def runTest(self):
self.maxDiff = None
my_test = {
'fn': genomic_manip.association.find_closest_features(),
'tracks': {'X': track_collections['Validation'][6]['path_sql'],
'Y': track_collections['Validation'][5]['path_sql']},
'input': {'max_length':0, 'utr_cutoff':0, 'prom_cutoff': 45},
'expected': [0]
}
run_one(self, my_test)
def runTest(self):
self.maxDiff = None
tests = [
{'fn': genomic_manip.basic.bounded(),
'input': {'min_bound': 0, 'max_bound': 45},
'tracks': {'X': [(-2,10),(20,30),(26,23),(40,50)]},
'expected': [(0,10),(20,30),(40,45)]}
,
{'fn': genomic_manip.basic.concatenate(),
'input': {'list_of_tracks': [track_collections['Validation'][1]['path_sql'],
track_collections['Validation'][4]['path_sql']]},
'expected': [
( 0, 10, u'Validation feature 1', 10.0, 0),
( 2, 8, u'Validation feature 2', 0.0, 0),
(10, 20, u'Lorem', 1.0, 1),
(20, 30, u'Validation feature 3', 10.0, 0),
(25, 30, u'Validation feature 4', 0.0, 0),
(30, 40, u'Ipsum', 2.0, 1),
(40, 45, u'Validation feature 5', 0.0, 0),
(40, 50, u'Validation feature 6', 10.0, 0),
(60, 70, u'Validation feature 7', 10.0, 0),
(70, 80, u'Validation feature 8', 10.0, 0),
(90, 100, u'Validation feature 9', 10.0, 0),
(90, 110, u'Validation feature 10', 10.0, 0),
(120, 130, u'Validation feature 11', 10.0, 0),
(125, 135, u'Validation feature 12', 5.0, 0)]}
,
{'fn': genomic_manip.basic.concatenate(),
'input': {'list_of_tracks': [[(0,10),(0,10),(5,10)],
[(0,5),(0,12),(5,8)]]},
'expected': [(0,5),(0,10),(0,10),(0,12),(5,8),(5,10)]}
,
{'fn': genomic_manip.basic.flatten(),
'input': {'stop_val': 10},
'tracks': {'X': [(0,2,10.0),(2,4,20.0),(6,8,10.0)]},
'expected': [10.0, 10.0, 20.0, 20.0, 0.0, 0.0, 10.0, 10.0, 0.0, 0.0]}
,
{'fn': genomic_manip.basic.qual_to_quan(),
'tracks': {'X': track_collections['Validation'][4]['path_sql']},
'fields': {'X': ['start', 'end', 'score']},
'expected': [(10, 20, 1.0), (30, 40, 2.0)]}
,
{'fn': genomic_manip.basic.qual_to_quan(),
'tracks': {'X': [(0,2),(2,4),(6,8)]},
'expected': [(0,2,1.0),(2,4,1.0),(6,8,1.0)]}
]
for t in tests: run_one(self, t)
def runTest(self):
self.maxDiff = None
tests = [
{'fn': genomic_manip.boolean.bool_not(),
'input': {'stop_val': 200},
'tracks': {'X': track_collections['Validation'][1]['path_sql']},
'expected': [( 10, 20, '', 0.0, 0),
( 30, 40, '', 0.0, 0),
( 50, 60, '', 0.0, 0),
( 80, 90, '', 0.0, 0),
(110, 120,'', 0.0, 0),
(135, 200,'', 0.0, 0)]}
,
{'fn': genomic_manip.boolean.bool_and(),
'tracks': {'X': track_collections['Validation'][2]['path_sql'],
'Y': track_collections['Validation'][3]['path_sql']},
'expected': [( 15, 20, u'NameA + Name1', 0.1, 0),
( 32, 38, u'NameB + Name2', 0.2, 1),
( 95, 100, u'NameD + Name5', 0.0, 1),
(110, 115, u'Name6 + NameD', 0.2, 1),
(130, 135, u'Name7 + NameE', 0.4, 0),
(140, 145, u'NameF + Name7', 0.4, 0),
(185, 190, u'NameG + Name8', 0.2, 1),
(185, 195, u'NameG + Name9', 0.2, 1),
(210, 215, u'Name10 + NameH', 0.45-5e-17, 1),
(215, 220, u'NameI + Name10', 0.25, 1),
(235, 240, u'NameJ + Name11', 0.1, 1),
(250, 254, u'Name12 + NameJ', 0.15+2e-17, 1),
(252, 258, u'NameK + Name12', 0.25, 1),
(256, 260, u'NameL + Name12', 0.2, 1),
(270, 275, u'Name13 + NameL', 0.1, 1)]}
,
{'fn': genomic_manip.boolean.bool_or(),
'tracks': {'X': track_collections['Validation'][2]['path_sql'],
'Y': track_collections['Validation'][3]['path_sql']},
'expected': [( 10, 25, u'Name1 + NameA', 0.2, 0),
( 30, 40, u'Name2 + NameB', 0.4, 1),
( 50, 80, u'Name3 + NameC + Name4', 0.4, 1),
( 90, 120, u'Name5 + NameD + Name6', 0.4, 1),
(125, 150, u'NameE + Name7 + NameF', 1.2+2e-16, 0),
(180, 200, u'Name8 + Name9 + NameG', 0.5, 1),
(205, 225, u'NameH + Name10 + NameI', 1.2, 1),
(230, 280, u'Name11 + NameJ + Name12 + NameK + NameL + Name13', 0.9-1e-16, 1),
(290, 300, u'Name14', 0.7, -1),
(310, 330, u'NameM', 0.3, 1)]}
,
{'fn': genomic_manip.boolean.bool_xor(),
'input': {'stop_val': 135},
'tracks': {'X1': [( 5,10,'GeneA1',0.0,-1), (20,30,'GeneA2',0.0,0), (40,50,'GeneA3',0.0,1)],
'X2': [( 5,10,'GeneA1',0.0,-1), (20,30,'GeneA2',0.0,0), (40,50,'GeneA3',0.0,1)],
'Y1': [(15,20,'GeneB1',0.0,0), (25,35,'GeneB2',0.0,0), (45,65,'GeneB3',0.0,1)],
'Y2': [(15,20,'GeneB1',0.0,0), (25,35,'GeneB2',0.0,0), (45,65,'GeneB3',0.0,1)]},
'expected': [( 5, 10, 'GeneA1', 0.0, 0),
( 15, 25, 'GeneB1 + GeneA2 + GeneB2', 0.0, 0),
( 30, 35, 'GeneB1 + GeneA2 + GeneB2', 0.0, 0),
( 40, 45, 'GeneA3 + GeneB3', 0.0, 0),
( 50, 65, 'GeneA3 + GeneB3', 0.0, 0)]}
]
for t in tests: run_one(self, t)
def runTest(self):
tests = [{'fn': genomic_manip.scores.merge_scores(),
'input': {'list_of_tracks': [[],[]]},
'expected': []}
,
{'fn': genomic_manip.scores.merge_scores(),
'input': {'list_of_tracks': [track_collections['Scores'][1]['path_sql'],
track_collections['Scores'][2]['path_sql'],
track_collections['Scores'][3]['path_sql']]},
'expected': [( 0, 5, 2.0 + 0.6666666666666666),
( 5, 10, 4.0),
( 20, 30, 10.0),
( 30, 40, 30.0),
( 40, 50, 26.0 + 0.666666666666666),
( 50, 60, 120.0),
( 60, 68, 100.0),
( 68, 70, 200.0),
( 70, 80, 100.0),
( 90, 110, 3.0),
(120, 130, 10.0)]}
,
{'fn': genomic_manip.scores.merge_scores(),
'input': {'geometric': True,
'list_of_tracks': [track_collections['Scores'][1]['path_sql'],
track_collections['Scores'][2]['path_sql'],
track_collections['Scores'][3]['path_sql']]},
'expected': [(0, 5, 2.0),
( 5, 10, 2.2894284851066637),
(20, 30, 3.1072325059538586),
(30, 40, 4.481404746557164),
(40, 50, 4.3088693800637667),
(50, 60, 7.1137866089801252),
(60, 68, 6.6943295008216941),
(68, 70, 8.4343266530174912),
(70, 80, 6.6943295008216941),
(90, 110, 2.0800838230519041),
(120, 130, 3.1072325059538586)]}
,
{'fn': genomic_manip.scores.mean_score_by_feature(),
'tracks': {'X': track_collections['Scores' ][4]['path_sql'],
'Y': track_collections['Validation'][2]['path_sql']},
'input': {},
'expected': [(10, 20, u'Name1', 15.0, -1),
(30, 40, u'Name2', 50.0, 1),
(50, 60, u'Name3', 30.0, 1),
(70, 80, u'Name4', 25.0, 1),
(90, 100, u'Name5', 8.0, 1),
(110, 120, u'Name6', 0.0, 1),
(130, 150, u'Name7', 1.0, -1),
(180, 190, u'Name8', 5.0, 1),
(180, 200, u'Name9', 8.0, 1),
(210, 220, u'Name10', 0.0, 1),
(230, 240, u'Name11', 0.0, 1),
(250, 260, u'Name12', 0.0, 1),
(270, 280, u'Name13', 0.0, 1),
(290, 300, u'Name14', 0.0, -1)]},
{'fn': genomic_manip.scores.threshold(),
'tracks': {'X': track_collections['Validation'][1]['path_sql']},
'fields': {'X': ['start', 'end', 'score', 'name']},
'input': {'s': 4.0,
'in_type' : 'qualitative',
'out_type': 'qualitative'},
'expected': [( 0, 10, 10.0, u'Validation feature 1'),
(20, 30, 10.0, u'Validation feature 3'),
(40, 50, 10.0, u'Validation feature 6'),
(60, 70, 10.0, u'Validation feature 7'),
(70, 80, 10.0, u'Validation feature 8'),
(90, 100, 10.0, u'Validation feature 9'),
(90, 110, 10.0, u'Validation feature 10'),
(120, 130, 10.0, u'Validation feature 11'),
(125, 135, 5.0, u'Validation feature 12')]},
]
for t in tests: run_one(self, t)
def runTest(self):
self.maxDiff = None
tests = [
{'fn': genomic_manip.standard.filter(),
'tracks': {'X': track_collections['Validation'][2]['path_sql'],
'Y': track_collections['Validation'][3]['path_sql']},
'expected': [( 10, 20, u'Name1', 0.1, -1),
( 30, 40, u'Name2', 0.2, 1),
( 90, 100, u'Name5', 0.0, 1),
(110, 120, u'Name6', 0.4, 1),
(130, 150, u'Name7', 0.4, -1),
(180, 190, u'Name8', 0.1, 1),
(180, 200, u'Name9', 0.1, 1),
(210, 220, u'Name10', 0.2, 1),
(230, 240, u'Name11', 0.1, 1),
(250, 260, u'Name12', 0.2, 1),
(270, 280, u'Name13', 0.0, 1)]}
,
{'fn': genomic_manip.standard.neighborhood(),
'input': {'stop_val': 135, 'before_start':-2, 'after_end':2, 'in_type': 'qualitative'},
'tracks': {'X': track_collections['Validation'][1]['path_sql']},
'expected': [( 0, 12, u'Validation feature 1', 10.0, 0),
( 0, 10, u'Validation feature 2', 0.0, 0),
(18, 32, u'Validation feature 3', 10.0, 0),
(23, 32, u'Validation feature 4', 0.0, 0),
(38, 47, u'Validation feature 5', 0.0, 0),
(38, 52, u'Validation feature 6', 10.0, 0),
(58, 72, u'Validation feature 7', 10.0, 0),
(68, 82, u'Validation feature 8', 10.0, 0),
(88, 102, u'Validation feature 9', 10.0, 0),
(88, 112, u'Validation feature 10', 10.0, 0),
(118, 132, u'Validation feature 11', 10.0, 0),
(123, 135, u'Validation feature 12', 5.0, 0)]}
,
{'fn': genomic_manip.standard.merge().quan,
'tracks': {'X': track_collections['Scores'][4]['path_sql']},
'expected': [( 10, 20, 15.0),
( 25, 35, 100.0),
( 45, 65, 30.0),
( 72, 77, 50.0),
( 85, 105, 8.0),
(120, 122, 9000.0),
(130, 131, 20.0),
(180, 183, 10.0),
(188, 193, 10.0),
(198, 200, 40.0)]}
,
{'fn': genomic_manip.standard.merge().quan,
'tracks': {'X': [(10, 40, 10.0), (40, 50, 20.0)]},
'expected': [(10, 50, 12.5)]}
,
{'fn': genomic_manip.standard.merge().qual,
'tracks': {'X': [( 10, 20, u'Name1', 1.0, 1),
( 10, 15, u'Name2', 1.0, 1),
( 17, 23, u'Name3', 1.0, 1),
( 50, 60, u'Name4', 1.0, -1),
( 70, 80, u'Name5', 1.0, -1),
( 72, 78, u'Name6', 1.0, 1)]},
'expected': [( 10, 23, u'Name1 + Name2 + Name3', 3.0, 1),
( 50, 60, u'Name4', 1.0, -1),
( 70, 80, u'Name5 + Name6', 2.0, 0)]}
]
for t in tests: run_one(self, t)
