Leveraging Scripting Languages For Math On Linux Command Line
The Problem: Doing Math Without a GUI
Working without a graphical user interface poses challenges for performing mathematical and scientific computations. The lack of interactive visualizations, plotting capabilities, and other features often relied upon in math applications can make calculations tedious and counterintuitive. However, Linux’s built-in scripting languages provide powerful programmatic interfaces for a wide range of numerical operations.
Powerful Scripting Languages at Your Fingertips
Linux distributions ship with diverse, general-purpose scripting languages that lend themselves well to computational work. Bash provides capabilities for basic math and financial operations using familiar syntax. Python contains extensive math libraries and interactive features for advanced calculations. Perl offers string processing functions ideal for fast data crunching and statistics. These languages lower the barrier to automating workflows that would be painful or impossible with only command line utilities.
Bash Math Operations
Bash, the default shell on most Linux systems, has infix math syntax using familiar operators like +, -, *, and / for integer arithmetic on variables. Floating point decimals can be approximated using the scale variable. The expr and let commands offer mathematical functions including modulo, absolute value, rounding, powers, roots, trigonometry, and more. Built-in programming constructs like loops allow iterating over mathematical computations.
Python as a Scientific Calculator
Python provides extensive math libraries, arbitrary precision decimal and fractions modules, advanced functions like statistics and linear algebra operations. As an interpreted language, Python enables an interactive REPL for doing math, with dynamic typing and support for complex numbers. Python can plot mathematical relationships, fit data to statistical models, and integrate with visualization apps for engaging with computations.
Perl for Quick Math Tasks
Perl offers many shorthand math operations on the command line using options like -E, -n, -p and -i for one liners. Perl variables automatically function as numbers or strings based on context, enabling concise data munging workflows. Regular expressions help match patterns in equation strings for batch processing. Date math can determine intervals or calendars. Statistics on data sets can generate counts, means, distributions and more. Libraries like PDL provide fast mathematical functions.
Using bc for Precision Math Operations
bc is a terminal-based numerical processing language adept at handling numbers of arbitrary size and precision. With bc, you can conduct integer math, decimal floating point operations, scientific notation, exponents, roots, scales, logarithms and more with guaranteed precision. bc has built-in functions for advanced math like sin, cos, atan, and prime testing. It can read equations from standard input, files or strings and output solutions. bc seamlessly integrates in shells and is commonly leveraged for financial applications.
Setting Precision
A key capability provided by bc is setting the number of decimal places or significant figures to round computations. This allows accuracy to be tailored to use cases from simple rounding to advanced numerical analysis requiring hundreds of digits. The global scale variable controls precision, or the precision modifier can prefix specific operations.
Running Calculations
In addition to an interactive prompt, bc can execute math statements provided on the command line, via stdin, or read from a file containing equations. This flexibility combines well with other CLI utilities to build math pipelines. Performance exceeds awk and most scripting languages for numerical computations since bc compiles to bytecode.
Leveraging bc Interactively
The REPL environment of bc interactive mode enables entering expressions and seeing results immediately. Infix notation works similarly to most calculator applications. The REPL maintains state like variables between invocations without needing to redeclare. Delimiting blocks of equations is also supported to group operations.
Python as an Advanced Scientific Calculator
Python’s math libraries like NumPy and SciPy activate capabilities resembling an advanced graphing calculator. The interpreter enables an interactive shell accepting math syntax. Mathematical operations work on a range of number types – integers, floats, decimals, fractions, complex, matrices, uncertainty values. Visualizing data and models helps inform next steps.
Numeric Operations
Out of the box, Python supports common math operations like sin, cos, tan, logs, exponents, square roots on all number types with arbitrary precision decimals and fractions. The math and cmath modules provide advanced special functions unavailable in hardware calculators like gamma, zeta, factorial, GCDs, combinations, Bubble sort, Fibonacci sequence generation and more for spot calculations.
Math Package Ecosystem
Python’s huge ecosystem of math packages significantly extends calculation capabilities. NumPy enables optimized arrays for linear algebra – matrix math, statistical aggregations, FFTs, integrations, differentials, random sampling, and polynomial handling exceeding spreadsheet software. SciPy adds more advanced math functions. Pandas manages structured data common for math use cases. Specialized domains like graph theory have custom libraries.
Visualization Capabilities
Matplotlib, seaborn, bokeh and other Python plotting libraries provide publication quality 2D and 3D visualizations useful for mathematical work. Notebooks like Jupyter enable interactive graphs updated dynamically alongside computations. Statistical models can be plotted against datasets for best fit analysis. Plots convey quantitative information and equations visually for enhanced understanding.
Perl for Quick Math Tasks
Perl’s string processing prowess aids math calculations on formatted numeric data or equations for flexible, one-off solutions. Conversion between bases, calculating statistics, sequence generation, numeric comparisons, date math, array handling, RPN expressions, series expansions leverage Perl’s specialty.
One-liners
Rapid math solutions one the command line take advantage of Perl one-liners. Infix and postfix equations parse input strings for evaluation. Statistics like means, variance, regression come from filtered numeric data streams via regular expressions. Date periods can be calculated and formatted. Code golfing exercises create compact math formulas within Perl’s rich syntax.
Command Line Scripts
Short Perl scripts make reusable math utilities. Functions model common numeric operations – combinatorics, discounting cashflows, delta differences, array slicing and dicing. CLI arguments or stdin input drive ad hoc calculations like unit conversions, math constant generations, RNG sequences, algebra, matrix math. Inline documentation explains scripts for modification.
Math Modules
CPAN provides specialized math Perl modules. PDL enables fast statistical analysis via vectorized operations familiar to mathematicians. Date::Manip precisely calculates calendaring math. Algorithm::Combinatorics generates combinations and permutations. Math::Symbolic computable algebra and calculus operations. Gtk2::Perl::Graph plots 2D/3D visualizations of equation data.
Putting It All Together: A Math Pipeline Example
Bash, Python, Perl, bc and traditional Linux utilities can combine into toolchains performing multi-stage mathematical operations not feasible in a calculator application. Each language handles computations suited for its strengths – bc and Python for precision calculations, Perl for data munging, Bash for orchestration and glue logic.
Project Setup
Consider calculating a 5 period exponential moving average from an input price time series. Bash scripts set up the pipeline, call utilities, pass data between stages. Temporary storage and outputs are defined. Here documents and stdout streams handle dataflow. Shebang lines invoke perfomant interpreters – python3, perl, bc.
Input Processing
A Perl one-liner normalizes the raw stdin numeric stream – filtering out non-numeric lines, removing commas and currency symbols, handling missing values. The preprocessed data is streamed into a Python script. Pandas converts the series into a timestamp indexed DataFrame enabling timeseries math.
Math Operations
Python’s pandas library handles the exponential moving average calculation across the structured dataset, leveraging C-optimized array math. Output averages per period stream into bc for rounding to 4 significant figures consistent with input precision. Calculations complete without temporary files touching disk.
Output and Next Steps
Bash wraps the pipeline stages, redirecting streams through the sequence of operations. Results output to screen or file for spreadsheet loading, graphing, analysis in a separate tool like R or MATLAB, or further processing in another CLI utility run.
Beyond Command Line Math: Integrations and Extensibilities
While mastering scripting languages unlocks mathematical capabilities on Linux terminals, headless batch operations limit interactive exploration. Visualization, model evaluation, collaborative features require external software integrations enumerated below.
Graphics
Terminal output constrains interpreting data mathematically. GUI spreadsheet tools assist plotting charts, leveraging mouse/touch navigation. Statistical programming languages render publication quality graphs of equations, data fits. Julia integrates nonlinear equation solving with 2D/3D animation for intuitive comprehension. Notebooks provide live coding environments mixing text, math, interactive visuals.
Desktop Computational Software
Packages like MATLAB, Mathematica, Maple handle symbolic and numerical math at scale – factorization, algebra solvers, ODEs, matrix decompositions, calculus – exceeding command line interfaces. Interactive troubleshooting refines models. Documentation and IDEs assist novices. Math notation entry leverages keyboard/mouse. Toolboxes customize domain-specific needs.
Collaborative Features
Cloud computational notebooks enable teams to simultaneously develop math algorithms + data visualizations, sharing worksheets. Version control history tracks who modified code. Widget dashboards parameterize groups of equations for dynamic inputs. APIs connect statistical languages to enterprise data for automation. Containerization scales containerized microservices solving subsets of complex math problems.
