Install Python, Num. Py, Sci. Py, and matplotlib on Mac OS X – Pen and Pants. Update: These instructions are over a year old, though they may still work for you. See the “Install Python” page for the most recent instructions.
A bit ago a friend and I both had fresh Mac OS X Lion installs so I helped him set up his computers with a scientific Python setup and did mine at the same time. These instructions are for Lion but should work on Snow Leopard or Mountain Lion without much trouble. On Snow Leopard you won’t install Xcode via the App Store, you’ll have to download it from Apple. After I’d helped my friend I found this blog post describing a procedure pretty much the same as below. Update: If doing all the stuff below doesn’t seem like your cup of tea, it’s also possible to install Python, Num.
Python (>= 2.6 or >= 3.3), NumPy (>= 1.6.1), SciPy (>= 0.9). If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is.
Py, Sci. Py, and matplotlib using double- click binary installers (resulting in a much less flexible installation), see this post to learn how. Xcode. You will need Apple’s developer tools in order to compile Python and the other installs. On Lion you can install Xcode from the App Store, on Snow Leopard you’ll have to get an older Xcode from developer.
I use the Xcode editor because I like its syntax highlighting, code completion, and organizer. However, I use hardly any of its features and unless you’re an i. OS or Mac developer you probably won’t either.
SciPy is package of tools for science and engineering for Python. It includes modules for statistics, optimization, integration, linear algebra,
If you prefer another editor it’s possible to get only the libraries and compilers that you need with the Command Line Tools for Xcode. To install it simply launch a terminal and enterruby - e . To add Homebrew installed executables and Python scripts to your path you’ll want to add the following line to your . Don’t be surprised if this takes a couple minutes. Important: You should close your terminal and open a fresh one right now so that it has the updated PATH from the previous section.
Otherwise you run the risk of executing the wrong scripts during the rest of these instructions. At this point you should be able to get a fresh terminal and typewhich pythonand see/usr/local/bin/python. Homebrew is for installing system packages and tools; for managing Python add- ons we want pip. We need gfortran to compile Sci.
For supervised learning learning of HMMs and. Scipy 2011 Time Series Analysis in Python 1. Time Series Analysis in Python with statsmodels Wes McKinney1. Download Extreme Bass Test 2013 Warning Zippy. Package Weight* Description; matplotlib 1.5.3: 9: Python plotting package: matplotlib-colorbar 0.3.2: 9: Artist for matplotlib to display a color bar: matplotlib. On Debian and derivatives (Ubuntu): python, python-dev. As other have said, make sure your.whl file matches the version and 32/64bit of the python distribution you're using. Next, the problem I was having was I forgot to. Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course.
Matplotlib is probably the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication.
Py but it is not included with the other Xcode tools. Luckily, Homebrew can help us out again: brew install gfortran.
When that’s done it’s a cinch to install Sci. Py: pip install scipy. This should install Sci.
Py 0. 1. 0. To install matplotlib we need to revisit Homebrew one more time: brew install pkg- config. And the usual pip command: pip install matplotlib. This should install matplotlib 1.
If it doesn’t you can try installing from the matplotlib development repo: pip install git+git: //github. Congratulations! You should now have the basics of a scientific Python installation that’s easy to manage and upgrade using Homebrew and pip. Fire up Python and make sure things worked. The following should work in Python with no errors: import numpy.
Welcome to Python.
Building From Source on Linux — Sci. Py. org. ATLAS is a BLAS/LAPACK implementation which tuned itself on the machine to provide ideal performances, and often match vendor specific implementations. Unfortunately, building ATLAS is not easy. It will try and find g. Then, use the following: rpm - ivh atlas- version.
This will NOT install atlas, just uncompress all the necessary files for building the rpm in /usr/src/packages. Before building atlas, you must disable dynamic change of CPU frequency (used to decrease battery consumption): cpufreq- selector - g performance. If this fails telling you no cpufreq support, this is fine.
Now, to build the rpm, go into the directory /usr/src/packages/SPEC, and execute. This will build the rpm: this can take a long time, even on a powerful machine. What matters is whether atlas has arch defaults for your machine: if not, it can take several hours (it takes 2 hours and a half on a P4 @3. Ghz, but takes ~1. If successfull, you will get an installable rpm in /usr/src/packages/RPMS/ARCH (where ARCH can be x. They are meant to be drop- out for the standard BLAS and LAPACK (the ones in refblas. To use the atlas libraries, once you installed numpy and scipy, you should tell the OS to use atlas instead of default libraries by using LD.
That is, normally, you can use numpy by : python - c.