Skip to main content

labfis.py

Travis - CI PyPI License

Description

Small library for uncertainty calculations and error propagation.

Error propagation:

The uncertainty is calculated analytically in accordance with the gaussian propagation aproximation established by the International Bureau of Weights and Measures (BIPM):

To compare two labfloats it is used the following methods:

Assuming:

  • If they are equal they must satisfy:
  • If they are different they must satisfy:

NOTE: Two labfloats can be not different and not equal at the same time by these methods.

Made by and for Physics Laboratory students in IFSC, who can't use uncertainties.py because of mean’s absolute deviation used in its calculation.

Usage

Just import with from labfis import labfloat and create an labfloat object, as this exemple below:

>>> from labfis import labfloat
>>> a = labfloat(1,3)
>>> b = labfloat(2,4)
>>> a*b
(2 ± 7)

Check the Wiki for more details.

Instalation

Intstall main releases with:

pip install labfis

Install development version with:

pip install git+https://github.com/phisgroup/labfis.py@development

References

  1. Kirchner, James. "Data Analysis Toolkit #5: Uncertainty Analysis and Error Propagation" (PDF). Berkeley Seismology Laboratory. University of California. Retrieved 22 April 2016.
  2. Goodman, Leo (1960). "On the Exact Variance of Products". Journal of the American Statistical Association. 55 (292): 708–713. doi:10.2307/2281592. JSTOR 2281592.
  3. Ochoa1,Benjamin; Belongie, Serge "Covariance Propagation for Guided Matching"
  4. Ku, H. H. (October 1966). "Notes on the use of propagation of error formulas". Journal of Research of the National Bureau of Standards. 70C (4): 262. doi:10.6028/jres.070c.025. ISSN 0022-4316. Retrieved 3 October 2012.
  5. Clifford, A. A. (1973). Multivariate error analysis: a handbook of error propagation and calculation in many-parameter systems. John Wiley & Sons. ISBN 978-0470160558.
  6. Lee, S. H.; Chen, W. (2009). "A comparative study of uncertainty propagation methods for black-box-type problems". Structural and Multidisciplinary Optimization. 37 (3): 239–253. doi:10.1007/s00158-008-0234-7.
  7. Johnson, Norman L.; Kotz, Samuel; Balakrishnan, Narayanaswamy (1994). Continuous Univariate Distributions, Volume 1. Wiley. p. 171. ISBN 0-471-58495-9.
  8. Lecomte, Christophe (May 2013). "Exact statistics of systems with uncertainties: an analytical theory of rank-one stochastic dynamic systems". Journal of Sound and Vibrations. 332 (11): 2750–2776. doi:10.1016/j.jsv.2012.12.009.
  9. "A Summary of Error Propagation" (PDF). p. 2. Retrieved 2016-04-04.
  10. "Propagation of Uncertainty through Mathematical Operations" (PDF). p. 5. Retrieved 2016-04-04.
  11. "Strategies for Variance Estimation" (PDF). p. 37. Retrieved 2013-01-18.
  12. Harris, Daniel C. (2003), Quantitative chemical analysis(6th ed.), Macmillan, p. 56, ISBN 978-0-7167-4464-1
  13. "Error Propagation tutorial" (PDF). Foothill College. October 9, 2009. Retrieved 2012-03-01.
  14. Helene, O.; Vanin, V.. Tratamento estatístico de dados em física experimental. São Paulo: Editora Edgard Blücher, 1981.
  15. Vuolo, J. E.. Fundamentos da teoria de erros. 2. ed. São Paulo: Editora Edgard Blücher, 1993.

Metadata

Release files for labfis 1.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for labfis 1.2.1
File Size Uploaded
labfis-1.2.1.tar.gz 14.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for labfis 1.2.1
File Interpreter ABI Platform
labfis-1.2.1-py3-none-any.whl Python 3 none any Details

Total release size: 25.5 kB

Release files / labfis-1.2.1.tar.gz

Download URL labfis-1.2.1.tar.gz
Size 14.1 kB
Tags Source
SHA-256 checksum
How to use checksums
ac353efb386ba9402a0a53baf35a2b2d010ca4e500c6c4a9391de8a2954cc4ea
BLAKE2b-256 checksum
How to use checksums
d06b806b3ee51d2146c071d40b8e167e40000789b6df73dab5c63369d82ea257
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.5

Release files / labfis-1.2.1-py3-none-any.whl

Download URL labfis-1.2.1-py3-none-any.whl
Size 11.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
522d9eba563439e8c31f0a819a8c7c534d90cc129dba67fae610b7d4ae3707d3
BLAKE2b-256 checksum
How to use checksums
fda2779a8029bb3be9ababd25a1a0ae782ffd72b5234e524fff4e0585cb441aa
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.5

Release history Release notifications | RSS feed

This release

1.2.1 This release

2 release files

1.2.0

2 release files

1.1.6

2 release files

1.1.5

1 release file

1.1.4

2 release files

1.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page