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",
enddate="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",
enddate="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(self, df_with_response):
""" Ensure the NRL is used to pull responses. """
inv = df_to_inventory(df_with_response)
assert isinstance(inv, obspy.Inventory)
for net in inv:
for sta in net:
for chan in sta:
assert chan.response is not None
def test_column_dtypes(self, dummy_df):
""" Make sure data types get set (particularly for NSLC columns) """
inv = df_to_inventory(dummy_df)
for network in inv.networks:
assert network.code == "1"
for station in network:
assert station.code == "1"
for channel in station:
assert channel.code == "1"
assert channel.location_code == "1"
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 inv_from_df(self, df_from_inv):
""" convert the station df back into an inventory. """
return df_to_inventory(df_from_inv)