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Copy pathstroke_data_parser.py
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305 lines (284 loc) · 14.5 KB
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import csv
from random import shuffle
class StrokeData():
def __init__(self, batch_size, process_id, is_shuffled, included_patients):
with open("actigraph_reads/DW-001-both_merged.csv") as pat_01: csv_1_list = list(csv.reader(pat_01, delimiter=","))
with open("actigraph_reads/DW-002-both_merged.csv") as pat_02: csv_2_list = list(csv.reader(pat_02, delimiter=","))
with open("actigraph_reads/DW-003-both_merged.csv") as pat_03: csv_3_list = list(csv.reader(pat_03, delimiter=","))
with open("actigraph_reads/DW-004-both_merged.csv") as pat_04: csv_4_list = list(csv.reader(pat_04, delimiter=","))
with open("actigraph_reads/DW-005-both_merged.csv") as pat_05: csv_5_list = list(csv.reader(pat_05, delimiter=","))
with open("actigraph_reads/DW-006-both_merged.csv") as pat_06: csv_6_list = list(csv.reader(pat_06, delimiter=","))
with open("actigraph_reads/DW-007-both_merged.csv") as pat_07: csv_7_list = list(csv.reader(pat_07, delimiter=","))
with open("actigraph_reads/DW-009-both_merged.csv") as pat_09: csv_9_list = list(csv.reader(pat_09, delimiter=","))
with open("actigraph_reads/DW-010-both_merged.csv") as pat_10: csv_10_list = list(csv.reader(pat_10, delimiter=","))
with open("actigraph_reads/DW-011-both_merged.csv") as pat_11: csv_11_list = list(csv.reader(pat_11, delimiter=","))
with open("actigraph_reads/DW-013-both_merged.csv") as pat_13: csv_13_list = list(csv.reader(pat_13, delimiter=","))
with open("actigraph_reads/DW-014-both_merged.csv") as pat_14: csv_14_list = list(csv.reader(pat_14, delimiter=","))
with open("actigraph_reads/DW-015-both_merged.csv") as pat_15: csv_15_list = list(csv.reader(pat_15, delimiter=","))
with open("actigraph_reads/DW-016-both_merged.csv") as pat_16: csv_16_list = list(csv.reader(pat_16, delimiter=","))
with open("actigraph_reads/DW-020-both_merged.csv") as pat_20: csv_20_list = list(csv.reader(pat_20, delimiter=","))
pat_to_csv_list = {
1 : csv_1_list,
2 : csv_2_list,
3 : csv_3_list,
4 : csv_4_list,
5 : csv_5_list,
6 : csv_6_list,
7 : csv_7_list,
9 : csv_9_list,
10 : csv_10_list,
11 : csv_11_list,
13 : csv_13_list,
14 : csv_14_list,
15 : csv_15_list,
16 : csv_16_list,
20 : csv_20_list
}
csv_file_list = []
for i in included_patients:
csv_file_list.append(pat_to_csv_list[i])
self.bsz = batch_size
self.is_shuffled = is_shuffled
self.process_id = process_id
self.current_position = 0
self.labelsDict = {
1 : [1,1,1,1,1,1,1,1],
2 : [0,0,0,0],
3 : [0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0],
4 : [0,0,0,0],
5 : [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1],
6 : [1,1,1,0,0,1,0,1],
7 : [0,0,0,0,0,0],
9 : [0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],
10 : [0,1,1,1,1,1,0,1,1,1,1,1,1,0,0,1,0,1,0,1,0,0],
11 : [1,1,1,0,1,1],
12 : [0,0,0,0],
13 : [0,0,0,0,0,0,0,0,0,0,0],
14 : [0,0,1,0,0,1,0,1,0,0,0,0,0],
15 : [1,1,1,1,1,1,1,0,1,1],
16 : [0,0,1,1,1,1,1,1,1,1,1,1,1,1],
20 : [1,1,1,1,1,1]
}
self.onLeftDict = {1: False, 2: False, 3: False, 4: False, 5: True, 6: False, 7: False, 9:False, 10:False, 11:True, 12 : False, 13:False, 14:False, 15: True, 16:False, 20:False}
running_batches, running_labels = [], []
available_patients = [1,2,3,4,5,6,7,9,10,11,13,14,15,16,20]
for i in included_patients:
patientId = i
single_patient_batches, single_patient_labels = self.create_batches_and_labels(self.bsz, pat_to_csv_list[patientId], self.labelsDict[patientId], patientId)
running_batches = running_batches + single_patient_batches
running_labels = running_labels + single_patient_labels
self.batches, self.labels = running_batches, running_labels
if self.is_shuffled:
self.batches, self.labels = self.shuffle_days(self.batches, self.labels)
else:
self.batches, self.labels = self.batches, self.labels
def place_paretic_side_first(self, days_list, patient_id):
if self.onLeftDict[patient_id]:
return days_list
else:
for i in range(len(days_list)):
for j in range(len(days_list[i])):
left_measures = days_list[i][j][2:9]
right_measures = days_list[i][j][11:]
days_list[i][j] = days_list[i][j][:2] + right_measures + days_list[i][j][9:11] + left_measures
return days_list
def split_by_day(self, csv_file, patient_id):
csv_file.pop(0)
single_day_list, days_list, current_date = [], [], csv_file[0][0]
for entry in csv_file:
if entry[0] == current_date:
single_day_list.append(entry)
else:
current_date = entry[0]
days_list.append(single_day_list)
single_day_list = [entry]
return self.place_paretic_side_first(days_list, patient_id)
def create_batches_and_labels(self, bsz, csv_file, labels_list, patient_id):
batches, labels = [], []
days_list = self.split_by_day(csv_file, patient_id)
for i in range(len(days_list)):
if i == 0:
c = 1
if len(days_list) == len(labels_list):
lst = days_list[i]
label = labels_list[i]
# line incompatible with python 2 and earlier
day_split_batches = [lst[j:j + bsz] for j in range(0, len(lst), bsz)]
day_split_labels = [label] * len(day_split_batches)
batches, labels = batches + day_split_batches, labels + day_split_labels
return batches, labels
def get_next_batch(self):
bch = self.batches.pop()
l = self.labels.pop()
lbl = [l] * len(bch)
return self.process(self.process_id, bch, lbl)
def shuffle_days(self, batches, labels):
a = [i for i in range(0, len(batches))]
shuffle(a)
shuffled_batches = []
shuffled_labels = []
for index in a:
shuffled_batches.append(batches[index])
shuffled_labels.append(labels[index])
return shuffled_batches, shuffled_labels
def process(self, process_id, batch, lbl):
processed_reads = []
processed_labels = []
if process_id == 1:
for read in batch:
try:
paretic_pos1 = float(read[2])
paretic_pos2 = float(read[3])
paretic_pos3 = float(read[4])
paretic_pos4 = float(read[5])
paretic_pos5 = float(read[6])
paretic_pos6 = float(read[7])
paretic_pos7 = float(read[8])
non_paretic_pos1 = float(read[11])
non_paretic_pos2 = float(read[12])
non_paretic_pos3 = float(read[13])
non_paretic_pos4 = float(read[14])
non_paretic_pos5 = float(read[15])
non_paretic_pos6 = float(read[16])
non_paretic_pos7 = float(read[17])
t = [paretic_pos1 - non_paretic_pos1, paretic_pos2 - non_paretic_pos2,
paretic_pos3 - non_paretic_pos3, paretic_pos4 - non_paretic_pos4,
paretic_pos5 - non_paretic_pos5, paretic_pos6 - non_paretic_pos6,
paretic_pos7 - non_paretic_pos7]
processed_reads.append(t)
except ValueError:
pass
l = lbl[0]
for label in range(len(processed_reads)):
if l == 0:
s = [1,0]
else:
s = [0, 1]
processed_labels.append(s)
elif process_id == 2:
for read in batch:
try:
paretic_pos1 = float(read[2])
paretic_pos2 = float(read[3])
paretic_pos3 = float(read[4])
paretic_pos4 = float(read[5])
paretic_pos5 = float(read[6])
paretic_pos6 = float(read[7])
paretic_pos7 = float(read[8])
non_paretic_pos1 = float(read[11])
non_paretic_pos2 = float(read[12])
non_paretic_pos3 = float(read[13])
non_paretic_pos4 = float(read[14])
non_paretic_pos5 = float(read[15])
non_paretic_pos6 = float(read[16])
non_paretic_pos7 = float(read[17])
t = [non_paretic_pos6, paretic_pos6, non_paretic_pos6 - paretic_pos6]
processed_reads.append(t)
except ValueError:
pass
l = lbl[0]
for label in range(len(processed_reads)):
if l == 0:
s = [1,0]
else:
s = [0, 1]
processed_labels.append(s)
elif process_id == 3:
for read in batch:
try:
paretic_pos1 = float(read[2])
paretic_pos2 = float(read[3])
paretic_pos3 = float(read[4])
paretic_pos4 = float(read[5])
paretic_pos5 = float(read[6])
paretic_pos6 = float(read[7])
paretic_pos7 = float(read[8])
non_paretic_pos1 = float(read[11])
non_paretic_pos2 = float(read[12])
non_paretic_pos3 = float(read[13])
non_paretic_pos4 = float(read[14])
non_paretic_pos5 = float(read[15])
non_paretic_pos6 = float(read[16])
non_paretic_pos7 = float(read[17])
t = [paretic_pos1, non_paretic_pos1, paretic_pos1 - non_paretic_pos1, non_paretic_pos6, paretic_pos6, non_paretic_pos6 - paretic_pos6]
processed_reads.append(t)
except ValueError:
pass
l = lbl[0]
for label in range(len(processed_reads)):
if l == 0:
s = [1,0]
else:
s = [0, 1]
processed_labels.append(s)
elif process_id == 4:
for read in batch:
try:
paretic_pos1 = float(read[2])
paretic_pos2 = float(read[3])
paretic_pos3 = float(read[4])
paretic_pos4 = float(read[5])
paretic_pos5 = float(read[6])
paretic_pos6 = float(read[7])
paretic_pos7 = float(read[8])
non_paretic_pos1 = float(read[11])
non_paretic_pos2 = float(read[12])
non_paretic_pos3 = float(read[13])
non_paretic_pos4 = float(read[14])
non_paretic_pos5 = float(read[15])
non_paretic_pos6 = float(read[16])
non_paretic_pos7 = float(read[17])
t = [paretic_pos1, non_paretic_pos1, paretic_pos1 - non_paretic_pos1,
paretic_pos2, non_paretic_pos2, paretic_pos2 - non_paretic_pos2,
paretic_pos3, non_paretic_pos3, paretic_pos3 - non_paretic_pos3,
paretic_pos4, non_paretic_pos4, paretic_pos4 - non_paretic_pos4,
paretic_pos5, non_paretic_pos5, paretic_pos5 - non_paretic_pos5,
non_paretic_pos6, paretic_pos6, non_paretic_pos6 - paretic_pos6,
paretic_pos7, non_paretic_pos7, paretic_pos7 - non_paretic_pos7]
processed_reads.append(t)
except ValueError:
pass
l = lbl[0]
for label in range(len(processed_reads)):
if l == 0:
s = [1,0]
else:
s = [0, 1]
processed_labels.append(s)
elif process_id == 5:
for read in batch:
try:
read_date = read[0]
read_time = read[1]
paretic_pos1 = float(read[2])
paretic_pos2 = float(read[3])
paretic_pos3 = float(read[4])
paretic_pos4 = float(read[5])
paretic_pos5 = float(read[6])
paretic_pos6 = float(read[7])
paretic_pos7 = float(read[8])
non_paretic_pos1 = float(read[11])
non_paretic_pos2 = float(read[12])
non_paretic_pos3 = float(read[13])
non_paretic_pos4 = float(read[14])
non_paretic_pos5 = float(read[15])
non_paretic_pos6 = float(read[16])
non_paretic_pos7 = float(read[17])
t = [read_date, read_time, paretic_pos1, non_paretic_pos1, paretic_pos1 - non_paretic_pos1,
paretic_pos2, non_paretic_pos2, paretic_pos2 - non_paretic_pos2,
paretic_pos3, non_paretic_pos3, paretic_pos3 - non_paretic_pos3,
paretic_pos4, non_paretic_pos4, paretic_pos4 - non_paretic_pos4,
paretic_pos5, non_paretic_pos5, paretic_pos5 - non_paretic_pos5,
non_paretic_pos6, paretic_pos6, non_paretic_pos6 - paretic_pos6,
paretic_pos7, non_paretic_pos7, paretic_pos7 - non_paretic_pos7]
processed_reads.append(t)
except ValueError:
pass
l = lbl[0]
for label in range(len(processed_reads)):
if l == 0:
s = [1,0]
else:
s = [0, 1]
processed_labels.append(s)
return processed_reads, processed_labels