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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,224 @@ | ||
| # %% | ||
| import argparse | ||
| import json | ||
| import multiprocessing as mp | ||
| import os | ||
| import pickle | ||
| from contextlib import nullcontext | ||
|
|
||
| import numpy as np | ||
| import pandas as pd | ||
| from args import parse_args | ||
| from pyproj import Proj | ||
| from sklearn.neighbors import NearestNeighbors | ||
| from tqdm import tqdm | ||
|
|
||
|
|
||
| # %% | ||
| def pairing_picks(event_pairs, picks, config): | ||
| picks = picks[["idx_eve", "idx_sta", "phase_type", "phase_score", "phase_time"]].copy() | ||
| merged = pd.merge( | ||
| event_pairs, | ||
| picks, | ||
| left_on="idx_eve1", | ||
| right_on="idx_eve", | ||
| ) | ||
| merged = pd.merge( | ||
| merged, | ||
| picks, | ||
| left_on=["idx_eve2", "idx_sta", "phase_type"], | ||
| right_on=["idx_eve", "idx_sta", "phase_type"], | ||
| suffixes=("_1", "_2"), | ||
| ) | ||
| merged = merged.rename(columns={"phase_time_1": "phase_time1", "phase_time_2": "phase_time2"}) | ||
| merged["phase_score"] = (merged["phase_score_1"] + merged["phase_score_2"]) / 2.0 | ||
|
|
||
| merged["travel_time1"] = (merged["phase_time1"] - merged["event_time1"]).dt.total_seconds() | ||
| merged["travel_time2"] = (merged["phase_time2"] - merged["event_time2"]).dt.total_seconds() | ||
| merged["phase_dtime"] = merged["travel_time1"] - merged["travel_time2"] | ||
|
|
||
| # filtering | ||
| # merged = merged.sort_values("phase_score", ascending=False) | ||
| merged = ( | ||
| merged.groupby(["idx_eve1", "idx_eve2"], group_keys=False) | ||
| .apply(lambda x: (x.nlargest(config["MAX_OBS"], "phase_score") if len(x) > config["MIN_OBS"] else None)) | ||
| .reset_index(drop=True) | ||
| ) | ||
|
|
||
| return merged[["idx_eve1", "idx_eve2", "idx_sta", "phase_type", "phase_score", "phase_dtime"]] | ||
|
|
||
|
|
||
| # %% | ||
| if __name__ == "__main__": | ||
|
|
||
| args = parse_args() | ||
| root_path = args.root_path | ||
| region = args.region | ||
|
|
||
| data_path = f"{root_path}/{region}/adloc" | ||
| result_path = f"{root_path}/{region}/adloc_dd" | ||
| if not os.path.exists(result_path): | ||
| os.makedirs(result_path) | ||
|
|
||
| # %% | ||
| pick_file = os.path.join(data_path, "ransac_picks.csv") | ||
| event_file = os.path.join(data_path, "ransac_events.csv") | ||
| station_file = os.path.join(data_path, "ransac_stations.csv") | ||
|
|
||
| # %% | ||
| MAX_PAIR_DIST = 10 # km | ||
| MAX_NEIGHBORS = 50 | ||
| MIN_NEIGHBORS = 8 | ||
| MIN_OBS = 8 | ||
| MAX_OBS = 100 | ||
| config = {} | ||
| config["MAX_PAIR_DIST"] = MAX_PAIR_DIST | ||
| config["MAX_NEIGHBORS"] = MAX_NEIGHBORS | ||
| config["MIN_NEIGHBORS"] = MIN_NEIGHBORS | ||
| config["MIN_OBS"] = MIN_OBS | ||
| config["MAX_OBS"] = MAX_OBS | ||
| mapping_phase_type_int = {"P": 0, "S": 1} | ||
|
|
||
| # %% | ||
| stations = pd.read_csv(station_file) | ||
| picks = pd.read_csv(pick_file, parse_dates=["phase_time"]) | ||
| events = pd.read_csv(event_file, parse_dates=["time"]) | ||
|
|
||
| picks = picks[picks["event_index"] != -1] | ||
| # check phase_type is P/S or 0/1 | ||
| if set(picks["phase_type"].unique()).issubset(set(mapping_phase_type_int.keys())): # P/S | ||
| picks["phase_type"] = picks["phase_type"].map(mapping_phase_type_int) | ||
|
|
||
| # %% | ||
| if "idx_eve" in events.columns: | ||
| events = events.drop("idx_eve", axis=1) | ||
| if "idx_sta" in stations.columns: | ||
| stations = stations.drop("idx_sta", axis=1) | ||
| if "idx_eve" in picks.columns: | ||
| picks = picks.drop("idx_eve", axis=1) | ||
| if "idx_sta" in picks.columns: | ||
| picks = picks.drop("idx_sta", axis=1) | ||
|
|
||
| # %% | ||
| # reindex in case the index does not start from 0 or is not continuous | ||
| stations = stations[stations["station_id"].isin(picks["station_id"].unique())] | ||
| events = events[events["event_index"].isin(picks["event_index"].unique())] | ||
| stations["idx_sta"] = np.arange(len(stations)) | ||
| events["idx_eve"] = np.arange(len(events)) | ||
|
|
||
| picks = picks.merge(events[["event_index", "idx_eve"]], on="event_index") | ||
| picks = picks.merge(stations[["station_id", "idx_sta"]], on="station_id") | ||
|
|
||
| # %% | ||
| lon0 = stations["longitude"].median() | ||
| lat0 = stations["latitude"].median() | ||
| proj = Proj(f"+proj=sterea +lon_0={lon0} +lat_0={lat0} +units=km") | ||
|
|
||
| stations[["x_km", "y_km"]] = stations.apply( | ||
| lambda x: pd.Series(proj(longitude=x.longitude, latitude=x.latitude)), axis=1 | ||
| ) | ||
| stations["depth_km"] = -stations["elevation_m"] / 1000 | ||
| stations["z_km"] = stations["depth_km"] | ||
|
|
||
| events[["x_km", "y_km"]] = events.apply( | ||
| lambda x: pd.Series(proj(longitude=x.longitude, latitude=x.latitude)), axis=1 | ||
| ) | ||
| events["z_km"] = events["depth_km"] | ||
|
|
||
| picks = picks.merge(events[["idx_eve", "time"]], on="idx_eve") | ||
| picks["travel_time"] = (picks["phase_time"] - picks["time"]).dt.total_seconds() | ||
| picks.drop("time", axis=1, inplace=True) | ||
|
|
||
| # %% | ||
| picks_by_event = picks.groupby("idx_eve") | ||
|
|
||
| # Option 1: | ||
| neigh = NearestNeighbors(radius=MAX_PAIR_DIST, n_jobs=-1) | ||
| neigh.fit(events[["x_km", "y_km", "z_km"]].values) | ||
| pairs = set() | ||
| neigh_ind = neigh.radius_neighbors(sort_results=True)[1] | ||
| for i, neighs in enumerate(tqdm(neigh_ind, desc="Generating pairs")): | ||
| if len(neighs) < MIN_NEIGHBORS: | ||
| continue | ||
| for j in neighs[:MAX_NEIGHBORS]: | ||
| if i < j: | ||
| pairs.add((i, j)) | ||
| pairs = list(pairs) | ||
| event_pairs = pd.DataFrame(list(pairs), columns=["idx_eve1", "idx_eve2"]) | ||
| print(f"Number of events: {len(events)}") | ||
| print(f"Number of event pairs: {len(event_pairs)}") | ||
| event_pairs["event_time1"] = events["time"].iloc[event_pairs["idx_eve1"]].values | ||
| event_pairs["event_time2"] = events["time"].iloc[event_pairs["idx_eve2"]].values | ||
|
|
||
| # Option 2: | ||
| # neigh = NearestNeighbors(radius=MAX_PAIR_DIST, n_jobs=-1) | ||
| # neigh.fit(events[["x_km", "y_km", "z_km"]].values) | ||
| # pairs = set() | ||
| # neigh_ind = neigh.radius_neighbors()[1] | ||
| # for i, neighs in enumerate(tqdm(neigh_ind, desc="Generating pairs")): | ||
| # if len(neighs) < MIN_NEIGHBORS: | ||
| # continue | ||
| # neighs = neighs[np.argsort(events.loc[neighs, "num_picks"])] ## TODO: check if useful | ||
| # for j in neighs[:MAX_NEIGHBORS]: | ||
| # if i > j: | ||
| # pairs.add((j, i)) | ||
| # else: | ||
| # pairs.add((i, j)) | ||
| # pairs = list(pairs) | ||
|
|
||
| # %% | ||
| chunk_size = 10_000 | ||
| num_chunk = len(event_pairs) // chunk_size | ||
| pbar = tqdm(total=num_chunk, desc="Pairing picks") | ||
| results = [] | ||
| jobs = [] | ||
| ctx = mp.get_context("spawn") | ||
| ncpu = min(num_chunk, min(32, mp.cpu_count())) | ||
| picks = picks.set_index("idx_eve") | ||
| with ctx.Pool(processes=ncpu) as pool: | ||
| for i in np.array_split(np.arange(len(event_pairs)), num_chunk): | ||
| event_pairs_ = event_pairs.iloc[i] | ||
| idx = np.unique(event_pairs_[["idx_eve1", "idx_eve2"]].values.flatten()) | ||
| picks_ = picks.loc[idx].reset_index() | ||
| job = pool.apply_async(pairing_picks, args=(event_pairs_, picks_, config), callback=lambda x: pbar.update()) | ||
| jobs.append(job) | ||
| pool.close() | ||
| pool.join() | ||
| for job in jobs: | ||
| results.append(job.get()) | ||
|
|
||
| event_pairs = pd.concat(results, ignore_index=True) | ||
| event_pairs = event_pairs.drop_duplicates() | ||
|
|
||
| print(f"Number of pick pairs: {len(event_pairs)}") | ||
|
|
||
| dtypes = np.dtype( | ||
| [ | ||
| ("event_index1", np.int32), | ||
| ("event_index2", np.int32), | ||
| ("station_index", np.int32), | ||
| ("phase_type", np.int32), | ||
| ("phase_score", np.float32), | ||
| ("phase_dtime", np.float32), | ||
| ] | ||
| ) | ||
| pairs_array = np.memmap( | ||
| os.path.join(result_path, "pair_dt.dat"), | ||
| mode="w+", | ||
| shape=(len(event_pairs),), | ||
| dtype=dtypes, | ||
| ) | ||
| pairs_array["event_index1"] = event_pairs["idx_eve1"].values | ||
| pairs_array["event_index2"] = event_pairs["idx_eve2"].values | ||
| pairs_array["station_index"] = event_pairs["idx_sta"].values | ||
| pairs_array["phase_type"] = event_pairs["phase_type"].values | ||
| pairs_array["phase_score"] = event_pairs["phase_score"].values | ||
| pairs_array["phase_dtime"] = event_pairs["phase_dtime"].values | ||
| with open(os.path.join(result_path, "pair_dtypes.pkl"), "wb") as f: | ||
| pickle.dump(dtypes, f) | ||
|
|
||
| events.to_csv(os.path.join(result_path, "pair_events.csv"), index=False) | ||
| stations.to_csv(os.path.join(result_path, "pair_stations.csv"), index=False) | ||
| picks.to_csv(os.path.join(result_path, "pair_picks.csv"), index=False) | ||
|
|
||
| # %% |
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