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Visualization-python - Project Hosting on Google Code. Python for scientific use. Part I: Data Visualization LG #114. By Anders Andreasen Motivation and outline A first step towards qualitative understanding and interpretation of scientific data is visualization of the data. Also, in order to reach a quantitative understanding, the data needs to be analyzed, e.g. by fitting a physical model to the data. The raw data may also require some initial processing in order to become useful, e.g. filtering, scaling, calibration etc. Several open source programs for data analysis and visualization exist: gnuplot, grace, octave, R, and scigraphica.

Each of these has its own pros and cons. Recently, I came across Python and found it to be a very powerful tool. Python: a brief review Python was originally created by Guido van Rossum and is an interpreted programming language (like e.g. The standard introduction to any programming language is the Hello world! Then the following code is typed: print "Hello world! " Python code can also be stored in a file e.g. named . #! Plotting 2-D data Plotting 3D-data Example 3: Matplotlib: python plotting — Matplotlib v1.0.1 documentation. Examples. These are some simple examples showing the core functionality of the currently available pyvisi plotting objects. simplePlot This is a very simple example showing how one would produce a line plot using pyvisi. It shows a graph of y = x^2. Example pyvisi output singleArrayPlot This example shows a graph of y = sin(x). multiCurvePlot Here we have an example of how to plot multiple curves with the same x-coordinate variable. contourPlot This shows a two-dimensional contour map of a three-dimensional surface. surfacePlot This shows a three-dimensional surface. surfaceAndContourPlot This shows a three-dimensional surface, with its contour shown on the base of the axes. scatterPlot Here we have randomly generated x-y data plotted as a scatter plot. scatterPlot3D Here we have a three-dimensional scatter plot. arrowPlot arrowPlot2D offsetPlot.


Django. Python & semantic. Livres.