Control Panel Graphing - Revision history

Mparadis at 16:19, 11 October 2022

2022-10-11T16:19:12Z

← Older revision		Revision as of 16:19, 11 October 2022
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	__NOTOC__		__NOTOC__

	In the [[Phidget Control Panel]], open the channel for your device and click on the [[Image:plot.jpg\|link=]] icon next to the data type that you want to plot. This will open up a new window:		In the Windows version of the [[Phidget Control Panel]], open the channel for your device and click on the [[Image:plot.jpg\|link=]] icon next to the data type that you want to plot. This will open up a new window:

	[[Image:plot2.jpg\|link=\|850px]]		[[Image:plot2.jpg\|link=\|850px]]

Mparadis at 21:11, 24 February 2022

2022-02-24T21:11:39Z

← Older revision		Revision as of 21:11, 24 February 2022
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	In the [[Phidget Control Panel]], open the channel for your device and click on the [[Image:plot.jpg\|link=]] icon next to the data type that you want to plot. This will open up a new window:		In the [[Phidget Control Panel]], open the channel for your device and click on the [[Image:plot.jpg\|link=]] icon next to the data type that you want to plot. This will open up a new window:

	[[Image:plot2.jpg\|link=]]		[[Image:plot2.jpg\|link=\|850px]]

	== Raw Data Graph ==		== Raw Data Graph ==

Jdecoux: /* More Complex Graphing */

2022-02-24T19:56:13Z

More Complex Graphing

← Older revision		Revision as of 19:56, 24 February 2022
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	If you need more complex functionality such as logging multiple sensors to the same sheet or performing calculations on the data, you'll need to write your own program. This graphing tool is intended as a jumping-off point to better understand your sensors individually, so you can get a feel for how to use them in your applications.		If you need more complex functionality such as logging multiple sensors to the same sheet or performing calculations on the data, you'll need to write your own program. This graphing tool is intended as a jumping-off point to better understand your sensors individually, so you can get a feel for how to use them in your applications.

	The quickest way to get started is to download some [{{SERVER}}/?view=code_samples sample code] for your desired programming language and then search ~~Google~~ for information on logging or plotting data in that language (e.g. "how to log to csv in python").		The quickest way to get started is to download some [{{SERVER}}/?view=code_samples sample code] for your desired programming language and then search the internet for information on logging or plotting data in that language (e.g. "how to log to csv in python").

Jdecoux: /* Fast Fourier Transform (FFT) */

2022-02-24T19:55:04Z

Fast Fourier Transform (FFT)

← Older revision		Revision as of 19:55, 24 February 2022
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	The FFT plot reveals that a significant fraction of the noise is coming from a 60Hz source; in this case, the signal wire was left disconnected and picking up noise from the power grid~~. You can increase the graph length to sharpen the frequency spikes, just know this will take the graph longer to react to changes in the incoming data~~.		The FFT plot reveals that a significant fraction of the noise is coming from a 60Hz source; in this case, the signal wire was left disconnected and picking up noise from the power grid.

	In general, the FFT plot is most useful for analyzing sensor data where frequency has useful real-world implications, such as measuring vibrations of an accelerometer, or checking for 50/60Hz interference.		You can increase the graph length to sharpen the frequency spikes, just know this will take the graph longer to react to changes in the incoming data. In general, the FFT plot is most useful for analyzing sensor data where frequency has useful real-world implications, such as measuring vibrations of an accelerometer, or checking for 50/60Hz interference.

	=== Allan Deviation ===		=== Allan Deviation ===

Jdecoux: /* Fast Fourier Transform (FFT) */

2022-02-24T19:50:09Z

Fast Fourier Transform (FFT)

← Older revision		Revision as of 19:50, 24 February 2022
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	The FFT plot reveals that a significant fraction of the noise is coming from a 60Hz source; in this case, the signal wire was left disconnected and picking up noise from the power grid. You can increase the graph length to sharpen the frequency spikes, just know this will take the graph longer to react to changes in the incoming data.		The FFT plot reveals that a significant fraction of the noise is coming from a 60Hz source; in this case, the signal wire was left disconnected and picking up noise from the power grid. You can increase the graph length to sharpen the frequency spikes, just know this will take the graph longer to react to changes in the incoming data.

	In general, the FFT plot is most useful for analyzing sensor data where frequency has useful real-world implications, such as measuring vibrations of an accelerometer, or checking for 50/60Hz interference ~~form the power grid~~.		In general, the FFT plot is most useful for analyzing sensor data where frequency has useful real-world implications, such as measuring vibrations of an accelerometer, or checking for 50/60Hz interference.

	=== Allan Deviation ===		=== Allan Deviation ===

Jdecoux: /* Other Chart Types */

2022-02-24T19:48:39Z

Other Chart Types

← Older revision		Revision as of 19:48, 24 February 2022
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	You can change the Chart Type selection to apply transforms to the data that may provide further insights into your sensor data.		You can change the Chart Type selection to apply transforms to the data that may provide further insights into your sensor data.

	[[Image:ChartTypes.jpg\|~~400px~~\|center\|link=]]		[[Image:ChartTypes.jpg\|600px\|center\|link=]]

	=== Fast Fourier Transform (FFT) ===		=== Fast Fourier Transform (FFT) ===

Jdecoux: /* Other Chart Types */

2022-02-24T19:45:37Z

Other Chart Types

← Older revision		Revision as of 19:45, 24 February 2022
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	[[Image:ChartTypes.jpg\|400px\|center\|link=]]		[[Image:ChartTypes.jpg\|400px\|center\|link=]]


	=== Fast Fourier Transform (FFT) ===		=== Fast Fourier Transform (FFT) ===

Jdecoux: /* Other Chart Types */

2022-02-24T19:45:21Z

Other Chart Types

← Older revision		Revision as of 19:45, 24 February 2022
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	You can change the Chart Type selection to apply transforms to the data that may provide further insights into your sensor data.		You can change the Chart Type selection to apply transforms to the data that may provide further insights into your sensor data.

			[[Image:ChartTypes.jpg\|400px\|center\|link=]]


	=== Fast Fourier Transform (FFT) ===		=== Fast Fourier Transform (FFT) ===

Jdecoux: /* Filtering */

2022-02-24T19:41:22Z

Filtering

← Older revision		Revision as of 19:41, 24 February 2022
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	[[Image:lowpass.jpg\|820px\|center\|link=]]		[[Image:lowpass.jpg\|820px\|center\|link=]]


	==== High-Pass Filter ====		==== High-Pass Filter ====

Jdecoux: /* Raw Data Graph */

2022-02-24T19:36:21Z

Raw Data Graph

← Older revision		Revision as of 19:36, 24 February 2022
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	The default graph is the Raw Data graph. This graph displays the readings from your sensor over time. This can be very helpful in visualizing sensor data in a much more concrete way than just reading numbers from the '''Control Panel Example'''.		The default graph is the Raw Data graph. This graph displays the readings from your sensor over time. This can be very helpful in visualizing sensor data in a much more concrete way than just reading numbers from the '''Control Panel Example'''.

			=== Chart Length ===

			You can adjust the amount of time displayed on your graph by adjusting the Chart Length slider. This can be helpful for setting up your graph to display data on timescales you are most interested in.

			[[Image:chartLength.jpg\|400px\|center\|link=]]


			=== Longer Graphs ===

			If you want to monitor a sensor over a period of hours using the Phidget Control Panel Graphing tool, you can do so by slowing down the ''Data Interval'' of your device, which will increase the amount of time that you can display on the graph.

			[[Image:GraphingLongInterval.jpg\|600px\|center\|link=]]

	=== Filtering ===		=== Filtering ===
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	You can perform filtering on the raw data in order to reduce noise in your graph.		You can perform filtering on the raw data in order to reduce noise in your graph.

	[[Image:filtertype.jpg\|link=]]		[[Image:filtertype.jpg\|400px\|center\|link=]]

	==== Low-Pass Filter ====		==== Low-Pass Filter ====
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	[[Image:highpass.jpg\|850px\|center\|link=]]		[[Image:highpass.jpg\|850px\|center\|link=]]


	== Other Chart Types ==		== Other Chart Types ==