Metadata-Version: 2.1
Name: labfis
Version: 1.2.1
Summary: Adds a new float type with uncertainty
Home-page: https://github.com/phisgroup/labfis.py
Author: Hendrik Dumith Louzada, João Carlos Rodrigues Júnior
Author-email: hendriklouzada@gmail.com, jc.rodrigues1997@usp.br
License: MIT
Download-URL: https://pypi.org/project/labfis/
Description: # labfis.py
        
        [![Travis - CI](https://img.shields.io/travis/com/phisgroup/labfis.py/master?style=for-the-badge)](https://travis-ci.com/phisgroup/labfis.py) [![PyPI](https://img.shields.io/pypi/v/labfis?style=for-the-badge)](https://pypi.org/project/labfis/) [![License](https://img.shields.io/github/license/phisgroup/labfis.py?style=for-the-badge)](https://github.com/phisgroup/labfis.py/blob/master/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)](https://www.bipm.org/):
        
        <p align="center">
        <img src="https://latex.codecogs.com/svg.image?%5Cbg_white%20%5Csigma_f%20=%20%5Csqrt%7B%5Cleft%20(%20%5Cfrac%7B%5Cpartial%20f%7D%7B%5Cpartial%20x%7D%20%5Cright%20)%5E2%20%5Csigma_x%5E2%20&plus;%20%5Cleft%20(%20%5Cfrac%7B%5Cpartial%20f%7D%7B%5Cpartial%20y%7D%20%5Cright%20)%5E2%20%5Csigma_y%5E2%20&plus;%20%5Cleft%20(%20%5Cfrac%7B%5Cpartial%20f%7D%7B%5Cpartial%20z%7D%20%5Cright%20)%5E2%20%5Csigma_z%5E2%20&plus;%20...%7D">
        </p>
        
        
        ### To compare two labfloats it is used the following methods:
        
        Assuming:  
        <img src="https://latex.codecogs.com/svg.image?%5Cbg_white%20%5Cinline%20labfloat(x_i,%5Csigma_i)%5Cequiv%20x_i%20%5Cpm%20%5Csigma_i">
        
        - If they are equal they must satisfy:  
        <img src="https://latex.codecogs.com/svg.image?%5Cbg_white%20%5Cinline%20%5Cleft%7Cx_1%20-%20x_2%5Cright%7C%20%3C%202(%5Csigma_1%20&plus;%20%5Csigma_2)">
         
        - If they are different they must satisfy:  
        <img src="https://latex.codecogs.com/svg.image?%5Cbg_white%20%5Cinline%20%5Cleft%7Cx_1%20-%20x_2%5Cright%7C%20%3E%203(%5Csigma_1%20&plus;%20%5Csigma_2)">
        
        > **_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:
        
        ```py
        >>> 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"](http://seismo.berkeley.edu/~kirchner/eps_120/Toolkits/Toolkit_05.pdf)  (PDF). _Berkeley Seismology Laboratory_. University of California. Retrieved  22 April  2016.
         2. [Goodman, Leo](https://en.wikipedia.org/wiki/Leo_Goodman "Leo Goodman") (1960). "On the Exact Variance of Products". _Journal of the American Statistical Association_. **55** (292): 708–713. [doi](https://en.wikipedia.org/wiki/Doi_(identifier) "Doi (identifier)"):[10.2307/2281592](https://doi.org/10.2307%2F2281592). [JSTOR](https://en.wikipedia.org/wiki/JSTOR_(identifier) "JSTOR (identifier)")  [2281592](https://www.jstor.org/stable/2281592).
         3. Ochoa1,Benjamin; Belongie, Serge ["Covariance Propagation for Guided Matching"](http://vision.ucsd.edu/sites/default/files/ochoa06.pdf)
         4. Ku, H. H. (October 1966). ["Notes on the use of propagation of error formulas"](http://nistdigitalarchives.contentdm.oclc.org/cdm/compoundobject/collection/p16009coll6/id/99848/rec/1). _Journal of Research of the National Bureau of Standards_. **70C** (4): 262. [doi](https://en.wikipedia.org/wiki/Doi_(identifier) "Doi (identifier)"):[10.6028/jres.070c.025](https://doi.org/10.6028%2Fjres.070c.025). [ISSN](https://en.wikipedia.org/wiki/ISSN_(identifier) "ISSN (identifier)")  [0022-4316](https://www.worldcat.org/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](https://en.wikipedia.org/wiki/ISBN_(identifier) "ISBN (identifier)")  [978-0470160558](https://en.wikipedia.org/wiki/Special:BookSources/978-0470160558 "Special:BookSources/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](https://en.wikipedia.org/wiki/Doi_(identifier) "Doi (identifier)"):[10.1007/s00158-008-0234-7](https://doi.org/10.1007%2Fs00158-008-0234-7).
         7. Johnson, Norman L.; Kotz, Samuel; Balakrishnan, Narayanaswamy (1994). _Continuous Univariate Distributions, Volume 1_. Wiley. p. 171. [ISBN](https://en.wikipedia.org/wiki/ISBN_(identifier) "ISBN (identifier)")  [0-471-58495-9](https://en.wikipedia.org/wiki/Special:BookSources/0-471-58495-9 "Special:BookSources/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](https://en.wikipedia.org/wiki/Doi_(identifier) "Doi (identifier)"):[10.1016/j.jsv.2012.12.009](https://doi.org/10.1016%2Fj.jsv.2012.12.009).
         9. ["A Summary of Error Propagation"](http://ipl.physics.harvard.edu/wp-uploads/2013/03/PS3_Error_Propagation_sp13.pdf)  (PDF). p. 2. Retrieved  2016-04-04.
         10. ["Propagation of Uncertainty through Mathematical Operations"](http://web.mit.edu/fluids-modules/www/exper_techniques/2.Propagation_of_Uncertaint.pdf)  (PDF). p. 5. Retrieved  2016-04-04. 
         11. ["Strategies for Variance Estimation"](http://www.sagepub.com/upm-data/6427_Chapter_4__Lee_%28Analyzing%29_I_PDF_6.pdf)  (PDF). p. 37. Retrieved  2013-01-18.
         12. Harris, Daniel C. (2003), [_Quantitative chemical analysis_](https://books.google.com/books?id=csTsQr-v0d0C&pg=PA56)(6th ed.), Macmillan, p. 56, [ISBN](https://en.wikipedia.org/wiki/ISBN_(identifier) "ISBN (identifier)")  [978-0-7167-4464-1](https://en.wikipedia.org/wiki/Special:BookSources/978-0-7167-4464-1 "Special:BookSources/978-0-7167-4464-1")
         13. ["Error Propagation tutorial"](http://www.foothill.edu/psme/daley/tutorials_files/10.%20Error%20Propagation.pdf)  (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. 
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/markdown
