From 0852511175f8d71a352549264de1681a5a89eefc Mon Sep 17 00:00:00 2001 From: Gabriel Stefanini Vicente Date: Thu, 8 Feb 2024 09:29:26 -0500 Subject: [PATCH] Revert "Add NTL Gaza example (#34)" (#35) This reverts commit 577995e8bac1bbdeb0ad931ec77ab5d713fe6e03. --- .env.example | 1 + docs/CONTRIBUTING.md => CONTRIBUTING.md | 0 README.md | 253 +- docs/_config.yml | 11 +- docs/_toc.yml | 35 +- docs/bibliography.bib | 62 +- docs/gallery.md | 114 + docs/introduction-to-data-goods.md | 47 - docs/peer-review.md | 70 - docs/requirements.txt | 2 - environment.yml | 11 + notebooks/bibliography.ipynb | 173 + notebooks/conda-lock.yml | 12517 -------------- notebooks/environment.yml | 13 - notebooks/nasa-apod.ipynb | 283 + .../nighttime-lights/nighttime-lights.ipynb | 14194 ---------------- notebooks/world-bank-api.ipynb | 715 + notebooks/world-bank-package.ipynb | 509 + pyproject.toml | 43 + src/datalab/VERSION | 1 + src/datalab/__init__.py | 4 + src/datalab/indicators.py | 84 + 22 files changed, 2249 insertions(+), 26893 deletions(-) create mode 100644 .env.example rename docs/CONTRIBUTING.md => CONTRIBUTING.md (100%) create mode 100644 docs/gallery.md delete mode 100644 docs/introduction-to-data-goods.md delete mode 100644 docs/peer-review.md delete mode 100644 docs/requirements.txt create mode 100644 environment.yml create mode 100644 notebooks/bibliography.ipynb delete mode 100644 notebooks/conda-lock.yml delete mode 100644 notebooks/environment.yml create mode 100644 notebooks/nasa-apod.ipynb delete mode 100644 notebooks/nighttime-lights/nighttime-lights.ipynb create mode 100644 notebooks/world-bank-api.ipynb create mode 100644 notebooks/world-bank-package.ipynb create mode 100644 pyproject.toml create mode 100644 src/datalab/VERSION create mode 100644 src/datalab/__init__.py create mode 100644 src/datalab/indicators.py diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..5982ee0 --- /dev/null +++ b/.env.example @@ -0,0 +1 @@ +NASA_API_KEY = \ No newline at end of file diff --git a/docs/CONTRIBUTING.md b/CONTRIBUTING.md similarity index 100% rename from docs/CONTRIBUTING.md rename to CONTRIBUTING.md diff --git a/README.md b/README.md index fb043d3..8245f35 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,255 @@ -# Data Goods: World Bank Reusable Data Products +# Project Template -Welcome to the Data Goods Handbook! This manual is designed to assist data scientists, analysts students or enthusiasts in creating and contributing impactful reusable data products: a [_Data Good_](docs/introduction-to-data-goods.md). +The template is a standardized, but flexible *project* and *documentation* structure of folders and files for sharing your data science work. -## Table of Contents +Inspired by [literate programming](http://literateprogramming.com), maintained by the [Development Data Group](https://www.worldbank.org/en/about/unit/unit-dec/dev) and built as [GitHub template repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template), the template contains: -- [What is a Data Good?](docs/introduction-to-data-goods.md) -- [Peer Reviewing a Data Good](docs/peer-review.md) +- [**README**](README), [**CODE_OF_CONDUCT**](docs/CODE_OF_CONDUCT.md), [**CONTRIBUTING**](CONTRIBUTING.md) templates + > README files are important and often neglected. The files should inform anyone about the first steps to use, learn and contribute to your project. + +- **LICENSE** + > The LICENSE is a document that determines what others can and cannot do with contents of the repository. If no license is present, no one has permission to use and/or modify your code. The template is licensed under the [**Mozilla Public License**](https://www.mozilla.org/en-US/MPL/). And so will projects generated from it. + +- **docs/** + + > Documentation is often never priotized until last minute. The template aims to revert the malpractice by setting up the documentation as an integral part, inspired by [literate programming](http://literateprogramming.com). With the power of [Jupyter Book](https://jupyterbook.org), data practioners have a way to share [Jupyter notebooks](https://jupyter.org) on [GitHub Pages](https://pages.github.com) in a standardized and effortless way. + +- **docs/bibliography.bib** + > A `bibliography` using the [BibTeX](https://www.bibtex.org/Format/) format. + +- **data/** + > Placeholder folder for data. Data is immutable. By default, the data folder is present but ignored from version control, in order to prevent files of being mistakenly versioned in the code repository. + +- **src/** + > Placeholder folder for source code. If Python, it is recommended the package is made pip-installable. + +- **notebooks/** + > Placeholder folder for [Jupyter notebooks](https://jupyter.org). Markdown files and Jupyter notebooks can be added to `docs/_toc.yml` (Table of Contents) to compose the *documentation*. + +- [Issues and Pull Requests GitHub templates](https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/configuring-issue-templates-for-your-repository) + > GitHub allows to customize how issues and pull requests are presented to the public. Custom templates encourage collaboration and maintainability. + +```{important} +*With flexibility comes great responsibility*. The template makes a few opiniated choices for the structure and code/documentation management of a project for what we envision to be most cases. However, even the best of the templates would never be perfect for the universe of cases out there. All in all, the template aims to encourage teams to start thinking and assimilate **collaborative coding**, **documentation**​, **enginerring**, **reproducibility​** and **best practices** as an integral part of the project. *In a standardized way*. + +In this spirit, if the template is not for you or in case you have feedback, please consider [opening an issue](https://github.com/worldbank/template/issues) or [submitting a pull request](https://github.com/worldbank/template/pulls) to share your ideas and suggestions. Your contributions would be appreciated immensely. +``` + +## Benefits + +Project templates on GitHub are essential for streamlining the data science and collaboration processes, and they offer several key benefits: + +- 🛠️ **Consistency and Best Practices:** Project templates encourage consistency in project structure, coding standards, and best practices. They provide a standardized starting point, ensuring that all team members follow the same guidelines and reduce the risk of introducing errors. + +- ⏳ **Time and Effort Savings:** Templates save time by eliminating the need to set up a project from scratch. Developers can quickly start working on their projects without the overhead of configuring the initial project structure, dependencies, or workflows. + +- 🚀 **Faster Onboarding:** New team members or contributors can easily get up to speed by using project templates. It simplifies the onboarding process, allowing them to understand the project structure and development practices more quickly. + +- 🎨 **Customization and Adaptability:** GitHub project templates can be customized to suit the specific needs of different types of projects or organizations. They serve as a foundation that can be adapted to meet unique requirements. + +- 🤝 **Community Engagement:** Open-source projects can attract more contributors when they provide accessible project templates. These templates facilitate contributions by reducing the barriers to entry for potential collaborators. + +- 🔄 **Version Control Integration:** GitHub project templates are tightly integrated with Git version control. This makes it easier to manage changes, collaborate, and track the history of project configurations. + +- 📖 **Documentation and Guidance:** Templates often include documentation and guidance to help developers understand the project's structure and how to get started. This can include README files, code comments, and links to relevant resources. + +- 🔍 **Discoverability:** Templates are discoverable on GitHub, making it easy for developers to find and use project templates for their preferred programming languages, frameworks, and tools. This helps build a supportive ecosystem. + +- ✍️ **Continual Improvement:** Project templates can evolve and improve over time as best practices, technology, and requirements change. This ensures that projects remain up to date and maintainable. + +In summary, GitHub project templates are valuable resources that enhance project management, development practices, and collaboration. They promote consistency, efficiency, and quality in software development, whether for individual projects, open-source contributions, or within organizational contexts. + +## Usage + +### Getting Started + +```{margin} ✨ Can't see the template ? +Please ensure you are logged in on [GitHub](https://github.com) and have permissions to create a repository. +``` + +1. **Create new repository from template** + + The template is a [GitHub template repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template); in other words, you can generate a new GitHub repository with the same files and folders to use as the starting point for your project. + + > 🌟 [Create new repository from **template**](https://github.com/worldbank/template/generate) + + ```{figure} docs/images/github-template.png + --- + --- + ``` + + Now, give your repository a name, choose the **visibility** (Public or Private) and click **Create repository from template**. + + ```{figure} docs/images/github-template-create.png + --- + --- + ``` + + *Voilà!* The repository has been created with the same files and folders of the template. + + ```{seealso} + For additional information, see the [GitHub documentation](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template) + ``` + +2. **Enable [GitHub Actions](https://github.com/features/actions) and [GitHub Pages](https://pages.github.com)** + + After creating the repository from the template, you will have to enable [GitHub Actions](https://github.com/features/actions) and [GitHub Pages](https://pages.github.com) to allow the [Jupyter Book](https://jupyterbook.org) to be built and published. + + To activate the workflow, please enable [GitHub Actions](https://github.com/features/actions) by going to the repository's settings (`Settings > Actions > General`), and selecting **read and write permissions** as shown below. + + ```{figure} docs/images/github-template-action-enable.png + --- + --- + ``` + + To publish, please enable [GitHub Pages](https://pages.github.com) by going to the repository's settings (`Settings > Pages`), and selecting to deploy from the `gh-pages` branch. + + ```{figure} docs/images/github-template-pages.png + --- + --- + ``` + + On the next push to `main`, the [Jupyter Book](https://jupyterbook.org) will be automatically built and published. You can check the progress on the `Actions` tab. + + ```{figure} docs/images/github-template-action.png + --- + --- + ``` + + ```{caution} + The *documentation* can be published from either *public* and *private* repositories. If publishing private content, please remember to carefully select the content to be made public and to abide by your organization's Data Privacy Policy. + ``` + +3. **Update configurations** + + The template comes with a default `docs/_config.yml` Jupyter Book configuration file. Remember to update it to reflect your project's name and details. + + ``` + repository: + url: https://github.com/worldbank/template + branch: main + ``` + + ```{seealso} + [Jupyter Book Configuration Reference](https://jupyterbook.org/en/stable/customize/config.html) + ``` + +4. **Review and update README files** + + The template comes with README files - including [this **README**](README) - that should provide anyone with the information about the first steps to use, learn and contribute to your project. Please **replace** and/or **repurpose** the files with instructions and detailed information about your project. + + > - **CODE_OF_CONDUCT** + > - **CONTRIBUTING** + > - **README** + > - Issues and Pull Requests GitHub templates + + ```{seealso} + [Awesome README](https://github.com/matiassingers/awesome-readme) + ``` + +5. **Choose a license** + + The template is licensed under the [**World Bank Master Community License Agreement**](LICENSE). A LICENSE is the document that guarantees the repository can be shared, modified and receive contributions. Otherwise, if no license is present, all rights are reserved. + +
+ +**Congratulations!** You just created a beautiful home for your project. To access your project page, use (and share) the link as shown below. + +> 🌟 `https://.github.io/` + +For example, see this template as a live demo. + +> 🌟 [worldbank.github.io/template](http://worldbank.github.io/template) (Live Demo) + +### Adding Content + +The template is created as a [Jupyter Book](https://jupyterbook.org/intro.html) - an open-source project to build beautiful, publication-quality books and documents from computational content. Let's see below how to add, execute and publish new content for your project. + +#### Table of Contents + +When ready to publish the *documentation* on [GitHub Pages](https://pages.github.com/), all you need to do is edit the [table of contents](#table-of-contents) and add and/or update content you would like to display. [Jupyter Book](https://jupyterbook.org) supports content written as [Markdown](https://daringfireball.net/projects/markdown/), [Jupyter](https://jupyter.org) notebooks and [reStructuredText](https://docutils.sourceforge.io/rst.html) files and the `docs/_toc.yml` file controls the [table of contents](#table-of-contents) of your book. + +The template comes with the [table of contents](#table-of-contents) below as an example. + +``` + +format: jb-book +root: README + +parts: + +- caption: Documentation + numbered: True + chapters: + - file: notebooks/world-bank-api.ipynb +- caption: Additional Resources + chapters: + - url: + title: Development Data Partnership + - url: + title: World Bank DEC + - url: + title: World Bank DIME + +``` + +```{seealso} +[Jupyter Book Structure and organize content](https://jupyterbook.org/en/stable/basics/organize.html) +``` + +#### Dependencies + +The next step is ensure your code is maintainable, realiable and reproducible by including +any dependencies and requirements, such as packages, configurations, secrets (template) and addtional instructions. + +The template suggests to use [conda](https://docs.conda.io/) (or [mamba](https://mamba.readthedocs.io/en/latest/)) as environment manager and, as [conventional](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html), the environment is controlled by the `environment.yml` file. + +The `environment.yml` file is where you specify any packages available on the [Anaconda repository](https://anaconda.org) as well as from the Anaconda Cloud (including [conda-forge](https://conda-forge.org)) to install for your project. Ensure to include the pinned version of packages required by your project (including by Jupyter notebooks). + +``` +channels: + - conda-forge + - defaults +dependencies: + - python=3.9 + - bokeh=2.4.3 + - pandas=1.4.3 + - pip: + - requests==2.28.1 +``` + +To (re)create the environment on your installation of [conda](https://conda.io) via [anaconda](https://docs.anaconda.com/anaconda/install/), [miniconda](https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/) or preferably [miniforge](https://github.com/conda-forge/miniforge), you only need to pass the `environment.yml` file, which will install requirements and guarantee that whoever uses your code has the necessary packages (and correct versions). By default, the template uses [Python 3.9](https://www.python.org). + +``` +conda env create -n -f environment.yml +``` + +In case your project uses Python, it is *strongly* recommended to distribute it as a [package](https://packaging.python.org/). + +```{important} +The template contains an example - the [datalab](https://github.com/worldbank/template/tree/main/src/datalab) Python package - and will automatically find and install any `src` packages as long as `pyproject.yml` is kept up-to-date. +``` + +```{seealso} +[Conda Managing Environments](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) +``` + +#### Jupyter Notebooks + +[Jupyter Notebooks](https://jupyter.org) can be beautifully rendered and downloaded from your book. By default, the template will render any files listed on the [table of contents](#table-of-contents) that have a notebook structure. The template comes with a Jupyter notebook example, `notebooks/world-bank-api.ipynb`, to illustrate. + +```{important} +Optionally, [Jupyter Book](https://jupyterbook.org) can execute notebooks during the build (on GitHub) and display **code outputs** and **interactive visualizations** as part of the *documentation* on the fly. In this case, Jupyter notebooks will be executed by [GitHub Actions](https://github.com/features/actions) during build on each commit to the `main` branch. Thus, it is important to include all [requirements and dependencies](#dependencies) in the repository. In case you would like to ignore a notebook, you can [exclude files from execution](https://jupyterbook.org/en/stable/content/execute.html#exclude-files-from-execution). +``` + +```{seealso} +[Jupyter Book Write executable content](https://jupyterbook.org/en/stable/content/executable/index.html) +``` ## Code of Conduct -This project maintains a [Code of Conduct](docs/CODE_OF_CONDUCT.md) to ensure an inclusive and respectful environment for everyone. Please adhere to it in all interactions within our community. +The template maintains a [Code of Conduct](docs/CODE_OF_CONDUCT.md) to ensure an inclusive and respectful environment for everyone. Please adhere to it in all interactions within our community. ## License -This projects is licensed under the [**Mozilla Public License**](https://www.mozilla.org/en-US/MPL). Remember to replace the [license](LICENSE) if necessary. If open source, [choose an open source license](https://choosealicense.com). +The template is licensed under the [**Mozilla Public License**](https://www.mozilla.org/en-US/MPL). Remember to replace the [license](LICENSE) if necessary. If open source, [choose an open source license](https://choosealicense.com). diff --git a/docs/_config.yml b/docs/_config.yml index a174674..d5bd00a 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -2,23 +2,24 @@ title: author: Development Data Group logo: docs/images/logo.png -only_build_toc_files: false +only_build_toc_files: true repository: - url: https://github.com/worldbank/data-good-template + url: https://github.com/worldbank/template branch: main ####################################################################################### # HTML-specific settings html: - home_page_in_navbar: true + home_page_in_navbar: false + extra_navbar: "" use_edit_page_button: true use_repository_button: true use_issues_button: true - baseurl: https://worldbank.github.io/data-good-template + baseurl: https://worldbank.github.io/template extra_footer: |
- Country borders or names do not necessarily reflect the World Bank Group’s official position. All maps are for illustrative purposes and do not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries. + For illustrative purposes and does not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.

diff --git a/docs/_toc.yml b/docs/_toc.yml index 89605b8..eebaf02 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -2,18 +2,27 @@ format: jb-book root: README parts: - - caption: Getting Started + - caption: Examples + numbered: True chapters: - - file: docs/introduction-to-data-goods - - file: docs/peer-review - - caption: Data Goods Examples + - file: notebooks/world-bank-api.ipynb + - file: notebooks/world-bank-package.ipynb + - file: notebooks/nasa-apod.ipynb + - file: notebooks/bibliography.ipynb + - caption: Gallery chapters: - - file: notebooks/nighttime-lights/nighttime-lights.ipynb - - url: https://datapartnership.github.io/lebanon-economic-monitor/notebooks/air-pollution/air-pollution.html - title: Air Pollution in Lebanon - - url: https://datapartnership.github.io/lebanon-economic-monitor/notebooks/population/population.html - title: Population in Lebanon - - url: https://datapartnership.github.io/morocco-earthquake-impact/docs/drought-index.html - title: Drought in Morocco - - url: https://datapartnership.github.io/morocco-earthquake-impact/notebooks/conflict-acled.html - title: Drought and Conflict in Morocco + - file: docs/gallery + - caption: Templates + chapters: + - file: docs/CODE_OF_CONDUCT + - file: CONTRIBUTING + - caption: Additional Resources + chapters: + - url: https://datapartnership.org + title: Development Data Partnership + - url: https://wbdatalab.org + title: World Bank Data Lab + - url: https://www.worldbank.org/en/about/unit/unit-dec + title: World Bank DEC + - url: https://www.worldbank.org/en/research/dime + title: World Bank DIME diff --git a/docs/bibliography.bib b/docs/bibliography.bib index 5d149bb..c9b5d3f 100644 --- a/docs/bibliography.bib +++ b/docs/bibliography.bib @@ -1,27 +1,39 @@ -@article{ROMAN2018113, -title = {NASA's Black Marble nighttime lights product suite}, -journal = {Remote Sensing of Environment}, -volume = {210}, -pages = {113-143}, -year = {2018}, -issn = {0034-4257}, -doi = {https://doi.org/10.1016/j.rse.2018.03.017}, -url = {https://www.sciencedirect.com/science/article/pii/S003442571830110X}, -author = {Miguel O. Román and Zhuosen Wang and Qingsong Sun and Virginia Kalb and Steven D. Miller and Andrew Molthan and Lori Schultz and Jordan Bell and Eleanor C. Stokes and Bhartendu Pandey and Karen C. Seto and Dorothy Hall and Tomohiro Oda and Robert E. Wolfe and Gary Lin and Navid Golpayegani and Sadashiva Devadiga and Carol Davidson and Sudipta Sarkar and Cid Praderas and Jeffrey Schmaltz and Ryan Boller and Joshua Stevens and Olga M. {Ramos González} and Elizabeth Padilla and José Alonso and Yasmín Detrés and Roy Armstrong and Ismael Miranda and Yasmín Conte and Nitza Marrero and Kytt MacManus and Thomas Esch and Edward J. Masuoka}, -keywords = {Suomi-NPP, JPSS, NASA black marble, VIIRS, Night lights, NTL, Urban dynamics, Long-term monitoring, Lunar BRDF, Albedo, Atmospheric correction}, -abstract = {NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 m resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.} +@book{WorldBank2021WorldDevelopmentReport, + author = {{World Bank}}, + publisher = {World Bank}, + url = {http://hdl.handle.net/10986/35218}, + date = {2021}, + note = {License: CC BY 3.0 IGO}, + title = {{W}orld {D}evelopment {R}eport 2021 : {D}ata for {B}etter {L}ives}, + type = {World Development Report}, + year = {2021}, } - -@article{BITTNER201734, -title = {OpenStreetMap in Israel and Palestine – ‘Game changer’ or reproducer of contested cartographies?}, -journal = {Political Geography}, -volume = {57}, -pages = {34-48}, -year = {2017}, -issn = {0962-6298}, -doi = {https://doi.org/10.1016/j.polgeo.2016.11.010}, -url = {https://www.sciencedirect.com/science/article/pii/S096262981630035X}, -author = {Christian Bittner}, -keywords = {OpenStreetMap, Israel, Palestine, Critical cartography, Web 2.0 cartographies, Volunteered geographic information}, -abstract = {In Israel and Palestine, map-making practices were always entangled with contradictive spatial identities and imbalanced power resources. Although an Israeli narrative has largely dominated the ‘cartographic battlefield’, the latest chapter of this story has not been written yet: collaborative forms of web 2.0 cartographies have restructured power relations in mapping practices and challenged traditional monopolies on map and spatial data production. Thus, we can expect web 2.0 cartographies to be a ‘game changer’ for cartography in Palestine and Israel. In this paper, I review this assumption with the popular example of OpenStreetMap (OSM). Following a mixed methods approach, I comparatively analyze the genesis of OSM in Israel and Palestine. Although nationalist motives do not play a significant role on either side, it turns out that the project is dominated by Israeli and international mappers, whereas Palestinians have hardly contributed to OSM. As a result, social fragmentations and imbalances between Israel and Palestine are largely reproduced through OSM data. Discussing the low involvement of Palestinians, I argue that OSM's ground truth paradigm might be a watershed for participation. Presumably, the project's data are less meaningful in some local contexts than in others. Moreover, the seemingly apolitical approach to map only ‘facts on the ground’ reaffirms present spatio-social order and thus the power relations behind it. Within a Palestinian narrative, however, many aspects of the factual material space might appear not as neutral physical objects but as results of suppression, in which case, any ‘accurate’ spatial representation, such as OSM, becomes objectionable.} +@book{WorldBank2022WorldDevelopmentReport, + author = {{World Bank}}, + publisher = {World Bank}, + url = {http://hdl.handle.net/10986/36883}, + date = {2022}, + note = {License: CC BY 3.0 IGO}, + title = {{W}orld {D}evelopment {R}eport 2022 : {F}inance for an {E}quitable {R}ecovery}, + type = {World Development Report}, + year = {2022}, +}, +@book{WorldBank2023WorldDevelopmentReport, + author = {{World Bank}}, + publisher = {World Bank}, + url = {https://openknowledge.worldbank.org/handle/10986/39696}, + date = {2023}, + note = {License: CC BY 3.0 IGO}, + title = {{W}orld {D}evelopment {R}eport 2021 : {M}igrants, {R}efugees, and {S}ocieties}, + type = {World Development Report}, + year = {2023}, } +@misc{sdgatlas2020, + author = {Pirlea, A. F. and U. Serajuddin and D. Wadhwa and M. Welch and A. Whitby}, + publisher = {World Bank}, + url = {https://datatopics.worldbank.org/sdgatlas.}, + date = {2020}, + note = {License: CC BY 3.0 IGO}, + title = {{A}tlas of the {S}ustainable {D}evelopment {G}oals 2020: {F}rom {W}orld {D}evelopment {I}ndicators}, + year = {2020}, +} \ No newline at end of file diff --git a/docs/gallery.md b/docs/gallery.md new file mode 100644 index 0000000..a54da18 --- /dev/null +++ b/docs/gallery.md @@ -0,0 +1,114 @@ +## Projects using the template + +> A curated list of projects/repositories using the template. + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [Syria Economic Monitor](https://datapartnership.github.io/syria-economic-monitor) +Using Alternative Data to Understand Changing Trends in Trade and Economic Activity in Syria +::: + +:::{grid-item-card} [Türkiye Earthquake Impact](https://datapartnership.github.io/turkiye-earthquake-impact) +Using Alternative Data to Understand Economic Impacts of the 2023 Turkey–Syria Earthquake +::: +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [Lebanon Economic Monitor](https://datapartnership.github.io/lebanon-economic-monitor) +Understanding Lebanon’s Economy through Alternative Data +::: + +:::{grid-item-card} [Mocorro Earthquake Impact](https://datapartnership.github.io/morocco-earthquake-impact) +Using Alternative Data to Understand Economic Impacts of the 2023 Morocco earthquake. +::: +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [Myanmar Economic Monitor](https://datapartnership.github.io/myanmar-economic-monitor) +Understanding Myanmar's Economy through Alternative Data +::: + +:::{grid-item-card} [Pacific Observatory](https://worldbank.github.io/pacific-observatory) +The Pacific Observatory is the World Bank analytical program to explore and develop new information sources to mitigate the impact of data gaps in official statistics for Papua New Guinea (PNG) and the Pacific Island Countries (PICs). + +::: +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [REaLTabFormer](https://worldbank.github.io/REaLTabFormer) +A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation. +::: +:::{grid-item-card} [iQual](https://worldbank.github.io/iQmual) +iQual is a package that leverages natural language processing to scale up interpretative qualitative analysis. It also provides methods to assess the bias, interpretability and efficiency of the machine-enhanced codes. iQual has been applied to analyse interviews on parents' aspirations for their children in Cox's Bazaar, Bangladesh. +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [GEE Zonal](https://worldbank.github.io/GEE_Zonal/) +This python package provides a wrapper function to request temporal and zonal statistics from Google Earth Engine (GEE) datasets. +::: + +:::{grid-item-card} [Geospatial Operations Support Team (GOST)](https://worldbank.github.io/GOST) +We document important resources, methods, and sources in real time via this live wiki. This repository is the evolving, growing workspace where GOST stores libraries and scripts for operationalizing the many initatives urrenctly on going in the geospatial realm within the World Bank Group. +::: +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [DECAT_Space2Stats](https://worldbank.github.io/DECAT_Space2Stats) +Consistent, comparable, authoritative data describing sub-national variation is a constant point of complication for World Bank teams, our development partners, and client countries when assessing and investigating economic issues and national policy. This project will focus on creating and disseminating such data through aggregation of geospatial information at standard administrative divisions, and through the attribution of household survey data with foundational geospatial variables. +::: + +:::{grid-item-card} [ZAF_Econ_Diversification](https://worldbank.github.io/ZAF_Econ_Diversification) +Mapping and quantifying opportunities for economic diversification in Mpumalanga in South Africa +::: +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [GFF Health Equity](https://worldbank.github.io/health-equity-diagnostics) +This repository contains scripts developed by GOST to map health facilities and understand differences in population access in support of the GFF Country Equity Diagnostic and other projects. +::: + +:::{grid-item-card} [Heatwaves Data Collaborative](https://datapartnership.org/heatwaves/PH) +The Heatwaves Data Collaborative enables teams across sectors and +organizations to simultaneously focus on their areas of expertise, while +collaborating through use of common frameworks and datasets, standards for +production and review of reusable methods, and through regular check-ins to +ensure the different work streams are aligned and build upon each other. +::: +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [Geospatial Analysis - MENA Poverty and Equity Group](https://worldbank.github.io/mena-pov) +The Geospatial Analysis repository aims to centrally maintain cleaning and analysis of data that leverage geospatial data conducted by the Poverty and Equity Group in the MENA region. +::: + +:::{grid-item-card} [Health Planning in the Philippines](https://datapartnership.org/health-planning-in-philippines) +The team conducted geospatial analysis to advise on key questions that could inform a more integrated health care system, starting with three LGUs (Bohol, Baguio City, and Maguindanao). +::: +:::: + +::::{grid} +:gutter: 2 + +:::{grid-item-card} [Understanding societal responses to policies undertaken during emergencies: Lessons from COVID-19’s Second Wave in Maharashtra](https://datapartnership.github.io/covid-19-maharashtra) +Understanding societal responses to policies undertaken during emergencies: Lessons from COVID-19's Second Wave in Maharashtra +::: + +:::{grid-item-card} [Understanding the vulnerability of New Delhi to Heatwaves](https://datapartnership.org/vulnerability-to-heatwaves-in-india) +Part of Understanding the vulnerability of New Delhi to Heatwaves, this repository holds a collection of (experimental) Jupyter notebooks exploring data available through the Development Data Partnership. +::: +:::: diff --git a/docs/introduction-to-data-goods.md b/docs/introduction-to-data-goods.md deleted file mode 100644 index 1c7657f..0000000 --- a/docs/introduction-to-data-goods.md +++ /dev/null @@ -1,47 +0,0 @@ -# What is a _Data Good_? - -**Data Goods** are comprised of data, reproducible methods (code), sample insights, and training guidance. Unlike a traditional data analysis, which results in a single-use report or visualization, Data Goods are designed to be customized, reused, and updated, thereby building the capacity of the World Bank and partner organizations to quickly and effectively deliver complex data science solutions to pressing global challenges. - -Data Goods packages include: - -1. **Datasets**. Data Goods provide guidance on how to access the data underpinning all data products, indicators, and insights. This transparency in data sources supports reproducibility and, critically, re-use in new countries and contexts, over time. The Datasets section includes three parts: - - - - > Existing Data. Each Data Goods starts with a curation of datasets -- public and private -- that will support project objectives. The team prepares this curated list as a table, which includes data type, update frequency, access links, and contact information. - - > Digitized Government Data. Where needed, a Data Good may also include guidance on government data digitalization and/or management, leveraging AI methods to make disaggregated government records readily searchable and usable. - - > New Data Collection. A Data Good may also incude a field data collection plan (and implementation of that plan, as needed) that includes some combination of household surveys, remote sensing (including drones), and crowdsourcing. Data Goods may also include guidance (and again, implementation of that guidance) on processing, storage, and cataloguing of all collected data. - - - - All Bank-produced datasets as part of the Data Good will be hosted as a special collection on the World Bank's Data Catalogue, managed by the Development Economics Data Group (DECDG). The Catalogue receives more than 14 million unique users per month and will ensure value of the investment in data collection will be multiplied. - - - -2. **Reusable Data Products**. These are analytical products derived from the Datasets, which can be further used to generate indicators and insights. All data products include original code, documentation, links to original data sources (and/or information on how to access them), and a description of their limitations. Reference resources are also cited, where relevant. - - - -3. **Insights and Indicators**. Each Data Goods package may also include additional analytical work, such as dynamic maps, data visualizaations, and/or sample indicators. Indicators can be derived from a combination of **Datasets** and **Reusable Data Products**. By combining these two inputs, teams are empowered to develop a large array of indicators to meet their project needs. - - - -4. **Training and Dissemination**. Each Data Good is packaged as a readily translated and distributed web book and includes guidance for further training and capcity building. - - - -5. **Data Lab Team**. For each project, the [World Bank Data Lab](https://wbdatalab.org/) recruits colleagues from throughout the World Bank, pooling our collective data talents in support of our lending and technical assistance operations. Data Goods packages include names and contact information for the unique teams that prepared the Goods. - -## How Data Goods are Managed - -1. **Dynamic, Web-Hosted Documentation**. Unless specified otherwise, all code and documentation used to produce the Data Goods is hosted in a project GitHub repository to facilitate reuse for future updates and projects, as well as to support collaboration and capacity building activities. - - - -2. **Data Catalogue**. Where possible, all datasets used in the production of Data Goods are added as entries to the World Bank’s [Development Data Hub](https://datacatalog.worldbank.org/home), where they are tagged with meta data, license attributes, and access information. - - - -3. **Internal Project Management and File Sharing System**. To facilitate project management across teams, the Lab creates a Project SharePoint, which includes project management information (work plan, milestones, check-in slides, log of hours charged, final report), related literature, data files, indicator tables, and links to resources, such as this documentation. The advantage of SharePoint for World Bank usage is that all contents are automatically encrypted and tagged as Official Use Only. The project SharePoint is accessible to project team members and, with permission, can be replicated as a basis for future project updates or for similar projects. \ No newline at end of file diff --git a/docs/peer-review.md b/docs/peer-review.md deleted file mode 100644 index e2c2716..0000000 --- a/docs/peer-review.md +++ /dev/null @@ -1,70 +0,0 @@ -# Peer Reviewing a _Data Good_ - -Welcome to the peer review process on GitHub! Peer review is a crucial step in collaborative development, ensuring code quality, correctness, and adherence to project standards. This guide outlines the steps to perform an effective peer review on GitHub. - -## Prerequisites - -- **GitHub Account:** Ensure you have a GitHub account. If not, [join now](https://github.com/join). - -- **Access to Repository:** Obtain access to the repository you'll be reviewing. If it's a public repository, you can directly contribute. For private repositories, request access from the repository owner. - -## Peer Review Steps - -### 1. Clone the Repository - -```shell -git clone https://github.com//.git -cd repository -``` - -### 2. Create a New Branch - -```shell -git checkout -b feature/peer-review -``` - -### 3. Review Code Changes - -- **Files Changed**: Identify the files modified by the pull request. - -- **Read Code**: Understand the proposed changes. Look for clarity, adherence to coding standards, and logical structure. - -### 4. Test the Changes - -- **Local Testing**: If possible, test the changes locally to ensure they work as intended. - -- **Review Tests**: Check if the changes include new tests or update existing ones. - -### 5. Provide Constructive Feedback - -- **Comments**: Use inline comments on specific lines of code to provide feedback. - -- **General Comments**: Add general comments summarizing your review at the end of the pull request. - -### 6. Verify Documentation - -- **Update Docs**: Ensure that documentation is updated to reflect the changes. - -- **Accuracy**: Check if the documentation accurately describes the new features or changes. - -### 7. Check for Code Style - -- **Consistency**: Verify that the code follows the established coding style guide. - -- **Naming Conventions**: Check if variable and function names are clear and follow conventions. - -### 8. Security and Performance - -- **Security**: Identify potential security vulnerabilities and suggest improvements. - -- **Performance**: Consider the impact on performance and suggest optimizations if necessary. - -### 9. Submit Your Review - -- **Review Summary**: Provide a summary of your review in the GitHub interface. - -### 10. Follow Up - -- **Discussion**: Engage in any discussion arising from your comments. - -- **Re-review**: If changes are requested, re-review the modified code. diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 873d7b8..0000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -docutils==0.17.1 -jupyter-book==1.0.0 \ No newline at end of file diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..d21a456 --- /dev/null +++ b/environment.yml @@ -0,0 +1,11 @@ +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - bokeh=2.4.3 + - pandas=1.4.3 + - pip + - pip: + - -e . + - requests==2.28.1 diff --git a/notebooks/bibliography.ipynb b/notebooks/bibliography.ipynb new file mode 100644 index 0000000..eba8449 --- /dev/null +++ b/notebooks/bibliography.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "90700fdc-fcc7-4e54-8c9e-449879d8c66d", + "metadata": { + "tags": [] + }, + "source": [ + "(bibliography)=\n", + "\n", + "# Including Bibliography" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "14e89727", + "metadata": {}, + "source": [ + "Including a bibliography is of utmost importance in any academic or research work. A bibliography serves as a comprehensive list of all the sources consulted and referenced during the creation of a paper, essay, or any scholarly project. It not only adds credibility to the work but also demonstrates the depth of research conducted by the author. By providing a bibliography, the author acknowledges the contributions of other scholars and researchers, thereby showing respect for intellectual property and avoiding plagiarism. \n", + "\n", + "Furthermore, a bibliography allows readers to delve deeper into the subject matter, explore related sources, and verify the accuracy and reliability of the information presented. In essence, the inclusion of a well-constructed bibliography is an essential aspect of scholarly writing that promotes transparency, authenticity, and the advancement of knowledge." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b4c0f3e8-7756-41bb-aa21-cc2eee5ff67f", + "metadata": {}, + "source": [ + "## Usage\n", + "\n", + "```{seealso}\n", + "[Jupyter Book: Citations and bibliographies](https://jupyterbook.org/en/stable/content/citations.html)\n", + "```\n", + "\n", + "The template includes [`docs/bibliography.bib`](docs/bibliography.bib) as example, containing an entry to the World Bank flagship publication [World Development Report 2021](https://www.worldbank.org/en/publication/wdr2021) in [BibTeX Format](http://www.bibtex.org/Format). \n", + "\n", + "To include a **citation**, we use the syntax as shown below.\n", + " \n", + "````md\n", + "{cite}`WorldBank2021WorldDevelopmentReport`\n", + "````\n", + "\n", + "Additionally, we can use different citation styles.\n", + "\n", + "- **{cite:t}**: {cite:t}`WorldBank2021WorldDevelopmentReport`\n", + "\n", + "- **{cite:p}**: {cite:p}`WorldBank2021WorldDevelopmentReport`\n", + "\n", + "\n", + "```{seealso}\n", + "For a more complete list of in-line citation styles, check out the [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/en/latest/usage.html#roles-and-directives).\n", + "```\n", + "\n", + "To include the **bibliography**, we use the syntax as shown below. Note that this will include all citations throughout the book. \n", + "\n", + "````md\n", + "```{bibliography}\n", + "```\n", + "````\n", + "\n", + "```{bibliography}\n", + "```\n", + "\n", + "To include only the **local bibliography**, we use hte syntax as shown below. \n", + "\n", + "````md\n", + "```{bibliography}\n", + ":filter: docname in docnames\n", + "```\n", + "````\n", + "\n", + "```{bibliography}\n", + ":filter: docname in docnames\n", + "```\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d0066241", + "metadata": {}, + "source": [ + "## Bibliography Styles\n", + "\n", + "\n", + "### `alpha`\n", + "\n", + "````md\n", + "```{bibliography}\n", + ":style: alpha\n", + "```\n", + "````\n", + "\n", + "```{bibliography}\n", + ":style: alpha\n", + "```\n", + "\n", + "### `plain`\n", + "\n", + "````md\n", + "```{bibliography}\n", + ":style: plain\n", + "```\n", + "````\n", + "\n", + "```{bibliography}\n", + ":style: plain\n", + "```\n", + "\n", + "### `unsrt`\n", + "\n", + "````md\n", + "```{bibliography}\n", + ":style: unsrt\n", + "```\n", + "````\n", + "\n", + "```{bibliography}\n", + ":style: unsrt\n", + "```\n", + "\n", + "### `unsrtalpha`\n", + "\n", + "````md\n", + "```{bibliography}\n", + ":style: unsrtalpha\n", + "```\n", + "````\n", + "\n", + "```{bibliography}\n", + ":style: unsrtalpha\n", + "```\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b0e84fdb", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "b6702b69e93007336b96338c5a331192f07cedff01d36d4dcfa0f842adb718ad" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/conda-lock.yml b/notebooks/conda-lock.yml deleted file mode 100644 index 1390428..0000000 --- a/notebooks/conda-lock.yml +++ /dev/null @@ -1,12517 +0,0 @@ -# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). 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hash: - sha256: 848cd2a1df56ddbffeb375535fb62c9d1645dde33ca4d51341378b3f5954429b - category: main - optional: false diff --git a/notebooks/environment.yml b/notebooks/environment.yml deleted file mode 100644 index b82519c..0000000 --- a/notebooks/environment.yml +++ /dev/null @@ -1,13 +0,0 @@ -channels: - - conda-forge -dependencies: - - python=3.10 - - bokeh - - colorcet - - folium - - geopandas - - jupyterlab - - pandas - - pip - - pip: - - blackmarblepy diff --git a/notebooks/nasa-apod.ipynb b/notebooks/nasa-apod.ipynb new file mode 100644 index 0000000..c0ad4a7 --- /dev/null +++ b/notebooks/nasa-apod.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "90700fdc-fcc7-4e54-8c9e-449879d8c66d", + "metadata": { + "tags": [] + }, + "source": [ + "# Securely Using API Keys\n", + "\n", + "> The following are (opinionated) best practices to store and use API keys in your source code. If you disagree, please consider [contributing](https://github.com/worldbank/template/issues/new/choose). " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ac0ed1c0", + "metadata": {}, + "source": [ + "## Environment Variables\n", + "\n", + "An [environment variable](https://en.wikipedia.org/wiki/Environment_variable) is a dynamic-named value that can be used to store information on a computer. For instance, an environment variable can be used to store settings and/or privileged information (e.g. API keys) on your local computer or server.\n", + "\n", + "To set a environment variable to a new value, in **Unix-like** systems, you must pass a `name` and a `value` pair as shown below in the terminal.\n", + "\n", + "```shell\n", + "export SECRET_API_KEY = \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "080bd097-f128-4759-946d-793368230804", + "metadata": { + "tags": [] + }, + "source": [ + "The `value` is acessible by the `name` without being exposed throughout the sytem. In particular, in [Python](https://python.org), the value can be retrieve as follows." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8d023b4e-496b-440c-91a7-199bceb44d7d", + "metadata": { + "tags": [] + }, + "source": [ + "```python\n", + "secret_api_key = os.getenv(\"SECRET_API_KEY\")\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "54a99582-d509-4ab8-be42-ddb4921c0f45", + "metadata": { + "tags": [] + }, + "source": [ + "Alternatively, it is customary to use a `.env` file to organize and load environments variables as needed. Packages such as [dotenv](https://www.npmjs.com/package/dotenv) and [python-dotenv](https://pypi.org/project/python-dotenv/) will automatically load environments variables for you from the `.env` file.\n", + "\n", + "```shell\n", + "source .env\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "3c0cc26a-2a99-49b0-a406-d57f31fff8ee", + "metadata": {}, + "source": [ + "With [Python](https://python.org)," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "960398ce-eadb-45e3-b160-53e6c9250dd0", + "metadata": { + "tags": [ + "remove_output" + ] + }, + "outputs": [], + "source": [ + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv()" + ] + }, + { + "cell_type": "markdown", + "id": "bda573d0-c877-42e6-8ee5-3000b780b4b7", + "metadata": { + "tags": [] + }, + "source": [ + "With [Jupyter](https://jupyter.org)," + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2e700464-b50d-4b06-b0aa-afaafa17e68e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "660db869", + "metadata": {}, + "source": [ + "The template includes `.env.example` as an example; to use, simply rename it to `.env` and add your settings and secrets to it. Please note that `.env` **must** never be commited/versioned (for example, to GitHub) and **should** be ignored on `.gitignore`. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "74484f7e", + "metadata": {}, + "source": [ + "```{tip}\n", + "While environments variables are a convenient way to minimize the security risk, it is important to emphasize secrets are still stored in plaintext in your computer. It is strongly recommended to use instead a secret manager, such as [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/) or [1Password](https://developer.1password.com/docs/cli/secret-references).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "14e89727", + "metadata": {}, + "source": [ + "## Astronomy Picture of the Day" + ] + }, + { + "cell_type": "markdown", + "id": "b4c0f3e8-7756-41bb-aa21-cc2eee5ff67f", + "metadata": {}, + "source": [ + "One of the most popular APIs is NASA's [Astronomy Picture of the Day](https://apod.nasa.gov/apod/astropix.html). Let's see in the following example how to use the NASA API with a secret API key." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d797ef77-6ca4-4f9d-a1f8-abbfd9884b07", + "metadata": { + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import httpx\n", + "from IPython.core.display import HTML\n", + "from IPython.display import Image" + ] + }, + { + "cell_type": "markdown", + "id": "ece37244", + "metadata": {}, + "source": [ + "First, you will have to [generate your API key](https://api.nasa.gov) and set up the environment variable `NASA_API_KEY` with its value. Now you are ready to use it in your code. For instance, in this example, we assign it to `api_key`. Please note that the value is never exposed and the notebook can be securely shared with anyone. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7b914e66-7ae8-4d8b-9621-d6dc5ec49631", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "api_key = os.getenv(\"NASA_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "10b5b12a", + "metadata": {}, + "source": [ + "Now, we are ready to make the request to the NASA API. According to the [documentation](https://github.com/nasa/apod-api#docs), the `api_key` is passed a parameter to the GET request. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1990c3b9-f145-4c1f-bbb5-82f50801a011", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "async with httpx.AsyncClient() as client:\n", + " r = await client.get(\n", + " \"https://api.nasa.gov/planetary/apod\", params={\"api_key\": api_key}\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "e952e343", + "metadata": {}, + "source": [ + "Voilà!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bd1cb597-0144-43e8-bed8-12145a831a0c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(url=r.json()[\"hdurl\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c7cb67e-c7ba-4ed3-bee0-36d303c1517d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "ce6d896885f4e28373aa2ff7c44f136ed5a497e2abd203a79a632f5859ed7bb5" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nighttime-lights/nighttime-lights.ipynb b/notebooks/nighttime-lights/nighttime-lights.ipynb deleted file mode 100644 index d9dbb14..0000000 --- a/notebooks/nighttime-lights/nighttime-lights.ipynb +++ /dev/null @@ -1,14194 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7776d467-20ad-4044-b10e-37b67bf0d914", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Nighttime Lights Trends in Gaza and West Bank\n", - "\n", - "Analyzing conflict dynamics through the lens of [NASA's Black Marble Nighttime Lights](https://blackmarble.gsfc.nasa.gov) dataset opens a unique window into the often-hidden facets of global unrest. In a world marked by diverse forms of conflict, from armed confrontations to civil unrest, the dataset offers an unconventional yet powerful tool for understanding the ripple effects of these conflicts on human settlements and infrastructure. By tracking nighttime light variations and disruptions, we can unearth vital insights into population displacement, economic destabilization, and the societal impacts of conflict. This analysis explores the potential of the Black Marble Nighttime Lights dataset to not only detect areas affected by conflict but also to quantify the extent of its influence on human lives and livelihoods, providing a valuable perspective on the multifaceted consequences of conflict worldwide." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5bcd9136-77df-4f63-ba42-bfa97a477e2a", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import os\n", - "from datetime import datetime\n", - "\n", - "import colorcet as cc\n", - "import folium\n", - "import geopandas\n", - "import pandas as pd\n", - "from blackmarble.extract import bm_extract\n", - "from bokeh.models import (\n", - " HoverTool,\n", - " Legend,\n", - " Range1d,\n", - " Span,\n", - " Title,\n", - ")\n", - "from bokeh.plotting import figure, output_notebook, show" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "98fe76c2-88ec-47e1-bf82-5ea1ba820a46", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# https://papermill.readthedocs.io/en/latest/usage-parameterize.html\n", - "bearer = os.environ.get(\"BLACKMARBLE_TOKEN\")\n", - "VAR = \"ntl_mean\"" - ] - }, - { - "cell_type": "markdown", - "id": "6edaf441-87a3-409a-bd07-6f99adb3a14f", - "metadata": {}, - "source": [ - "## Data" - ] - }, - { - "cell_type": "markdown", - "id": "23970210-dfe3-4dfb-a0ac-7ad651bc573d", - "metadata": {}, - "source": [ - "### Define Region of Interest\n", - "\n", - "Define region of interest for where NASA Black Marble will be downloaded." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "62f8e6b9-f8b2-483d-a3e0-9e239666c4d3", - "metadata": { - "editable": true, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PSE = geopandas.read_file(\"../../data/boundaries/gadm41_PSE_shp/gadm41_PSE_2.shp\")\n", - "\n", - "PSE.explore()" - ] - }, - { - "cell_type": "markdown", - "id": "71533d45-6933-4a84-85eb-0c9efb946688", - "metadata": {}, - "source": [ - "```{figure} ../../docs/images/logo.png\n", - "---\n", - "height: 0px\n", - "---\n", - "Region of Interest. Country borders or names do not necessarily reflect the World Bank Group's official position. This map is for illustrative purposes and does not imply the expression of any opinion on the part of the World Bank, conceming the legal status of any country or territory or concerning the delimitation of frontiers or boundaries. \n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "dc9751be-048d-4a41-ad17-6a23bbad7dc4", - "metadata": {}, - "source": [ - "### Black Marble \n", - "\n", - "[NASA's Black Marble](https://blackmarble.gsfc.nasa.gov) VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights product suite represents a remarkable advancement in our ability to monitor and understand nocturnal light emissions on a global scale. By utilizing cutting-edge satellite technology and image processing techniques, the Black Marble VIIRS dataset offers a comprehensive and high-resolution view of the Earth's nighttime illumination patterns. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "82b0b10b-b94b-479d-920f-00307da0c84e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-output" - ] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b900a2d7dc5b4485b8bd71c244bf9371", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "GETTING MANIFEST...: 0%| | 0/4 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ntl_mean
NAME_2BethlehemDeir Al-BalahGazaGaza ash ShamaliyahHebronJeninJerichoJerusalemKhan YunisNablusQalqilyaRafahRamallah and Al-BirehSalfitTubasTulkarm
date
2022-01-0215.2046507.97956613.6736678.2756105.5095544.9638825.52608626.56229912.3400927.09797911.070501NaN9.82951814.3044723.7009415.659215
2022-01-093.93723913.33558320.10696612.4001836.7957717.5215705.59858921.75160113.6036696.98695017.08368622.9261389.66452815.1487512.43991011.244419
2022-01-169.06718211.66678315.24734311.1853644.9521666.4385544.49460929.34699813.0051587.00442914.89024622.2348689.46328812.2012091.95026811.332975
2022-01-2311.88556313.39817419.64851013.6277236.9768297.2569744.37201722.75630212.4094887.99700117.84844220.6348018.74713213.9809352.78036511.759398
2022-01-303.98476710.13558214.4382179.7848704.5179484.6743714.45162918.47918110.8097774.88201512.37646616.9433096.50666410.6235852.2168198.362207
...................................................
2023-12-316.6128080.4829351.1466961.4108979.2611567.3879245.26608723.1765130.8835029.24642817.4309914.48353410.75102616.0225472.86671312.376930
2024-01-077.3464612.0355261.4619381.2705148.9204807.9571575.80591824.7937001.12238910.81786818.6356214.1767259.98239315.2100603.77308810.613670
2024-01-144.5134381.7998500.9676161.2399626.4697046.5098855.24609223.7275611.1976028.01411414.6600344.9336067.81220712.9119323.36119410.792833
2024-01-216.9761930.6614840.6833570.7808098.9759368.0981655.09928126.6263790.9222078.87745014.9431004.57642010.47983114.7825992.78723312.302222
2024-01-284.0473421.8380050.7183330.9996825.5894387.3025834.99708621.3146730.9992456.67578414.0818625.8481217.86901812.9571133.78809411.383841
\n", - "

109 rows × 16 columns

\n", - "
" - ], - "text/plain": [ - " ntl_mean \\\n", - "NAME_2 Bethlehem Deir Al-Balah Gaza Gaza ash Shamaliyah Hebron \n", - "date \n", - "2022-01-02 15.204650 7.979566 13.673667 8.275610 5.509554 \n", - "2022-01-09 3.937239 13.335583 20.106966 12.400183 6.795771 \n", - "2022-01-16 9.067182 11.666783 15.247343 11.185364 4.952166 \n", - "2022-01-23 11.885563 13.398174 19.648510 13.627723 6.976829 \n", - "2022-01-30 3.984767 10.135582 14.438217 9.784870 4.517948 \n", - "... ... ... ... ... ... \n", - "2023-12-31 6.612808 0.482935 1.146696 1.410897 9.261156 \n", - "2024-01-07 7.346461 2.035526 1.461938 1.270514 8.920480 \n", - "2024-01-14 4.513438 1.799850 0.967616 1.239962 6.469704 \n", - "2024-01-21 6.976193 0.661484 0.683357 0.780809 8.975936 \n", - "2024-01-28 4.047342 1.838005 0.718333 0.999682 5.589438 \n", - "\n", - " \\\n", - "NAME_2 Jenin Jericho Jerusalem Khan Yunis Nablus Qalqilya \n", - "date \n", - "2022-01-02 4.963882 5.526086 26.562299 12.340092 7.097979 11.070501 \n", - "2022-01-09 7.521570 5.598589 21.751601 13.603669 6.986950 17.083686 \n", - "2022-01-16 6.438554 4.494609 29.346998 13.005158 7.004429 14.890246 \n", - "2022-01-23 7.256974 4.372017 22.756302 12.409488 7.997001 17.848442 \n", - "2022-01-30 4.674371 4.451629 18.479181 10.809777 4.882015 12.376466 \n", - "... ... ... ... ... ... ... \n", - "2023-12-31 7.387924 5.266087 23.176513 0.883502 9.246428 17.430991 \n", - "2024-01-07 7.957157 5.805918 24.793700 1.122389 10.817868 18.635621 \n", - "2024-01-14 6.509885 5.246092 23.727561 1.197602 8.014114 14.660034 \n", - "2024-01-21 8.098165 5.099281 26.626379 0.922207 8.877450 14.943100 \n", - "2024-01-28 7.302583 4.997086 21.314673 0.999245 6.675784 14.081862 \n", - "\n", - " \n", - "NAME_2 Rafah Ramallah and Al-Bireh Salfit Tubas Tulkarm \n", - "date \n", - "2022-01-02 NaN 9.829518 14.304472 3.700941 5.659215 \n", - "2022-01-09 22.926138 9.664528 15.148751 2.439910 11.244419 \n", - "2022-01-16 22.234868 9.463288 12.201209 1.950268 11.332975 \n", - "2022-01-23 20.634801 8.747132 13.980935 2.780365 11.759398 \n", - "2022-01-30 16.943309 6.506664 10.623585 2.216819 8.362207 \n", - "... ... ... ... ... ... \n", - "2023-12-31 4.483534 10.751026 16.022547 2.866713 12.376930 \n", - "2024-01-07 4.176725 9.982393 15.210060 3.773088 10.613670 \n", - "2024-01-14 4.933606 7.812207 12.911932 3.361194 10.792833 \n", - "2024-01-21 4.576420 10.479831 14.782599 2.787233 12.302222 \n", - "2024-01-28 5.848121 7.869018 12.957113 3.788094 11.383841 \n", - "\n", - "[109 rows x 16 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PSE_2 = (\n", - " VNP46A2.pivot_table(values=[VAR], index=\"date\", columns=[\"NAME_2\"])\n", - " .resample(\"W-SUN\", label=\"right\")\n", - " .mean()\n", - ")\n", - "PSE_1 = (\n", - " VNP46A2.pivot_table(values=[VAR], index=\"date\", columns=[\"NAME_1\"])\n", - " .resample(\"W-SUN\", label=\"right\")\n", - " .mean()\n", - ")\n", - "\n", - "PSE_2" - ] - }, - { - "cell_type": "markdown", - "id": "56870a8d-5b0f-46af-849a-6a902ebac336", - "metadata": {}, - "source": [ - "#### Monthly\n", - "\n", - "In this step, we compute a monthy aggregation of the zonal statistics by for each second-level administrative division and for each month. Additionaly, we add the [VNP46A3](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP46A3/#data-availability) monthly composite, when available." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "dd2ddc14-a031-4d84-addf-2244563f67ae", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "PSE_2_MS = (\n", - " VNP46A2.pivot_table(index=\"date\", columns=[\"NAME_2\"], values=[VAR], aggfunc=\"mean\")\n", - " .resample(\"MS\", label=\"left\")\n", - " .mean()\n", - ")\n", - "PSE_2_MS.to_csv(\"../../data/ntl/PSE_2-VNP46A2-M-zs.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9ba011a6-595e-4c25-95ff-33e495f851c7", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - "
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*/\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(\"f9c123e6-eaf5-41df-b0e0-51fbef0044dd\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"f9c123e6-eaf5-41df-b0e0-51fbef0044dd\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"f9c123e6-eaf5-41df-b0e0-51fbef0044dd\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"f9c123e6-eaf5-41df-b0e0-51fbef0044dd\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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division\",\"text_font_size\":\"12pt\",\"text_font_style\":\"italic\"}}],\"below\":[{\"type\":\"object\",\"name\":\"DatetimeAxis\",\"id\":\"p1013\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"DatetimeTicker\",\"id\":\"p1014\",\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1015\",\"attributes\":{\"num_minor_ticks\":0,\"mantissas\":[1,2,5],\"max_interval\":500.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1016\",\"attributes\":{\"num_minor_ticks\":0,\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"min_interval\":1000.0,\"max_interval\":1800000.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p1017\",\"attributes\":{\"num_minor_ticks\":0,\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"min_interval\":3600000.0,\"max_interval\":43200000.0}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1018\",\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1019\",\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1020\",\"attributes\":{\"days\":[1,8,15,22]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p1021\",\"attributes\":{\"days\":[1,15]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1022\",\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1023\",\"attributes\":{\"months\":[0,2,4,6,8,10]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1024\",\"attributes\":{\"months\":[0,4,8]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p1025\",\"attributes\":{\"months\":[0,6]}},{\"type\":\"object\",\"name\":\"YearsTicker\",\"id\":\"p1026\"}]}},\"formatter\":{\"type\":\"object\",\"name\":\"DatetimeTickFormatter\",\"id\":\"p1027\"},\"axis_label\":\"Date\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1028\"}}},{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1052\",\"attributes\":{\"text\":\"Data Source: NASA Black Marble. Creation date: 05 February 2024. Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1029\",\"attributes\":{\"axis\":{\"id\":\"p1013\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1034\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1030\"}}}]}}]}};\n", - " const render_items = [{\"docid\":\"ee8bc98a-002a-4b20-8d32-4c82113c6978\",\"roots\":{\"p1001\":\"b3ac8500-d918-4afc-b551-e0d890af1fd5\"},\"root_ids\":[\"p1001\"]}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " let attempts = 0;\n", - " const timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " clearInterval(timer);\n", - " embed_document(root);\n", - " } else {\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " clearInterval(timer);\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " }\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p1001" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "p = figure(\n", - " title=\"Palestine: Monthly Nighttime Lights (2022-2023)\",\n", - " width=800,\n", - " height=600,\n", - " x_axis_label=\"Date\",\n", - " x_axis_type=\"datetime\",\n", - " y_axis_label=r\"Radiance [nW $$cm^{-2}$$ $$sr^{-1}$$]\",\n", - " tools=\"pan,wheel_zoom,box_zoom,reset,save,box_select\",\n", - ")\n", - "p.add_layout(\n", - " Title(\n", - " text=\"Monthly NTL Radiance Average (VNP46A2 and VNP46A3) for each second-level administrative division\",\n", - " text_font_size=\"12pt\",\n", - " text_font_style=\"italic\",\n", - " ),\n", - " \"above\",\n", - ")\n", - "p.add_layout(\n", - " Title(\n", - " text=f\"Data Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.\",\n", - " text_font_size=\"10pt\",\n", - " text_font_style=\"italic\",\n", - " ),\n", - " \"below\",\n", - ")\n", - "p.add_layout(Legend(), \"right\")\n", - "\n", - "p.add_tools(\n", - " HoverTool(\n", - " tooltips=[\n", - " (\"Month\", \"@x{%B %Y}\"),\n", - " (\"Radiance\", \"@y{0.00}\"),\n", - " ],\n", - " formatters={\"@x\": \"datetime\"},\n", - " )\n", - ")\n", - "\n", - "for column, color in zip(PSE_2_MS.columns, cc.b_glasbey_category10):\n", - " r = p.line(\n", - " PSE_2_MS.index,\n", - " PSE_2_MS[column],\n", - " legend_label=column[1],\n", - " line_color=color,\n", - " line_width=2,\n", - " )\n", - " r.visible = False\n", - "\n", - " # Except Gaza visible\n", - " if str(column[1]) == \"Gaza\":\n", - " r.visible = True\n", - "\n", - "for column, color in zip(VNP46A3.columns, cc.b_cyclic_grey_15_85_c0):\n", - " r = p.line(\n", - " VNP46A3.index,\n", - " VNP46A3[column],\n", - " legend_label=column[1],\n", - " line_color=color,\n", - " line_width=2,\n", - " )\n", - " r.visible = False\n", - "\n", - " # Except Gaza visible\n", - " if str(column[1]) == \"Gaza\":\n", - " r.visible = True\n", - "\n", - "p.legend.location = \"bottom_left\"\n", - "p.legend.click_policy = \"hide\"\n", - "p.title.text_font_size = \"16pt\"\n", - "\n", - "output_notebook()\n", - "show(p)" - ] - }, - { - "cell_type": "markdown", - "id": "cd9e128a-c7a8-44de-bca9-044c7f087598", - "metadata": {}, - "source": [ - "```{figure} ../../docs/images/logo.png\n", - "---\n", - "height: 0px\n", - "---\n", - "Monthly NTL radiance average derived from the VNP46A2 daily composite and, in grey, derived from the VNP46A3 monthly composite.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "98e51b8c-4b6b-4181-a781-24815425a2cb", - "metadata": {}, - "source": [ - "## Findings" - ] - }, - { - "cell_type": "markdown", - "id": "86165371-b848-4235-b4f3-5c354c8b7ac0", - "metadata": {}, - "source": [ - "### Percent Change in NTL Radiance \n", - "\n", - "#### Benchmark Comparison\n", - "\n", - "In this exploratory analysis, we conducted analysis of NTL radiance trends, comparing the observed average radiance levels to a benchmark established in the year 2022 for each second-level administrative division." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0b22c9bd-6b30-45b4-8047-add75fe5671f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "PERCENTCHANGE_2 = 100 * (\n", - " PSE_2 / PSE_2[(PSE_2.index >= \"2022-01-01\") & (PSE_2.index < \"2023-01-01\")].mean()\n", - " - 1\n", - ")\n", - "PERCENTCHANGE_1 = 100 * (\n", - " PSE_1 / PSE_1[(PSE_1.index >= \"2022-01-01\") & (PSE_2.index < \"2023-01-01\")].mean()\n", - " - 1\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c997b93d-3680-49de-864c-8ac5331cd148", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "PERCENTCHANGE_2.to_csv(\"../../data/ntl/PSE_2-VNP46A2-WSUN-percentchange.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "26c263cd-d679-4b7d-896b-86e424b46793", - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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 ntl_mean
NAME_2BethlehemDeir Al-BalahGazaGaza ash ShamaliyahHebronJeninJerichoJerusalemKhan YunisNablusQalqilyaRafahRamallah and Al-BirehSalfitTubasTulkarm
date                
2023-10-01 00:00:00-1.036671-5.176193-0.94803417.16972623.82542716.1487949.14661613.1554295.24042323.910822-1.6835306.81433813.9584375.48115336.6282511.838384
2023-10-08 00:00:00-15.460339-23.601012-13.2175210.40662922.50252216.33470223.188619-18.918551-19.251201-0.12658689.428368-5.4613364.5274834.09627347.43544714.639749
2023-10-15 00:00:00-28.738148-84.990641-84.737613-86.2951707.02339316.1034615.234816-8.937742-79.4751605.1823280.334092-74.8057768.3623686.21682310.471731-7.134540
2023-10-22 00:00:002.589614-87.887605-85.547573-84.46272124.01921312.93915117.27259110.637407-85.65693014.4119979.635253-76.23160815.86138110.02948827.9682694.552786
2023-10-29 00:00:00-33.228641-84.899959-71.589192-79.4165374.37366626.02109222.101463-38.325125-78.75818823.40900722.102664-68.93989343.61872117.61619760.56852012.916775
2023-11-05 00:00:0031.840591-83.361935-67.098409-72.98975131.95240037.85461416.8265757.884103-76.80671534.92247221.287162-69.79714228.69411524.66619648.75627418.704953
2023-11-12 00:00:0010.707432-88.084733-87.231798-83.93956642.28419531.52329112.808116-7.058684-85.12165419.730373-1.670999-59.68889111.52698429.53635431.6526224.416223
2023-11-19 00:00:00-6.505106-90.766412-93.044013-76.0974558.9700457.8086947.31253713.671693-88.850664-4.30768030.739551-77.34590425.49246822.82990518.46617126.871471
2023-11-26 00:00:002.140390-89.456731-92.147935-68.86517117.40066823.08211618.250061-5.227394-87.32819927.28610616.763595-75.14592320.81681012.39003027.6760133.721364
2023-12-03 00:00:0010.755868-92.292800-90.129065-49.73573016.12537619.25546010.091243-4.122347-89.9009475.30658822.665316-74.880719-1.62046737.79185617.148207-1.032048
2023-12-10 00:00:00-27.778308-92.140833-94.562775-84.6137794.7319700.929518-1.824031-16.815768-88.865193-30.247339-3.611129-75.155288-4.995364-0.336748-17.544758-25.185401
2023-12-17 00:00:00-29.911173-92.699088-88.513792-92.29163816.4116879.431974-7.2793286.723104-91.59562921.33525214.573100-80.57796514.7336377.7196946.9367232.147263
2023-12-24 00:00:003.496513-92.941141-92.821373-87.8358076.7213468.40233045.399533-1.798316-91.398088-7.664691-2.114718-75.6759669.0847563.9275694.985050-6.837770
2023-12-31 00:00:00-5.615821-96.182853-94.691191-90.58484620.687287-3.763185-3.150697-2.323018-93.14941815.8290277.898747-78.33972914.59506511.681975-6.6435062.076317
2024-01-07 00:00:004.855568-83.911092-93.231731-91.52164516.2477433.6517756.7774074.492586-91.29711235.51428115.355475-79.8219426.4022126.01869522.873217-12.465832
2024-01-14 00:00:00-35.579987-85.773887-95.520273-91.725524-15.689690-15.200722-3.518413-0.000636-90.7139130.391955-9.253621-76.165399-16.729773-9.9999479.459606-10.988212
2024-01-21 00:00:00-0.429245-94.771595-96.836293-94.78953216.9704155.488577-6.21844112.216378-92.84930211.206870-7.501423-77.89098911.7043963.039164-9.2318341.460175
2024-01-28 00:00:00-42.232540-85.472312-96.674364-93.328954-27.160916-4.874859-8.097918-10.169709-92.251955-16.373157-12.832538-71.747311-16.124229-9.68502623.361902-6.113997
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administrative division." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "25ca306b-9bce-438a-9cd0-3a3136ca6090", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "WOW_2 = 100 * (PSE_2.pct_change(1, axis=\"rows\", fill_method=None))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "0e8b10f7-7f19-46e2-8cc9-f938241148a0", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "WOW_2.to_csv(\"../../data/ntl/PSE_2-VNP46A2-WOW-percentchange.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "9e8accf1-6810-413c-97cf-7c29e4e4a530", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Source: NASA Black Marble (VNP46A1).\n", - "```\n", - "\n", - "```{figure} ./figures/pse_ntl_VNP46A1_2023-10-21.png\n", - "---\n", - "height: 400px\n", - "---\n", - "Nighttime lights on October 21, 2023. Source: NASA Black Marble (VNP46A1).\n", - "```\n", - "\n", - "#### Weekly\n", - "We visualize below weekly snapshots of the percent change (compared to 2022) in NTL radiance average for each second-level administrative division. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "db2e0ec3-491d-4687-800e-5d06142516b5", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "group = lambda flat, size: [flat[i : i + size] for i in range(0, len(flat), size)]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "40a89914-7f7e-4744-a2d4-b9595995ea28", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = PSE.merge(\n", - " PERCENTCHANGE_2.iloc[1]\n", - " .to_frame(\"percent change in radiance average (%)\")\n", - " .reset_index(),\n", - " on=\"NAME_2\",\n", - ").explore(\n", - " column=\"percent change in radiance average (%)\", cmap=\"cividis\", vmin=-100, vmax=0\n", - ")\n", - "\n", - "title_html = f\"\"\"

Week of {PERCENTCHANGE_2.iloc[0].name.strftime(\"%F\")}

\"\"\"\n", - "m.get_root().html.add_child(folium.Element(title_html))\n", - "m" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6bc5e49e-0d5a-4eae-8d7c-e3002420d897", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = PSE.merge(\n", - " PERCENTCHANGE_2.loc[\"2023-10-08\"]\n", - " .to_frame(\"percent change in radiance average (%)\")\n", - " .reset_index(),\n", - " on=\"NAME_2\",\n", - ").explore(\n", - " column=\"percent change in radiance average (%)\", cmap=\"cividis\", vmin=-100, vmax=0\n", - ")\n", - "\n", - "title_html = f\"\"\"

Week of {PERCENTCHANGE_2.loc[\"2023-10-08\"].name.strftime(\"%F\")}

\"\"\"\n", - "m.get_root().html.add_child(folium.Element(title_html))\n", - "m" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "716d106d-70d0-4594-81e8-b8d5d2655784", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = PSE.merge(\n", - " PERCENTCHANGE_2.iloc[-1]\n", - " .to_frame(\"percent change in radiance average (%)\")\n", - " .reset_index(),\n", - " on=\"NAME_2\",\n", - ").explore(\n", - " column=\"percent change in radiance average (%)\", cmap=\"cividis\", vmin=-100, vmax=0\n", - ")\n", - "\n", - "title_html = f\"\"\"

Week of {PERCENTCHANGE_2.iloc[-1].name.strftime(\"%F\")}

\"\"\"\n", - "m.get_root().html.add_child(folium.Element(title_html))\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "511afbb2-e745-42ad-9a47-f294780ae7ab", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Limitations \n", - "\n", - "Using nighttime lights to estimate macroeconomic indicators during conflict may be a valuable approach, but it comes with several assumptions and limitations. Here's a list of some of the key assumptions and limitations:\n", - "\n", - "```{caution}\n", - "**Assumptions:**\n", - "\n", - "- **Luminosity Reflects Economic Activity:** The approach assumes that the level of nighttime lights is a reliable proxy for economic activity. It presupposes that areas with brighter lights correspond to higher economic productivity.\n", - "\n", - "- **Baseline Data Availability:** It assumes the availability of baseline nighttime lights data before the onset of the conflict. The accuracy of the estimates depends on the quality and relevance of this baseline data.\n", - "\n", - "- **Spatial Distribution:** The method assumes that nighttime lights are evenly distributed within a given geographic area and that changes in luminosity accurately reflect changes in economic activity across all locations.\n", - " \n", - "\n", - "**Limitations:**\n", - "\n", - "- **Confounding Factors and Data Interpretation:** The approach may require subjective interpretation, as it may not distinguish between reduced lighting due to conflict and reduced lighting due to other factors. Changes in nighttime lights can be influenced by factors other than economic activity, such as energy conservation measures, urban development, or seasonal variations.\n", - "\n", - "- **Generalization:** The approach might lead to overgeneralization, as a reduction in nighttime lights can be associated with various economic outcomes, from minor disruptions to severe economic downturns.\n", - "\n", - "- **Alternative Explanations:** Changes in nighttime lights can result from factors other than conflict, such as urban development, changes in economic activities, or natural disasters. Therefore, it may not always be clear whether a decline in nighttime lights is solely due to conflict.\n", - "\n", - "- **Geopolitical Factors:** The dataset may be subject to geopolitical biases, with some areas having less comprehensive coverage due to political reasons.\n", - "\n", - "- **Data Lag:** There can be a significant time lag between the occurrence of a conflict event and its reflection in the nighttime lights dataset. This lag may limit the dataset's utility for real-time conflict monitoring.\n", - "\n", - "- **Resolution and Urban Bias:** The dataset's spatial resolution may not be fine enough to capture small villages or isolated conflict events. It may also have an urban bias, making it less suitable for analyzing rural or remote conflicts.\n", - "```\n", - "\n", - "To address these assumptions and limitations, it is crucial to complement nighttime lights data analysis with other sources of information and adopt a cautious and context-aware approach when interpreting the findings." - ] - }, - { - "cell_type": "markdown", - "id": "7922d168-87ab-4fe3-af83-feabd14a3a81", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "{cite:empty}`ROMAN2018113`\n", - "\n", - "```{bibliography}\n", - ":filter: docname in docnames\n", - ":style: plain\n", - "```" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/world-bank-api.ipynb b/notebooks/world-bank-api.ipynb new file mode 100644 index 0000000..d510e51 --- /dev/null +++ b/notebooks/world-bank-api.ipynb @@ -0,0 +1,715 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "90700fdc-fcc7-4e54-8c9e-449879d8c66d", + "metadata": { + "tags": [] + }, + "source": [ + "# Indicators Example\n", + "\n", + "> The following is an example of a [Jupyter notebook](https://jupyter.org) - a tutorial of how to retrieve data from the [World Bank Indicators API](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation) - that illustrates how to use computational content with the [template](https://worldbank.github.io/template). " + ] + }, + { + "cell_type": "markdown", + "id": "e0d992a6-f656-45ce-a025-f824901e8797", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1811080b-c4c6-43cb-9e46-5cfa65d54abf", + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "import bokeh\n", + "import pandas\n", + "import requests\n", + "from bokeh.palettes import Spectral6\n", + "from bokeh.plotting import figure, output_notebook, show" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "fb8d2738-535e-4957-b82a-987891955a7f", + "metadata": {}, + "source": [ + "## Retrieve Data\n", + "\n", + "https://data.worldbank.org/indicator\n", + "\n", + "\n", + "https://databank.worldbank.org/reports.aspx?source=World-Development-Indicators\n", + "\n", + "\n", + "In this example, we retrieve **Population, total** (`SP.POP.TOTL`) from the [World Bank Indicators](https://data.worldbank.org/indicator) for [BRICS](https://infobrics.org)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c955864a-1889-4f7f-a29e-108b0534846b", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://api.worldbank.org/v2/country/chn;bra;ind;rus;zaf/indicator/SP.POP.TOTL?format=json&per_page=10000\"" + ] + }, + { + "cell_type": "markdown", + "id": "6b5aac7c-bf80-4daa-a4a4-eebe8edc97bb", + "metadata": {}, + "source": [ + "Let's use [requests](https://requests.readthedocs.io) to send a GET request," + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8d699f28-853a-40a1-8ea9-8dd566962454", + "metadata": {}, + "outputs": [], + "source": [ + "r = requests.get(url)" + ] + }, + { + "cell_type": "markdown", + "id": "a21cf193-ec13-45b8-9726-bb960ac8586a", + "metadata": {}, + "source": [ + "Now, let's normalize and create `pandas.DataFrame` from the response," + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3dc152b2-95ba-473a-a416-2c4d5bac7622", + "metadata": {}, + "outputs": [], + "source": [ + "# normalize\n", + "data = pandas.json_normalize(r.json()[-1])\n", + "\n", + "# create dataframe\n", + "df = pandas.DataFrame.from_dict(data)" + ] + }, + { + "cell_type": "markdown", + "id": "241904c0-35b9-4e43-a3f9-f97738ea9fd1", + "metadata": {}, + "source": [ + "```{tip}\n", + "Alternatively, the World Bank API supports downloading the data as an [archive](http://api.worldbank.org/v2/country/all/indicator/SP.POP.TOTL?date=2000&source=2&downloadformat=csv). \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "bae3462b-f49c-4b8a-badb-8f580b4fc268", + "metadata": {}, + "source": [ + "Let's take a look at the dataframe, " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c0bbef2d-495c-4140-b8ac-45ee47772142", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryiso3codeBRACHNINDRUSZAF
date
196073.092515667.070445.954579119.89700016.520441
196175.330008660.330456.351876121.23600016.989464
196277.599218665.770467.024193122.59100017.503133
196379.915555682.335477.933619123.96000018.042215
196482.262794698.355489.059309125.34500018.603097
..................
2017208.5049601396.2151354.195680144.49673956.641209
2018210.1665921402.7601369.003306144.47785957.339635
2019211.7828781407.7451383.112050144.40626158.087055
2020213.1963041411.1001396.387127144.07313958.801927
2021214.3262231412.3601407.563842143.44928659.392255
\n", + "

62 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + "countryiso3code BRA CHN IND RUS ZAF\n", + "date \n", + "1960 73.092515 667.070 445.954579 119.897000 16.520441\n", + "1961 75.330008 660.330 456.351876 121.236000 16.989464\n", + "1962 77.599218 665.770 467.024193 122.591000 17.503133\n", + "1963 79.915555 682.335 477.933619 123.960000 18.042215\n", + "1964 82.262794 698.355 489.059309 125.345000 18.603097\n", + "... ... ... ... ... ...\n", + "2017 208.504960 1396.215 1354.195680 144.496739 56.641209\n", + "2018 210.166592 1402.760 1369.003306 144.477859 57.339635\n", + "2019 211.782878 1407.745 1383.112050 144.406261 58.087055\n", + "2020 213.196304 1411.100 1396.387127 144.073139 58.801927\n", + "2021 214.326223 1412.360 1407.563842 143.449286 59.392255\n", + "\n", + "[62 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.pivot_table(values=\"value\", index=\"date\", columns=\"countryiso3code\")\n", + "df = df / 1e6 # scaling\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "27f26a03-6d7a-4c86-8a02-1ea341d7ac5b", + "metadata": {}, + "source": [ + "## Visualization" + ] + }, + { + "cell_type": "markdown", + "id": "38e8582b-2a51-4908-8356-76cb6158fdc3", + "metadata": {}, + "source": [ + "Let's now plot the data as a time series using [Bokeh](https://docs.bokeh.org)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "78041d94-56a6-43ff-a307-8e8a3b377858", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
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\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"1002\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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Application\",\"version\":\"2.4.3\"}};\n", + " const render_items = [{\"docid\":\"76a88bd2-d1e2-4b96-a4aa-feed715948c8\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"4d4ccb98-b0a7-4ec4-9327-87b553eb7e33\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1003" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "output_notebook()\n", + "\n", + "p = figure(title=\"Population, total (World Bank)\", width=700, height=600)\n", + "\n", + "# colors\n", + "colors = itertools.cycle(Spectral6)\n", + "\n", + "# plotting the line graph\n", + "for column, color in zip(df.columns, colors):\n", + " p.line(\n", + " df.index,\n", + " df[column],\n", + " legend_label=column,\n", + " color=color,\n", + " line_width=2,\n", + " )\n", + "\n", + "p.legend.location = \"right\"\n", + "p.legend.click_policy = \"mute\"\n", + "p.title.text_font_size = \"12pt\"\n", + "\n", + "p.xaxis.axis_label = \"Year\"\n", + "p.yaxis.axis_label = \"Population, total (in millions)\"\n", + "\n", + "show(p)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + }, + "vscode": { + "interpreter": { + "hash": "b6702b69e93007336b96338c5a331192f07cedff01d36d4dcfa0f842adb718ad" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/world-bank-package.ipynb b/notebooks/world-bank-package.ipynb new file mode 100644 index 0000000..5c38153 --- /dev/null +++ b/notebooks/world-bank-package.ipynb @@ -0,0 +1,509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "90700fdc-fcc7-4e54-8c9e-449879d8c66d", + "metadata": { + "tags": [] + }, + "source": [ + "# Package Example\n", + "\n", + "> The following is an example of on how to use and distribute the source code of your project as a [Python package](https://packaging.python.org) using the template." + ] + }, + { + "cell_type": "markdown", + "id": "14e89727", + "metadata": {}, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "markdown", + "id": "b4c0f3e8-7756-41bb-aa21-cc2eee5ff67f", + "metadata": {}, + "source": [ + "Unlike the [previous example](https://worldbank.github.io/template/notebooks/world-bank-api.html), where the source code was contained on the Jupyter notebok itself, we (re)use a Python package - the [datalab](https://github.com/worldbank/template/tree/main/src/datalab) Python package - which will let us (re)use any attributes and methods in the following example.\n", + "\n", + "Let's start by importing `WorldBankIndicatorsAPI`, a Python API wrapper class created to facilitate the usage of the [World Bank Indicators API](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d797ef77-6ca4-4f9d-a1f8-abbfd9884b07", + "metadata": {}, + "outputs": [], + "source": [ + "from datalab.indicators import WorldBankIndicatorsAPI" + ] + }, + { + "cell_type": "markdown", + "id": "17f380e4-3854-4af6-940c-7afe9723a59a", + "metadata": {}, + "source": [ + "Let's continue by creating the API object. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f911a5c3-6994-45a6-a049-4b398f5890c0", + "metadata": {}, + "outputs": [], + "source": [ + "api = WorldBankIndicatorsAPI()" + ] + }, + { + "cell_type": "markdown", + "id": "7fa96741-f4cd-4504-a5f8-6467f9a2345e", + "metadata": {}, + "source": [ + "The `api` wrapper object is now ready to use! We will invoke its `query` method to retrieve data from the [World Bank Indicators API](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation). To learn how to use it, such as information about method signature, valid parameters and return value, we read `help`. Since [PEP 257](https://peps.python.org/pep-0257), Python offers *doctrings*, which are an easy and standard to create code documentation and it is a good practice adopt it. Documentating the source code is crucial to create a maintainable reliable and reproducicle code base and project.\n", + "\n", + "Let's see the `query` method's *docstring* as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fb6ca314-d161-40e1-a376-a3013a0711eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method query in module datalab.indicators:\n", + "\n", + "query(indicator, country: list = 'all', params: dict = {}) method of datalab.indicators.WorldBankIndicatorsAPI instance\n", + " Retrieve a response, valid JSON response or error, from the World Bank Indicators API.\n", + " \n", + " See also:\n", + " https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation\n", + " \n", + " Parameters\n", + " ----------\n", + " indicator : str\n", + " World Bank API Indicator.\n", + " country : list, optional\n", + " List of countries. The country name is converted to ISO 3166-1 alpha-3 country code.\n", + " params : dict, optional\n", + " World Bank API Indicator Query Strings.\n", + " \n", + " Returns\n", + " -------\n", + " pandas.core.frame.DataFrame\n", + " Return a Pandas DataFrame obtained with response data from World Bank Indicators API.\n", + "\n" + ] + } + ], + "source": [ + "help(api.query)" + ] + }, + { + "cell_type": "markdown", + "id": "e82fc342-165d-42d6-b3dc-7534c215ca1f", + "metadata": {}, + "source": [ + "The `query` method allows us to select an **indicator** (e.g, [World Development Indicators](https://datatopics.worldbank.org/world-development-indicators)), a list of countries and [query parameters](https://datahelpdesk.worldbank.org/knowledgebase/articles/898581#query-strings). Note that contrary to the [previous example](https://worldbank.github.io/template/notebooks/world-bank-api.html), the method expects a list of country names and converts them to [ISO 3166-1 alpha-3](https://www.iso.org/iso-3166-country-codes.html) automatically." + ] + }, + { + "cell_type": "markdown", + "id": "23b0a1eb-73c1-42e7-8903-98e362ef86de", + "metadata": {}, + "source": [ + "Let's invoke the `query` method and retrieve the results for `SP.POP.TOTL` for the [BRICS](https://infobrics.org) (as before)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7fb7daea-c5cf-42ea-b746-a565dd9ac4e1", + "metadata": {}, + "outputs": [], + "source": [ + "df = api.query(\n", + " \"SP.POP.TOTL\", country=[\"Brazil\", \"China\", \"India\", \"Russia\", \"South Africa\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "46662c1b-4c19-424b-8a61-f651cb486c5b", + "metadata": {}, + "source": [ + "**Voilà!** We just (re)used the [datalab](https://github.com/worldbank/template/tree/main/src/datalab) Python package in our example delegating the maintenance and logic, making the notebook easier to understand and reproduce. \n", + "\n", + "```{tip}\n", + "In addition, the `template` makes any Python package automatically [pip installable](https://packaging.python.org/en/latest/tutorials/installing-packages/) and acessible to *anyone* and from *anywhere*!\n", + "\n", + "To install from source:\n", + "\n", + "\tpip install git+https://github.com/worldbank/template.git\n", + "\n", + "To install from version:\n", + "\n", + "\tpip install git+https://github.com/worldbank/template.git@v0.1.0\n", + "\t\n", + "\n", + "When distributing a project release, it is strongly recommended to adhere to release management good practices. It is recommended to create checklists, adopt versioning (e.g, [semantic versioning](https://semver.org/) and to release on [Python Package Index](https://pypi.org/) (instead of GitHub).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "80887da5-0474-48b3-8c71-fbe3dbd3a8e8", + "metadata": {}, + "source": [ + "```{tip}\n", + "The template will automatically find and install any local `src` packages as long as the `setup.cfg` file is up-to-date.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "daa4319a-8936-4195-b1fc-aad9c008325b", + "metadata": {}, + "source": [ + "```{caution}\n", + "The `datalab` Python package shoudl be used for demonstration purposes only. For support, please see the [World Bank Indicators API Documentation](https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e9f14239", + "metadata": {}, + "source": [ + "Finally, let's take a look at the retrieved data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b1d7cf70-bf0e-4c12-ae0d-fd26349291db", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "df = df.pivot_table(values=\"value\", index=\"date\", columns=\"country.value\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "699a0495-4f06-479c-b517-58336110547f", + "metadata": { + "tags": [ + "output_scroll" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "country.value Brazil China India Russian Federation \\\n", + "date \n", + "1960 73092515 667070000 445954579 119897000 \n", + "1961 75330008 660330000 456351876 121236000 \n", + "1962 77599218 665770000 467024193 122591000 \n", + "1963 79915555 682335000 477933619 123960000 \n", + "1964 82262794 698355000 489059309 125345000 \n", + "... ... ... ... ... \n", + "2017 208504960 1396215000 1354195680 144496739 \n", + "2018 210166592 1402760000 1369003306 144477859 \n", + "2019 211782878 1407745000 1383112050 144406261 \n", + "2020 213196304 1411100000 1396387127 144073139 \n", + "2021 214326223 1412360000 1407563842 143449286 \n", + "\n", + "country.value South Africa \n", + "date \n", + "1960 16520441 \n", + "1961 16989464 \n", + "1962 17503133 \n", + "1963 18042215 \n", + "1964 18603097 \n", + "... ... \n", + "2017 56641209 \n", + "2018 57339635 \n", + "2019 58087055 \n", + "2020 58801927 \n", + "2021 59392255 \n", + "\n", + "[62 rows x 5 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "c5daa85a-004d-4e93-be84-72d064d0b83b", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "As before, let's now plot the data as a time series using [Bokeh](https://docs.bokeh.org)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5d6be794-9e37-4ee2-94df-b40dff3ace6a", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
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    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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Application\",\"version\":\"2.4.3\"}};\n const render_items = [{\"docid\":\"27a5602b-a100-49a8-be71-4c1a30cbf69c\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"756bcecf-5d05-4b33-b3f9-817acf206cf6\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1003" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "\n", + "import bokeh\n", + "from bokeh.palettes import Spectral6\n", + "from bokeh.plotting import figure, output_notebook, show\n", + "\n", + "output_notebook()\n", + "\n", + "# instantiating the figure object\n", + "p = figure(title=\"Population, total (World Bank)\", width=700, height=600)\n", + "\n", + "# colors\n", + "colors = itertools.cycle(Spectral6)\n", + "\n", + "# plotting the line graph\n", + "for column, color in zip(df.columns, colors):\n", + " p.line(\n", + " df.index,\n", + " df[column],\n", + " legend_label=column,\n", + " color=color,\n", + " line_width=2,\n", + " )\n", + "\n", + "p.legend.location = \"right\"\n", + "p.legend.click_policy = \"mute\"\n", + "p.title.text_font_size = \"12pt\"\n", + "\n", + "p.xaxis.axis_label = \"Year\"\n", + "p.yaxis.axis_label = \"Population, total (in millions)\"\n", + "\n", + "show(p)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "b6702b69e93007336b96338c5a331192f07cedff01d36d4dcfa0f842adb718ad" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..db59961 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,43 @@ +[build-system] +requires = ["setuptools>=61.0.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "datalab" +dynamic = ["version"] +description = "An Python exampe package from the World Bank Data Lab" +readme = { file = "README.md", content-type = "text/markdown" } +license = { file = "LICENSE" } +keywords = ["template", "reproducible research"] +authors = [{ name = "Development Data Group", email = "datalab@worldbank.org" }] +maintainers = [ + { name = "Gabriel Stefanini Vicente", email = "gvicente@worldbank.org" }, +] +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3 :: Only", + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Operating System :: OS Independent", +] +requires-python = ">=3.7" +dependencies = ["requests>=2.28.1", "pycountry>=22.3.5"] +[project.optional-dependencies] +docs = [ + "docutils==0.17.1", # https://jupyterbook.org/en/stable/content/citations.html?highlight=docutils#citations-and-bibliographies + "jupyter-book >=1,<2", +] +notebook = ["notebook>=6.5.2"] + +[project.urls] +"Homepage" = "https://github.com/worldbank/template" +"Bug Reports" = "https://github.com/worldbank/template/issues" +"Source" = "https://github.com/worldbank/template" + +[tool.setuptools.dynamic] +version = { attr = "datalab.__version__" } diff --git a/src/datalab/VERSION b/src/datalab/VERSION new file mode 100644 index 0000000..6c6aa7c --- /dev/null +++ b/src/datalab/VERSION @@ -0,0 +1 @@ +0.1.0 \ No newline at end of file diff --git a/src/datalab/__init__.py b/src/datalab/__init__.py new file mode 100644 index 0000000..41740c0 --- /dev/null +++ b/src/datalab/__init__.py @@ -0,0 +1,4 @@ +from pathlib import Path + +__version__ = (Path(__file__).parent / "VERSION").read_text().strip() + diff --git a/src/datalab/indicators.py b/src/datalab/indicators.py new file mode 100644 index 0000000..4cf79a2 --- /dev/null +++ b/src/datalab/indicators.py @@ -0,0 +1,84 @@ +import pandas +import pycountry +import requests + + +class WorldBankIndicatorsAPI: + + URL = "https://api.worldbank.org/v2/country" + + def _get_country_code(self, country): + """ + Using `pycountry`, return the ISO 3166-1 alpha-3 country code for corresponding query term. + + See also: + https://github.com/flyingcircusio/pycountry + + Parameters + ---------- + country : str + + Returns + ------- + str + ISO 3166-1 alpha-3 country code for corresponding query term. + + Raises + ------ + LookupError + If the query term is not a valid country. + """ + return pycountry.countries.search_fuzzy(country)[0].alpha_3 + + def _get(self, indicator, country: str = "all", params: dict = {}): + """ + Retrieve a response, valid JSON response or error, from the World Bank Indicators API. + + See also: + https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation + + Parameters + ---------- + indicator : str + country : str, optional + params : dict, optional + + Returns + ------- + requests.models.Response + Return JSON response from the World Bank Indicators API. + """ + url = f"{self.URL}/{country}/indicator/{indicator}" + + return requests.get(url, params) + + def query(self, indicator, country: list = "all", params: dict = {}): + """ + Retrieve a response, valid JSON response or error, from the World Bank Indicators API. + + See also: + https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation + + Parameters + ---------- + indicator : str + World Bank API Indicator. + country : list, optional + List of countries. The country name is converted to ISO 3166-1 alpha-3 country code. + params : dict, optional + World Bank API Indicator Query Strings. + + Returns + ------- + pandas.core.frame.DataFrame + Return a Pandas DataFrame obtained with response data from World Bank Indicators API. + """ + if isinstance(country, list): + country = ";".join([self._get_country_code(c) for c in country]) + + params.update({"format": "json", "per_page": 1000}) + + response = self._get(indicator, country, params) + data = response.json()[-1] + + return pandas.json_normalize(data)