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1. #Check scipy version install
2. #Check scipy version software
3. #Check scipy version code
4. #Check scipy version plus

#Check scipy version install

Run command sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose Some tests are very slow and need to be separately enabled. There are also a number of alternatives to it, for example in-place build or installing to a virtual environment. Using runtests.py is the recommended approach to running tests. Specifying -n will run the tests against the version of Scipy (if any) found on the current PYTHONPATH. This builds Scipy first, so it may take a while the first time. $ python runtests.py -python somescript.py $ python runtests.py -v -t scipy/special/tests/test_basic.py:test_xlogy Do one of the following: $ python runtests.py -v To build the development version of Scipy and run tests, spawn interactive shells with the Python import paths properly set up, and so on. $ git remote add upstream git:///scipy/scipy.git

#Check scipy version code

Having compiled code also means that Scipy needs additional steps to import from development sources, which are explained below.įork a copy of the main Scipy repository in Github onto your own account, then create your local repository via: $ git clone scipy Therefore make sure the necessary compilers and Python development headers are installed. Scipy contains parts written in C, C++, and Fortran that need to be compiled before use. Imsave('assets/cat_tinted.jpg', img_tinted) # Resize the tinted image to be 300 by 300 pixels. # and multiplies the green and blue channels by 0.95 and 0.9 # numpy broadcasting means that this leaves the red channel unchanged, # we multiply it by the array of shape (3,) # We can tint the image by scaling each of the color channels Print img.dtype, img.shape # Prints "uint8 (400, 248, 3)" Both original and resulting images are shown below: import numpy as np //scipy is numpy-dependentįrom scipy.misc import imread, imsave, imresize //image resizing functions In the following code, only one image is used. These include functions to read images from disk into numpy arrays, to write numpy arrays to disk as images, and to resize images. SciPy provides basic image manipulation functions. Image Manipulation using Scipy (Basic Image resize) With 3 stored elements in Compressed Sparse Row format> #this will also output Hello World, only this time with an exclamation mark.Ĭonvert a sparse matrix to a dense matrix using SciPy from scipy.sparse import csr_matrix

#Check scipy version plus

If you wish you can use two variables, e.g one for hello and one for world and concatenate them using the plus (+) sign: h = 'Hello ' #display info on variable wld (name/type/value) In this instance, you can use run your saved file 'hello_world.py' in IPython like so: In : %run hello_world.py The output of the above two lines of code is that the string "Hello World" will be displayed.įunctions written in Python can be used in iPython also. Note that python variables do not need to be explicitly declared the declaration happens when you assign a value with the equal (=) sign to a variable. hello_world.py) in a text editor or a python editor if you have one installed ( pick one if you don't - SublimeText, Eclipse, NetBeans, SciTe. All of this power is available in addition to the mathematical libraries in SciPy. Everything from parallel programming to web and data-base subroutines and classes have been made available to the Python programmer.

#Check scipy version software

Scientific applications using SciPy benefit from the development of additional modules in numerous niches of the software landscape by developers across the world. The additional benefit of basing SciPy on Python is that this also makes a powerful programming language available for use in developing sophisticated programs and specialized applications. With SciPy an interactive Python session becomes a data-processing and system-prototyping environment rivaling systems such as MATLAB, IDL, Octave, R-Lab, and SciLab. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python.