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Top Forums Programming Python Screen Capture of RIGOL 1054Z on macOS Catalina Using NI-VISA Post 303043073 by Neo on Friday 17th of January 2020 10:40:08 AM
Old 01-17-2020
More beautiful noise..... I really like noise Smilie This is A/C hum from the 240v (plus or minus) standard line voltage here (using an "unplugged Arduino UNO" as my "antenna"):

Python Screen Capture of RIGOL 1054Z on macOS Catalina Using NI-VISA-more_noisejpg


I've always liked noise on scopes and analyzers.... it's kinda hypnotic ....

Have you noticed how much more beautiful noise is, compared to a "clean" 1 KHz reference signal?
 

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WZIP(1) 						      General Commands Manual							   WZIP(1)

NAME
wzip - lossy data compression and denoising SYNOPSIS
wzip [ -c | -d | -dn | -hdn ] num sf DESCRIPTION
This manual page documents the wzip command. wzip is a program that can be used for LOSSY data compression and denoising. It reads from STDIN and writes to STDOUT. In compression mode the input is a sequence of ascii floating-point values. num is the number of these data values. The output is a sequence of small inte- gers, most of them zero in typical application. This is ready for effective compression with a standard loss-less compression program like gzip. The program can also be used for denoising. In this case both input and output are sequences of ascii floating-point values. The scale factor sf determines the strength of compression or denoising. A higher scale factor means heavier compression and stronger denoising. Four times the standard deviation of the noise content is a good start. Otherwise 5 percent of the overall signal amplitude might be used as a first estimation of a suitable scale factor. If the noise content of the input data is strongly non-Gaussian-distributed, like Poisson noise. The input data should be transformed to approximate Gaussian-distributed noise. If the input values are Poisson-distributed, that means for example raw counts per channel in EDX or XPD, they can be transformed to approximate Gaussian-distributed noise by transformation of each data point with y:=2.0*sqrt(x+0.25109). Back transformation is done with y:=(x/2)^2. The summand 0.25109 compensates for the bias caused by the asymmetry of the Poisson-distribu- tion. Invoking the program without any options writes examples of the use of the program to STDERR. OPTIONS
There must be given exactly one option. -c Compression, reads num ascii floating-point values from STDIN and writes a sequence of integers with high redundancy to STDOUT. -d Decompression, reads from STDIN and writes a sequence of num ascii floating-point values to STDOUT. These are more or less similar to the original data. -dn Denoising, reads num ascii floating-point values from STDIN and writes a sequence of num ascii floating-point values to STDOUT. These are more or less similar to the original data. -hdn Denoising with hard thresholding instead of wavelet shrinkage. Single untouched noise peaks may be visible with this mode. On the other hand, there is much less impact on the signal slope. SEE ALSO
Donoho, D.L.; Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage, technical report 425, Department of Statistics, Stan- ford University, Stanford, June 1993, ftp://playfair.stanford.edu/pub/donoho/ausws.ps.Z Franzen, A.: Compression of process data with a wavelet method, steel res. 69 (1998), No. 1, pp. 28/30 Franzen, A.: Non-linear denoising with wavelet transformation, Z. Metallkd. 89 (1998), No. 4, pp. 297/302 AUTHOR
This manual page was written by Andreas Franzen <anfra@debian.org>, for the Debian GNU/Linux system (but may be used by others). Copyright (C) 1997 Andreas Franzen, placed under the GNU General Public License, see the file copyright for details. 24 December 1997 WZIP(1)
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