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attribute e_Q removed

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Fabian Joswig 2021-10-18 10:08:48 +01:00
parent e73a99409c
commit 4edea2c3a8
3 changed files with 3 additions and 15 deletions
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1
CHANGELOG.md
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@ -14,6 +14,7 @@ All notable changes to this project will be documented in this file.
### Deprecated ### Deprecated
- The function `plot_corrs` was deprecated as all its functionality is now contained within `Corr.show` - The function `plot_corrs` was deprecated as all its functionality is now contained within `Corr.show`
- Obs no longer have an attribute e_Q
## [1.1.0] - 2021-10-11 ## [1.1.0] - 2021-10-11
### Added ### Added

1
pyerrors/misc.py
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@ -56,6 +56,7 @@ def ks_test(obs=None):
else: else:
obs_list = obs obs_list = obs
# TODO: Rework to apply to Q-values of all fits in memory
Qs = [] Qs = []
for obs_i in obs_list: for obs_i in obs_list:
for ens in obs_i.e_names: for ens in obs_i.e_names:

16
pyerrors/pyerrors.py
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@ -86,7 +86,6 @@ class Obs:
self.e_tauint = {} self.e_tauint = {}
self.e_dtauint = {} self.e_dtauint = {}
self.e_windowsize = {} self.e_windowsize = {}
self.e_Q = {}
self.e_rho = {} self.e_rho = {}
self.e_drho = {} self.e_drho = {}
self.e_n_tauint = {} self.e_n_tauint = {}
@ -298,19 +297,6 @@ class Obs:
self.e_windowsize[e_name] = n self.e_windowsize[e_name] = n
break break
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]
e_mean /= e_N
xi2 = 0
for r_name in self.e_content[e_name]:
xi2 += self.shape[r_name] * (self.r_values[r_name] - e_mean) ** 2
xi2 /= self.e_dvalue[e_name] ** 2 * e_N
self.e_Q[e_name] = 1 - scipy.special.gammainc((len(self.e_content[e_name]) - 1.0) / 2.0, xi2 / 2.0)
else:
self.e_Q[e_name] = None
self.dvalue += self.e_dvalue[e_name] ** 2 self.dvalue += self.e_dvalue[e_name] ** 2
self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2 self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
@ -421,7 +407,7 @@ class Obs:
for r, r_name in enumerate(self.e_content[e_name]): for r, r_name in enumerate(self.e_content[e_name]):
arr[r] = (self.r_values[r_name] - sub_r_mean) / (self.e_dvalue[e_name] * np.sqrt(e_N / self.shape[r_name] - 1)) arr[r] = (self.r_values[r_name] - sub_r_mean) / (self.e_dvalue[e_name] * np.sqrt(e_N / self.shape[r_name] - 1))
plt.hist(arr, rwidth=0.8, bins=len(self.e_content[e_name])) plt.hist(arr, rwidth=0.8, bins=len(self.e_content[e_name]))
plt.title('Replica distribution' + e_name + ' (mean=0, var=1), Q=' + str(np.around(self.e_Q[e_name], decimals=2))) plt.title('Replica distribution' + e_name + ' (mean=0, var=1)')
plt.draw() plt.draw()
def plot_history(self): def plot_history(self):