def test_result_attributes(self):
np.random.seed(1234567)
outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
res = mstats.ttest_rel(outcome[:, 0], outcome[:, 1])
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_result_attributes(self):
np.random.seed(1234567)
outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
res = mstats.ttest_rel(outcome[:, 0], outcome[:, 1])
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def get_test(self, test, pbase, pcomp, multi_test=False): if np.array_equal(pbase.values, pcomp.values): pvalue = 1. else: if test == 'student': (tvalue, pvalue) = ttest_rel(pbase, pcomp) elif test == 'wilcoxon': (tvalue, pvalue) = wilcoxon(pbase, pcomp) elif test == 'welcht': (tvalue, pvalue) = ttest_ind(pbase, pcomp, equal_var=False) if pvalue < 0.05: pbase_mean = pbase.mean() pcomp_mean = pcomp.mean() if pvalue < 0.01: if pbase_mean > pcomp_mean: result_test = '▼ ' else: result_test = '▲ ' else: if pbase_mean > pcomp_mean: result_test = 'ᐁ ' else: result_test = 'ᐃ ' else: if not multi_test: result_test = ' ' else: result_test = '⏺ ' return result_test
def test_vs_nonmasked(self):
np.random.seed(1234567)
outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
# 1-D inputs
res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1])
res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1])
assert_allclose(res1, res2)
# 2-D inputs
res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None)
res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None)
assert_allclose(res1, res2)
res1 = stats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0)
res2 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0)
assert_allclose(res1, res2)
# Check default is axis=0
res3 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:])
assert_allclose(res2, res3)
def test_vs_nonmasked(self):
np.random.seed(1234567)
outcome = np.random.randn(20, 4) + [0, 0, 1, 2]
# 1-D inputs
res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1])
res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1])
assert_allclose(res1, res2)
# 2-D inputs
res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None)
res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None)
assert_allclose(res1, res2)
res1 = stats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0)
res2 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0)
assert_allclose(res1, res2)
# Check default is axis=0
res3 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:])
assert_allclose(res2, res3)
def test_empty(self):
res1 = mstats.ttest_rel([], [])
assert_(np.all(np.isnan(res1)))
def test_empty(self):
res1 = mstats.ttest_rel([], [])
assert_(np.all(np.isnan(res1)))
def ttests(path_to_dict, name_dict='\\TOTresults'):
import numpy as np
import fnmatch
from dictmanager import load_obj
#NOTES TO UNDERSTAND NOTATIONS
#d = list of distances from reference point
#fl = list of FL
#PAs = list of PA sup
#PAi = list of PA inf
#mt = list of MT
#_s : simple images
#_p : panoramic images
#_m : manual
#_a : automated
#_filtered: matched fascicles only
participants=['01_Kevin', '02_rafaelopes', '03_charlesbarrand', '04_guilhem',\
'05_leandre', '06_thomasmartine', '10_victor',\
'11_youssouf', '12_sufyan', '16_julien', '34_nicolas']
'************************************************************************'
'*****************************INITIALIZATION*****************************'
d_s_m = [[] for par in range(len(participants))]
mt_s_m = [[] for par in range(len(participants))]
d_s_m_filtered = [[] for par in range(len(participants))]
fl_s_m_filtered = [[] for par in range(len(participants))]
PAs_s_m_filtered = [[] for par in range(len(participants))]
PAi_s_m_filtered = [[] for par in range(len(participants))]
d_s_a = [[] for par in range(len(participants))]
mt_s_a = [[] for par in range(len(participants))]
d_s_a_filtered = [[] for par in range(len(participants))]
fl_s_a_filtered = [[] for par in range(len(participants))]
PAs_s_a_filtered = [[] for par in range(len(participants))]
PAi_s_a_filtered = [[] for par in range(len(participants))]
d_p_m = [[] for par in range(len(participants))]
mt_p_m = [[] for par in range(len(participants))]
d_p_m_filtered = [[] for par in range(len(participants))]
fl_p_m_filtered = [[] for par in range(len(participants))]
PAs_p_m_filtered = [[] for par in range(len(participants))]
PAi_p_m_filtered = [[] for par in range(len(participants))]
d_p_a = [[] for par in range(len(participants))]
mt_p_a = [[] for par in range(len(participants))]
d_p_a_filtered = [[] for par in range(len(participants))]
fl_p_a_filtered = [[] for par in range(len(participants))]
PAs_p_a_filtered = [[] for par in range(len(participants))]
PAi_p_a_filtered = [[] for par in range(len(participants))]
#stats on the number of fascicles detected
nb_fasc_tot_s = 0
nb_fasc_in_s = 0
nb_fasc_filt_s = 0
nb_images_s = 0
nb_fasc_tot_p = 0
nb_fasc_in_p = 0
nb_fasc_filt_p = 0
nb_images_p = 0
'************************************************************************'
'*****************************DATA RETRIEVAL*****************************'
dictio = load_obj(name_dict, path_to_dict)
l2 = ['fasc*', 'fsc_*']
for par in range(len(participants)):
participant = participants[par]
fam_folders = [str(d) for d in dictio[participant].keys()]
s_manuFasc = []
s_autoFasc = []
p_manuFasc = []
p_autoFasc = []
for fam in fam_folders:
###################################################################
# simple images
dictioS = dictio[participant][fam]['BF']['simple']
images = [str(im) for im in dictioS.keys()]
for i in images:
# if par == 9 and fam =='fam_2' and i=='img_2':
# print(par, fam, i)
# else:
nb_images_s = nb_images_s + 1
###############################################################
# SIMPLE - manual
dictioM = dictioS[i]['architecture manual']
fascicles = [
str(fa) for fa in dictioM if any(
fnmatch.fnmatch(fa, p) for p in l2)
]
for f in fascicles:
dictioF = dictioM[f]
idf = fam + '/' + i + '/' + f
if len(dictioF.keys()) > 1:
s_manuFasc.append(
(idf,
dictioF['dist from (0,0) of RGB image, in mm']))
d_s_m[par].append(
dictioF['dist from (0,0) of RGB image, in mm'])
###############################################################
# SIMPLE - automatic
if ('architecture auto' in dictioS[i]):
dictioA = dictioS[i]['architecture auto']
midRow = np.mean(dictioA['crop']['lines'])
midCol = np.mean(dictioA['crop']['columns'])
if dictioA and ('MT' in dictioA):
fascicles = [
fa for fa in dictioA if any(
fnmatch.fnmatch(fa, p) for p in l2)
]
nb_fasc_tot_s = nb_fasc_tot_s + len(fascicles)
for f in fascicles:
dictioF = dictioA[f]
idf = fam + '/' + i + '/' + f
if len(dictioF.keys()) > 1:
#keep the fascicles that are in the lower half of the image,
#to compare with manual data - often taken in that region
PAi = dictioF['PAinf']['intersection with apo']
PAs = dictioF['PAsup']['intersection with apo']
fasc_row = (PAs[0] - PAi[0]) / (
PAs[1] - PAi[1]) * (midCol -
PAs[1]) + PAs[0]
if fasc_row <= midRow:
s_autoFasc.append((idf, dictioF[
'dist from (0,0) of RGB image, in mm']
))
d_s_a[par].append(dictioF[
'dist from (0,0) of RGB image, in mm'])
nb_fasc_in_s = nb_fasc_in_s + 1
if ('MT for labelled points' in dictioM['MT']):
for ind0 in range(
len(dictioM['MT']
['MT for labelled points'])):
elem = dictioM['MT']['MT for labelled points'][
ind0]
if elem != 'error':
mt_s_m[par].append(elem) #MT in mm
for ind0 in range(
len(dictioA['MT']
['MT for labelled points'])):
elem = dictioA['MT']['MT for labelled points'][
ind0]
if elem != 'error':
mt_s_a[par].append(elem)
###################################################################
# panoramic images
dictioP = dictio[participant][fam]['BF']['panoramic']
images = [str(im) for im in dictioP.keys()]
for i in images:
nb_images_p = nb_images_p + 1
###############################################################
# PANORAMIC - manual
dictioM = dictioP[i]['architecture manual']
fascicles = [
fa for fa in dictioM if any(
fnmatch.fnmatch(fa, p) for p in l2)
]
for f in fascicles:
dictioF = dictioM[f]
idf = fam + '/' + i + '/' + f
if len(dictioF.keys()) > 1:
p_manuFasc.append(
(idf, dictioF['dist from insertion in mm']))
d_p_m[par].append(dictioF['dist from insertion in mm'])
###############################################################
# PANORAMIC - automatic
if ('architecture auto' in dictioP[i]):
dictioA = dictioP[i]['architecture auto']
if dictioA and ('MT' in dictioA):
fascicles = [
fa for fa in dictioA if any(
fnmatch.fnmatch(fa, p) for p in l2)
]
nb_fasc_tot_p = nb_fasc_tot_p + len(fascicles)
for f in fascicles:
dictioF = dictioA[f]
idf = fam + '/' + i + '/' + f
#only keep fascicles that are entirely within the cropped image,
#to compare with manually identified fascicles
if len(dictioF.keys()) > 1 and dictioF['FL'][
'in/out of the image'] == 'in image':
nb_fasc_in_p = nb_fasc_in_p + 1
p_autoFasc.append(
(idf,
dictioF['dist from insertion in mm']))
d_p_a[par].append(
dictioF['dist from insertion in mm'])
if ('MT for labelled points' in dictioM['MT']):
for ind0 in range(
len(dictioM['MT']
['MT for labelled points'])):
elem = dictioM['MT']['MT for labelled points'][
ind0]
if elem != 'error':
mt_p_m[par].append(elem) #MT in mm
for ind0 in range(
len(dictioA['MT']
['MT for labelled points'])):
elem = dictioA['MT']['MT for labelled points'][
ind0]
if elem != 'error':
mt_p_a[par].append(elem)
'************************************************************************'
'********************MATCHING AUTO & MANUAL FASCICLES*******************'
listePair_manuF_s = []
for n in range(len(s_manuFasc)):
mf = s_manuFasc[n]
subtr = [(tup, abs(tup[1] - mf[1])) for tup in s_autoFasc]
subtr.sort(key=lambda x: x[1])
closest = subtr[0]
listePair_manuF_s.append(
(mf[0], closest[0][0], closest[1])
) #tuple = ( ID manu fasc, ID auto fasc, distance entre les deux)
listePair_manuF_s.sort(key=lambda x: x[1])
uniqueMatching = []
counterL = 0
while counterL < len(listePair_manuF_s):
currentAutoFasc = listePair_manuF_s[counterL][1]
correspondingAutoFasc = [(listePair_manuF_s[counterL][0],
listePair_manuF_s[counterL][2])]
rank = counterL + 1
while rank < len(listePair_manuF_s) and listePair_manuF_s[rank][
1] == currentAutoFasc:
correspondingAutoFasc.append(
(listePair_manuF_s[rank][0], listePair_manuF_s[rank][2]))
rank = rank + 1
correspondingAutoFasc.sort(key=lambda x: x[1])
uniqueMatching.append(
(correspondingAutoFasc[0][0], currentAutoFasc,
correspondingAutoFasc[0][1]))
counterL = rank
for element in uniqueMatching:
pathA = element[1].split('/')
pathM = element[0].split('/')
nb_fasc_filt_s = nb_fasc_filt_s + 1
d_s_m_filtered[par].append(dictio[participant][
pathM[0]]['BF']['simple'][pathM[1]]['architecture manual'][
pathM[2]]['dist from (0,0) of RGB image, in mm'])
fl_s_m_filtered[par].append(
dictio[participant][pathM[0]]['BF']['simple'][pathM[1]]
['architecture manual'][pathM[2]]['FL']['length in mm'])
PAs_s_m_filtered[par].append(
dictio[participant][pathM[0]]['BF']['simple'][pathM[1]]
['architecture manual'][pathM[2]]['PAsup']['value in degree'])
PAi_s_m_filtered[par].append(
dictio[participant][pathM[0]]['BF']['simple'][pathM[1]]
['architecture manual'][pathM[2]]['PAinf']['value in degree'])
d_s_a_filtered[par].append(dictio[participant][
pathA[0]]['BF']['simple'][pathA[1]]['architecture auto'][
pathA[2]]['dist from (0,0) of RGB image, in mm'])
fl_s_a_filtered[par].append(
dictio[participant][pathA[0]]['BF']['simple'][pathA[1]]
['architecture auto'][pathA[2]]['FL']['length in mm'])
PAs_s_a_filtered[par].append(
dictio[participant][pathA[0]]['BF']['simple'][pathA[1]]
['architecture auto'][pathA[2]]['PAsup']['value in degree'])
PAi_s_a_filtered[par].append(
dictio[participant][pathA[0]]['BF']['simple'][pathA[1]]
['architecture auto'][pathA[2]]['PAinf']['value in degree'])
listePair_manuF_p = []
for n in range(len(p_manuFasc)):
mf = p_manuFasc[n]
subtr = [(tup, abs(tup[1] - mf[1])) for tup in p_autoFasc]
subtr.sort(key=lambda x: x[1])
closest = subtr[0]
listePair_manuF_p.append(
(mf[0], closest[0][0], closest[1])
) #tuple = ( ID manu fasc, ID auto fasc, distance entre les deux)
listePair_manuF_p.sort(key=lambda x: x[1])
uniqueMatching = []
counterL = 0
while counterL < len(listePair_manuF_p):
currentAutoFasc = listePair_manuF_p[counterL][1]
correspondingAutoFasc = [(listePair_manuF_p[counterL][0],
listePair_manuF_p[counterL][2])]
rank = counterL + 1
while rank < len(listePair_manuF_p) and listePair_manuF_p[rank][
1] == currentAutoFasc:
correspondingAutoFasc.append(
(listePair_manuF_p[rank][0], listePair_manuF_p[rank][2]))
rank = rank + 1
correspondingAutoFasc.sort(key=lambda x: x[1])
uniqueMatching.append(
(correspondingAutoFasc[0][0], currentAutoFasc,
correspondingAutoFasc[0][1]))
counterL = rank
for element in uniqueMatching:
pathA = element[1].split('/')
pathM = element[0].split('/')
nb_fasc_filt_p = nb_fasc_filt_p + 1
d_p_m_filtered[par].append(
dictio[participant][pathM[0]]['BF']['panoramic'][pathM[1]]
['architecture manual'][pathM[2]]['dist from insertion in mm'])
fl_p_m_filtered[par].append(
dictio[participant][pathM[0]]['BF']['panoramic'][pathM[1]]
['architecture manual'][pathM[2]]['FL']['length in mm'])
PAs_p_m_filtered[par].append(
dictio[participant][pathM[0]]['BF']['panoramic'][pathM[1]]
['architecture manual'][pathM[2]]['PAsup']['value in degree'])
PAi_p_m_filtered[par].append(
dictio[participant][pathM[0]]['BF']['panoramic'][pathM[1]]
['architecture manual'][pathM[2]]['PAinf']['value in degree'])
d_p_a_filtered[par].append(
dictio[participant][pathA[0]]['BF']['panoramic'][pathA[1]]
['architecture auto'][pathA[2]]['dist from insertion in mm'])
fl_p_a_filtered[par].append(
dictio[participant][pathA[0]]['BF']['panoramic'][pathA[1]]
['architecture auto'][pathA[2]]['FL']['length in mm'])
PAs_p_a_filtered[par].append(
dictio[participant][pathA[0]]['BF']['panoramic'][pathA[1]]
['architecture auto'][pathA[2]]['PAsup']['value in degree'])
PAi_p_a_filtered[par].append(
dictio[participant][pathA[0]]['BF']['panoramic'][pathA[1]]
['architecture auto'][pathA[2]]['PAinf']['value in degree'])
#t_tests
print('paired samples t-tests resuts: ')
from scipy.stats.mstats import ttest_rel
#NOTES: we cannot user '..._filtered' arrays directly because of their structure
#we need to flatten them to 1-D lists
t, p = ttest_rel(
[item for sublist in PAs_s_m_filtered for item in sublist],
[item for sublist in PAs_s_a_filtered for item in sublist],
axis=None)
print('PAS s', p)
t2, p2 = ttest_rel(
[item for sublist in PAs_p_m_filtered for item in sublist],
[item for sublist in PAs_p_a_filtered for item in sublist],
axis=None)
print('PAS p', p2)
t3, p3 = ttest_rel(
[item for sublist in PAi_s_m_filtered for item in sublist],
[item for sublist in PAi_s_a_filtered for item in sublist],
axis=None)
print('PAI s', p3)
t4, p4 = ttest_rel(
[item for sublist in PAi_p_m_filtered for item in sublist],
[item for sublist in PAi_p_a_filtered for item in sublist],
axis=None)
print('PAI p', p4)
t5, p5 = ttest_rel(
[item for sublist in fl_s_m_filtered for item in sublist],
[item for sublist in fl_s_a_filtered for item in sublist],
axis=None)
print('FL s', p5)
t6, p6 = ttest_rel(
[item for sublist in fl_p_m_filtered for item in sublist],
[item for sublist in fl_p_a_filtered for item in sublist],
axis=None)
print('FL p', p6)
t7, p7 = ttest_rel([item for sublist in mt_s_m for item in sublist],
[item for sublist in mt_s_a for item in sublist],
axis=None)
print('mt s', p7)
t7_2, p7_2 = ttest_rel([np.mean(sublist) for sublist in mt_s_m],
[np.mean(sublist) for sublist in mt_s_a],
axis=None)
print('mt s for means', p7_2)
t8, p8 = ttest_rel([item for sublist in mt_p_m for item in sublist],
[item for sublist in mt_p_a for item in sublist],
axis=None)
print('mt p', p8)
t8_2, p8_2 = ttest_rel([np.mean(sublist) for sublist in mt_p_m],
[np.mean(sublist) for sublist in mt_p_a],
axis=None)
print('mt p for means', p8_2)
print('independent samples t-tests resuts: ')
from scipy.stats.mstats import ttest_rel, ttest_ind
#NOTES: we cannot user '..._filtered' arrays directly because of their structure
#we need to flatten them to 1-D lists
t, p = ttest_ind(
[item for sublist in PAs_s_m_filtered for item in sublist],
[item for sublist in PAs_s_a_filtered for item in sublist],
axis=None)
print('PAS s', p)
t2, p2 = ttest_ind(
[item for sublist in PAs_p_m_filtered for item in sublist],
[item for sublist in PAs_p_a_filtered for item in sublist],
axis=None)
print('PAS p', p2)
t3, p3 = ttest_ind(
[item for sublist in PAi_s_m_filtered for item in sublist],
[item for sublist in PAi_s_a_filtered for item in sublist],
axis=None)
print('PAI s', p3)
t4, p4 = ttest_ind(
[item for sublist in PAi_p_m_filtered for item in sublist],
[item for sublist in PAi_p_a_filtered for item in sublist],
axis=None)
print('PAI p', p4)
t5, p5 = ttest_ind(
[item for sublist in fl_s_m_filtered for item in sublist],
[item for sublist in fl_s_a_filtered for item in sublist],
axis=None)
print('FL s', p5)
t6, p6 = ttest_ind(
[item for sublist in fl_p_m_filtered for item in sublist],
[item for sublist in fl_p_a_filtered for item in sublist],
axis=None)
print('FL p', p6)
t7, p7 = ttest_ind([item for sublist in mt_s_m for item in sublist],
[item for sublist in mt_s_a for item in sublist],
axis=None)
print('mt s', p7)
t7_2, p7_2 = ttest_ind([np.mean(sublist) for sublist in mt_s_m],
[np.mean(sublist) for sublist in mt_s_a],
axis=None)
print('mt s for means', p7_2)
t8, p8 = ttest_ind([item for sublist in mt_p_m for item in sublist],
[item for sublist in mt_p_a for item in sublist],
axis=None)
print('mt p', p8)
t8_2, p8_2 = ttest_ind([np.mean(sublist) for sublist in mt_p_m],
[np.mean(sublist) for sublist in mt_p_a],
axis=None)
print('mt p for means', p8_2)
#size effects
s1 = sizeEffect(PAs_s_m_filtered, PAs_s_a_filtered)
s2 = sizeEffect(PAs_p_m_filtered, PAs_p_a_filtered)
s3 = sizeEffect(PAi_s_m_filtered, PAi_s_a_filtered)
s4 = sizeEffect(PAi_p_m_filtered, PAi_p_a_filtered)
s5 = sizeEffect(fl_s_m_filtered, fl_s_a_filtered)
s6 = sizeEffect(fl_p_m_filtered, fl_p_a_filtered)
s7 = sizeEffect(mt_s_m, mt_s_a)
s8 = sizeEffect(mt_p_m, mt_p_a)
print('Size effects: ')
print('PAS s', s1)
print('PAS p', s2)
print('PAi s', s3)
print('PAi p', s4)
print('fl s', s5)
print('fl p', s6)
print('mt s', s7)
print('mt p', s8)
mt_s_a_filt = [[] for par in range(len(participants))]
mt_s_m_filt = [[] for par in range(len(participants))]
for p in range(len(mt_s_a)):
for val in range(len(mt_s_a[p])):
if p == 9:
if mt_s_a[p][val] > mt_s_m[p][val] + 2.37 or mt_s_a[p][
val] < mt_s_m[p][val] - 2.08:
print('aberrante valeur: participant ', p, ' , place val ',
val)
else:
mt_s_a_filt[p].append(mt_s_a[p][val])
mt_s_m_filt[p].append(mt_s_m[p][val])
else:
mt_s_a_filt[p].append(mt_s_a[p][val])
mt_s_m_filt[p].append(mt_s_m[p][val])
print('apres avoir enleve les valeurs out of LoA: ')
t7, p7 = ttest_rel([item for sublist in mt_s_m_filt for item in sublist],
[item for sublist in mt_s_a_filt for item in sublist],
axis=None)
print('mt s', p7)
