R. Discrete Mathematics for Dummies. Finite Mathematics and Applied Calculus. Sage: Open Source Mathematics Software. Angularjs. How Quantum Computers and Machine Learning Will Revolutionize Big Data - Wired Science. When subatomic particles smash together at the Large Hadron Collider in Switzerland, they create showers of new particles whose signatures are recorded by four detectors. The LHC captures 5 trillion bits of data — more information than all of the world’s libraries combined — every second. After the judicious application of filtering algorithms, more than 99 percent of those data are discarded, but the four experiments still produce a whopping 25 petabytes (25×1015 bytes) of data per year that must be stored and analyzed.
That is a scale far beyond the computing resources of any single facility, so the LHC scientists rely on a vast computing grid of 160 data centers around the world, a distributed network that is capable of transferring as much as 10 gigabytes per second at peak performance. The LHC’s approach to its big data problem reflects just how dramatically the nature of computing has changed over the last decade. Since Intel co-founder Gordon E. Memory and Movement. Brief history of statistics. Science Simulations. 1.4. Matplotlib: plotting — Scipy lecture notes. 1.4.2. Simple plot Tip In this section, we want to draw the cosine and sine functions on the same plot. Starting from the default settings, we’ll enrich the figure step by step to make it nicer.
First step is to get the data for the sine and cosine functions: import numpy as np X = np.linspace(-np.pi, np.pi, 256, endpoint=True)C, S = np.cos(X), np.sin(X) X is now a numpy array with 256 values ranging from -π to +π (included). To run the example, you can type them in an IPython interactive session: This brings us to the IPython prompt: IPython 0.13 -- An enhanced Interactive Python. ? You can also download each of the examples and run it using regular python, but you will loose interactive data manipulation: You can get source for each step by clicking on the corresponding figure. 1.4.2.1.
Matplotlib comes with a set of default settings that allow customizing all kinds of properties. 1.4.2.2. 1.4.2.3. 1.4.2.4. ...pl.xlim(X.min() * 1.1, X.max() * 1.1)pl.ylim(C.min() * 1.1, C.max() * 1.1)... Hint.