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Author SHA1 Message Date
Krystyna Milian
f40ac35cc8 fixed PhaseNet normalization, added reducing lr on plateau 2024-07-14 10:32:37 +02:00
Krystyna Milian
ebc759c215 Added Phasenet finetunig option and input validation 2024-05-13 14:33:19 +02:00

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