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experimental cartography using Openstreetmap data.
Influenced by the concept of the Flaneur, someone who wanders a city (in this case, Bergen in Norway) at random, deciding on their next move using chance, or without a specific destination in mind.
A connected network is created from OpenStreetMap data, and hundreds of thousands of random journeys are made through the network by software agents following very simple rules:-
- move to a neighbouring node
- don't backtrack
- stop at a dead-end
- stop if you've visited a node for a second time
- repeat
The number of times each route is covered is shown using colour; red are the roads most travelled, blue the least travelled.
Originally I thought this might show up major arteries, but it doesn't seem to work that way...
Data Copyright OpenStreetMap and contributors, CC-BY-SA.
This is my attempt at remixing the Tiles@Home Heatmap by Ævar Arnfjörð Bjarmason (on flickr)
This is a matplotlib/python implementation (the original is in Perl)
The heatmap uses the file size of each tile, which should give a good measure of how much real data there is in a tile. Red areas have lots of data, green areas less, and blue the least.
Data sources: www.openstreetmap.org/, Tiles@Home
Source code on OSM repository : original, mine.
I've used log10 scaling for the colours, so the rural areas get a bit more prominence.
A bit of photoshop work in this case to mask out the ocean areas.
A graph created with networkx and matplotlib using data from Freebase that shows the influence of programming languages within the object-oriented paradigm.
Find more programming paradigm influence graphs at:
visualizations/programming-language-influence-by-paradigm-gallery
Artist(s): Aaron Javier Juarez
Title: Pi Spiral
Medium: digital art - Python scripting language (Numpy and Matplotlib)
The Artist Says: This work contains 3000 digits of pi, approximated will a simple generator function. The background is 314 stacked circles with different radii and colors, to give the illusion of a color gradient.
Artist(s): Aaron Javier Juarez
Title: Special Triangles of Pi
Medium: digital art - Python scripting language (Numpy and Matplotlib)
The Artist Says: This work conveys the special right triangles [red (30-60-90 deg), green (45-45-90 deg), and blue (60-30-90 deg) triangles] and their angles found from the unit circle. Along the vertical center of the piece are equilateral triangles whose sides are scaled by a factor of pi from one another. The "fog" effect of the color in the background is actually many encircling circles with varying opacity. In fact, all the gradients you see are illusions, created by many stacked shapes with varying colors.
终于找到一种dirty and quick way 来实现matplotlib 统计图中显示中文了。
python23\share\matplotlib\.matplotlibrc 文件有一部分设计字体设置的。
可以将该行：font.sans-serif : 后面的字体改成 nothing 。这样当matplotlib无法找到该字体时会自动用同一个目录下面的Vera.ttf 字体代替。
这时只需将一个中文字体复制到该目录下，替换掉Vera.ttf 即可。不过部分中文字体显示时仍然有一些问题。
Re-processed and cropped version the previous Saturn image.
I used the Lucy Deconvolution algorithm to deconvolve the image, modeling the PSF with the (very faint and unresolved) nearby moon Titan, which was barely visible (again, very, very faint) in the previous image.
Notice that the Cassini Gap is almost visible on the rings!
Approximately a year of GPS data from Amber Case using geoloqi.com/
This is a polar plot of heading and velocity. The closer to the outside edge of the plot the faster; the center is stopped. The point's direction is around the circle according to the labels.
You can see heavy preference for cardinal directions because Portland's roads are on a grid!
Plotted with python and matplotlib
Measuring the waves using the Phoenix setup and a simple program to render on screen using matplotlib
Adding the ability to dynamically select a chunk of a graph showing real-time home energy usage.
Click-and-drag to highlight a span of the graph, and a Python script calculates the area under that part of the curve - using this to calculate how much energy you used during this time.
Take your machine learning to the next level with these artificial intelligence technologies.
Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation, we seem to be getting increasingly addicted to them.
Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies.
Here is a list of 8 best open sources AI technologies you can use to take your machine learning projects to the next level.
1. TensorFlow
Initially released in 2015, TensorFlow is an open source machine learning framework that is easy to use and deploy across a variety of platforms. It is one of the most well-maintained and extensively used frameworks for machine learning.
Created by Google for supporting its research and production objectives, TensorFlow is now widely used by several companies, including Dropbox, eBay, Intel, Twitter, and Uber.
TensorFlow is available in Python, C++, Haskell, Java, Go, Rust, and most recently, JavaScript. You can also find third-party packages for other
programming languages.
The framework allows you to develop neural networks (and even other computational models) using flowgraphs.
2. Keras
Initially released in 2015, Keras is an open source software library designed to simplify the creation of deep learning models. It is written in Python
and can be deployed on top of other AI technologies such as TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano.
Keras is known for its user-friendliness, modularity, and ease of extensibility. It is suitable if you need a machine learning library that allows for easy and fast prototyping, supports both convolutional and recurrent networks, and runs optimally on both CPUs (central processing units) and GPUs (graphics processing units).
3. Scikit-learn
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Initially released in 2007, sci-kit-learn is an open source library developed for machine learning. This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction.
Scikit-learn is designed on three other open source projects—Matplotlib, NumPy, and SciPy—and it focuses on data mining and data analysis.
4. Microsoft Cognitive Toolkit
Initially released in 2016, the Microsoft Cognitive Toolkit (previously referred to as CNTK), is an AI solution that can empower you to take your
machine learning projects to the next level.
Microsoft says that the open source framework is capable of "training deep learning algorithms to function like the human brain."
Some of the vital features of the Microsoft Cognitive Toolkit include highly optimized components capable of handling data from Python, C++, or BrainScript, ability to provide efficient resource usage, ease of integration with Microsoft Azure, and interoperation with NumPy.
5. Theano
Initially released in 2007, Theano is an open source Python library that allows you to easily fashion various machine learning models. Since it's one of the oldest libraries, it is regarded as an industry standard that has inspired developments in deep learning.
At its core, it enables you to simplify the process of defining, optimizing, and assessing mathematical expressions.
Theano is capable of taking your structures and transforming them into very efficient code that integrates with NumPy, efficient native libraries such as BLAS, and native code (C++).
Furthermore, it is optimized for GPUs, provides efficient symbolic differentiation, and comes with extensive code-testing capabilities.
6. Caffe
Initially released in 2017, Caffe (Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on
expressiveness, speed, and modularity. The open source framework is written in C++ and comes with a Python interface.
Caffe's main features include an expressive architecture that inspires innovation, extensive code that facilitates active development, a fast performance that accelerates industry deployment, and a vibrant community that stimulates growth.
7. Torch
Initially released in 2002, Torch is a machine learning library that offers a wide array of algorithms for deep learning. The open source framework provides you with optimized flexibility and speed when handling machine learning projects—without causing unnecessary complexities in the process.
It is written using the scripting language Lua and comes with an underlying C implementation. Some of Torch's key features include N-dimensional arrays, linear algebra routines, numeric optimization routines, efficient GPU support, and support for iOS and Android platforms.
8. Accord.NET
Initially released in 2010, Accord. NET is a machine learning framework entirely written in C#.
The open source framework is suitable for production-grade scientific computing. With its extensive range of libraries, you can build
various applications in artificial neural networks, statistical data processing, image processing, and many others.
One of the diagrams that I'll be presenting tomorrow night, at CS night.
This visualizes one of the sets of endogenous retrovirus sequences we found in the mouse genome. They've been clustered, based on pair-wise similarities, with a basic self-organizing map. For the SOM and the graph, I used the python libraries C Cluster for python and matplotlib.
How to use matplotlib for scientific plotting on Linux
If you would like to use this photo, be sure to place a proper attribution linking to xmodulo.com
How to use matplotlib for scientific plotting on Linux
If you would like to use this photo, be sure to place a proper attribution linking to xmodulo.com
A portion of the Mandelbrot set enlarged 9 million times. Generated with the help of numpy and matplotlib for Python.
How to use matplotlib for scientific plotting on Linux
If you would like to use this photo, be sure to place a proper attribution linking to xmodulo.com
Obwohl PlotDevice nicht mit meinem Anaconda-Python (ein Python 2.7.9) arbeitet, sondern sich aus den Tiefen des Systems ein Python 2.7.5 herholt -- vermutlich das von Apple mitgelieferte Python, kennt dieses Python Numpy und die Matplotlib (habe ich vielleicht da mal installiert). Was ich damit anfangen kann, weiß ich aber auch noch nicht. Numpy ist gut, Matplotlib wohl eher eine Spielerei.