def plot_fits(ids, posdir=None, outdir='fit'):
if not os.path.exists(outdir):
os.makedirs(outdir)
for id in ids:
obsid = id['obsid']
data_id = id['data_id']
ax = id['ax']
pf = open(os.path.join(posdir, 'pos.pkl'), 'r')
pos = cPickle.load(pf)
pf.close()
pos_data = pos[ax]
fig = plt.figure(figsize=(6,4))
axplot = ui.get_model_plot(data_id)
plt.plot( axplot.x/1000., axplot.y, 'b.')
[ts, ds, dsminus, dsplus] = binned_mean(
pos_data-np.mean(pos_data),
pos['time']-pos['time'][0] )
if not len(ts):
continue
plt.errorbar((ts-ts[0])/1000., ds,
yerr=dsminus, color='k', marker='.', linestyle='')
plt.xlabel('Time (ksec)')
plt.ylabel('%s' % ax)
plt.subplots_adjust(left=.15)
plt.title('Model(Blue), BinMean Pos(Black) (obs=%s)' % (obsid))
plt.ylim(-.3,.3)
plt.grid()
plt.savefig('%s/%s_%s_sfit.png' % (outdir, obsid, ax))
plt.close()
def _fit_poly(fit_data, evt_times, degree, data_id=0):
"""
Given event data transformed into Y or Z angle positions, and a degree of the desired
fit polynomial, fit a polynomial to the data.
:param fit_data: event y or z angle position data
:param evt_times: times of event/fit_data
:param degree: degree of polynomial to use for the fit model
:param data_id: sherpa dataset id to use for the fit
:returns: (sherpa model plot, sherpa model)
"""
# Set initial value for fit data position error
init_error = 1
ui.clean()
ui.load_arrays(data_id, evt_times - evt_times[0], fit_data,
np.zeros_like(fit_data) + init_error)
v2("Fitting a line to the data to get reduced stat errors")
# First just fit a line to get reduced errors on this set
ui.polynom1d.line
ui.set_model(data_id, 'line')
ui.thaw('line.c1')
ui.fit(data_id)
fit = ui.get_fit_results()
calc_error = init_error * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_error)
# Then fit the specified model
v2("Fitting a polynomial of degree {} to the data".format(degree))
ui.polynom1d.fitpoly
ui.freeze('fitpoly')
# Thaw the coefficients requested by the degree of the desired polynomial
ui.thaw('fitpoly.c0')
fitpoly.c0.val = 0
for deg in range(1, 1 + degree):
ui.thaw("fitpoly.c{}".format(deg))
ui.set_model(data_id, 'fitpoly')
ui.fit(data_id)
# Let's screw up Y on purpose
if data_id == 0:
fitpoly.c0.val = 0
fitpoly.c1.val = 7.5e-05
fitpoly.c2.val = -1.0e-09
fitpoly.c3.val = 0
fitpoly.c4.val = 0
mp = ui.get_model_plot(data_id)
model = ui.get_model(data_id)
return mp, model
def _fit_poly(fit_data, evt_times, degree, data_id=0):
"""
Given event data transformed into Y or Z angle positions, and a degree of the desired
fit polynomial, fit a polynomial to the data.
:param fit_data: event y or z angle position data
:param evt_times: times of event/fit_data
:param degree: degree of polynomial to use for the fit model
:param data_id: sherpa dataset id to use for the fit
:returns: (sherpa model plot, sherpa model)
"""
# Set initial value for fit data position error
init_error = 1
ui.clean()
ui.load_arrays(data_id, evt_times - evt_times[0], fit_data,
np.zeros_like(fit_data) + init_error)
v2("Fitting a line to the data to get reduced stat errors")
# First just fit a line to get reduced errors on this set
ui.polynom1d.line
ui.set_model(data_id, 'line')
ui.thaw('line.c1')
ui.fit(data_id)
fit = ui.get_fit_results()
calc_error = init_error * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_error)
# Then fit the specified model
v2("Fitting a polynomial of degree {} to the data".format(degree))
ui.polynom1d.fitpoly
ui.freeze('fitpoly')
# Thaw the coefficients requested by the degree of the desired polynomial
ui.thaw('fitpoly.c0')
fitpoly.c0.val = 0
for deg in range(1, 1 + degree):
ui.thaw("fitpoly.c{}".format(deg))
ui.set_model(data_id, 'fitpoly')
ui.fit(data_id)
mp = ui.get_model_plot(data_id)
model = ui.get_model(data_id)
return mp, model
def plot_fits(ids, posdir=None, outdir='fit'):
if not os.path.exists(outdir):
os.makedirs(outdir)
for id in ids:
obsid = id['obsid']
data_id = id['data_id']
ax = id['ax']
pf = open(os.path.join(posdir, 'pos.pkl'), 'r')
pos = cPickle.load(pf)
pf.close()
pos_data = pos[ax]
fig = plt.figure(figsize=(6, 4))
axplot = ui.get_model_plot(data_id)
plt.plot(axplot.x / 1000., axplot.y, 'b.')
[ts, ds, dsminus, dsplus] = binned_mean(pos_data - np.mean(pos_data),
pos['time'] - pos['time'][0])
if not len(ts):
continue
plt.errorbar((ts - ts[0]) / 1000.,
ds,
yerr=dsminus,
color='k',
marker='.',
linestyle='')
plt.xlabel('Time (ksec)')
plt.ylabel('%s' % ax)
plt.subplots_adjust(left=.15)
plt.title('Model(Blue), BinMean Pos(Black) (obs=%s)' % (obsid))
plt.ylim(-.3, .3)
plt.grid()
plt.savefig('%s/%s_%s_sfit.png' % (outdir, obsid, ax))
plt.close()
ax.relim()
ax.autoscale_view()
else:
ax.plot(x, y)
ax.texts = []
ax.annotate("{}".format(y.name),
xy=(0.5, 0.5),
xycoords="axes fraction",
ha='center',
va='center',
color='grey')
t_ccd = dat['TEMPCD']
try:
fit, modpars = fit_pix_values(t_ccd, y, id=i_col)
fits[y.name] = {'fit': fit, 'modpars': modpars}
fitmod = ui.get_model_plot(i_col)
except Exception as exception:
pix_log.warn('Sherpa fit failed on {}'.format(y.name))
pix_log.warn(exception)
pix_log.warn('Continuing')
fits[y.name] = None
continue
if len(ax.lines) > 1:
l1 = ax.lines[1]
l1.set_data(x, fitmod.y)
ax.relim()
ax.autoscale_view()
else:
ax.plot(x, fitmod.y, color='red')
if opt.plot_fit_curves:
fitax = fitaxes[r][c]
ui.set_method('simplex')
ui.load_arrays(data_id,
rates['time'],
rates['rate'])
ui.set_staterror(data_id,
rates['err'])
ftype_poly = ui.polynom1d(ftype)
ui.set_model(data_id, ftype_poly)
ui.thaw(ftype_poly.c0)
ui.thaw(ftype_poly.c1)
ui.notice(DateTime(trend_date_start).frac_year)
ui.fit(data_id)
ui.notice()
myfit = ui.get_fit_results()
axplot = ui.get_model_plot(data_id)
if myfit.succeeded:
b = ftype_poly.c1.val * DateTime(trend_date_start).frac_year + ftype_poly.c0.val
m = ftype_poly.c1.val
rep_file = open('%s_fitfile.json' % ftype, 'w')
rep_file.write(json.dumps(dict(time0=DateTime(trend_date_start).frac_year,
datestart=trend_date_start,
datestop=data_stop,
bin=trend_type,
m=m,
b=b,
comment="mx+b with b at time0 and m = (delta rate)/year"),
sort_keys=True,
indent=4))
rep_file.close()
else:
y = dat[best_pix] * GAIN / integ
# ax.yaxis.set_label_position("right")
# ax.yaxis.tick_right()
# ax.grid()
# plot_cxctime(dat['time'], y, 'k', ax=ax)
# ax.set_ylabel('Dark Current (e-/sec)')
# ccdax.set_ylabel('CCD Temp (DegC)')
# ax.texts = []
# ax.annotate("{}".format(y.name),
# xy=(0.5, 0.5), xycoords="axes fraction",
# ha='center', va='center',
# color='lightgrey')
t_ccd = dat["TEMPCD"]
fit, modpars = fit_pix_values(t_ccd, y, id=i_col)
fits[y.name] = {"fit": fit, "modpars": modpars}
fitmod = ui.get_model_plot(i_col)
# plot_cxctime(DateTime(dat['time'][0]).secs + x, fitmod.y, color='red', ax=ax)
# plt.tight_layout()
plot2 = plot_two(fig_id="overplots", x2=dat["time"], y2=y, x=dat["time"], y=t_ccd, color="blue", color2="black")
plot_cxctime(
DateTime(dat["time"][0]).secs + x,
fitmod.y,
color="red",
ax=plot2["ax2"],
linewidth=6,
alpha=0.5,
label="Dark current model",
)
plot2["ax2"].legend(loc="upper left", fontsize=10)
plot2["ax"].yaxis.label.set_color("blue")
plot2["ax"].tick_params(axis="y", colors="blue")