Conky Clock

For a few months now, I have been a vivid user of the ArchLabs distribution which — in a recent release — added the system monitor Conky to display various pieces of information such as uptime, CPU usage and UTC time.

However, Conky does not statically produce a wall of text and plops it on your desktop; it periodically updates itself as to be able to display time-dependent information.
Furthermore, it allows to be fully configured through a simple ~/.config/conky/ArchLabs.conkyrc file.

I wanted to display a useful time-dependent piece of information which does not require user interaction of any kind and found it — an analogue ASCII-art clock.

Conky Clock
Time smiley optional.

For installation, download conky_clock.py and add a ${exec python <chosen_path>/conky_clock.py} line to your conky configuration file.

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Truth

Proposition calculus deals with statements and the relation between statements, where each of them can only be in one of two states; \vdash p \lor \lnot p. Therefore, when working with finitely many connected propositions, one can algorithmically determine all possible truth values of all atomic and thus connected propositions.

Truth is command-line tool which was written to precisely perform those computations; computing a logical expression’s truth value. Download link: truth.py
A list of all supportet operators can be seen by invoking the tool with a --help flag.
This project was inspired by Albert Menne’s Einführung in die Logik1; the operator syntax used is similar to his, translated to be 7-bit-ASCII-compatible.

Truth can be used to either verify universally true statements, e.g. tertium non datur and a property of the replication, verum sequitur ex quodlibet.

-(p&-p) <-> 1  ,  1 <- p
1 0010   1  1     1 1  0
1 1001   1  1     1 1  1

Though not only absolute truth, but also complete relational equivalence between two expressions can be shown.

(p->q)|(r>-<s) <-> q|(r|s)&-(r&s)|-p
 01 0 1 0 0 0   1  00 000 01 000 110
 10 0 0 0 0 0   1  00 000 01 000 001
 01 1 1 0 0 0   1  11 000 01 000 110
 11 1 1 0 0 0   1  11 000 01 000 101
 01 0 1 1 1 0   1  01 110 11 100 110
 10 0 1 1 1 0   1  01 110 11 100 101
 01 1 1 1 1 0   1  11 110 11 100 110
 11 1 1 1 1 0   1  11 110 11 100 101
 01 0 1 0 1 1   1  01 011 11 001 110
 10 0 1 0 1 1   1  01 011 11 001 101
 01 1 1 0 1 1   1  11 011 11 001 110
 11 1 1 0 1 1   1  11 011 11 001 101
 01 0 1 1 0 1   1  00 111 00 111 110
 10 0 0 1 0 1   1  00 111 00 111 001
 01 1 1 1 0 1   1  11 111 00 111 110
 11 1 1 1 0 1   1  11 111 00 111 101

Complete contravalence can also be shown.

-(p/-p>-<0)|p->q<-r >-< p&-q&r
0 0110 1 0 101 01 0  1  001000
0 1101 1 0 110 01 0  1  111000
0 0110 1 0 101 11 0  1  000100
0 1101 1 0 111 11 0  1  100100
0 0110 1 0 101 01 1  1  001001
0 1101 1 0 010 00 1  1  111011
0 0110 1 0 101 11 1  1  000101
0 1101 1 0 111 11 1  1  100101

1Menne, Albert: Einführung in die Logik. Bern: Franke, 1966. (= Dalp-Taschenbücher; 384 D)

Python Matrix Module

Matrices are an important part of linear algebra. By arranging scalars in a rectangular manner, one can elegantly encode vector transformations like scaling, rotating, shearing and squashing, solve systems of linear equations and represent general vector space homomorphisms.
However, as powerful as matrices are, when actually applying the theoretical tools one has to calculate specific values. Doing so by hand can be done, yet gets cumbersome quite quickly when dealing with any matrices which contain more than a few rows and columns.

So, even though there are a lot of other implementations already present, I set out to write a Python matrix module containing a matrix class capable of basic matrix arithmetic (matrix multiplication, transposition, …) together with a set of functions which perform some higher-level matrix manipulation like applying Gaussian elimination, calculating the reduced row echelon form, determinant, inversion and rank of a given matrix.

Module source code can be seen below and downloaded. When saved as matrix.py in the current working directory, one can import the module as follows.

>>> import matrix
>>> A = matrix.Matrix([[13,  1, 20, 18],
...                    [ 9, 24,  0,  9],
...                    [14, 22,  5, 18],
...                    [19,  9, 15, 14]])
>>> print A**-1
        -149/1268  -67/634   83/1268 171/1268
          51/1268 239/1902 -105/1268 -33/1268
Matrix(    73/634 803/4755  -113/634 -87/3170 )
          13/1268  -75/634  197/1268 -83/1268

Matrices are defined over a field, typically \mathbb{F} = \mathbb{R} in theoretical use, though for my implementation I chose not to use a double data structure, as it lacked the conceptual precision in numbers like a third. As one cannot truly represent a large portion of the reals anyways, I chose to use \mathbb{F} = \mathbb{Q}, which also is a field though can be — to a certain scalar size and precision — accurately represented using fractional data types (Python’s built-in Fraction is used here).

To simplify working with matrices, the implemented matrix class supports operator overloading such that the following expressions — A[i,j], A[i,j]=l, A*B, A*l, A+B, -A, A/l, A+B, A-B, A**-1, A**"t", ~A, A==B, A!=B — all behave in a coherent and intuitive way for matrices A, B, scalars l and indices i, j.

When working with matrices, there are certain rules that must be obeyed, like proper size when adding or multiplying, invertibility when inverting and others. To minimize potential bug track down problems, I tried to include a variety of detailed exceptions (ValueErrors) explaining the program’s failure at that point.

Apart from basic matrix arithmetic, a large part of my module centers around Gaussian elimination and the functions that follow from it. At their heart lies the implementation of GaussianElimination, a function which calculates the reduced row echelon form rref(A) of a matrix together with the transformation matrix T such that T*A = rref(A), a list of all matrix pivot coordinates, the number of row transpositions used to achieve row echelon form and a product of all scalars used to achieve reduced row echelon form.
From this function, rref(A) simply returns the first, rrefT(A) the second parameter. Functions rcef(A) (reduced column echelon form) and rcefS(A) (A*S=rcef(A)) follow from repeated transposition.
Determinant calculation uses characteristic determinant properties (multilinear, alternating and the unit hypercube has hypervolume one).

  • \det \begin{pmatrix}  a_{1 1}&a_{1 2}&\dots&a_{1 n}\\  \vdots&\vdots&\ddots&\vdots&\\  \lambda \cdot a_{i 1}&\lambda \cdot a_{i 2}&\dots&\lambda \cdot a_{i n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{n 1}&a_{n 2}&\dots&a_{n n}  \end{pmatrix} = \lambda \cdot \det \begin{pmatrix}  a_{1 1}&a_{1 2}&\dots&a_{1 n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{i 1}&a_{i 2}&\dots&a_{i n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{n 1}&a_{n 2}&\dots&a_{n n}  \end{pmatrix}
  • \det \begin{pmatrix}  a_{1 1}&a_{1 2}&\dots&a_{1 n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{i 1}&a_{i 2}&\dots&a_{i n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{j 1}&a_{j 2}&\dots&a_{j n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{n 1}&a_{n 2}&\dots&a_{n n}  \end{pmatrix} = -\det \begin{pmatrix}  a_{1 1}&a_{1 2}&\dots&a_{1 n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{j 1}&a_{j 2}&\dots&a_{j n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{i 1}&a_{i 2}&\dots&a_{i n}\\  \vdots&\vdots&\ddots&\vdots&\\  a_{n 1}&a_{n 2}&\dots&a_{n n}  \end{pmatrix}
  • \det \begin{pmatrix}1&a_{1 2}&a_{1 3}&\dots&a_{1 n}\\0&1&a_{2 3}&\dots&a_{2 n}\\0&0&1&\dots&a_{3 n}\\\vdots&\vdots&\vdots&\ddots&\vdots\\0&0&0&\dots&1\end{pmatrix} = \det \bold{1}_n = 1

Using these properties, the determinant is equal to the product of the total product of all factors used in transforming the matrix into reduced row echelon form and the permutation parity (minus one to the power of the number of transpositions used).
Questions regarding invertibility and rank can also conveniently be implemented using the above described information.

All in all, this Python module implements basic matrix functionality paired with a bit more involved matrix transformations to build a usable environment for manipulating matrices in Python.


# Python 2.7 code; Jonathan Frech; 9th, 10th, 11th, 12th, 14th of December 2017
# 4th of January 2018: added support for matrix inequality, implemented __ne__

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