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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 kafka import KafkaProducer
from pydantic import BaseModel
from pyproj import Proj
from gamma.utils import association
app = FastAPI()
@app.get("/")
def greet_json():
return {"message": "Hello, World!"}
@app.post("/predict/")
def predict(picks: dict, stations: dict, config: dict):
picks = picks["data"]
stations = stations["data"]
picks = pd.DataFrame(picks)
picks["phase_time"] = pd.to_datetime(picks["phase_time"])
stations = pd.DataFrame(stations)
print(stations)
events_, picks_ = run_gamma(picks, stations, config)
picks_ = picks_.to_dict(orient="records")
events_ = events_.to_dict(orient="records")
return {"picks": picks_, "events": events_}
def set_config(region="ridgecrest"):
config = {
"min_picks": 8,
"min_picks_ratio": 0.2,
"max_residual_time": 1.0,
"max_residual_amplitude": 1.0,
"min_score": 0.6,
"min_s_picks": 2,
"min_p_picks": 2,
"use_amplitude": False,
}
# ## Domain
if region.lower() == "ridgecrest":
config.update(
{
"region": "ridgecrest",
"minlongitude": -118.004,
"maxlongitude": -117.004,
"minlatitude": 35.205,
"maxlatitude": 36.205,
"mindepth_km": 0.0,
"maxdepth_km": 30.0,
}
)
lon0 = (config["minlongitude"] + config["maxlongitude"]) / 2
lat0 = (config["minlatitude"] + config["maxlatitude"]) / 2
proj = Proj(f"+proj=sterea +lon_0={lon0} +lat_0={lat0} +units=km")
xmin, ymin = proj(config["minlongitude"], config["minlatitude"])
xmax, ymax = proj(config["maxlongitude"], config["maxlatitude"])
zmin, zmax = config["mindepth_km"], config["maxdepth_km"]
xlim_km = (xmin, xmax)
ylim_km = (ymin, ymax)
zlim_km = (zmin, zmax)
config.update(
{
"xlim_km": xlim_km,
"ylim_km": ylim_km,
"zlim_km": zlim_km,
"proj": proj,
}
)
config.update(
{
"min_picks_per_eq": 5,
"min_p_picks_per_eq": 0,
"min_s_picks_per_eq": 0,
"max_sigma11": 3.0,
"max_sigma22": 1.0,
"max_sigma12": 1.0,
}
)
config["use_dbscan"] = False
config["use_amplitude"] = True
config["oversample_factor"] = 8.0
config["dims"] = ["x(km)", "y(km)", "z(km)"]
config["method"] = "BGMM"
config["ncpu"] = 1
vel = {"p": 6.0, "s": 6.0 / 1.75}
config["vel"] = vel
config["bfgs_bounds"] = (
(xlim_km[0] - 1, xlim_km[1] + 1), # x
(ylim_km[0] - 1, ylim_km[1] + 1), # y
(0, zlim_km[1] + 1), # z
(None, None), # t
)
config["event_index"] = 0
return config
config = set_config()
def run_gamma(picks, stations, config_):
# %%
config.update(config_)
proj = config["proj"]
picks = picks.rename(
columns={
"station_id": "id",
"phase_time": "timestamp",
"phase_type": "type",
"phase_score": "prob",
"phase_amplitude": "amp",
}
)
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)
stations = stations.rename(columns={"station_id": "id"})
events, assignments = association(picks, stations, config, 0, config["method"])
print(events)
events = pd.DataFrame(events)
events[["longitude", "latitude"]] = events.apply(
lambda x: pd.Series(proj(longitude=x["x(km)"], latitude=x["y(km)"], inverse=True)), axis=1
)
events["depth_km"] = events["z(km)"]
events.drop(columns=["x(km)", "y(km)", "z(km)"], inplace=True, errors="ignore")
picks = picks.rename(
columns={
"id": "station_id",
"timestamp": "phase_time",
"type": "phase_type",
"prob": "phase_score",
"amp": "phase_amplitude",
}
)
assignments = pd.DataFrame(assignments, columns=["pick_index", "event_index", "gamma_score"])
picks = picks.join(assignments.set_index("pick_index")).fillna(-1).astype({"event_index": int})
return events, picks
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