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Noise fitting frequency limits from hwp freq #1085

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Noise fitting frequency limits from hwp freq #1085

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ed48224
add check for hwp freq in noise fitting
Jan 6, 2025
7cb6436
rework frequency checks
Jan 6, 2025
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39 changes: 35 additions & 4 deletions sotodlib/tod_ops/fft_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import numdifftools as ndt
import numpy as np
import pyfftw
import logging
import so3g
from scipy.optimize import minimize
from scipy.signal import welch
Expand All @@ -12,6 +13,8 @@
from . import detrend_tod


logger = logging.getLogger(__name__)

def _get_num_threads():
# Guess how many threads we should be using in FFT ops...
return so3g.useful_info().get("omp_num_threads", 4)
Expand Down Expand Up @@ -356,6 +359,18 @@ def calc_wn(aman, pxx=None, freqs=None, low_f=5, high_f=10):
if pxx is None:
pxx = aman.Pxx

# limit upper frequency cutoffs to hwp freq
if 'hwp_angle' in aman._fields:
hwp_freq = (np.sum(np.abs(np.diff(np.unwrap(aman.hwp_angle)))) /
(aman.timestamps[-1] - aman.timestamps[0])) / (2 * np.pi)
hwp_freq_limit = 0.95 * hwp_freq
if high_f >= hwp_freq and hwp_freq > 0:
logger.warning(f"high frequency cutoff={high_f} > hwp_freq={hwp_freq}. Limiting to hwp_freq.")
high_f = hwp_freq_limit
# make sure low_f < high_f
if low_f >= hwp_freq_limit:
raise ValueError(f"lower frequency cutoff={low_f} >= lpf freq limit={hwp_freq_limit}")

fmsk = np.all([freqs >= low_f, freqs <= high_f], axis=0)
if pxx.ndim == 1:
wn2 = np.median(pxx[fmsk])
Expand Down Expand Up @@ -459,12 +474,28 @@ def fit_noise_model(
subscan=subscan,
**psdargs,
)

# limit upper frequency cutoffs to hwp freq
if 'hwp_angle' in aman._fields:
hwp_freq = (np.sum(np.abs(np.diff(np.unwrap(aman.hwp_angle)))) /
(aman.timestamps[-1] - aman.timestamps[0])) / (2 * np.pi)
if hwp_freq > 0:
hwp_freq_limit = 0.95 * hwp_freq
if f_max >= hwp_freq:
logger.warning(f"f_max={f_max} > hwp_freq={hwp_freq}. Limiting to hwp_freq.")
f_max = hwp_freq_limit
if fwhite[1] >= hwp_freq:
logger.warning(f"upper white noise freq={fwhite[1]} > hwp_freq={hwp_freq}. Limiting to hwp_freq.")
fwhite[1] = hwp_freq_limit
# make sure fwhite[0] < fwhite[1]
if fwhite[0] >= hwp_freq_limit:
raise ValueError(f"lower white noise freq={fwhite[0]} >= lpf freq={hwp_freq_limit}")
if subscan:
fitout, covout = _fit_noise_model_subscan(aman, signal, f, pxx, psdargs=psdargs,
fitout, covout = _fit_noise_model_subscan(aman, signal, f, pxx, psdargs=psdargs,
fwhite=fwhite, lowf=lowf, f_max=f_max,
freq_spacing=freq_spacing)
axis_map_fit = [(0, "dets"), (1, "noise_model_coeffs"), (2, aman.subscans)]
axis_map_cov = [(0, "dets"), (1, "noise_model_coeffs"), (2, "noise_model_coeffs"), (3, aman.subscans)]
axis_map_fit = [(0, "dets"), (1, "noise_model_coeffs"), (2, aman.subscans)]
axis_map_cov = [(0, "dets"), (1, "noise_model_coeffs"), (2, "noise_model_coeffs"), (3, aman.subscans)]
else:
eix = np.argmin(np.abs(f - f_max))
f = f[1:eix]
Expand All @@ -479,7 +510,7 @@ def fit_noise_model(
pfit = np.polyfit(np.log10(f[f < lowf]), np.log10(p[f < lowf]), 1)
fidx = np.argmin(np.abs(10 ** np.polyval(pfit, np.log10(f)) - wnest))
p0 = [f[fidx], wnest, -pfit[0]]
bounds = [(0, None), (sys.float_info.min, None), (None, None)]
bounds = [(sys.float_info.min, None), (sys.float_info.min, None), (None, None)]
res = minimize(neglnlike, p0, args=(f, p), bounds=bounds, method="Nelder-Mead")
try:
Hfun = ndt.Hessian(lambda params: neglnlike(params, f, p), full_output=True)
Expand Down
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