diff --git a/.readthedocs.yml b/.readthedocs.yml index b0c36ed98b..eea4e96d22 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -22,3 +22,6 @@ python: formats: - pdf + +sphinx: + configuration: doc/conf.py diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f21b73e951..11a8ecc246 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -12,7 +12,7 @@ For orientation, these are some categories of possible contributions we can thin * **New Modules and Utility Functions:** Did you create a function or an entire module you find useful for your work? Maybe you are not the only one! Feel free to simply raise a pull request for functions that improve, e.g., plotting or data handling. As an entire module has to be carefully integrated into the framework, it might help if you talk to us first so we can design the module and plan the next steps. You can do that by raising an issue or starting a [discussion](https://github.com/CLIMADA-project/climada_python/discussions) on GitHub. A good place to start a personal discussion is our monthly CLIMADA developers call. -Please contact the [lead developers](https://wcr.ethz.ch/research/climada.html) if you want to join. +Please contact the [lead developers](https://climada.ethz.ch/team/) if you want to join. ## Why Should You Contribute? diff --git a/climada/engine/unsequa/calc_base.py b/climada/engine/unsequa/calc_base.py index 4ec8e55b06..a024d1e12f 100644 --- a/climada/engine/unsequa/calc_base.py +++ b/climada/engine/unsequa/calc_base.py @@ -207,7 +207,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): sampling_method : str, optional The sampling method as defined in SALib. Possible choices: 'saltelli', 'latin', 'morris', 'dgsm', 'fast_sampler', 'ff', 'finite_diff', - https://salib.readthedocs.io/en/latest/api.html + https://salib.readthedocs.io/en/latest/api.html The default is 'saltelli'. sampling_kwargs : kwargs, optional Optional keyword arguments passed on to the SALib sampling_method. @@ -223,7 +223,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): The 'ff' sampling method does not require a value for the N parameter. The inputed N value is hence ignored in the sampling process in the case of this method. - The 'ff' sampling method requires a number of uncerainty parameters to be + The 'ff' sampling method requires a number of uncertainty parameters to be a power of 2. The users can generate dummy variables to achieve this requirement. Please refer to https://salib.readthedocs.io/en/latest/api.html for more details. @@ -232,7 +232,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): See Also -------- SALib.sample: sampling methods from SALib SALib.sample - https://salib.readthedocs.io/en/latest/api.html + https://salib.readthedocs.io/en/latest/api.html """ diff --git a/climada/engine/unsequa/calc_cost_benefit.py b/climada/engine/unsequa/calc_cost_benefit.py index b42e76da11..3bdfe46bba 100644 --- a/climada/engine/unsequa/calc_cost_benefit.py +++ b/climada/engine/unsequa/calc_cost_benefit.py @@ -77,7 +77,7 @@ class CalcCostBenefit(Calc): _metric_names : tuple(str) Names of the cost benefit output metrics ('tot_climate_risk', 'benefit', 'cost_ben_ratio', - 'imp_meas_present', 'imp_meas_future') + 'imp_meas_present', 'imp_meas_future') """ diff --git a/climada/engine/unsequa/calc_delta_climate.py b/climada/engine/unsequa/calc_delta_climate.py index 0ec1fb3afc..93fdfec969 100644 --- a/climada/engine/unsequa/calc_delta_climate.py +++ b/climada/engine/unsequa/calc_delta_climate.py @@ -81,7 +81,7 @@ class CalcDeltaImpact(Calc): _input_var_names : tuple(str) Names of the required uncertainty input variables ('exp_initial_input_var', 'impf_initial_input_var', 'haz_initial_input_var', - 'exp_final_input_var', 'impf_final_input_var', 'haz_final_input_var'') + 'exp_final_input_var', 'impf_final_input_var', 'haz_final_input_var'') _metric_names : tuple(str) Names of the impact output metrics ('aai_agg', 'freq_curve', 'at_event', 'eai_exp') diff --git a/climada/engine/unsequa/unc_output.py b/climada/engine/unsequa/unc_output.py index d9c68fe69d..80a385395e 100644 --- a/climada/engine/unsequa/unc_output.py +++ b/climada/engine/unsequa/unc_output.py @@ -84,20 +84,9 @@ class UncOutput: samples_df : pandas.DataFrame Values of the sampled uncertainty parameters. It has n_samples rows and one column per uncertainty parameter. - sampling_method : str - Name of the sampling method from SAlib. - https://salib.readthedocs.io/en/latest/api.html# - n_samples : int - Effective number of samples (number of rows of samples_df) - param_labels : list - Name of all the uncertainty parameters distr_dict : dict Comon flattened dictionary of all the distr_dict of all input variables. It represents the distribution of all the uncertainty parameters. - problem_sa : dict - The description of the uncertainty variables and their - distribution as used in SALib. - https://salib.readthedocs.io/en/latest/basics.html. """ _metadata = [ @@ -192,6 +181,7 @@ def check_salib(self, sensitivity_method): def sampling_method(self): """ Returns the sampling method used to generate self.samples_df + See: https://salib.readthedocs.io/en/latest/api.html# Returns ------- diff --git a/climada/hazard/base.py b/climada/hazard/base.py index 51d90cbbf7..507b79312b 100644 --- a/climada/hazard/base.py +++ b/climada/hazard/base.py @@ -225,15 +225,16 @@ def check_matrices(self): -------- :py:func:`climada.util.checker.prune_csr_matrix` - Todo - ----- - * Check consistency with centroids - Raises ------ ValueError If matrices are ill-formed or ill-shaped in relation to each other """ + + # Todo (Previously in docstring) + # ----- + # * Check consistency with centroids + u_check.prune_csr_matrix(self.intensity) u_check.prune_csr_matrix(self.fraction) if self.fraction.nnz > 0: diff --git a/climada/hazard/tc_clim_change.py b/climada/hazard/tc_clim_change.py index 576cb38bde..9aad4b6032 100644 --- a/climada/hazard/tc_clim_change.py +++ b/climada/hazard/tc_clim_change.py @@ -71,11 +71,11 @@ def get_knutson_scaling_factor( in Jewson et al., (2021). Related publications: + - Knutson et al., (2020): Tropical cyclones and climate change assessment. Part II: Projected response to anthropogenic warming. Bull. Amer. Meteor. Soc., 101 (3), E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1. - - Jewson (2021): Conversion of the Knutson et al. (2020) Tropical Cyclone Climate Change Projections to Risk Model Baselines, https://doi.org/10.1175/JAMC-D-21-0102.1 @@ -94,15 +94,15 @@ def get_knutson_scaling_factor( the provided percentiles are the 10th, 25th, 50th, 75th and 90th. Please refer to the mentioned publications for more details. possible percentiles: - '5/10' either the 5th or 10th percentile depending on variable (see text above) - '25' for the 25th percentile - '50' for the 50th percentile - '75' for the 75th percentile - '90/95' either the 90th or 95th percentile depending on variable (see text above) + - '5/10' either the 5th or 10th percentile depending on variable (see text above) + - '25' for the 25th percentile + - '50' for the 50th percentile + - '75' for the 75th percentile + - '90/95' either the 90th or 95th percentile depending on variable (see text above) Default: '50' basin : str region of interest, possible choices are: - 'NA', 'WP', 'EP', 'NI', 'SI', 'SP' + 'NA', 'WP', 'EP', 'NI', 'SI', 'SP' baseline : tuple of int the starting and ending years that define the historical baseline. The historical baseline period must fall within diff --git a/climada/hazard/tc_tracks.py b/climada/hazard/tc_tracks.py index 963d282cd3..4a6b03d2e3 100644 --- a/climada/hazard/tc_tracks.py +++ b/climada/hazard/tc_tracks.py @@ -198,6 +198,7 @@ class TCTracks: ---------- data : list(xarray.Dataset) List of tropical cyclone tracks. Each track contains following attributes: + - time (coords) - lat (coords) - lon (coords) @@ -216,9 +217,12 @@ class TCTracks: - data_provider (attrs) - id_no (attrs) - category (attrs) + Computed during processing: + - on_land (bool for each track position) - dist_since_lf (in km) + Additional data variables such as "nature" (specifiying, for each track position, whether a system is a disturbance, tropical storm, post-transition extratropical storm etc.) might be included, depending on the data source and on use cases. diff --git a/climada/hazard/trop_cyclone/trop_cyclone.py b/climada/hazard/trop_cyclone/trop_cyclone.py index ae01332ca0..f4f0a7b0d0 100644 --- a/climada/hazard/trop_cyclone/trop_cyclone.py +++ b/climada/hazard/trop_cyclone/trop_cyclone.py @@ -408,20 +408,25 @@ def apply_climate_scenario_knu( are the 10th, 25th, 50th, 75th and 90th. Please refer to the mentioned publications for more details. possible percentiles: - '5/10' either the 5th or 10th percentile depending on variable (see text above) - '25' for the 25th percentile - '50' for the 50th percentile - '75' for the 75th percentile - '90/95' either the 90th or 95th percentile depending on variable (see text above) + + - '5/10' either the 5th or 10th percentile depending on variable (see text above) + - '25' for the 25th percentile + - '50' for the 50th percentile + - '75' for the 75th percentile + - '90/95' either the 90th or 95th percentile depending on variable (see text above) + Default: '50' scenario : str possible scenarios: - '2.6' for RCP 2.6 - '4.5' for RCP 4.5 - '6.0' for RCP 6.0 - '8.5' for RCP 8.5 + + - '2.6' for RCP 2.6 + - '4.5' for RCP 4.5 + - '6.0' for RCP 6.0 + - '8.5' for RCP 8.5 + target_year : int future year to be simulated, between 2000 and 2100. Default: 2050. + Returns ------- haz_cc : climada.hazard.TropCyclone diff --git a/climada/util/api_client.py b/climada/util/api_client.py index 3857cf0d88..4fb1c92be5 100644 --- a/climada/util/api_client.py +++ b/climada/util/api_client.py @@ -1143,6 +1143,7 @@ def purge_cache(self, target_dir=SYSTEM_DIR, keep_testfiles=True): """Removes downloaded dataset files from the given directory if they have been downloaded with the API client, if they are beneath the given directory and if one of the following is the case: + - there status is neither 'active' nor 'test_dataset' - their status is 'test_dataset' and keep_testfiles is set to False - their status is 'active' and they are outdated, i.e., there is a dataset with the same diff --git a/climada/util/coordinates.py b/climada/util/coordinates.py index cec74b512c..5a13edf176 100644 --- a/climada/util/coordinates.py +++ b/climada/util/coordinates.py @@ -2940,9 +2940,11 @@ def set_df_geometry_points(df_val, scheduler=None, crs=None): contains latitude and longitude columns scheduler : str, optional Scheduler type for dask map_partitions. + .. deprecated:: 5.0 This function does not use dask features anymore. The parameter has no effect and will be removed in a future version. + crs : object (anything readable by pyproj4.CRS.from_user_input), optional Coordinate Reference System, if omitted or None: df_val.geometry.crs """ diff --git a/doc/_static/css/custom.css b/doc/_static/css/custom.css new file mode 100644 index 0000000000..aa76131f59 --- /dev/null +++ b/doc/_static/css/custom.css @@ -0,0 +1,22 @@ +:root { + + .navbar-brand { + height: 7rem; + max-height: 7rem; + } + +} + +.bd-main .bd-content .bd-article-container { + max-width: 100%; /* default is 60em */ +} + +.bd-page-width { + max-width: 100rem; +} + + +html { + --pst-font-size-base: 16px; + --pst-header-height: 7rem; +} diff --git a/doc/climada/climada.engine.rst b/doc/api/climada/climada.engine.rst similarity index 100% rename from doc/climada/climada.engine.rst rename to doc/api/climada/climada.engine.rst diff --git a/doc/climada/climada.engine.unsequa.rst b/doc/api/climada/climada.engine.unsequa.rst similarity index 100% rename from doc/climada/climada.engine.unsequa.rst rename to doc/api/climada/climada.engine.unsequa.rst diff --git a/doc/climada/climada.entity.disc_rates.rst b/doc/api/climada/climada.entity.disc_rates.rst similarity index 100% rename from doc/climada/climada.entity.disc_rates.rst rename to doc/api/climada/climada.entity.disc_rates.rst diff --git a/doc/climada/climada.entity.exposures.litpop.rst b/doc/api/climada/climada.entity.exposures.litpop.rst similarity index 100% rename from doc/climada/climada.entity.exposures.litpop.rst rename to doc/api/climada/climada.entity.exposures.litpop.rst diff --git a/doc/climada/climada.entity.exposures.rst b/doc/api/climada/climada.entity.exposures.rst similarity index 100% rename from doc/climada/climada.entity.exposures.rst rename to doc/api/climada/climada.entity.exposures.rst diff --git a/doc/climada/climada.entity.impact_funcs.rst b/doc/api/climada/climada.entity.impact_funcs.rst similarity index 100% rename from doc/climada/climada.entity.impact_funcs.rst rename to doc/api/climada/climada.entity.impact_funcs.rst diff --git a/doc/climada/climada.entity.measures.rst b/doc/api/climada/climada.entity.measures.rst similarity index 100% rename from doc/climada/climada.entity.measures.rst rename to doc/api/climada/climada.entity.measures.rst diff --git a/doc/climada/climada.entity.rst b/doc/api/climada/climada.entity.rst similarity index 100% rename from doc/climada/climada.entity.rst rename to doc/api/climada/climada.entity.rst diff --git a/doc/climada/climada.hazard.centroids.rst b/doc/api/climada/climada.hazard.centroids.rst similarity index 100% rename from doc/climada/climada.hazard.centroids.rst rename to doc/api/climada/climada.hazard.centroids.rst diff --git a/doc/climada/climada.hazard.rst b/doc/api/climada/climada.hazard.rst similarity index 100% rename from doc/climada/climada.hazard.rst rename to doc/api/climada/climada.hazard.rst diff --git a/doc/climada/climada.hazard.trop_cyclone.rst b/doc/api/climada/climada.hazard.trop_cyclone.rst similarity index 100% rename from doc/climada/climada.hazard.trop_cyclone.rst rename to doc/api/climada/climada.hazard.trop_cyclone.rst diff --git a/doc/climada/climada.rst b/doc/api/climada/climada.rst similarity index 100% rename from doc/climada/climada.rst rename to doc/api/climada/climada.rst diff --git a/doc/climada/climada.util.calibrate.rst b/doc/api/climada/climada.util.calibrate.rst similarity index 100% rename from doc/climada/climada.util.calibrate.rst rename to doc/api/climada/climada.util.calibrate.rst diff --git a/doc/climada/climada.util.rst b/doc/api/climada/climada.util.rst similarity index 100% rename from doc/climada/climada.util.rst rename to doc/api/climada/climada.util.rst diff --git a/doc/api/index.rst b/doc/api/index.rst new file mode 100644 index 0000000000..562fd27de5 --- /dev/null +++ b/doc/api/index.rst @@ -0,0 +1,11 @@ +============== +API Reference +============== + +Could be nice to have an API section homepage + +.. toctree:: + :caption: API Reference + :hidden: + + Modules diff --git a/doc/conf.py b/doc/conf.py index b4ef1dc69d..195603a1fb 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -40,6 +40,7 @@ "sphinx.ext.viewcode", "sphinx.ext.napoleon", "sphinx.ext.ifconfig", + "sphinx_design", "myst_nb", "sphinx_markdown_tables", "readthedocs_ext.readthedocs", @@ -123,12 +124,27 @@ # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. -html_theme = "sphinx_book_theme" +html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -# html_theme_options = {} +html_theme_options = { + "header_links_before_dropdown": 8, + "navbar_align": "left", + # "icon_links": [ + # { + # # Label for this link + # "name": "GitHub", + # # URL where the link will redirect + # "url": "https://github.com/CLIMADA-project", # required + # # Icon class (if "type": "fontawesome"), or path to local image (if "type": "local") + # "icon": "fa-brands fa-square-github", + # # The type of image to be used (see below for details) + # "type": "fontawesome", + # } + # ], +} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] @@ -154,6 +170,9 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] +html_css_files = [ + "css/custom.css", +] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' diff --git a/doc/guide/Guide_Git_Development.ipynb b/doc/development/Guide_CLIMADA_Development.ipynb similarity index 59% rename from doc/guide/Guide_Git_Development.ipynb rename to doc/development/Guide_CLIMADA_Development.ipynb index 08eb92c0cc..8e23160497 100644 --- a/doc/guide/Guide_Git_Development.ipynb +++ b/doc/development/Guide_CLIMADA_Development.ipynb @@ -2,288 +2,77 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "# Development and Git and CLIMADA" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Git and GitHub\n", - "\n", - "- Git's not that scary\n", - " - 95% of your work on Git will be done with the same handful of commands (the other 5% will always be done with careful Googling)\n", - " - Almost everything in Git can be undone by design (but use `rebase`, `--force` and `--hard` with care!)\n", - " - Your favourite IDE (Spyder, PyCharm, ...) will have a GUI for working with Git, or you can download a standalone one.\n", - "- The [Git Book](https://git-scm.com/book/en/v2) is a great introduction to how Git works and to using it on the command line.\n", - "- Consider using a GUI program such as “git desktop” or “Gitkraken” to have a visual git interface, in particular at the beginning. Your python IDE is also likely to have a visual git interface. \n", - "- Feel free to ask for help" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, + "metadata": {}, "source": [ - "![](img/git_gui.png)" + "# CLIMADA Development\n", + "\n", + "This is a guide about how to contribute to the development of CLIMADA. We first explain some general guidelines about when and how one can contribute to CLIMADA, and then describe the steps in detail. We assume that you are familiar with Git, Github and their commands. If you are not familiar with these, you can refer to our instructions for [Development with Git](Guide_Git_Development.ipynb). " ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ - "### What we assume you know\n", + "## Is CLIMADA the right place for your contribution? \n", "\n", - "We're assuming you're all familiar with the basics of Git.\n", + "When developing for CLIMADA, it is important to distinguish between core content and particular applications. Core content is meant to be included into the [climada_python](https://github.com/CLIMADA-project/climada_python) repository and will be subject to a code review. Any new addition should first be discussed with one of the [repository admins](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board). The purpose of this discussion is to see\n", "\n", - "- What (and why) is version control\n", - "- How to clone a repository\n", - "- How to make a commit and push it to GitHub\n", - "- What a branch is, and how to make one\n", - "- How to merge two branches\n", - "- The basics of the GitHub website\n", + "- How does the planned module fit into CLIMADA?\n", + "- What is an optimal architecture for the new module?\n", + "- What parts might already exist in other parts of the code?\n", "\n", - "If you're not feeling great about this, we recommend\n", - "- sending me a message so we can arrange an introduction with CLIMADA\n", - "- exploring the [Git Book](https://git-scm.com/book/en/v2)" + "Applications made with CLIMADA, such as an [ECA study](https://eca-network.org/) can be stored in the [paper repository](https://github.com/CLIMADA-project/climada_papers) once they have been published. For other types of work, consider making a separate repository that imports CLIMADA as an external package." ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ - "### Terms we'll be using today\n", + "## Planning a new feature\n", "\n", - "These are terms that will come up a lot, so let's make sure we know them\n", + "Here we're talking about large features such as new modules, new data sources, or big methodological changes. Any extension to CLIMADA that might affect other developers' work, modify the CLIMADA core, or need a big code review.\n", "\n", - "- local versus remote\n", - " - Our **remote** repository is hosted on GitHub. This is the central location where all updates to CLIMADA that we want to share end up. If you're updating CLIMADA for the community, your code will end up here too.\n", - " - Your **local** repository is the copy you have on the machine you're working on, and where you do your work.\n", - " - Git calls the (first, default) remote the `origin`\n", - " - (It's possible to set more than one remote repository, e.g. you might set one up on a network-restricted computing cluster)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- push, pull and pull request\n", - " - You **push** your work when you send it from your local machine to the remote repository\n", - " - You **pull** from the remote repository to update the code on your local machine\n", - " - A **pull request** is a standardised review process on GitHub. Usually it ends with one branch merging into another" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- Conflict resolution\n", - " - Sometimes two people have made changes to the same bit of code. Usually this comes up when you're trying to merge branches. The changes have to be manually compared and the code edited to make sure the 'correct' version of the code is kept. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Gitflow " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Gitflow is a particular way of using git to organise projects that have\n", - "- multiple developers\n", - "- working on different features\n", - "- with a release cycle\n", - "\n", - "It means that\n", - "- there's always a stable version of the code available to the public\n", - "- the chances of two developers' code conflicting are reduced\n", - "- the process of adding and reviewing features and fixes is more standardised for everyone\n", - "\n", - "Gitflow is a _convention_, so you don't need any additional software.\n", - "- ... but if you want you can get some: a popular extension to the git command line tool allows you to issue more intuitive commands for a Gitflow workflow.\n", - "- Mac/Linux users can install git-flow from their package manager, and it's included with Git for Windows " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Gitflow works on the `develop` branch instead of `main`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "![](img/flow_1.png)\n", + "Smaller feature branches don't need such formalities. Use your judgment, and if in doubt, let people know.\n", "\n", - "- The critical difference between Gitflow and 'standard' git is that almost all of your work takes place on the `develop` branch, instead of the `main` (formerly `master`) branch.\n", - "- The `main` branch is reserved for planned, stable product releases, and it's what the general public download when they install CLIMADA. The developers almost never interact with it." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Gitflow is a feature-based workflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](img/flow_2.png)\n", + "### Talk to the group\n", + " - Before starting coding a module, do not forget to coordinate with one of the repo admins (Emanuel, Chahan or Lukas)\n", + " - This is the chance to work out the Big Picture stuff that is better when it's planned with the group - possible intersections with other projects, possible conflicts, changes to the CLIMADA core, additional dependencies\n", + " - Also talk with others from the core development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", + " - Bring it to a developers meeting - people may be able to help/advise and are always interested in hearing about new projects. You can also find reviewers!\n", + " - Also, keep talking! Your plans _will_ change :)\n", "\n", - "- This is common to many workflows: when you want to add something new to the model you start a new branch, work on it locally, and then merge it back into `develop` **with a pull request** (which we'll cover later)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- By convention we name all CLIMADA feature branches `feature/*` (e.g. `feature/meteorite`).\n", - "- Features can be anything, from entire hazard modules to a smarter way to do one line of a calculation. Most of the work you'll do on CLIMADA will be a features of one size or another.\n", - "- We'll talk more about developing CLIMADA features later!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Gitflow enables a regular release cycle" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](img/flow_3.png)\n", + "### Formulate the feature's data flow and workflow\n", "\n", - "- A release is usually more complex than merging `develop` into `main`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- So for this a `release-*` branch is created from `develop`. We'll all be notified repeatedly when the deadline is to submit (and then to review) pull requests so that you can be included in a release.\n", - "- The core developer team (mostly Emanuel) will then make sure tests, bugfixes, documentation and compatibility requirements are met, merging any fixes back into `develop`.\n", - "- On release day, the release branch is merged into `main`, the commit is tagged as a release and the release notes are published on the GitHub at " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Everything else is hotfixes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](img/flow_4.png)\n", + "To optimize implementation and usefulness of the new feature, first conceptualize its data flow and workflow. It makes sense to discuss these with a CLIMADA core developer before starting to work on the feature's implementation.\n", + "- **Data flow**: Outline of how data moves through the system — where it is created or input, how it is processed, and if and where it is stored. This helps to improve the computational efficiency and to identify potential bottlenecks. \n", + "- **Workflow**: Plan about where and how the user and other CLIMADA components can interact with the new feature. This ensures that the new feature couples seamlessly to the existing code base of CLIMADA and that the new feaute is easily and clearly accessible to users.\n", "\n", - "- The other type of branch you'll create is a hotfix." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- Hotfixes are generally small changes to code that do one thing, fixing typos, small bugs, or updating docstrings. They're done in much the same way as features, and are usually merged with a pull request.\n", - "- The difference between features and hotfixes is fuzzy and you don't need to worry about getting it right.\n", - "- Hotfixes will occasionally be used to fix bugs on the `main` branch, in which case they will merge into both `main` and `develop`.\n", - "- Some hotfixes are so simple - e.g. fixing a typo or a docstring - that they don't need a pull request. Use your judgement, but as a rule, if you change what the code does, or how, you should be merging with a pull request." + "### Planning the work\n", + "\n", + "- Does the project go in its own repository and import CLIMADA, or does it extend the main CLIMADA repository. The way this is done is slowly changing, so definitely discuss it with the group.\n", + "- Find a few people who will help to review your code.\n", + " - Ask in a developers' meeting, on Slack (for WCR developers) or message people on the development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", + " - Let them know roughly how much code will be in the reviews, and when you'll be creating pull requests.\n", + "- How can the work split into manageable chunks?\n", + " - A series of smaller pull requests is far more manageable than one big one (and takes off some of the pre-release pressure)\n", + " - Reviewing and spotting issues/improvements/generalisations early is always a good thing.\n", + " - It encourages modularisation of the code: smaller self-contained updates, with documentation and tests.\n", + "- Will there be any changes to the CLIMADA core? These should be planned carefully\n", + "- Will you need any new dependencies? Are you sure?" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Installing CLIMADA for development\n", - "\n", - "See [Installation](install.rst) for instructions on how to install CLIMADA for developers. You might need to install additional environments contained in ``climada_python/requirements`` when using specific functionalities. Also see [Apps for working with CLIMADA](../guide/Guide_get_started.ipynb#apps-for-working-with-climada) for an overview of which tools are useful for CLIMADA developers. " + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "(guide-pre-commit-hooks)=\n", "### Pre-Commit Hooks\n", "\n", "Climada developer dependencies include pre-commit hooks to help ensure code linting and formatting.\n", @@ -295,7 +84,7 @@ "- the correct sorting of imports using ``isort``\n", "- the correct formatting of the code using ``black``\n", "\n", - "If you have installed the pre-commit hooks (see [Install developer dependencies](install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", + "If you have installed the pre-commit hooks (see [Install developer dependencies](../getting-started/install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", "\n", "If any check fails, you will be warned and these hooks **will apply** corrections (such as formatting the code with black if it is not).\n", "As files are modified, you are required to stage them again (hooks cannot stage their modification, only you can) and commit again.\n", @@ -372,90 +161,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Does it belong in CLIMADA? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When developing for CLIMADA, it is important to distinguish between core content and particular applications. Core content is meant to be included into the [climada_python](https://github.com/CLIMADA-project/climada_python) repository and will be subject to a code review. Any new addition should first be discussed with one of the [repository admins](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board). The purpose of this discussion is to see\n", - "\n", - "- How does the planned module fit into CLIMADA?\n", - "- What is an optimal architecture for the new module?\n", - "- What parts might already exist in other parts of the code?\n", - "\n", - "Applications made with CLIMADA, such as an [ECA study](https://eca-network.org/) can be stored in the [paper repository](https://github.com/CLIMADA-project/climada_papers) once they have been published. For other types of work, consider making a separate repository that imports CLIMADA as an external package." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Features and branches" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Planning a new feature\n", - "\n", - "Here we're talking about large features such as new modules, new data sources, or big methodological changes. Any extension to CLIMADA that might affect other developers' work, modify the CLIMADA core, or need a big code review.\n", - "\n", - "Smaller feature branches don't need such formalities. Use your judgment, and if in doubt, let people know.\n", - "\n", - "### Talk to the group\n", - " - Before starting coding a module, do not forget to coordinate with one of the repo admins (Emanuel, Chahan or Lukas)\n", - " - This is the chance to work out the Big Picture stuff that is better when it's planned with the group - possible intersections with other projects, possible conflicts, changes to the CLIMADA core, additional dependencies\n", - " - Also talk with others from the core development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", - " - Bring it to a developers meeting - people may be able to help/advise and are always interested in hearing about new projects. You can also find reviewers!\n", - " - Also, keep talking! Your plans _will_ change :)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Planning the work\n", - "\n", - "- Does the project go in its own repository and import CLIMADA, or does it extend the main CLIMADA repository?\n", - " - The way this is done is slowly changing, so definitely discuss it with the group.\n", - " - Chahan will discuss this later!\n", - "- Find a few people who will help to review your code.\n", - " - Ask in a developers' meeting, on Slack (for WCR developers) or message people on the development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", - " - Let them know roughly how much code will be in the reviews, and when you'll be creating pull requests.\n", - "- How can the work split into manageable chunks?\n", - " - A series of smaller pull requests is far more manageable than one big one (and takes off some of the pre-release pressure)\n", - " - Reviewing and spotting issues/improvements/generalisations early is always a good thing.\n", - " - It encourages modularisation of the code: smaller self-contained updates, with documentation and tests.\n", - "- Will there be any changes to the CLIMADA core?\n", - " - These should be planned carefully\n", - "- Will you need any new dependencies? Are you sure?\n", - " - Chahan will discuss this later!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Working on feature branches\n", + "## Working on feature branches\n", "\n", "When developing a big new feature, consider creating a feature branch and merging smaller branches into that feature branch with pull requests, keeping the whole process separate from `develop` until it's completed. This makes step-by-step code review nice and easy, and makes the final merge more easily tracked in the history.\n", "\n", @@ -475,8 +181,6 @@ "\n", " git checkout -b branch_name\n", "\n", - "### Follow the [python do's and don't](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_PythonDos-n-Donts.ipynb) and [performance](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", - "\n", "get the latest data from the remote repository and update your branch\n", " \n", " git pull\n", @@ -494,28 +198,15 @@ " git commit -m \"new functionality of .. implemented\"\n", " \n", "### Make unit and integration tests on your code, preferably during development\n", - "see [Guide on unit and integration tests](../guide/Guide_Testing.ipynb)\n" + "see [Guide on unit and integration tests](Guide_Testing.ipynb)\n" ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Pull requests" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ + "## Pull requests\n", + "\n", "We want every line of code that goes into the CLIMADA repository to be reviewed!\n", "\n", "Code review:\n", @@ -523,17 +214,8 @@ "- lets you draw on the experience of the rest of the team\n", "- makes sure that more than one person knows how your code works\n", "- helps to unify and standardise CLIMADA's code, so new users find it easier to read and navigate\n", - "- creates an archived description and discussion of the changes you've made" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- creates an archived description and discussion of the changes you've made\n", + "\n", "### When to make a pull request\n", "\n", "- When you've finished writing a big new class or method (and its tests)\n", @@ -543,17 +225,8 @@ "\n", "Not all pull requests have to be into `develop` - you can make a pull request into any active branch that suits you.\n", "\n", - "Pull requests need to be made latest two weeks before a release, see [releases](https://github.com/CLIMADA-project/climada_python/releases)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "Pull requests need to be made latest two weeks before a release, see [releases](https://github.com/CLIMADA-project/climada_python/releases).\n", + "\n", "### Step by step pull request!\n", "\n", "Let's suppose you've developed a cool new module on the `feature/meteorite` branch and you're ready to merge it into `develop`.\n", @@ -566,17 +239,8 @@ "- Updated dependencies (if need be)\n", "- Added your name to the AUTHORS file\n", "- Added an entry to the ``CHANGELOG.md`` file. See for information on how this shoud look like.\n", - "- (Advanced, optional) interactively rebase/squash recent commits that _aren't yet on GitHub_.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- (Advanced, optional) interactively rebase/squash recent commits that _aren't yet on GitHub_.\n", + "\n", "### Steps\n", "\n", "1) Make sure the `develop` branch is up to date on your own machine\n", @@ -600,17 +264,8 @@ " ```\n", "\n", "4) Perform a static code analysis using pylint with CLIMADA's configuration `.pylintrc` (in the climada root directory). Jenkins executes it after every push.\\\n", - " To do it locally, your IDE probably provides a tool, or you can run `make lint` and see the output in `pylint.log`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + " To do it locally, your IDE probably provides a tool, or you can run `make lint` and see the output in `pylint.log`.\n", + "\n", "5) Push to GitHub.\n", " If you're pushing this branch for the first time, use\n", " ```\n", @@ -621,17 +276,8 @@ " git push\n", " ```\n", "\n", - "6) Check all the tests pass on the WCR Jenkins server (). See Emanuel's presentation for how to do this! You should regularly be pushing your code and checking this!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "6) Check all the tests pass on the WCR Jenkins server (). See Emanuel's presentation for how to do this! You should regularly be pushing your code and checking this!\n", + "\n", "7) Create the pull request!\n", "\n", " - On the CLIMADA GitHub page, navigate to your feature branch (there's a drop-down menu above the file structure, pointing by default to `main`).\n", @@ -642,13 +288,7 @@ " - Assign reviewers in the page's right hand sidebar. Tag anyone who might be interested in reading the code. You should already have found one or two people who are happy to read the whole request and\n", " sign it off (they could also be added to 'Assignees').\n", " - Create the pull request.\n", - " - Contact the reviewers to let them know the request is live. GitHub's settings mean that they may not be alerted automatically. Maybe also let people know on the WCR Slack!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + " - Contact the reviewers to let them know the request is live. GitHub's settings mean that they may not be alerted automatically. Maybe also let people know on the WCR Slack!\n", "\n", "8) Talk with your reviewers\n", "\n", @@ -656,17 +296,8 @@ " - Take comments and suggestions on board, but you don't need to agree with everything and you don't need to implement everything.\n", " - If you feel someone is asking for too many changes, prioritise, especially if you don't have time for complex rewrites.\n", " - If the suggested changes and or features don't block functionality and you don't have time to fix them, they can be moved to Issues.\n", - " - Chase people up if they're slow. People are slow." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + " - Chase people up if they're slow. People are slow.\n", + "\n", "\n", "9) Once you implement the requested changes, respond to the comments with the corresponding commit implementing each requested change.\n", "\n", @@ -679,41 +310,21 @@ " \n", "12) Update the `develop` branch on your local machine.\n", "\n", - "Also see the [**Reviewer Guide**](../guide/Guide_Review.ipynb) and [**Reviewer Checklist**](../guide/Guide_Review.ipynb#reviewer-checklist)!" + "Also see the [**Reviewer Guide**](Guide_Review.ipynb) and [**Reviewer Checklist**](Guide_Review.ipynb#reviewer-checklist)!" ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## General tips and tricks" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ + "## General tips and tricks\n", + "\n", + "Follow the [python do's and don't](Guide_PythonDos-n-Donts) and [performance](Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", + "\n", "### Ask for help with Git\n", "\n", - "- Git isn't intuitive, and rewinding or resetting is always work. If you're not certain what you're doing, or if you think you've messed up, send someone a message." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- Git isn't intuitive, and rewinding or resetting is always work. If you're not certain what you're doing, or if you think you've messed up, send someone a message. See also our instructions for [Development with Git](Guide_Git_Development.ipynb).\n", + "\n", "### Don't push or commit to develop or main\n", "\n", "- Almost all new additions to CLIMADA should be merged into the `develop` branch with a pull request.\n", @@ -723,17 +334,8 @@ "\n", "So if you find yourself on the `main` or `develop` branches typing `git merge ...` or `git push` stop and think again - you should probably be making a pull request.\n", "\n", - "This can be difficult to undo, so contact someone on the team if you're unsure!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "This can be difficult to undo, so contact someone on the team if you're unsure!\n", + "\n", "### Commit more often than you think, and use informative commit messages\n", "\n", "- Committing often makes mistakes less scary to undo\n", @@ -741,13 +343,8 @@ "git reset --hard HEAD\n", "```\n", "- Detailed commit messages make writing pull requests really easy\n", - "- Yes it's boring, but _trust me_, everyone (usually your future self) will love you when they're rooting through the git history to try and understand why something was changed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "- Yes it's boring, but _trust me_, everyone (usually your future self) will love you when they're rooting through the git history to try and understand why something was changed\n", + "\n", "### Commit message syntax guidelines\n", "\n", "Basic syntax guidelines taken from here (on 17.06.2020)\n", @@ -763,17 +360,8 @@ " do it directly with the git command)\n", "- Put the name of the function/class/module/file that was edited\n", "- When fixing an issue, add the reference gh-ISSUENUMBER to the commit message \n", - " e.g. “fixes gh-40.” or “Closes gh-40.” For more infos see here ." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + " e.g. “fixes gh-40.” or “Closes gh-40.” For more infos see here .\n", + "\n", "### What not to commit\n", "\n", "There are a lot of things that don't belong in the Git repository: \n", @@ -790,43 +378,16 @@ "To avoid committing changes of unrelated metadata, open Jupyter Notebooks in a text editor instead of your browser renderer. When committing changes, make sure that you indeed only commit things you *did* change, and revert any changes to metadata that are not related to your code updates.\n", "\n", "Several code editors use plugins to render Jupyter Notebooks. Here we collect the instructions to inspect Jupyter Notebooks as plain text when using them:\n", - "- **VSCode**: Open the Jupyter Notebook. Then open the internal command prompt (`Ctrl` + `Shift` + `P` or `Cmd` + `Shift` + `P` on macOS) and type/select 'View: Reopen Editor with Text Editor'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- **VSCode**: Open the Jupyter Notebook. Then open the internal command prompt (`Ctrl` + `Shift` + `P` or `Cmd` + `Shift` + `P` on macOS) and type/select 'View: Reopen Editor with Text Editor'\n", + "\n", "### Log ideas and bugs as GitHub Issues\n", "\n", "If there's a change you might want to see in the code - something that generalises, something that's not quite right, or a cool new feature - it can be set up as a GitHub Issue. Issues are pages for conversations about changes to the codebase and for logging bugs, and act as a 'backlog' for the CLIMADA project.\n", "\n", - "For a bug, or a question about functionality, make a minimal working example, state which version of CLIMADA you are using, and post it with the Issue." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### How not to mess up the timeline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ + "For a bug, or a question about functionality, make a minimal working example, state which version of CLIMADA you are using, and post it with the Issue.\n", + "\n", + "### How not to mess up the timeline\n", + "\n", "Git builds the repository through incremental edits. This means it's great at keeping track of its history. But there are a few commands that _edit_ this history, and if histories get out of sync on different copies of the repository you're going to have a bad time.\n", "\n", "- Don't rebase any commits that already exist remotely!\n", @@ -834,17 +395,8 @@ "- Otherwise, you're unlikely to do anything irreversible\n", "- You can do what you like with commits that only exist on your machine.\n", "\n", - "That said, doing an interactive rebase to tidy up your commit history _before_ you push it to GitHub is a nice friendly gesture :)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "That said, doing an interactive rebase to tidy up your commit history _before_ you push it to GitHub is a nice friendly gesture :)\n", + "\n", "### Do not fast forward merges \n", "\n", "(This shouldn't be relevant - all your merges into `develop` should be through pull requests, which doesn't fast forward. But:)\n", @@ -852,17 +404,8 @@ "Don't fast forward your merges unless your branch is a single commit. Use\n", "`git merge --no-ff ...`\n", "\n", - "The exceptions is when you're merging `develop` into your feature branch." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "The exceptions is when you're merging `develop` into your feature branch.\n", + "\n", "### Merge the remote develop branch into your feature branch every now and again\n", "\n", "- This way you'll find conflicts early\n", @@ -871,96 +414,39 @@ "git pull\n", "git checkout feature/myfeature\n", "git merge develop\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "```\n", + "\n", "### Create frequent pull requests\n", "\n", "I said this already:\n", "- It structures your workflow\n", "- It's easier for reviewers\n", "- If you're going to break something for other people you all know sooner\n", - "- It saves work for the rest of the team right before a release" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- It saves work for the rest of the team right before a release\n", + "\n", "### Whenever you do something with CLIMADA, make a new local branch \n", "\n", - "You never know when a quick experiment will become something you want to save for later." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "You never know when a quick experiment will become something you want to save for later.\n", + "\n", "### But do not do everything in the CLIMADA repository\n", "\n", "- If you're running CLIMADA rather than developing it, create a new folder, initialise a new repository with `git init` and store your scripts and data there\n", - "- If you're writing an extension to CLIMADA that doesn't change the model core, create a new folder, initialise a new repository with `git init` and import CLIMADA. You can always add it to the model later if you need to." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Questions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "- If you're writing an extension to CLIMADA that doesn't change the model core, create a new folder, initialise a new repository with `git init` and import CLIMADA. You can always add it to the model later if you need to.\n", + "\n", + "### Questions\n", + "\n", "![Git and Github logos](img/xkcd_git.png)\\\n", "" ] } ], "metadata": { - "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + "display_name": "", + "name": "" }, "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" - }, - "vscode": { - "interpreter": { - "hash": "fe76ddefd4ac3b756bca82b2809865e7c67c346a46477cb9eec4ead581742ab6" - } + "name": "python" } }, "nbformat": 4, diff --git a/doc/guide/Guide_CLIMADA_Tutorial.ipynb b/doc/development/Guide_CLIMADA_Tutorial.ipynb similarity index 100% rename from doc/guide/Guide_CLIMADA_Tutorial.ipynb rename to doc/development/Guide_CLIMADA_Tutorial.ipynb diff --git a/doc/guide/Guide_CLIMADA_conventions.ipynb b/doc/development/Guide_CLIMADA_conventions.ipynb similarity index 98% rename from doc/guide/Guide_CLIMADA_conventions.ipynb rename to doc/development/Guide_CLIMADA_conventions.ipynb index 6b4e4c2909..28a6f18f67 100644 --- a/doc/guide/Guide_CLIMADA_conventions.ipynb +++ b/doc/development/Guide_CLIMADA_conventions.ipynb @@ -132,8 +132,8 @@ "\n", "Note that most text editors usually take care of 1. and 2. by default.\n", "\n", - "Please note that pull requests will not be merged if these checks fail. The easiest way to ensure this, is to use [pre-commit hooks](guide-pre-commit-hooks), which will allow you to both run the checks and apply fixes when creating a new commit.\n", - "Following the [advanced installation instructions](install.rst#advanced-instructions) will set up these hooks for you." + "Please note that pull requests will not be merged if these checks fail. The easiest way to ensure this, is to use [pre-commit hooks](Guide_CLIMADA_Development.ipynb#pre-commit-hooks), which will allow you to both run the checks and apply fixes when creating a new commit.\n", + "Following the [advanced installation instructions](../getting-started/install.rst#advanced-instructions) will set up these hooks for you." ] }, { @@ -505,7 +505,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/guide/Guide_Configuration.ipynb b/doc/development/Guide_Configuration.ipynb similarity index 98% rename from doc/guide/Guide_Configuration.ipynb rename to doc/development/Guide_Configuration.ipynb index 69056eba61..ad8ccb36f4 100644 --- a/doc/guide/Guide_Configuration.ipynb +++ b/doc/development/Guide_Configuration.ipynb @@ -439,7 +439,7 @@ "source": [ "### Test Configuration \n", "\n", - "The configuration values for unit and integration tests are not part of the [default configuration](#Default-Configuration), since they are irrelevant for the regular CLIMADA user and only aimed for developers.\\\n", + "The configuration values for unit and integration tests are not part of the [default configuration](#default-configuration), since they are irrelevant for the regular CLIMADA user and only aimed for developers.\\\n", "The default test configuration is defined in the `climada.conf` file of the installation directory.\n", "This file contains paths to files that are read during tests. If they are part of the GitHub repository, their path i.g. starts with the `climada` folder within the installation directory:\n", "```json\n", @@ -509,7 +509,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -523,7 +523,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:47) \n[Clang 12.0.1 ]" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/guide/Guide_Euler.ipynb b/doc/development/Guide_Euler.ipynb similarity index 95% rename from doc/guide/Guide_Euler.ipynb rename to doc/development/Guide_Euler.ipynb index 2f2cfa7f47..ad7e6b0a9f 100644 --- a/doc/guide/Guide_Euler.ipynb +++ b/doc/development/Guide_Euler.ipynb @@ -92,7 +92,7 @@ "\n", "### 3. Adjust the Climada configuration\n", "\n", - "Edit a configuration file according to your needs (see [Guide_Configuration](../guide/Guide_Configuration.ipynb)).\n", + "Edit a configuration file according to your needs (see [Guide_Configuration](Guide_Configuration.ipynb)).\n", "Create a climada.conf file e.g., in /cluster/home/$USER/.config with the following content:\n", "\n", "```json\n", @@ -140,7 +140,7 @@ "\n", "### 1. Load dependencies \n", "\n", - "See [Load dependencies](#1.-load-dependencies) above." + "See [Load dependencies](#load-dependencies) above." ] }, { @@ -159,7 +159,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", "### 3. Checkout sources\n", "\n", "```bash\n", @@ -208,7 +207,7 @@ "\n", "### 6. Adjust the Climada configuration\n", "\n", - "See [Adjust the Climada configuration](#3.-adjust-the-climada-configuration) above." + "See [Adjust the Climada configuration](#adjust-the-climada-configuration) above." ] }, { @@ -218,7 +217,7 @@ "\n", "### 7. Run a job\n", "\n", - "See [Run a job](#4.-run-a-job) above." + "See [Run a job](#run-a-job) above." ] }, { @@ -261,9 +260,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2. Checkout sources \n", + "#### 2. Work with a different branch\n", "\n", - "See [Checkout sources](#3.-Checkout-sources) above." + "See [Checkout sources](#checkout-sources) above." ] }, { @@ -289,9 +288,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 4. Adjust the Climada configuration\n", + "#### 4. Adjust configuration\n", "\n", - "See [Adjust the Climada configuration](#3.-Adjust-the-Climada-configuration) above." + "See [Adjust the Climada configuration](#adjust-the-climada-configuration) above." ] }, { @@ -359,7 +358,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Deinstallation" + "### Uninstallation" ] }, { @@ -469,7 +468,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -483,7 +482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:47) \n[Clang 12.0.1 ]" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/guide/Guide_Exception_Logging.ipynb b/doc/development/Guide_Exception_Logging.ipynb similarity index 100% rename from doc/guide/Guide_Exception_Logging.ipynb rename to doc/development/Guide_Exception_Logging.ipynb diff --git a/doc/development/Guide_Git_Development.ipynb b/doc/development/Guide_Git_Development.ipynb new file mode 100644 index 0000000000..b71d408fe5 --- /dev/null +++ b/doc/development/Guide_Git_Development.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "# Development with Git\n", + "\n", + " Here we provide a detailed instruction to the use of Git and GitHub and their workflows, which are essential to the code development of CLIMADA. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Git and GitHub\n", + "\n", + "- Git's not that scary\n", + " - 95% of your work on Git will be done with the same handful of commands (the other 5% will always be done with careful Googling)\n", + " - Almost everything in Git can be undone by design (but use `rebase`, `--force` and `--hard` with care!)\n", + " - Your favourite IDE (Spyder, PyCharm, ...) will have a GUI for working with Git, or you can download a standalone one.\n", + "- The [Git Book](https://git-scm.com/book/en/v2) is a great introduction to how Git works and to using it on the command line.\n", + "- Consider using a GUI program such as “git desktop” or “Gitkraken” to have a visual git interface, in particular at the beginning. Your python IDE is also likely to have a visual git interface. \n", + "- Feel free to ask for help" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "![](img/git_gui.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### What we assume you know\n", + "\n", + "We're assuming you're all familiar with the basics of Git.\n", + "\n", + "- What (and why) is version control\n", + "- How to clone a repository\n", + "- How to make a commit and push it to GitHub\n", + "- What a branch is, and how to make one\n", + "- How to merge two branches\n", + "- The basics of the GitHub website\n", + "\n", + "If you're not feeling great about this, we recommend\n", + "- sending me a message so we can arrange an introduction with CLIMADA\n", + "- exploring the [Git Book](https://git-scm.com/book/en/v2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Terms we'll be using today\n", + "\n", + "These are terms that will come up a lot, so let's make sure we know them\n", + "\n", + "- local versus remote\n", + " - Our **remote** repository is hosted on GitHub. This is the central location where all updates to CLIMADA that we want to share end up. If you're updating CLIMADA for the community, your code will end up here too.\n", + " - Your **local** repository is the copy you have on the machine you're working on, and where you do your work.\n", + " - Git calls the (first, default) remote the `origin`\n", + " - (It's possible to set more than one remote repository, e.g. you might set one up on a network-restricted computing cluster)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- push, pull and pull request\n", + " - You **push** your work when you send it from your local machine to the remote repository\n", + " - You **pull** from the remote repository to update the code on your local machine\n", + " - A **pull request** is a standardised review process on GitHub. Usually it ends with one branch merging into another" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- Conflict resolution\n", + " - Sometimes two people have made changes to the same bit of code. Usually this comes up when you're trying to merge branches. The changes have to be manually compared and the code edited to make sure the 'correct' version of the code is kept. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Gitflow " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Gitflow is a particular way of using git to organise projects that have\n", + "- multiple developers\n", + "- working on different features\n", + "- with a release cycle\n", + "\n", + "It means that\n", + "- there's always a stable version of the code available to the public\n", + "- the chances of two developers' code conflicting are reduced\n", + "- the process of adding and reviewing features and fixes is more standardised for everyone\n", + "\n", + "Gitflow is a _convention_, so you don't need any additional software.\n", + "- ... but if you want you can get some: a popular extension to the git command line tool allows you to issue more intuitive commands for a Gitflow workflow.\n", + "- Mac/Linux users can install git-flow from their package manager, and it's included with Git for Windows " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Gitflow works on the `develop` branch instead of `main`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "![](img/flow_1.png)\n", + "\n", + "- The critical difference between Gitflow and 'standard' git is that almost all of your work takes place on the `develop` branch, instead of the `main` (formerly `master`) branch.\n", + "- The `main` branch is reserved for planned, stable product releases, and it's what the general public download when they install CLIMADA. The developers almost never interact with it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Gitflow is a feature-based workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](img/flow_2.png)\n", + "\n", + "- This is common to many workflows: when you want to add something new to the model you start a new branch, work on it locally, and then merge it back into `develop` **with a pull request** (which we'll cover later)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- By convention we name all CLIMADA feature branches `feature/*` (e.g. `feature/meteorite`).\n", + "- Features can be anything, from entire hazard modules to a smarter way to do one line of a calculation. Most of the work you'll do on CLIMADA will be a features of one size or another.\n", + "- We'll talk more about developing CLIMADA features later!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Gitflow enables a regular release cycle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](img/flow_3.png)\n", + "\n", + "- A release is usually more complex than merging `develop` into `main`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- So for this a `release-*` branch is created from `develop`. We'll all be notified repeatedly when the deadline is to submit (and then to review) pull requests so that you can be included in a release.\n", + "- The core developer team (mostly Emanuel) will then make sure tests, bugfixes, documentation and compatibility requirements are met, merging any fixes back into `develop`.\n", + "- On release day, the release branch is merged into `main`, the commit is tagged as a release and the release notes are published on the GitHub at " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Everything else is hotfixes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](img/flow_4.png)\n", + "\n", + "- The other type of branch you'll create is a hotfix." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- Hotfixes are generally small changes to code that do one thing, fixing typos, small bugs, or updating docstrings. They're done in much the same way as features, and are usually merged with a pull request.\n", + "- The difference between features and hotfixes is fuzzy and you don't need to worry about getting it right.\n", + "- Hotfixes will occasionally be used to fix bugs on the `main` branch, in which case they will merge into both `main` and `develop`.\n", + "- Some hotfixes are so simple - e.g. fixing a typo or a docstring - that they don't need a pull request. Use your judgement, but as a rule, if you change what the code does, or how, you should be merging with a pull request." + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "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" + }, + "vscode": { + "interpreter": { + "hash": "fe76ddefd4ac3b756bca82b2809865e7c67c346a46477cb9eec4ead581742ab6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/guide/Guide_Py_Performance.ipynb b/doc/development/Guide_Py_Performance.ipynb similarity index 100% rename from doc/guide/Guide_Py_Performance.ipynb rename to doc/development/Guide_Py_Performance.ipynb diff --git a/doc/guide/Guide_PythonDos-n-Donts.ipynb b/doc/development/Guide_PythonDos-n-Donts.ipynb similarity index 100% rename from doc/guide/Guide_PythonDos-n-Donts.ipynb rename to doc/development/Guide_PythonDos-n-Donts.ipynb diff --git a/doc/guide/Guide_Review.ipynb b/doc/development/Guide_Review.ipynb similarity index 100% rename from doc/guide/Guide_Review.ipynb rename to doc/development/Guide_Review.ipynb diff --git a/doc/guide/Guide_Testing.ipynb b/doc/development/Guide_Testing.ipynb similarity index 100% rename from doc/guide/Guide_Testing.ipynb rename to doc/development/Guide_Testing.ipynb diff --git a/doc/guide/Guide_continuous_integration_GitHub_actions.ipynb b/doc/development/Guide_continuous_integration_GitHub_actions.ipynb similarity index 100% rename from doc/guide/Guide_continuous_integration_GitHub_actions.ipynb rename to doc/development/Guide_continuous_integration_GitHub_actions.ipynb diff --git a/doc/development/coding-in-python.rst b/doc/development/coding-in-python.rst new file mode 100644 index 0000000000..39912c73ec --- /dev/null +++ b/doc/development/coding-in-python.rst @@ -0,0 +1,10 @@ +################ +Coding in python +################ + +.. toctree:: + :maxdepth: 1 + + Guide_PythonDos-n-Donts + Guide_Exception_Logging + Performance and Best Practices diff --git a/doc/development/dev-git.rst b/doc/development/dev-git.rst new file mode 100644 index 0000000000..44ba858fc0 --- /dev/null +++ b/doc/development/dev-git.rst @@ -0,0 +1,10 @@ +############################### +Using git and GitHub to develop +############################### + +.. toctree:: + :maxdepth: 1 + :hidden: + + Development with Git + Guide_continuous_integration_GitHub_actions diff --git a/doc/guide/img/CLIMADA_logo_QR.png b/doc/development/img/CLIMADA_logo_QR.png similarity index 100% rename from doc/guide/img/CLIMADA_logo_QR.png rename to doc/development/img/CLIMADA_logo_QR.png diff --git a/doc/guide/img/FileSystem-1.png b/doc/development/img/FileSystem-1.png similarity index 100% rename from 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rename to doc/development/img/git_gui.png diff --git a/doc/guide/img/pylint.png b/doc/development/img/pylint.png similarity index 100% rename from doc/guide/img/pylint.png rename to doc/development/img/pylint.png diff --git a/doc/guide/img/xkcd_git.png b/doc/development/img/xkcd_git.png similarity index 100% rename from doc/guide/img/xkcd_git.png rename to doc/development/img/xkcd_git.png diff --git a/doc/guide/img/zen_of_python.png b/doc/development/img/zen_of_python.png similarity index 100% rename from doc/guide/img/zen_of_python.png rename to doc/development/img/zen_of_python.png diff --git a/doc/development/index.rst b/doc/development/index.rst new file mode 100644 index 0000000000..177b169291 --- /dev/null +++ b/doc/development/index.rst @@ -0,0 +1,15 @@ +.. include:: ../../CONTRIBUTING.md + :parser: myst_parser.sphinx_ + +.. toctree:: + :maxdepth: 2 + :hidden: + + Developer guide + Development with Git + Coding in python + CLIMADA Coding Conventions + Documenting your code + Writing tests for your code + Guide_Review + Guide_Euler diff --git a/doc/development/write-documentation.rst b/doc/development/write-documentation.rst new file mode 100644 index 0000000000..cfa4baa323 --- /dev/null +++ b/doc/development/write-documentation.rst @@ -0,0 +1,9 @@ +########################### +Documentation writing +########################### + +.. toctree:: + :maxdepth: 1 + + Guide_CLIMADA_Tutorial + Building the Documentation <../README> diff --git a/doc/tutorial/0_intro_python.ipynb b/doc/getting-started/0_intro_python.ipynb similarity index 100% rename from doc/tutorial/0_intro_python.ipynb rename to doc/getting-started/0_intro_python.ipynb diff --git a/doc/guide/Guide_Introduction.ipynb b/doc/getting-started/Guide_Introduction.ipynb similarity index 98% rename from doc/guide/Guide_Introduction.ipynb rename to doc/getting-started/Guide_Introduction.ipynb index 3f3a9ff134..a88accdddf 100644 --- a/doc/guide/Guide_Introduction.ipynb +++ b/doc/getting-started/Guide_Introduction.ipynb @@ -70,7 +70,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -84,7 +84,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/guide/Guide_get_started.ipynb b/doc/getting-started/Guide_get_started.ipynb similarity index 63% rename from doc/guide/Guide_get_started.ipynb rename to doc/getting-started/Guide_get_started.ipynb index 6fa55047b2..463d238377 100644 --- a/doc/guide/Guide_get_started.ipynb +++ b/doc/getting-started/Guide_get_started.ipynb @@ -5,7 +5,7 @@ "id": "trying-bronze", "metadata": {}, "source": [ - "# Getting started with CLIMADA" + "# Climada documentation" ] }, { @@ -42,31 +42,6 @@ "It is best to have some basic knowledge of Python programming before starting with CLIMADA. But if you need a quick introduction or reminder, have a look at the short [Python Tutorial](../tutorial/0_intro_python.ipynb). Also have a look at the python [Python Dos and Don't](../guide/Guide_PythonDos-n-Donts.ipynb) guide and at the [Python Performance Guide](../guide/Guide_Py_Performance.ipynb) for best practice tips." ] }, - { - "cell_type": "markdown", - "id": "c6ae7939", - "metadata": {}, - "source": [ - "## Apps for working with CLIMADA\n", - "\n", - "To work with CLIMADA, you will need an application that supports Jupyter Notebooks.\n", - "There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/).\n", - "It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what:" - ] - }, - { - "cell_type": "markdown", - "id": "25ab3b98", - "metadata": {}, - "source": [ - "| Use | Tools | Description | Useful for |\n", - "|:----------------------------------|:---------------------|:------------|:-----------|\n", - "| Distribution /
manage virtual environment
& packages | Recommended:
Mamba
Alternatives:
Anaconda|
  • Install climada, manage & use the climada virtual environment, install packages
  • Anaconda includes Anaconda navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder etc.
    • | Climada Users
    & Developers|\n", - "| IDE
    (Integrated Development Environment)|Recommended:
    VSCode
    Alternatives:
    Spyder
    JupyterLab
    PyCharm
    & many more|
  • Write and run code
    • Useful for Developers:
  • VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code)
  • VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon)
    • |Climada Users
    & Developers|\n", - "| Git GUI
    (Graphical User Interface)|GitHub Desktop
    Gitkraken|
  • Provides an interface which keeps track of the branch you’re working on, changes you made etc.
  • Allows you to commit changes, push to GitHub etc. without having to use command line
  • The code itself is not written using these applications but with your IDE of choice(see above)
    • |Climada Developers|\n", - "| Continuous integration
    (CI) server|Jenkins|
  • Automatically checks code changes in GitHub repositories, e.g. when you create a pull request for the develop branch
  • Performs static code analysis using pylint
  • you don't need to do any installations yourself, this runs automatically when you push new code to GitHub
  • see [Continuous Integration and GitHub Actions](../guide/Guide_continuous_integration_GitHub_actions.ipynb)
    • |Climada Developers|" - ] - }, { "cell_type": "markdown", "id": "touched-penetration", @@ -108,7 +83,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -122,7 +97,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15 | packaged by conda-forge | (default, Nov 22 2022, 08:49:06) \n[Clang 14.0.6 ]" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst new file mode 100644 index 0000000000..4d9829ef6d --- /dev/null +++ b/doc/getting-started/index.rst @@ -0,0 +1,67 @@ +=================== +Getting started +=================== + +Quick installation +-------------------- + +Are you already working with mamba or conda? proceed to install CLIMADA by executing the following line in the terminal:: + + mamba create -n climada_env -c conda-forge climada + +Each time you will want to work with CLIMADA, simply activate the environnment:: + + mamba activate climada_env + +You are good to go! + +.. seealso:: + + You don't have mamba or conda installed or you are looking for advanced installation instructions? Look up our :doc:`detailed instructions ` on CLIMADA installation. + + +.. dropdown:: How does CLIMADA compute impacts ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: How do you create an Hazard ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: How do we define an exposure ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: How do we model vulnerability ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: Do you want to quantify the uncertainties ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: Compare adaptation measures and assess their cost effectiveness + :color: primary + :icon: unlock + + And some content! + +.. toctree:: + :maxdepth: 1 + :hidden: + + Introduction + Navigate this documentation + In depth installation instructions + How to cite CLIMADA <../misc/citation> + Python introduction <0_intro_python> diff --git a/doc/guide/install.rst b/doc/getting-started/install.rst similarity index 85% rename from doc/guide/install.rst rename to doc/getting-started/install.rst index 5e05d25ca1..28ec6377a8 100644 --- a/doc/guide/install.rst +++ b/doc/getting-started/install.rst @@ -15,26 +15,40 @@ All following instructions should work on any operating system (OS) that is supp .. hint:: If you need help with the vocabulary used on this page, refer to the :ref:`Glossary `. ------------- -Prerequisites +Install Conda ------------- -* Make sure you are using the **latest version** of your OS. Install any outstanding **updates**. -* Free up at least 10 GB of **free storage space** on your machine. - Conda and the CLIMADA dependencies will require around 5 GB of free space, and you will need at least that much additional space for storing the input and output data of CLIMADA. -* Ensure a **stable internet connection** for the installation procedure. - All dependencies will be downloaded from the internet. - Do **not** use a metered, mobile connection! -* Install the `Conda`_ environment management system. - We highly recommend you use `Miniforge`_, which includes the potent `Mamba`_ package manager. - Download the installer suitable for your system and follow the respective installation instructions. - We do **not** recommend using the ``conda`` command anymore, rather use ``mamba`` (see :ref:`conda-instead-of-mamba`). +If you haven't already installed an environment management system like `Mamba`_ or `Conda`_, you have to do so now. +We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge, and can be installed as follows. -.. note:: When mentioning the terms "terminal" or "command line" in the following, we are referring to the "Terminal" apps on macOS or Linux and the "Miniforge Prompt" on Windows. +macOS and Linux +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Open the "Terminal" app, copy-paste the two commands below, and hit enter: + + .. code-block:: shell + + curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" + bash Miniforge3-$(uname)-$(uname -m).sh + +* Accept the license terms. +* You can confirm the default location. +* Answer 'yes' when asked if if you wish to update your shell profile to automatically initialize conda. **Do not just hit ENTER but first type 'yes'** +* If at some point you encounter ``command not found: mamba``, open a new terminal window. +* If you encounter ``Run 'mamba init' to be able to run mamba activate/deactivate ...``, please run ``mamba init zsh`` or ``mamba init``. + +Windows +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Download the Windows installer at the Install section from `Miniforge`_. +* Execute the installer. This will install Mamba and provide the "Miniforge Prompt" program as a command line replacement. .. _install-choice: +--------------------------------------- Decide on Your Entry Level! -^^^^^^^^^^^^^^^^^^^^^^^^^^^ +--------------------------------------- + Depening on your level of expertise, we provide two different approaches: @@ -211,7 +225,7 @@ For advanced Python users or developers of CLIMADA, we recommed cloning the CLIM If this test passes, great! You are good to go. -.. _install-dev: +.. _devdeps: Install Developer Dependencies (Optional) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -293,6 +307,10 @@ To install CLIMADA Petals, we assume you have already installed CLIMADA Core wit python -m pip install -e ./ +--------------------------------------- +Code Editors +--------------------------------------- + JupyterLab ^^^^^^^^^^ @@ -420,6 +438,56 @@ Therefore, we recommend installing Spyder in a *separate* environment, and then #. Set the Python interpreter used by Spyder to the one of ``climada_env``. Select *Preferences* > *Python Interpreter* > *Use the following interpreter* and paste the iterpreter path you copied from the ``climada_env``. +--------------------------------------- +Apps for working with CLIMADA +--------------------------------------- + +To work with CLIMADA, you will need an application that supports Jupyter Notebooks. +There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/). +It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what: + +.. list-table:: + :header-rows: 1 + :widths: auto + + * - Use + - Tools + - Description + - Useful for + * - Distribution / manage virtual environment & packages + - **Recommended:** + Mamba + **Alternatives:** + Anaconda + - - Install climada, manage & use the climada virtual environment, install packages + - Anaconda includes Anaconda Navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder, etc. + - Climada Users + & Developers + * - IDE (Integrated Development Environment) + - **Recommended:** + VSCode + **Alternatives:** + Spyder, JupyterLab, PyCharm, & many more + - - Write and run code + - Useful for Developers: + - VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code) + - VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon) + - Climada Users + & Developers + * - Git GUI (Graphical User Interface) + - GitHub Desktop, GitKraken + - - Provides an interface which keeps track of the branch you’re working on, changes you made, etc. + - Allows you to commit changes, push to GitHub, etc. without having to use the command line + - The code itself is not written using these applications but with your IDE of choice (see above) + - Climada Developers + * - Continuous integration (CI) server + - Jenkins + - - Automatically checks code changes in GitHub repositories, e.g., when you create a pull request for the develop branch + - Performs static code analysis using pylint + - You don't need to do any installations yourself; this runs automatically when you push new code to GitHub + - See `Continuous Integration and GitHub Actions <../guide/Guide_continuous_integration_GitHub_actions.ipynb>`_ + - Climada Developers + ---- FAQs ---- diff --git a/doc/index.rst b/doc/index.rst index 4ad14dd788..cba11fa18d 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -6,22 +6,96 @@ Welcome to CLIMADA! :align: center :alt: CLIMADA Logo -CLIMADA stands for CLIMate ADAptation and is a probabilistic natural catastrophe impact model, that also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioural, etc.). +CLIMADA (CLIMate ADAptation) is a free and open-source software framework for +comprehensive climate risk assessment. Designed by a large scientific community, +CLIMADA offers a robust and flexible platform to analyse the impacts of natural +hazards and explore adaptation strategies, and it can be used by researchers, +policy and decision-makers. -CLIMADA is primarily developed and maintained by the `Weather and Climate Risks Group `_ at `ETH Zürich `_. +CLIMADA is primarily developed and maintained by the `Weather and Climate Risks +Group `_ at `ETH Zürich `_. -If you use CLIMADA for your own scientific work, please reference the appropriate publications according to the :doc:`misc/citation`. +If you use CLIMADA for your own scientific work, please reference the +appropriate publications according to the :doc:`misc/citation`. -This is the documentation of the CLIMADA core module which contains all functionalities necessary for performing climate risk analysis and appraisal of adaptation options. Modules for generating different types of hazards and other specialized applications can be found in the `CLIMADA Petals `_ module. +This is the documentation of the CLIMADA core module which contains all +functionalities necessary for performing climate risk analysis and appraisal of +adaptation options. Modules for generating different types of hazards and other +specialized applications can be found in the `CLIMADA Petals +`_ module. -Jump right in: +.. grid:: 1 2 2 2 + :gutter: 4 + :padding: 2 2 0 0 + :class-container: sd-text-center -* :doc:`README ` -* :doc:`Getting Started ` -* :doc:`Installation ` -* :doc:`Overview ` -* `GitHub Repository `_ -* :doc:`Module Reference ` + .. grid-item-card:: Getting Started + :shadow: md + + Getting started with CLIMADA: How to install? + What are the basic concepts and functionalities? + + +++ + + .. button-ref:: getting-started/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + + .. grid-item-card:: User Guide + :shadow: md + + Want to go more in depth? Check out the User guide. It contains detailed + tutorials on the different concepts, modules and possible usage of CLIMADA. + + +++ + + .. button-ref:: user-guide/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the user guide! + + + + .. grid-item-card:: Implementation API reference + :shadow: md + + The reference guide contains a detailed description of + the CLIMADA API. The API reference describes each module, class, + methods and functions. + + +++ + + .. button-ref:: api/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the reference guide! + + .. grid-item-card:: Developer guide + :shadow: md + + Saw a typo in the documentation? Want to improve + existing functionalities? Want to extend them? + The contributing guidelines will guide you through + the process of improving CLIMADA. + + +++ + + .. button-ref:: development/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the development guide! .. ifconfig:: readthedocs @@ -31,6 +105,8 @@ Jump right in: Use the drop-down menu on the bottom left to switch versions. ``stable`` refers to the most recent release, whereas ``latest`` refers to the latest development version. +**Date**: |today| **Version**: |version| + .. admonition:: Copyright Notice Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in :doc:`AUTHORS.md `. @@ -47,73 +123,15 @@ Jump right in: with CLIMADA. If not, see https://www.gnu.org/licenses/. -.. toctree:: - :hidden: - - GitHub Repositories - CLIMADA Petals - Weather and Climate Risks Group - - .. toctree:: :maxdepth: 1 - :caption: User Guide - :hidden: - - guide/Guide_Introduction - Getting Started - guide/install - Running CLIMADA on Euler - - -.. toctree:: - :caption: API Reference - :hidden: - - Python Modules - - -.. toctree:: - :maxdepth: 2 - :caption: Tutorials :hidden: - Overview - Python Introduction - Hazard - Exposures - Impact - Uncertainty Quantification - tutorial/climada_engine_Forecast - tutorial/climada_util_calibrate - Google Earth Engine - tutorial/climada_util_api_client - - -.. toctree:: - :maxdepth: 1 - :caption: Developer Guide - :hidden: - - Development with Git - guide/Guide_CLIMADA_Tutorial - guide/Guide_Configuration - guide/Guide_Testing - guide/Guide_continuous_integration_GitHub_actions - guide/Guide_Review - guide/Guide_PythonDos-n-Donts - guide/Guide_Exception_Logging - Performance and Best Practices - CLIMADA Coding Conventions - Building the Documentation - - -.. toctree:: - :caption: Miscellaneous - :hidden: - - README + Getting started + User Guide + Developer Guide + API Reference + About Changelog - List of Authors - Contribution Guide - misc/citation + CLIMADA Petals + WCR Group diff --git a/doc/misc/AUTHORS.md b/doc/misc/AUTHORS.md deleted file mode 120000 index 561ed5cd36..0000000000 --- a/doc/misc/AUTHORS.md +++ /dev/null @@ -1 +0,0 @@ -../../AUTHORS.md diff --git a/doc/misc/CHANGELOG.md b/doc/misc/CHANGELOG.md deleted file mode 120000 index 03cb731062..0000000000 --- a/doc/misc/CHANGELOG.md +++ /dev/null @@ -1 +0,0 @@ -../../CHANGELOG.md diff --git a/doc/misc/CONTRIBUTING.md b/doc/misc/CONTRIBUTING.md deleted file mode 120000 index bcac999a8e..0000000000 --- a/doc/misc/CONTRIBUTING.md +++ /dev/null @@ -1 +0,0 @@ -../../CONTRIBUTING.md diff --git a/doc/user-guide/0_10min_climada.ipynb b/doc/user-guide/0_10min_climada.ipynb new file mode 100644 index 0000000000..705e0da0f2 --- /dev/null +++ b/doc/user-guide/0_10min_climada.ipynb @@ -0,0 +1,452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10 minutes CLIMADA\n", + "\n", + "This is a brief introduction to CLIMADA that showcases CLIMADA's key building block, the impact calculation. For more details and features of the impact calculation, please check out the more detailed [CLIMADA Overview](../tutorial/1_main_climada.ipynb). TBDnaming\n", + "\n", + "## Key ingredients in a CLIMADA impact calculation\n", + "\n", + "For CLIMADA's impact calculation, we have to specify the following ingredients:\n", + "- **Hazard**: The hazard object entails event-based and spatially-resolved information of the intensity of a natural hazard. It contains a probabilistic event set, meaning that is a set of several events, each of which is associated to a frequency corresponding to the estimated probability of the occurence of the event.\n", + "- **Exposure**: The exposure information provides the location and the number and/or value of objects (e.g., humans, buildings, ecosystems) that are exposed to the hazard.\n", + "- **Vulnerability**: The impact or vunerability function models the average impact that is expected for a given exposure value and given hazard intensity.\n", + "\n", + "## Exemplary impact calculation\n", + "\n", + "We exemplify the impact calculation and its key ingredients with an analysis of the risk of tropical cyclones on several assets in Florida.\n", + "\n", + "\n", + "### Hazard objects\n", + "\n", + "First, we read a demo hazard file that includes information about several tropical cyclone events. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.hazard import Hazard\n", + "from climada.util import HAZ_DEMO_H5\n", + "\n", + "haz = Hazard.from_hdf5(HAZ_DEMO_H5)\n", + "\n", + "# to hide the warnings\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can infer some information from the Hazard object. The central piece of the hazard object is a sparse matrix at `haz.intensity` that contains the hazard intensity values for each event (axis 0) and each location (axis 1). " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The hazard object contains 216 events. \n", + "The maximal intensity contained in the Hazard object is 72.75 m/s. \n", + "The first event was observed in a time series of 185 years, \n", + "which is why CLIMADA estimates an annual probability of 0.0054 for the occurence of this event.\n" + ] + } + ], + "source": [ + "print(\n", + " f\"The hazard object contains {haz.intensity.shape[0]} events. \\n\"\n", + " f\"The maximal intensity contained in the Hazard object is {haz.intensity.max():.2f} {haz.units}. \\n\"\n", + " f\"The first event was observed in a time series of {int(1/haz.frequency[0])} {haz.frequency_unit[2:]}s, \\n\"\n", + " f\"which is why CLIMADA estimates an annual probability of {haz.frequency[0]:.4f} for the occurence of this event.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The probabilistic event set and its single events can be plotted. For instance, below we plot maximal intensity per grid point over the whole event set." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "haz.plot_intensity(0, figsize=(6, 6));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exposure objects\n", + "Now, we read a demo expopure file containing the location and value of a number of exposed assets in Florida." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:38:13,269 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/demo/data/exp_demo_today.h5\n" + ] + } + ], + "source": [ + "from climada.entity import Exposures\n", + "from climada.util.constants import EXP_DEMO_H5\n", + "\n", + "exp = Exposures.from_hdf5(EXP_DEMO_H5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print some basic information about the exposure object. The central information of the exposure object is contained in a geopandas.GeoDataFrame at `exp.gdf`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In the exposure object, a total amount of USD 657.05B is distributed among 50 points.\n" + ] + } + ], + "source": [ + "print(\n", + " f\"In the exposure object, a total amount of {exp.value_unit} {exp.gdf.value.sum() / 1_000_000_000:.2f}B\"\n", + " f\" is distributed among {exp.gdf.shape[0]} points.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the different exposure points on a map." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:39:38,249 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:39:38,498 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "image/png": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp.plot_basemap(figsize=(6, 6));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Impact Functions\n", + "\n", + "To model the impact to the exposure that is caused by the hazard, CLIMADA makes use of an impact function. This function relates both percentage of assets affected (PAA, red line below) and the mean damage degree (MDD, blue line below), to the hazard intensity. The multiplication of PAA and MDD result in the mean damage ratio (MDR, black dashed line below), that relates the hazard intensity to corresponding relative impact values. Finally, a multiplication with the exposure values results in the total impact.\n", + "\n", + "Below, we read and plot a standard impact function for tropical cyclones." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", + "\n", + "impf_tc = ImpfTropCyclone.from_emanuel_usa()\n", + "impf_set = ImpactFuncSet([impf_tc])\n", + "impf_set.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Impact calculation \n", + "\n", + "Having defined hazard, exposure, and impact function, we can finally perform the impact calcuation. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:43:22,682 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:43:22,683 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:43:22,686 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:43:22,687 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" + ] + } + ], + "source": [ + "from climada.engine import ImpactCalc\n", + "\n", + "imp = ImpactCalc(exp, impf_set, haz).impact(save_mat=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Impact object contains the results of the impact calculation (including event- and location-wise impact information when `save_mat=True`)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total expected annual impact over all exposure points is USD 288.90 M. \n", + "The largest estimated single-event impact is USD 20.96 B. \n", + "The largest expected annual impact for a single location is USD 9.58 M. \n", + "\n" + ] + } + ], + "source": [ + "print(\n", + " f\"The total expected annual impact over all exposure points is {imp.unit} {imp.aai_agg / 1_000_000:.2f} M. \\n\"\n", + " f\"The largest estimated single-event impact is {imp.unit} {max(imp.at_event) / 1_000_000_000:.2f} B. \\n\"\n", + " f\"The largest expected annual impact for a single location is {imp.unit} {max(imp.eai_exp) / 1_000_000:.2f} M. \\n\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Several visualizations of impact objects are available. For instance, we can plot the expected annual impact per location on a map." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:44:16,514 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2025-01-21 15:44:16,518 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:44:16,771 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "imp.plot_basemap_eai_exposure(figsize=(6, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further CLIMADA features\n", + "\n", + "CLIMADA offers several additional features and modules that complement its basic impact and risk calculation, among which are\n", + "- uncertainty and sensitivity analysis\n", + "- adaptation option appraisal and cost benefit analysis\n", + "- several tools for providing hazard objects such as tropical cyclones, floods, or winter storms; and exposure objects such as Litpop, or open street maps\n", + "- impact function calibration methods\n", + "\n", + "We end this introduction with a simple adaptation measure analysis. \n", + "\n", + "### Adaptation measure analysis\n", + "\n", + "Consider a simple adaptation measure that results in a 10% decrease in the percentage of affected assets (PAA) decreases and a 20% decrease in the mean damage degree (MDD). We apply this measure and recompute the impact." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:49:48,642 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:49:48,645 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" + ] + } + ], + "source": [ + "from climada.entity.measures import Measure\n", + "\n", + "meas = Measure(haz_type=\"TC\", paa_impact=(0.9, 0), mdd_impact=(0.8, 0))\n", + "\n", + "new_exp, new_impfs, new_haz = meas.apply(exp, impf_set, haz)\n", + "new_imp = ImpactCalc(new_exp, new_impfs, new_haz).impact()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To analyze the effect of the adaptation measure, we can, for instance, plot the impact exceedance frequency curves that describe, according to the given data, how frequent different impacts thresholds are expected to be exceeded." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = imp.calc_freq_curve().plot(label=\"Without measure\")\n", + "new_imp.calc_freq_curve().plot(axis=ax, label=\"With measure\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "hide_input": false, + "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": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/tutorial/1_main_climada.ipynb b/doc/user-guide/1_main_climada.ipynb similarity index 99% rename from doc/tutorial/1_main_climada.ipynb rename to doc/user-guide/1_main_climada.ipynb index 7a9b45ab83..1e5fee2732 100644 --- a/doc/tutorial/1_main_climada.ipynb +++ b/doc/user-guide/1_main_climada.ipynb @@ -1235,7 +1235,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1249,7 +1249,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/tutorial/climada_engine_CostBenefit.ipynb b/doc/user-guide/climada_engine_CostBenefit.ipynb similarity index 100% rename from doc/tutorial/climada_engine_CostBenefit.ipynb rename to doc/user-guide/climada_engine_CostBenefit.ipynb diff --git a/doc/tutorial/climada_engine_Forecast.ipynb b/doc/user-guide/climada_engine_Forecast.ipynb similarity index 100% rename from doc/tutorial/climada_engine_Forecast.ipynb rename to doc/user-guide/climada_engine_Forecast.ipynb diff --git a/doc/tutorial/climada_engine_Impact.ipynb b/doc/user-guide/climada_engine_Impact.ipynb similarity index 99% rename from doc/tutorial/climada_engine_Impact.ipynb rename to doc/user-guide/climada_engine_Impact.ipynb index a342a43b39..03683b3b3c 100644 --- a/doc/tutorial/climada_engine_Impact.ipynb +++ b/doc/user-guide/climada_engine_Impact.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Goal of this tutorial" + "## Goal of this tutorial" ] }, { @@ -30,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is an Impact?" + "## What is an Impact?" ] }, { @@ -44,7 +44,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Impact class data structure" + "## Impact class data structure" ] }, { @@ -97,7 +97,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### How do I compute an impact in CLIMADA?" + "### How do I compute an impact in CLIMADA?" ] }, { @@ -2039,7 +2039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_engine_impact_data.ipynb b/doc/user-guide/climada_engine_impact_data.ipynb similarity index 99% rename from doc/tutorial/climada_engine_impact_data.ipynb rename to doc/user-guide/climada_engine_impact_data.ipynb index 40ead3d807..6f6972f3b3 100644 --- a/doc/tutorial/climada_engine_impact_data.ipynb +++ b/doc/user-guide/climada_engine_impact_data.ipynb @@ -62,6 +62,7 @@ "metadata": {}, "source": [ "### clean_emdat_df()\n", + "\n", "read CSV from EM-DAT into a DataFrame and clean up.\n", "\n", "Use the parameters countries, hazard, and year_range to filter. These parameters are the same for most functions shown here." @@ -184,11 +185,11 @@ "### emdat_to_impact()\n", "function to load EM-DAT impact data and return impact set with impact per event\n", "\n", - "##### Parameters:\n", + "#### Parameters:\n", "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", "- hazard_type_climada (str): Hazard type abbreviation used in CLIMADA, e.g. 'TC'\n", "\n", - "##### Optional parameters:\n", + "#### Optional parameters:\n", "\n", "- hazard_type_emdat (list or str): List of Disaster (sub-)type according EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", @@ -196,7 +197,7 @@ "- reference_year (int): reference year of exposures for normalization. Impact is scaled proportional to GDP to the value of the reference year. No scaling for reference_year=0 (default)\n", "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", "\n", - "##### Returns:\n", + "#### Returns:\n", "- impact_instance (instance of climada.engine.Impact):\n", " Impact() instance (same format as output from CLIMADA impact computations).\n", " Values are scaled with GDP to reference_year if reference_year not equal 0.\n", @@ -322,10 +323,10 @@ "\n", "function to load EM-DAT impact data and return DataFrame with impact summed per year and country\n", "\n", - "##### Parameters:\n", + "#### Parameters:\n", "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", "\n", - "##### Optional parameters:\n", + "#### Optional parameters:\n", "\n", "- hazard (list or str): List of Disaster (sub-)type according EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", @@ -334,7 +335,7 @@ "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", "- version (int): given EM-DAT data format version (i.e. year of download), changes naming of columns/variables (default: 2020)\n", "\n", - "##### Returns:\n", + "#### Returns:\n", "- pandas.DataFrame with impact per year and country" ] }, @@ -430,9 +431,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/tutorial/climada_engine_unsequa.ipynb b/doc/user-guide/climada_engine_unsequa.ipynb similarity index 100% rename from doc/tutorial/climada_engine_unsequa.ipynb rename to doc/user-guide/climada_engine_unsequa.ipynb diff --git a/doc/tutorial/climada_engine_unsequa_helper.ipynb b/doc/user-guide/climada_engine_unsequa_helper.ipynb similarity index 100% rename from doc/tutorial/climada_engine_unsequa_helper.ipynb rename to doc/user-guide/climada_engine_unsequa_helper.ipynb diff --git a/doc/tutorial/climada_entity_DiscRates.ipynb b/doc/user-guide/climada_entity_DiscRates.ipynb similarity index 100% rename from doc/tutorial/climada_entity_DiscRates.ipynb rename to doc/user-guide/climada_entity_DiscRates.ipynb diff --git a/doc/tutorial/climada_entity_Exposures.ipynb b/doc/user-guide/climada_entity_Exposures.ipynb similarity index 99% rename from doc/tutorial/climada_entity_Exposures.ipynb rename to doc/user-guide/climada_entity_Exposures.ipynb index a57079ef20..aa1b39fd38 100644 --- a/doc/tutorial/climada_entity_Exposures.ipynb +++ b/doc/user-guide/climada_entity_Exposures.ipynb @@ -12,21 +12,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### What is an exposure?\n", + "## What is an exposure?\n", "\n", "Exposure describes the set of assets, people, livelihoods, infrastructures, etc. within an area of interest in terms of their geographic location, their value etc.; in brief - everything potentially exposed to hazards. \n", "\n", - "\n", - "\n", - "### What options does CLIMADA offer for me to create an exposure?\n", + "## What options does CLIMADA offer for me to create an exposure?\n", "\n", "CLIMADA has an `Exposures` class for this purpuse. An `Exposures` instance can be filled with your own data, or loaded from available default sources implemented through some Exposures-type classes from CLIMADA.
    \n", "If you have your own data, they can be provided in the formats of a `pandas.DataFrame`, a `geopandas.GeoDataFrame` or simply an `Excel` file. \n", "If you didn't collect your own data, exposures can be generated on the fly using CLIMADA's [LitPop](climada_entity_LitPop.ipynb), [BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html) or [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html) modules. See the respective tutorials to learn what exactly they contain and how to use them.\n", "\n", - "\n", - "\n", - "### What does an exposure look like in CLIMADA?\n", + "## What does an exposure look like in CLIMADA?\n", "\n", "An exposure is represented in the class `Exposures`, which contains a [geopandas](https://geopandas.readthedocs.io/en/latest/gallery/cartopy_convert.html) [GeoDataFrame](https://geopandas.readthedocs.io/en/latest/docs/user_guide/data_structures.html#geodataframe) that is accessible through the `Exposures.data` attribute.\n", "A \"geometry\" column is initialized in the `GeoDataFrame` of the `Exposures` object, other columns are optional at first but some have to be present or make a difference when it comes to do calculations.\n", @@ -1689,7 +1685,6 @@ }, { "cell_type": "markdown", - "id": "5d078d09", "metadata": {}, "source": [ "Optionally use climada's save option to save it in pickle format. This allows fast to quickly restore the object in its current state and take up your work right were you left it the next time.\n", @@ -1727,7 +1722,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_entity_Exposures_polygons_lines.ipynb b/doc/user-guide/climada_entity_Exposures_polygons_lines.ipynb similarity index 100% rename from doc/tutorial/climada_entity_Exposures_polygons_lines.ipynb rename to doc/user-guide/climada_entity_Exposures_polygons_lines.ipynb diff --git a/doc/tutorial/climada_entity_ImpactFuncSet.ipynb b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb similarity index 99% rename from doc/tutorial/climada_entity_ImpactFuncSet.ipynb rename to doc/user-guide/climada_entity_ImpactFuncSet.ipynb index 6df482925f..fd349487cd 100644 --- a/doc/tutorial/climada_entity_ImpactFuncSet.ipynb +++ b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is an impact function?\n", + "## What is an impact function?\n", "\n", "An impact function relates the percentage of damage in the exposure to the hazard intensity, also commonly referred to as a \"vulnerability curve\" in the modelling community. Every hazard and exposure types are characterized by an impact function." ] @@ -20,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is the difference between `ImpactFunc` and `ImpactFuncSet`?\n", + "## What is the difference between `ImpactFunc` and `ImpactFuncSet`?\n", "\n", "An `ImpactFunc` is a class for a single impact function. E.g. a function that relates the percentage of damage of a reinforced concrete building (exposure) to the wind speed of a tropical cyclone (hazard intensity). \n", "\n", @@ -31,7 +31,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What does an `ImpactFunc` look like in CLIMADA?\n", + "### What does an `ImpactFunc` look like in CLIMADA?\n", "\n", "The `ImpactFunc` class requires users to define the following attributes.\n", "\n", @@ -52,7 +52,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What does an `ImpactFuncSet` look like in CLIMADA?\n", + "### What does an `ImpactFuncSet` look like in CLIMADA?\n", "\n", "The `ImpactFuncSet` class contains all the `ImpactFunc` classes. Users are not required to define any attributes in `ImpactFuncSet`. \n", "\n", @@ -77,7 +77,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Generate a dummy impact function from scratch.\n", + "### Generate a dummy impact function from scratch.\n", "\n", "Here we generate an impact function with random dummy data for illustrative reasons. Assuming this impact function is a function that relates building damage to tropical cyclone (TC) wind, with an arbitrary id 3." ] @@ -187,7 +187,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Loading CLIMADA in-built impact function for tropical cyclones\n", + "### Loading CLIMADA in-built impact function for tropical cyclones\n", "\n", "`ImpfTropCyclone` is a derivated class of `ImpactFunc`. This in-built impact function estimates the insured property damages by tropical cyclone wind in USA, following the reference paper [Emanuel (2011)](https://doi.org/10.1175/WCAS-D-11-00007.1).
    \n", "\n", @@ -289,7 +289,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Plotting all the impact functions in an `ImpactFuncSet`\n", + "### Plotting all the impact functions in an `ImpactFuncSet`\n", "\n", "The method `plot()` in `ImpactFuncSet` also uses the the [matplotlib's axes plot function](https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.axes.Axes.plot.html) to visualise the impact functions, returning a figure with all the subplots of impact functions. Users may modify these plots." ] @@ -321,7 +321,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Retrieving an impact function from the `ImpactFuncSet`\n", + "### Retrieving an impact function from the `ImpactFuncSet`\n", "User may want to retrive a particular impact function from `ImpactFuncSet`. Using the method `get_func(haz_type, id)`, it returns an `ImpactFunc` class of the desired impact function. Below is an example of extracting the TC impact function with id 1, and using `plot()` to visualise the function." ] }, @@ -354,7 +354,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Removing an impact function from the `ImpactFuncSet`\n", + "### Removing an impact function from the `ImpactFuncSet`\n", "\n", "If there is an unwanted impact function from the `ImpactFuncSet`, we may remove it using the method `remove_func(haz_type, id)` to remove it from the set. \n", "\n", @@ -423,7 +423,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Reading impact functions from an Excel file\n", + "### Reading impact functions from an Excel file\n", "\n", "Impact functions defined in an excel file following the template provided in sheet `impact_functions` of `climada_python/climada/data/system/entity_template.xlsx` can be ingested directly using the method `from_excel()`." ] @@ -471,7 +471,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Write impact functions\n", + "### Write impact functions\n", "\n", "Users may write the impact functions in Excel format using `write_excel()` method." ] @@ -570,7 +570,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -584,7 +584,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_entity_LitPop.ipynb b/doc/user-guide/climada_entity_LitPop.ipynb similarity index 100% rename from doc/tutorial/climada_entity_LitPop.ipynb rename to doc/user-guide/climada_entity_LitPop.ipynb diff --git a/doc/tutorial/climada_entity_MeasureSet.ipynb b/doc/user-guide/climada_entity_MeasureSet.ipynb similarity index 100% rename from doc/tutorial/climada_entity_MeasureSet.ipynb rename to doc/user-guide/climada_entity_MeasureSet.ipynb diff --git a/doc/tutorial/climada_hazard_Hazard.ipynb b/doc/user-guide/climada_hazard_Hazard.ipynb similarity index 99% rename from doc/tutorial/climada_hazard_Hazard.ipynb rename to doc/user-guide/climada_hazard_Hazard.ipynb index aebd85792c..412346d041 100644 --- a/doc/tutorial/climada_hazard_Hazard.ipynb +++ b/doc/user-guide/climada_hazard_Hazard.ipynb @@ -6,17 +6,13 @@ "source": [ "# Hazard class\n", "\n", - "#### What is a hazard?\n", + "## What is a hazard?\n", "A hazard describes weather events such as storms, floods, droughts, or heat waves both in terms of probability of occurrence as well as physical intensity.\n", "\n", - "
    \n", - "\n", - "#### How are hazards embedded in the CLIMADA architecture?\n", + "## How are hazards embedded in the CLIMADA architecture?\n", "Hazards are defined by the base class `Hazard` which gathers the required attributes that enable the impact computation (such as centroids, frequency per event, and intensity per event and centroid) and common methods such as readers and visualization functions. Each hazard class collects historical data or model simulations and transforms them, if necessary, in order to construct a coherent event database. Stochastic events can be generated taking into account the frequency and main intensity characteristics (such as local water depth for floods or gust speed for storms) of historical events, producing an ensemble of probabilistic events for each historical event. CLIMADA provides therefore an event-based probabilistic approach which does not depend on a hypothesis of a priori general probability distribution choices. Note that one can also reduce the probabilistic approach to a deterministic approach (e.g., story-line or forecasting) by defining the frequency to be 1. The source of the historical data (e.g. inventories or satellite images) or model simulations (e.g. synthetic tropical cyclone tracks) and the methodologies used to compute the hazard attributes and its stochastic events depend on each hazard type and are defined in its corresponding Hazard-derived class (e.g. `TropCylcone` for tropical cyclones, explained in the tutorial [TropCyclone](climada_hazard_TropCyclone.ipynb)). This procedure provides a solid and homogeneous methodology to compute impacts worldwide. In the case where the risk analysis comprises a specific region where good quality data or models describing the hazard intensity and frequency are available, these can be directly ingested by the platform through the reader functions, skipping the hazard modelling part (in total or partially), and allowing us to easily and seamlessly combine CLIMADA with external sources. Hence the impact model can be used for a wide variety of applications, e.g. deterministically to assess the impact of a single (past or future) event or to quantify risk based on a (large) set of probabilistic events. Note that since the `Hazard` class is not an abstract class, any hazard that is not defined in CLIMADA can still be used by providing the `Hazard` attributes.\n", "\n", - "
    \n", - "\n", - "#### What do hazards look like in CLIMADA?\n", + "## What do hazards look like in CLIMADA?\n", "\n", "A `Hazard` contains events of some hazard type defined at `centroids`. There are certain variables in a `Hazard` instance that _are needed_ to compute the impact, while others are _descriptive_ and can therefore be set with default values. The full list of looks like this:\n", "\n", @@ -779,9 +775,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1009,7 +1003,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('climada_env')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1023,7 +1017,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/tutorial/climada_hazard_StormEurope.ipynb b/doc/user-guide/climada_hazard_StormEurope.ipynb similarity index 100% rename from doc/tutorial/climada_hazard_StormEurope.ipynb rename to doc/user-guide/climada_hazard_StormEurope.ipynb diff --git a/doc/tutorial/climada_hazard_TropCyclone.ipynb b/doc/user-guide/climada_hazard_TropCyclone.ipynb similarity index 99% rename from doc/tutorial/climada_hazard_TropCyclone.ipynb rename to doc/user-guide/climada_hazard_TropCyclone.ipynb index 47df87fb75..b4f1ef2ffb 100644 --- a/doc/tutorial/climada_hazard_TropCyclone.ipynb +++ b/doc/user-guide/climada_hazard_TropCyclone.ipynb @@ -13,7 +13,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What do tropical cyclones look like in CLIMADA?\n", + "## What do tropical cyclones look like in CLIMADA?\n", "\n", "`TCTracks` reads and handles historical tropical cyclone tracks of the [IBTrACS](https://www.ncdc.noaa.gov/ibtracs/) repository or synthetic tropical cyclone tracks simulated using fully statistical or coupled statistical-dynamical modeling approaches. It also generates synthetic tracks from the historical ones using Wiener processes.\n", "\n", @@ -2183,7 +2183,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### REFERENCES:\n", + "## REFERENCES:\n", "\n", "- Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., & Aerts, J. C. J. H. (2020). Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0381-2\n", "\n", @@ -2195,6 +2195,13 @@ "\n", "- Lee, C. Y., Tippett, M. K., Sobel, A. H., & Camargo, S. J. (2018). An environmentally forced tropical cyclone hazard model. Journal of Advances in Modeling Earth Systems, 10(1), 223–241. https://doi.org/10.1002/2017MS001186" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -2213,7 +2220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" }, "toc": { "base_numbering": 1, diff --git a/doc/tutorial/climada_util_api_client.ipynb b/doc/user-guide/climada_util_api_client.ipynb similarity index 100% rename from doc/tutorial/climada_util_api_client.ipynb rename to doc/user-guide/climada_util_api_client.ipynb diff --git a/doc/tutorial/climada_util_calibrate.ipynb b/doc/user-guide/climada_util_calibrate.ipynb similarity index 100% rename from doc/tutorial/climada_util_calibrate.ipynb rename to doc/user-guide/climada_util_calibrate.ipynb diff --git a/doc/tutorial/climada_util_earth_engine.ipynb b/doc/user-guide/climada_util_earth_engine.ipynb similarity index 99% rename from doc/tutorial/climada_util_earth_engine.ipynb rename to doc/user-guide/climada_util_earth_engine.ipynb index 10811ce4d7..bf773ef7d4 100644 --- a/doc/tutorial/climada_util_earth_engine.ipynb +++ b/doc/user-guide/climada_util_earth_engine.ipynb @@ -88,10 +88,10 @@ "metadata": {}, "source": [ "If you have a collection, specification of the time range and area of interest. Then, use methods of the series **obtain_image_type(collection,time_range,area)** depending the type of product needed.\n", - "#### Time range\n", + "### Time range\n", "It depends on the image acquisition period of the targeted satellite and type of images desired (without clouds, from a specific period...) \n", "\n", - "#### Area\n", + "### Area\n", "GEE needs a special format for defining an area of interest. It has to be a GeoJSON Polygon and the coordinates should be first defined in a list and then converted using ee.Geometry. It is possible to use data obtained via Exposure layer. Some examples are given below." ] }, @@ -558,9 +558,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/tutorial/climada_util_yearsets.ipynb b/doc/user-guide/climada_util_yearsets.ipynb similarity index 100% rename from doc/tutorial/climada_util_yearsets.ipynb rename to doc/user-guide/climada_util_yearsets.ipynb diff --git a/doc/tutorial/exposures.rst b/doc/user-guide/exposures.rst similarity index 100% rename from doc/tutorial/exposures.rst rename to doc/user-guide/exposures.rst diff --git a/doc/tutorial/hazard.rst b/doc/user-guide/hazard.rst similarity index 100% rename from doc/tutorial/hazard.rst rename to doc/user-guide/hazard.rst diff --git a/doc/tutorial/img/UncertaintySensitivity.jpg b/doc/user-guide/img/UncertaintySensitivity.jpg similarity index 100% rename from doc/tutorial/img/UncertaintySensitivity.jpg rename to doc/user-guide/img/UncertaintySensitivity.jpg diff --git a/doc/tutorial/impact.rst b/doc/user-guide/impact.rst similarity index 100% rename from doc/tutorial/impact.rst rename to doc/user-guide/impact.rst diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst new file mode 100644 index 0000000000..bf1a922e10 --- /dev/null +++ b/doc/user-guide/index.rst @@ -0,0 +1,21 @@ +==================== +User guide +==================== + +Landing page of the user guide + +.. toctree:: + :maxdepth: 2 + :caption: User guides + :hidden: + + 10 minutes CLIMADA <0_10min_climada> + Overview <1_main_climada> + Hazard + Exposures + Impact + Uncertainty Quantification + climada_engine_Forecast + climada_util_calibrate + Google Earth Engine + climada_util_api_client diff --git a/doc/tutorial/unsequa.rst b/doc/user-guide/unsequa.rst similarity index 100% rename from doc/tutorial/unsequa.rst rename to doc/user-guide/unsequa.rst diff --git a/setup.py b/setup.py index 94514cf74c..0a1c3b69fd 100644 --- a/setup.py +++ b/setup.py @@ -19,6 +19,7 @@ "sphinx", "sphinx-book-theme", "sphinx-markdown-tables", + "sphinx-design", ] # Requirements for testing