minor fixes

Author: mbruno46

pyobs/core/error.py

@@ -332,11 +332,11 @@ def covar(self):
 
 
 class errinfo:
-    def __init__(self, Stau=1.5, k=0, W=None):
+    def __init__(self, Stau=1.5, k=0, W=None, gamma_bias=True):
         self.Stau = Stau
         self.k = k
         self.W = W
-
+        self.gamma_bias = gamma_bias
 
 def gamma_error(x, name, plot=False, pfile=None, einfo=None):
     if einfo is None:
@@ -349,7 +349,8 @@ def gamma_error(x, name, plot=False, pfile=None, einfo=None):
         v.set_opt(einfo.W)
 
     if not v.ismf:
-        # v.correct_gamma_bias()
+        if einfo.gamma_bias:
+            v.correct_gamma_bias()
         tau = v.tauint() * 0.5
     else:
         tau = v.tauint()
@@ -363,7 +364,7 @@ def covariance(x, name, W):
     cov = covar(x, name)
     cov.set_opt(W)
 
-    # if not cov.ismf:
-    #     cov.correct_gamma_bias()
+    if not cov.ismf:
+        cov.correct_gamma_bias()
 
     return cov.covar()

tests/core/error.py

@@ -9,46 +9,18 @@
 N=1000
 tau=0.0
 
-rng = pyobs.random.generator(46)
-
-data = rng.markov_chain(val,sig**2,tau,N)
-obsA = pyobs.observable()
-obsA.create('EnsA',data)
-
-[a,da] = obsA.error()
-dda = obsA.error_of_error()
-assert abs(a-val) < da
-assert abs(da - sig) < dda
-print(f'Estimated error {da[0]:g} vs expeceted {sig:g}')
-
-N=10000
-tau=0.0
-
-data = rng.markov_chain(val,sig**2,tau,N)
-obsA = pyobs.observable()
-obsA.create('EnsA',data)
-
-[a,da] = obsA.error()
-dda = obsA.error_of_error()
-assert abs(a-val) <da
-assert abs(da - sig) < dda
-print(f'Estimated error {da[0]:g} vs expeceted {sig:g}')
-
-[a, da] = obsA.error(errinfo={'EnsA': pyobs.errinfo(W=0)})
-assert abs(a-val) < da
-
-N=4000
-tau=4.0
-
-data = rng.markov_chain(val,sig**2,tau,N)
-obsA = pyobs.observable()
-obsA.create('EnsA',data)
-
-[a,da] = obsA.error()
-dda = obsA.error_of_error()
-assert abs(a-val) < da
-assert abs(da - sig) < dda
-print(f'Estimated error {da[0]:g} vs expeceted {sig:g}')
+rng = pyobs.random.generator(67)
+
+for tau, N in zip([0.0,4.0],[10000,4000]):
+    data = rng.markov_chain(val,sig**2,tau,N)
+    obsA = pyobs.observable()
+    obsA.create('EnsA',data)
+
+    [a,da] = obsA.error()
+    dda = obsA.error_of_error()
+    assert abs(a-val) < da
+    assert abs(da - sig) < dda
+    print(f'Estimated error {da[0]:g} vs expeceted {sig:g}')
 
 _tau = obsA.tauint()
 assert abs(_tau['EnsA'][0]-tau) < 1.5*_tau['EnsA'][1]

tests/core/slice.py

@@ -2,6 +2,7 @@
 import numpy
 
 pyobs.set_verbose('slice', True)
+pyobs.set_verbose('transform', True)
 
 T=16
 L=16
@@ -44,6 +45,11 @@
 assert numpy.all(abs(v0-v1) < 1e-12)
 assert numpy.all(abs(e0-e1) < 1e-12)
 
+[v0, e0] = obs.error()
+[v1, e1] = obs[::-1,:].error()
+assert numpy.all(abs(v0[::-1,:]-v1) < 1e-12)
+assert numpy.all(abs(e0[::-1,:]-e1) < 1e-12)
+
 pyobs.set_verbose('slice', False)
 
 data = rng.markov_chain(1.23,0.01,tau,N)