update

Dockerfile

@@ -12,5 +12,9 @@ WORKDIR /app
 COPY --chown=user ./requirements.txt requirements.txt
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
+RUN git clone https://github.com/AI4EPS/GaMMA.git
+RUN pip install --no-cache-dir -e GaMMA
+WORKDIR /app/GaMMA
+
 COPY --chown=user . /app
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,155 +0,0 @@
-import pandas as pd
-from fastapi import FastAPI
-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