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process_sensor_caldata.py: skip accel or gyro if temperature is NAN

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Daniel Agar
2020-08-28 16:54:19 -04:00
parent fe391c0af8
commit 0b0d23e8fa
1 changed files with 7 additions and 6 deletions
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Tools/process_sensor_caldata.py
+7 -6
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@@ -4,6 +4,7 @@ from __future__ import print_function
import argparse import argparse
import os import os
import math
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
@@ -160,7 +161,7 @@ gyro_0_params = {
} }
# curve fit the data for gyro 0 corrections # curve fit the data for gyro 0 corrections
if num_gyros >= 1: if num_gyros >= 1 and not math.isnan(sensor_gyro_0['temperature'][0]):
gyro_0_params['TC_G0_ID'] = int(np.median(sensor_gyro_0['device_id'])) gyro_0_params['TC_G0_ID'] = int(np.median(sensor_gyro_0['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
@@ -268,7 +269,7 @@ gyro_1_params = {
} }
# curve fit the data for gyro 1 corrections # curve fit the data for gyro 1 corrections
if num_gyros >= 2: if num_gyros >= 2 and not math.isnan(sensor_gyro_1['temperature'][0]):
gyro_1_params['TC_G1_ID'] = int(np.median(sensor_gyro_1['device_id'])) gyro_1_params['TC_G1_ID'] = int(np.median(sensor_gyro_1['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
@@ -376,7 +377,7 @@ gyro_2_params = {
} }
# curve fit the data for gyro 2 corrections # curve fit the data for gyro 2 corrections
if num_gyros >= 3: if num_gyros >= 3 and not math.isnan(sensor_gyro_2['temperature'][0]):
gyro_2_params['TC_G2_ID'] = int(np.median(sensor_gyro_2['device_id'])) gyro_2_params['TC_G2_ID'] = int(np.median(sensor_gyro_2['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
@@ -484,7 +485,7 @@ accel_0_params = {
} }
# curve fit the data for accel 0 corrections # curve fit the data for accel 0 corrections
if num_accels >= 1: if num_accels >= 1 and not math.isnan(sensor_accel_0['temperature'][0]):
accel_0_params['TC_A0_ID'] = int(np.median(sensor_accel_0['device_id'])) accel_0_params['TC_A0_ID'] = int(np.median(sensor_accel_0['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
@@ -595,7 +596,7 @@ accel_1_params = {
} }
# curve fit the data for accel 1 corrections # curve fit the data for accel 1 corrections
if num_accels >= 2: if num_accels >= 2 and not math.isnan(sensor_accel_1['temperature'][0]):
accel_1_params['TC_A1_ID'] = int(np.median(sensor_accel_1['device_id'])) accel_1_params['TC_A1_ID'] = int(np.median(sensor_accel_1['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature
@@ -707,7 +708,7 @@ accel_2_params = {
} }
# curve fit the data for accel 2 corrections # curve fit the data for accel 2 corrections
if num_accels >= 3: if num_accels >= 3 and not math.isnan(sensor_accel_2['temperature'][0]):
accel_2_params['TC_A2_ID'] = int(np.median(sensor_accel_2['device_id'])) accel_2_params['TC_A2_ID'] = int(np.median(sensor_accel_2['device_id']))
# find the min, max and reference temperature # find the min, max and reference temperature