def setup(self):
x1 = [0.0, 1.0, 2.0, 3.0, 4.0]
y1 = [0.1, 1.3, 1.8, 3.3, 4.2]
luf1 = LookupFunction(x1, y1)
x2 = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0]
y2 = [0.6, 0.8, 1.3, 1.9, 2.6, 2.8, 3.3, 3.9]
luf2 = LookupFunction(x2, y2)
x3 = [1.0, 2.0]
y3 = [1.0, 2.0]
luf3 = LookupFunction(x3, y3)
y4 = [1.1, 1.9]
luf4 = LookupFunction(x3, y4)
y5 = [1.2, 2.2]
luf5 = LookupFunction(x3, y5)
self.group = LookupFunctionGroup([luf1, luf2, luf3, luf4, luf5])
def total_crossing_probability(self,
steps=None,
method="wham",
force=False):
"""Return the total crossing probability using `method`
Parameters
----------
steps : iterable of :class:`.MCStep`
cycles to be analyzed
method : "wham" (later: or "mbar" or "tram")
approach to use to combine the histograms
force : bool (False)
if true, cached results are overwritten
"""
if method == "wham":
run_ensembles = False
for ens in self.ensembles:
try:
hist = self.histograms['max_lambda'][ens]
except KeyError:
run_ensembles = True
if run_ensembles or force:
if steps is None:
raise RuntimeError(
"Unable to build histograms without steps source")
self.all_statistics(steps, force=True)
df = histograms_to_pandas_dataframe(
self.histograms['max_lambda'].values(),
fcn="reverse_cumulative").sort_index(axis=1)
# if lambdas not set, returns None and WHAM uses fallback
lambdas = self.interfaces.lambdas
wham = WHAM(interfaces=lambdas)
# wham.load_from_dataframe(df)
# wham.clean_leading_ones()
tcp = wham.wham_bam_histogram(df).to_dict()
elif method == "mbar":
pass
else:
raise ValueError(
"Only supported methods are 'wham' and 'mbar'. " +
"Whereas 'mbar' is not yet implemented!")
self.tcp = LookupFunction(tcp.keys(), tcp.values())
return self.tcp
def from_ensemble_histograms(self, hists):
"""Calculate results from a dict of ensemble histograms.
Parameters
----------
hists : dict of {:class:`.Ensemble`: :class:`.numerics.Histogram`}
histogram for each ensemble (from ``self.max_lambda_calc``)
Returns
-------
:class:`.LookupFunction`
the total crossing probability function
"""
tcp_results = {}
input_hists = [hists[ens] for ens in self.transition.ensembles]
df = paths.numerics.histograms_to_pandas_dataframe(
input_hists, fcn="reverse_cumulative").sort_index(axis=1)
# TODO: remove WHAM-specific name here
tcp = self.combiner.wham_bam_histogram(df).to_dict()
return LookupFunction(tcp.keys(), tcp.values())
def test_append(self):
luf = LookupFunction([0.0, 1.0, 2.0], [0.5, 1.5, 2.5])
self.group.append(luf)
assert_almost_equal(self.group(1.0), 1.15)
def test_setitem(self):
luf = LookupFunction([0.0, 1.0, 2.0], [0.5, 1.5, 2.5])
self.group[3] = luf
assert_almost_equal(self.group(1.0), 1.16)