mirror of
https://github.com/ArduPilot/ardupilot.git
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Tools/LogAnalyzer: pass flake8
`TestDualGyroDrift.py` ignored because it is mostly commented out code
This commit is contained in:
Luiz Georg
Peter Barker
File diff suppressed because it is too large
Load Diff
@@ -5,13 +5,15 @@
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# Initial code by Andrew Chapman (amchapman@gmail.com), 16th Jan 2014
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#
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# AP_FLAKE8_CLEAN
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# some logging oddities noticed while doing this, to be followed up on:
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# - tradheli MOT labels Mot1,Mot2,Mot3,Mot4,GGain
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# - Pixhawk doesn't output one of the FMT labels... forget which one
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# - MAG offsets seem to be constant (only seen data on Pixhawk)
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# - MAG offsets seem to be cast to int before being output? (param is -84.67, logged as -84)
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# - copter+plane use 'V' in their vehicle type/version/build line, rover uses lower case 'v'. Copter+Rover give a build number, plane does not
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# - copter+plane use 'V' in their vehicle type/version/build line, rover uses lower case 'v'.
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# Copter+Rover give a build number, plane does not
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# - CTUN.ThrOut on copter is 0-1000, on plane+rover it is 0-100
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# TODO: add test for noisy baro values
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@@ -25,7 +27,6 @@ import glob
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import imp
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import inspect
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import os
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import pprint # temp
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import sys
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import time
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from xml.sax.saxutils import escape
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@@ -38,7 +39,8 @@ class TestResult(object):
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'''all tests return a standardized result type'''
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class StatusType:
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# NA means not applicable for this log (e.g. copter tests against a plane log), UNKNOWN means it is missing data required for the test
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# NA means not applicable for this log (e.g. copter tests against a plane log)
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# UNKNOWN means it is missing data required for the test
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GOOD, FAIL, WARN, UNKNOWN, NA = range(5)
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status = None
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@@ -46,7 +48,10 @@ class TestResult(object):
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class Test(object):
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'''base class to be inherited by log tests. Each test should be quite granular so we have lots of small tests with clear results'''
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"""
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Base class to be inherited by log tests. Each test should be quite granular so we have lots of small tests with
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clear results
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"""
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def __init__(self):
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self.name = ""
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@@ -140,7 +145,8 @@ class TestSuite(object):
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print('\n')
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print(
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'The Log Analyzer is currently BETA code.\nFor any support or feedback on the log analyzer please email Andrew Chapman (amchapman@gmail.com)'
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"The Log Analyzer is currently BETA code."
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"\nFor any support or feedback on the log analyzer please email Andrew Chapman (amchapman@gmail.com)"
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)
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print('\n')
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@@ -218,8 +224,6 @@ class TestSuite(object):
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def main():
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dirName = os.path.dirname(os.path.abspath(__file__))
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# deal with command line arguments
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parser = argparse.ArgumentParser(description='Analyze an APM Dataflash log for known issues')
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parser.add_argument('logfile', type=argparse.FileType('r'), help='path to Dataflash log file (or - for stdin)')
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@@ -5,6 +5,8 @@
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#
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#
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# AP_FLAKE8_CLEAN
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# TODO: implement more unit+regression tests
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from __future__ import print_function
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@@ -397,6 +399,6 @@ try:
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print("All unit/regression tests GOOD\n")
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except Exception as e:
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except Exception:
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print("Error found: " + traceback.format_exc())
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print("UNIT TEST FAILED\n")
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@@ -1,3 +1,6 @@
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# AP_FLAKE8_CLEAN
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class VehicleType:
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Plane = 17
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Copter = 23
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@@ -1,4 +1,5 @@
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import DataflashLog
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# AP_FLAKE8_CLEAN
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from LogAnalyzer import Test, TestResult
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from VehicleType import VehicleType
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@@ -70,7 +71,7 @@ class TestAutotune(Test):
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for i in ['EV', 'ATDE', 'ATUN']:
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r = False
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if not i in logdata.channels:
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if i not in logdata.channels:
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self.result.status = TestResult.StatusType.UNKNOWN
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self.result.statusMessage = "No {} log data".format(i)
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r = True
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@@ -126,7 +127,9 @@ class TestAutotune(Test):
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for key in logdata.channels['ATUN']:
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setattr(atun, key, logdata.channels['ATUN'][key].getNearestValueFwd(linenext)[0])
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linenext = logdata.channels['ATUN'][key].getNearestValueFwd(linenext)[1] + 1
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self.result.statusMessage += 'ATUN Axis:{atun.Axis} TuneStep:{atun.TuneStep} RateMin:{atun.RateMin:5.0f} RateMax:{atun.RateMax:5.0f} RPGain:{atun.RPGain:1.4f} RDGain:{atun.RDGain:1.4f} SPGain:{atun.SPGain:1.1f} (@line:{l})\n'.format(
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l=linenext, s=s, atun=atun
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)
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self.result.statusMessage += (
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"ATUN Axis:{atun.Axis} TuneStep:{atun.TuneStep} RateMin:{atun.RateMin:5.0f}"
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" RateMax:{atun.RateMax:5.0f} RPGain:{atun.RPGain:1.4f} RDGain:{atun.RDGain:1.4f}"
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" SPGain:{atun.SPGain:1.1f} (@line:{l})\n"
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).format(l=linenext, atun=atun)
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self.result.statusMessage += '\n'
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@@ -1,6 +1,5 @@
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import collections
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# AP_FLAKE8_CLEAN
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import DataflashLog
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from LogAnalyzer import Test, TestResult
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@@ -19,7 +18,8 @@ class TestBrownout(Test):
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# FIXME: cope with LOG_ARM_DISARM_MSG message
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if "EV" in logdata.channels:
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# step through the arm/disarm events in order, to see if they're symmetrical
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# note: it seems landing detection isn't robust enough to rely upon here, so we'll only consider arm+disarm, not takeoff+land
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# note: it seems landing detection isn't robust enough to rely upon here, so we'll only consider arm+disarm,
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# not takeoff+land
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for line, ev in logdata.channels["EV"]["Id"].listData:
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if ev == 10:
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isArmed = True
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@@ -1,7 +1,9 @@
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# AP_FLAKE8_CLEAN
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import math
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from functools import reduce
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import DataflashLog
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from LogAnalyzer import Test, TestResult
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@@ -1,6 +1,8 @@
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# AP_FLAKE8_CLEAN
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from __future__ import print_function
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import DataflashLog
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from LogAnalyzer import Test, TestResult
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@@ -25,12 +27,10 @@ class TestDupeLogData(Test):
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# c
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data = logdata.channels["ATT"]["Pitch"].listData
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for i in range(sampleStartIndex, len(data)):
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# print("Checking against index %d" % i)
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if i == sampleStartIndex:
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continue # skip matching against ourselves
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j = 0
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while j < 20 and (i + j) < len(data) and data[i + j][1] == sample[j][1]:
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# print("### Match found, j=%d, data=%f, sample=%f, log data matched to sample at line %d" % (j,data[i+j][1],sample[j][1],data[i+j][0]))
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j += 1
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if j == 20: # all samples match
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return data[i][0]
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@@ -41,7 +41,8 @@ class TestDupeLogData(Test):
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self.result = TestResult()
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self.result.status = TestResult.StatusType.GOOD
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# this could be made more flexible by not hard-coding to use ATT data, could make it dynamic based on whatever is available as long as it is highly variable
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# this could be made more flexible by not hard-coding to use ATT data, could make it dynamic based on whatever
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# is available as long as it is highly variable
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if "ATT" not in logdata.channels:
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self.result.status = TestResult.StatusType.UNKNOWN
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self.result.statusMessage = "No ATT log data"
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@@ -49,22 +50,18 @@ class TestDupeLogData(Test):
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# pick 10 sample points within the range of ATT data we have
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sampleStartIndices = []
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attStartIndex = 0
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attEndIndex = len(logdata.channels["ATT"]["Pitch"].listData) - 1
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step = int(attEndIndex / 11)
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for i in range(step, attEndIndex - step, step):
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sampleStartIndices.append(i)
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# print("Dupe data sample point index %d at line %d" % (i, logdata.channels["ATT"]["Pitch"].listData[i][0]))
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# get 20 datapoints of pitch from each sample location and check for a match elsewhere
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sampleIndex = 0
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for i in range(sampleStartIndices[0], len(logdata.channels["ATT"]["Pitch"].listData)):
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if i == sampleStartIndices[sampleIndex]:
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# print("Checking sample %d" % i)
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sample = logdata.channels["ATT"]["Pitch"].listData[i : i + 20]
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matchedLine = self.__matchSample(sample, i, logdata)
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if matchedLine:
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# print("Data from line %d found duplicated at line %d" % (sample[0][0],matchedLine))
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self.result.status = TestResult.StatusType.FAIL
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self.result.statusMessage = "Duplicate data chunks found in log (%d and %d)" % (
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sample[0][0],
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@@ -1,3 +1,6 @@
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# AP_FLAKE8_CLEAN
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import DataflashLog
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from LogAnalyzer import Test, TestResult
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@@ -1,4 +1,5 @@
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import DataflashLog
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# AP_FLAKE8_CLEAN
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from LogAnalyzer import Test, TestResult
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@@ -1,3 +1,6 @@
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# AP_FLAKE8_CLEAN
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from LogAnalyzer import Test, TestResult
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@@ -1,8 +1,10 @@
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# AP_FLAKE8_CLEAN
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from __future__ import print_function
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from math import sqrt
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import DataflashLog
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from LogAnalyzer import Test, TestResult
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@@ -23,12 +25,12 @@ class TestIMUMatch(Test):
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self.result = TestResult()
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self.result.status = TestResult.StatusType.GOOD
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if ("IMU" in logdata.channels) and (not "IMU2" in logdata.channels):
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if ("IMU" in logdata.channels) and ("IMU2" not in logdata.channels):
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self.result.status = TestResult.StatusType.NA
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self.result.statusMessage = "No IMU2"
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return
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if (not "IMU" in logdata.channels) or (not "IMU2" in logdata.channels):
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if ("IMU" not in logdata.channels) or ("IMU2" not in logdata.channels):
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self.result.status = TestResult.StatusType.UNKNOWN
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self.result.statusMessage = "No IMU log data"
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return
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@@ -115,7 +117,6 @@ class TestIMUMatch(Test):
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diff_filtered = sqrt(xdiff_filtered**2 + ydiff_filtered**2 + zdiff_filtered**2)
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max_diff_filtered = max(max_diff_filtered, diff_filtered)
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# print(max_diff_filtered)
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last_t = t
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if max_diff_filtered > fail_threshold:
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@@ -1,4 +1,5 @@
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import DataflashLog
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# AP_FLAKE8_CLEAN
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from LogAnalyzer import Test, TestResult
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from VehicleType import VehicleType
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@@ -19,7 +20,7 @@ class TestBalanceTwist(Test):
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return
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self.result.status = TestResult.StatusType.UNKNOWN
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if not "RCOU" in logdata.channels:
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if "RCOU" not in logdata.channels:
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return
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ch = []
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@@ -50,7 +51,7 @@ class TestBalanceTwist(Test):
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/ (logdata.parameters["RC3_MAX"] - logdata.parameters["RC3_MIN"])
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/ 1000.0
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)
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except KeyError as e:
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except KeyError:
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min_throttle = (
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logdata.parameters["MOT_PWM_MIN"]
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/ (logdata.parameters["MOT_PWM_MAX"] - logdata.parameters["RC3_MIN"])
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@@ -1,3 +1,6 @@
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# AP_FLAKE8_CLEAN
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import math
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from LogAnalyzer import Test, TestResult
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@@ -36,5 +39,5 @@ class TestNaN(Test):
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field,
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)
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raise ValueError()
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except ValueError as e:
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except ValueError:
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continue
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@@ -1,6 +1,8 @@
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# AP_FLAKE8_CLEAN
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from math import sqrt
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import DataflashLog
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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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@@ -53,8 +55,12 @@ class TestFlow(Test):
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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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param_abs_threshold = 200 # max/min allowable scale factor parameter. Values of FLOW_FXSCALER and FLOW_FYSCALER outside the range of +-param_abs_threshold indicate a sensor configuration problem.
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min_num_points = 100 # minimum number of points required for a curve fit - this is necessary, but not sufficient condition - the standard deviation estimate of the fit gradient is also important.
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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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@@ -204,7 +210,8 @@ class TestFlow(Test):
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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 achieve a slope of 1
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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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@@ -212,7 +219,8 @@ class TestFlow(Test):
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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 achieve a unity slope
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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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@@ -220,8 +228,9 @@ class TestFlow(Test):
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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\nFLOW_FXSCALER 1STD = %u\nFLOW_FYSCALER 1STD = %u\n"
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% (round(1000 * sqrt(cov_x[0][0])), round(1000 * sqrt(cov_y[0][0])))
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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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@@ -234,7 +243,12 @@ class TestFlow(Test):
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# display recommended scale factors
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self.result.statusMessage = (
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"Set FLOW_FXSCALER to %i\nSet FLOW_FYSCALER to %i\n\nCal plots saved to flow_calibration.pdf\nCal parameters saved to flow_calibration.param\n\nFLOW_FXSCALER 1STD = %u\nFLOW_FYSCALER 1STD = %u\n"
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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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@@ -1,6 +1,8 @@
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# AP_FLAKE8_CLEAN
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import math # for isnan()
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import DataflashLog
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from LogAnalyzer import Test, TestResult
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from VehicleType import VehicleType
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@@ -54,8 +56,10 @@ class TestParams(Test):
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self.result.statusMessage = self.result.statusMessage + name + " is NaN\n"
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try:
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# add parameter checks below using the helper functions, any failures will trigger a FAIL status and accumulate info in statusMessage
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# if more complex checking or correlations are required you can access parameter values directly using the logdata.parameters[paramName] dict
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# add parameter checks below using the helper functions, any failures will trigger a FAIL status and
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# accumulate info in statusMessage.
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# If more complex checking or correlations are required you can access parameter values directly using the
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# logdata.parameters[paramName] dict
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if logdata.vehicleType == VehicleType.Copter:
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self.__checkParamIsEqual("MAG_ENABLE", 1, logdata)
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if "THR_MIN" in logdata.parameters:
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||||
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||||
@@ -1,6 +1,8 @@
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# AP_FLAKE8_CLEAN
|
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|
||||
|
||||
from __future__ import print_function
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||||
|
||||
import DataflashLog
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||||
from LogAnalyzer import Test, TestResult
|
||||
from VehicleType import VehicleType
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||||
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@@ -21,7 +23,8 @@ class TestPerformance(Test):
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self.result.status = TestResult.StatusType.NA
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return
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||||
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||||
# NOTE: we'll ignore MaxT altogether for now, it seems there are quite regularly one or two high values in there, even ignoring the ones expected after arm/disarm events
|
||||
# NOTE: we'll ignore MaxT altogether for now, it seems there are quite regularly one or two high values in
|
||||
# there, even ignoring the ones expected after arm/disarm events.
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||||
# gather info on arm/disarm lines, we will ignore the MaxT data from the first line found after each of these
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||||
# armingLines = []
|
||||
# for line,ev in logdata.channels["EV"]["Id"].listData:
|
||||
@@ -32,7 +35,6 @@ class TestPerformance(Test):
|
||||
# if not armingLines:
|
||||
# break
|
||||
# if maxT[0] > armingLines[0]:
|
||||
# #print("Ignoring maxT from line %d, as it is the first PM line after arming on line %d" % (maxT[0],armingLines[0]))
|
||||
# ignoreMaxTLines.append(maxT[0])
|
||||
# armingLines.pop(0)
|
||||
|
||||
@@ -55,8 +57,6 @@ class TestPerformance(Test):
|
||||
if percentSlow > maxPercentSlow:
|
||||
maxPercentSlow = percentSlow
|
||||
maxPercentSlowLine = line
|
||||
# if (maxT > 13000) and line not in ignoreMaxTLines:
|
||||
# print("MaxT of %d detected on line %d" % (maxT,line))
|
||||
if (maxPercentSlow > 10) or (slowLoopLineCount > 6):
|
||||
self.result.status = TestResult.StatusType.FAIL
|
||||
self.result.statusMessage = "%d slow loop lines found, max %.2f%% on line %d" % (
|
||||
|
||||
@@ -1,3 +1,6 @@
|
||||
# AP_FLAKE8_CLEAN
|
||||
|
||||
|
||||
import collections
|
||||
|
||||
import DataflashLog
|
||||
@@ -8,12 +11,12 @@ from VehicleType import VehicleType
|
||||
class TestPitchRollCoupling(Test):
|
||||
'''test for divergence between input and output pitch/roll, i.e. mechanical failure or bad PID tuning'''
|
||||
|
||||
# TODO: currently we're only checking for roll/pitch outside of max lean angle, will come back later to analyze roll/pitch in versus out values
|
||||
# TODO: currently we're only checking for roll/pitch outside of max lean angle, will come back later to analyze
|
||||
# roll/pitch in versus out values
|
||||
|
||||
def __init__(self):
|
||||
Test.__init__(self)
|
||||
self.name = "Pitch/Roll"
|
||||
self.enable = True # TEMP
|
||||
|
||||
def run(self, logdata, verbose):
|
||||
self.result = TestResult()
|
||||
@@ -23,12 +26,12 @@ class TestPitchRollCoupling(Test):
|
||||
self.result.status = TestResult.StatusType.NA
|
||||
return
|
||||
|
||||
if not "ATT" in logdata.channels:
|
||||
if "ATT" not in logdata.channels:
|
||||
self.result.status = TestResult.StatusType.UNKNOWN
|
||||
self.result.statusMessage = "No ATT log data"
|
||||
return
|
||||
|
||||
if not "CTUN" in logdata.channels:
|
||||
if "CTUN" not in logdata.channels:
|
||||
self.result.status = TestResult.StatusType.UNKNOWN
|
||||
self.result.statusMessage = "No CTUN log data"
|
||||
return
|
||||
@@ -38,7 +41,8 @@ class TestPitchRollCoupling(Test):
|
||||
else:
|
||||
self.ctun_baralt_att = 'BAlt'
|
||||
|
||||
# figure out where each mode begins and ends, so we can treat auto and manual modes differently and ignore acro/tune modes
|
||||
# figure out where each mode begins and ends, so we can treat auto and manual modes differently and ignore
|
||||
# acro/tune modes
|
||||
autoModes = [
|
||||
"RTL",
|
||||
"AUTO",
|
||||
|
||||
@@ -1,6 +1,8 @@
|
||||
# AP_FLAKE8_CLEAN
|
||||
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import DataflashLog
|
||||
from LogAnalyzer import Test, TestResult
|
||||
from VehicleType import VehicleType
|
||||
|
||||
@@ -20,11 +22,11 @@ class TestThrust(Test):
|
||||
self.result.status = TestResult.StatusType.NA
|
||||
return
|
||||
|
||||
if not "CTUN" in logdata.channels:
|
||||
if "CTUN" not in logdata.channels:
|
||||
self.result.status = TestResult.StatusType.UNKNOWN
|
||||
self.result.statusMessage = "No CTUN log data"
|
||||
return
|
||||
if not "ATT" in logdata.channels:
|
||||
if "ATT" not in logdata.channels:
|
||||
self.result.status = TestResult.StatusType.UNKNOWN
|
||||
self.result.statusMessage = "No ATT log data"
|
||||
return
|
||||
@@ -49,7 +51,8 @@ class TestThrust(Test):
|
||||
|
||||
highThrottleSegments = []
|
||||
|
||||
# find any contiguous chunks where CTUN.ThrOut > highThrottleThreshold, ignore high throttle if tilt > tiltThreshold, and discard any segments shorter than minSampleLength
|
||||
# find any contiguous chunks where CTUN.ThrOut > highThrottleThreshold, ignore high throttle if
|
||||
# tilt > tiltThreshold, and discard any segments shorter than minSampleLength
|
||||
start = None
|
||||
data = logdata.channels["CTUN"][throut_key].listData
|
||||
for i in range(0, len(data)):
|
||||
@@ -61,11 +64,10 @@ class TestThrust(Test):
|
||||
if (abs(roll) > tiltThreshold) or (abs(pitch) > tiltThreshold):
|
||||
isBelowTiltThreshold = False
|
||||
if (value > highThrottleThreshold) and isBelowTiltThreshold:
|
||||
if start == None:
|
||||
if start is None:
|
||||
start = i
|
||||
elif start != None:
|
||||
elif start is not None:
|
||||
if (i - start) > minSampleLength:
|
||||
# print("Found high throttle chunk from line %d to %d (%d samples)" % (data[start][0],data[i][0],i-start+1))
|
||||
highThrottleSegments.append((start, i))
|
||||
start = None
|
||||
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import collections
|
||||
# AP_FLAKE8_CLEAN
|
||||
|
||||
import DataflashLog
|
||||
from LogAnalyzer import Test, TestResult
|
||||
|
||||
|
||||
@@ -15,7 +14,7 @@ class TestVCC(Test):
|
||||
self.result = TestResult()
|
||||
self.result.status = TestResult.StatusType.GOOD
|
||||
|
||||
if not "CURR" in logdata.channels:
|
||||
if "CURR" not in logdata.channels:
|
||||
self.result.status = TestResult.StatusType.UNKNOWN
|
||||
self.result.statusMessage = "No CURR log data"
|
||||
return
|
||||
@@ -24,7 +23,7 @@ class TestVCC(Test):
|
||||
try:
|
||||
vccMin = logdata.channels["CURR"]["Vcc"].min()
|
||||
vccMax = logdata.channels["CURR"]["Vcc"].max()
|
||||
except KeyError as e:
|
||||
except KeyError:
|
||||
vccMin = logdata.channels["POWR"]["Vcc"].min()
|
||||
vccMax = logdata.channels["POWR"]["Vcc"].max()
|
||||
vccMin *= 1000
|
||||
|
||||
@@ -1,3 +1,6 @@
|
||||
# AP_FLAKE8_CLEAN
|
||||
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import DataflashLog
|
||||
@@ -21,13 +24,12 @@ class TestVibration(Test):
|
||||
return
|
||||
|
||||
# constants
|
||||
gravity = -9.81
|
||||
aimRangeWarnXY = 1.5
|
||||
aimRangeFailXY = 3.0
|
||||
aimRangeWarnZ = 2.0 # gravity +/- aim range
|
||||
aimRangeFailZ = 5.0 # gravity +/- aim range
|
||||
|
||||
if not "IMU" in logdata.channels:
|
||||
if "IMU" not in logdata.channels:
|
||||
self.result.status = TestResult.StatusType.UNKNOWN
|
||||
self.result.statusMessage = "No IMU log data"
|
||||
return
|
||||
@@ -40,11 +42,11 @@ class TestVibration(Test):
|
||||
return
|
||||
|
||||
# for now we'll just use the first (largest) chunk of LOITER data
|
||||
# TODO: ignore the first couple of secs to avoid bad data during transition - or can we check more analytically that we're stable?
|
||||
# TODO: ignore the first couple of secs to avoid bad data during transition - or can we check more analytically
|
||||
# that we're stable?
|
||||
# TODO: accumulate all LOITER chunks over min size, or just use the largest one?
|
||||
startLine = chunks[0][0]
|
||||
endLine = chunks[0][1]
|
||||
# print("TestVibration using LOITER chunk from lines %s to %s" % (repr(startLine), repr(endLine)))
|
||||
|
||||
def getStdDevIMU(logdata, channelName, startLine, endLine):
|
||||
loiterData = logdata.channels["IMU"][channelName].getSegment(startLine, endLine)
|
||||
|
||||
Reference in New Issue
Block a user