def test_set_filter_masked_wrong(clean_ui):
"""What happens when we call set_filter to a filtered dataset with the wrong size?"""
x = np.asarray([10, 20, 30])
y = np.asarray([2, 3, 4])
ui.load_arrays(1, x, y)
ui.ignore(lo=15, hi=45)
with pytest.raises(DataErr) as err:
ui.set_filter(np.asarray([True, False]))
assert str(err.value) == "size mismatch between 3 and 2"
def test_set_filter_masked(clean_ui):
"""What happens when we call set_filter to a filtered dataset?"""
x = np.asarray([10, 20, 30, 40, 50])
y = np.asarray([2, 3, 4, 5, 6])
ui.load_arrays(1, x, y)
ui.ignore(lo=15, hi=45)
data = ui.get_data()
assert data.mask == pytest.approx([True, False, False, False, True])
ui.set_filter(np.asarray([True, False, True, False, False]))
assert data.mask == pytest.approx([True, False, True, False, True])
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_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 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