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Immerse is a wrapper that adds graphical interactivity to Julia plots. Currently, Immerse supports Gadfly.
Usage
By and large, you plot just as you would in Gadfly:
using Immerse, Distributions
X = rand(MultivariateNormal([0.0, 0.0], [1.0 0.5; 0.5 1.0]), 10000)
plot(x=X[1,:], y=X[2,:], Geom.hexbin)
However, rather than being displayed in a browser window, you'll see your figure in a Gtk window:
The toolbar at the top supports saving your figure to a file, zooming and panning, and lasso selection.
Zooming and panning uses the defaults set by GtkUtilities. The left mouse button allows you to rubberband-select a zoom region. Use your mouse wheel or arrow-keys to pan or change the zoom level. Double-click, or press the 1:1 button, to restore the full view.
The right-most button on the toolbar allows you to select a group of points for further analysis by drawing a "lasso" around them:
By default, this pops up a dialog asking you which variable in Main
you want to save the selected indexes to:
You can alternatively define a custom callback function; see the help for lasso_initialize
by typing ?lasso_initialize
at the REPL.
Lasso selection is currently implemented only for Geom.point
and Geom.line
. If you want to try this feature, the demonstration in test/faces.jl
can be fun.
You can add extra interactivity by setting up callbacks that run whenever the user clicks on an object. A demonstration of this capability is exhibited in the test/hittesting.jl
test script:
Here the red circles are drawn around the dots that the user clicked on; see also the console output that showed the results of clicking on the line segments between the dots.
Note that hit testing is disabled while the "zoom" button is active. Like lasso selection, this is currently implemented only for Geom.point
and Geom.line
.
Objects can be modified interactively after their creation:
julia> using Immerse, Colors
julia> hfig = figure()
1
julia> x = linspace(0,4pi,101);
julia> p = plot(x=x, y=sin(x), Geom.line(tag=:line))
julia> setproperty!((hfig,:line), rand(1:5), :linewidth)
3
julia> setproperty!((hfig,:line), RGB(rand(),rand(),rand()), :stroke)
RGB{Float64}(0.9563599683564541,0.20964995278692222,0.997388106654052)
julia> setproperty!((hfig,:line), false, :visible)
false
julia> setproperty!((hfig,:line), true, :visible)
true
julia> getproperty((hfig,:line), :visible)
1-element Array{Bool,1}:
true
Compose Form
and Property
objects apply to a vector of objects, which is why getproperty
returns a vector.
Each figure is addressed by an integer; for a window displaying a single Gadfly figure, by default this integer appears in the window title.
There are a few simple utilities for working with figure windows:
figure()
opens a new figure window. This will become the default plotting window.figure(3)
raises the corresponding window and makes it the default.gcf()
returns the index of the current default figure.scf()
shows the current figure (raising the window to the top).closefig(3)
destroys Figure 3, closing the window.closeall()
closes all open figure windows.scf()
, nothing happensYour window manager may have "focus stealing prevention" enabled. For example, under KDE, go to the Kmenu->System Settings->Window behavior->Window behavior (pane)->Focus (tab) and set "Focus stealing prevent" to "None". Alternatively, if you want to limit this change to julia, use the "Window rules" pane and add a new setting where "Window class (application)" is set to "Regular Expression" with value "^julia.*".
Author: JuliaGraphics
Source Code: https://github.com/JuliaGraphics/Immerse.jl
License: View license
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
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In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
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Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt
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CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data
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Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:
Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.
#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data