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+# 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