text: import requests
  import obspy
  import numpy as np
  import matplotlib.pyplot as plt
  from datetime import datetime
  
  ### Start running the model first:
  ### FLASK_ENV=development FLASK_APP=app.py flask run
  
  def read_data(mseed):
      data = []
      mseed = mseed.sort()
      for c in ["E", "N", "Z"]:
          data.append(mseed.select(channel="*"+c)[0].data)
      return np.array(data).T
  
  timestamp = lambda x: x.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]
  
  ## prepare some test data
  mseed = obspy.read()
  data = []
  for i in range(1): 
      data.append(read_data(mseed))
  data = {
      "id": ["test01"],
      "timestamp": [timestamp(datetime.now())],
      "vec": np.array(data).tolist(),
      "dt": 0.01
      }
  
  ## run prediction
  print(data["id"])
  resp = requests.get("http://localhost:8000/predict", json=data)
  # picks = resp.json()["picks"]
  print(resp.json())
  
  
  ## plot figure
  plt.figure()
  plt.plot(np.array(data["data"])[0,:,1])
  ylim = plt.ylim()
  plt.plot([picks[0][0][0], picks[0][0][0]], ylim, label="P-phase")
  plt.text(picks[0][0][0], ylim[1]*0.9, f"{picks[0][1][0]:.2f}")
  plt.plot([picks[0][2][0], picks[0][2][0]], ylim, label="S-phase")
  plt.text(picks[0][2][0], ylim[1]*0.9, f"{picks[0][1][0]:.2f}")
  plt.legend()
  plt.savefig("test.png")