def nearest(a, v):
"""Alternative (not optimized) implementation of find_nearest
Used for testing purpose only
"""
nr = a[0]
diff = 1e+9
for x in a:
if np.abs(x - v) < diff:
nr = x
diff = np.abs(x - v)
return nr
def test_simple_find_nearest():
x = np.arange(100)
assert find_nearest(x, 75.6) == exactly(76)
assert find_nearest(x, 75.5) == exactly(75)
def test_gauss_find_nearest():
e = np.random.normal(100, 10, 100)
for x in range(1, 100, 10):
assert find_nearest(e, x) == approx(nearest(e, x), rel=1e-3)
@given(float_arrays(min_value=1, max_value=100))
def test_find_nearest(data):
assert find_nearest(data, 10) == approx(nearest(data, 10), rel=1e-3)
"""
maps = read_maps(map_filename)
assert np.all(maps.chi2 == 1)
assert np.all(maps.e0 == 13000)
assert np.all(maps.e0u == 2)
assert np.all(maps.lt == 5000)
assert np.all(maps.ltu == 3)
@given(random_length_float_arrays(min_value=0, max_value=3e4))
def test_correct_geometry_properly(x):
assert_array_equal(correct_geometry_(x), (1 / x))
@given(float_arrays(min_value=0, max_value=530),
float_arrays(min_value=1, max_value=1e4))
def test_correct_geometry_properly(z, lt):
compute_corr = np.exp(z / lt)
assert_array_equal(correct_lifetime_(z, lt), compute_corr)
@given(floats(min_value=0, max_value=1e4))
def test_time_coefs_corr(map_filename, time):
"""
In the map taken as input, none of the parameters
changes with time, thus all outputs of
time_coefs_corr function must be 1.
"""
maps = read_maps(map_filename)
from .fit_functions import sigmoid
from .fit_functions import compute_drift_v
from .fit_functions import relative_errors
from .fit_functions import to_relative
from .fit_functions import fit_profile_1d_expo
from .fit_functions import fit_slices_2d_gauss
from .fit_functions import fit_slices_2d_expo
from .kr_types import Measurement
sensible_floats = floats(-1e4, +1e4)
fractions = floats(0, 1)
sensible_arrays_variable = random_length_float_arrays(1,
10,
min_value=-1e4,
max_value=1e4)
sensible_arrays_fixed = float_arrays(5, min_value=-1e4, max_value=1e4)
measurements = builds(Measurement, sensible_arrays_fixed,
sensible_arrays_fixed.map(abs))
def test_get_chi2f_when_data_equals_error_and_fit_equals_zero():
Nevt = int(1e6)
xdata = np.zeros(Nevt) # Dummy value, not needed
ydata = np.random.uniform(1, 100, Nevt)
errs = ydata
f = lambda x: np.zeros_like(x)
chi2 = chi2f(f, 0, xdata, ydata, errs)
assert chi2 == approx(1, rel=1e-3)
def test_get_chi2f_when_data_equals_fit():