File size: 2,801 Bytes
4b876a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import astropy.table
import numpy as np
import pandas as pd
def load(path,
x_cols=('psfMag_u', 'psfMag_g', 'psfMag_r', 'psfMag_i', 'psfMag_z'),
y_col='redshift',
class_col='class',
class_val='Galaxy'):
# Cast x_cols to list so Pandas doesn't complain…
x_cols_l = list(x_cols)
if '.h5' in path or '.hdf' in path:
# We have an HDF5 file
data = pd.read_hdf(path)
return data, x_cols_l, y_col
elif '.fits' in path:
x_cols_l = ['umag', 'gmag', 'rmag', 'imag', 'zmag']
y_col = 'z'
dat = astropy.table.Table.read(path, format='fits')
data = dat.to_pandas()
data = data[x_cols_l + [y_col]]
return data, x_cols_l, y_col
else:
# We have a CSV file
data_iter = pd.read_csv(
path,
iterator=True,
chunksize=100000,
usecols=x_cols_l + [y_col, class_col])
# Filter out anything that is not a galaxy without loading the
# whole file into memory.
data = pd.concat(chunk[chunk[class_col] == class_val]
for chunk in data_iter)
return data[x_cols_l + [y_col]], x_cols_l, y_col
def split(data, train_n, test_n):
data, x_cols, y_col = data
X_data = data[x_cols].as_matrix()
y_data = data[y_col].as_matrix()
assert X_data.shape[0] == y_data.shape[0] == data.shape[0]
assert X_data.shape[1] == data.shape[1] - 1
assert len(y_data.shape) == 1
# Shuffle data
indices = list(range(data.shape[0]))
np.random.seed(seed=12)
np.random.shuffle(indices)
X_data = X_data[indices]
y_data = y_data[indices]
train_X = X_data[:train_n]
test_X = X_data[train_n:train_n+test_n]
train_y = y_data[:train_n]
test_y = y_data[train_n:train_n+test_n]
assert train_X.shape == (train_n, len(x_cols))
assert train_y.shape == (train_n,)
assert test_X.shape == (test_n, len(x_cols))
assert test_y.shape == (test_n,)
return (train_X, train_y), (test_X, test_y)
def save_as_hdf5(path, data):
data.to_hdf(path, '👀')
if __name__ == '__main__':
# TODO: Use argparse if this gets any longer...
import sys
if len(sys.argv) == 3 and sys.argv[2] == '--test':
# Tiny test
data = load(sys.argv[1])
data = split(data, 2, 1)
print(data)
elif len(sys.argv) == 4 and sys.argv[2] == '--to-hdf5':
data, _, _ = load(sys.argv[1])
save_as_hdf5(sys.argv[3], data)
elif len(sys.argv) == 3 and sys.argv[2] == '--time':
from time import time
start_time = time()
data = load(sys.argv[1])
total_time = time() - start_time
print('Took {}s'.format(total_time))
else:
print('No options specified.')
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