Manual workflows

.github/workflows/manual_build.yml

@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2024 Shell Global Solutions International B.V. All Rights Reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+name: ManualBuild
+
+on:
+  workflow_dispatch:
+
+jobs:
+  Build:
+    uses: sede-open/pyELQ/.github/workflows/build_package.yml@main
+    secrets: inherit
+
+  PublishDocs:
+    uses: sede-open/pyELQ/.github/workflows/publish_docs.yml@main
+    needs: Build

.github/workflows/manual_publish_docs.yml

@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2024 Shell Global Solutions International B.V. All Rights Reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+name: ManualPublishDocs
+
+on:
+  workflow_dispatch:
+
+jobs:
+  PublishDocs:
+    uses: sede-open/pyELQ/.github/workflows/publish_docs.yml@main
+    needs: Build

README.md

@@ -26,7 +26,6 @@ SPDX-License-Identifier: Apache-2.0
 This repository contains the Python Emission Localization and Quantification software we call pyELQ. It is code used 
 for gas dispersion modelling, in particular methane emissions detection, localization and quantification. 
 
-The main code is structured in the src folder in an object-oriented way. 
 ***
 # Background
 The **py**thon **E**mission **L**ocalization and **Q**uantification (pyELQ) code aims to maximize effective use of 

docs/index.md

@@ -4,4 +4,59 @@ SPDX-FileCopyrightText: 2024 Shell Global Solutions International B.V. All Right
 SPDX-License-Identifier: Apache-2.0
 -->
 
---8<-- "README.md"
+# pyELQ
+This repository contains the Python Emission Localization and Quantification software we call pyELQ. It is code used 
+for gas dispersion modelling, in particular methane emissions detection, localization and quantification. 
+
+***
+# Background
+The **py**thon **E**mission **L**ocalization and **Q**uantification (pyELQ) code aims to maximize effective use of 
+existing measurement data, especially from continuous monitoring solutions. The code has been developed to detect, 
+localize, and quantify methane emissions from concentration and wind measurements. It can be used in combination with 
+point or beam sensors that are placed strategically on an area of interest.
+
+The algorithms in the pyELQ code are based a Bayesian statistics framework. pyELQ can ingest long-term concentration 
+and wind data, and it performs an inversion to predict the likely strengths and locations of persistent methane sources. 
+The goal is to arrive at a plausible estimate of methane emissions from an area of interest that matches the measured 
+data. The predictions from pyELQ come with uncertainty ranges that are representative of probability density functions 
+sampled by a Markov Chain Monte Carlo method. Time series of varying length can be processed by pyELQ: in general, 
+the Bayesian inversion leads to a more constrained solution if more high-precision measurement data is available. 
+We have tested our code under controlled conditions as well as in operating oil and gas facilities.
+
+The information on the strength and the approximate location of methane emission sources provided by pyELQ can help 
+operators with more efficient identification and quantification of (unexpected) methane sources, in order to start 
+appropriate mitigating actions accordingly. The pyELQ code is being made available in an open-source environment, 
+to support various assets in their quest to reduce methane emissions.
+
+Use cases where the pyELQ code has been applied are described in the following papers:
+
+* IJzermans, R., Jones, M., Weidmann, D. et al. "Long-term continuous monitoring of methane emissions at an oil and gas facility using a multi-open-path laser dispersion spectrometer." Sci Rep 14, 623 (2024). (https://doi.org/10.1038/s41598-023-50081-9)
+
+* Weidmann, D., Hirst, B. et al. "Locating and Quantifying Methane Emissions by Inverse Analysis of Path-Integrated Concentration Data Using a Markov-Chain Monte Carlo Approach." ACS Earth and Space Chemistry 2022 6 (9), 2190-2198  (https://doi.org/10.1021/acsearthspacechem.2c00093)
+***
+# Installing pyELQ as a package
+Suppose you want to use this pyELQ package in a different project.
+You can install it from [PyPi](https://pypi.org/project/pyelq-sdk/) through pip 
+`pip install pyelq-sdk`.
+Or you could clone the repository and install it from the source code. 
+After activating the environment you want to install pyELQ in, open a terminal, move to the main pyELQ folder
+where pyproject.toml is located and run `pip install .`, optionally you can pass the `-e` flag is for editable mode.
+All the main options, info and settings for the package are found in the pyproject.toml file which sits in this repo
+as well.
+
+***
+
+# Examples
+For some examples on how to use this package please check out these [Examples](https://github.com/sede-open/pyELQ/blob/main/examples)
+
+***
+
+# Contribution
+This project welcomes contributions and suggestions. If you have a suggestion that would make this better you can simply open an issue with a relevant title. Don't forget to give the project a star! Thanks again!
+
+For more details on contributing to this repository, see the [Contributing guide](https://github.com/sede-open/pyELQ/blob/main/CONTRIBUTING.md).
+
+***
+# Licensing
+
+Distributed under the Apache License Version 2.0. See the [license file](https://github.com/sede-open/pyELQ/blob/main/LICENSE.md) for more information.