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ICAize.py
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ICAize.py
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import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from astropy.table import Column
from astropy.table import vstack
from astropy.table import join
from sklearn.decomposition import FastICA
from sklearn.decomposition import PCA
from sklearn.decomposition import SparsePCA
from sklearn import preprocessing as skpp
import fnmatch
import os
import os.path
import sys
import random
import pickle
from astropy.utils.compat import argparse
random_state=1234975
data_file = "{}_{}_sources_and_mixing.npz"
pickle_file = "{}_{}_pickle.pkl"
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Compute ICA components over set of stacked spectra, save those out, and pickle ICA model')
parser.add_argument(
'--pattern', type=str, default='stacked*exp??????.*', metavar='PATTERN',
help='File pattern for stacked sky fibers.'
)
parser.add_argument(
'--path', type=str, default='.', metavar='PATH',
help='Path to work from, if not ''.'''
)
parser.add_argument(
'--n_components', type=int, default=40, metavar='N_COMPONENTS',
help='Number of ICA/PCA/etc. components'
)
parser.add_argument(
'--method', type=str, default='ICA', metavar='METHOD', choices=['ICA', 'PCA', 'SPCA', 'NMF']
help='Which dim. reduction method to use'
)
parser.add_argument(
'--ivar_cutoff', type=float, default=0.001, metavar='IVAR_CUTOFF',
help='data with inverse variace below cutoff is masked as if ivar==0'
)
parser.add_argument(
'--max_iter', type=int, default=1200, metavar='MAX_ITER',
help='Maximum number of iterations to allow for convergence. For SDSS data 1000 is a safe number of ICA, while SPCA requires larger values e.g. ~2000 to ~2500'
)
parser.add_argument(
'--filter_split_path', type=str, default=None, metavar='FILTER_SPLIT_PATH',
help='Path on which to find filter_split file'
)
parser.add_argument(
'--filter_cutpoint', type=str, default=None, metavar='FILTER_CUTPOINT',
help='Point at which to divide between ''normal'' flux and emission flux'
)
parser.add_argument(
'--which_filter', type=str, default='both', metavar='WHICH_FILTER',
help='Whether to use ''em''isson, ''nonem''isson, or ''both'''
)
args = parser.parse_args()
comb_flux_arr, comb_exposure_arr, comb_ivar_arr, comb_masks, comb_wavelengths = \
load_all_in_dir(args.path, use_con_flux=False, recombine_flux=False,
pattern=args.pattern, ivar_cutoff=args.ivar_cutoff)
filter_split_arr = None
if args.filter_split_path is not None:
fstable = Table.read(args.filter_split_path, format="ascii.csv")
filter_split_arr = fstable["flux_kurtosis_per_wl"] < args.filter_cutpoint
mask_summed = np.sum(comb_masks, axis=0)
min_val_ind = np.min(np.where(mask_summed == 0))
max_val_ind = np.max(np.where(mask_summed == 0))
print "For data set, minimum and maximum valid indecies are:", (min_val_ind, max_val_ind)
flux_arr = comb_flux_arr
if filter_split_arr is not None and args.which_filter != "both":
flux_arr = np.array(comb_flux_arr, copy=True)
if args.which_filter == "nonem":
new_flux_arr[:,filter_split_arr] = 0
elif args.which_filter == "em":
new_flux_arr[:,~filter_split_arr] = 0
sources, components, model = dim_reduce(flux_arr, args.n_components, args.method, args.max_iter, random_state)
np.savez(data_file.format(args.method, args.which_filter), sources=sources, components=components,
exposures=comb_exposure_arr, wavelengths=comb_wavelengths)
pickle(model, args.path, args.method, args.which_filter)
def pickle(model, path='.', method='ICA', filter_str='both', filename=None):
if filename is None:
filename = pickle_file.format(method, filter_str)
output = open(os.path.join(path, filename), 'wb')
pickle.dump(model, output)
output.close()
def unpickle(path='.', method='ICA', filter_str='both', filename=None):
if filename is None:
filename = pickle_file.format(method, filter_str)
output = open(os.path.join(path, filename), 'rb')
model = pickle.load(output)
output.close()
return model
def dim_reduce(flux_arr, n, method, max_iter, random_state):
model = None
if method == 'ICA':
model = FastICA(n_components = n, whiten=True, max_iter=max_iter,
random_state=random_state, w_init=mixing)
elif method == 'PCA':
model = PCA(n_components = n)
elif method == 'SPCA':
model = SparsePCA(n_components = n, max_iter=max_iter,
random_state=random_state, n_jobs=-1)
elif method == 'NMF':
model = NMF(n_components = n, solver='cd', max_iter=max_iter,
random_state=random_state)
sources = model.fit_transform(flux_arr)
if method == 'ICA':
components_ = model.mixing_
else:
components_ = model.components_
return source, components_, model
def load_all_in_dir(path, pattern, ivar_cutoff=0):
flux_list = []
exp_list = []
mask_list = []
ivar_list = []
wavelengths = None
for file in os.listdir(path):
if fnmatch.fnmatch(file, pattern):
if file.endswith(".csv"):
data = Table(Table.read(os.path.join(path, file), format="ascii.csv"), masked=True)
elif file.endswith(".fits"):
data = Table(Table.read(os.path.join(path, file), format="fits"), masked=True)
mask = data['ivar'] <= ivar_cutoff
ivar_list.append(np.array(data['ivar'], copy=False))
exp = file.split("-")[2][3:]
if exp.endswith("csv"):
exp = int(exp[:-4])
elif exp.endswith("fits"):
exp = int(exp[:-5])
else:
exp = int(exp)
if wavelengths is None:
wavelengths = np.array(data['wavelength'], copy=False)
flux_list.append(np.array(data['flux'], copy=False))
mask_list.append(mask)
exp_list.append(exp)
flux_arr = np.array(flux_list)
exp_arr = np.array(exp_list)
mask_arr = np.array(mask_list)
ivar_arr = np.array(ivar_list)
return flux_arr, exp_arr, ivar_arr, mask_arr, wavelengths
if __name__ == '__main__':
main()