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import os
from json import dumps
from typing import Dict, List, Union
import numpy as np
import pandas as pd
from fastapi import FastAPI
from gamma.utils import association
from pydantic import BaseModel
from pyproj import Proj
app = FastAPI()
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), ".."))
STATION_CSV = os.path.join(PROJECT_ROOT, "tests/stations_hawaii.csv")
# STATION_CSV = os.path.join(PROJECT_ROOT, "tests/stations.csv") ## ridgecrest
def default_config(config):
if "degree2km" not in config:
config["degree2km"] = 111.195
if "use_amplitude" not in config:
config["use_amplitude"] = True
if "use_dbscan" not in config:
config["use_dbscan"] = True
if "dbscan_eps" not in config:
config["dbscan_eps"] = 30.0
if "dbscan_min_samples" not in config:
config["dbscan_min_samples"] = 3
if "method" not in config:
config["method"] = "BGMM"
if "oversample_factor" not in config:
config["oversample_factor"] = 5
if "min_picks_per_eq" not in config:
config["min_picks_per_eq"] = 10
if "max_sigma11" not in config:
config["max_sigma11"] = 2.0
if "max_sigma22" not in config:
config["max_sigma22"] = 1.0
if "max_sigma12" not in config:
config["max_sigma12"] = 1.0
if "dims" not in config:
config["dims"] = ["x(km)", "y(km)", "z(km)"]
return config
## set config
config = {"xlim_degree": [-156.32, -154.32], "ylim_degree": [18.39, 20.39], "z(km)": [0, 41]} ## hawaii
# config = {'xlim_degree': [-118.004, -117.004], 'ylim_degree': [35.205, 36.205], "z(km)": [0, 41]} ## ridgecrest
config = default_config(config)
config["center"] = [np.mean(config["xlim_degree"]), np.mean(config["ylim_degree"])]
config["x(km)"] = (np.array(config["xlim_degree"]) - config["center"][0]) * config["degree2km"]
config["y(km)"] = (np.array(config["ylim_degree"]) - config["center"][1]) * config["degree2km"]
config["bfgs_bounds"] = [list(config[x]) for x in config["dims"]] + [[None, None]]
for k, v in config.items():
print(f"{k}: {v}")
## read stations
stations = pd.read_csv(STATION_CSV, delimiter="\t")
stations = stations.rename(columns={"station": "id"})
stations["x(km)"] = stations["longitude"].apply(lambda x: (x - config["center"][0]) * config["degree2km"])
stations["y(km)"] = stations["latitude"].apply(lambda x: (x - config["center"][1]) * config["degree2km"])
stations["z(km)"] = stations["elevation(m)"].apply(lambda x: -x / 1e3)
print(stations)
class Data(BaseModel):
picks: List[Dict[str, Union[float, str]]]
stations: List[Dict[str, Union[float, str]]]
config: Dict[str, Union[List[float], List[int], List[str], float, int, str]]
class Pick(BaseModel):
picks: List[Dict[str, Union[float, str]]]
def run_gamma(picks, config, stations):
proj = Proj(f"+proj=sterea +lon_0={config['center'][0]} +lat_0={config['center'][1]} +units=km")
stations[["x(km)", "y(km)"]] = stations.apply(
lambda x: pd.Series(proj(longitude=x.longitude, latitude=x.latitude)), axis=1
)
stations["z(km)"] = stations["elevation(m)"].apply(lambda x: -x / 1e3)
catalogs, assignments = association(picks, stations, config, 0, config["method"])
catalogs = pd.DataFrame(
catalogs,
columns=["time"]
+ config["dims"]
+ ["magnitude", "sigma_time", "sigma_amp", "cov_time_amp", "event_index", "gamma_score"],
)
catalogs[["longitude", "latitude"]] = catalogs.apply(
lambda x: pd.Series(proj(longitude=x["x(km)"], latitude=x["y(km)"], inverse=True)), axis=1
)
catalogs["depth(m)"] = catalogs["z(km)"].apply(lambda x: x * 1e3)
assignments = pd.DataFrame(assignments, columns=["pick_index", "event_index", "gamma_score"])
picks_gamma = picks.join(assignments.set_index("pick_index")).fillna(-1).astype({"event_index": int})
return catalogs, picks_gamma
@app.post("/predict_stream")
def predict(data: Pick):
picks = pd.DataFrame(data.picks)
if len(picks) == 0:
return {"catalog": [], "picks": []}
catalogs, picks_gamma = run_gamma(data, config, stations)
if use_kafka:
print("Push events to kafka...")
for event in catalogs.to_dict(orient="records"):
producer.send("gmma_events", key=event["time"], value=event)
return {"catalog": catalogs.to_dict(orient="records"), "picks": picks_gamma.to_dict(orient="records")}
@app.post("/predict")
def predict(data: Data):
picks = pd.DataFrame(data.picks)
if len(picks) == 0:
return {"catalog": [], "picks": []}
stations = pd.DataFrame(data.stations)
if len(stations) == 0:
return {"catalog": [], "picks": []}
assert "latitude" in stations
assert "longitude" in stations
assert "elevation(m)" in stations
config = data.config
config = default_config(config)
if "xlim_degree" not in config:
config["xlim_degree"] = (stations["longitude"].min(), stations["longitude"].max())
if "ylim_degree" not in config:
config["ylim_degree"] = (stations["latitude"].min(), stations["latitude"].max())
if "center" not in config:
config["center"] = [np.mean(config["xlim_degree"]), np.mean(config["ylim_degree"])]
if "x(km)" not in config:
config["x(km)"] = (
(np.array(config["xlim_degree"]) - config["center"][0])
* config["degree2km"]
* np.cos(np.deg2rad(config["center"][1]))
)
if "y(km)" not in config:
config["y(km)"] = (np.array(config["ylim_degree"]) - config["center"][1]) * config["degree2km"]
if "z(km)" not in config:
config["z(km)"] = (0, 41)
if "bfgs_bounds" not in config:
config["bfgs_bounds"] = [list(config[x]) for x in config["dims"]] + [[None, None]]
catalogs, picks_gamma = run_gamma(picks, config, stations)
if use_kafka:
print("Push events to kafka...")
for event in catalogs.to_dict(orient="records"):
producer.send("gamma_events", key=event["time"], value=event)
return {"catalog": catalogs.to_dict(orient="records"), "picks": picks_gamma.to_dict(orient="records")}
@app.get("/healthz")
def healthz():
return {"status": "ok"}
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