Demo of drawing on top of the sprites. You may need to click the green flag twice to correct starting issues. I wrote this to support the suggestion of setting the pen layer to either the front or the back. I made a set of pen commands that mimic the motion and pen commands which can be used to draw on top of sprites. The usual workarounds of using clones or having a system of redrawing everything in on the screen in each frame does not always work well in a project. So having pen commands that could draw on the front layer would be very helpful. My custom blocks are just front ends to the SciSnap blocks. I used some of the tree drawing logic from this project. https://snap.berkeley.edu/project?username=manuelv24&projectname=tree The tadpole script is a modified version of this Scratch project, Tadpole (pen) /おたまじゃくし by Ceratophrys https://scratch.mit.edu/projects/779412450/data:image/png;base64,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 of drawing on top of the sprites. You may need to click the green flag twice to correct starting issues. I wrote this to support the suggestion of setting the pen layer to either the front or the back. I made a set of pen commands that mimic the motion and pen commands which can be used to draw on top of sprites. The usual workarounds of using clones or having a system of redrawing everything in on the screen in each frame does not always work well in a project. So having pen commands that could draw on the front layer would be very helpful. My custom blocks are just front ends to the SciSnap blocks. I used some of the tree drawing logic from this project. https://snap.berkeley.edu/project?username=manuelv24&projectname=tree The tadpole script is a modified version of this Scratch project, Tadpole (pen) /おたまじゃくし by Ceratophrys https://scratch.mit.edu/projects/779412450/This is an awful hack but I count not get the equations right.

Initialization of SciSnap!

Creates global variables "SciSnap!Properties", "SciSnap!Data", and "SciSnap!Messages". Sets the default values of global SciSnap!-properties for items typeOfData width height minValue maxValue columns rows maxSetValue

Sets the value of an existing global SciSnap!-property or inserts it anew. Items: typeOfData width height minValue maxValue columns rows maxSetValue

minValuetypeOfData width height minValue maxValue columns rows maxSetValueShows the message with headline in the middle of the window.

headlinetheMessageAdds a new entry to SciSnap!Messages and shows the error message.

something wrong!Reports a copy of a costume.

Reports the costume of a sprite.

Returns a random number between 0 and 1.

Reports Pi.

Reports Euler's number.

Rounds value x to n digits.

1.23572Reports n faculty.

50Reports a binomial coefficient.

531result1n1i1Test on vector, matrix, table, predicate, interval, list of intervals, set, or complex-number.

vectorvector transposed-vector matrix table complex-number complex-number-Cartesian-style complex-number-polar-style set comparisonOperator interval listOfIntervals numericalListlistcomplex-numbercomplex-number-Cartesian-stylecomplex-number-polar-styletransposed-vectorvectormatrixwidthlength11table1listwidthlength1SciS_isTable(data)intervalfalselistOfIntervalsset2Boolean3list44listOfIntervals55predicatenumericalListfalseReports a substring of a string.

thisString14Reports a string without first/all instances of substring.

allall firstthisthisStringReports a string in upper case.

thisStringReports a string in lower case.

ThisStringStores a string in a file.

this textthis fileReports the index of first appearance of substring in string.

ringthisStringReports a string with replacement instead of substring.

allall firstthisthatthisStringReports date and time in standard notation.

daydate10hourhour10minuteminute10secondsecond10year--T::

seconds todayJulian Date decimal years days this year hours this year minutes this year seconds this year hours today minutes today seconds todayYYYY17MM54DD21h12m45s7daysi11402Julian Date2JDfloor365.25floor30.6001172099531121531101215821260360024Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE only if all of the inputs are TRUE, otherwise FALSE. Like AND but for multiple inputs.

pt:todas as afirmações _ são verdadeiras 1Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE if at least one input is TRUE, otherwise FALSE.

pt:alguma das afirmações _ é verdadeira 1Returns an empty costume of the given width and height with background color r/g/b.

400300245245245Reports the row or column name of the row or column with the number entered, or reports an error message.

columncolumn row1SciSnap!Datatablelength0columnrowEvaluates the predicate with a given parameter.

ringified predicateEvaluates the intervals with a given parameter.

ringified predicateitem3<trueReports the intersection of two sets.

set1set2are these two sets?setresultset2234element34element344Reports the union of two sets.

set1set2are these two sets?setresultset223344Reports the difference between two sets.

set1set2are these two sets?setresultset2234element32set1set24433m1length4n1m1compare the mth interval with all intervals of set144Reports the Cartesian product between two sets.

set1set2setlength40length40resultset22item_13list3implication

10equivalence

10Converts numbers to booleans

1Converts booleans to numbers

trueIntervals should be of structure [leftLimit,operator1,operator2,rightLimit]

length2resultn1length

1111111111212121212121212Intervals should be of structure [leftLimit,operator1,operator2,rightLimit]

11define left edge and operator44define right edge and operator

length1length1resultn1lengthm1n1

length1length1resultn1length

length0listOfIntervalsitemfalseReturns all elements of a list not covered by the predicate.

Returns all elements of a list not covered by the intervals.

result3lst_sort(list, fn)34Test wether the predicate is translatable to a list of intervals.

ringified predicate1comparisonOperator1122number33numberfalseIntervals should be of structure [leftLimit,operator1,operator2,rightLimit]

interval1interval2intersectionlength0result1144341444341444

1141413<2≤3≤2<3<2<4414142<3≤2≤3<2<3<trueCreates a new empty costume of the specified size and color and sets the local properties

thisSpritethisSprite theStage anotherSprite400300245245245costumeWidthcostumeHeightscaleProperties2212126-10-102211600falsefalsefalsefalsetruetruefalseStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage 8: front color red 9: front color green 10: front color blue line properties: 1: line width 2: line style 3: line color red 4: line color green 5: lineColor blue marker properties: 1: marker style 2: marker width 3: marker connected 4: marker color red 5: marker color green 6: marker color blue data properties: 1: minValue 2: maxValue 3: columns 4: rows scale offsets: 1:left offset 2: right offset 3: upper offset 4: lower offset labels: 1: title 2: x label 3: y label 4: title height 5: x label height 6: y label height 7: x unit 8: y unit ranges: 1: x left 2: x right 3: y lower 4: y upper scale properties: 1: scales x precision 2: scales y precision 3: scales x textheight 4: scales y textheight 5: scales x intervals 6: scales y intervals 7: scales x start 8: scales y start 9: scales x step 10: scales y step 11: scales x scaling 12: scales y scaling 13: scales ticlength 14: scales x minitics 15: scales y minitics 16: scales x grid 17: scales y grid 18: scales x centered 19: scales y centered 20: scales show x scale 21: scales show y scale 22: plot border

thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.

costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties markerProperties dataProperties scaleOffsets labels ranges scalePropertiesthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.

costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties markerProperties dataProperties scaleOffsets labels ranges scalePropertiesthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage 8: front color red 9: front color green 10: front color blue

40030024524524518018018000thisSpritethisSprite theStage anotherSpriteStagea simple way to set some net attributes: line properties: 1: line style 2: lineWidth 3: line color red 4: line color green 5: lineColor blue

continuouscontinuous dashed dash-dot dot-dot rainbow inverse-rainbow1000thisSpritethisSprite theStage anotherSpritelinePropertiesa simple way to set some net attributes: marker properties: 1: marker style 2: marker width 3: marker connected 4: marker color red 5: marker color green 6: marker color blue

squarenone o_circle ._point +_plus x_ex square triangle5000falsethisSpritethisSprite theStage anotherSpritemarkerPropertiesSets some properties for scales.

2212121010thisSpritethisSprite theStage anotherSpritescalePropertiesscaleProperties123456scalePropertiesSets the labels and textheights of the diagrams of a PlotPad.

thisSpritethisSprite theStage anotherSpriteDiagram Title18x-label16y-label16labelsSets the distances of the diagram axes to the edges of the Sketchpad.

thisSpritethisSprite theStage anotherSpriteupperOffset13scalePropertiesUPPER OFFSET1labels013scaleProperties0lowerOffset13scalePropertiesLOWER OFFSET19scalePropertiesrightOffset1.54scalePropertiesRIGHT OFFSET.13scaleProperties0leftOffset14scalePropertiesLEFT OFFSET18scaleProperties13scaleProperties0scaleOffsetsSets the ranges for the axes of the diagrams of a Sketchpad.

-1010-1010false0.1truethisSpritethisSprite theStage anotherSpriterangesrangesDraws the graph of a function given as rigified Snap!-function or coefficient-list of a polynomial.

ringified operator or polynomialthisSpritethisSprite theStage anotherSpritereportervectorSciS_addGraphToPlotpad(costume,ranges,offsets,lineattributes,aFunction,proc)costumerangesscaleOffsetslinePropertiesDraws the data points of a two-dimensional table with numerical values. With a lot of additions from Rick Hessman. Thanks!

myDatathisSpritethisSprite theStage anotherSpritemyDatamatrixlength12SciS_addNumericDataplotToPlotpad(costume,ranges,offsets,labels,lineattributes,dataattributes,data)costumerangesscaleOffsetslabelslinePropertiesmarkerPropertiesDraws the data points of a two-dimensional table with numerical values in column 2. Text values should be used to set the x-label.

myDatatruetruethisSpritethisSprite theStage anotherSpritemyDatatableSciS_addMixedDataplotToPlotpad(costume,ranges,offsets,labels,lineattributes,dataattributes,data)costumerangesscaleOffsetslabelslinePropertiesmarkerPropertiesscalePropertiesscalePropertieswasXscaled?20wasYscaled?212021scalePropertiesDraws a histogram of histogram data.With a lot of additions by Rick Hessman. Thanks!

myData10truethisSpritethisSprite theStage anotherSpritemyDatavectorlength0transposed-vectorminmaxdeltah2resulti1lengthnumber221i1source0.1SciS_addHistogramToPlotpad(costume,ranges,offsets,lineattributes,data,datapointattributes)costumerangesscaleOffsetslinePropertiesmarkerPropertiesDraws and labels the axes of a PlotPad. With lot of additions of Rick Hessman. Thanks!

thisSpritethisSprite theStage anotherSpriteSciS_addAxesAndScalesToPlotpad(costume,scaleattributes,labels,offsets,ranges,plotcolors)costumescalePropertieslabelsscaleOffsetsrangescostumeProperties

thisSpriteSciS_newcostume(w,h,r,g,b)widthheight3costumeProperties4costumeProperties5costumePropertiesSets the ranges to "pretty" values. Many thanks to Rick Hessman!

thisSpritethisSprite theStage anotherSpritescalePropertiesscalePropertiesrangesrangespretty125get dictionary of pretty values.72192311245ceilingwidthcurrent631261427pretty346get dictionary of pretty values.821102312246ceilingheightcurrent842261527rangesscaleProperties"Pretty" scaling from Rick Hessman. Many thanks!

-10106abs2000abs2000Numbers are too big for pretty printing.nintpretty0.0010.0020.0050.010.020.050.10.20.512510205010020050010002000between91341341341341341341places33322211100000000000signsign2index1i2lengthlengthabsabs1dticnintfloorabsresultdeltanfloorstartstart*scaling is the actual starting value (start is the value displayed next to the tic)stop1stepstep*scaling is the actual stepping value (step is the difference between the tic labels).scalingintervalsprecisionminitics

thisSpritethisSprite theStage anotherSpritemyData0.1myDatalength2dy43ranges1234scalePropertiesscaleProperties7192151111418310436121151scalePropertiesCalculates the ranges of two columns of a table.

Conversion of coordinates.

100xpxp yp x ythisSpritethisSprite theStage anotherSpriteoffsetsscaleOffsetsrangesrangesdiagramWidthwidthcurrent1diagramHeightheightcurrent34x0112y0443coordinate1 xpypxyObtaining coordinates of a PlotPad by mouse.

costume-coordinatescostume-coordinates graph-coordinatesthisSpritethisSprite theStage anotherSpriteselection1 targetCostumecostumetargetCostumeWidthwidthtargetCostumeHeightheighttargetXPositionx positiontargetYPositiony positiontargetSizesizecostume-coordinatescostume coordinates.graph-coordinatescostume coordinates.

400300xycontinuouso_circle000

400300xycontinuous000

00600400continuousnone continuous dashed dash-dot dot-dot rainbow inverse rainbowsquarenone o_circle ._point +_plus x_ex square triangle000Changes the Snap!-Logo. ;-)

Creates SQL variables and sets some properties.

Establishes connection and sets the corresponding properties. Change parameters if necessary.

connectionhttps://snapextensions.uni-goettingen.de/mysqlquery.php?server=db1&user=snapexuser&password=snap!userconnection&type=connect12oklength0115ERRORImports the results of an SQL-query to SQLData and sets the corresponding properties.

listtypeOfDatatablelength0minValuenotSetmaxValuenotSetReports a list of databases on the server and sets the corresponding properties.

dbsconnection&type=getDBslinelength0last0length0115ERRORSelects one of the databases and sets the corresponding properties.

2lengthdatabasescurrentDatabasedatabasesresultconnection&command=USE currentDatabasetablestablesconnection&command=SHOW TABLES FROM currentDatabase&type=getTableslinelength0last0length0115ERRORReports a list of tables of the chosen database and sets the corresponding properties.

tblsconnection&command=SHOW TABLES FROM currentDatabase&type=getTableslinelength0last0length0115ERRORReads the attributes of the specified table and sets the corresponding properties.

1lengthtableslength0115ERRORSelects one of the tables and sets the corresponding properties.

1lengthtableslength0115ERRORGenerates a simplified SQL query.

* DISTINCTi1length1result FROM length0result2Generates an almost complete SQL query.

* DISTINCTASCASC DESC10i1length1result FROM length0result2length0result2length0resultASCnumberExecutes an SQL query.

1:ERRORreplaces "%" with "%25" to prevent trouble with the url-block.resultconnection&type=query&query=&database=currentDatabaselinelastReturns an SQL predicate.

Returns an SQL predicate.

Returns an SQL predicate.

Returns an SQL predicate.

Returns an SQL predicate.

Returns an SQL predicate.

Returns an SQL predicate.

Returns an SQL predicate.

Returns an SQL value.

Returns an SQL value.

Returns an SQL value.

Returns an SQL value.

Returns an SQL value.

Creates a new empty costume of the specified size and color and sets local properties. If sprite is the stage, the old costume# of stage is stored. So you can draw graphs on maps, for example.

thisSpritethisSprite theStage anotherSprite400300245245245targettheStageStagethisSpritemyselfStagetheStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue vertex properties: 1: ranges for random coordinates (xLeft,xRight, yUpper,yLower) 2: minimal radius of a vertex, should be changed is vertices are not growing with number of edges 3: boolean: vertices grow with number of connected edges 4: boolean: vertex content is shown. If there is no content, the vertex number is shown edge properties: 1: line width 2: line color red 3: line color green 4: line color blue 5: boolean: edge is directed 6: boolean: edge has a weight 7: boolean: edge shows weight

thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.

costumePropertiestypeOfConfiguration typeOfData costumeProperties vertexProperties edgePropertiesthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.

costumePropertiestypeOfConfiguration typeOfData costumeProperties vertexProperties edgePropertiesthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes. 1: width 2: height 3: back color red 4: back color green 5: back color blue

400300245245245thisSpritethisSprite theStage anotherSpritecostumePropertiesa simple way to set some vertex attributes: 1: ranges for random coordinates (xLeft,xRight, yUpper,yLower) 2: minimal radius of a vertex, should be changed is vertices are not growing with number of edges 3: boolean: vertices grow with number of connected edges 4: boolean: vertex content is shown. If there is no content, the vertex number is shown

3truefalsethisSpritethisSprite theStage anotherSpritevertexProperties1vertexPropertiesThe ranges are maintained. a simple way to set some edge attributes: 1: line width 2: line color red 3: line color green 4: line color blue 5: boolean: edge is directed 6: boolean: edge has a weight 7: boolean: edge shows weight

1000falsefalsefalsethisSpritethisSprite theStage anotherSpriteedgeProperties

100100thisSpritethisSprite theStage anotherSprite1lengthvertexListlengthvertexListadds n vertices to the vertexList of a GraphPad: Vertex attributes have the following order: 1: x 2: y 3: size 4: content 5: isMarked 6: colorNr 7: numberOfLinks

1thisSpritethisSprite theStage anotherSpritevertexListSciS_addVerticesToVertexlist(n,vlist,vAttributes)vertexListvertexPropertiesadjacencyMatrixSciS_addVerticesToAdjacencymatrix(n,amatrix)adjacencyMatrixMoves a vertex to another position measured in Snap! sprite-coordinates

1thisSpritethisSprite theStage anotherSprite100100lengthvertexListCreates n new edges randomly.

1thisSpritethisSprite theStage anotherSpriteresultSciS_addRandomEdgesToGraph(amatrix,n,lAttributes,vlist)adjacencyMatrixedgePropertiesvertexListadjacencyMatrix1vertexList2Creates a new edge between two vertices.

12thisSpritethisSprite theStage anotherSprite0lengthvertexList0lengthvertexListadjacencyMatrixXadjacencyMatrixX5edgePropertiesdraws the graph on a GraphPad.

thisSpritethisSprite theStage anotherSpriteStageoldCostumeStageSciS_drawGraph(amatrix,vlist,cAttributes,vAttributes,lAttributes,oldCostume)adjacencyMatrixvertexListcostumePropertiesvertexPropertiesedgePropertiesDeletes a vertex.

1thisSpritethisSprite theStage anotherSprite0lengthvertexListvertexListadjacencyMatrixDeletes an edge.

12thisSpritethisSprite theStage anotherSprite0lengthvertexList0lengthvertexListadjacencyMatrix05edgeProperties

12thisSpritethisSprite theStage anotherSprite0lengthvertexList0lengthvertexListSets the weight of an edge.

121thisSpritethisSprite theStage anotherSprite0lengthvertexList0lengthvertexList

thisSpritethisSprite theStage anotherSpritefrom vertex nrnumberInput for start vertex.

thisSpritethisSprite theStage anotherSpritenew start vertexwidth (1....20)number

1thisSpritethisSprite theStage anotherSprite0lengthvertexListSets the content of a vertex.

1thisSpritethisSprite theStage anotherSprite0lengthvertexListInput for a new vertex content.

thisSpritethisSprite theStage anotherSpritevertex numbernumberTells a vertex to be marked.

1thisSpritethisSprite theStage anotherSprite0lengthvertexListDeletes a marker.

1thisSpritethisSprite theStage anotherSprite0lengthvertexListDeletes all markers.

thisSpritethisSprite theStage anotherSpriteitemvertexListDepth first search in a graph.

1thisSpritethisSprite theStage anotherSprite0lengthvertexListBreadth first search in a graph.

1thisSpritethisSprite theStage anotherSprite0lengthvertexListReports the distance of two vertices.

thisSpritethisSprite theStage anotherSprite120lengthvertexList0lengthvertexListShortes path between two vertices, using Dijkstra-method.

12thisSpritethisSprite theStage anotherSprite0lengthvertexList0lengthvertexListShortes paths between startVertex and all connected vertices, using Dijkstra-method.

1thisSpritethisSprite theStage anotherSprite0lengthvertexListReturns the vertexnumber of a vertex near (x|y), if there is one.

10050thisSpritethisSprite theStage anotherSpriteSciS_vertexnumberAtGraph(vlist,cAttributes,vAttributes,x,y)vertexListcostumePropertiesvertexProperties

00thisSpritethisSprite theStage anotherSpritetargetSizesize100x position1100y position1Reports the vertexnumber of the vertex with the specified content. If not found then reports 0.

PeterthisSpritethisSprite theStage anotherSpritenr0itemvertexListFamilyTree

numberXdiagramSpriteselfDiagramSpriteDiagramSprite400300245245245DiagramSpriteEdges per Node18Number of Edges16Number of Vertices1610trueDiagramSpriteDiagramSprite

spriteName

spriteNameImports a stored sprite from a file.

new nameImports stored blocks to another palette. All blocks should be from the same category!

Lookslibrarybegincategory=10end"50oldcat2libraryall""""SciS_importLibrary2(src)Creates a new empty costume of the specified size and color ans sets local properties for a NeuralNet.

thisSpritethisSprite theStage anotherSprite400300245245245costumeWidthcostumeHeightStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for NN position on stage 7: upper offset for NN position on stage neural net properties: 1: number of layers 2: layer width 3: width of NN image 4: height of NN image

thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.

netPropertiestypeOfConfiguration typeOfData costumeProperties netPropertiesthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.

netPropertiestypeOfConfiguration typeOfData costumeProperties netPropertiesthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for NN position on stage 7: upper offset for NN position on stage

40030024524524500thisSpritethisSprite theStage anotherSpritecostumePropertiesa simple way to set some net attributes: 1: number of layers 2: layer width 3: width of NN image 4: height of NN image

23400300thisSpritethisSprite theStage anotherSpritenetPropertiesReports the output of the nth layer of a neural network with given input.

last1 lastthisSpritethisSprite theStage anotherSpritevector2netPropertieslengthCreates a fully connected neural network of perceptrons. Inputs should be connected to layer 1, outputs to last layer.

23thisSpritethisSprite theStage anotherSpritemyDataone matrix for each layer1myDatapropertiesnetPropertiesnetProperties34typeOfDataweightsDraws the nodes and current status of connections of an NN as new costume.

thisSpritethisSprite theStage anotherSpritenetWidth2netPropertieslengthSciS_NNshowStatus(cAttributes,nAttributes,weights,outputs,costume,sprite)costumePropertiesnetPropertiesmyDatacostumeA block for teaching a neural net.

0.1thisSpritethisSprite theStage anotherSpritewidth2netPropertieslengthlengthmyDataSciS_NNteach(weights,width,depth,input,output,eta)myData1netProperties

thisSpritethisSprite theStage anotherSpritecreates a temporary or static Sprite.

truenewSprite

Removes the calling sprite.

Reports the row or column number of the row or column with the name entered, or reports an error message. If the the name is a number You can mark this with a # (e.g. #123).

columncolumn rownameSciSnap!Datatablelength0length10columnfirstlast1#n0i10lengthi100ERROR: name not found!rowfirstlast1#n0i10lengthi10ERROR: name not found!ERROR: something wrong!Input dialog for a list of items. Click item by mouse and then ok. Returns the selected item.

thisSpritethisSprite theStage anotherSpritetitlewidth9costumecostume400245245245continuous1000245245245 (click on it)102018truei1length2530505281218true70301005ok751218trueokfalse70100305Returns an empty table.

Reports a new table initialized with value.

23resultReports a table with labeled columns.

20i1rowERROR: labels required!Reports the copy of a list or an empty list.

Imports table-CSV-data, costume-data, SQL-data or FITS-data to SciSnap!Data.

costume-(RGB)-datatable-(CSV)-data costume-(RGB)-data SQL-(query)-data FITS-datacurrent-costumecurrent-costume filepicker other-sourceFITS-datatable-(CSV)-datacolumns10rows1000hmaxcolumnSciSnap!DatatrueminValuemaxValuecostume-(RGB)-datacostumehminValuemaxValueSQL-(query)-datatypeOfDatatablelength0columns10rows1000hmaxcolumntrueminValuemaxValueReads a file using the filepicker.

Stores a list in a CSV file. JS-code copied from Snap!-code.

SciSnap!DatafilenamelistReports randomly distributed points in the form of a rectangle, circle, or ring.

100-100100-100100squaresquare circle ringcircleringCreates new random data in the specified ranges swaying by a straight.

10-55102Generates a list of points "near" a given graph.

20-552Creates the transpose of a table or a list.

length01listvaluevaluetableERROR: data are not transposable!Adds a row, a column, or column headers to a table.

rowrow column column-headersSciSnap!Datachoice1 tablerowi1columnlength0i1column-headersi11Reports the row or column of a table identified by number or name, or reports an error message. If the the name is a number You can mark this with a # (e.g. #123).

rowrow columnnumberOrNamefirst last numberOrNameSciSnap!Datatruechoice1 tablerowlastnumber1lengthcolumnlastnumber1length1ERROR: something wrong!Deletes a row or column of a table. identified by number or name, or reports an error message. If the the name is a number You can mark this with a # (e.g. #123).

rowrow columnnumberOrNamefirst last numberOrNameSciSnap!Datatablerowlastnumbercolumnlastnumber1length1itemReports the element a position x|y of a table, identified by numbers or names.

numberOrNamenumberOrNameSciSnap!Datatablexcolumnnumberyrownumber0length0length1Replaces the value at position x|y of a table.

numberOrNamenumberOrNameSciSnap!Datatablexcolumnnumberyrownumber0length0length1Reports the columns from row startnr to row endnr.

SciSnap!DatanumberOrNamelastlast numberOrNametablelaststartnrrownumberendnrrownumbercolNumbersitemSciS_columncopy(data,cols,start,stop)Reports a subsection of an image or table.

RGB-datatable-data matrix-data list-data RGB-data FITS-dataSciSnap!DatanumberOrNamenumberOrNamenumberOrNamenumberOrNamelist-datanumberlistresultdummyvalueindextable-datamatrix-datab1columnnumberb2rownumbere1columnnumbere2rownumberbeginendSciS_subsection(data,begin,end)RGB-dataFITS-dataRGB-datawidthwidthheightheightFITS-datawidthwidthheightheightlistnumbernumbernumbernumberbeginendSciS_subsectionImage(data,begin,end,width,height)Determines the rows of a table with certain properties.

SciSnap!DatanumberOrNameless-thanless-than greater-than equal-to different-fromtablencolumnnumber1length1op1whitespaceless-thangreater-thanequal-todifferent-fromcounts the appearances of the values of a list.

sortedDatacountedValuesvalue1n0itemreturns the entropy of a list of data

Copy of corresponding Snap!-library-block: Reports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

SciSnap!DatatablelistNormalizes a table by dividing it by the specified value.

SciSnap!Datameanmean max number sum median softmaxchoice1 vector0Reports a compressed version of a vector or matrix.

SciSnap!Data2isMatrixmatrixvectorlength11resulti1length1iresultcolumnNr1length1Pooling operation with matrices, images and vectors.

maxmax meanSciSnap!Data21typeOfPooling1 matrixvectorFITSRGBmatrixFITSRGBSciS_pooling(data,width,height,typeOfPooling,stride,typeOfData)Reports a sorted list.

Sorts a list or table.

SciSnap!DatanumberOrNametruetrueisTabletablelistnumberresult1listDetermines a group of properties of a table.

meanmin max number sum meannumberOrNameSciSnap!DatanumberOrNametruetablegroupcolumncolumnnumber1length1valuecolumncolumnnumber1length1choice1 copytruecolumnlength0SciS_groupeddata(aTable,compaircolumn,operation,groupcolumn)Calculates the ranges, the covariance or the correlation between two columns of a table.

rangesranges covariance correlationnumberOrNamenumberOrNameSciSnap!Datatruetablencolumnnumber1length1mcolumnnumber1length1copychoice1 length1SciS_propertiesoftable(selection,table,x,y)1 Calculates the parameters of a regression line.

SciSnap!DatamatrixsumX0sumY0numerator0denominator0dummymeanXlengthmeanYlengthdummymbDetermines the next k neighbours of a point in data. The class of the point has to be in column 3.

5SciSnap!Datatablepoint2neighbors3truetruetable-data114Applies a convolution to a table with FITS, RGB, or table values.

tableimage tableSciSnap!Data100100listmatrixSciS_convolution(kernel,data,width,height,typeOfData,mIndex,kWidth)Custering of n-dimensional data with k-means-method. Cluster numbers are attached to the data.

3SciSnap!DatamatrixlengthSciS_k-means-clustering(k,data)Custering of n-dimensional data with k-means-method. A metric has to be delivered as a ringified term. Cluster numbers are attached to the data.

3SciSnap!Dataringified operatormatrixlengthreporterresultadd cluster number 0 to datadimensionlength1minmaxi1calculate min and max for all columnscentersi1choose k random centerscalculate min and max for all columnsanyChangestruen0anyChangesfalsedummycenter1pointbuild clustersadjust centersMetric for Strtings.

SaturdaySundaylabels of data: -1 not visited, 0 visited, -2 noise, >0 clusternr

SciSnap!Data505matrixlength2SciS_DBSCAN(data,r,minMembers)-1reports a ID3 decision tree constructed on the base of labeled data

length0empty datasetcountedValuescolumnlasttruelength1only one result --> readylength11only one attribut left --> readyinformation gainsi1length11calculate information gains for all attributesinformation gains2falsetruecompareColumn11take attribute with max information gain as compare criterioncountedValuescolumntruelinksitemrecursive construction of ID3 trees for all other attributesnode11

thisSpritethisSprite theStage anotherSpriteReturns the vertexnumber of a vertex near mouse position, if there is one.

thisSpritethisSprite theStage anotherSpriteSciS_vertexnumberAtGraph(vlist,cAttributes,vAttributes,x,y)vertexListcostumePropertiesvertexPropertiesx positiony positionreturns the class of a dataset using an ID3-decision-tree

1leaf (unambiguous)1nodei1ERROR: no sensefull data to compare found!Reports a vector, built by the items.

Returns a new vector of dimension n with random elements.

3110Returns a matrix formed from the input vectors.

lengthlength1resultitemReturns a new nxm-matrix with random elements.

32110i1Creates the transpose of a vector or a matrix.

valuevalueDetermines a property of a vector.

minmin max minpos maxpos number sum mean median variance standard-deviation softmaxdatatransposed-vectorlength1choice1 softmaxe^medianminmaxnumbersummeanminposmaxposvariancestandard-deviationvariancestandard-deviationERROR: unknown option!Linear operations with scalars, vectors and matrices.

*+ - * X-*ERROR: illegal operation between numbers!vectormatrixERROR: illegal operation with numbers!vectorvector+-*Xmatrixvector matrix operationERROR: illegal operation with vectors!matrixvectormatrix vector operationmatrixmatrix matrix operation-*ERROR: wrong operation!ERROR: illegal operationApplies a mapping matrix to an object that is given as a matrix of points.

Soves a linear system of max. 50 equations.

some tests for correct datalength0length1lengthtransposed-vectormatrixcreates matrix with attached column bsolvedtruenlength50i1dlengthTry to solve matrixr1rowswap rowshelpnormalize row1k11k1k1diagonalizer1Reports a list of matrix A in triangle form, the rank of A, column change and column positions.

length0Aba copy of A is used to let A unchangedranklengthrow1columchangefalsecolumn positions1length1lengthlengthi1search downwardsi1search to the rightc0row1row1row-1transform to diagonal form ECalculates the coefficients of the interpolation polynomial for a 2xn-matrix of points.

length2length12matrixcreate matrix of powers of x and y-valuesolvedtruenlength50i1j-12dlengthTry to solve matrixr1rowswap rowshelpnormalize row1k11k1k1diagonaizer1Calculates the value of a polynomial for input x.

polynomial as list of coefficients11result12i3Applies an affine transformation to a list of points.

SciSnap!DataoktruelistlistP11P22P33Q11Q22Q33S1221221220ERROR: denominator is zeroReports a complex number in Cartesian style.

23Reports a complex number in polar style.

230Reports a complex number in polar style.

Reports a complex number in Cartesian style.

Reports properties of complex numbers.

real-partabsolute-value real-part imaginary-part phase conjugatereal-partimaginary-partphaseconjugateERROR: unknown operator!Reports arithmetic results of two complex numbers.

++ - * /complex-numbercomplex-number-polar-stylecomplex-number-polar-style+-*/complexNumberCartesianStyle22333223Creates a new empty costume of the specified size and color and sets local properties

thisSpritethisSprite theStage anotherSprite400300245245245theStagecostumeWidthcostumeHeighttheStagecostume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for MathPad position on stage 7: upper offset for MathPad position on stage other attributes: 1: line width 2: draw only ponts, not arrows 3: dimension of coordinate system 4: max. value on scales 5: start point of arrows

thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.

costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dimension maxValue startPointthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.

costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dimension maxValue startPointthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for MathPad position on stage 7: upper offset for MathPad position on stage

40030024524524500thisSpritethisSprite theStage anotherSpritecostumePropertiesa simple way to set some MathPad attributes: 1: line width 2: draw only ponts, not arrows 3: dimension of coordinate system 4: max. value on scales 5: start point of arrows

1false310000thisSpritethisSprite theStage anotherSpriteoldDimensiondimensionoldMaxValuemaxValuelinePropertiesdimensionmaxValuestartPointDraws axes on a MathPad in 2 or 3 dimensions.

thisSpritethisSprite theStage anotherSpriteSciS_mathpadaddaxes(cAttributes,type,maxValue,dimension,costume)costumePropertiestypeOfDatamaxValuedimensioncostumeDraws a vector, complex number or object of points from the start point stored in the properties.

vectorvector complex-number line-to object-of25500thisSpritethisSprite theStage anotherSpritefalselistdataobject-ofdimensionlength1SciS_mathpadplot(costume,data,r,g,b,maxValue,linewidth,dimension,onlypoints,cAttributes,startpoint,choice)costumemaxValue1linePropertiesdimension2linePropertiescostumePropertiesstartPointthis blockcomplex-numberdata23dimensionlengthSciS_mathpadplot(costume,data,r,g,b,maxValue,linewidth,dimension,onlypoints,cAttributes,startpoint,choice)costumemaxValue1linePropertiesdimension2linePropertiescostumePropertiesstartPoint

Returns a root of an equation calculated with Newton's method.

ringified term1epsilon0.00001fi11000abs1000Reports an element of a sequence.

ringified term1Returns a list of the n first elements of a sequence.

1ringified termresulti1Returns a list of secant slopes calculated with the given sequence.

ringified term21/ntextresulti1lengthReports the secant slope "nearby" the given point.

ringified term1epsilon0.00001Returns a finite sum.

10ringified term10result0iReturns the integral of a term calculated with n trapezoids.

2ringified term11000result0dxx1Reports either - data Fourier transformed to frequency-space - "raw" FFT (Fast Forier Transformation)data formatted as SciSnap! complex numbers - or iFFT real data

frequency_spectrumfrequency_spectrum complex_FFTdata iFFT_of_FFTdata100iFFT_of_FFTdataSciS_FFTops(data,freq,choice)Reports coefficient of the binomial distribution b(N,p,k)=(N choose k)*p^k(1-p)^(N-k)

100.12ERROR: wrong parameter!Reports binomial distribution B(N,p,k).

1100.1ERROR: wrong parameter!Reports coefficient of the hypergeometric distribution.

10352ERROR: wrong parameter!Reports hypergeometric distribution.

11035ERROR: wrong parameter!Reports coefficient of the Poisson distribution.

0.052ERROR: wrong parameter!Reports Poisson distribution.

20.05ERROR: wrong parameter!Reports coefficient of the Pareto distribution.

132Reports coefficient of the normal distribution.

101The empty set.

The real (computer-) numbers.

Returns a set by listing the elements, predicates, and defined intervals. The sequence is: set isNumerical list of elements list of intervals predicate.

Creation of a set by a predicate. Intervals and elements should be defined by another block. If possible the predicate is translated to a list of intervals. The sequence is: set isNumerical list of elements list of intervals predicate.

ringified predicatepredicateTreeblocksERROR: pure predicate sets are not supported yet.Returns a set with one numerical interval. The sequence is: set isNumerical list of elements list of intervals predicate.

-Infinity-Infinity≤≤ <≤≤ <InfinityInfinitysettrueReports whether "element" is element of "set".

elementset43Reports the intersection, union, difference, or cross product of two sets.

set1∩∩ ∪ \ Xset2are these two sets?set∩∪\set1set24433m1length4n1m1compare the mth interval with all intervals of set144Xresultset22item_13list3Is set1 subset of set2?

set1set2are these two sets?setinterval4element3Compare elementstrueAre theses two sets equal?

set1set2setReports up to n elements of a set. Numers are natural numbers smaller than maxSetValue in SciSnap!-properties.

10setresult3length40i1maxSetValue should be close to infinity ;-)resultvalueindexlistclean uplength1listReports simple types, sets and lists as string.

Booleantextlisti1maxSetValue should be close to infinity ;-)elementsvalueindexlistlength20itemlength18is first elementtrueitemlength18ERROR: no simple type!Converts a string to a list of elements.

1,2,[3,4],{6,7,8..12}]10]0]0}10}0}0find listsCreates a new empty costume of the specified size and color and sets local properties

thisSpritethisSprite theStage anotherSprite400300245245245costumeWidthcostumeHeightStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage line properties: 1: lineWidth 2: line color red 3: line color green 4: lineColor blue data properties: 1: minValue 2: maxValue 3: columns 4: rows image properties: 1: type (RGB,FITS) 2: imageWidth 3: imageHeight grid colors: list of usable colors: 1:black 2:gray 3:white 4: red 5:yellow 6:magenta 7:green 8:cyan 9:blue

thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.

costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dataProperties imageProperties gridProperties gridColorsthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.

costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dataProperties imageProperties gridProperties gridColorsthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage

40030024524524500thisSpritethisSprite theStage anotherSpriteStagea simple way to set some net attributes: line properties: 1: line style 2: lineWidth 3: line color red 4: line color green 5: lineColor blue 6: fill color red 6: fill color green 8: fill color blue

continuouscontinuous dashed dash-dot dot-dot1000180180180thisSpritethisSprite theStage anotherSpritelinePropertiesa simple way to set some grid attributes: 1: number of horizontal cells 2: number of vertical cells 3: cell width 4: cell height grid data are stored in myData

thisSpritethisSprite theStage anotherSprite400400whfloor1costumePropertieswsfloor2costumeProperties11gridPropertiesmyData2Imports costume-data or FITS-data to a data list.

costume(RGB)datacostume(RGB)data FITSDatacurrentCostumecurrentCostume filepicker list with dimensions at topthisSpritethisSprite theStage anotherSpritechoice1 filepickerFITSData6typeOfConfigurationImagePadmyPropertiestypeOfDataFITSmyPropertieslineProperties1000myPropertiesdataProperties1length6myPropertiesimagePropertiesFITSmyPropertiesgridProperties4040floor40floor40myPropertiesgridColors000black150150150gray255255255white25500red2552550yellow2550255magenta02550green0255255cyan00255bluemyPropertiesheader5length011Stagecostume(RGB)dataw1h211costumeDatacostumePropertiesminmin123maxmax123typeOfConfigurationImagePadmyPropertiestypeOfDataRGBmyPropertieslineProperties1000myPropertiesdataPropertieslength1lengthmyPropertiesimagePropertiesRGBmyPropertiesgridProperties4040floor40floor40myPropertiesgridColors000black150150150gray255255255white25500red2552550yellow2550255magenta02550green0255255cyan00255bluemyPropertiesStageGenerates an image from the FITS or RGB data of the DataSprite in gray or false colors

graygray false-color RGBmyData0255falsethisSpritethisSprite theStage anotherSpritecolor1 sourcemyDatamyDatatypeOfDataFITS1costumePropertiestheStage2costumePropertiestheStage1costumeProperties2costumeProperties1costumeProperties2costumeProperties1costumePropertiestheStage2costumePropertiestheStage1costumeProperties2costumePropertiesDraws a grid on an ImagePad.

myDatathisSpritethisSprite theStage anotherSpritetruemyDatamatrix1gridPropertieslength12gridPropertieslengthDraws a line on present costume using line properties. Attention: JS coordinates are used.

1010100100thisSpritethisSprite theStage anotherSpriteDraws a rectangle on present costume using line properties. Attention: JS coordinates are used.

1010100100thisSpritethisSprite theStage anotherSpriteSciS_drawRectangleOnImagepad(costume,x1,y1,width,height,lineAttributes)costumelinePropertiesFills a rectangle on present costume using surface properties. Attention: JS coordinates are used.

1010100100thisSpritethisSprite theStage anotherSpriteSciS_fillRectangleOnImagepad(costume,x1,y1,width,height,lineAttributes)costumelinePropertiesDraws a circle on present costume using line properties. Attention: JS coordinates are used.

10010020thisSpritethisSprite theStage anotherSpriteSciS_drawCircleOnImagepad(costume,x,y,radius,lineAttributes)costumelinePropertiesFills a circle on present costume using surface properties. Attention: JS coordinates are used.

10010020thisSpritethisSprite theStage anotherSpriteSciS_fillCircleOnImagepad(costume,x,y,radius,lineAttributes)costumelinePropertiesDraws a text on present costume using line properties. Attention: JS coordinates are used.

my text1005012truethisSpritethisSprite theStage anotherSpriteSciS_drawTextOnImagepad(costume,x,y,text,height,horizontal,lineAttributes)costumelinePropertiesDraws a list of "point" as "balls". Attention: JS-coordinates are used!

myDatacirclescircles squares5thisSpritethisSprite theStage anotherSpritesourcemyDatamatrixlength0length12SciS_drawListOfPoints(costume,data,shape,size,lineAttributes)costume121lastlinePropertiesSets the RGB value at (x|y) on costume. Attention: JS-coordinates are used!

11thisSpritethisSprite theStage anotherSprite255100301costumeProperties2costumeProperties11Reports the RGB value at the position (x|y) of the costume. JS-code-snippets copied from Snap!-code. Attention: JS-coordinates are used!

11thisSpritethisSprite theStage anotherSprite1costumeProperties2costumeProperties11Sets the value at (x|y) in myData. Attention: JS-coordinates are used!

11thisSpritethisSprite theStage anotherSpritetypeOfDataRGBtypeOfDataFITSReads the data value at the position (x/y) of an image stored in myData. Attention: JS-coordinates are used!

11thisSpritethisSprite theStage anotherSpritetypeOfDataRGBtypeOfDataFITSObtaining data with the mouse from an image or grid stored in myData.

image-valueimage-value costume-coordinates slice-data line-data circle-data brightness cell-valuethisSpritethisSprite theStage anotherSpriteselection1 targetCostumecostumetargetCostumeWidthwidthtargetCostumeHeightheightStagecostume-coordinatescostume coordinates.line-datadraggable?okfalseokfalsepixelsxold1yold21212Stagedraggable?line dataimage-value11image valueslice-dataslice datacircle-datacircle datadraggable?okfalseokfalsepixelscontinuous32552000180180180112112continuous1255200018018018012Stagedraggable?brightnessbrightnesscell-valuevalue of a grid cellApplies an affine transformation to a costume.

currentCostumeoktruelistlistP11P22P33Q11Q22Q33S1221221220ERROR: denominator is zeroReports the brightness of a FITS or RGB image around x|y in a circle with radius r. Attention: JS-coordinates are used!

10010010thisSpritethisSprite theStage anotherSpritedatamyDatalisttypeOfDataFITStypeOfDataRGBFills a grid or a part of a grid with one of the specified values.

thisSpritethisSprite theStage anotherSprite1xMax1yMaxgridPropsgridPropertiesxMaxyMaxmyDataSciS_fillOnImagePadGridRandomlyOnImagePad(xMin,xMax,yMin,yMax,numbers,data)myDatasets the value of a grid cell

thisSpritethisSprite theStage anotherSprite1targetCostumecostumetargetCostumeWidthwidthtargetCostumeHeightheightStage11myDatatrueSets the value of a grid cell an shows the result.

thisSpritethisSprite theStage anotherSprite112truegridPropsgridProperties1112Reports the Moore- or v.-Neumann-neighborhood of a cell. Order: north,east,south,west

MooreMoore vonNeumannfalse11thisSpritethisSprite theStage anotherSpriteSciS_neighborhoodInGridOnImagePad(data,gridProperties,x,y,isTorus,typeOfNeighborhood)myDatagridPropertiesSwaps the cell values of a grid randomly.

thisSpritethisSprite theStage anotherSpritetrue111xMax1yMaxgridPropsgridPropertiesxMaxyMaxSciS_swapCellsOfGridOnImagePad(data,gridProperties,n,isTorus,range,xMin,xMax,yMin,yMax)myDatagridPropertiesabsCounts the surrounding values and changes it if necessary.

thisSpritethisSprite theStage anotherSpritetrueany or numberany22greater-thangreater-than equal-to smaller-than different-from41false51xMax1yMaxgridPropsgridPropertiesxMaxyMaxSciS_changeSurroundingValuesOfGridOnImagePag(data,gridProperties,ifValue,elseValue,surrValue,op,n,isTorus,withNoise,noise,xMin,xMax,yMin,yMax,oldValue)myDatagridProperties1 1 Replaces the cell values of a grid depending on the features of the neighbors.

thisSpritethisSprite theStage anotherSpritetruesumsum min max mean1xMax1yMax1gridPropsgridPropertiesxMaxyMaxSciS_replaceValuesOfGridOnImagePad(data,gridProperties,operation,isTorus,xMin,xMax,yMin,yMax,range)myDatagridProperties1 absCombines two grids.

1orand or xor not-and not-or not-xor minus212xMaxlength1length1length1length1yMaxlengthlengthlengthlengthSciS_combineGridsOnImagePad(grid1,grid2,value1,operator,value2,ifValue,elseValue,xMax,yMax)Applies a Wolfram Cellular Automaton to a grid.

3031SciS_applyWolframAutomatonToAgridOnImagePad(no,grid,color0,color1)

An image should be loaded as costume of the sprite!

An image of Albategnius should be loaded as costume of the sprite!

Tests the value of a property.

falsetypeOfConfigurationMathPadReturns the value of an existing global SciSnap!-property or "not found". Items: typeOfData width height minValue maxValue columns rows maxSetValue

minValuetypeOfData width height minValue maxValue columns rows maxSetValue

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.5-43-633start underwater testBlue25Red-15Steps-2Y Change8X Change0Blue25Red-15Steps-5Y Change30X Change0Sprite(2)90.550-100typeOfData,not setwidth,not setheight,not setminValue,not setmaxValue,not setcolumns,0rows,0maxSetValue,500