update

Dockerfile

@@ -12,6 +12,10 @@ 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/ADLoc.git
+RUN pip install --no-cache-dir -e ADLoc
+WORKDIR /app/ADLoc
+
 COPY --chown=user . /app
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
 

app.py

@@ -1,175 +0,0 @@
-# %%
-import json
-import multiprocessing as mp
-import os
-from dataclasses import dataclass, asdict
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from fastapi import FastAPI
-from pyproj import Proj
-
-import adloc
-
-from adloc.eikonal2d import init_eikonal2d
-from adloc.sacloc2d import ADLoc
-from adloc.utils import invert_location, invert_location_iter
-
-app = FastAPI()
-
-
-@app.get("/")
-def greet_json():
-    return {"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)
-    picks_, events_ = run_adloc(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,
-        }
-    )
-
-    ## Eikonal for 1D velocity model
-    zz = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 30.0]
-    vp = [4.746, 4.793, 4.799, 5.045, 5.721, 5.879, 6.504, 6.708, 6.725, 7.800]
-    vs = [2.469, 2.470, 2.929, 2.930, 3.402, 3.403, 3.848, 3.907, 3.963, 4.500]
-    h = 0.3
-
-    vel = {"Z": zz, "P": vp, "S": vs}
-    eikonal = {
-        "vel": vel,
-        "h": h,
-        "xlim_km": xlim_km,
-        "ylim_km": ylim_km,
-        "zlim_km": zlim_km,
-    }
-    eikonal = init_eikonal2d(eikonal)
-    config["eikonal"] = eikonal
-
-    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_adloc(picks, stations, config_):
-
-
-    # %%
-    config.update(config_)
-    
-    proj = config["proj"]
-
-    # %%
-    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)
-
-
-    # %%
-    mapping_phase_type_int = {"P": 0, "S": 1}
-    picks["phase_type"] = picks["phase_type"].map(mapping_phase_type_int)
-    if "phase_amplitude" in picks.columns:
-        picks["phase_amplitude"] = picks["phase_amplitude"].apply(lambda x: np.log10(x) + 2.0)  # convert to log10(cm/s)
-
-    # %%
-    # reindex in case the index does not start from 0 or is not continuous
-    stations["idx_sta"] = np.arange(len(stations))
-    picks = picks.merge(stations[["station_id", "idx_sta"]], on="station_id")
-    picks["idx_eve"] = config["event_index"]
-
-    # %%
-    estimator = ADLoc(config, stations=stations[["x_km", "y_km", "z_km"]].values, eikonal=config["eikonal"])
-
-    # %%
-    picks, events = invert_location_iter(picks, stations, config, estimator, events_init=None, iter=0)
-
-    if (picks is None) or (events is None):
-        return None, None
-        
-    # %%
-    if "event_index" not in events.columns:
-        events["event_index"] = events.merge(picks[["idx_eve", "event_index"]], on="idx_eve")["event_index"]
-    events[["longitude", "latitude"]] = events.apply(
-        lambda x: pd.Series(proj(x["x_km"], x["y_km"], inverse=True)), axis=1
-    )
-    events["depth_km"] = events["z_km"]
-    events.drop(["idx_eve", "x_km", "y_km", "z_km"], axis=1, inplace=True, errors="ignore")
-    events.sort_values(["time"], inplace=True)
-
-    picks.rename({"mask": "adloc_mask", "residual_s": "adloc_residual_s"}, axis=1, inplace=True)
-    picks["phase_type"] = picks["phase_type"].map({0: "P", 1: "S"})
-    picks.drop(["idx_eve", "idx_sta"], axis=1, inplace=True, errors="ignore")
-    picks.sort_values(["phase_time"], inplace=True)
-
-    return picks, events
-
-