<phyphox version="1.6">
    <title>Two Car Study 2</title>
    <category>PocketLab Voyager</category>
    <description>Momentum Conservation with Rangefinders
	
	Voyager A is scanned first, then Voyager B is scanned.  They are identified as A and B in the graphs.
	
	https://www.thepocketlab.com/support/lesson/two-voyagers-connected-single-device-phyphox-conservation-momentum-experiment
	
	</description>
	<icon format="base64">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
	</icon>
    <data-containers>
        <container size="0">dA</container>
        <container size="0">dRawA</container>
		<container size="0">dCalA</container>
		<container size="0">tRawA</container>
		<container size="0">tA</container>
		<container size="0">dB</container>
        <container size="0">dRawB</container>
		<container size="0">dCalB</container>
		<container size="0">tRawB</container>
		<container size="0">tB</container>
		<container size="1">nA</container>
        <container size="1">nA-1</container>
        <container size="1">tmaxA</container>
    </data-containers>
    <input>
        <bluetooth name="PL Voyager" mode="notification" id="A Rangefinder">
            <config char="F000AA12-0452-4000-B000-000000000000" conversion="hexadecimal">0132</config>
            <output char="F000AA13-0452-4000-B000-000000000000" conversion="int16BigEndian">dRawA</output>
			<output char="F000AA13-0452-4000-B000-000000000000" extra="time">tRawA</output>

		</bluetooth>
		<bluetooth name="PL Voyager" mode="notification" id="B Rangefinder">
            <config char="F000AA12-0452-4000-B000-000000000000" conversion="hexadecimal">0132</config>
            <output char="F000AA13-0452-4000-B000-000000000000" conversion="int16BigEndian">dRawB</output>
			<output char="F000AA13-0452-4000-B000-000000000000" extra="time">tRawB</output>
		</bluetooth>
	</input>
    <views>
        <view label="Rangefinder">
            <graph label="PocketLab Voyager (A) Rangefinder" aspectRatio="4" labelX="t (s)" labelY="distance (m)" partialUpdate="true">
                <input axis="x">tA</input>
                <input axis="y">dA</input>
            </graph>
			<graph label="PocketLab Voyager (B) Rangefinder" aspectRatio="4" labelX="t (s)" labelY="distance (m)" partialUpdate="true">
                <input axis="x">tB</input>
                <input axis="y">dB</input>
            </graph>
        </view>
    </views>
    <analysis>
        <multiply>
            <input clear="false">dRawA</input>
            <input type="value">0.001</input> 
            <output clear="true">dCalA</output>
        </multiply>
		<count>
            <input clear="false">dCalA</input>
            <output>nA</output>
        </count>
        <subtract>
            <input clear="false">nA</input>
            <input type="value">1</input>
            <output>nA-1</output>
        </subtract>
        <multiply>
            <input type="value">0.02</input>
            <input clear="false">nA-1</input>
            <output>tmaxA</output>
        </multiply>
        <ramp>
            <input as="start" type="value">0</input>
            <input as="stop" clear="false">tmaxA</input>
            <input as="length" clear="false">nA</input>
            <output>tRawA</output>
        </ramp>
		<rangefilter>
            <input clear="false">tRawA</input>
            <input clear="false">dCalA</input>
            <input as="max" type="value">2.2</input>
            <output>tA</output>
            <output>dA</output>
        </rangefilter>
		<multiply>
            <input clear="false">dRawB</input>
            <input type="value">0.001</input> 
            <output clear="true">dCalB</output>
        </multiply>
		<rangefilter>
            <input clear="false">tRawB</input>
            <input clear="false">dCalB</input>
            <input as="max" type="value">2.2</input>
            <output>tB</output>
            <output>dB</output>
        </rangefilter>
    </analysis>
    <export>
        <set name="Two Car Study 2 (Rangefinders)">
            <data name="Voyager A Time (s)">tA</data>
            <data name="Voyager A Position (m)">dA</data>
            <data name="Voyager B Position (m)">dB</data>
        </set>
    </export>	
</phyphox>
