lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-03-15 14:50:25 +01:00
Code Issues Projects Releases Packages Wiki Activity

fix: corrected bug that prevented combined fits with multiple x-obs in some cases (#241)

Browse source 
* fix: corrected bug that prevented combined fits with multiple x-obs in some cases

* made test more complex

* [Fix] Slightly increase tolerance for matrix function test.

* Adapt test_merge_idx to compare lists

---------

Co-authored-by: Simon Kuberski <simon.kuberski@cern.ch>
Co-authored-by: Fabian Joswig <fjosw@users.noreply.github.com>
This commit is contained in:
s-kuberski 2024-09-14 02:15:59 +09:00 • committed by GitHub
parent 1d6f7f65c0
commit 4b1bb0872a
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
4 changed files with 21 additions and 7 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
pyerrors/fits.py
View file

@ -256,7 +256,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
if sorted(list(funcd.keys())) != key_ls: if sorted(list(funcd.keys())) != key_ls:
raise ValueError('x and func dictionaries do not contain the same keys.') raise ValueError('x and func dictionaries do not contain the same keys.')
x_all = np.concatenate([np.array(xd[key]) for key in key_ls]) x_all = np.concatenate([np.array(xd[key]).transpose() for key in key_ls]).transpose()
y_all = np.concatenate([np.array(yd[key]) for key in key_ls]) y_all = np.concatenate([np.array(yd[key]) for key in key_ls])
y_f = [o.value for o in y_all] y_f = [o.value for o in y_all]

14
tests/fits_test.py
View file

@ -1082,6 +1082,20 @@ def test_combined_resplot_qqplot():
fr = pe.least_squares(xd, yd, fd, resplot=True, qqplot=True) fr = pe.least_squares(xd, yd, fd, resplot=True, qqplot=True)
plt.close('all') plt.close('all')
def test_combined_fit_xerr():
fitd = {
'a' : lambda p, x: p[0] * x[0] + p[1] * x[1],
'b' : lambda p, x: p[0] * x[0] + p[2] * x[1],
'c' : lambda p, x: p[0] * x[0] + p[3] * x[1],
}
yd = {
'a': [pe.cov_Obs(3 + .1 * np.random.uniform(), .1**2, 'a' + str(i)) for i in range(5)],
'b': [pe.cov_Obs(1 + .1 * np.random.uniform(), .1**2, 'b' + str(i)) for i in range(6)],
'c': [pe.cov_Obs(3 + .1 * np.random.uniform(), .1**2, 'c' + str(i)) for i in range(3)],
}
xd = {k: np.transpose([[1 + .01 * np.random.uniform(), 2] for i in range(len(yd[k]))]) for k in fitd}
pe.fits.least_squares(xd, yd, fitd)
def test_x_multidim_fit(): def test_x_multidim_fit():
x1 = np.arange(1, 10) x1 = np.arange(1, 10)

6
tests/linalg_test.py
View file

@ -276,10 +276,10 @@ def test_matrix_functions():
for (i, j), entry in np.ndenumerate(check_inv): for (i, j), entry in np.ndenumerate(check_inv):
entry.gamma_method() entry.gamma_method()
if(i == j): if(i == j):
assert math.isclose(entry.value, 1.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value) assert math.isclose(entry.value, 1.0, abs_tol=2e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
else: else:
assert math.isclose(entry.value, 0.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value) assert math.isclose(entry.value, 0.0, abs_tol=2e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
assert math.isclose(entry.dvalue, 0.0, abs_tol=1e-9), 'dvalue ' + str(i) + ',' + str(j) + ' ' + str(entry.dvalue) assert math.isclose(entry.dvalue, 0.0, abs_tol=2e-9), 'dvalue ' + str(i) + ',' + str(j) + ' ' + str(entry.dvalue)
# Check Cholesky decomposition # Check Cholesky decomposition
sym = np.dot(matrix, matrix.T) sym = np.dot(matrix, matrix.T)

4
tests/obs_test.py
View file

@ -554,11 +554,11 @@ def test_merge_idx():
for j in range(5): for j in range(5):
idll = [range(1, int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)] idll = [range(1, int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
assert pe.obs._merge_idx(idll) == sorted(set().union(*idll)) assert list(pe.obs._merge_idx(idll)) == sorted(set().union(*idll))
for j in range(5): for j in range(5):
idll = [range(int(round(np.random.uniform(1, 28))), int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)] idll = [range(int(round(np.random.uniform(1, 28))), int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
assert pe.obs._merge_idx(idll) == sorted(set().union(*idll)) assert list(pe.obs._merge_idx(idll)) == sorted(set().union(*idll))
idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)] idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)]
new_idx = pe.obs._merge_idx(idl) new_idx = pe.obs._merge_idx(idl)