Setup and general usage

Setup and general usage

• Setting up XlCalcNet
  • Downloading and installing the “right” version of CPython
  • Installing XlCalcNet
  • Installing and using Pythonnet: Calling C# from Python
  • The 3 folder concept: user, application local data, installation
  • Copying and exploring the DataXlCalcNet folder
  • Installing and using the Tiny IDE as a Python application
  • Building a library of user defined python functions
  • Building a library of user defined functions in C#
  • Sphinx: building documentation for software with Python (html and pdf)
• General and user interface functions
  • Starting the socket server
    • gui.socketserver()
  • Starting the output monitor
    • gui.outputmonitor()
  • Starting an additional instance of the IDE
    • gui.tinyide()
  • Starting the gallery of plots
    • gui.plot2d()
  • Starting the interactive 3D wpf plots
    • gui.plot3d()
  • Starting the data viewer
    • gui.dataviewer()
  • Starting IDLE
    • gui.idle()
  • Functions related to folders
    • gui.get_local_appdata()
    • gui.get_local_appdata_xlcalcnet()
    • gui.get_my_documents()
  • Information about installation status of supporting python packages
    • gui.info()
    • gui.has_gpm()
    • gui.has_apm()
    • gui.has_xlcalcnet2()
  • Information about context lists, and setting global precision
    • gui.ctxlistreal
    • gui.ctxlistcplx
    • gui.setdps()
  • Calling the socketserver from Python
  • Support for running interactive Matplotlib output in a separate process
    • gui.plot
• Calling Python from C#
  • Using Pythonnet from C#
  • Calling the socket server from C#
• Using XlCalcNet with spreadsheet formulas
  • Installation of the MS Excel XlNet addin
  • Starting and stopping the socket server
  • Exploring with an example spreadsheet: simple formulas, double precision
  • Exploring with an example spreadsheet: simple formulas, multiple precision
  • Exploring with an example spreadsheet: array formulas
  • Exploring with an example spreadsheet: small Python programs, syntax rules
  • Entering multi-precision data into a spreadsheet (text formatting and AsDouble)
  • Managing procedures instead of functions
• Mathematical functions based on Mpmath, Gmpy2 and Python-Flint (only Python)
  • Overview
    • ctx.mpf()
    • ctx.mpc()
    • ctx.t()
    • ctx.convert()
    • ctx.mpmathify()
  • Double-precision arithmetic (fpm)
  • Binary floating-point in arbitrary-precision and with arbitrary exponent (mpm)
  • Interval arithmetic in arbitrary-precision and with arbitrary exponent (ipm)
    • mpi_from_str()
    • mpi_to_str()
  • Decimal floating-point in arbitrary-precision with limited exponent (dpm)
  • Rational numbers (qpm)
  • Binary floating-point in arbitrary-precision with limited exponent (gpm)
  • Binary balls in arbitrary-precision and with arbitrary exponent (apm)
  • Writing portable code for interval and ball arithmetic
    • ctx.mid()
    • ctx.radius()
    • ctx.left()
    • ctx.right()
    • ctx.absmin()
    • ctx.absmax()
    • ctx.union()
  • Reasons for using multiprecision arithmetic
• Mathematical functions in fixed precision
  • Scalar functions with Double and Complex: math53 and cmath53
  • Binary floating point, single precision: sreal and scplx
  • Binary floating point, double precision: dreal and dcplx
  • Binary floating point, extended precision: ereal and ecplx
  • Binary floating point, quadruple precision: qreal and qcplx
  • Binary floating point, octuple precision: oreal and ocplx
• Mathematical functions based on XlCalcNet2
  • Binary floating point, multiple precision: mreal, mcplx
  • Binary floating point, single precision: sflint and sflintc
  • Binary floating point, double precision: dflint and dflintc
  • Binary floating point, extended precision: eflint and eflintc
  • Binary floating point, quadruple precision: qflint and qflintc
  • Binary floating point, octuple precision: oflint and oflintc
  • Binary floating point, multiple precision: mflint, mflintc
  • Binary arbitrary precision ball: aflint and aflintc
• A quick look at Numpy
  • Overview
  • Using Numpy for vectorization of scalar functions
  • Using Numpy with Numba
  • Use of Numpy with data types based on Mpmath, Gmpy2 and Python-Flint
  • Use of Numpy with scalar functions written in C#:: np53, np53c
  • Using Numpy in a spreadsheet formula
• A quick look at Matplotlib and related libraries
  • Overview
  • Using Matplotlib in a spreadsheet formula (without blocking function return)
  • Seaborn
  • Cartopy
  • Networkx
  • Matplotlib and 3D plots (based on S3dlib)
• A quick look at Pandas and Xlxswriter
  • Reading dataframes from *.xlsx files
  • Writing a dataframe object to an *.xlsx file
  • Reading a dataframe from a *.csv file
  • Writing a dataframe to a *.csv file
  • Transforming data in a dataframe
  • Xlsxwriter: creating files in the *.xlsx format
  • Python-docx: creating files in the *.docx format
  • Python-pptx: creating files in the *.pptx format
• A quick look at Scipy
  • Example: Numerical integration
  • Example: One-dimensional root-finding
  • Example: Special functions
  • Example: Linear regression
  • Statsmodels
  • Scikit-learn
• A quick look at R, RStudio and Rpy2
  • Installing R, RStudio, and the reticulate package
  • Installing additional R packges
  • Using the python command line in the RStudio console
  • Using the python editor line in the RStudio editor panel