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saveDataSets_v2.py
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from PIL import Image
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
def saveDataSets(train_data, train_data_noisy, test_data, test_data_noisy):
for i,j in enumerate(train_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_v2/trainB/%s.png' %str(i))
for i,j in enumerate(train_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_v2/trainA/%s.png' %str(i))
for i,j in enumerate(test_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_v2/testB/%s.png' %str(i))
for i,j in enumerate(test_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_v2/testA/%s.png' %str(i))
def max_min_mapper(test_data, test_data_noisy):
dic = {}
itr = 0
# print(test_data_noisy)
for i, j in enumerate(test_data):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
ppg = pd.DataFrame.from_dict(dic, orient='index')
ppg.to_csv('noise2ppg_v2/ppg.csv', index=False)
dic = {}
itr = 0
for i, j in enumerate(test_data_noisy):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
abp = pd.DataFrame.from_dict(dic, orient='index')
abp.to_csv('noise2ppg_v2/abp.csv', index=False)
def saveDataSets_mimicIII(train_data, train_data_noisy, test_data, test_data_noisy):
for i,j in enumerate(train_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII/trainB/%s.png' %str(i))
for i,j in enumerate(train_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII/trainA/%s.png' %str(i))
for i,j in enumerate(test_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII/testB/%s.png' %str(i))
for i,j in enumerate(test_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII/testA/%s.png' %str(i))
def max_min_mapper_mimicIII(test_data, test_data_noisy):
dic = {}
itr = 0
# print(test_data_noisy)
for i, j in enumerate(test_data):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
ppg = pd.DataFrame.from_dict(dic, orient='index')
ppg.to_csv('noise2ppg_mimicIII/ppg.csv', index=False)
dic = {}
itr = 0
for i, j in enumerate(test_data_noisy):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
abp = pd.DataFrame.from_dict(dic, orient='index')
abp.to_csv('noise2ppg_mimicIII/abp.csv', index=False)
def saveDataSets_mimicIII_subject(train_data, train_data_noisy, test_data, test_data_noisy, subject_id):
for i,j in enumerate(train_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_s'+str(subject_id)+'/trainB/%s.png' %str(i))
for i,j in enumerate(train_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_s'+str(subject_id)+'/trainA/%s.png' %str(i))
for i,j in enumerate(test_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_s'+str(subject_id)+'/testB/%s.png' %str(i))
for i,j in enumerate(test_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_s'+str(subject_id)+'/testA/%s.png' %str(i))
def max_min_mapper_mimicIII_subject(test_data, test_data_noisy, subject_id):
dic = {}
itr = 0
# print(test_data_noisy)
for i, j in enumerate(test_data):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
ppg = pd.DataFrame.from_dict(dic, orient='index')
ppg.to_csv('noise2ppg_mimicIII_s'+str(subject_id)+'/ppg.csv', index=False)
dic = {}
itr = 0
for i, j in enumerate(test_data_noisy):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
abp = pd.DataFrame.from_dict(dic, orient='index')
abp.to_csv('noise2ppg_mimicIII_s'+str(subject_id)+'/abp.csv', index=False)
def saveDataSets_mimicIII_all(train_data, train_data_noisy, test_data, test_data_noisy):
for i,j in enumerate(train_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all/trainB/%s.png' %str(i))
for i,j in enumerate(train_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all/trainA/%s.png' %str(i))
for i,j in enumerate(test_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all/testB/%s.png' %str(i))
for i,j in enumerate(test_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all/testA/%s.png' %str(i))
def max_min_mapper_mimicIII_all(test_data, test_data_noisy):
dic = {}
itr = 0
# print(test_data_noisy)
for i, j in enumerate(test_data):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
ppg = pd.DataFrame.from_dict(dic, orient='index')
ppg.to_csv('noise2ppg_mimicIII_all/ppg.csv', index=False)
dic = {}
itr = 0
for i, j in enumerate(test_data_noisy):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
abp = pd.DataFrame.from_dict(dic, orient='index')
abp.to_csv('noise2ppg_mimicIII_all/abp.csv', index=False)
def saveDataSets_mimicIII_all_subject(train_data, train_data_noisy, test_data, test_data_noisy, subject_id):
for i,j in enumerate(train_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all_s'+str(subject_id)+'/trainB/%s.png' %str(i))
for i,j in enumerate(train_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all_s'+str(subject_id)+'/trainA/%s.png' %str(i))
for i,j in enumerate(test_data):
rescaled = ((255.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all_s'+str(subject_id)+'/testB/%s.png' %str(i))
for i,j in enumerate(test_data_noisy):
rescaled = ((155.0 / (j.max() - j.min())) * (j - j.min())).astype(np.uint8)
#rescaled = np.array(j).astype(np.uint8)
im = Image.fromarray(rescaled)
im.save('noise2ppg_mimicIII_all_s'+str(subject_id)+'/testA/%s.png' %str(i))
def max_min_mapper_mimicIII_all_subject(test_data, test_data_noisy, subject_id):
dic = {}
itr = 0
# print(test_data_noisy)
for i, j in enumerate(test_data):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
ppg = pd.DataFrame.from_dict(dic, orient='index')
ppg.to_csv('noise2ppg_mimicIII_all_s'+str(subject_id)+'/ppg.csv', index=False)
dic = {}
itr = 0
for i, j in enumerate(test_data_noisy):
dic[itr] = {'min': j.min(), 'max': j.max(), 'key': i}
itr += 1
abp = pd.DataFrame.from_dict(dic, orient='index')
abp.to_csv('noise2ppg_mimicIII_all_s'+str(subject_id)+'/abp.csv', index=False)