def test_inversion(self): self.failUnlessEqual((invert_intervalframe(self.tier1[0:4], 0, 600) == \ self.inversion).all().all(), True) #self.failUnlessEqual(invert_intervalframe(self.tier1, 0, # self.tier1['end_time'].iloc[-1])\ # ['start_time'].ix[len(invert_intervalframe\ # (self.tier1, 0, self.tier1['end_time'].\ # iloc[-1]))-1], 1306.064) self.failUnlessEqual((self.inv1_str == self.inverted1).all().all(), True) self.failUnlessEqual((self.inv_def_pd == self.inv_def_str).all().\ all(), True) #self.failUnlessEqual((invert_intervalframe(self.tier1[0:10], 0) == \ # self.invert_tier1).all().all(), True) # self.failUnlessEqual(str(type(invert_intervalframe(self.empty))), # '<type \'NoneType\'>') self.failUnlessEqual(str(type(invert_intervalframe(pd.DataFrame()))), '<type \'NoneType\'>')
def setUp(self): self.dict1 = open_intervalframe_from_textgrid('data/r1-20120704-cam1-' 'head-sk' '.TextGrid', 'utf-8') self.dict2 = open_intervalframe_from_textgrid('data/r1-20120704-cam1-' 'head-zm.TextGrid', 'utf-8') self.dict3 = open_intervalframe_from_textgrid('data/mytextgrid' '.TextGrid', 'utf-8') self.dict4 = dict() self.dict4['speaker A'] = self.dict3['speaker A'][0:4].copy(deep=True) self.dict4['speaker B'] = self.dict3['speaker B'][0:4].copy(deep=True) shift_tiers(self.dict4, 1) self.tier1 = self.dict1['HeadSK'] self.tier2 = self.dict2['HeadZM'] self.stream = open_streamframe_from_xiofile('data/fseeksmaller.xio.gz', "lab-labtop/irioKinect 2", window_size=5, with_fields=None, without_fields=None, discard_duplicates=True, start_time=0, end_time=500, timestamp_offset=None) self.ifr = create_intervalframe_from_streamframe(self.stream, 'soundAngle', lambda x: True \ if x <- 0.7323895 \ else False, 10) self.iframe = open_intervalframe_from_textgrid('data/mytextgrid.Text' 'Grid', 'utf-8') self.speaker_a = self.iframe['speaker A'] self.speaker_b = self.iframe['speaker B'] self.overlap_ab_shouldbe = [{'end_time': 3.47134775993, 'start_time': 3.4540353413, 'text': u'exactly and at ' 'the very end of the corridor is just ' 'the bathroom/I see yes'}, {'end_time': 3.8781895978200001, 'start_' 'time': 3.7223778301200001, 'text': u'exactly and at the very end of ' 'the corridor is just the bathroom/yes'}, {'end_time': 7.3163672878900003, 'start_time': 6.9095254500000003, 'text': u'that you quasi such have so ' 'zack zack zack/yes'}, {'end_time': 7.5933659860300002, 'start_time': 7.4029293810599999, 'text': u'that you quasi such have so ' 'zack zack zack/yes'}, {'end_time': 11.168380433799999, 'start_time': 10.8740693171, 'text': u'know you ?/yes I think I'}, {'end_time': 11.644471946299999, 'start_time': 11.2895673643, 'text': u'if this the corridor is here a ' 'room there a room there a room ' 'there and above/yes I think I'}, {'end_time': 14.892213031400001, 'start_time': 13.1073713208, 'text': u'if this the corridor is here a ' 'room there a room there a room there ' 'and above/there a room there there and ' 'then there yes'}, {'end_time': 15.558741148799999, 'start_time': 15.376960753100001, 'text': u'that is perfect/yes'}, {'end_time': 17.679512431399999, 'start_time': 17.4544509891, 'text': u'yes/sure'}, {'end_time': 27.8435647606, 'start_time': 27.5492536438, 'text': u'twentyfive/so I would already ' 'gladly a large room have'}] self.right_overlap_ab = pd.DataFrame(self.overlap_ab_shouldbe, columns=['start_time', 'end_time', 'text']) self.union_dict = [{'end_time': 2.5191647350899999, 'start_time': 0.0, 'text': 'union'}, {'end_time': 6.1304666115000002, 'start_time': \ 2.80481964254, 'text': 'union'}, {'end_time': 9.5669817102499994, 'start_time': 6.7796823102500001, 'text': 'union'}, {'end_time': 15.0393685898, 'start_time': \ 10.7528823866, 'text': 'union'}, {'end_time': 16.1646758009, 'start_time': 15.281742450599999, 'text': 'union'}, {'end_time': 17.3159516401, 'start_time': 17.064921569900001, 'text': 'union'}, {'end_time': 17.714137268599998, 'start_time': 17.4457947798, 'text': 'union'}, {'end_time': 19.393441876099999, 'start_time': 17.826667989699999, 'text': 'union'}] self.union_mustbe = pd.DataFrame(self.union_dict, columns=['start_' 'time', 'end_time', 'text']) self.invert = [{'start_time': 0., 'end_time': 547.016, 'text': 'nod'}, {'start_time': 547.464, 'end_time': 549.507, 'text': 'nod/turn-aw'}, {'start_time': 549.988, 'end_time': 556.808, 'text': 'turn-aw/nod-2'}, {'start_time': 557.404, 'end_time': 561.428, 'text': 'nod-2/nod-3'}, {'start_time': 563.345, 'end_time': 600.000, 'text': 'nod-3'}] self.inversion = pd.DataFrame(self.invert, columns=['start_' 'time', 'end_time', 'text']) self.tier1_inverted = [{'end_time': 547.01599999999996, 'start_time': 0.0, 'text': u'nod'}, {'end_time': 549.50699999999995, 'start_time': 547.46400000000006, 'text': u'nod/turn-aw'}, {'end_time': 556.80799999999999, 'start_time': 549.98800000000006, 'text': u'turn-aw/nod-2'}, {'end_time': 561.428, 'start_time': 557.404, 'text': u'nod-2/nod-3'}, {'end_time': 567.51099999999997, 'start_time': 563.34500000000003, 'text': u'nod-3/slide-right'}, {'end_time': 578.17100000000005, 'start_time': 568.02300000000002, 'text': u'slide-right/nod-4'}, {'end_time': 586.03599999999994, 'start_time': 579.53499999999997, 'text': u'nod-4/nod-3'}, {'end_time': 604.73699999999997, 'start_time': 587.10699999999997, 'text': u'nod-3/nod-4-turn-aw-tw'}, {'end_time': 609.11800000000005, 'start_time': 607.35699999999997, 'text': u'nod-4-turn-aw-tw/nod-4'}, {'end_time': 617.30899999999997, 'start_time': 611.16600000000005, 'text': u'nod-4/nod-5'}] self.invert_tier1 = pd.DataFrame(self.tier1_inverted, columns=['start_time', 'end_time', 'text']) self.empty = pd.DataFrame([]) self.withna = [{'start_time': 3, 'end_time': 6, 'text': 'hi'}, {'start_time': 7, 'end_time': 8.5}, {'start_time': 9, 'end_time': 13, 'text': 'bye'}] self.emptyint = pd.DataFrame(self.withna, columns=['start_time', 'end_time', 'text']) self.emptyshouldbe = [{'end_time': 3.0, 'start_time': 0, 'text': 'hi'}, {'end_time': 7.0, 'start_time': 6, 'text': 'hi/empty'}, {'end_time': 9.0, 'start_time': 8.5, 'text': 'empty/bye'}] self.emptyshouldbepd = pd.DataFrame(self.emptyshouldbe, columns= \ ['start_time', 'end_time', 'text']) self.no_overlap1 = [{'start_time': 3, 'end_time': 5, 'text': 'eins'}, {'start_time': 9, 'end_time': 15, 'text': 'zwei'}, {'start_time': 19, 'end_time': 21, 'text': 'drei'}] self.no_overlap2 = [{'start_time': 0, 'end_time': 2.5, 'text': 'vier'}, {'start_time': 6, 'end_time': 7, 'text': 'fuenf'}, {'start_time': 23, 'end_time': 27, 'text': 'sechs'}] self.pd_no_overlap1 = pd.DataFrame(self.no_overlap1, columns=['start_time', 'end_time', 'text']) self.pd_no_overlap2 = pd.DataFrame(self.no_overlap2, columns=['start_time', 'end_time', 'text']) self.convert = [{'start_time': 4, 'end_time': 5, 'text': 'eins'}, {'start_time': 9, 'end_time': 15, 'text': 'zwei'}, {'start_time': 19, 'end_time': 21, 'text': 'drei'}] self.convert_pd = pd.DataFrame(self.convert, columns=['start_time', 'end_time', 'text']) convert_times_of_tier(self.convert_pd, lambda y: int(1000 * y)) self.converted = [{'start_time': 4000, 'end_time': 5000, 'text': 'eins'}, {'start_time': 9000, 'end_time': 15000, 'text': 'zwei'}, {'start_time': 19000, 'end_time': 21000, 'text': 'drei'}] self.converted_pd = pd.DataFrame(self.converted, columns=['start_time', 'end_time', 'text']) self.tiers_shifted_a = [{'end_time': 4.47134775993, 'start_time': 3.80481964254, 'text': u'I see yes'}, {'end_time':4.8781895978200001, 'start_time': 4.7223778301200001, 'text': u'yes'}, {'end_time': 8.3163672878900003, 'start_time': 7.9095254500000003, 'text': u'yes'}, {'end_time': 8.5933659860300002, 'start_time': 8.4029293810599999, 'text': u'yes'}] self.tiers_shifted_pd = pd.DataFrame(self.tiers_shifted_a, columns=['start_time', 'end_time', 'text']) self.shift_dict = [{'start_time': 3, 'end_time': 5, 'text': 'eins'}, {'start_time': 9, 'end_time': 15, 'text': 'zwei'}, {'start_time': 19, 'end_time': 21, 'text': 'drei'}] self.shift = pd.DataFrame(self.shift_dict, columns=['start_time', 'end_time', 'text']) shift_tier(self.shift, 1) self.shifted_dict = [{'start_time': 4, 'end_time': 6, 'text': 'eins'}, {'start_time': 10, 'end_time': 16, 'text': 'zwei'}, {'start_time': 20, 'end_time': 22, 'text': 'drei'}] self.shifted = pd.DataFrame(self.shifted_dict, columns=['start_time', 'end_time', 'text']) for col in self.dict4['speaker A'].columns: self.dict4['speaker A'].loc[:, col] = \ self.dict4['speaker A'][col].map(lambda x: str(x)) for col in self.tiers_shifted_pd.columns: self.tiers_shifted_pd.loc[:, col] = \ self.tiers_shifted_pd[col].map(lambda x: str(x)) self.withPoint = open_intervalframe_from_textgrid('data/r1_12_15with' 'Point.TextGrid', 'utf-8') self.points = self.withPoint['P'][0:4].copy(deep=True) shift_tier(self.points, 10) for col in self.points.columns: self.points.loc[:, col] = self.points[col].map(lambda x: str(x)) self.points_shifted = [{'mark': 'A', 'time': 12.804819642542952}, {'mark': 'B', 'time': 13.454035341299345}, {'mark': 'A', 'time': 13.722377830118717}, {'mark': 'B', 'time': 16.779682310252952}] self.points_shifted_pd = pd.DataFrame(self.points_shifted, columns=['time', 'mark']) for col in self.points_shifted_pd.columns: self.points_shifted_pd.loc[:, col] = \ self.points_shifted_pd[col].map(lambda x: str(x)) self.labeljoin = open_intervalframe_from_textgrid('data/joinlabels' '.TextGrid', 'utf-8') self.streamdict1 = [{'value': u'nod_start'}, {'value': u'nod_end'}, {'value': u'turn-aw_start'}, {'value': u'turn-aw_end'}, {'value': u'nod-2_start'}, {'value': u'nod-2_end'}, {'value': u'nod-3_start'}, {'value': u'nod-3'}, {'value': u'nod-3_end'}] self.stream1 = pd.DataFrame(self.streamdict1, columns=['value']) self.stream1.index = [547.016, 547.464, 549.507, 549.988, 556.808, 557.404, 561.428, 562.428, 563.345] self.inv1 = [{'end_time': '547.016', 'start_time': '300.0', 'text': u'nod'}, {'end_time': '549.507', 'start_time': '547.464', 'text': u'nod/turn-aw'}, {'end_time': '556.808', 'start_time': '549.988', 'text': u'turn-aw/nod-2'}, {'end_time': '561.428', 'start_time': '557.404', 'text': u'nod-2/nod-3'}, {'end_time': '700.0', 'start_time': '563.345', 'text': u'nod-3'}] self.inverted1 = pd.DataFrame(self.inv1, columns=['start_time', 'end_time', 'text']) self.inv1_str = invert_intervalframe(self.tier1[0:4], 300, 700) for col in self.inv1_str: self.inv1_str.loc[:, col] = \ self.inv1_str[col].map(lambda x: str(x)) self.inv_default_conc = [{'end_time': '549.507', 'start_time': '547.464', 'text': u'nod'}, {'end_time': '556.808', 'start_time': '549.988', 'text': u'turn-aw+nod-2'}, {'end_time': '561.428', 'start_time': '557.404', 'text': u'nod-2'}] self.inv_def_pd = pd.DataFrame(self.inv_default_conc, columns=['start_time', 'end_time', 'text']) self.inv_def_str = invert_intervalframe(self.tier1[0:4], concat_delimiter='+') for col in self.inv_def_str: self.inv_def_str.loc[:, col] = \ self.inv_def_str[col].map(lambda x: str(x))
def setUp(self): self.dict1 = open_intervalframe_from_textgrid( 'data/r1-20120704-cam1-' 'head-sk' '.TextGrid', 'utf-8') self.dict2 = open_intervalframe_from_textgrid( 'data/r1-20120704-cam1-' 'head-zm.TextGrid', 'utf-8') self.dict3 = open_intervalframe_from_textgrid( 'data/mytextgrid' '.TextGrid', 'utf-8') self.dict4 = dict() self.dict4['speaker A'] = self.dict3['speaker A'][0:4].copy(deep=True) self.dict4['speaker B'] = self.dict3['speaker B'][0:4].copy(deep=True) shift_tiers(self.dict4, 1) self.tier1 = self.dict1['HeadSK'] self.tier2 = self.dict2['HeadZM'] self.stream = open_streamframe_from_xiofile('data/fseeksmaller.xio.gz', "lab-labtop/irioKinect 2", window_size=5, with_fields=None, without_fields=None, discard_duplicates=True, start_time=0, end_time=500, timestamp_offset=None) self.ifr = create_intervalframe_from_streamframe(self.stream, 'soundAngle', lambda x: True \ if x <- 0.7323895 \ else False, 10) self.iframe = open_intervalframe_from_textgrid( 'data/mytextgrid.Text' 'Grid', 'utf-8') self.speaker_a = self.iframe['speaker A'] self.speaker_b = self.iframe['speaker B'] self.overlap_ab_shouldbe = [{ 'end_time': 3.47134775993, 'start_time': 3.4540353413, 'text': u'exactly and at ' 'the very end of the corridor is just ' 'the bathroom/I see yes' }, { 'end_time': 3.8781895978200001, 'start_' 'time': 3.7223778301200001, 'text': u'exactly and at the very end of ' 'the corridor is just the bathroom/yes' }, { 'end_time': 7.3163672878900003, 'start_time': 6.9095254500000003, 'text': u'that you quasi such have so ' 'zack zack zack/yes' }, { 'end_time': 7.5933659860300002, 'start_time': 7.4029293810599999, 'text': u'that you quasi such have so ' 'zack zack zack/yes' }, { 'end_time': 11.168380433799999, 'start_time': 10.8740693171, 'text': u'know you ?/yes I think I' }, { 'end_time': 11.644471946299999, 'start_time': 11.2895673643, 'text': u'if this the corridor is here a ' 'room there a room there a room ' 'there and above/yes I think I' }, { 'end_time': 14.892213031400001, 'start_time': 13.1073713208, 'text': u'if this the corridor is here a ' 'room there a room there a room there ' 'and above/there a room there there and ' 'then there yes' }, { 'end_time': 15.558741148799999, 'start_time': 15.376960753100001, 'text': u'that is perfect/yes' }, { 'end_time': 17.679512431399999, 'start_time': 17.4544509891, 'text': u'yes/sure' }, { 'end_time': 27.8435647606, 'start_time': 27.5492536438, 'text': u'twentyfive/so I would already ' 'gladly a large room have' }] self.right_overlap_ab = pd.DataFrame( self.overlap_ab_shouldbe, columns=['start_time', 'end_time', 'text']) self.union_dict = [{'end_time': 2.5191647350899999, 'start_time': 0.0, 'text': 'union'}, {'end_time': 6.1304666115000002, 'start_time': \ 2.80481964254, 'text': 'union'}, {'end_time': 9.5669817102499994, 'start_time': 6.7796823102500001, 'text': 'union'}, {'end_time': 15.0393685898, 'start_time': \ 10.7528823866, 'text': 'union'}, {'end_time': 16.1646758009, 'start_time': 15.281742450599999, 'text': 'union'}, {'end_time': 17.3159516401, 'start_time': 17.064921569900001, 'text': 'union'}, {'end_time': 17.714137268599998, 'start_time': 17.4457947798, 'text': 'union'}, {'end_time': 19.393441876099999, 'start_time': 17.826667989699999, 'text': 'union'}] self.union_mustbe = pd.DataFrame( self.union_dict, columns=['start_' 'time', 'end_time', 'text']) self.invert = [{ 'start_time': 0., 'end_time': 547.016, 'text': 'nod' }, { 'start_time': 547.464, 'end_time': 549.507, 'text': 'nod/turn-aw' }, { 'start_time': 549.988, 'end_time': 556.808, 'text': 'turn-aw/nod-2' }, { 'start_time': 557.404, 'end_time': 561.428, 'text': 'nod-2/nod-3' }, { 'start_time': 563.345, 'end_time': 600.000, 'text': 'nod-3' }] self.inversion = pd.DataFrame( self.invert, columns=['start_' 'time', 'end_time', 'text']) self.tier1_inverted = [{ 'end_time': 547.01599999999996, 'start_time': 0.0, 'text': u'nod' }, { 'end_time': 549.50699999999995, 'start_time': 547.46400000000006, 'text': u'nod/turn-aw' }, { 'end_time': 556.80799999999999, 'start_time': 549.98800000000006, 'text': u'turn-aw/nod-2' }, { 'end_time': 561.428, 'start_time': 557.404, 'text': u'nod-2/nod-3' }, { 'end_time': 567.51099999999997, 'start_time': 563.34500000000003, 'text': u'nod-3/slide-right' }, { 'end_time': 578.17100000000005, 'start_time': 568.02300000000002, 'text': u'slide-right/nod-4' }, { 'end_time': 586.03599999999994, 'start_time': 579.53499999999997, 'text': u'nod-4/nod-3' }, { 'end_time': 604.73699999999997, 'start_time': 587.10699999999997, 'text': u'nod-3/nod-4-turn-aw-tw' }, { 'end_time': 609.11800000000005, 'start_time': 607.35699999999997, 'text': u'nod-4-turn-aw-tw/nod-4' }, { 'end_time': 617.30899999999997, 'start_time': 611.16600000000005, 'text': u'nod-4/nod-5' }] self.invert_tier1 = pd.DataFrame( self.tier1_inverted, columns=['start_time', 'end_time', 'text']) self.empty = pd.DataFrame([]) self.withna = [{ 'start_time': 3, 'end_time': 6, 'text': 'hi' }, { 'start_time': 7, 'end_time': 8.5 }, { 'start_time': 9, 'end_time': 13, 'text': 'bye' }] self.emptyint = pd.DataFrame( self.withna, columns=['start_time', 'end_time', 'text']) self.emptyshouldbe = [{ 'end_time': 3.0, 'start_time': 0, 'text': 'hi' }, { 'end_time': 7.0, 'start_time': 6, 'text': 'hi/empty' }, { 'end_time': 9.0, 'start_time': 8.5, 'text': 'empty/bye' }] self.emptyshouldbepd = pd.DataFrame(self.emptyshouldbe, columns= \ ['start_time', 'end_time', 'text']) self.no_overlap1 = [{ 'start_time': 3, 'end_time': 5, 'text': 'eins' }, { 'start_time': 9, 'end_time': 15, 'text': 'zwei' }, { 'start_time': 19, 'end_time': 21, 'text': 'drei' }] self.no_overlap2 = [{ 'start_time': 0, 'end_time': 2.5, 'text': 'vier' }, { 'start_time': 6, 'end_time': 7, 'text': 'fuenf' }, { 'start_time': 23, 'end_time': 27, 'text': 'sechs' }] self.pd_no_overlap1 = pd.DataFrame( self.no_overlap1, columns=['start_time', 'end_time', 'text']) self.pd_no_overlap2 = pd.DataFrame( self.no_overlap2, columns=['start_time', 'end_time', 'text']) self.convert = [{ 'start_time': 4, 'end_time': 5, 'text': 'eins' }, { 'start_time': 9, 'end_time': 15, 'text': 'zwei' }, { 'start_time': 19, 'end_time': 21, 'text': 'drei' }] self.convert_pd = pd.DataFrame( self.convert, columns=['start_time', 'end_time', 'text']) convert_times_of_tier(self.convert_pd, lambda y: int(1000 * y)) self.converted = [{ 'start_time': 4000, 'end_time': 5000, 'text': 'eins' }, { 'start_time': 9000, 'end_time': 15000, 'text': 'zwei' }, { 'start_time': 19000, 'end_time': 21000, 'text': 'drei' }] self.converted_pd = pd.DataFrame( self.converted, columns=['start_time', 'end_time', 'text']) self.tiers_shifted_a = [{ 'end_time': 4.47134775993, 'start_time': 3.80481964254, 'text': u'I see yes' }, { 'end_time': 4.8781895978200001, 'start_time': 4.7223778301200001, 'text': u'yes' }, { 'end_time': 8.3163672878900003, 'start_time': 7.9095254500000003, 'text': u'yes' }, { 'end_time': 8.5933659860300002, 'start_time': 8.4029293810599999, 'text': u'yes' }] self.tiers_shifted_pd = pd.DataFrame( self.tiers_shifted_a, columns=['start_time', 'end_time', 'text']) self.shift_dict = [{ 'start_time': 3, 'end_time': 5, 'text': 'eins' }, { 'start_time': 9, 'end_time': 15, 'text': 'zwei' }, { 'start_time': 19, 'end_time': 21, 'text': 'drei' }] self.shift = pd.DataFrame(self.shift_dict, columns=['start_time', 'end_time', 'text']) shift_tier(self.shift, 1) self.shifted_dict = [{ 'start_time': 4, 'end_time': 6, 'text': 'eins' }, { 'start_time': 10, 'end_time': 16, 'text': 'zwei' }, { 'start_time': 20, 'end_time': 22, 'text': 'drei' }] self.shifted = pd.DataFrame(self.shifted_dict, columns=['start_time', 'end_time', 'text']) for col in self.dict4['speaker A'].columns: self.dict4['speaker A'].loc[:, col] = \ self.dict4['speaker A'][col].map(lambda x: str(x)) for col in self.tiers_shifted_pd.columns: self.tiers_shifted_pd.loc[:, col] = \ self.tiers_shifted_pd[col].map(lambda x: str(x)) self.withPoint = open_intervalframe_from_textgrid( 'data/r1_12_15with' 'Point.TextGrid', 'utf-8') self.points = self.withPoint['P'][0:4].copy(deep=True) shift_tier(self.points, 10) for col in self.points.columns: self.points.loc[:, col] = self.points[col].map(lambda x: str(x)) self.points_shifted = [{ 'mark': 'A', 'time': 12.804819642542952 }, { 'mark': 'B', 'time': 13.454035341299345 }, { 'mark': 'A', 'time': 13.722377830118717 }, { 'mark': 'B', 'time': 16.779682310252952 }] self.points_shifted_pd = pd.DataFrame(self.points_shifted, columns=['time', 'mark']) for col in self.points_shifted_pd.columns: self.points_shifted_pd.loc[:, col] = \ self.points_shifted_pd[col].map(lambda x: str(x)) self.labeljoin = open_intervalframe_from_textgrid( 'data/joinlabels' '.TextGrid', 'utf-8') self.streamdict1 = [{ 'value': u'nod_start' }, { 'value': u'nod_end' }, { 'value': u'turn-aw_start' }, { 'value': u'turn-aw_end' }, { 'value': u'nod-2_start' }, { 'value': u'nod-2_end' }, { 'value': u'nod-3_start' }, { 'value': u'nod-3' }, { 'value': u'nod-3_end' }] self.stream1 = pd.DataFrame(self.streamdict1, columns=['value']) self.stream1.index = [ 547.016, 547.464, 549.507, 549.988, 556.808, 557.404, 561.428, 562.428, 563.345 ] self.inv1 = [{ 'end_time': '547.016', 'start_time': '300.0', 'text': u'nod' }, { 'end_time': '549.507', 'start_time': '547.464', 'text': u'nod/turn-aw' }, { 'end_time': '556.808', 'start_time': '549.988', 'text': u'turn-aw/nod-2' }, { 'end_time': '561.428', 'start_time': '557.404', 'text': u'nod-2/nod-3' }, { 'end_time': '700.0', 'start_time': '563.345', 'text': u'nod-3' }] self.inverted1 = pd.DataFrame( self.inv1, columns=['start_time', 'end_time', 'text']) self.inv1_str = invert_intervalframe(self.tier1[0:4], 300, 700) for col in self.inv1_str: self.inv1_str.loc[:, col] = \ self.inv1_str[col].map(lambda x: str(x)) self.inv_default_conc = [{ 'end_time': '549.507', 'start_time': '547.464', 'text': u'nod' }, { 'end_time': '556.808', 'start_time': '549.988', 'text': u'turn-aw+nod-2' }, { 'end_time': '561.428', 'start_time': '557.404', 'text': u'nod-2' }] self.inv_def_pd = pd.DataFrame( self.inv_default_conc, columns=['start_time', 'end_time', 'text']) self.inv_def_str = invert_intervalframe(self.tier1[0:4], concat_delimiter='+') for col in self.inv_def_str: self.inv_def_str.loc[:, col] = \ self.inv_def_str[col].map(lambda x: str(x))