mirror of https://github.com/ArduPilot/ardupilot
337 lines
16 KiB
Python
337 lines
16 KiB
Python
# AP_FLAKE8_CLEAN
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from math import sqrt
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import matplotlib.pyplot as plt
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import numpy as np
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from LogAnalyzer import Test, TestResult
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class TestFlow(Test):
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'''test optical flow sensor scale factor calibration'''
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#
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# Use the following procedure to log the calibration data. is assumed that the optical flow sensor has been
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# correctly aligned, is focussed and the test is performed over a textured surface with adequate lighting.
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# Note that the strobing effect from non incandescent artifical lighting can produce poor optical flow measurements.
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#
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# 1) Set LOG_DISARMED and FLOW_TYPE to 10 and verify that ATT and OF messages are being logged onboard
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# 2) Place on level ground, apply power and wait for EKF to complete attitude alignment
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# 3) Keeping the copter level, lift it to shoulder height and rock between +-20 and +-30 degrees
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# in roll about an axis that passes through the flow sensor lens assembly. The time taken to rotate from
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# maximum left roll to maximum right roll should be about 1 second.
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# 4) Repeat 3) about the pitch axis
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# 5) Holding the copter level, lower it to the ground and remove power
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# 6) Transfer the logfile from the sdcard.
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# 7) Open a terminal and cd to the ardupilot/Tools/LogAnalyzer directory
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# 8) Enter to run the analysis 'python LogAnalyzer.py <log file name including full path>'
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# 9) Check the OpticalFlow test status printed to the screen. The analysis plots are saved to
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# flow_calibration.pdf and the recommended scale factors to flow_calibration.param
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def __init__(self):
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Test.__init__(self)
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self.name = "OpticalFlow"
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def run(self, logdata, verbose):
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self.result = TestResult()
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self.result.status = TestResult.StatusType.GOOD
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def FAIL():
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self.result.status = TestResult.StatusType.FAIL
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def WARN():
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if self.result.status != TestResult.StatusType.FAIL:
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self.result.status = TestResult.StatusType.WARN
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try:
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# tuning parameters used by the algorithm
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tilt_threshold = 15 # roll and pitch threshold used to start and stop calibration (deg)
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quality_threshold = 124 # minimum flow quality required for data to be used by the curve fit (N/A)
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min_rate_threshold = (
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0.0 # if the gyro rate is less than this, the data will not be used by the curve fit (rad/sec)
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)
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max_rate_threshold = (
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2.0 # if the gyro rate is greter than this, the data will not be used by the curve fit (rad/sec)
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)
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param_std_threshold = 5.0 # maximum allowable 1-std uncertainty in scaling parameter (scale factor * 1000)
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# max/min allowable scale factor parameter. Values of FLOW_FXSCALER and FLOW_FYSCALER outside the range
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# of +-param_abs_threshold indicate a sensor configuration problem.
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param_abs_threshold = 200
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# minimum number of points required for a curve fit - this is necessary, but not sufficient condition - the
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# standard deviation estimate of the fit gradient is also important.
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min_num_points = 100
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# get the existing scale parameters
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flow_fxscaler = logdata.parameters["FLOW_FXSCALER"]
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flow_fyscaler = logdata.parameters["FLOW_FYSCALER"]
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# load required optical flow data
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if "OF" in logdata.channels:
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flowX = np.zeros(len(logdata.channels["OF"]["flowX"].listData))
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for i in range(len(logdata.channels["OF"]["flowX"].listData)):
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(line, flowX[i]) = logdata.channels["OF"]["flowX"].listData[i]
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bodyX = np.zeros(len(logdata.channels["OF"]["bodyX"].listData))
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for i in range(len(logdata.channels["OF"]["bodyX"].listData)):
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(line, bodyX[i]) = logdata.channels["OF"]["bodyX"].listData[i]
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flowY = np.zeros(len(logdata.channels["OF"]["flowY"].listData))
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for i in range(len(logdata.channels["OF"]["flowY"].listData)):
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(line, flowY[i]) = logdata.channels["OF"]["flowY"].listData[i]
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bodyY = np.zeros(len(logdata.channels["OF"]["bodyY"].listData))
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for i in range(len(logdata.channels["OF"]["bodyY"].listData)):
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(line, bodyY[i]) = logdata.channels["OF"]["bodyY"].listData[i]
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flow_time_us = np.zeros(len(logdata.channels["OF"]["TimeUS"].listData))
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for i in range(len(logdata.channels["OF"]["TimeUS"].listData)):
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(line, flow_time_us[i]) = logdata.channels["OF"]["TimeUS"].listData[i]
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flow_qual = np.zeros(len(logdata.channels["OF"]["Qual"].listData))
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for i in range(len(logdata.channels["OF"]["Qual"].listData)):
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(line, flow_qual[i]) = logdata.channels["OF"]["Qual"].listData[i]
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else:
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FAIL()
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self.result.statusMessage = "FAIL: no optical flow data\n"
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return
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# load required attitude data
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if "ATT" in logdata.channels:
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Roll = np.zeros(len(logdata.channels["ATT"]["Roll"].listData))
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for i in range(len(logdata.channels["ATT"]["Roll"].listData)):
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(line, Roll[i]) = logdata.channels["ATT"]["Roll"].listData[i]
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Pitch = np.zeros(len(logdata.channels["ATT"]["Pitch"].listData))
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for i in range(len(logdata.channels["ATT"]["Pitch"].listData)):
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(line, Pitch[i]) = logdata.channels["ATT"]["Pitch"].listData[i]
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att_time_us = np.zeros(len(logdata.channels["ATT"]["TimeUS"].listData))
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for i in range(len(logdata.channels["ATT"]["TimeUS"].listData)):
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(line, att_time_us[i]) = logdata.channels["ATT"]["TimeUS"].listData[i]
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else:
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FAIL()
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self.result.statusMessage = "FAIL: no attitude data\n"
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return
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# calculate the start time for the roll calibration
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startTime = int(0)
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startRollIndex = int(0)
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for i in range(len(Roll)):
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if abs(Roll[i]) > tilt_threshold:
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startTime = att_time_us[i]
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break
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for i in range(len(flow_time_us)):
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if flow_time_us[i] > startTime:
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startRollIndex = i
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break
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# calculate the end time for the roll calibration
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endTime = int(0)
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endRollIndex = int(0)
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for i in range(len(Roll) - 1, -1, -1):
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if abs(Roll[i]) > tilt_threshold:
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endTime = att_time_us[i]
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break
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for i in range(len(flow_time_us) - 1, -1, -1):
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if flow_time_us[i] < endTime:
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endRollIndex = i
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break
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# check we have enough roll data points
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if endRollIndex - startRollIndex <= min_num_points:
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FAIL()
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self.result.statusMessage = "FAIL: insufficient roll data pointsa\n"
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return
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# resample roll test data excluding data before first movement and after last movement
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# also exclude data where there is insufficient angular rate
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flowX_resampled = []
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bodyX_resampled = []
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flowX_time_us_resampled = []
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for i in range(len(Roll)):
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if (
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(i >= startRollIndex)
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and (i <= endRollIndex)
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and (abs(bodyX[i]) > min_rate_threshold)
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and (abs(bodyX[i]) < max_rate_threshold)
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and (flow_qual[i] > quality_threshold)
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):
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flowX_resampled.append(flowX[i])
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bodyX_resampled.append(bodyX[i])
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flowX_time_us_resampled.append(flow_time_us[i])
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# calculate the start time for the pitch calibration
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startTime = 0
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startPitchIndex = int(0)
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for i in range(len(Pitch)):
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if abs(Pitch[i]) > tilt_threshold:
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startTime = att_time_us[i]
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break
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for i in range(len(flow_time_us)):
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if flow_time_us[i] > startTime:
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startPitchIndex = i
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break
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# calculate the end time for the pitch calibration
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endTime = 0
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endPitchIndex = int(0)
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for i in range(len(Pitch) - 1, -1, -1):
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if abs(Pitch[i]) > tilt_threshold:
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endTime = att_time_us[i]
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break
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for i in range(len(flow_time_us) - 1, -1, -1):
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if flow_time_us[i] < endTime:
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endPitchIndex = i
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break
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# check we have enough pitch data points
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if endPitchIndex - startPitchIndex <= min_num_points:
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FAIL()
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self.result.statusMessage = "FAIL: insufficient pitch data pointsa\n"
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return
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# resample pitch test data excluding data before first movement and after last movement
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# also exclude data where there is insufficient or too much angular rate
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flowY_resampled = []
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bodyY_resampled = []
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flowY_time_us_resampled = []
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for i in range(len(Roll)):
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if (
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(i >= startPitchIndex)
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and (i <= endPitchIndex)
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and (abs(bodyY[i]) > min_rate_threshold)
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and (abs(bodyY[i]) < max_rate_threshold)
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and (flow_qual[i] > quality_threshold)
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):
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flowY_resampled.append(flowY[i])
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bodyY_resampled.append(bodyY[i])
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flowY_time_us_resampled.append(flow_time_us[i])
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# fit a straight line to the flow vs body rate data and calculate the scale factor parameter required to
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# achieve a slope of 1
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coef_flow_x, cov_x = np.polyfit(
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bodyX_resampled, flowX_resampled, 1, rcond=None, full=False, w=None, cov=True
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)
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coef_flow_y, cov_y = np.polyfit(
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bodyY_resampled, flowY_resampled, 1, rcond=None, full=False, w=None, cov=True
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)
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# taking the exisiting scale factor parameters into account, calculate the parameter values reequired to
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# achieve a unity slope
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flow_fxscaler_new = int(1000 * (((1 + 0.001 * float(flow_fxscaler)) / coef_flow_x[0] - 1)))
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flow_fyscaler_new = int(1000 * (((1 + 0.001 * float(flow_fyscaler)) / coef_flow_y[0] - 1)))
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# Do a sanity check on the scale factor variance
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if sqrt(cov_x[0][0]) > param_std_threshold or sqrt(cov_y[0][0]) > param_std_threshold:
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FAIL()
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self.result.statusMessage = (
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"FAIL: inaccurate fit - poor quality or insufficient data"
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"\nFLOW_FXSCALER 1STD = %u"
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"\nFLOW_FYSCALER 1STD = %u\n" % (round(1000 * sqrt(cov_x[0][0])), round(1000 * sqrt(cov_y[0][0])))
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)
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# Do a sanity check on the scale factors
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if abs(flow_fxscaler_new) > param_abs_threshold or abs(flow_fyscaler_new) > param_abs_threshold:
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FAIL()
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self.result.statusMessage = (
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"FAIL: required scale factors are excessive\nFLOW_FXSCALER=%i\nFLOW_FYSCALER=%i\n"
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% (flow_fxscaler, flow_fyscaler)
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)
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# display recommended scale factors
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self.result.statusMessage = (
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"Set FLOW_FXSCALER to %i"
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"\nSet FLOW_FYSCALER to %i"
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"\n\nCal plots saved to flow_calibration.pdf"
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"\nCal parameters saved to flow_calibration.param"
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"\n\nFLOW_FXSCALER 1STD = %u"
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"\nFLOW_FYSCALER 1STD = %u\n"
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% (
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flow_fxscaler_new,
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flow_fyscaler_new,
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round(1000 * sqrt(cov_x[0][0])),
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round(1000 * sqrt(cov_y[0][0])),
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)
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)
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# calculate fit display data
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body_rate_display = [-max_rate_threshold, max_rate_threshold]
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fit_coef_x = np.poly1d(coef_flow_x)
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flowX_display = fit_coef_x(body_rate_display)
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fit_coef_y = np.poly1d(coef_flow_y)
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flowY_display = fit_coef_y(body_rate_display)
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# plot and save calibration test points to PDF
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from matplotlib.backends.backend_pdf import PdfPages
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output_plot_filename = "flow_calibration.pdf"
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pp = PdfPages(output_plot_filename)
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plt.figure(1, figsize=(20, 13))
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plt.subplot(2, 1, 1)
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plt.plot(bodyX_resampled, flowX_resampled, 'b', linestyle=' ', marker='o', label="test points")
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plt.plot(body_rate_display, flowX_display, 'r', linewidth=2.5, label="linear fit")
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plt.title('X axis flow rate vs gyro rate')
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plt.ylabel('flow rate (rad/s)')
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plt.xlabel('gyro rate (rad/sec)')
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plt.grid()
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plt.legend(loc='upper left')
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# draw plots
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plt.subplot(2, 1, 2)
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plt.plot(bodyY_resampled, flowY_resampled, 'b', linestyle=' ', marker='o', label="test points")
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plt.plot(body_rate_display, flowY_display, 'r', linewidth=2.5, label="linear fit")
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plt.title('Y axis flow rate vs gyro rate')
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plt.ylabel('flow rate (rad/s)')
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plt.xlabel('gyro rate (rad/sec)')
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plt.grid()
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plt.legend(loc='upper left')
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pp.savefig()
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plt.figure(2, figsize=(20, 13))
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plt.subplot(2, 1, 1)
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plt.plot(flow_time_us, flowX, 'b', label="flow rate - all")
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plt.plot(flow_time_us, bodyX, 'r', label="gyro rate - all")
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plt.plot(flowX_time_us_resampled, flowX_resampled, 'c', linestyle=' ', marker='o', label="flow rate - used")
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plt.plot(flowX_time_us_resampled, bodyX_resampled, 'm', linestyle=' ', marker='o', label="gyro rate - used")
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plt.title('X axis flow and body rate vs time')
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plt.ylabel('rate (rad/s)')
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plt.xlabel('time (usec)')
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plt.grid()
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plt.legend(loc='upper left')
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# draw plots
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plt.subplot(2, 1, 2)
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plt.plot(flow_time_us, flowY, 'b', label="flow rate - all")
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plt.plot(flow_time_us, bodyY, 'r', label="gyro rate - all")
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plt.plot(flowY_time_us_resampled, flowY_resampled, 'c', linestyle=' ', marker='o', label="flow rate - used")
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plt.plot(flowY_time_us_resampled, bodyY_resampled, 'm', linestyle=' ', marker='o', label="gyro rate - used")
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plt.title('Y axis flow and body rate vs time')
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plt.ylabel('rate (rad/s)')
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plt.xlabel('time (usec)')
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plt.grid()
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plt.legend(loc='upper left')
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pp.savefig()
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# close the pdf file
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pp.close()
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# close all figures
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plt.close("all")
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# write correction parameters to file
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test_results_filename = "flow_calibration.param"
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file = open(test_results_filename, "w")
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file.write("FLOW_FXSCALER" + " " + str(flow_fxscaler_new) + "\n")
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file.write("FLOW_FYSCALER" + " " + str(flow_fyscaler_new) + "\n")
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file.close()
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except KeyError as e:
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self.result.status = TestResult.StatusType.FAIL
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self.result.statusMessage = str(e) + ' not found'
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