Animating the Lorenz System in 3D | Pythonic Perambulations
One of the things I really enjoy about Python is how easy it makes it to solve interesting problems and visualize those solutions in a compelling way. I've done several posts on creating animations using matplotlib's relatively new animation toolkit: (some examples are a chaotic double pendulum, the collisions of particles in a box, the time-evolution of a quantum-mechanical wavefunction, and even
matplotlibを使ってみた - まどろみの思考空間
matplotlibというのはscipyに入っているグラフ描写用のライブラリです。普通は計算結果を適当なファイルに出力してそれでgnuplotなりで描写させるとおもうのですが、matplotlibを使えればPython一つでデータの解析から出力までできちゃいます。またPythonはネットワーク系のライブラリも充実しているのでデータサーバと通信してデータを落とすってとこまでできちゃいそうです(実際にやったわけではないんで実際にどういう感じにデータが配信されるとかはわからないのですが...) さて地殻構造学(構造地質学の授業。岩石の変形とかの解析を扱う)で変形機構図の作成をする課題がでたのでせっかくなのでmatplotlibでやってみることにしました。 とりあえず教科書に乗っていたものを試しにやってみます。 今回の変形機構は転位クリープ(DLC)と粒界滑り(GBS)を扱っています。 それぞれの
Daft — Daft 0.0.3 documentation
Summary¶ Daft is a Python package that uses matplotlib to render pixel-perfect probabilistic graphical models for publication in a journal or on the internet. With a short Python script and an intuitive model-building syntax you can design directed (Bayesian Networks, directed acyclic graphs) and undirected (Markov random fields) models and save them in any formats that matplotlib supports (includ
Minesweeper in Matplotlib | Pythonic Perambulations
Lately I've been playing around with interactivity in matplotlib. A couple weeks ago, I discussed briefly how to use event callbacks to implement simple 3D visualization and later used this as a base for creating a working 3D Rubik's cube entirely in matplotlib. Today I have a different goal: re-create minesweeper, that ubiquitous single-player puzzle game that most of us will admit to having bing
XKCD-style plots in Matplotlib | Pythonic Perambulations
from IPython.display import Image Image('http://jakevdp.github.com/figures/xkcd_version.png') Sometimes when showing schematic plots, this is the type of figure I want to display. But drawing it by hand is a pain: I'd rather just use matplotlib. The problem is, matplotlib is a bit too precise. Attempting to duplicate this figure in matplotlib leads to something like this: It just doesn't have the
computer vision in early stage product development (with code) | Product Development Notebook
Followers of the blog know that I’ve been playing around with python and computer vision. Well I recently wrote an article describing how computer vision can be used in product development. In the video below I go through the steps necessary to extract data from someone walking. And of course, the code (developed within ipython). import cv,cv2 import math import numpy as np import matplotlib.pyplo
Matplotlib Animation Tutorial | Pythonic Perambulations
Matplotlib version 1.1 added some tools for creating animations which are really slick. You can find some good example animations on the matplotlib examples page. I thought I'd share here some of the things I've learned when playing around with these tools. Basic Animation The animation tools center around the matplotlib.animation.Animation base class, which provides a framework around which the a
Messy Mind » Making matplotlib look like ggplot
When I first started using matplotlib, the output looked very crisp and polished compared to excel, however after seeing ggplot2, I realized that matplotlib’s default presentation settings leave a lot to be desired. I have put together a quick script that will restyle an axes to look more or less like ggplot2′s. def rstyle(ax): """Styles an axes to appear like ggplot2 Must be called after all pl
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GitHub - dmcdougall/mpl_binutils: A command-line front-end to matplotlib for quick line-plotting
3D Interactive Rubik's Cube in Python | Pythonic Perambulations
Quaternions and Key Bindings: Simple 3D Visualization in Matplotlib | Pythonic Perambulations
DataViz in Python: Chernoff Faces with Matplotlib
http://fseoane.net/blog/2012/learning-to-rank-with-scikit-learn-the-pairwise-transform/
Updating a matplotlib bar graph?