 'phase5t_psr'
short_name['Jits_Phase_8_mperiod_noreplace'] = 'phase8j_mpk' #post shift, kept nans
short_name['Jits_Phase_8_mperiod_replaced'] = 'phase8j_mpr' #post shift, replaced nans
short_name['Ticks_Phase_8_mperiod_noreplace'] = 'phase8tp_mpk'
short_name['Ticks_Phase_8_mperiod_replaced'] = 'phase8t_mpr'
short_name['Jits_Phase_5_mperiod_noreplace'] = 'phase5j_mpk'
short_name['Jits_Phase_5_mperiod_replaced'] = 'phase5j_mpr'
short_name['Ticks_Phase_5_mperiod_noreplace'] = 'phase5t_mpk'
short_name['Ticks_Phase_5_mperiod_replaced'] = 'phase5t_mpr'
short_name['Ticks_Phase_5_normal'] = 'phase5t_nrm'
short_name['Ticks_Phase_8_normal'] = 'phase8t_nrm'
short_name['Jits_Phase_5_normal'] = 'phase5j_nrm'
short_name['Jits_Phase_8_normal'] = 'phase8j_nrm'
short_name['Tick_Lin_500600'] = 'lint_500600'
short_name['Tick_Lin_610690'] = 'lint_610690'
short_name['Tick_Lin_700800'] = 'lint_700800'
short_name['Jit_Lin_500600'] = 'linj_500600'
short_name['Jit_Lin_610690'] = 'linj_610690'
short_name['Jit_Lin_700800'] = 'linj_700800'
tasknames = task_frames.keys()
# create a dataframe with an index for every PID that is represented
# in at least one of the tasks' data
pids_all_sms = set()
for (t, df) in task_frames.items():
pids = df.index.get_level_values('pid').unique()
pids_not_excl = [p for p in pids if p not in excluded_all_tasks]
pids_all_sms = pids_all_sms.union(pids_not_excl)
dfo = pd.DataFrame(index = sorted(pids_all_sms))
dfo.index.name = 'pid'
# Iterate through all combinations of tasks, measures, statistics,
# participants. (the valid PIDs are different between tasks, so
# this selects the correct pids. But it might be more elegant to use an
# all-possible-combinations function across all four lists, and handle
# an exception when a PID isn't represented in a given task's data.)
def stat_combo_gen(tasks, measures, statfuncs, statkwargs):
kwargs_all = {k: {} for k in statfuncs} #default, no kwargs
for k, v in statkwargs.items():
kwargs_all[k] = v
for task in tasks:
for measure in measures:
for statfunc in statfuncs:
for p in task_pids[task]:
yield (task, measure, statfunc, kwargs_all, p)
filt_tasknames = taps_filtered.keys()
#stat_combos = stat_combo_gen(tasks = tasknames, # ['T1_SMS_8'],
stat_combos = stat_combo_gen(tasks = filt_tasknames, # ['T1_SMS_8'],
measures = ['dev_perc', 'dev', 'ints'],
statfuncs = ['mean', 'std', 'count'],
statkwargs = {'std': {'ddof': 1}}
)
for (task, measure, statfunc, kwargs, p) in stat_combos:
#print((task, measure, statfunc, p))
ptaps = taps_filtered[task].xs(p, level='pid')
mseries = ptaps[measure]
statfunction = getattr(mseries, statfunc)
result = statfunction(**kwargs[statfunc])
output_varname = '_'.join([short_name[task], measure, statfunc])
output_varname = output_varname.replace("dev_perc_mean", "DPm")
output_varname = output_varname.replace("dev_perc_std", "DPsd")
output_varname = output_varname.replace("dev_perc_count", "DPct")
if output_varname not in dfo:
print(output_varname, end=", ")
dfo[output_varname] = np.nan
dfo[output_varname].loc[p] = result
iso5t1_DPm, iso5t1_DPsd, iso5t1_DPct, iso5t1_dev_mean, iso5t1_dev_std, iso5t1_dev_count, iso5t1_ints_mean, iso5t1_ints_std, iso5t1_ints_count, iso8t1_DPm, iso8t1_DPsd, iso8t1_DPct, iso8t1_dev_mean, iso8t1_dev_std, iso8t1_dev_count, iso8t1_ints_mean, iso8t1_ints_std, iso8t1_ints_count, iso5t2_DPm, iso5t2_DPsd, iso5t2_DPct, iso5t2_dev_mean, iso5t2_dev_std, iso5t2_dev_count, iso5t2_ints_mean, iso5t2_ints_std, iso5t2_ints_count, iso8t2_DPm, iso8t2_DPsd, iso8t2_DPct, iso8t2_dev_mean, iso8t2_dev_std, iso8t2_dev_count, iso8t2_ints_mean, iso8t2_ints_std, iso8t2_ints_count, lin5t_DPm, lin5t_DPsd, lin5t_DPct, lin5t_dev_mean, lin5t_dev_std, lin5t_dev_count, lin5t_ints_mean, lin5t_ints_std, lin5t_ints_count, lin8t_DPm, lin8t_DPsd, lin8t_DPct, lin8t_dev_mean, lin8t_dev_std, lin8t_dev_count, lin8t_ints_mean, lin8t_ints_std, lin8t_ints_count, phase5t_DPm, phase5t_DPsd, phase5t_DPct, phase5t_dev_mean, phase5t_dev_std, phase5t_dev_count, phase5t_ints_mean, phase5t_ints_std, phase5t_ints_count, phase8t_DPm, phase8t_DPsd, phase8t_DPct, phase8t_dev_mean, phase8t_dev_std, phase8t_dev_count, phase8t_ints_mean, phase8t_ints_std, phase8t_ints_count, iso5j_DPm, iso5j_DPsd, iso5j_DPct, iso5j_dev_mean, iso5j_dev_std, iso5j_dev_count, iso5j_ints_mean, iso5j_ints_std, iso5j_ints_count, iso8j_DPm, iso8j_DPsd, iso8j_DPct, iso8j_dev_mean, iso8j_dev_std, iso8j_dev_count, iso8j_ints_mean, iso8j_ints_std, iso8j_ints_count, lin5j_DPm, lin5j_DPsd, lin5j_DPct, lin5j_dev_mean, lin5j_dev_std, lin5j_dev_count, lin5j_ints_mean, lin5j_ints_std, lin5j_ints_count, lin8j_DPm, lin8j_DPsd, lin8j_DPct, lin8j_dev_mean, lin8j_dev_std, lin8j_dev_count, lin8j_ints_mean, lin8j_ints_std, lin8j_ints_count, phase5j_DPm, phase5j_DPsd, phase5j_DPct, phase5j_dev_mean, phase5j_dev_std, phase5j_dev_count, phase5j_ints_mean, phase5j_ints_std, phase5j_ints_count, phase8j_DPm, phase8j_DPsd, phase8j_DPct, phase8j_dev_mean, phase8j_dev_std, phase8j_dev_count, phase8j_ints_mean, phase8j_ints_std, phase8j_ints_count, phase8j_mpk_DPm, phase8j_mpk_DPsd, phase8j_mpk_DPct, phase8j_mpk_dev_mean, phase8j_mpk_dev_std, phase8j_mpk_dev_count, phase8j_mpk_ints_mean, phase8j_mpk_ints_std, phase8j_mpk_ints_count, phase8j_mpr_DPm, phase8j_mpr_DPsd, phase8j_mpr_DPct, phase8j_mpr_dev_mean, phase8j_mpr_dev_std, phase8j_mpr_dev_count, phase8j_mpr_ints_mean, phase8j_mpr_ints_std, phase8j_mpr_ints_count, phase8tp_mpk_DPm, phase8tp_mpk_DPsd, phase8tp_mpk_DPct, phase8tp_mpk_dev_mean, phase8tp_mpk_dev_std, phase8tp_mpk_dev_count, phase8tp_mpk_ints_mean, phase8tp_mpk_ints_std, phase8tp_mpk_ints_count, phase8t_mpr_DPm, phase8t_mpr_DPsd, phase8t_mpr_DPct, phase8t_mpr_dev_mean, phase8t_mpr_dev_std, phase8t_mpr_dev_count, phase8t_mpr_ints_mean, phase8t_mpr_ints_std, phase8t_mpr_ints_count, phase5j_mpk_DPm, phase5j_mpk_DPsd, phase5j_mpk_DPct, phase5j_mpk_dev_mean, phase5j_mpk_dev_std, phase5j_mpk_dev_count, phase5j_mpk_ints_mean, phase5j_mpk_ints_std, phase5j_mpk_ints_count, phase5j_mpr_DPm, phase5j_mpr_DPsd, phase5j_mpr_DPct, phase5j_mpr_dev_mean, phase5j_mpr_dev_std, phase5j_mpr_dev_count, phase5j_mpr_ints_mean, phase5j_mpr_ints_std, phase5j_mpr_ints_count, phase5t_mpk_DPm, phase5t_mpk_DPsd, phase5t_mpk_DPct, phase5t_mpk_dev_mean, phase5t_mpk_dev_std, phase5t_mpk_dev_count, phase5t_mpk_ints_mean, phase5t_mpk_ints_std, phase5t_mpk_ints_count, phase5t_mpr_DPm, phase5t_mpr_DPsd, phase5t_mpr_DPct, phase5t_mpr_dev_mean, phase5t_mpr_dev_std, phase5t_mpr_dev_count, phase5t_mpr_ints_mean, phase5t_mpr_ints_std, phase5t_mpr_ints_count, phase8j_psk_DPm, phase8j_psk_DPsd, phase8j_psk_DPct, phase8j_psk_dev_mean, phase8j_psk_dev_std, phase8j_psk_dev_count, phase8j_psk_ints_mean, phase8j_psk_ints_std, phase8j_psk_ints_count, phase8j_psr_DPm, phase8j_psr_DPsd, phase8j_psr_DPct, phase8j_psr_dev_mean, phase8j_psr_dev_std, phase8j_psr_dev_count, phase8j_psr_ints_mean, phase8j_psr_ints_std, phase8j_psr_ints_count, phase8tp_psk_DPm, phase8tp_psk_DPsd, phase8tp_psk_DPct, phase8tp_psk_dev_mean, phase8tp_psk_dev_std, phase8tp_psk_dev_count, phase8tp_psk_ints_mean, phase8tp_psk_ints_std, phase8tp_psk_ints_count, phase8t_psr_DPm, phase8t_psr_DPsd, phase8t_psr_DPct, phase8t_psr_dev_mean, phase8t_psr_dev_std, phase8t_psr_dev_count, phase8t_psr_ints_mean, phase8t_psr_ints_std, phase8t_psr_ints_count, phase5j_psk_DPm, phase5j_psk_DPsd, phase5j_psk_DPct, phase5j_psk_dev_mean, phase5j_psk_dev_std, phase5j_psk_dev_count, phase5j_psk_ints_mean, phase5j_psk_ints_std, phase5j_psk_ints_count, phase5j_psr_DPm, phase5j_psr_DPsd, phase5j_psr_DPct, phase5j_psr_dev_mean, phase5j_psr_dev_std, phase5j_psr_dev_count, phase5j_psr_ints_mean, phase5j_psr_ints_std, phase5j_psr_ints_count, phase5t_psk_DPm, phase5t_psk_DPsd, phase5t_psk_DPct, phase5t_psk_dev_mean, phase5t_psk_dev_std, phase5t_psk_dev_count, phase5t_psk_ints_mean, phase5t_psk_ints_std, phase5t_psk_ints_count, phase5t_psr_DPm, phase5t_psr_DPsd, phase5t_psr_DPct, phase5t_psr_dev_mean, phase5t_psr_dev_std, phase5t_psr_dev_count, phase5t_psr_ints_mean, phase5t_psr_ints_std, phase5t_psr_ints_count, phase5t_nrm_DPm, phase5t_nrm_DPsd, phase5t_nrm_DPct, phase5t_nrm_dev_mean, phase5t_nrm_dev_std, phase5t_nrm_dev_count, phase5t_nrm_ints_mean, phase5t_nrm_ints_std, phase5t_nrm_ints_count, phase8t_nrm_DPm, phase8t_nrm_DPsd, phase8t_nrm_DPct, phase8t_nrm_dev_mean, phase8t_nrm_dev_std, phase8t_nrm_dev_count, phase8t_nrm_ints_mean, phase8t_nrm_ints_std, phase8t_nrm_ints_count, phase5j_nrm_DPm, phase5j_nrm_DPsd, phase5j_nrm_DPct, phase5j_nrm_dev_mean, phase5j_nrm_dev_std, phase5j_nrm_dev_count, phase5j_nrm_ints_mean, phase5j_nrm_ints_std, phase5j_nrm_ints_count, phase8j_nrm_DPm, phase8j_nrm_DPsd, phase8j_nrm_DPct, phase8j_nrm_dev_mean, phase8j_nrm_dev_std, phase8j_nrm_dev_count, phase8j_nrm_ints_mean, phase8j_nrm_ints_std, phase8j_nrm_ints_count, lint_610690_DPm, lint_610690_DPsd, lint_610690_DPct, lint_610690_dev_mean, lint_610690_dev_std, lint_610690_dev_count, lint_610690_ints_mean, lint_610690_ints_std, lint_610690_ints_count, linj_610690_DPm, linj_610690_DPsd, linj_610690_DPct, linj_610690_dev_mean, linj_610690_dev_std, linj_610690_dev_count, linj_610690_ints_mean, linj_610690_ints_std, linj_610690_ints_count, lint_700800_DPm, lint_700800_DPsd, lint_700800_DPct, lint_700800_dev_mean, lint_700800_dev_std, lint_700800_dev_count, lint_700800_ints_mean, lint_700800_ints_std, lint_700800_ints_count, lint_500600_DPm, lint_500600_DPsd, lint_500600_DPct, lint_500600_dev_mean, lint_500600_dev_std, lint_500600_dev_count, lint_500600_ints_mean, lint_500600_ints_std, lint_500600_ints_count, linj_700800_DPm, linj_700800_DPsd, linj_700800_DPct, linj_700800_dev_mean, linj_700800_dev_std, linj_700800_dev_count, linj_700800_ints_mean, linj_700800_ints_std, linj_700800_ints_count, linj_500600_DPm, linj_500600_DPsd, linj_500600_DPct, linj_500600_dev_mean, linj_500600_dev_std, linj_500600_dev_count, linj_500600_ints_mean, linj_500600_ints_std, linj_500600_ints_count,
dfo.T
# We need a good way of dealing with missed beats in the post-shift periods.
# We want missed beats to "count against" the participant there.
# So: we'll use an outlier criterion for truncating (not removing) here, and
# but we'll apply it identically across participants (rather than relative to
# individual participants' overall variability). When a tap is missed, we
# apply the maximum (truncation) value, so that a missed tap is as bad as the
# worst inaccuracy that's kept on record for participants.
pid | 015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
iso5t1_DPm | -3.202915 | -2.840413 | -2.290274 | -3.022502 | -0.588655 | -10.975295 | -3.609441 | -1.953836 | -5.568016 | -5.484477 | ... | -10.934370 | -0.918811 | -1.107983 | -0.594511 | -0.838101 | -7.211448 | -3.449031 | -0.357402 | -5.634339 | -4.897263 |
iso5t1_DPsd | 8.582088 | 3.423481 | 3.607109 | 3.291210 | 2.583675 | 7.004012 | 4.608983 | 3.167766 | 3.170995 | 3.186261 | ... | 6.123362 | 2.799212 | 4.586392 | 4.075651 | 2.487950 | 5.184207 | 5.405130 | 2.827062 | 5.538719 | 4.245075 |
iso5t1_DPct | 109.000000 | 116.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 | 116.000000 | 110.000000 | ... | 114.000000 | 115.000000 | 113.000000 | 112.000000 | 113.000000 | 114.000000 | 114.000000 | 114.000000 | 98.000000 | 114.000000 |
iso5t1_dev_mean | -16.010606 | -14.202517 | -11.450393 | -15.112000 | -2.943828 | -54.878940 | -18.047483 | -9.768456 | -27.838379 | -27.423273 | ... | -54.671333 | -4.593843 | -5.538513 | -2.973321 | -4.189770 | -36.057789 | -17.244000 | -1.786596 | -28.172776 | -24.488526 |
iso5t1_dev_std | 42.896551 | 17.118586 | 18.032705 | 16.457877 | 12.918372 | 35.020673 | 23.046491 | 15.833194 | 15.852717 | 15.927936 | ... | 30.617894 | 13.995923 | 22.925903 | 20.374066 | 12.438061 | 25.919974 | 27.024790 | 14.135781 | 27.700888 | 21.226033 |
iso5t1_dev_count | 109.000000 | 116.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 | 116.000000 | 110.000000 | ... | 114.000000 | 115.000000 | 113.000000 | 112.000000 | 113.000000 | 114.000000 | 114.000000 | 114.000000 | 98.000000 | 114.000000 |
iso5t1_ints_mean | 502.308731 | 499.983374 | 500.089812 | 499.661138 | 500.006000 | 499.371726 | 499.922966 | 500.060070 | 499.864241 | 500.063491 | ... | 500.144246 | 499.777649 | 499.598582 | 500.773345 | 499.961214 | 500.053333 | 500.755895 | 500.177404 | 501.673633 | 500.111434 |
iso5t1_ints_std | 35.124004 | 19.831489 | 21.902181 | 20.380895 | 14.309757 | 24.981757 | 26.865811 | 19.949975 | 18.868627 | 15.898406 | ... | 27.714262 | 12.555693 | 28.246736 | 27.138166 | 16.746550 | 18.086702 | 19.358602 | 15.098443 | 21.476434 | 17.214456 |
iso5t1_ints_count | 104.000000 | 115.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 | 116.000000 | 110.000000 | ... | 114.000000 | 114.000000 | 110.000000 | 110.000000 | 112.000000 | 114.000000 | 114.000000 | 114.000000 | 98.000000 | 113.000000 |
iso8t1_DPm | -3.198754 | -2.827224 | -13.311147 | -0.553203 | -2.327200 | -3.092538 | -1.458121 | 0.968610 | -2.741571 | 0.354671 | ... | -5.737524 | 0.205687 | -0.826352 | -2.226755 | -1.372491 | -1.247007 | 1.162443 | -0.060845 | -9.056157 | -3.304496 |
iso8t1_DPsd | 7.944039 | 2.964738 | 9.624027 | 5.005393 | 2.431597 | 6.505625 | 4.433459 | 2.268350 | 4.452189 | 4.964179 | ... | 8.144077 | 2.344456 | 6.235354 | 3.659721 | 2.334648 | 5.438937 | 3.613148 | 2.767637 | 4.298762 | 5.691151 |
iso8t1_DPct | 84.000000 | 104.000000 | 103.000000 | 106.000000 | 107.000000 | 101.000000 | 106.000000 | 104.000000 | 107.000000 | 103.000000 | ... | 98.000000 | 104.000000 | 104.000000 | 106.000000 | 106.000000 | 103.000000 | 106.000000 | 104.000000 | 102.000000 | 105.000000 |
iso8t1_dev_mean | -25.590571 | -22.616038 | -106.491573 | -4.426755 | -18.618243 | -24.742257 | -11.667094 | 7.748077 | -21.934579 | 2.835883 | ... | -45.901959 | 1.644308 | -6.616038 | -17.817962 | -10.979925 | -9.973903 | 9.298755 | -0.490923 | -72.449020 | -26.437486 |
iso8t1_dev_std | 63.544930 | 23.714597 | 76.990767 | 40.042257 | 19.452715 | 52.040415 | 35.461231 | 18.145349 | 35.619188 | 39.708958 | ... | 65.151177 | 18.754777 | 49.879442 | 29.279861 | 18.677167 | 43.512120 | 28.906756 | 22.143410 | 34.388699 | 45.531691 |
iso8t1_dev_count | 84.000000 | 104.000000 | 103.000000 | 106.000000 | 107.000000 | 101.000000 | 106.000000 | 104.000000 | 107.000000 | 103.000000 | ... | 98.000000 | 104.000000 | 104.000000 | 106.000000 | 106.000000 | 103.000000 | 106.000000 | 104.000000 | 102.000000 | 105.000000 |
iso8t1_ints_mean | 806.249253 | 799.617663 | 801.918118 | 800.000264 | 800.274131 | 798.427515 | 800.581811 | 799.969538 | 800.026542 | 799.152549 | ... | 801.884884 | 800.040308 | 800.386824 | 799.707276 | 799.953547 | 800.741255 | 800.792264 | 800.469731 | 800.220745 | 800.108155 |
iso8t1_ints_std | 49.683214 | 32.389188 | 36.390481 | 40.611161 | 20.218406 | 61.034256 | 39.983867 | 21.396505 | 41.784418 | 45.136511 | ... | 58.127908 | 21.335290 | 50.067403 | 38.509564 | 21.827709 | 38.341611 | 29.683219 | 20.671748 | 26.000545 | 41.996412 |
iso8t1_ints_count | 83.000000 | 101.000000 | 102.000000 | 106.000000 | 107.000000 | 99.000000 | 106.000000 | 104.000000 | 107.000000 | 102.000000 | ... | 95.000000 | 104.000000 | 102.000000 | 105.000000 | 106.000000 | 102.000000 | 106.000000 | 104.000000 | 102.000000 | 103.000000 |
iso5t2_DPm | -7.320309 | -0.609046 | -2.155283 | -2.628721 | -0.035660 | -3.025222 | -5.794925 | -1.195840 | -1.939757 | 0.793761 | ... | -10.848408 | 1.998485 | -2.249983 | -0.615855 | -2.109468 | -10.922451 | -1.735612 | 1.315051 | -11.177051 | -7.462378 |
iso5t2_DPsd | 8.438450 | 3.522687 | 3.871627 | 3.373599 | 2.612512 | 7.021192 | 5.208270 | 3.488723 | 3.281671 | 5.276439 | ... | 5.511892 | 2.801852 | 6.319350 | 4.716004 | 3.143240 | 4.653159 | 4.076174 | 3.167036 | 5.855743 | 4.506521 |
iso5t2_DPct | 102.000000 | 115.000000 | 113.000000 | 116.000000 | 117.000000 | 112.000000 | 116.000000 | 117.000000 | 116.000000 | 98.000000 | ... | 114.000000 | 113.000000 | 109.000000 | 116.000000 | 117.000000 | 114.000000 | 114.000000 | 116.000000 | 114.000000 | 110.000000 |
iso5t2_dev_mean | -36.598431 | -3.044383 | -10.779327 | -13.145345 | -0.181265 | -15.130071 | -28.975138 | -5.979350 | -9.698759 | 3.969143 | ... | -54.241544 | 9.990088 | -11.252771 | -3.081966 | -10.550188 | -54.611789 | -8.680772 | 6.572207 | -55.885614 | -37.312109 |
iso5t2_dev_std | 42.201264 | 17.610315 | 19.359850 | 16.867665 | 13.060263 | 35.105208 | 26.040483 | 17.438377 | 16.406360 | 26.378954 | ... | 27.552623 | 14.014877 | 31.599656 | 23.574756 | 15.720240 | 23.267520 | 20.386118 | 15.837025 | 29.279216 | 22.531627 |
iso5t2_dev_count | 102.000000 | 115.000000 | 113.000000 | 116.000000 | 117.000000 | 112.000000 | 116.000000 | 117.000000 | 116.000000 | 98.000000 | ... | 114.000000 | 113.000000 | 109.000000 | 116.000000 | 117.000000 | 114.000000 | 114.000000 | 116.000000 | 114.000000 | 110.000000 |
iso5t2_ints_mean | 497.762511 | 500.728283 | 499.561036 | 500.063862 | 500.227111 | 500.431927 | 499.685241 | 499.951966 | 499.913724 | 502.111625 | ... | 499.233789 | 500.881607 | 500.193231 | 499.434793 | 500.534427 | 499.803930 | 500.239579 | 499.958034 | 500.085965 | 499.497000 |
iso5t2_ints_std | 33.280437 | 21.690589 | 28.073822 | 20.327823 | 15.633583 | 31.042419 | 25.163016 | 24.080754 | 21.041502 | 25.667839 | ... | 22.156106 | 12.782811 | 38.134461 | 23.337360 | 23.550592 | 17.140675 | 22.179854 | 17.412732 | 18.091992 | 22.827675 |
iso5t2_ints_count | 94.000000 | 113.000000 | 112.000000 | 116.000000 | 117.000000 | 109.000000 | 116.000000 | 117.000000 | 116.000000 | 96.000000 | ... | 114.000000 | 112.000000 | 104.000000 | 116.000000 | 117.000000 | 114.000000 | 114.000000 | 116.000000 | 114.000000 | 108.000000 |
iso8t2_DPm | -0.419035 | -4.064043 | -4.258572 | -0.759809 | -1.704668 | -1.823981 | -1.913083 | 0.890989 | -2.384110 | 0.150065 | ... | -1.732022 | -0.082369 | -6.790021 | -1.854593 | -1.226942 | -3.161301 | 0.728819 | -1.877539 | -4.290588 | -5.760608 |
iso8t2_DPsd | 10.221017 | 3.101403 | 5.063099 | 5.568829 | 3.404020 | 6.388830 | 4.368410 | 2.339890 | 4.170302 | 4.422124 | ... | 5.838884 | 2.772427 | 6.077328 | 3.511975 | 2.276168 | 4.564913 | 2.928230 | 3.214585 | 3.713862 | 5.989694 |
iso8t2_DPct | 63.000000 | 105.000000 | 105.000000 | 107.000000 | 107.000000 | 102.000000 | 106.000000 | 106.000000 | 107.000000 | 101.000000 | ... | 96.000000 | 104.000000 | 102.000000 | 102.000000 | 105.000000 | 105.000000 | 104.000000 | 105.000000 | 106.000000 | 101.000000 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
lint_700800_ints_mean | 707.267833 | 744.007313 | 745.237859 | 745.107542 | 743.529720 | 744.607458 | 744.768158 | 743.742920 | 744.671843 | 743.349118 | ... | 723.414209 | 741.961011 | 745.099621 | 739.514976 | 745.440768 | 750.220474 | 741.015232 | 742.645918 | 738.399804 | 728.258476 |
lint_700800_ints_std | 82.818315 | 54.798394 | 52.689766 | 55.143067 | 55.159627 | 67.120715 | 54.335688 | 49.462457 | 56.974034 | 59.425360 | ... | 54.015745 | 48.338952 | 70.882980 | 56.621315 | 51.530964 | 57.018765 | 48.128471 | 47.497360 | 54.678046 | 52.953405 |
lint_700800_ints_count | 48.000000 | 99.000000 | 99.000000 | 96.000000 | 100.000000 | 96.000000 | 101.000000 | 100.000000 | 102.000000 | 93.000000 | ... | 67.000000 | 95.000000 | 95.000000 | 82.000000 | 99.000000 | 76.000000 | 99.000000 | 98.000000 | 102.000000 | 84.000000 |
lint_500600_DPm | -4.508464 | -2.935850 | -3.679656 | -3.081273 | -4.239167 | -4.696701 | -6.162629 | -2.058697 | -1.059976 | -0.138641 | ... | -1.890201 | 1.884941 | -6.121897 | -3.185709 | -2.553906 | -8.668644 | -0.270897 | -2.702535 | -3.943322 | -6.960489 |
lint_500600_DPsd | 13.727942 | 3.763389 | 7.094297 | 4.338958 | 3.428162 | 6.109977 | 7.413205 | 3.699728 | 4.834872 | 6.247794 | ... | 9.205697 | 3.589876 | 9.466895 | 3.924627 | 2.970704 | 5.239500 | 6.837897 | 5.603176 | 5.913442 | 4.700504 |
lint_500600_DPct | 63.000000 | 102.000000 | 96.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 | 102.000000 | 98.000000 | ... | 96.000000 | 99.000000 | 97.000000 | 95.000000 | 101.000000 | 94.000000 | 96.000000 | 100.000000 | 98.000000 | 99.000000 |
lint_500600_dev_mean | -27.902730 | -16.399294 | -21.205958 | -17.334863 | -23.248040 | -26.180475 | -35.337030 | -11.428317 | -5.656667 | -1.489388 | ... | -12.282250 | 10.195960 | -35.716371 | -17.735705 | -14.411683 | -48.703617 | -2.624208 | -16.333320 | -21.997673 | -39.677980 |
lint_500600_dev_std | 83.320041 | 20.923966 | 40.142477 | 24.338434 | 18.819031 | 34.302793 | 41.788892 | 20.884789 | 26.707172 | 33.959895 | ... | 52.459121 | 19.771509 | 53.866754 | 22.147641 | 17.221546 | 31.064133 | 37.023904 | 32.238741 | 32.237428 | 27.672641 |
lint_500600_dev_count | 63.000000 | 102.000000 | 96.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 | 102.000000 | 98.000000 | ... | 96.000000 | 99.000000 | 97.000000 | 95.000000 | 101.000000 | 94.000000 | 96.000000 | 100.000000 | 98.000000 | 99.000000 |
lint_500600_ints_mean | 589.466897 | 556.850667 | 559.668758 | 556.426157 | 556.957703 | 557.516554 | 557.297347 | 557.415050 | 555.666627 | 563.118694 | ... | 565.873895 | 563.661737 | 557.844652 | 558.886681 | 559.346800 | 562.442426 | 560.347750 | 558.371160 | 556.748122 | 559.526531 |
lint_500600_ints_std | 99.278525 | 45.952089 | 51.940173 | 45.734692 | 41.687211 | 58.564666 | 44.114686 | 46.886870 | 48.359597 | 54.134013 | ... | 65.921180 | 47.043223 | 60.446082 | 48.785817 | 45.647486 | 50.764789 | 48.124082 | 45.020388 | 48.266569 | 46.900139 |
lint_500600_ints_count | 58.000000 | 102.000000 | 95.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 | 102.000000 | 98.000000 | ... | 95.000000 | 99.000000 | 92.000000 | 91.000000 | 100.000000 | 94.000000 | 96.000000 | 100.000000 | 98.000000 | 98.000000 |
linj_700800_DPm | -4.543913 | -5.536776 | -6.491154 | NaN | 3.699780 | -9.080940 | -5.285475 | -1.128587 | -6.143924 | 1.101778 | ... | -11.383132 | -0.760195 | -9.589756 | -5.754093 | -2.759417 | -4.056123 | -2.236343 | -2.155906 | -6.996401 | -8.532782 |
linj_700800_DPsd | 8.344733 | 5.658566 | 8.351930 | NaN | 4.806744 | 7.484739 | 7.572755 | 5.879357 | 6.601137 | 6.673417 | ... | 8.888837 | 5.307915 | 12.374330 | 5.832539 | 5.775269 | 5.086778 | 5.948107 | 5.018592 | 4.189604 | 10.555065 |
linj_700800_DPct | 82.000000 | 102.000000 | 94.000000 | NaN | 55.000000 | 95.000000 | 101.000000 | 99.000000 | 100.000000 | 93.000000 | ... | 55.000000 | 98.000000 | 69.000000 | 101.000000 | 98.000000 | 99.000000 | 97.000000 | 97.000000 | 91.000000 | 98.000000 |
linj_700800_dev_mean | -35.060195 | -42.212392 | -50.175064 | NaN | 27.178618 | -67.877284 | -41.259426 | -9.124000 | -46.663340 | 7.180817 | ... | -81.578618 | -6.505041 | -74.535884 | -43.754970 | -21.539571 | -30.123838 | -17.068227 | -16.877381 | -52.320901 | -65.728122 |
linj_700800_dev_std | 62.242545 | 43.082708 | 62.982782 | NaN | 34.647911 | 56.002782 | 56.409904 | 43.056576 | 50.023199 | 48.922786 | ... | 63.241005 | 39.205492 | 94.913012 | 44.286645 | 43.048863 | 37.060101 | 44.493454 | 37.388890 | 32.548486 | 80.381359 |
linj_700800_dev_count | 82.000000 | 102.000000 | 94.000000 | NaN | 55.000000 | 95.000000 | 101.000000 | 99.000000 | 100.000000 | 93.000000 | ... | 55.000000 | 98.000000 | 69.000000 | 101.000000 | 98.000000 | 99.000000 | 97.000000 | 97.000000 | 91.000000 | 98.000000 |
linj_700800_ints_mean | 736.345333 | 744.903255 | 745.610851 | NaN | 722.833745 | 746.594652 | 744.664000 | 746.651111 | 745.658240 | 740.928430 | ... | 721.253018 | 745.253567 | 739.948358 | 745.138297 | 745.435093 | 742.805616 | 740.081768 | 743.881361 | 743.331121 | 737.000898 |
linj_700800_ints_std | 44.098428 | 56.340198 | 57.037814 | NaN | 39.627906 | 76.006378 | 53.222050 | 55.484002 | 63.494353 | 61.906997 | ... | 58.177540 | 52.599923 | 71.436837 | 58.200095 | 48.848304 | 58.916788 | 51.131245 | 50.041618 | 51.864642 | 51.738302 |
linj_700800_ints_count | 81.000000 | 102.000000 | 94.000000 | NaN | 55.000000 | 92.000000 | 101.000000 | 99.000000 | 100.000000 | 93.000000 | ... | 55.000000 | 97.000000 | 67.000000 | 101.000000 | 97.000000 | 99.000000 | 95.000000 | 97.000000 | 91.000000 | 98.000000 |
linj_500600_DPm | -9.168428 | -4.356788 | -6.390442 | NaN | -6.003091 | -9.124480 | -4.398286 | -5.446709 | -3.476588 | -3.543392 | ... | -16.546086 | -0.700534 | -14.919772 | -5.130468 | -1.959767 | -12.389941 | -2.258212 | -1.509382 | -4.102402 | -2.914103 |
linj_500600_DPsd | 9.660953 | 6.813903 | 14.502102 | NaN | 9.550193 | 11.361092 | 10.270239 | 11.104630 | 8.447056 | 6.763741 | ... | 11.726403 | 7.186386 | 13.197983 | 8.916590 | 6.994755 | 8.385819 | 9.242493 | 10.248535 | 5.844371 | 8.385738 |
linj_500600_DPct | 88.000000 | 102.000000 | 83.000000 | NaN | 80.000000 | 91.000000 | 83.000000 | 99.000000 | 99.000000 | 91.000000 | ... | 79.000000 | 99.000000 | 96.000000 | 100.000000 | 101.000000 | 98.000000 | 94.000000 | 101.000000 | 99.000000 | 98.000000 |
linj_500600_dev_mean | -53.029568 | -24.775451 | -38.339446 | NaN | -42.624475 | -52.709231 | -26.508048 | -32.033333 | -20.173616 | -20.190198 | ... | -96.469038 | -5.094970 | -85.952854 | -29.802260 | -12.301505 | -68.682184 | -14.450128 | -9.628119 | -23.822990 | -18.087224 |
linj_500600_dev_std | 54.936544 | 37.683056 | 81.355827 | NaN | 58.473561 | 64.806756 | 58.026878 | 63.987436 | 45.420654 | 36.986215 | ... | 72.700623 | 39.058540 | 76.778872 | 49.062198 | 39.111930 | 45.051978 | 51.936293 | 56.013779 | 32.027880 | 47.448913 |
linj_500600_dev_count | 88.000000 | 102.000000 | 83.000000 | NaN | 80.000000 | 91.000000 | 83.000000 | 99.000000 | 99.000000 | 91.000000 | ... | 79.000000 | 99.000000 | 96.000000 | 100.000000 | 101.000000 | 98.000000 | 94.000000 | 101.000000 | 99.000000 | 98.000000 |
linj_500600_ints_mean | 571.447325 | 556.815961 | 559.101037 | NaN | 628.861250 | 565.440133 | 564.736771 | 560.137778 | 556.286788 | 572.908923 | ... | 562.167899 | 558.697616 | 562.803326 | 556.283120 | 559.007604 | 560.142286 | 559.093234 | 562.570099 | 568.848889 | 560.828204 |
linj_500600_ints_std | 61.388153 | 54.382222 | 42.027804 | NaN | 85.549107 | 63.945218 | 40.622070 | 65.184352 | 55.394485 | 51.324183 | ... | 68.640366 | 46.355399 | 74.581856 | 47.391112 | 54.433873 | 52.854432 | 54.006056 | 51.513329 | 49.383151 | 53.254857 |
linj_500600_ints_count | 83.000000 | 102.000000 | 81.000000 | NaN | 80.000000 | 90.000000 | 83.000000 | 99.000000 | 99.000000 | 91.000000 | ... | 79.000000 | 99.000000 | 95.000000 | 100.000000 | 101.000000 | 98.000000 | 94.000000 | 101.000000 | 99.000000 | 98.000000 |
360 rows × 97 columns
dfo_output_updated = '2014-10-20a'
prefix = "c:/db_pickles/pickle - dfo-sms - "
import cPickle as pickle
output_file= prefix + dfo_output_updated + '.pickle'
pickle.dump(dfo, open(output_file, "wb"))
# Proceed with pickle to Part 5
dfo.head(8).T
pid | 015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 |
---|---|---|---|---|---|---|---|---|
iso5t1_DPm | -3.202915 | -2.840413 | -2.290274 | -3.022502 | -0.588655 | -10.975295 | -3.609441 | -1.953836 |
iso5t1_DPsd | 8.582088 | 3.423481 | 3.607109 | 3.291210 | 2.583675 | 7.004012 | 4.608983 | 3.167766 |
iso5t1_DPct | 109.000000 | 116.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 |
iso5t1_dev_mean | -16.010606 | -14.202517 | -11.450393 | -15.112000 | -2.943828 | -54.878940 | -18.047483 | -9.768456 |
iso5t1_dev_std | 42.896551 | 17.118586 | 18.032705 | 16.457877 | 12.918372 | 35.020673 | 23.046491 | 15.833194 |
iso5t1_dev_count | 109.000000 | 116.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 |
iso5t1_ints_mean | 502.308731 | 499.983374 | 500.089812 | 499.661138 | 500.006000 | 499.371726 | 499.922966 | 500.060070 |
iso5t1_ints_std | 35.124004 | 19.831489 | 21.902181 | 20.380895 | 14.309757 | 24.981757 | 26.865811 | 19.949975 |
iso5t1_ints_count | 104.000000 | 115.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 |
iso8t1_DPm | -3.198754 | -2.827224 | -13.311147 | -0.553203 | -2.327200 | -3.092538 | -1.458121 | 0.968610 |
iso8t1_DPsd | 7.944039 | 2.964738 | 9.624027 | 5.005393 | 2.431597 | 6.505625 | 4.433459 | 2.268350 |
iso8t1_DPct | 84.000000 | 104.000000 | 103.000000 | 106.000000 | 107.000000 | 101.000000 | 106.000000 | 104.000000 |
iso8t1_dev_mean | -25.590571 | -22.616038 | -106.491573 | -4.426755 | -18.618243 | -24.742257 | -11.667094 | 7.748077 |
iso8t1_dev_std | 63.544930 | 23.714597 | 76.990767 | 40.042257 | 19.452715 | 52.040415 | 35.461231 | 18.145349 |
iso8t1_dev_count | 84.000000 | 104.000000 | 103.000000 | 106.000000 | 107.000000 | 101.000000 | 106.000000 | 104.000000 |
iso8t1_ints_mean | 806.249253 | 799.617663 | 801.918118 | 800.000264 | 800.274131 | 798.427515 | 800.581811 | 799.969538 |
iso8t1_ints_std | 49.683214 | 32.389188 | 36.390481 | 40.611161 | 20.218406 | 61.034256 | 39.983867 | 21.396505 |
iso8t1_ints_count | 83.000000 | 101.000000 | 102.000000 | 106.000000 | 107.000000 | 99.000000 | 106.000000 | 104.000000 |
iso5t2_DPm | -7.320309 | -0.609046 | -2.155283 | -2.628721 | -0.035660 | -3.025222 | -5.794925 | -1.195840 |
iso5t2_DPsd | 8.438450 | 3.522687 | 3.871627 | 3.373599 | 2.612512 | 7.021192 | 5.208270 | 3.488723 |
iso5t2_DPct | 102.000000 | 115.000000 | 113.000000 | 116.000000 | 117.000000 | 112.000000 | 116.000000 | 117.000000 |
iso5t2_dev_mean | -36.598431 | -3.044383 | -10.779327 | -13.145345 | -0.181265 | -15.130071 | -28.975138 | -5.979350 |
iso5t2_dev_std | 42.201264 | 17.610315 | 19.359850 | 16.867665 | 13.060263 | 35.105208 | 26.040483 | 17.438377 |
iso5t2_dev_count | 102.000000 | 115.000000 | 113.000000 | 116.000000 | 117.000000 | 112.000000 | 116.000000 | 117.000000 |
iso5t2_ints_mean | 497.762511 | 500.728283 | 499.561036 | 500.063862 | 500.227111 | 500.431927 | 499.685241 | 499.951966 |
iso5t2_ints_std | 33.280437 | 21.690589 | 28.073822 | 20.327823 | 15.633583 | 31.042419 | 25.163016 | 24.080754 |
iso5t2_ints_count | 94.000000 | 113.000000 | 112.000000 | 116.000000 | 117.000000 | 109.000000 | 116.000000 | 117.000000 |
iso8t2_DPm | -0.419035 | -4.064043 | -4.258572 | -0.759809 | -1.704668 | -1.823981 | -1.913083 | 0.890989 |
iso8t2_DPsd | 10.221017 | 3.101403 | 5.063099 | 5.568829 | 3.404020 | 6.388830 | 4.368410 | 2.339890 |
iso8t2_DPct | 63.000000 | 105.000000 | 105.000000 | 107.000000 | 107.000000 | 102.000000 | 106.000000 | 106.000000 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
lint_700800_ints_mean | 707.267833 | 744.007313 | 745.237859 | 745.107542 | 743.529720 | 744.607458 | 744.768158 | 743.742920 |
lint_700800_ints_std | 82.818315 | 54.798394 | 52.689766 | 55.143067 | 55.159627 | 67.120715 | 54.335688 | 49.462457 |
lint_700800_ints_count | 48.000000 | 99.000000 | 99.000000 | 96.000000 | 100.000000 | 96.000000 | 101.000000 | 100.000000 |
lint_500600_DPm | -4.508464 | -2.935850 | -3.679656 | -3.081273 | -4.239167 | -4.696701 | -6.162629 | -2.058697 |
lint_500600_DPsd | 13.727942 | 3.763389 | 7.094297 | 4.338958 | 3.428162 | 6.109977 | 7.413205 | 3.699728 |
lint_500600_DPct | 63.000000 | 102.000000 | 96.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 |
lint_500600_dev_mean | -27.902730 | -16.399294 | -21.205958 | -17.334863 | -23.248040 | -26.180475 | -35.337030 | -11.428317 |
lint_500600_dev_std | 83.320041 | 20.923966 | 40.142477 | 24.338434 | 18.819031 | 34.302793 | 41.788892 | 20.884789 |
lint_500600_dev_count | 63.000000 | 102.000000 | 96.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 |
lint_500600_ints_mean | 589.466897 | 556.850667 | 559.668758 | 556.426157 | 556.957703 | 557.516554 | 557.297347 | 557.415050 |
lint_500600_ints_std | 99.278525 | 45.952089 | 51.940173 | 45.734692 | 41.687211 | 58.564666 | 44.114686 | 46.886870 |
lint_500600_ints_count | 58.000000 | 102.000000 | 95.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 |
linj_700800_DPm | -4.543913 | -5.536776 | -6.491154 | NaN | 3.699780 | -9.080940 | -5.285475 | -1.128587 |
linj_700800_DPsd | 8.344733 | 5.658566 | 8.351930 | NaN | 4.806744 | 7.484739 | 7.572755 | 5.879357 |
linj_700800_DPct | 82.000000 | 102.000000 | 94.000000 | NaN | 55.000000 | 95.000000 | 101.000000 | 99.000000 |
linj_700800_dev_mean | -35.060195 | -42.212392 | -50.175064 | NaN | 27.178618 | -67.877284 | -41.259426 | -9.124000 |
linj_700800_dev_std | 62.242545 | 43.082708 | 62.982782 | NaN | 34.647911 | 56.002782 | 56.409904 | 43.056576 |
linj_700800_dev_count | 82.000000 | 102.000000 | 94.000000 | NaN | 55.000000 | 95.000000 | 101.000000 | 99.000000 |
linj_700800_ints_mean | 736.345333 | 744.903255 | 745.610851 | NaN | 722.833745 | 746.594652 | 744.664000 | 746.651111 |
linj_700800_ints_std | 44.098428 | 56.340198 | 57.037814 | NaN | 39.627906 | 76.006378 | 53.222050 | 55.484002 |
linj_700800_ints_count | 81.000000 | 102.000000 | 94.000000 | NaN | 55.000000 | 92.000000 | 101.000000 | 99.000000 |
linj_500600_DPm | -9.168428 | -4.356788 | -6.390442 | NaN | -6.003091 | -9.124480 | -4.398286 | -5.446709 |
linj_500600_DPsd | 9.660953 | 6.813903 | 14.502102 | NaN | 9.550193 | 11.361092 | 10.270239 | 11.104630 |
linj_500600_DPct | 88.000000 | 102.000000 | 83.000000 | NaN | 80.000000 | 91.000000 | 83.000000 | 99.000000 |
linj_500600_dev_mean | -53.029568 | -24.775451 | -38.339446 | NaN | -42.624475 | -52.709231 | -26.508048 | -32.033333 |
linj_500600_dev_std | 54.936544 | 37.683056 | 81.355827 | NaN | 58.473561 | 64.806756 | 58.026878 | 63.987436 |
linj_500600_dev_count | 88.000000 | 102.000000 | 83.000000 | NaN | 80.000000 | 91.000000 | 83.000000 | 99.000000 |
linj_500600_ints_mean | 571.447325 | 556.815961 | 559.101037 | NaN | 628.861250 | 565.440133 | 564.736771 | 560.137778 |
linj_500600_ints_std | 61.388153 | 54.382222 | 42.027804 | NaN | 85.549107 | 63.945218 | 40.622070 | 65.184352 |
linj_500600_ints_count | 83.000000 | 102.000000 | 81.000000 | NaN | 80.000000 | 90.000000 | 83.000000 | 99.000000 |
360 rows × 8 columns
#st = 'Ticks_Linear_5ptB'
for st in ['Ticks_Linear_5ptA',
'Ticks_Linear_5ptB',
'Ticks_Linear_5ptC', ]#linear_part_names:
print('\n\n %s \n===================\n' % st)
task_taps = linear_part_taps[st]
for p in task_pids[st]:
print(p)
taps = task_taps[p].dev_perc
print('\t unfiltered')
taps.hist(figsize=(5,1.2), color='blue')
plt.show() #remove this to plot both in the same figure
print('\t filtered')
filt = taps[(taps <= 20) & (taps >= -35)]
try:
filt.hist(figsize=(5,1.2), color='green')
plt.show()
except ValueError:
print("ZERO SIZE, CANNOT PLOT")
#if p=="025": break
Ticks_Linear_5ptA =================== 015 unfiltered
filtered
016 unfiltered
filtered
017 unfiltered
filtered
018 unfiltered
filtered
019 unfiltered
filtered
020 unfiltered
filtered
021 unfiltered
filtered
022 unfiltered
filtered
024 unfiltered
filtered
025 unfiltered
filtered
026 unfiltered
filtered
027 unfiltered
filtered
028 unfiltered
filtered
029 unfiltered
filtered
030 unfiltered
filtered
032 unfiltered
filtered
033 unfiltered
filtered
034 unfiltered
filtered
035 unfiltered
filtered
036 unfiltered
filtered
037 unfiltered
filtered
038 unfiltered
filtered
039 unfiltered
filtered
040 unfiltered
filtered
041 unfiltered
filtered
043 unfiltered
filtered
044 unfiltered
filtered
046 unfiltered
filtered
047 unfiltered
filtered
048 unfiltered
filtered
049 unfiltered
filtered
051 unfiltered
filtered
052 unfiltered
filtered
053 unfiltered
filtered
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-239-2d81ae26af78> in <module>() 17 18 print('\t filtered') ---> 19 filt = taps[(taps <= 20) & (taps >= -35)] 20 try: 21 filt.hist(figsize=(5,1.2), color='green') C:\Applications\_Data analysis\Anaconda\lib\site-packages\pandas\core\ops.pyc in wrapper(self, other) 546 elif isinstance(other, pd.DataFrame): # pragma: no cover 547 return NotImplemented --> 548 elif isinstance(other, (pa.Array, pd.Series)): 549 if len(self) != len(other): 550 raise ValueError('Lengths must match to compare') KeyboardInterrupt:
#NO LONGER USING THIS-- SECTIONS ARE COLLAPSED TOGETHER ABOVE
# First pass: determine the truncation value for each task. What's
# z = 2.97 for all taps across all participants within a given
# section of a given task?
phase_tasks = ['Ticks_Phase_5', 'Ticks_Phase_8',
'Jits_Phase_5', 'Jits_Phase_8', ]
psection_list = ['0a', #0b isn't a separate section
'1a', '1b', '2a', '2b',
'3a', '3b', '4a', '4b']
trunc_value = {t: {} for t in phase_tasks}
reindexed_sections = {t: {} for t in phase_tasks}
reindexed_stacked = {}
for t in phase_tasks:
tdata = taps_filtered[t]
#not splitting by participant-- calc across all p's
for section in psection_list:
#print(section)
section_ident_column = 'is_range_' + section
section_taps = tdata[tdata[section_ident_column]==True]
#print(section_taps.head())
section_dps = section_taps.dev_perc
#print(section_dps.count())
#print(section_dps.std())
section_mean = section_dps.mean()
section_sd = section_dps.std()
trunc = {'upper': section_mean + 2.97 * section_sd,
'lower': section_mean - 2.97 * section_sd}
trunc_value[t][section] = trunc
print("{}, {}: <{}, >{}".format(t, section,
trunc['lower'], trunc['upper']))
#fill in all beat values (they were skipped when we built
# this dataframe, so there aren't any NaN values in place yet)
sec_start = section_dps.index.get_level_values('beat').min()
sec_end = section_dps.index.get_level_values('beat').max()
dps = {pid: section_dps.xs(pid) for pid in task_pids[t]}
p_rows = pd.DataFrame(dps)
p_cols = p_rows.T
dps_reindexed = p_cols.reindex(columns=range(sec_start,sec_end+1))
dps_reindexed.index.name = 'pid'
reindexed_sections[t][section] = dps_reindexed
#Not actually doing this yet: just looking at what happens to the values
# in each task based on the trunc values we just found.
stacked = dps_reindexed.stack(dropna=False)
print('trunc <: %s' % stacked[stacked < trunc['lower']].count())
print('trunc >: %s' % stacked[stacked > trunc['upper']].count())
print('blank: %s' % len(stacked[stacked.isnull()])) #count() excludes NaNs!
#print(section.isnull().count())
Ticks_Phase_5, 0a: <-24.3217065947, >20.2101140066 trunc <: 38 trunc >: 34 blank: 122 Ticks_Phase_5, 1a: <-25.9276919285, >20.6768485979 trunc <: 2 trunc >: 1 blank: 2 Ticks_Phase_5, 1b: <-27.4342199428, >19.3622797233 trunc <: 2 trunc >: 5 blank: 7 Ticks_Phase_5, 2a: <-33.5755750907, >28.2188889548 trunc <: 4 trunc >: 1 blank: 4 Ticks_Phase_5, 2b: <-26.7849634393, >21.5750891194 trunc <: 4 trunc >: 7 blank: 16 Ticks_Phase_5, 3a: <-40.303058949, >43.5521542269 trunc <: 5 trunc >: 4 blank: 16 Ticks_Phase_5, 3b: <-28.916376226, >24.0387863914 trunc <: 13 trunc >: 5 blank: 24 Ticks_Phase_5, 4a: <-43.0920098425, >43.4288759719 trunc <: 0 trunc >: 5 blank: 12 Ticks_Phase_5, 4b: <-29.4435795667, >21.5599080268 trunc <: 3 trunc >: 10 blank: 54 Ticks_Phase_8, 0a: <-27.9954792074, >23.3477283308 trunc <: 82 trunc >: 56 blank: 121 Ticks_Phase_8, 1a: <-29.5778426712, >21.3643421533 trunc <: 1 trunc >: 5 blank: 7 Ticks_Phase_8, 1b: <-33.7229198208, >27.6623063138 trunc <: 10 trunc >: 18 blank: 11 Ticks_Phase_8, 2a: <-29.3249526278, >27.0598983218 trunc <: 5 trunc >: 1 blank: 9 Ticks_Phase_8, 2b: <-29.2395038666, >25.4520071649 trunc <: 15 trunc >: 14 blank: 19 Ticks_Phase_8, 3a: <-39.0413277472, >40.2586460022 trunc <: 8 trunc >: 2 blank: 13 Ticks_Phase_8, 3b: <-32.2407757032, >27.9080776362 trunc <: 12 trunc >: 19 blank: 21 Ticks_Phase_8, 4a: <-46.4513011995, >43.2554648724 trunc <: 2 trunc >: 7 blank: 23 Ticks_Phase_8, 4b: <-28.5689956191, >24.8779424276 trunc <: 3 trunc >: 26 blank: 43 Jits_Phase_5, 0a: <-32.183391232, >23.0458001343 trunc <: 31 trunc >: 62 blank: 102 Jits_Phase_5, 1a: <-38.3792970034, >27.1509886645 trunc <: 1 trunc >: 4 blank: 3 Jits_Phase_5, 1b: <-38.2519814012, >27.4841084086 trunc <: 7 trunc >: 17 blank: 12 Jits_Phase_5, 2a: <-34.6356334388, >25.0539290282 trunc <: 3 trunc >: 1 blank: 1 Jits_Phase_5, 2b: <-30.6721568742, >24.8559146094 trunc <: 3 trunc >: 10 blank: 5 Jits_Phase_5, 3a: <-44.4234575766, >30.8466647363 trunc <: 0 trunc >: 0 blank: 3 Jits_Phase_5, 3b: <-32.3039080486, >30.2344224288 trunc <: 11 trunc >: 8 blank: 11 Jits_Phase_5, 4a: <-32.2752608158, >39.7226139904 trunc <: 3 trunc >: 3 blank: 7 Jits_Phase_5, 4b: <-35.7201722912, >23.7358238179 trunc <: 5 trunc >: 27 blank: 17 Jits_Phase_8, 0a: <-36.1393156131, >25.6880837029 trunc <: 66 trunc >: 64 blank: 77 Jits_Phase_8, 1a: <-32.0026595602, >22.8623228212 trunc <: 4 trunc >: 1 blank: 2 Jits_Phase_8, 1b: <-34.5718812977, >20.1980401312 trunc <: 8 trunc >: 8 blank: 8 Jits_Phase_8, 2a: <-39.9529181142, >28.2903260977 trunc <: 5 trunc >: 4 blank: 3 Jits_Phase_8, 2b: <-39.9992033576, >28.3002646375 trunc <: 16 trunc >: 18 blank: 14 Jits_Phase_8, 3a: <-41.384366441, >33.4910731398 trunc <: 4 trunc >: 4 blank: 3 Jits_Phase_8, 3b: <-37.0236726141, >24.7503018455 trunc <: 15 trunc >: 21 blank: 12 Jits_Phase_8, 4a: <-41.0662553619, >32.4894698153 trunc <: 5 trunc >: 6 blank: 1 Jits_Phase_8, 4b: <-40.7729011293, >28.4572354624 trunc <: 11 trunc >: 24 blank: 7
phase_tasks = ['Ticks_Phase_5', 'Ticks_Phase_8',
'Jits_Phase_5', 'Jits_Phase_8', ]
psection_list = ['0a', #0b isn't a separate section
'1a', '1b', '2a', '2b',
'3a', '3b', '4a', '4b']
for task in phase_tasks:
print(task)
task_sections = reindexed_sections[task]
for section in psection_list:
print(section)
truncs = trunc_value[task][section]
for pid in task_pids[task]:
print(pid, end=':')
ptaps = task_sections[section].loc[pid]
# only measure we're using is dev_perc now, since that's
#Truncate outliers and impute missing beats with truncation value
# (randomly select upper or lower truncation value, I guess?)
def trunc_and_replace_nans(i):
#def pick_trunc_side():
# import random
# r = random.randint(0, 1)
# if r==1:
# return truncs['upper']
# else:
# return truncs['lower']
if i > truncs['upper']:
i = truncs['upper']
elif i < truncs['lower']:
i = truncs['lower']
elif np.isnan(i):
i = truncs[prev_trunc]
#avoid randomization.... was pick_trunc_side()
return i
stats_before = (ptaps.max(), ptaps.min(), ptaps.mean())
prev_trunc = 'upper' #default
ptaps_post = ptaps.apply(trunc_and_replace_nans)
stats_after = (ptaps_post.max(), ptaps_post.min(), ptaps_post.mean())
if stats_before==stats_after:
print("no change: {:f} {:f} {:f}"
.format(stats_before[0], stats_before[1], stats_before[2]))
else:
print("changed. before: {:f} {:f} {:f}, after: {:f} {:f} {:f}"
.format(stats_before[0], stats_before[1], stats_before[2],
stats_after[0], stats_after[1], stats_after[2]))
result_m = ptaps_post.mean()
result_sd = ptaps_post.std()
result_ct_pre = ptaps.count() #AFTER replacements!
result_ct_post = ptaps_post.count() #AFTER replacements!
task_sec_name = '_'.join([short_name[task], 's' + section])
output_varname_m = task_sec_name + '_DPm'
output_varname_sd = task_sec_name + '_DPsd'
output_varname_ct_pre = task_sec_name + '_DPctPre'
output_varname_ct_post = task_sec_name + '_DPctPost'
if output_varname_m not in dfo:
dfo[output_varname_m] = np.nan
print('added varname: %s' % output_varname_m)
if output_varname_sd not in dfo:
dfo[output_varname_sd] = np.nan
print('added varname: %s' % output_varname_sd)
if output_varname_ct_pre not in dfo:
dfo[output_varname_ct_pre] = np.nan
print('added varname: %s' % output_varname_ct_pre)
if output_varname_ct_post not in dfo:
dfo[output_varname_ct_post] = np.nan
print('added varname: %s' % output_varname_ct_post)
dfo[output_varname_m].loc[pid] = result_m
dfo[output_varname_sd].loc[pid] = result_sd
dfo[output_varname_ct_pre].loc[pid] = output_varname_ct_pre
dfo[output_varname_ct_post].loc[pid] = output_varname_ct_post
print()
print()
print()
#change output: max, min, mean
dfo.T
pid | 015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
iso5t1_dev_perc_mean | -2.342489 | -2.933012 | -2.366698 | -2.636451 | -0.6459543 | -10.87183 | -3.749141 | -2.046958 | -5.678476 | -5.635096 | ... | -10.97467 | -0.725504 | -1.218539 | -0.4310472 | -0.7957316 | -7.734171 | -3.822602 | -0.5959113 | -11.5089 | -4.767391 |
iso5t1_dev_perc_std | 7.0247 | 3.474803 | 3.466094 | 3.367212 | 2.436263 | 7.004568 | 3.878841 | 2.964041 | 3.198982 | 3.268974 | ... | 5.979733 | 2.706513 | 3.97258 | 4.055465 | 2.436333 | 4.548844 | 5.543061 | 3.012047 | 13.92394 | 4.008097 |
iso5t1_dev_perc_count | 111 | 120 | 120 | 120 | 120 | 119 | 118 | 118 | 121 | 115 | ... | 120 | 119 | 115 | 118 | 119 | 117 | 120 | 120 | 119 | 117 |
iso5t1_dev_mean | -11.71178 | -14.66553 | -11.8327 | -13.18133 | -3.230433 | -54.36128 | -18.74698 | -10.23386 | -28.39117 | -28.17659 | ... | -54.87277 | -3.626689 | -6.092209 | -2.155932 | -3.978151 | -38.67097 | -19.112 | -2.979167 | -57.54306 | -23.83976 |
iso5t1_dev_std | 35.12227 | 17.3746 | 17.32853 | 16.83581 | 12.18129 | 35.02349 | 19.3971 | 14.81583 | 15.99327 | 16.34244 | ... | 29.89959 | 13.53027 | 19.85418 | 20.27347 | 12.17995 | 22.74279 | 27.71425 | 15.06088 | 69.61694 | 20.0439 |
iso5t1_dev_count | 111 | 120 | 120 | 120 | 120 | 119 | 118 | 118 | 121 | 115 | ... | 120 | 119 | 115 | 118 | 119 | 117 | 120 | 120 | 119 | 117 |
iso5t1_ints_mean | 501.2866 | 500.0634 | 499.5734 | 499.9619 | 499.678 | 498.3869 | 499.0367 | 500.1896 | 500.3116 | 500.666 | ... | 500.4185 | 499.9379 | 499.5406 | 500.6955 | 500.022 | 500.172 | 500.7644 | 500.3504 | 500.3232 | 500.3592 |
iso5t1_ints_std | 33.0584 | 20.1298 | 21.10341 | 19.74623 | 14.13903 | 26.08191 | 24.11722 | 19.45059 | 18.73578 | 15.83903 | ... | 27.26917 | 12.25516 | 27.28585 | 26.55751 | 16.37401 | 18.04187 | 19.25761 | 15.398 | 21.10637 | 16.65214 |
iso5t1_ints_count | 107 | 119 | 120 | 120 | 120 | 119 | 118 | 118 | 121 | 115 | ... | 120 | 118 | 113 | 116 | 118 | 117 | 120 | 120 | 118 | 116 |
iso8t1_dev_perc_mean | -0.3507087 | -2.768806 | -13.83518 | -0.3748164 | -2.380208 | -3.240543 | -1.631161 | 0.9656163 | -3.15535 | 0.3272409 | ... | -6.187546 | 0.103829 | -0.6655429 | -2.124959 | -1.499217 | -0.8652373 | 0.8952681 | -0.2166827 | -8.813291 | -2.878531 |
iso8t1_dev_perc_std | 19.27442 | 2.817007 | 9.791912 | 4.662476 | 2.211189 | 6.115085 | 4.21753 | 2.051336 | 3.648294 | 5.014253 | ... | 7.168626 | 2.154212 | 6.265936 | 3.635931 | 2.387929 | 4.77778 | 3.791858 | 2.799014 | 4.393185 | 5.529805 |
iso8t1_dev_perc_count | 106 | 107 | 110 | 109 | 109 | 104 | 110 | 108 | 108 | 108 | ... | 101 | 108 | 109 | 110 | 111 | 107 | 111 | 110 | 107 | 108 |
iso8t1_dev_mean | -2.803811 | -22.14841 | -110.6818 | -2.999486 | -19.04235 | -25.925 | -13.05131 | 7.724333 | -25.24526 | 2.616519 | ... | -49.49917 | 0.8292222 | -5.329761 | -17.00367 | -11.99362 | -6.920636 | 7.160685 | -1.737091 | -70.50587 | -23.02874 |
iso8t1_dev_std | 154.2057 | 22.5323 | 78.33074 | 37.29964 | 17.68978 | 48.92324 | 33.73431 | 16.40954 | 29.1858 | 40.10902 | ... | 57.35578 | 17.23347 | 50.12318 | 29.08938 | 19.10303 | 38.22541 | 30.33776 | 22.39343 | 35.1443 | 44.23512 |
iso8t1_dev_count | 106 | 107 | 110 | 109 | 109 | 104 | 110 | 108 | 108 | 108 | ... | 101 | 108 | 109 | 110 | 111 | 107 | 111 | 110 | 107 | 108 |
iso8t1_ints_mean | 816.4497 | 799.9578 | 800.6367 | 799.956 | 800.6078 | 797.3068 | 799.9141 | 799.7408 | 796.5956 | 798.564 | ... | 800.5167 | 800.4482 | 800.4901 | 799.6149 | 799.8858 | 801.1369 | 800.3997 | 799.7916 | 798.9361 | 800.6235 |
iso8t1_ints_std | 90.25246 | 31.57134 | 36.49612 | 40.04827 | 18.20076 | 58.96032 | 38.95553 | 20.81468 | 35.61002 | 45.78635 | ... | 54.59722 | 20.73257 | 50.80378 | 37.98372 | 21.79104 | 37.48624 | 30.28434 | 20.68354 | 26.30323 | 41.38953 |
iso8t1_ints_count | 102 | 104 | 109 | 109 | 109 | 102 | 110 | 108 | 108 | 106 | ... | 99 | 108 | 107 | 109 | 111 | 107 | 111 | 110 | 107 | 106 |
iso5t2_dev_perc_mean | -7.221713 | -0.6491488 | -2.207171 | -2.524806 | -0.09163417 | -2.99653 | -5.3928 | -0.9960761 | -1.848489 | 0.01383371 | ... | -11.06501 | 2.185041 | -2.023009 | -0.6997633 | -2.061946 | -10.94477 | -1.803022 | 1.212337 | -10.24515 | -7.981646 |
iso5t2_dev_perc_std | 8.030122 | 3.485702 | 3.874653 | 3.179624 | 2.562351 | 6.924439 | 4.686902 | 3.154822 | 2.884471 | 4.490689 | ... | 4.865246 | 2.126718 | 6.184123 | 4.374259 | 3.002337 | 4.560788 | 3.913104 | 3.002175 | 5.024712 | 4.592551 |
iso5t2_dev_perc_count | 107 | 119 | 118 | 119 | 120 | 118 | 119 | 119 | 118 | 101 | ... | 118 | 117 | 113 | 119 | 120 | 120 | 119 | 120 | 116 | 115 |
iso5t2_dev_mean | -36.10673 | -3.244874 | -11.03814 | -12.62595 | -0.4612 | -14.9858 | -26.96511 | -4.981412 | -9.244068 | 0.07009901 | ... | -55.32346 | 10.92393 | -10.11681 | -3.498924 | -10.31163 | -54.7234 | -9.016403 | 6.057433 | -51.22714 | -39.90824 |
iso5t2_dev_std | 40.15388 | 17.42533 | 19.37432 | 15.89819 | 12.80935 | 34.62109 | 23.43611 | 15.77046 | 14.42232 | 22.44893 | ... | 24.32096 | 10.63167 | 30.92471 | 21.87286 | 15.01284 | 22.80487 | 19.56731 | 15.00997 | 25.12571 | 22.96157 |
iso5t2_dev_count | 107 | 119 | 118 | 119 | 120 | 118 | 119 | 119 | 118 | 101 | ... | 118 | 117 | 113 | 119 | 120 | 120 | 119 | 120 | 116 | 115 |
iso5t2_ints_mean | 499.0438 | 500.9173 | 498.821 | 499.9667 | 499.734 | 500.2146 | 500.0998 | 500.9514 | 500.1624 | 501.0322 | ... | 498.7641 | 500.9702 | 500.435 | 498.5331 | 500.833 | 499.6811 | 500.5165 | 499.6543 | 500.2213 | 499.5084 |
iso5t2_ints_std | 33.00661 | 21.37603 | 28.48595 | 20.21623 | 15.45374 | 31.35915 | 24.49632 | 22.56536 | 20.8047 | 24.62363 | ... | 22.5625 | 12.63029 | 39.51406 | 22.45964 | 22.94002 | 16.98884 | 21.55657 | 17.08963 | 18.05534 | 22.458 |
iso5t2_ints_count | 99 | 117 | 117 | 119 | 120 | 115 | 119 | 119 | 118 | 99 | ... | 118 | 117 | 108 | 119 | 120 | 120 | 119 | 120 | 116 | 113 |
iso8t2_dev_perc_mean | 7.318768 | -3.873531 | -4.46869 | -0.9058396 | -1.537087 | -1.986273 | -2.000616 | 0.8646689 | -2.354792 | -0.08663725 | ... | -2.51537 | -0.1075894 | -7.0438 | -1.640711 | -1.052971 | -3.531019 | 0.4584651 | -2.05007 | -4.373602 | -6.520124 |
iso8t2_dev_perc_std | 24.48115 | 2.925917 | 5.125628 | 5.254976 | 2.781922 | 5.282071 | 4.24902 | 2.260226 | 4.131377 | 4.51932 | ... | 5.790562 | 2.703154 | 5.754412 | 3.41057 | 2.209201 | 4.03179 | 2.724487 | 3.271753 | 3.794736 | 5.426415 |
iso8t2_dev_perc_count | 103 | 108 | 110 | 110 | 107 | 102 | 110 | 110 | 111 | 105 | ... | 101 | 110 | 105 | 106 | 109 | 109 | 108 | 110 | 107 | 105 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | ... | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre | phase8j_s1a_DPctPre |
phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | ... | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost | phase8j_s1a_DPctPost |
phase8j_s1b_DPm | -10.37741 | -5.82779 | -10.11072 | -1.371796 | -6.842059 | -13.86932 | -11.10534 | 1.02878 | -7.499895 | 0.6469573 | ... | -12.8413 | -9.046528 | -12.0093 | -9.852106 | -3.88275 | -4.251878 | -3.209898 | -7.186987 | -9.765089 | -14.29951 |
phase8j_s1b_DPsd | 3.811483 | 4.414938 | 6.067769 | 4.363318 | 2.852727 | 12.16675 | 3.68715 | 3.107706 | 9.097075 | 6.099366 | ... | 5.165977 | 5.045151 | 5.129981 | 3.908623 | 10.49753 | 11.11674 | 7.455754 | 6.248663 | 7.42547 | 4.434402 |
phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | ... | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre | phase8j_s1b_DPctPre |
phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | ... | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost | phase8j_s1b_DPctPost |
phase8j_s2a_DPm | 0.8378159 | -0.246566 | -9.664952 | 1.482057 | -15.754 | 1.490469 | -7.859603 | 0.1435588 | -4.683328 | 4.086119 | ... | -23.40428 | -7.357001 | -15.16863 | -14.28948 | -7.157177 | 3.090689 | 0.9337939 | -2.66188 | -2.810831 | -11.7101 |
phase8j_s2a_DPsd | 18.56051 | 3.105036 | 10.32693 | 8.967237 | 3.796017 | 3.666267 | 9.061089 | 7.307256 | 8.717242 | 7.196873 | ... | 3.437502 | 5.859396 | 1.387135 | 3.996148 | 1.065331 | 8.61172 | 3.411674 | 7.477207 | 7.427063 | 15.9339 |
phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | ... | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre | phase8j_s2a_DPctPre |
phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | ... | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost | phase8j_s2a_DPctPost |
phase8j_s2b_DPm | -11.38455 | -4.896362 | -5.172745 | 3.322383 | -14.17191 | -9.135897 | -12.44877 | 2.857706 | -5.327714 | -1.326417 | ... | -14.21686 | -5.33427 | -4.339154 | -5.583813 | -9.407973 | 3.569284 | 3.351438 | -4.726624 | -10.36414 | -10.98684 |
phase8j_s2b_DPsd | 6.901526 | 3.523893 | 13.20319 | 5.708262 | 5.056977 | 29.14859 | 5.53419 | 3.596764 | 5.405394 | 4.638411 | ... | 4.321816 | 3.650546 | 13.16747 | 6.339592 | 4.855249 | 9.976189 | 4.237302 | 4.578618 | 5.15124 | 7.938525 |
phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | ... | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre | phase8j_s2b_DPctPre |
phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | ... | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost | phase8j_s2b_DPctPost |
phase8j_s3a_DPm | -11.87298 | -0.6591388 | -5.826845 | -5.956262 | -4.618697 | 1.564391 | -9.407485 | 0.1098456 | -3.706344 | 7.323124 | ... | -5.603968 | 3.035721 | -12.7133 | -1.951633 | -6.105533 | 4.436911 | -0.2370999 | 2.45805 | -13.18259 | -8.136433 |
phase8j_s3a_DPsd | 5.261061 | 8.228678 | 5.389029 | 2.579079 | 13.24063 | 3.519445 | 5.210719 | 5.66116 | 4.732085 | 9.604177 | ... | 8.667053 | 9.924554 | 3.486555 | 9.146751 | 5.262778 | 4.270543 | 5.59554 | 5.651438 | 5.178901 | 10.45921 |
phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | ... | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre | phase8j_s3a_DPctPre |
phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | ... | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost | phase8j_s3a_DPctPost |
phase8j_s3b_DPm | -7.73547 | -4.714102 | -12.54583 | -2.60899 | 7.114538 | -12.8673 | -8.406937 | -3.334 | -5.370262 | 1.201136 | ... | -13.09757 | -2.987564 | -12.07881 | -9.071119 | -6.917003 | -3.590599 | -6.976546 | -2.979848 | -14.0467 | -11.90929 |
phase8j_s3b_DPsd | 4.812512 | 3.231291 | 7.283943 | 3.450616 | 2.935276 | 7.503993 | 6.556869 | 5.839274 | 6.230284 | 7.522506 | ... | 4.456931 | 4.32498 | 7.972343 | 5.348321 | 10.8415 | 4.810308 | 4.019082 | 4.79915 | 8.144518 | 4.816552 |
phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | ... | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre | phase8j_s3b_DPctPre |
phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | ... | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost | phase8j_s3b_DPctPost |
phase8j_s4a_DPm | -8.206735 | -4.461307 | -1.597212 | -3.681431 | 18.88049 | -12.62337 | -7.272361 | -1.243286 | -7.47664 | 0.8416677 | ... | -8.376577 | -2.618764 | -15.69055 | -9.754653 | -9.505631 | -6.054791 | -4.901113 | 1.540526 | -3.085345 | -9.682805 |
phase8j_s4a_DPsd | 7.050488 | 4.376005 | 11.17015 | 2.116713 | 4.342071 | 10.96371 | 8.190328 | 1.90655 | 7.2157 | 9.080357 | ... | 7.068173 | 8.133509 | 10.26543 | 7.274737 | 7.783784 | 9.047441 | 6.3804 | 11.06732 | 6.212393 | 7.001689 |
phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | ... | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre | phase8j_s4a_DPctPre |
phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | ... | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost | phase8j_s4a_DPctPost |
phase8j_s4b_DPm | -9.901036 | -5.317269 | -12.74985 | 0.3329968 | 4.937623 | -9.305246 | -7.988606 | -5.367575 | -2.83348 | -5.569427 | ... | -9.484041 | -1.867257 | -16.16994 | -5.776393 | -5.162722 | -4.136471 | -7.075807 | -3.107556 | -7.946916 | -14.24405 |
phase8j_s4b_DPsd | 9.064814 | 3.607806 | 6.98562 | 5.100792 | 3.723088 | 12.51339 | 5.423732 | 3.801082 | 3.230428 | 4.900465 | ... | 7.278874 | 2.75119 | 7.299305 | 10.23416 | 9.994217 | 7.482837 | 5.481531 | 4.037751 | 5.19513 | 5.48739 |
phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | ... | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre | phase8j_s4b_DPctPre |
phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | ... | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost | phase8j_s4b_DPctPost |
270 rows × 99 columns
#sms_tasknames = ['Ticks_Linear_5',
# 'Ticks_Linear_8',
# 'Ticks_Phase_5',
# 'Ticks_Phase_8',
# 'T1_SMS_5',
# 'Ticks_ISO_T2_5',
# 'T1_SMS_8',
# 'Ticks_ISO_T2_8',]
def cool_sms_plot(participant_task_df, ISI):
from pandas.stats.moments import rolling_mean
lb_test = participant_task_df.xs('target', level='stamp')
tap_test = participant_task_df.xs('tap', level='stamp')
lb_test.plot(x = 'task_ms', y = 'tinterval', figsize=(13,7), linewidth=4)
tap_test['dev_relative'] = tap_test.dev + ISI
tap_test.plot(x = 'task_ms', y = 'dev_relative', marker="o", linewidth=0) #, figsize=(14,9))
tap_test['rmean'] = rolling_mean(tap_test.dev_relative, window=10, center=True, min_periods=2)
tap_test.plot(x = 'task_ms', y = 'rmean', linewidth=2)
#print(tap_test.dev)
#need to do this in MPL directly so I can return a plot variable
#cool_sms_plot(sms_tasknames[0], '102')
#cool_sms_plot('Ticks_ISO_T2_5', '103')
#could use this in manuscript to demonstrate pre- and post-filtered data...
task = long_name['phase8j']
pid = '116'
print(pid)
cool_sms_plot(task_frames[task].xs(pid), 800)
116
def fig_dims(width, factor):
#WIDTH = 350.0 # the number latex spits out
#FACTOR = 0.45 # the fraction of the width you'd like the figure to occupy
fig_width_pt = width * factor
inches_per_pt = 1.0 / 72.27
golden_ratio = (np.sqrt(5) - 1.0) / 2.0 # because it looks good
fig_width_in = fig_width_pt * inches_per_pt # figure width in inches
fig_height_in = fig_width_in * golden_ratio # figure height in inches
fig_dims = [fig_width_in, fig_height_in] # fig dims as a list
return fig_dims
def task_variability(taskname):
#tdata = db_taps[taskname]
tdata = taps_filtered[taskname]
#plt.suptitle(short_name[taskname])
ISI = sms_params[taskname]['ISI']
if ISI == '(varies)': ISI = 650
avgdevs = tdata.mean(level='beat')[5:]
SD_devs = tdata.std(level='beat')[5:]
avgtargs = task_frames[taskname].xs('target',level='stamp').mean(level='beat')
figsize = fig_dims(2000, 0.45)
ax = avgtargs.plot(y = 'tinterval', linewidth=2, color='black', figsize=figsize)
#ax.plot(avgdevs)
adjust_avgdevs = avgdevs + ISI
#adjust_avgdevs.plot(y = 'dev', linewidth=2)
#upper_sd = adjust_avgdevs + (SD_devs)
#lower_sd = adjust_avgdevs - (SD_devs)
#plt.fill_between(x='task_ms', y1=upper_sd.dev, y2=lower_sd.dev, color='grey', alpha='0.5')
#upper_sd.plot(y = 'dev', linewidth=1)
#lower_sd.plot(y = 'dev', linewidth=1)
#upper_sd = ISI + SD_devs
#lower_sd = ISI - SD_devs
avg_tap = avgtargs.tinterval + avgdevs.dev
upper_sd = avg_tap + SD_devs.dev
lower_sd = avg_tap - SD_devs.dev
#upper_sd.plot(y = 'dev', linewidth=3, color='black', linestyle="--")
#lower_sd.plot(y = 'dev', linewidth=3, color='black', linestyle="--")
#ax.plot(upper_sd.dev, linewidth=3, color='black', linestyle="--")
#ax.plot(lower_sd.dev, linewidth=3, color='black', linestyle="--")
avg_tap.plot(linewidth=1, color='black', linestyle="--", dashes=(5,3))
upper_sd.plot(linewidth=1, color='black', linestyle="-", marker="o", markersize=4)
lower_sd.plot(linewidth=1, color='black', linestyle="-", marker="o", markersize=4)
ax.set_ylabel("Milliseconds")
ax.set_xlabel("Interval number")
ax.grid(b=False, which='major', axis='both')
# set number of labeled "ticks" on each axis (overriding auto setting)
ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(15))
ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(10))
# (it will sometimes decide to show fewer than this, hence "max")
# Or to be precise:
ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(15))
#ax.xaxis.tick_bottom()
#ax.yaxis.tick_left()
#ax.spines["right"].set_color("none")
#ax.spines["top"].set_color("none")
ax.legend(["Target IOI",
"IOI + mean of absolute asynchrony values",
u"Between-participants variability in mean asynchrony (IOI ± 1 SD)"], loc="best")
ax.get_legend().set_title("")
ax.get_legend().draw_frame(False)
plt.savefig("c:/_Sync/1020a_postfilt_varlines_" + short_name[t] + '.png',
format='png',
)
plt.show()
#don't adjust MPL defaults to pandas's preferred defaults
pd.options.display.mpl_style = None
mpl.rcdefaults()
from matplotlib import rcParams
#rcParams['axes.titlesize'] = 22
rcParams['font.size'] = 14
rcParams['xtick.labelsize'] = 12
rcParams['ytick.labelsize'] = 12
rcParams['legend.fontsize'] = 12
rcParams['font.family'] = 'serif'
rcParams['figure.facecolor'] = '1.0' # 0 black --> 1 white; grays
for t in sms_tasknames:
print(short_name[t])
print("N = ", len(db_taps[t].index.get_level_values('pid').unique()))
task_variability(t) #long_name['iso5t1'])
#break
# iso5t1 and iso8t1: Need to remove the extra intervals at the
# end of the task from the first few subs! (after beat 130-ish?)
# (Probably easiest and less confusing for future readers if they're just
# chopped out of the CSV file at the start.)
iso5t1 N = 97
iso8t1 N = 97
iso5t2 N = 97
iso8t2 N = 97
lin5t N = 97
lin8t N = 97
phase5t N = 97
phase8t N = 97
iso5j N = 97
iso8j N = 97
lin5j N = 96
lin8j N = 97
phase5j N = 97
phase8j N = 97
mpl.rcdefaults()
rcParams #.keys()
#avgdevs = db_taps[long_name['phase8t']].mean(level='beat').dev
RcParams({'agg.path.chunksize': 0, 'animation.avconv_args': '', 'animation.avconv_path': 'avconv', 'animation.bitrate': -1, 'animation.codec': 'mpeg4', 'animation.convert_args': '', 'animation.convert_path': 'convert', 'animation.ffmpeg_args': '', 'animation.ffmpeg_path': 'ffmpeg', 'animation.frame_format': 'png', 'animation.mencoder_args': '', 'animation.mencoder_path': 'mencoder', 'animation.writer': 'ffmpeg', 'axes.axisbelow': False, 'axes.color_cycle': ['b', 'g', 'r', 'c', 'm', 'y', 'k'], 'axes.edgecolor': 'k', 'axes.facecolor': 'w', 'axes.formatter.limits': [-7, 7], 'axes.formatter.use_locale': False, 'axes.formatter.use_mathtext': False, 'axes.grid': False, 'axes.hold': True, 'axes.labelcolor': 'k', 'axes.labelsize': 'medium', 'axes.labelweight': 'normal', 'axes.linewidth': 1.0, 'axes.titlesize': 'large', 'axes.unicode_minus': True, 'axes.xmargin': 0, 'axes.ymargin': 0, 'axes3d.grid': True, 'backend': 'Agg', 'backend.qt4': 'PyQt4', 'backend_fallback': True, 'contour.negative_linestyle': 'dashed', 'datapath': 'C:\\Applications\\_Data analysis\\Anaconda\\lib\\site-packages\\matplotlib\\mpl-data', 'docstring.hardcopy': False, 'examples.directory': '', 'figure.autolayout': False, 'figure.dpi': 80, 'figure.edgecolor': 'w', 'figure.facecolor': '0.75', 'figure.figsize': [8.0, 6.0], 'figure.frameon': True, 'figure.max_open_warning': 20, 'figure.subplot.bottom': 0.1, 'figure.subplot.hspace': 0.2, 'figure.subplot.left': 0.125, 'figure.subplot.right': 0.9, 'figure.subplot.top': 0.9, 'figure.subplot.wspace': 0.2, 'font.cursive': ['Apple Chancery', 'Textile', 'Zapf Chancery', 'Sand', 'cursive'], 'font.family': 'sans-serif', 'font.fantasy': ['Comic Sans MS', 'Chicago', 'Charcoal', 'ImpactWestern', 'fantasy'], 'font.monospace': ['Bitstream Vera Sans Mono', 'DejaVu Sans Mono', 'Andale Mono', 'Nimbus Mono L', 'Courier New', 'Courier', 'Fixed', 'Terminal', 'monospace'], 'font.sans-serif': ['Bitstream Vera Sans', 'DejaVu Sans', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Arial', 'Helvetica', 'Avant Garde', 'sans-serif'], 'font.serif': ['Bitstream Vera Serif', 'DejaVu Serif', 'New Century Schoolbook', 'Century Schoolbook L', 'Utopia', 'ITC Bookman', 'Bookman', 'Nimbus Roman No9 L', 'Times New Roman', 'Times', 'Palatino', 'Charter', 'serif'], 'font.size': 12, 'font.stretch': 'normal', 'font.style': 'normal', 'font.variant': 'normal', 'font.weight': 'normal', 'grid.alpha': 1.0, 'grid.color': 'k', 'grid.linestyle': ':', 'grid.linewidth': 0.5, 'image.aspect': 'equal', 'image.cmap': 'jet', 'image.interpolation': 'bilinear', 'image.lut': 256, 'image.origin': 'upper', 'image.resample': False, 'interactive': False, 'keymap.all_axes': 'a', 'keymap.back': ['left', 'c', 'backspace'], 'keymap.forward': ['right', 'v'], 'keymap.fullscreen': ('f', 'ctrl+f'), 'keymap.grid': 'g', 'keymap.home': ['h', 'r', 'home'], 'keymap.pan': 'p', 'keymap.quit': ('ctrl+w', 'cmd+w'), 'keymap.save': ('s', 'ctrl+s'), 'keymap.xscale': ['k', 'L'], 'keymap.yscale': 'l', 'keymap.zoom': 'o', 'legend.borderaxespad': 0.5, 'legend.borderpad': 0.4, 'legend.columnspacing': 2.0, 'legend.fancybox': False, 'legend.fontsize': 'large', 'legend.frameon': True, 'legend.handleheight': 0.7, 'legend.handlelength': 2.0, 'legend.handletextpad': 0.8, 'legend.isaxes': True, 'legend.labelspacing': 0.5, 'legend.loc': 'upper right', 'legend.markerscale': 1.0, 'legend.numpoints': 2, 'legend.scatterpoints': 3, 'legend.shadow': False, 'lines.antialiased': True, 'lines.color': 'b', 'lines.dash_capstyle': 'butt', 'lines.dash_joinstyle': 'round', 'lines.linestyle': '-', 'lines.linewidth': 1.0, 'lines.marker': 'None', 'lines.markeredgewidth': 0.5, 'lines.markersize': 6, 'lines.solid_capstyle': 'projecting', 'lines.solid_joinstyle': 'round', 'mathtext.bf': 'serif:bold', 'mathtext.cal': 'cursive', 'mathtext.default': 'it', 'mathtext.fallback_to_cm': True, 'mathtext.fontset': 'cm', 'mathtext.it': 'serif:italic', 'mathtext.rm': 'serif', 'mathtext.sf': 'sans\\-serif', 'mathtext.tt': 'monospace', 'patch.antialiased': True, 'patch.edgecolor': 'k', 'patch.facecolor': 'b', 'patch.linewidth': 1.0, 'path.effects': [], 'path.simplify': True, 'path.simplify_threshold': 0.1111111111111111, 'path.sketch': None, 'path.snap': True, 'pdf.compression': 6, 'pdf.fonttype': 3, 'pdf.inheritcolor': False, 'pdf.use14corefonts': False, 'pgf.debug': False, 'pgf.preamble': [''], 'pgf.rcfonts': True, 'pgf.texsystem': 'xelatex', 'plugins.directory': '.matplotlib_plugins', 'polaraxes.grid': True, 'ps.distiller.res': 6000, 'ps.fonttype': 3, 'ps.papersize': 'letter', 'ps.useafm': False, 'ps.usedistiller': False, 'savefig.bbox': None, 'savefig.directory': '~', 'savefig.dpi': 100, 'savefig.edgecolor': 'w', 'savefig.extension': 'png', 'savefig.facecolor': 'w', 'savefig.format': 'png', 'savefig.frameon': True, 'savefig.jpeg_quality': 95, 'savefig.orientation': 'portrait', 'savefig.pad_inches': 0.1, 'svg.embed_char_paths': True, 'svg.fonttype': 'path', 'svg.image_inline': True, 'svg.image_noscale': False, 'text.antialiased': True, 'text.color': 'k', 'text.dvipnghack': None, 'text.hinting': True, 'text.hinting_factor': 8, 'text.latex.preamble': [''], 'text.latex.preview': False, 'text.latex.unicode': False, 'text.usetex': False, 'timezone': 'UTC', 'tk.pythoninspect': False, 'tk.window_focus': False, 'toolbar': 'toolbar2', 'verbose.fileo': 'sys.stdout', 'verbose.level': 'silent', 'webagg.open_in_browser': True, 'webagg.port': 8988, 'webagg.port_retries': 50, 'xtick.color': 'k', 'xtick.direction': 'in', 'xtick.labelsize': 'medium', 'xtick.major.pad': 4, 'xtick.major.size': 4, 'xtick.major.width': 0.5, 'xtick.minor.pad': 4, 'xtick.minor.size': 2, 'xtick.minor.width': 0.5, 'ytick.color': 'k', 'ytick.direction': 'in', 'ytick.labelsize': 'medium', 'ytick.major.pad': 4, 'ytick.major.size': 4, 'ytick.major.width': 0.5, 'ytick.minor.pad': 4, 'ytick.minor.size': 2, 'ytick.minor.width': 0.5})
def plot_settings():
#don't adjust MPL defaults to pandas's preferred defaults
pd.options.display.mpl_style = None
mpl.rcdefaults()
from matplotlib import rcParams
#rcParams['axes.titlesize'] = 22
rcParams['font.size'] = 14
rcParams['xtick.labelsize'] = 12
rcParams['ytick.labelsize'] = 12
rcParams['legend.fontsize'] = 12
rcParams['font.family'] = 'serif'
rcParams['figure.facecolor'] = '1.0' # 0 black --> 1 white; grays
plot_settings()
phase5t = db_taps[long_name['phase5t']]
phase8t = db_taps[long_name['phase5t']]
avgdevs = {5: phase5t.mean(level='beat')[5:],
8: phase8t.mean(level='beat')[5:], }
sd_devs = {5: phase5t.std(level='beat')[5:],
8: phase8t.std(level='beat')[5:], }
sd_devs[5].dev_perc.plot(color='red')
for shift_beat in [97, 114, 131, 150]:
plt.axvline(x=shift_beat, color='black', ymin=0, ymax=1.0, linewidth=1)
import re
def col_find(df, regex):
cols = list(enumerate(df.columns))
matches = [#'%d. %s' %
(i, c)
for (i, c) in cols
#if filt in c
if re.findall(regex, c)
]
#print('\n'.join(matches))
return matches
#filt = r"(^J)(.*)(d$)"
#cf = col_find(dfo, filt)
#import itertools
#list(itertools.combinations(cf, 2))
def inverse_scatter(dfo, ilocx, ilocy, *args, **kwargs):
inversed = lambda df: 1.0/df
df_temp = pd.concat([inversed(dfo.T.iloc[ilocx]),
inversed(dfo.T.iloc[ilocy])],
axis=1)
df_temp.plot(x=0,y=1, kind='scatter', **kwargs)
plt.show()
print('r = %f' % df_temp.corr().iloc[0,1])
def inverse_correl(dfo, ilocx, ilocy, **kwargs):
inversed = lambda df: 1.0/df
df_temp = pd.concat([inversed(dfo.T.iloc[ilocx]),
inversed(dfo.T.iloc[ilocy])],
axis=1)
print('r = %f' % df_temp.corr().iloc[0,1])
inverse_scatter(dfo, 73, 79, figsize=(5,5))
r = 0.618713
#NEW DATA VERSION....
percstds = col_find(dfo, r'.*perc_std')
import itertools
pairs = list(itertools.combinations(percstds, 2))
pair_nums = [(x[0], y[0]) for x, y in pairs]
for (x, y) in pair_nums:
inverse_scatter(dfo, x, y, figsize=(5, 5))
r = 0.765077
r = 0.821947
r = 0.726245
r = 0.718186
r = 0.770297
r = 0.759170
r = 0.707261
r = 0.515730
r = 0.545702
r = 0.432605
r = 0.243781
r = 0.398977
r = 0.511366
r = 0.784661
r = 0.842805
r = 0.781268
r = 0.817396
r = 0.757109
r = 0.844245
r = 0.448423
r = 0.595536
r = 0.498796
r = 0.306525
r = 0.363971
r = 0.495897
r = 0.767854
r = 0.735698
r = 0.794601
r = 0.764939
r = 0.767359
r = 0.471036
r = 0.615309
r = 0.558016
r = 0.281440
r = 0.376552
r = 0.538143
r = 0.851002
r = 0.816263
r = 0.760070
r = 0.872057
r = 0.515063
r = 0.566111
r = 0.583157
r = 0.353279
r = 0.420687
r = 0.554770
r = 0.841310
r = 0.787802
r = 0.878939
r = 0.497393
r = 0.641265
r = 0.687896
r = 0.481159
r = 0.508746
r = 0.679792
r = 0.824611
r = 0.862888
r = 0.521231
r = 0.686177
r = 0.569833
r = 0.392198
r = 0.520752
r = 0.602643
r = 0.779487
r = 0.482428
r = 0.565928
r = 0.558194
r = 0.321351
r = 0.463462
r = 0.502468
r = 0.526930
r = 0.667784
r = 0.574635
r = 0.503814
r = 0.496453
r = 0.634663
r = 0.479808
r = 0.532108
r = 0.427598
r = 0.566822
r = 0.574748
r = 0.538733
r = 0.445605
r = 0.474810
r = 0.683507
r = 0.501346
r = 0.525872
r = 0.575659
r = 0.573054
r = 0.576472
r = 0.618713
# original scale plots for comparison
def scatter_by_colnum(dfo, ilocx, ilocy, *args, **kwargs):
#inversed = lambda df: 1.0/df
df_temp = pd.concat([dfo.T.iloc[ilocx],
dfo.T.iloc[ilocy]],
axis=1)
df_temp.plot(x=0,y=1, kind='scatter', **kwargs)
plt.show()
print('r = %f' % df_temp.corr().iloc[0,1])
import itertools
pairs = list(itertools.combinations(percstds, 2))
pair_nums = [(x[0], y[0]) for x, y in pairs]
for (x, y) in pair_nums:
scatter_by_colnum(dfo, x, y, figsize=(5, 5))
r = 0.598121
r = 0.828544
r = 0.664704
r = 0.558165
r = 0.722386
r = 0.741657
r = 0.592727
r = 0.267684
r = 0.405239
r = 0.357660
r = 0.308328
r = 0.399985
r = 0.588676
r = 0.529275
r = 0.582498
r = 0.607872
r = 0.764485
r = 0.646210
r = 0.624153
r = 0.155338
r = 0.389675
r = 0.347034
r = 0.309237
r = 0.294634
r = 0.448726
r = 0.754742
r = 0.710131
r = 0.756830
r = 0.674557
r = 0.719420
r = 0.293740
r = 0.443736
r = 0.462588
r = 0.464724
r = 0.558051
r = 0.664373
r = 0.765101
r = 0.770047
r = 0.847746
r = 0.831036
r = 0.318473
r = 0.535361
r = 0.502814
r = 0.358193
r = 0.446816
r = 0.582852
r = 0.717852
r = 0.710793
r = 0.926027
r = 0.254892
r = 0.576605
r = 0.736836
r = 0.631216
r = 0.434963
r = 0.760646
r = 0.807721
r = 0.741037
r = 0.185137
r = 0.658507
r = 0.430533
r = 0.393690
r = 0.527222
r = 0.597971
r = 0.770588
r = 0.218466
r = 0.540778
r = 0.480212
r = 0.335500
r = 0.385980
r = 0.596735
r = 0.263878
r = 0.643864
r = 0.589094
r = 0.590682
r = 0.432036
r = 0.779257
r = 0.264134
r = 0.515194
r = 0.330317
r = 0.677801
r = 0.483918
r = 0.336498
r = 0.317066
r = 0.355100
r = 0.577531
r = 0.534783
r = 0.441617
r = 0.603131
r = 0.528399
r = 0.728432
r = 0.613616
def sideplots(series_top, series_bottom,
plotname_top="pre-filter",
plotname_bottom="post-filter"):
from matplotlib import pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=3)
fig.set_figheight(7)
fig.set_figwidth(15)
#fig.suptitle('t', fontsize=25)
#plt.xlabel('xlabel', fontsize=18)
#plt.ylabel('ylabel', fontsize=16)
ax1 = plt.subplot2grid((2,3), (0,0), colspan=2)
ax2 = plt.subplot2grid((2,3), (1,0), colspan=2)
ax3 = plt.subplot2grid((2,3), (0, 2)) #, rowspan=2)
ax4 = plt.subplot2grid((2,3), (1, 2))
#ax5 = plt.subplot2grid((4,4), (2, 1))
ax1.set_title(plotname_top, fontsize=16)
ax2.set_title(plotname_bottom, fontsize=16)
ax3.set_title(plotname_top, fontsize=16)
ax4.set_title(plotname_bottom, fontsize=16)
#ax5.set_title('ax5 title', fontsize=35)
# series_l.plot(ax=axes[0,0], linewidth=3)
# series_r.plot(ax=axes[0,1], linewidth=3)
# series_l.hist(ax=axes[1,0])
# series_r.hist(ax=axes[1,1])
series_top.plot(ax=ax1, linewidth=3)
series_bottom.plot(ax=ax2, linewidth=3)
series_top.hist(ax=ax3, bins=20)
series_bottom.hist(ax=ax4, bins=20)
fig.tight_layout()
#not using this at the moment
test_params = {'ISI': 500,
'filter_outliers_beyond_x_stdevs': 3,
#'min_percentISI_deviation_counted_as_failure': 40,
'stdev_calcs_exclude_n_from_left': 2,
'stdev_calcs_exclude_n_from_right': 2,
#'stimulus_style': 'tick',
#'stimulus_timing': 'iso',
'wait_beats_after_subj_start': 6,
'wait_beats_after_task_start': 9, }
#test_task = long_name['iso5t2'] #'Ticks_ISO_T2_5'
#test_pids = ['101', '102', '103', '104', '105', '107']
def before_after_plots(taskname, pid):
unfilt = db_taps[taskname].xs(pid)
filtered = filter_taps(unfilt, task_params=sms_params[taskname])
print("{0} taps ==> {1} taps".format(len(unfilt), len(filtered)))
#fig = sideplots(test_taps.dev_perc, filtered.dev_perc)
outliers_removed = filtered[filtered.is_outlier != True]
print('pre-filter:\t' +
'sd= {sd} \t mean= {mean} \t md= {md}'.format(sd=unfilt.dev_perc.std(),
mean=unfilt.dev_perc.mean(),
md=unfilt.dev_perc.median()))
print('post-filter:\t' +
'sd= {sd} \t mean= {mean} \t md= {md}'.format(sd=filtered.dev_perc.std(),
mean=filtered.dev_perc.mean(),
md=filtered.dev_perc.median()))
fig = sideplots(unfilt.dev_perc,
outliers_removed.dev_perc,
plotname_top = "%s, P. %s, pre-filter" % (short_name[taskname], pid),
plotname_bottom = "P. {}, post-filter".format(pid))
#plt.show()
return fig
def too_many_plots(**kwargs):
gen_tasks_pids = general_task_pid_iterator(**kwargs)
for t, pid in gen_tasks_pids:
#plt.figure()
fig = before_after_plots(t, pid)
#plt.show()
yield fig
next_plot = too_many_plots()
for i in range(2):
# plt.figure()
# fig = next_plot.next()
plt.show(next_plot.next())
#skip_to = ('T1_SMS_8', '115')
#start_later = too_many_plots(skip_to_task='T1_SMS_8', skip_to_pid='080')
#
#for i in range(2):
# plt.figure()
# fig = start_later.next()
# plt.show()
================================================================================ T1_SMS_5 ================================================================================ ------------------------------------------------------------ P. 011 150 taps ==> 140 taps pre-filter: sd= 4.53454486945 mean= 0.12922621229 md= -0.369563627668 post-filter: sd= 4.43198730634 mean= 0.0753302472248 md= -0.317041022576
------------------------------------------------------------ P. 012 150 taps ==> 141 taps pre-filter: sd= 7.24981257029 mean= -7.46174224007 md= -8.75978169705 post-filter: sd= 5.81067336948 mean= -8.05403337968 md= -8.88020326944
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('C:/db_pickles/multipage-big.pdf')
tpid_plots = too_many_plots()
#for i in tpid_plots:
plotgrid = next_plot.next()
pp.savefig(plotgrid)
plt.close() #prevents output from displaying to user
else:
pp.close()
================================================================================ T1_SMS_5 ================================================================================ ------------------------------------------------------------ P. 011 150 taps ==> 140 taps pre-filter: sd= 4.53454486945 mean= 0.12922621229 md= -0.369563627668 post-filter: sd= 4.43198730634 mean= 0.0753302472248 md= -0.317041022576 ------------------------------------------------------------ P. 015 150 taps ==> 140 taps pre-filter: sd= 9.29939990333 mean= -2.48911777898 md= -2.64350932771 post-filter: sd= 9.3237759278 mean= -2.23567421956 md= -2.57728032758 ------------------------------------------------------------ P. 012 150 taps ==> 141 taps pre-filter: sd= 7.24981257029 mean= -7.46174224007 md= -8.75978169705 post-filter: sd= 5.81067336948 mean= -8.05403337968 md= -8.88020326944 ------------------------------------------------------------ P. 016 130 taps ==> 121 taps pre-filter: sd= 5.18261654315 mean= -2.8238407723 md= -2.89548039402 post-filter: sd= 3.47480270867 mean= -2.9330124916 md= -2.77263161983 ------------------------------------------------------------ P. 015 150 taps ==> 140 taps pre-filter: sd= 9.29939990333 mean= -2.48911777898 md= -2.64350932771 post-filter: sd= 9.3237759278 mean= -2.23567421956 md= -2.57728032758 ------------------------------------------------------------ P. 017 130 taps ==> 121 taps pre-filter: sd= 5.97095559662 mean= -2.41712694821 md= -2.38912408829 post-filter: sd= 3.57560868935 mean= -2.28184065777 md= -2.1981181808 ------------------------------------------------------------ P. 016 130 taps ==> 121 taps pre-filter: sd= 5.18261654315 mean= -2.8238407723 md= -2.89548039402 post-filter: sd= 3.47480270867 mean= -2.9330124916 md= -2.77263161983 ------------------------------------------------------------ P. 018 130 taps ==> 121 taps pre-filter: sd= 4.40172850023 mean= -2.6719034793 md= -2.72777653974 post-filter: sd= 3.51807479538 mean= -2.73322650724 md= -2.86018390326 ------------------------------------------------------------ P. 017 130 taps ==> 121 taps pre-filter: sd= 5.97095559662 mean= -2.41712694821 md= -2.38912408829 post-filter: sd= 3.57560868935 mean= -2.28184065777 md= -2.1981181808 ------------------------------------------------------------ P. 019 130 taps ==> 121 taps pre-filter: sd= 2.94844013277 mean= -0.720503083349 md= -0.820212150342 post-filter: sd= 2.56051427157 mean= -0.571524340657 md= -0.855997572544 ------------------------------------------------------------ P. 018 130 taps ==> 121 taps pre-filter: sd= 4.40172850023 mean= -2.6719034793 md= -2.72777653974 post-filter: sd= 3.51807479538 mean= -2.73322650724 md= -2.86018390326 ------------------------------------------------------------ P. 020 130 taps ==> 121 taps pre-filter: sd= 9.52224499213 mean= -9.63780183808 md= -10.9225239617 post-filter: sd= 8.07706019611 mean= -10.3473351506 md= -10.9287955433 ------------------------------------------------------------ P. 019 130 taps ==> 121 taps pre-filter: sd= 2.94844013277 mean= -0.720503083349 md= -0.820212150342 post-filter: sd= 2.56051427157 mean= -0.571524340657 md= -0.855997572544 ------------------------------------------------------------ P. 021 130 taps ==> 121 taps pre-filter: sd= 5.02306673398 mean= -3.20878677067 md= -3.61829692119 post-filter: sd= 4.56030828713 mean= -3.57627290227 md= -3.77631779007 ------------------------------------------------------------ P. 020 130 taps ==> 121 taps pre-filter: sd= 9.52224499213 mean= -9.63780183808 md= -10.9225239617 post-filter: sd= 8.07706019611 mean= -10.3473351506 md= -10.9287955433 ------------------------------------------------------------ P. 022 130 taps ==> 120 taps pre-filter: sd= 3.32553889043 mean= -2.29471633547 md= -2.01959585704 post-filter: sd= 3.16870041155 mean= -2.05690045312 md= -1.85448391205 ------------------------------------------------------------ P. 021 130 taps ==> 121 taps pre-filter: sd= 5.02306673398 mean= -3.20878677067 md= -3.61829692119 post-filter: sd= 4.56030828713 mean= -3.57627290227 md= -3.77631779007 ------------------------------------------------------------ P. 024 130 taps ==> 121 taps pre-filter: sd= 4.04764943808 mean= -6.23729710415 md= -5.46972641324 post-filter: sd= 3.19898227917 mean= -5.67847567609 md= -5.21597848619 ------------------------------------------------------------ P. 022 130 taps ==> 120 taps pre-filter: sd= 3.32553889043 mean= -2.29471633547 md= -2.01959585704 post-filter: sd= 3.16870041155 mean= -2.05690045312 md= -1.85448391205 ------------------------------------------------------------ P. 025 130 taps ==> 116 taps pre-filter: sd= 3.58864076695 mean= -5.8673397433 md= -5.61484972828 post-filter: sd= 3.44503327167 mean= -5.73993492579 md= -5.50218244933 ------------------------------------------------------------ P. 024 130 taps ==> 121 taps pre-filter: sd= 4.04764943808 mean= -6.23729710415 md= -5.46972641324 post-filter: sd= 3.19898227917 mean= -5.67847567609 md= -5.21597848619 ------------------------------------------------------------ P. 026 130 taps ==> 120 taps pre-filter: sd= 3.17937128969 mean= 0.924450977964 md= 0.818420201393 post-filter: sd= 2.70267552681 mean= 0.672031630714 md= 0.774812554712 ------------------------------------------------------------ P. 025 130 taps ==> 116 taps pre-filter: sd= 3.58864076695 mean= -5.8673397433 md= -5.61484972828 post-filter: sd= 3.44503327167 mean= -5.73993492579 md= -5.50218244933
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-24-de81c874ee88> in <module>() 5 tpid_plots = too_many_plots() 6 ----> 7 for i in tpid_plots: 8 plotgrid = next_plot.next() 9 pp.savefig(plotgrid) <ipython-input-23-55a28285d417> in too_many_plots(**kwargs) 44 for t, pid in gen_tasks_pids: 45 #plt.figure() ---> 46 fig = before_after_plots(t, pid) 47 #plt.show() 48 yield fig <ipython-input-23-55a28285d417> in before_after_plots(taskname, pid) 33 outliers_removed.dev_perc, 34 plotname_top = "%s, P. %s, pre-filter" % (short_name[taskname], pid), ---> 35 plotname_bottom = "P. {}, post-filter".format(pid)) 36 #plt.show() 37 return fig <ipython-input-22-2f5719cf3724> in sideplots(series_top, series_bottom, plotname_top, plotname_bottom) 13 14 ax1 = plt.subplot2grid((2,3), (0,0), colspan=2) ---> 15 ax2 = plt.subplot2grid((2,3), (1,0), colspan=2) 16 17 ax3 = plt.subplot2grid((2,3), (0, 2)) #, rowspan=2) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\pyplot.pyc in subplot2grid(shape, loc, rowspan, colspan, **kwargs) 1139 rowspan=rowspan, 1140 colspan=colspan) -> 1141 a = fig.add_subplot(subplotspec, **kwargs) 1142 bbox = a.bbox 1143 byebye = [] C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\figure.pyc in add_subplot(self, *args, **kwargs) 912 self._axstack.remove(ax) 913 --> 914 a = subplot_class_factory(projection_class)(self, *args, **kwargs) 915 916 self._axstack.add(key, a) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axes.pyc in __init__(self, fig, *args, **kwargs) 9257 9258 # _axes_class is set in the subplot_class_factory -> 9259 self._axes_class.__init__(self, fig, self.figbox, **kwargs) 9260 9261 def __reduce__(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axes.pyc in __init__(self, fig, rect, axisbg, frameon, sharex, sharey, label, xscale, yscale, **kwargs) 447 448 # this call may differ for non-sep axes, eg polar --> 449 self._init_axis() 450 451 if axisbg is None: C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axes.pyc in _init_axis(self) 508 self.spines['top'].register_axis(self.xaxis) 509 self.yaxis = maxis.YAxis(self) --> 510 self.spines['left'].register_axis(self.yaxis) 511 self.spines['right'].register_axis(self.yaxis) 512 self._update_transScale() C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\spines.pyc in register_axis(self, axis) 151 self.axis = axis 152 if self.axis is not None: --> 153 self.axis.cla() 154 155 def cla(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in cla(self) 740 self._set_artist_props(self.label) 741 --> 742 self.reset_ticks() 743 744 self.converter = None C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in reset_ticks(self) 754 755 self.majorTicks.extend([self._get_tick(major=True)]) --> 756 self.minorTicks.extend([self._get_tick(major=False)]) 757 self._lastNumMajorTicks = 1 758 self._lastNumMinorTicks = 1 C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in _get_tick(self, major) 1909 else: 1910 tick_kw = self._minor_tick_kw -> 1911 return YTick(self.axes, 0, '', major=major, **tick_kw) 1912 1913 def _get_label(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in __init__(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major) 138 self.apply_tickdir(tickdir) 139 --> 140 self.tick1line = self._get_tick1line() 141 self.tick2line = self._get_tick2line() 142 self.gridline = self._get_gridline() C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\axis.pyc in _get_tick1line(self) 524 markersize=self._size, 525 markeredgewidth=self._width, --> 526 zorder=self._zorder, 527 ) 528 l.set_transform(self.axes.get_yaxis_transform(which='tick1')) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\lines.pyc in __init__(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs) 209 self.set_color(color) 210 self._marker = MarkerStyle() --> 211 self.set_marker(marker) 212 self.set_markevery(markevery) 213 self.set_antialiased(antialiased) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\lines.pyc in set_marker(self, marker) 851 852 """ --> 853 self._marker.set_marker(marker) 854 855 def set_markeredgecolor(self, ec): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\markers.pyc in set_marker(self, marker) 237 238 self._marker = marker --> 239 self._recache() 240 241 def get_path(self): C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\markers.pyc in _recache(self) 174 175 def _recache(self): --> 176 self._path = Path(np.empty((0, 2))) 177 self._transform = IdentityTransform() 178 self._alt_path = None C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\path.pyc in __init__(self, vertices, codes, _interpolation_steps, closed, readonly) 151 self._codes = codes 152 self._interpolation_steps = _interpolation_steps --> 153 self._update_values() 154 155 if readonly: C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\path.pyc in _update_values(self) 195 def _update_values(self): 196 self._should_simplify = ( --> 197 rcParams['path.simplify'] and 198 (len(self._vertices) >= 128 and 199 (self._codes is None or np.all(self._codes <= Path.LINETO)))) C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\__init__.pyc in __getitem__(self, key) 799 warnings.warn(self.msg_depr % (key, alt)) 800 key = alt --> 801 elif key in _deprecated_ignore_map: 802 alt = _deprecated_ignore_map[key] 803 warnings.warn(self.msg_depr_ignore % (key, alt)) KeyboardInterrupt:
import psutil
free_megs = psutil.virtual_memory()[1] / 1000000
free_megs
5962L
#separate files for tasks
from matplotlib.backends.backend_pdf import PdfPages
iterator = general_task_pid_iterator(concise_labels=False,
skip_to_task='Ticks_Phase_8')
prev_t = None
pp = PdfPages('C:/db_pickles/empty.pdf')
while True:
try:
t, pid = iterator.next()
except StopIteration:
pp.close()
break
if prev_t != t:
pp.close()
pp = PdfPages('C:/db_pickles/sideplots - 2014-09-23b2 - %s.pdf' % short_name[t])
print("{} megs free memory".format(psutil.virtual_memory()[1] / 1000000))
fig = before_after_plots(t, pid)
pp.savefig(fig)
plt.close()
prev_t = t
pp.close()
def task_side_plotter_pdf(task_name):
from matplotlib.backends.backend_pdf import PdfPages
t = task_name
file_out = 'C:/db_pickles/sideplots - 2014-09-26c1 - %s.pdf' % short_name[t]
with PdfPages(file_out) as pp:
for pid in task_pids[task_name]:
print("{} megs free memory".format(psutil.virtual_memory()[1] / 1000000))
fig = before_after_plots(t, pid)
pp.savefig(fig)
plt.close()
pp.close()
for t in sms_tasknames:
task_side_plotter_pdf(t)
================================================================================ Ticks_Phase_8 ================================================================================ ------------------------------------------------------------ P. 011 4774 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.15133638106 mean= -0.628754511522 md= -0.794645906348 post-filter: sd= 6.23690831333 mean= -0.610119453288 md= -0.737832024411 ------------------------------------------------------------ P. 012 4762 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.53297312887 mean= -4.81414899212 md= -5.22643733269 post-filter: sd= 5.34034611159 mean= -5.03070577063 md= -5.25281222022 ------------------------------------------------------------ P. 015 4756 megs free memory 170 taps ==> 161 taps pre-filter: sd= 29.0355981008 mean= -4.56767508401 md= -3.3266597522 post-filter: sd= 29.7380079121 mean= -4.86429311506 md= -4.51010108589 ------------------------------------------------------------ P. 016 4763 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.38803923719 mean= -3.62745117209 md= -3.30887311491 post-filter: sd= 4.21444084736 mean= -3.46573461026 md= -3.22866231843 ------------------------------------------------------------ P. 017 4750 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.12435650014 mean= -10.7032150783 md= -10.2612238377 post-filter: sd= 6.96207930094 mean= -11.0318910292 md= -10.4963555575 ------------------------------------------------------------ P. 018 4754 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.27805484611 mean= 1.8229582983 md= 1.32495325931 post-filter: sd= 7.04995955427 mean= 2.1145938952 md= 1.54366763993 ------------------------------------------------------------ P. 019 4738 megs free memory 170 taps ==> 161 taps pre-filter: sd= 3.8037112577 mean= -0.544815283453 md= -0.667807191308 post-filter: sd= 3.84737751058 mean= -0.496771202485 md= -0.67224494782 ------------------------------------------------------------ P. 020 4733 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.70810244407 mean= -2.1467597151 md= -2.35367899187 post-filter: sd= 7.78832810139 mean= -2.22492566333 md= -2.86214330056 ------------------------------------------------------------ P. 021 4724 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.43371055594 mean= -3.1895128658 md= -3.59338370865 post-filter: sd= 5.45192605357 mean= -3.14067315333 md= -3.67195576803 ------------------------------------------------------------ P. 022 4715 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.36368704161 mean= 0.117148012367 md= -0.199513498657 post-filter: sd= 5.44372157501 mean= 0.188723529209 md= -0.199076977558 ------------------------------------------------------------ P. 024 4709 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.71499236607 mean= -1.81307538035 md= -2.14854088784 post-filter: sd= 5.2646450969 mean= -2.32574598416 md= -2.14854088784 ------------------------------------------------------------ P. 025 4702 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.64091975308 mean= 1.47804811186 md= 0.614715150304 post-filter: sd= 6.69940584845 mean= 1.528196051 md= 0.670769293367 ------------------------------------------------------------ P. 026 4693 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.35790710536 mean= 0.858750520096 md= 0.43550352598 post-filter: sd= 4.392191971 mean= 0.835905046071 md= 0.43550352598 ------------------------------------------------------------ P. 027 4683 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.38778323204 mean= -0.532796962891 md= -1.02843981932 post-filter: sd= 4.4127738471 mean= -0.614607184908 md= -1.03574585543 ------------------------------------------------------------ P. 028 4674 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.85077501147 mean= -2.37280678454 md= -2.8262792469 post-filter: sd= 4.92013870696 mean= -2.43514310914 md= -3.13761541229 ------------------------------------------------------------ P. 029 4665 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.7547212219 mean= 0.133608596758 md= 0.120873686998 post-filter: sd= 9.47934646093 mean= 0.286245551855 md= -0.0180780045311 ------------------------------------------------------------ P. 030 4654 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.67809651717 mean= -0.91367409061 md= -0.463169056706 post-filter: sd= 5.19936765172 mean= -0.680345716206 md= -0.397535778221 ------------------------------------------------------------ P. 032 4646 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.05591813338 mean= -1.4038928555 md= -1.63378012951 post-filter: sd= 4.12248629714 mean= -1.38701081948 md= -1.63378012951 ------------------------------------------------------------ P. 033 4635 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.58679730969 mean= -1.24867330458 md= -1.75498245018 post-filter: sd= 4.64930027499 mean= -1.29695927354 md= -1.78314265141 ------------------------------------------------------------ P. 034 4625 megs free memory 170 taps ==> 161 taps pre-filter: sd= 3.98371270762 mean= -0.978799297223 md= -0.734194561557 post-filter: sd= 3.89044371261 mean= -1.0052129079 md= -0.855808090912 ------------------------------------------------------------ P. 035 4616 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.40347650707 mean= -8.25638648025 md= -8.27322295033 post-filter: sd= 5.35011862176 mean= -8.368625357 md= -8.40740108298 ------------------------------------------------------------ P. 036 4607 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.3316815669 mean= -5.78501649271 md= -5.52216810291 post-filter: sd= 11.3760208882 mean= -5.60399847188 md= -5.39841643783 ------------------------------------------------------------ P. 037 4597 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.74538709862 mean= -4.73357088465 md= -4.38044433789 post-filter: sd= 6.61262245917 mean= -4.67503703413 md= -4.2224886358 ------------------------------------------------------------ P. 038 4590 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.2872910987 mean= -2.09550182777 md= -2.35168587107 post-filter: sd= 5.35871162431 mean= -2.10488399753 md= -2.40946385804 ------------------------------------------------------------ P. 039 4585 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.31346695223 mean= -0.0783974672355 md= -0.912072965837 post-filter: sd= 7.20266234207 mean= -0.136224650806 md= -0.912072965837 ------------------------------------------------------------ P. 040 4572 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.69187737926 mean= 0.361812119887 md= -0.208827258848 post-filter: sd= 4.70801856115 mean= 0.474275810011 md= -0.0233098134369 ------------------------------------------------------------ P. 041 4562 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.41186613196 mean= -2.25902816462 md= -2.75289851091 post-filter: sd= 5.19899210217 mean= -2.23178042832 md= -2.53153162164 ------------------------------------------------------------ P. 043 4553 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.08506353174 mean= -9.41228209998 md= -9.65574433859 post-filter: sd= 5.88646407833 mean= -9.40804575559 md= -9.80398042806 ------------------------------------------------------------ P. 044 4551 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.00925448893 mean= 0.960612506629 md= 0.511827569645 post-filter: sd= 5.04179366638 mean= 1.04269908142 md= 0.633164622802 ------------------------------------------------------------ P. 046 4534 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.45722702507 mean= -1.09957114354 md= -1.70404272211 post-filter: sd= 5.62330589075 mean= -0.784424084956 md= -1.53853882041 ------------------------------------------------------------ P. 047 4523 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.43867011273 mean= -4.52982393042 md= -4.59918872197 post-filter: sd= 7.55035084655 mean= -4.66654744639 md= -4.7435121302 ------------------------------------------------------------ P. 048 4518 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.25666335444 mean= -1.5059519172 md= -2.36164205582 post-filter: sd= 8.01518001566 mean= -1.56354459027 md= -2.55066055055 ------------------------------------------------------------ P. 049 4511 megs free memory 170 taps ==> 161 taps pre-filter: sd= 28.6159707618 mean= -7.26971970242 md= -14.7223082858 post-filter: sd= 29.1349593853 mean= -7.23063379017 md= -14.8729098394 ------------------------------------------------------------ P. 051 4501 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.52121165581 mean= 0.814345814012 md= -0.259593050455 post-filter: sd= 6.60265934103 mean= 0.919405945651 md= -0.206202038055 ------------------------------------------------------------ P. 052 4489 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.57206504148 mean= -0.645827585237 md= -0.921283928694 post-filter: sd= 4.59206869956 mean= -0.613681808442 md= -0.921283928694 ------------------------------------------------------------ P. 053 4937 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.85115361656 mean= 1.96462996812 md= 1.00565557778 post-filter: sd= 6.72803785215 mean= 1.80545666196 md= 0.782977616346 ------------------------------------------------------------ P. 054 4922 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.67235758967 mean= 1.06489891761 md= 1.37386245618 post-filter: sd= 6.1543533848 mean= 0.827840075505 md= 1.30384037938 ------------------------------------------------------------ P. 055 4909 megs free memory 170 taps ==> 161 taps pre-filter: sd= 32.9675345998 mean= -6.50674312245 md= -26.6876428775 post-filter: sd= 31.7827208203 mean= -9.02793280052 md= -27.0885126179 ------------------------------------------------------------ P. 056 4899 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.20307198877 mean= 0.463459259798 md= 0.458580251544 post-filter: sd= 5.03260975278 mean= 0.288963916002 md= 0.314995275071 ------------------------------------------------------------ P. 057 4897 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.92348361903 mean= -2.50700411802 md= -2.72969108064 post-filter: sd= 5.97181111558 mean= -2.47790691605 md= -2.73874612949 ------------------------------------------------------------ P. 058 4886 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.19544355101 mean= -4.08964189598 md= -3.82318817387 post-filter: sd= 6.20793069049 mean= -3.86486892328 md= -3.58762534994 ------------------------------------------------------------ P. 059 4876 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.61486053158 mean= -2.25022772744 md= -2.26893170336 post-filter: sd= 4.65544460888 mean= -2.17885087925 md= -2.14056437263 ------------------------------------------------------------ P. 060 4858 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.01413617111 mean= -1.70658349817 md= -1.81133321328 post-filter: sd= 6.12056229975 mean= -1.75420283953 md= -1.96620140239 ------------------------------------------------------------ P. 061 4857 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.56357214044 mean= -3.92982711562 md= -3.37594117245 post-filter: sd= 6.59897195009 mean= -4.11936972441 md= -3.67180097356 ------------------------------------------------------------ P. 062 4845 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.57168697178 mean= -3.29680182943 md= -2.85577962314 post-filter: sd= 8.2599570326 mean= -3.65554000217 md= -2.9168345544 ------------------------------------------------------------ P. 063 4836 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.66021373816 mean= -0.660812389102 md= -0.740092581252 post-filter: sd= 4.68032215864 mean= -0.598597798632 md= -0.7343304487 ------------------------------------------------------------ P. 064 4825 megs free memory 170 taps ==> 161 taps pre-filter: sd= 16.3328019847 mean= -0.218187707321 md= 1.31576424997 post-filter: sd= 15.2770163144 mean= 0.56323541062 md= 1.70979921486 ------------------------------------------------------------ P. 065 4812 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.15619627516 mean= -5.52887981974 md= -5.88470941118 post-filter: sd= 9.30579828239 mean= -5.54779241927 md= -5.93795890585 ------------------------------------------------------------ P. 066 4802 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.39844961909 mean= -3.35843777564 md= -4.63799014858 post-filter: sd= 7.28385447855 mean= -3.21086162906 md= -4.57613562211 ------------------------------------------------------------ P. 067 4793 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.6266607306 mean= -1.75463496653 md= -1.61543616777 post-filter: sd= 5.65696407825 mean= -1.915643789 md= -1.93476738486 ------------------------------------------------------------ P. 068 4764 megs free memory 170 taps ==> 161 taps pre-filter: sd= 16.0024475588 mean= 0.0713092020437 md= -0.332589799245 post-filter: sd= 15.5027444776 mean= -0.567368843246 md= -0.397605365422 ------------------------------------------------------------ P. 069 4748 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.66307922597 mean= -4.72785832651 md= -4.79354800137 post-filter: sd= 5.67693989156 mean= -4.60754941662 md= -4.77427749562 ------------------------------------------------------------ P. 071 4733 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.8387687531 mean= -0.817982733938 md= -0.187138117608 post-filter: sd= 9.97266992957 mean= -0.890434918227 md= -0.187138117608 ------------------------------------------------------------ P. 072 4731 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.28953448765 mean= -4.76819226164 md= -4.89935738754 post-filter: sd= 6.90103615971 mean= -4.50776376782 md= -4.81515503901 ------------------------------------------------------------ P. 073 4718 megs free memory 170 taps ==> 158 taps pre-filter: sd= 23.7922473723 mean= -6.37566994817 md= -8.48369404907 post-filter: sd= 24.2237770816 mean= -6.53816211317 md= -9.39494587727 ------------------------------------------------------------ P. 074 4710 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.88628179402 mean= -0.924994239983 md= -0.878331846227 post-filter: sd= 5.87642670255 mean= -0.855581997887 md= -0.968261749956 ------------------------------------------------------------ P. 075 4702 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.55932162338 mean= -2.77622507999 md= -2.7755126544 post-filter: sd= 4.6300037408 mean= -2.82981102274 md= -2.85824295065 ------------------------------------------------------------ P. 076 4695 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.7003472049 mean= -2.49820263606 md= -2.17578328198 post-filter: sd= 5.09392992979 mean= -2.28179268266 md= -1.98280733514 ------------------------------------------------------------ P. 077 4686 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.83713545429 mean= -8.36067595633 md= -8.53039865603 post-filter: sd= 6.82713217511 mean= -8.5471894547 md= -8.73759173201 ------------------------------------------------------------ P. 078 4676 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.03921483561 mean= 0.253941708305 md= -0.15780826296 post-filter: sd= 4.10278340807 mean= 0.206874323606 md= -0.198109951023 ------------------------------------------------------------ P. 079 4672 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.83692140625 mean= -0.305981097216 md= -0.557510121761 post-filter: sd= 5.91159057479 mean= -0.437972338843 md= -0.658961717081 ------------------------------------------------------------ P. 080 4663 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.75061924482 mean= -3.28352236522 md= -3.59991698363 post-filter: sd= 5.8114358509 mean= -3.16192640463 md= -3.46881096493 ------------------------------------------------------------ P. 081 4647 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.31296821125 mean= -1.13342082504 md= -1.48679311549 post-filter: sd= 5.33394818112 mean= -1.24833041794 md= -1.52933348675 ------------------------------------------------------------ P. 082 4639 megs free memory 170 taps ==> 161 taps pre-filter: sd= 3.97875919904 mean= -0.674598320243 md= -0.66855014126 post-filter: sd= 3.98150146931 mean= -0.527713746963 md= -0.449126296768 ------------------------------------------------------------ P. 083 4627 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.98153125162 mean= -0.23150563929 md= -0.157527417746 post-filter: sd= 5.08682626028 mean= -0.145795261512 md= -0.194826604322 ------------------------------------------------------------ P. 084 4616 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.5133788165 mean= -4.93095959906 md= -5.50272631187 post-filter: sd= 6.02594424436 mean= -4.9335798361 md= -5.54221992975 ------------------------------------------------------------ P. 085 4607 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.1752122914 mean= -2.24055269162 md= -1.91851886078 post-filter: sd= 5.20308804985 mean= -2.33204130666 md= -2.09135553044 ------------------------------------------------------------ P. 086 4595 megs free memory 170 taps ==> 151 taps pre-filter: sd= 9.57328988926 mean= -0.886471275478 md= -0.332558197685 post-filter: sd= 9.75952046221 mean= -0.87042161599 md= -0.332558197685 ------------------------------------------------------------ P. 087 4585 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.12706228423 mean= 1.9246993775 md= 2.20067656241 post-filter: sd= 6.69577086322 mean= 1.94382103067 md= 2.34663607786 ------------------------------------------------------------ P. 089 4566 megs free memory 170 taps ==> 161 taps pre-filter: sd= 33.984833863 mean= -1.05190284156 md= -6.63279127522 post-filter: sd= 34.7631017024 mean= -1.15635741975 md= -7.88104647091 ------------------------------------------------------------ P. 090 4555 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.89461019412 mean= -2.35012044138 md= -2.78384029544 post-filter: sd= 4.85605585441 mean= -2.44263407146 md= -2.8310191669 ------------------------------------------------------------ P. 091 4546 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.8483090355 mean= -0.996446514943 md= -0.84669473979 post-filter: sd= 4.63902104729 mean= -0.749337207528 md= -0.82743596569 ------------------------------------------------------------ P. 092 4540 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.84481341274 mean= -0.99722295407 md= -1.69008165226 post-filter: sd= 5.87066969849 mean= -0.985986708571 md= -1.657182951 ------------------------------------------------------------ P. 093 4533 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.69711462922 mean= -2.05632508513 md= -2.23994687624 post-filter: sd= 5.60282809715 mean= -2.21575732455 md= -2.28826753064 ------------------------------------------------------------ P. 094 4522 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.65931864397 mean= -6.21775714 md= -5.83882521812 post-filter: sd= 7.71190996838 mean= -6.33472177154 md= -6.12032905068 ------------------------------------------------------------ P. 095 4512 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.36007482872 mean= -2.98913824778 md= -3.03864086607 post-filter: sd= 4.91470360218 mean= -3.29347464102 md= -3.0658277735 ------------------------------------------------------------ P. 096 4504 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.28497115993 mean= -2.01969535067 md= -2.32337316402 post-filter: sd= 5.32326884817 mean= -2.14547927374 md= -2.44734633523 ------------------------------------------------------------ P. 097 4493 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.3090517306 mean= -0.820621759667 md= -0.808718353956 post-filter: sd= 5.45630206412 mean= -0.398587504272 md= -0.599250552346 ------------------------------------------------------------ P. 098 4471 megs free memory 170 taps ==> 159 taps pre-filter: sd= 4.61216460891 mean= -1.85135080807 md= -2.02762533206 post-filter: sd= 4.68643714015 mean= -1.85617657815 md= -2.02762533206 ------------------------------------------------------------ P. 099 4463 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.31193592446 mean= -3.26395009706 md= -2.40455161879 post-filter: sd= 8.46695631217 mean= -3.38869776904 md= -2.55018855091 ------------------------------------------------------------ P. 100 4451 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.99567220674 mean= -1.77328065502 md= -1.67200997882 post-filter: sd= 5.03557480147 mean= -1.64609036077 md= -1.51005037992 ------------------------------------------------------------ P. 101 4442 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.45520963612 mean= 0.353471441376 md= 0.660609000485 post-filter: sd= 6.76582473056 mean= 0.970676887196 md= 0.787283001335 ------------------------------------------------------------ P. 102 4430 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.25143371863 mean= -0.63375402349 md= -0.916175455379 post-filter: sd= 5.24027310482 mean= -0.848970600564 md= -1.16409951125 ------------------------------------------------------------ P. 103 4418 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.66274932934 mean= -1.6459504946 md= -1.67569823741 post-filter: sd= 4.6901936191 mean= -1.63968070886 md= -1.68121790902 ------------------------------------------------------------ P. 104 4405 megs free memory 170 taps ==> 161 taps pre-filter: sd= 18.9303681766 mean= -2.03292053417 md= -4.11175908562 post-filter: sd= 18.6809172238 mean= -1.62335725449 md= -3.81014345009 ------------------------------------------------------------ P. 105 4400 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.9108345063 mean= -5.01294991885 md= -4.18249048069 post-filter: sd= 8.86366423462 mean= -4.74872081105 md= -4.00316530268 ------------------------------------------------------------ P. 107 4383 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.05642857098 mean= -0.374305518067 md= -0.250330934231 post-filter: sd= 4.05923041376 mean= -0.444866776823 md= -0.288915081053 ------------------------------------------------------------ P. 108 4374 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.26335365234 mean= -1.90723269782 md= -2.05163344311 post-filter: sd= 5.25853643397 mean= -1.80761585115 md= -1.98970634575 ------------------------------------------------------------ P. 109 4358 megs free memory 170 taps ==> 156 taps pre-filter: sd= 7.30687327931 mean= 1.47084164203 md= 0.40866801651 post-filter: sd= 7.40463837097 mean= 1.59044114185 md= 0.534950506106 ------------------------------------------------------------ P. 110 4348 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.49828503798 mean= 0.488717501177 md= 0.176575044393 post-filter: sd= 4.20674533705 mean= 0.304971130678 md= 0.158026864566 ------------------------------------------------------------ P. 111 4340 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.77584366104 mean= -0.0484886027477 md= -0.593859740122 post-filter: sd= 6.88179719039 mean= -0.0700152725728 md= -0.602815804063 ------------------------------------------------------------ P. 112 4329 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.05954415812 mean= -6.00153904429 md= -5.77513414581 post-filter: sd= 7.14201043369 mean= -5.93469099441 md= -5.64742710205 ------------------------------------------------------------ P. 113 4317 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.00644815813 mean= 1.00624030622 md= 1.02284730834 post-filter: sd= 5.09143900583 mean= 0.958131243615 md= 0.999859949582 ------------------------------------------------------------ P. 114 4306 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.01586001501 mean= -6.57826132283 md= -6.86974401049 post-filter: sd= 6.9481404941 mean= -6.92822708421 md= -7.16281685605 ------------------------------------------------------------ P. 115 4294 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.69372070127 mean= -3.14458208374 md= -3.33793405292 post-filter: sd= 5.76838013281 mean= -3.22700808971 md= -3.49496505035 ------------------------------------------------------------ P. 116 4288 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.32117601925 mean= -2.14696449025 md= -2.03466497648 post-filter: sd= 4.12429489325 mean= -1.74312798732 md= -1.7992943494 ------------------------------------------------------------ P. 117 4280 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.35523237943 mean= -0.686350411431 md= -1.83966227842 post-filter: sd= 7.14340848863 mean= -1.01332811097 md= -1.93251211571 ------------------------------------------------------------ P. 118 4263 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.0331026499 mean= 2.0971614768 md= 1.82699560966 post-filter: sd= 6.09312918296 mean= 2.19915003116 md= 1.90623415147 ------------------------------------------------------------ P. 119 4255 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.29500755527 mean= -0.557052782158 md= -0.378996210038 post-filter: sd= 4.31064795398 mean= -0.441971862288 md= -0.312059961321 ------------------------------------------------------------ P. 120 4250 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.65158378596 mean= -5.87870840261 md= -6.53241440069 post-filter: sd= 5.54060458374 mean= -5.65866083967 md= -6.47343307021 ------------------------------------------------------------ P. 121 4233 megs free memory 170 taps ==> 160 taps pre-filter: sd= 11.4260172677 mean= -3.02060862422 md= -1.90380860834 post-filter: sd= 11.6371457148 mean= -3.11031302103 md= -2.10460865943 ================================================================================ Jits_ISO_5 ================================================================================ ------------------------------------------------------------ P. 011 4220 megs free memory 120 taps ==> 105 taps pre-filter: sd= 23.2817213864 mean= 35.3613969996 md= 40.2557326847 post-filter: sd= 22.4477044404 mean= 35.645900338 md= 40.2557326847 ------------------------------------------------------------ P. 012 4203 megs free memory 120 taps ==> 111 taps pre-filter: sd= 34.7929345872 mean= 26.7451617799 md= 39.8835901069 post-filter: sd= 34.9419386777 mean= 26.9129211068 md= 39.9653748919 ------------------------------------------------------------ P. 015 4204 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.25265571364 mean= -7.83670935136 md= -7.2198878433 post-filter: sd= 4.29786221269 mean= -7.78645594454 md= -7.2198878433 ------------------------------------------------------------ P. 016 4189 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.87949588044 mean= -6.07873341677 md= -6.74678436229 post-filter: sd= 5.14073839892 mean= -6.89898862151 md= -7.17717857181 ------------------------------------------------------------ P. 017 4180 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.10068540785 mean= -4.87084959964 md= -4.3277438763 post-filter: sd= 5.20846909822 mean= -5.37329342646 md= -4.67674277114 ------------------------------------------------------------ P. 018 4138 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.80657980129 mean= 0.427344100354 md= 0.430171548547 post-filter: sd= 6.84731289892 mean= -0.439441449361 md= 0.0579878605828 ------------------------------------------------------------ P. 019 4131 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.49478052021 mean= -2.97239223683 md= -4.29493028542 post-filter: sd= 6.54212309973 mean= -3.53581511757 md= -5.18563583676 ------------------------------------------------------------ P. 020 4120 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.33140598558 mean= -5.86357192928 md= -6.04248711299 post-filter: sd= 7.23355543591 mean= -6.1624458506 md= -6.51467099114 ------------------------------------------------------------ P. 021 4111 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.82675888966 mean= -3.33574817045 md= -4.42607948902 post-filter: sd= 5.83771709712 mean= -3.2594088015 md= -4.42690651735 ------------------------------------------------------------ P. 022 4103 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.97610891192 mean= -2.18003858666 md= -3.11651167993 post-filter: sd= 5.1830081465 mean= -2.75245256918 md= -3.32447447207 ------------------------------------------------------------ P. 024 4091 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.9405868706 mean= -4.51411301913 md= -5.01038504554 post-filter: sd= 6.40299042425 mean= -5.08402527666 md= -5.3780826523 ------------------------------------------------------------ P. 025 4079 megs free memory 120 taps ==> 106 taps pre-filter: sd= 6.18136652003 mean= -6.41940636984 md= -7.56068626123 post-filter: sd= 5.39766766088 mean= -7.16532061413 md= -7.96242334972 ------------------------------------------------------------ P. 026 4070 megs free memory 120 taps ==> 106 taps pre-filter: sd= 3.94582963624 mean= -8.44324040373 md= -8.61581693734 post-filter: sd= 3.52667926256 mean= -8.87956686219 md= -8.81270763274 ------------------------------------------------------------ P. 027 4078 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.22077485273 mean= 1.46085825409 md= 1.78629918115 post-filter: sd= 5.21081435631 mean= 1.61155501121 md= 2.18771715429 ------------------------------------------------------------ P. 028 4062 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.37687289898 mean= -10.4413315382 md= -10.7979533077 post-filter: sd= 5.17882086162 mean= -10.8036857525 md= -11.1498800173 ------------------------------------------------------------ P. 029 4055 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.19208293779 mean= -7.3555634133 md= -7.38790092742 post-filter: sd= 5.48505556687 mean= -7.92424021585 md= -7.77648205243 ------------------------------------------------------------ P. 030 4046 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.59435699699 mean= 4.01383872905 md= 3.76472933204 post-filter: sd= 6.48288678862 mean= 3.69176160464 md= 3.4881716647 ------------------------------------------------------------ P. 032 4028 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.4430757925 mean= -2.52804554881 md= -2.42863303736 post-filter: sd= 4.14209144973 mean= -2.89297924677 md= -2.80868561589 ------------------------------------------------------------ P. 033 4022 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.53674198497 mean= -5.5494380238 md= -5.82773637679 post-filter: sd= 4.56584599519 mean= -5.65072491343 md= -6.15645060592 ------------------------------------------------------------ P. 034 4012 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.29735717525 mean= -3.18986630284 md= -3.29280688976 post-filter: sd= 4.0691775582 mean= -3.12327376401 md= -3.42881230477 ------------------------------------------------------------ P. 035 4035 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.33601053221 mean= -11.2889004937 md= -11.5476971841 post-filter: sd= 6.15021160234 mean= -11.693611448 md= -11.7289917477 ------------------------------------------------------------ P. 036 4023 megs free memory 120 taps ==> 111 taps pre-filter: sd= 10.2434764416 mean= -4.30081501026 md= -5.43291453669 post-filter: sd= 7.38215386035 mean= -6.0267260484 md= -6.09976173385 ------------------------------------------------------------ P. 037 4016 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.01028929598 mean= -10.5629068776 md= -11.3019674388 post-filter: sd= 5.28390310128 mean= -10.6205964581 md= -10.9130493945 ------------------------------------------------------------ P. 038 4006 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.08778146641 mean= 0.127984673935 md= 0.00440134403521 post-filter: sd= 5.1994203606 mean= 0.0664842337355 md= -0.263590906914 ------------------------------------------------------------ P. 039 3993 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.73449900649 mean= -8.55718538497 md= -9.41114321618 post-filter: sd= 6.22230219251 mean= -10.0540476024 md= -10.1923523338 ------------------------------------------------------------ P. 040 3983 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.62623036064 mean= -8.04144938342 md= -7.96478278211 post-filter: sd= 5.23590448604 mean= -8.61043187995 md= -8.33039802747 ------------------------------------------------------------ P. 041 3971 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.5663117738 mean= 5.92691647038 md= 5.41578951581 post-filter: sd= 5.28684183814 mean= 5.672752893 md= 5.37148071951 ------------------------------------------------------------ P. 043 3961 megs free memory 120 taps ==> 111 taps pre-filter: sd= 10.0499886629 mean= -4.72061628161 md= -6.95578619342 post-filter: sd= 9.39466652935 mean= -5.26470769025 md= -7.64813426382 ------------------------------------------------------------ P. 044 3952 megs free memory 120 taps ==> 106 taps pre-filter: sd= 5.54914302763 mean= -2.17808363318 md= -2.8406763771 post-filter: sd= 5.29741074072 mean= -2.61483609197 md= -3.3488928766 ------------------------------------------------------------ P. 046 3945 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.35553768068 mean= -3.68753782884 md= -3.63393805769 post-filter: sd= 5.61224986725 mean= -4.61693757529 md= -3.95629027739 ------------------------------------------------------------ P. 047 3932 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.10784924455 mean= -7.77738130018 md= -8.06144947913 post-filter: sd= 5.65443374596 mean= -8.78607773654 md= -8.74134549966 ------------------------------------------------------------ P. 048 3920 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.59058650389 mean= -2.91122260654 md= -3.45788081844 post-filter: sd= 4.57900621238 mean= -3.0715454546 md= -3.79675043535 ------------------------------------------------------------ P. 049 3912 megs free memory 120 taps ==> 111 taps pre-filter: sd= 17.8811963638 mean= -22.9967646544 md= -26.3125035019 post-filter: sd= 18.3426095751 mean= -23.492543135 md= -27.3014098007 ------------------------------------------------------------ P. 051 3902 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.26261470319 mean= -0.420238195008 md= 0.341381613923 post-filter: sd= 5.27051921366 mean= -0.681824225281 md= 0.00560282382326 ------------------------------------------------------------ P. 052 3891 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.13060657623 mean= -3.21141486265 md= -3.72523308421 post-filter: sd= 4.68621511684 mean= -3.53579359058 md= -3.96001594799 ------------------------------------------------------------ P. 053 3882 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.10782447439 mean= -2.36614955628 md= -3.4211724485 post-filter: sd= 5.64467647439 mean= -2.99789872952 md= -3.50547154556 ------------------------------------------------------------ P. 054 3873 megs free memory 120 taps ==> 111 taps pre-filter: sd= 12.774727623 mean= -2.3105627855 md= -4.32170759374 post-filter: sd= 13.0286715839 mean= -2.12338298832 md= -3.9703186661 ------------------------------------------------------------ P. 055 3862 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.85937431533 mean= -14.3578967248 md= -14.4469616311 post-filter: sd= 9.04057585295 mean= -14.7292106108 md= -15.4063508073 ------------------------------------------------------------ P. 056 3854 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.52426800009 mean= -3.08271337087 md= -2.99518145581 post-filter: sd= 4.79985367796 mean= -3.39046503035 md= -3.25691004953 ------------------------------------------------------------ P. 057 3844 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.53959228897 mean= -5.04753285909 md= -5.58388230303 post-filter: sd= 4.87978647409 mean= -5.6764691828 md= -5.98312492995 ------------------------------------------------------------ P. 058 3832 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.48499765814 mean= -10.6342964906 md= -10.8323769764 post-filter: sd= 5.46907797641 mean= -10.8068783929 md= -10.8542027781 ------------------------------------------------------------ P. 059 3822 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.73402606805 mean= 1.54442537596 md= 1.38513462558 post-filter: sd= 4.68386711122 mean= 1.44767145615 md= 1.19911176589 ------------------------------------------------------------ P. 060 3811 megs free memory 120 taps ==> 110 taps pre-filter: sd= 6.14812457247 mean= -3.18242604577 md= -3.87451208496 post-filter: sd= 5.99416614976 mean= -2.91033147682 md= -3.70872886725 ------------------------------------------------------------ P. 061 3802 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.78068816164 mean= -4.51797689208 md= -4.79489258811 post-filter: sd= 4.80987429742 mean= -4.46739160794 md= -4.72472168493 ------------------------------------------------------------ P. 062 3792 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.09343860733 mean= -5.80680071782 md= -5.60368161525 post-filter: sd= 6.51174839827 mean= -5.29576802719 md= -5.26362038664 ------------------------------------------------------------ P. 063 3780 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.07738982081 mean= -2.80731292502 md= -2.77782067609 post-filter: sd= 4.86633249405 mean= -2.53527043403 md= -2.67981494229 ------------------------------------------------------------ P. 064 3766 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.3074501882 mean= -6.38025500992 md= -6.89883769793 post-filter: sd= 4.07211183066 mean= -7.1569572481 md= -7.0130148065 ------------------------------------------------------------ P. 065 3760 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.52444083434 mean= -5.22161068359 md= -6.09386858398 post-filter: sd= 6.78479354353 mean= -6.38560308706 md= -6.32448321033 ------------------------------------------------------------ P. 066 3749 megs free memory 120 taps ==> 106 taps pre-filter: sd= 6.24876688891 mean= -1.96818528831 md= -2.4405524113 post-filter: sd= 5.25820877142 mean= -2.77731342447 md= -2.76041401073 ------------------------------------------------------------ P. 067 3736 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.72687807025 mean= -5.59225052594 md= -5.30427335377 post-filter: sd= 6.98935120943 mean= -6.82679451642 md= -5.96188173894 ------------------------------------------------------------ P. 068 3725 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.44650751633 mean= -11.8023716373 md= -12.2957080556 post-filter: sd= 6.7650480001 mean= -13.1863479361 md= -13.116630514 ------------------------------------------------------------ P. 069 3716 megs free memory 120 taps ==> 110 taps pre-filter: sd= 7.46904568656 mean= -5.08334224718 md= -5.20817489733 post-filter: sd= 7.59374019182 mean= -4.96260118611 md= -5.20817489733 ------------------------------------------------------------ P. 071 3700 megs free memory 120 taps ==> 110 taps pre-filter: sd= 12.170933131 mean= -1.52201853607 md= -2.44400863855 post-filter: sd= 12.2803564686 mean= -1.88062653805 md= -3.36074554303 ------------------------------------------------------------ P. 072 3688 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.98181007848 mean= -16.0449239958 md= -15.2252261003 post-filter: sd= 7.92093913882 mean= -17.0545547629 md= -15.8638299235 ------------------------------------------------------------ P. 073 3681 megs free memory 120 taps ==> 106 taps pre-filter: sd= 12.7168922813 mean= -7.87903147802 md= -7.14298401507 post-filter: sd= 12.8406558439 mean= -8.32409832531 md= -7.91372005875 ------------------------------------------------------------ P. 074 3671 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.3221130279 mean= -7.93426929127 md= -8.48331342386 post-filter: sd= 5.47488855938 mean= -8.75393425611 md= -8.76921968672 ------------------------------------------------------------ P. 075 3664 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.3576069748 mean= -0.389545313873 md= -0.206118997185 post-filter: sd= 4.20397465041 mean= -0.448058614534 md= -0.414593697073 ------------------------------------------------------------ P. 076 3649 megs free memory 120 taps ==> 111 taps pre-filter: sd= 10.1182752276 mean= 0.203411358147 md= -1.19504054173 post-filter: sd= 10.220635421 mean= 0.763658174525 md= -0.353789631243 ------------------------------------------------------------ P. 077 3639 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.21785209025 mean= -22.6634716825 md= -22.9212691704 post-filter: sd= 7.78989879538 mean= -23.4498554185 md= -23.2510704963 ------------------------------------------------------------ P. 078 3629 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.88263760015 mean= -3.28177065803 md= -3.29372263632 post-filter: sd= 4.74662071812 mean= -3.37933232108 md= -3.32128982485 ------------------------------------------------------------ P. 079 3618 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.21979473604 mean= -1.16535932022 md= -0.754061168265 post-filter: sd= 5.2863003919 mean= -1.27620298667 md= -0.806039303403 ------------------------------------------------------------ P. 080 3607 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.16209912184 mean= -7.96325315846 md= -7.75504896591 post-filter: sd= 5.74271398825 mean= -8.52809068397 md= -8.18470011848 ------------------------------------------------------------ P. 081 3597 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.2707807275 mean= -11.301248839 md= -11.4093583637 post-filter: sd= 5.1727858419 mean= -11.6192214628 md= -11.7232656067 ------------------------------------------------------------ P. 082 3589 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.60589644419 mean= -0.457572092627 md= -0.640343223969 post-filter: sd= 4.39698001695 mean= -0.871487064458 md= -1.46471381445 ------------------------------------------------------------ P. 083 3576 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.73360302869 mean= -1.80028518618 md= -2.73054254006 post-filter: sd= 5.84665504042 mean= -1.844711072 md= -2.74970641571 ------------------------------------------------------------ P. 084 3569 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.76176014052 mean= -8.35377842784 md= -9.07199001073 post-filter: sd= 5.46127670339 mean= -8.82044642675 md= -9.15735991947 ------------------------------------------------------------ P. 085 3555 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.04003264636 mean= -5.43161489119 md= -6.35444368607 post-filter: sd= 6.06768073814 mean= -5.33004229914 md= -6.05690277645 ------------------------------------------------------------ P. 086 3531 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.86607327798 mean= 5.94077063557 md= 6.45296056605 post-filter: sd= 5.65732070374 mean= 5.9754619517 md= 6.33980424297 ------------------------------------------------------------ P. 087 3522 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.70405172771 mean= -3.34525997798 md= -3.01999217246 post-filter: sd= 5.54802245836 mean= -4.23018473949 md= -3.94015321929 ------------------------------------------------------------ P. 089 3508 megs free memory 120 taps ==> 110 taps pre-filter: sd= 8.44682968815 mean= -6.98977042976 md= -6.27164137265 post-filter: sd= 8.03998238715 mean= -6.30815754956 md= -5.50465595677 ------------------------------------------------------------ P. 090 3497 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.45937231133 mean= -1.97986539641 md= -1.46479684949 post-filter: sd= 6.21946617268 mean= -2.84946975413 md= -2.08182987669 ------------------------------------------------------------ P. 091 3486 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.0681443283 mean= -12.1208191503 md= -13.1470468171 post-filter: sd= 4.45024004905 mean= -12.9243233354 md= -13.6023820577 ------------------------------------------------------------ P. 092 3478 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.47996110567 mean= -2.21168979378 md= -1.91394254174 post-filter: sd= 5.36637148541 mean= -2.43921891482 md= -2.1155088673 ------------------------------------------------------------ P. 093 3464 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.76954777184 mean= -12.212496362 md= -12.08698153 post-filter: sd= 5.65281681212 mean= -11.8594887005 md= -11.8789699485 ------------------------------------------------------------ P. 094 3453 megs free memory 120 taps ==> 110 taps pre-filter: sd= 6.92828151103 mean= -5.44240715573 md= -6.19646490359 post-filter: sd= 7.00450758873 mean= -5.59627381683 md= -6.4347615701 ------------------------------------------------------------ P. 095 3445 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.95059333851 mean= -8.11703494668 md= -8.49670069821 post-filter: sd= 6.05784413471 mean= -8.23001231886 md= -8.52629278262 ------------------------------------------------------------ P. 096 3432 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.42357772929 mean= -5.33445125135 md= -4.35030692685 post-filter: sd= 6.55892707514 mean= -5.52093141312 md= -4.72012998967 ------------------------------------------------------------ P. 097 3452 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.34479610662 mean= -5.47179801895 md= -5.70527545883 post-filter: sd= 5.39508265641 mean= -6.14889656711 md= -5.81975922094 ------------------------------------------------------------ P. 098 3444 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.80893457127 mean= -4.2847464237 md= -4.66423642919 post-filter: sd= 4.72526530715 mean= -4.1779960478 md= -4.71210892204 ------------------------------------------------------------ P. 099 3428 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.15805658681 mean= -2.28144083985 md= -1.61858991717 post-filter: sd= 7.15212285912 mean= -2.6097644127 md= -1.76435741847 ------------------------------------------------------------ P. 100 3428 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.28668188832 mean= -1.42847751504 md= -1.66614079173 post-filter: sd= 5.30311088876 mean= -1.63851031486 md= -1.73528163836 ------------------------------------------------------------ P. 101 3411 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.33660983478 mean= -1.37699418577 md= -2.37659333345 post-filter: sd= 7.72538311892 mean= -1.95561805794 md= -2.81908801344 ------------------------------------------------------------ P. 102 3401 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.13474153589 mean= -5.15796452356 md= -5.60446948838 post-filter: sd= 5.84031196264 mean= -5.61657695707 md= -5.87039186647 ------------------------------------------------------------ P. 103 3391 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.97760408715 mean= 0.0172618425187 md= 0.0936621916947 post-filter: sd= 6.47119030942 mean= -0.60535413916 md= -0.493048496442 ------------------------------------------------------------ P. 104 3384 megs free memory 120 taps ==> 110 taps pre-filter: sd= 22.8084796721 mean= 3.24716502604 md= 4.92574633125 post-filter: sd= 23.3857931008 mean= 3.5718468885 md= 5.17477367869 ------------------------------------------------------------ P. 105 3368 megs free memory 120 taps ==> 110 taps pre-filter: sd= 6.39174585361 mean= -2.63040440707 md= -2.23378021365 post-filter: sd= 6.32425928372 mean= -2.88670923688 md= -2.28667864701 ------------------------------------------------------------ P. 107 3366 megs free memory 120 taps ==> 106 taps pre-filter: sd= 5.07926734157 mean= -2.32467740266 md= -2.4670939078 post-filter: sd= 5.1787562851 mean= -2.36877221536 md= -2.9528434082 ------------------------------------------------------------ P. 108 3345 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.05214663343 mean= -5.03858710012 md= -4.7429437668 post-filter: sd= 4.95351973453 mean= -5.39871112062 md= -5.07952262413 ------------------------------------------------------------ P. 109 3332 megs free memory 120 taps ==> 102 taps pre-filter: sd= 5.4615272947 mean= -1.94237447698 md= -2.2098071319 post-filter: sd= 5.35460242592 mean= -1.60325880126 md= -2.15381633761 ------------------------------------------------------------ P. 110 3325 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.59118746367 mean= -2.99100734171 md= -2.48626921386 post-filter: sd= 4.60006023727 mean= -2.99766093788 md= -2.45607012769 ------------------------------------------------------------ P. 111 3314 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.77942859336 mean= -5.01827292886 md= -4.49850101615 post-filter: sd= 5.78276202709 mean= -5.1991589853 md= -4.87121878594 ------------------------------------------------------------ P. 112 3305 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.17441269578 mean= -11.1899577229 md= -12.1373045818 post-filter: sd= 6.00547337537 mean= -11.4997372984 md= -12.3564023536 ------------------------------------------------------------ P. 113 3303 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.04775162108 mean= 0.778002758471 md= 0.784415607579 post-filter: sd= 5.11060483549 mean= 0.748570107393 md= 0.405035022804 ------------------------------------------------------------ P. 114 3294 megs free memory 120 taps ==> 111 taps pre-filter: sd= 10.785459929 mean= -14.4805892263 md= -13.6443521329 post-filter: sd= 8.850918372 mean= -15.8361204288 md= -14.7576212693 ------------------------------------------------------------ P. 115 3283 megs free memory 120 taps ==> 110 taps pre-filter: sd= 6.32837454574 mean= -1.65977846968 md= -1.67461185426 post-filter: sd= 6.27643421925 mean= -1.97013580635 md= -1.85215588559 ------------------------------------------------------------ P. 116 3270 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.83781396172 mean= -2.31046324925 md= -2.51646830002 post-filter: sd= 4.83181801988 mean= -2.25849543249 md= -2.51646830002 ------------------------------------------------------------ P. 117 3262 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.85072684297 mean= -15.2315550625 md= -14.9313116434 post-filter: sd= 5.744898046 mean= -14.8441996368 md= -14.4987751573 ------------------------------------------------------------ P. 118 3247 megs free memory 120 taps ==> 110 taps pre-filter: sd= 8.07931237749 mean= 2.13870812993 md= 2.34060066088 post-filter: sd= 8.17992742933 mean= 2.30055940764 md= 3.11540480306 ------------------------------------------------------------ P. 119 3231 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.1817566883 mean= 0.61982389831 md= 0.746070009401 post-filter: sd= 4.98992295093 mean= 1.0050746421 md= 0.859236727591 ------------------------------------------------------------ P. 120 3221 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.57627612848 mean= -8.08354836197 md= -8.56757991724 post-filter: sd= 4.94931589475 mean= -8.63675626775 md= -9.07952867116 ------------------------------------------------------------ P. 121 3207 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.20361530418 mean= -1.08690242269 md= -1.07655119421 post-filter: sd= 5.52364122764 mean= -1.39064018775 md= -1.37326644085 ================================================================================ Jits_ISO_8 ================================================================================ ------------------------------------------------------------ P. 011 3198 megs free memory 120 taps ==> 110 taps pre-filter: sd= 37.2594665325 mean= 20.9834818038 md= 38.3012390588 post-filter: sd= 37.9812173505 mean= 19.824526148 md= 38.3010007352 ------------------------------------------------------------ P. 012 3185 megs free memory 120 taps ==> 111 taps pre-filter: sd= 26.9185195359 mean= 32.6207392155 md= 40.8617528257 post-filter: sd= 27.2652975212 mean= 32.7993507532 md= 41.1292919848 ------------------------------------------------------------ P. 015 3178 megs free memory 120 taps ==> 111 taps pre-filter: sd= 24.9552599086 mean= -12.4601007346 md= -11.4294767315 post-filter: sd= 25.6537590627 mean= -13.031652833 md= -12.5049390558 ------------------------------------------------------------ P. 016 3167 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.77621612587 mean= -6.32768888355 md= -7.11820096164 post-filter: sd= 5.76735143144 mean= -6.11289525776 md= -6.67086820547 ------------------------------------------------------------ P. 017 3156 megs free memory 120 taps ==> 111 taps pre-filter: sd= 9.18020149963 mean= -9.64561110992 md= -9.21554112857 post-filter: sd= 8.34749517604 mean= -10.128649963 md= -9.45766831889 ------------------------------------------------------------ P. 018 3133 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.18530654964 mean= -4.74915181029 md= -5.36116196021 post-filter: sd= 5.90733110883 mean= -4.86049949941 md= -5.56486422952 ------------------------------------------------------------ P. 019 3126 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.20027953924 mean= -8.87062558484 md= -9.01416339922 post-filter: sd= 5.6499679292 mean= -9.48081028203 md= -9.16784203103 ------------------------------------------------------------ P. 020 3119 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.48500986158 mean= -7.88487390714 md= -8.13883491143 post-filter: sd= 6.21301861777 mean= -7.7489362207 md= -7.95227427923 ------------------------------------------------------------ P. 021 3111 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.38151099084 mean= -8.8318699361 md= -9.03893765474 post-filter: sd= 5.43002810302 mean= -8.89584519101 md= -9.07090268886 ------------------------------------------------------------ P. 022 3098 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.7556422241 mean= 1.42185927925 md= 1.20787595213 post-filter: sd= 5.67997346193 mean= 1.66588523929 md= 1.54000050016 ------------------------------------------------------------ P. 024 3092 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.05036470222 mean= -7.12999823882 md= -5.90215428631 post-filter: sd= 6.08938055529 mean= -6.79003246441 md= -5.7577273182 ------------------------------------------------------------ P. 025 3077 megs free memory 120 taps ==> 106 taps pre-filter: sd= 6.54457174897 mean= -2.38464908267 md= -2.10822194724 post-filter: sd= 6.66500480032 mean= -2.47673042954 md= -2.10822194724 ------------------------------------------------------------ P. 026 3068 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.04679797955 mean= -4.27276037244 md= -4.3033902623 post-filter: sd= 4.09461260231 mean= -4.27831406331 md= -4.3033902623 ------------------------------------------------------------ P. 027 3054 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.90821617278 mean= 0.804916490977 md= 0.806498998394 post-filter: sd= 4.78214629761 mean= 1.01895477708 md= 0.806498998394 ------------------------------------------------------------ P. 028 3046 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.02943697695 mean= -9.12619749463 md= -9.640828639 post-filter: sd= 5.09376379372 mean= -9.24227310444 md= -9.8740415367 ------------------------------------------------------------ P. 029 3039 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.87750042244 mean= -7.5072502529 md= -8.41690065704 post-filter: sd= 6.94338291923 mean= -7.5706277221 md= -8.4310747897 ------------------------------------------------------------ P. 030 3026 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.61910685325 mean= -6.5436053145 md= -7.11545675061 post-filter: sd= 5.20192561591 mean= -6.37327448871 md= -7.12045655751 ------------------------------------------------------------ P. 032 3015 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.7374258385 mean= -2.2015103863 md= -2.35760429528 post-filter: sd= 4.80597534314 mean= -2.19344893537 md= -2.49813002222 ------------------------------------------------------------ P. 033 3004 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.83284209322 mean= -5.4959807784 md= -5.34859073581 post-filter: sd= 4.64355782367 mean= -5.23713099201 md= -5.31033163008 ------------------------------------------------------------ P. 034 2966 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.54094280954 mean= -5.16266658103 md= -5.07671178736 post-filter: sd= 4.40473170504 mean= -5.15702020732 md= -5.12371644501 ------------------------------------------------------------ P. 035 2956 megs free memory 120 taps ==> 111 taps pre-filter: sd= 9.20302053881 mean= -15.8420795443 md= -16.6361937402 post-filter: sd= 8.91563322735 mean= -16.0946276412 md= -16.7429368783 ------------------------------------------------------------ P. 036 2941 megs free memory 120 taps ==> 111 taps pre-filter: sd= 15.7415771849 mean= -16.2123282035 md= -16.8840308692 post-filter: sd= 15.8145285924 mean= -16.4139431696 md= -16.7381469617 ------------------------------------------------------------ P. 037 2931 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.47984353129 mean= -10.1673442507 md= -9.91232062741 post-filter: sd= 6.27249306776 mean= -9.85400540763 md= -9.47050302213 ------------------------------------------------------------ P. 038 2922 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.80127008315 mean= -0.922432994167 md= 0.525679820663 post-filter: sd= 8.30995844189 mean= -0.410565751925 md= 0.934149923569 ------------------------------------------------------------ P. 039 2910 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.55955229409 mean= -8.41539735831 md= -8.4324470131 post-filter: sd= 4.33674650422 mean= -8.38351670658 md= -8.52685649693 ------------------------------------------------------------ P. 040 2902 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.64452838623 mean= -5.41699811651 md= -6.23943422461 post-filter: sd= 6.71369493246 mean= -5.30645183546 md= -6.20208509824 ------------------------------------------------------------ P. 041 2891 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.07732398807 mean= -3.44988109699 md= -4.10429285225 post-filter: sd= 5.29864656145 mean= -3.72748487197 md= -4.10429285225 ------------------------------------------------------------ P. 043 2879 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.26385620413 mean= -7.62606422472 md= -7.92885438758 post-filter: sd= 6.97317929856 mean= -7.27927425753 md= -7.82566072465 ------------------------------------------------------------ P. 044 2873 megs free memory 120 taps ==> 110 taps pre-filter: sd= 6.72096750998 mean= 1.4016226844 md= 1.44315188515 post-filter: sd= 6.5577767737 mean= 1.77440426924 md= 1.86488961381 ------------------------------------------------------------ P. 046 2862 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.8401196403 mean= -3.92647957938 md= -3.96984974891 post-filter: sd= 6.44704106646 mean= -3.37501138072 md= -3.7842488021 ------------------------------------------------------------ P. 047 2851 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.3720291974 mean= -11.4561795625 md= -11.551042865 post-filter: sd= 6.26277309334 mean= -11.1877376366 md= -11.4495645954 ------------------------------------------------------------ P. 048 2842 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.90957580775 mean= -6.11975992089 md= -6.06998714685 post-filter: sd= 4.57125127787 mean= -6.18762214011 md= -6.02659393794 ------------------------------------------------------------ P. 049 2838 megs free memory 120 taps ==> 111 taps pre-filter: sd= 20.4302986757 mean= -16.4995901037 md= -17.1969567228 post-filter: sd= 21.0143587121 mean= -17.0856199781 md= -18.1288932605 ------------------------------------------------------------ P. 051 2823 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.69528309576 mean= -4.21511398214 md= -4.44825677908 post-filter: sd= 4.64164800074 mean= -4.27928140036 md= -4.90191670788 ------------------------------------------------------------ P. 052 2814 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.17974404657 mean= -4.34330612354 md= -4.38130656077 post-filter: sd= 5.17592647039 mean= -4.15650219631 md= -4.27483988047 ------------------------------------------------------------ P. 053 2802 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.85510945802 mean= -6.28032645828 md= -6.95036249391 post-filter: sd= 5.56842192773 mean= -5.95253822828 md= -6.76932857906 ------------------------------------------------------------ P. 054 2793 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.10116927045 mean= 0.0148407452677 md= 0.517175839785 post-filter: sd= 4.99500995671 mean= 0.253183544109 md= 0.689827418268 ------------------------------------------------------------ P. 055 2781 megs free memory 120 taps ==> 111 taps pre-filter: sd= 28.1705458705 mean= -3.18330984689 md= -13.7868981059 post-filter: sd= 28.9623541249 mean= -3.17141064333 md= -14.9042460563 ------------------------------------------------------------ P. 056 2777 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.95805086448 mean= -3.03314410197 md= -3.55355305354 post-filter: sd= 5.43041735624 mean= -3.28284537036 md= -3.4807530711 ------------------------------------------------------------ P. 057 2764 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.56260645338 mean= -4.58349304303 md= -4.71178470197 post-filter: sd= 4.29739332993 mean= -4.2535454593 md= -3.98477802979 ------------------------------------------------------------ P. 058 2756 megs free memory 120 taps ==> 111 taps pre-filter: sd= 9.35458400776 mean= -8.7875940355 md= -7.81022591438 post-filter: sd= 7.88886826731 mean= -7.43469785262 md= -7.59812135187 ------------------------------------------------------------ P. 059 2740 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.79271497712 mean= -4.21864578276 md= -4.15260043912 post-filter: sd= 4.81235897673 mean= -4.27204931501 md= -4.33234302634 ------------------------------------------------------------ P. 060 2730 megs free memory 120 taps ==> 110 taps pre-filter: sd= 7.81084585447 mean= -8.2798919318 md= -7.30702179419 post-filter: sd= 7.54893873503 mean= -8.62771163587 md= -7.69394166655 ------------------------------------------------------------ P. 061 2721 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.58929770625 mean= -7.52111793626 md= -7.23209442807 post-filter: sd= 5.70898320596 mean= -7.54924467575 md= -7.08900890328 ------------------------------------------------------------ P. 062 2710 megs free memory 120 taps ==> 111 taps pre-filter: sd= 11.0276070855 mean= -8.57157053956 md= -10.6823322714 post-filter: sd= 10.2405115735 mean= -8.66488074827 md= -10.5368196772 ------------------------------------------------------------ P. 063 2701 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.02115908995 mean= -3.66615003921 md= -3.47358647596 post-filter: sd= 5.0278827743 mean= -3.703847413 md= -3.49972490372 ------------------------------------------------------------ P. 064 2689 megs free memory 120 taps ==> 111 taps pre-filter: sd= 14.214789623 mean= -9.34449756126 md= -3.54812475393 post-filter: sd= 14.3052839087 mean= -10.1055189655 md= -3.94730926584 ------------------------------------------------------------ P. 065 2678 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.62808747349 mean= -5.09517079405 md= -4.88034209408 post-filter: sd= 8.69580125551 mean= -4.83415003223 md= -4.16864637775 ------------------------------------------------------------ P. 066 2668 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.23375307374 mean= -3.2653094875 md= -3.75893059806 post-filter: sd= 5.13330409838 mean= -3.01356031805 md= -3.66115370942 ------------------------------------------------------------ P. 067 2651 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.88038665334 mean= -7.90039675283 md= -8.43885650347 post-filter: sd= 5.3798221035 mean= -7.96487901022 md= -8.31837073566 ------------------------------------------------------------ P. 068 2640 megs free memory 120 taps ==> 111 taps pre-filter: sd= 9.66122226177 mean= -4.0206431369 md= -3.98641517739 post-filter: sd= 9.66346328611 mean= -4.38780685398 md= -4.28995858592 ------------------------------------------------------------ P. 069 2632 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.59843507709 mean= -6.82390045704 md= -6.81119964033 post-filter: sd= 6.47661078278 mean= -6.6498065838 md= -6.66783365426 ------------------------------------------------------------ P. 071 2622 megs free memory 120 taps ==> 110 taps pre-filter: sd= 8.76982833852 mean= -3.31893271139 md= -3.54048835018 post-filter: sd= 8.88571004251 mean= -3.5025741361 md= -3.67172908039 ------------------------------------------------------------ P. 072 2613 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.748844396 mean= -10.0504291152 md= -10.5504036494 post-filter: sd= 6.94146602982 mean= -10.454168132 md= -10.6304542208 ------------------------------------------------------------ P. 073 2605 megs free memory 120 taps ==> 108 taps pre-filter: sd= 10.2224564416 mean= -5.62498280851 md= -6.78973507902 post-filter: sd= 10.4137794915 mean= -5.90834475666 md= -7.29033538283 ------------------------------------------------------------ P. 074 2597 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.31756421495 mean= -11.2508074627 md= -11.9283767224 post-filter: sd= 7.1682173502 mean= -11.2749485789 md= -11.8407231838 ------------------------------------------------------------ P. 075 2592 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.80487903206 mean= -3.1376295203 md= -3.38802256189 post-filter: sd= 4.65761516301 mean= -2.95738125803 md= -3.24471372196 ------------------------------------------------------------ P. 076 2587 megs free memory 120 taps ==> 111 taps pre-filter: sd= 9.29789545988 mean= -9.42943852084 md= -10.5599319779 post-filter: sd= 7.81291574817 mean= -10.0528173927 md= -10.410799942 ------------------------------------------------------------ P. 077 2583 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.77724005411 mean= -15.0148139754 md= -14.7740557231 post-filter: sd= 7.57381995584 mean= -15.2256130639 md= -14.6701810016 ------------------------------------------------------------ P. 078 2576 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.87257913689 mean= -3.61526442151 md= -4.35603416891 post-filter: sd= 4.92389425253 mean= -3.56140928397 md= -4.3049634229 ------------------------------------------------------------ P. 079 2571 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.95503899786 mean= -1.17307442197 md= -0.61529717707 post-filter: sd= 5.23436904299 mean= -1.71794394415 md= -0.985578777328 ------------------------------------------------------------ P. 080 2562 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.93370160215 mean= -8.78600700889 md= -8.77774776667 post-filter: sd= 6.00013346418 mean= -8.67895498931 md= -8.48242122537 ------------------------------------------------------------ P. 081 2578 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.46689878932 mean= -1.6314256267 md= -1.54673021907 post-filter: sd= 4.46205724139 mean= -1.49811177416 md= -1.3879510841 ------------------------------------------------------------ P. 082 2572 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.78008258391 mean= -0.0819873384438 md= -0.37954792148 post-filter: sd= 4.86422236591 mean= 0.0322185629738 md= -0.244579488333 ------------------------------------------------------------ P. 083 2566 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.50913957406 mean= -2.59372871991 md= -2.86236596351 post-filter: sd= 6.52807698289 mean= -2.68345772782 md= -2.85975783583 ------------------------------------------------------------ P. 084 2553 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.71517978636 mean= -2.46081687077 md= -2.92423105776 post-filter: sd= 6.48676675506 mean= -1.66322072538 md= -2.62699122338 ------------------------------------------------------------ P. 085 2550 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.50040850883 mean= -5.93485364959 md= -6.79244064878 post-filter: sd= 6.60170232471 mean= -6.02148292351 md= -6.80260880873 ------------------------------------------------------------ P. 086 2543 megs free memory 120 taps ==> 108 taps pre-filter: sd= 12.1811898948 mean= -1.96370497979 md= -2.56829230646 post-filter: sd= 12.1914694989 mean= -2.51984468191 md= -3.29643684364 ------------------------------------------------------------ P. 087 2532 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.9532435852 mean= -1.55372445602 md= -0.988286603114 post-filter: sd= 6.71106369666 mean= -1.56447192327 md= -0.942329815434 ------------------------------------------------------------ P. 089 2524 megs free memory 120 taps ==> 111 taps pre-filter: sd= 20.8930047689 mean= -9.27424414131 md= -10.5313864405 post-filter: sd= 21.5023659387 mean= -8.93968669474 md= -10.0607667108 ------------------------------------------------------------ P. 090 2523 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.27133998411 mean= -3.92106786858 md= -3.47103902835 post-filter: sd= 5.2542088939 mean= -3.97776722357 md= -3.38585115842 ------------------------------------------------------------ P. 091 2515 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.69716493314 mean= -5.25006697596 md= -4.85526139232 post-filter: sd= 4.37608207487 mean= -4.78826035047 md= -4.67349008207 ------------------------------------------------------------ P. 092 2515 megs free memory 120 taps ==> 111 taps pre-filter: sd= 9.43009080323 mean= -9.59675099824 md= -8.83466533467 post-filter: sd= 9.3690849857 mean= -9.12315283469 md= -8.07959103054 ------------------------------------------------------------ P. 093 2508 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.78933817539 mean= -9.65921973068 md= -10.3357208595 post-filter: sd= 7.26642260565 mean= -9.73901303165 md= -10.1526549558 ------------------------------------------------------------ P. 094 2501 megs free memory 120 taps ==> 106 taps pre-filter: sd= 7.68789701955 mean= -10.4195176515 md= -10.7409936654 post-filter: sd= 7.67398994891 mean= -10.774825473 md= -11.2961795864 ------------------------------------------------------------ P. 095 2498 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.25984018874 mean= -7.04528432335 md= -7.25200194067 post-filter: sd= 4.15988361747 mean= -7.21904790934 md= -7.33196452086 ------------------------------------------------------------ P. 096 2495 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.73269821244 mean= -7.06127856222 md= -7.47410824645 post-filter: sd= 5.53989838499 mean= -7.52185935228 md= -7.71966328383 ------------------------------------------------------------ P. 097 2493 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.05605045865 mean= -5.81438092205 md= -6.25021882768 post-filter: sd= 5.8007339987 mean= -5.47691154498 md= -6.04773766294 ------------------------------------------------------------ P. 098 2488 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.51523291168 mean= -4.88714041323 md= -5.38265536756 post-filter: sd= 4.28811538583 mean= -4.86692088396 md= -5.37792515016 ------------------------------------------------------------ P. 099 2483 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.0801884084 mean= -7.32843963557 md= -7.15772370228 post-filter: sd= 6.09162854865 mean= -7.54816243511 md= -7.45410893813 ------------------------------------------------------------ P. 100 2477 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.57686073 mean= -6.21050874481 md= -6.27519088785 post-filter: sd= 4.63379209308 mean= -6.22496274259 md= -6.5250449977 ------------------------------------------------------------ P. 101 2472 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.74200383583 mean= -4.45200608447 md= -3.74363455981 post-filter: sd= 6.77891438143 mean= -3.60154193925 md= -2.47267568486 ------------------------------------------------------------ P. 102 2465 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.01512978944 mean= -1.64598990795 md= -2.17408700349 post-filter: sd= 5.19757119045 mean= -1.61464115826 md= -1.9746911419 ------------------------------------------------------------ P. 103 2457 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.85262367694 mean= -8.57833872572 md= -8.92780352011 post-filter: sd= 5.90942400828 mean= -8.58039150891 md= -8.97436538702 ------------------------------------------------------------ P. 104 2448 megs free memory 120 taps ==> 108 taps pre-filter: sd= 4.78661673971 mean= -6.14249197427 md= -6.08790426563 post-filter: sd= 4.80246099302 mean= -6.04342883179 md= -5.8099671105 ------------------------------------------------------------ P. 105 2445 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.24695611136 mean= -7.62213260351 md= -8.20045216278 post-filter: sd= 8.3932438169 mean= -7.63370459811 md= -8.20045216278 ------------------------------------------------------------ P. 107 2435 megs free memory 120 taps ==> 106 taps pre-filter: sd= 5.13827105641 mean= -0.518686273756 md= -0.939826598502 post-filter: sd= 4.92348198922 mean= -0.305858289653 md= -0.939826598502 ------------------------------------------------------------ P. 108 2420 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.35203986186 mean= -6.94487072792 md= -7.14874382068 post-filter: sd= 5.47603079113 mean= -7.03946626712 md= -7.21584514967 ------------------------------------------------------------ P. 109 2412 megs free memory 120 taps ==> 106 taps pre-filter: sd= 5.50298597272 mean= -1.42691605338 md= -1.56269614499 post-filter: sd= 5.49482705788 mean= -1.65527254246 md= -1.77318190055 ------------------------------------------------------------ P. 110 2403 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.34393635393 mean= -1.09469603059 md= -0.629787234231 post-filter: sd= 4.29657125328 mean= -0.859087469649 md= -0.502482443082 ------------------------------------------------------------ P. 111 2206 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.07344396541 mean= -3.26369980196 md= -3.36215151691 post-filter: sd= 5.01428128679 mean= -3.23082048444 md= -3.35415718423 ------------------------------------------------------------ P. 112 2194 megs free memory 120 taps ==> 111 taps pre-filter: sd= 8.85300795215 mean= -11.6475348791 md= -12.2752247863 post-filter: sd= 8.90905510389 mean= -11.8725518536 md= -12.3165515832 ------------------------------------------------------------ P. 113 2189 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.12911670422 mean= -2.07830352088 md= -1.91125189431 post-filter: sd= 3.90121701611 mean= -2.0322627179 md= -2.064888241 ------------------------------------------------------------ P. 114 2168 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.84309566119 mean= -11.1276470523 md= -11.1776357252 post-filter: sd= 6.17650168003 mean= -11.7404697925 md= -11.7114503511 ------------------------------------------------------------ P. 115 2163 megs free memory 120 taps ==> 110 taps pre-filter: sd= 5.08270286896 mean= -5.49943478944 md= -5.08263985255 post-filter: sd= 5.10043087186 mean= -5.65841890176 md= -5.24659336893 ------------------------------------------------------------ P. 116 2137 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.10592733315 mean= -5.19078444538 md= -6.28666907152 post-filter: sd= 6.08708351933 mean= -5.2864928543 md= -6.30373782693 ------------------------------------------------------------ P. 117 2121 megs free memory 120 taps ==> 111 taps pre-filter: sd= 5.02496403163 mean= -4.65815955257 md= -4.72074667627 post-filter: sd= 4.96619117333 mean= -4.63588736083 md= -4.55757914823 ------------------------------------------------------------ P. 118 2109 megs free memory 120 taps ==> 111 taps pre-filter: sd= 7.02256269844 mean= -0.69820788412 md= -0.293601292446 post-filter: sd= 7.02852935017 mean= -0.346246372366 md= -0.174547127724 ------------------------------------------------------------ P. 119 2102 megs free memory 120 taps ==> 111 taps pre-filter: sd= 4.09495308979 mean= -5.24048236691 md= -5.33446749076 post-filter: sd= 4.10777681626 mean= -5.24718980071 md= -5.32798553497 ------------------------------------------------------------ P. 120 2079 megs free memory 120 taps ==> 110 taps pre-filter: sd= 4.33217614876 mean= -6.99287124898 md= -7.20413541981 post-filter: sd= 4.3185056165 mean= -7.20794383889 md= -7.56006043285 ------------------------------------------------------------ P. 121 2070 megs free memory 120 taps ==> 111 taps pre-filter: sd= 6.66241537338 mean= -10.2636976706 md= -10.1517917427 post-filter: sd= 5.48980998965 mean= -10.6103563871 md= -10.1517917427 ================================================================================ Jits_Linear_5 ================================================================================ ------------------------------------------------------------ P. 011 2064 megs free memory 170 taps ==> 160 taps pre-filter: sd= 24.8992228376 mean= 33.6114498151 md= 40.7445391526 post-filter: sd= 25.3630322054 mean= 33.4302227543 md= 40.8725481943 ------------------------------------------------------------ P. 013 2057 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.62701898348 mean= 39.646840313 md= 40.8702189944 post-filter: sd= 8.70370979989 mean= 39.5894165467 md= 40.8702189944 ------------------------------------------------------------ P. 015 2046 megs free memory 170 taps ==> 160 taps pre-filter: sd= 11.6954901604 mean= -16.4358661145 md= -14.3242198482 post-filter: sd= 11.8983298451 mean= -16.3741676166 md= -14.2039029889 ------------------------------------------------------------ P. 016 2038 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.71083611206 mean= -7.07843221959 md= -7.55386844758 post-filter: sd= 5.14538771894 mean= -7.54542173129 md= -7.8940515325 ------------------------------------------------------------ P. 017 2034 megs free memory 170 taps ==> 160 taps pre-filter: sd= 10.259103 mean= -13.4724827651 md= -14.6541760922 post-filter: sd= 10.2324031173 mean= -13.8196663438 md= -14.9089359411 ------------------------------------------------------------ P. 019 2022 megs free memory 170 taps ==> 161 taps pre-filter: sd= 24.1818656052 mean= -13.8021293455 md= -11.6908731311 post-filter: sd= 24.682865206 mean= -14.0209907779 md= -11.9837690181 ------------------------------------------------------------ P. 020 2015 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.72817841602 mean= -10.4576093373 md= -9.65730042017 post-filter: sd= 8.20764643481 mean= -11.0222763047 md= -9.96194703194 ------------------------------------------------------------ P. 021 2004 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.17887576575 mean= -10.0070680121 md= -9.53989961767 post-filter: sd= 5.04209291603 mean= -9.57773146001 md= -9.53076628752 ------------------------------------------------------------ P. 022 1991 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.80192073045 mean= -7.20497682845 md= -6.60983353422 post-filter: sd= 7.84321398683 mean= -7.24207852666 md= -6.60983353422 ------------------------------------------------------------ P. 024 1985 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.99928890512 mean= -7.22050542259 md= -7.62989459568 post-filter: sd= 6.99344740952 mean= -7.33339053251 md= -7.77758843308 ------------------------------------------------------------ P. 025 1976 megs free memory 170 taps ==> 156 taps pre-filter: sd= 7.21105890131 mean= -4.37274800291 md= -4.43334799859 post-filter: sd= 7.31226225256 mean= -4.32577758421 md= -4.43334799859 ------------------------------------------------------------ P. 026 1972 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.89906684775 mean= -2.94191865776 md= -3.44357763713 post-filter: sd= 5.95212365239 mean= -2.89903295416 md= -3.44357763713 ------------------------------------------------------------ P. 027 1964 megs free memory 170 taps ==> 160 taps pre-filter: sd= 16.616077408 mean= -6.13270098568 md= -6.82184590768 post-filter: sd= 16.9173337878 mean= -6.15946807579 md= -7.05673179106 ------------------------------------------------------------ P. 028 1954 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.9842583784 mean= -8.56649288277 md= -8.08333162381 post-filter: sd= 6.0604232981 mean= -8.59583685225 md= -8.12882876011 ------------------------------------------------------------ P. 029 1945 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.8835333705 mean= -12.5863032784 md= -13.9061168942 post-filter: sd= 14.0059817146 mean= -12.7741722067 md= -13.9164148828 ------------------------------------------------------------ P. 030 1936 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.94763158331 mean= -9.12113308427 md= -10.2159712038 post-filter: sd= 4.77806096239 mean= -9.89813837214 md= -10.5544352567 ------------------------------------------------------------ P. 032 1929 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.49603030525 mean= -6.49600664316 md= -6.36738824423 post-filter: sd= 5.55068076986 mean= -6.48861216779 md= -6.39319183678 ------------------------------------------------------------ P. 033 1907 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.55421341194 mean= -5.37470280598 md= -6.02109683245 post-filter: sd= 5.50099935278 mean= -5.56187321412 md= -6.4401154971 ------------------------------------------------------------ P. 034 1897 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.96389135108 mean= -5.47257251256 md= -5.98094719982 post-filter: sd= 4.9539246988 mean= -5.30856272788 md= -5.7239936385 ------------------------------------------------------------ P. 035 1893 megs free memory 170 taps ==> 160 taps pre-filter: sd= 21.9900795835 mean= -14.9153486705 md= -16.9520356418 post-filter: sd= 22.3030297066 mean= -15.2919047793 md= -17.9012477535 ------------------------------------------------------------ P. 036 1884 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.5595325309 mean= -15.7056261346 md= -14.9470933826 post-filter: sd= 12.4696011915 mean= -14.410201214 md= -14.5707094587 ------------------------------------------------------------ P. 037 1873 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.88956694883 mean= -8.3518881451 md= -8.35744646379 post-filter: sd= 5.80986783703 mean= -8.36079079762 md= -8.35744646379 ------------------------------------------------------------ P. 038 1862 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.64361607299 mean= -8.64044754228 md= -7.60370441278 post-filter: sd= 6.400519211 mean= -9.06955690327 md= -8.21469565028 ------------------------------------------------------------ P. 039 1854 megs free memory 170 taps ==> 161 taps pre-filter: sd= 17.1778516796 mean= -13.7217995994 md= -14.2052903365 post-filter: sd= 16.4436716346 mean= -14.8133082595 md= -14.7283957833 ------------------------------------------------------------ P. 040 1846 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.7111646732 mean= -8.99576893905 md= -9.65639674612 post-filter: sd= 10.3884226028 mean= -9.67871483947 md= -9.84861976848 ------------------------------------------------------------ P. 041 1836 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.08860080266 mean= -3.92802344086 md= -4.15528529564 post-filter: sd= 4.66905785485 mean= -4.33853741427 md= -4.27202008272 ------------------------------------------------------------ P. 043 1828 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.46333890489 mean= -9.0276096111 md= -10.1407616457 post-filter: sd= 8.22742619493 mean= -9.49522200721 md= -10.2748468554 ------------------------------------------------------------ P. 044 1816 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.82497673887 mean= -7.28700176335 md= -6.61775626384 post-filter: sd= 9.89880069913 mean= -7.49305167539 md= -6.74516049327 ------------------------------------------------------------ P. 046 1807 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.09188039307 mean= -3.52752039884 md= -4.65910415938 post-filter: sd= 6.11276410465 mean= -3.3036139971 md= -4.0175846273 ------------------------------------------------------------ P. 047 1802 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.38228290192 mean= -13.1364837615 md= -13.0780327302 post-filter: sd= 5.44837693096 mean= -13.8512443607 md= -13.402081291 ------------------------------------------------------------ P. 048 1792 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.71139083898 mean= -4.87197259437 md= -4.24145787397 post-filter: sd= 6.10390402728 mean= -3.75098100327 md= -4.17536039454 ------------------------------------------------------------ P. 049 1781 megs free memory 170 taps ==> 161 taps pre-filter: sd= 23.0800821962 mean= -9.4964018209 md= -13.1550025982 post-filter: sd= 23.2652649054 mean= -10.1594809503 md= -13.6462569561 ------------------------------------------------------------ P. 051 1771 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.70030621386 mean= -5.24014839083 md= -4.6964331682 post-filter: sd= 4.68260548133 mean= -5.2772431337 md= -4.7439333503 ------------------------------------------------------------ P. 052 1762 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.53688011514 mean= -3.38743371545 md= -3.44722360582 post-filter: sd= 4.39473978332 mean= -3.57886035098 md= -3.65201750889 ------------------------------------------------------------ P. 053 1756 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.7434994302 mean= -4.47935993652 md= -5.06040312667 post-filter: sd= 4.6002942447 mean= -5.11920343158 md= -5.35804850336 ------------------------------------------------------------ P. 054 1750 megs free memory 170 taps ==> 160 taps pre-filter: sd= 10.3032431003 mean= -3.59649614289 md= -4.43988520267 post-filter: sd= 10.3982078111 mean= -3.75696056679 md= -4.72142575162 ------------------------------------------------------------ P. 055 1740 megs free memory 170 taps ==> 161 taps pre-filter: sd= 23.0446029061 mean= -16.2555359645 md= -21.7271030529 post-filter: sd= 22.8064122401 mean= -16.3563054774 md= -21.9535661576 ------------------------------------------------------------ P. 056 1731 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.27388393285 mean= -4.7809481294 md= -4.83074125114 post-filter: sd= 5.14885852489 mean= -5.03329649751 md= -5.06570415163 ------------------------------------------------------------ P. 057 1718 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.97435275756 mean= -2.0048262766 md= -3.01477908643 post-filter: sd= 6.6929810689 mean= -2.43295823536 md= -3.17251272583 ------------------------------------------------------------ P. 058 1716 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.99968059132 mean= -13.2007392489 md= -13.3138057094 post-filter: sd= 7.57819767283 mean= -14.4495134859 md= -13.9922274741 ------------------------------------------------------------ P. 059 1708 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.75740054957 mean= -7.90315276432 md= -8.33221827369 post-filter: sd= 6.59612709173 mean= -8.24888094581 md= -8.774765029 ------------------------------------------------------------ P. 060 1698 megs free memory 170 taps ==> 156 taps pre-filter: sd= 8.23023519653 mean= -5.19001223687 md= -5.40246555475 post-filter: sd= 8.30701894553 mean= -5.25805366071 md= -5.40246555475 ------------------------------------------------------------ P. 061 1691 megs free memory 170 taps ==> 160 taps pre-filter: sd= 11.9532741006 mean= -11.8907541586 md= -12.8014768077 post-filter: sd= 12.110320619 mean= -12.0867706202 md= -13.32943032 ------------------------------------------------------------ P. 062 1683 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.2064064966 mean= -9.24386644441 md= -10.0448635085 post-filter: sd= 11.1931989269 mean= -9.67668733519 md= -10.5283703689 ------------------------------------------------------------ P. 063 1668 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.60759602684 mean= -2.88063362703 md= -2.76783557897 post-filter: sd= 4.68546720867 mean= -2.86801844736 md= -2.78335767073 ------------------------------------------------------------ P. 064 1657 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.25049195501 mean= -3.31836222599 md= -3.97760629063 post-filter: sd= 5.4526857466 mean= -3.97074255873 md= -4.0652426902 ------------------------------------------------------------ P. 065 1645 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.89190099341 mean= -3.28980575773 md= -3.22788222404 post-filter: sd= 7.06648379652 mean= -3.97702505767 md= -3.58477255507 ------------------------------------------------------------ P. 066 1634 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.91287013924 mean= -3.59905658983 md= -3.84152183995 post-filter: sd= 6.15619452212 mean= -3.9544384415 md= -4.00647611804 ------------------------------------------------------------ P. 067 1622 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.82725471318 mean= -5.80591167561 md= -5.56972458981 post-filter: sd= 5.64813453152 mean= -6.13960659373 md= -5.9329825418 ------------------------------------------------------------ P. 068 1612 megs free memory 170 taps ==> 161 taps pre-filter: sd= 28.7744565212 mean= -4.95290009191 md= -12.7957837757 post-filter: sd= 29.2164469534 mean= -5.75132450223 md= -13.3874047962 ------------------------------------------------------------ P. 069 1604 megs free memory 170 taps ==> 159 taps pre-filter: sd= 8.08603625361 mean= -10.0384451303 md= -10.7072711547 post-filter: sd= 8.123848645 mean= -9.84830066021 md= -10.351639222 ------------------------------------------------------------ P. 071 1594 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.3439329389 mean= -15.7722889672 md= -16.1084931938 post-filter: sd= 8.71923910639 mean= -16.5171442067 md= -16.6256423272 ------------------------------------------------------------ P. 072 1583 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.7101286244 mean= -15.1995103898 md= -17.0409499135 post-filter: sd= 7.11978171452 mean= -16.8139288202 md= -17.8978331209 ------------------------------------------------------------ P. 073 1573 megs free memory 170 taps ==> 160 taps pre-filter: sd= 27.2976217492 mean= -4.97982668741 md= -7.15159058088 post-filter: sd= 27.3904066226 mean= -5.31220313341 md= -7.39534098 ------------------------------------------------------------ P. 074 1563 megs free memory 170 taps ==> 161 taps pre-filter: sd= 12.5615162778 mean= -12.4865003442 md= -9.17234408001 post-filter: sd= 12.7142457435 mean= -12.7943821968 md= -10.2474266729 ------------------------------------------------------------ P. 075 1552 megs free memory 170 taps ==> 160 taps pre-filter: sd= 3.63653667618 mean= -4.52368765954 md= -4.36227155544 post-filter: sd= 3.64607798382 mean= -4.58742996368 md= -4.60215921518 ------------------------------------------------------------ P. 076 1545 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.00895338104 mean= -8.54868055498 md= -7.80249770478 post-filter: sd= 8.07852413997 mean= -9.39255062275 md= -8.43921234018 ------------------------------------------------------------ P. 077 1541 megs free memory 170 taps ==> 161 taps pre-filter: sd= 27.2626858501 mean= -12.0013569031 md= -19.8413756661 post-filter: sd= 27.8216167333 mean= -12.2076546699 md= -21.2692308905 ------------------------------------------------------------ P. 078 1530 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.07335931734 mean= -5.20364894931 md= -5.06716423939 post-filter: sd= 4.97775915626 mean= -5.43155798481 md= -5.21092476543 ------------------------------------------------------------ P. 079 1521 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.44570850687 mean= -8.14244477132 md= -7.89143164356 post-filter: sd= 5.32317437108 mean= -8.42050623029 md= -8.19652653207 ------------------------------------------------------------ P. 080 1516 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.3207174502 mean= -9.90701479761 md= -9.69555415522 post-filter: sd= 5.20088962134 mean= -10.1510855943 md= -9.97249633267 ------------------------------------------------------------ P. 081 1503 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.8862350402 mean= -16.1872842207 md= -15.0881116203 post-filter: sd= 9.98169475889 mean= -17.079631035 md= -15.5737138954 ------------------------------------------------------------ P. 082 1497 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.78584828812 mean= -3.20275345782 md= -2.91440618305 post-filter: sd= 5.77980408263 mean= -3.45512467412 md= -3.1067911997 ------------------------------------------------------------ P. 083 1487 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.12356659161 mean= -5.73899367971 md= -5.26722747204 post-filter: sd= 6.18888332364 mean= -5.76506784242 md= -5.33597232256 ------------------------------------------------------------ P. 084 1478 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.82680544587 mean= -9.82793838429 md= -10.2635563761 post-filter: sd= 6.03665898559 mean= -10.5458117541 md= -10.5771783049 ------------------------------------------------------------ P. 085 1471 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.29017362464 mean= -8.19210492174 md= -8.214208301 post-filter: sd= 5.23735312971 mean= -8.35551667504 md= -8.57359607176 ------------------------------------------------------------ P. 086 1459 megs free memory 170 taps ==> 154 taps pre-filter: sd= 7.62946467962 mean= -3.37783878163 md= -4.02611091755 post-filter: sd= 6.68130433506 mean= -3.95092980097 md= -4.1850969909 ------------------------------------------------------------ P. 087 1449 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.69866038753 mean= -7.36737548764 md= -6.86636461446 post-filter: sd= 6.03055737818 mean= -7.10407648612 md= -6.94313112018 ------------------------------------------------------------ P. 089 1442 megs free memory 170 taps ==> 160 taps pre-filter: sd= 30.90575377 mean= 9.79282283093 md= 19.8852274284 post-filter: sd= 30.9943145139 mean= 10.8520457421 md= 21.3055804833 ------------------------------------------------------------ P. 090 1432 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.94716165328 mean= -4.60788761677 md= -4.68363725841 post-filter: sd= 5.82905183622 mean= -4.77851629223 md= -4.75744197454 ------------------------------------------------------------ P. 091 1428 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.93236587869 mean= -11.6627371451 md= -11.699096571 post-filter: sd= 4.95737911896 mean= -11.8189343246 md= -12.0431844967 ------------------------------------------------------------ P. 092 1422 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.34221751007 mean= -9.97466529476 md= -10.3804936051 post-filter: sd= 6.15918974201 mean= -10.3390583218 md= -10.6559441247 ------------------------------------------------------------ P. 093 1408 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.37366137095 mean= -13.6402276341 md= -13.6580005937 post-filter: sd= 8.29781623371 mean= -13.9148844704 md= -13.9016655715 ------------------------------------------------------------ P. 094 1418 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.62324340893 mean= -11.7264172224 md= -11.8918223343 post-filter: sd= 7.55904562768 mean= -11.9069200382 md= -11.9557991278 ------------------------------------------------------------ P. 095 1402 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.7705469714 mean= -13.9750395763 md= -12.6933814442 post-filter: sd= 11.879359394 mean= -14.2716500962 md= -13.3496330788 ------------------------------------------------------------ P. 096 1393 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.79303004658 mean= -10.9487650563 md= -10.2954638356 post-filter: sd= 7.8661728428 mean= -11.0867509748 md= -10.2983961209 ------------------------------------------------------------ P. 097 1383 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.895673773 mean= -2.89512746055 md= -3.83423513942 post-filter: sd= 6.35956360026 mean= -3.07544369555 md= -3.85862307819 ------------------------------------------------------------ P. 098 1374 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.86387457384 mean= -5.42198465515 md= -4.96511877181 post-filter: sd= 4.80636014627 mean= -5.57204068678 md= -5.09430094485 ------------------------------------------------------------ P. 099 1357 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.82843794816 mean= -8.43216288492 md= -8.99841914345 post-filter: sd= 5.78598210243 mean= -8.54833127558 md= -9.03944133416 ------------------------------------------------------------ P. 100 1348 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.97718407739 mean= -5.52776927268 md= -5.42178837447 post-filter: sd= 6.06374977693 mean= -5.53527991128 md= -5.42178837447 ------------------------------------------------------------ P. 101 1334 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.96810684867 mean= -7.65864429668 md= -8.25696560244 post-filter: sd= 6.27775421426 mean= -7.92816303866 md= -8.35495300501 ------------------------------------------------------------ P. 102 1324 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.79376479712 mean= -6.30471639875 md= -6.53452193145 post-filter: sd= 5.62673592261 mean= -6.58953708943 md= -6.8519214131 ------------------------------------------------------------ P. 103 1316 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.06022719704 mean= -8.74195657043 md= -9.34374962121 post-filter: sd= 6.01704250786 mean= -8.93462315069 md= -9.55557049505 ------------------------------------------------------------ P. 104 1303 megs free memory 170 taps ==> 160 taps pre-filter: sd= 30.0668480423 mean= -0.0289142136426 md= -10.4680192132 post-filter: sd= 30.6150908777 mean= 0.118177806714 md= -10.857270862 ------------------------------------------------------------ P. 105 1292 megs free memory 170 taps ==> 160 taps pre-filter: sd= 19.0880960917 mean= -18.0362952659 md= -20.6918817792 post-filter: sd= 19.0908562375 mean= -18.7371798984 md= -21.3393732067 ------------------------------------------------------------ P. 107 1281 megs free memory 170 taps ==> 156 taps pre-filter: sd= 5.87590593182 mean= -5.00377058292 md= -4.49568867457 post-filter: sd= 5.87963000447 mean= -4.96876263138 md= -4.50158087162 ------------------------------------------------------------ P. 108 2085 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.78123982123 mean= -8.50207098961 md= -8.15217690656 post-filter: sd= 6.6091050571 mean= -8.86521614183 md= -8.38818914172 ------------------------------------------------------------ P. 109 2347 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.98726125402 mean= -6.3025514775 md= -6.21659234993 post-filter: sd= 4.93869320276 mean= -6.48200387968 md= -6.37559142507 ------------------------------------------------------------ P. 110 2341 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.26583650204 mean= -6.18126827965 md= -6.06585196714 post-filter: sd= 5.20780505612 mean= -6.43454253254 md= -6.30864915982 ------------------------------------------------------------ P. 111 2327 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.84868180899 mean= -3.50975171888 md= -4.32172827592 post-filter: sd= 5.89485677502 mean= -3.59397615603 md= -4.32172827592 ------------------------------------------------------------ P. 112 2320 megs free memory 170 taps ==> 160 taps pre-filter: sd= 37.5412284769 mean= -1.94177697738 md= -20.8163458114 post-filter: sd= 38.2252334135 mean= -1.62884749301 md= -22.2709123146 ------------------------------------------------------------ P. 113 2305 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.03535693282 mean= -3.2379255566 md= -2.91229258018 post-filter: sd= 4.02596446999 mean= -3.3673250641 md= -3.007276498 ------------------------------------------------------------ P. 114 2299 megs free memory 170 taps ==> 161 taps pre-filter: sd= 18.5654436295 mean= -17.4046094641 md= -21.7260525945 post-filter: sd= 18.7679740837 mean= -17.8706656097 md= -22.5602140615 ------------------------------------------------------------ P. 115 2285 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.6439716696 mean= -8.58572380005 md= -8.73190860746 post-filter: sd= 5.56549305742 mean= -8.72174621304 md= -8.87620980971 ------------------------------------------------------------ P. 116 2272 megs free memory 170 taps ==> 160 taps pre-filter: sd= 4.95199492142 mean= -5.86560298897 md= -5.98765581248 post-filter: sd= 4.8763551483 mean= -6.04891877463 md= -6.01813088231 ------------------------------------------------------------ P. 117 2261 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.69648914189 mean= -9.29366679878 md= -7.90700285489 post-filter: sd= 7.66855978691 mean= -9.00551985399 md= -7.66581321409 ------------------------------------------------------------ P. 118 2251 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.7580768675 mean= -5.31608421703 md= -5.57005291389 post-filter: sd= 5.81849839881 mean= -5.35464009805 md= -5.75961648247 ------------------------------------------------------------ P. 119 2240 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.05644110114 mean= -5.69132175651 md= -4.93656415901 post-filter: sd= 7.16281115131 mean= -5.68557384762 md= -4.86763388322 ------------------------------------------------------------ P. 120 2228 megs free memory 170 taps ==> 152 taps pre-filter: sd= 5.49044097124 mean= -7.48468524383 md= -7.3246422927 post-filter: sd= 5.28329864383 mean= -7.78324124106 md= -7.70290030086 ------------------------------------------------------------ P. 121 2219 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.65386646754 mean= -8.9544047408 md= -8.91568072301 post-filter: sd= 9.55796691162 mean= -9.33969911045 md= -9.36274137207 ================================================================================ Jits_Linear_8 ================================================================================ ------------------------------------------------------------ P. 011 2204 megs free memory 170 taps ==> 161 taps pre-filter: sd= 26.4282626323 mean= 32.9544023323 md= 40.9633225157 post-filter: sd= 26.8947473507 mean= 32.6310923186 md= 40.9633225157 ------------------------------------------------------------ P. 015 2191 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.1698645349 mean= -2.92268950056 md= -3.12098020989 post-filter: sd= 11.3235672575 mean= -2.7553652177 md= -2.9413681085 ------------------------------------------------------------ P. 016 2179 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.91697854992 mean= -2.87833205306 md= -3.48301688899 post-filter: sd= 6.56254651029 mean= -2.8370444156 md= -3.15541544626 ------------------------------------------------------------ P. 017 2169 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.2124852054 mean= 3.25208379302 md= 3.40800312319 post-filter: sd= 13.2577034236 mean= 3.67517429391 md= 3.79762521686 ------------------------------------------------------------ P. 018 2159 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.63302228834 mean= 0.24436597435 md= 0.0199986452548 post-filter: sd= 8.63219078618 mean= 0.354974190792 md= 0.0871846162582 ------------------------------------------------------------ P. 019 2146 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.49905654 mean= 6.66171512764 md= 6.3260934965 post-filter: sd= 13.461284986 mean= 6.94317998857 md= 6.40082321259 ------------------------------------------------------------ P. 020 2127 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.1138514979 mean= -4.20603700236 md= -5.60576584873 post-filter: sd= 13.230448066 mean= -4.07787241688 md= -5.14360697804 ------------------------------------------------------------ P. 021 2100 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.803708142 mean= 4.75342700913 md= 2.85975533914 post-filter: sd= 11.687535831 mean= 5.3409155704 md= 3.7136257524 ------------------------------------------------------------ P. 022 2090 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.91991017594 mean= 0.393529866981 md= 0.157944758872 post-filter: sd= 8.03201179852 mean= 0.541882430865 md= 0.566911304995 ------------------------------------------------------------ P. 024 2079 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.88770945908 mean= -1.89999948112 md= -2.36701024928 post-filter: sd= 8.85058170129 mean= -1.97120192047 md= -2.36701024928 ------------------------------------------------------------ P. 025 2068 megs free memory 170 taps ==> 160 taps pre-filter: sd= 10.5768268954 mean= 0.192018524973 md= -0.570311671389 post-filter: sd= 10.7324732999 mean= 0.212909440817 md= -0.730266314779 ------------------------------------------------------------ P. 026 2059 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.88372981369 mean= -2.11007606281 md= -2.09312711465 post-filter: sd= 5.83657555495 mean= -1.96785645945 md= -2.0479128207 ------------------------------------------------------------ P. 027 2047 megs free memory 170 taps ==> 160 taps pre-filter: sd= 19.8538301614 mean= 3.14515727026 md= 2.12673188097 post-filter: sd= 20.1586550807 mean= 3.33500753745 md= 2.5800949026 ------------------------------------------------------------ P. 028 2035 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.32269757842 mean= -5.76308492102 md= -5.6160927994 post-filter: sd= 6.00983884604 mean= -5.52817446526 md= -5.61097467556 ------------------------------------------------------------ P. 029 2026 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.68554918123 mean= -7.39349600621 md= -6.96994665336 post-filter: sd= 7.49893683761 mean= -7.27401241215 md= -6.96994665336 ------------------------------------------------------------ P. 030 2015 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.75519485916 mean= -4.16306134637 md= -5.60580915585 post-filter: sd= 8.72761061531 mean= -3.82847427326 md= -5.58381655972 ------------------------------------------------------------ P. 032 2004 megs free memory 170 taps ==> 160 taps pre-filter: sd= 12.8313795987 mean= 4.53024422544 md= 2.39682164835 post-filter: sd= 12.8996320222 mean= 4.90467946003 md= 3.03967430663 ------------------------------------------------------------ P. 033 1999 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.94729385994 mean= -1.64597586967 md= -1.15405353478 post-filter: sd= 5.86961982485 mean= -1.41056648447 md= -1.00599317379 ------------------------------------------------------------ P. 034 1988 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.53474701487 mean= 1.97465946907 md= 2.28500798156 post-filter: sd= 6.50347499157 mean= 2.2415371101 md= 2.67607912346 ------------------------------------------------------------ P. 035 1976 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.57593141032 mean= -9.96348219424 md= -10.2448745888 post-filter: sd= 7.66553915922 mean= -10.0681114849 md= -10.2112373548 ------------------------------------------------------------ P. 036 1966 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.38470529049 mean= -6.25062570167 md= -7.39224302749 post-filter: sd= 9.2170629407 mean= -6.55232000342 md= -7.85324065762 ------------------------------------------------------------ P. 037 1957 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.33032247427 mean= -7.89261807043 md= -8.45280686919 post-filter: sd= 7.24811870643 mean= -7.86382029588 md= -8.47621587273 ------------------------------------------------------------ P. 038 1946 megs free memory 170 taps ==> 161 taps pre-filter: sd= 18.8743821552 mean= 3.53662370359 md= 1.7288209874 post-filter: sd= 19.2790291747 mean= 3.60633809723 md= 1.70166348514 ------------------------------------------------------------ P. 039 1934 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.5592827861 mean= -9.9400352091 md= -8.98828484729 post-filter: sd= 11.6260701692 mean= -10.1604476213 md= -8.98828484729 ------------------------------------------------------------ P. 040 1921 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.6040729026 mean= 3.95141290707 md= 3.22421067201 post-filter: sd= 10.725337834 mean= 4.03769853144 md= 3.24627118246 ------------------------------------------------------------ P. 041 1909 megs free memory 170 taps ==> 161 taps pre-filter: sd= 16.2358983437 mean= 6.42519100875 md= 4.94587605421 post-filter: sd= 16.3712417166 mean= 6.97960638812 md= 5.71754567298 ------------------------------------------------------------ P. 043 1900 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.2485597011 mean= -4.57827210623 md= -5.99699685754 post-filter: sd= 9.92246345534 mean= -4.81315437314 md= -6.17621808746 ------------------------------------------------------------ P. 044 1890 megs free memory 170 taps ==> 160 taps pre-filter: sd= 12.0951065215 mean= 3.56176057375 md= 1.15191377708 post-filter: sd= 12.2628373801 mean= 3.73454218436 md= 1.52974189426 ------------------------------------------------------------ P. 046 1881 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.44546276594 mean= 0.713437554802 md= 0.692208205484 post-filter: sd= 9.14972590477 mean= 0.970096614009 md= 0.692208205484 ------------------------------------------------------------ P. 047 1867 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.09860624795 mean= -5.68448556162 md= -5.09332583801 post-filter: sd= 8.92833404922 mean= -5.92472201956 md= -5.11859148097 ------------------------------------------------------------ P. 048 1856 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.92881246129 mean= 5.31482577421 md= 5.67193682412 post-filter: sd= 7.4498683158 mean= 5.80148636121 md= 6.02715943811 ------------------------------------------------------------ P. 049 1842 megs free memory 170 taps ==> 161 taps pre-filter: sd= 23.3142617902 mean= -10.6142869163 md= -12.4130706802 post-filter: sd= 23.1276179889 mean= -11.0828865257 md= -12.6495353679 ------------------------------------------------------------ P. 051 1834 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.62790757853 mean= 2.80982790105 md= 2.70929198601 post-filter: sd= 7.50953477619 mean= 3.16475903669 md= 3.17557063921 ------------------------------------------------------------ P. 052 1826 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.02358624548 mean= -0.719470354598 md= -0.421370103116 post-filter: sd= 8.05733736182 mean= -0.514777653007 md= -0.161616836897 ------------------------------------------------------------ P. 053 1809 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.44630675679 mean= -1.11583210505 md= -1.67942748211 post-filter: sd= 6.40380881083 mean= -0.84198163457 md= -1.65496416804 ------------------------------------------------------------ P. 054 1797 megs free memory 170 taps ==> 161 taps pre-filter: sd= 17.8000938379 mean= 4.23228555861 md= 4.03919698068 post-filter: sd= 18.1128952654 mean= 4.20634509348 md= 3.76660728054 ------------------------------------------------------------ P. 055 1790 megs free memory 170 taps ==> 161 taps pre-filter: sd= 15.9978041691 mean= -6.04881788193 md= -7.14958909569 post-filter: sd= 15.9990632232 mean= -5.32630172209 md= -6.05718664702 ------------------------------------------------------------ P. 056 1776 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.29678391193 mean= 0.162892667547 md= 0.721614686597 post-filter: sd= 7.14451240908 mean= 0.348796656283 md= 0.799924192771 ------------------------------------------------------------ P. 057 1765 megs free memory 170 taps ==> 160 taps pre-filter: sd= 18.3307090564 mean= 0.550443245869 md= 0.0427162846853 post-filter: sd= 18.5704057565 mean= 0.857676667198 md= 0.933016345025 ------------------------------------------------------------ P. 058 1757 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.0346549837 mean= -4.22949676683 md= -5.02805993353 post-filter: sd= 10.7458116348 mean= -3.95280221861 md= -5.2674742379 ------------------------------------------------------------ P. 059 1747 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.35106197177 mean= 2.11754256061 md= 2.54788798342 post-filter: sd= 6.8179503474 mean= 2.67244977872 md= 2.93018309696 ------------------------------------------------------------ P. 060 1729 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.79246633625 mean= 0.559039317965 md= -0.109797053933 post-filter: sd= 6.84222277189 mean= 0.453447260788 md= -0.276501627813 ------------------------------------------------------------ P. 061 1717 megs free memory 170 taps ==> 161 taps pre-filter: sd= 14.9520030791 mean= 4.32923514399 md= 2.51871064515 post-filter: sd= 15.2125079879 mean= 4.61514307813 md= 3.02894557971 ------------------------------------------------------------ P. 062 1712 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.4068757402 mean= -3.55397541494 md= -5.49662693651 post-filter: sd= 13.3184820913 mean= -3.29054502475 md= -5.22021255004 ------------------------------------------------------------ P. 063 1700 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.27682486309 mean= 0.338344488941 md= -0.0914146707327 post-filter: sd= 6.33502503183 mean= 0.448611112551 md= -0.0869989168934 ------------------------------------------------------------ P. 064 1688 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.4084210867 mean= 2.35560864459 md= 1.48214911215 post-filter: sd= 9.96524482728 mean= 2.64531122544 md= 1.62595707298 ------------------------------------------------------------ P. 065 1675 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.25632923774 mean= -4.72105301403 md= -3.4786780668 post-filter: sd= 9.41143451703 mean= -4.73650325517 md= -3.40399338283 ------------------------------------------------------------ P. 066 1664 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.99199809666 mean= 1.87159557972 md= 1.2585909715 post-filter: sd= 9.09092407865 mean= 1.9784475194 md= 1.37825887551 ------------------------------------------------------------ P. 067 1653 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.33680008308 mean= -2.14242781654 md= -2.54491889139 post-filter: sd= 8.05851212521 mean= -2.07760510085 md= -2.5322689337 ------------------------------------------------------------ P. 068 1642 megs free memory 170 taps ==> 161 taps pre-filter: sd= 21.885248612 mean= 2.92415867973 md= 0.976976865471 post-filter: sd= 22.379785841 mean= 3.01584049237 md= 0.987023623687 ------------------------------------------------------------ P. 069 1635 megs free memory 170 taps ==> 158 taps pre-filter: sd= 7.97352621384 mean= -6.62560865517 md= -6.89426156631 post-filter: sd= 8.04652421962 mean= -6.5354912305 md= -6.89426156631 ------------------------------------------------------------ P. 071 1625 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.58871759807 mean= -6.04896196548 md= -6.52206729253 post-filter: sd= 7.57952680305 mean= -5.99557420375 md= -6.29683975137 ------------------------------------------------------------ P. 072 1618 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.3494918616 mean= -6.19422912773 md= -7.9032716464 post-filter: sd= 12.5151606259 mean= -6.61958192397 md= -7.9032716464 ------------------------------------------------------------ P. 073 1610 megs free memory 170 taps ==> 159 taps pre-filter: sd= 25.3978791826 mean= -4.14221128548 md= -8.4691020253 post-filter: sd= 25.8399105707 mean= -3.85623137185 md= -8.30346563981 ------------------------------------------------------------ P. 074 1588 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.43637469009 mean= -4.02834552181 md= -4.25333068576 post-filter: sd= 7.81052488024 mean= -3.40519958792 md= -3.95266506676 ------------------------------------------------------------ P. 075 1577 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.84068977516 mean= 3.43818047102 md= 2.74639297573 post-filter: sd= 7.85471614328 mean= 3.6834188821 md= 3.01513843904 ------------------------------------------------------------ P. 076 1571 megs free memory 170 taps ==> 161 taps pre-filter: sd= 12.1680025639 mean= -2.52736304267 md= -2.86354348048 post-filter: sd= 12.0810268232 mean= -1.94648208483 md= -1.64677834758 ------------------------------------------------------------ P. 077 1561 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.91400024378 mean= -10.9050993975 md= -10.2840486941 post-filter: sd= 8.79784221986 mean= -10.6146530587 md= -10.1509718815 ------------------------------------------------------------ P. 078 1548 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.4797157801 mean= 3.11125534202 md= 2.88969904965 post-filter: sd= 7.32939258229 mean= 3.49064339711 md= 3.26326357456 ------------------------------------------------------------ P. 079 1549 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.22034655977 mean= 2.00437503018 md= 1.32682402212 post-filter: sd= 8.30130101703 mean= 2.03813534401 md= 1.26121473561 ------------------------------------------------------------ P. 080 1545 megs free memory 170 taps ==> 160 taps pre-filter: sd= 11.1412412683 mean= 3.55861065495 md= 3.34251432869 post-filter: sd= 11.1667269241 mean= 3.81268442046 md= 3.53743166161 ------------------------------------------------------------ P. 081 1539 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.84039832821 mean= -0.417462290146 md= -0.471679830881 post-filter: sd= 6.9047811911 mean= -0.253939846636 md= -0.160696138209 ------------------------------------------------------------ P. 082 1534 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.0173265588 mean= 2.56282950076 md= 2.89980503931 post-filter: sd= 6.99318299771 mean= 2.69232863492 md= 2.92065668359 ------------------------------------------------------------ P. 083 1521 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.71747527435 mean= -4.71815300351 md= -4.59640497616 post-filter: sd= 5.78972604924 mean= -4.72833660854 md= -4.57246399101 ------------------------------------------------------------ P. 084 1517 megs free memory 170 taps ==> 161 taps pre-filter: sd= 12.6422214982 mean= -2.26084550428 md= -3.6909873246 post-filter: sd= 12.7237275489 mean= -1.84108426396 md= -3.29356791633 ------------------------------------------------------------ P. 085 1512 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.09429750581 mean= -0.38994507481 md= -0.775535527691 post-filter: sd= 8.10165970732 mean= -0.162155571356 md= -0.463084785464 ------------------------------------------------------------ P. 086 1494 megs free memory 170 taps ==> 157 taps pre-filter: sd= 11.4181629692 mean= -1.22207910656 md= -2.38907592204 post-filter: sd= 11.6065618813 mean= -1.26378405408 md= -2.39039790176 ------------------------------------------------------------ P. 087 1493 megs free memory 170 taps ==> 161 taps pre-filter: sd= 16.4610541576 mean= 4.75248465103 md= 3.49343952281 post-filter: sd= 16.578259263 mean= 5.06649145628 md= 3.64403626718 ------------------------------------------------------------ P. 089 1477 megs free memory 170 taps ==> 161 taps pre-filter: sd= 27.5270018445 mean= 6.73640213265 md= 8.54833743843 post-filter: sd= 27.435830285 mean= 7.97099578377 md= 9.09505847225 ------------------------------------------------------------ P. 090 1471 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.41226562261 mean= 1.21337840866 md= 0.710951848955 post-filter: sd= 7.39909531407 mean= 1.49124615977 md= 0.911724563486 ------------------------------------------------------------ P. 091 1462 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.25114297892 mean= -6.19340954369 md= -6.724153524 post-filter: sd= 6.22594714056 mean= -6.0771584415 md= -6.60235534759 ------------------------------------------------------------ P. 092 1493 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.97940239506 mean= -5.77287520765 md= -5.92670044924 post-filter: sd= 6.49416939716 mean= -5.79091226525 md= -5.62456263121 ------------------------------------------------------------ P. 093 1485 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.63811475026 mean= -8.12828298136 md= -8.41101599241 post-filter: sd= 7.723564582 mean= -8.25941573463 md= -8.69848743893 ------------------------------------------------------------ P. 094 1476 megs free memory 170 taps ==> 159 taps pre-filter: sd= 9.7440693458 mean= -5.43324279635 md= -7.24559870488 post-filter: sd= 9.92137336125 mean= -5.4192577026 md= -7.67732668784 ------------------------------------------------------------ P. 095 1464 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.7711477452 mean= 2.29422255312 md= 1.06168394412 post-filter: sd= 10.8887171324 mean= 2.59798058473 md= 1.3128320778 ------------------------------------------------------------ P. 096 1449 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.28379166912 mean= -6.68008962559 md= -7.16488418757 post-filter: sd= 6.32078702031 mean= -6.74162590633 md= -7.1650221138 ------------------------------------------------------------ P. 097 1440 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.8426960907 mean= 2.1722029056 md= 0.358338876311 post-filter: sd= 13.7124107585 mean= 2.79186955045 md= 1.70256916687 ------------------------------------------------------------ P. 098 1428 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.7671156264 mean= -3.28005920862 md= -3.5760410799 post-filter: sd= 6.74933478073 mean= -3.16829225332 md= -3.5760410799 ------------------------------------------------------------ P. 099 1418 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.9764831497 mean= -2.40146942889 md= -3.40506534471 post-filter: sd= 11.1570499889 mean= -2.2655715674 md= -3.37018819372 ------------------------------------------------------------ P. 100 1410 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.14543859061 mean= 2.336414363 md= 1.97174587592 post-filter: sd= 6.95734722739 mean= 2.71413502015 md= 2.14626362974 ------------------------------------------------------------ P. 101 1395 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.41003503297 mean= -3.86903647787 md= -3.42516711323 post-filter: sd= 8.23160955475 mean= -3.49868240509 md= -2.95325072548 ------------------------------------------------------------ P. 102 1387 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.4126737518 mean= -0.220642546137 md= -0.907934423539 post-filter: sd= 7.07307236887 mean= 0.196863431323 md= -0.323032250061 ------------------------------------------------------------ P. 103 1377 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.05653856021 mean= -0.510299530021 md= -1.46611190758 post-filter: sd= 7.09488051185 mean= -0.297910881349 md= -1.23010157032 ------------------------------------------------------------ P. 104 1360 megs free memory 170 taps ==> 160 taps pre-filter: sd= 13.3096983448 mean= -6.72471811742 md= -8.35899787628 post-filter: sd= 13.5268929487 mean= -6.5690289244 md= -8.16965650114 ------------------------------------------------------------ P. 105 1351 megs free memory 170 taps ==> 161 taps pre-filter: sd= 12.434875313 mean= -8.79088327225 md= -8.06813458103 post-filter: sd= 12.5102891722 mean= -8.74497717456 md= -8.0127156139 ------------------------------------------------------------ P. 107 1341 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.84257670521 mean= 0.523245391058 md= 1.41427500321 post-filter: sd= 7.88272875637 mean= 0.654253625789 md= 1.55329649989 ------------------------------------------------------------ P. 108 1332 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.77812958197 mean= 0.73255523747 md= -0.557484320753 post-filter: sd= 7.82519003897 mean= 0.938840634994 md= -0.379608765376 ------------------------------------------------------------ P. 109 1318 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.38151698822 mean= 0.546617406085 md= -0.0226038472911 post-filter: sd= 7.44836238595 mean= 0.582050563307 md= -0.0226038472911 ------------------------------------------------------------ P. 110 1309 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.55861025165 mean= 1.70296728028 md= 1.33709081279 post-filter: sd= 7.34491981958 mean= 1.96053827777 md= 1.72785029673 ------------------------------------------------------------ P. 111 1299 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.47752613036 mean= 2.84859887074 md= 2.5442185568 post-filter: sd= 6.47874123692 mean= 3.0577025474 md= 2.71120039408 ------------------------------------------------------------ P. 112 1288 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.40747813678 mean= -10.2782871895 md= -9.34936338828 post-filter: sd= 9.44104148284 mean= -10.3763223391 md= -9.34936338828 ------------------------------------------------------------ P. 113 1279 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.32193881098 mean= 3.29132982906 md= 3.35990993242 post-filter: sd= 6.31351843673 mean= 3.39223266415 md= 3.35990993242 ------------------------------------------------------------ P. 114 1262 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.2320458423 mean= -9.19493788978 md= -11.6194099572 post-filter: sd= 11.1657680929 mean= -9.34023438781 md= -11.6194099572 ------------------------------------------------------------ P. 115 1254 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.23778947381 mean= -1.90932948239 md= -2.72937704169 post-filter: sd= 7.31803788732 mean= -1.79404113791 md= -2.65972210884 ------------------------------------------------------------ P. 116 1245 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.03940936431 mean= -0.232677787871 md= -0.21399805217 post-filter: sd= 6.05467144673 mean= -0.0469395571912 md= -0.0446929471014 ------------------------------------------------------------ P. 117 1229 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.74132130402 mean= -9.44928202167 md= -8.73260161134 post-filter: sd= 8.72761872617 mean= -9.68066281454 md= -9.17530339037 ------------------------------------------------------------ P. 118 1221 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.11782522451 mean= 0.872252177843 md= -0.0167924175037 post-filter: sd= 8.96015659556 mean= 1.32116351067 md= 0.483960471416 ------------------------------------------------------------ P. 119 1215 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.97602095523 mean= 0.831143357863 md= 0.554087775158 post-filter: sd= 6.85282665911 mean= 1.16750515604 md= 0.914757674926 ------------------------------------------------------------ P. 120 1202 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.09221305527 mean= -2.64938324003 md= -3.20074192745 post-filter: sd= 6.13344503751 mean= -2.76783325614 md= -3.354088033 ------------------------------------------------------------ P. 121 1188 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.355772158 mean= -2.7924927798 md= -3.61443266319 post-filter: sd= 11.3406488944 mean= -2.43892021213 md= -3.34642069289 ================================================================================ Jits_Phase_5 ================================================================================ ------------------------------------------------------------ P. 011 1175 megs free memory 170 taps ==> 160 taps pre-filter: sd= 19.7197757069 mean= 35.0366049344 md= 38.2921168337 post-filter: sd= 20.0697566584 mean= 34.9663832135 md= 38.5352738803 ------------------------------------------------------------ P. 012 1169 megs free memory 170 taps ==> 161 taps pre-filter: sd= 31.2471259974 mean= 27.1041681611 md= 38.1220544772 post-filter: sd= 30.0481452401 mean= 28.654207732 md= 39.327479529 ------------------------------------------------------------ P. 015 1157 megs free memory 170 taps ==> 160 taps pre-filter: sd= 10.9458929299 mean= -7.60995328588 md= -8.1742582499 post-filter: sd= 11.1438590309 mean= -7.67659982936 md= -8.29797793823 ------------------------------------------------------------ P. 016 1142 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.94020284476 mean= -3.57288350708 md= -3.46805294094 post-filter: sd= 5.88230237837 mean= -3.40972533094 md= -3.3860587237 ------------------------------------------------------------ P. 017 1132 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.54191544375 mean= -3.64976951976 md= -5.34420797349 post-filter: sd= 9.48783335748 mean= -3.95230683613 md= -5.83945695852 ------------------------------------------------------------ P. 018 1118 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.52554592963 mean= -2.6509066113 md= -3.3266793651 post-filter: sd= 8.40978366642 mean= -3.0223746537 md= -3.5771459674 ------------------------------------------------------------ P. 019 1105 megs free memory 170 taps ==> 161 taps pre-filter: sd= 15.0663327621 mean= -5.20822423654 md= -7.32351001261 post-filter: sd= 15.3380687617 mean= -4.96674197271 md= -6.99241920894 ------------------------------------------------------------ P. 020 1175 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.5400216246 mean= -8.87062525445 md= -8.82812062216 post-filter: sd= 7.97929780706 mean= -9.30000848354 md= -8.97134165866 ------------------------------------------------------------ P. 021 1161 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.20397601607 mean= -4.65482317461 md= -5.68282543476 post-filter: sd= 6.01874043604 mean= -5.03424236792 md= -5.77128243748 ------------------------------------------------------------ P. 022 1156 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.79888624029 mean= -8.40035302842 md= -7.82444650661 post-filter: sd= 7.69353953986 mean= -8.71507320758 md= -8.30202042124 ------------------------------------------------------------ P. 024 1142 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.7834979612 mean= -2.01295900142 md= -2.43634995718 post-filter: sd= 7.89215135679 mean= -2.06729754541 md= -2.43647198856 ------------------------------------------------------------ P. 025 1128 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.58548743775 mean= -2.88767040625 md= -3.90083412049 post-filter: sd= 8.64424480916 mean= -3.07112309426 md= -4.14504669831 ------------------------------------------------------------ P. 026 1120 megs free memory 170 taps ==> 156 taps pre-filter: sd= 7.72968873046 mean= -6.82982839712 md= -7.9502152917 post-filter: sd= 7.23506972379 mean= -7.41786405191 md= -8.22355544549 ------------------------------------------------------------ P. 027 1107 megs free memory 170 taps ==> 160 taps pre-filter: sd= 11.9986876254 mean= 5.16203113283 md= 2.7313043339 post-filter: sd= 12.1880173053 mean= 5.29738274233 md= 2.7313043339 ------------------------------------------------------------ P. 028 1101 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.85965905345 mean= -5.58042757502 md= -5.93544573752 post-filter: sd= 7.28002580272 mean= -5.75957439376 md= -5.90375193369 ------------------------------------------------------------ P. 029 1083 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.44446932658 mean= -9.59449116241 md= -9.80547550432 post-filter: sd= 6.7491610831 mean= -10.1250559315 md= -10.0844018425 ------------------------------------------------------------ P. 030 1074 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.26795720191 mean= -0.446986957247 md= -1.59691498588 post-filter: sd= 7.34729090261 mean= -0.446940544267 md= -1.62988224181 ------------------------------------------------------------ P. 032 1065 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.76411396412 mean= -0.892447241047 md= -0.722434300992 post-filter: sd= 5.84112637285 mean= -0.956326688974 md= -0.722434300992 ------------------------------------------------------------ P. 033 1049 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.26038812815 mean= -5.33501406906 md= -4.73189220667 post-filter: sd= 7.36969718276 mean= -5.4004336223 md= -5.06826052503 ------------------------------------------------------------ P. 034 1042 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.09870946795 mean= -3.19052098232 md= -3.35591414774 post-filter: sd= 5.10261558583 mean= -3.15287793925 md= -3.44160877042 ------------------------------------------------------------ P. 035 1028 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.41472292113 mean= -13.5667694927 md= -13.6033237804 post-filter: sd= 6.32421455786 mean= -13.7990959962 md= -13.7157127192 ------------------------------------------------------------ P. 036 1021 megs free memory 170 taps ==> 161 taps pre-filter: sd= 16.4116302144 mean= 0.902108278729 md= 0.566712825479 post-filter: sd= 15.6178712622 mean= 0.279146939181 md= 0.511811304841 ------------------------------------------------------------ P. 037 1012 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.67157968584 mean= -9.9607342966 md= -10.5549196795 post-filter: sd= 6.29937247391 mean= -9.99254986575 md= -10.5259366571 ------------------------------------------------------------ P. 038 996 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.52808485864 mean= -1.28909441954 md= -1.78098310267 post-filter: sd= 6.622986346 mean= -1.27515985826 md= -1.78098310267 ------------------------------------------------------------ P. 039 988 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.31049095996 mean= -9.74777829776 md= -10.3164820388 post-filter: sd= 8.11857050171 mean= -10.6537176399 md= -10.7043087784 ------------------------------------------------------------ P. 040 978 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.49382859439 mean= -3.34331190659 md= -3.5983605245 post-filter: sd= 6.47480183316 mean= -3.62389177318 md= -3.99835394084 ------------------------------------------------------------ P. 041 968 megs free memory 170 taps ==> 161 taps pre-filter: sd= 16.5680357196 mean= 11.7103791242 md= 11.9281664453 post-filter: sd= 16.8987236438 mean= 11.8533761563 md= 12.03639352 ------------------------------------------------------------ P. 043 956 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.45889309601 mean= -12.2815921303 md= -11.8701724389 post-filter: sd= 6.44151064503 mean= -12.0837528425 md= -11.8165169555 ------------------------------------------------------------ P. 044 943 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.11762041309 mean= -1.75944730372 md= -1.9355996876 post-filter: sd= 7.11241875553 mean= -1.98736694014 md= -2.41811957076 ------------------------------------------------------------ P. 046 935 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.8133939538 mean= -1.8547877838 md= -1.64582699605 post-filter: sd= 8.44802720715 mean= -2.27509744913 md= -2.04927874994 ------------------------------------------------------------ P. 047 920 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.56660061137 mean= -7.95727690047 md= -8.51542365013 post-filter: sd= 6.37807009795 mean= -8.26132413704 md= -9.01094151452 ------------------------------------------------------------ P. 048 910 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.14745261358 mean= 2.20787423735 md= 1.27824423509 post-filter: sd= 6.28725948542 mean= 1.45137694025 md= 1.15571776156 ------------------------------------------------------------ P. 049 897 megs free memory 170 taps ==> 161 taps pre-filter: sd= 28.2964998015 mean= -15.3719533784 md= -27.0149994397 post-filter: sd= 28.9597560008 mean= -15.5481192853 md= -28.265742327 ------------------------------------------------------------ P. 051 890 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.52784975092 mean= 0.75830074379 md= 0.893716808584 post-filter: sd= 7.61190453268 mean= 0.638562176413 md= 0.870964384921 ------------------------------------------------------------ P. 052 877 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.56713505388 mean= -2.25173594448 md= -2.25547993354 post-filter: sd= 5.49281866964 mean= -2.16293695777 md= -2.13724557055 ------------------------------------------------------------ P. 053 1142 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.67451165981 mean= -1.80285313349 md= -2.7991021145 post-filter: sd= 5.40688348359 mean= -2.33720669593 md= -3.03998079001 ------------------------------------------------------------ P. 054 1269 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.5554344918 mean= 3.32140860483 md= 2.72157882098 post-filter: sd= 10.6923349659 mean= 3.4631788197 md= 2.81086343898 ------------------------------------------------------------ P. 055 1256 megs free memory 170 taps ==> 161 taps pre-filter: sd= 14.4241054795 mean= -13.7493246952 md= -13.4149676881 post-filter: sd= 14.591304056 mean= -14.2456129647 md= -13.7151573304 ------------------------------------------------------------ P. 056 1246 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.29510023065 mean= -2.5756575062 md= -2.88659533652 post-filter: sd= 5.3193829511 mean= -2.63214126228 md= -2.96742793561 ------------------------------------------------------------ P. 057 1240 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.16618516948 mean= 0.0309880567409 md= -0.794044741647 post-filter: sd= 9.29655126252 mean= 0.177500255989 md= -0.61393744378 ------------------------------------------------------------ P. 058 1226 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.9309189588 mean= -5.5948370405 md= -7.0031372549 post-filter: sd= 7.59339233581 mean= -6.54487586486 md= -7.16343845046 ------------------------------------------------------------ P. 059 1213 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.91360850951 mean= -0.517725338998 md= -0.277329654359 post-filter: sd= 6.98555103326 mean= -0.382227257195 md= 0.385816572039 ------------------------------------------------------------ P. 060 1207 megs free memory 170 taps ==> 156 taps pre-filter: sd= 10.1696394247 mean= 5.1104675442 md= 2.85262375882 post-filter: sd= 10.0509226465 mean= 4.66456126742 md= 2.69598836931 ------------------------------------------------------------ P. 061 1190 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.9177915309 mean= -2.75169012422 md= -3.91821454767 post-filter: sd= 6.84053811193 mean= -2.98590853807 md= -4.1221009336 ------------------------------------------------------------ P. 062 1181 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.46386083184 mean= -2.25319673624 md= -3.69456160532 post-filter: sd= 9.64097822366 mean= -2.14394741042 md= -3.25000200123 ------------------------------------------------------------ P. 063 1171 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.82112112057 mean= -1.57765284759 md= -1.71916959926 post-filter: sd= 5.75594315186 mean= -1.70626306749 md= -1.74606217451 ------------------------------------------------------------ P. 064 1159 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.09798638764 mean= -1.11799873134 md= -1.19816553413 post-filter: sd= 6.95378150536 mean= -1.52775373383 md= -1.54101090315 ------------------------------------------------------------ P. 065 1145 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.02300589463 mean= -1.82665855091 md= -2.33102467627 post-filter: sd= 7.48053532988 mean= -2.44158208621 md= -2.51915179751 ------------------------------------------------------------ P. 066 1135 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.09396411302 mean= 3.22365725352 md= 3.61862378639 post-filter: sd= 7.9059629553 mean= 3.24308291379 md= 3.43494340468 ------------------------------------------------------------ P. 067 1126 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.36530289893 mean= -5.97157743164 md= -7.36002817848 post-filter: sd= 6.74945666648 mean= -6.93668707311 md= -7.56368981522 ------------------------------------------------------------ P. 068 1110 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.38028912569 mean= 5.56084185715 md= 3.28728308894 post-filter: sd= 9.34400653871 mean= 5.43776673209 md= 3.28610310599 ------------------------------------------------------------ P. 069 1101 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.03738044903 mean= -8.928383281 md= -9.5721493843 post-filter: sd= 8.08327902442 mean= -8.91517107474 md= -9.32235051709 ------------------------------------------------------------ P. 071 1085 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.7361922894 mean= -6.63977736223 md= -6.73092318038 post-filter: sd= 11.7799390886 mean= -6.99407707105 md= -7.27031116163 ------------------------------------------------------------ P. 072 1078 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.3328146293 mean= -9.64896514303 md= -9.35964919287 post-filter: sd= 8.91489150458 mean= -10.6946341278 md= -10.2774264812 ------------------------------------------------------------ P. 073 1070 megs free memory 170 taps ==> 158 taps pre-filter: sd= 19.4959387272 mean= -10.3958472904 md= -12.7578524454 post-filter: sd= 19.6134765921 mean= -10.205767747 md= -12.7575584489 ------------------------------------------------------------ P. 074 1057 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.56463904967 mean= -7.75884894712 md= -7.56121624234 post-filter: sd= 6.52911368998 mean= -7.95574213447 md= -7.64445418381 ------------------------------------------------------------ P. 075 1046 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.33888525082 mean= 0.261062101121 md= -0.445821127278 post-filter: sd= 5.39461173624 mean= 0.289645683944 md= -0.493036657597 ------------------------------------------------------------ P. 076 1038 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.1736350366 mean= 2.03540420247 md= 0.3107555622 post-filter: sd= 10.0510565991 mean= 2.06708970925 md= 0.526648578129 ------------------------------------------------------------ P. 077 1027 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.65408910255 mean= -15.0822714709 md= -14.8976877754 post-filter: sd= 7.53682970072 mean= -15.4152514579 md= -15.276377083 ------------------------------------------------------------ P. 078 1016 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.59271840851 mean= -2.83624541342 md= -3.33406715371 post-filter: sd= 5.62113664783 mean= -2.81969824528 md= -3.33406715371 ------------------------------------------------------------ P. 079 1008 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.12249174833 mean= -3.53018833232 md= -3.89934233284 post-filter: sd= 6.19585039818 mean= -3.50471491878 md= -3.8897516771 ------------------------------------------------------------ P. 080 995 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.69128683175 mean= -7.39810665705 md= -7.61469575568 post-filter: sd= 6.45906204483 mean= -7.77496548879 md= -7.80432298355 ------------------------------------------------------------ P. 081 984 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.02568970396 mean= -8.8984046489 md= -9.15363485788 post-filter: sd= 5.85579387897 mean= -9.25995455876 md= -9.2652038521 ------------------------------------------------------------ P. 082 976 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.44833579639 mean= 0.9783163512 md= 1.684590888 post-filter: sd= 5.51477268865 mean= 0.870269246318 md= 1.59436529534 ------------------------------------------------------------ P. 083 958 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.25084962333 mean= -1.91269316258 md= -2.37914168381 post-filter: sd= 5.35434963069 mean= -2.45760126412 md= -2.59359765616 ------------------------------------------------------------ P. 084 953 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.02142585654 mean= -7.27911496087 md= -7.46539868559 post-filter: sd= 6.78324555325 mean= -7.61595925078 md= -7.55426778511 ------------------------------------------------------------ P. 085 948 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.20616782431 mean= -4.97379288452 md= -5.28176227705 post-filter: sd= 6.02755921884 mean= -5.11682958566 md= -5.4091261835 ------------------------------------------------------------ P. 086 931 megs free memory 170 taps ==> 147 taps pre-filter: sd= 21.7532681916 mean= 3.37923023271 md= 3.76620210534 post-filter: sd= 22.1857221891 mean= 3.47556467902 md= 4.07575889302 ------------------------------------------------------------ P. 087 923 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.09309828792 mean= -0.949598287468 md= -1.15351056251 post-filter: sd= 6.8353833806 mean= -1.22302571994 md= -1.29813069181 ------------------------------------------------------------ P. 089 915 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.19223274159 mean= -4.17310009087 md= -3.27412739103 post-filter: sd= 9.1488178576 mean= -3.83727645297 md= -2.89415153577 ------------------------------------------------------------ P. 090 901 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.15152972821 mean= -2.74605467856 md= -2.85604565753 post-filter: sd= 6.9524917892 mean= -2.9181837278 md= -2.86503362152 ------------------------------------------------------------ P. 091 882 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.38789528812 mean= -5.92132750231 md= -6.40079409552 post-filter: sd= 6.34399000788 mean= -6.14378453355 md= -6.57825230907 ------------------------------------------------------------ P. 092 875 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.3716837369 mean= -1.00767086098 md= -1.37349224009 post-filter: sd= 7.49005659078 mean= -0.972146500626 md= -1.47074752877 ------------------------------------------------------------ P. 093 865 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.358125268 mean= -17.9884281423 md= -17.2552922888 post-filter: sd= 8.49291335784 mean= -17.9434125934 md= -17.1192830556 ------------------------------------------------------------ P. 094 850 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.22839485167 mean= -2.76443851185 md= -2.70029110935 post-filter: sd= 8.23870266689 mean= -2.83032591345 md= -2.70029110935 ------------------------------------------------------------ P. 095 840 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.60667741418 mean= -6.635304303 md= -6.6911647228 post-filter: sd= 6.66566933295 mean= -6.75425370299 md= -6.7601793703 ------------------------------------------------------------ P. 096 904 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.27729687986 mean= -7.4211393896 md= -7.41858528932 post-filter: sd= 6.29992076881 mean= -7.57973577284 md= -7.76285620701 ------------------------------------------------------------ P. 097 892 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.1678946846 mean= -1.03910633506 md= -1.18759069695 post-filter: sd= 13.1977213796 mean= -1.26088885321 md= -1.43352249534 ------------------------------------------------------------ P. 098 882 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.11903298704 mean= 0.72989470778 md= -0.00879972013615 post-filter: sd= 7.20505228954 mean= 0.715985292269 md= -0.0259751472921 ------------------------------------------------------------ P. 099 866 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.99722292324 mean= -0.234370636062 md= -0.83479122231 post-filter: sd= 6.99593907426 mean= -0.444094482445 md= -1.23680144734 ------------------------------------------------------------ P. 100 856 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.61244520865 mean= -0.70220640758 md= -1.01824767278 post-filter: sd= 6.68200399961 mean= -0.63912420519 md= -1.01824767278 ------------------------------------------------------------ P. 101 846 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.83947630739 mean= -6.54247856786 md= -6.67136293047 post-filter: sd= 7.93047444803 mean= -6.46520510116 md= -6.62441266679 ------------------------------------------------------------ P. 102 838 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.62647459322 mean= -4.43053742065 md= -4.5354922771 post-filter: sd= 6.56242068677 mean= -5.00356209546 md= -4.92798606684 ------------------------------------------------------------ P. 103 821 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.55349862471 mean= -1.92998686093 md= -1.64322061655 post-filter: sd= 7.62253558685 mean= -2.07051515586 md= -1.89641438453 ------------------------------------------------------------ P. 104 811 megs free memory 170 taps ==> 161 taps pre-filter: sd= 13.2915894398 mean= -4.93030518524 md= -5.88927490754 post-filter: sd= 13.3767686891 mean= -5.22479498871 md= -5.90373115809 ------------------------------------------------------------ P. 105 800 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.62794418886 mean= -3.91254577089 md= -3.4002407593 post-filter: sd= 9.70858839578 mean= -4.03971293471 md= -3.49732650349 ------------------------------------------------------------ P. 107 790 megs free memory 170 taps ==> 156 taps pre-filter: sd= 7.24640477217 mean= -0.895861379345 md= -0.717246184257 post-filter: sd= 7.33492154725 mean= -1.00245521998 md= -0.880822375258 ------------------------------------------------------------ P. 108 779 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.30337528806 mean= -2.07981354834 md= -1.31447018046 post-filter: sd= 7.32001550539 mean= -2.32662147215 md= -1.44971622065 ------------------------------------------------------------ P. 109 768 megs free memory 170 taps ==> 155 taps pre-filter: sd= 6.0473499413 mean= -2.89056614444 md= -3.26644628893 post-filter: sd= 6.02861338368 mean= -3.02132170557 md= -3.34208546133 ------------------------------------------------------------ P. 110 756 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.8657609695 mean= -4.74788285828 md= -5.03834392661 post-filter: sd= 5.92710419474 mean= -5.31903472142 md= -5.12477789694 ------------------------------------------------------------ P. 111 742 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.84674548583 mean= -3.33396629209 md= -2.91619595423 post-filter: sd= 5.78827262661 mean= -3.51687417822 md= -3.03754332477 ------------------------------------------------------------ P. 112 735 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.63782036771 mean= -16.5913992575 md= -17.5426473312 post-filter: sd= 8.54207040132 mean= -16.9385883358 md= -17.8835875295 ------------------------------------------------------------ P. 113 726 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.81390746233 mean= 2.56819437767 md= -0.364621099067 post-filter: sd= 9.87340264006 mean= 2.33963357693 md= -0.978541358675 ------------------------------------------------------------ P. 114 722 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.21394778403 mean= -8.54435102633 md= -8.57595722028 post-filter: sd= 9.27611599037 mean= -8.80656493599 md= -8.79807794781 ------------------------------------------------------------ P. 115 713 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.97722566057 mean= -2.724732514 md= -1.82404635751 post-filter: sd= 7.04046597524 mean= -2.83320434818 md= -1.98741755779 ------------------------------------------------------------ P. 116 700 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.82973768796 mean= -3.14987672911 md= -3.32399551785 post-filter: sd= 6.08332782562 mean= -3.54617222195 md= -3.5193174269 ------------------------------------------------------------ P. 117 686 megs free memory 170 taps ==> 160 taps pre-filter: sd= 9.10965067986 mean= -6.80049665724 md= -7.51068495782 post-filter: sd= 9.2100254391 mean= -6.64247236945 md= -7.40595485833 ------------------------------------------------------------ P. 118 679 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.49084961576 mean= -8.22503828463 md= -8.44730466323 post-filter: sd= 6.56260415659 mean= -8.14436138299 md= -8.08152218676 ------------------------------------------------------------ P. 119 664 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.74544641481 mean= -4.10673170326 md= -3.8029296406 post-filter: sd= 5.7102702038 mean= -3.86947011282 md= -3.44173463426 ------------------------------------------------------------ P. 120 651 megs free memory 170 taps ==> 152 taps pre-filter: sd= 7.76805723466 mean= -6.19122559615 md= -5.62639068978 post-filter: sd= 7.74876175694 mean= -6.48109042977 md= -5.93258985601 ------------------------------------------------------------ P. 121 641 megs free memory 170 taps ==> 158 taps pre-filter: sd= 10.083551823 mean= -2.6256850587 md= -4.88592744951 post-filter: sd= 10.1418606011 mean= -2.83593917201 md= -5.09589850629 ================================================================================ Jits_Phase_8 ================================================================================ ------------------------------------------------------------ P. 011 630 megs free memory 170 taps ==> 161 taps pre-filter: sd= 18.9780391165 mean= 34.5754107825 md= 38.0128936614 post-filter: sd= 19.2635428485 mean= 34.4161970655 md= 38.0128936614 ------------------------------------------------------------ P. 012 620 megs free memory 170 taps ==> 161 taps pre-filter: sd= 28.2624764585 mean= 28.5764086202 md= 37.6701752128 post-filter: sd= 28.6774263671 mean= 28.5989952648 md= 37.674028704 ------------------------------------------------------------ P. 015 613 megs free memory 170 taps ==> 161 taps pre-filter: sd= 12.3180093171 mean= -8.71285868407 md= -8.12856049656 post-filter: sd= 12.5471942595 mean= -8.79328995565 md= -8.18872380352 ------------------------------------------------------------ P. 016 596 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.03480040166 mean= -5.21756764978 md= -5.14693027368 post-filter: sd= 5.05803955099 mean= -5.03993301718 md= -4.77760049617 ------------------------------------------------------------ P. 017 583 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.75704113581 mean= -10.7730187164 md= -10.1101791975 post-filter: sd= 8.81881829335 mean= -10.9400933013 md= -10.4375075027 ------------------------------------------------------------ P. 018 574 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.31451300157 mean= -1.50083464095 md= -2.6196744707 post-filter: sd= 5.63710122672 mean= -1.91503394263 md= -2.67440929916 ------------------------------------------------------------ P. 019 571 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.4404055758 mean= -2.10538200058 md= -3.4789567131 post-filter: sd= 10.5017156269 mean= -2.3412477164 md= -3.62683647841 ------------------------------------------------------------ P. 020 558 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.2835303086 mean= -9.42810992617 md= -8.5667266919 post-filter: sd= 11.4161453836 mean= -9.55351666764 md= -8.76956984279 ------------------------------------------------------------ P. 021 550 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.88013026768 mean= -9.01731427275 md= -9.1925739207 post-filter: sd= 5.87602492741 mean= -8.79502004445 md= -8.9615540429 ------------------------------------------------------------ P. 022 533 megs free memory 170 taps ==> 161 taps pre-filter: sd= 4.94706760307 mean= -0.754002507409 md= -0.792473975866 post-filter: sd= 4.97633693471 mean= -0.745775514175 md= -0.796735134271 ------------------------------------------------------------ P. 024 535 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.15906093738 mean= -5.76473777438 md= -5.49044910943 post-filter: sd= 6.31777899152 mean= -6.0491953695 md= -5.93572589721 ------------------------------------------------------------ P. 025 520 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.76818339607 mean= -0.54685666431 md= -0.849254587826 post-filter: sd= 6.75431284943 mean= -0.322097064018 md= -0.661972352422 ------------------------------------------------------------ P. 026 513 megs free memory 170 taps ==> 158 taps pre-filter: sd= 7.39693054295 mean= -5.63277949702 md= -6.08710270977 post-filter: sd= 7.44512078374 mean= -5.84148992681 md= -6.26981406999 ------------------------------------------------------------ P. 027 501 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.36898522266 mean= 1.99691772418 md= 1.69601018348 post-filter: sd= 5.334592862 mean= 2.00102750669 md= 1.79321650337 ------------------------------------------------------------ P. 028 491 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.74201587093 mean= -8.03517629115 md= -7.64133060583 post-filter: sd= 5.76305784564 mean= -7.96493401545 md= -7.67024318472 ------------------------------------------------------------ P. 029 478 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.37369547122 mean= -7.61131271906 md= -8.18417956345 post-filter: sd= 7.29408361841 mean= -7.80711750025 md= -8.30207176655 ------------------------------------------------------------ P. 030 467 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.27245623682 mean= -6.75962339746 md= -6.78318290118 post-filter: sd= 6.19774629538 mean= -6.77156232988 md= -6.78318290118 ------------------------------------------------------------ P. 032 455 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.18227733927 mean= -0.351594283739 md= -0.487806804023 post-filter: sd= 5.21464216432 mean= -0.270157521356 md= -0.237126065405 ------------------------------------------------------------ P. 033 444 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.21434979424 mean= -4.82753886334 md= -4.81459112354 post-filter: sd= 6.24811757647 mean= -4.7149278468 md= -4.70380041571 ------------------------------------------------------------ P. 034 433 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.14682681938 mean= -2.47250385545 md= -2.55843881608 post-filter: sd= 6.23243746084 mean= -2.52120648143 md= -2.78279309603 ------------------------------------------------------------ P. 035 420 megs free memory 170 taps ==> 160 taps pre-filter: sd= 10.6943126989 mean= -13.9284277772 md= -13.9576873062 post-filter: sd= 10.7295537176 mean= -13.6148074272 md= -13.7507627364 ------------------------------------------------------------ P. 036 410 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.7256987492 mean= -14.4479682182 md= -14.5773497635 post-filter: sd= 11.3343024736 mean= -14.6283343287 md= -14.6598246483 ------------------------------------------------------------ P. 037 398 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.12930598601 mean= -7.34156528206 md= -7.18676690526 post-filter: sd= 6.01020249506 mean= -7.07817162315 md= -6.89361957544 ------------------------------------------------------------ P. 038 391 megs free memory 170 taps ==> 161 taps pre-filter: sd= 16.3210033549 mean= 3.7188281852 md= 2.13938798094 post-filter: sd= 16.6560752256 mean= 3.74819798806 md= 2.23898491358 ------------------------------------------------------------ P. 039 380 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.38424588379 mean= -8.06816739499 md= -7.43111790779 post-filter: sd= 8.11003640312 mean= -7.74109175596 md= -7.1200648188 ------------------------------------------------------------ P. 040 413 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.14470133761 mean= -4.20538779489 md= -4.30115859758 post-filter: sd= 6.1378377001 mean= -4.03330873641 md= -4.2567219944 ------------------------------------------------------------ P. 041 401 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.93575095062 mean= -4.57276753268 md= -3.83855510874 post-filter: sd= 8.02823613612 mean= -4.55238953459 md= -3.82480043215 ------------------------------------------------------------ P. 043 389 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.68594872411 mean= -10.577000089 md= -10.7918454721 post-filter: sd= 8.72512496636 mean= -10.5796135985 md= -10.7918454721 ------------------------------------------------------------ P. 044 374 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.48585230572 mean= -1.43749768879 md= -1.98459216682 post-filter: sd= 6.46074007151 mean= -1.214344899 md= -1.56856409551 ------------------------------------------------------------ P. 046 364 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.4543851249 mean= -4.72372127135 md= -4.53737707717 post-filter: sd= 5.93302820653 mean= -4.55333985411 md= -4.58851498151 ------------------------------------------------------------ P. 047 354 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.8853014639 mean= -7.54981406851 md= -8.02444745647 post-filter: sd= 7.2163324012 mean= -7.43357805239 md= -8.04659794021 ------------------------------------------------------------ P. 048 347 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.46195196462 mean= -5.17040429867 md= -5.11313620358 post-filter: sd= 6.54070026112 mean= -4.84817013214 md= -5.06651995599 ------------------------------------------------------------ P. 049 332 megs free memory 170 taps ==> 161 taps pre-filter: sd= 26.8614134899 mean= -1.93752157067 md= -9.31654864027 post-filter: sd= 26.9401741913 mean= -1.11878646421 md= -8.88791200008 ------------------------------------------------------------ P. 051 318 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.12067375511 mean= -4.0742403042 md= -4.64016144904 post-filter: sd= 5.71143659744 mean= -4.4145861983 md= -4.88180626832 ------------------------------------------------------------ P. 052 308 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.31981982313 mean= -2.89637647176 md= -3.28773289984 post-filter: sd= 6.37505398673 mean= -2.80260150829 md= -3.23948804385 ------------------------------------------------------------ P. 053 290 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.55945960978 mean= -4.85979978484 md= -5.25403493698 post-filter: sd= 6.04638575801 mean= -5.04025710148 md= -5.20358709406 ------------------------------------------------------------ P. 054 286 megs free memory 170 taps ==> 160 taps pre-filter: sd= 12.497918099 mean= 1.00040736314 md= -0.544177515739 post-filter: sd= 12.6969541274 mean= 1.08531893684 md= -0.541885313834 ------------------------------------------------------------ P. 055 274 megs free memory 170 taps ==> 161 taps pre-filter: sd= 32.7961427297 mean= 5.08500349017 md= 11.7161032567 post-filter: sd= 32.485300544 mean= 4.45453082934 md= -3.92146985811 ------------------------------------------------------------ P. 056 263 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.06550435827 mean= -1.55392979591 md= -1.8120708023 post-filter: sd= 5.72230507542 mean= -1.5838088726 md= -1.8120708023 ------------------------------------------------------------ P. 057 256 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.26794964294 mean= -6.42931230509 md= -6.99267697921 post-filter: sd= 6.30069650274 mean= -6.62899827063 md= -7.20388155262 ------------------------------------------------------------ P. 058 240 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.4070273275 mean= -11.4309648368 md= -10.6902761104 post-filter: sd= 9.90065366505 mean= -12.1366311782 md= -11.0922722203 ------------------------------------------------------------ P. 059 234 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.75064039757 mean= -4.47566057048 md= -4.33370248232 post-filter: sd= 5.73223795487 mean= -4.67473416377 md= -4.79666676549 ------------------------------------------------------------ P. 060 222 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.65940719809 mean= -8.89037324551 md= -9.17827583279 post-filter: sd= 5.71363665631 mean= -8.96384111665 md= -9.32215681469 ------------------------------------------------------------ P. 061 223 megs free memory 170 taps ==> 160 taps pre-filter: sd= 6.08878954227 mean= -6.81620387055 md= -7.11966774332 post-filter: sd= 6.10937913441 mean= -6.87907522592 md= -7.31164417969 ------------------------------------------------------------ P. 062 214 megs free memory 170 taps ==> 161 taps pre-filter: sd= 12.2627283702 mean= -5.3849275832 md= -5.80727121772 post-filter: sd= 11.7826288271 mean= -5.70407178314 md= -5.9408169186 ------------------------------------------------------------ P. 063 201 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.10375977772 mean= -2.42590447597 md= -2.57305691935 post-filter: sd= 6.17721016081 mean= -2.43492046383 md= -2.61848193072 ------------------------------------------------------------ P. 064 193 megs free memory 170 taps ==> 161 taps pre-filter: sd= 10.3235957245 mean= -4.68126184035 md= -5.39466556583 post-filter: sd= 10.3842395991 mean= -4.60577042206 md= -5.39466556583 ------------------------------------------------------------ P. 065 178 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.80030625953 mean= -0.998638416471 md= -1.3427713642 post-filter: sd= 7.87505335291 mean= -0.90981124155 md= -1.3427713642 ------------------------------------------------------------ P. 066 191 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.30073570403 mean= -2.62771631489 md= -2.62803807504 post-filter: sd= 8.37756615989 mean= -2.70136130065 md= -2.75844521759 ------------------------------------------------------------ P. 067 177 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.14316632171 mean= -6.17777872383 md= -6.39838752432 post-filter: sd= 6.98229856557 mean= -6.06889286023 md= -6.39838752432 ------------------------------------------------------------ P. 068 184 megs free memory 170 taps ==> 161 taps pre-filter: sd= 17.2848784447 mean= -5.86440587774 md= -3.67555052185 post-filter: sd= 17.3709404767 mean= -6.610235469 md= -4.49207379932 ------------------------------------------------------------ P. 069 197 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.03261554447 mean= -8.15534945392 md= -8.27102266191 post-filter: sd= 7.0501288528 mean= -8.01997983804 md= -8.01568470741 ------------------------------------------------------------ P. 071 187 megs free memory 170 taps ==> 160 taps pre-filter: sd= 10.7575973944 mean= -1.11819050794 md= -0.932067932067 post-filter: sd= 10.8844149386 mean= -1.02274778279 md= -0.717767876765 ------------------------------------------------------------ P. 072 400 megs free memory 170 taps ==> 161 taps pre-filter: sd= 11.6126072203 mean= -11.1947455888 md= -11.3480384811 post-filter: sd= 11.6290704224 mean= -11.6125108971 md= -11.9168367143 ------------------------------------------------------------ P. 073 496 megs free memory 170 taps ==> 160 taps pre-filter: sd= 33.4536205318 mean= -6.94800709752 md= -19.9170664119 post-filter: sd= 33.7887230264 mean= -6.22960638591 md= -19.7197117092 ------------------------------------------------------------ P. 074 480 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.08303184702 mean= -5.32111464444 md= -5.81098562987 post-filter: sd= 7.1212011804 mean= -5.10252633642 md= -5.45832407414 ------------------------------------------------------------ P. 075 467 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.67995343862 mean= -6.00582350949 md= -6.21105021942 post-filter: sd= 5.74577418289 mean= -5.90724360921 md= -6.00806731129 ------------------------------------------------------------ P. 076 456 megs free memory 170 taps ==> 161 taps pre-filter: sd= 9.46768539844 mean= -9.74713323402 md= -9.76793037911 post-filter: sd= 8.96156108022 mean= -9.07132844466 md= -9.14135864136 ------------------------------------------------------------ P. 077 446 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.85336585866 mean= -15.9820161251 md= -16.0248754562 post-filter: sd= 7.93535126773 mean= -16.2822100752 md= -16.0248754562 ------------------------------------------------------------ P. 078 433 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.71366001981 mean= -4.32544110384 md= -5.02067933474 post-filter: sd= 5.62043390847 mean= -4.25267911425 md= -5.02067933474 ------------------------------------------------------------ P. 079 420 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.66560985028 mean= -1.10841106811 md= -2.07509140105 post-filter: sd= 5.48883155837 mean= -1.25173469569 md= -2.11560294684 ------------------------------------------------------------ P. 080 409 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.80833903608 mean= -8.79232466734 md= -8.68136204477 post-filter: sd= 7.80723479203 mean= -8.63283343361 md= -8.68136204477 ------------------------------------------------------------ P. 081 400 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.80470354543 mean= -1.63912955615 md= -2.06564034851 post-filter: sd= 5.59473342859 mean= -1.77423267729 md= -2.06564034851 ------------------------------------------------------------ P. 082 390 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.91858727344 mean= -1.43657197009 md= -1.78862601911 post-filter: sd= 6.73716670001 mean= -1.26650581633 md= -1.78862601911 ------------------------------------------------------------ P. 083 373 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.93886842176 mean= -2.99934993307 md= -3.12634411291 post-filter: sd= 5.4981278159 mean= -2.92269654825 md= -3.15906207227 ------------------------------------------------------------ P. 084 364 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.88190223202 mean= -4.81899579695 md= -4.93540091043 post-filter: sd= 7.74427206845 mean= -4.50102150661 md= -4.81676137922 ------------------------------------------------------------ P. 085 335 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.33351353568 mean= -4.91380365121 md= -5.50891629932 post-filter: sd= 5.35961799138 mean= -4.86396736498 md= -5.39751130317 ------------------------------------------------------------ P. 086 318 megs free memory 170 taps ==> 152 taps pre-filter: sd= 9.31394917169 mean= -1.51158745361 md= -2.16923634049 post-filter: sd= 9.40863472535 mean= -1.42536226783 md= -2.19840952286 ------------------------------------------------------------ P. 087 307 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.07474537656 mean= -3.68690968327 md= -4.04715343873 post-filter: sd= 6.05198744391 mean= -3.56136565352 md= -3.88167179704 ------------------------------------------------------------ P. 089 298 megs free memory 170 taps ==> 161 taps pre-filter: sd= 38.2049282536 mean= -10.8763737534 md= -22.7051172419 post-filter: sd= 39.1125369439 mean= -10.798124743 md= -25.7596723031 ------------------------------------------------------------ P. 090 291 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.71625813996 mean= -3.99011559613 md= -4.68131518748 post-filter: sd= 5.7097664616 mean= -3.84772580575 md= -4.60385011139 ------------------------------------------------------------ P. 091 275 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.31631529894 mean= -5.50803215428 md= -5.28308492548 post-filter: sd= 5.31270128778 mean= -5.31298345232 md= -5.18691916009 ------------------------------------------------------------ P. 092 265 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.26556452376 mean= -4.76851308968 md= -4.19523759509 post-filter: sd= 5.83126328292 mean= -4.38870636388 md= -3.9937380588 ------------------------------------------------------------ P. 093 262 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.43512398299 mean= -12.7259997332 md= -12.846510139 post-filter: sd= 7.85555560924 mean= -13.2114054557 md= -13.1548451548 ------------------------------------------------------------ P. 094 251 megs free memory 170 taps ==> 160 taps pre-filter: sd= 8.28985017014 mean= -10.7636587998 md= -11.112276653 post-filter: sd= 8.35694384862 mean= -10.6727500777 md= -11.112276653 ------------------------------------------------------------ P. 095 244 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.29998533654 mean= -7.75576995567 md= -7.90766178828 post-filter: sd= 5.5150092447 mean= -8.18167220471 md= -7.99550799615 ------------------------------------------------------------ P. 096 271 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.96318160752 mean= -8.78208304957 md= -8.79590269368 post-filter: sd= 6.88675568522 mean= -9.10658318674 md= -9.26010099749 ------------------------------------------------------------ P. 097 255 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.70316589257 mean= -5.05876244799 md= -5.4994023476 post-filter: sd= 6.50767833608 mean= -5.11724964845 md= -5.47869147659 ------------------------------------------------------------ P. 098 468 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.39566063562 mean= -3.30890293759 md= -3.30038262966 post-filter: sd= 5.32345412865 mean= -3.17534229106 md= -3.23146944083 ------------------------------------------------------------ P. 099 462 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.65892520729 mean= -5.37311775449 md= -5.7096346122 post-filter: sd= 6.61036816883 mean= -5.29340466494 md= -5.64855335217 ------------------------------------------------------------ P. 100 451 megs free memory 170 taps ==> 160 taps pre-filter: sd= 7.58804411186 mean= -3.6921164563 md= -3.71907709376 post-filter: sd= 7.69626838209 mean= -3.59718217142 md= -3.55487380039 ------------------------------------------------------------ P. 101 441 megs free memory 170 taps ==> 161 taps pre-filter: sd= 8.4916642288 mean= -0.135941740128 md= 0.386053309106 post-filter: sd= 7.52153990179 mean= 0.110043688052 md= 0.508842961838 ------------------------------------------------------------ P. 102 427 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.04204521746 mean= -3.08745099939 md= -3.76366673669 post-filter: sd= 5.7366755277 mean= -3.11610614531 md= -3.76366673669 ------------------------------------------------------------ P. 103 427 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.14054554536 mean= -7.44733954979 md= -7.12468621221 post-filter: sd= 7.22392761969 mean= -7.50105893469 md= -7.21765353719 ------------------------------------------------------------ P. 104 411 megs free memory 170 taps ==> 161 taps pre-filter: sd= 12.026683409 mean= -6.41280956866 md= -6.48831343611 post-filter: sd= 12.1437332529 mean= -6.48854742958 md= -6.57400507155 ------------------------------------------------------------ P. 105 398 megs free memory 170 taps ==> 159 taps pre-filter: sd= 17.6277059486 mean= -9.22959536085 md= -11.9727053561 post-filter: sd= 17.5973147617 mean= -8.62614327061 md= -11.7045413525 ------------------------------------------------------------ P. 107 389 megs free memory 170 taps ==> 156 taps pre-filter: sd= 6.45780741788 mean= -4.6166806644 md= -4.82285341552 post-filter: sd= 6.55100968335 mean= -4.63238834605 md= -4.82285341552 ------------------------------------------------------------ P. 108 417 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.15393527462 mean= -6.19127571886 md= -6.44252239155 post-filter: sd= 7.2596588067 mean= -6.25436288241 md= -6.44916350703 ------------------------------------------------------------ P. 109 402 megs free memory 170 taps ==> 159 taps pre-filter: sd= 6.19742361716 mean= -6.72640726089 md= -6.36651477334 post-filter: sd= 6.22962988706 mean= -6.60976654005 md= -6.357970971 ------------------------------------------------------------ P. 110 405 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.41523241192 mean= 0.180462795063 md= -0.132790095859 post-filter: sd= 5.40212598771 mean= 0.368843905396 md= 0.00550156794617 ------------------------------------------------------------ P. 111 433 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.50151314374 mean= -4.94716612401 md= -5.06746143591 post-filter: sd= 6.59140804463 mean= -4.85180950464 md= -4.93094772435 ------------------------------------------------------------ P. 112 423 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.29975124187 mean= -10.1232425542 md= -10.5711721529 post-filter: sd= 7.21989628929 mean= -10.4607657694 md= -10.9840102702 ------------------------------------------------------------ P. 113 441 megs free memory 170 taps ==> 160 taps pre-filter: sd= 5.6674279713 mean= -3.15874933049 md= -3.25035490387 post-filter: sd= 5.58052067768 mean= -2.94475928922 md= -3.11963628914 ------------------------------------------------------------ P. 114 435 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.96784854324 mean= -11.3709139401 md= -12.3948111187 post-filter: sd= 7.46591233557 mean= -11.8118558627 md= -12.7841205392 ------------------------------------------------------------ P. 115 428 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.58720034527 mean= -6.25546455121 md= -6.64332171081 post-filter: sd= 5.4364673151 mean= -6.47492913791 md= -6.72565551308 ------------------------------------------------------------ P. 116 443 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.50904594422 mean= -7.6166014975 md= -7.95800677213 post-filter: sd= 6.51562550259 mean= -7.38695669084 md= -7.81888492014 ------------------------------------------------------------ P. 117 434 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.50955088498 mean= -2.40227064337 md= -2.15997689476 post-filter: sd= 7.50476302835 mean= -2.50878779434 md= -2.25433410362 ------------------------------------------------------------ P. 118 424 megs free memory 170 taps ==> 161 taps pre-filter: sd= 6.6340645376 mean= -5.95146064176 md= -7.15254293629 post-filter: sd= 6.71738545747 mean= -5.86857854366 md= -6.84756783794 ------------------------------------------------------------ P. 119 413 megs free memory 170 taps ==> 161 taps pre-filter: sd= 5.49257972222 mean= -3.55186041639 md= -3.52073654142 post-filter: sd= 5.47030702073 mean= -3.37131165103 md= -3.41595646781 ------------------------------------------------------------ P. 120 402 megs free memory 170 taps ==> 158 taps pre-filter: sd= 6.66385443053 mean= -7.93401269668 md= -8.0329601256 post-filter: sd= 6.60876041207 mean= -8.19839139885 md= -8.30712047635 ------------------------------------------------------------ P. 121 394 megs free memory 170 taps ==> 161 taps pre-filter: sd= 7.94198962435 mean= -10.5669251776 md= -10.6967625126 post-filter: sd= 7.99028655285 mean= -10.758833322 md= -11.0833779343
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-37-13c64b9741b0> in <module>() 28 prev_t = t 29 ---> 30 pp.close() C:\Applications\_Data analysis\Anaconda\lib\site-packages\matplotlib\backends\backend_pdf.pyc in close(self) 2282 PDF file. 2283 """ -> 2284 self._file.close() 2285 self._file = None 2286 AttributeError: 'NoneType' object has no attribute 'close'
for i in general_task_pid_iterator(concise_labels=True):
pass
T1_SMS_5 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, T1_SMS_8 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Ticks_ISO_T2_5 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Ticks_ISO_T2_8 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Ticks_Linear_5 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Ticks_Linear_8 012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Ticks_Phase_5 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Ticks_Phase_8 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Jits_ISO_5 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Jits_ISO_8 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Jits_Linear_5 011,013,015,016,017,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Jits_Linear_8 011,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Jits_Phase_5 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121, Jits_Phase_8 011,012,015,016,017,018,019,020,021,022,024,025,026,027,028,029,030,032,033,034,035,036,037,038,039,040,041,043,044,046,047,048,049,051,052,053,054,055,056,057,058,059,060,061,062,063,064,065,066,067,068,069,071,072,073,074,075,076,077,078,079,080,081,082,083,084,085,086,087,089,090,091,092,093,094,095,096,097,098,099,100,101,102,103,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,
# OUTLIER LABELING - removed for now
adjusted_devperc_mean = rem_worst.mean()
taps['outlier_metric'] = taps.dev_perc / adjusted_devperc_mean
devperc_limit = rem_worst.std() * rem_beyond_stds
taps['is_outlier'] = ( (taps.dev_perc - adjusted_devperc_mean > devperc_limit)
| (taps.dev_perc - adjusted_devperc_mean < -1 * devperc_limit) )
if print_results:
print('worst deviations (% of ISI):')
print(list(temp_devperc_ordered[:nworst_left]))
print(list(temp_devperc_ordered[-1 * nworst_right:]))
print('original mean: %s' % round(taps.dev_perc.mean(), 1))
print('adjusted mean: %s' % round(adjusted_devperc_mean), 1)
print('original stdv: %s' % round(taps.dev_perc.std(), 1))
print('adjusted stdv: %s' % round(rem_worst.std(), 1))
outliers = taps[taps.is_outlier]
if len(outliers) > 0:
print('outlier deviations (% of ISI):')
print(list(outliers.dev_perc.round(decimals=2)))
else:
print('No outliers.')
taps.set_index('beat', inplace=True)
return taps
File "<ipython-input-260-c2f73415d29c>", line 3 adjusted_devperc_mean = rem_worst.mean() ^ IndentationError: unexpected indent
#filtering would have to take place here, since we aren't assigning "outliers"
#in the earlier processing steps anymore
db_taps_filt = {t: df[df.is_outlier==False]
for (t, df) in db_taps.items()}
db_taps_filt['T1_SMS_5']
beat_end | beat_start | beat_target | channel | dev | dev_perc | i | interval | ints | is_outlier | micros | pitch | task_ms | velocity | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pid | beat | ||||||||||||||
011 | 9 | 4750.394 | 4250.062 | 4500.528 | 1 | -22.132 | -4.418165 | 14 | NaN | 490.560 | False | 88211308 | 48 | 4478.396 | 56 |
10 | 5250.118 | 4750.394 | 5000.260 | 1 | -17.716 | -3.545100 | 16 | NaN | 504.148 | False | 88715456 | 48 | 4982.544 | 54 | |
11 | 5749.754 | 5250.118 | 5499.976 | 1 | 12.532 | 2.507824 | 19 | NaN | 529.964 | False | 89245420 | 48 | 5512.508 | 51 | |
12 | 6249.884 | 5749.754 | 5999.532 | 1 | 36.988 | 7.404175 | 21 | NaN | 524.012 | False | 89769432 | 48 | 6036.520 | 52 | |
13 | 6750.176 | 6249.884 | 6500.236 | 1 | -28.596 | -5.711159 | 22 | NaN | 435.120 | False | 90204552 | 48 | 6471.640 | 47 | |
14 | 7249.908 | 6750.176 | 7000.116 | 1 | -17.168 | -3.434424 | 24 | NaN | 511.308 | False | 90715860 | 48 | 6982.948 | 54 | |
15 | 7750.044 | 7249.908 | 7499.700 | 1 | -11.688 | -2.339547 | 26 | NaN | 505.064 | False | 91220924 | 48 | 7488.012 | 51 | |
16 | 8250.222 | 7750.044 | 8000.388 | 1 | -17.096 | -3.414502 | 29 | NaN | 495.280 | False | 91716204 | 48 | 7983.292 | 51 | |
17 | 8750.034 | 8250.222 | 8500.056 | 1 | 12.000 | 2.401595 | 32 | NaN | 528.764 | False | 92244968 | 48 | 8512.056 | 52 | |
18 | 9249.766 | 8750.034 | 9000.012 | 1 | 0.968 | 0.193617 | 34 | NaN | 488.924 | False | 92733892 | 48 | 9000.980 | 48 | |
19 | 9749.908 | 9249.766 | 9499.520 | 1 | 3.212 | 0.643033 | 36 | NaN | 501.752 | False | 93235644 | 48 | 9502.732 | 48 | |
20 | 10250.200 | 9749.908 | 10000.296 | 1 | -4.692 | -0.936946 | 37 | NaN | 492.872 | False | 93728516 | 48 | 9995.604 | 44 | |
21 | 10750.012 | 10250.200 | 10500.104 | 1 | -28.972 | -5.796626 | 39 | NaN | 475.528 | False | 94204044 | 48 | 10471.132 | 52 | |
22 | 11250.196 | 10750.012 | 10999.920 | 1 | 3.288 | 0.657842 | 42 | NaN | 532.076 | False | 94736120 | 48 | 11003.208 | 47 | |
23 | 11750.304 | 11250.196 | 11500.472 | 1 | -7.020 | -1.402452 | 43 | NaN | 490.244 | False | 95226364 | 48 | 11493.452 | 56 | |
24 | 12250.000 | 11750.304 | 12000.136 | 1 | 8.400 | 1.681130 | 46 | NaN | 515.084 | False | 95741448 | 48 | 12008.536 | 41 | |
25 | 12749.804 | 12250.000 | 12499.864 | 1 | -38.144 | -7.632952 | 47 | NaN | 453.184 | False | 96194632 | 48 | 12461.720 | 45 | |
26 | 13249.988 | 12749.804 | 12999.744 | 1 | -20.924 | -4.185805 | 49 | NaN | 517.100 | False | 96711732 | 48 | 12978.820 | 54 | |
27 | 13750.134 | 13249.988 | 13500.232 | 1 | -17.244 | -3.445437 | 51 | NaN | 504.168 | False | 97215900 | 48 | 13482.988 | 55 | |
28 | 14249.902 | 13750.134 | 14000.036 | 1 | -24.908 | -4.983554 | 53 | NaN | 492.140 | False | 97708040 | 48 | 13975.128 | 44 | |
29 | 14750.240 | 14249.902 | 14499.768 | 1 | -2.124 | -0.425028 | 55 | NaN | 522.516 | False | 98230556 | 48 | 14497.644 | 44 | |
30 | 15250.610 | 14750.240 | 15000.712 | 1 | 0.900 | 0.179661 | 58 | NaN | 503.968 | False | 98734524 | 48 | 15001.612 | 47 | |
31 | 15750.226 | 15250.610 | 15500.508 | 1 | -13.560 | -2.713107 | 59 | NaN | 485.336 | False | 99219860 | 48 | 15486.948 | 41 | |
32 | 16249.760 | 15750.226 | 15999.944 | 1 | -5.392 | -1.079618 | 61 | NaN | 507.604 | False | 99727464 | 48 | 15994.552 | 48 | |
33 | 16749.968 | 16249.760 | 16499.576 | 1 | -43.784 | -8.763250 | 63 | NaN | 461.240 | False | 100188704 | 48 | 16455.792 | 41 | |
34 | 17250.188 | 16749.968 | 17000.360 | 1 | -16.192 | -3.233330 | 65 | NaN | 528.376 | False | 100717080 | 48 | 16984.168 | 43 | |
35 | 17749.886 | 17250.188 | 17500.016 | 1 | -13.640 | -2.729878 | 67 | NaN | 502.208 | False | 101219288 | 48 | 17486.376 | 40 | |
36 | 18249.768 | 17749.886 | 17999.756 | 1 | 13.808 | 2.763037 | 70 | NaN | 527.188 | False | 101746476 | 48 | 18013.564 | 44 | |
37 | 18750.160 | 18249.768 | 18499.780 | 1 | -35.136 | -7.026863 | 71 | NaN | 451.080 | False | 102197556 | 48 | 18464.644 | 43 | |
38 | 19250.218 | 18750.160 | 19000.540 | 1 | -10.932 | -2.183082 | 74 | NaN | 524.964 | False | 102722520 | 48 | 18989.608 | 43 | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
121 | 98 | 49250.198 | 48750.090 | 49000.424 | 1 | -49.628 | -9.912357 | 195 | NaN | 488.024 | False | 422182876 | 48 | 48950.796 | 25 |
99 | 49749.856 | 49250.198 | 49499.972 | 1 | -23.592 | -4.722669 | 197 | NaN | 525.584 | False | 422708460 | 48 | 49476.380 | 22 | |
100 | 50249.664 | 49749.856 | 49999.740 | 1 | -20.748 | -4.151526 | 199 | NaN | 502.612 | False | 423211072 | 48 | 49978.992 | 22 | |
101 | 50749.996 | 50249.664 | 50499.588 | 1 | -24.196 | -4.840672 | 201 | NaN | 496.400 | False | 423707472 | 48 | 50475.392 | 27 | |
102 | 51250.140 | 50749.996 | 51000.404 | 1 | -4.220 | -0.842625 | 203 | NaN | 520.792 | False | 424228264 | 48 | 50996.184 | 23 | |
103 | 51749.724 | 51250.140 | 51499.876 | 1 | -20.084 | -4.021046 | 205 | NaN | 483.608 | False | 424711872 | 48 | 51479.792 | 22 | |
104 | 52249.494 | 51749.724 | 51999.572 | 1 | -13.496 | -2.700842 | 207 | NaN | 506.284 | False | 425218156 | 48 | 51986.076 | 20 | |
105 | 52749.898 | 52249.494 | 52499.416 | 1 | -15.592 | -3.119373 | 209 | NaN | 497.748 | False | 425715904 | 48 | 52483.824 | 19 | |
106 | 53250.080 | 52749.898 | 53000.380 | 1 | -11.552 | -2.305954 | 211 | NaN | 505.004 | False | 426220908 | 48 | 52988.828 | 19 | |
107 | 53749.626 | 53250.080 | 53499.780 | 1 | -6.468 | -1.295154 | 213 | NaN | 504.484 | False | 426725392 | 48 | 53493.312 | 21 | |
108 | 54249.980 | 53749.626 | 53999.472 | 1 | -12.472 | -2.495937 | 215 | NaN | 493.688 | False | 427219080 | 48 | 53987.000 | 23 | |
109 | 54750.224 | 54249.980 | 54500.488 | 1 | -16.044 | -3.202293 | 217 | NaN | 497.444 | False | 427716524 | 48 | 54484.444 | 26 | |
110 | 55249.770 | 54750.224 | 54999.960 | 1 | -8.040 | -1.609700 | 219 | NaN | 507.476 | False | 428224000 | 48 | 54991.920 | 21 | |
111 | 55749.464 | 55249.770 | 55499.580 | 1 | -25.244 | -5.052640 | 221 | NaN | 482.416 | False | 428706416 | 48 | 55474.336 | 26 | |
112 | 56249.830 | 55749.464 | 55999.348 | 1 | -12.300 | -2.461142 | 223 | NaN | 512.712 | False | 429219128 | 48 | 55987.048 | 22 | |
113 | 56750.048 | 56249.830 | 56500.312 | 1 | -31.148 | -6.217612 | 225 | NaN | 482.116 | False | 429701244 | 48 | 56469.164 | 33 | |
114 | 57249.706 | 56750.048 | 56999.784 | 1 | -38.332 | -7.674504 | 227 | NaN | 492.288 | False | 430193532 | 48 | 56961.452 | 19 | |
115 | 57749.962 | 57249.706 | 57499.628 | 1 | -32.116 | -6.425205 | 229 | NaN | 506.060 | False | 430699592 | 48 | 57467.512 | 21 | |
116 | 58250.256 | 57749.962 | 58000.296 | 1 | -65.036 | -12.989846 | 231 | NaN | 467.748 | False | 431167340 | 48 | 57935.260 | 25 | |
117 | 58749.952 | 58250.256 | 58500.216 | 1 | -54.112 | -10.824132 | 233 | NaN | 510.844 | False | 431678184 | 48 | 58446.104 | 13 | |
119 | 59749.900 | 59249.534 | 59499.380 | 1 | -53.064 | -10.619342 | 237 | NaN | 537.016 | False | 432678396 | 48 | 59446.316 | 24 | |
120 | 60250.120 | 59749.900 | 60000.420 | 1 | -56.652 | -11.306882 | 239 | NaN | 497.452 | False | 433175848 | 48 | 59943.768 | 27 | |
121 | 60749.704 | 60250.120 | 60499.820 | 1 | -49.852 | -9.982379 | 241 | NaN | 506.200 | False | 433682048 | 48 | 60449.968 | 27 | |
122 | 61249.996 | 60749.704 | 60999.588 | 1 | -50.824 | -10.169519 | 243 | NaN | 498.796 | False | 434180844 | 48 | 60948.764 | 27 | |
123 | 61750.326 | 61249.996 | 61500.404 | 1 | -24.564 | -4.904795 | 245 | NaN | 527.076 | False | 434707920 | 48 | 61475.840 | 21 | |
124 | 62249.984 | 61750.326 | 62000.248 | 1 | -11.928 | -2.386345 | 247 | NaN | 512.480 | False | 435220400 | 48 | 61988.320 | 21 | |
125 | 62749.646 | 62249.984 | 62499.720 | 1 | -16.116 | -3.226607 | 249 | NaN | 495.284 | False | 435715684 | 48 | 62483.604 | 20 | |
128 | 64249.856 | 63750.262 | 64000.192 | 1 | -19.924 | -3.985916 | 253 | NaN | NaN | False | 437212348 | 48 | 63980.268 | 23 | |
126 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | NaN | NaN | NaN | NaN | |
127 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | False | NaN | NaN | NaN | NaN |
12101 rows × 14 columns
task_frames.keys()
['T1_SMS_5', 'T1_SMS_8', 'Ticks_ISO_T2_5', 'Ticks_ISO_T2_8', 'Ticks_Linear_5', 'Ticks_Linear_8', 'Ticks_Phase_5', 'Ticks_Phase_8', 'Jits_ISO_5', 'Jits_ISO_8', 'Jits_Linear_5', 'Jits_Linear_8', 'Jits_Phase_5', 'Jits_Phase_8']
# Goofing off
def list_summary(ls, head=5, tail=5):
lhead = list(ls[:head])
ltail = list(ls[-tail:])
return ' '.join(lhead + ['...'] + ltail)
def tabbed_dict(d):
maxlength = max([len(h) for h in d.keys()])
set_tabs = 1 + maxlength//8
outd = {}
for k, v in d.items():
add_tabs = set_tabs - len(k)//8
outk = k + '\t' * add_tabs
outd[outk] = v
return outd
for k, v in tabbed_dict(task_pids).items():
print(k + list_summary(v))
T1_SMS_5 011 012 015 016 017 ... 117 118 119 120 121 Jits_ISO_8 011 012 015 016 017 ... 117 118 119 120 121 Ticks_Linear_5 011 012 015 016 017 ... 117 118 119 120 121 Jits_Phase_8 011 012 015 016 017 ... 117 118 119 120 121 T1_SMS_8 011 012 015 016 017 ... 117 118 119 120 121 Jits_Linear_8 011 015 016 017 018 ... 117 118 119 120 121 Ticks_ISO_T2_5 011 012 015 016 017 ... 117 118 119 120 121 Ticks_Phase_8 011 012 015 016 017 ... 117 118 119 120 121 Ticks_ISO_T2_8 011 012 015 016 017 ... 117 118 119 120 121 Jits_Linear_5 011 013 015 016 017 ... 117 118 119 120 121 Jits_ISO_5 011 012 015 016 017 ... 117 118 119 120 121 Ticks_Phase_5 011 012 015 016 017 ... 117 118 119 120 121 Ticks_Linear_8 012 015 016 017 018 ... 117 118 119 120 121 Jits_Phase_5 011 012 015 016 017 ... 117 118 119 120 121
def tapxs(df):
return df.xs('tap', level='stamp')
def tapxsp(df, pid):
return (df.xs(pid, level='pid')
.xs('tap', level='stamp'))
def db_ptap(task, pid):
return (task_frames[task].xs(pid, level='pid')
.xs('tap', level='stamp'))
df = task_frames[t].xs('098') #.xs('tap', level='stamp')
df[-30:] #.sort().iloc[-30:]
beat_end | beat_start | beat_target | channel | dev | dev_perc | i | interval | ints | is_outlier | micros | pitch | task_ms | velocity | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
beat | stamp | ||||||||||||||
155 | tap | 124400.738 | 123601.010 | 124000.852 | 1 | -38.868 | -4.860420 | 2 | NaN | 781.020 | False | 1591273288 | 48 | 123961.984 | 49 |
target | 124400.738 | 123601.010 | 124000.852 | NaN | 0.000 | 0.000000 | 155 | 799.684 | 799.684 | NaN | 1591312156 | NaN | 124000.852 | NaN | |
156 | tap | 125200.502 | 124400.738 | 124800.624 | 1 | -3.224 | -0.403115 | 1 | NaN | 835.416 | False | 1592108704 | 48 | 124797.400 | 46 |
target | 125200.502 | 124400.738 | 124800.624 | NaN | 0.000 | 0.000000 | 156 | 799.772 | 799.772 | NaN | 1592111928 | NaN | 124800.624 | NaN | |
157 | tap | 126000.748 | 125200.502 | 125600.380 | 1 | -42.484 | -5.312120 | 0 | NaN | 760.496 | False | 1592869200 | 48 | 125557.896 | 47 |
target | 126000.748 | 125200.502 | 125600.380 | NaN | 0.000 | 0.000000 | 157 | 799.756 | 799.756 | NaN | 1592911684 | NaN | 125600.380 | NaN | |
158 | tap | 126800.994 | 126000.748 | 126401.116 | 1 | -79.796 | -9.965332 | 0 | NaN | 763.424 | False | 1593632624 | 48 | 126321.320 | 50 |
target | 126800.994 | 126000.748 | 126401.116 | NaN | 0.000 | 0.000000 | 158 | 800.736 | 800.736 | NaN | 1593712420 | NaN | 126401.116 | NaN | |
159 | tap | 127600.762 | 126800.994 | 127200.872 | 1 | -31.728 | -3.967210 | 1 | NaN | 847.824 | False | 1594480448 | 48 | 127169.144 | 45 |
target | 127600.762 | 126800.994 | 127200.872 | NaN | 0.000 | 0.000000 | 159 | 799.756 | 799.756 | NaN | 1594512176 | NaN | 127200.872 | NaN | |
160 | tap | 128400.506 | 127600.762 | 128000.652 | 1 | -8.820 | -1.102803 | 0 | NaN | 822.688 | False | 1595303136 | 48 | 127991.832 | 42 |
target | 128400.506 | 127600.762 | 128000.652 | NaN | 0.000 | 0.000000 | 160 | 799.780 | 799.780 | NaN | 1595311956 | NaN | 128000.652 | NaN | |
161 | tap | 129200.768 | 128400.506 | 128800.360 | 1 | -24.796 | -3.100632 | 1 | NaN | 783.732 | False | 1596086868 | 48 | 128775.564 | 52 |
target | 129200.768 | 128400.506 | 128800.360 | NaN | 0.000 | 0.000000 | 161 | 799.708 | 799.708 | NaN | 1596111664 | NaN | 128800.360 | NaN | |
162 | tap | 130001.016 | 129200.768 | 129601.176 | 1 | -76.660 | -9.572736 | 0 | NaN | 748.952 | False | 1596835820 | 48 | 129524.516 | 44 |
target | 130001.016 | 129200.768 | 129601.176 | NaN | 0.000 | 0.000000 | 162 | 800.816 | 800.816 | NaN | 1596912480 | NaN | 129601.176 | NaN | |
163 | tap | 130800.734 | 130001.016 | 130400.856 | 1 | -22.076 | -2.760604 | 2 | NaN | 854.264 | False | 1597690084 | 48 | 130378.780 | 47 |
target | 130800.734 | 130001.016 | 130400.856 | NaN | 0.000 | 0.000000 | 163 | 799.680 | 799.680 | NaN | 1597712160 | NaN | 130400.856 | NaN | |
164 | tap | 131600.492 | 130800.734 | 131200.612 | 1 | -89.004 | -11.128894 | 0 | NaN | 732.828 | False | 1598422912 | 48 | 131111.608 | 47 |
target | 131600.492 | 130800.734 | 131200.612 | NaN | 0.000 | 0.000000 | 164 | 799.756 | 799.756 | NaN | 1598511916 | NaN | 131200.612 | NaN | |
165 | tap | 132400.738 | 131600.492 | 132000.372 | 1 | -70.544 | -8.820646 | 0 | NaN | 818.220 | False | 1599241132 | 48 | 131929.828 | 53 |
target | 132400.738 | 131600.492 | 132000.372 | NaN | 0.000 | 0.000000 | 165 | 799.760 | 799.760 | NaN | 1599311676 | NaN | 132000.372 | NaN | |
166 | tap | 133200.982 | 132400.738 | 132801.104 | 1 | -35.740 | -4.463416 | 1 | NaN | 835.536 | False | 1600076668 | 48 | 132765.364 | 47 |
target | 133200.982 | 132400.738 | 132801.104 | NaN | 0.000 | 0.000000 | 166 | 800.732 | 800.732 | NaN | 1600112408 | NaN | 132801.104 | NaN | |
167 | tap | 134000.738 | 133200.982 | 133600.860 | 1 | -63.992 | -8.001440 | 0 | NaN | 771.504 | False | 1600848172 | 48 | 133536.868 | 50 |
target | 134000.738 | 133200.982 | 133600.860 | NaN | 0.000 | 0.000000 | 167 | 799.756 | 799.756 | NaN | 1600912164 | NaN | 133600.860 | NaN | |
168 | tap | 134800.476 | 134000.738 | 134400.616 | 1 | -1.008 | -0.126038 | 2 | NaN | 862.740 | False | 1601710912 | 48 | 134399.608 | 42 |
target | 134800.476 | 134000.738 | 134400.616 | NaN | 0.000 | 0.000000 | 168 | 799.756 | 799.756 | NaN | 1601711920 | NaN | 134400.616 | NaN | |
169 | tap | 136000.056 | 134800.476 | 135200.336 | 1 | -25.640 | -3.206122 | 0 | NaN | 775.088 | False | 1602486000 | 48 | 135174.696 | 48 |
target | 136000.056 | 134800.476 | 135200.336 | NaN | 0.000 | 0.000000 | 169 | 799.720 | 799.720 | NaN | 1602511640 | NaN | 135200.336 | NaN |
import re
def col_find(df, regex):
cols = list(enumerate(df.columns))
matches = [#'%d. %s' %
(i, c)
for (i, c) in cols
#if filt in c
if re.findall(regex, c)
]
#print('\n'.join(matches))
return matches
filt = r"(^J)(.*)(d$)"
cf = col_find(dfo, filt)
import itertools
list(itertools.combinations(cf, 2))
[((49, 'Jits_ISO_5_dev_perc_std'), (51, 'Jits_ISO_5_dev_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (53, 'Jits_ISO_5_ints_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (55, 'Jits_ISO_8_dev_perc_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (57, 'Jits_ISO_8_dev_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (59, 'Jits_ISO_8_ints_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (61, 'Jits_Linear_5_dev_perc_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (63, 'Jits_Linear_5_dev_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (65, 'Jits_Linear_5_ints_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (69, 'Jits_Linear_8_dev_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (71, 'Jits_Linear_8_ints_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (75, 'Jits_Phase_5_dev_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (77, 'Jits_Phase_5_ints_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (81, 'Jits_Phase_8_dev_std')), ((49, 'Jits_ISO_5_dev_perc_std'), (83, 'Jits_Phase_8_ints_std')), ((51, 'Jits_ISO_5_dev_std'), (53, 'Jits_ISO_5_ints_std')), ((51, 'Jits_ISO_5_dev_std'), (55, 'Jits_ISO_8_dev_perc_std')), ((51, 'Jits_ISO_5_dev_std'), (57, 'Jits_ISO_8_dev_std')), ((51, 'Jits_ISO_5_dev_std'), (59, 'Jits_ISO_8_ints_std')), ((51, 'Jits_ISO_5_dev_std'), (61, 'Jits_Linear_5_dev_perc_std')), ((51, 'Jits_ISO_5_dev_std'), (63, 'Jits_Linear_5_dev_std')), ((51, 'Jits_ISO_5_dev_std'), (65, 'Jits_Linear_5_ints_std')), ((51, 'Jits_ISO_5_dev_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((51, 'Jits_ISO_5_dev_std'), (69, 'Jits_Linear_8_dev_std')), ((51, 'Jits_ISO_5_dev_std'), (71, 'Jits_Linear_8_ints_std')), ((51, 'Jits_ISO_5_dev_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((51, 'Jits_ISO_5_dev_std'), (75, 'Jits_Phase_5_dev_std')), ((51, 'Jits_ISO_5_dev_std'), (77, 'Jits_Phase_5_ints_std')), ((51, 'Jits_ISO_5_dev_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((51, 'Jits_ISO_5_dev_std'), (81, 'Jits_Phase_8_dev_std')), ((51, 'Jits_ISO_5_dev_std'), (83, 'Jits_Phase_8_ints_std')), ((53, 'Jits_ISO_5_ints_std'), (55, 'Jits_ISO_8_dev_perc_std')), ((53, 'Jits_ISO_5_ints_std'), (57, 'Jits_ISO_8_dev_std')), ((53, 'Jits_ISO_5_ints_std'), (59, 'Jits_ISO_8_ints_std')), ((53, 'Jits_ISO_5_ints_std'), (61, 'Jits_Linear_5_dev_perc_std')), ((53, 'Jits_ISO_5_ints_std'), (63, 'Jits_Linear_5_dev_std')), ((53, 'Jits_ISO_5_ints_std'), (65, 'Jits_Linear_5_ints_std')), ((53, 'Jits_ISO_5_ints_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((53, 'Jits_ISO_5_ints_std'), (69, 'Jits_Linear_8_dev_std')), ((53, 'Jits_ISO_5_ints_std'), (71, 'Jits_Linear_8_ints_std')), ((53, 'Jits_ISO_5_ints_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((53, 'Jits_ISO_5_ints_std'), (75, 'Jits_Phase_5_dev_std')), ((53, 'Jits_ISO_5_ints_std'), (77, 'Jits_Phase_5_ints_std')), ((53, 'Jits_ISO_5_ints_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((53, 'Jits_ISO_5_ints_std'), (81, 'Jits_Phase_8_dev_std')), ((53, 'Jits_ISO_5_ints_std'), (83, 'Jits_Phase_8_ints_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (57, 'Jits_ISO_8_dev_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (59, 'Jits_ISO_8_ints_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (61, 'Jits_Linear_5_dev_perc_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (63, 'Jits_Linear_5_dev_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (65, 'Jits_Linear_5_ints_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (69, 'Jits_Linear_8_dev_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (71, 'Jits_Linear_8_ints_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (75, 'Jits_Phase_5_dev_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (77, 'Jits_Phase_5_ints_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (81, 'Jits_Phase_8_dev_std')), ((55, 'Jits_ISO_8_dev_perc_std'), (83, 'Jits_Phase_8_ints_std')), ((57, 'Jits_ISO_8_dev_std'), (59, 'Jits_ISO_8_ints_std')), ((57, 'Jits_ISO_8_dev_std'), (61, 'Jits_Linear_5_dev_perc_std')), ((57, 'Jits_ISO_8_dev_std'), (63, 'Jits_Linear_5_dev_std')), ((57, 'Jits_ISO_8_dev_std'), (65, 'Jits_Linear_5_ints_std')), ((57, 'Jits_ISO_8_dev_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((57, 'Jits_ISO_8_dev_std'), (69, 'Jits_Linear_8_dev_std')), ((57, 'Jits_ISO_8_dev_std'), (71, 'Jits_Linear_8_ints_std')), ((57, 'Jits_ISO_8_dev_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((57, 'Jits_ISO_8_dev_std'), (75, 'Jits_Phase_5_dev_std')), ((57, 'Jits_ISO_8_dev_std'), (77, 'Jits_Phase_5_ints_std')), ((57, 'Jits_ISO_8_dev_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((57, 'Jits_ISO_8_dev_std'), (81, 'Jits_Phase_8_dev_std')), ((57, 'Jits_ISO_8_dev_std'), (83, 'Jits_Phase_8_ints_std')), ((59, 'Jits_ISO_8_ints_std'), (61, 'Jits_Linear_5_dev_perc_std')), ((59, 'Jits_ISO_8_ints_std'), (63, 'Jits_Linear_5_dev_std')), ((59, 'Jits_ISO_8_ints_std'), (65, 'Jits_Linear_5_ints_std')), ((59, 'Jits_ISO_8_ints_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((59, 'Jits_ISO_8_ints_std'), (69, 'Jits_Linear_8_dev_std')), ((59, 'Jits_ISO_8_ints_std'), (71, 'Jits_Linear_8_ints_std')), ((59, 'Jits_ISO_8_ints_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((59, 'Jits_ISO_8_ints_std'), (75, 'Jits_Phase_5_dev_std')), ((59, 'Jits_ISO_8_ints_std'), (77, 'Jits_Phase_5_ints_std')), ((59, 'Jits_ISO_8_ints_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((59, 'Jits_ISO_8_ints_std'), (81, 'Jits_Phase_8_dev_std')), ((59, 'Jits_ISO_8_ints_std'), (83, 'Jits_Phase_8_ints_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (63, 'Jits_Linear_5_dev_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (65, 'Jits_Linear_5_ints_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (69, 'Jits_Linear_8_dev_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (71, 'Jits_Linear_8_ints_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (75, 'Jits_Phase_5_dev_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (77, 'Jits_Phase_5_ints_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (81, 'Jits_Phase_8_dev_std')), ((61, 'Jits_Linear_5_dev_perc_std'), (83, 'Jits_Phase_8_ints_std')), ((63, 'Jits_Linear_5_dev_std'), (65, 'Jits_Linear_5_ints_std')), ((63, 'Jits_Linear_5_dev_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((63, 'Jits_Linear_5_dev_std'), (69, 'Jits_Linear_8_dev_std')), ((63, 'Jits_Linear_5_dev_std'), (71, 'Jits_Linear_8_ints_std')), ((63, 'Jits_Linear_5_dev_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((63, 'Jits_Linear_5_dev_std'), (75, 'Jits_Phase_5_dev_std')), ((63, 'Jits_Linear_5_dev_std'), (77, 'Jits_Phase_5_ints_std')), ((63, 'Jits_Linear_5_dev_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((63, 'Jits_Linear_5_dev_std'), (81, 'Jits_Phase_8_dev_std')), ((63, 'Jits_Linear_5_dev_std'), (83, 'Jits_Phase_8_ints_std')), ((65, 'Jits_Linear_5_ints_std'), (67, 'Jits_Linear_8_dev_perc_std')), ((65, 'Jits_Linear_5_ints_std'), (69, 'Jits_Linear_8_dev_std')), ((65, 'Jits_Linear_5_ints_std'), (71, 'Jits_Linear_8_ints_std')), ((65, 'Jits_Linear_5_ints_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((65, 'Jits_Linear_5_ints_std'), (75, 'Jits_Phase_5_dev_std')), ((65, 'Jits_Linear_5_ints_std'), (77, 'Jits_Phase_5_ints_std')), ((65, 'Jits_Linear_5_ints_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((65, 'Jits_Linear_5_ints_std'), (81, 'Jits_Phase_8_dev_std')), ((65, 'Jits_Linear_5_ints_std'), (83, 'Jits_Phase_8_ints_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (69, 'Jits_Linear_8_dev_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (71, 'Jits_Linear_8_ints_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (75, 'Jits_Phase_5_dev_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (77, 'Jits_Phase_5_ints_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (81, 'Jits_Phase_8_dev_std')), ((67, 'Jits_Linear_8_dev_perc_std'), (83, 'Jits_Phase_8_ints_std')), ((69, 'Jits_Linear_8_dev_std'), (71, 'Jits_Linear_8_ints_std')), ((69, 'Jits_Linear_8_dev_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((69, 'Jits_Linear_8_dev_std'), (75, 'Jits_Phase_5_dev_std')), ((69, 'Jits_Linear_8_dev_std'), (77, 'Jits_Phase_5_ints_std')), ((69, 'Jits_Linear_8_dev_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((69, 'Jits_Linear_8_dev_std'), (81, 'Jits_Phase_8_dev_std')), ((69, 'Jits_Linear_8_dev_std'), (83, 'Jits_Phase_8_ints_std')), ((71, 'Jits_Linear_8_ints_std'), (73, 'Jits_Phase_5_dev_perc_std')), ((71, 'Jits_Linear_8_ints_std'), (75, 'Jits_Phase_5_dev_std')), ((71, 'Jits_Linear_8_ints_std'), (77, 'Jits_Phase_5_ints_std')), ((71, 'Jits_Linear_8_ints_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((71, 'Jits_Linear_8_ints_std'), (81, 'Jits_Phase_8_dev_std')), ((71, 'Jits_Linear_8_ints_std'), (83, 'Jits_Phase_8_ints_std')), ((73, 'Jits_Phase_5_dev_perc_std'), (75, 'Jits_Phase_5_dev_std')), ((73, 'Jits_Phase_5_dev_perc_std'), (77, 'Jits_Phase_5_ints_std')), ((73, 'Jits_Phase_5_dev_perc_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((73, 'Jits_Phase_5_dev_perc_std'), (81, 'Jits_Phase_8_dev_std')), ((73, 'Jits_Phase_5_dev_perc_std'), (83, 'Jits_Phase_8_ints_std')), ((75, 'Jits_Phase_5_dev_std'), (77, 'Jits_Phase_5_ints_std')), ((75, 'Jits_Phase_5_dev_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((75, 'Jits_Phase_5_dev_std'), (81, 'Jits_Phase_8_dev_std')), ((75, 'Jits_Phase_5_dev_std'), (83, 'Jits_Phase_8_ints_std')), ((77, 'Jits_Phase_5_ints_std'), (79, 'Jits_Phase_8_dev_perc_std')), ((77, 'Jits_Phase_5_ints_std'), (81, 'Jits_Phase_8_dev_std')), ((77, 'Jits_Phase_5_ints_std'), (83, 'Jits_Phase_8_ints_std')), ((79, 'Jits_Phase_8_dev_perc_std'), (81, 'Jits_Phase_8_dev_std')), ((79, 'Jits_Phase_8_dev_perc_std'), (83, 'Jits_Phase_8_ints_std')), ((81, 'Jits_Phase_8_dev_std'), (83, 'Jits_Phase_8_ints_std'))]
#df_X = dfo.T.iloc[49] #'Jits_ISO_5_dev_perc_std'
#df_Y = dfo.T.iloc[55] #'Jits_ISO_8_dev_perc_std'
def inverse_scatter(dfo, ilocx, ilocy, *args, **kwargs):
inversed = lambda df: 1.0/df
df_temp = pd.concat([inversed(dfo.T.iloc[ilocx]),
inversed(dfo.T.iloc[ilocy])],
axis=1)
df_temp.plot(x=0,y=1, kind='scatter', **kwargs)
plt.show()
print('r = %f' % df_temp.corr().iloc[0,1])
def inverse_correl(dfo, ilocx, ilocy, **kwargs):
inversed = lambda df: 1.0/df
df_temp = pd.concat([inversed(dfo.T.iloc[ilocx]),
inversed(dfo.T.iloc[ilocy])],
axis=1)
print('r = %f' % df_temp.corr().iloc[0,1])
inverse_scatter(dfo, 73, 79, figsize=(5,5))
r = 0.620315