numpy

NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.

Sample histogram

From http://bespokeblog.wordpress.com/2011/07/11/basic-data-plotting-with-matplotlib-part-3-histograms/

   1 import matplotlib.pyplot as plt
   2 from numpy.random import normal
   3 gaussian_numbers = normal(size=1000)
   4 plt.hist(gaussian_numbers)
   5 plt.title("Gaussian Histogram")
   6 plt.xlabel("Value")
   7 plt.ylabel("Frequency")
   8 plt.show()

SlackBuilds

numpy

• su
• cd /tmp

• wget http://slackbuilds.org/slackbuilds/14.0/development/numpy.tar.gz

• tar xvzf numpy.tar.gz
• cd numpy

• wget http://freefr.dl.sourceforge.net/project/numpy/NumPy/1.6.2/numpy-1.6.2.tar.gz

• ./numpy.SlackBuild

• installpkg /tmp/numpy-1.6.2-i486-1_SBo.tgz

Package 32 bit: numpy-1.6.2-i486-1_SBo.tgz

Package 64 bit: numpy-1.6.2-x86_64-1_SBo.tgz

pytz

• su
• cd /tmp

• wget http://slackbuilds.org/slackbuilds/14.0/python/pytz.tar.gz

• tar xvzf pytz.tar.gz
• cd pytz

• wget https://launchpad.net/pytz/main/2012h/+download/pytz-2012h.tar.bz2

• ./pytz.SlackBuild

• installpkg /tmp/pytz-2012h-i486-1_SBo.tgz

Package 32 bit: pytz-2012h-i486-1_SBo.tgz

Package 64 bit: pytz-2012h-x86_64-1_SBo.tgz

python-dateutil

• su
• cd /tmp

• wget http://slackbuilds.org/slackbuilds/14.0/python/python-dateutil.tar.gz

• tar xvzf python-dateutil.tar.gz
• cd python-dateutil

• wget http://pypi.python.org/packages/source/p/python-dateutil/python-dateutil-2.1.tar.gz

• ./python-dateutil.SlackBuild

• installpkg /tmp/python-dateutil-2.1-i486-1_SBo.tgz

Package 32 bit: python-dateutil-2.1-i486-1_SBo.tgz

Package 64 bit: python-dateutil-2.1-x86_64-1_SBo.tgz

six

• su
• cd /tmp

• wget http://slackbuilds.org/slackbuilds/14.0/python/six.tar.gz

• tar xvzf six.tar.gz
• cd six

• wget http://pypi.python.org/packages/source/s/six/six-1.4.1.tar.gz

• ./six.SlackBuild

• installpkg /tmp/six-1.4.1-i486-1_SBo.tgz

Package 32 bit: six-1.3.0-i486-1_SBo.tgz

Package 64 bit: six-1.4.1-x86_64-1_SBo.tgz

pysetuptools

• su
• cd /tmp

• wget http://slackbuilds.org/slackbuilds/14.0/python/pysetuptools.tar.gz

• tar xvzf pysetuptools.tar.gz
• cd pysetuptools

• wget https://pypi.python.org/packages/source/s/setuptools/setuptools-0.9.8.tar.gz

• ./pysetuptools.SlackBuild

• installpkg /tmp/pysetuptools-0.9.8-i486-1_SBo.tgz

Package 32 bit: pysetuptools-0.8-i486-1_SBo.tgz

Package 64 bit: pysetuptools-0.9.8-x86_64-1_SBo.tgz

matplotlib

• su
• cd /tmp

• wget http://slackbuilds.org/slackbuilds/14.0/libraries/matplotlib.tar.gz

• tar xvzf matplotlib.tar.gz
• cd matplotlib

• wget http://downloads.sourceforge.net/matplotlib/matplotlib-1.1.1.tar.gz

• ./matplotlib.SlackBuild

• installpkg /tmp/matplotlib-1.1.1-i486-1_SBo.tgz

Package 32 bit: matplotlib-1.1.1-i486-1_SBo.tgz

Package 64 bit: matplotlib-1.1.1-x86_64-1_SBo.tgz

In windows

• pip install numpy
• pip install matplotlib

Simple plot

• python3 -m venv plotEnv
• source plotEnv/bin/activate
• pip install --upgrade pip
• pip install numpy matplotlib pyqt5

simple_plot.py

   1 # python3 simple_plot.py
   2 import matplotlib.pyplot as plt
   3 import numpy as np
   4 # Data for plotting
   5 start=0.0
   6 end=2.0
   7 step=0.01
   8 x_axis = np.arange(start, end, step) # <class 'numpy.ndarray'>
   9 y_axis = 1 + np.sin(2 * np.pi * x_axis) # <class 'numpy.ndarray'>
  10 image, window = plt.subplots()
  11 window.plot(x_axis, y_axis)
  12 window.set(xlabel='x', ylabel='sin(2*pi*x)',
  13        title='Sin wave from 0 to 2')
  14 window.grid()
  15 image.savefig("test.png")
  16 plt.show()

test_chart.py

   1 # python3 test_chart.py
   2 import matplotlib.pyplot as plt
   3 import numpy as np
   4 x = np.array(['a', 'b', 'c'])
   5 y = np.array([1, 2, 3])
   6 image, window = plt.subplots()
   7 window.set(xlabel='x', ylabel='y',
   8        title='Test chart')
   9 window.grid()
  10 window.plot(x,y)
  11 plt.show()