def test_save_filter_data1d(tmp_path, clean_ui):
"""Check save_filter [Data1D]"""
x = np.arange(1, 11, dtype=np.int16)
ui.load_arrays(1, x, x)
ui.notice(2, 4)
ui.notice(6, 8)
outfile = tmp_path / "filter.dat"
ui.save_filter(str(outfile))
expected = [0, 1, 1, 1, 0, 1, 1, 1, 0, 0]
d = ui.unpack_data(str(outfile), colkeys=['X', 'FILTER'])
assert isinstance(d, ui.Data1D)
assert d.x == pytest.approx(x)
assert d.y == pytest.approx(expected)
assert d.staterror is None
assert d.syserror is None
def mwl_fit_high_level():
"""Use high-level Sherpa API.
High-level = session and convenience functions
Example: http://cxc.harvard.edu/sherpa/threads/simultaneous/
Example: http://python4astronomers.github.io/fitting/spectrum.html
"""
import sherpa.ui as ui
fermi_data = FermiData()
ui.load_arrays(fermi_data.name, fermi_data.x, fermi_data.y,
fermi_data.staterror)
ui.load_user_stat('fermi_stat', FermiStat.calc_stat,
FermiStat.calc_staterror)
# TODO: is there a good way to get the stat??
# ui.get_stat('fermi_stat')
# fermi_stat = ui._session._get_stat_by_name('fermi_stat')
ui.set_stat(fermi_stat)
# IPython.embed()
iact_data = IACTData()
ui.load_arrays(iact_data.name, iact_data.x, iact_data.y,
iact_data.staterror)
spec_model = ui.logparabola.spec_model
spec_model.c1 = 0.5
spec_model.c2 = 0.2
spec_model.ampl = 5e-11
ui.set_source(fermi_data.name, spec_model)
ui.set_source(iact_data.name, spec_model)
ui.notice(lo=1e-3, hi=None)
# IPython.embed()
ui.fit()
return dict(results=ui.get_fit_results(), model=spec_model)
def mwl_fit_high_level():
"""Use high-level Sherpa API.
High-level = session and convenience functions
Example: http://cxc.harvard.edu/sherpa/threads/simultaneous/
Example: http://python4astronomers.github.io/fitting/spectrum.html
"""
import sherpa.ui as ui
fermi_data = FermiData()
ui.load_arrays(fermi_data.name, fermi_data.x, fermi_data.y, fermi_data.staterror)
ui.load_user_stat('fermi_stat', FermiStat.calc_stat, FermiStat.calc_staterror)
# TODO: is there a good way to get the stat??
# ui.get_stat('fermi_stat')
# fermi_stat = ui._session._get_stat_by_name('fermi_stat')
ui.set_stat(fermi_stat)
# IPython.embed()
iact_data = IACTData()
ui.load_arrays(iact_data.name, iact_data.x, iact_data.y, iact_data.staterror)
spec_model = ui.logparabola.spec_model
spec_model.c1 = 0.5
spec_model.c2 = 0.2
spec_model.ampl = 5e-11
ui.set_source(fermi_data.name, spec_model)
ui.set_source(iact_data.name, spec_model)
ui.notice(lo=1e-3, hi=None)
# IPython.embed()
ui.fit()
return Bunch(results=ui.get_fit_results(), model=spec_model)
rates = asciitable.read(filename)
data_id = fail_types[ftype]
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"),
def fit_evol(dateglob='20?????', rootdir='darkhist_peaknorm', outroot='', xmin=25.0, xmax=4000,
conf=True, gauss=False):
results = {}
fileglob = os.path.join(rootdir, '{}.dat'.format(dateglob))
for i, filename in enumerate(glob(fileglob)):
filedate = re.search(r'(\d{7})', filename).group(1)
print "\n\n*************** {} *****************".format(filename)
plt.figure(1)
ui.load_data(1, filename, 2)
data = ui.get_data()
ui.ignore(None, xmin)
ui.ignore(xmax, None)
dark_models.xall = data.x
# dark_models.imin = np.where(xall > xmin)[0][0]
# dark_models.imax = np.where(xall > xmax)[0][0]
sbp.gamma1 = 0.05
sbp.gamma2 = 3.15
sbp.gamma2.min = 2.
sbp.gamma2.max = 4.
sbp.x_b = 130.
sbp.x_b.min = 100.
sbp.x_b.max = 160.
sbp.x_r = 50.
ok = (data.x > 40) & (data.x < 60)
sbp.ampl1 = np.mean(data.y[ok])
if gauss:
fit_gauss_sbp()
else:
fit_sbp()
pars = (sbp.gamma1.val, sbp.gamma2.val, sbp.x_b.val, sbp.x_r.val, sbp.ampl1.val)
fit_y = dark_models.smooth_broken_pow(pars, data.x)
if conf:
ui.set_conf_opt('numcores', 1)
ui.conf()
res = ui.get_conf_results()
result = dict((x, getattr(res, x))
for x in ('parnames', 'parmins', 'parvals', 'parmaxes'))
result['x'] = data.x
result['y'] = data.y
result['y_fit'] = fit_y
results[filedate] = result
if outroot is not None:
ui.notice(0, xmax)
ui.set_xlog()
ui.set_ylog()
ui.plot_fit()
plt.xlim(1, 1e4)
plt.ylim(0.5, 1e5)
plt.grid(True)
plt.xlabel('Dark current (e-/sec)')
outfile = os.path.join(rootdir, '{}{}.png'.format(outroot, filedate))
print 'Writing', outfile
plt.savefig(outfile)
if conf:
outfile = os.path.join(rootdir, '{}{}.pkl'.format(outroot, filedate))
print 'Writing', outfile
pickle.dump(result, open(outfile, 'w'), protocol=-1)
if outroot is not None:
outfile = os.path.join(rootdir, '{}fits.pkl'.format(outroot))
print 'Writing', outfile
pickle.dump(results, open(outfile, 'w'), protocol=-1)
return results