spaces

app.py

@@ -0,0 +1,98 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import os
+import obspy
+import pandas as pd
+from phasenet.model import ModelConfig, UNet
+from phasenet.postprocess import extract_picks
+
+tf.compat.v1.disable_eager_execution()
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+## load model
+tf.compat.v1.reset_default_graph()
+model = UNet(mode="pred")
+sess_config = tf.compat.v1.ConfigProto()
+sess_config.gpu_options.allow_growth = True
+sess = tf.compat.v1.Session(config=sess_config)
+saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables())
+init = tf.compat.v1.global_variables_initializer()
+sess.run(init)
+latest_check_point = tf.train.latest_checkpoint("./model/190703-214543")
+print(f"restoring model {latest_check_point}")
+saver.restore(sess, latest_check_point)
+
+
+def normalize(vec):
+    mu = np.mean(vec, axis=1, keepdims=True)
+    std = np.std(vec, axis=1, keepdims=True)
+    std[std == 0] = 1.0
+    vec = (vec - mu) / std
+    return vec
+
+
+def reshape_input(vec):
+    if len(vec.shape) == 2:
+        vec = vec[np.newaxis, :, np.newaxis, :]
+    elif len(vec.shape) == 3:
+        vec = vec[np.newaxis, :, :, :]
+    else:
+        pass
+    return vec
+
+
+# inference fn
+def predict(inputs):
+
+    picks = []
+    for input in inputs:
+        file = input.name  # if not isinstance(input, str) else input
+        mseed = obspy.read(file)
+        begin_time = min([tr.stats.starttime for tr in mseed])
+        end_time = max([tr.stats.endtime for tr in mseed])
+        mseed = mseed.trim(begin_time, end_time)
+        vec = np.asarray([tr.data for tr in mseed]).T
+        vec = reshape_input(vec)  # (nb, nt, nsta, nch)
+        vec = normalize(vec)
+
+        feed = {model.X: vec, model.drop_rate: 0, model.is_training: False}
+        preds = sess.run(model.preds, feed_dict=feed)
+        tmp_picks = extract_picks(
+            preds, begin_times=[begin_time.datetime.isoformat(timespec="milliseconds")]
+        )  # , station_ids=data.id, begin_times=data.timestamp, waveforms=vec_raw)
+        tmp_picks = [
+            {
+                "phase_time": x["phase_time"],
+                "phase_index": x["phase_index"],
+                "phase_score": x["phase_score"],
+                "phase_type": x["phase_type"],
+            }
+            for x in tmp_picks
+        ]
+
+        picks.extend(tmp_picks)
+
+    picks = pd.DataFrame(picks)
+    picks.to_csv("picks.csv", index=False)
+
+    return picks, "picks.csv"
+
+
+# gradio components
+inputs = gr.File(file_count="multiple")
+# outputs = gr.outputs.JSON()
+outputs = [gr.Dataframe(headers=["phase_time", "phase_score", "phase_type"]), gr.File()]
+gr.Interface(
+    predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="PhaseNet",
+    description="PhaseNet",
+    examples=[[[os.path.join(os.path.dirname(__file__), "tests/test.mseed")]]],
+    allow_flagging="never",
+).launch()
+
+# if __name__ == "__main__":
+#    picks = predict(["tests/test.mseed"])
+#    print(picks)

tests/test.mseed

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53efa249668e9abddded9798cc3d8d450e5c1f841537170c2804a4347cbe2dd1
+size 73728