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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,166 @@ | ||
| # %% | ||
| import json | ||
| import multiprocessing as mp | ||
| import os | ||
| from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed | ||
| from datetime import datetime, timedelta, timezone | ||
| from threading import Lock, Thread | ||
|
|
||
| import fsspec | ||
| import numpy as np | ||
| import pandas as pd | ||
| import pyproj | ||
| from obspy import read_inventory | ||
| from obspy.clients.fdsn import Client | ||
| from sklearn.cluster import DBSCAN | ||
| from tqdm import tqdm | ||
| from args import parse_args | ||
| from glob import glob | ||
| import matplotlib.pyplot as plt | ||
| from utils.plotting import plotting_ransac | ||
|
|
||
| # %% | ||
| if __name__ == "__main__": | ||
|
|
||
| args = parse_args() | ||
| root_path = args.root_path | ||
| region = args.region | ||
| iter = args.iter | ||
|
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||
| data_path = f"{region}/adloc" | ||
| result_path = f"{region}/adloc" | ||
| figure_path = f"{region}/adloc/figures" | ||
| if not os.path.exists(figure_path): | ||
| os.makedirs(figure_path) | ||
|
|
||
| # %% | ||
| # protocol = "gs" | ||
| # token_json = f"{os.environ['HOME']}/.config/gcloud/application_default_credentials.json" | ||
| # with open(token_json, "r") as fp: | ||
| # token = json.load(fp) | ||
| # fs = fsspec.filesystem(protocol, token=token) | ||
|
|
||
| # %% | ||
| event_csvs = sorted(glob(f"{root_path}/{data_path}/????/????.???.events_sst_{iter}.csv")) | ||
|
|
||
| # %% | ||
| events = [] | ||
| picks = [] | ||
| stations = [] | ||
| for event_csv in tqdm(event_csvs, desc="Load event csvs"): | ||
| pick_csv = event_csv.replace(f"events_sst_{iter}.csv", f"picks_sst_{iter}.csv") | ||
| station_csv = event_csv.replace(f"events_sst_{iter}.csv", f"stations_sst_{iter}.csv") | ||
|
|
||
| year, jday = event_csv.split("/")[-1].split(".")[:2] | ||
| events_ = pd.read_csv(event_csv, dtype=str) | ||
| picks_ = pd.read_csv(pick_csv, dtype=str) | ||
| stations_ = pd.read_csv(station_csv) | ||
| events_["year"] = year | ||
| events_["jday"] = jday | ||
| picks_["year"] = year | ||
| picks_["jday"] = jday | ||
| stations_["year"] = year | ||
| stations_["jday"] = jday | ||
| events.append(events_) | ||
| picks.append(picks_) | ||
| stations.append(stations_) | ||
|
|
||
| events = pd.concat(events, ignore_index=True) | ||
| picks = pd.concat(picks, ignore_index=True) | ||
| stations = pd.concat(stations, ignore_index=True) | ||
|
|
||
| station_terms = ( | ||
| stations.groupby(["station_id"]) | ||
| .apply( | ||
| lambda x: pd.Series( | ||
| { | ||
| "station_term_time_p": ( | ||
| (x.station_term_time_p * x.num_pick_p).sum() / x.num_pick_p.sum() | ||
| if x.num_pick_p.sum() > 0 | ||
| else 0 | ||
| ), | ||
| "station_term_time_s": ( | ||
| (x.station_term_time_s * x.num_pick_s).sum() / x.num_pick_s.sum() | ||
| if x.num_pick_s.sum() > 0 | ||
| else 0 | ||
| ), | ||
| "station_term_amplitude": ( | ||
| (x.station_term_amplitude * x.num_pick).sum() / x.num_pick.sum() if x.num_pick.sum() > 0 else 0 | ||
| ), | ||
| } | ||
| ) | ||
| ) | ||
| .reset_index() | ||
| ) | ||
| if iter > 0: | ||
| stations_prev = pd.read_csv(f"{root_path}/{result_path}/adloc_stations_sst_{iter-1}.csv") | ||
| stations_prev.set_index("station_id", inplace=True) | ||
|
|
||
| station_terms["station_term_time_p"] += ( | ||
| station_terms["station_id"].map(stations_prev["station_term_time_p"]).fillna(0) | ||
| ) | ||
| station_terms["station_term_time_s"] += ( | ||
| station_terms["station_id"].map(stations_prev["station_term_time_s"]).fillna(0) | ||
| ) | ||
| station_terms["station_term_amplitude"] += ( | ||
| station_terms["station_id"].map(stations_prev["station_term_amplitude"]).fillna(0) | ||
| ) | ||
|
|
||
| stations = stations.groupby(["station_id"]).first().reset_index() | ||
| stations.drop(["station_term_time_p", "station_term_time_s", "station_term_amplitude"], axis=1, inplace=True) | ||
| stations = stations.merge(station_terms, on="station_id") | ||
|
|
||
| events["dummy_id"] = events["year"] + "." + events["jday"] + "." + events["event_index"] | ||
| picks["dummy_id"] = picks["year"] + "." + picks["jday"] + "." + picks["event_index"] | ||
|
|
||
| events["event_index"] = np.arange(len(events)) | ||
| picks = picks.drop("event_index", axis=1) | ||
| picks = picks.merge(events[["dummy_id", "event_index"]], on="dummy_id") | ||
|
|
||
| events.drop(["year", "jday", "dummy_id"], axis=1, inplace=True) | ||
| picks.drop(["year", "jday", "dummy_id"], axis=1, inplace=True) | ||
| stations.drop(["year", "jday"], axis=1, inplace=True) | ||
|
|
||
| events.to_csv(f"{root_path}/{result_path}/adloc_events_sst_{iter}.csv", index=False) | ||
| picks.to_csv(f"{root_path}/{result_path}/adloc_picks_sst_{iter}.csv", index=False) | ||
| stations.to_csv(f"{root_path}/{result_path}/adloc_stations_sst_{iter}.csv", index=False) | ||
|
|
||
| # %% | ||
|
|
||
| events = pd.read_csv(f"{root_path}/{result_path}/adloc_events_sst_{iter}.csv") | ||
| picks = pd.read_csv(f"{root_path}/{result_path}/adloc_picks_sst_{iter}.csv") | ||
| stations = pd.read_csv(f"{root_path}/{result_path}/adloc_stations_sst_{iter}.csv") | ||
|
|
||
| fig, ax = plt.subplots(3, 3, figsize=(12, 10)) | ||
| ax[0, 0].scatter(events["longitude"], events["latitude"], c=events["depth_km"], s=1, cmap="viridis_r") | ||
| ax[0, 0].set_title(f"Events {len(events)}") | ||
| ax[0, 1].scatter(events["longitude"], events["depth_km"], c=events["depth_km"], s=1, cmap="viridis_r") | ||
| ax[0, 1].invert_yaxis() | ||
| ax[0, 1].set_title(f"Events depth") | ||
| ax[0, 2].scatter(events["latitude"], events["depth_km"], c=events["depth_km"], s=1, cmap="viridis_r") | ||
| ax[0, 2].invert_yaxis() | ||
| ax[0, 2].set_title(f"Events latitude") | ||
| ax[1, 0].scatter( | ||
| stations["longitude"], stations["latitude"], c=stations["station_term_time_p"], marker="^", cmap="viridis_r" | ||
| ) | ||
| ax[1, 0].set_title(f"Station term time P {stations['station_term_time_p'].mean():.2f} s") | ||
| ax[1, 1].scatter( | ||
| stations["longitude"], stations["latitude"], c=stations["station_term_time_s"], marker="^", cmap="viridis_r" | ||
| ) | ||
| ax[1, 1].set_title(f"Station term time S {stations['station_term_time_s'].mean():.2f} s") | ||
| ax[1, 2].scatter( | ||
| stations["longitude"], stations["latitude"], c=stations["station_term_amplitude"], marker="^", cmap="viridis_r" | ||
| ) | ||
| ax[1, 2].set_title(f"Station term amplitude {stations['station_term_amplitude'].mean():.2f} m") | ||
| ax[2, 0].hist(events["adloc_residual_time"], bins=30, edgecolor="white") | ||
| ax[2, 0].set_title(f"Event residual time") | ||
| ax[2, 1].hist(events["adloc_residual_amplitude"], bins=30, edgecolor="white") | ||
| ax[2, 1].set_title(f"Event residual amplitude") | ||
| idx = picks["adloc_mask"] == 1 | ||
| ax[2, 2].hist(picks.loc[idx, "adloc_residual_time"], bins=30, edgecolor="white") | ||
| ax[2, 2].set_title(f"Pick residual time") | ||
| # ax[2, 2].hist(picks["adloc_residual_amplitude"], bins=30, edgecolor="white") | ||
| # ax[2, 2].set_title(f"Pick residual amplitude") | ||
| plt.tight_layout() | ||
| plt.savefig(f"{root_path}/{figure_path}/adloc_summary_{iter}.png") | ||
| plt.close() |
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