def test_update_bounds_with_datapattern(self):
patt = np.ones((100, 100))
x = np.linspace(-2, 2, 100)
y = np.linspace(-0.5, 3.5, 100)
x_mesh, y_mesh = np.meshgrid(x, y)
dp = pyfdd.DataPattern(pattern_array=patt)
dp.set_xymesh(xmesh=x_mesh, ymesh=y_mesh)
fp = FitParameters(n_sites=1)
fp.update_bounds_with_datapattern(datapattern=dp)
result_bounds = fp.get_bounds()
# Assumes default bounds are (-3, 3)
self.assertEqual(result_bounds['dx'], (-2, 2))
self.assertEqual(result_bounds['dy'], (-0.5, 3))
def test_get_scale_for_fit(self):
patt = np.ones((100, 100)) * 100 # 10e6
total_cts = patt.sum()
dp = pyfdd.DataPattern(pattern_array=patt)
fp = FitParameters(n_sites=1)
fp.update_initial_values_with_datapattern(datapattern=dp)
keys = fp.get_keys()
scale = fp.get_scale_for_fit(cost_function='chi2')
for k, scale_val in zip(keys, scale):
if k == 'total_cts':
self.assertEqual(scale_val, total_cts * FitParameters.default_scale['total_cts'])
scale = fp.get_scale_for_fit(cost_function='ml')
for k, scale_val in zip(keys, scale):
if k == 'total_cts':
self.assertEqual(scale_val, -1)
def test_update_initial_values_with_datapattern(self):
# Dummy DataPattern
patt = np.ones((100, 100))
x = np.linspace(-2, 2, 100)
y = np.linspace(-2, 2, 100)
x_mesh, y_mesh = np.meshgrid(x, y)
dp = pyfdd.DataPattern(pattern_array=patt)
dp.set_xymesh(xmesh=x_mesh, ymesh=y_mesh)
dp.set_pattern_angular_pos((-0.1, 0.5), 3)
# Test
fitp = FitParameters(n_sites=1)
fitp.update_initial_values_with_datapattern(datapattern=dp)
initial_values = fitp.get_initial_values()
self.assertEqual(initial_values['dx'], -0.1)
self.assertEqual(initial_values['dy'], 0.5)
self.assertEqual(initial_values['phi'], 3)
self.assertEqual(initial_values['total_cts'], dp.pattern_matrix.sum())
def make_dummy_pattern():
pattern = np.random.poisson(1000, (22, 22))
dp = pyfdd.DataPattern(pattern_array=pattern)
dp.manip_create_mesh(pixel_size=1.4, distance=300)
return dp
def get_datapattern():
print(os.getcwd())
datapattern = pyfdd.DataPattern(
'../../test_pyfdd/data_files/pad_dp_2M.json')
return datapattern
pd.set_option('display.max_colwidth', None)
# In[2]:
filename1 = "/home/user/path/to/pattern_1.txt"
filename2 = "/home/user/path/to/pattern_2.txt"
# ## Creating the DataPattern
#
# The arguments to create a data pattern are the path to the data matrix, the number of chips (2 for the time quad 1 for the others) and the real size of the central pixels.
#
# Two DataPattern can be added together.
# In[3]:
dp1 = pyfdd.DataPattern(file_path=filename1, nChipsX=2, nChipsY=2, real_size=3)
dp2 = pyfdd.DataPattern(file_path=filename2, nChipsX=2, nChipsY=2, real_size=3)
dp = dp1 + dp2
# ## Manipulation of the data pattern
# In[4]:
# Manipulation methods
# Mask
mask = load_tpxquad_mask("/home/user/path/to/detector_mask.txt")
dp.set_mask(
mask, expand_by=0) # Set expand by > 0 when pixels are not added together.
# mask pixels for which its number of counts is over 4 standard deviations from averagage
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_colwidth', None)
# In[2]:
filename = "../test_pyfdd/data_files/pad_dp_2M.txt"
# ## Creating the DataPattern
#
# The arguments to create a data pattern are the path to the data matrix, the number of chips (2 for the time quad 1 for the others) and the real size of the central pixels.
#
# Two DataPattern can be added together.
# In[3]:
dp1 = pyfdd.DataPattern(file_path=filename)
dp2 = dp1 * 1.5
dp = dp1 + dp2
# ## Manipulation of the data pattern
# In[4]:
# Manipulation methods
# Manipulation methods
# -Orient
dp.manip_orient('cw') # PAD6 orientation
# use 'cw','cc','mh',mv' for rotate clockwise, rotate counterclockwise, mirror horizontal and mirror vertical
# in the desired order