def test_path_calculations(self):
path_output = tpt.find_top_paths(self.sources, self.sinks, self.tprob)
paths_ref = io.loadh( tpt_get("dijkstra_paths.h5"), 'Data')
fluxes_ref = io.loadh( tpt_get("dijkstra_fluxes.h5"), 'Data')
bottlenecks_ref = io.loadh( tpt_get("dijkstra_bottlenecks.h5"), 'Data')
#npt.assert_array_almost_equal(path_output[0], paths_ref)
npt.assert_array_almost_equal(path_output[1], bottlenecks_ref)
npt.assert_array_almost_equal(path_output[2], fluxes_ref)
def test_path_calculations(self):
path_output = tpt.find_top_paths(self.sources, self.sinks, self.tprob)
paths_ref = io.loadh(os.path.join(self.tpt_ref_dir,"dijkstra_paths.h5"), 'Data')
fluxes_ref = io.loadh(os.path.join(self.tpt_ref_dir,"dijkstra_fluxes.h5"), 'Data')
bottlenecks_ref = io.loadh(os.path.join(self.tpt_ref_dir,"dijkstra_bottlenecks.h5"), 'Data')
#npt.assert_array_almost_equal(path_output[0], paths_ref)
npt.assert_array_almost_equal(path_output[1], bottlenecks_ref)
npt.assert_array_almost_equal(path_output[2], fluxes_ref)
def test_path_calculations(self):
path_output = tpt.find_top_paths(self.sources, self.sinks, self.tprob)
paths_ref = io.loadh( tpt_get("dijkstra_paths.h5"), 'Data')
fluxes_ref = io.loadh( tpt_get("dijkstra_fluxes.h5"), 'Data')
bottlenecks_ref = io.loadh( tpt_get("dijkstra_bottlenecks.h5"), 'Data')
for i in range(len(paths_ref)):
npt.assert_array_almost_equal(path_output[0][i], paths_ref[i])
npt.assert_array_almost_equal(path_output[1], bottlenecks_ref)
npt.assert_array_almost_equal(path_output[2], fluxes_ref)
def run(tprob, A, B, n):
(Paths, Bottlenecks, Fluxes) = tpt.find_top_paths(A, B, tprob, num_paths=n)
# We have to pad the paths with -1s to make a square array
maxi = 0 # the maximum path length
for path in Paths:
if len(path) > maxi: maxi = len(path)
PaddedPaths = -1 * np.ones( (len(Paths), maxi ) )
for i, path in enumerate(Paths):
PaddedPaths[i,:len(path)] = np.array(path)
return PaddedPaths, np.array(Bottlenecks), np.array(Fluxes)
def run(tprob, A, B, n):
Paths, Bottlenecks, Fluxes = tpt.find_top_paths(A, B, tprob, num_paths=n)
# We have to pad the paths with -1s to make a square array
maxi = 0 # the maximum path length
for path in Paths:
if len(path) > maxi:
maxi = len(path)
PaddedPaths = -1 * np.ones((len(Paths), maxi))
for i, path in enumerate(Paths):
PaddedPaths[i, :len(path)] = np.array(path)
return PaddedPaths, np.array(Bottlenecks), np.array(Fluxes)
