Events to DataFrame

ObsPlus provides a way to extract useful summary information from ObsPy objects in order to create dataframes. This transformation is lossy but very useful when the full complexity of the Catalog object isn’t warranted. By default the events_to_df function collects information the authors of ObsPlus have found useful, but it is fully extensible/customizable using the functionality of the DataFrameExtractor.

To demonstrate how the Catalog can be flattened into a table, let’s again use the Crandall catalog.

[1]:

from matplotlib import pyplot as plt

import obsplus

[2]:

crandall = obsplus.load_dataset("crandall_test")
cat = crandall.event_client.get_events()
ev_df = obsplus.events_to_df(cat)

ev_df.head()

[2]:
time latitude longitude depth magnitude event_description associated_phase_count azimuthal_gap event_id horizontal_uncertainty ... standard_error used_phase_count station_count vertical_uncertainty updated author agency_id creation_time version stations
0 2007-08-06 01:44:48.810 39.4617 -111.2378 6570.0 1.78 LR 0.0 NaN smi:local/248828 NaN ... 0.8936 0.0 23.0 NaN 2018-10-10 20:26:49.642650112 DC NIOSH 2018-10-10 20:26:49.642650 DUG, MVCO, O15A, P14A, P15A, P16A, P17A, P18A,...
1 2007-08-06 10:47:25.600 39.4615 -111.2317 2050.0 1.57 LR 0.0 NaN smi:local/248843 NaN ... 0.8237 0.0 29.0 NaN 2018-10-10 20:33:27.110914048 DC NIOSH 2018-10-10 20:33:27.110914 DUG, M14A, MVCO, N17A, O18A, P14A, P15A, P16A,...
2 2007-08-06 08:48:40.010 39.4635 -111.2277 410.0 4.37 LR 0.0 NaN smi:local/248839 NaN ... 1.7356 0.0 134.0 NaN 2018-10-10 20:33:13.618110976 DC NIOSH 2018-10-10 20:33:13.618111 DUG, GMR, H13A, HLID, I11A, I13A, J11A, J12A, ...
3 2007-08-07 21:42:51.130 39.4627 -111.2200 4620.0 1.65 0.0 NaN smi:local/248925 NaN ... 0.5704 0.0 19.0 NaN 2018-10-11 22:08:54.236915968 DC NIOSH 2018-10-11 22:08:54.236916 M15A, O15A, P14A, P15A, P16A, P17A, P18A, Q14A...
4 2007-08-07 07:13:05.760 39.4605 -111.2242 3240.0 2.24 0.0 NaN smi:local/248891 NaN ... 0.9901 0.0 35.0 NaN 2012-04-12 10:19:26.228029952 NaT DUG, L15A, M14A, M15A, M16A, N14A, N15A, N16A,...

5 rows × 28 columns

events_to_df can also operate on other event_clients, like the EventBank.

[3]:

bank = crandall.event_client
ev_df2 = obsplus.events_to_df(bank)
ev_df2.head()

[3]:
time latitude longitude depth magnitude event_description associated_phase_count azimuthal_gap event_id horizontal_uncertainty ... standard_error used_phase_count station_count vertical_uncertainty updated author agency_id creation_time version stations
0 2007-08-06 01:44:48.810 39.4617 -111.2378 6570.0 1.78 LR 0.0 NaN smi:local/248828 NaN ... 0.8936 0.0 23.0 NaN 2018-10-10 20:26:49.642650112 DC NIOSH 2018-10-10 20:26:49.642650 DUG, MVCO, O15A, P14A, P15A, P16A, P17A, P18A,...
1 2007-08-06 10:47:25.600 39.4615 -111.2317 2050.0 1.57 LR 0.0 NaN smi:local/248843 NaN ... 0.8237 0.0 29.0 NaN 2018-10-10 20:33:27.110914048 DC NIOSH 2018-10-10 20:33:27.110914 DUG, M14A, MVCO, N17A, O18A, P14A, P15A, P16A,...
2 2007-08-06 08:48:40.010 39.4635 -111.2277 410.0 4.37 LR 0.0 NaN smi:local/248839 NaN ... 1.7356 0.0 134.0 NaN 2018-10-10 20:33:13.618110976 DC NIOSH 2018-10-10 20:33:13.618111 DUG, GMR, H13A, HLID, I11A, I13A, J11A, J12A, ...
3 2007-08-07 21:42:51.130 39.4627 -111.2200 4620.0 1.65 0.0 NaN smi:local/248925 NaN ... 0.5704 0.0 19.0 NaN 2018-10-11 22:08:54.236915968 DC NIOSH 2018-10-11 22:08:54.236916 M15A, O15A, P14A, P15A, P16A, P17A, P18A, Q14A...
4 2007-08-07 07:13:05.760 39.4605 -111.2242 3240.0 2.24 0.0 NaN smi:local/248891 NaN ... 0.9901 0.0 35.0 NaN 2012-04-12 10:19:26.228029952 NaT DUG, L15A, M14A, M15A, M16A, N14A, N15A, N16A,...

5 rows × 28 columns

Now we have access to all the wonderful Pandas magics. Here are a few contrived examples of things we may want to do:

[4]:

# plot a histogram of magnitudes
ev_df.magnitude.hist()
plt.show()
../../_images/notebooks_datastructures_events_to_pandas_7_0.png

Since there aren’t a lot of events let’s look at the picks to make things slightly more interesting:

[5]:

# get pick info into a dataframe
picks = obsplus.picks_to_df(cat)

[6]:

# count the types of phase picks made on all events
picks.phase_hint.value_counts()

[6]:

phase_hint
P     384
S      85
Pb     72
Sb     40
?      33
Name: count, dtype: int64

[7]:

# calculate the max pick_time for each event
picks.groupby("event_id")["time"].max()

[7]:

event_id
smi:local/248828   2007-08-06 01:46:30.424280
smi:local/248839   2007-08-06 08:50:18.697470
smi:local/248843   2007-08-06 10:49:02.491970
smi:local/248882   2007-08-07 02:06:40.963320
smi:local/248883   2007-08-07 02:15:24.573490
smi:local/248887   2007-08-07 03:45:22.084660
smi:local/248891   2007-08-07 07:14:24.078630
smi:local/248925   2007-08-07 21:43:54.936950
Name: time, dtype: datetime64[ns]

The example below demonstrates how to calculate travel time stats grouped by stations on stations with at least 3 P picks. Since all the events are coming from roughly the same location (within a few km) we might look for stations that have high standard deviations or obvious outliers as on the the first steps in quality control.

[8]:

# get only P picks
df = picks[picks.phase_hint.str.upper() == "P"]

# add columns for travel time
df["travel_time"] = df["time"] - df["event_time"]

# filter out stations that aren't used at least 3 times
station_count = df["station"].value_counts()
stations_with_three = station_count[station_count > 2]

# only include picks that are used on at least 3 stations
df = df[df.station.isin(stations_with_three.index)]

# get stats of travel times
df.groupby("station")["travel_time"].describe()

[8]:
count mean std min 25% 50% 75% max
station
DUG 6 0 days 00:00:26.450391666 0 days 00:00:00.271624748 0 days 00:00:26.071740 0 days 00:00:26.329110 0 days 00:00:26.405940 0 days 00:00:26.613517500 0 days 00:00:26.832520
L15A 4 0 days 00:00:44.281470 0 days 00:00:00.395213333 0 days 00:00:43.904210 0 days 00:00:43.958120 0 days 00:00:44.299440 0 days 00:00:44.622790 0 days 00:00:44.622790
M14A 3 0 days 00:00:42.733800 0 days 00:00:00.232198731 0 days 00:00:42.465680 0 days 00:00:42.666770 0 days 00:00:42.867860 0 days 00:00:42.867860 0 days 00:00:42.867860
M15A 6 0 days 00:00:37.389573333 0 days 00:00:00.395734547 0 days 00:00:36.924550 0 days 00:00:37.155292500 0 days 00:00:37.276860 0 days 00:00:37.723365 0 days 00:00:37.872200
N14A 4 0 days 00:00:34.753022500 0 days 00:00:00.309191321 0 days 00:00:34.382170 0 days 00:00:34.556087500 0 days 00:00:34.810995 0 days 00:00:35.007930 0 days 00:00:35.007930
N15A 4 0 days 00:00:30.787567500 0 days 00:00:00.678323974 0 days 00:00:30.187990 0 days 00:00:30.206282500 0 days 00:00:30.793665 0 days 00:00:31.374950 0 days 00:00:31.374950
N16A 3 0 days 00:00:27.035340 0 days 00:00:00.319788540 0 days 00:00:26.666080 0 days 00:00:26.943025 0 days 00:00:27.219970 0 days 00:00:27.219970 0 days 00:00:27.219970
N17A 3 0 days 00:00:28.347476666 0 days 00:00:00.111948217 0 days 00:00:28.218210 0 days 00:00:28.315160 0 days 00:00:28.412110 0 days 00:00:28.412110 0 days 00:00:28.412110
N18A 3 0 days 00:00:34.095483333 0 days 00:00:00.503778524 0 days 00:00:33.513770 0 days 00:00:33.950055 0 days 00:00:34.386340 0 days 00:00:34.386340 0 days 00:00:34.386340
O15A 5 0 days 00:00:24.222288400 0 days 00:00:00.158953507 0 days 00:00:24.049170 0 days 00:00:24.049170 0 days 00:00:24.314742 0 days 00:00:24.335500 0 days 00:00:24.362860
O16A 3 0 days 00:00:14.805791333 0 days 00:00:00.214392546 0 days 00:00:14.558760 0 days 00:00:14.737037 0 days 00:00:14.915314 0 days 00:00:14.929307 0 days 00:00:14.943300
O18A 5 0 days 00:00:23.389030200 0 days 00:00:00.425951696 0 days 00:00:22.925160 0 days 00:00:23.116750 0 days 00:00:23.224960 0 days 00:00:23.792720 0 days 00:00:23.885561
P14A 8 0 days 00:00:26.757380 0 days 00:00:00.369050089 0 days 00:00:26.272320 0 days 00:00:26.410845 0 days 00:00:26.791710 0 days 00:00:27.140000 0 days 00:00:27.143660
P15A 9 0 days 00:00:16.204747666 0 days 00:00:00.692561976 0 days 00:00:15.271110 0 days 00:00:15.920450 0 days 00:00:16.195300 0 days 00:00:16.614280 0 days 00:00:17.513850
P16A 11 0 days 00:00:07.927102 0 days 00:00:00.215584258 0 days 00:00:07.682190 0 days 00:00:07.795410 0 days 00:00:07.859270 0 days 00:00:08.034686 0 days 00:00:08.397620
P17A 6 0 days 00:00:07.174232666 0 days 00:00:00.289962934 0 days 00:00:06.905530 0 days 00:00:06.943467500 0 days 00:00:07.091821500 0 days 00:00:07.364075500 0 days 00:00:07.607380
P18A 11 0 days 00:00:14.736734454 0 days 00:00:00.293448302 0 days 00:00:14.258420 0 days 00:00:14.580390 0 days 00:00:14.690940 0 days 00:00:14.856656 0 days 00:00:15.265000
Q14A 6 0 days 00:00:30.549576666 0 days 00:00:00.335974466 0 days 00:00:30.212890 0 days 00:00:30.306100 0 days 00:00:30.498545 0 days 00:00:30.690990 0 days 00:00:31.090390
Q15A 7 0 days 00:00:18.810036142 0 days 00:00:00.297412245 0 days 00:00:18.382690 0 days 00:00:18.592790 0 days 00:00:18.931850 0 days 00:00:18.977141500 0 days 00:00:19.215850
Q16A 10 0 days 00:00:10.165477800 0 days 00:00:00.328986240 0 days 00:00:09.618000 0 days 00:00:10.006930 0 days 00:00:10.153105 0 days 00:00:10.248371250 0 days 00:00:10.690000
Q18A 11 0 days 00:00:17.135285636 0 days 00:00:00.256429698 0 days 00:00:16.894450 0 days 00:00:16.975725 0 days 00:00:17.010560 0 days 00:00:17.186767 0 days 00:00:17.665000
Q19A 3 0 days 00:00:29.604353333 0 days 00:00:00.383435634 0 days 00:00:29.161600 0 days 00:00:29.493665 0 days 00:00:29.825730 0 days 00:00:29.825730 0 days 00:00:29.825730
R15A 4 0 days 00:00:27.445802500 0 days 00:00:00.545923743 0 days 00:00:26.713610 0 days 00:00:27.186507500 0 days 00:00:27.603435 0 days 00:00:27.862730 0 days 00:00:27.862730
R17A 11 0 days 00:00:20.501155545 0 days 00:00:00.236867827 0 days 00:00:20.280000 0 days 00:00:20.351515 0 days 00:00:20.397810 0 days 00:00:20.510261 0 days 00:00:20.965000
R18A 8 0 days 00:00:27.290727500 0 days 00:00:00.298790493 0 days 00:00:26.900100 0 days 00:00:27.151197500 0 days 00:00:27.153725 0 days 00:00:27.449397500 0 days 00:00:27.732590
SRU 14 0 days 00:00:12.069393500 0 days 00:00:00.243053585 0 days 00:00:11.719920 0 days 00:00:11.960260250 0 days 00:00:12.020410 0 days 00:00:12.112279500 0 days 00:00:12.505000

In addition to events_to_df and picks_to_df, the following extractors are defined:

  • arrivals_to_df extracts arrival information from an Origin object (or from the preferred origin of each event in a catalog)

  • amplitudes_to_df extracts amplitude information

  • station_magnitudes_to_df extracts station magnitude information from a catalog, event, or magnitude

  • magnitudes_to_df extracts magnitude information