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Update to V1.0
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‎BehaviorDEPOT.prj‎

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<deployment-project plugin="plugin.apptool" plugin-version="1.0">
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<configuration build-checksum="83420990" file="C:\BehaviorDEPOT\BehaviorDEPOT.prj" location="C:\BehaviorDEPOT" name="BehaviorDEPOT" target="target.mlapps" target-name="Package App">
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<param.appname>BehaviorDEPOT</param.appname>
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<param.authnamewatermark>C. Gabriel, Z. Zeidler, B. Jin, L. DeNardo</param.authnamewatermark>
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<param.email>[email protected]</param.email>
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<param.company />
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<param.icon />
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<param.icons />
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<param.summary>BehaviorDEPOT v1.0 application for analyzing top-down rodent pose tracking and automatic behavior classification</param.summary>
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<param.description />
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<param.screenshot />
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<param.version>1.0</param.version>
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<param.products.name>
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<item>MATLAB</item>
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<item>Curve Fitting Toolbox</item>
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<item>Image Processing Toolbox</item>
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<item>Signal Processing Toolbox</item>
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<item>Statistics and Machine Learning Toolbox</item>
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</param.products.name>
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<param.products.id>
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<item>1</item>
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<item>60</item>
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<item>17</item>
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<item>8</item>
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<item>19</item>
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</param.products.id>
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<param.products.version>
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<item>9.8</item>
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<item>3.5.11</item>
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<item>11.1</item>
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<item>8.4</item>
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<item>11.7</item>
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</param.products.version>
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<param.platforms />
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<param.output>${PROJECT_ROOT}\installation</param.output>
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<param.guid>786e8f67-a371-44fa-8876-10a81a869d58</param.guid>
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<unset>
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<param.company />
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<param.icon />
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<param.icons />
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<param.description />
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<param.screenshot />
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<param.version />
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<param.platforms />
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</unset>
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<fileset.main>
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<file>${PROJECT_ROOT}\BehaviorDEPOT_GUI_v1_0.mlapp</file>
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</fileset.main>
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<fileset.depfun />
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<fileset.resources>
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<file>${PROJECT_ROOT}\code</file>
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<file>${PROJECT_ROOT}\code\classifiers</file>
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<file>${PROJECT_ROOT}\code\functions</file>
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<file>${PROJECT_ROOT}\code\modules</file>
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<file>${PROJECT_ROOT}\code\plotting</file>
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</fileset.resources>
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<fileset.package />
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<build-deliverables>
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<file location="${PROJECT_ROOT}" name="installation" optional="false">C:\BehaviorDEPOT\installation</file>
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</build-deliverables>
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<workflow />
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<matlab>
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<root>C:\Program Files\MATLAB\R2020a</root>
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<toolboxes>
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<toolbox name="matlabcoder" />
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<toolbox name="gpucoder" />
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<toolbox name="neuralnetwork" />
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</toolboxes>
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<toolbox>
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<matlabcoder>
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<enabled>true</enabled>
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</matlabcoder>
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</toolbox>
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<toolbox>
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<gpucoder>
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<enabled>true</enabled>
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</gpucoder>
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</toolbox>
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<toolbox>
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<neuralnetwork>
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<enabled>true</enabled>
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</neuralnetwork>
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</toolbox>
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</matlab>
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<platform>
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<unix>false</unix>
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<mac>false</mac>
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<windows>true</windows>
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<win2k>false</win2k>
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<winxp>false</winxp>
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<vista>false</vista>
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<linux>false</linux>
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<solaris>false</solaris>
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<osver>10.0</osver>
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<os32>false</os32>
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<os64>true</os64>
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<arch>win64</arch>
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<matlab>true</matlab>
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</platform>
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</configuration>
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</deployment-project>

‎BehaviorDEPOTApp.m‎

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classdef BehaviorDEPOTApp < handle
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%
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% Usage:
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% app.BehaviorDEPOTApp
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properties
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AppHandle;
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AppCount = 0;
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Output;
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CurrClass;
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end
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properties (Constant)
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AppPathBase = {'/BehaviorDEPOT'};
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AppClass = 'BehaviorDEPOTApp';
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Increment = 1;
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Decrement = 0;
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Version = '13a';
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end
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properties (Dependent)
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AppPath;
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end
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methods (Static)
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function count = refcount(increment)
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persistent AppCount;
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if(isempty(AppCount))
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AppCount = 1;
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else
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if(increment)
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AppCount = plus(AppCount,1);
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else
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AppCount = minus(AppCount,1);
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if AppCount < 0
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AppCount = 0;
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end
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end
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end
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count = AppCount;
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end
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end
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methods
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% Create the application object
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function obj = BehaviorDEPOTApp()
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obj.CurrClass = metaclass(obj);
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startApp(obj)
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end
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function value = get.AppPath(obj)
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myAppsLocation = char(com.mathworks.appmanagement.MlappinstallUtil.getAppInstallationFolder);
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value = cellfun(@(x) fullfile(myAppsLocation, x), obj.AppPathBase, 'UniformOutput', false);
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end
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% Start the application
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function startApp(obj)
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% Increment the reference count by one and lock the file
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mlock;
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BehaviorDEPOTApp.refcount(obj.Increment);
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% Verify we are about to execute the correct function - if not we
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% should error and exit now. We need to make sure the paths are
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% equal using canonical paths.
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existVal = exist(fullfile(pwd,'BehaviorDEPOT_GUI_v1_0')); %#ok<EXIST>
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doesShadowExist = existVal >= 2 && existVal <= 6;
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pathOne = java.io.File(pwd);
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pathOne = pathOne.getCanonicalPath();
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pathTwo = java.io.File(obj.AppPath{1});
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pathTwo = pathTwo.getCanonicalPath();
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if (doesShadowExist && ~pathOne.equals(pathTwo))
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% We are trying to execute the wrong BehaviorDEPOT_GUI_v1_0
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errordlg(message('MATLAB:apps:runapp:WrongEntryPoint', 'BehaviorDEPOT_GUI_v1_0').getString, ...
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message('MATLAB:apps:runapp:WrongEntryPointTitle').getString);
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appinstall.internal.stopapp([],[],obj)
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return;
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end
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% Must load function (force by using function handle) or nargout lies.
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% Check if the app is a GUIDE app
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if nargout(@BehaviorDEPOT_GUI_v1_0) == 0
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eval('BehaviorDEPOT_GUI_v1_0');
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else
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obj.AppHandle = eval('BehaviorDEPOT_GUI_v1_0');
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end
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if(ishandle(obj.AppHandle))
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% Traditional graphics handle based app
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obj.attachOncleanupToFigure(obj.AppHandle);
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elseif isa(obj.AppHandle, 'matlab.apps.AppBase')
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% appdesigner based app
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obj.attachOncleanupToFigure(appdesigner.internal.service.AppManagementService.getFigure(obj.AppHandle));
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elseif isa(obj.AppHandle,'handle') && ~isvalid(obj.AppHandle)
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% Cleanup in the case where the handle was invalidated before here
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appinstall.internal.stopapp([],[],obj)
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else
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% There will be no call to stopapp, instead decrease the refcount
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% now to prevent future clearing issues
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BehaviorDEPOTApp.refcount(obj.Increment);
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munlock;
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end
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end
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function attachOncleanupToFigure(obj, fig)
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% Setup cleanup code on figure handle using onCleanup object
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cleanupObj = onCleanup(@()appinstall.internal.stopapp([],[],obj));
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appdata = getappdata(fig);
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appfields = fields(appdata);
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found = cellfun(@(x) strcmp(x,'AppCleanupCode'), appfields);
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if(~any(found))
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setappdata(fig, 'AppCleanupCode', cleanupObj);
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end
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end
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end
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end

‎BehaviorDEPOT_GUI_v1_0.mlapp‎

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‎README.md‎

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# BehaviorDEPOT ReadME
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- BehaviorDEPOT is currently pre-release and is undergoing a substantial update (v1.0), which is now available on the 'development' branch of the repository! Check it out and feel free to provide feedback on bugs and features you'd like to see!
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- BehaviorDEPOT v1.0 is now live and under initial testing! Please give it a try and let us know of any issues you may encounter.
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- To find out more about BehaviorDEPOT or to cite the software in your own paper, see our pre-print on bioRxiv: https://www.biorxiv.org/content/10.1101/2021.06.20.449150v2
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- Demo data is now available that can be used to test installation, classifiers, spatial/temporal filtering, and modules. Due to limitations on file size, the original video file (necessary to run the analysis module from scratch) is not available on Github but can be downloaded from: https://drive.google.com/drive/folders/1VNv9FuXyiI4xgt-RokcVvWk-1dBovuqO?usp=sharing
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- Sample DeepLabCut networks that have been trained on our data are available for use. These may work on your own data, depending on the individual camera/chamber setup, but these models can serve as starting points for quickly training your own new networks! Find them here: https://drive.google.com/drive/folders/1Fl4PmLz6CWQcqOjfQ1q_60ZCZjI5949-?usp=sharing
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**Patch Notes v0.51**
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- Updated smoothing algorithm to better perform on behavior videos
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- Updated freezing algorithm sliding convolution values
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- Removed option to select video file type; changed code to automatically detect video
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- Patched minor bugs in analysis, exploration, and optimization modules
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- Updated code structure of validation and inter-rater modules
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- Updated inter-rater module to match current directory structure
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- Updated inter-rater module to automatically identify 'hB' and 'analyzed folders'
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- Updated validation module to match current directory structure
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- Updated validation module with enhanced plotting features
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- Updated prepBatch.m to automatically ignore results folders in data directories
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**Patch Notes v1.0**
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- Integrate pipeline with custom classifier selection
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- Update GUI design for support modules
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- Update GUI design for analysis module
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- Add readout of classifiers in GUI with interactive selectivity
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- Write function that runs the classifier functions using the info stored in P and the Params, Tracking, and Metrics structs
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- Add additional behavior classifiers
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- Re-work classifier files to use separate 'P_' files to store parameters
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- Write a function that can apply a minDuration threshold to selected Behavior data
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- Edit GUI to allow custom classifier usage
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- Add convertHumanAnnotations functionality to GUI
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- Update convertHumanAnnotations_BD code
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- Update Metrics structure; make more straightforward to add or modify metrics
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- Merge mainscript files
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- Add new metrics to calculateMetrics
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- Clean up metric naming in calculateMetrics
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- Update GUI to set [batch=0 or 1] to replace mainscript_single
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- Reformat rearing classifier
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- Reformat freezing classifier
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- Reformat moving classifier
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- Write function to standardize generation of behavior structures (genBehStruct)
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- Split freezing scripts into separate files (jitter and velocity)
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- Write function to detect start/stop inds (findStartStop)
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- Write function to apply min time threshold to behaviors (applyMinThreshold)
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- Add smoothing options to GUI
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- Add hampel filtering option to GUI
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- Update spatiotemporal filtering options
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**----------------------------------------------------------------------------------------------------**
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If you’re curious about any of the code, want to tweak it, or create your own classifier, you can access the underlying scripts by simply right clicking on the app and navigating to where the app files are installed. To edit the GUI, you can open the BehaviorDEPOT.mlapp file in MATLAB’s ‘App Designer’.
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## Credits
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BehaviorDEPOT was developed by Chris Gabriel, Benita Jin, Zachary Zeidler, and Laura DeNardo. If you find this tool useful, please cite the work:
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BehaviorDEPOT was developed by Chris Gabirel, Benita Jin, Zachary Zeidler, and Laura DeNardo. If you find this tool useful, please cite the work:
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{will be updated upon publication}
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## License

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