def test_unsupported_column_warns(self, df_from_inv):
"""Ensure an unsupported column issues a warning."""
df_from_inv["bob"] = 1
with pytest.warns(UserWarning) as w:
df_to_inventory(df_from_inv)
assert len(w) == 1
assert "found unexpected columns" in w.list[0].message.args[0]
def test_mixing_nrl_with_station_client(self, df_with_both_response_cols):
"""
Ensure mixing the two types of responses raises an Error.
"""
df = df_with_both_response_cols
with pytest.raises(AmbiguousResponseError):
df_to_inventory(df)
def test_make_station_level_inventory(self, df_from_inv):
"""Ensure station level invs can be constructed."""
df = df_from_inv.drop(columns="channel")
inv = df_to_inventory(df)
for net in inv:
for sta in net:
assert not sta.channels, "there should be no channels"
def test_00_location_code(self, df_from_inv):
"""Ensure a 00 location code makes it into the inventory."""
df = df_from_inv.copy()
df["location"] = "00"
inv = df_to_inventory(df)
for channel in inv.get_contents()["channels"]:
assert channel.split(".")[2] == "00"
def inv_issue_115(self):
"""Get an inventory for testing issue 115."""
sta1 = dict(
network="LF",
location="",
station="BOB",
channel="HHZ",
start_date="2019-01-01",
end_date="2100-01-01",
sample_rate=250,
latitude=0,
longitude=0,
elevation=0,
depth=0,
)
sta2 = dict(
network="01",
location="",
station="01",
channel="BHZ",
start_date="2019-01-01",
end_date="2100-01-01",
sample_rate=1000,
latitude=0,
longitude=0,
elevation=0,
depth=0,
)
return df_to_inventory(pd.DataFrame([sta1, sta2]))
def test_response_one_missing(self, df_with_partial_responses):
"""Ensure responses which can be got are fetched."""
df = df_with_partial_responses
with suppress_warnings():
inv = df_to_inventory(df)
missing = df["sensor_keys"].isnull() | df["datalogger_keys"].isnull()
missing_seed_ids = set(get_seed_id_series(df[missing]))
assert self.has_valid_response(inv, missing_seed_ids)
def test_get_stations_client(self, df_with_get_stations_kwargs):
"""Ensure get_station_kwargs results responses."""
df = df_with_get_stations_kwargs
col = "get_station_kwargs"
missing = df[col].isnull() | (df[col] == "")
missing_seed_ids = set(get_seed_id_series(df[missing]))
with suppress_warnings():
inv = df_to_inventory(df)
assert self.has_valid_response(inv, missing_seed_ids)
def test_nslc_variations_float(self, nslc_dtype_variation):
"""Make sure data types get set (particularly for NSLC columns)"""
inp = nslc_dtype_variation[0]
expect = nslc_dtype_variation[1]
inv = df_to_inventory(inp)
for network in inv.networks:
assert network.code == expect
for station in network:
assert station.code == expect
for channel in station:
assert channel.code == expect
assert channel.location_code == expect
def split_inventory(self, inv_df, cat):
"""
Split the inventory and duplicate so first have encompasses half of
the events and second gets the second half.
"""
edf = obsplus.events_to_df(cat).sort_values("time")
ser = edf.loc[len(edf) // 2]
inv1, inv2 = inv_df.copy(), inv_df.copy()
inv1["end_date"] = ser["time"]
inv2["start_date"] = ser["time"]
new = pd.concat([inv1, inv2], ignore_index=True, axis=0).reset_index()
return df_to_inventory(new)
def test_duplicate_stations(self, inv_df_duplicate_channels):
"""
Ensure duplicate stations create Station objects with correct
time range.
"""
df = inv_df_duplicate_channels
fur_df = df[df["station"] == "FUR"]
inv = df_to_inventory(fur_df).select(station="FUR")
stations = inv.networks
assert len(stations) == 1
fur = stations[0]
assert fur.start_date == to_utc(fur_df["start_date"].min())
assert fur.end_date == to_utc(fur_df["end_date"].max())
def test_NaN_in_non_time_columns(self, df_from_inv):
"""
If there are NaN values in non-time these should just be interp.
as None.
"""
df_from_inv.loc[2, "dip"] = np.NaN
df_from_inv.loc[3, "azimuth"] = np.NaN
# convert to inv
inv = df_to_inventory(df_from_inv)
# make sure dip is None
dip_row = df_from_inv.loc[2]
kwargs = {x: getattr(dip_row, x) for x in NSLC}
inv_sub = inv.get_stations(**kwargs)
assert inv_sub[0][0][0].dip is None
# make sure azimuth is None
dip_row = df_from_inv.loc[3]
kwargs = {x: getattr(dip_row, x) for x in NSLC}
inv_sub = inv.get_stations(**kwargs)
assert inv_sub[0][0][0].azimuth is None
def test_ambiguous_query_raises(self, df_ambiguous_client_query):
"""Ensure a query that returns multiple channels will raise."""
df = df_ambiguous_client_query
with pytest.raises(AmbiguousResponseError):
df_to_inventory(df)
def inv_from_df(self, df_from_inv):
"""convert the station df back into an inventory."""
return df_to_inventory(df_from_inv)
def test_nrl_responses(self, df_with_nrl_response):
"""Ensure the NRL is used to pull responses."""
with suppress_warnings():
inv = df_to_inventory(df_with_nrl_response)
assert self.has_valid_response(inv)
def test_make_network_level_inventory(self, df_from_inv):
"""Ensure station level invs can be constructed."""
df = df_from_inv.drop(columns=["channel", "station"])
inv = df_to_inventory(df)
for net in inv:
assert not net.stations
def test_invalid_nslc(self, invalid_nslc):
"""Make sure data types get set (particularly for NSLC columns)"""
with pytest.raises(TypeError, match="cannot contain '.'"):
df_to_inventory(invalid_nslc)
