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mattDataAug.py
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mattDataAug.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 9 01:55:15 2021
@author: rodrigosandon
"""
import dataloader
from itertools import chain
import pandas as pd
import numpy as np
import os, random
import csv
class DataAugmentation:
def __init__(self, data_path, amount_to_make, regions):
self.data_path = data_path
self.amount_to_make = amount_to_make
self.regions = regions
def chooseRandomFile(list_of_csv_dirs):
rand_csvfile = random.choice(list_of_csv_dirs)
return rand_csvfile
def random_noise(self, number_to_be_generated, rows, columns): #random chosen file as input
self.loader.generateClassSummaries()
for region in self.loader.regions:
region_mean_matrix = self.loader.summaries[region][0]
region_std_matrix = self.loader.summaries[region][1]
#mean and std matrix for summarizing each region
for i in range(0, number_to_be_generated):
generated = np.zeros((rows, columns))
for row in range(0, rows):
for col in range(0, columns):
generated[row][col] = max(-1, min(1, np.random.normal(region_mean_matrix[row][col], region_std_matrix[row][col])))
#np.savetxt("Data_generated\\{Region}\\{Region}_generated_{index}.csv".format(Region=region, index=i), generated, delimiter=",")
def combineDataOfFolders(base_path):
list_of_csv_paths = []
for folders in os.listdir(base_path):
for csvs in os.listdir(base_path+ folders):
if not csvs.startswith("._"):
list_of_csv_paths.append(base_path+ folders + "/" + csvs)
return list_of_csv_paths
def makeNameForNewNoisyCSV(csv_path):
#/Volumes/Passport/ResearchDataChen/Code/Data/allBrainRegionsnorm/visrl/553568031_visrl_normalized_corrmap.csv
pieces = csv_path.split("/")
piece = pieces[8]
new_name = "noisy_"+ piece
return new_name
def folderPathOfCSV(csv_path):
pieces = csv_path.split("/")
folder_path = pieces[7] + "/"
return folder_path
def oneDimToTwoDim(lst, num_rows):
return [lst[i:i+num_rows] for i in range(0, len(lst), num_rows)]
#nvm its not a problem, this is just called additive noise
num_files_to_generate = 245
regions = ['visal', 'visam', 'visl', 'visp', 'vispm','visrl' ] #this needs to be kept the same if we want unsupervised
data_path = "/Volumes/Passport/ResearchDataChen/Code/Data/allBrainRegionsnorm/" #needs to be basename for the different regions folder
files = DataAugmentation.combineDataOfFolders(data_path)
mylist = list(files)
num_generated_files = 0
while num_generated_files < num_files_to_generate:
csv_path = DataAugmentation.chooseRandomFile(mylist)
reader = csv.reader(open(csv_path, "rt"), delimiter = ",")
x = list(reader)
flat = list(chain.from_iterable(x))
csv_to_arr = np.array(flat).astype(float) #convert all numbers in list to floats
print(csv_to_arr)
new_arr = DataAugmentation.random_noise(csv_to_arr)
unflat_arr = DataAugmentation.oneDimToTwoDim(new_arr, 32)
df = pd.DataFrame(unflat_arr, index = None, columns = None)
df.to_csv(data_path + DataAugmentation.folderPathOfCSV(csv_path) + DataAugmentation.makeNameForNewNoisyCSV(csv_path), header=False, index=False)
#np.savetxt(data_path + DataAugmentation.folderPathOfCSV(csv_path) + DataAugmentation.makeNameForNewNoisyCSV(csv_path), [unflat_arr], delimiter=',')
num_generated_files += 1
# import dataloader
# import numpy as np
# class DataGenerator:
# def __init__(self, path, to_search):
# self.loader = dataloader.DataLoader(path, to_search)
# def generateData(self):
# self.loader.generateClassSummaries()
# total_number_of_generated = 300
# rows = 32
# columns = 32
# for region in self.loader.regions:
# print(region)
# region_mean_matrix = self.loader.summaries[region][0]
# region_std_matrix = self.loader.summaries[region][1]
# for i in range(0, total_number_of_generated):
# generated = np.zeros((rows, columns))
# for row in range(0, rows):
# for col in range(0, columns):
# generated[row][col] = max(-1, min(1, np.random.normal(region_mean_matrix[row][col], region_std_matrix[row][col])))
# np.savetxt("Data_generated\\{Region}\\{Region}_generated_{index}.csv".format(Region=region, index=i), generated, delimiter=",")
# generator = DataGenerator("Data\\allBrainRegionsraw", ["visal", "visam", "visl", "visp", "vispm", "visrl"])
# generator.generateData()