def fit(self, method='simplex'):
"""Initiate a fit of the model using Sherpa.
:param method: Method to be used to fit the model (e.g. simplex, levmar, or moncar)
"""
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(self.model, self.fit_logger),
'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.fit_logger)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
ui.fit(1)
self.save_snapshot(fit_stat=calc_stat.min_fit_stat, method=method)
def fit(self):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(self.method)
ui.get_method().config.update(sherpa_configs.get(self.method, {}))
ui.load_user_model(CalcModel(self.model), 'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.child_pipe, self.maxiter)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
if any(not par.frozen for par in self.model.pars):
try:
ui.fit(1)
calc_stat.message['status'] = 'finished'
fit_logger.info('Fit finished normally')
except FitTerminated as err:
calc_stat.message['status'] = 'terminated'
fit_logger.warning('Got FitTerminated exception {}'.format(err))
self.child_pipe.send(calc_stat.message)
def fit(self):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(self.method)
ui.get_method().config.update(sherpa_configs.get(self.method, {}))
ui.load_user_model(CalcModel(self.model), 'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.child_pipe)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
if any(not par.frozen for par in self.model.pars):
try:
ui.fit(1)
calc_stat.message['status'] = 'finished'
logging.debug('Fit finished normally')
except FitTerminated as err:
calc_stat.message['status'] = 'terminated'
logging.debug('Got FitTerminated exception {}'.format(err))
self.child_pipe.send(calc_stat.message)
def test_no_covar(stat, clean_ui, setup_covar):
"Test an exception is thrown if covar is not run"
ui.set_stat(stat)
with pytest.raises(SessionErr) as exc:
ui.get_draws()
assert NO_COVAR_MSG == str(exc.value)
def test_covar_wstat_no_background(clean_ui, setup_covar):
"Test an exception is thrown when wstat is used without background"
ui.covar()
ui.set_stat("wstat")
with pytest.raises(StatErr) as exc:
ui.get_draws()
assert WSTAT_ERR_MSG == str(exc.value)
def test_no_covar(self):
for stat in self.right_stats:
ui.set_stat(stat)
try:
ui.get_draws()
except SessionErr as ve:
self.assertEqual(self.no_covar_msg, str(ve))
return
self.fail(self.fail_msg)
def test_covar_wrong_stat(stat, clean_ui, setup_covar):
"Test an exception is thrown is the proper stat is not set"
ui.covar()
ui.set_stat(stat)
with pytest.raises(ValueError) as exc:
ui.get_draws()
assert WRONG_STAT_MSG.format(stat) == str(exc.value)
def test_no_covar(self):
for stat in self.right_stats:
ui.set_stat(stat)
try:
ui.get_draws()
except SessionErr as ve:
self.assertEqual(self.no_covar_msg, ve.message)
return
self.fail(self.fail_msg)
def test_covar_wstat_no_background(self):
ui.covar()
ui.set_stat("wstat")
try:
ui.get_draws()
except StatErr as ve:
self.assertEqual(self.wstat_err_msg, str(ve))
return
self.fail(self.fail_msg)
def test_covar_wstat_no_background(self):
ui.covar()
ui.set_stat("wstat")
try:
ui.get_draws()
except StatErr as ve:
self.assertEqual(self.wstat_err_msg, ve.message)
return
self.fail(self.fail_msg)
def test_covar_as_none(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
ui.covar()
niter = 10
stat, accept, params = ui.get_draws(niter=niter)
self.assertEqual(niter + 1, stat.size)
self.assertEqual(niter + 1, accept.size)
self.assertEqual((2, niter + 1), params.shape)
self.assertTrue(numpy.any(accept))
def test_covar_as_argument(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
matrix = [[0.00064075, 0.01122127], [0.01122127, 0.20153251]]
niter = 10
stat, accept, params = ui.get_draws(niter=niter, covar_matrix=matrix)
self.assertEqual(niter + 1, stat.size)
self.assertEqual(niter + 1, accept.size)
self.assertEqual((2, niter + 1), params.shape)
self.assertTrue(numpy.any(accept))
def test_covar_as_none(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
ui.covar()
niter = 10
stat, accept, params = ui.get_draws(niter=niter)
self.assertEqual(niter+1, stat.size)
self.assertEqual(niter+1, accept.size)
self.assertEqual((2, niter+1), params.shape)
self.assertTrue(numpy.any(accept))
def test_covar_as_argument(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
matrix = [[0.00064075, 0.01122127], [0.01122127, 0.20153251]]
niter = 10
stat, accept, params = ui.get_draws(niter=niter, covar_matrix=matrix)
self.assertEqual(niter+1, stat.size)
self.assertEqual(niter+1, accept.size)
self.assertEqual((2, niter+1), params.shape)
self.assertTrue(numpy.any(accept))
def test_set_error_array_wrong(field, clean_ui):
"""What happens when we set the stat/syserror to an array of the wrong length?"""
y = np.asarray([2, 3, 4])
ui.load_arrays(1, np.arange(3), y)
ui.set_stat('cstat')
setfunc = getattr(ui, f"set_{field}")
# this does not error out
setfunc(np.asarray([1, 2, 3, 4]))
getfunc = getattr(ui, f"get_{field}")
assert getfunc() == pytest.approx([1, 2, 3, 4])
def test_user_model1d_fit():
"""Check can use in a fit."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"],
parvals = [1.0, 1.0])
mdl = ui.get_model_component(mname)
x = numpy.asarray([-2.4, 2.3, 5.4, 8.7, 12.3])
# Set up the data to be scattered around y = -0.2 x + 2.8
# Pick the deltas so that they sum to 0 (except for central
# point)
#
slope = -0.2
intercept = 2.8
dy = numpy.asarray([0.1, -0.2, 0.14, -0.1, 0.2])
ydata = x * slope + intercept + dy
ui.load_arrays(1, x, ydata)
ui.set_source(mname)
ui.ignore(5.0, 6.0) # drop the central bin
ui.set_stat('leastsq')
ui.set_method('simplex')
ui.fit()
fres = ui.get_fit_results()
assert fres.succeeded
assert fres.parnames == ('test_model.slope', 'test_model.intercept')
assert fres.numpoints == 4
assert fres.dof == 2
# Tolerance has been adjusted to get the tests to pass on my
# machine. It's really just to check that the values have chanegd
# from their default values.
#
assert fres.parvals[0] == pytest.approx(slope, abs=0.01)
assert fres.parvals[1] == pytest.approx(intercept, abs=0.05)
# Thse should be the same values, so no need to use pytest.approx
# (unless there's some internal translation between types done
# somewhere?).
#
assert mdl.slope.val == fres.parvals[0]
assert mdl.intercept.val == fres.parvals[1]
def test_covar_as_argument(stat, clean_ui, setup_covar):
ui.set_stat(stat)
ui.fit()
matrix = [[0.00064075, 0.01122127], [0.01122127, 0.20153251]]
niter = 10
stat, accept, params = ui.get_draws(niter=niter, covar_matrix=matrix)
n = niter + 1
assert stat.size == n
assert accept.size == n
assert params.shape == (2, n)
assert np.any(accept)
def test_user_model1d_fit():
"""Check can use in a fit."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"],
parvals = [1.0, 1.0])
mdl = ui.get_model_component(mname)
x = numpy.asarray([-2.4, 2.3, 5.4, 8.7, 12.3])
# Set up the data to be scattered around y = -0.2 x + 2.8
# Pick the deltas so that they sum to 0 (except for central
# point)
#
slope = -0.2
intercept = 2.8
dy = numpy.asarray([0.1, -0.2, 0.14, -0.1, 0.2])
ydata = x * slope + intercept + dy
ui.load_arrays(1, x, ydata)
ui.set_source(mname)
ui.ignore(5.0, 6.0) # drop the central bin
ui.set_stat('leastsq')
ui.set_method('simplex')
ui.fit()
fres = ui.get_fit_results()
assert fres.succeeded
assert fres.parnames == ('test_model.slope', 'test_model.intercept')
assert fres.numpoints == 4
assert fres.dof == 2
# Tolerance has been adjusted to get the tests to pass on my
# machine. It's really just to check that the values have chanegd
# from their default values.
#
assert fres.parvals[0] == pytest.approx(slope, abs=0.01)
assert fres.parvals[1] == pytest.approx(intercept, abs=0.05)
# Thse should be the same values, so no need to use pytest.approx
# (unless there's some internal translation between types done
# somewhere?).
#
assert mdl.slope.val == fres.parvals[0]
assert mdl.intercept.val == fres.parvals[1]
def test_covar_as_none(stat, clean_ui, setup_covar):
ui.set_stat(stat)
ui.fit()
ui.covar()
niter = 10
stat, accept, params = ui.get_draws(niter=niter)
n = niter + 1
assert stat.size == n
assert accept.size == n
assert params.shape == (2, n)
assert np.any(accept)
def test_covar_wrong_stat(self):
ui.covar()
fail = False
wrong_stats = set(ui.list_stats()) - self.right_stats
for stat in wrong_stats:
ui.set_stat(stat)
try:
ui.get_draws()
except ValueError as ve:
self.assertEqual(self.wrong_stat_msg.format(stat), ve.message)
continue
fail = True
break
if fail:
self.fail(self.fail_msg)
def test_set_syserror_array(clean_ui):
"""What happens when we set the syserror to an array?"""
staterror = 0.1 * np.ones(3)
syserror = 0.5 * np.ones(3)
combo = np.sqrt(0.01 + 0.25) * np.ones(3)
y = np.asarray([2, 3, 4])
ui.load_arrays(1, np.arange(3), y, staterror, None)
ui.set_stat('cstat')
ui.set_syserror(syserror)
assert ui.get_staterror() == pytest.approx(staterror)
assert ui.get_syserror() == pytest.approx(syserror)
assert ui.get_error() == pytest.approx(combo)
def test_covar_wrong_stat(self):
ui.covar()
fail = False
wrong_stats = set(ui.list_stats()) - self.right_stats
for stat in wrong_stats:
ui.set_stat(stat)
try:
ui.get_draws()
except ValueError as ve:
self.assertEqual(self.wrong_stat_msg.format(stat), str(ve))
continue
fail = True
break
if fail:
self.fail(self.fail_msg)
def test_set_syserror_scalar_no_fractional(clean_ui):
"""What happens when we set the syserror to a scalar fractional=False?"""
staterror = 0.1 * np.ones(3)
syserror = 0.5 * np.ones(3)
combo = np.sqrt(0.01 + 0.25) * np.ones(3)
ui.load_arrays(1, np.arange(3), np.ones(3), staterror, syserror)
ui.set_stat('cstat')
ui.set_syserror(3)
assert ui.get_staterror() == pytest.approx(staterror)
assert ui.get_syserror() == pytest.approx(3 * np.ones(3))
combo = np.sqrt(0.01 + 9) * np.ones(3)
assert ui.get_error() == pytest.approx(combo)
def setup_err_estimate_multi_ids(strings=False):
"""Create the environment used in test_err_estimate_xxx tests.
The model being fit is polynom1d with c0=50 c1=-2
and was evaluated and passed through sherpa.utils.poisson_noise
to create the datasets.
Since we can have string or integer ids we allow either,
but do not try to mix them.
"""
if strings:
id1 = "1"
id2 = "2"
id3 = "3"
else:
id1 = 1
id2 = 2
id3 = 3
ui.load_arrays(id1, [1, 3, 7, 12], [50, 40, 27, 20])
ui.load_arrays(id2, [-3, 4, 5], [55, 34, 37])
ui.load_arrays(id3, [10, 12, 20], [24, 26, 7])
# NOTE: dataset "not-used" is not used in the fit and is not
# drawn from the distributino used to create the other datasets.
#
ui.load_arrays("not-used", [2000, 2010, 2020], [10, 12, 14])
mdl = ui.create_model_component("polynom1d", "mdl")
mdl.c1.thaw()
ui.set_source(id1, mdl)
ui.set_source(id2, mdl)
ui.set_source(id3, mdl)
# apply the model to dataset not-used just so we can check we
# don't end up using it
mdl_not_used = ui.create_model_component("scale1d", "mdl_not_used")
ui.set_source("not-used", mdl + mdl_not_used)
# use cstat so we have an approximate goodness-of-fit just to
# check we are getting sensible results.
#
ui.set_stat("cstat")
ui.set_method("simplex")
def test_set_syserror_scalar_fractional(clean_ui):
"""What happens when we set the syserror to a scalar fractional=True?"""
staterror = 0.1 * np.ones(3)
syserror = 0.5 * np.ones(3)
combo = np.sqrt(0.01 + 0.25) * np.ones(3)
y = np.asarray([2, 3, 4])
ui.load_arrays(1, np.arange(3), y, staterror, syserror)
ui.set_stat('cstat')
ui.set_syserror(0.4, fractional=True)
assert ui.get_staterror() == pytest.approx(staterror)
assert ui.get_syserror() == pytest.approx(0.4 * y)
combo = np.sqrt(0.01 + 0.16 * y * y)
assert ui.get_error() == pytest.approx(combo)
def fit_model(
model,
comm=None,
method='simplex',
config=None,
nofit=None,
freeze_pars=freeze_pars,
thaw_pars=[],
):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(config or sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(model, comm), 'xijamod')
ui.add_user_pars('xijamod', model.parnames)
ui.set_model(1, 'xijamod')
fit_parnames = set()
for parname, parval in zip(model.parnames, model.parvals):
getattr(xijamod, parname).val = parval
fit_parnames.add(parname)
if any([re.match(x + '$', parname) for x in freeze_pars]):
fit_logger.info('Freezing ' + parname)
ui.freeze(getattr(xijamod, parname))
fit_parnames.remove(parname)
if any([re.match(x + '$', parname) for x in thaw_pars]):
fit_logger.info('Thawing ' + parname)
ui.thaw(getattr(xijamod, parname))
fit_parnames.add(parname)
if 'tau' in parname:
getattr(xijamod, parname).min = 0.1
calc_stat = CalcStat(model, comm)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
if fit_parnames and not nofit:
ui.fit(1)
else:
model.calc()
def fit_model(model,
comm=None,
method='simplex',
config=None,
nofit=None,
freeze_pars=freeze_pars,
thaw_pars=[],
):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(config or sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(model, comm), 'xijamod')
ui.add_user_pars('xijamod', model.parnames)
ui.set_model(1, 'xijamod')
fit_parnames = set()
for parname, parval in zip(model.parnames, model.parvals):
getattr(xijamod, parname).val = parval
fit_parnames.add(parname)
if any([re.match(x + '$', parname) for x in freeze_pars]):
fit_logger.info('Freezing ' + parname)
ui.freeze(getattr(xijamod, parname))
fit_parnames.remove(parname)
if any([re.match(x + '$', parname) for x in thaw_pars]):
fit_logger.info('Thawing ' + parname)
ui.thaw(getattr(xijamod, parname))
fit_parnames.add(parname)
if 'tau' in parname:
getattr(xijamod, parname).min = 0.1
calc_stat = CalcStat(model, comm)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
if fit_parnames and not nofit:
ui.fit(1)
else:
model.calc()
def test_set_syserror_none(clean_ui):
"""What happens when we set the syserror to None?"""
staterror = 0.1 * np.ones(3)
syserror = 0.5 * np.ones(3)
combo = np.sqrt(0.01 + 0.25) * np.ones(3)
ui.load_arrays(1, np.arange(3), np.ones(3), staterror, syserror)
ui.set_stat('cstat')
ui.set_syserror(None)
assert ui.get_staterror() == pytest.approx(staterror)
with pytest.raises(DataErr) as err:
ui.get_syserror()
assert str(err.value) == "data set '1' does not specify systematic errors"
combo = staterror
assert ui.get_error() == pytest.approx(combo)
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)
def test_set_staterror_none(clean_ui):
"""What happens when we set the staterror to None?"""
staterror = 0.1 * np.ones(3)
syserror = 0.5 * np.ones(3)
combo = np.sqrt(0.01 + 0.25) * np.ones(3)
ui.load_arrays(1, np.arange(3), np.ones(3), staterror, syserror)
ui.set_stat('cstat')
assert ui.get_staterror() == pytest.approx(staterror)
assert ui.get_syserror() == pytest.approx(syserror)
assert ui.get_error() == pytest.approx(combo)
# removing the statistical error means that the statistic is used;
# for the likelihood stats we just get 1's
#
ui.set_staterror(None)
assert ui.get_staterror() == pytest.approx(np.ones(3))
assert ui.get_syserror() == pytest.approx(syserror)
combo = np.sqrt(1 + 0.25) * np.ones(3)
assert ui.get_error() == pytest.approx(combo)
ui.load_user_model(lim_line, '%s_mod' % ftype)
ui.add_user_pars('%s_mod' % ftype, ['m', 'b'])
ui.set_model(data_id, '%s_mod' % ftype)
ui.load_arrays(data_id, times, failures[ftype])
fmod = ui.get_model_component('%s_mod' % ftype)
fmod.b.min = 0
fmod.b.max = 1
fmod.m.min = 0
fmod.m.max = 0.5
fmod.b.val = 1e-7
ui.load_user_stat("loglike", llh, my_err)
ui.set_stat(loglike)
# the tricky part here is that the "model" is the probability polynomial
# we've defined evaluated at the data x values.
# the model and the data are passed to the user stat/ llh
# function as it is minimized.
ui.fit(data_id)
myfit = ui.get_fit_results()
#axplot[ftype] = ui.get_model_plot(data_id)
if myfit.succeeded:
import pickle
pickle.dump(myfit, open('%s_fitfile.pkl' % ftype, 'w'))
rep_file = open('%s_fitfile.json' % ftype, 'w')
rep_file.write(
json.dumps(dict(
time0=trend_start,
import re
import os
from glob import glob
import pickle
import matplotlib.pyplot as plt
import numpy as np
from sherpa import ui
import dark_models
sbp = None # for pychecker
g1 = None
method = 'levmar'
ui.set_stat('cash')
ui.set_method('simplex')
ui.load_user_model(dark_models.smooth_broken_pow, 'sbp')
ui.add_user_pars('sbp', ('gamma1', 'gamma2', 'x_b', 'x_r', 'ampl1'))
def fit_gauss_sbp():
g1 = ui.gauss1d.g1
ui.set_model(sbp + g1)
ui.set_method('simplex')
g1.fwhm = 5.0
g1.pos = 7.0
g1.ampl = 30000.
ui.freeze(sbp.gamma1)
ui.freeze(sbp.gamma2)
for limit in [50, 75, 100, 125, 150, 200, 1000]:
warm_frac = data[range_type][mag][ok]["n{}".format(limit)]
print "range_type {}".format(range_type)
print "mag {}".format(mag)
print "limit is {}".format(limit)
extent = np.max(warm_frac) - np.min(warm_frac)
wp_min = np.min(warm_frac)
warm_frac = warm_frac - wp_min
def scaled_warm_frac(pars, x):
scaled = pars[1] + warm_frac * pars[0]
return scaled
data_id = 1
ui.set_method("simplex")
ui.set_stat("chi2datavar")
# ui.set_stat('leastsq')
# ui.load_user_stat("chi2custom", my_chi2, my_err)
# ui.set_stat(chi2custom)
ui.load_user_model(scaled_warm_frac, "model")
ui.add_user_pars("model", ["scale", "offset"])
ui.set_model(data_id, "model")
ui.load_arrays(data_id, np.array(times), np.array(bad_frac))
fmod = ui.get_model_component("model")
fmod.scale.min = 1e-9
max_err = np.max([data[range_type][mag][ok]["err_high"], data[range_type][mag][ok]["err_low"]], axis=0)
ui.set_staterror(data_id, max_err)
ui.fit(data_id)
f = ui.get_fit_results()
scale = f.rstat ** 0.5
ui.set_staterror(data_id, max_err * scale)
ui.load_arrays(data_id,
times,
failures[ftype])
fmod = ui.get_model_component('%s_mod' % ftype)
fmod.b.min = 0
fmod.b.max = 1
fmod.m.min = 0
fmod.m.max = 0.5
fmod.b.val=1e-7
ui.load_user_stat("loglike", llh, my_err)
ui.set_stat(loglike)
# the tricky part here is that the "model" is the probability polynomial
# we've defined evaluated at the data x values.
# the model and the data are passed to the user stat/ llh
# function as it is minimized.
ui.fit(data_id)
myfit = ui.get_fit_results()
#axplot[ftype] = ui.get_model_plot(data_id)
if myfit.succeeded:
import pickle
pickle.dump(myfit, open('%s_fitfile.pkl' % ftype, 'w'))
rep_file = open('%s_fitfile.json' % ftype, 'w')
rep_file.write(json.dumps(dict(time0=trend_start,
datestop=trend_date_stop,
datestart=trend_date_start,
err_low[err_low == 0] = .0001
for limit in warm_limits:
print "range type {}".format(range_type)
print "mag {}".format(mag)
print "limit is {}".format(limit)
print "ftype {}".format(limit)
warm_frac = data[range_type][mag][ok]['n{}'.format(limit)]
extent = np.max(warm_frac) - np.min(warm_frac)
wp_min = np.min(warm_frac)
warm_frac = warm_frac - wp_min
def scaled_warm_frac(pars, x):
scaled = pars[1] + warm_frac * pars[0]
return scaled
data_id = 1
ui.set_method('simplex')
ui.set_stat('chi2datavar')
#ui.set_stat('leastsq')
#ui.load_user_stat("chi2custom", my_chi2, my_err)
#ui.set_stat(chi2custom)
ui.load_user_model(scaled_warm_frac, 'model')
ui.add_user_pars('model', ['scale', 'offset'])
ui.set_model(data_id, 'model')
ui.load_arrays(data_id,
np.array(times),
np.array(bad_frac))
fmod = ui.get_model_component('model')
fmod.scale.min = 1e-9
fmod.offset.val = 0
ui.freeze(fmod.offset)
max_err = np.max([err_high, err_low], axis=0)
ui.set_staterror(data_id, max_err)