### PyFluxconserving
#### Fully based on a Fortran legacy package to easily compute the flux-density conservation
Obs.: A Fortran legacy Interpolation routines also furnished <br>
email: [antineutrinomuon@gmail.com](mailto:antineutrinomuon@gmail.com), [jean@astro.up.pt](mailto:jean@astro.up.pt)
github repository: <a href="https://github.com/neutrinomuon/PyFluxconserving">PyFluxconserving</a>
last stable version: 0.0.14
© Copyright ®
J.G. - Jean Gomes @ 2023
<hr>
<img src="https://skillicons.dev/icons?i=python,fortran,c,numpy&theme=light"><br>
<img src="https://img.shields.io/pypi/pyversions/PyFluxconserving"><img src="https://anaconda.org/neutrinomuon/PyFluxconserving/badges/license.svg">
<hr>
<div align="center">
<img src='https://raw.githubusercontent.com/neutrinomuon/PyFluxconserving/main/tutorials/PyFluxconserving.png' width='60%'>
</div>
<hr>
#### <b>RESUME</b>
Original Fortran 2003+ routines date back to 2003-2004. Read the <a
href='https://github.com/neutrinomuon/PyFluxconserving/blob/main/LICENSE.txt'>LICENSE.txt</a>
file. When analyzing astronomical spectra, astronomers often bin the data to
increase the signal-to-noise ratio and reduce the effects of noise in the
data. Binning refers to the process of averaging the intensity of adjacent
spectral channels, or pixels, to produce a new, coarser set of data.
In the process of binning, it is important to ensure that the principle of
flux density conservation is maintained. This means that the total energy
emitted by the object, and hence its flux density, must remain constant after
binning.
To conserve flux density, the intensity of each binned pixel should be scaled
by the number of pixels it represents. For example, if two adjacent pixels are
binned together, the intensity of the resulting bin should be the sum of the
intensities of the two original pixels, divided by two. This ensures that the
total energy in the bin is conserved, and that the flux density of the object
remains the same.
It's worth noting that binning can introduce errors in the spectral data,
especially if the signal-to-noise ratio is low or if the binning is too
coarse. In general, astronomers choose a binning size that balances the need
for a high signal-to-noise ratio with the desire to maintain the spectral
resolution and avoid introducing significant errors in the data.
In summary, the principle of flux density conservation is important to
consider when binning astronomical spectra, and astronomers need to scale the
intensity of each binned pixel to ensure that the total energy emitted by the
object is conserved. SpectRes from A. Carnall is *NOT* part of the distribution,
but used as a comparison: <a
href='https://github.com/ACCarnall/SpectRes'>https://github.com/ACCarnall/SpectRes</a>. If you want to install for comparison then:
<pre>
pip install spectres
</pre>
However, it is not necessary for the usage of this package. Accompanying there are several routines for interpolations.
<hr>
#### <b>INSTALLATION</b>
You can easily install <a
href=https://pypi.org/project/PyFluxconserving/>PyFluxconserving</a> by using pip -
<a href='https://pypi.org/'>PyPI - The Python Package Index</a>:
<pre>
pip install PyFluxconserving
</pre>
<br>or by using a generated conda repository <a
href='https://anaconda.org/neutrinomuon/PyFluxconserving'>https://anaconda.org/neutrinomuon/PyFluxconserving</a>:
<img src="https://anaconda.org/neutrinomuon/PyFluxconserving/badges/version.svg"><img src="https://anaconda.org/neutrinomuon/PyFluxconserving/badges/latest_release_date.svg"><img src="https://anaconda.org/neutrinomuon/PyFluxconserving/badges/platforms.svg">
<pre>
conda install -c neutrinomuon PyFluxconserving
</pre>
<br>OBS.: Linux, OS-X and Windows pre-compilations available in conda.
You can also clone the repository and install by yourself in your machine:
<pre>
git clone https://github.com/neutrinomuon/PyFluxconserving
python setup.py install
</pre>
<hr>
#### <b>METHOD & REFERENCES</b>
Here, the method used is with the Cumulative function to produce a new
flux-conserved, some options can be chosen for the interpolation:
<table border="1">
<tr><td width='20%'>Integer Number</td><td width='33%'>Option: Interpolation Schemes</td><td>Brief Description</td></tr>
<tr><td>0)</td><td>AkimaSpline</td><td>Akima Spline interpolation. The Akima spline is a C1 differentiable function (that is, has a continuous first
derivative) but, in general, will have a discontinuous second derivative at the knot points.<p>
<ul>
<il>Akima, H. (1970). A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures. <i>Journal of the ACM</i>, 17(4),
589-602. Association for Computing Machinery. DOI: 10.1145/321607.321609. Link: <a href="https://dl.acm.org/doi/10.1145/321607.321609">https://dl.acm.org/doi/10.1145/321607.321609</a></il><p>
<il>De Boor, C. (1978). A Practical Guide to Splines. Springer-Verlag. ISBN: 978-0-387-95366-3. Link: <a href="https://www.springer.com/gp/book/9780387953663">https://www.springer.com/gp/book/9780387953663</a></il><p>
<il>Forsythe, G.E. (1979) <i>Computer Methods for Mathematical Computations</i>. Prentice-Hall, Inc. DOI: 10.1002/zamm.19790590235. Link: <a href="https://onlinelibrary.wiley.com/doi/10.1002/zamm.19790590235">https://onlinelibrary.wiley.com/doi/10.1002/zamm.19790590235</a></il><p>
<il>Bartels, R.H., Beatty, J.C., and Barsky, B.A. (1987). An Introduction to Splines for Use in Computer Graphics and Geometric Modeling. Morgan Kaufmann
Publishers. Link: <a href="https://www.osti.gov/biblio/5545263">https://www.osti.gov/biblio/5545263</a></il><p>
<il>Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical Recipes: The Art of Scientific Computing (3rd
ed.). Cambridge University Press. ISBN: 978-0521880688. Link: <a href="http://numerical.recipes/">http://numerical.recipes/</a></il><p>
<il>Cheney, W. and Kincaid, D. (2008). Numerical Mathematics and Computing (6th ed.). Brooks/Cole. ISBN: 978-0495114758. Link: <a href="https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758">https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758</a></il><p>
<il>Burden, R. L., & Faires, J. D. (2010). Numerical Analysis (9th ed.). Brooks/Cole. ISBN: 978-0538733519. Link: <a href="https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/0538733519">https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/0538733519</a></il><p>
</ul>
</td></tr>
<tr><td>1)</td><td>Interpolado</td><td>Based on a linear interpolation within
a table of pair values.<p>
<ul>
<il>Atkinson, K. E. (1991). <i>An introduction to numerical analysis</i> (2nd ed.). John Wiley & Sons. ISBN: 978-0-471-62489-9. Link: <a href="https://www.wiley.com/en-us/An+Introduction+to+Numerical+Analysis%2C+2nd+Edition-p-9780471624899">https://www.wiley.com/en-us/An+Introduction+to+Numerical+Analysis%2C+2nd+Edition-p-9780471624899</a></il><p>
<il>Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). <em>Numerical Recipes: The Art of Scientific Computing</em> (3rd
ed.). Cambridge University Press. ISBN: 978-0521880688. Link: <a href="http://numerical.recipes/">http://numerical.recipes/</a></il><p>
<il>Chapra, S. C., & Canale, R. P. (2010). <i>Numerical methods for engineers</i> (6th ed.). McGraw-Hill. ISBN: 978-0073401065. Link: <a href="https://www.amazon.com/Numerical-Methods-Engineers-Steven-Chapra/dp/0073401064">https://www.amazon.com/Numerical-Methods-Engineers-Steven-Chapra/dp/0073401064</a></il><p>
<il>Burden, R. L., Faires, J. D. & A.M. Burden (2015). <i>Numerical analysis (10th ed.)</i> (9th ed.). Cengage Learning. ISBN: 978-1305253667. Link: <a href="https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/1305253663">https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/1305253663</a></il><p>
</ul>
</td></tr>
<tr><td>2)</td><td>LINdexerpol</td><td>Based on a linear interpolation within
a table of pair values using indexing. The references are the same as in
1).</td></tr>
<tr><td>3)</td><td>LINinterpol</td><td>Based on a linear interpolation within
a table of pair values. The references are the same as in 1).</td></tr>
<tr><td>4)</td><td>SPLINE1DArr</td><td>This is a Fortran 2003 subroutine
called SPLINE1DArr that takes an array of values x to interpolate from the
arrays t and y. It has ten input arguments, six output arguments, and two
optional arguments. The interpolation is linear.<p>
<ul>
<il>Forsythe, G.E. (1977) <em>Computer Methods For Mathematical Computations</em>. Ed. Prentice-Hall, Inc. <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/zamm.19790590235">https://onlinelibrary.wiley.com/doi/abs/10.1002/zamm.19790590235</a></il><p>
<il>De Boor, C. (1978). <em>A Practical Guide to Splines</em>. Springer-Verlag. ISBN: 978-0-387-95366-3. Link: <a href="https://www.springer.com/gp/book/9780387953663">https://www.springer.com/gp/book/9780387953663</a></il><p>
<il>Bartels, R.H., Beatty, J.C., and Barsky, B.A. (1998). <em>An Introductionto Splines for Use in Computer Graphics and Geometric Modeling</em>. Morgan
Kaufmann Publishers. ISBN: 978-1558604000. Link: <a href="https://www.amazon.com/Introduction-Computer-Graphics-Geometric-Modeling/dp/1558604006">https://www.amazon.com/Introduction-Computer-Graphics-Geometric-Modeling/dp/1558604006</a></il><p>
<il>Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). <em>Numerical Recipes: The Art of Scientific Computing</em> (3rd
ed.). Cambridge University Press. ISBN: 978-0521880688. Link: <a href="http://numerical.recipes/">http://numerical.recipes/</a></il><p>
<il>Cheney, W. and Kincaid, D. (2007). <em>Numerical Mathematics and Computing</em> (6th ed.). Brooks/Cole. ISBN: 978-0495114758. Link: <a href="https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758">https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758</a></il><p>
</td></tr>
<tr><td>5)</td><td>SPLINE3DFor</td><td>This function evaluates the cubic
spline interpolation. The same references as in 4).</td></tr>
</table>
<hr>
#### <b>STRUCTURE</b>
The main structure of the directories and files are:
<pre>
PyFluxconserving
├── dist
│ ├── PyFluxconserving-0.0.4.tar.gz
│ ├── PyFluxconserving-0.0.7.tar.gz
│ ├── PyFluxconserving-0.0.3.tar.gz
│ ├── PyFluxconserving-0.0.6.tar.gz
│ ├── PyFluxconserving-0.0.8.tar.gz
│ ├── PyFluxconserving-0.0.9.tar.gz
│ ├── PyFluxconserving-0.0.1.tar.gz
│ ├── PyFluxconserving-0.0.2.tar.gz
│ ├── PyFluxconserving-0.0.10.tar.gz
│ ├── PyFluxconserving-0.0.11.tar.gz
│ └── PyFluxconserving-0.0.5.tar.gz
├── README.md
├── .#README.md
├── showdown.min.js
├── scripts
│ └── update_readme.py
├── index.html
├── LICENSE.txt
├── setup.py
├── PyFluxconserving
│ ├── calculate_sha256.py
│ ├── meta.yaml
│ └── README.txt
├── tutorials
│ ├── PyFluxconserving.png
│ ├── .ipynb_checkpoints
│ │ └── PyFluxconserving Example-checkpoint.ipynb
│ └── PyFluxconserving Example.ipynb
├── PyFluxconserving.egg-info
│ ├── PKG-INFO
│ ├── dependency_links.txt
│ ├── SOURCES.txt
│ ├── top_level.txt
│ └── requires.txt
├── src
│ ├── python
│ │ ├── PyFluxConSpec.py
│ │ ├── califa_cmap_alternative.py
│ │ ├── PyLinear__int.py
│ │ ├── PyLINinterpol.py
│ │ ├── fluxconserve.py
│ │ ├── __init__.py
│ │ ├── PySPLINE1DArr.py
│ │ ├── PySPLINE3DFor.py
│ │ ├── fluxconserving.png
│ │ ├── PyAkimaSpline.py
│ │ ├── PyInterpolado.py
│ │ └── PyLINdexerpol.py
│ └── fortran
│ ├── SPLINE3DFor.cpython-39-darwin.so
│ ├── LINdexerpol.cpython-311-darwin.so
│ ├── SPLINE3DFor.cpython-310-darwin.so
│ ├── LINdexerpol.f90
│ ├── LINinterpol.cpython-39-darwin.so
│ ├── SPLINE3DFor.cpython-39-x86_64-linux-gnu.so
│ ├── SPLINE1DFlt.cpython-39-x86_64-linux-gnu.so
│ ├── SPLINE3DFor.compile
│ ├── FluxConSpec.compile
│ ├── FluxConSpec.cpython-39-darwin.so
│ ├── SPLINE1DArr.compile
│ ├── SPLINE1DFlt.cpython-39-darwin.so
│ ├── AkimaSpline.cpython-310-darwin.so
│ ├── Interpolado.cpython-39-x86_64-linux-gnu.so
│ ├── Interpolado.cpython-38-x86_64-linux-gnu.so
│ ├── SPLINE1DFlt.f90
│ ├── FluxConSpec.cpython-39-x86_64-linux-gnu.so
│ ├── SPLINE1DFlt.cpython-310-darwin.so
│ ├── FluxConSpec.cpython-311-darwin.so
│ ├── AkimaSpline.f90
│ ├── SPLINE1DArr.f90
│ ├── SPLINE1DFlt.cpython-311-darwin.so
│ ├── FluxConSpec.cpython-38-x86_64-linux-gnu.so
│ ├── AkimaSpline.cpython-39-darwin.so
│ ├── SPLINE1DFlt.cpython-38-x86_64-linux-gnu.so
│ ├── SPLINE3DFor.cpython-38-x86_64-linux-gnu.so
│ ├── AkimaSpline.compile
│ ├── DataTypes.f90
│ ├── SPLINE1DArr.cpython-39-darwin.so
│ ├── LINdexerpol.cpython-310-darwin.so
│ ├── LINdexerpol.compile
│ ├── SPLINE3DFor.f90
│ ├── SPLINE1DFlt.compile
│ ├── Interpolado.f90
│ ├── LINinterpol.cpython-311-darwin.so
│ ├── SPLINE1DArr.cpython-310-darwin.so
│ ├── FluxConSpec.cpython-310-x86_64-linux-gnu.so
│ ├── LINinterpol.cpython-310-darwin.so
│ ├── SPLINE1DFlt.cpython-310-x86_64-linux-gnu.so
│ ├── LINinterpol.compile
│ ├── SPLINE1DArr.cpython-311-darwin.so
│ ├── SPLINE3DFor.cpython-311-darwin.so
│ ├── LINinterpol.compile~
│ ├── FluxConSpec.cpython-310-darwin.so
│ ├── FluxConSpec.f90
│ ├── Interpolado.cpython-310-x86_64-linux-gnu.so
│ ├── LINdexerpol.cpython-310-x86_64-linux-gnu.so
│ ├── README.txt
│ ├── Interpolado.compile
│ ├── SPLINE3DFor.cpython-310-x86_64-linux-gnu.so
│ ├── LINdexerpol.cpython-39-darwin.so
│ ├── LINinterpol.f90
│ ├── LINdexerpol.cpython-39-x86_64-linux-gnu.so
│ ├── LINdexerpol.cpython-38-x86_64-linux-gnu.so
│ └── AkimaSpline.cpython-311-darwin.so
├── version.txt
└── build
├── lib.linux-x86_64-3.9
│ └── PyFluxconserving
├── lib.macosx-11.1-arm64-cpython-39
│ └── PyFluxconserving
├── temp.macosx-11.1-arm64-cpython-39
│ ├── __pycache__
│ ├── PyFluxconserving
│ ├── ccompiler_opt_cache_ext.py
│ ├── src
│ └── build
├── src.linux-x86_64-3.9
│ ├── PyFluxconserving
│ ├── build
│ └── numpy
├── temp.linux-x86_64-3.9
│ ├── __pycache__
│ ├── PyFluxconserving
│ ├── ccompiler_opt_cache_ext.py
│ ├── src
│ └── build
└── src.macosx-11.1-arm64-3.9
├── PyFluxconserving
├── build
└── numpy
32 directories, 99 files
</pre>
<br>PyFluxConSPec.py is a python wrapper to the library in fortran called
PyFluxconserving.flib. The fortran directory can be compiled separately for
each individual subroutine.
<hr>
#### ISSUES AND CONTRIBUTIONS
If you encounter any issues with this project, please feel free to submit an issue on the GitHub repository. We appreciate your feedback and are committed to improving the quality of our codebase.
If you'd like to contribute to this project, we welcome pull requests from the community. Before submitting a pull request, please make sure to fork the repository and create a new branch for your changes. Once your changes are complete, submit a pull request and we'll review your code as soon as possible.
For any questions or concerns about contributing, please contact the project maintainer at antineutrinomuon@gmail.com. Thank you for your interest in contributing to our project!
<hr>
#### <b>LICENSE</b>
This software is provided "AS IS" (see DISCLAIMER below). Permission to
use, for non-commercial purposes is granted. Permission to modify for personal
or internal use is granted, provided this copyright and disclaimer are
included in ALL copies of the software. All other rights are reserved. In
particular, redistribution of the code is not allowed without explicit
permission by the author.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Raw data
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"maintainer_email": "antineutrinomuon@gmail.com",
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"author": "Jean Gomes",
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"platform": null,
"description": "### PyFluxconserving\n#### Fully based on a Fortran legacy package to easily compute the flux-density conservation\nObs.: A Fortran legacy Interpolation routines also furnished <br>\nemail: [antineutrinomuon@gmail.com](mailto:antineutrinomuon@gmail.com), [jean@astro.up.pt](mailto:jean@astro.up.pt)\n\ngithub repository: <a href=\"https://github.com/neutrinomuon/PyFluxconserving\">PyFluxconserving</a>\n\nlast stable version: 0.0.14\n\n\u00a9 Copyright \u00ae\n\nJ.G. - Jean Gomes @ 2023\n\n<hr>\n\n<img src=\"https://skillicons.dev/icons?i=python,fortran,c,numpy&theme=light\"><br>\n<img src=\"https://img.shields.io/pypi/pyversions/PyFluxconserving\"><img src=\"https://anaconda.org/neutrinomuon/PyFluxconserving/badges/license.svg\">\n\n<hr>\n\n<div align=\"center\">\n<img src='https://raw.githubusercontent.com/neutrinomuon/PyFluxconserving/main/tutorials/PyFluxconserving.png' width='60%'>\n</div>\n\n<hr>\n\n#### <b>RESUME</b>\n\nOriginal Fortran 2003+ routines date back to 2003-2004. Read the <a\nhref='https://github.com/neutrinomuon/PyFluxconserving/blob/main/LICENSE.txt'>LICENSE.txt</a>\nfile. When analyzing astronomical spectra, astronomers often bin the data to\nincrease the signal-to-noise ratio and reduce the effects of noise in the\ndata. Binning refers to the process of averaging the intensity of adjacent\nspectral channels, or pixels, to produce a new, coarser set of data.\n\nIn the process of binning, it is important to ensure that the principle of\nflux density conservation is maintained. This means that the total energy\nemitted by the object, and hence its flux density, must remain constant after\nbinning.\n\nTo conserve flux density, the intensity of each binned pixel should be scaled\nby the number of pixels it represents. For example, if two adjacent pixels are\nbinned together, the intensity of the resulting bin should be the sum of the\nintensities of the two original pixels, divided by two. This ensures that the\ntotal energy in the bin is conserved, and that the flux density of the object\nremains the same.\n\nIt's worth noting that binning can introduce errors in the spectral data,\nespecially if the signal-to-noise ratio is low or if the binning is too\ncoarse. In general, astronomers choose a binning size that balances the need\nfor a high signal-to-noise ratio with the desire to maintain the spectral\nresolution and avoid introducing significant errors in the data.\n\nIn summary, the principle of flux density conservation is important to\nconsider when binning astronomical spectra, and astronomers need to scale the\nintensity of each binned pixel to ensure that the total energy emitted by the\nobject is conserved. SpectRes from A. Carnall is *NOT* part of the distribution,\nbut used as a comparison: <a\nhref='https://github.com/ACCarnall/SpectRes'>https://github.com/ACCarnall/SpectRes</a>. If you want to install for comparison then:\n\n<pre>\npip install spectres\n</pre>\n\nHowever, it is not necessary for the usage of this package. Accompanying there are several routines for interpolations.\n\n<hr>\n\n#### <b>INSTALLATION</b>\n\nYou can easily install <a\nhref=https://pypi.org/project/PyFluxconserving/>PyFluxconserving</a> by using pip -\n<a href='https://pypi.org/'>PyPI - The Python Package Index</a>:\n\n<pre>\npip install PyFluxconserving\n</pre>\n\n<br>or by using a generated conda repository <a\nhref='https://anaconda.org/neutrinomuon/PyFluxconserving'>https://anaconda.org/neutrinomuon/PyFluxconserving</a>:\n\n<img src=\"https://anaconda.org/neutrinomuon/PyFluxconserving/badges/version.svg\"><img src=\"https://anaconda.org/neutrinomuon/PyFluxconserving/badges/latest_release_date.svg\"><img src=\"https://anaconda.org/neutrinomuon/PyFluxconserving/badges/platforms.svg\">\n\n<pre>\nconda install -c neutrinomuon PyFluxconserving\n</pre>\n\n<br>OBS.: Linux, OS-X and Windows pre-compilations available in conda.\n\nYou can also clone the repository and install by yourself in your machine:\n\n<pre>\ngit clone https://github.com/neutrinomuon/PyFluxconserving\npython setup.py install\n</pre>\n\n<hr>\n\n#### <b>METHOD & REFERENCES</b>\n\nHere, the method used is with the Cumulative function to produce a new\nflux-conserved, some options can be chosen for the interpolation:\n\n<table border=\"1\">\n\n<tr><td width='20%'>Integer Number</td><td width='33%'>Option: Interpolation Schemes</td><td>Brief Description</td></tr>\n\n<tr><td>0)</td><td>AkimaSpline</td><td>Akima Spline interpolation. The Akima spline is a C1 differentiable function (that is, has a continuous first\nderivative) but, in general, will have a discontinuous second derivative at the knot points.<p>\n\n<ul>\n<il>Akima, H. (1970). A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures. <i>Journal of the ACM</i>, 17(4),\n589-602. Association for Computing Machinery. DOI: 10.1145/321607.321609. Link: <a href=\"https://dl.acm.org/doi/10.1145/321607.321609\">https://dl.acm.org/doi/10.1145/321607.321609</a></il><p>\n\n<il>De Boor, C. (1978). A Practical Guide to Splines. Springer-Verlag. ISBN: 978-0-387-95366-3. Link: <a href=\"https://www.springer.com/gp/book/9780387953663\">https://www.springer.com/gp/book/9780387953663</a></il><p>\n\n<il>Forsythe, G.E. (1979) <i>Computer Methods for Mathematical Computations</i>. Prentice-Hall, Inc. DOI: 10.1002/zamm.19790590235. Link: <a href=\"https://onlinelibrary.wiley.com/doi/10.1002/zamm.19790590235\">https://onlinelibrary.wiley.com/doi/10.1002/zamm.19790590235</a></il><p>\n\n<il>Bartels, R.H., Beatty, J.C., and Barsky, B.A. (1987). An Introduction to Splines for Use in Computer Graphics and Geometric Modeling. Morgan Kaufmann\nPublishers. Link: <a href=\"https://www.osti.gov/biblio/5545263\">https://www.osti.gov/biblio/5545263</a></il><p>\n\n<il>Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical Recipes: The Art of Scientific Computing (3rd\ned.). Cambridge University Press. ISBN: 978-0521880688. Link: <a href=\"http://numerical.recipes/\">http://numerical.recipes/</a></il><p>\n\n<il>Cheney, W. and Kincaid, D. (2008). Numerical Mathematics and Computing (6th ed.). Brooks/Cole. ISBN: 978-0495114758. Link: <a href=\"https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758\">https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758</a></il><p>\n\n<il>Burden, R. L., & Faires, J. D. (2010). Numerical Analysis (9th ed.). Brooks/Cole. ISBN: 978-0538733519. Link: <a href=\"https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/0538733519\">https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/0538733519</a></il><p>\n\n</ul>\n\n</td></tr>\n\n<tr><td>1)</td><td>Interpolado</td><td>Based on a linear interpolation within\na table of pair values.<p>\n\n<ul>\n\n<il>Atkinson, K. E. (1991). <i>An introduction to numerical analysis</i> (2nd ed.). John Wiley & Sons. ISBN: 978-0-471-62489-9. Link: <a href=\"https://www.wiley.com/en-us/An+Introduction+to+Numerical+Analysis%2C+2nd+Edition-p-9780471624899\">https://www.wiley.com/en-us/An+Introduction+to+Numerical+Analysis%2C+2nd+Edition-p-9780471624899</a></il><p>\n\n<il>Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). <em>Numerical Recipes: The Art of Scientific Computing</em> (3rd\ned.). Cambridge University Press. ISBN: 978-0521880688. Link: <a href=\"http://numerical.recipes/\">http://numerical.recipes/</a></il><p>\n\n<il>Chapra, S. C., & Canale, R. P. (2010). <i>Numerical methods for engineers</i> (6th ed.). McGraw-Hill. ISBN: 978-0073401065. Link: <a href=\"https://www.amazon.com/Numerical-Methods-Engineers-Steven-Chapra/dp/0073401064\">https://www.amazon.com/Numerical-Methods-Engineers-Steven-Chapra/dp/0073401064</a></il><p>\n\n<il>Burden, R. L., Faires, J. D. & A.M. Burden (2015). <i>Numerical analysis (10th ed.)</i> (9th ed.). Cengage Learning. ISBN: 978-1305253667. Link: <a href=\"https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/1305253663\">https://www.amazon.com/Numerical-Analysis-Richard-L-Burden/dp/1305253663</a></il><p>\n\n</ul>\n\n</td></tr>\n\n<tr><td>2)</td><td>LINdexerpol</td><td>Based on a linear interpolation within\na table of pair values using indexing. The references are the same as in\n1).</td></tr>\n\n<tr><td>3)</td><td>LINinterpol</td><td>Based on a linear interpolation within\na table of pair values. The references are the same as in 1).</td></tr>\n\n<tr><td>4)</td><td>SPLINE1DArr</td><td>This is a Fortran 2003 subroutine\ncalled SPLINE1DArr that takes an array of values x to interpolate from the\narrays t and y. It has ten input arguments, six output arguments, and two\noptional arguments. The interpolation is linear.<p>\n\n<ul>\n<il>Forsythe, G.E. (1977) <em>Computer Methods For Mathematical Computations</em>. Ed. Prentice-Hall, Inc. <a href=\"https://onlinelibrary.wiley.com/doi/abs/10.1002/zamm.19790590235\">https://onlinelibrary.wiley.com/doi/abs/10.1002/zamm.19790590235</a></il><p>\n\n<il>De Boor, C. (1978). <em>A Practical Guide to Splines</em>. Springer-Verlag. ISBN: 978-0-387-95366-3. Link: <a href=\"https://www.springer.com/gp/book/9780387953663\">https://www.springer.com/gp/book/9780387953663</a></il><p>\n\n<il>Bartels, R.H., Beatty, J.C., and Barsky, B.A. (1998). <em>An Introductionto Splines for Use in Computer Graphics and Geometric Modeling</em>. Morgan\nKaufmann Publishers. ISBN: 978-1558604000. Link: <a href=\"https://www.amazon.com/Introduction-Computer-Graphics-Geometric-Modeling/dp/1558604006\">https://www.amazon.com/Introduction-Computer-Graphics-Geometric-Modeling/dp/1558604006</a></il><p>\n\n<il>Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). <em>Numerical Recipes: The Art of Scientific Computing</em> (3rd\ned.). Cambridge University Press. ISBN: 978-0521880688. Link: <a href=\"http://numerical.recipes/\">http://numerical.recipes/</a></il><p>\n\n<il>Cheney, W. and Kincaid, D. (2007). <em>Numerical Mathematics and Computing</em> (6th ed.). Brooks/Cole. ISBN: 978-0495114758. Link: <a href=\"https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758\">https://www.amazon.com/Numerical-Mathematics-Computing-Ward-Cheney/dp/0495114758</a></il><p>\n\n</td></tr>\n\n<tr><td>5)</td><td>SPLINE3DFor</td><td>This function evaluates the cubic\nspline interpolation. The same references as in 4).</td></tr>\n\n</table>\n\n<hr>\n\n#### <b>STRUCTURE</b>\n\nThe main structure of the directories and files are:\n\n<pre>\nPyFluxconserving\n\u251c\u2500\u2500 dist\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.4.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.7.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.3.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.6.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.8.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.9.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.1.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.2.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.10.tar.gz\n\u2502 \u251c\u2500\u2500 PyFluxconserving-0.0.11.tar.gz\n\u2502 \u2514\u2500\u2500 PyFluxconserving-0.0.5.tar.gz\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 .#README.md\n\u251c\u2500\u2500 showdown.min.js\n\u251c\u2500\u2500 scripts\n\u2502 \u2514\u2500\u2500 update_readme.py\n\u251c\u2500\u2500 index.html\n\u251c\u2500\u2500 LICENSE.txt\n\u251c\u2500\u2500 setup.py\n\u251c\u2500\u2500 PyFluxconserving\n\u2502 \u251c\u2500\u2500 calculate_sha256.py\n\u2502 \u251c\u2500\u2500 meta.yaml\n\u2502 \u2514\u2500\u2500 README.txt\n\u251c\u2500\u2500 tutorials\n\u2502 \u251c\u2500\u2500 PyFluxconserving.png\n\u2502 \u251c\u2500\u2500 .ipynb_checkpoints\n\u2502 \u2502 \u2514\u2500\u2500 PyFluxconserving Example-checkpoint.ipynb\n\u2502 \u2514\u2500\u2500 PyFluxconserving Example.ipynb\n\u251c\u2500\u2500 PyFluxconserving.egg-info\n\u2502 \u251c\u2500\u2500 PKG-INFO\n\u2502 \u251c\u2500\u2500 dependency_links.txt\n\u2502 \u251c\u2500\u2500 SOURCES.txt\n\u2502 \u251c\u2500\u2500 top_level.txt\n\u2502 \u2514\u2500\u2500 requires.txt\n\u251c\u2500\u2500 src\n\u2502 \u251c\u2500\u2500 python\n\u2502 \u2502 \u251c\u2500\u2500 PyFluxConSpec.py\n\u2502 \u2502 \u251c\u2500\u2500 califa_cmap_alternative.py\n\u2502 \u2502 \u251c\u2500\u2500 PyLinear__int.py\n\u2502 \u2502 \u251c\u2500\u2500 PyLINinterpol.py\n\u2502 \u2502 \u251c\u2500\u2500 fluxconserve.py\n\u2502 \u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2502 \u251c\u2500\u2500 PySPLINE1DArr.py\n\u2502 \u2502 \u251c\u2500\u2500 PySPLINE3DFor.py\n\u2502 \u2502 \u251c\u2500\u2500 fluxconserving.png\n\u2502 \u2502 \u251c\u2500\u2500 PyAkimaSpline.py\n\u2502 \u2502 \u251c\u2500\u2500 PyInterpolado.py\n\u2502 \u2502 \u2514\u2500\u2500 PyLINdexerpol.py\n\u2502 \u2514\u2500\u2500 fortran\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.cpython-39-darwin.so\n\u2502 \u251c\u2500\u2500 LINdexerpol.cpython-311-darwin.so\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.cpython-310-darwin.so\n\u2502 \u251c\u2500\u2500 LINdexerpol.f90\n\u2502 \u251c\u2500\u2500 LINinterpol.cpython-39-darwin.so\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.cpython-39-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.cpython-39-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.compile\n\u2502 \u251c\u2500\u2500 FluxConSpec.compile\n\u2502 \u251c\u2500\u2500 FluxConSpec.cpython-39-darwin.so\n\u2502 \u251c\u2500\u2500 SPLINE1DArr.compile\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.cpython-39-darwin.so\n\u2502 \u251c\u2500\u2500 AkimaSpline.cpython-310-darwin.so\n\u2502 \u251c\u2500\u2500 Interpolado.cpython-39-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 Interpolado.cpython-38-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.f90\n\u2502 \u251c\u2500\u2500 FluxConSpec.cpython-39-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.cpython-310-darwin.so\n\u2502 \u251c\u2500\u2500 FluxConSpec.cpython-311-darwin.so\n\u2502 \u251c\u2500\u2500 AkimaSpline.f90\n\u2502 \u251c\u2500\u2500 SPLINE1DArr.f90\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.cpython-311-darwin.so\n\u2502 \u251c\u2500\u2500 FluxConSpec.cpython-38-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 AkimaSpline.cpython-39-darwin.so\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.cpython-38-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.cpython-38-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 AkimaSpline.compile\n\u2502 \u251c\u2500\u2500 DataTypes.f90\n\u2502 \u251c\u2500\u2500 SPLINE1DArr.cpython-39-darwin.so\n\u2502 \u251c\u2500\u2500 LINdexerpol.cpython-310-darwin.so\n\u2502 \u251c\u2500\u2500 LINdexerpol.compile\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.f90\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.compile\n\u2502 \u251c\u2500\u2500 Interpolado.f90\n\u2502 \u251c\u2500\u2500 LINinterpol.cpython-311-darwin.so\n\u2502 \u251c\u2500\u2500 SPLINE1DArr.cpython-310-darwin.so\n\u2502 \u251c\u2500\u2500 FluxConSpec.cpython-310-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 LINinterpol.cpython-310-darwin.so\n\u2502 \u251c\u2500\u2500 SPLINE1DFlt.cpython-310-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 LINinterpol.compile\n\u2502 \u251c\u2500\u2500 SPLINE1DArr.cpython-311-darwin.so\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.cpython-311-darwin.so\n\u2502 \u251c\u2500\u2500 LINinterpol.compile~\n\u2502 \u251c\u2500\u2500 FluxConSpec.cpython-310-darwin.so\n\u2502 \u251c\u2500\u2500 FluxConSpec.f90\n\u2502 \u251c\u2500\u2500 Interpolado.cpython-310-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 LINdexerpol.cpython-310-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 README.txt\n\u2502 \u251c\u2500\u2500 Interpolado.compile\n\u2502 \u251c\u2500\u2500 SPLINE3DFor.cpython-310-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 LINdexerpol.cpython-39-darwin.so\n\u2502 \u251c\u2500\u2500 LINinterpol.f90\n\u2502 \u251c\u2500\u2500 LINdexerpol.cpython-39-x86_64-linux-gnu.so\n\u2502 \u251c\u2500\u2500 LINdexerpol.cpython-38-x86_64-linux-gnu.so\n\u2502 \u2514\u2500\u2500 AkimaSpline.cpython-311-darwin.so\n\u251c\u2500\u2500 version.txt\n\u2514\u2500\u2500 build\n \u251c\u2500\u2500 lib.linux-x86_64-3.9\n \u2502 \u2514\u2500\u2500 PyFluxconserving\n \u251c\u2500\u2500 lib.macosx-11.1-arm64-cpython-39\n \u2502 \u2514\u2500\u2500 PyFluxconserving\n \u251c\u2500\u2500 temp.macosx-11.1-arm64-cpython-39\n \u2502 \u251c\u2500\u2500 __pycache__\n \u2502 \u251c\u2500\u2500 PyFluxconserving\n \u2502 \u251c\u2500\u2500 ccompiler_opt_cache_ext.py\n \u2502 \u251c\u2500\u2500 src\n \u2502 \u2514\u2500\u2500 build\n \u251c\u2500\u2500 src.linux-x86_64-3.9\n \u2502 \u251c\u2500\u2500 PyFluxconserving\n \u2502 \u251c\u2500\u2500 build\n \u2502 \u2514\u2500\u2500 numpy\n \u251c\u2500\u2500 temp.linux-x86_64-3.9\n \u2502 \u251c\u2500\u2500 __pycache__\n \u2502 \u251c\u2500\u2500 PyFluxconserving\n \u2502 \u251c\u2500\u2500 ccompiler_opt_cache_ext.py\n \u2502 \u251c\u2500\u2500 src\n \u2502 \u2514\u2500\u2500 build\n \u2514\u2500\u2500 src.macosx-11.1-arm64-3.9\n \u251c\u2500\u2500 PyFluxconserving\n \u251c\u2500\u2500 build\n \u2514\u2500\u2500 numpy\n\n32 directories, 99 files\n</pre>\n\n<br>PyFluxConSPec.py is a python wrapper to the library in fortran called\nPyFluxconserving.flib. The fortran directory can be compiled separately for\neach individual subroutine.\n\n<hr>\n\n#### ISSUES AND CONTRIBUTIONS\n\nIf you encounter any issues with this project, please feel free to submit an issue on the GitHub repository. We appreciate your feedback and are committed to improving the quality of our codebase.\n\nIf you'd like to contribute to this project, we welcome pull requests from the community. Before submitting a pull request, please make sure to fork the repository and create a new branch for your changes. Once your changes are complete, submit a pull request and we'll review your code as soon as possible.\n\nFor any questions or concerns about contributing, please contact the project maintainer at antineutrinomuon@gmail.com. Thank you for your interest in contributing to our project!\n\n<hr>\n\n#### <b>LICENSE</b>\n\nThis software is provided \"AS IS\" (see DISCLAIMER below). Permission to\nuse, for non-commercial purposes is granted. Permission to modify for personal\nor internal use is granted, provided this copyright and disclaimer are\nincluded in ALL copies of the software. All other rights are reserved. In\nparticular, redistribution of the code is not allowed without explicit\npermission by the author.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\n\n",
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