entities don't handle being set to np.inf

control script に示されている Python cmd_interface.set 呼び出しから開始し、entity の値が service によってどのように処理されるかを追跡してください。np.inf のケースを再現し、その後、service がハングするのではなくエラーを報告するか意図的にクラッシュすることを確認してください。issue では特定のソースファイルやテストは指定されていません。

My data taking scripts ran into an edge case where it tried to set an entity to np.inf. The script just hung. I didn't even see an error in the service logs. I think the dripline should catch this case and try to intentionally crash or throw an error.

To go into more detail, here is the relevant portion of my control script.

                popt_reflection, pcov_reflection = data_lorentzian_fit(s11_pow, freq, 'reflection')
                perr_reflection = np.sqrt(np.diag(pcov_reflection))

                print('Reflection lorentzian fitted parameters')
                print(popt_reflection)
                self.cmd_interface.set('f_reflection', popt_reflection[0])
                self.cmd_interface.set('sig_f_reflection', perr_reflection[0])
                self.cmd_interface.set('Q_reflection', popt_reflection[1])
                self.cmd_interface.set('sig_Q_reflection', perr_reflection[1])
                self.cmd_interface.set('dy_reflection', popt_reflection[2])
                self.cmd_interface.set('sig_dy_reflection', perr_reflection[2])
                self.cmd_interface.set('C_reflection', popt_reflection[3])
                self.cmd_interface.set('sig_C_reflection', perr_reflection[3])

My script couldn't perform the fit on the VNA trace.

 Setting na_measurement_status to start_measurement
Logging list of endpoints
Switching to transmission path
Switching to reflection path
Switching to transmission path
VNA reflection measurement
Setting na_measurement_status to start_measurement
Logging list of endpoints
Switching to transmission path
Transmission lorentzian fitted parameters
[1.61968618e+10 1.59075036e+04 2.62957230e-01 3.45835612e-03]
Switching to reflection path
/usr/local/lib/python3.7/site-packages/scipy/optimize/minpack.py:829: OptimizeWarning: Covariance of the parameters could not be estimated
  category=OptimizeWarning)
Reflection lorentzian fitted parameters
[1.61903696e+10 4.84379319e+00 2.84176343e+02 2.84231409e+02]

I think when I get an OptimizeWarning error, the values of my pcov are np.inf, so it tried to set sig_f_reflection to np.inf and just hung without making any sort of progress.

➜  ~ kubectl logs -f double-precision-logger-dripline-python-deployment-6b7fbf8d4b7h --tail 20 
{'timestamp': '2021-03-22T16:32:18.386845Z', 'sensor_name': 'Q_transmission', 'value_cal': 15907.503649437182, 'value_raw': 15907.503649437182}
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(46) -> insert data are:
{'timestamp': '2021-03-22T16:32:18.399052Z', 'sensor_name': 'sig_Q_transmission', 'value_cal': 570.3355496234211, 'value_raw': 570.3355496234211}
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(46) -> insert data are:
{'timestamp': '2021-03-22T16:32:18.411096Z', 'sensor_name': 'dy_transmission', 'value_cal': 0.26295723019139855, 'value_raw': 0.26295723019139855}
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(46) -> insert data are:
{'timestamp': '2021-03-22T16:32:18.423272Z', 'sensor_name': 'sig_dy_transmission', 'value_cal': 0.010107652156098478, 'value_raw': 0.010107652156098478}
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(46) -> insert data are:
{'timestamp': '2021-03-22T16:32:18.436062Z', 'sensor_name': 'C_transmission', 'value_cal': 0.003458356118361542, 'value_raw': 0.003458356118361542}
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(46) -> insert data are:
{'timestamp': '2021-03-22T16:32:18.449049Z', 'sensor_name': 'sig_C_transmission', 'value_cal': 0.00015618094767875254, 'value_raw': 0.00015618094767875254}
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:26[INFO    ] dripline.implementations.postgres_sensor_logger(46) -> insert data are:
{'timestamp': '2021-03-22T16:32:26.161902Z', 'sensor_name': 'f_reflection', 'value_cal': 16190369623.81609, 'value_raw': 16190369623.81609}
2021-03-22T16:32:26[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:18[INFO    ] dripline.implementations.postgres_sensor_logger(49) -> finished processing data
2021-03-22T16:32:26[INFO    ] dripline.implementations.postgres_sensor_logger(46) -> insert data are:
{'timestamp': '2021-03-22T16:32:26.161902Z', 'sensor_name': 'f_reflection', 'value_cal': 16190369623.81609, 'value_raw': 16190369623.81609}