Plot the acceleration - Revision history

Mol: /* Example script 2: LabQuest */

2020-09-29T20:40:56Z

Example script 2: LabQuest

← Older revision		Revision as of 23:40, 29 September 2020
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	=== Example script 2: LabQuest ===		=== Example script 2: LabQuest ===

	The data files are [[File:Vernier_LinearAcceleration.csv]], [[File:Vernier_SineAcceleration.csv]] [[File:~~Vernier_LogisticAcceleration~~.csv]] and [[File:Vernier_BumbAcceleration.csv]].		The data files are [[File:Vernier_LinearAcceleration.csv]], [[File:Vernier_SineAcceleration.csv]] [[File:Vernier_LogAcceleration.csv]] and [[File:Vernier_BumbAcceleration.csv]].

	As the LabQuest is manually triggered, the zero point position need to be adjusted by hand. There is an option to trigger on the measured value, but this was not used.		As the LabQuest is manually triggered, the zero point position need to be adjusted by hand. There is an option to trigger on the measured value, but this was not used.
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	# Read CSV file into DataFrame df		# Read CSV file into DataFrame df

	f = '~~LinearAcceleration~~.csv'		f = 'Vernier_LinearAcceleration.csv'
	#f = '~~SineAcceleration~~.csv'		#f = 'Vernier_SineAcceleration.csv'
	f = '~~LogAcceleration~~.csv'		f = 'Vernier_LogAcceleration.csv'
	#f = '~~BumbAcceleration~~.csv'		#f = 'Vernier_BumbAcceleration.csv'
	df = pd.read_csv( f )		df = pd.read_csv( f )
	df.columns = ["t1", "acc1", "t2", "acc2","t3", "acc3"]		df.columns = ["t1", "acc1", "t2", "acc2","t3", "acc3"]

Mol: /* Example script 2: LabQuest */

2020-09-29T20:39:53Z

Example script 2: LabQuest

← Older revision		Revision as of 23:39, 29 September 2020
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	=== Example script 2: LabQuest ===		=== Example script 2: LabQuest ===

	The data files are .		The data files are [[File:Vernier_LinearAcceleration.csv]], [[File:Vernier_SineAcceleration.csv]] [[File:Vernier_LogisticAcceleration.csv]] and [[File:Vernier_BumbAcceleration.csv]].

	~~Finally,~~ the ~~grouped data~~ is ~~smoothed and plotted~~, ~~and~~ the ~~average~~ is ~~drawn~~.		As the LabQuest is manually triggered, the zero point position need to be adjusted by hand. There is an option to trigger on the measured value, but this was not used.

	See the images at https://www.cod3v.info/index.php?title=Velocity,_acceleration_and_jerk#Analysis_Scripts.		See the images at https://www.cod3v.info/index.php?title=Velocity,_acceleration_and_jerk#Analysis_Scripts.

Mol at 20:34, 29 September 2020

2020-09-29T20:34:43Z

@@ Line 1: / Line 1: @@
 === Introduction ===
-We use Python Pandas to read the csv file, plot the data and to draw the average value.
+We use Python Pandas to read the csv file, plot the data and to draw the average value. There are two different scripts, other for plotting the LEGO data, and the other for LabQuest data.
 The data files are [[File:vlin.csv]], [[File:vsin.csv]], [[File:vlog.csv]] and [[File:Vbum.csv]]. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.
@@ Line 9: / Line 12: @@
 See the images at https://www.cod3v.info/index.php?title=Velocity,_acceleration_and_jerk#Analysis_Scripts.
 <syntaxhighlight lang="python">
@@ Line 77: / Line 79: @@
 ax.set_ylabel('Value',fontdict={'fontsize':24})
 </syntaxhighlight>

Mol: Mol moved page Plot acceleration to Plot the acceleration

2020-09-29T19:56:18Z

Mol moved page Plot acceleration to Plot the acceleration

← Older revision	Revision as of 22:56, 29 September 2020
(No difference)

Mol: /* Introduction */

2020-09-29T19:54:45Z

Introduction

← Older revision		Revision as of 22:54, 29 September 2020
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	We use Python Pandas to read the csv file, plot the data and to draw the average value.		We use Python Pandas to read the csv file, plot the data and to draw the average value.

	The data files are [[vlin.csv]], [[File:vsin.csv]], [[File:vlog.csv]] and [[File:Vbum.csv]]		The data files are [[File:vlin.csv]], [[File:vsin.csv]], [[File:vlog.csv]] and [[File:Vbum.csv]]. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.
	. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.

	Finally, the grouped data is smoothed and plotted, and the average is drawn.		Finally, the grouped data is smoothed and plotted, and the average is drawn.

Mol: /* Introduction */

2020-09-29T19:54:05Z

Introduction

← Older revision		Revision as of 22:54, 29 September 2020
Line 3:		Line 3:
	We use Python Pandas to read the csv file, plot the data and to draw the average value.		We use Python Pandas to read the csv file, plot the data and to draw the average value.

	The data files are [[~~File:~~vlin.csv]]		The data files are [[vlin.csv]], [[File:vsin.csv]], [[File:vlog.csv]] and [[File:Vbum.csv]]
	, [[File:vsin.csv]]
	, [[File:vlog.csv]]
	and [[File:Vbum.csv]]
	. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.		. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.

Mol: /* Introduction */

2020-09-29T19:53:43Z

Introduction

← Older revision		Revision as of 22:53, 29 September 2020
Line 3:		Line 3:
	We use Python Pandas to read the csv file, plot the data and to draw the average value.		We use Python Pandas to read the csv file, plot the data and to draw the average value.

	The data files are vlin.csv, vsin.csv, vlog.csv and [[File:Vbum.csv]]		The data files are [[File:vlin.csv]]
			, [[File:vsin.csv]]
			, [[File:vlog.csv]]
			and [[File:Vbum.csv]]
	. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.		. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.

Mol: /* Introduction */

2020-09-29T19:52:14Z

Introduction

← Older revision		Revision as of 22:52, 29 September 2020
Line 3:		Line 3:
	We use Python Pandas to read the csv file, plot the data and to draw the average value.		We use Python Pandas to read the csv file, plot the data and to draw the average value.

	The data files are vlin.csv, vsin.csv, vlog.csv and [[File:~~Vbum.csv\|~~Vbum.csv]]		The data files are vlin.csv, vsin.csv, vlog.csv and [[File:Vbum.csv]]
	. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.		. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.

Mol: /* Introduction */

2020-09-29T19:51:57Z

Introduction

← Older revision		Revision as of 22:51, 29 September 2020
Line 3:		Line 3:
	We use Python Pandas to read the csv file, plot the data and to draw the average value.		We use Python Pandas to read the csv file, plot the data and to draw the average value.

	The data files are vlin.csv, vsin.csv, vlog.csv and [[File:Vbum.csv\|~~thumb~~]]		The data files are vlin.csv, vsin.csv, vlog.csv and [[File:Vbum.csv\|Vbum.csv]]
	. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.		. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.

Mol at 19:51, 29 September 2020

2020-09-29T19:51:39Z

← Older revision		Revision as of 22:51, 29 September 2020
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	=== Introduction ===		=== Introduction ===

	We use Python Pandas to read the csv file, plot the data and to draw the average value.		We use Python Pandas to read the csv file, plot the data and to draw the average value.

	The data files are ~~called~~ vlin.csv, vsin.csv, vlog.csv and ~~vbum~~.csv. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.		The data files are vlin.csv, vsin.csv, vlog.csv and [[File:Vbum.csv\|thumb]]
			. The files contains data from multiple runs, appended after each other. First the data is grouped by using the time difference, and the corresponding wall clock times are changed to running time.

	Finally, the grouped data is smoothed and plotted, and the average is drawn.		Finally, the grouped data is smoothed and plotted, and the average is drawn.

	See the images at https://www.cod3v.info/index.php?title=Velocity,_acceleration_and_jerk#Analysis_Scripts.		See the images at https://www.cod3v.info/index.php?title=Velocity,_acceleration_and_jerk#Analysis_Scripts.