_images/app_simlytiks.png

24. VizLib Visualizer Library

The following showcases each visualization on the platform with icons, descriptions and placeholders for each.

Table

This is the table view that helps to get a 2D view of the data. You can choose columns that are text or number.

The following is a list of Table options with their placeholder illustration:

Option Name Illustration
columns
primaryKeys
fontSize
fontWeight
summaryRowColumns
staticRowAtTop
computeDifference
excludeColumns
reverse_column_colors
baselineType
baselineRow
baselineColumn
baselineCriteria
differenceType
curveDifferenceType
digitizePoints
normalize
xmin
xmax
sync
showDifferenceByArrows
variableFontSizes
showArrowsForNegativeValues
colorTexts
colorLessThanBaseline
colorMoreThanBaseline
baselineColor
flipTable
thresholds
useThresholdColors

Vertical Bar Chart

A graph that visually displays data using vertical bars going up from the bottom, whose lengths are proportional to quantities they represent. It can be used when one axis cannot have a numerical scale.

The following is a list of Vertical Bar Chart options with their placeholder illustration:

Option Name Illustration
x
y
colorBy
aggregationType
groupType
ymin
ymax
showTooltip
tooltipBy
showLegends
showBarLabels
showDatumLines
showAggregatePercent
showImagesInAxes
sortBy
sortByValue
sortOrder
binScale
showSummary

Bubble Chart

Scatter plots are used to plot data points on a horizontal and a vertical axis in the attempt to show how much one variable is affected by another. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X and Y axes.

The following is a list of Bubble Chart options with their placeholder illustration:

Option Name Illustration
x
y
colorBy
separateColorScales
strokeColorBy
labelBy
size_by
opacity_by
thickness_by
imageBy
imageW
symbol_by
tooltipBy
showImagesInAxes
xAxisPosition
tickCenterLabels
set_min_to_zero
trendLine
order
showImageLegends
showLegendLabels
showLegendLines
autofit
labelsAroundBubbles
labelsAsImages
labelDateFormat
hideCircles
boundingBoxes
boundingBoxesColorBy
minMaxIndicators
xmin
xmax
ymin
ymax
xTicksCount
yTicksCount
showSummaryCount
autoSizeBasedOnZoom
collisionType
sortBy
sortOrder
groupDataBy
groupLimit
parent_child_relationship
groupingMiniView
limitGroupingChar
parentIdBy
primaryKeyBy
useOnlyUniqueValues
grayOutFilteredData
updateDataOnGroupSelection
dynamicGroupFiltering
showGroupingCount

Parallel Coordinates

Parallel Coordinates shows how data columns and their values directly relate to one another, as each column in the chart represents a column from the dataset. Each line represents a data row which is linked between all its column values.

The following is a list of Parallel Coordinates options with their placeholder illustration:

Option Name Illustration
fields
color_by
imageBy
line_width_by
colorMin
colorMax
showTable
set_min_to_zero
normalize

Line Chart

Line charts are best when you want to show how the value of something changes over time or compare how several things change over time relative to each other.

The following is a list of Line Chart options with their placeholder illustration:

Option Name Illustration
xBy
yBy
yAggregate
groupDateBy
ymin
ymax

Donut Chart

Donut charts are used to show the proportions of categorical data, with the size of each piece representing the proportion of each category.

The following is a list of Donut Chart options with their placeholder illustration:

Option Name Illustration
groupBy
valueBy
groupType
groupLimit
nestedGroupBy
sortBy
sortOrder
polyLines

Curve Plot

The Curve plot displays a simple group of X-Y pair data such as that output by 1D simulations or data produced by Lineouts of 2D or 3D datasets. Curve plots are useful for visualizations where it is useful to plot 1D quantities that evolve over time.

The following is a list of Curve Plot options with their placeholder illustration:

Option Name Illustration
curve_by
colorByCriteria
colorBy
datasetColumn
showDatasetColAsSeparateLegends
chartType
opacityBy
imageBy
lineWidthBy
datumLineBy
curveBy2
curveBy3
differentiateMultiCurvesBy
forceIndividualColors
forceSolidLines
xLabel
yLabel
xSubLabel
ySubLabel
xmin
xmax
ymin
ymax
showLegends
showDatumLines
showAverageCircles
legendsFontSize
tooltip
tooltipBy
xType
x_unit_type
upperYType
colorMin
colorMax
computeDifference
baseline
curveDifferenceType
groupingFactors
aggregation_mode
datum_area
average_lines

Box Plot

This visualizer helps to show the distribution of the data with quartiles and to aid in identifying the outliers

The following is a list of Box Plot options with their placeholder illustration:

Option Name Illustration
column

Multi Box Plot

This visualizer helps to show the distribution of the data with quartiles and to aid in identifying the outliers

The following is a list of Multi Box Plot options with their placeholder illustration:

Option Name Illustration
x
y

Frequency Plot

This visualizer helps to show the distribution of the data with quartiles and to aid in identifying the outliers

The following is a list of Frequency Plot options with their placeholder illustration:

Option Name Illustration
column
groupLimit

Summary Table

Customize the number of rows and columns for a table with customizable cells that support all forms of data

The following is a list of Summary Table options with their placeholder illustration:

Option Name Illustration
rows
cols
fixedRowsTop
fixedColumnsLeft
headerValue
headerTitle
itemView
fontSize
fontWeight

Decision Table

This is the table view that helps to get a 2D view of the data. You can choose columns that are text or number

The following is a list of Decision Table options with their placeholder illustration:

Option Name Illustration
columns
primaryKeys
baseline
fontSize
fontSize
reverse_column_colors

Filterable Table

This table view allows for creating custom filters based on data columns.

The following is a list of Filterable Table options with their placeholder illustration:

Option Name Illustration
columns
filterableColumns
primaryKeys
fontSize
fontWeight

Ranking Table

This is the table view that helps to rank a dataset based on weights assigned to numerical columns.

The following is a list of Ranking Table options with their placeholder illustration:

Option Name Illustration
columns
primaryKeys
weights

Difference Table

This visualizer helps to show the distribution of the data with quartiles and to aid in identifying the outliers

The following is a list of Difference Table options with their placeholder illustration:

Option Name Illustration
row
col
fontSize
fontWeight

Distribution Plot

This visualizer helps to show the distribution of the data with quartiles and to aid in identifying the outliers

The following is a list of Distribution Plot options with their placeholder illustration:

Option Name Illustration
columns
showMaturityTable
showSimpleTexts
useVerticalBarCharts
limit
sortBy
sortOrder

IIHS Intrusions

This visualizer helps to visualize numbers based on certain criteria for each of the columns. The criteria can be a one or more.

The following is a list of IIHS Intrusions options with their placeholder illustration:

Option Name Illustration
columns
color_by
thresholds
line_width_by
lineWidth
baselineRow
baselineWidth
baselineColor
baselinePinned
showDefaultImage
normalize
showLegends
yLabel
showGridLines
ticksFontSize
numTicks
rotatedLabels
lowerOcc
upperOcc

Hexagonal Chart

This visualizer helps to visualize binned data. This is useful to visualize pattens in large datasets.

The following is a list of Hexagonal Chart options with their placeholder illustration:

Option Name Illustration
x_by
y_by
color_by

Gantt Plot

This visualizer helps to visualize binned data. This is useful to visualize pattens in large datasets.

The following is a list of Gantt Plot options with their placeholder illustration:

Option Name Illustration
key
startDate
endDate
colorBy

Gantt Chart Guide

This user guide provides introduction to create and modify a Gantt Chart on d3VIEW.

Dataset for creating gantt chart with default rules

Here is an example of a dataset that we can use for creating a Gantt Chart. Among these columns, the columns “Default Duration”, “Parent”, “Delay”, “Delay From” and “ID” are used for generating the rules. An example of a “rule” : Task Y starts two months after task X ends. Here we know task Y(ID) depends on task X (Parent). Task Y only starts after task X ends (Delay From). And there are two months “waiting period” before task Y starts after task X is complete (Delay).After we have the dataset ready, we can start creating the chart on d3VIEW.

  • Task
    • Unique names for each task
  • Start Date
    • Start date of the task
  • End Date
    • End date of the task
  • Default Duration
    • Default task duration
  • Parent
    • ID of the parent task from which the current task depends on
  • Delay
    • Number of months to postpone the current task from parent task
  • Delay From
    • where we start to count the delay, either “start” or “end” (of the parent task)
  • ID
    • Unique ID for each task. ID will be used to refer to each task in Gantt Chart
_images/input_dataset.png

A sample dataset to be used for creating a Gantt Chart.

Login to d3VIEW and Open Simlytiks

Click on the link https://d3view-web.intra.chrysler.com/. Login with username and password.

d3VIEW Login page; Enter username and password to login

After login, we are at the “Dashboard” application by default. The Dashboard shows the physical tests application, from where we see the tests published/uploaded under the current account. New users usually don’t have anything published at their account. There would no record to show.

default landing page after login

Click on the Apps icon on the left corner and click on the Simlytiks app.

_images/open_simlytiks.png

Upload the dataset

Create a new Simlytiks dataset with a csv file on our computer with either one of the approaches. After uploading the dataset, we have a new Simlytiks dataset to work with.

  1. Click on the designated area to choose the file to upload or directly drag the file and drop it at the same area.
  2. Click “New Dataset” on the top left corner and drop or click file.
_images/new_dataset_drag_and_drop.png

Two methods we can upload and create a dataset record on Simlytiks.

Create a Gantt Chart

In the “Overview” page, we can see the dataset we are working with. Right next to it, we can click on “New Page” to place our Gantt Chart.

  • Click on “New Page” to create a new page

  • Click “Add Chart” and search for “Timeline Chart”

  • Configurate required items for creating a Gantt Chart
    • Build Data Using Different Columns
      • By default, the timeline chart uses “start date” and “end date” to build the chart. Choose “yes” to configurate the rules.
    • Inter dependency between parent and child
      • By default, when we modify any task, the other tasks will NOT update. Choosing “unlocked” allows the rules to be applied to the current task we are editing and all its child tasks. The chart will automatically update.
  • Click on “color” tab and congifure rate the color for task bars and the y axis labels
    • In order to fill the block with color, click on the “appearance” tab and choose “yes” for “Fill Blocks with color”
  • Click the green “Accept” icon to complete setting

_images/new_page.png

A new Simlytiks dataset with the dataset we uploaded. Click on “New Page” to create a new page for our Gantt Chart.

_images/add_chart.png

Click “Add Chart”.

_images/timeline_chart.png

Search “Timeline Chart” and Click on the “Timeline Chart” icon.

_images/gantt_chart_config.png

Settings required for creating a Gantt Chart with rules.

_images/color_options.png

Choose the columns to color the task bars and the y-axis labels.

_images/fill_blocks_with_color.png

Click on the “appearance” tab and choose “yes” for “Fill Blocks with color”.

_images/accept_gantt_chart.png

After “Accept” the settings, the Gantt Chart is complete.

Make Modifications

When we want to make additional changes, we can move our mouse to the chart. Then, we can see a pencil icon on the left corner of the chart area. Click on the icon to make changes on the settings and click “Accept” or “Cancel” after we are done.

_images/pencil_icon.png

Click on the pencil icon to make changes on settings.

Drag the tasks to move it and drag the start or end of the task bar to edit the start and end dates. NOTE: if “unlocked” is chosen, any child tasks will reset based on the rules set up by the dataset.

Add Comments

  • Click on “view” and choose “Show Viz Footnotes”
  • Resize the text area at the bottom of the chart and enter any comments
  • Click on chart title to edit
_images/add_comments.png

Modify Dataset

If we want to make changes in the dataset, go back to the “Overview” page.

  • Click on any cell to make changes on the cell values

  • Right click and choose “Insert row above/below” to add a blank new record

  • Right click on a record to copy its information

  • Right click on a record to paste copied information
    • NOTE: “Paste row” will overwrite cell values from current row. It does NOT automatically create a new row
  • Right click on a record and choose “remove row” to remove the record from the dataset

_images/insert_row.png
_images/copy_paste_row.png

Save

Click on “File” and choose “Save” to save the work on Simlytiks.

_images/save.png

Saved Simlytiks dataset will show up as a record in the Simlytiks app.

_images/saved_record.png

Saved Simlytiks record. In case the record saved is not showing up, try 1) Click “All Datasets” on the left panel; 2) Remove any filters in the search bar; 3) Refresh the page.

Radar Chart

This multi dimensional plot explores layers of data based on the area between data columns.

The following is a list of Radar Chart options with their placeholder illustration:

Option Name Illustration
fields
colorBy
fillArea

Calendar

This visualizer plots the calendar within a range and the color of the days are by another column

The following is a list of Calendar options with their placeholder illustration:

Option Name Illustration
date_by
color_by
color_mode

3D Model

View and explore a model file in 3D via the Peacock application.

The following is a list of Peacock options with their placeholder illustration:

Option Name Illustration
nameBy
modelBy

Geo Map

Geo-mapping is the process of taking location-based data and using it to create a map.

The following is a list of Geo Map options with their placeholder illustration:

Option Name Illustration
mapType
countryBy
colorBy
groupType

Key Value

This visualizer helps to visualize binned data. This is useful to visualize pattens in large datasets.

The following is a list of Key Value options with their placeholder illustration:

Option Name Illustration
field
fieldtype
ignoreColsWithSameValue
ignorePartsWithSameValue
thresholdValue
thresholdMin
thresholdMax

Tree

This parent-child relationship chart explores and tracks connections between a tree of nodes in reference to a timeline.

The following is a list of Tree options with their placeholder illustration:

Option Name Illustration
parentIdBy
primaryKeyBy
useSingleColumnData
separator
dateColumn
timeline
enableTracker
trackingAttrs
colorBy
viewType
labelBy
labelsAsImages
imageBy
imageW
sizeBy
strokeWidthBy
lineWidthBy
tooltipBy
highlightNodes
expansionLevels

Treemap

This visualizer uses groups to explore multiple layers of data in a drill-down fashion.

The following is a list of Treemap options with their placeholder illustration:

Option Name Illustration
groupingFactors
value_by
aggregation_mode
id_by

Force Directed Graph

This parent-child relationship chart explores connections between a web of nodes.

The following is a list of Force Directed Graph options with their placeholder illustration:

Option Name Illustration
parentIdBy
primaryKeyBy
useSingleColumnData
separator
colorBy
sizeBy
lineWidthBy

Card

This parent-child relationsip chart shows a summary along with the node web to explore connections between nodes.

The following is a list of Card options with their placeholder illustration:

Option Name Illustration
parentKey
childKey
useSingleColumnData
separator
colorBy
sizeBy
strokeWidthBy
lineWidthBy
parentImageBy
childImageBy
imageW
labelBy
tooltipBy

Survival Space

This visualizer plots the curves for pre and post crash data.

The following is a list of Survival Space options with their placeholder illustration:

Option Name Illustration
preCrashBy
postCrashBy
isSample
orientation
datumareas

Whiplash

Whiplash illustrates if passenger and driver sensors meet thresholds based on a body map.

The following is a list of Whiplash options with their placeholder illustration:

Option Name Illustration
color_by
nameBy

Heatmap

A heat map is a data visualization technique that shows magnitude of a phenomenon as color in two dimensions. The variation in color may be by hue or intensity, giving obvious visual cues to the reader about how the phenomenon is clustered or varies over space.

The following is a list of Heatmap options with their placeholder illustration:

Option Name Illustration
y
x
colorBy
bins
circularHeatMap

Pivot Table

A PivotTable is an interactive way to quickly summarize large amounts of data. You can use a PivotTable to analyze numerical data in detail and answer unanticipated questions about your data.

The following is a list of Pivot Table options with their placeholder illustration:

Option Name Illustration
rows
columns
values
stopLevel
aggregationType

PedPro

PedPro includes the means to export your pedigree data for transfer to other programs or for display as a table.

The following is a list of PedPro options with their placeholder illustration:

Option Name Illustration
useSampleData
titleBy
XcoordBy
YcoordBy
hicBy
accCurveBy
t1By
t2By
peakAccBy
leftSectionAnimationBy
sizeBy
reflection
colorGroups

PedPro Table

PedPro includes the means to export your pedigree data for transfer to other programs or for display as a table.

The following is a list of PedProTable options with their placeholder illustration:

Option Name Illustration
xBy
yBy
hicBy
modelBy
imageBy
xmin
xBins
x_step
xmax
ymin
yBins
y_step
ymax
customBinRows
xLabel
yLabel
binning
predictedScoreLabel
headFormLabel
showBinAxes
showHicValues
showLocation
shape
trimCommonString
showNullValuesAsBlanks
showPointsTable
colorGroups

Circle Chart

Circle chart visualizes different aspects of data into different groups based on a timeline.

The following is a list of Circle Chart options with their placeholder illustration:

Option Name Illustration
primary_name
columns
primary_key
date

3D Scatter Plot

3D scatter plots are used to plot data points on three axes in the attempt to show the relationship between three variables.

The following is a list of 3D Scatter Plot options with their placeholder illustration:

Option Name Illustration
x_axis
y_axis
z_axis
color_by
radius_by
opacity_by

Image Analyzer

Analyze and compare two images to explore similarities and differences.

The following is a list of Image Analyzer options with their placeholder illustration:

Option Name Illustration
component
valuerange

Pie Chart

Pie charts can be used to show percentages of a whole and represents percentages at a set point in time.

The following is a list of Pie Chart options with their placeholder illustration:

Option Name Illustration
groupBy
highlightArcs
radiusBy
radiusAggregateBy
nestedGroupBy
groupLimit
innerRadius

Scatter Matrix

A scatter plot matrix is a grid of scatter plots used to visualize bivariate relationships between combinations of variables. Each scatter plot in the matrix visualizes the relationship between a pair of variables.

The following is a list of Scatter Matrix options with their placeholder illustration:

Option Name Illustration
columns
color_by

Vector Scatter Matrix

A vector scatter matrix is a grid of vector plots used to visualize bivariate relationships between combinations of curves. Each curve plot in the matrix shows the relationship between a pair of vectors.

The following is a list of Vector Scatter Matrix options with their placeholder illustration:

Option Name Illustration
columns
colorBy
pearsonCoeffInTable

Multi Line Chart

A multi-line graph shows the relationship between independent and dependent values of multiple sets of data. Usually, multi-line graphs are used to show trends over time.

The following is a list of Multi Line Chart options with their placeholder illustration:

Option Name Illustration
lines

Filtered Line Chart

A filtered line chart is used to depict two or more variables that change over the same period for specifically filtered data.

The following is a list of Filtered Line Chart options with their placeholder illustration:

Option Name Illustration
xBy
yBy
lines
yBy2
duplicateLinesForDatasets
sortdata
ignoreX
xScaleType

Text Compare

Compare text-based data side by side.

The following is a list of Text Compare options with their placeholder illustration:

Option Name Illustration
nameBy
textBy

Horizontal Barchart

A horizontal bar graph shows comparisons among discrete categories. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value.

The following is a list of Horizontal Barchart options with their placeholder illustration:

Option Name Illustration
y
x
colorBy
aggregationType
grouping
xmin
xmax
nestedGroupBy
groupLimit
tooltipBy
showLegends
axisTicksFontSize
axisLabelsFontSize

Newton Manager

This visualizer plots time-history data and is helpful when both X and Y data are numbers. The color, line and opacity of the curves can be based on other columns which allows visualizing up to 5 dimensions.

The following is a list of Newton Manager options with their placeholder illustration:

Option Name Illustration
curveBy
colorBy
opacityBy
imageBy
lineWidthBy
curveBy2
xLabel
yLabel
xmin
xmax
ymin
ymax
tooltipBy
baseline

Histogram

A histogram is an approximate representation of the distribution of numerical data. It can also help detect outliers or any gaps in the data.

The following is a list of Histogram options with their placeholder illustration:

Option Name Illustration
columns
colorBy
xLabel
yLabel

Sparkline Matrix

Explore a grid of vectors or a grid of vector groups with color ranges or panels. This matrix is generally used for time series data and allows for animation of time via the sparklines and color panels.

The following is a list of Sparkline Matrix options with their placeholder illustration:

Option Name Illustration
column
segments
colorMin
colorMax
xDigitize
ymin
ymax
threshold
wrap
displayNames
displayIndices
overlay
initBox
initDirection
displacementColumn
colorBy
showImage

Occupant Rating

Visualize the marginal color values for driver and passenger test analysis directly on a body sketch.

The following is a list of Occupant Rating options with their placeholder illustration:

Option Name Illustration
occupantType
headColorBy
neckColorBy
chestColorBy
rightThighColorBy
rightLegColorBy
rightFootColorBy
leftThighColorBy
leftLegColorBy
leftFootColorBy

Area Chart

Diplay the evolution of value for a column of data based on another.

The following is a list of Area Chart options with their placeholder illustration:

Option Name Illustration
x
y
yAggregate

Stacked Area Chart

A stacked area chart is the extension of a basic area chart. It displays the evolution of the value of several groups on the same graphic. A stacked area chart can show how part to whole relationships change over time.

The following is a list of Stacked Area Chart options with their placeholder illustration:

Option Name Illustration
x
y
yAggregate
sortByLimit

Circle Pack

Circle Packs represent hierarchical data sets, showing both each level in the hierarchy and how they interact with each other. They are used for identifying patterns of performance, and outliers within peer groups.

The following is a list of Circle Pack options with their placeholder illustration:

Option Name Illustration
nodes

Chord Diagram

Chord diagram shows dependencies amongst chosen columns

The following is a list of Chord Diagram options with their placeholder illustration:

Option Name Illustration
columns
groupLimit
sortBy
sortOrder
colorBy
offset
offsetBy
highlightNodes

Sankey Plot

This chart explores connections of values from one data collumn to other data columns.

The following is a list of Sankey Plot options with their placeholder illustration:

Option Name Illustration
columns
useClassifier
sortBy
sortOrder
colorBy

Arc Diagram

This chart explores connections between individual values from different columns.

The following is a list of Arc Diagram options with their placeholder illustration:

Option Name Illustration
columns
sortBy
sortOrder
colorBy

Gauge

A gauge chart is a type of data visualization often used to display a single data value with a quantitative context. This chart aims to track the progress of a KPI in comparison to a set target or to other time periods.

The following is a list of Gauge options with their placeholder illustration:

Option Name Illustration
column
value
numBins
colorMin
colorMax

K-Means Cluster

The K-means cluster is used to find groups which have not been explicitly labeled in the data.

The following is a list of K-Means Cluster options with their placeholder illustration:

Option Name Illustration
num_clusters
fields

Data Viewer

View any data files associated with the dataset.

The following is a list of Data Viewer options with their placeholder illustration:

Option Name Illustration
files

Image Annotator

This visualizer helps to upload images and annotate them

The following is a list of Image Annotator options with their placeholder illustration:

Option Name Illustration
base64
annotation

Image Positioner

This visualizer helps to overlay images and animate them simultaneously.

The following is a list of Image Positioner options with their placeholder illustration:

Option Name Illustration
column

Data Classifier

This plugin helps with search predication based on categories and classification of the dataset. This plugin uses Machine Learning to produce the results.

The following is a list of Data Classifier options with their placeholder illustration:

Option Name Illustration
learn_columns
predict_columns
min_max

Text Viewer

This plugin helps view large text-based data.

The following is a list of Text Viewer options with their placeholder illustration:

Option Name Illustration
column