This document provides information on tools for research plotting in Python and R. It discusses matplotlib and R for creating plots in Python and R respectively. It provides examples of different plot types that can be created such as line plots, bar plots, scatter plots, and histograms. It also discusses installing and working with matplotlib and R Studio, and provides code examples to generate various plots from data.
% plot sin cos and tan
d=0:1:360;
r=(pi/180)*d;
s1=sin(r);
c1=cos(r);
t1=tan(r);
grid on
plot(r,s1,'-')
hold on
xlim([0,2*pi])
ylim([-2,2])
plot(r,c1,'r-')
plot(r,t1,'b--')
title("Graph of Sin(x) Cos(x) & Tan(x)")
xlabel("Angles")
ylabel("Values")
grid on
grid minor
legend("sin(x)","cos(x)","tan(x)")
% plot sin cos and tan
d=0:1:360;
r=(pi/180)*d;
s1=sin(r);
c1=cos(r);
t1=tan(r);
grid on
plot(r,s1,'-')
hold on
xlim([0,2*pi])
ylim([-2,2])
plot(r,c1,'r-')
plot(r,t1,'b--')
title("Graph of Sin(x) Cos(x) & Tan(x)")
xlabel("Angles")
ylabel("Values")
grid on
grid minor
legend("sin(x)","cos(x)","tan(x)")
Forecasting through ARIMA Modeling using R
ref:http://ucanalytics.com/blogs/step-by-step-graphic-guide-to-forecasting-through-arima-modeling-in-r-manufacturing-case-study-example/
We consider the problem of counting motifs in bipartite affiliation networks, such as author-paper, user-product, and actor-movie relations. We focus on counting the number of occurrences of a ``butterfly'', a complete $2 \times 2$ biclique, the simplest cohesive higher-order structure in a bipartite graph. Our main contribution is a suite of randomized algorithms that can quickly approximate the number of butterflies in a graph with a provable guarantee on accuracy. An experimental evaluation on large real-world networks shows that our algorithms return accurate estimates within a few seconds, even for networks with trillions of butterflies and hundreds of millions of edges.
Loom & Functional Graphs in Clojure @ LambdaConf 2015Aysylu Greenberg
Graphs are ubiquitous data structures, and the algorithms for analyzing them are fascinating. Loom is an open-source Clojure library that provides many graph algorithms and visualizations. We will discuss how graphs are represented in a functional world, bridge the gap between procedural description of algorithms and their functional implementation, and learn about the way Loom integrates with other graph representations.
Graph algorithms are cool and fascinating. We'll look at a graph algorithms and visualization library, Loom, which is written in Clojure. We will discuss the graph API, look at implementation of the algorithms and learn about the integration of Loom with Titanium, which allows us to run the algorithms on and visualize data in graph databases.
A survey of data visualization functions and packages in R. In particular, I discuss three approaches for data visualization in R: (i) the built-in base graphics functions, (ii) the ggplot2 package, and (iii) the lattice package. I also discuss some methods for visualizing large data sets.
Forecasting through ARIMA Modeling using R
ref:http://ucanalytics.com/blogs/step-by-step-graphic-guide-to-forecasting-through-arima-modeling-in-r-manufacturing-case-study-example/
We consider the problem of counting motifs in bipartite affiliation networks, such as author-paper, user-product, and actor-movie relations. We focus on counting the number of occurrences of a ``butterfly'', a complete $2 \times 2$ biclique, the simplest cohesive higher-order structure in a bipartite graph. Our main contribution is a suite of randomized algorithms that can quickly approximate the number of butterflies in a graph with a provable guarantee on accuracy. An experimental evaluation on large real-world networks shows that our algorithms return accurate estimates within a few seconds, even for networks with trillions of butterflies and hundreds of millions of edges.
Loom & Functional Graphs in Clojure @ LambdaConf 2015Aysylu Greenberg
Graphs are ubiquitous data structures, and the algorithms for analyzing them are fascinating. Loom is an open-source Clojure library that provides many graph algorithms and visualizations. We will discuss how graphs are represented in a functional world, bridge the gap between procedural description of algorithms and their functional implementation, and learn about the way Loom integrates with other graph representations.
Graph algorithms are cool and fascinating. We'll look at a graph algorithms and visualization library, Loom, which is written in Clojure. We will discuss the graph API, look at implementation of the algorithms and learn about the integration of Loom with Titanium, which allows us to run the algorithms on and visualize data in graph databases.
A survey of data visualization functions and packages in R. In particular, I discuss three approaches for data visualization in R: (i) the built-in base graphics functions, (ii) the ggplot2 package, and (iii) the lattice package. I also discuss some methods for visualizing large data sets.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
Presentation with a brief history of C, C++ and their ancestors along with an introduction to latest version C++11 and futures such as C++17. The presentation covers applications that use C++, C++11 compilers such as LLVM/Clang, some of the new language features in C++11 and C++17 and examples of modern idioms such as the new form compressions, initializer lists, lambdas, compile time type identification, improved memory management and improved standard library (threads, math, random, chrono, etc). (less == more) || (more == more)
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
6. 6
matplotlip
Most used Python
package for 2D-
graphics
pyplot provides a
convenient interface
to the matplotlib
very quick
Publication-
quality figures
in many formats
22. R Studio
22
• An IDE for R
• Runs on Windows, Mac OS X and Linux
• RStudio project has following folders for default content
types:
code: source code
data: raw data, cleaned data
figures: charts and graphs
docs: documents and reports
models: analytics models
24. • cars <- c(1, 3, 6, 4, 9)
• # Graph cars using blue points overlayed by a
line
• plot(cars, type="o", col="blue")
• # Create a title with a red, bold/italic font
• title(main="Autos", col.main="red", font.main=4)
24
25. • # Define 2 vectors
• cars <- c(1, 3, 6, 4, 9)
• trucks <- c(2, 5, 4, 5, 12)
• # Graph cars using a y axis that ranges from 0 to 12
• plot(cars, type="o", col="blue", ylim=c(0,12))
• # Graph trucks with red dashed line and square points
• lines(trucks, type="o", pch=22, lty=2, col="red")
• # Create a title with a red, bold/italic font
• title(main="Autos", col.main="red", font.main=4)
25
27. • # Define 2 vectors
• cars <- c(1, 3, 6, 4, 9)
• trucks <- c(2, 5, 4, 5, 12)
• # Calculate range from 0 to max value of cars and trucks
• g_range <- range(0, cars, trucks)
• # Graph autos using y axis that ranges from 0 to max value in cars or trucks vector.
#Turn off axes and annotations (axis labels) so we can specify them our self
• plot(cars, type="o", col="blue", ylim=g_range, axes=FALSE, ann=FALSE)
• # Make x axis using Mon-Fri labels
• axis(1, at=1:5, lab=c("Mon","Tue","Wed","Thu","Fri"))
• # Make y axis with horizontal labels that display ticks at every 4 marks.
#4*0:g_range[2] is equivalent to c(0,4,8,12).
• axis(2, las=1, at=4*0:g_range[2])
27
28. • # Create box around plot
• box()
• # Graph trucks with red dashed line and square points
• lines(trucks, type="o", pch=22, lty=2, col="red")
• # Create a title with a red, bold/italic font
• title(main="Autos", col.main="red", font.main=4)
• # Label the x and y axes with dark green text
• title(xlab="Days", col.lab=rgb(0,0.5,0))
• title(ylab="Total", col.lab=rgb(0,0.5,0))
• # Create a legend at (1, g_range[2]) that is slightly smaller # (cex) and uses the same
#line colors and points used by the actual plots
• legend(1, g_range[2], c("cars","trucks"), cex=0.8,
• col=c("blue","red"), pch=21:22, lty=1:2);
28
30. Plotting from a file• # Read car and truck values from tab-delimited autos.dat
• autos_data <- read.table("C:/R/autos.dat", header=T, sep="t")
• # Compute the largest y value used in the data (or we could # just use range again)
• max_y <- max(autos_data)
• # Define colors to be used for cars, trucks, suvs
• plot_colors <- c("blue","red","forestgreen")
• # Start PNG device driver to save output to figure.png
• png(filename="C:/R/figure.png", height=295, width=300, bg="white")
• # Graph autos using y axis that ranges from 0 to max_y. # Turn off axes and annotations (axis labels) so we can
• # specify them ourself
• plot(autos_data$cars, type="o", col=plot_colors[1], ylim=c(0,max_y), axes=FALSE, ann=FALSE)
• # Make x axis using Mon-Fri labels
• axis(1, at=1:5, lab=c("Mon", "Tue", "Wed", "Thu", "Fri"))
• # Make y axis with horizontal labels that display ticks at every 4 marks. 4*0:max_y is equivalent to c(0,4,8,12).
• axis(2, las=1, at=4*0:max_y)
30
31. • # Create box around plot
• box()
• # Graph trucks with red dashed line and square points
• lines(autos_data$trucks, type="o", pch=22, lty=2, col=plot_colors[2])
• # Graph suvs with green dotted line and diamond points
• lines(autos_data$suvs, type="o", pch=23, lty=3, col=plot_colors[3])
• # Create a title with a red, bold/italic font
• title(main="Autos", col.main="red", font.main=4)
• # Label the x and y axes with dark green text
• title(xlab= "Days", col.lab=rgb(0,0.5,0))
• title(ylab= "Total", col.lab=rgb(0,0.5,0))
• # Create a legend at (1, max_y) that is slightly smaller # (cex) and uses the same line colors and points used by
• # the actual plots
• legend(1, max_y, names(autos_data), cex=0.8, col=plot_colors, pch=21:23, lty=1:3);
•
• # Turn off device driver (to flush output to png)
• dev.off()
31
