def datarows(cls):
# make segment dictionary
segs = {}
for level_num, segment_list in cls.project.segmenttree.getLeveledSegments().items():
for segment in segment_list:
segpath = cls.segpathsep.join([u'{}'.format(sp) for sp in segment.path])
segs[segment.id] = (level_num,segpath,segment.name)
# Iterate through the pen data array, yielding each pen sample
# as a list.
getSeriesForSample = cls.project.getSeriesForSample
getPressedRunForSample = cls.project.getPressedRunForSample
getStrokeForSample = cls.project.getStrokeForSample
for i, pensample in enumerate(cls.project.pendata):
r = [i,]
r.extend(pensample.tolist())
r.extend((convertSampleStateValue(pensample['state']),
getSeriesForSample(i),
getPressedRunForSample(i),
getStrokeForSample(i),
segs.get(pensample['segment_id'],('','',''))[0],
segs.get(pensample['segment_id'],('','',''))[1],
segs.get(pensample['segment_id'],('','',''))[2]
))
yield r
def datarows(cls):
# make segment dictionary
segs = {}
for level_num, segment_list in cls.project.segmenttree.getLeveledSegments(
).items():
for segment in segment_list:
segpath = cls.segpathsep.join(
[u'{}'.format(sp) for sp in segment.path])
segs[segment.id] = (level_num, segpath, segment.name)
# Iterate through the pen data array, yielding each pen sample
# as a list.
getSeriesForSample = cls.project.getSeriesForSample
getPressedRunForSample = cls.project.getPressedRunForSample
getStrokeForSample = cls.project.getStrokeForSample
getStrokeTypeForSample = cls.project.getStrokeTypeForSample
for i, pensample in enumerate(cls.project.pendata):
r = [
i,
]
r.extend(pensample.tolist())
r.extend((convertSampleStateValue(pensample['state']),
getSeriesForSample(i), getPressedRunForSample(i),
getStrokeForSample(i), getStrokeTypeForSample(i),
segs.get(pensample['segment_id'], ('', '', ''))[0],
segs.get(pensample['segment_id'], ('', '', ''))[1],
segs.get(pensample['segment_id'], ('', '', ''))[2]))
yield r
def datarows(cls):
pendata = cls.project.pendata
ss = cls.project.segmenttree
sfile = ss.name
lvls = range(1, ss.getLevelCount() + 1)
segs_by_lvl = ss.getLeveledSegments()
catname = ss.name
cvcolcount = 0
if len(cls.project.trial_cond_vars):
cvcolcount = len(cls.project.trial_cond_vars.dtype.names)
for i in xrange(pendata.shape[0]):
dp = pendata[i]
rowdata = [
sfile, i, dp['time'], dp['x'], dp['y'], dp['pressure'],
convertSampleStateValue(dp['state']), catname
]
# Go through segment levels finding matching seg at each level for
# current data point. Use '' for levels where no seg matched
# data point time, otherwise use segment name.
for l in lvls:
dpsegname = cls.missingval
for seg in segs_by_lvl[l]:
if seg.contains(time=dp['time']):
dpsegname = seg.name
break
# If no seg was found at this level, none will exist at lower
# levels for this point, so break the loop. Remaining col vals
# will automatically be filled in by ReportExporter
if dpsegname == cls.missingval:
break
rowdata.append(dpsegname)
if cvcolcount:
tcv = cls.project.getTrialConditionsForSample(i)
if tcv is not None:
rowdata = tcv + rowdata
else:
rowdata = [cls.missingval
for i in range(cvcolcount)] + rowdata
yield rowdata
def datarows(cls):
# Iterate through the pen data array, yielding each pen sample
# as a list.
getSeriesForSample = cls.project.getSeriesForSample
getPressedRunForSample = cls.project.getPressedRunForSample
getStrokeForSample = cls.project.getStrokeForSample
for i, pensample in enumerate(cls.project.pendata):
r = [i,]
r.extend(pensample.tolist())
r.extend((convertSampleStateValue(pensample['state']),
getSeriesForSample(i),
getPressedRunForSample(i),
getStrokeForSample(i)
))
yield r
def datarows(cls):
pendata = cls.project.pendata
ss = cls.project.segmenttree
sfile=ss.name
lvls = range(1,ss.getLevelCount()+1)
segs_by_lvl=ss.getLeveledSegments()
catname = ss.name
cvcolcount=0
if len(cls.project.trial_cond_vars):
cvcolcount=len(cls.project.trial_cond_vars.dtype.names)
for i in xrange(pendata.shape[0]):
dp=pendata[i]
rowdata = [sfile,i,dp['time'],dp['x'],dp['y'],dp['pressure'],
convertSampleStateValue(dp['state']), catname]
# Go through segment levels finding matching seg at each level for
# current data point. Use '' for levels where no seg matched
# data point time, otherwise use segment name.
for l in lvls:
dpsegname=cls.missingval
for seg in segs_by_lvl[l]:
if seg.contains(time=dp['time']):
dpsegname=seg.name
break
# If no seg was found at this level, none will exist at lower
# levels for this point, so break the loop. Remaining col vals
# will automatically be filled in by ReportExporter
if dpsegname == cls.missingval:
break
rowdata.append(dpsegname)
if cvcolcount:
tcv=cls.project.getTrialConditionsForSample(i)
if tcv is not None:
rowdata.extend(list(tcv))
else:
rowdata.extend([cls.missingval for i in range(cvcolcount)])
yield rowdata
def datarows(cls):
# Iterate through the pen data array, yielding each pen sample
# as a list.
getSeriesForSample = cls.project.getSeriesForSample
getPressedRunForSample = cls.project.getPressedRunForSample
getStrokeForSample = cls.project.getStrokeForSample
getStrokeTypeForSample = cls.project.getStrokeTypeForSample
for i, pensample in enumerate(cls.project.pendata):
r = [
i,
]
r.extend(pensample.tolist())
r.extend((convertSampleStateValue(pensample['state']),
getSeriesForSample(i), getPressedRunForSample(i),
getStrokeForSample(i), getStrokeTypeForSample(i)))
yield r
def datarows(cls):
# make segment dictionary
segs = {}
for level_num, segment_list in cls.project.segmenttree.getLeveledSegments(
).items():
for segment in segment_list:
segpath = cls.segpathsep.join(
[u'{}'.format(sp) for sp in segment.path])
segs[segment.id] = (level_num, segpath, segment.name)
# Iterate through the pen data array, yielding each pen sample
# as a list.
getSeriesForSample = cls.project.getSeriesForSample
getPressedRunForSample = cls.project.getPressedRunForSample
getStrokeForSample = cls.project.getStrokeForSample
getStrokeTypeForSample = cls.project.getStrokeTypeForSample
vcparser_dat = cls.project.vc_parser_dat
prev_series_id = 0
series_sample_index = -1
for i, pensample in enumerate(cls.project.pendata):
r = [
i,
]
r.extend(pensample.tolist())
series_id = getSeriesForSample(i)
series_parser_dat = None
if series_id > 0 and vcparser_dat:
series_parser_dat = vcparser_dat.get(series_id - 1)
if series_id == 0:
prev_series_id = 0
series_sample_index = -1
if series_id != prev_series_id:
series_sample_index = 0
prev_series_id = series_id
else:
series_sample_index += 1
x_fc10 = cls.missingval
y_fc10 = cls.missingval
x_fc5 = cls.missingval
y_fc5 = cls.missingval
vxy_fc10 = cls.missingval
vxy_fc5 = cls.missingval
vxy_fc10_extrema = cls.missingval
vxy_fc5_extrema = cls.missingval
vxy_fc10_minima_dalpha = cls.missingval
vxy_fc10_minima_pre = cls.missingval
vxy_fc10_minima_post = cls.missingval
if series_parser_dat:
x_fc10 = series_parser_dat['x.fc10'][series_sample_index]
y_fc10 = series_parser_dat['y.fc10'][series_sample_index]
x_fc5 = series_parser_dat['x.fc5'][series_sample_index]
y_fc5 = series_parser_dat['y.fc5'][series_sample_index]
vxy_fc10 = series_parser_dat['vxy.fc10'][series_sample_index]
vxy_fc5 = series_parser_dat['vxy.fc5'][series_sample_index]
vxy_fc10_extrema = series_parser_dat['vxy.fc10.extrema'][
series_sample_index]
vxy_fc5_extrema = series_parser_dat['vxy.fc5.extrema'][
series_sample_index]
vxy_fc10_minima_dalpha = series_parser_dat[
'vxy.fc10.minima.dalpha'][series_sample_index]
vxy_fc10_minima_pre = series_parser_dat['vxy.fc10.minima.pre'][
series_sample_index]
vxy_fc10_minima_post = series_parser_dat[
'vxy.fc10.minima.post'][series_sample_index]
r.extend((convertSampleStateValue(pensample['state']), series_id,
getPressedRunForSample(i), x_fc10, y_fc10, x_fc5, y_fc5,
vxy_fc10, vxy_fc5, vxy_fc10_extrema, vxy_fc5_extrema,
vxy_fc10_minima_dalpha, series_sample_index,
vxy_fc10_minima_pre, vxy_fc10_minima_post,
getStrokeForSample(i), getStrokeTypeForSample(i),
segs.get(pensample['segment_id'], ('', '', ''))[0],
segs.get(pensample['segment_id'], ('', '', ''))[1],
segs.get(pensample['segment_id'], ('', '', ''))[2]))
yield r