Fit values here were computed 2018-Apr-03. This is a candidate for the FLIGHT model.
This notebook fits the flight acquisition data using the poly-spline-tccd model.
This uses starting fit values from the accompanying fit_acq_prob_model-2018-04-poly-tccd.ipynb notebook.
This model is a 15-parameter fit for acquisition probability as a function of magnitude and CCD temperature.
See the final cells for best-fit values.
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
from scipy.interpolate import CubicSpline
from Chandra.Time import DateTime
%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.
#
# UPDATE: is ion_rad being ignored on-board?
#
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]:
# Allow for defining a 'mag' column that is the observed mag
# if available else the catalog mag. This is a tempting thing
# to do for calibration, but it is incorrect because we want
# probabilities based on the information we have (catalog mag)
# not the information we wish we had (actual mag). Thus the
# acq prob model folds in catalog mag uncertainty. This is
# especially apparent for color=1.5.
USE_OBSERVED_MAG = False
if USE_OBSERVED_MAG:
acqs['mag'] = np.where(acqs['fail_mask'], acqs['mag_aca'], acqs['mag_obs'])
else:
acqs['mag'] = acqs['mag_aca']
In [6]:
# Filter for year and mag (previously used data through 2007:001)
#
# UPDATE this to be between 4 to 5 years from time of recalibration.
#
# The mag range is restricted to 8.5 < mag < 10.7 because the model
# is only calibrated in that range. Above 10.7 there is concern that
# stats are actually unreliable (fraction of imposters that happen to
# is high?) This upper limit is something to play with.
#
ok = (acqs['year'] > 2014.0) & (acqs['mag'] > 8.5) & (acqs['mag'] < 10.7)
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')
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]:
spline_mags = np.array([8.5, 9.25, 10.0, 10.4, 10.7])
def p_fail(pars, mag,
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 wp: warm fraction
:param box_delta: search box half width (arcsec)
"""
p_bright_fail = 0.03 # For now
p0s, p1s, p2s = pars[0:5], pars[5:10], pars[10:15]
if tc2 is None:
tc2 = tc ** 2
# Make sure box_delta has right dimensions
tc, box_delta = np.broadcast_arrays(tc, box_delta)
p0 = CubicSpline(spline_mags, p0s, bc_type=((1, 0.0), (2, 0.0)))(mag)
p1 = CubicSpline(spline_mags, p1s, bc_type=((1, 0.0), (2, 0.0)))(mag)
p2 = CubicSpline(spline_mags, p2s, bc_type=((1, 0.0), (2, 0.0)))(mag)
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']
mag = data['mag']
def sherpa_func(pars, x=None):
return p_fail(pars, mag, tc, tc2, box_delta)
return sherpa_func
In [12]:
def fit_poly_spline_model(data_mask=None):
from sherpa import ui
data = data_all if data_mask is None else data_all[data_mask]
comp_names = [f'p{i}{j}' for i in range(3) for j in range(5)]
# Approx starting values based on plot of p0, p1, p2
# spline_mags = np.array([8.5, 9.25, 10.0, 10.4, 10.7])
spline_p = {}
spline_p[0] = np.array([-2.6, -2.0, -0.7, 0.3, 1.1])
spline_p[1] = np.array([0.0, 0.0, 0.4, 0.7, 0.5])
spline_p[2] = np.array([0.0, 0.0, 0.1, 0.1, 0.1])
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 i in range(3):
for j in range(5):
comp_name = f'p{i}{j}'
setattr(fmod, comp_name, spline_p[i][j])
comp = getattr(fmod, comp_name)
comp.max = 10
comp.min = -4.0 if i == 0 else 0.0
ui.fit(data_id)
# conf = ui.get_confidence_results()
return ui.get_fit_results()
In [13]:
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])
line, = plt.plot(data_mean[group_col], model_fail, '-')
plt.errorbar(data_mean[group_col], data_fail, yerr=fail_sigmas,
fmt='.', color=line.get_color(), label=label, markersize=8)
if log:
ax = plt.gca()
ax.set_yscale('log')
In [14]:
def mag_filter(mag0, mag1):
ok = (data_all['mag'] > mag0) & (data_all['mag'] < mag1)
return ok
In [15]:
def t_ccd_filter(t_ccd0, t_ccd1):
ok = (data_all['t_ccd'] > t_ccd0) & (data_all['t_ccd'] < t_ccd1)
return ok
In [16]:
def wp_filter(wp0, wp1):
ok = (data_all['warm_pix'] > wp0) & (data_all['warm_pix'] < wp1)
return ok
In [17]:
def plot_fit_all(parvals, mask=None, probit=False):
if mask is None:
mask = np.ones(len(data_all), dtype=bool)
mt = mag_filter(8.5, 10.7) & mask
plt.figure(figsize=(12, 4))
for probit in True, False:
plt.subplot(1, 2, int(probit) + 1)
for v0, v1, colors in ((-12, -11, 'gk'),
(-13, -12, 'cm'),
(-14, -13, 'br'),
(-15, -14, 'gk')):
plot_fit_grouped(parvals, 'mag', 0.25, t_ccd_filter(v0, v1) & mt,
colors=colors, label=f'{v0} < t_ccd < {v1}', probit=probit)
plt.legend(loc='upper left')
plt.ylim(-3, 3) if probit else plt.ylim(-0.1, 1.1)
plt.ylabel('p_fail')
plt.xlabel('year')
plt.tight_layout()
plt.grid()
mt = t_ccd_filter(-16, -10) & mask
plt.figure(figsize=(12, 4))
for probit in True, False:
plt.subplot(1, 2, int(probit) + 1)
for v0, v1, colors in ((10.3, 10.7, 'gk'),
(10, 10.3, 'cm'),
(9.5, 10, 'br'),
(9, 9.5, 'gk')):
plot_fit_grouped(parvals, 'year', 0.25, mag_filter(v0, v1) & mt,
colors=colors, label=f'{v0} < mag < {v1}', probit=probit)
plt.legend(loc='upper left')
plt.ylim(-3, 3) if probit else plt.ylim(-0.1, 1.1)
plt.ylabel('p_fail')
plt.xlabel('mag_aca')
plt.tight_layout()
plt.grid()
mt = t_ccd_filter(-16, -10) & mask
plt.figure(figsize=(12, 4))
for probit in True, False:
plt.subplot(1, 2, int(probit) + 1)
for v0, v1, colors in ((10.3, 10.7, 'gk'),
(10, 10.3, 'cm'),
(9.5, 10, 'br'),
(9, 9.5, 'gk')):
plot_fit_grouped(parvals, 't_ccd', 0.5, mag_filter(v0, v1) & mt,
colors=colors, label=f'{v0} < mag < {v1}', probit=probit)
plt.legend(loc='upper left')
plt.ylim(-3, 3) if probit else plt.ylim(-0.1, 1.1)
plt.xlabel('t_ccd')
plt.ylabel('p_fail')
plt.tight_layout()
plt.grid()
In [19]:
def plot_splines(pars):
mag = np.arange(8.5, 11.01, 0.1)
n = len(spline_mags)
p0 = CubicSpline(spline_mags, pars[0:n], bc_type=((1, 0.0), (2, 0.0)))(mag)
p1 = CubicSpline(spline_mags, pars[n:2*n], bc_type=((1, 0.0), (2, 0.0)))(mag)
p2 = CubicSpline(spline_mags, pars[2*n:3*n], bc_type=((1, 0.0), (2, 0.0)))(mag)
plt.plot(mag, p0, label='p0')
plt.plot(mag, p1, label='p1')
plt.plot(mag, p2, label='p2')
plt.grid()
plt.legend();
In [20]:
def print_model_coeffs(parvals):
n = len(spline_mags)
print(f'spline_mags = np.array({spline_mags.tolist()})')
ln = 'spline_vals = np.array(['
print(ln, end='')
print(', '.join(f'{val:.4f}' for val in parvals[0:n]) + ',')
print(' ' * len(ln) + ', '.join(f'{val:.4f}' for val in parvals[n:2*n]) + ',')
print(' ' * len(ln) + ', '.join(f'{val:.4f}' for val in parvals[2*n:3*n]) + '])')
In [22]:
mask_no_1p5 = data_all['color'] != 1.5
In [23]:
fit_no_1p5 = fit_poly_spline_model(mask_no_1p5)
In [24]:
plot_splines(fit_no_1p5.parvals)
In [25]:
plot_fit_all(fit_no_1p5.parvals, mask_no_1p5)
In [26]:
plot_fit_grouped(fit_no_1p5.parvals, 'year', 0.10, mag_filter(10.3, 10.6) & mask_no_1p5,
colors='gk', label='10.3 < mag < 10.6')
plt.xlim(2016.0, None)
y0, y1 = plt.ylim()
x = DateTime('2017-10-01T00:00:00').frac_year
plt.plot([x, x], [y0, y1], '--r', alpha=0.5)
plt.grid();
In [27]:
plot_fit_grouped(fit_no_1p5.parvals, 'year', 0.10, mag_filter(10.5, 10.7) & mask_no_1p5,
colors='gk', label='10.5 < mag < 10.7')
plt.xlim(2016.0, None)
y0, y1 = plt.ylim()
x = DateTime('2017-10-01T00:00:00').frac_year
plt.plot([x, x], [y0, y1], '--r', alpha=0.5)
plt.grid();
In [28]:
plot_fit_grouped(fit_no_1p5.parvals, 'year', 0.10, mag_filter(10.0, 10.3) & mask_no_1p5,
colors='gk', label='10.0 < mag < 10.3')
plt.xlim(2016.0, None)
y0, y1 = plt.ylim()
x = DateTime('2017-10-01T00:00:00').frac_year
plt.plot([x, x], [y0, y1], '--r', alpha=0.5)
plt.grid();
In [29]:
print('Hang tight, this could take a few minutes')
mask_1p5 = data_all['color'] == 1.5
fit_1p5 = fit_poly_spline_model(mask_1p5)
In [30]:
plot_splines(fit_1p5.parvals)
In [31]:
plot_fit_all(fit_1p5.parvals, mask=mask_1p5)
In [32]:
plt.plot(data_all['year'], data_all['t_ccd'])
plt.title('ACA CCD temperature trend')
plt.grid();
In [33]:
plt.hist(data_all['t_ccd'], bins=24)
plt.grid()
plt.xlabel('ACA CCD temperature');
In [34]:
plt.hist(data_all['mag'], bins=np.arange(8.0, 11.1, 0.1))
plt.grid()
plt.xlabel('Mag_aca');
In [35]:
ok = ~data_all['fail'].astype(bool)
dok = data_all[ok]
plt.plot(dok['mag_aca'], dok['mag_obs'] - dok['mag_aca'], '.')
plt.plot(dok['mag_aca'], dok['mag_obs'] - dok['mag_aca'], ',', alpha=0.3)
plt.grid()
In [36]:
da = data_all
ok = ~da['fail'].astype(bool)
fuzz = np.random.uniform(-0.3, 0.3, np.count_nonzero(ok))
plt.plot(da['t_ccd'][ok] + fuzz, da['mag_aca'][ok], '.C0', markersize=4)
ok = da['fail'].astype(bool)
fuzz = np.random.uniform(-0.3, 0.3, np.count_nonzero(ok))
plt.plot(da['t_ccd'][ok] + fuzz, da['mag_aca'][ok], '.C1')
plt.xlabel('T_ccd (C)')
plt.ylabel('Mag_aca')
plt.grid()
In [37]:
from chandra_aca.star_probs import acq_success_prob
In [38]:
t_ccds = np.linspace(-18, -10, 20)
mag_acas = [8.5, 9.5, 10.0, 10.3, 10.6]
for ii, mag_aca in enumerate(reversed(mag_acas)):
flight_probs = 1 - acq_success_prob(date='2018-05-01T00:00:00', t_ccd=t_ccds, mag=mag_aca)
new_probs = p_fail(fit_no_1p5.parvals, mag_aca, t_ccds + 12)
plt.plot(t_ccds, flight_probs, '--', label=f'mag_aca={mag_aca}', color=f'C{ii}')
plt.plot(t_ccds, new_probs, '-', color=f'C{ii}')
plt.xlim(-16, None)
plt.legend()
plt.xlabel('T_ccd')
plt.ylabel('Probability')
plt.grid()
In [44]:
def print_parvals(parvals, label):
print('{:s} = np.array([{:s}, # P0 values'
.format(label, ', '.join(str(round(x, 5)) for x in parvals[0:5])))
print(' ' * len(label) + ' {:s}, # P1 values'
.format(', '.join(str(round(x, 5)) for x in parvals[5:10])))
print(' ' * len(label) + ' {:s}]) # P2 values'
.format(', '.join(str(round(x, 5)) for x in parvals[0:5])))
In [46]:
print_parvals(fit_no_1p5.parvals, 'fit_no_1p5')
In [47]:
print_parvals(fit_1p5.parvals, 'fit_1p5')
In [51]:
print('spline_mags = np.array([{:s}])'.format(', '.join(str(x) for x in spline_mags)))
In [ ]: