import numpy as np
from astropy.stats import median_absolute_deviation
[docs]def db(x):
"""
Converts to dB.
"""
return 10 * np.log10(x)
[docs]def normalize(data, cols=0, exclude=0.0, to_db=False, use_median=False):
"""
Normalize data per frequency channel so that the noise level in data is
controlled; using mean or median filter.
Uses a sliding window to calculate mean and standard deviation
to preserve non-drifted signals. Excludes a fraction of brightest pixels to
better isolate noise.
Parameters
----------
data : ndarray
Time-frequency data
cols : int
Number of columns on either side of the current frequency bin. The
width of the sliding window is thus 2 * cols + 1
exclude : float, optional
Fraction of brightest samples in each frequency bin to exclude in
calculating mean and standard deviation
to_db : bool, optional
Convert values to decibel equivalents *before* normalization
use_median : bool, optional
Use median and median absolute deviation instead of mean and standard
deviation
Returns
-------
normalized_data : ndarray
Normalized data
"""
# Width of normalization window = 2 * cols + 1
t_len, f_len = data.shape
mean = np.empty(f_len)
std = np.empty(f_len)
if to_db:
data = db(data)
for i in np.arange(f_len):
if i < cols:
start = 0
else:
start = i - cols
if i > f_len - 1 - cols:
end = f_len
else:
end = i + cols + 1
temp = np.sort(data[:, start:end].flatten())
noise = temp[0:int(np.ceil(t_len * (end - start) * (1 - exclude)))]
if use_median:
mean[i] = np.median(noise)
std[i] = median_absolute_deviation(noise)
else:
mean[i] = np.mean(noise)
std[i] = np.std(noise)
return np.nan_to_num((data - mean) / std)
[docs]def normalize_by_max(data):
"""
Simple normalization by dividing out by the brightest pixel.
"""
return data / np.max(data)