def test_template_read():
# Now lets check out our reading Chain
# Create our fitter object
fitter = TemplateFitter()
# Get our example data file (10 events of 1 TeV at 0 Alt, 0 Az)
data_dir = pkg_resources.resource_filename('template_builder', 'data/')
# Which needs to actually be there
assert data_dir is not None
data_dir += "gamma_HESS_example.simhess.gz"
# Read in the file
amp, raw_x, raw_y = fitter.read_templates(data_dir)
# First check our output is empty
assert amp.keys() is not None
# Check our template parameters are correct
assert list(amp.keys())[0][0] == 0. # Alt
assert list(amp.keys())[0][1] == 0. # Az
assert list(amp.keys())[0][2] == 1. # Energy
# Can't be sure of the exact template content, but at least check the values make
# sense
test_template = (0., 0., 1., 0., 50.)
assert np.max(amp[test_template]) > 100. # Max amplitude is reasonable
# Average y value is about 0.
assert np.average(raw_y[test_template], weights=amp[test_template]) < 0.05
def test_template_fitting():
# Now lets check out our reading Chain
# Create our fitter object
fitter = TemplateFitter(min_fit_pixels=0)
# Get our example data file (10 events of 1 TeV at 0 Alt, 0 Az)
data_dir = pkg_resources.resource_filename('template_builder', 'data/')
# Which needs to actually be there
data_dir += "gamma_HESS_example.simhess.gz"
# Read in the file
amp, raw_x, raw_y = fitter.read_templates(data_dir)
test_template = (0., 0., 1., 0., 50.)
# Then lets fit our example template using the different options
fit_options = ["sklearn", "KNN"]
for option in fit_options:
fitter.training_library = option
template, var_template = fitter.fit_templates(
{test_template: amp[test_template]},
{test_template: raw_x[test_template]},
{test_template: raw_y[test_template]}, True, 1000)
assert template is not None
assert var_template is not None
x = np.linspace(fitter.bounds[0][0], fitter.bounds[0][1], fitter.bins[0])
y = np.linspace(fitter.bounds[1][0], fitter.bounds[1][1], fitter.bins[1])
# Make sure the template is the expected shape
assert template[test_template].shape[0] == fitter.bins[1]
assert template[test_template].shape[1] == fitter.bins[0]
assert var_template[test_template].shape[0] == fitter.bins[1]
assert var_template[test_template].shape[1] == fitter.bins[0]
# For now we will assume the fit just works
# Finally we will check that the range extension functions work
extended_template = fitter.extend_xmax_range(template)
xmax_range = np.array(list(extended_template.keys())).T[4]
# Check the bins are right
assert np.sort(xmax_range).all() == fitter.xmax_bins.all()
# And that all templates are the same
for key in extended_template:
assert extended_template[key].all() == template[test_template].all()
template = {test_template: template[test_template]}
# Finally check the distance extension works
template[0., 0., 1., 50., 50.] = template[test_template]
template[0., 0., 1., 100., 0.] = template[test_template]
template[0., 0., 1., 200., 0.] = template[test_template]
extended_template = fitter.extend_distance_range(template)
assert (0., 0., 1., 100., 50.) in extended_template
assert (0., 0., 1., 200., 50.) in extended_template