Moving README.txt to README.md.

1 files changed, 0 insertions, 177 deletions

diff --git a/scipy/basic/README.txt b/scipy/basic/README.txt
deleted file mode 100644
index 59c8dc9..0000000
--- a/scipy/basic/README.txt
+++ /dev/null
@@ -1,177 +0,0 @@
-# Introductory Scientific Computing with Python
-
-This is an entirely hands-on workshop. At the end of this workshop, attendees
-should be able to use the basic tools and libraries for Python-based
-scientific computing. We imagine that an undergraduate/graduate
-engineering/science student would be able to *start* doing their basic
-computing tasks using Python.
-
-The course features multiple simple quizzes that students take online. Based
-on the performance in these quizzes students are given grades and a
-certificate.
-
-
-## Intended audience and pre-requisites
-
-This course is designed to be taken by folks who do not have any programming
-experience. The students should be comfortable using a computer and editing
-text files.
-
-Prior experience with Scilab/Matlab/octave or similar tools would be useful
-but not necessary.
-
-For those of you who do not have much experience using a keyboard to type or do
-not know touch typing, it would be a good idea to practice typing using online
-typing tutors.  Here is a good one:
-
-https://www.speedtypingonline.com/typing-tutor
-
-Just practice and go through the default lessons and learn the basic keys.  The
-more you practice the better you become.  If you are not good at typing quickly,
-you might benefit from a few hours of practice.  Try to log two to four hours on
-this and you will find that your typing speed improves significantly.
-
-
-## Software and hardware requirements:
-
-A laptop or reasonably configured desktop is recommended since this will be a
-hands-on session.
-
-The following packages need to be installed:
-
-- Python (2.x or 3.x)
-- IPython/Jupyter
-- NumPy
-- SciPy
-- Matplotlib
-- Optionally install Mayavi.
-
-On Linux, Windows and Mac OS X it is easiest to install these by installing
-the Enthought Canopy. Download the Canopy Python distribution for your OS and
-architecture from here: https://store.enthought.com/downloads.
-
-You could also use Anaconda or Conda along with the Spyder editor to obtain
-the requirements.
-
-On many Linux distributions, these packages are easy to install.
-
-
-## Detailed outline
-
-The session is entirely hands-on. We focus on common tasks and introduce the
-Python language in the context of these common tasks. Here is an outline
-of what is covered:
-
-- Introduction to Python and some preliminaries.
-
-- Getting started with IPython and creating basic plots with `pylab`.
-  - Using IPython effectively, reading documentation interactively.
-  - Basic plots.
-  - Decorating plots with labels, legends, annotation, and titles.
-  - Multiple plots and separate figures.
-  - Saving plots.
-
-- Saving Python scripts and running them using IPython and Python.
-  - Using `%hist` and `%save`
-  - Creating new Python scripts on an editor.
-  - Running scripts in IPython using `%run`.
-  - Running scripts from the terminal with `python`.
-
-- Creating and using lists, list slicing, list operations.
-  - Creating data and storing them in lists.
-  - Plotting data in lists.
-  - Initializing and accessing list elements with indexing
-  - List slicing, striding, and list operations
-  - Looping over a list with `for`.
-
-- Defining functions in Python.
-  - Functions accepting arguments and returning values.
-
-- Timing operations using IPython's `%timeit` and `%time` magic.
-
-- NumPy array basics:
-  - Importing numpy.
-  - Basic array attributes and operations.
-  - 1-D and multi-dimensional arrays.
-  - Array slicing and striding.
-  - Other array creation functions.
-  - Basic array math.
-  - Reading data files with `loadtxt`.
-  - Exercise on plotting data from a file.
-
-- More on NumPy.
-  - Creating matrices using numpy arrays.
-  - Special kinds of matrices, `ones`, `identity` etc.
-  - Accessing elements, accessing rows and columns.
-  - Setting elements, setting rows and columns.
-  - Multi-dimensional slicing and striding.
-
-- Elementary image processing using numpy arrays.
-  - Reading an image and matrix as a numpy array.
-  - Viewing an image/matrix.
-  - Basic cropping, sub-sampling images.
-
-- More matrix operations.
-  - Transposition.
-  - Elementwise addition/multiplication.
-  - Matrix multiplication with `numpy.dot`.
-  - Inverse, determinant, sum of elements.
-  - Computing norms, eigenvalues, and eigenvectors.
-  - Computing the singular value decomposition.
-
-- Performing a least squares fit for some experimental data.
-  - Read data from a file.
-  - Perform a least square fit from first principles.
-
-- Introduction to random number generation with `numpy.random`.
-
-- Introduction to Jupyter/IPython notebooks.
-  - Starting up the notebook.
-  - Using `%pylab` and `%matplotlib`.
-  - A sample notebook with a demonstration of images, equations, code, and
-    simple widgets.
-
-The above are covered first, additional material is also made available to
-users that they can see at their leisure. These are optional but students are
-suggested to go over the material on their own.
-
-- Introduction to the SciPy package. (52 minutes)
-
-  - Solving a system of linear equations.
-  - Finding the roots of a polynomial using `numpy.roots`.
-  - Finding the roots of non-polynomial equations using scipy's `fsolve`.
-  - Numerical integration of Ordinary Differential Equations (ODES).
-    - Example of a 1D ODE.
-    - Example of a coupled 2D ODE or a second order DE.
-  - Using scipy's `fft` module for basic signal processing
-    - Finding the FFT and inverse FFT.
-    - Simple noise filtering using `scipy.signal`.
-
-- Exercises: (65 minutes)
-  - We solve 5 different problems using the tools we have learned.
-  - Users are given time to solve the problem.
-  - The solution is shown and explained before the next problem.
-
-- Simple 3D plots with Mayavi's `mayavi.mlab`. (50 minutes)
-  - Introduction to Mayavi.
-  - Getting started with `mlab`
-    - Using `mlab` in a console and in an IPython notebook.
-  - Basic plotting with Mayavi
-  - 0D data, 1D data, 2D data, and 3D data.
-  - Simple scalar and vector plots.
-  - Utility functions to annotate the plot and save images.
-
-
-Session wise slide breakup:
-
-1. intro.pdf
-2. prelims.pdf
-3. ipython_plotting.pdf
-4. saving_scripts.pdf
-5. lists_arrays.pdf
-6. numpy.pdf
-7. more_numpy.pdf
-8. scipy.pdf
-9. exercises.pdf
-10. notebook.pdf
-11. mlab.pdf