def aggregated_with_group_1d(self, dar_disjoint_seeds):
""" Test aggregation with a dependent coordinate along one dim """
# swap out time series
dar = dar_disjoint_seeds.copy(deep=True)
dar.values = rand.rand(*dar.values.shape)
# add group coord
dar.coords["group"] = "bob_" + dar.seed_id
return aggregate(dar, method="mean", level="station", coord="group")
def aggregated_with_group_2d(self, dar_disjoint_seeds):
""" Test aggregation with a dependent coordinate along one dim """
# swap out time series
dar = dar_disjoint_seeds.copy(deep=True)
dar.values = rand.rand(*dar.values.shape)
# create groups with dims of seed_id and stream_id
df = dar.coords["starttime"].to_pandas()
df[0] = "1" + df.index
df[1] = "2" + df.index
dar.coords["group"] = df
return aggregate(dar, method="mean", level="station", coord="group")
def zoo_max_network(self, dar_disjoint_seeds):
""" aggregate with max on the network level """
return aggregate(dar_disjoint_seeds, "max", "network")
def zoo_std_all(self, dar_disjoint_seeds):
""" aggregate with max on the network level """
return aggregate(dar_disjoint_seeds, "std", "all")
def zoo_mean_station(self, dar_disjoint_seeds):
""" aggregate with mean on the station level """
return aggregate(dar_disjoint_seeds, "mean", "station")