| Name | gpr1d JSON |
| Version |
2.0.0
JSON |
| download |
| home_page | None |
| Summary | Classes for Gaussian Process Regression fitting of 1D data with errorbars |
| upload_time | 2024-12-07 22:07:42 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.7 |
| license | MIT License Copyright (c) 2017 Aaron Ho Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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. |
| keywords |
gaussian process regression
1d data fitting
regression analysis
kriging
noisy input
heteroscedastic error
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
gpr1d
=====
Installing the gpr1d program
----------------------------
*Author: Aaron Ho (01/06/2018)*
Installation is **mandatory** for this package!
For first time users, it is strongly recommended to use the GUI
developed for this Python package. To obtain the Python package
dependencies needed to use this capability, install this package
by using the following on the command line::
pip install [--user] gpr1d[guis]
Use the :code:`--user` flag if you do not have root access on the system
that you are working on. If you have already cloned the
repository, enter the top level of the repository directory and
use the following instead::
pip install [--user] -e .[guis]
Removal of the :code:`[guis]` portion will no longer check for
the GUI generation and plotting packages needed for this
functionality. However, these packages are not crucial for the
base classes and algorithms.
Documentation
=============
Documentation of the equations used in the algorithm, along with
the available kernels and optimizers, can be found in docs/.
Documentation of the :code:`gpr1d` module can be found on
`GitLab pages <https://aaronkho.gitlab.io/GPR1D>`_
Using the gpr1d program
-----------------------
For those who wish to include the functionality of this package
into their own Python scripts, a demo script is provided in
:code:`src/gpr1d/scripts`. The basic syntax used to create kernels,
select settings, and perform GP regression fits are outlined there.
In addition, a simplified :code:`gpr1d` class is available for those
wishing to distill the parameters into a subset of the most
crucial ones.
For any questions or to report bugs, please do so through the
proper channels in the GitLab repository.
*Important note for users!*
The following runtime warnings are common within this routine,
but they are filtered out by default::
RuntimeWarning: overflow encountered in double_scalars
RuntimeWarning: invalid value encountered in true_divide
RuntimeWarning: invalid value encountered in sqrt
They normally occur when using the kernel restarts option (as
in the demo) and do not necessarily mean that the resulting
fit is poor.
Plotting the resulting fit and errors is the recommended way to
check its quality. The log-marginal-likelihood metric can also
be used, but is only valuable when comparing different fits of
the same data, ie. its absolute value is meaningless.
From v1.1.1, the adjusted R\ :sup:`2` and pseudo R\ :sup:`2`
metrics are now available. The adjusted R\ :sup:`2` metric provides
a measure of how close the fit is to the input data points. The
pseudo R\ :sup:`2` provides a measure of this closeness accounting
for the input data uncertainties.
Raw data
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"description": "gpr1d\n=====\n\nInstalling the gpr1d program\n----------------------------\n\n*Author: Aaron Ho (01/06/2018)*\n\nInstallation is **mandatory** for this package!\n\nFor first time users, it is strongly recommended to use the GUI\ndeveloped for this Python package. To obtain the Python package\ndependencies needed to use this capability, install this package\nby using the following on the command line::\n\n pip install [--user] gpr1d[guis]\n\nUse the :code:`--user` flag if you do not have root access on the system\nthat you are working on. If you have already cloned the\nrepository, enter the top level of the repository directory and\nuse the following instead::\n\n pip install [--user] -e .[guis]\n\nRemoval of the :code:`[guis]` portion will no longer check for\nthe GUI generation and plotting packages needed for this\nfunctionality. However, these packages are not crucial for the\nbase classes and algorithms.\n\n\nDocumentation\n=============\n\nDocumentation of the equations used in the algorithm, along with\nthe available kernels and optimizers, can be found in docs/.\nDocumentation of the :code:`gpr1d` module can be found on\n`GitLab pages <https://aaronkho.gitlab.io/GPR1D>`_\n\n\nUsing the gpr1d program\n-----------------------\n\nFor those who wish to include the functionality of this package\ninto their own Python scripts, a demo script is provided in\n:code:`src/gpr1d/scripts`. The basic syntax used to create kernels,\nselect settings, and perform GP regression fits are outlined there.\n\nIn addition, a simplified :code:`gpr1d` class is available for those\nwishing to distill the parameters into a subset of the most\ncrucial ones.\n\nFor any questions or to report bugs, please do so through the\nproper channels in the GitLab repository.\n\n\n*Important note for users!*\n\nThe following runtime warnings are common within this routine,\nbut they are filtered out by default::\n\n RuntimeWarning: overflow encountered in double_scalars\n RuntimeWarning: invalid value encountered in true_divide\n RuntimeWarning: invalid value encountered in sqrt\n\n\nThey normally occur when using the kernel restarts option (as\nin the demo) and do not necessarily mean that the resulting\nfit is poor.\n\nPlotting the resulting fit and errors is the recommended way to\ncheck its quality. The log-marginal-likelihood metric can also\nbe used, but is only valuable when comparing different fits of\nthe same data, ie. its absolute value is meaningless.\n\nFrom v1.1.1, the adjusted R\\ :sup:`2` and pseudo R\\ :sup:`2`\nmetrics are now available. The adjusted R\\ :sup:`2` metric provides\na measure of how close the fit is to the input data points. The\npseudo R\\ :sup:`2` provides a measure of this closeness accounting\nfor the input data uncertainties.\n",
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"license": "MIT License Copyright (c) 2017 Aaron Ho Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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. ",
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