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

further instances of np.float removed

Browse source
This commit is contained in:
Fabian Joswig 2021-10-12 14:12:21 +01:00
parent ef35dd8684
commit cddf6ddf6b
3 changed files with 5 additions and 5 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

6
pyerrors/fits.py
View file

@ -256,7 +256,7 @@ def odr_fit(x, y, func, silent=False, **kwargs):
data = RealData(x_f, y_f, sx=dx_f, sy=dy_f)
model = Model(func)
odr = ODR(data, model, x0, partol=np.finfo(np.float).eps)
odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=0, deriv=1)
output = odr.run()
@ -610,7 +610,7 @@ def covariance_matrix(y):
def error_band(x, func, beta):
"""Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
cov = covariance_matrix(beta)
if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float).eps):
if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
print('Warning, Covariance matrix is not symmetric within floating point precision')
print('cov - cov.T:')
print(cov - cov.T)
@ -716,7 +716,7 @@ def fit_general(x, y, func, silent=False, **kwargs):
model = Model(func)
odr = ODR(data, model, beta0, partol=np.finfo(np.float).eps)
odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=fit_type, deriv=1)
output = odr.run()
if print_output and not silent:

2
pyerrors/misc.py
View file

@ -71,7 +71,7 @@ def ks_test(obs=None):
plt.ylabel('Cumulative probability')
plt.title(str(bins) + ' Q values')
n = np.arange(1, bins + 1) / np.float(bins)
n = np.arange(1, bins + 1) / np.float64(bins)
Xs = np.sort(Qs)
plt.step(Xs, n)
diffs = n - Xs

2
pyerrors/pyerrors.py
View file

@ -297,7 +297,7 @@ class Obs:
self.e_windowsize[e_name] = n
break
if len(self.e_content[e_name]) > 1 and self.e_dvalue[e_name] > np.finfo(np.float).eps:
if len(self.e_content[e_name]) > 1 and self.e_dvalue[e_name] > np.finfo(np.float64).eps:
e_mean = 0
for r_name in self.e_content[e_name]:
e_mean += self.shape[r_name] * self.r_values[r_name]