def notice(self, emin_ener, emax_ener):
"""Notice energy range.
if minimal value required below threshold, use threshold instead
if maximal value required beyond emax, use emax instead
"""
sau.notice_id(self.name, max(self.threshold, emin_ener), min(self.emax, emax_ener))
def test_grouped_pha_all_bad_response_bg_warning(elo, ehi, nbins, bkg_id,
caplog, make_data_path,
clean_astro_ui):
"""Check we get the warning messages with background filtering"""
ui.load_pha('check', make_data_path('3c273.pi'))
ui.set_quality('check', 2 * numpy.ones(1024, dtype=numpy.int16), bkg_id=1)
ui.ignore_bad('check', bkg_id=1)
with caplog.at_level(logging.INFO, logger='sherpa'):
ui.notice_id('check', elo, ehi, bkg_id=bkg_id)
# filtering has or hasn't happened
nsrc = ui.get_dep('check', filter=True).size
nback = ui.get_dep('check', filter=True, bkg_id=1).size
if bkg_id is None:
assert nsrc == nbins
assert nback == 0
else:
assert nsrc == 46 # ie no filter
assert nback == 0
# did we get a warning message from the background?
assert len(caplog.records) == 1
name, lvl, msg = caplog.record_tuples[0]
assert name == 'sherpa.astro.data'
assert lvl == logging.INFO
assert msg.startswith('Skipping dataset ')
assert msg.endswith('/3c273_bg.pi: mask excludes all data')
def notice(self, emin_ener, emax_ener):
"""Notice energy range.
if minimal value required below threshold, use threshold instead
if maximal value required beyond emax, use emax instead
"""
sau.notice_id(self.name, max(self.threshold, emin_ener),
min(self.emax, emax_ener))
def run_hspec_fit(self, model, thres_low, thres_high):
"""Run the gammapy.hspec fit
Parameters
----------
model : str
Sherpa model
thres_high : `~gammapy.spectrum.Energy`
Upper threshold of the spectral fit
thres_low : `~gammapy.spectrum.Energy`
Lower threshold of the spectral fit
"""
log.info("Starting HSPEC")
import sherpa.astro.ui as sau
from ..hspec import wstat
from sherpa.models import PowLaw1D
if model == 'PL':
p1 = PowLaw1D('p1')
p1.gamma = 2.2
p1.ref = 1e9
p1.ampl = 6e-19
else:
raise ValueError('Desired Model is not defined')
thres = thres_low.to('keV').value
emax = thres_high.to('keV').value
sau.freeze(p1.ref)
sau.set_conf_opt("max_rstat", 100)
list_data = []
for obs in self.observations:
datid = obs.phafile.parts[-1][7:12]
sau.load_data(datid, str(obs.phafile))
sau.notice_id(datid, thres, emax)
sau.set_source(datid, p1)
list_data.append(datid)
wstat.wfit(list_data)
sau.covar()
fit_val = sau.get_covar_results()
fit_attrs = ('parnames', 'parvals', 'parmins', 'parmaxes')
fit = dict((attr, getattr(fit_val, attr)) for attr in fit_attrs)
fit = self.apply_containment(fit)
sau.clean()
self.fit = fit
def _run_hspec_fit(self):
"""Run the gammapy.hspec fit
"""
log.info("Starting HSPEC")
import sherpa.astro.ui as sau
from ..hspec import wstat
sau.set_conf_opt("max_rstat", 100)
thres_lo = self.energy_threshold_low.to('keV').value
thres_hi = self.energy_threshold_high.to('keV').value
sau.freeze(self.model.ref)
list_data = []
for pha in self.pha:
datid = pha.parts[-1][7:12]
sau.load_data(datid, str(pha))
sau.notice_id(datid, thres_lo, thres_hi)
sau.set_source(datid, self.model)
list_data.append(datid)
wstat.wfit(list_data)
def test_bug38(self):
ui.load_pha('3c273', self.pha3c273)
ui.notice_id('3c273', 0.3, 2)
ui.group_counts('3c273', 30)
ui.group_counts('3c273', 15)
def test_more_ui_bug38(make_data_path):
ui.load_pha('3c273', make_data_path('3c273.pi'))
ui.notice_id('3c273', 0.3, 2)
ui.group_counts('3c273', 30)
ui.group_counts('3c273', 15)
def test_bug38(self):
ui.load_pha('3c273', self.pha3c273)
ui.notice_id('3c273', 0.3, 2)
ui.group_counts('3c273', 30)
ui.group_counts('3c273', 15)
def test_bug38(self):
ui.load_pha("3c273", self.pha3c273)
ui.notice_id("3c273", 0.3, 2)
ui.group_counts("3c273", 30)
ui.group_counts("3c273", 15)
def test_load_pha2_compare_meg_order1(make_data_path):
"""Do we read in the MEG +/-1 orders?"""
# The MEG -1 order is dataset 9
# The MEG +1 order is dataset 10
#
pha2file = make_data_path('3c120_pha2')
meg_p1file = make_data_path('3c120_meg_1.pha')
meg_m1file = make_data_path('3c120_meg_-1.pha')
ui.load_pha('meg_p1', meg_p1file)
ui.load_pha('meg_m1', meg_m1file)
orig_ids = set(ui.list_data_ids())
assert 'meg_p1' in orig_ids
assert 'meg_m1' in orig_ids
ui.load_pha(pha2file)
for n, lbl in zip([9, 10], ["-1", "1"]):
h = '3c120_meg_{}'.format(lbl)
ui.load_arf(n, make_data_path(h + '.arf'))
ui.load_rmf(n, make_data_path(h + '.rmf'))
# check that loading the pha2 file doesn't overwrite existing
# data
new_ids = set(ui.list_data_ids())
for i in range(1, 13):
orig_ids.add(i)
assert orig_ids == new_ids
# Check that the same model gives the same statistic
# value; this should check that the data and response are
# read in, that grouping and filtering work, and that
# model evaluation is the same, without having to
# check these steps individually.
#
# The model is not meant to be physically meaningful,
# just one that reasonably represents the data and
# can be evaluated without requiring XSPEC.
#
pmdl = ui.create_model_component('powlaw1d', 'pmdl')
pmdl.gamma = 0.318
pmdl.ampl = 2.52e-3
ncts = 20
for i in [9, 10, "meg_m1", "meg_p1"]:
ui.set_analysis(i, 'wave')
ui.group_counts(i, ncts)
ui.notice_id(i, 2, 12)
ui.set_source(i, pmdl)
ui.set_stat('chi2datavar')
s9 = ui.calc_stat(9)
s10 = ui.calc_stat(10)
sm1 = ui.calc_stat('meg_m1')
sp1 = ui.calc_stat('meg_p1')
# Since these should be the same, we use an equality test
# rather than approximation. At least until it becomes
# a problem.
#
assert s9 == sm1
assert s10 == sp1
# The values were calculated using CIAO 4.9, Linux64, with
# Python 3.5.
#
assert s9 == pytest.approx(1005.4378559390879)
assert s10 == pytest.approx(1119.980439489647)
def fit_with_sherpa(model, data, trans, rows,
ranges=[], errs=None, shmod=1, method='levmar'):
""" This is probably going to be one of the slightly more complicated
functions, so it'll probably need a relatively good and comprehensive
docstring.
Parameters
-----------
model : pandas DataFrame object.
Must either have a 3-level index as created by grism, or at least a
one-level index identifying the different components. If a three-level
index is passed, it must either contain only one value of the
Transition and Row values, or the 'trans' and 'row' kwargs must be
passed in the call.
data : numpy array or pandas Series or DataFrame.
The data is assumed to have at least two columns containing first
wavelength, then data. An optional third column can contain errors.
If no such column exists, the errors are assumed to be 1.
trans : string
The desired value of the 'Transition' level of the model dataframe.
rows : string
The desired value of the 'Rows' level of the model index.
shmod : integer
Identifier for the Sherpa model in case one wants to have more models
loaded in memory simultaneously. If not, it will be overwritten each
time fit_with_sherpa() is run.
Default: 1
ranges : list of (min, max) tuples.
The determines which wavelength ranges will be included in the fit. If
an empty list is passed, the entire range of the data passed will be
used.
Default: [] (Empty list).
method : string
optimization to be used by Sherpa. Can be either 'levmar', 'neldermead'
or 'moncar'. See Sherpa documentation for more detail.
Default: 'levmar'.
"""
# Should this perhaps be an instancemethod of one of the major classes
# instead? On the upside it would mean direct access to all said class's
# attributes, which is good because it means less mandatory input
# parameters. On the downside, it is not generally useable. I want things
# to be as general as possible. But not at any price. Cost/benifit analysis
# not yet conclusive.
# First of all, check if Sherpa is even installed.
try:
import sherpa.astro.ui as ai
import sherpa.models as sm
except ImportError:
print " ".join("The Sherpa fitting software must be installed to use \
this functionality.".split())
raise
# Sherpa isn't good at staying clean, need to help it.
for i in ai.list_model_ids():
ai.delete_model(shmod)
ai.delete_data(shmod)
# Load data first, 'cause Sherpa wants it so.
if data.shape[0] == 2:
ai.load_arrays(shmod, data[:, 0], data[:, 1])
if data.shape[0] > 2:
ai.load_arrays(shmod, data[:, 0], data[:, 1], data[:, 2])
# Initialize model by setting continuum
Contin = sm.Const1D('Contin')
Contin.c0 = model.xs('Contin')['Ampl']
ai.set_model(shmod, Contin)
for i in model.index:
if i == 'Contin':
continue
else:
# use the identifier as letter (good idea?)
name = model.ix[i]['Identifier']
comp = ai.gauss1d(name)
comp.ampl = model.ix[i]['Ampl']
comp.pos = model.ix[i]['Pos']
comp.fwhm = model.ix[i]['Sigma']
ai.set_model(shmod, ai.get_model(shmod) + comp)
ai.show_model(shmod) # For testing...
print ' '
print ai.get_model(shmod)
# Set ranges included in fit.
# First, unset all.
ai.ignore_id(shmod)
if len(ranges) == 0:
ai.notice_id(shmod)
else:
for r in ranges:
ai.notice_id(shmod, r[0], r[1])
# Set optimization algorithm
ai.set_method(method)
# Create copy of model
new_model = model.copy()
# Perform the fit:
ai.fit(shmod)
print ai.get_fit_results()
print model
return new_model
