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data_file_util.py
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data_file_util.py
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import csv
from astropy.io import fits
import os
import numpy as np
from scipy import misc
import shutil
##############################################################################################################
############################################# Labeled Data ###################################################
##############################################################################################################
NUM_DISTINCT_LABELS = 2
label_dict = {'noise':0, 'broad':0, 'signa':1, 'lowsnr':-1}
labeled_file_dir = "./Combined"
label_filename = labeled_file_dir + "/labels.csv"
NUM_NOISE_TRAINING = 45459
NUM_SIGNAL_TRAINING = 4824
SN_DATA_RATIO = float(NUM_NOISE_TRAINING)/NUM_SIGNAL_TRAINING #change when we add more data
def prepare_training_data():
"""
ARG1 - Directory name in which to find .fits files
RET1 - array of normalized images (ndarrays)
RET2 - array of ndarrays containing one-hot label vectors which also map image to a unique numeric id for use in dictionary d
RET3 - dictionary which maps unique numeric id of image to (fullpath, class_label, fname)
Note: bootstraps signal data so that RET1 has roughly as many signal images as noise images
Note: assumes following directory structure with three levels below working directory i.e.: ./Combined/noise/HIP1324_1
"""
print "Reading and training on files from {0}".format(labeled_file_dir)
xs = []
ys = []
d = {}
file_label_map = make_file_label_map()
file_tuples = get_all_fits_info(labeled_file_dir, file_label_map)
for unique_id, (fullpath, fname) in enumerate(file_tuples):
x = get_and_normalize_data(fullpath)
if x is None:
continue
class_label = file_label_map[fname]
d[unique_id] = (fullpath, class_label, fname)
y_vec = make_y_vec(class_label, unique_id)
if class_label == 0:
xs.append(x)
ys.append(y_vec)
if class_label == 1:
bootstrap_signal(xs, ys, x, y_vec)
return np.array(xs), np.array(ys), d
def make_file_label_map(filename = label_filename):
"""
ARGS - csv filename with labels
RET - dictionary to map filename -> signal/noise label
Side effects -
reads from csv file
"""
file_label_map = {} #fname-> label
with open(filename, 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
first = True
for row in reader:
if first:
first = False
continue
fname, label = row
file_label_map[fname] = int(label)
return file_label_map
def make_y_vec(class_label, unique_id):
"""
ARG1 - integer class label
ARG2 - unique numeric id for particular file
RET - 2x2 matrix where first row is one-hot label vector, second row first column has unique id
"""
y_vec = np.zeros((NUM_DISTINCT_LABELS, NUM_DISTINCT_LABELS))
y_vec[0][class_label] = 1
y_vec[1][0] = unique_id
return y_vec
def bootstrap_signal(xs, ys, x, y_vec):
"""
ARG1 - list of image ndarrays
ARG2 - list of label ndarrays
ARG3 - new image (labeled signal) to be added to ndarray some number of times according to a normal distribution
ARG4 - new image label to be added the same number of times
"""
std_dev = SN_DATA_RATIO/3.5 # set so that getting random value < 0 is very rare
number_to_add = int(np.random.normal(SN_DATA_RATIO, std_dev))
xs += number_to_add * [x]
ys += number_to_add * [y_vec]
##############################################################################################################
########################################### Unlabeled Data ###################################################
##############################################################################################################
def prepare_unlabeled_data(root_file_dir):
"""
ARG1 - Directory name in which to find .fits files
RET1 - array of normalized images (ndarrays)
RET2 - array of files from which those images came
"""
xs = []
filenames = []
for subdir, dirs, files in os.walk(root_file_dir):
for file in files:
fullpath = os.path.join(subdir, file)
x = get_and_normalize_data(fullpath)
if x is None:
continue
xs.append(x)
filenames.append(fullpath)
return np.array(xs), np.array(filenames)
##############################################################################################################
########################################### Data/File Util ##################################################
##############################################################################################################
def get_and_normalize_data(fullpath):
"""
ARG1 - String - Relative filename of .fits file of dimension (16, 512)
RET - (ndarray - normalized image)
Error RET - None
"""
if not fullpath.endswith(".fits"):
return None
x = file_contents(fullpath)
try:
assert x.shape == (16,512)
except AssertionError:
print "File {0} is of size {1} instead of (16,512), so it won't be used".format(fullpath, x.shape)
return None
x = normalize_image(x) #trying whitening on single image level
return x
def get_all_fits_info(directory, file_label_map, stop_short_count=None):
"""
ARG1 - root directory in which to look for .fits files
ARG2 (optional) - For testing purposes, a number of files to find so we can stop short
RET - List of tuples of (fullpath, fname)
Ex: If path of file found is ./Combined/noise/HIP1324_1/455634944.fits then
fullpath = ./Combined/noise/HIP1324_1/455634944.fits
fname = HIP1324_1_455634944
"""
file_tuples = []
for subdir, dirs, files in os.walk(directory):
dir_names = subdir.split("/")
lowest_dir_name = dir_names[-1]
for file in files:
if stop_short_count and len(file_tuples) >= stop_short_count:
break
if not file.endswith(".fits"):
continue
filenumber = file[:-5]
fname = lowest_dir_name + "_" + filenumber
if fname in file_label_map:
fullpath = os.path.join(subdir, file)
file_tuples.append((fullpath, fname))
return file_tuples
def file_contents(fname):
"""
ARG1 - String - relative filename of .fits file
RET - ndarray
"""
hdulist = fits.open(fname, memmap=False)
data = hdulist[0].data
hdulist.close()
return data
def normalize_image(image):
"""
ARG1 - ndarray - 2d image
RET - de-meaned image scaled so that its pixel values have std. dev. of 1
"""
avg = np.mean(image)
stddev = np.std(image)
image = (image-avg)/stddev
return image
def copy_files_to_folder(files, directory, png = True):
"""
ARG1 - Either: list of tuples where first index is filename, second is filename with directory or list of filenames without directory
ex: [(46969856, GJ1002_1_46969856)] or [46969856]
ARG2 - Directory to which we're copying files
ARG3 - Boolean for whether we're copying existing png or existing .fits
RET - None
Side Effects -
deletes given directory
copies .fits or .png with paths given in files to a given directory
"""
def change_extension_function_generator(old_extension = ".fits", new_extension = ".png"):
if old_extension == new_extension:
return lambda string: string
return lambda string: string[:len(string) - len(old_extension)] + new_extension
if png:
change_extension = change_extension_function_generator(".fits", ".png")
else:
change_extension = change_extension_function_generator(".fits", ".fits")
shutil.rmtree(directory, ignore_errors=True)
os.makedirs(directory)
files = [(change_extension(file), None) if type(file) != tuple else change_extension(file) for file in files]
for file, fname_with_dir in files:
try:
shutil.copy2(file, directory)
file_number = file.split("/")[-1]
if fname_with_dir is not None:
os.rename(directory + "/" + file_number, directory + "/" + fname_with_dir + ".png")
except IOError:
with open(no_image_found_file, "a") as f:
f.write(file)
def find_all_fil(directory = "."):
"""
ARG1 - Directory to look in
RET - List of paths where filterbank files can be found
"""
fils = []
for subdir, dirs, files in os.walk(directory):
for file in files:
if file.endswith(".fil"):
fullpath = os.path.join(subdir, file)
fils.append(fullpath)
return fils
def recent_model_directory(directory):
"""
ARG1 - Directory in which to look
RET - Directory where the most recently modified file containing saved model was found
"""
last_dir = None
max_time = 0
for subdir, dirs, files in os.walk(directory):
if subdir == directory:
continue
modified_time = os.path.getmtime(subdir)
if modified_time > max_time:
last_dir = subdir
max_time = modified_time
return last_dir
def write_image_arrs_to_png(image_tups, directory, remake_dir = False):
"""
ARG1 - list of image tuples of format (data array, filename)
ARG2 - directory to which we write png
ARG3 - (optional) boolean for whether we're deleting the given directory first
RET - None
Side effect - possibly deletes directory
writes images in png format to given directory
if "images" are of greater than 2 dimensions, treats ndarray as 2d images stacked on top of each other
"""
if remake_dir or not os.path.isdir(directory):
shutil.rmtree(directory, ignore_errors=True)
os.makedirs(directory)
for data_arr, fname in image_tups:
if len(data_arr.shape) == 1:
data_arr = np.reshape(data_arr, (data_arr.shape[0], 1))
if len(data_arr.shape) == 3:
for dim in range(data_arr.shape[2]):
write_image_arr_to_png(data_arr[:,:,dim], directory+"/"+fname+"-"+str(dim))
else:
write_image_arr_to_png(data_arr, directory + "/" + fname)
def write_image_arr_to_png(data_arr, fullpath):
misc.imsave(fullpath, data_arr, format = "png")