###### Read participants
nbprocess = cpu_count()
ps = read_participants_Basic_multiprocessing(nbprocess, user_list = ul,pids = uids, log_time_offsets = alogoffset, datadir=params.EYELOGDATAFOLDER,
prune_length = None,
aoifile = "./sampledata/general.aoi",
# aoifile = "./sampledata/Dynamic_1.aoi",
require_valid_segs = False, auto_partition_low_quality_segments = True,
rpsfile = "./sampledata/all_rest_pupil_sizes.tsv")
print
######
if params.DEBUG or params.VERBOSE == "VERBOSE":
#explore_validation_threshold_segments(ps, auto_partition_low_quality_segments = False)
output_Validity_info_Segments(ps, auto_partition_low_quality_segments_flag = False, validity_method = 3)
output_percent_discarded(ps,'./outputfolder/disc_multiprocessing.csv')
output_Validity_info_Segments(ps, auto_partition_low_quality_segments_flag = False, validity_method = 2, threshold_gaps_list = [100, 200, 250, 300],output_file = "./outputfolder/Seg_val_multiprocessing.csv")
output_Validity_info_Participants(ps, include_restored_samples =True, auto_partition_low_quality_segments_flag = False)
##### WRITE features to file
print
aoi_feat_names = (map(lambda x:x, params.aoigeneralfeat))
print "Exporting features:\n--General:", params.featurelist, "\n--AOI:", aoi_feat_names, "\n--Sequences:", params.aoisequencefeat
write_features_tsv(ps, './outputfolder/sample_features_multiprocessing.tsv',featurelist = params.featurelist, aoifeaturelist=aoi_feat_names, id_prefix = False)
##### WRITE AOI sequences to file
write_features_tsv(ps, './outputfolder/sample_sequences_multiprocessing.tsv',featurelist = params.aoisequencefeat, aoifeaturelist=aoi_feat_names, id_prefix = False)
#### Export pupil dilations for each scene to a separate file
#print "--pupil dilation trends"
# Read participants
ps = read_participants_Basic(user_list=ul,
pids=uids,
log_time_offsets=alogoffset,
datadir=params.EYELOGDATAFOLDER,
prune_length=None,
aoifile="./sampledata/general.aoi",
require_valid_segs=False,
auto_partition_low_quality_segments=False)
if params.DEBUG or params.VERBOSE == "VERBOSE":
# explore_validation_threshold_segments(ps, auto_partition_low_quality_segments = False)
output_Validity_info_Segments(
ps, auto_partition_low_quality_segments_flag=False, validity_method=3)
output_percent_discarded(ps, './outputfolder/smi_disc.csv')
output_Validity_info_Segments(
ps,
auto_partition_low_quality_segments_flag=False,
validity_method=2,
threshold_gaps_list=[100, 200, 250, 300],
output_file="./outputfolder/tobiiv3_Seg_val.csv")
output_Validity_info_Participants(
ps,
include_restored_samples=True,
auto_partition_low_quality_segments_flag=False)
# WRITE features to file
#if params.VERBOSE != "QUIET":#
# print#
# print "Exporting:\n--General:", params.featurelist
@author: Mike Wu (creator), Sebastien Lalle
Institution: The University of British Columbia.
"""
from BasicParticipant import *
from EMDAT_core.Participant import export_features_all, write_features_tsv
from EMDAT_core.ValidityProcessing import output_Validity_info_Segments, output_percent_discarded, output_Validity_info_Participants
ul = [67]
uids = ul
alogoffset = [0]
# Read participants
ps = read_participants_Basic(user_list=ul, pids=uids, log_time_offsets=alogoffset, datadir=params.EYELOGDATAFOLDER,
prune_length=None, require_valid_segs=False, auto_partition_low_quality_segments=True)
if params.DEBUG or params.VERBOSE == "VERBOSE":
# explore_validation_threshold_segments(ps, auto_partition_low_quality_segments = False)
output_Validity_info_Segments(ps, auto_partition_low_quality_segments_flag=False, validity_method=3)
output_percent_discarded(ps, './outputfolder/smi_disc.csv')
output_Validity_info_Segments(ps, auto_partition_low_quality_segments_flag=False, validity_method=2,
threshold_gaps_list=[100, 200, 250, 300], output_file="./outputfolder/smi_Seg_val.csv")
output_Validity_info_Participants(ps, include_restored_samples=True, auto_partition_low_quality_segments_flag=False)
# WRITE features to file
if params.VERBOSE != "QUIET":
print "Exporting features:\n--General:", params.featurelist
write_features_tsv(ps, './outputfolder/smi_sample_features.tsv', featurelist=params.featurelist, id_prefix=False)
prune_length=None,
aoifile="./sampledata/general.aoi",
# aoifile = "./sampledata/Dynamic_1.aoi",
require_valid_segs=False,
auto_partition_low_quality_segments=True,
rpsfile="./sampledata/all_rest_pupil_sizes.tsv")
print
######
if params.DEBUG or params.VERBOSE == "VERBOSE":
#explore_validation_threshold_segments(ps, auto_partition_low_quality_segments = False)
output_Validity_info_Segments(
ps,
auto_partition_low_quality_segments_flag=False,
validity_method=3)
output_percent_discarded(ps, './outputfolder/disc_multiprocessing.csv')
output_Validity_info_Segments(
ps,
auto_partition_low_quality_segments_flag=False,
validity_method=2,
threshold_gaps_list=[100, 200, 250, 300],
output_file="./outputfolder/Seg_val_multiprocessing.csv")
output_Validity_info_Participants(
ps,
include_restored_samples=True,
auto_partition_low_quality_segments_flag=False)
##### WRITE features to file
print
aoi_feat_names = (map(lambda x: x, params.aoigeneralfeat))
print "Exporting features:\n--General:", params.featurelist, "\n--AOI:", aoi_feat_names, "\n--Sequences:", params.aoisequencefeat
