def test0(self):
R = {
'd': [
9.0, 8.0, 6.0, 8.0, 10.0, 4.0, 6.0, 5.0, 7.0, 7.0, 7.0, 9.0,
6.0, 6.0, 6.0, 11.0, 6.0, 3.0, 8.0, 7.0, 11.0, 13.0, 8.0, 6.0,
14.0, 11.0, 13.0, 13.0, 10.0, 11.0, 12.0, 11.0, 16.0, 11.0,
9.0, 23.0, 12.0, 10.0, 19.0, 11.0, 10.0, 19.0, 14.0, 5.0, 10.0,
11.0, 14.0, 15.0, 11.0, 11.0, 8.0, 6.0, 4.0, 6.0, 7.0, 6.0,
5.0, 7.0, 9.0, 7.0, 10.0, 7.0, 8.0, 10.0, 4.0, 7.0, 10.0, 6.0,
7.0, 7.0, 14.0, 11.0, 18.0, 14.0, 13.0, 22.0, 17.0, 16.0, 12.0,
11.0, 20.0, 16.0, 16.0, 15.0, 18.0, 16.0, 20.0, 22.0, 14.0,
19.0, 21.0, 19.0, 17.0, 15.0, 22.0, 16.0, 22.0, 22.0, 18.0,
21.0
],
'fname':
'output\\box(WORDS).png',
'maintitle':
'WORDS',
'val':
'WORDS'
}
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.box_plot('WORDS', output_dir='output')
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['val'], R['val'])
for d, r in zip(np.array(D['d']).flat, np.array(R['d']).flat):
self.assertAlmostEqual(d, r)
def test1(self):
R = {'d': [np.array([ 9, 8, 6, 8, 10, 4, 6, 5, 7, 7,
7, 9, 6, 6, 6, 11, 6, 3, 8, 7,
11, 13, 8, 6, 14, 11, 13, 13, 10, 11,
12, 11, 16, 11, 9, 23, 12, 10, 19, 11,
10, 19, 14, 5, 10, 11, 14, 15, 11, 11]),
np.array([ 8, 6, 4, 6, 7, 6, 5, 7, 9, 7,
10, 7, 8, 10, 4, 7, 10, 6, 7, 7,
14, 11, 18, 14, 13, 22, 17, 16, 12, 11,
20, 16, 16, 15, 18, 16, 20, 22, 14, 19,
21, 19, 17, 15, 22, 16, 22, 22, 18, 21])],
'fname': 'output\\box(WORDS~AGE).png',
'maintitle': 'WORDS by AGE',
'xlabels': [u'AGE = old', u'AGE = young']}
df=DataFrame()
df.TESTMODE=True
df.read_tbl('data/words~ageXcondition.csv')
D=df.box_plot('WORDS',['AGE'], output_dir='output')
self.assertEqual(D['fname'],R['fname'])
self.assertEqual(D['maintitle'],R['maintitle'])
self.assertEqual(D['xlabels'],R['xlabels'])
for d,r in zip(np.array(D['d']).flat,
np.array(R['d']).flat):
self.assertAlmostEqual(d,r)
def test01(self):
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/iqbrainsize.txt', delimiter='\t')
D = df.scatter_plot('TOTVOL', 'FIQ', output_dir='output')
self.assertEqual(None, D['trend'])
def test1(self):
R = {
'd': [
np.array([
9, 8, 6, 8, 10, 4, 6, 5, 7, 7, 7, 9, 6, 6, 6, 11, 6, 3, 8,
7, 11, 13, 8, 6, 14, 11, 13, 13, 10, 11, 12, 11, 16, 11, 9,
23, 12, 10, 19, 11, 10, 19, 14, 5, 10, 11, 14, 15, 11, 11
]),
np.array([
8, 6, 4, 6, 7, 6, 5, 7, 9, 7, 10, 7, 8, 10, 4, 7, 10, 6, 7,
7, 14, 11, 18, 14, 13, 22, 17, 16, 12, 11, 20, 16, 16, 15,
18, 16, 20, 22, 14, 19, 21, 19, 17, 15, 22, 16, 22, 22, 18,
21
])
],
'fname':
'output\\box(WORDS~AGE).png',
'maintitle':
'WORDS by AGE',
'xlabels': ['AGE = old', 'AGE = young']
}
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.box_plot('WORDS', ['AGE'], output_dir='output')
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['xlabels'], R['xlabels'])
for d, r in zip(np.array(D['d']).flat, np.array(R['d']).flat):
self.assertAlmostEqual(d, r)
def test0(self):
R = {'d': [9.0, 8.0, 6.0, 8.0, 10.0, 4.0, 6.0, 5.0, 7.0, 7.0,
7.0, 9.0, 6.0, 6.0, 6.0, 11.0, 6.0, 3.0, 8.0, 7.0,
11.0, 13.0, 8.0, 6.0, 14.0, 11.0, 13.0, 13.0, 10.0,
11.0, 12.0, 11.0, 16.0, 11.0, 9.0, 23.0, 12.0, 10.0,
19.0, 11.0, 10.0, 19.0, 14.0, 5.0, 10.0, 11.0, 14.0,
15.0, 11.0, 11.0, 8.0, 6.0, 4.0, 6.0, 7.0, 6.0, 5.0,
7.0, 9.0, 7.0, 10.0, 7.0, 8.0, 10.0, 4.0, 7.0, 10.0,
6.0, 7.0, 7.0, 14.0, 11.0, 18.0, 14.0, 13.0, 22.0, 17.0,
16.0, 12.0, 11.0, 20.0, 16.0, 16.0, 15.0, 18.0, 16.0,
20.0, 22.0, 14.0, 19.0, 21.0, 19.0, 17.0, 15.0, 22.0,
16.0, 22.0, 22.0, 18.0, 21.0],
'fname': 'output\\box(WORDS).png',
'maintitle': 'WORDS',
'val': 'WORDS'}
df=DataFrame()
df.TESTMODE=True
df.read_tbl('data/words~ageXcondition.csv')
D=df.box_plot('WORDS', output_dir='output')
self.assertEqual(D['fname'],R['fname'])
self.assertEqual(D['maintitle'],R['maintitle'])
self.assertEqual(D['val'],R['val'])
for d,r in zip(np.array(D['d']).flat,
np.array(R['d']).flat):
self.assertAlmostEqual(d,r)
def test1(self):
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.histogram_plot('WORDS', cumulative=True, output_dir='output')
self.assertEqual(D['fname'], 'output\\hist(WORDS,cumulative=True).png')
def test1(self):
df=DataFrame()
df.TESTMODE=True
df.read_tbl('data/words~ageXcondition.csv')
D=df.histogram_plot('WORDS', cumulative=True, output_dir='output')
self.assertEqual(D['fname'],'output\\hist(WORDS,cumulative=True).png')
def test01(self):
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data/iqbrainsize.txt', delimiter='\t')
D = df.scatter_plot('TOTVOL','FIQ',
output_dir='output')
self.assertEqual(None, D['trend'])
def test3(self):
R = {'aggregate': 'ci',
'clevels': ['I', 'II'],
'fname': 'output\\whereGROUPnotLAB.png',
'maintitle': 'SUPPRESSION by CYCLE * AGE * PHASE * GROUP',
'numcols': 2,
'numrows': 2,
'rlevels': ['AA', 'AB'],
'subplot_titles': ['GROUP = AA, PHASE = AA',
'GROUP = AA, PHASE = AA',
'GROUP = AB, PHASE = AB',
'GROUP = AB, PHASE = AB'],
'xmaxs': [4.1500000000000004,
4.1500000000000004,
4.1500000000000004,
4.1500000000000004],
'xmins': [0.84999999999999998,
0.84999999999999998,
0.84999999999999998,
0.84999999999999998],
'y': [[[ 17.75 , 22.375, 23.125, 20.25 ],
[ 8.675, 10.225, 10.5 , 9.925]],
[[ 20.875, 28.125, 20.75 , 24.25 ],
[ 8.3 , 10.25 , 9.525, 11.1 ]],
[[ 12.625, 23.5 , 20. , 15.625],
[ 5.525, 8.825, 9.125, 7.75 ]],
[[ 22.75 , 41.125, 46.125, 51.75 ],
[ 8.675, 13.1 , 14.475, 12.85 ]]],
'ymax': 64.8719707118471,
'ymin': 0.0}
# separate y plots and separate x plots
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION','CYCLE',
seplines='AGE',
sepxplots='PHASE',
sepyplots='GROUP',yerr='ci',
where=[('GROUP','not in',['LAB'])],
fname='whereGROUPnotLAB.png',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d,r in zip(np.array(D['y']).flat,np.array(R['y']).flat):
self.assertAlmostEqual(d,r)
def test2(self):
R = {
'aggregate':
'ci',
'clevels': [1],
'fname':
'output\\interaction_plot(SUPPRESSION~CYCLE_X_AGE_X_PHASE,yerr=95% ci).png',
'maintitle':
'SUPPRESSION by CYCLE * AGE * PHASE',
'numcols':
1,
'numrows':
2,
'rlevels': ['I', 'II'],
'subplot_titles': ['I', 'II'],
'xmaxs': [4.1749999999999998, 4.1749999999999998],
'xmins': [0.32499999999999996, 0.32499999999999996],
'y': [[[17.33333333, 22.41666667, 22.29166667, 20.75],
[7.34166667, 9.65, 9.70833333, 9.10833333]],
[[26.625, 38.70833333, 39.08333333, 40.83333333],
[10.24166667, 12.575, 13.19166667, 12.79166667]]],
'yerr': [[1.81325589, 1.44901936, 1.60883063, 1.57118871],
[2.49411239, 1.34873573, 1.95209851, 1.35412572]],
'ymax':
64.8719707118471,
'ymin':
0.0
}
# generate yerr
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION',
'CYCLE',
seplines='AGE',
sepyplots='PHASE',
yerr='ci',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d, r in zip(np.array(D['y']).flat, np.array(R['y']).flat):
self.assertAlmostEqual(d, r)
for d, r in zip(np.array(D['yerr']).flat, np.array(R['yerr']).flat):
self.assertAlmostEqual(d, r)
def test1(self):
R = {
'aggregate':
None,
'clevels': ['M1', 'M2', 'M3'],
'fname':
'output\\interaction_plot(ERROR~TIMEOFDAY_X_COURSE_X_MODEL,yerr=1.0).png',
'maintitle':
'ERROR by TIMEOFDAY * COURSE * MODEL',
'numcols':
3,
'numrows':
1,
'rlevels': [1],
'subplot_titles': ['M1', 'M2', 'M3'],
'xmaxs': [1.5, 1.5, 1.5],
'xmins': [-0.5, -0.5, -0.5],
'y': [[[9., 4.33333333], [8.66666667, 3.66666667],
[4.66666667, 1.66666667]],
[[7.5, 2.66666667], [6., 2.66666667], [5., 1.66666667]],
[[5., 2.66666667], [3.5, 2.33333333],
[2.33333333, 1.33333333]]],
'yerr': [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]],
'ymax':
11.119188627248182,
'ymin':
0.0
}
# specify yerr
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/error~subjectXtimeofdayXcourseXmodel_MISSING.csv')
D = df.interaction_plot('ERROR',
'TIMEOFDAY',
seplines='COURSE',
sepxplots='MODEL',
yerr=1.,
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d, r in zip(np.array(D['y']).flat, np.array(R['y']).flat):
self.assertAlmostEqual(d, r)
for d, r in zip(np.array(D['yerr']).flat, np.array(R['yerr']).flat):
self.assertAlmostEqual(d, r)
def test4(self):
R = {'aggregate': None,
'clevels': ['adjective',
'counting',
'imagery',
'intention',
'rhyming'],
'fname': 'output\\interaction_plot(WORDS~AGE_X_CONDITION).png',
'maintitle': 'WORDS by AGE * CONDITION',
'numcols': 5,
'numrows': 1,
'rlevels': [1],
'subplot_titles': ['adjective',
'counting',
'imagery',
'intention',
'rhyming'],
'xmaxs': [1.5, 1.5, 1.5, 1.5, 1.5],
'xmins': [-0.5, -0.5, -0.5, -0.5, -0.5],
'y': [[ 11. , 14.8],
[ 7. , 6.5],
[ 13.4, 17.6],
[ 12. , 19.3],
[ 6.9, 7.6]],
'yerr': [[], [], [], [], []],
'ymax': 27.183257964740832,
'ymin': 0.0}
# a simple plot
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.interaction_plot('WORDS','AGE',
sepxplots='CONDITION',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d,r in zip(np.array(D['y']).flat,
np.array(R['y']).flat):
self.assertAlmostEqual(d,r)
for d,r in zip(np.array(D['yerr']).flat,
np.array(R['yerr']).flat):
self.assertAlmostEqual(d,r)
def test1(self):
R = {'aggregate': None,
'clevels': ['M1', 'M2', 'M3'],
'fname': 'output\\interaction_plot(ERROR~TIMEOFDAY_X_COURSE_X_MODEL,yerr=1.0).png',
'maintitle': 'ERROR by TIMEOFDAY * COURSE * MODEL',
'numcols': 3,
'numrows': 1,
'rlevels': [1],
'subplot_titles': ['M1', 'M2', 'M3'],
'xmaxs': [1.5, 1.5, 1.5],
'xmins': [-0.5, -0.5, -0.5],
'y': [[[ 9. , 4.33333333],
[ 8.66666667, 3.66666667],
[ 4.66666667, 1.66666667]],
[[ 7.5 , 2.66666667],
[ 6. , 2.66666667],
[ 5. , 1.66666667]],
[[ 5. , 2.66666667],
[ 3.5 , 2.33333333],
[ 2.33333333, 1.33333333]]],
'yerr': [[1.0, 1.0],
[1.0, 1.0],
[1.0, 1.0]],
'ymax': 11.119188627248182,
'ymin': 0.0}
# specify yerr
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data/error~subjectXtimeofdayXcourseXmodel_MISSING.csv')
D=df.interaction_plot('ERROR','TIMEOFDAY',
seplines='COURSE',
sepxplots='MODEL',
yerr=1.,
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d,r in zip(np.array(D['y']).flat,
np.array(R['y']).flat):
self.assertAlmostEqual(d,r)
for d,r in zip(np.array(D['yerr']).flat,
np.array(R['yerr']).flat):
self.assertAlmostEqual(d,r)
def test01(self):
"""confidence interval error bars specified"""
R = {
'aggregate':
'ci',
'clevels': [1],
'fname':
'output\\interaction_plot(WORDS~AGE_X_CONDITION,yerr=95% ci).png',
'maintitle':
'WORDS by AGE * CONDITION',
'numcols':
1,
'numrows':
1,
'rlevels': [1],
'subplot_titles': [''],
'xmaxs': [1.5],
'xmins': [-0.5],
'y': [[[11.0, 14.8], [7.0, 6.5], [13.4, 17.6], [12.0, 19.3],
[6.9, 7.6]]],
'yerr': [[]],
'ymin':
0.0,
'ymax':
27.183257964740832
}
# a simple plot
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.interaction_plot('WORDS',
'AGE',
seplines='CONDITION',
output_dir='output',
yerr='ci')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d, r in zip(np.array(D['y']).flat, np.array(R['y']).flat):
self.assertAlmostEqual(d, r)
for d, r in zip(np.array(D['yerr']).flat, np.array(R['yerr']).flat):
self.assertAlmostEqual(d, r)
def test6(self):
R = {
'aggregate':
'ci',
'clevels': [1],
'fname':
'output\\interaction_plot(SUPPRESSION~CYCLE_X_PHASE,yerr=95% ci).png',
'maintitle':
'SUPPRESSION by CYCLE * PHASE',
'numcols':
1,
'numrows':
2,
'rlevels': ['I', 'II'],
'subplot_titles': ['I', 'II'],
'xmaxs': [4.1749999999999998, 4.1749999999999998],
'xmins': [0.82499999999999996, 0.82499999999999996],
'y': [[12.3375, 16.03333333, 16., 14.92916667],
[18.43333333, 25.64166667, 26.1375, 26.8125]],
'yerr': [[3.18994762, 3.20528834, 3.26882751, 3.53477953],
[3.98429064, 4.5950803, 4.9514978, 4.97429769]],
'ymax':
64.8719707118471,
'ymin':
0.0
}
# generate yerr
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION',
'CYCLE',
sepyplots='PHASE',
yerr='ci',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d, r in zip(np.array(D['y']).flat, np.array(R['y']).flat):
self.assertAlmostEqual(d, r)
for d, r in zip(np.array(D['yerr']).flat, np.array(R['yerr']).flat):
self.assertAlmostEqual(d, r)
def test4(self):
R = {
'aggregate':
None,
'clevels':
['adjective', 'counting', 'imagery', 'intention', 'rhyming'],
'fname':
'output\\interaction_plot(WORDS~AGE_X_CONDITION).png',
'maintitle':
'WORDS by AGE * CONDITION',
'numcols':
5,
'numrows':
1,
'rlevels': [1],
'subplot_titles':
['adjective', 'counting', 'imagery', 'intention', 'rhyming'],
'xmaxs': [1.5, 1.5, 1.5, 1.5, 1.5],
'xmins': [-0.5, -0.5, -0.5, -0.5, -0.5],
'y': [[11., 14.8], [7., 6.5], [13.4, 17.6], [12., 19.3],
[6.9, 7.6]],
'yerr': [[], [], [], [], []],
'ymax':
27.183257964740832,
'ymin':
0.0
}
# a simple plot
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.interaction_plot('WORDS',
'AGE',
sepxplots='CONDITION',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d, r in zip(np.array(D['y']).flat, np.array(R['y']).flat):
self.assertAlmostEqual(d, r)
for d, r in zip(np.array(D['yerr']).flat, np.array(R['yerr']).flat):
self.assertAlmostEqual(d, r)
def test31(self):
# separate y plots and separate x plots
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION','CYCLE',
seplines='AGE',
sepxplots='GROUP',
sepyplots='PHASE',
yerr='sem',
output_dir='output')
def test31(self):
# separate y plots and separate x plots
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION',
'CYCLE',
seplines='AGE',
sepxplots='GROUP',
sepyplots='PHASE',
yerr='sem',
output_dir='output')
def test01(self):
"""confidence interval error bars specified"""
R = {'aggregate': 'ci',
'clevels': [1],
'fname': 'output\\interaction_plot(WORDS~AGE_X_CONDITION,yerr=95% ci).png',
'maintitle': 'WORDS by AGE * CONDITION',
'numcols': 1,
'numrows': 1,
'rlevels': [1],
'subplot_titles': [''],
'xmaxs': [1.5],
'xmins': [-0.5],
'y': [[[11.0, 14.8],
[7.0, 6.5],
[13.4, 17.6],
[12.0, 19.3],
[6.9, 7.6]]],
'yerr': [[]],
'ymin': 0.0,
'ymax': 27.183257964740832}
# a simple plot
df=DataFrame()
df.TESTMODE=True
df.read_tbl('data/words~ageXcondition.csv')
D=df.interaction_plot('WORDS','AGE',
seplines='CONDITION',
output_dir='output',
yerr='ci')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d,r in zip(np.array(D['y']).flat,
np.array(R['y']).flat):
self.assertAlmostEqual(d,r)
for d,r in zip(np.array(D['yerr']).flat,
np.array(R['yerr']).flat):
self.assertAlmostEqual(d,r)
def test0(self):
R = {'bins': np.array([ 4, 14, 17, 12, 15, 10, 9, 5, 6, 8]),
'counts': np.array([ 3., 5., 7., 9., 11., 13., 15., 17., 19., 21., 23.]),
'fname': 'output\\hist(WORDS).png'}
df=DataFrame()
df.TESTMODE=True
df.read_tbl('data/words~ageXcondition.csv')
D=df.histogram_plot('WORDS', output_dir='output')
self.assertEqual(D['fname'],R['fname'])
for d,r in zip(D['bins'].flat,R['bins'].flat):
self.assertAlmostEqual(d,r)
for d,r in zip(D['counts'].flat,R['counts'].flat):
self.assertAlmostEqual(d,r)
def test2(self):
R = {'aggregate': 'ci',
'clevels': [1],
'fname': 'output\\interaction_plot(SUPPRESSION~CYCLE_X_AGE_X_PHASE,yerr=95% ci).png',
'maintitle': 'SUPPRESSION by CYCLE * AGE * PHASE',
'numcols': 1,
'numrows': 2,
'rlevels': ['I', 'II'],
'subplot_titles': ['I', 'II'],
'xmaxs': [4.1749999999999998, 4.1749999999999998],
'xmins': [0.32499999999999996, 0.32499999999999996],
'y': [[[ 17.33333333, 22.41666667, 22.29166667, 20.75 ],
[ 7.34166667, 9.65 , 9.70833333, 9.10833333]],
[[ 26.625 , 38.70833333, 39.08333333, 40.83333333],
[ 10.24166667, 12.575 , 13.19166667, 12.79166667]]],
'yerr': [[ 1.81325589, 1.44901936, 1.60883063, 1.57118871],
[ 2.49411239, 1.34873573, 1.95209851, 1.35412572]],
'ymax': 64.8719707118471,
'ymin': 0.0}
# generate yerr
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION','CYCLE',
seplines='AGE',
sepyplots='PHASE',
yerr='ci',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d,r in zip(np.array(D['y']).flat,np.array(R['y']).flat):
self.assertAlmostEqual(d,r)
for d,r in zip(np.array(D['yerr']).flat,np.array(R['yerr']).flat):
self.assertAlmostEqual(d,r)
def test6(self):
R = {'aggregate': 'ci',
'clevels': [1],
'fname': 'output\\interaction_plot(SUPPRESSION~CYCLE_X_PHASE,yerr=95% ci).png',
'maintitle': 'SUPPRESSION by CYCLE * PHASE',
'numcols': 1,
'numrows': 2,
'rlevels': ['I', 'II'],
'subplot_titles': ['I', 'II'],
'xmaxs': [4.1749999999999998, 4.1749999999999998],
'xmins': [0.82499999999999996, 0.82499999999999996],
'y': [[ 12.3375 , 16.03333333, 16. , 14.92916667],
[ 18.43333333, 25.64166667, 26.1375 , 26.8125 ]],
'yerr': [[ 3.18994762, 3.20528834, 3.26882751, 3.53477953],
[ 3.98429064, 4.5950803 , 4.9514978 , 4.97429769]],
'ymax': 64.8719707118471,
'ymin': 0.0}
# generate yerr
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION','CYCLE',
sepyplots='PHASE',
yerr='ci',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d,r in zip(np.array(D['y']).flat,
np.array(R['y']).flat):
self.assertAlmostEqual(d,r)
for d,r in zip(np.array(D['yerr']).flat,
np.array(R['yerr']).flat):
self.assertAlmostEqual(d,r)
def test2(self):
R = {
'd': [
np.array([11, 13, 8, 6, 14, 11, 13, 13, 10, 11]),
np.array([9, 8, 6, 8, 10, 4, 6, 5, 7, 7]),
np.array([12, 11, 16, 11, 9, 23, 12, 10, 19, 11]),
np.array([10, 19, 14, 5, 10, 11, 14, 15, 11, 11]),
np.array([7, 9, 6, 6, 6, 11, 6, 3, 8, 7]),
np.array([14, 11, 18, 14, 13, 22, 17, 16, 12, 11]),
np.array([8, 6, 4, 6, 7, 6, 5, 7, 9, 7]),
np.array([20, 16, 16, 15, 18, 16, 20, 22, 14, 19]),
np.array([21, 19, 17, 15, 22, 16, 22, 22, 18, 21]),
np.array([10, 7, 8, 10, 4, 7, 10, 6, 7, 7])
],
'fname':
'output\\box(WORDS~AGE_X_CONDITION).png',
'maintitle':
'WORDS by AGE * CONDITION',
'xlabels': [
'AGE = old\nCONDITION = adjective',
'AGE = old\nCONDITION = counting',
'AGE = old\nCONDITION = imagery',
'AGE = old\nCONDITION = intention',
'AGE = old\nCONDITION = rhyming',
'AGE = young\nCONDITION = adjective',
'AGE = young\nCONDITION = counting',
'AGE = young\nCONDITION = imagery',
'AGE = young\nCONDITION = intention',
'AGE = young\nCONDITION = rhyming'
]
}
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.box_plot('WORDS', ['AGE', 'CONDITION'], output_dir='output')
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['xlabels'], R['xlabels'])
for d, r in zip(np.array(D['d']).flat, np.array(R['d']).flat):
self.assertAlmostEqual(d, r)
def test0(self):
R = {
'bins': np.array([4, 14, 17, 12, 15, 10, 9, 5, 6, 8]),
'counts':
np.array([3., 5., 7., 9., 11., 13., 15., 17., 19., 21., 23.]),
'fname': 'output\\hist(WORDS).png'
}
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data/words~ageXcondition.csv')
D = df.histogram_plot('WORDS', output_dir='output')
self.assertEqual(D['fname'], R['fname'])
for d, r in zip(D['bins'].flat, R['bins'].flat):
self.assertAlmostEqual(d, r)
for d, r in zip(D['counts'].flat, R['counts'].flat):
self.assertAlmostEqual(d, r)
def test2(self):
R = {'d': [np.array([11, 13, 8, 6, 14, 11, 13, 13, 10, 11]),
np.array([ 9, 8, 6, 8, 10, 4, 6, 5, 7, 7]),
np.array([12, 11, 16, 11, 9, 23, 12, 10, 19, 11]),
np.array([10, 19, 14, 5, 10, 11, 14, 15, 11, 11]),
np.array([ 7, 9, 6, 6, 6, 11, 6, 3, 8, 7]),
np.array([14, 11, 18, 14, 13, 22, 17, 16, 12, 11]),
np.array([8, 6, 4, 6, 7, 6, 5, 7, 9, 7]),
np.array([20, 16, 16, 15, 18, 16, 20, 22, 14, 19]),
np.array([21, 19, 17, 15, 22, 16, 22, 22, 18, 21]),
np.array([10, 7, 8, 10, 4, 7, 10, 6, 7, 7])],
'fname': 'output\\box(WORDS~AGE_X_CONDITION).png',
'maintitle': 'WORDS by AGE * CONDITION',
'xlabels': [u'AGE = old\nCONDITION = adjective',
u'AGE = old\nCONDITION = counting',
u'AGE = old\nCONDITION = imagery',
u'AGE = old\nCONDITION = intention',
u'AGE = old\nCONDITION = rhyming',
u'AGE = young\nCONDITION = adjective',
u'AGE = young\nCONDITION = counting',
u'AGE = young\nCONDITION = imagery',
u'AGE = young\nCONDITION = intention',
u'AGE = young\nCONDITION = rhyming']}
df=DataFrame()
df.TESTMODE=True
df.read_tbl('data/words~ageXcondition.csv')
D=df.box_plot('WORDS',['AGE','CONDITION'], output_dir='output')
self.assertEqual(D['fname'],R['fname'])
self.assertEqual(D['maintitle'],R['maintitle'])
self.assertEqual(D['xlabels'],R['xlabels'])
for d,r in zip(np.array(D['d']).flat,
np.array(R['d']).flat):
self.assertAlmostEqual(d,r)
def test3(self):
R = {
'aggregate':
'ci',
'clevels': ['I', 'II'],
'fname':
'output\\whereGROUPnotLAB.png',
'maintitle':
'SUPPRESSION by CYCLE * AGE * PHASE * GROUP',
'numcols':
2,
'numrows':
2,
'rlevels': ['AA', 'AB'],
'subplot_titles': [
'GROUP = AA, PHASE = AA', 'GROUP = AA, PHASE = AA',
'GROUP = AB, PHASE = AB', 'GROUP = AB, PHASE = AB'
],
'xmaxs': [
4.1500000000000004, 4.1500000000000004, 4.1500000000000004,
4.1500000000000004
],
'xmins': [
0.84999999999999998, 0.84999999999999998, 0.84999999999999998,
0.84999999999999998
],
'y':
[[[17.75, 22.375, 23.125, 20.25], [8.675, 10.225, 10.5, 9.925]],
[[20.875, 28.125, 20.75, 24.25], [8.3, 10.25, 9.525, 11.1]],
[[12.625, 23.5, 20., 15.625], [5.525, 8.825, 9.125, 7.75]],
[[22.75, 41.125, 46.125, 51.75], [8.675, 13.1, 14.475, 12.85]]],
'ymax':
64.8719707118471,
'ymin':
0.0
}
# separate y plots and separate x plots
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION',
'CYCLE',
seplines='AGE',
sepxplots='PHASE',
sepyplots='GROUP',
yerr='ci',
where=[('GROUP', 'not in', ['LAB'])],
fname='whereGROUPnotLAB.png',
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d, r in zip(np.array(D['y']).flat, np.array(R['y']).flat):
self.assertAlmostEqual(d, r)
def test7(self):
R = {
'aggregate':
'ci',
'clevels': ['I', 'II'],
'fname':
'output\\interaction_plot(SUPPRESSION~CYCLE_X_PHASE_X_GROUP,yerr=95% ci).png',
'maintitle':
'SUPPRESSION by CYCLE * PHASE * GROUP',
'numcols':
2,
'numrows':
2,
'rlevels': ['AA', 'AB'],
'subplot_titles': [
'GROUP = AA, PHASE = AA', 'GROUP = AA, PHASE = AA',
'GROUP = AB, PHASE = AB', 'GROUP = AB, PHASE = AB'
],
'xmaxs': [
4.1500000000000004, 4.1500000000000004, 4.1500000000000004,
4.1500000000000004
],
'xmins': [
0.84999999999999998, 0.84999999999999998, 0.84999999999999998,
0.84999999999999998
],
'y': [[13.2125, 16.3, 16.8125, 15.0875],
[14.5875, 19.1875, 15.1375, 17.675],
[9.075, 16.1625, 14.5625, 11.6875],
[15.7125, 27.1125, 30.3, 32.3]],
'yerr': [[6.41377058, 4.90274323, 6.52638491, 4.723284],
[7.98351964, 7.01554694, 5.50066923, 4.7712851],
[4.06006718, 6.15225848, 4.21669129, 6.23708923],
[4.55687267, 7.52964629, 8.43210133, 10.3156968]],
'ymax':
64.8719707118471,
'ymin':
0.0
}
# separate y plots and separate x plots
df = DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION',
'CYCLE',
sepxplots='PHASE',
sepyplots='GROUP',
yerr='ci',
where=[('GROUP', 'not in', ['LAB'])],
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d, r in zip(np.array(D['y']).flat, np.array(R['y']).flat):
self.assertAlmostEqual(d, r)
for d, r in zip(np.array(D['yerr']).flat, np.array(R['yerr']).flat):
self.assertAlmostEqual(d, r)
def test7(self):
R = {'aggregate': 'ci',
'clevels': ['I', 'II'],
'fname': 'output\\interaction_plot(SUPPRESSION~CYCLE_X_PHASE_X_GROUP,yerr=95% ci).png',
'maintitle': 'SUPPRESSION by CYCLE * PHASE * GROUP',
'numcols': 2,
'numrows': 2,
'rlevels': ['AA', 'AB'],
'subplot_titles': ['GROUP = AA, PHASE = AA',
'GROUP = AA, PHASE = AA',
'GROUP = AB, PHASE = AB',
'GROUP = AB, PHASE = AB'],
'xmaxs': [4.1500000000000004,
4.1500000000000004,
4.1500000000000004,
4.1500000000000004],
'xmins': [0.84999999999999998,
0.84999999999999998,
0.84999999999999998,
0.84999999999999998],
'y': [[ 13.2125, 16.3 , 16.8125, 15.0875],
[ 14.5875, 19.1875, 15.1375, 17.675 ],
[ 9.075 , 16.1625, 14.5625, 11.6875],
[ 15.7125, 27.1125, 30.3 , 32.3 ]],
'yerr': [[ 6.41377058, 4.90274323, 6.52638491, 4.723284 ],
[ 7.98351964, 7.01554694, 5.50066923, 4.7712851 ],
[ 4.06006718, 6.15225848, 4.21669129, 6.23708923],
[ 4.55687267, 7.52964629, 8.43210133, 10.3156968 ]],
'ymax': 64.8719707118471,
'ymin': 0.0}
# separate y plots and separate x plots
df=DataFrame()
df.TESTMODE = True
df.read_tbl('data\suppression~subjectXgroupXageXcycleXphase.csv')
D = df.interaction_plot('SUPPRESSION','CYCLE',
sepxplots='PHASE',
sepyplots='GROUP',
yerr='ci',
where=[('GROUP','not in',['LAB'])],
output_dir='output')
self.assertEqual(D['aggregate'], R['aggregate'])
self.assertEqual(D['clevels'], R['clevels'])
self.assertEqual(D['rlevels'], R['rlevels'])
self.assertEqual(D['numcols'], R['numcols'])
self.assertEqual(D['numrows'], R['numrows'])
self.assertEqual(D['fname'], R['fname'])
self.assertEqual(D['maintitle'], R['maintitle'])
self.assertEqual(D['subplot_titles'], R['subplot_titles'])
self.assertAlmostEqual(D['ymin'], R['ymin'])
self.assertAlmostEqual(D['ymax'], R['ymax'])
for d,r in zip(np.array(D['y']).flat,
np.array(R['y']).flat):
self.assertAlmostEqual(d,r)
for d,r in zip(np.array(D['yerr']).flat,
np.array(R['yerr']).flat):
self.assertAlmostEqual(d,r)
