From 5b54baa142cbac8268d62886e342707ebb0d5ddc Mon Sep 17 00:00:00 2001 From: Manuel del Jesus Date: Sat, 12 Mar 2022 17:03:44 +0100 Subject: [PATCH] Updated version. Ready to Release MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit En la rama main Tu rama está actualizada con 'origin/main'. Cambios a ser confirmados: modificados: .zenodo.json modificados: CITATION.cff modificados: setup.py modificados: utils/zenodo.json --- .zenodo.json | 5 +++-- CITATION.cff | 2 +- setup.py | 2 +- utils/Zenodo_File_Template | 2 +- utils/zenodo.json | 3 ++- 5 files changed, 8 insertions(+), 6 deletions(-) diff --git a/.zenodo.json b/.zenodo.json index 327b43f..79e0ba1 100644 --- a/.zenodo.json +++ b/.zenodo.json @@ -17,10 +17,11 @@ } ], "license": { - "id": "General Public License v3 (GPLv3)" + "id": "GPL-3.0+" }, "title": "NEOPRENE: Neyman-Scott Process Rainfall Emulator", "upload_type": "software", "keywords": ["Rainfall emulator", "Neyman-Scott"], - "description": "

NEOPRENE: Neyman-Scott Process Rainfall Emulator

\"DOI\"

The NEOPRENE library implements a rectangular pulses model for rainfall emulation based on the Neyman-Scott process. The emulator may be used to generate multi-site synthetic rainfall time series that reproduce observed statistics at different temporal aggregations. It has been designed with rainfall dissaggregation and extreme rainfall analysis in mind.

The description of the Neyman-Scott Process -or Space-time Neyman-Scott Rectangular Pulses Model- can be found in the doc folder.

A paper describing the library has been sent for review to Environmental Modelling & Software.

Other papers by the authors where -previous incarnations of- the NEOPRENE library has been used and the mathematical model has been described are:

Contents

DirectoryContents
NSRPPython code to calibrate the NSRPM (Neyman-Scott Rectangular Pulse Model) and simulate single-site synthetic rainfall series.
STNSRPPython code for calibrate the STNSRPM (Space-Time Neyman-Scott Rectangular Pulse Model) and simulate multi-site synthetic rainfall series.
docDescription of the model.
notebooksJupyter notebooks with examples on how to calibrate, simulate and validate a Neyman-Scott model using the library. Examples on how to perform a daily-to-hourly rainfall disaggregation using the synthetic series are also included.

Requirements

Scripts and (jupyter) notebooks are provided in Python to ensure reproducibility and reusability of the results. The simplest way to match all these requirements is by using a dedicated conda environment, which can be easily installed by issuing:

conda create -n NEOPRENE pip jupyterconda activate NEOPRENEpip install NEOPRENE

Examples of use

Examples of use of the NEOPRENE library are available in the form of jupyter notebooks. To run the examples follow the following steps:

  1. Download the folder notebooks from the github repository, or navigate to the folder should you have cloned the repo.
  2. Open jupyter notebook of Jupyter Lab (type jupyter notebook or jupyter lab in the terminal)
  3. Open one of the tests available in the notebooks folder with jupyter notebook (e.g. NSRP_test.ipynb, STNSRP_test.ipynb)

Errata and problem reporting

To report an issue with the library, please fill a GitHub issue.

Contributors

The original version of the library was developed by:

License

Copyright 2021 Instituto de Hidráulica Ambiental “IHCantabria”. Universidad de Cantabria.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

" + "description": +"

NEOPRENE: Neyman-Scott Process Rainfall Emulator

\"DOI\"

The NEOPRENE library implements a rectangular pulses model for rainfall emulation based on the Neyman-Scott process. The emulator may be used to generate multi-site synthetic rainfall time series that reproduce observed statistics at different temporal aggregations. It has been designed with rainfall dissaggregation and extreme rainfall analysis in mind.

The description of the Neyman-Scott Process -or Space-time Neyman-Scott Rectangular Pulses Model- can be found in the doc folder.

A paper describing the library has been sent for review to Environmental Modelling & Software.

Other papers by the authors where -previous incarnations of- the NEOPRENE library has been used and the mathematical model has been described are:

Test the library

If you are curious about how the library works or what it can do, I invite you to go to the Releases section of this webpage (on the right-hand side of the page) and download the executable file NEOPRENE-Setup for your operative system. This executable file will check if Jupyterlab Desktop is installed in your computer. If it is not, it will download the installation program for you to install Jupyterlab. After Jupyterlab is installed, NEOPRENE-Setup will launch the example notebooks for you. Then you can test the library and check its functionality in action.

Contents

DirectoryContents
NSRPPython code to calibrate the NSRPM (Neyman-Scott Rectangular Pulse Model) and simulate single-site synthetic rainfall series.
STNSRPPython code for calibrate the STNSRPM (Space-Time Neyman-Scott Rectangular Pulse Model) and simulate multi-site synthetic rainfall series.
docDescription of the model.
notebooksJupyter notebooks with examples on how to calibrate, simulate and validate a Neyman-Scott model using the library. Examples on how to perform a daily-to-hourly rainfall disaggregation using the synthetic series are also included.

Requirements

Scripts and (jupyter) notebooks are provided in Python to ensure reproducibility and reusability of the results. The simplest way to match all these requirements is by using a dedicated conda environment, which can be easily installed by issuing:

conda create -n NEOPRENE pip jupyterconda activate NEOPRENEpip install NEOPRENE

Examples of use

Examples of use of the NEOPRENE library are available in the form of jupyter notebooks. To run the examples follow the following steps:

  1. Download the folder notebooks from the github repository, or navigate to the folder should you have cloned the repo.
  2. Open jupyter notebook of Jupyter Lab (type jupyter notebook or jupyter lab in the terminal)
  3. Open one of the tests available in the notebooks folder with jupyter notebook (e.g. NSRP_test.ipynb, STNSRP_test.ipynb)

Errata and problem reporting

To report an issue with the library, please fill a GitHub issue.

Contributors

The original version of the library was developed by:

License

Copyright 2021 Instituto de Hidráulica Ambiental “IHCantabria”. Universidad de Cantabria.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

" } diff --git a/CITATION.cff b/CITATION.cff index b43610b..8993aab 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -14,7 +14,7 @@ authors: orcid: "https://orcid.org/0000-0003-0703-8960" affiliation: "Environmental Hydraulics Institute \"IHCantabria\". Universidad de Cantabria. Santander, Spain." title: "NEOPRENE: Neyman-Scott Process Rainfall Emulator" -version: "0.99.1" +version: "1.0.0" license: GPLv3 repository-code: "https://github.com/IHCantabria/NEOPRENE" doi: 10.5281/zenodo.5549811 diff --git a/setup.py b/setup.py index ab470f2..b957db5 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ setup( name='NEOPRENE', packages = find_packages(), license = "GPLv3", - version='0.99.1', + version='1.0.0', description='🌎 Scripts and information to synthetic generation of precipitation based on Point Processes.', long_description=long_description, long_description_content_type='text/markdown', diff --git a/utils/Zenodo_File_Template b/utils/Zenodo_File_Template index 9b95172..18bc7cb 100644 --- a/utils/Zenodo_File_Template +++ b/utils/Zenodo_File_Template @@ -17,7 +17,7 @@ } ], "license": { - "id": "General Public License v3 (GPLv3)" + "id": "GPL-3.0+" }, "title": "NEOPRENE: Neyman-Scott Process Rainfall Emulator", "upload_type": "software", diff --git a/utils/zenodo.json b/utils/zenodo.json index 327b43f..71deefe 100644 --- a/utils/zenodo.json +++ b/utils/zenodo.json @@ -22,5 +22,6 @@ "title": "NEOPRENE: Neyman-Scott Process Rainfall Emulator", "upload_type": "software", "keywords": ["Rainfall emulator", "Neyman-Scott"], - "description": "

NEOPRENE: Neyman-Scott Process Rainfall Emulator

\"DOI\"

The NEOPRENE library implements a rectangular pulses model for rainfall emulation based on the Neyman-Scott process. The emulator may be used to generate multi-site synthetic rainfall time series that reproduce observed statistics at different temporal aggregations. It has been designed with rainfall dissaggregation and extreme rainfall analysis in mind.

The description of the Neyman-Scott Process -or Space-time Neyman-Scott Rectangular Pulses Model- can be found in the doc folder.

A paper describing the library has been sent for review to Environmental Modelling & Software.

Other papers by the authors where -previous incarnations of- the NEOPRENE library has been used and the mathematical model has been described are:

Contents

DirectoryContents
NSRPPython code to calibrate the NSRPM (Neyman-Scott Rectangular Pulse Model) and simulate single-site synthetic rainfall series.
STNSRPPython code for calibrate the STNSRPM (Space-Time Neyman-Scott Rectangular Pulse Model) and simulate multi-site synthetic rainfall series.
docDescription of the model.
notebooksJupyter notebooks with examples on how to calibrate, simulate and validate a Neyman-Scott model using the library. Examples on how to perform a daily-to-hourly rainfall disaggregation using the synthetic series are also included.

Requirements

Scripts and (jupyter) notebooks are provided in Python to ensure reproducibility and reusability of the results. The simplest way to match all these requirements is by using a dedicated conda environment, which can be easily installed by issuing:

conda create -n NEOPRENE pip jupyterconda activate NEOPRENEpip install NEOPRENE

Examples of use

Examples of use of the NEOPRENE library are available in the form of jupyter notebooks. To run the examples follow the following steps:

  1. Download the folder notebooks from the github repository, or navigate to the folder should you have cloned the repo.
  2. Open jupyter notebook of Jupyter Lab (type jupyter notebook or jupyter lab in the terminal)
  3. Open one of the tests available in the notebooks folder with jupyter notebook (e.g. NSRP_test.ipynb, STNSRP_test.ipynb)

Errata and problem reporting

To report an issue with the library, please fill a GitHub issue.

Contributors

The original version of the library was developed by:

License

Copyright 2021 Instituto de Hidráulica Ambiental “IHCantabria”. Universidad de Cantabria.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

" + "description": +"

NEOPRENE: Neyman-Scott Process Rainfall Emulator

\"DOI\"

The NEOPRENE library implements a rectangular pulses model for rainfall emulation based on the Neyman-Scott process. The emulator may be used to generate multi-site synthetic rainfall time series that reproduce observed statistics at different temporal aggregations. It has been designed with rainfall dissaggregation and extreme rainfall analysis in mind.

The description of the Neyman-Scott Process -or Space-time Neyman-Scott Rectangular Pulses Model- can be found in the doc folder.

A paper describing the library has been sent for review to Environmental Modelling & Software.

Other papers by the authors where -previous incarnations of- the NEOPRENE library has been used and the mathematical model has been described are:

Test the library

If you are curious about how the library works or what it can do, I invite you to go to the Releases section of this webpage (on the right-hand side of the page) and download the executable file NEOPRENE-Setup for your operative system. This executable file will check if Jupyterlab Desktop is installed in your computer. If it is not, it will download the installation program for you to install Jupyterlab. After Jupyterlab is installed, NEOPRENE-Setup will launch the example notebooks for you. Then you can test the library and check its functionality in action.

Contents

DirectoryContents
NSRPPython code to calibrate the NSRPM (Neyman-Scott Rectangular Pulse Model) and simulate single-site synthetic rainfall series.
STNSRPPython code for calibrate the STNSRPM (Space-Time Neyman-Scott Rectangular Pulse Model) and simulate multi-site synthetic rainfall series.
docDescription of the model.
notebooksJupyter notebooks with examples on how to calibrate, simulate and validate a Neyman-Scott model using the library. Examples on how to perform a daily-to-hourly rainfall disaggregation using the synthetic series are also included.

Requirements

Scripts and (jupyter) notebooks are provided in Python to ensure reproducibility and reusability of the results. The simplest way to match all these requirements is by using a dedicated conda environment, which can be easily installed by issuing:

conda create -n NEOPRENE pip jupyterconda activate NEOPRENEpip install NEOPRENE

Examples of use

Examples of use of the NEOPRENE library are available in the form of jupyter notebooks. To run the examples follow the following steps:

  1. Download the folder notebooks from the github repository, or navigate to the folder should you have cloned the repo.
  2. Open jupyter notebook of Jupyter Lab (type jupyter notebook or jupyter lab in the terminal)
  3. Open one of the tests available in the notebooks folder with jupyter notebook (e.g. NSRP_test.ipynb, STNSRP_test.ipynb)

Errata and problem reporting

To report an issue with the library, please fill a GitHub issue.

Contributors

The original version of the library was developed by:

License

Copyright 2021 Instituto de Hidráulica Ambiental “IHCantabria”. Universidad de Cantabria.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

" }