def compute_reciprocal_errors(self, key="r"):
# JG: BaseContainer?
r"""
Compute reciprocal erros following LaBrecque et al. (1996) according
to:
.. math::
\epsilon = \left|\frac{2(|R_n| - |R_r|)}{|R_n| + |R_r|}\right|
Parameters
----------
key : str
Parameter to calculate the reciprocal error for (default is "r").
Examples
--------
>>> import reda
>>> ert = reda.ERT()
>>> ert.data = reda.utils.norrec.get_test_df()
>>> ert.data = pd.DataFrame([
... [1,2,3,4,95],
... [3,4,2,1,-105]], columns=list("abmnr")
... )
>>> ert.compute_reciprocal_errors()
generating ids
assigning ids
>>> ert.data["error"].mean() == 0.1
True
"""
# Assign norrec ids if not already present
if "id" not in self.data.keys():
self.data = assign_norrec_to_df(self.data)
# Average repetitions
data = average_repetitions(self.data, "r")
# Get configurations with reciprocals
data = data.groupby("id").filter(lambda b: not b.shape[0] == 1)
n = self.data.shape[0] - data.shape[0]
if n > 0:
print("Could not find reciprocals for %d configurations" % n)
# Calc reciprocal error
grouped = data.groupby("id")
def _error(group):
R_n = group["r"].iloc[0]
R_r = group["r"].iloc[1]
return abs(2 * (abs(R_n) - abs(R_r)) / (abs(R_n) + abs(R_r)))
error = grouped.apply(_error)
error.name = "error"
self.data = pd.merge(self.data,
error.to_frame().reset_index(),
how='outer',
on='id')
def test_norrec_assignments1():
import reda.utils.norrec as redanr
df = redanr.get_test_df()
redanr.assign_norrec_to_df(df)
df1 = redanr.average_repetitions(df, ['r', ])
g = df1.groupby('id')
diffs_R = g['r'].diff()
def apply_nr_diff(row):
return diffs_R.iloc[row['id']]
df1['norrec_diff'] = df1.apply(apply_nr_diff, axis=1)
