This is an intermediate model which collects the probabilities within narrow magnitude bins and fits a quadratic polynomial model to the data as a function of CCD temperature.
The fit and plot of polynomial coefficients in each mag bin are used as starting values
in the fit_acq_prob_model-2018-04-poly-spline-tccd notebook.
In [1]:
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from astropy.time import Time
import tables
from scipy import stats
import tables3_api
%matplotlib inline
In [2]:
with tables.open_file('/proj/sot/ska/data/acq_stats/acq_stats.h5', 'r') as h5:
cols = h5.root.data.cols
names = {'tstart': 'guide_tstart',
'obsid': 'obsid',
'obc_id': 'acqid',
'halfwidth': 'halfw',
'warm_pix': 'n100_warm_frac',
'mag_aca': 'mag_aca',
'mag_obs': 'mean_trak_mag',
'known_bad': 'known_bad',
'color': 'color1',
'img_func': 'img_func',
'ion_rad': 'ion_rad',
'sat_pix': 'sat_pix',
'agasc_id': 'agasc_id',
't_ccd': 'ccd_temp',
'slot': 'slot'}
acqs = Table([getattr(cols, h5_name)[:] for h5_name in names.values()],
names=list(names.keys()))
year_q0 = 1999.0 + 31. / 365.25 # Jan 31 approximately
In [3]:
acqs['year'] = Time(acqs['tstart'], format='cxcsec').decimalyear.astype('f4')
acqs['quarter'] = (np.trunc((acqs['year'] - year_q0) * 4)).astype('f4')
acqs['color_1p5'] = np.where(acqs['color'] == 1.5, 1, 0)
In [4]:
# Create 'fail' column, rewriting history as if the OBC always
# ignore the MS flag in ID'ing acq stars. Set ms_disabled = False
# to not do this
obc_id = acqs['obc_id']
obc_id_no_ms = (acqs['img_func'] == 'star') & ~acqs['sat_pix'] & ~acqs['ion_rad']
acqs['fail'] = np.where(obc_id | obc_id_no_ms, 0.0, 1.0)
acqs['fail_mask'] = acqs['fail'].astype(bool)
In [5]:
# Define a 'mag' column that is the observed mag if available else the catalog mag
acqs['mag'] = np.where(acqs['fail_mask'], acqs['mag_aca'], acqs['mag_obs'])
In [6]:
# Filter for year and mag (previously used data through 2007:001)
ok = (acqs['year'] > 2012.0) & (acqs['mag'] > 6.0) & (acqs['mag'] < 11.0)
In [7]:
# Filter known bad obsids
print('Filtering known bad obsids, start len = {}'.format(np.count_nonzero(ok)))
bad_obsids = [
# Venus
2411,2414,6395,7306,7307,7308,7309,7311,7312,7313,7314,7315,7317,7318,7406,583,
7310,9741,9742,9743,9744,9745,9746,9747,9749,9752,9753,9748,7316,15292,16499,
16500,16501,16503,16504,16505,16506,16502,
]
for badid in bad_obsids:
ok = ok & (acqs['obsid'] != badid)
print('Filtering known bad obsids, end len = {}'.format(np.count_nonzero(ok)))
In [8]:
data_all = acqs[ok]
del data_all['img_func']
data_all.sort('year')
data_all['mag10'] = data_all['mag'] - 10.0
In [9]:
# Adjust probability (in probit space) for box size. See:
# https://github.com/sot/skanb/blob/master/pea-test-set/fit_box_size_acq_prob.ipynb
b1 = 0.96
b2 = -0.30
box0 = (data_all['halfwidth'] - 120) / 120 # normalized version of box, equal to 0.0 at nominal default
data_all['box_delta'] = b1 * box0 + b2 * box0**2
In [10]:
data_all = data_all.group_by('quarter')
data_mean = data_all.groups.aggregate(np.mean)
In [11]:
def p_fail(pars,
tc, tc2=None,
box_delta=0):
"""
Acquisition probability model
:param pars: 7 parameters (3 x offset, 3 x scale, p_fail for bright stars)
:param tc, tc2: t_ccd, t_ccd ** 2
:param box_delta: search box half width (arcsec)
"""
p0, p1, p2 = pars
if tc2 is None:
tc2 = tc ** 2
# Make sure box_delta has right dimensions
tc, box_delta = np.broadcast_arrays(tc, box_delta)
probit_p_fail = p0 + p1 * tc + p2 * tc2 + box_delta
p_fail = stats.norm.cdf(probit_p_fail) # transform from probit to linear probability
return p_fail
def p_acq_fail(data=None):
"""
Sherpa fit function wrapper to ensure proper use of data in fitting.
"""
if data is None:
data = data_all
tc = (data['t_ccd'] - (-12))
tc2 = tc ** 2
box_delta = data['box_delta']
def sherpa_func(pars, x=None):
return p_fail(pars, tc, tc2, box_delta)
return sherpa_func
In [12]:
def fit_poly_model(data_mask=None):
from sherpa import ui
data = data_all if data_mask is None else data_all[data_mask]
comp_names = ['p0', 'p1', 'p2']
data_id = 1
ui.set_method('simplex')
ui.set_stat('cash')
ui.load_user_model(p_acq_fail(data), 'model')
ui.add_user_pars('model', comp_names)
ui.set_model(data_id, 'model')
ui.load_arrays(data_id, np.array(data['year']), np.array(data['fail'], dtype=np.float))
# Initial fit values from fit of all data
fmod = ui.get_model_component('model')
for comp_name in comp_names[:3]:
setattr(fmod, comp_name, 0.0)
comp = getattr(fmod, comp_name)
comp.max = 10
fmod.p0.min = -10
fmod.p1.min = 0
fmod.p2.min = 0
fmod.p2.max = 0.5
ui.fit(data_id)
return ui.get_fit_results()
In [13]:
def plot_fails_mag_aca_vs_t_ccd(mag_bins, data_all=data_all, year0=2014.0):
ok = (data_all['year'] > year0) & ~data_all['fail'].astype(bool)
da = data_all[ok]
fuzz = np.random.uniform(-0.3, 0.3, len(da))
plt.plot(da['t_ccd'] + fuzz, da['mag_aca'], '.C0', markersize=4)
ok = (data_all['year'] > year0) & data_all['fail'].astype(bool)
da = data_all[ok]
fuzz = np.random.uniform(-0.3, 0.3, len(da))
plt.plot(da['t_ccd'] + fuzz, da['mag_aca'], '.C1')
plt.xlim(-18, -10)
plt.ylim(8.4, 11.1)
x0, x1 = plt.xlim()
for y in mag_bins:
plt.plot([x0, x1], [y, y], '-', color='r', linewidth=2, alpha=0.8)
plt.xlabel('T_ccd (C)')
plt.ylabel('Mag_aca')
plt.title(f'Acq successes (blue) and failures (orange) since {year0}')
plt.grid()
In [14]:
def plot_fit_grouped(pars, group_col, group_bin, mask=None, log=False, colors='br', label=None, probit=False):
data = data_all if mask is None else data_all[mask]
data['model'] = p_acq_fail(data)(pars)
group = np.trunc(data[group_col] / group_bin)
data = data.group_by(group)
data_mean = data.groups.aggregate(np.mean)
len_groups = np.diff(data.groups.indices)
data_fail = data_mean['fail']
model_fail = np.array(data_mean['model'])
fail_sigmas = np.sqrt(data_fail * len_groups) / len_groups
# Possibly plot the data and model probabilities in probit space
if probit:
dp = stats.norm.ppf(np.clip(data_fail + fail_sigmas, 1e-6, 1-1e-6))
dm = stats.norm.ppf(np.clip(data_fail - fail_sigmas, 1e-6, 1-1e-6))
data_fail = stats.norm.ppf(data_fail)
model_fail = stats.norm.ppf(model_fail)
fail_sigmas = np.vstack([data_fail - dm, dp - data_fail])
plt.errorbar(data_mean[group_col], data_fail, yerr=fail_sigmas,
fmt='.' + colors[1:], label=label, markersize=8)
plt.plot(data_mean[group_col], model_fail, '-' + colors[0])
if log:
ax = plt.gca()
ax.set_yscale('log')
In [15]:
def mag_filter(mag0, mag1):
ok = (data_all['mag'] > mag0) & (data_all['mag'] < mag1)
return ok
In [16]:
def t_ccd_filter(t_ccd0, t_ccd1):
ok = (data_all['t_ccd'] > t_ccd0) & (data_all['t_ccd'] < t_ccd1)
return ok
In [17]:
def wp_filter(wp0, wp1):
ok = (data_all['warm_pix'] > wp0) & (data_all['warm_pix'] < wp1)
return ok
In [18]:
mag_bins = np.concatenate([[6], np.arange(8.5, 11.001, 0.25)])
In [19]:
plot_fails_mag_aca_vs_t_ccd(mag_bins)
In [20]:
# fit = fit_sota_model(data_all['color'] == 1.5, ms_disabled=True)
mask_no_1p5 = ((data_all['color'] != 1.5) &
(data_all['t_ccd'] > -17) &
(data_all['t_ccd'] < -10))
In [21]:
mag0s, mag1s = mag_bins[:-1], mag_bins[1:]
fits = {}
for m0, m1 in zip(mag0s, mag1s):
print(m0, m1)
fits[m0, m1] = fit_poly_model(mask_no_1p5 & mag_filter(m0, m1))
In [22]:
colors = [f'kC{i}' for i in range(9)]
for m0_m1, color in zip(list(fits)[3:], colors):
fit = fits[m0_m1]
m0, m1 = m0_m1
plot_fit_grouped(fit.parvals, 't_ccd', 1.0,
mask=mask_no_1p5 & mag_filter(m0, m1),
probit=True, colors=color, label=str(m0_m1))
plt.grid()
# plt.ylim(-3.5, 0.5)
plt.ylabel('Probit(p_fail)')
plt.xlabel('T_ccd')
plt.legend();
In [23]:
colors = [f'kC{i}' for i in range(9)]
for m0_m1, color in zip(list(fits)[3:], colors):
fit = fits[m0_m1]
m0, m1 = m0_m1
plot_fit_grouped(fit.parvals, 't_ccd', 1.0,
mask=mask_no_1p5 & mag_filter(m0, m1),
probit=False, colors=color, label=str(m0_m1))
plt.grid()
plt.ylabel('p_fail')
plt.xlabel('T_ccd')
plt.legend(fontsize='small', loc='upper left');
In [24]:
p0s = []
p1s = []
p2s = []
mags = []
for m0_m1, fit in fits.items():
ps = fit.parvals
m0, m1 = m0_m1
mags.append((m0 + m1) / 2)
p0s.append(ps[0])
p1s.append(ps[1])
p2s.append(ps[2])
plt.plot(mags, p0s, '.-', label='p0')
plt.plot(mags, p1s, '.-', label='p1')
plt.plot(mags, p2s, '.-', label='p2')
plt.legend(fontsize='small')
plt.grid()
In [25]:
plt.hist(data_all['warm_pix'], bins=100)
plt.grid()
plt.xlabel('Warm pixel fraction');
In [26]:
plt.hist(data_all['mag'], bins=np.arange(6, 11.1, 0.1))
plt.grid()
plt.xlabel('Mag_aca')
Out[26]:
In [27]:
plt.plot(data_all['year'], data_all['warm_pix'])
plt.ylim(0, None)
plt.grid();
In [28]:
plt.plot(data_all['year'], data_all['t_ccd'])
# plt.ylim(0, None)
plt.xlim(2017.0, None)
plt.grid();