150 lines
4.2 KiB
Python
150 lines
4.2 KiB
Python
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import json
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import pathlib
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import numpy as np
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import obspy
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import pandas as pd
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import seisbench.data as sbd
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import seisbench.models as sbm
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from seisbench.models.team import itertools
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from sklearn.metrics import precision_recall_curve, roc_auc_score, roc_curve
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datasets = [
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# path to datasets in seisbench format
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]
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models = [
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# model names
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]
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def find_keys_phase(meta, phase):
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phases = []
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for k in meta.keys():
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if k.startswith("trace_" + phase) and k.endswith("_arrival_sample"):
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phases.append(k)
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return phases
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def create_stream(meta, raw, start, length=30):
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st = obspy.Stream()
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for i in range(3):
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tr = obspy.Trace(raw[i, :])
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tr.stats.starttime = meta["trace_start_time"]
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tr.stats.sampling_rate = meta["trace_sampling_rate_hz"]
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tr.stats.network = meta["station_network_code"]
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tr.stats.station = meta["station_code"]
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tr.stats.channel = meta["trace_channel"][:2] + meta["trace_component_order"][i]
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stop = start + length
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tr = tr.slice(start, stop)
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st.append(tr)
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return st
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def get_pred(model, stream):
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ann = model.annotate(stream)
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noise = ann.select(channel="PhaseNet_N")[0]
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pred = max(1 - noise.data)
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return pred
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def to_short(stream):
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short = [tr for tr in stream if tr.data.shape[0] < 3001]
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return any(short)
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for ds, model_name in itertools.product(datasets, models):
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data = sbd.WaveformDataset(ds, sampling_rate=100).test()
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data_name = pathlib.Path(ds).stem
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fname = f"roc___{model_name}___{data_name}.csv"
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print(f"{fname:.<50s}.... ", flush=True, end="")
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if pathlib.Path(fname).is_file():
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print(" ready, skipping", flush=True)
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continue
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p_labels = find_keys_phase(data.metadata, "P")
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s_labels = find_keys_phase(data.metadata, "S")
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model = sbm.PhaseNet().from_pretrained(model_name)
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label_true = []
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label_pred = []
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for i in range(len(data)):
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waveform, metadata = data.get_sample(i)
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m = pd.Series(metadata)
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has_p_label = m[p_labels].notna()
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has_s_label = m[s_labels].notna()
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if any(has_p_label):
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trace_start_time = obspy.UTCDateTime(m["trace_start_time"])
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pick_sample = m[p_labels][has_p_label][0]
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start = trace_start_time + pick_sample / m["trace_sampling_rate_hz"] - 15
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try:
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st_p = create_stream(m, waveform, start)
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if not (to_short(st_p)):
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pred_p = get_pred(model, st_p)
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label_true.append(1)
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label_pred.append(pred_p)
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except IndexError:
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pass
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try:
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st_n = create_stream(m, waveform, trace_start_time + 1)
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if not (to_short(st_n)):
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pred_n = get_pred(model, st_n)
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label_true.append(0)
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label_pred.append(pred_n)
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except IndexError:
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pass
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if any(has_s_label):
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trace_start_time = obspy.UTCDateTime(m["trace_start_time"])
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pick_sample = m[s_labels][has_s_label][0]
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start = trace_start_time + pick_sample / m["trace_sampling_rate_hz"] - 15
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try:
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st_s = create_stream(m, waveform, start)
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if not (to_short(st_s)):
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pred_s = get_pred(model, st_s)
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label_true.append(1)
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label_pred.append(pred_s)
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except IndexError:
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pass
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fpr, tpr, roc_thresholds = roc_curve(label_true, label_pred)
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df = pd.DataFrame({"fpr": fpr, "tpr": tpr, "thresholds": roc_thresholds})
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df.to_csv(fname)
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precision, recall, prc_thresholds = precision_recall_curve(label_true, label_pred)
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prc_thresholds_extra = np.append(prc_thresholds, -999)
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df = pd.DataFrame(
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{"pre": precision, "rec": recall, "thresholds": prc_thresholds_extra}
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)
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df.to_csv(fname.replace("roc", "pr"))
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stats = {
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"model": str(model_name),
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"data": str(data_name),
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"auc": float(roc_auc_score(label_true, label_pred)),
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}
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with open(f"stats___{model_name}___{data_name}.json", "w") as fp:
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json.dump(stats, fp)
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print(" finished", flush=True)
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