MCS 507: Mathematical, Statistical and Scientific Software

Catalog description: The design, analysis, and use of mathematical, statistical, and scientific software.

Slides for the lectures will be posted below.

1. overview of research software and Python

• L-1 08/21/23: welcome to mcs 507
          Jupyter notebook: hex4pi.ipynb.
          Prerecorded lecture: youtube link.
• L-2 08/23/23: Introduction to Python
          Jupyter notebook: intro2python.ipynb.
          Prerecorded lecture: youtube link.
• L-3 08/25/23: object-oriented programming in Python
          Jupyter notebook: oopinpython.ipynb.
          Prerecorded lecture: youtube link.

2. numpy, sympy, scipy, and matplotlib

• L-4 08/28/23: numpy, linear algebra, and vectorization
          some Python scripts: walk.py and walkvector.py ,
          Jupyter notebook: numpylinalgvector.ipynb.
          Prerecorded lecture: youtube link.
• L-5 08/30/23: sympy, scipy, and integration
          Jupyter notebook: sympyscipyintegration.ipynb.
          Prerecorded lecture: youtube link.

3. arithmetic, roots, wrapping code

• L-6 09/01/23: numerical root finding and extending Python
          a Python module: call_mpsolve.py
          Prerecorded lecture: youtube link.
• L-7 09/06/23: interval and multiple double arithmetic
          some Python scripts intvalmotiv.py, usempmathiv.py, usempmathivfun.py, intvalsolved.py, and intvalnewton.py.
          Jupyter notebook: intervalarithmetic.ipynb.
          Prerecorded lecture: youtube link.
• L-8 09/08/23: overloading and extending
          a Python script differential_numbers.py
          Jupyter notebook: differential_numbers.ipynb.
          C code revertString.c and revertStrings.c
          to build the extension module: setup.py
          Prerecorded lecture: youtube link.

4. Julia

• L-9 09/11/23: introduction to Julia
          a Jupyter notebook intro2julia.ipynb
          C code for Laguerre's method: laguerre.c, the Python script laguerre.py, and the Julia program laguerre.jl
          Prerecorded lecture: youtube link.
• L-10 09/13/23: Julia ecosystems;
          a Jupyter notebook juliasystem.ipynb
          a Python module hellopython.py
          some Julia programs: pipe4ls.jl, polyroots.jl, polyrootscall.jl, showhostname.jl, and callqsort.jl.
          Prerecorded lecture: youtube link.
• L-11 09/15/23: parallel computing with Julia;
          some Julia programs: mpi_hello_world.jl, mpi_point2point.jl, estimatepi1.jl, estimatepidp.jl, estimatepimt.jl, and matmatmulmt.jl.
          Prerecorded lecture: youtube link.

5. parallel programming

• L-12 09/18/23: multiprocessing and multithreading;
          Python scripts multiprocess.py, simpson4pi.py, simpson4pi1.py, simpson4pi2.py, hello_threading.py, classproducer.py, classconsumer.py, and prodcons.py.
          Prerecorded lecture: youtube link.
• L-13 02/20/23: Message Passing Interface for Python
          with Python scripts mpi4py_hello_world.py, mpi4py_point2point.py, mpi4py_broadcast.py, mpi4py_scatter.py, mpi4py_gather.py, mpi4py_estimate_pi_2.py, mpi4py_estimate_pi_n.py, mpi4py_parallel_sum.py, and mpi4py_dynamic.py.
          Prerecorded lecture: youtube link.
• L-14 09/22/23: GPU Acceleration in Python and Julia
          with Python scripts matmatmulocl.py, matmatmulsdk.py, and matmatmulcuda.py;
          and a Julia program gpuadd.jl.
          Prerecorded lecture: youtube link.

6. cython, testing, documenting, packaging

• L-15 09/25/23: running Cython
          a Jupyter notebook: running_cython.ipynb
          with needs the Cython code and Python scripts: hello.pyx, hello_setup.py, integral4pi.py, integral4pi_typed.pyx, integral4pi_typed_setup.py, integral4pi_typed_apply.py, integral4pi_cdeffun.pyx, integral4pi_cdeffun_setup.py, integral4pi_cdeffun_apply.py, integral4pi_extcfun.pyx, integral4pi_extcfun_setup.py, integral4pi_extcfun_apply.py,
          and native C code: integral4pi_native.c.
          Code for improving the game of life: run_game_of_life.py, run_game_better.pyx, run_game_better_setup.py, run_game_better_apply.py, game_of_life.py, and run_game_better_show.py.
          Prerecorded lecture: youtube link.
• L-16 09/27/23: pexpect and testing
          a Jupyter notebook: pexpect_testing.ipynb
          Python scripts: pexpect_hello_julia.py, sage_version_test.py, game_oracle_number.py, pexpect_interactive_game.py, pexpect_guessing_game.py, factorial50_sympy.py, factorial50_sympy_assert.py, and factorial50_sympy_test.py.
          Prerecorded lecture: youtube link.
• L-17 09/29/23: packaging and documenting software
          Our module: ourfirstmodule.py
          Our package: ourfirstpackage-0.0.1.tar.gz
          Prerecorded lecture: youtube link.

7. graphical user interfaces, solving ODEs, web interfaces

• L-18 10/02/23: scientific movies and animations
          Jupyter notebooks: billiards_basic.ipynb and fourbargui.ipynb.
          with Python scripts: pylabsinplot.py, animatedsine.py, billiards_basic.py, fourbarforms.py, fourbar.py, and fourbargui.py.
          Prerecorded lecture: youtube link.
• L-19 10/04/23: solving ordinary differential equations
          a Jupyter notebook with a Python kernel: solvingODEsPython.ipynb
          a Jupyter notebook with a Julia kernel: solvingODEsJulia.ipynb
          Prerecorded lecture: youtube link.
• L-20 10/06/23: web interfaces
          with Python scripts: python_works.py, myserver.py, web_det.py, and compute_det.py
          Prerecorded lecture: youtube link.

8. web servers, review, and midterm exam

• L-21 10/09/23: web servers
          with Python script: emailauth.py.
• L-22 10/11/23: review of the first 21 lectures
• L-23 10/13/23: midterm exam

9. data retrieval and processing

• L-24 10/16/23: parsing html and web crawlers
          Python scripts forecast.py, tallytags.py, htmlrefs.py, courselistings.py, scanquotes.py, scanhttplinks.py, and webcrawler.py.
          Prerecorded lecture: youtube link.
• L-25 10/18/23: data analysis with pandas
          a Jupyter notebook pandasexamples.ipynb
• L-26 10/20/23: machine learning in Python and Julia
          a Jupyter notebook with Python kernel machineLearningPython.ipynb
          a Jupyter notebook with Julia kernel machineLearningJulia.ipynb

10. image processing, networks

• L-27 10/23/23: image processing
          using the familiar image buildingseo.jpg
          a Jupyter notebook imageprocessing.ipynb
• L-28 10/25/23: graphs and networks with networkx
          a Jupyter notebook networkx.ipynb
• L-29 10/27/23: image procesing with scikit-image
          the tiger in the lake picture bsdstiger.jpg
          a Jupyter notebook skimage.ipynb

11. computational geometry

• L-30 10/29/23: computational geometry software
          a Python script read_poly.py
          a Jupyter notebook geometry.ipynb
          data for the lake Superior polygon superior.poly
• L-31 11/01/23: geometric queries
          a Jupyter notebook queries.ipynb
• L-32 11/03/23: computer aided geometric design
          a Jupyter notebook cagd.ipynb

12. probability and statistics

• L-33 11/06/23: probability and statistics
          a Jupyter notebook probstats.ipynb
• L-34 11/08/23: statistical data exploration
          a Jupyter notebook datastats.ipynb
• L-35 11/10/23: statistical computing with R
          a Jupyter notebook rSageMath.ipynb

13. groups, numbers, and polynomials

• L-36 11/13/23: computing with groups
          a Python script defines a GUI rubic_magic_cube.py
          a Jupyter notebook gapSageMath.ipynb
• L-37 11/15/23: higher arithmetic with Pari/GP
          a Jupyter notebook parigpSageMath.ipynb
• L-38 11/17/23: computing with polynomials with Singular
          two Jupyter notebooks singularinsage.ipynb and fourbarformulas.ipynb
• L-39 11/20/23: computational algebraic geometry with Macaulay2
          a Jupyter notebook macaulay2SageMath.ipynb
• L-40 11/22/23: solving polynomial systems with PHCpack and phcpy