diff --git a/.github/codecov.yml b/.github/codecov.yml index f6db6cba29..503302a905 100644 --- a/.github/codecov.yml +++ b/.github/codecov.yml @@ -8,6 +8,11 @@ coverage: project: default: if_ci_failed: success + # Currently, CI runs on PR branches do not include tests of (a) snapshot + # handling or (b) legacy reporting; these are only run on schedule + # triggers. This resuls in an apparent coverage drop of about 90 lines, + # or 0.46 percent. Allow this. + threshold: 0.5% patch: default: if_ci_failed: success diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml index 07b5ffba5b..902efc2305 100644 --- a/.github/workflows/pytest.yaml +++ b/.github/workflows/pytest.yaml @@ -13,7 +13,17 @@ concurrency: cancel-in-progress: true jobs: + warm-lfs-cache: + strategy: + matrix: + os: [ macos-latest, ubuntu-latest, windows-latest ] + runs-on: ${{ matrix.os }} + steps: + - uses: nschloe/action-cached-lfs-checkout@v1 + pytest: + needs: warm-lfs-cache + strategy: matrix: os: @@ -67,10 +77,7 @@ jobs: key: ${{ matrix.os }} - name: Check out message-ix-models - uses: actions/checkout@v4 - with: - lfs: true - fetch-depth: ${{ env.depth }} + uses: nschloe/action-cached-lfs-checkout@v1 - uses: actions/setup-python@v5 with: diff --git a/MANIFEST.in b/MANIFEST.in index 46b272c504..f07c8148d7 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,10 +1,13 @@ +# Data for testing prune message_ix_models/data/test/advance +prune message_ix_models/data/test/gea prune message_ix_models/data/test/iea -prune message_ix_models/data/test/MESSAGEix-GLOBIOM_1.1_R11_no-policy_baseline +prune message_ix_models/data/test/shape +prune message_ix_models/data/test/snapshot-* prune message_ix_models/data/test/ssp -prune message_ix_models/data/water/* # Larger package data # - Not distributed on PyPI. # - Should be fetched with Pooch from GitHub. exclude message_ix_models/data/ssp/*.gz +prune message_ix_models/data/water/* exclude message_ix_models/data/water/*.tar.xz diff --git a/README.rst b/README.rst index b3e03986cf..37d6cdfd2f 100644 --- a/README.rst +++ b/README.rst @@ -1,21 +1,6 @@ Tools for MESSAGEix-GLOBIOM models ********************************** - -.. image:: https://img.shields.io/pypi/v/message-ix-models.svg - :alt: PyPI version - :target: https://pypi.python.org/pypi/message-ix-models/ - -.. image:: https://readthedocs.com/projects/iiasa-energy-program-message-ix-models/badge/?version=latest - :alt: Documentation status - :target: https://docs.messageix.org/projects/models/en/latest/?badge=latest - -.. image:: https://github.com/iiasa/message-ix-models/actions/workflows/pytest.yaml/badge.svg - :alt: Test status - :target: https://github.com/iiasa/message-ix-models/actions/workflows/pytest.yaml - -.. image:: https://codecov.io/gh/iiasa/message-ix-models/branch/main/graph/badge.svg - :alt: Test coverage - :target: https://codecov.io/gh/iiasa/message-ix-models +|pypi| |rtd| |gha| |codecov| ``message_ix_models`` provides tools for research using the MESSAGEix-GLOBIOM family of models. These models are built in the `MESSAGEix framework `_ and on the `ix modeling platform (ixmp) `_. @@ -28,3 +13,19 @@ License Copyright © 2020–2024 IIASA Energy, Climate, and Environment (ECE) program Licensed under the Apache License, version 2.0. + +.. |pypi| image:: https://img.shields.io/pypi/v/message-ix-models.svg + :alt: PyPI version + :target: https://pypi.python.org/pypi/message-ix-models/ + +.. |rtd| image:: https://readthedocs.com/projects/iiasa-energy-program-message-ix-models/badge/?version=latest + :alt: Documentation status + :target: https://docs.messageix.org/projects/models/en/latest/?badge=latest + +.. |gha| image:: https://github.com/iiasa/message-ix-models/actions/workflows/pytest.yaml/badge.svg + :alt: Test status + :target: https://github.com/iiasa/message-ix-models/actions/workflows/pytest.yaml + +.. |codecov| image:: https://codecov.io/gh/iiasa/message-ix-models/branch/main/graph/badge.svg + :alt: Test coverage + :target: https://codecov.io/gh/iiasa/message-ix-models diff --git a/doc/api/data-sources.rst b/doc/api/data-sources.rst index 57e1816867..c5f96d3151 100644 --- a/doc/api/data-sources.rst +++ b/doc/api/data-sources.rst @@ -1,6 +1,16 @@ Tools for specific data sources ******************************* +.. _tools-gfei: + +Global Fuel Economy Initiative (GFEI) (:mod:`.tools.gfei`) +========================================================== + +.. currentmodule:: message_ix_models.tools.gfei + +.. automodule:: message_ix_models.tools.gfei + :members: + .. _tools-iea: International Energy Agency (IEA) (:mod:`.tools.iea`) @@ -20,6 +30,26 @@ Documentation for all module contents: iea +Energy efficiency indicators (:mod:`.tools.iea.eei`) +---------------------------------------------------- + +See :class:`.IEA_EEI`. +This data is produced by the IEA and retrieved from the Energy Efficiency Indicators database. +It is proprietary. + +The data: + +- Has the geographic resolution of individual countries, and scope including 41 countries: + + - 24 IEA member countries for which data covering most end-uses area available: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Italy, Japan, Korea, Luxembourg, the Netherlands, New Zealand, Poland, Portugal, Slovak Republic, Spain, Switzerland, the United Kingdom and the United States. + - Others including Brazil, Chile, Lithuania, Morocco, Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Ukraine, Uzbekistan. + +- Includes measures/variables for energy consumption, efficiency, carbon emissions, and others for four conceptual sectors: Residential, Services, Industry and Transport. +- The **December 2020 edition** covers the time periods 2000–2018 with annual resolution. + +.. note:: Currently, :mod:`.iea.eei` mainly retrieves and processes data useful for MESSAGEix-Transport. + To retrieve other end-use sectoral data, the code can be extended. + .. _tools-iea-web: (Extended) World Energy Balances (:mod:`.tools.iea.web`) diff --git a/doc/api/disutility.rst b/doc/api/disutility.rst index 8f06370214..78d653b032 100644 --- a/doc/api/disutility.rst +++ b/doc/api/disutility.rst @@ -14,15 +14,15 @@ Method & usage Use this code by calling :func:`add`, which takes arguments that describe the concrete usage: Consumer groups - This is a list of |Code| objects describing the consumer groups. + This is a list of :class:`.Code` objects describing the consumer groups. The list must be 1-dimensional, but can be composed (as in :mod:`message_data.model.transport`) from multiple dimensions. Technologies - This is a list of |Code| objects describing the technologies for which the consumers in the different groups experience disutility. + This is a list of :class:`.Code` objects describing the technologies for which the consumers in the different groups experience disutility. Each object must be have 'input' and 'output' annotations (:attr:`~.Code.annotations`); each of these is a :class:`dict` with the keys 'commodity', 'input', and 'unit', describing the source or sink for the technology. Template - This is also a |Code| object, similar to those in ``technologies``; see below. + This is also a :class:`.Code` object, similar to those in ``technologies``; see below. The code creates a source technology for the “disutility” commodity. The code does *not* perform the following step(s) needed to completely parametrize the formulation: diff --git a/doc/api/report/index.rst b/doc/api/report/index.rst index ed783a84e3..4f2b5a336f 100644 --- a/doc/api/report/index.rst +++ b/doc/api/report/index.rst @@ -37,7 +37,7 @@ In short, for instance: The basic **design pattern** of :mod:`message_ix_models.report` is: -- :func:`~.report.prepare_reporter` populates a new :class:`~.message_ix.Reporter` for a given |Scenario| with many keys to report all quantities of interest in a MESSAGEix-GLOBIOM–family model. +- :func:`~.report.prepare_reporter` populates a new :class:`~.message_ix.Reporter` for a given :class:`.Scenario` with many keys to report all quantities of interest in a MESSAGEix-GLOBIOM–family model. - This function relies on *callbacks* defined in multiple submodules to add keys and tasks for general or tailored reporting calculations and actions. Additional modules **should** define callback functions and register them with :func:`~report.register` when they are to be used. For example: @@ -145,7 +145,7 @@ Operators - :mod:`message_ix.report.operator` - :mod:`ixmp.report.operator` - - :mod:`genno.computations` + - :mod:`genno.operator` - Other submodules: diff --git a/doc/cli.rst b/doc/cli.rst index 7341da56a8..f16d040d1e 100644 --- a/doc/cli.rst +++ b/doc/cli.rst @@ -172,7 +172,7 @@ Further information about the top-level options: These options direct it to work with a different Platform. :program:`--model MODEL --scenario SCENARIO` or :program:`--url` - Many commands use an *existing* |Scenario| as a starting point, and begin by cloning that Scenario to a new (model name, scenario name). + Many commands use an *existing* :class:`.Scenario` as a starting point, and begin by cloning that Scenario to a new (model name, scenario name). For any such command, these top-level options define the starting point/initial Scenario to clone/‘baseline’. In contrast, see :program:`--output-model`, below. diff --git a/doc/conf.py b/doc/conf.py index 5bcb32c841..3748442910 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -22,7 +22,6 @@ # Add any Sphinx extension module names here, as strings. They can be extensions coming # with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ - # "ixmp.util.sphinx_linkcode_github", "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.extlinks", @@ -30,6 +29,9 @@ "sphinx.ext.napoleon", "sphinx.ext.todo", "sphinx.ext.viewcode", + # Others + "genno.compat.sphinx.rewrite_refs", + # "ixmp.util.sphinx_linkcode_github", "sphinxcontrib.bibtex", ] @@ -43,12 +45,8 @@ nitpicky = True nitpick_ignore_regex = { - # These occur because there is no .. py:module:: directive for the *top-level* - # module or package in the respective documentation and inventories. - # TODO Remove once the respective docs are fixed - ("py:mod", "ixmp"), - ("py:mod", "message_ix"), - ("py:mod", "message_data"), + # Legacy reporting docstrings are not formatted + ("py:.*", r"boolean \(default|str \(default|None\)|False\)"), # iam-units has no Sphinx docs ("py:.*", "iam_units.*"), # These are a consequence of autosummary-class.rst @@ -59,12 +57,6 @@ .. role:: py(code) :language: python -.. |Annotation| replace:: :class:`~sdmx.model.common.Annotation` -.. |Code| replace:: :class:`~sdmx.model.common.Code` -.. |Codelist| replace:: :class:`~sdmx.model.common.Codelist` -.. |Platform| replace:: :class:`~ixmp.Platform` -.. |Scenario| replace:: :class:`~message_ix.Scenario` - .. |n| replace:: :math:`n` .. |y| replace:: :math:`y` .. |y0| replace:: :math:`y_0` @@ -101,6 +93,34 @@ def setup(app: "sphinx.application.Sphinx") -> None: # builtin themes. html_theme = "sphinx_rtd_theme" +# -- Options for genno.compat.sphinx.rewrite_refs -------------------------------------- + +# When base classes in upstream (genno, ixmp) packages are inherited in message_ix, +# Sphinx will not properly resolve relative references within docstrings of methods of +# the former. Some of these aliases are to allow Sphinx to locate the correct targets. +reference_aliases = { + # genno + "Computer": "genno.Computer", + "KeyLike": ":data:`genno.core.key.KeyLike`", + r"(genno\.|)Key(?=Seq|[^\w]|$)": "genno.core.key.Key", + r"(genno\.|)Quantity": "genno.core.attrseries.AttrSeries", + # ixmp + "Platform": "ixmp.Platform", + "TimeSeries": "ixmp.TimeSeries", + # message_ix + r"Scenario(?=[^\w]|$)": "message_ix.Scenario", + "Reporter": "message_ix.report.Reporter", + "make_df": "message_ix.util.make_df", + # sdmx + "Code": "sdmx.model.common.Code", + # + # Many projects (including Sphinx itself!) do not have a py:module target in for the + # top-level module in objects.inv. Resolve these using :doc:`index` or similar for + # each project. + "pint$": ":std:doc:`pint `", + "plotnine$": ":class:`plotnine.ggplot`", +} + # -- Options for sphinx.ext.autosummary ------------------------------------------------ autosummary_generate = True @@ -145,6 +165,7 @@ def local_inv(name: str, *parts: str) -> Optional[str]: "pint": ("https://pint.readthedocs.io/en/stable/", None), "platformdirs": ("https://platformdirs.readthedocs.io/en/latest", None), "pooch": ("https://www.fatiando.org/pooch/latest/", None), + "pyam": ("https://pyam-iamc.readthedocs.io/en/stable", None), "pytest": ("https://docs.pytest.org/en/stable/", None), "python": ("https://docs.python.org/3/", None), "sdmx": ("https://sdmx1.readthedocs.io/en/stable/", None), @@ -158,7 +179,8 @@ def local_inv(name: str, *parts: str) -> Optional[str]: napoleon_preprocess_types = True napoleon_type_aliases = { - "Code": ":class:`~sdmx.model.common.Code`", + "iterable": ":class:`~collections.abc.Iterable`", + "sequence": ":class:`~collections.abc.Sequence`", "Path": ":class:`~pathlib.Path`", "PathLike": ":class:`os.PathLike`", } diff --git a/doc/data.rst b/doc/data.rst index 655a322907..fc26dc9a1d 100644 --- a/doc/data.rst +++ b/doc/data.rst @@ -201,7 +201,7 @@ Fetch directly from a remote source This corresponds to section (1) above. Preferably, do this via :mod:`message_ix_models.util.pooch`: -- Extend :mod:`.pooch.SOURCE` to store the Internet location, file name(s), and hash(es) of the file(s). +- Extend :data:`.pooch.SOURCE` to store the Internet location, file name(s), and hash(es) of the file(s). - Call :func:`.pooch.fetch` to retrieve the file and cache it locally. - Write code in :mod:`message_ix_models` that processes the data into a common format, for instance by subclassing :class:`.ExoDataSource`. diff --git a/doc/develop.rst b/doc/develop.rst index 99cabf1373..f98df13ed5 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -45,3 +45,45 @@ Code that will only work with certain structures… Code **may** also check a :class:`.Context` instance and automatically adapt data from certain structures to others, e.g. by interpolating data for certain periods or areas. To help with validation, code that does this **should** log on the :data:`logging.INFO` level to advertise these steps. + +.. _policy-upstream-versions: + +Upstream version policy +======================= + +:mod:`message_ix_models` is developed to be compatible with the following versions of its upstream dependencies. + +:mod:`ixmp` and :mod:`message_ix` + + The most recent 4 minor versions, or all minor versions released in the past two (2) years—whichever is greater. + + For example, as of 2024-04-08: + + - The most recent release of :mod:`ixmp` and :mod:`message_ix` are versions 3.8.0 of each project. + These are supported by :mod:`message_ix_models`. + - The previous 3 minor versions are 3.7.0, 3.6.0, and 3.5.0. + All were released since 2022-04-08. + All are supported by :mod:`message_ix_models.` + - :mod:`ixmp` and :mod:`message_ix` versions 3.4.0 were released 2022-01-24. + These this is the fifth-most-recent minor version *and* was released more than 2 years before 2024-04-08, so it is not supported. + +Python + All currently-maintained versions of Python. + + The Python website displays a list of these versions (`1 `__, `2 `__). + + For example, as of 2024-04-08: + + - Python 3.13 is in "prerelease" or "feature" development status, and is *not* supported by :mod:`message_ix_models`. + - Python 3.12 through 3.8 are in "bugfix" or "security" maintenance status, and are supported by :mod:`message_ix_models`. + - Python 3.7 and earlier are in "end-of-life" status, and are not supported by the Python community or by :mod:`message_ix_models`. + +- Support for older versions of dependencies **may** be dropped as early as the first :mod:`message_ix_models` version released after changes in upstream versions. + + - Conversely, some parts of :mod:`message_ix_models` **may** continue to be compatible with older upstream versions, but this compatibility is not tested and may break at any time. + - Users **should** upgrade their dependencies and other code to newer versions; we recommend the latest. +- Some newer code is marked with a :func:`.minimum_version` decorator. + + - This indicates that the marked code relies on features only available in certain upstream versions (of one of the packages mentioned above, or another package), newer than those listed in `pyproject.toml `__. + - These minima **must** be mentioned in the :mod:`message_ix_models` documentation. + - Users wishing to use this marked code **must** use compatible versions of those packages. diff --git a/doc/index.rst b/doc/index.rst index 73a11cfca0..7767a8277a 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -49,6 +49,7 @@ Commonly used classes may be imported directly from :mod:`message_ix_models`. - :doc:`api/disutility` - :doc:`api/report/index` - :doc:`api/tools` +- :doc:`api/data-sources` - :doc:`api/util` - :doc:`api/testing` - :doc:`api/workflow` @@ -73,10 +74,18 @@ Commonly used classes may be imported directly from :mod:`message_ix_models`. .. toctree:: :maxdepth: 2 - :caption: Variants and projects + :caption: Model variants water/index - api/project + +.. toctree:: + :maxdepth: 2 + :caption: Research projects + + project/advance + project/gea + project/shape + project/ssp .. toctree:: :maxdepth: 2 diff --git a/doc/project/advance.rst b/doc/project/advance.rst new file mode 100644 index 0000000000..77a1adbd94 --- /dev/null +++ b/doc/project/advance.rst @@ -0,0 +1,17 @@ +.. currentmodule:: message_ix_models.project.advance + +ADVANCE (:mod:`.project.advance`) +********************************* + +.. automodule:: message_ix_models.project.advance + +.. automodule:: message_ix_models.project.advance.data + :members: + + Although free of charge, the ADVANCE data can not be downloaded automatically. + This source requires that users first submit personal information to register before being able to retrieve the data. + :mod:`message_ix_models` does not circumvent this requirement. + Thus: + + - A copy of the data are stored in :mod:`message_data`. + - :mod:`message_ix_models` contains only a ‘fuzzed’ version of the data (same structure, random values) for testing purposes. diff --git a/doc/project/gea.rst b/doc/project/gea.rst new file mode 100644 index 0000000000..ff8a5f63bb --- /dev/null +++ b/doc/project/gea.rst @@ -0,0 +1,17 @@ +.. currentmodule:: message_ix_models.project.gea + +Global Energy Assessment (:mod:`.project.gea`) +********************************************** + +.. automodule:: message_ix_models.project.gea + +.. automodule:: message_ix_models.project.gea.data + :members: + + Although free of charge, the GEA data can not be downloaded automatically. + This source requires that users first submit personal information to register before being able to retrieve the data. + :mod:`message_ix_models` does not circumvent this requirement. + Thus: + + - A copy of the data are stored in :mod:`message_data`. + - :mod:`message_ix_models` contains only a ‘fuzzed’ version of the data (same structure, random values) for testing purposes. diff --git a/doc/project/shape.rst b/doc/project/shape.rst new file mode 100644 index 0000000000..0645c96190 --- /dev/null +++ b/doc/project/shape.rst @@ -0,0 +1,98 @@ +.. currentmodule:: message_ix_models.project.shape + +.. module:: message_ix_models.project.shape + +SHAPE +***** + +.. image:: http://shape-project.org/@@site-logo/Logo_Shape_RGB_72dpi_small.png + :alt: SHAPE Logo + :width: 50% + :align: right + +“Sustainable development pathways achieving Human well-being while safeguarding the climate And Planet Earth” + +The SHAPE project introduces the new term **Sustainable Development Pathway** or **SDP** for its scenarios, roughly analogous to SSPs. +The SDPs simultaneously achieve the Sustainable Development Goals in 2030, maintain sustainable development thereafter, and meet the climate targets set out in the Paris Agreement. + +Uses: + +- :doc:`m-data:/model/buildings` +- :doc:`m-data:/reference/model/material` +- :doc:`m-data:/reference/model/transport` + +Project information +=================== + +- Website: http://shape-project.org +- Part of AXIS, an ERA-NET initiated by JPI Climate and funded by DLR/BMBF (DE, Grant No. 01LS1907A-B-C), Formas (SE), FFG/BMWFW (AT), NWO (NL) and RCN (NO) with co-funding by the European Union (Grant No. 776608). +- Project period: 2019-09 to 2022-08. + +.. _shape-data: + +​Data +===== + +:file:`data/shape/` contains common data for constructing scenarios. +These data quantify the SDP narratives (see link above) as future projections for certain quantities. +Modeling tools [1]_ should use :func:`.exo_data.prepare_computer` to configure :class:`.SHAPE` to retrieve these quantities and select an appropriate scenario's data for model setup calculations. + +.. [1] e.g. the IIASA standalone demand models as well as the MESSAGEix-GLOBIOM family of models. + +The files include: + +:file:`gdp_v*.mif`, :file:`gdp_v*.xlsx` + Gross Domestic Product. + + Generated by Bjoern Soergel at PIK, using stylized GDP/capita growth rate modifications to represent three narratives from the “Economic paradigm, growth, inequality and finance” section of the SHAPE description. + The narratives are identified by codes for the IAMC "Scenario" dimension: + + - "innovation": “Cornucopia (market-driven innovation)” + - "service": “Human Service Economy (managing the global commons)” + - "society": “A New Public Economy (resilient communities)” + + The files have multiple **versions**: + + - "v1p0" version 1.0. + - "v1p1" version 1.1. + + The extension :file:`.mif` apparently means "modeling interchange file"; this is a CSV file with semicolon (;) delimiters. + +:file:`population_v*.mif`, :file:`population_v*.xlsx` + Population. + + The values are the same across all SDPs; the single code "all_SHAPE_SDPs" is used for the "Scenario" dimension. + + The values are identical to the SSP1 population projection. + + .. todo:: This is may not be true, because the data differ in some (unspecified) way between version 1.0 and 1.1. + Locate an accurate description for these data. + + .. note:: If true, this would imply that the same population data could be retrieved with :class:`.SSPOriginal` and underlying files, also stored in the :mod:`message_data` repository; so the files :file:`data/shape/population_*` are duplicates. + +:file:`gini_v*.csv`: + Gini coefficients. + Generated by Jihoon Min. + + - Normative Gini trajectories that will achieve SDG1 (No poverty) and SDG11 (Reduced inequalities) for **most** countries under the corresponding GDP projections (above), also in a stylized way. + - Same dimensions as the GDP data. + - v1.1 uses a slower rate of maximum Gini decrease to alleviate concerns about feasibility. + +:file:`urbanization.csv` + Generated by Alessio Mastrucci. + + Three different codes are used for the "Scenario" dimension: + + - "urb_distr" + - "urb_green" + - "urb_tech" + + .. todo:: Describe what these values represent. + +Code reference +============== + +.. currentmodule:: message_ix_models.project.shape.data + +.. automodule:: message_ix_models.project.shape.data + :members: diff --git a/doc/api/project.rst b/doc/project/ssp.rst similarity index 56% rename from doc/api/project.rst rename to doc/project/ssp.rst index 1282fcfece..7946d20370 100644 --- a/doc/api/project.rst +++ b/doc/project/ssp.rst @@ -1,40 +1,10 @@ -.. currentmodule:: message_ix_models.project - -Specific research projects (:mod:`~message_ix_models.project`) -************************************************************** - -.. contents:: - :local: - :backlinks: none - -.. automodule:: message_ix_models.project - :members: - -.. currentmodule:: message_ix_models.project.advance - -ADVANCE (:mod:`.project.advance`) -================================= - -.. automodule:: message_ix_models.project.advance - -.. automodule:: message_ix_models.project.advance.data - :members: - - Although free of charge, the ADVANCE data can not be downloaded automatically. - This source requires that users first submit personal information to register before being able to retrieve the data. - :mod:`message_ix_models` does not circumvent this requirement. - Thus: - - - A copy of the data are stored in :mod:`message_data`. - - :mod:`message_ix_models` contains only a ‘fuzzed’ version of the data (same structure, random values) for testing purposes. - .. currentmodule:: message_ix_models.project.ssp Shared Socioeconomic Pathways (:mod:`.project.ssp`) -=================================================== +*************************************************** Structure ---------- +========= The enumerations :obj:`SSP_2017` and :obj:`SSP_2024` contain one member from the corresponding SDMX code lists. These can be used to uniquely identify both an SSP narrative *and* the set in which it occurs, in applications where this distinction is meaningful: @@ -53,7 +23,7 @@ These can be used to uniquely identify both an SSP narrative *and* the set in wh :members: Data ----- +==== .. automodule:: message_ix_models.project.ssp.data :members: diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst index 0775ce0348..3dd0d44695 100644 --- a/doc/whatsnew.rst +++ b/doc/whatsnew.rst @@ -5,7 +5,9 @@ Next release ============ - Migrate :doc:`/api/report/legacy` to provide post-processing functionality for the :doc:`global model snapshot ` (:pull:`159`). +- Migrate and improve code for four sources of exogenous data (:pull:`162`): :mod:`.project.gea`, :mod:`.project.shape`, :mod:`.tools.gfei`, and :mod:`.tools.iea.eei`. - Expand :doc:`data` (:pull:`161`). +- Add an explicit :ref:`policy-upstream-versions` (:pull:`162`). v2024.4.2 ========= @@ -49,7 +51,7 @@ Update code that imports from the following modules: Code that imports from the old locations will continue to work, but will raise :class:`DeprecationWarning`. -Data for :doc:`water` is no longer included in the PyPI distributions for :mod:`message_ix_models`. +Data for :doc:`water/index` is no longer included in the PyPI distributions for :mod:`message_ix_models`. This reduces the package size from >20 MB to <5 MB. To automatically download and unpack these data into a local directory, use :program:`mix-models fetch MESSAGEix-Nexus`. @@ -87,15 +89,15 @@ v2023.10.16 - New providers for exogenous data from the :class:`.SSPOriginal` and :class:`.SSPUpdate` (:pull:`125`) sources. - Improved :class:`.ScenarioInfo` (:pull:`125`): - - New attributes :attr:`~.ScenarioInfo.model`, :attr:`~.ScenarioInfo.scenario`, :attr:`~.ScenarioInfo.version`, and (settable) :attr:`~.ScenarioInfo.url`; class method :meth:`~.ScenarioInfo.from_url` to allow storing |Scenario| identifiers on ScenarioInfo objects. + - New attributes :attr:`~.ScenarioInfo.model`, :attr:`~.ScenarioInfo.scenario`, :attr:`~.ScenarioInfo.version`, and (settable) :attr:`~.ScenarioInfo.url`; class method :meth:`~.ScenarioInfo.from_url` to allow storing :class:`.Scenario` identifiers on ScenarioInfo objects. - New property :attr:`~.ScenarioInfo.path`, giving a valid path name for scenario-specific file I/O. - Improvements to :mod:`~message_ix_models.report` (:pull:`125`): - New :class:`.report.Config` class collecting recognized settings for the module. - :py:`context["report"]` always exists as an instance of :class:`.report.Config`. - - New submodule :class:`.report.plot` with base class and 5 plots of time-series data stored on Scenarios. - - Submodule :class:`.report.sim` provides :func:`add_simulated_solution` for testing reporting configuration. + - New submodule :mod:`.report.plot` with base class and 5 plots of time-series data stored on Scenarios. + - Submodule :mod:`.report.sim` provides :func:`.add_simulated_solution` for testing reporting configuration. - New operator :func:`.filter_ts`. - New reusable command-line option :program:`--urls-from-file` in :mod:`.util.click` (:pull:`125`). @@ -196,7 +198,7 @@ v2023.5.13 2022.8.17 ========= -- Add :func:`.nodes_ex_world` and use this in :func:`.disutility.data_conversion` instead of expected a "World" node ID to be the first element in :attr:`.ScenarioInfo.N` (:pull:`78`). +- Add :func:`~.util.node.nodes_ex_world` and use this in :func:`.disutility.data_conversion` instead of expected a "World" node ID to be the first element in :attr:`.ScenarioInfo.N` (:pull:`78`). - Add example files and documentation for :doc:`pkg-data/iiasa-se` (:pull:`78`). - Expand :file:`~` (i.e. ``$HOME``) in the ``"message local data"`` :ref:`configuration setting ` (:pull:`78`). @@ -244,7 +246,7 @@ v2023.5.13 - New :class:`.Spec` class for easier handling of specifications of model (or model variant) structure (:pull:`39`) - New utility function :func:`.util.local_data_path` (:pull:`39`). - :func:`.repr` of :class:`.Context` no longer prints a (potentially very long) list of all keys and settings (:pull:`39`). -- :func:`.as_codes` accepts a :class:`.dict` with |Code| values (:pull:`39`). +- :func:`.as_codes` accepts a :class:`.dict` with :class:`.Code` values (:pull:`39`). Earlier releases ================ @@ -263,15 +265,15 @@ Earlier releases 2021.7.22 --------- -- Migrate utilities :func:`cached`, :func:`.check_support`, :func:`.convert_units`, :func:`.maybe_query`, :func:`.series_of_pint_quantity` (:pull:`27`) +- Migrate utilities :func:`.cached`, :func:`.check_support`, :func:`.convert_units`, :func:`.maybe_query`, :func:`.series_of_pint_quantity` (:pull:`27`) - Add :data:`.testing.NIE`. - Add the ``--jvmargs`` option to :command:`pytest` (see :func:`.pytest_addoption`). -- Remove :meth:`.Context.get_config_file`, :meth:`.get_path`, :meth:`.load_config`, and :meth:`.units`, all deprecated since 2021-02-28. +- Remove :py:`.Context.get_config_file()`, :py:`.get_path()`, :py:`.load_config()`, and :py:`.units`, all deprecated since 2021-02-28. 2021.7.6 -------- -- Add :func:`identify_nodes`, a function for identifying a :doc:`pkg-data/node` based on a |Scenario| (:pull:`24`). +- Add :func:`.identify_nodes`, a function for identifying a :doc:`pkg-data/node` based on a :class:`.Scenario` (:pull:`24`). - Add :obj:`.adapt_R11_R14`, a function for adapting data from the :ref:`R11` to the :ref:`R14` node lists (:pull:`24`). - Add :func:`.export_test_data` and :command:`mix-models export-test-data` command (:pull:`16`). See :ref:`export-test-data`. @@ -298,8 +300,8 @@ Earlier releases 2021.3.3 -------- -- Migrate :mod:`.util.click`, :mod:`.util.logging`; expand documentation (:pull:`8`:). -- :meth:`.Context.clone_to_dest` method replaces :func:`clone_to_dest` function. +- Migrate :mod:`.util.click`, :mod:`.util.logging <.util._logging>`; expand documentation (:pull:`8`:). +- :meth:`.Context.clone_to_dest` method replaces :py:`clone_to_dest()` function. - Build PDF documentation on ReadTheDocs. - Allow CLI commands from both :mod:`message_ix_models` and :mod:`message_data` via :program:`mix-models`. - Migrate :program:`mix-models techs` CLI command. diff --git a/message_ix_models/data/gea/model-scenario.json b/message_ix_models/data/gea/model-scenario.json new file mode 100644 index 0000000000..eee325389e --- /dev/null +++ b/message_ix_models/data/gea/model-scenario.json @@ -0,0 +1,186 @@ +[ + { + "model": "GEA", + "scenario": "geaha_450_atr_full" + }, + { + "model": "GEA", + "scenario": "geaha_450_atr_limbe" + }, + { + "model": "GEA", + "scenario": "geaha_450_atr_limir" + }, + { + "model": "GEA", + "scenario": "geaha_450_atr_nbecs" + }, + { + "model": "GEA", + "scenario": "geaha_450_atr_nonuc" + }, + { + "model": "GEA", + "scenario": "geaha_450_atr_nsink" + }, + { + "model": "GEA", + "scenario": "geaha_450_btr_full" + }, + { + "model": "GEA", + "scenario": "geaha_450_btr_nonuc" + }, + { + "model": "GEA", + "scenario": "geah_counterfactual" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_full" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_limbe" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_limbe_limir" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_limir" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_nbecs" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_nbecs_nsink_limbe" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_noccs" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_noccs_nonuc" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_nonuc" + }, + { + "model": "GEA", + "scenario": "geala_450_atr_nsink" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_full" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_limbe" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_limbe_limir" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_limir" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_nbecs" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_nbecs_nsink_limbe" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_noccs" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_noccs_nonuc" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_nonuc" + }, + { + "model": "GEA", + "scenario": "geala_450_btr_nsink" + }, + { + "model": "GEA", + "scenario": "geama_450_atr_full" + }, + { + "model": "GEA", + "scenario": "geama_450_atr_limbe" + }, + { + "model": "GEA", + "scenario": "geama_450_atr_limir" + }, + { + "model": "GEA", + "scenario": "geama_450_atr_nbecs" + }, + { + "model": "GEA", + "scenario": "geama_450_atr_noccs" + }, + { + "model": "GEA", + "scenario": "geama_450_atr_nonuc" + }, + { + "model": "GEA", + "scenario": "geama_450_atr_nsink" + }, + { + "model": "GEA", + "scenario": "geama_450_btr_full" + }, + { + "model": "GEA", + "scenario": "geama_450_btr_limbe" + }, + { + "model": "GEA", + "scenario": "geama_450_btr_limir" + }, + { + "model": "GEA", + "scenario": "geama_450_btr_nbecs" + }, + { + "model": "GEA", + "scenario": "geama_450_btr_nonuc" + }, + { + "model": "GEA", + "scenario": "geama_450_btr_nsink" + }, + { + "model": "IMAGE", + "scenario": "GEA_high" + }, + { + "model": "IMAGE", + "scenario": "GEA_high_450" + }, + { + "model": "IMAGE", + "scenario": "GEA_low_450" + }, + { + "model": "IMAGE", + "scenario": "GEA_med_450" + } +] diff --git a/message_ix_models/data/test/gea/GEADB_ARCHIVE_20171108.zip b/message_ix_models/data/test/gea/GEADB_ARCHIVE_20171108.zip new file mode 100644 index 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b/message_ix_models/data/test/shape/gini_v1p0.csv @@ -0,0 +1,550 @@ +Model,Scenario,Region,Variable,Unit,2020,2025,2030,2035,2040,2045,2050,2055,2060,2065,2070,2075,2080,2085,2090,2095,2100,Base gini imputed,Share of final consumption among GDP imputed +SHAPE_Gini,service,LBR,Gini,NA,0.72,0.27635333082427294,0.19729957948884103,0.24567617159973099,0.5010966131997685,0.28722154269437106,0.5995622005157154,0.5959477983076396,0.11277542949399222,0.08343722429515599,0.6398178876512371,0.031016334179337912,0.4573250274747993,0.9092471838062317,0.4569543897818267,0.02936645851667652,0.5502658882324315,False,False +SHAPE_Gini,innovation,LBR,Gini,NA,0.16,0.14321945755317655,0.7774170411869618,0.18465379463277232,0.7504885130308037,0.5733247567034063,0.2987599917213549,0.06187992742483095,0.28970859266381355,0.521200313197659,0.1398648705351524,0.43855105774024217,0.5651828468156771,0.7833237262954843,0.9612942340597849,0.16447390363964365,0.9026305598693198,False,False 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+SHAPE_Gini,innovation,GUY,Gini,NA,True,0.79,0.03,0.71,0.41,0.58,0.51,0.04,0.36,0.73,0.84,0.68,0.37,0.74,0.09,0.92,0.43,0.60,False,False +SHAPE_Gini,service,KAZ,Gini,NA,False,0.98,0.05,0.77,0.21,0.99,0.18,0.01,0.53,0.88,0.42,0.85,0.62,0.70,0.44,0.31,0.68,0.17,False,False +SHAPE_Gini,service,CRI,Gini,NA,True,0.07,0.52,0.35,0.87,0.17,0.66,0.28,0.80,0.75,0.26,0.51,0.29,0.53,0.69,0.44,0.42,0.48,False,False +SHAPE_Gini,service,BLZ,Gini,NA,True,0.19,0.37,0.81,0.03,0.15,0.30,0.92,0.59,0.25,0.37,0.39,0.03,0.08,0.38,0.99,0.07,0.38,False,False +SHAPE_Gini,service,SVK,Gini,NA,False,0.65,0.03,0.23,0.58,0.24,0.48,0.77,0.25,0.98,0.82,0.45,0.98,0.44,0.10,0.56,0.54,0.56,False,False +SHAPE_Gini,innovation,DZA,Gini,NA,True,0.32,0.25,0.10,0.70,0.27,0.98,0.37,0.51,0.32,0.74,0.57,0.73,0.04,0.69,0.02,0.27,0.13,False,False +SHAPE_Gini,society,MRT,Gini,NA,True,0.56,0.84,0.06,0.32,0.12,0.53,0.41,0.51,0.97,0.52,0.03,0.25,0.93,0.85,0.72,0.34,0.81,False,False +SHAPE_Gini,society,DOM,Gini,NA,True,0.23,0.67,0.84,0.75,0.65,0.82,0.63,0.16,0.45,0.85,0.23,0.16,0.73,0.83,0.00,0.76,0.38,False,False +SHAPE_Gini,service,STP,Gini,NA,True,0.41,1.00,0.15,0.06,0.24,0.84,0.41,0.27,0.62,0.01,0.95,0.62,0.15,0.76,0.78,0.06,0.21,False,True +SHAPE_Gini,innovation,MWI,Gini,NA,True,0.47,0.18,0.70,0.28,0.65,0.79,0.16,0.22,0.11,0.87,0.67,0.46,0.39,0.79,0.63,0.55,0.61,False,True +SHAPE_Gini,society,SLE,Gini,NA,True,0.67,0.96,0.18,0.52,0.29,0.96,0.45,0.39,0.63,0.65,0.29,0.45,0.72,0.73,0.06,0.02,0.12,False,False +SHAPE_Gini,service,PRY,Gini,NA,True,0.50,0.63,0.28,0.67,0.31,0.92,0.46,0.26,0.97,0.10,0.26,0.40,0.38,0.93,0.45,0.05,0.40,False,False +SHAPE_Gini,service,BDI,Gini,NA,True,0.13,0.98,0.19,0.07,0.86,0.32,0.54,0.73,0.14,0.60,0.73,0.58,0.25,0.61,0.75,0.15,0.22,False,False +SHAPE_Gini,service,IDN,Gini,NA,True,0.92,0.80,0.56,0.54,0.03,0.55,0.73,0.69,0.60,0.25,0.31,0.11,0.23,0.61,0.53,0.49,0.24,False,False +SHAPE_Gini,innovation,HTI,Gini,NA,True,0.22,0.94,0.25,0.46,0.08,0.80,0.61,0.14,0.85,0.34,0.20,0.70,0.89,0.93,0.77,0.75,0.36,False,False +SHAPE_Gini,innovation,HUN,Gini,NA,False,0.88,0.43,0.74,0.32,0.34,0.81,0.36,0.37,0.27,0.35,0.87,0.32,0.70,0.14,0.34,0.65,0.97,False,False +SHAPE_Gini,society,MDV,Gini,NA,True,0.45,0.45,0.96,0.95,0.35,0.69,0.87,0.20,0.24,0.51,0.52,0.95,0.26,0.87,0.64,0.35,0.36,False,False +SHAPE_Gini,society,BOL,Gini,NA,True,0.54,0.92,0.67,0.19,0.04,0.73,0.16,0.09,0.55,0.56,0.24,0.43,0.99,0.18,0.23,0.40,0.28,False,False +SHAPE_Gini,society,TUV,Gini,NA,False,0.86,0.59,0.93,0.91,0.92,0.18,0.59,0.11,0.74,0.42,0.71,0.22,0.71,0.68,0.67,0.92,0.15,True,True +SHAPE_Gini,service,ZAF,Gini,NA,True,0.22,0.11,0.83,0.17,0.90,0.42,0.27,0.46,0.86,0.30,0.78,0.51,0.33,0.00,0.55,0.47,1.00,False,False +SHAPE_Gini,society,RWA,Gini,NA,True,0.26,0.69,0.32,0.58,0.95,0.73,0.32,0.97,0.46,0.83,0.03,0.36,0.79,0.03,0.61,0.01,0.44,False,False +SHAPE_Gini,society,HRV,Gini,NA,True,0.53,0.13,0.74,0.56,0.48,0.46,0.17,0.13,0.08,0.14,0.11,0.77,0.93,0.38,0.33,0.84,0.00,False,False +SHAPE_Gini,innovation,FSM,Gini,NA,False,0.99,0.01,0.85,0.79,0.44,0.02,0.78,0.15,0.28,0.48,0.52,0.27,0.05,0.29,0.76,0.33,0.93,True,True +SHAPE_Gini,society,UGA,Gini,NA,True,0.28,0.14,0.83,0.50,0.81,0.30,0.44,0.05,0.30,0.92,0.43,0.38,0.67,0.42,0.14,0.16,0.26,False,False +SHAPE_Gini,service,DOM,Gini,NA,True,0.08,0.41,0.25,0.51,0.79,0.73,0.38,0.23,0.43,0.62,0.59,0.92,0.84,0.03,0.27,0.68,0.46,False,False +SHAPE_Gini,service,MUS,Gini,NA,True,0.45,0.89,0.01,0.60,0.63,0.41,0.64,0.04,0.82,0.45,0.23,0.38,0.25,0.02,0.62,0.26,0.99,False,False +SHAPE_Gini,service,BLR,Gini,NA,True,0.88,1.00,0.62,0.52,0.53,0.00,0.41,0.61,0.24,0.81,0.56,0.43,0.28,0.28,0.73,0.50,0.60,False,False +SHAPE_Gini,innovation,LBN,Gini,NA,True,0.82,0.91,0.11,0.63,0.05,0.48,0.78,0.12,0.71,0.52,0.02,0.78,0.39,0.95,0.26,0.23,0.11,False,False +SHAPE_Gini,innovation,PRI,Gini,NA,True,0.70,0.71,0.56,0.18,0.86,0.65,0.02,0.33,0.17,1.00,0.28,0.75,0.12,0.06,0.55,1.00,0.83,False,False +SHAPE_Gini,innovation,LCA,Gini,NA,True,0.58,0.58,0.50,0.47,0.95,0.31,0.02,0.49,0.10,0.23,0.14,0.31,0.72,0.21,0.59,0.34,0.31,False,True diff --git a/message_ix_models/data/test/shape/population_v1p0.mif b/message_ix_models/data/test/shape/population_v1p0.mif new file mode 100644 index 0000000000..7dee53e740 --- /dev/null +++ b/message_ix_models/data/test/shape/population_v1p0.mif @@ -0,0 +1,250 @@ +Model;Scenario;Region;Variable;Unit;2010;2015;2020;2025;2030;2035;2040;2045;2050;2055;2060;2065;2070;2075;2080;2085;2090;2095;2100; +SHAPE_GDP_POP;all_SHAPE_SDPs;GMB;Population;million;0.29;0.91;0.87;0.66;0.41;0.68;0.35;0.25;0.47;0.62;0.68;0.44;0.14;0.73;0.43;0.89;0.60;0.09;0.74; +SHAPE_GDP_POP;all_SHAPE_SDPs;LIE;Population;million;0.81;0.90;0.69;0.48;0.27;0.65;0.17;0.54;0.48;0.80;0.41;0.63;0.48;0.45;0.55;0.45;0.71;0.33;0.85; +SHAPE_GDP_POP;all_SHAPE_SDPs;FJI;Population;million;0.53;0.18;0.56;0.40;0.93;0.90;0.56;0.67;0.35;0.75;0.85;0.42;0.12;0.07;0.86;0.82;0.33;0.77;0.64; +SHAPE_GDP_POP;all_SHAPE_SDPs;FLK;Population;million;0.62;0.12;0.24;0.28;0.35;0.48;0.27;0.39;0.60;0.89;0.17;0.46;0.09;0.59;0.52;0.97;0.05;0.11;0.13; +SHAPE_GDP_POP;all_SHAPE_SDPs;NLD;Population;million;0.94;1.00;0.05;0.72;0.89;0.07;0.43;0.57;0.96;0.41;0.42;0.71;0.97;0.56;0.04;0.75;0.98;0.17;0.49; +SHAPE_GDP_POP;all_SHAPE_SDPs;PNG;Population;million;0.64;0.37;0.65;0.37;0.92;0.29;0.84;0.83;0.62;0.27;0.49;0.26;0.21;0.56;0.07;0.94;0.63;0.45;0.17; +SHAPE_GDP_POP;all_SHAPE_SDPs;ARM;Population;million;0.71;0.44;0.63;0.65;0.47;0.47;0.21;0.95;0.48;0.98;0.30;0.32;0.50;0.96;0.38;0.36;0.59;0.45;0.74; +SHAPE_GDP_POP;all_SHAPE_SDPs;NIU;Population;million;0.26;0.33;0.93;0.47;0.74;0.36;0.17;0.23;0.95;0.01;0.01;0.76;0.70;0.87;0.90;0.41;0.80;0.18;0.21; +SHAPE_GDP_POP;all_SHAPE_SDPs;NIC;Population;million;0.56;0.52;0.78;0.57;0.34;0.05;0.63;0.50;0.05;0.79;0.88;0.49;0.33;0.20;0.33;0.42;0.63;0.66;0.94; +SHAPE_GDP_POP;all_SHAPE_SDPs;KGZ;Population;million;0.62;0.38;0.23;0.24;0.29;0.28;0.53;0.23;0.37;0.91;0.30;0.46;0.20;0.50;0.16;0.12;0.31;0.26;0.72; +SHAPE_GDP_POP;all_SHAPE_SDPs;CHL;Population;million;0.51;0.99;0.61;0.59;0.26;0.42;0.66;0.29;0.68;0.27;0.45;0.95;0.23;0.06;0.75;0.54;0.93;0.70;0.78; +SHAPE_GDP_POP;all_SHAPE_SDPs;VIR;Population;million;0.72;0.87;0.57;0.36;0.17;0.55;0.87;0.58;0.63;0.07;0.24;0.66;0.18;0.36;0.08;0.62;0.32;0.91;0.30; +SHAPE_GDP_POP;all_SHAPE_SDPs;UGA;Population;million;0.12;0.19;0.69;0.69;0.64;0.53;0.32;0.46;0.76;0.19;0.13;0.10;0.65;0.01;0.11;0.12;0.89;0.70;0.05; +SHAPE_GDP_POP;all_SHAPE_SDPs;PRK;Population;million;0.87;0.10;0.47;0.72;0.57;0.68;0.67;0.89;0.31;0.79;1.00;0.51;0.65;0.16;0.04;0.69;0.20;0.94;0.46; +SHAPE_GDP_POP;all_SHAPE_SDPs;FSM;Population;million;0.36;0.24;0.18;0.27;0.07;0.94;0.14;0.30;0.15;0.92;0.97;0.30;0.72;0.28;0.73;0.10;0.17;0.08;0.27; +SHAPE_GDP_POP;all_SHAPE_SDPs;GUF;Population;million;0.04;0.52;0.97;0.50;0.66;0.68;0.72;0.66;0.91;0.10;0.07;0.78;0.01;0.81;0.62;0.88;0.64;0.60;0.14; +SHAPE_GDP_POP;all_SHAPE_SDPs;TCA;Population;million;0.03;0.90;0.49;0.21;0.83;0.68;0.36;0.05;0.71;0.27;0.95;0.83;0.02;0.73;0.17;0.63;0.82;0.06;0.05; +SHAPE_GDP_POP;all_SHAPE_SDPs;BRN;Population;million;0.53;0.30;0.24;0.25;0.87;0.30;0.33;0.22;0.17;0.33;0.13;0.53;0.09;0.55;0.71;0.88;0.74;0.89;0.34; +SHAPE_GDP_POP;all_SHAPE_SDPs;TGO;Population;million;0.87;0.34;0.30;0.60;0.83;0.34;0.48;0.49;0.87;0.16;0.63;0.31;0.37;0.98;0.17;0.02;0.37;0.09;0.92; +SHAPE_GDP_POP;all_SHAPE_SDPs;MLI;Population;million;0.25;0.81;0.86;0.88;0.15;0.06;0.64;0.93;0.51;0.65;0.76;0.34;0.15;0.93;0.64;0.11;0.59;0.43;0.57; +SHAPE_GDP_POP;all_SHAPE_SDPs;DEU;Population;million;0.96;0.13;0.08;0.55;0.43;0.49;0.50;0.57;0.12;0.20;0.06;0.41;0.55;0.78;0.52;0.35;0.50;0.96;0.21; +SHAPE_GDP_POP;all_SHAPE_SDPs;PAK;Population;million;0.79;0.22;0.04;0.74;0.73;0.66;0.82;0.47;0.98;0.69;0.20;0.64;0.86;0.10;0.90;0.75;0.34;0.59;0.63; +SHAPE_GDP_POP;all_SHAPE_SDPs;MOZ;Population;million;0.98;0.74;0.06;0.76;0.84;0.21;0.96;0.98;0.63;0.14;0.99;0.42;0.02;0.34;0.70;0.08;0.03;0.03;0.06; +SHAPE_GDP_POP;all_SHAPE_SDPs;POL;Population;million;0.63;0.63;0.77;0.69;0.74;0.08;0.81;0.48;0.19;0.06;0.85;0.43;0.33;0.85;0.80;0.70;0.66;0.71;0.29; +SHAPE_GDP_POP;all_SHAPE_SDPs;COL;Population;million;0.56;0.30;0.38;0.67;0.50;0.23;0.67;0.37;0.10;0.66;0.78;0.29;0.06;0.47;0.57;0.82;0.71;0.49;0.53; +SHAPE_GDP_POP;all_SHAPE_SDPs;BFA;Population;million;0.29;0.01;0.20;0.11;0.13;0.15;0.42;0.85;0.14;0.77;0.35;0.41;0.95;0.50;0.77;0.18;0.44;0.17;0.85; +SHAPE_GDP_POP;all_SHAPE_SDPs;SDN;Population;million;0.20;0.44;0.96;0.92;0.99;0.78;0.09;0.32;0.17;0.16;0.99;0.26;0.62;0.21;0.60;0.36;0.72;0.53;0.63; +SHAPE_GDP_POP;all_SHAPE_SDPs;BGD;Population;million;0.98;0.73;0.33;0.53;0.14;0.86;0.21;0.89;0.62;0.48;0.23;0.20;0.17;0.91;0.46;0.50;0.96;0.83;0.37; +SHAPE_GDP_POP;all_SHAPE_SDPs;ATG;Population;million;0.58;0.52;0.83;0.02;0.66;0.28;0.33;0.20;0.43;0.77;0.52;0.69;0.97;0.19;0.33;0.09;0.11;0.47;0.59; +SHAPE_GDP_POP;all_SHAPE_SDPs;LUX;Population;million;0.23;0.17;0.23;0.32;0.57;0.23;0.40;0.13;0.78;0.76;0.65;0.13;0.18;0.98;0.55;0.40;0.28;0.49;0.72; +SHAPE_GDP_POP;all_SHAPE_SDPs;SMR;Population;million;0.88;0.73;0.95;0.09;0.63;0.92;0.81;0.10;0.86;0.08;0.68;0.82;0.34;0.58;0.52;0.70;0.95;0.35;0.65; +SHAPE_GDP_POP;all_SHAPE_SDPs;GLP;Population;million;0.70;0.24;0.17;0.40;0.54;0.89;0.42;0.18;0.02;0.47;0.07;0.42;0.96;0.88;0.39;0.85;0.98;0.00;0.34; +SHAPE_GDP_POP;all_SHAPE_SDPs;KWT;Population;million;0.12;0.02;0.64;0.44;0.95;0.97;0.92;0.78;0.88;0.86;0.42;0.37;0.92;0.76;0.89;0.60;0.38;0.96;0.26; +SHAPE_GDP_POP;all_SHAPE_SDPs;ASM;Population;million;0.10;0.72;0.73;0.91;0.70;0.93;0.93;0.93;0.58;0.91;0.40;0.82;0.14;0.79;0.07;0.44;0.34;0.25;0.96; +SHAPE_GDP_POP;all_SHAPE_SDPs;GNQ;Population;million;0.95;0.27;0.41;0.94;0.24;0.15;0.37;0.97;0.55;0.42;0.41;0.11;0.90;0.57;0.61;0.32;0.06;0.06;0.81; +SHAPE_GDP_POP;all_SHAPE_SDPs;TKM;Population;million;0.38;0.59;0.98;0.90;0.47;0.50;0.59;0.48;0.27;0.16;0.33;0.69;0.48;0.40;0.71;0.39;0.79;0.31;0.49; +SHAPE_GDP_POP;all_SHAPE_SDPs;GRL;Population;million;0.59;0.55;0.80;0.62;0.19;0.83;0.74;0.22;0.29;0.80;0.88;0.48;0.23;0.27;0.72;0.79;0.07;0.85;0.05; +SHAPE_GDP_POP;all_SHAPE_SDPs;SUR;Population;million;0.36;0.51;0.10;0.95;0.72;0.62;0.29;0.04;0.13;0.37;0.49;0.59;0.11;0.51;0.59;0.06;0.32;0.16;0.35; 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+SHAPE_GDP_POP;all_SHAPE_SDPs;LKA;Population;million;0.92;0.69;0.72;0.50;0.85;0.55;0.58;0.44;0.49;0.18;0.85;0.84;0.48;0.35;0.72;0.82;0.87;0.35;0.98; +SHAPE_GDP_POP;all_SHAPE_SDPs;SVN;Population;million;0.30;0.76;0.44;0.69;0.81;0.75;0.80;0.01;0.74;0.50;0.16;0.86;0.36;0.54;0.93;0.90;0.73;0.70;0.54; +SHAPE_GDP_POP;all_SHAPE_SDPs;CYM;Population;million;0.38;0.75;0.48;0.79;0.72;0.83;0.62;0.41;0.18;0.58;0.24;0.52;0.39;0.26;0.25;0.90;0.53;0.47;0.30; +SHAPE_GDP_POP;all_SHAPE_SDPs;GIN;Population;million;0.24;0.57;0.52;0.43;0.53;0.57;0.80;0.71;0.61;0.33;0.07;0.86;0.01;0.39;0.76;0.75;0.39;0.79;0.17; +SHAPE_GDP_POP;all_SHAPE_SDPs;URY;Population;million;0.11;0.99;0.78;0.95;0.64;0.89;0.52;0.87;0.62;0.09;0.71;0.12;0.73;0.91;0.72;0.88;0.72;0.61;0.52; +SHAPE_GDP_POP;all_SHAPE_SDPs;PRI;Population;million;0.69;0.95;0.85;0.22;0.99;0.53;0.14;0.16;0.06;0.89;0.08;0.27;0.30;0.76;0.80;0.57;0.30;0.41;0.30; +SHAPE_GDP_POP;all_SHAPE_SDPs;ZMB;Population;million;0.81;0.49;0.64;0.11;0.12;0.58;0.69;1.00;0.87;0.29;0.27;0.00;0.01;0.86;0.70;0.50;0.71;0.80;0.25; +SHAPE_GDP_POP;all_SHAPE_SDPs;KEN;Population;million;0.31;0.10;0.95;0.94;0.67;0.23;0.46;0.86;0.57;0.30;0.94;0.44;0.05;0.39;0.74;0.09;0.12;0.29;0.19; +SHAPE_GDP_POP;all_SHAPE_SDPs;BRB;Population;million;0.39;0.13;0.00;0.85;0.84;0.58;0.43;0.46;0.88;0.69;0.48;0.37;0.83;0.13;0.08;0.60;0.93;0.52;0.90; +SHAPE_GDP_POP;all_SHAPE_SDPs;EST;Population;million;0.67;0.14;0.11;0.66;0.99;0.84;0.43;0.26;0.40;0.09;0.74;0.58;0.84;0.74;0.43;0.25;0.78;0.79;0.04; +SHAPE_GDP_POP;all_SHAPE_SDPs;SYC;Population;million;0.53;0.83;0.66;0.93;0.29;0.54;0.34;0.79;0.58;0.62;0.07;0.85;0.74;0.93;0.17;0.93;0.82;0.67;0.30; +SHAPE_GDP_POP;all_SHAPE_SDPs;TUN;Population;million;0.99;0.17;0.07;0.38;0.87;0.95;0.35;0.56;0.40;0.82;0.59;0.24;0.15;0.51;0.29;0.16;0.76;0.76;0.07; +SHAPE_GDP_POP;all_SHAPE_SDPs;IDN;Population;million;0.78;0.03;0.91;0.06;0.60;0.63;0.32;0.34;0.56;0.71;0.55;0.74;0.79;0.58;0.04;0.47;0.03;0.18;0.73; diff --git a/message_ix_models/data/test/shape/population_v1p1.mif b/message_ix_models/data/test/shape/population_v1p1.mif new file mode 100644 index 0000000000..5b1614e0a4 --- /dev/null +++ b/message_ix_models/data/test/shape/population_v1p1.mif @@ -0,0 +1,250 @@ +Model;Scenario;Region;Variable;Unit;2010;2015;2020;2025;2030;2035;2040;2045;2050;2055;2060;2065;2070;2075;2080;2085;2090;2095;2100; +SHAPE_GDP_POP;all_SHAPE_SDPs;COD;Population;million;0.95;0.68;0.63;0.30;0.33;0.08;0.90;0.97;0.91;0.72;0.18;0.83;0.30;0.29;0.36;0.55;0.88;0.74;0.96; +SHAPE_GDP_POP;all_SHAPE_SDPs;CZE;Population;million;0.65;0.71;0.00;0.98;0.75;0.00;0.57;0.67;0.44;0.97;0.56;0.29;0.94;0.18;0.51;0.92;0.74;0.73;0.84; +SHAPE_GDP_POP;all_SHAPE_SDPs;HRV;Population;million;0.64;0.92;0.30;0.39;0.26;0.61;0.39;0.07;0.14;0.05;0.58;0.98;0.73;0.13;0.13;0.07;0.70;0.55;0.10; +SHAPE_GDP_POP;all_SHAPE_SDPs;CMR;Population;million;0.82;0.34;0.52;0.78;0.90;0.40;0.96;0.22;0.19;0.34;0.61;0.88;0.26;0.78;0.25;0.53;0.48;0.10;0.08; +SHAPE_GDP_POP;all_SHAPE_SDPs;COG;Population;million;0.79;0.31;0.39;0.15;0.75;0.84;0.34;0.69;0.66;0.45;0.99;0.59;0.96;0.87;0.62;0.73;0.38;0.21;0.95; +SHAPE_GDP_POP;all_SHAPE_SDPs;LUX;Population;million;0.24;0.40;0.90;0.54;0.72;0.05;0.52;0.88;0.01;0.90;0.59;0.69;0.06;0.72;0.54;0.32;0.87;0.09;0.77; +SHAPE_GDP_POP;all_SHAPE_SDPs;HND;Population;million;0.69;0.50;0.40;1.00;0.39;0.65;0.17;0.08;0.56;0.49;0.44;0.71;0.79;0.68;0.76;0.63;0.87;0.63;0.39; +SHAPE_GDP_POP;all_SHAPE_SDPs;UZB;Population;million;0.39;0.82;0.82;0.41;0.33;0.17;0.33;0.43;0.80;0.11;0.80;0.50;0.61;0.66;0.85;0.26;0.98;0.16;0.77; +SHAPE_GDP_POP;all_SHAPE_SDPs;NOR;Population;million;0.88;0.62;0.94;0.65;0.10;0.74;0.26;0.14;0.83;0.04;0.14;0.84;0.13;0.27;0.67;0.11;0.60;0.20;0.42; +SHAPE_GDP_POP;all_SHAPE_SDPs;PYF;Population;million;0.14;0.23;0.52;0.93;0.52;0.80;0.88;0.03;1.00;0.84;0.98;0.75;0.59;0.83;0.58;0.07;0.72;0.54;0.65; +SHAPE_GDP_POP;all_SHAPE_SDPs;KEN;Population;million;0.40;0.21;0.87;0.02;0.67;0.68;0.28;0.53;0.41;0.40;0.76;0.48;0.62;0.02;0.84;0.37;0.20;0.93;0.93; +SHAPE_GDP_POP;all_SHAPE_SDPs;BVT;Population;million;0.61;0.92;0.63;0.15;0.87;0.32;0.85;0.83;0.12;0.71;0.69;0.49;0.99;0.46;0.80;0.34;0.13;0.62;0.34; +SHAPE_GDP_POP;all_SHAPE_SDPs;EGY;Population;million;0.61;0.19;0.97;0.77;0.18;0.39;0.54;0.60;0.18;0.65;0.52;0.11;0.23;0.23;0.73;0.75;0.60;0.63;0.46; +SHAPE_GDP_POP;all_SHAPE_SDPs;TZA;Population;million;0.90;0.86;0.86;0.46;0.62;0.10;0.59;0.51;0.98;0.37;0.44;0.79;0.86;0.57;0.87;0.82;0.32;0.41;0.22; +SHAPE_GDP_POP;all_SHAPE_SDPs;MYS;Population;million;0.92;0.40;0.38;0.63;0.43;0.13;0.43;0.30;0.01;0.20;0.69;0.26;0.04;0.14;0.48;0.43;0.87;0.32;0.18; +SHAPE_GDP_POP;all_SHAPE_SDPs;TKM;Population;million;0.18;0.51;0.11;0.93;0.02;0.10;0.67;0.54;0.80;0.62;0.94;0.22;0.62;0.20;0.81;0.21;0.57;0.67;0.68; +SHAPE_GDP_POP;all_SHAPE_SDPs;HKG;Population;million;0.82;0.60;0.58;0.10;0.38;0.87;0.09;0.30;0.99;0.56;0.51;0.13;0.44;0.39;0.27;0.27;0.29;0.19;0.01; +SHAPE_GDP_POP;all_SHAPE_SDPs;AGO;Population;million;0.37;0.21;0.89;0.63;0.41;0.22;0.71;0.63;0.86;0.10;0.84;0.40;0.76;0.06;0.79;0.64;0.70;0.37;0.94; +SHAPE_GDP_POP;all_SHAPE_SDPs;ALB;Population;million;0.95;0.32;0.64;0.13;0.55;0.84;0.21;0.28;0.35;0.74;0.15;0.78;0.15;0.33;0.85;0.52;0.66;0.85;0.67; +SHAPE_GDP_POP;all_SHAPE_SDPs;KWT;Population;million;0.03;0.99;0.87;0.30;0.63;0.76;0.95;0.00;0.96;0.31;0.73;0.83;0.02;0.96;0.48;0.94;0.70;0.48;0.09; +SHAPE_GDP_POP;all_SHAPE_SDPs;LSO;Population;million;0.43;0.79;0.05;0.40;0.67;0.12;0.78;0.42;0.30;0.68;0.90;0.20;0.41;0.92;0.51;0.32;0.43;0.15;0.42; +SHAPE_GDP_POP;all_SHAPE_SDPs;TLS;Population;million;0.60;0.41;0.77;0.40;0.70;0.86;0.82;0.51;0.14;0.71;0.20;0.57;0.46;0.64;0.51;0.16;0.98;0.91;0.63; +SHAPE_GDP_POP;all_SHAPE_SDPs;THA;Population;million;0.44;0.41;0.41;0.43;0.55;0.74;0.34;0.17;0.20;0.32;0.02;0.81;0.06;0.11;0.75;0.20;0.07;0.43;0.85; +SHAPE_GDP_POP;all_SHAPE_SDPs;MKD;Population;million;0.57;0.26;0.08;0.09;0.65;0.18;0.44;0.94;0.63;0.61;0.69;0.35;0.49;0.61;0.75;0.23;0.82;0.43;0.64; +SHAPE_GDP_POP;all_SHAPE_SDPs;LCA;Population;million;0.27;0.82;0.52;0.95;0.77;0.22;0.72;0.22;0.92;0.59;0.50;0.38;0.27;0.53;0.78;0.74;0.26;0.39;0.31; +SHAPE_GDP_POP;all_SHAPE_SDPs;ROU;Population;million;0.42;0.04;0.25;0.24;0.48;0.59;0.86;0.40;0.04;0.87;0.77;0.94;0.84;0.80;0.32;0.53;0.75;0.66;0.23; +SHAPE_GDP_POP;all_SHAPE_SDPs;CCK;Population;million;0.57;0.11;0.31;0.17;0.77;0.24;0.67;0.15;0.51;0.30;0.47;0.91;0.76;0.32;0.53;0.75;0.68;0.75;0.25; +SHAPE_GDP_POP;all_SHAPE_SDPs;PCN;Population;million;0.87;0.27;0.35;0.61;0.53;0.27;0.18;0.29;0.89;0.61;0.28;0.06;0.20;0.07;0.94;0.33;0.23;0.12;0.70; +SHAPE_GDP_POP;all_SHAPE_SDPs;ALA;Population;million;0.64;0.64;0.81;0.29;0.42;0.56;0.06;0.28;0.13;0.56;0.94;0.12;0.02;0.33;0.65;0.70;0.70;0.56;0.11; +SHAPE_GDP_POP;all_SHAPE_SDPs;HUN;Population;million;0.95;0.98;0.93;0.34;0.36;0.26;0.60;0.73;0.32;0.54;0.75;0.78;0.46;0.14;0.73;0.84;0.29;0.60;0.08; +SHAPE_GDP_POP;all_SHAPE_SDPs;SMR;Population;million;0.07;0.53;0.22;0.72;0.81;0.92;0.65;0.03;0.48;0.06;0.76;0.23;0.30;0.09;0.46;0.21;0.60;0.47;0.47; +SHAPE_GDP_POP;all_SHAPE_SDPs;NPL;Population;million;0.19;0.00;0.16;0.13;0.77;0.50;0.57;0.22;0.90;0.44;0.03;1.00;0.81;0.52;0.36;0.71;0.67;0.26;0.29; +SHAPE_GDP_POP;all_SHAPE_SDPs;SLB;Population;million;0.21;0.37;0.19;0.87;0.40;0.15;0.05;0.54;0.55;0.74;0.38;0.74;0.13;0.45;0.58;0.62;0.96;0.86;0.93; +SHAPE_GDP_POP;all_SHAPE_SDPs;MEX;Population;million;0.43;0.51;0.42;0.06;0.80;0.82;0.28;0.86;0.16;0.57;0.23;0.13;0.15;0.78;0.09;0.01;0.87;0.51;0.88; +SHAPE_GDP_POP;all_SHAPE_SDPs;ARM;Population;million;0.23;0.55;0.05;0.88;0.24;0.61;0.58;0.16;0.73;0.35;0.51;0.35;0.03;0.78;0.98;0.56;0.34;0.10;0.73; +SHAPE_GDP_POP;all_SHAPE_SDPs;VGB;Population;million;0.18;0.04;0.10;0.66;0.40;0.68;0.62;0.45;0.90;0.71;0.73;0.67;0.32;0.56;0.12;0.23;0.20;0.22;0.58; +SHAPE_GDP_POP;all_SHAPE_SDPs;YEM;Population;million;0.44;0.54;0.29;0.91;0.71;0.27;1.00;1.00;0.22;0.42;0.51;0.31;0.96;0.65;0.59;0.88;0.19;0.11;0.38; +SHAPE_GDP_POP;all_SHAPE_SDPs;TUR;Population;million;0.08;0.63;0.91;0.85;0.74;0.74;0.73;0.43;0.62;0.30;0.24;0.31;0.83;0.83;0.14;0.54;0.68;0.13;0.57; +SHAPE_GDP_POP;all_SHAPE_SDPs;MUS;Population;million;0.90;0.85;0.39;0.67;0.84;0.93;0.81;0.76;0.70;0.65;0.44;0.18;0.56;0.58;0.69;0.61;0.31;0.44;0.08; 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+SHAPE_GDP_POP;all_SHAPE_SDPs;DEU;Population;million;0.89;0.20;0.23;0.18;0.53;0.19;0.94;0.70;0.06;0.16;0.71;1.00;0.01;0.66;0.17;0.30;0.58;0.28;0.17; +SHAPE_GDP_POP;all_SHAPE_SDPs;ETH;Population;million;0.53;0.87;0.64;0.13;0.87;0.72;0.31;0.86;0.90;0.12;0.23;0.66;0.54;0.72;0.56;0.10;0.87;0.23;0.85; +SHAPE_GDP_POP;all_SHAPE_SDPs;CHN;Population;million;0.06;0.53;0.58;0.34;0.53;0.45;0.32;0.47;0.07;0.70;0.48;0.52;0.97;0.94;1.00;0.86;0.00;0.66;0.72; +SHAPE_GDP_POP;all_SHAPE_SDPs;CUB;Population;million;0.32;0.71;0.43;0.74;0.89;0.73;0.88;0.63;0.18;0.79;0.51;0.44;0.75;0.67;0.60;0.42;0.80;0.26;0.45; +SHAPE_GDP_POP;all_SHAPE_SDPs;MWI;Population;million;0.83;0.04;0.06;0.01;0.90;0.32;0.89;0.78;0.52;0.28;0.38;0.18;0.12;0.20;0.41;0.35;0.39;0.72;0.69; +SHAPE_GDP_POP;all_SHAPE_SDPs;LBR;Population;million;0.67;0.73;0.26;0.41;0.08;0.52;0.19;0.61;0.95;0.63;0.68;0.48;0.98;0.43;0.52;0.81;0.80;0.51;0.38; +SHAPE_GDP_POP;all_SHAPE_SDPs;PSE;Population;million;0.70;0.84;0.83;0.16;0.49;0.26;0.91;0.95;0.49;0.84;0.89;0.59;0.95;0.07;0.53;0.73;0.08;0.81;0.18; +SHAPE_GDP_POP;all_SHAPE_SDPs;ASM;Population;million;0.79;0.23;0.56;0.17;0.61;0.20;0.30;0.47;0.99;0.19;0.37;0.71;0.35;0.17;0.26;0.89;0.52;0.63;0.27; +SHAPE_GDP_POP;all_SHAPE_SDPs;MLI;Population;million;0.55;0.74;0.59;0.50;0.82;0.93;0.19;0.09;0.70;0.91;0.93;0.81;0.53;0.06;0.29;0.49;0.82;0.94;0.02; +SHAPE_GDP_POP;all_SHAPE_SDPs;PRK;Population;million;0.73;0.34;0.18;0.06;0.69;0.92;0.40;0.32;0.99;0.78;0.68;0.63;0.99;0.59;0.79;0.39;0.53;0.02;0.99; +SHAPE_GDP_POP;all_SHAPE_SDPs;BGD;Population;million;0.61;0.65;0.82;0.97;0.34;0.64;0.22;0.23;0.45;0.70;0.50;0.07;0.95;0.47;0.95;0.53;0.62;0.17;0.85; +SHAPE_GDP_POP;all_SHAPE_SDPs;KOR;Population;million;0.68;0.55;0.67;0.42;0.39;0.64;0.22;0.98;0.63;0.81;0.71;0.30;0.06;0.74;0.21;0.07;0.97;0.34;0.53; diff --git a/message_ix_models/data/test/shape/population_v1p2.mif b/message_ix_models/data/test/shape/population_v1p2.mif new file mode 100644 index 0000000000..e329f28299 --- /dev/null +++ b/message_ix_models/data/test/shape/population_v1p2.mif @@ -0,0 +1,250 @@ +Model;Scenario;Region;Variable;Unit;2010;2011;2012;2013;2014;2015;2016;2017;2018;2019;2020;2021;2022;2023;2024;2025;2026;2030;2035;2040;2045;2050;2055;2060;2065;2070;2075;2080;2085;2090;2095;2100; +SHAPE_GDP_POP;all_SHAPE_SDPs;UKR;Population;million;0.53;0.30;0.77;0.09;0.76;0.43;0.57;0.74;0.26;0.03;0.99;0.00;0.07;0.17;0.29;0.93;0.33;0.17;0.57;0.87;0.30;0.95;0.83;0.73;0.02;1.00;0.27;0.86;0.45;0.30;0.76;0.53; +SHAPE_GDP_POP;all_SHAPE_SDPs;FRO;Population;million;0.33;0.49;0.85;0.66;0.34;0.18;0.46;0.55;0.43;0.76;0.90;0.77;0.62;0.46;0.96;0.38;0.17;0.97;0.53;0.71;0.66;0.77;0.53;0.36;0.52;0.92;0.75;0.88;0.28;0.61;0.86;0.50; +SHAPE_GDP_POP;all_SHAPE_SDPs;NIU;Population;million;0.51;0.04;0.64;0.41;0.27;0.04;0.85;0.93;0.11;0.49;0.64;0.48;0.55;0.92;0.39;0.19;0.16;0.55;0.86;0.30;0.62;0.66;0.79;0.04;0.01;0.00;0.38;0.42;0.37;0.81;0.42;0.18; +SHAPE_GDP_POP;all_SHAPE_SDPs;ARG;Population;million;0.29;0.38;0.49;0.26;0.90;0.63;0.57;0.79;0.81;0.15;0.74;0.61;0.30;0.06;0.39;0.75;0.68;0.63;0.29;0.00;0.94;0.46;0.92;0.91;0.91;0.02;0.89;0.01;0.85;0.45;0.34;0.34; +SHAPE_GDP_POP;all_SHAPE_SDPs;GEO;Population;million;0.24;0.27;0.80;0.23;0.96;0.92;0.88;0.06;0.50;0.88;0.14;0.52;0.93;0.26;0.58;0.91;0.42;0.63;1.00;0.77;0.79;0.04;0.18;0.51;0.15;0.97;0.04;0.79;0.52;0.87;0.03;0.32; +SHAPE_GDP_POP;all_SHAPE_SDPs;TKM;Population;million;0.16;0.40;0.71;0.02;0.30;0.23;0.80;0.46;0.15;0.09;0.57;0.43;0.48;0.67;0.59;0.82;0.05;0.79;0.29;0.73;0.46;0.24;0.00;0.01;0.79;0.94;0.01;0.72;0.89;0.09;0.22;0.43; +SHAPE_GDP_POP;all_SHAPE_SDPs;ABW;Population;million;0.76;0.19;0.82;0.10;0.05;0.22;0.52;0.82;0.16;0.12;0.45;0.38;0.14;0.46;0.40;0.67;0.08;0.42;0.63;0.25;0.21;0.50;0.06;0.27;0.03;0.56;0.74;0.03;0.61;0.70;0.60;0.40; +SHAPE_GDP_POP;all_SHAPE_SDPs;LBN;Population;million;0.58;0.80;0.79;0.00;0.23;0.59;0.19;0.46;0.70;0.98;0.81;0.94;0.44;0.43;0.86;0.29;0.82;0.71;0.64;0.97;0.60;0.06;0.20;0.51;0.10;0.20;0.16;0.69;0.72;0.51;0.75;0.19; +SHAPE_GDP_POP;all_SHAPE_SDPs;GNQ;Population;million;0.49;0.60;0.20;0.31;0.76;0.22;0.15;0.29;0.70;0.75;0.07;0.31;0.95;0.28;0.27;0.44;0.14;0.86;0.97;0.59;0.79;0.87;0.43;0.37;0.40;0.99;0.08;0.16;0.10;0.02;0.95;0.89; +SHAPE_GDP_POP;all_SHAPE_SDPs;ZMB;Population;million;0.85;0.74;0.54;0.10;0.35;0.60;0.93;0.40;0.23;0.84;0.03;0.88;0.75;0.77;0.55;0.06;0.67;0.03;0.89;0.03;0.84;0.83;0.05;0.48;0.38;0.90;0.39;0.17;0.63;0.77;0.17;0.31; +SHAPE_GDP_POP;all_SHAPE_SDPs;CZE;Population;million;0.19;0.01;0.87;0.70;0.32;0.62;0.99;0.37;0.03;0.53;0.69;0.64;0.90;0.51;0.48;0.30;0.59;0.47;0.82;0.40;0.36;0.09;0.35;0.40;0.17;0.83;0.40;0.68;0.15;0.24;0.95;0.70; +SHAPE_GDP_POP;all_SHAPE_SDPs;BVT;Population;million;0.21;0.10;0.05;0.82;0.60;0.04;0.14;0.90;0.71;0.59;0.67;0.56;0.05;0.21;0.90;0.73;0.02;0.49;0.70;0.70;0.83;0.49;0.78;0.20;0.33;0.95;0.35;0.16;0.33;0.69;0.80;0.84; +SHAPE_GDP_POP;all_SHAPE_SDPs;MDA;Population;million;0.21;0.89;0.27;0.01;0.39;0.77;0.01;0.11;0.85;0.85;0.25;0.36;0.30;0.59;0.44;0.09;0.18;0.52;0.16;0.80;0.69;0.84;0.84;0.14;0.23;0.37;0.18;0.77;0.21;0.02;0.50;0.28; +SHAPE_GDP_POP;all_SHAPE_SDPs;MYT;Population;million;0.96;0.37;0.72;0.17;0.34;0.54;0.04;0.39;0.44;0.24;0.18;0.30;0.33;0.94;0.98;0.81;0.90;0.32;0.73;0.22;0.77;0.41;0.92;0.04;0.93;0.77;0.85;0.00;0.15;0.23;0.81;0.25; +SHAPE_GDP_POP;all_SHAPE_SDPs;ZWE;Population;million;0.07;0.52;0.89;0.86;0.65;0.97;0.74;0.78;0.59;0.80;0.75;0.63;0.47;0.61;0.53;0.27;0.84;0.10;0.17;0.06;0.32;0.10;0.56;0.14;0.90;0.98;0.83;0.19;0.14;0.83;0.12;0.06; +SHAPE_GDP_POP;all_SHAPE_SDPs;KNA;Population;million;0.64;0.34;0.27;0.16;0.35;0.55;0.36;0.79;0.43;0.69;0.13;0.31;0.77;0.61;0.54;0.79;0.66;0.84;0.82;0.95;0.98;0.66;0.77;0.70;0.76;1.00;0.09;0.85;0.96;0.74;0.89;0.32; +SHAPE_GDP_POP;all_SHAPE_SDPs;TGO;Population;million;0.14;0.12;0.73;0.07;0.98;0.75;0.39;0.85;0.57;0.15;0.98;0.95;0.91;0.37;0.71;0.50;0.02;0.12;0.11;0.44;0.91;0.99;0.92;0.50;0.36;0.60;0.93;0.63;0.41;0.46;0.36;0.28; +SHAPE_GDP_POP;all_SHAPE_SDPs;GIB;Population;million;0.27;0.67;0.24;0.69;0.69;0.80;1.00;0.58;0.84;0.73;0.44;0.16;0.10;0.29;0.08;0.33;0.90;0.81;0.84;0.62;0.33;0.35;0.61;0.75;0.03;0.86;0.39;0.58;0.92;0.47;0.42;0.32; +SHAPE_GDP_POP;all_SHAPE_SDPs;SHN;Population;million;0.05;0.27;0.35;0.22;0.80;0.86;0.55;0.79;0.29;0.25;0.50;0.79;0.64;0.71;0.63;0.76;0.18;0.62;0.06;0.31;0.08;0.78;0.72;0.01;0.72;0.62;0.96;0.40;0.58;0.72;0.90;0.31; +SHAPE_GDP_POP;all_SHAPE_SDPs;BGR;Population;million;0.05;0.29;0.48;0.89;0.62;0.75;0.28;0.81;0.53;0.99;0.51;0.70;0.43;0.83;0.80;0.52;0.18;0.29;0.47;0.53;0.98;0.25;0.28;0.75;0.17;0.91;0.86;0.59;0.83;0.62;0.59;0.08; +SHAPE_GDP_POP;all_SHAPE_SDPs;ESP;Population;million;0.82;0.55;0.45;0.88;0.66;0.63;0.14;0.75;0.79;0.19;0.35;0.07;0.22;0.54;0.71;0.20;0.96;0.95;0.76;0.73;0.23;0.95;0.11;0.31;0.06;0.37;0.88;0.75;0.34;0.19;0.93;0.84; +SHAPE_GDP_POP;all_SHAPE_SDPs;PNG;Population;million;0.81;0.44;0.17;0.71;0.40;0.03;0.73;0.54;0.28;0.80;0.54;0.11;0.41;0.73;0.67;0.03;0.06;0.87;0.76;0.66;0.43;0.75;0.53;0.66;0.50;0.07;0.36;0.46;0.51;0.51;0.03;0.79; +SHAPE_GDP_POP;all_SHAPE_SDPs;RUS;Population;million;0.43;0.49;0.97;0.02;0.12;0.10;0.15;0.06;0.55;0.32;0.81;0.99;0.52;0.85;0.72;0.75;0.87;0.36;0.13;0.80;0.97;0.78;0.71;0.16;0.64;0.09;0.93;0.99;0.18;0.54;0.34;0.27; +SHAPE_GDP_POP;all_SHAPE_SDPs;NCL;Population;million;0.13;0.27;0.16;0.82;0.20;0.84;0.25;0.98;0.66;0.04;0.63;0.31;0.38;0.92;0.98;0.62;0.94;0.58;0.69;0.94;0.40;0.18;0.07;0.28;0.02;0.14;0.18;0.59;0.10;0.66;0.93;0.21; +SHAPE_GDP_POP;all_SHAPE_SDPs;VAT;Population;million;0.35;0.17;0.28;0.82;0.02;0.35;0.13;0.65;0.19;0.78;0.99;0.05;0.70;0.66;1.00;0.53;0.74;0.58;0.72;0.30;0.01;0.03;0.57;0.09;0.24;0.25;0.89;0.06;0.50;0.89;0.98;0.56; +SHAPE_GDP_POP;all_SHAPE_SDPs;TUV;Population;million;0.72;0.60;0.64;0.62;0.37;0.10;0.08;0.11;0.16;0.45;0.59;0.85;0.56;0.17;0.39;0.26;0.68;0.28;0.87;0.02;0.14;0.79;0.57;0.18;0.90;0.85;0.45;0.42;0.96;0.61;0.46;0.15; +SHAPE_GDP_POP;all_SHAPE_SDPs;MNP;Population;million;0.47;0.46;0.68;0.59;0.69;0.10;0.45;0.38;0.69;0.69;0.03;0.89;0.96;0.73;0.77;0.21;0.90;0.78;0.14;0.82;0.88;0.06;0.36;0.99;0.50;0.12;0.46;0.66;0.98;0.57;0.50;0.14; +SHAPE_GDP_POP;all_SHAPE_SDPs;PRI;Population;million;0.31;0.93;0.81;0.14;0.03;0.85;0.93;0.99;0.09;0.53;0.03;0.68;0.19;0.67;0.62;0.14;0.19;0.08;0.45;0.47;0.86;0.65;0.68;0.38;0.15;0.83;0.82;0.18;0.83;0.70;0.66;0.85; +SHAPE_GDP_POP;all_SHAPE_SDPs;CMR;Population;million;0.37;0.33;0.69;0.75;1.00;0.90;0.29;0.32;0.30;0.71;0.36;0.57;0.90;0.51;0.80;0.68;0.32;0.80;0.03;0.19;0.71;0.47;0.34;1.00;0.41;0.15;0.28;0.79;0.04;0.21;0.21;0.56; +SHAPE_GDP_POP;all_SHAPE_SDPs;NFK;Population;million;0.15;0.69;0.68;0.52;0.06;0.41;0.15;0.96;0.13;0.75;0.23;0.19;0.02;0.65;0.98;0.63;0.92;0.27;0.87;0.06;0.81;0.85;0.60;0.70;0.87;0.88;0.28;0.49;0.46;0.44;0.03;0.85; +SHAPE_GDP_POP;all_SHAPE_SDPs;FSM;Population;million;0.03;0.15;0.38;0.31;0.05;0.17;0.09;0.63;0.26;0.67;0.63;0.28;0.66;0.49;0.17;0.21;0.20;0.92;0.10;0.82;0.30;0.50;0.16;0.20;0.25;0.78;0.26;0.14;0.70;0.98;0.05;0.27; +SHAPE_GDP_POP;all_SHAPE_SDPs;HUN;Population;million;0.90;0.13;0.38;0.10;0.18;0.68;0.17;0.72;0.89;0.55;0.95;0.95;0.72;0.60;0.01;0.41;0.57;0.94;0.50;0.62;0.46;0.16;0.14;0.27;0.53;0.11;0.03;0.92;0.75;0.11;0.89;0.60; +SHAPE_GDP_POP;all_SHAPE_SDPs;AFG;Population;million;0.17;0.78;0.99;0.33;0.65;0.03;0.20;0.17;0.97;0.14;0.95;0.30;0.57;0.70;0.99;0.38;0.52;0.29;0.59;0.68;0.99;0.09;0.88;0.04;0.16;0.63;0.89;0.01;0.80;0.19;0.73;0.03; +SHAPE_GDP_POP;all_SHAPE_SDPs;MEX;Population;million;0.59;0.70;0.91;0.00;0.91;0.38;0.80;0.38;0.37;0.38;0.95;0.65;0.36;0.33;0.46;0.14;0.78;0.14;0.84;0.53;0.69;0.13;0.45;0.95;0.74;0.63;0.41;0.73;0.89;0.02;0.76;0.62; 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+SHAPE_GDP_POP;all_SHAPE_SDPs;ASM;Population;million;0.15;0.10;0.80;0.29;0.36;0.93;0.55;0.55;0.21;0.63;0.99;0.36;0.80;0.48;0.01;0.05;0.60;0.70;0.64;0.31;0.13;0.46;0.06;0.10;0.44;0.28;0.96;0.74;0.19;0.93;0.22;0.48; +SHAPE_GDP_POP;all_SHAPE_SDPs;BEL;Population;million;0.58;0.03;0.95;0.92;0.89;0.49;0.07;0.80;0.66;0.57;0.68;0.36;0.21;0.58;0.92;0.53;0.48;0.68;0.34;0.28;0.87;0.24;0.37;0.49;0.09;0.32;0.54;0.26;0.70;0.46;0.21;0.17; +SHAPE_GDP_POP;all_SHAPE_SDPs;KAZ;Population;million;0.73;0.89;0.14;0.15;0.98;0.27;0.34;0.73;0.33;0.84;0.65;0.03;0.87;0.82;0.02;0.14;0.13;0.13;0.02;0.07;0.72;0.92;1.00;0.39;0.53;0.87;0.26;0.61;0.46;0.57;0.78;0.47; +SHAPE_GDP_POP;all_SHAPE_SDPs;GTM;Population;million;0.48;0.65;0.57;0.44;0.05;0.97;0.12;0.13;0.32;0.86;0.61;0.44;0.38;0.26;0.04;0.42;0.54;0.07;0.68;0.64;0.41;0.45;0.27;0.02;0.25;0.69;0.74;0.84;0.67;0.97;0.05;0.76; +SHAPE_GDP_POP;all_SHAPE_SDPs;KIR;Population;million;0.25;0.14;0.82;0.00;0.24;0.52;0.95;0.82;0.53;0.91;0.04;0.10;0.02;0.73;0.87;0.98;0.23;0.09;0.37;0.56;0.01;0.67;0.02;0.82;0.36;0.18;0.85;0.58;0.64;0.94;0.80;0.46; +SHAPE_GDP_POP;all_SHAPE_SDPs;HMD;Population;million;0.47;0.10;0.73;0.03;0.93;0.81;0.02;0.79;0.38;0.24;0.84;0.57;0.51;0.45;0.22;0.54;0.67;0.02;0.10;0.44;0.46;0.80;0.43;0.23;0.89;0.33;0.04;0.75;0.37;0.10;0.37;0.45; +SHAPE_GDP_POP;all_SHAPE_SDPs;CYM;Population;million;0.10;0.84;0.57;0.13;0.92;0.65;0.70;0.94;0.22;0.46;0.34;0.20;0.82;0.91;0.74;0.55;0.36;0.64;0.07;0.20;0.04;0.02;0.98;0.90;0.11;0.10;0.91;0.62;0.14;0.16;0.84;0.38; +SHAPE_GDP_POP;all_SHAPE_SDPs;PRK;Population;million;0.98;0.47;0.27;0.48;0.55;0.42;0.13;0.19;0.96;0.54;0.16;0.33;0.36;0.88;0.10;0.56;0.29;0.89;0.06;0.11;0.02;0.24;0.97;0.02;0.84;0.96;0.92;0.93;0.71;0.28;0.97;0.10; +SHAPE_GDP_POP;all_SHAPE_SDPs;MYS;Population;million;0.79;0.66;0.09;0.55;0.37;0.98;0.99;0.94;0.47;0.77;0.75;0.34;0.03;0.61;0.40;0.31;0.75;0.61;0.18;0.09;0.91;0.98;0.42;0.57;0.89;0.82;0.10;0.31;0.09;0.93;0.68;0.72; +SHAPE_GDP_POP;all_SHAPE_SDPs;ITA;Population;million;0.97;0.78;0.32;0.41;0.58;0.60;0.77;0.21;0.91;0.39;0.26;0.76;0.50;0.34;0.02;0.11;0.52;0.78;0.34;0.13;0.60;0.39;0.25;0.06;0.98;0.30;0.98;0.03;0.39;0.49;0.70;0.97; +SHAPE_GDP_POP;all_SHAPE_SDPs;RWA;Population;million;0.28;0.61;0.81;0.28;0.20;0.13;0.48;0.12;0.43;0.30;0.16;0.85;0.55;0.20;0.71;0.86;0.11;0.78;0.43;0.12;0.96;0.82;0.43;0.33;0.34;0.04;0.77;0.06;0.92;0.27;0.88;0.80; +SHAPE_GDP_POP;all_SHAPE_SDPs;NZL;Population;million;0.54;0.82;0.77;0.24;0.69;0.31;0.20;0.84;0.27;0.71;0.15;0.56;0.61;0.35;0.07;0.03;0.92;0.85;0.78;0.65;0.43;0.44;0.14;0.76;0.73;0.30;0.46;0.24;0.45;0.12;0.03;0.15; +SHAPE_GDP_POP;all_SHAPE_SDPs;CIV;Population;million;0.53;0.45;0.71;0.74;0.28;0.36;0.68;0.35;0.78;0.42;0.47;0.97;0.97;0.83;0.43;0.65;0.63;0.98;0.32;0.54;0.98;0.31;0.26;0.62;0.20;0.16;0.58;0.27;0.13;0.86;0.44;0.10; +SHAPE_GDP_POP;all_SHAPE_SDPs;NIC;Population;million;0.32;0.47;0.71;0.52;0.35;0.33;0.37;0.98;0.50;0.16;0.62;0.92;0.61;0.97;0.14;0.22;0.88;0.96;0.11;0.41;0.47;0.10;0.58;0.91;0.16;0.97;0.50;0.70;0.59;0.75;0.20;0.58; +SHAPE_GDP_POP;all_SHAPE_SDPs;SDN;Population;million;0.11;0.88;0.51;0.31;0.69;0.46;0.62;0.69;0.68;0.76;0.39;0.12;0.90;0.91;0.51;0.19;0.13;0.77;0.23;0.34;0.22;0.81;0.24;0.30;0.70;0.21;0.24;0.44;0.88;0.74;0.41;0.53; +SHAPE_GDP_POP;all_SHAPE_SDPs;NOR;Population;million;0.37;0.36;0.99;0.37;0.26;0.63;0.17;0.21;0.37;0.44;0.27;0.44;0.87;0.28;0.58;0.40;0.66;0.39;0.42;0.30;0.60;0.65;0.54;0.28;0.03;0.49;0.71;0.69;0.16;0.01;0.68;0.12; +SHAPE_GDP_POP;all_SHAPE_SDPs;NER;Population;million;0.80;0.86;0.97;0.34;0.34;0.85;0.60;0.12;0.78;0.93;0.81;0.46;0.47;0.28;0.24;0.84;0.69;0.13;0.41;0.25;0.85;0.93;0.47;0.66;0.72;0.24;0.11;0.02;0.67;0.69;0.82;0.61; +SHAPE_GDP_POP;all_SHAPE_SDPs;USA;Population;million;0.71;0.23;0.50;0.10;0.64;0.67;0.69;0.73;0.70;0.47;0.24;0.02;0.28;0.65;0.77;0.98;0.77;0.54;0.73;0.15;0.12;0.52;0.76;0.52;0.68;0.75;0.81;0.21;0.78;0.60;0.87;0.29; +SHAPE_GDP_POP;all_SHAPE_SDPs;JOR;Population;million;0.50;0.71;0.50;0.70;0.84;0.48;0.60;0.42;0.18;0.81;0.08;0.80;0.67;0.92;0.06;0.54;0.59;0.19;0.93;0.54;0.47;0.26;0.47;0.11;0.41;0.61;0.68;0.96;0.18;0.35;0.73;0.57; +SHAPE_GDP_POP;all_SHAPE_SDPs;MLI;Population;million;0.55;0.54;0.40;0.84;0.29;0.76;0.13;0.42;0.08;0.98;0.24;0.03;0.43;0.57;0.59;0.60;0.45;0.46;0.21;0.75;0.14;0.14;0.50;0.90;0.87;0.71;0.14;0.77;0.99;0.81;0.00;0.37; +SHAPE_GDP_POP;all_SHAPE_SDPs;CAF;Population;million;0.04;0.07;0.18;0.92;0.80;0.83;0.13;0.78;0.74;0.57;0.52;0.79;0.76;0.64;0.28;0.67;0.83;0.77;0.48;0.04;0.45;0.05;0.83;0.96;0.46;0.69;0.67;0.47;0.52;0.46;0.60;0.54; +SHAPE_GDP_POP;all_SHAPE_SDPs;UMI;Population;million;0.02;0.87;0.84;0.01;0.83;0.04;0.45;0.38;0.20;0.84;0.86;0.29;0.16;0.12;0.42;0.35;0.16;0.77;0.66;0.72;0.12;0.47;0.74;0.35;0.90;0.72;0.59;0.65;0.33;0.85;0.21;0.19; +SHAPE_GDP_POP;all_SHAPE_SDPs;BFA;Population;million;0.50;0.97;0.55;0.46;0.91;0.86;0.80;0.15;0.35;0.33;0.87;0.77;0.14;0.16;0.99;0.22;0.10;0.79;0.90;0.67;0.43;0.76;0.74;0.91;0.78;0.98;0.78;0.76;0.75;0.35;0.54;0.80; +SHAPE_GDP_POP;all_SHAPE_SDPs;HTI;Population;million;0.06;0.94;0.36;0.79;0.36;0.87;0.92;0.42;0.70;0.82;0.12;0.62;0.59;0.79;0.99;0.57;0.25;0.93;0.93;0.93;0.46;0.60;0.41;0.62;0.17;0.51;0.37;0.79;0.41;0.42;0.05;0.99; +SHAPE_GDP_POP;all_SHAPE_SDPs;URY;Population;million;0.17;0.64;1.00;0.92;0.46;0.67;0.53;0.68;0.54;0.71;0.32;0.88;0.88;0.19;0.62;0.64;0.14;0.30;0.18;0.07;0.83;0.90;0.54;0.65;0.40;0.90;0.41;0.96;0.36;0.30;0.22;0.81; +SHAPE_GDP_POP;all_SHAPE_SDPs;BDI;Population;million;0.60;0.22;0.28;0.69;0.02;0.90;0.36;0.96;0.22;0.39;0.48;0.54;0.58;0.46;0.77;0.16;0.31;0.63;0.38;0.73;0.46;0.11;0.62;0.26;0.49;0.71;0.87;0.19;0.34;0.46;0.48;0.95; +SHAPE_GDP_POP;all_SHAPE_SDPs;TUN;Population;million;0.43;0.41;0.09;0.46;0.48;0.88;0.04;0.78;0.05;0.01;0.76;0.79;0.41;0.93;0.39;0.00;0.16;0.12;0.43;0.85;0.63;0.22;0.41;0.35;0.73;0.24;0.91;0.10;0.04;0.45;0.25;0.21; +SHAPE_GDP_POP;all_SHAPE_SDPs;IRL;Population;million;0.66;0.62;0.57;0.63;0.62;0.63;0.86;0.90;0.23;0.24;0.16;0.35;0.79;0.84;0.65;0.90;0.06;0.07;0.04;0.72;0.46;0.14;0.67;0.53;0.21;0.15;0.03;0.95;0.13;0.61;0.47;0.33; +SHAPE_GDP_POP;all_SHAPE_SDPs;COL;Population;million;0.09;0.04;0.86;0.42;0.54;0.30;0.43;0.46;0.50;0.59;0.99;0.00;0.84;0.48;0.77;0.67;0.64;0.59;0.56;0.74;0.13;0.30;0.38;0.05;0.43;0.85;0.45;0.78;0.78;0.04;0.23;0.42; +SHAPE_GDP_POP;all_SHAPE_SDPs;SLE;Population;million;0.50;0.73;0.86;0.77;0.85;0.29;0.48;0.75;0.65;0.38;0.94;0.78;0.76;0.02;0.90;0.38;0.98;0.98;0.82;0.19;0.86;0.45;0.52;0.36;0.32;0.26;0.49;0.05;0.15;0.80;0.56;0.17; +SHAPE_GDP_POP;all_SHAPE_SDPs;AZE;Population;million;0.11;0.90;0.14;0.26;0.94;0.15;0.18;0.33;0.86;0.26;0.78;0.96;0.46;0.94;0.42;0.91;0.58;0.36;0.00;0.35;0.21;0.28;0.22;0.79;0.18;0.26;0.77;0.65;0.36;0.55;0.85;0.97; diff --git a/message_ix_models/data/test/shape/urbanisation_v1p0.csv b/message_ix_models/data/test/shape/urbanisation_v1p0.csv new file mode 100644 index 0000000000..43ecd9b6f9 --- /dev/null +++ b/message_ix_models/data/test/shape/urbanisation_v1p0.csv @@ -0,0 +1,580 @@ +Model,Scenario,Region,Variable,Unit,2010,2015,2020,2025,2030,2035,2040,2045,2050,2055,2060,2065,2070,2075,2080,2085,2090,2095,2100,Notes +SHAPE_urb,urb_tech,LVA,Population|Urban|Share,%,0.05,0.33,0.41,0.89,0.01,0.05,0.82,0.93,0.80,0.38,0.57,0.15,0.89,0.33,0.94,0.21,0.01,0.98,0.03,NA +SHAPE_urb,urb_tech,LSO,Population|Urban|Share,%,0.26,0.21,0.06,0.32,0.85,0.72,0.68,0.81,0.56,0.79,0.72,0.63,0.76,0.49,0.88,0.89,0.84,0.03,0.08,NA +SHAPE_urb,urb_green,AUS,Population|Urban|Share,%,0.82,0.28,0.51,0.01,0.20,0.69,0.65,0.84,0.48,0.46,0.03,0.44,0.74,0.15,0.32,0.20,0.86,0.87,0.01,NA +SHAPE_urb,urb_tech,SVN,Population|Urban|Share,%,0.45,0.54,0.95,0.39,0.52,0.54,0.94,0.45,0.44,0.54,0.98,0.20,0.11,0.62,0.67,0.43,0.13,0.81,0.03,NA +SHAPE_urb,urb_green,VIR,Population|Urban|Share,%,0.06,0.66,0.21,0.96,0.60,0.95,0.91,0.67,0.80,0.47,0.28,0.64,0.67,0.77,0.35,0.80,0.20,0.25,0.34,NA +SHAPE_urb,urb_tech,BHR,Population|Urban|Share,%,0.93,0.14,0.35,0.62,0.72,0.74,0.67,0.90,0.27,0.61,0.35,0.15,0.11,0.35,0.98,0.11,0.24,0.83,0.98,NA +SHAPE_urb,urb_green,SVN,Population|Urban|Share,%,0.56,0.46,0.67,0.72,0.31,0.33,0.11,0.63,0.87,0.90,0.19,0.54,0.71,0.43,0.04,0.41,0.03,0.73,0.84,NA +SHAPE_urb,urb_green,LBY,Population|Urban|Share,%,0.32,0.98,0.21,0.56,0.55,0.72,0.74,0.92,0.82,0.99,0.63,0.38,0.06,0.20,0.56,0.27,0.97,0.27,0.37,NA +SHAPE_urb,urb_green,GNB,Population|Urban|Share,%,0.09,0.10,0.31,0.55,0.28,0.71,0.75,0.77,0.07,0.87,0.24,0.61,0.58,0.40,0.44,0.46,0.37,0.80,0.82,NA +SHAPE_urb,urb_tech,POL,Population|Urban|Share,%,0.02,0.35,0.81,0.88,0.65,0.27,0.58,0.29,0.96,0.20,0.13,0.95,0.43,0.10,0.09,0.72,0.11,0.78,0.06,NA +SHAPE_urb,urb_tech,GLP,Population|Urban|Share,%,0.12,0.10,0.83,0.52,0.97,0.80,0.79,0.13,0.71,0.71,0.15,0.35,0.85,0.50,0.96,0.54,0.45,0.21,0.77,NA +SHAPE_urb,urb_distr,REU,Population|Urban|Share,%,0.33,0.56,0.60,0.47,0.64,0.25,0.96,0.74,0.40,0.51,0.63,0.35,0.42,0.79,0.07,0.64,0.42,0.83,0.29,NA +SHAPE_urb,urb_distr,VEN,Population|Urban|Share,%,0.07,0.99,0.24,0.31,0.42,0.46,0.89,0.88,0.98,0.98,0.72,0.91,0.91,0.82,0.96,0.79,0.45,0.16,0.37,NA +SHAPE_urb,urb_distr,HRV,Population|Urban|Share,%,0.40,0.16,0.22,0.10,0.00,0.93,0.03,0.41,0.90,0.33,0.24,0.03,0.33,0.12,0.89,0.80,0.37,0.88,0.17,NA +SHAPE_urb,urb_green,SUR,Population|Urban|Share,%,0.66,0.46,0.16,0.34,0.90,0.34,0.80,0.29,0.64,0.83,0.18,0.53,0.04,0.29,0.27,0.72,0.02,0.52,0.68,NA +SHAPE_urb,urb_green,BRN,Population|Urban|Share,%,0.52,0.97,0.11,0.09,0.42,0.15,0.47,0.86,0.77,0.26,0.86,0.38,0.11,0.90,0.99,0.41,0.93,0.36,0.81,NA +SHAPE_urb,urb_distr,BRA,Population|Urban|Share,%,0.68,0.57,0.60,0.83,0.06,0.96,0.85,0.38,0.20,0.40,0.39,0.14,0.24,0.50,0.91,0.68,0.69,0.59,0.16,NA +SHAPE_urb,urb_tech,SOM,Population|Urban|Share,%,0.96,0.93,0.08,0.02,0.44,0.51,0.79,0.05,0.50,0.49,0.53,0.72,0.38,0.84,0.46,0.79,0.19,0.03,0.55,NA +SHAPE_urb,urb_distr,AFG,Population|Urban|Share,%,0.49,0.80,0.91,0.58,0.79,0.57,0.49,0.10,0.38,0.76,0.01,0.15,0.34,0.79,0.69,0.67,0.01,0.42,0.95,NA +SHAPE_urb,urb_tech,TUN,Population|Urban|Share,%,0.24,0.92,0.43,0.82,0.42,0.32,0.91,0.55,0.28,0.06,0.87,0.09,0.16,0.30,0.18,0.18,0.91,0.09,0.31,NA +SHAPE_urb,urb_tech,NER,Population|Urban|Share,%,0.64,0.42,0.96,0.42,0.25,0.13,0.67,0.23,0.81,0.94,0.51,0.80,0.15,0.95,0.59,0.02,0.04,0.50,0.79,NA +SHAPE_urb,urb_distr,LTU,Population|Urban|Share,%,0.17,0.48,0.63,0.30,0.22,0.30,0.81,0.60,0.50,0.48,0.42,0.65,0.47,0.63,0.27,0.94,0.31,0.77,0.20,NA +SHAPE_urb,urb_distr,DJI,Population|Urban|Share,%,0.14,0.80,0.42,0.37,0.23,0.52,0.40,0.57,0.87,0.15,0.95,0.50,0.21,0.78,0.51,0.08,0.66,0.54,0.73,NA +SHAPE_urb,urb_green,NCL,Population|Urban|Share,%,0.62,0.73,0.01,0.58,0.57,0.46,0.85,0.05,0.45,0.27,0.68,0.95,0.14,0.75,0.56,0.72,0.61,0.31,0.58,NA +SHAPE_urb,urb_green,DOM,Population|Urban|Share,%,0.29,0.72,0.68,0.18,0.72,0.66,0.58,0.83,0.29,0.89,0.34,0.50,0.82,0.17,0.21,0.90,0.33,0.77,0.24,NA +SHAPE_urb,urb_green,MTQ,Population|Urban|Share,%,0.18,0.42,0.58,0.94,0.59,0.24,0.47,0.48,0.87,0.73,0.17,0.09,0.67,0.44,0.95,0.31,0.14,0.14,0.94,NA +SHAPE_urb,urb_green,MKD,Population|Urban|Share,%,0.68,0.47,0.16,0.18,0.24,0.15,0.32,0.37,0.36,0.20,0.93,0.38,0.09,0.78,0.63,0.64,0.62,0.79,0.06,NA +SHAPE_urb,urb_green,SWZ,Population|Urban|Share,%,0.36,0.98,0.35,0.12,0.15,0.54,0.79,0.41,0.33,0.06,0.72,0.89,0.30,0.73,0.61,0.20,0.05,0.90,0.63,NA +SHAPE_urb,urb_distr,SLB,Population|Urban|Share,%,0.28,0.51,0.83,1.00,0.22,0.20,0.31,0.23,0.26,0.09,0.50,0.81,0.96,0.95,0.48,0.12,0.09,0.09,0.08,NA +SHAPE_urb,urb_tech,QAT,Population|Urban|Share,%,0.00,0.54,0.72,0.82,0.91,0.19,0.16,0.09,0.66,0.16,0.62,0.86,0.45,0.01,0.94,0.06,0.10,0.53,0.22,NA +SHAPE_urb,urb_tech,STP,Population|Urban|Share,%,0.11,0.55,0.79,0.23,0.79,0.57,0.05,0.96,0.30,0.61,0.61,0.47,0.25,0.69,0.21,0.51,0.56,0.44,0.06,NA +SHAPE_urb,urb_green,CPV,Population|Urban|Share,%,0.24,0.39,0.51,0.86,0.78,0.74,0.85,0.48,0.49,0.27,0.82,0.76,0.08,0.58,0.52,0.50,0.49,0.42,0.17,NA +SHAPE_urb,urb_green,VUT,Population|Urban|Share,%,0.35,0.60,0.66,0.72,0.96,0.80,0.18,0.76,0.38,0.95,0.13,0.36,0.02,0.80,0.70,0.19,0.83,0.52,0.34,NA +SHAPE_urb,urb_distr,SUR,Population|Urban|Share,%,0.99,0.02,0.51,0.75,0.20,0.31,0.86,0.69,0.28,0.73,0.42,0.10,0.95,0.97,0.19,0.85,0.25,0.37,0.11,NA +SHAPE_urb,urb_distr,TZA,Population|Urban|Share,%,0.65,0.92,0.65,0.13,0.87,0.60,0.20,0.98,0.28,0.79,0.61,0.06,0.03,0.56,0.32,0.13,0.20,0.01,0.59,NA +SHAPE_urb,urb_green,SYR,Population|Urban|Share,%,0.19,0.37,0.27,0.43,0.69,0.27,0.64,0.88,0.96,0.04,0.42,0.01,0.64,0.91,0.99,0.89,0.53,0.46,0.92,NA +SHAPE_urb,urb_tech,MUS,Population|Urban|Share,%,0.37,0.02,0.39,0.10,0.38,0.13,0.85,0.13,0.71,0.72,0.49,0.46,0.26,1.00,0.85,0.78,0.74,0.83,0.93,NA +SHAPE_urb,urb_green,LBN,Population|Urban|Share,%,0.13,0.96,0.58,0.66,0.84,0.87,0.56,0.02,0.37,0.52,0.48,0.85,0.73,0.18,0.58,0.95,0.83,0.36,0.87,NA +SHAPE_urb,urb_green,ARE,Population|Urban|Share,%,0.93,0.72,0.53,0.38,0.69,0.98,0.27,0.72,0.18,0.68,0.94,0.15,1.00,0.97,0.63,0.57,0.99,0.71,0.54,NA +SHAPE_urb,urb_green,PYF,Population|Urban|Share,%,0.34,0.13,0.07,0.07,0.31,0.84,0.51,0.83,0.98,0.97,0.21,0.85,0.44,0.63,0.51,0.50,0.86,0.27,0.28,NA +SHAPE_urb,urb_tech,TLS,Population|Urban|Share,%,0.10,0.78,0.75,0.83,0.62,0.08,0.91,0.22,0.91,0.28,0.64,0.65,0.74,0.72,0.94,0.88,0.08,0.59,0.12,NA +SHAPE_urb,urb_green,MMR,Population|Urban|Share,%,0.14,0.60,0.37,0.59,0.81,0.63,0.75,0.72,0.75,0.13,0.36,0.14,0.35,0.48,0.73,0.01,0.61,0.58,0.28,NA +SHAPE_urb,urb_tech,NPL,Population|Urban|Share,%,0.41,0.70,0.41,0.59,0.01,0.02,0.23,0.77,0.44,0.65,0.54,0.77,0.30,0.62,0.86,0.45,0.96,0.09,0.90,NA +SHAPE_urb,urb_distr,GAB,Population|Urban|Share,%,1.00,0.32,0.47,0.35,0.40,0.71,0.86,0.82,0.07,0.39,0.61,0.18,0.15,0.24,0.11,0.83,0.01,0.22,0.30,NA +SHAPE_urb,urb_green,GUF,Population|Urban|Share,%,0.97,0.20,0.89,0.32,0.66,0.84,0.13,0.02,0.93,0.06,0.81,0.03,0.82,0.18,0.63,0.32,0.38,0.89,0.56,NA +SHAPE_urb,urb_green,RWA,Population|Urban|Share,%,0.90,0.55,0.95,0.46,0.63,0.10,0.69,0.75,0.53,0.88,0.49,0.30,0.59,0.84,0.28,0.03,0.69,0.88,0.16,NA +SHAPE_urb,urb_distr,DZA,Population|Urban|Share,%,0.75,0.53,0.66,0.27,0.33,0.46,0.33,0.82,0.42,0.16,0.24,0.69,0.10,0.60,0.75,0.33,0.86,0.58,0.87,NA +SHAPE_urb,urb_green,NOR,Population|Urban|Share,%,0.76,0.24,0.95,0.54,0.39,0.40,0.36,0.67,0.50,0.27,0.37,0.47,0.02,0.05,0.89,0.78,0.65,0.97,0.15,NA +SHAPE_urb,urb_distr,JAM,Population|Urban|Share,%,0.06,0.60,0.64,0.51,0.25,0.31,0.40,0.57,0.94,0.94,0.80,0.58,0.38,0.49,0.17,0.97,0.22,0.21,0.82,NA +SHAPE_urb,urb_green,ETH,Population|Urban|Share,%,0.22,0.33,0.17,0.17,0.51,0.54,0.92,0.71,0.54,0.75,0.13,0.12,0.84,0.96,0.61,0.51,0.31,0.36,0.92,NA +SHAPE_urb,urb_distr,NLD,Population|Urban|Share,%,0.26,0.20,0.62,0.89,0.16,0.26,0.65,0.09,0.68,0.35,0.53,0.37,0.50,0.38,0.57,0.36,0.68,0.87,0.92,NA +SHAPE_urb,urb_green,MNE,Population|Urban|Share,%,0.06,0.36,0.94,0.94,0.12,0.25,0.58,0.84,0.79,0.28,0.46,0.43,0.83,0.33,0.42,0.88,0.35,0.10,0.48,NA +SHAPE_urb,urb_tech,VEN,Population|Urban|Share,%,0.28,0.32,0.28,0.29,0.43,0.34,0.24,0.30,0.22,0.06,0.33,0.20,0.89,0.74,0.48,0.17,0.63,0.21,0.67,NA +SHAPE_urb,urb_tech,URY,Population|Urban|Share,%,0.33,0.12,0.10,0.57,0.19,0.54,0.54,0.90,0.60,0.35,0.81,0.19,0.34,0.24,0.65,0.92,0.66,0.77,0.77,NA +SHAPE_urb,urb_tech,HKG,Population|Urban|Share,%,0.86,0.22,0.05,0.31,0.72,0.34,0.72,0.85,0.65,0.51,0.18,0.18,0.72,0.84,0.97,0.94,0.70,0.75,0.74,NA +SHAPE_urb,urb_tech,PRT,Population|Urban|Share,%,0.72,0.07,0.83,0.01,0.81,0.57,0.44,0.79,0.05,0.69,0.96,0.73,0.63,0.17,0.65,0.65,0.04,0.92,1.00,NA +SHAPE_urb,urb_green,ISL,Population|Urban|Share,%,0.51,0.33,0.26,0.37,0.08,0.39,0.33,0.96,0.36,0.14,0.89,0.88,0.07,0.61,0.50,0.61,0.76,0.44,0.92,NA +SHAPE_urb,urb_tech,IRL,Population|Urban|Share,%,0.69,0.16,0.88,0.68,0.82,0.48,0.70,0.33,0.38,0.69,0.20,0.42,0.90,0.60,0.97,0.30,0.13,0.41,0.85,NA +SHAPE_urb,urb_green,FSM,Population|Urban|Share,%,0.01,0.50,0.49,0.44,0.49,0.84,0.07,0.80,0.41,0.39,0.58,0.02,0.78,0.42,0.00,0.84,0.26,0.60,0.37,NA +SHAPE_urb,urb_green,LBR,Population|Urban|Share,%,0.30,0.29,0.56,0.51,0.27,0.40,0.01,0.37,0.39,0.37,0.38,0.56,0.10,0.64,0.37,0.32,0.83,0.55,0.24,NA +SHAPE_urb,urb_green,GIN,Population|Urban|Share,%,0.27,0.90,0.37,0.97,0.01,0.92,0.51,0.35,0.70,0.77,0.89,0.21,0.38,0.96,0.92,0.77,0.60,0.62,0.62,NA +SHAPE_urb,urb_tech,MEX,Population|Urban|Share,%,0.02,0.48,0.93,0.88,0.89,0.86,0.74,0.37,0.39,0.32,0.26,0.44,0.51,0.85,0.06,0.85,0.40,0.02,0.62,NA +SHAPE_urb,urb_tech,ITA,Population|Urban|Share,%,0.18,0.76,0.91,1.00,0.62,0.09,0.39,0.22,0.31,0.43,0.45,0.54,0.62,0.42,0.55,0.82,0.82,0.99,0.46,NA +SHAPE_urb,urb_green,SLV,Population|Urban|Share,%,0.07,0.50,0.41,0.65,0.76,0.77,0.74,0.19,0.80,0.31,0.63,0.39,0.00,0.19,0.40,0.24,0.11,0.84,0.62,NA +SHAPE_urb,urb_distr,BEL,Population|Urban|Share,%,0.26,0.09,0.27,0.06,0.39,0.20,0.48,0.10,0.73,0.82,0.52,0.98,0.50,0.67,0.25,0.88,0.20,0.49,0.93,NA +SHAPE_urb,urb_green,LTU,Population|Urban|Share,%,0.92,0.25,0.92,0.22,0.11,0.37,0.21,0.22,0.31,0.39,0.25,0.45,0.42,0.77,0.18,0.17,0.17,0.41,0.09,NA +SHAPE_urb,urb_tech,JOR,Population|Urban|Share,%,0.65,0.26,0.34,0.60,0.92,0.78,0.17,0.04,0.21,0.62,0.35,0.40,0.42,0.37,0.12,0.09,0.88,0.56,0.46,NA +SHAPE_urb,urb_green,CYP,Population|Urban|Share,%,0.15,0.97,0.72,0.62,0.38,1.00,0.34,0.69,0.72,0.67,0.75,0.33,0.61,0.50,0.81,0.27,0.26,0.69,0.37,NA +SHAPE_urb,urb_distr,LKA,Population|Urban|Share,%,0.68,0.13,0.41,0.22,0.10,0.10,0.22,0.11,0.41,0.81,0.93,0.94,0.36,0.01,0.45,0.40,0.31,0.41,0.36,NA +SHAPE_urb,urb_green,TCD,Population|Urban|Share,%,0.53,0.44,0.11,0.16,0.29,0.36,0.33,0.89,0.70,0.22,0.18,0.60,0.07,0.26,0.33,0.31,0.03,0.73,0.17,NA +SHAPE_urb,urb_green,OMN,Population|Urban|Share,%,0.67,0.30,0.61,0.80,0.78,0.69,0.53,0.09,0.62,0.49,0.80,0.71,0.95,0.27,0.63,0.26,0.47,0.64,0.08,NA +SHAPE_urb,urb_distr,CPV,Population|Urban|Share,%,0.45,0.68,0.71,0.14,0.96,0.56,0.41,0.41,0.58,0.79,0.91,0.31,0.89,0.58,0.78,0.71,0.14,0.25,0.20,NA +SHAPE_urb,urb_tech,ERI,Population|Urban|Share,%,0.02,0.02,0.58,0.91,0.87,0.24,0.16,0.07,0.01,0.48,0.07,0.92,0.55,0.58,0.59,0.49,0.62,0.56,0.25,NA +SHAPE_urb,urb_distr,SVK,Population|Urban|Share,%,0.62,0.03,0.51,0.23,0.47,0.20,0.65,0.15,0.68,0.55,0.70,0.20,0.67,0.39,0.70,0.81,0.53,0.03,0.27,NA +SHAPE_urb,urb_green,FIN,Population|Urban|Share,%,0.49,0.83,0.36,0.18,0.17,0.62,0.38,0.51,0.81,0.86,0.61,0.46,0.18,0.85,0.22,0.29,0.96,0.86,0.68,NA +SHAPE_urb,urb_distr,BLZ,Population|Urban|Share,%,0.63,0.45,0.50,0.56,0.86,1.00,0.69,0.03,0.50,0.83,0.35,0.09,0.46,0.36,0.32,0.24,0.21,0.74,0.83,NA +SHAPE_urb,urb_green,AFG,Population|Urban|Share,%,0.25,0.69,0.15,0.19,0.66,0.84,0.96,0.40,0.88,0.30,0.91,0.58,0.21,0.41,0.99,0.04,0.66,0.52,0.41,NA +SHAPE_urb,urb_green,BLR,Population|Urban|Share,%,0.31,0.71,0.83,0.27,0.41,0.48,0.22,0.39,0.58,0.45,0.03,0.69,0.89,0.05,0.44,0.06,0.60,0.18,0.13,NA +SHAPE_urb,urb_green,SLB,Population|Urban|Share,%,0.98,0.30,0.15,0.84,0.76,0.54,0.04,0.05,0.29,0.58,0.44,0.33,0.65,0.98,0.33,0.06,0.53,0.39,0.59,NA +SHAPE_urb,urb_green,MDV,Population|Urban|Share,%,0.15,0.47,0.36,0.38,0.93,0.53,0.77,0.02,0.95,0.92,0.59,0.82,0.83,0.54,0.36,0.46,0.16,0.24,0.03,NA +SHAPE_urb,urb_green,ARM,Population|Urban|Share,%,0.85,0.45,0.80,0.87,0.91,0.62,0.88,0.25,0.29,0.42,0.42,0.67,0.20,0.89,0.36,0.15,0.02,0.93,0.57,NA +SHAPE_urb,urb_green,MYS,Population|Urban|Share,%,0.99,0.28,0.52,0.71,0.18,0.82,0.86,0.31,0.96,0.92,0.97,0.88,0.85,0.37,0.74,0.41,0.49,0.82,0.01,NA +SHAPE_urb,urb_tech,VNM,Population|Urban|Share,%,0.35,0.96,0.51,0.61,0.49,0.70,0.12,0.84,0.10,0.72,0.18,0.26,0.94,0.52,0.29,0.98,0.87,0.07,0.16,NA +SHAPE_urb,urb_green,ARG,Population|Urban|Share,%,0.05,0.26,0.52,0.21,0.03,0.76,0.38,0.12,0.88,1.00,0.09,0.26,0.56,0.68,0.31,0.73,0.15,0.71,0.25,NA +SHAPE_urb,urb_tech,MNE,Population|Urban|Share,%,0.14,0.38,0.21,0.54,0.06,0.88,0.11,0.01,0.25,0.73,0.75,0.62,0.87,0.70,0.10,0.06,0.69,0.27,0.83,NA +SHAPE_urb,urb_distr,NCL,Population|Urban|Share,%,0.17,0.67,0.18,0.37,0.01,0.32,0.11,0.27,0.71,0.98,0.19,0.84,0.01,0.47,0.45,0.28,0.68,0.97,0.36,NA +SHAPE_urb,urb_tech,TON,Population|Urban|Share,%,0.27,0.32,0.16,0.49,0.75,0.06,0.85,0.55,0.56,0.87,0.52,0.49,0.81,0.85,0.20,0.97,0.76,0.32,0.61,NA +SHAPE_urb,urb_tech,KAZ,Population|Urban|Share,%,0.88,0.74,0.59,0.88,0.51,0.11,0.32,0.10,0.83,0.01,0.24,0.17,0.88,0.10,0.02,0.42,0.67,0.39,0.44,NA +SHAPE_urb,urb_tech,AUS,Population|Urban|Share,%,0.65,0.98,0.44,0.89,0.28,0.62,0.83,0.67,0.23,0.29,0.81,0.80,0.51,0.56,0.23,0.91,0.34,0.74,0.07,NA +SHAPE_urb,urb_distr,DEU,Population|Urban|Share,%,0.35,0.79,0.56,0.88,0.35,0.07,0.45,0.95,0.70,1.00,0.34,0.84,0.51,0.01,0.16,0.05,0.37,0.15,0.32,NA +SHAPE_urb,urb_distr,SEN,Population|Urban|Share,%,0.53,0.54,0.46,0.91,0.50,0.42,0.79,0.49,0.14,0.07,0.30,0.93,0.24,0.73,0.22,0.18,0.71,0.43,0.75,NA +SHAPE_urb,urb_distr,MTQ,Population|Urban|Share,%,0.69,0.89,0.50,0.02,0.17,0.09,0.62,0.31,0.65,0.11,0.48,0.08,0.37,0.97,0.05,0.37,0.36,0.19,0.77,NA +SHAPE_urb,urb_distr,SOM,Population|Urban|Share,%,0.59,0.12,0.89,0.40,0.99,0.68,0.12,0.34,0.42,0.93,0.56,0.03,0.59,0.75,0.59,0.44,0.48,0.34,0.36,NA +SHAPE_urb,urb_distr,PRI,Population|Urban|Share,%,0.22,0.06,0.27,0.22,0.18,0.51,0.47,0.12,0.69,0.21,0.87,0.44,0.11,0.88,0.66,0.35,0.69,0.22,0.82,NA +SHAPE_urb,urb_distr,SLE,Population|Urban|Share,%,0.10,0.74,0.27,0.33,0.84,0.42,0.47,0.24,0.36,0.67,0.48,0.58,0.44,0.70,0.79,0.10,0.23,0.67,0.99,NA +SHAPE_urb,urb_tech,CHN,Population|Urban|Share,%,0.75,0.66,0.09,0.01,0.52,0.13,0.82,0.19,0.52,0.67,0.29,0.41,0.49,0.94,0.03,0.23,0.54,0.02,0.60,NA +SHAPE_urb,urb_tech,ALB,Population|Urban|Share,%,0.62,0.78,0.36,0.43,0.77,0.08,0.29,0.13,0.71,0.51,0.51,0.89,0.78,0.60,0.82,0.69,0.32,0.10,0.83,NA +SHAPE_urb,urb_distr,HTI,Population|Urban|Share,%,0.49,0.92,0.94,0.20,0.11,0.25,0.47,0.33,0.31,0.81,0.49,0.29,0.68,0.25,0.97,0.53,0.49,0.85,0.46,NA +SHAPE_urb,urb_green,BEL,Population|Urban|Share,%,0.91,0.90,0.24,0.84,0.63,0.11,0.17,0.99,0.76,0.37,0.76,0.56,0.29,0.94,0.04,0.33,0.16,0.80,0.64,NA +SHAPE_urb,urb_tech,GUM,Population|Urban|Share,%,0.90,0.55,0.76,0.11,0.91,0.01,0.62,0.66,0.04,0.86,0.11,0.60,0.48,0.46,0.07,0.02,0.19,0.73,0.64,NA +SHAPE_urb,urb_green,COG,Population|Urban|Share,%,0.78,0.49,0.37,0.11,0.49,1.00,0.71,0.71,0.70,0.85,0.38,0.55,0.83,0.58,0.76,0.04,0.74,0.74,0.95,NA +SHAPE_urb,urb_distr,SWE,Population|Urban|Share,%,0.86,0.51,0.23,0.19,0.38,0.16,0.75,0.09,0.57,0.31,0.14,0.14,0.16,0.26,0.68,0.98,0.91,0.37,0.82,NA +SHAPE_urb,urb_green,VNM,Population|Urban|Share,%,0.38,0.24,0.10,0.25,0.92,0.61,0.93,0.68,0.05,0.73,0.90,0.90,0.40,0.66,0.41,0.09,0.89,0.03,0.80,NA +SHAPE_urb,urb_tech,IDN,Population|Urban|Share,%,0.67,0.33,0.75,0.68,0.96,0.20,0.43,0.50,0.88,0.62,0.23,0.91,0.48,0.21,0.49,0.71,0.03,0.95,0.73,NA +SHAPE_urb,urb_distr,RWA,Population|Urban|Share,%,0.34,0.86,0.98,0.81,0.30,0.40,0.58,0.27,0.60,0.38,0.63,0.37,0.83,0.79,0.53,0.77,0.81,0.78,0.72,NA +SHAPE_urb,urb_distr,MNE,Population|Urban|Share,%,0.05,0.44,0.63,0.17,0.10,0.32,0.19,0.51,0.85,0.09,0.27,0.13,0.23,0.35,0.75,0.70,0.49,0.23,0.16,NA +SHAPE_urb,urb_distr,ABW,Population|Urban|Share,%,0.30,0.32,0.30,0.64,0.22,0.77,0.78,0.66,0.33,0.98,0.24,0.37,0.08,0.44,0.47,0.15,0.98,0.16,0.59,NA +SHAPE_urb,urb_tech,ARM,Population|Urban|Share,%,0.11,0.73,0.56,0.03,0.24,0.91,0.02,0.93,0.18,0.09,0.15,0.14,0.15,0.88,0.24,0.01,0.04,0.79,0.40,NA +SHAPE_urb,urb_green,KHM,Population|Urban|Share,%,0.78,0.37,0.08,0.33,0.11,0.66,0.90,0.84,0.47,0.42,0.60,0.26,0.38,0.35,0.14,0.10,0.92,0.37,0.37,NA +SHAPE_urb,urb_tech,ARG,Population|Urban|Share,%,0.44,0.86,0.28,0.30,0.55,0.71,0.03,0.43,0.02,0.88,0.86,0.67,0.57,0.07,0.72,0.77,0.09,0.31,0.63,NA +SHAPE_urb,urb_distr,CUB,Population|Urban|Share,%,0.66,0.29,0.30,0.26,0.14,0.08,0.13,0.15,0.99,0.37,0.80,0.55,0.22,0.72,0.33,0.27,0.26,0.98,0.95,NA +SHAPE_urb,urb_distr,MDA,Population|Urban|Share,%,0.66,0.07,0.36,0.17,0.24,0.46,0.04,0.85,0.12,0.59,0.14,0.79,0.55,0.29,0.97,0.69,0.73,0.51,0.30,NA +SHAPE_urb,urb_green,KWT,Population|Urban|Share,%,0.23,0.03,0.95,0.09,0.19,0.51,1.00,0.79,0.81,0.70,0.57,0.88,0.88,0.61,0.42,0.67,0.91,0.48,0.99,NA +SHAPE_urb,urb_green,BDI,Population|Urban|Share,%,0.27,0.66,0.49,0.02,0.22,0.62,0.07,0.74,0.30,0.34,0.48,0.72,0.27,0.48,0.50,0.76,0.53,0.80,0.29,NA +SHAPE_urb,urb_distr,HND,Population|Urban|Share,%,0.17,0.22,0.85,0.35,0.55,0.51,0.24,0.42,0.33,0.05,0.39,0.98,0.54,1.00,0.89,0.93,0.06,0.89,0.23,NA +SHAPE_urb,urb_distr,POL,Population|Urban|Share,%,0.61,0.58,0.07,0.42,0.54,0.94,0.11,0.39,0.28,0.95,0.86,0.91,0.76,0.81,0.34,0.72,0.75,0.03,0.93,NA +SHAPE_urb,urb_tech,PNG,Population|Urban|Share,%,0.65,0.86,0.29,0.16,0.94,0.67,0.13,0.30,0.58,0.09,0.91,0.29,0.97,0.64,0.35,0.44,0.19,0.92,0.06,NA +SHAPE_urb,urb_tech,AGO,Population|Urban|Share,%,0.77,0.21,0.61,0.60,0.19,0.46,0.14,0.89,0.35,0.90,0.30,0.48,0.88,0.70,0.89,0.15,0.62,0.72,0.73,NA +SHAPE_urb,urb_distr,MYT,Population|Urban|Share,%,0.31,0.73,0.99,0.06,0.11,0.12,0.18,0.93,0.42,0.40,0.36,0.42,0.50,0.22,0.42,0.41,0.92,0.63,0.61,NA +SHAPE_urb,urb_distr,MNG,Population|Urban|Share,%,0.31,0.23,0.25,0.40,0.74,0.25,0.61,0.58,0.49,0.35,0.55,0.31,0.17,0.43,0.14,0.08,0.63,0.17,0.32,NA +SHAPE_urb,urb_tech,KEN,Population|Urban|Share,%,0.93,0.64,0.04,0.37,0.48,0.43,0.69,0.59,0.56,0.86,0.23,0.16,0.60,0.26,0.48,0.48,0.86,0.91,0.93,NA +SHAPE_urb,urb_tech,BFA,Population|Urban|Share,%,0.91,0.20,0.57,0.32,0.28,0.26,0.03,0.01,0.20,0.18,0.06,0.80,0.72,0.84,0.34,0.52,0.30,0.59,0.06,NA +SHAPE_urb,urb_distr,IRL,Population|Urban|Share,%,0.34,0.20,0.24,0.84,0.05,0.21,0.45,0.45,0.70,0.38,0.76,0.53,0.00,0.30,0.03,0.16,0.65,0.79,0.36,NA +SHAPE_urb,urb_distr,ITA,Population|Urban|Share,%,0.18,0.64,0.15,0.33,0.00,0.61,0.07,0.65,0.88,0.63,0.41,0.34,0.84,0.71,0.37,0.26,0.34,0.20,0.14,NA +SHAPE_urb,urb_green,DJI,Population|Urban|Share,%,0.03,0.29,0.18,0.71,0.73,0.15,0.84,0.78,0.44,0.32,0.21,0.67,0.75,0.99,0.30,0.87,0.32,0.43,0.36,NA +SHAPE_urb,urb_distr,CHL,Population|Urban|Share,%,0.08,0.18,0.26,0.25,0.08,0.06,0.89,0.15,0.49,0.33,0.42,0.01,0.57,0.67,0.93,0.02,0.97,0.77,0.44,NA +SHAPE_urb,urb_distr,AUT,Population|Urban|Share,%,0.06,0.67,0.93,0.30,0.02,0.90,0.25,0.93,0.52,0.56,0.73,0.55,0.66,1.00,0.47,0.29,0.38,0.25,0.15,NA +SHAPE_urb,urb_tech,SLB,Population|Urban|Share,%,0.75,0.72,0.17,0.18,0.48,0.61,0.58,0.04,0.35,1.00,0.87,0.24,0.06,0.45,0.80,0.39,0.44,0.73,0.46,NA +SHAPE_urb,urb_green,ALB,Population|Urban|Share,%,0.97,0.17,0.74,0.60,0.32,0.32,0.85,0.89,0.65,0.16,0.36,0.11,0.14,0.84,0.51,0.76,0.24,0.30,0.67,NA +SHAPE_urb,urb_tech,SWE,Population|Urban|Share,%,0.22,0.97,0.59,0.53,0.94,0.49,0.55,0.78,0.57,0.69,0.70,0.59,0.48,0.94,0.32,0.66,0.57,0.41,0.47,NA +SHAPE_urb,urb_distr,KGZ,Population|Urban|Share,%,0.34,0.42,0.86,0.39,0.44,0.61,0.43,0.00,0.94,0.01,0.35,0.26,0.56,0.65,0.92,0.60,0.83,0.95,0.15,NA +SHAPE_urb,urb_green,AZE,Population|Urban|Share,%,0.48,0.73,0.01,0.15,0.73,0.90,0.39,0.81,0.34,0.67,0.64,0.85,0.65,0.76,0.01,0.77,0.11,0.14,0.45,NA +SHAPE_urb,urb_distr,VUT,Population|Urban|Share,%,0.28,0.47,0.94,0.04,0.07,0.73,0.00,0.88,0.25,0.03,0.60,0.14,0.09,0.19,0.35,0.20,0.21,0.50,0.98,NA +SHAPE_urb,urb_distr,FIN,Population|Urban|Share,%,0.55,0.08,0.25,0.05,0.64,0.01,0.98,0.09,0.12,0.33,0.10,0.58,0.26,0.06,0.76,0.29,0.76,0.39,0.45,NA +SHAPE_urb,urb_green,BGR,Population|Urban|Share,%,0.52,0.65,0.86,0.87,0.06,0.86,0.21,0.77,0.00,0.15,0.08,0.72,0.82,0.44,0.80,0.60,0.66,0.76,0.18,NA +SHAPE_urb,urb_tech,PER,Population|Urban|Share,%,0.23,0.86,0.13,0.61,0.54,0.64,0.02,0.20,0.65,0.00,0.92,0.63,0.43,0.81,0.21,0.62,0.36,0.85,0.20,NA +SHAPE_urb,urb_tech,BWA,Population|Urban|Share,%,0.86,0.01,0.94,0.79,0.26,0.35,0.55,0.84,0.09,1.00,0.44,0.38,0.83,0.98,0.83,0.77,0.09,0.96,0.08,NA +SHAPE_urb,urb_distr,NOR,Population|Urban|Share,%,0.25,0.42,0.31,0.93,0.95,0.74,0.68,0.87,0.04,0.15,0.91,0.12,0.75,0.65,0.62,0.44,0.70,0.27,0.28,NA +SHAPE_urb,urb_tech,MYS,Population|Urban|Share,%,0.00,0.48,0.28,0.26,0.16,0.56,0.74,0.77,0.60,0.57,0.40,0.65,0.25,0.43,0.54,0.08,0.51,0.19,0.51,NA +SHAPE_urb,urb_distr,TTO,Population|Urban|Share,%,0.64,0.43,0.87,0.28,0.76,0.81,0.07,0.86,0.53,0.06,0.53,0.91,0.63,0.65,0.15,0.59,0.23,0.96,0.03,NA +SHAPE_urb,urb_distr,ZWE,Population|Urban|Share,%,0.23,0.36,0.49,0.36,0.14,0.25,0.32,0.09,0.12,0.90,0.31,0.90,0.13,0.75,1.00,0.47,0.46,0.63,0.59,NA +SHAPE_urb,urb_distr,BHS,Population|Urban|Share,%,0.12,0.80,0.13,0.04,0.88,0.14,0.04,0.63,0.80,0.05,0.44,0.08,0.68,0.68,0.26,0.36,0.99,0.45,0.96,NA +SHAPE_urb,urb_green,MLI,Population|Urban|Share,%,0.67,0.64,0.96,0.55,0.57,0.54,0.40,0.47,0.56,0.26,0.52,0.06,0.57,0.99,0.10,0.68,0.78,0.44,0.16,NA +SHAPE_urb,urb_tech,BRB,Population|Urban|Share,%,0.32,0.59,0.03,0.21,0.06,0.15,0.56,0.25,0.69,0.21,0.41,0.90,0.16,0.97,0.88,0.75,0.37,0.80,0.09,NA +SHAPE_urb,urb_distr,SLV,Population|Urban|Share,%,0.26,0.03,0.06,0.75,0.88,0.69,0.82,0.47,0.53,0.72,0.57,0.32,0.29,0.75,0.59,0.50,0.50,0.55,0.31,NA +SHAPE_urb,urb_green,AUT,Population|Urban|Share,%,0.50,0.91,0.85,0.54,0.96,0.07,0.11,0.78,0.41,0.45,0.36,0.19,0.10,0.41,0.10,0.60,0.05,0.60,0.33,NA +SHAPE_urb,urb_tech,MAC,Population|Urban|Share,%,0.71,0.02,0.90,0.22,0.21,0.46,0.98,0.90,0.31,0.40,0.55,0.47,0.85,0.45,0.07,0.30,0.67,0.95,0.24,NA +SHAPE_urb,urb_tech,BHS,Population|Urban|Share,%,0.29,0.53,0.21,0.51,0.97,0.24,0.96,0.05,0.68,0.83,0.58,0.90,0.74,0.80,0.59,0.30,0.53,0.30,0.33,NA +SHAPE_urb,urb_green,VCT,Population|Urban|Share,%,0.98,0.71,0.14,0.38,0.09,0.40,0.59,0.55,0.41,0.03,0.58,0.90,0.70,0.66,0.19,0.90,0.40,0.95,0.10,NA +SHAPE_urb,urb_tech,TUR,Population|Urban|Share,%,0.46,0.43,0.80,0.77,0.75,0.84,0.11,0.78,0.42,0.63,0.62,0.86,0.33,0.69,0.75,0.70,0.29,0.57,0.33,NA +SHAPE_urb,urb_green,DZA,Population|Urban|Share,%,0.79,0.15,0.90,0.47,0.96,0.10,0.08,0.67,0.90,0.38,0.49,0.55,0.67,0.49,0.35,0.30,0.34,0.96,0.71,NA +SHAPE_urb,urb_green,URY,Population|Urban|Share,%,0.98,0.29,0.22,0.15,0.89,0.77,0.95,0.70,0.02,0.60,0.73,0.05,0.61,0.56,0.86,0.83,0.11,0.79,0.19,NA +SHAPE_urb,urb_tech,CRI,Population|Urban|Share,%,0.90,0.90,0.82,0.50,0.10,0.08,0.81,0.70,0.55,0.35,0.87,0.75,0.39,0.59,0.06,0.44,0.21,0.82,0.35,NA +SHAPE_urb,urb_tech,FRA,Population|Urban|Share,%,0.77,0.57,0.23,0.27,0.71,0.03,0.55,0.84,0.01,0.48,0.18,0.54,0.68,0.20,0.51,0.61,0.50,0.96,0.35,NA +SHAPE_urb,urb_distr,BFA,Population|Urban|Share,%,0.27,0.55,0.03,0.04,0.03,0.65,0.71,0.11,0.91,0.98,0.81,0.78,0.78,0.17,0.43,0.25,0.09,0.22,0.38,NA +SHAPE_urb,urb_tech,CHE,Population|Urban|Share,%,0.30,0.55,0.83,0.92,0.45,0.82,0.39,0.36,0.01,0.12,0.41,0.82,0.18,0.39,0.68,0.61,0.86,0.72,0.13,NA +SHAPE_urb,urb_green,HUN,Population|Urban|Share,%,0.77,0.62,0.25,0.71,0.57,0.64,0.72,0.19,0.19,0.56,0.35,0.49,0.85,0.93,0.75,0.56,0.40,0.50,0.50,NA +SHAPE_urb,urb_distr,JOR,Population|Urban|Share,%,0.31,0.29,0.84,0.59,0.59,0.58,0.02,0.62,0.95,1.00,0.48,0.06,0.95,0.50,0.67,0.26,0.21,0.74,0.58,NA +SHAPE_urb,urb_tech,MNG,Population|Urban|Share,%,0.02,0.39,0.36,0.07,0.35,0.51,0.55,0.74,0.59,0.93,0.59,0.89,0.21,0.23,0.20,0.75,0.27,0.35,0.50,NA +SHAPE_urb,urb_green,GUY,Population|Urban|Share,%,0.58,0.90,0.77,0.88,0.33,0.77,0.68,0.54,0.81,0.06,0.27,0.68,0.67,0.88,0.03,0.08,0.58,0.93,0.60,NA +SHAPE_urb,urb_green,LCA,Population|Urban|Share,%,0.72,0.27,0.92,0.16,0.24,0.26,0.42,0.46,0.60,0.09,0.16,0.11,0.55,0.87,0.23,0.96,0.87,0.94,0.37,NA 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+SHAPE_urb,urb_green,MYT,Population|Urban|Share,%,0.35,0.89,0.21,0.73,0.43,0.68,0.25,0.30,0.06,0.64,1.00,0.85,0.35,0.67,0.13,0.90,0.13,0.32,0.01,NA +SHAPE_urb,urb_green,PAN,Population|Urban|Share,%,0.34,0.33,0.83,0.77,0.93,0.37,0.89,0.63,0.91,0.95,0.61,0.70,0.30,0.76,0.27,0.67,0.78,0.49,0.42,NA +SHAPE_urb,urb_green,TUN,Population|Urban|Share,%,0.65,0.10,0.50,0.58,0.98,0.89,0.78,0.43,0.82,0.35,0.88,0.48,0.28,0.44,0.54,0.21,0.74,0.49,0.16,NA +SHAPE_urb,urb_green,HKG,Population|Urban|Share,%,0.95,0.51,0.58,0.80,0.13,0.45,0.35,0.14,0.96,0.53,0.76,0.88,0.05,0.72,0.60,0.64,0.45,0.95,0.59,NA +SHAPE_urb,urb_distr,GUY,Population|Urban|Share,%,0.17,0.15,0.16,0.15,0.68,0.62,0.35,0.76,0.45,0.33,0.24,0.88,0.48,0.23,0.04,0.73,0.41,0.32,1.00,NA +SHAPE_urb,urb_tech,AFG,Population|Urban|Share,%,0.48,0.20,0.54,0.53,0.67,0.41,0.68,0.98,0.18,0.88,0.32,0.91,0.53,0.53,0.99,0.51,0.67,0.16,0.02,NA 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+SHAPE_urb,urb_tech,KHM,Population|Urban|Share,%,0.78,0.78,0.97,0.37,0.92,0.72,0.36,0.13,0.56,0.30,0.86,0.68,0.78,0.16,0.40,0.29,0.61,0.63,0.57,NA +SHAPE_urb,urb_tech,CHL,Population|Urban|Share,%,0.26,0.60,0.52,0.28,0.50,0.58,0.91,0.93,0.91,0.25,0.25,0.59,0.85,0.44,0.55,0.82,0.82,0.17,0.22,NA +SHAPE_urb,urb_green,IDN,Population|Urban|Share,%,0.10,0.58,0.49,0.96,0.02,0.76,0.80,0.43,0.63,0.11,0.49,0.18,0.95,0.96,0.06,0.96,0.50,0.81,0.39,NA +SHAPE_urb,urb_tech,PAK,Population|Urban|Share,%,0.29,0.61,0.31,1.00,0.98,0.41,0.28,0.06,0.64,0.55,0.17,0.62,0.19,0.10,0.09,0.91,0.77,0.41,0.51,NA +SHAPE_urb,urb_tech,ROU,Population|Urban|Share,%,0.41,0.48,0.99,0.81,0.30,0.45,0.72,0.20,0.86,0.32,0.90,0.69,0.21,0.93,0.38,0.50,0.49,0.38,0.28,NA +SHAPE_urb,urb_distr,FRA,Population|Urban|Share,%,0.73,0.80,0.48,0.28,0.07,0.62,0.02,0.42,0.68,0.95,0.63,0.21,0.97,0.44,0.47,0.04,0.01,0.44,0.59,NA 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+SHAPE_urb,urb_tech,USA,Population|Urban|Share,%,0.84,0.45,0.24,0.20,0.50,0.32,0.77,0.36,0.08,0.06,0.20,0.32,0.91,0.34,1.00,0.02,0.46,0.21,0.01,NA +SHAPE_urb,urb_green,JAM,Population|Urban|Share,%,0.37,0.14,0.44,0.16,0.10,0.39,0.26,0.66,0.80,0.87,0.40,0.58,0.77,0.27,0.88,0.37,0.80,0.37,0.36,NA +SHAPE_urb,urb_distr,ISL,Population|Urban|Share,%,0.92,0.63,0.07,0.84,0.85,0.35,0.66,0.75,0.45,0.20,0.50,0.18,0.79,0.78,0.89,0.01,0.28,0.25,0.02,NA +SHAPE_urb,urb_green,CIV,Population|Urban|Share,%,0.68,0.67,0.41,0.07,0.14,0.81,0.44,0.78,0.53,0.05,0.98,0.87,0.95,0.53,0.11,0.08,0.44,0.08,0.28,NA +SHAPE_urb,urb_green,ERI,Population|Urban|Share,%,0.81,0.06,0.70,0.24,0.88,0.07,0.32,0.52,0.54,0.64,0.02,0.22,0.51,0.53,0.71,0.92,0.21,0.48,0.38,NA +SHAPE_urb,urb_distr,UGA,Population|Urban|Share,%,0.37,0.75,0.74,0.17,0.76,0.45,0.51,0.61,0.03,0.31,0.04,0.40,0.53,0.88,0.58,0.67,0.25,0.66,0.78,NA +SHAPE_urb,urb_distr,TUN,Population|Urban|Share,%,0.01,0.16,0.29,0.33,0.74,0.18,0.64,0.07,0.37,0.44,0.44,0.55,0.38,0.93,0.12,0.74,0.10,0.69,0.74,NA +SHAPE_urb,urb_green,EST,Population|Urban|Share,%,0.48,0.97,0.52,0.33,0.62,0.95,0.75,0.89,0.80,0.42,0.31,0.88,0.27,0.31,0.74,0.24,0.18,0.07,0.36,NA +SHAPE_urb,urb_distr,WSM,Population|Urban|Share,%,0.39,0.51,0.76,0.73,0.37,0.21,0.96,0.65,0.93,0.48,0.45,0.86,0.43,0.23,0.74,0.59,0.87,0.81,0.19,NA +SHAPE_urb,urb_tech,PHL,Population|Urban|Share,%,0.53,0.79,0.63,0.98,0.62,0.49,0.91,0.33,0.37,0.54,0.15,0.79,0.95,0.48,0.18,0.14,0.52,0.29,0.15,NA +SHAPE_urb,urb_green,SWE,Population|Urban|Share,%,0.58,0.07,0.19,0.95,0.30,0.18,0.78,0.22,0.89,0.57,0.19,0.41,0.07,0.72,0.56,0.36,0.50,0.18,0.83,NA +SHAPE_urb,urb_distr,SDN,Population|Urban|Share,%,0.06,0.87,0.12,0.32,0.49,0.27,0.08,0.29,0.37,0.51,0.34,0.07,0.81,0.07,0.14,0.64,0.19,0.58,0.90,NA +SHAPE_urb,urb_green,CHN,Population|Urban|Share,%,0.85,0.56,0.30,0.24,0.12,0.74,0.78,0.40,0.24,0.05,0.56,0.97,0.96,0.15,0.28,0.10,0.87,0.56,0.68,NA +SHAPE_urb,urb_green,BFA,Population|Urban|Share,%,0.85,0.07,0.60,0.40,0.99,0.18,0.93,0.47,0.94,0.90,0.65,0.40,0.02,0.39,0.21,0.07,0.97,0.72,0.86,NA +SHAPE_urb,urb_distr,MDG,Population|Urban|Share,%,0.38,0.20,0.66,0.67,0.52,0.25,0.68,0.79,0.70,0.16,0.86,0.91,0.21,0.78,0.28,0.25,0.55,0.56,0.77,NA +SHAPE_urb,urb_distr,BIH,Population|Urban|Share,%,0.77,0.95,0.06,0.76,0.18,0.22,0.06,0.06,0.16,0.77,0.37,0.47,0.44,0.37,0.73,0.57,0.99,0.12,0.92,NA +SHAPE_urb,urb_green,GLP,Population|Urban|Share,%,0.88,0.60,0.64,0.05,0.95,0.70,0.08,0.34,0.13,0.28,0.69,0.74,0.35,0.63,0.40,0.42,0.28,0.28,0.56,NA +SHAPE_urb,urb_tech,RWA,Population|Urban|Share,%,0.47,0.90,0.90,0.28,0.47,0.26,0.67,0.71,0.80,0.32,0.35,0.26,0.90,0.07,0.98,0.17,0.69,0.88,0.80,NA +SHAPE_urb,urb_green,TGO,Population|Urban|Share,%,0.80,0.97,0.06,0.96,0.71,0.52,0.30,0.28,0.67,0.12,0.62,0.34,0.51,0.41,0.20,0.15,0.72,0.45,0.76,NA +SHAPE_urb,urb_distr,CHN,Population|Urban|Share,%,0.34,0.94,0.21,0.96,0.68,0.56,0.99,0.60,0.79,0.29,0.65,0.27,0.61,0.22,0.59,0.34,0.11,0.64,0.89,NA +SHAPE_urb,urb_tech,BGD,Population|Urban|Share,%,0.38,0.59,0.48,0.69,0.66,0.88,0.47,0.90,0.07,0.19,0.16,0.45,0.54,0.80,0.21,0.64,0.22,0.27,0.99,NA +SHAPE_urb,urb_distr,MOZ,Population|Urban|Share,%,0.03,0.67,0.65,0.10,0.90,0.19,0.76,0.91,0.30,0.08,0.82,0.16,0.11,0.92,0.68,0.49,0.91,0.20,0.24,NA +SHAPE_urb,urb_green,SGP,Population|Urban|Share,%,0.28,0.34,0.62,0.14,0.57,0.87,0.66,0.77,0.99,0.67,0.78,0.60,0.25,0.85,0.35,0.10,0.63,0.80,0.38,NA +SHAPE_urb,urb_distr,GHA,Population|Urban|Share,%,0.56,0.16,0.44,0.92,0.16,0.73,0.37,0.42,0.06,0.96,0.43,0.07,0.43,0.11,0.77,0.89,0.92,0.32,0.03,NA +SHAPE_urb,urb_distr,URY,Population|Urban|Share,%,0.62,0.49,0.91,0.33,0.69,0.30,0.73,0.34,0.31,0.16,0.79,0.41,0.05,0.48,0.23,0.33,0.25,0.72,0.92,NA +SHAPE_urb,urb_tech,ESP,Population|Urban|Share,%,0.40,0.78,0.39,0.69,0.97,0.98,0.19,0.78,0.06,0.11,0.42,0.51,0.13,0.02,0.79,0.76,0.84,0.72,0.04,NA +SHAPE_urb,urb_green,AGO,Population|Urban|Share,%,0.74,0.32,0.48,0.54,0.17,0.13,0.73,0.06,0.96,0.31,0.43,0.48,0.14,0.39,0.05,0.19,0.24,0.62,0.54,NA +SHAPE_urb,urb_tech,DEU,Population|Urban|Share,%,0.44,0.63,0.56,0.20,0.59,0.39,0.87,0.68,0.10,0.19,0.19,0.50,0.58,0.38,0.78,0.89,0.33,0.74,0.82,NA +SHAPE_urb,urb_tech,GEO,Population|Urban|Share,%,0.69,0.97,0.67,0.84,0.73,0.06,0.40,0.07,0.91,0.39,0.14,0.78,0.75,0.95,0.04,0.38,0.38,0.97,0.85,NA +SHAPE_urb,urb_distr,GRD,Population|Urban|Share,%,0.90,0.07,0.50,0.34,0.65,0.05,0.36,0.13,0.09,0.31,0.49,0.28,0.79,0.52,0.91,0.82,0.07,0.34,0.43,NA +SHAPE_urb,urb_distr,EST,Population|Urban|Share,%,0.89,0.24,0.74,0.53,0.78,0.32,0.03,0.66,0.14,0.87,0.32,0.87,0.31,0.17,0.25,0.73,0.58,0.96,0.88,NA +SHAPE_urb,urb_distr,GBR,Population|Urban|Share,%,0.22,0.71,0.36,0.81,0.06,0.56,0.88,0.81,0.17,0.22,0.92,0.68,0.06,0.58,0.75,0.71,0.96,0.84,0.81,NA +SHAPE_urb,urb_green,GTM,Population|Urban|Share,%,0.89,0.89,0.27,0.13,0.76,0.80,0.29,0.42,0.24,0.39,0.94,0.88,0.48,0.79,0.70,0.60,0.25,0.62,0.16,NA +SHAPE_urb,urb_distr,BLR,Population|Urban|Share,%,0.73,0.40,0.48,0.61,0.37,0.24,0.19,0.99,0.14,0.87,0.44,0.66,0.18,0.86,0.49,0.94,0.31,0.39,0.14,NA +SHAPE_urb,urb_green,UKR,Population|Urban|Share,%,0.89,0.97,0.32,0.39,0.21,0.93,0.10,0.87,0.12,0.40,0.69,0.06,0.07,0.75,0.55,0.86,0.94,0.79,0.33,NA +SHAPE_urb,urb_tech,FIN,Population|Urban|Share,%,0.62,0.88,0.89,0.88,0.90,0.36,0.28,0.01,0.89,0.33,0.15,0.68,0.84,0.59,0.58,0.02,0.52,0.43,0.46,NA +SHAPE_urb,urb_tech,BTN,Population|Urban|Share,%,0.64,0.64,0.51,0.35,0.21,0.04,0.59,0.10,0.73,0.44,0.97,0.96,0.80,0.17,0.59,0.10,0.60,0.15,0.08,NA +SHAPE_urb,urb_green,SOM,Population|Urban|Share,%,0.24,0.53,0.96,0.67,0.35,0.47,0.56,0.75,0.81,0.10,0.37,0.99,0.65,0.61,0.27,0.06,0.00,0.13,0.91,NA +SHAPE_urb,urb_green,LSO,Population|Urban|Share,%,0.92,0.34,0.30,0.24,0.48,0.59,0.10,0.25,0.77,0.75,0.94,0.14,0.24,0.00,0.22,0.88,0.12,0.62,0.26,NA +SHAPE_urb,urb_distr,CAN,Population|Urban|Share,%,0.12,0.67,0.62,0.38,0.91,0.75,0.03,0.55,0.07,0.29,0.73,0.53,0.56,0.31,0.94,0.30,0.25,0.49,0.87,NA +SHAPE_urb,urb_tech,TZA,Population|Urban|Share,%,0.17,0.99,0.87,0.54,0.02,0.75,0.06,0.00,0.44,0.63,0.17,0.57,0.46,0.91,0.66,0.25,0.01,0.72,0.45,NA +SHAPE_urb,urb_tech,CYP,Population|Urban|Share,%,0.49,0.47,0.97,0.17,0.30,0.77,0.53,0.91,0.90,0.95,0.78,0.71,0.72,0.04,0.58,0.26,0.39,0.79,0.92,NA +SHAPE_urb,urb_distr,VNM,Population|Urban|Share,%,0.14,0.83,0.99,0.46,0.58,0.37,0.62,0.31,0.56,0.77,0.34,0.11,0.28,0.01,0.64,0.24,0.64,0.71,0.06,NA +SHAPE_urb,urb_tech,GNQ,Population|Urban|Share,%,0.07,0.48,0.06,0.20,0.06,0.55,0.03,0.67,0.33,0.89,0.57,0.61,0.26,0.85,0.29,0.45,0.25,0.84,0.08,NA +SHAPE_urb,urb_tech,KGZ,Population|Urban|Share,%,0.34,0.30,0.14,0.05,0.75,0.95,0.65,0.86,0.52,0.41,0.80,0.72,0.88,0.60,0.20,0.46,0.64,0.58,0.18,NA +SHAPE_urb,urb_green,CZE,Population|Urban|Share,%,0.47,0.97,0.35,0.10,0.65,0.15,0.61,0.13,0.95,0.06,0.99,0.13,0.75,0.56,0.58,0.80,0.79,0.62,0.55,NA +SHAPE_urb,urb_distr,BRN,Population|Urban|Share,%,0.30,0.51,0.93,0.37,0.35,0.81,0.79,0.27,0.39,0.40,0.80,0.05,0.01,0.19,0.78,0.63,0.23,0.47,0.66,NA +SHAPE_urb,urb_tech,BEN,Population|Urban|Share,%,0.07,0.94,0.27,0.12,0.69,0.62,0.15,0.23,0.61,0.44,0.71,0.41,0.46,0.20,0.95,0.51,0.30,0.49,0.68,NA +SHAPE_urb,urb_tech,YEM,Population|Urban|Share,%,0.18,0.78,0.29,0.97,0.40,0.20,0.64,0.10,0.81,0.92,0.64,0.98,0.43,0.48,0.21,0.74,0.92,0.12,0.75,NA +SHAPE_urb,urb_tech,CAF,Population|Urban|Share,%,0.71,0.59,0.03,0.17,0.96,0.28,0.92,0.77,0.26,0.22,0.19,0.46,0.24,0.39,0.04,0.47,0.44,0.80,0.00,NA +SHAPE_urb,urb_tech,DZA,Population|Urban|Share,%,0.70,0.73,0.78,0.93,0.27,0.59,0.45,0.73,0.87,0.49,0.16,0.34,0.27,0.73,0.79,0.11,0.76,0.41,0.47,NA +SHAPE_urb,urb_distr,CHE,Population|Urban|Share,%,0.69,0.61,0.14,0.49,0.94,0.71,0.16,0.10,0.20,0.41,0.73,0.34,0.60,0.21,0.33,0.42,0.35,0.86,0.07,NA +SHAPE_urb,urb_green,NAM,Population|Urban|Share,%,0.14,0.85,0.93,0.17,0.78,0.74,0.83,0.59,0.49,0.36,0.23,0.91,0.10,0.32,0.28,0.87,0.34,0.98,0.88,NA +SHAPE_urb,urb_distr,PRK,Population|Urban|Share,%,0.45,0.46,0.36,0.89,0.36,0.26,0.34,0.80,0.45,0.40,0.10,0.83,0.48,0.81,0.06,0.17,0.48,0.72,0.96,NA +SHAPE_urb,urb_distr,ARG,Population|Urban|Share,%,0.69,0.37,0.61,1.00,0.41,0.36,0.44,0.75,0.40,0.60,0.34,0.39,0.41,0.87,0.19,0.34,0.11,0.42,0.70,NA +SHAPE_urb,urb_tech,LCA,Population|Urban|Share,%,0.40,0.62,0.73,0.45,0.04,0.78,0.47,0.03,0.27,0.20,0.03,0.49,0.02,0.14,0.57,0.40,0.81,0.09,0.69,NA +SHAPE_urb,urb_tech,BEL,Population|Urban|Share,%,0.41,0.62,0.24,0.62,0.48,0.20,0.26,0.94,0.68,0.34,0.06,0.22,0.94,0.91,0.41,0.81,0.18,0.88,0.94,NA +SHAPE_urb,urb_tech,BGR,Population|Urban|Share,%,0.14,0.42,0.21,0.67,0.71,0.38,0.17,0.31,0.62,0.40,0.85,0.91,0.61,0.97,0.55,0.64,0.65,0.85,0.11,NA +SHAPE_urb,urb_tech,GNB,Population|Urban|Share,%,0.43,0.68,0.63,0.18,0.66,0.85,0.86,0.33,0.18,0.52,0.96,0.60,0.72,0.61,0.78,0.58,0.63,0.14,0.88,NA +SHAPE_urb,urb_green,WSM,Population|Urban|Share,%,0.01,0.56,0.88,0.83,0.76,0.25,0.33,0.67,0.52,0.57,0.83,0.71,0.96,0.33,0.11,0.93,0.80,0.25,0.67,NA +SHAPE_urb,urb_green,LVA,Population|Urban|Share,%,0.82,0.72,0.85,0.29,0.85,0.16,0.15,0.42,0.62,0.74,0.06,0.08,0.67,0.57,0.02,0.35,0.15,0.81,0.44,NA +SHAPE_urb,urb_tech,COG,Population|Urban|Share,%,0.88,0.88,0.42,0.20,0.14,0.74,0.97,0.34,0.86,0.97,0.71,0.63,0.81,0.48,0.34,0.30,0.95,0.16,0.89,NA +SHAPE_urb,urb_tech,ISL,Population|Urban|Share,%,0.08,0.22,0.06,0.92,0.28,0.88,0.88,0.08,0.01,0.28,0.49,0.14,0.86,0.04,0.31,0.71,0.35,0.39,0.34,NA +SHAPE_urb,urb_green,USA,Population|Urban|Share,%,0.73,1.00,0.30,0.70,0.16,0.84,0.63,0.63,0.86,0.93,0.03,0.64,0.60,0.58,0.07,0.11,0.70,0.63,0.48,NA +SHAPE_urb,urb_tech,HND,Population|Urban|Share,%,0.29,0.11,0.53,0.12,0.81,0.63,0.33,0.26,0.15,0.83,0.57,0.27,0.96,0.23,0.57,0.13,0.18,0.28,0.97,NA +SHAPE_urb,urb_green,VEN,Population|Urban|Share,%,0.13,0.57,0.11,0.88,0.49,0.45,0.47,0.12,0.72,0.92,0.56,0.68,0.18,0.17,0.28,0.75,0.31,0.64,0.78,NA +SHAPE_urb,urb_distr,KOR,Population|Urban|Share,%,0.19,0.64,0.74,0.96,0.83,0.31,0.62,0.08,0.65,0.99,0.13,0.84,0.04,0.57,0.33,0.28,0.52,0.63,0.57,NA +SHAPE_urb,urb_green,CAN,Population|Urban|Share,%,0.93,0.88,0.88,0.51,0.45,0.08,0.99,0.77,0.32,0.13,0.48,0.11,0.88,0.78,0.60,0.05,0.68,0.15,0.61,NA +SHAPE_urb,urb_tech,LUX,Population|Urban|Share,%,0.27,0.90,0.11,0.81,0.43,0.65,0.22,0.86,0.90,0.86,0.92,0.22,0.82,0.43,0.71,0.61,0.85,0.81,0.09,NA +SHAPE_urb,urb_distr,KHM,Population|Urban|Share,%,0.52,0.21,0.64,0.17,0.96,0.93,0.51,0.87,0.26,0.43,0.70,0.44,0.60,0.92,0.79,0.49,0.22,0.48,0.70,NA +SHAPE_urb,urb_green,KAZ,Population|Urban|Share,%,0.76,0.98,0.25,0.33,0.46,0.38,0.84,0.64,0.86,0.66,0.70,0.48,1.00,0.48,0.37,0.35,0.79,0.14,0.19,NA +SHAPE_urb,urb_tech,COM,Population|Urban|Share,%,0.91,0.12,0.30,0.91,0.53,0.10,0.98,0.10,0.93,0.32,0.32,0.08,0.29,0.67,0.66,0.09,0.43,0.92,0.35,NA +SHAPE_urb,urb_tech,CAN,Population|Urban|Share,%,0.51,0.88,0.03,0.09,0.55,0.01,0.12,0.79,0.87,0.43,0.12,0.18,0.28,0.77,0.48,0.81,0.57,0.70,0.04,NA +SHAPE_urb,urb_green,MWI,Population|Urban|Share,%,0.73,0.27,0.35,0.47,0.07,0.53,0.38,0.53,0.23,0.46,0.24,0.77,0.02,0.01,0.51,0.25,0.76,0.68,0.60,NA +SHAPE_urb,urb_distr,ECU,Population|Urban|Share,%,0.57,0.17,0.48,0.58,0.50,0.56,0.78,0.29,0.40,1.00,0.12,0.67,0.22,0.61,0.03,0.83,0.58,0.12,0.66,NA +SHAPE_urb,urb_tech,BLZ,Population|Urban|Share,%,0.41,0.90,0.91,0.11,0.73,0.78,0.61,0.85,0.44,0.40,0.28,0.65,0.54,0.65,0.82,0.53,0.58,0.76,0.08,NA +SHAPE_urb,urb_tech,BDI,Population|Urban|Share,%,0.70,0.20,0.22,0.78,0.96,0.40,0.38,0.33,0.74,0.58,0.46,0.38,0.55,0.54,0.65,0.30,0.95,0.80,0.09,NA +SHAPE_urb,urb_green,KGZ,Population|Urban|Share,%,0.63,0.34,0.33,0.01,0.43,0.84,0.08,0.67,0.02,0.95,0.95,0.30,0.47,0.80,0.75,0.68,0.71,0.27,0.93,NA +SHAPE_urb,urb_tech,GUY,Population|Urban|Share,%,0.68,0.35,0.21,0.83,0.75,0.26,0.47,0.01,0.55,0.70,0.92,0.16,0.83,0.66,0.85,1.00,0.24,0.92,0.22,NA +SHAPE_urb,urb_distr,BEN,Population|Urban|Share,%,0.42,0.80,0.76,0.28,0.17,0.62,0.26,0.43,0.40,0.03,0.86,0.29,0.12,0.20,0.55,0.91,0.46,0.33,0.16,NA +SHAPE_urb,urb_distr,MWI,Population|Urban|Share,%,0.41,0.43,0.56,0.71,0.71,0.62,0.09,0.48,0.19,0.38,0.49,0.85,0.77,0.79,0.66,0.15,0.65,0.81,0.01,NA +SHAPE_urb,urb_distr,NIC,Population|Urban|Share,%,0.03,0.14,0.72,0.56,0.79,0.69,0.72,0.05,0.61,0.65,0.18,0.82,0.22,0.86,0.11,0.66,0.24,0.81,0.88,NA +SHAPE_urb,urb_tech,IRN,Population|Urban|Share,%,0.74,0.67,0.35,0.35,0.53,0.76,0.97,0.65,0.72,0.74,0.76,0.88,0.51,0.78,0.86,0.69,0.89,0.18,0.78,NA +SHAPE_urb,urb_distr,BTN,Population|Urban|Share,%,0.23,0.95,0.58,0.00,0.28,0.96,0.02,0.39,0.13,0.83,0.90,0.23,0.75,0.17,0.65,0.63,0.68,0.36,0.55,NA +SHAPE_urb,urb_tech,CUB,Population|Urban|Share,%,0.42,0.91,0.96,0.98,0.57,0.98,0.82,0.31,0.63,0.15,0.13,0.57,0.91,0.25,0.24,0.09,0.50,0.47,0.55,NA +SHAPE_urb,urb_distr,MLI,Population|Urban|Share,%,0.52,0.12,0.55,0.21,0.53,0.33,0.26,0.80,0.85,0.17,0.93,0.25,0.90,0.34,0.31,0.51,1.00,0.12,0.93,NA +SHAPE_urb,urb_green,BLZ,Population|Urban|Share,%,0.12,0.94,0.41,0.29,0.28,0.31,0.98,0.81,0.81,0.04,0.82,0.58,0.69,0.44,0.32,0.52,0.87,0.62,0.48,NA +SHAPE_urb,urb_tech,KWT,Population|Urban|Share,%,0.51,0.03,0.04,0.82,0.11,0.55,0.25,0.55,0.69,0.06,0.83,0.14,0.30,0.12,0.21,0.82,0.90,0.40,0.43,NA +SHAPE_urb,urb_distr,FJI,Population|Urban|Share,%,0.36,0.99,0.16,0.48,0.10,0.18,0.22,0.14,0.34,0.42,0.05,0.90,0.33,0.78,0.82,0.57,0.89,0.98,0.68,NA +SHAPE_urb,urb_green,PHL,Population|Urban|Share,%,0.25,0.98,0.89,0.38,0.40,0.40,0.76,0.31,0.34,0.69,0.77,0.58,0.38,0.33,0.92,0.83,0.83,0.58,0.81,NA +SHAPE_urb,urb_green,ITA,Population|Urban|Share,%,0.93,0.76,0.73,0.22,0.22,0.46,0.03,0.40,0.66,0.81,0.29,0.59,0.93,0.44,0.31,0.34,0.06,0.92,0.05,NA +SHAPE_urb,urb_green,BIH,Population|Urban|Share,%,0.43,0.24,0.26,0.59,0.52,0.39,0.50,0.03,0.12,0.94,0.62,0.56,0.00,0.57,0.95,0.83,1.00,0.44,0.21,NA +SHAPE_urb,urb_green,NGA,Population|Urban|Share,%,0.07,0.22,0.67,0.18,0.81,0.73,0.96,0.71,0.41,0.74,0.98,0.28,0.57,0.36,0.24,0.05,0.30,0.31,0.26,NA +SHAPE_urb,urb_tech,CZE,Population|Urban|Share,%,0.96,0.49,0.39,0.31,0.02,0.77,0.81,0.82,0.23,0.01,0.81,0.37,0.78,0.27,0.63,0.36,0.38,0.97,0.44,NA +SHAPE_urb,urb_green,BRA,Population|Urban|Share,%,0.14,0.24,0.43,0.16,0.44,0.85,0.65,0.99,0.63,0.77,0.88,0.77,0.84,0.07,0.94,0.92,0.89,0.72,0.51,NA +SHAPE_urb,urb_distr,VCT,Population|Urban|Share,%,0.63,0.88,0.70,0.53,0.62,0.06,0.45,0.25,0.50,0.80,0.84,0.05,0.41,0.21,0.73,0.42,0.52,0.19,0.94,NA +SHAPE_urb,urb_distr,ESP,Population|Urban|Share,%,0.37,0.65,0.09,0.15,0.54,0.85,0.86,0.13,0.60,0.32,0.95,0.22,0.48,0.94,0.70,0.28,0.10,0.13,0.16,NA +SHAPE_urb,urb_distr,NAM,Population|Urban|Share,%,0.65,0.71,0.91,0.81,0.14,0.58,0.93,0.25,0.51,0.21,0.77,0.18,0.23,0.95,0.55,0.65,0.95,0.39,0.47,NA +SHAPE_urb,urb_green,BHS,Population|Urban|Share,%,0.00,0.14,0.59,0.82,0.69,0.58,0.60,0.47,0.56,0.43,0.08,0.29,0.62,0.68,0.91,0.72,0.67,0.91,0.04,NA +SHAPE_urb,urb_distr,NER,Population|Urban|Share,%,0.92,0.16,0.81,0.02,0.82,0.66,0.74,0.06,0.65,0.65,0.09,0.05,0.06,0.99,0.09,0.56,0.28,0.03,0.92,NA +SHAPE_urb,urb_tech,SLV,Population|Urban|Share,%,0.87,0.49,0.94,0.09,0.32,0.12,0.54,0.83,0.73,0.34,0.84,0.23,1.00,0.38,0.12,0.45,0.43,0.87,0.43,NA +SHAPE_urb,urb_tech,SVK,Population|Urban|Share,%,0.79,0.35,0.19,0.14,0.31,0.12,0.35,0.08,0.69,0.60,0.76,0.73,0.23,0.74,0.76,0.99,0.12,0.96,0.58,NA +SHAPE_urb,urb_green,QAT,Population|Urban|Share,%,0.87,0.84,0.70,0.93,0.02,0.27,0.95,0.40,0.69,0.82,0.10,0.60,0.47,0.15,0.76,0.90,0.54,0.31,0.85,NA +SHAPE_urb,urb_distr,UZB,Population|Urban|Share,%,0.74,0.21,0.03,0.61,0.80,0.09,0.66,0.66,0.14,0.78,0.45,0.83,0.66,0.11,0.91,0.20,0.93,0.34,0.89,NA +SHAPE_urb,urb_distr,CIV,Population|Urban|Share,%,0.10,0.73,0.47,0.45,0.02,0.80,0.28,0.38,0.02,0.29,0.23,0.66,0.07,0.68,0.44,0.99,0.94,0.96,0.07,NA +SHAPE_urb,urb_tech,OMN,Population|Urban|Share,%,0.46,0.00,0.82,0.62,0.20,0.41,0.42,0.73,0.00,0.01,0.73,0.72,0.50,0.21,0.98,0.22,0.14,0.17,0.71,NA +SHAPE_urb,urb_green,MNG,Population|Urban|Share,%,0.85,0.68,0.20,0.29,0.25,0.21,0.25,0.31,0.51,0.09,0.35,0.37,0.85,0.23,0.79,0.53,0.64,0.25,0.72,NA +SHAPE_urb,urb_green,GMB,Population|Urban|Share,%,0.18,0.95,0.92,0.59,0.79,0.33,0.87,0.11,0.18,0.73,0.58,0.00,0.25,0.51,0.91,0.64,0.21,0.57,0.45,NA +SHAPE_urb,urb_green,GNQ,Population|Urban|Share,%,0.77,0.27,0.90,0.09,0.01,0.73,0.30,0.02,0.50,0.65,0.15,0.50,0.24,0.06,0.74,0.68,0.37,0.02,0.51,NA +SHAPE_urb,urb_green,STP,Population|Urban|Share,%,0.73,0.96,0.76,0.53,0.71,0.97,0.19,0.72,0.73,0.43,0.45,0.33,0.02,0.67,0.38,0.17,0.98,0.85,0.67,NA +SHAPE_urb,urb_green,ISR,Population|Urban|Share,%,0.28,0.52,0.98,0.78,0.28,0.27,0.97,0.21,0.83,0.63,0.03,0.69,0.09,0.47,0.56,0.36,0.82,0.80,0.16,NA +SHAPE_urb,urb_distr,ZMB,Population|Urban|Share,%,0.50,0.86,0.01,0.80,0.85,0.94,0.61,0.60,0.99,0.05,0.04,0.84,0.92,0.31,0.55,0.95,0.22,0.82,0.57,NA +SHAPE_urb,urb_green,THA,Population|Urban|Share,%,0.36,0.64,0.83,0.11,0.04,0.61,0.05,0.85,0.84,0.38,0.84,0.14,0.76,0.61,0.07,0.49,0.83,0.08,0.77,NA +SHAPE_urb,urb_distr,CRI,Population|Urban|Share,%,0.07,0.19,0.03,0.38,0.80,0.41,0.91,0.31,0.00,0.05,0.54,0.09,0.26,0.90,0.45,0.45,0.04,0.66,0.09,NA +SHAPE_urb,urb_distr,KEN,Population|Urban|Share,%,0.85,0.19,0.48,0.96,0.70,0.34,0.86,0.72,0.88,0.42,0.60,0.81,0.56,0.76,0.49,0.27,0.26,0.96,0.53,NA +SHAPE_urb,urb_tech,TCD,Population|Urban|Share,%,0.79,0.72,0.81,0.63,0.77,0.02,0.28,0.11,0.21,1.00,0.97,0.06,0.79,0.02,0.70,0.10,0.22,0.04,0.26,NA +SHAPE_urb,urb_tech,BIH,Population|Urban|Share,%,0.01,0.90,0.04,0.89,0.96,0.43,0.14,0.71,0.05,0.76,0.51,0.72,0.11,0.75,0.16,0.29,0.71,0.86,0.00,NA +SHAPE_urb,urb_green,PNG,Population|Urban|Share,%,0.10,0.24,0.11,0.74,0.11,0.28,0.08,0.53,0.95,0.77,0.80,0.26,0.32,0.14,0.03,0.92,0.45,0.83,0.83,NA +SHAPE_urb,urb_distr,DNK,Population|Urban|Share,%,0.93,0.06,0.07,0.80,0.46,0.19,0.52,0.40,0.80,0.39,0.80,0.85,0.60,0.75,0.32,0.53,0.58,0.16,0.45,NA +SHAPE_urb,urb_tech,ARE,Population|Urban|Share,%,0.28,0.50,0.96,0.99,0.85,0.27,0.17,0.22,0.98,0.44,0.51,0.14,0.51,0.60,0.13,0.04,0.26,0.29,0.61,NA +SHAPE_urb,urb_distr,TON,Population|Urban|Share,%,0.33,0.32,0.96,0.51,0.60,0.11,0.52,0.53,0.75,0.71,0.76,0.70,0.58,0.38,0.52,0.57,0.34,0.02,0.89,NA +SHAPE_urb,urb_distr,BGD,Population|Urban|Share,%,0.16,0.77,0.05,0.95,0.53,0.74,0.96,0.79,0.35,0.21,0.79,0.39,0.96,0.39,0.15,0.23,0.93,0.28,0.24,NA +SHAPE_urb,urb_tech,UGA,Population|Urban|Share,%,0.66,0.13,0.24,0.19,0.10,0.79,0.31,0.51,0.16,0.33,0.44,0.99,0.45,0.67,0.38,0.03,0.01,0.60,0.55,NA +SHAPE_urb,urb_green,KOR,Population|Urban|Share,%,0.46,0.07,0.58,0.77,0.92,0.30,0.09,0.19,0.94,0.08,0.79,0.81,0.68,0.11,0.29,0.88,0.19,0.54,0.66,NA +SHAPE_urb,urb_tech,BOL,Population|Urban|Share,%,0.91,0.86,0.36,0.39,0.23,0.94,0.57,0.03,0.60,0.20,0.55,0.82,0.87,0.68,0.38,0.48,0.51,0.38,0.24,NA +SHAPE_urb,urb_distr,FSM,Population|Urban|Share,%,0.20,0.30,0.34,0.95,0.82,0.91,0.11,0.37,0.90,0.93,0.72,0.02,0.89,0.98,0.95,0.72,0.26,0.93,0.17,NA +SHAPE_urb,urb_distr,PHL,Population|Urban|Share,%,0.79,0.90,0.43,0.02,0.69,0.42,0.29,0.62,0.14,0.68,0.29,0.85,0.51,0.52,0.97,0.19,0.93,0.31,0.48,NA +SHAPE_urb,urb_distr,MDV,Population|Urban|Share,%,0.45,0.99,0.08,0.89,0.38,0.43,0.57,0.29,0.42,0.38,0.50,0.07,0.99,0.26,0.94,0.21,0.25,0.45,0.83,NA +SHAPE_urb,urb_tech,IND,Population|Urban|Share,%,0.38,0.54,0.41,0.21,0.34,0.29,0.84,0.16,0.67,0.76,0.48,0.67,0.89,0.74,0.77,0.90,0.51,0.78,0.88,NA +SHAPE_urb,urb_distr,IND,Population|Urban|Share,%,0.57,0.76,0.89,0.70,0.27,0.04,0.62,0.78,0.92,0.29,0.48,0.81,0.33,0.64,0.73,0.62,0.91,0.84,0.63,NA +SHAPE_urb,urb_tech,MTQ,Population|Urban|Share,%,0.38,0.64,0.56,0.66,0.45,0.27,0.02,0.41,0.31,0.03,0.48,0.70,0.21,0.13,0.02,0.40,0.46,0.20,0.95,NA +SHAPE_urb,urb_tech,LBN,Population|Urban|Share,%,0.77,0.99,0.46,0.63,0.73,0.20,0.46,0.50,0.78,0.54,0.82,0.89,0.99,0.17,0.24,0.05,0.58,0.69,0.00,NA +SHAPE_urb,urb_green,HRV,Population|Urban|Share,%,0.19,0.53,0.09,0.53,0.60,0.58,0.56,0.07,0.07,0.75,0.50,0.07,0.07,0.06,0.44,0.24,0.19,0.90,0.63,NA +SHAPE_urb,urb_distr,ERI,Population|Urban|Share,%,0.22,0.94,0.66,0.13,0.35,0.74,0.07,0.37,0.18,0.49,0.67,0.59,0.66,0.04,0.05,0.88,0.73,0.90,0.90,NA +SHAPE_urb,urb_green,POL,Population|Urban|Share,%,0.09,0.71,0.89,0.64,0.80,0.09,0.06,0.13,0.80,0.03,0.92,0.08,0.25,0.32,0.20,0.73,0.12,0.23,0.40,NA +SHAPE_urb,urb_green,IRQ,Population|Urban|Share,%,0.32,0.16,0.45,0.64,0.05,0.69,1.00,0.54,0.77,0.51,0.62,0.40,0.54,0.61,0.46,0.69,0.60,0.61,0.31,NA +SHAPE_urb,urb_tech,AZE,Population|Urban|Share,%,0.90,0.65,0.14,0.01,0.12,0.56,0.92,0.51,0.33,0.29,0.26,0.48,0.36,0.03,0.66,0.34,0.81,0.06,0.49,NA +SHAPE_urb,urb_tech,COL,Population|Urban|Share,%,0.81,0.10,0.30,0.22,0.52,0.47,0.85,0.84,0.00,0.81,0.92,0.61,0.10,0.31,0.73,0.05,0.13,0.67,0.09,NA +SHAPE_urb,urb_distr,BHR,Population|Urban|Share,%,0.59,0.55,0.20,0.52,1.00,0.67,0.00,0.21,0.27,0.89,0.10,0.27,0.31,0.29,0.12,0.30,0.73,0.95,0.35,NA +SHAPE_urb,urb_tech,BRA,Population|Urban|Share,%,0.05,0.73,0.45,0.95,0.19,0.73,0.48,0.83,0.80,0.67,0.22,0.27,0.71,0.73,0.08,0.77,0.52,0.56,0.62,NA +SHAPE_urb,urb_distr,SRB,Population|Urban|Share,%,0.93,0.08,0.93,0.03,0.82,0.92,0.53,0.48,0.36,0.87,0.25,0.60,0.52,0.41,0.07,0.37,0.04,0.95,0.52,NA +SHAPE_urb,urb_green,PRK,Population|Urban|Share,%,0.54,0.39,0.81,1.00,0.07,0.40,0.18,0.72,0.80,0.79,0.29,0.01,0.30,0.83,0.86,0.16,0.42,0.94,0.69,NA +SHAPE_urb,urb_distr,GMB,Population|Urban|Share,%,0.64,0.20,0.88,0.21,0.47,0.10,0.80,0.99,0.45,0.86,0.48,1.00,0.44,0.83,0.60,0.33,0.70,0.20,0.54,NA +SHAPE_urb,urb_tech,NZL,Population|Urban|Share,%,0.85,0.89,0.45,0.02,0.24,0.09,0.47,0.87,0.99,0.14,0.21,0.96,0.73,0.93,0.58,0.26,0.48,0.42,0.23,NA +SHAPE_urb,urb_distr,SGP,Population|Urban|Share,%,0.25,0.45,0.13,0.26,0.21,0.02,0.92,0.26,0.50,0.91,0.45,0.32,0.44,0.36,0.86,0.51,0.78,0.21,0.31,NA +SHAPE_urb,urb_green,MDA,Population|Urban|Share,%,0.85,0.90,0.57,0.02,0.04,0.69,0.23,0.45,0.74,0.79,0.77,0.10,0.92,0.69,0.78,0.31,0.08,0.33,0.68,NA +SHAPE_urb,urb_distr,TCD,Population|Urban|Share,%,0.29,0.17,0.71,0.31,0.85,0.95,0.48,0.79,0.04,0.53,0.60,0.37,0.08,0.86,0.66,0.85,0.65,0.97,0.60,NA +SHAPE_urb,urb_tech,PYF,Population|Urban|Share,%,0.74,0.13,0.58,0.76,0.21,0.09,0.93,0.23,0.45,0.02,0.67,0.11,0.79,0.29,0.49,0.16,0.23,0.06,0.91,NA +SHAPE_urb,urb_green,GRD,Population|Urban|Share,%,0.35,0.82,0.10,0.48,0.79,0.39,0.76,0.82,0.58,0.96,0.94,0.50,0.91,0.91,0.36,0.77,0.02,0.14,0.22,NA +SHAPE_urb,urb_green,SVK,Population|Urban|Share,%,0.71,0.50,0.36,0.74,0.21,0.76,0.87,0.66,0.60,0.86,0.35,0.93,0.66,0.44,0.87,0.14,0.25,0.98,0.12,NA +SHAPE_urb,urb_tech,WSM,Population|Urban|Share,%,0.10,0.77,0.05,0.01,0.20,0.37,0.93,0.58,0.88,0.74,0.84,0.87,0.01,0.23,0.33,0.97,0.93,0.69,0.54,NA +SHAPE_urb,urb_distr,THA,Population|Urban|Share,%,0.26,0.45,0.03,0.58,0.81,0.72,0.41,0.25,0.12,0.36,0.79,0.16,0.58,0.20,0.14,0.23,0.33,0.86,0.71,NA +SHAPE_urb,urb_distr,SWZ,Population|Urban|Share,%,0.23,0.73,0.64,0.05,0.31,0.87,0.85,0.67,0.98,0.28,0.88,0.80,0.08,0.31,0.45,0.88,0.37,0.40,0.73,NA +SHAPE_urb,urb_green,CHL,Population|Urban|Share,%,0.66,0.95,0.05,0.38,0.28,0.32,0.93,0.08,0.78,0.39,0.35,0.11,0.15,0.92,0.41,0.42,0.85,0.86,0.38,NA +SHAPE_urb,urb_distr,BRB,Population|Urban|Share,%,0.26,0.51,0.50,0.78,0.88,0.93,0.23,0.55,0.92,0.56,0.40,0.71,0.37,0.58,0.66,0.05,0.04,0.94,0.99,NA +SHAPE_urb,urb_distr,LBN,Population|Urban|Share,%,0.57,0.18,0.86,0.64,0.60,0.53,0.25,0.05,0.30,0.91,0.85,0.33,0.97,0.31,0.84,0.80,0.30,0.23,0.76,NA +SHAPE_urb,urb_green,FRA,Population|Urban|Share,%,0.59,0.98,0.88,0.56,0.73,0.12,0.42,0.96,0.81,0.82,0.83,0.25,0.30,0.18,0.57,0.76,0.30,0.65,0.03,NA +SHAPE_urb,urb_tech,VCT,Population|Urban|Share,%,0.19,0.43,0.50,0.50,0.36,0.07,0.08,0.80,0.76,0.59,0.27,0.51,0.86,0.33,0.31,0.05,0.40,0.45,0.48,NA +SHAPE_urb,urb_green,TJK,Population|Urban|Share,%,0.39,0.88,0.88,0.52,0.98,0.78,0.74,0.22,0.01,0.00,0.73,0.09,0.49,0.54,0.36,0.48,0.93,0.79,0.05,NA +SHAPE_urb,urb_distr,PSE,Population|Urban|Share,%,0.11,0.14,0.78,0.89,0.44,0.61,0.05,0.01,0.84,0.99,0.93,0.56,0.81,0.98,0.44,0.33,0.30,0.18,0.19,NA +SHAPE_urb,urb_green,COD,Population|Urban|Share,%,0.13,0.14,0.42,0.71,0.08,0.90,0.82,0.33,0.94,0.81,0.85,0.52,0.14,0.25,0.10,0.11,0.88,0.89,0.82,NA +SHAPE_urb,urb_tech,MOZ,Population|Urban|Share,%,0.66,0.43,0.18,0.59,0.39,0.10,0.31,0.37,0.98,0.55,0.50,0.25,0.82,0.28,0.59,0.38,0.13,0.40,0.20,NA +SHAPE_urb,urb_green,MAC,Population|Urban|Share,%,0.97,0.41,0.03,0.08,0.60,0.74,0.07,0.33,0.72,0.90,0.24,0.75,0.75,0.07,0.62,0.57,0.35,0.45,0.08,NA +SHAPE_urb,urb_tech,COD,Population|Urban|Share,%,1.00,0.24,0.91,0.00,0.71,0.11,0.56,0.27,0.75,0.25,0.83,0.26,0.17,0.90,0.92,0.95,0.48,0.01,0.96,NA +SHAPE_urb,urb_tech,SDN,Population|Urban|Share,%,0.95,0.89,0.04,0.35,0.99,0.48,0.54,0.67,0.84,0.09,0.59,0.11,0.30,0.70,0.22,0.06,0.11,0.67,0.40,NA +SHAPE_urb,urb_distr,CAF,Population|Urban|Share,%,0.18,0.18,0.19,0.24,0.54,0.79,0.29,0.67,0.03,0.47,0.17,0.09,0.21,0.02,0.32,0.93,0.58,0.52,0.18,NA +SHAPE_urb,urb_green,TUR,Population|Urban|Share,%,0.41,0.57,0.10,0.52,0.70,0.52,0.95,0.09,0.67,0.11,0.19,0.85,0.95,0.99,0.31,0.23,0.07,0.23,0.71,NA +SHAPE_urb,urb_green,DEU,Population|Urban|Share,%,0.40,0.59,0.58,0.52,0.89,0.25,0.75,0.40,0.56,0.66,0.86,0.43,0.98,0.88,0.06,0.69,0.35,0.56,0.38,NA +SHAPE_urb,urb_distr,UKR,Population|Urban|Share,%,0.58,0.71,0.28,0.99,0.20,0.91,0.98,0.18,0.06,0.45,0.68,0.96,0.08,0.47,0.07,0.19,0.22,0.32,0.39,NA +SHAPE_urb,urb_green,NZL,Population|Urban|Share,%,0.31,0.71,0.49,0.21,0.86,0.12,1.00,0.59,0.47,0.09,0.56,0.04,0.68,0.16,0.93,0.93,0.95,0.40,0.17,NA +SHAPE_urb,urb_distr,ALB,Population|Urban|Share,%,0.51,0.34,0.49,0.17,0.01,0.68,0.75,0.36,0.86,0.53,0.29,0.97,0.42,0.63,0.97,0.09,0.07,0.53,0.60,NA +SHAPE_urb,urb_distr,TUR,Population|Urban|Share,%,0.93,0.48,0.10,0.82,0.42,0.95,0.40,0.41,0.49,0.17,0.58,0.67,0.98,0.32,0.13,0.52,0.23,0.79,0.24,NA +SHAPE_urb,urb_tech,MKD,Population|Urban|Share,%,0.50,0.02,0.89,0.59,0.42,0.21,0.46,0.33,0.29,0.43,0.95,0.67,0.95,0.84,0.99,0.40,0.42,0.22,0.52,NA +SHAPE_urb,urb_green,GEO,Population|Urban|Share,%,0.20,0.01,0.70,0.48,0.98,0.01,0.41,0.94,0.32,0.26,0.99,0.64,0.93,0.57,0.83,0.12,0.61,0.53,0.05,NA +SHAPE_urb,urb_tech,LAO,Population|Urban|Share,%,0.42,0.03,0.58,0.82,0.82,0.35,0.76,0.51,0.33,0.81,0.03,0.05,0.70,0.23,0.23,0.74,0.07,0.51,0.98,NA +SHAPE_urb,urb_tech,SAU,Population|Urban|Share,%,0.18,0.35,0.50,0.73,0.56,0.83,0.83,0.31,0.13,0.33,0.52,0.20,0.75,0.44,0.79,0.85,0.60,0.28,0.74,NA +SHAPE_urb,urb_green,CMR,Population|Urban|Share,%,0.55,0.39,0.63,0.75,0.65,0.73,0.97,0.69,0.34,0.31,0.39,0.81,0.47,0.83,0.82,0.57,0.82,0.25,0.85,NA +SHAPE_urb,urb_green,COM,Population|Urban|Share,%,0.06,0.15,0.94,0.42,0.52,0.74,0.30,0.70,0.56,0.59,0.71,0.61,0.33,0.74,0.97,0.49,0.73,0.61,0.45,NA +SHAPE_urb,urb_distr,GNQ,Population|Urban|Share,%,0.64,0.01,0.42,0.54,0.09,0.37,0.81,0.31,0.00,0.13,0.37,0.24,0.08,0.13,0.77,0.86,0.88,0.97,0.84,NA +SHAPE_urb,urb_tech,SLE,Population|Urban|Share,%,0.39,0.67,0.62,0.59,0.76,0.83,0.72,0.17,0.21,0.51,0.61,0.70,0.03,0.52,0.50,0.73,0.24,0.81,0.03,NA +SHAPE_urb,urb_distr,GRC,Population|Urban|Share,%,0.40,0.09,0.72,0.88,0.45,0.42,0.67,0.64,0.97,0.26,0.47,0.65,0.59,0.58,0.11,0.67,0.86,0.50,0.97,NA +SHAPE_urb,urb_green,MOZ,Population|Urban|Share,%,0.19,0.13,0.86,0.92,0.08,0.80,0.30,0.26,0.34,0.43,0.39,0.83,0.77,0.99,0.32,0.41,0.61,0.28,0.13,NA +SHAPE_urb,urb_distr,ETH,Population|Urban|Share,%,0.15,0.33,0.06,0.29,0.20,0.40,0.90,0.63,0.98,0.46,0.33,0.96,0.07,0.37,0.44,0.70,0.25,0.99,0.50,NA +SHAPE_urb,urb_tech,SRB,Population|Urban|Share,%,0.75,0.04,0.83,0.19,0.14,0.76,0.80,0.06,0.21,0.57,0.84,0.03,0.92,0.33,0.62,0.58,0.02,0.75,0.75,NA +SHAPE_urb,urb_tech,VUT,Population|Urban|Share,%,0.28,0.26,0.42,0.54,0.60,0.86,0.70,0.84,0.80,0.72,0.89,0.86,0.00,0.43,0.42,0.57,0.72,0.48,0.57,NA +SHAPE_urb,urb_distr,DOM,Population|Urban|Share,%,0.37,0.26,0.33,0.08,0.32,0.49,0.86,0.46,0.25,0.84,0.02,0.65,0.80,0.83,0.93,0.28,0.56,0.38,0.93,NA 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+SHAPE_urb,urb_distr,MMR,Population|Urban|Share,%,0.58,0.41,0.27,0.14,0.45,0.14,0.55,0.46,0.60,0.58,0.71,0.25,0.41,0.29,0.48,0.31,0.92,0.60,0.62,NA +SHAPE_urb,urb_green,LUX,Population|Urban|Share,%,0.62,0.76,0.20,0.84,0.68,0.34,0.28,0.78,0.66,0.69,0.09,0.06,0.56,0.62,0.98,0.80,0.45,0.05,0.51,NA +SHAPE_urb,urb_distr,TLS,Population|Urban|Share,%,0.16,0.10,0.75,0.03,0.72,0.23,0.68,0.89,0.79,0.10,0.96,0.38,0.32,0.23,0.73,0.72,0.42,0.70,0.56,NA +SHAPE_urb,urb_tech,SGP,Population|Urban|Share,%,0.03,0.38,0.71,0.27,0.46,0.72,0.64,0.60,0.51,0.79,0.03,0.13,0.58,0.20,0.95,0.82,0.54,0.94,0.26,NA +SHAPE_urb,urb_green,EGY,Population|Urban|Share,%,0.72,0.68,0.76,0.66,0.02,0.86,0.11,0.64,0.53,0.96,0.09,0.26,0.36,0.37,0.60,0.81,0.59,0.60,0.31,NA +SHAPE_urb,urb_tech,GAB,Population|Urban|Share,%,0.10,0.28,0.89,0.53,0.37,0.63,0.40,0.23,0.94,0.37,0.63,0.04,0.81,0.82,0.52,0.65,0.36,0.76,0.75,NA +SHAPE_urb,urb_tech,EST,Population|Urban|Share,%,0.25,0.39,0.37,0.15,0.30,0.93,0.34,0.40,0.50,0.23,0.02,0.06,0.30,0.53,0.53,0.64,0.96,0.46,0.00,NA +SHAPE_urb,urb_green,MEX,Population|Urban|Share,%,0.04,0.80,0.37,0.90,0.28,0.04,0.87,0.88,0.26,0.58,0.21,0.11,0.93,0.38,0.55,0.60,0.94,0.21,0.37,NA +SHAPE_urb,urb_tech,MDG,Population|Urban|Share,%,0.53,0.20,0.46,0.14,0.89,0.58,0.97,0.57,0.82,0.68,0.98,0.79,0.18,0.01,0.36,0.63,0.22,0.51,0.94,NA +SHAPE_urb,urb_distr,AZE,Population|Urban|Share,%,0.51,0.98,0.18,0.50,0.08,0.79,0.64,0.26,0.48,0.64,0.16,0.78,0.50,0.95,0.37,0.08,0.76,0.30,0.70,NA +SHAPE_urb,urb_tech,AUT,Population|Urban|Share,%,0.29,0.64,0.57,0.65,0.32,0.89,0.90,0.44,0.57,0.01,0.26,0.99,0.79,0.75,0.74,0.26,0.34,0.97,0.09,NA +SHAPE_urb,urb_tech,TGO,Population|Urban|Share,%,0.51,0.58,0.94,0.10,0.13,0.82,0.37,0.49,0.72,0.51,0.61,0.14,0.14,0.84,0.43,0.36,0.44,0.65,0.01,NA 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+SHAPE_urb,urb_distr,GIN,Population|Urban|Share,%,0.57,0.16,0.98,0.89,0.22,0.67,0.64,0.17,0.05,0.57,0.64,0.18,0.67,0.66,0.02,0.51,0.72,0.33,0.39,NA +SHAPE_urb,urb_green,COL,Population|Urban|Share,%,0.74,0.23,0.02,0.79,0.04,0.50,0.52,0.63,0.80,0.76,0.08,0.52,0.42,0.61,0.02,0.29,0.72,0.21,0.49,NA +SHAPE_urb,urb_green,FJI,Population|Urban|Share,%,0.20,0.93,0.47,0.49,0.67,0.30,0.81,0.61,0.50,0.47,0.27,0.78,0.27,0.29,0.51,0.44,0.56,0.87,0.71,NA +SHAPE_urb,urb_green,PRT,Population|Urban|Share,%,0.56,0.34,0.10,0.03,0.46,0.23,0.25,0.29,0.93,0.44,0.53,0.30,0.98,0.31,0.77,0.24,0.17,0.36,0.99,NA +SHAPE_urb,urb_green,SAU,Population|Urban|Share,%,0.81,0.14,0.97,0.41,0.83,0.02,0.05,0.66,0.87,0.32,0.72,0.70,0.50,0.64,0.41,0.39,0.68,0.78,0.61,NA +SHAPE_urb,urb_distr,JPN,Population|Urban|Share,%,0.04,0.47,0.42,0.98,0.60,0.83,0.44,0.22,0.36,0.21,0.92,0.25,0.74,0.92,0.55,0.91,0.45,0.73,0.80,NA 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+SHAPE_urb,urb_distr,GTM,Population|Urban|Share,%,1.00,1.00,0.58,0.82,0.64,0.33,0.32,0.56,0.42,0.07,0.94,0.13,0.20,0.39,0.41,0.99,0.65,0.74,0.03,NA +SHAPE_urb,urb_tech,UKR,Population|Urban|Share,%,0.58,0.66,0.75,0.81,0.61,0.30,0.79,0.29,0.17,0.99,1.00,0.57,0.26,0.86,0.73,0.12,0.40,0.91,0.72,NA +SHAPE_urb,urb_green,BRB,Population|Urban|Share,%,0.25,0.82,0.56,0.99,0.38,0.91,0.95,0.56,0.55,0.93,0.64,0.20,0.27,0.74,0.82,0.03,0.50,0.53,0.04,NA +SHAPE_urb,urb_tech,DNK,Population|Urban|Share,%,0.48,0.85,0.98,0.94,0.48,0.28,0.44,0.07,0.46,0.57,0.70,0.43,0.60,0.22,0.17,0.13,0.06,0.92,0.92,NA +SHAPE_urb,urb_green,BHR,Population|Urban|Share,%,0.08,0.86,0.68,0.91,0.73,1.00,0.20,0.86,0.73,0.33,0.70,0.80,0.32,0.42,0.52,0.81,0.50,0.44,0.59,NA +SHAPE_urb,urb_distr,LBR,Population|Urban|Share,%,0.51,0.45,0.49,0.05,0.32,0.58,0.02,0.21,0.49,0.70,0.40,0.17,0.72,0.40,0.53,0.10,0.59,0.84,0.16,NA +SHAPE_urb,urb_tech,MLT,Population|Urban|Share,%,0.01,0.05,0.11,0.67,0.70,0.99,0.40,0.89,0.91,0.44,0.28,0.22,0.06,0.29,0.30,0.69,0.52,0.94,0.91,NA +SHAPE_urb,urb_distr,GUM,Population|Urban|Share,%,0.99,0.53,0.78,0.83,0.12,0.49,0.83,0.63,0.63,0.45,0.86,0.35,0.09,0.76,0.45,0.83,0.70,0.92,0.45,NA +SHAPE_urb,urb_tech,NGA,Population|Urban|Share,%,0.32,0.84,0.47,0.91,0.83,0.54,0.28,0.48,0.63,0.07,0.44,0.05,0.18,0.59,0.25,0.62,0.95,0.66,0.94,NA +SHAPE_urb,urb_tech,ECU,Population|Urban|Share,%,0.57,0.37,0.22,0.84,0.37,0.97,0.02,0.32,0.92,0.77,0.60,0.38,0.60,0.97,0.10,0.46,0.90,0.63,0.31,NA +SHAPE_urb,urb_tech,SWZ,Population|Urban|Share,%,0.67,0.05,0.23,0.79,0.56,0.64,0.17,0.12,0.93,0.44,0.20,0.05,0.83,0.78,0.43,0.82,0.75,0.89,0.67,NA +SHAPE_urb,urb_green,UZB,Population|Urban|Share,%,0.91,0.25,0.49,0.87,0.89,0.86,0.97,0.11,0.85,0.23,0.97,0.39,0.87,0.93,0.94,0.36,0.97,0.01,0.20,NA +SHAPE_urb,urb_green,PER,Population|Urban|Share,%,0.41,0.56,0.54,0.81,0.85,0.29,0.01,0.31,0.99,0.90,0.10,0.16,0.30,0.05,0.09,0.88,0.10,0.76,0.58,NA +SHAPE_urb,urb_green,CUB,Population|Urban|Share,%,0.10,0.33,1.00,0.94,0.30,0.56,0.26,0.37,0.40,0.86,0.55,0.89,0.86,0.70,0.65,0.13,0.77,0.05,0.12,NA +SHAPE_urb,urb_green,JOR,Population|Urban|Share,%,0.63,0.45,0.51,0.68,0.58,0.63,0.47,0.35,0.10,0.47,0.68,0.38,0.41,0.96,0.67,0.55,0.69,0.20,0.49,NA +SHAPE_urb,urb_distr,PYF,Population|Urban|Share,%,0.55,0.14,0.27,0.22,0.22,0.27,0.12,0.32,0.60,0.55,0.72,0.03,0.35,0.84,0.77,0.89,0.92,0.55,0.73,NA +SHAPE_urb,urb_tech,HRV,Population|Urban|Share,%,0.70,0.56,0.08,0.49,0.42,0.79,0.11,0.02,0.12,0.05,0.92,0.14,0.66,0.76,0.19,0.01,0.86,0.69,0.27,NA +SHAPE_urb,urb_distr,IRN,Population|Urban|Share,%,0.32,0.48,0.43,0.85,0.16,0.85,0.96,0.17,0.26,0.92,0.11,0.49,0.31,0.87,0.20,0.43,0.78,0.12,0.48,NA +SHAPE_urb,urb_green,NLD,Population|Urban|Share,%,0.36,0.66,0.67,0.46,0.01,0.96,0.35,0.40,0.53,0.56,0.81,0.66,0.82,0.52,0.12,0.50,0.00,0.64,0.17,NA +SHAPE_urb,urb_tech,IRQ,Population|Urban|Share,%,0.82,0.23,0.77,0.62,0.77,0.95,0.31,0.88,0.67,0.24,0.96,1.00,0.74,0.13,0.80,0.69,0.48,0.92,0.53,NA +SHAPE_urb,urb_distr,USA,Population|Urban|Share,%,0.99,0.67,0.17,0.68,0.12,0.80,0.97,0.21,0.69,0.43,0.23,0.29,0.19,0.37,0.97,0.37,0.83,0.10,0.37,NA +SHAPE_urb,urb_distr,PER,Population|Urban|Share,%,0.61,0.96,0.19,0.25,0.49,0.17,0.29,0.45,0.21,0.72,0.78,0.60,0.84,0.02,0.76,0.73,0.80,0.63,0.83,NA +SHAPE_urb,urb_distr,ZAF,Population|Urban|Share,%,0.92,0.97,0.57,0.76,0.06,0.59,0.09,0.74,0.94,0.09,0.97,0.76,0.22,0.43,0.53,0.47,0.16,0.22,0.45,NA +SHAPE_urb,urb_tech,NLD,Population|Urban|Share,%,0.90,0.56,0.93,0.71,0.24,0.70,0.50,0.07,0.97,0.07,0.66,0.79,0.79,0.75,0.51,0.96,0.88,0.52,0.59,NA +SHAPE_urb,urb_green,GAB,Population|Urban|Share,%,0.08,0.90,0.86,0.10,0.46,0.06,0.75,0.64,0.60,0.16,0.25,0.41,0.89,0.94,0.01,0.88,0.83,0.96,0.34,NA +SHAPE_urb,urb_distr,YEM,Population|Urban|Share,%,0.85,0.19,0.60,0.90,0.96,0.45,0.75,0.89,0.73,0.92,0.14,0.18,0.31,0.34,0.92,0.85,0.57,0.44,0.56,NA +SHAPE_urb,urb_green,PRY,Population|Urban|Share,%,0.38,0.77,0.63,0.23,0.86,0.97,0.68,0.55,0.01,0.25,0.57,0.42,0.05,0.77,0.38,0.99,0.43,0.23,0.62,NA +SHAPE_urb,urb_green,HTI,Population|Urban|Share,%,0.06,0.40,0.41,0.22,0.19,0.10,0.79,0.79,0.97,0.49,0.74,0.88,0.65,0.89,0.03,0.35,0.55,0.50,0.47,NA +SHAPE_urb,urb_distr,MKD,Population|Urban|Share,%,0.43,0.77,0.97,0.91,0.29,0.03,0.50,0.99,0.84,0.91,0.03,0.99,0.48,0.29,0.83,0.89,0.21,0.32,0.85,NA +SHAPE_urb,urb_green,TON,Population|Urban|Share,%,0.89,0.14,0.23,0.60,0.83,0.49,0.42,0.90,0.38,0.08,0.07,0.66,0.82,0.24,0.04,0.47,0.61,0.57,0.05,NA +SHAPE_urb,urb_tech,PRY,Population|Urban|Share,%,0.64,0.88,0.37,0.17,0.82,0.70,0.30,0.89,0.43,0.19,0.62,0.66,0.68,0.44,0.69,0.18,0.93,0.99,0.38,NA +SHAPE_urb,urb_distr,NGA,Population|Urban|Share,%,0.99,0.75,0.40,0.09,0.37,0.23,0.89,0.06,0.30,0.77,0.95,0.48,0.09,0.48,0.90,0.27,0.33,0.89,0.56,NA +SHAPE_urb,urb_tech,MAR,Population|Urban|Share,%,0.26,0.81,0.06,0.90,0.49,0.37,0.90,0.02,0.71,0.08,0.87,0.19,0.65,0.51,0.94,0.11,0.19,0.46,0.77,NA +SHAPE_urb,urb_distr,IDN,Population|Urban|Share,%,0.39,0.56,0.01,0.32,0.29,0.93,0.78,0.68,0.65,0.28,0.84,0.60,0.78,0.26,0.45,0.67,0.83,0.95,0.26,NA +SHAPE_urb,urb_distr,LAO,Population|Urban|Share,%,0.40,0.24,0.91,0.93,0.44,0.98,0.68,0.60,0.93,0.60,0.74,0.99,0.07,0.60,0.66,0.89,0.15,0.30,0.52,NA +SHAPE_urb,urb_tech,SEN,Population|Urban|Share,%,0.43,0.26,0.96,0.66,0.17,0.76,0.80,0.55,0.81,0.62,0.77,0.43,0.30,0.67,0.49,0.77,0.22,0.61,0.01,NA +SHAPE_urb,urb_green,CHE,Population|Urban|Share,%,0.35,0.79,0.18,0.03,0.35,0.33,0.46,0.77,0.12,0.44,0.59,0.98,0.13,0.07,0.58,0.94,0.58,0.30,0.76,NA +SHAPE_urb,urb_tech,LKA,Population|Urban|Share,%,0.05,0.37,0.51,0.05,0.88,0.76,0.53,0.91,0.94,0.64,0.15,0.36,1.00,0.36,0.32,0.36,0.15,0.41,0.91,NA +SHAPE_urb,urb_distr,CYP,Population|Urban|Share,%,0.12,0.32,0.71,0.68,0.36,0.60,0.85,0.72,0.66,0.07,0.71,0.75,0.81,0.44,0.54,0.30,0.81,0.80,0.86,NA +SHAPE_urb,urb_tech,PAN,Population|Urban|Share,%,0.25,0.76,0.58,0.85,0.67,0.04,0.72,0.29,0.40,0.97,0.05,0.54,0.78,0.17,0.73,0.31,0.19,0.41,0.58,NA +SHAPE_urb,urb_tech,CPV,Population|Urban|Share,%,0.56,0.74,0.80,0.49,0.68,0.56,0.50,0.36,0.82,0.29,0.90,0.13,0.57,0.73,0.47,0.73,0.79,0.48,0.46,NA +SHAPE_urb,urb_distr,GLP,Population|Urban|Share,%,0.65,0.10,0.91,0.50,0.15,0.33,0.34,0.63,0.06,0.71,0.31,0.77,0.25,0.70,0.93,0.79,0.68,0.20,0.22,NA +SHAPE_urb,urb_tech,JAM,Population|Urban|Share,%,0.73,0.30,0.33,0.68,0.93,0.75,0.86,0.34,0.97,0.64,0.11,0.97,0.40,0.11,0.08,0.08,0.31,0.93,0.84,NA +SHAPE_urb,urb_green,KEN,Population|Urban|Share,%,0.80,0.72,0.70,0.30,0.05,0.89,0.05,0.36,0.01,0.57,0.97,0.89,0.95,0.78,0.66,0.84,0.49,0.91,0.65,NA +SHAPE_urb,urb_distr,BDI,Population|Urban|Share,%,0.29,0.78,0.84,0.29,0.99,0.95,0.83,0.67,0.61,0.89,0.67,0.01,0.67,0.01,0.33,0.37,0.71,0.13,0.60,NA +SHAPE_urb,urb_tech,ZWE,Population|Urban|Share,%,0.50,0.01,0.64,0.79,0.55,0.63,1.00,0.58,0.21,1.00,0.24,0.22,0.40,0.13,0.39,0.76,0.78,0.15,0.68,NA +SHAPE_urb,urb_distr,TJK,Population|Urban|Share,%,0.83,0.39,0.64,0.71,0.64,0.31,0.28,0.53,0.13,0.29,0.58,0.02,0.36,0.69,0.73,0.46,0.46,0.54,0.06,NA +SHAPE_urb,urb_green,GBR,Population|Urban|Share,%,0.07,0.88,0.29,0.23,0.53,0.42,0.23,0.74,0.96,0.69,0.18,0.41,0.17,0.12,0.80,0.55,0.87,0.23,0.55,NA 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+SHAPE_urb,urb_green,ROU,Population|Urban|Share,%,0.94,0.97,0.19,0.28,0.48,0.77,0.10,0.90,0.73,0.46,0.41,0.68,0.30,0.18,0.72,0.92,0.47,0.52,0.94,NA +SHAPE_urb,urb_tech,JPN,Population|Urban|Share,%,0.32,0.84,0.40,0.72,0.27,0.19,0.47,0.08,0.47,0.88,0.49,0.16,0.90,0.96,0.09,0.05,0.43,0.76,0.54,NA +SHAPE_urb,urb_green,ZWE,Population|Urban|Share,%,0.06,0.96,0.71,0.30,0.95,0.77,0.59,0.85,0.80,0.24,0.42,0.07,0.51,0.97,0.53,0.62,0.81,0.08,0.22,NA +SHAPE_urb,urb_distr,MLT,Population|Urban|Share,%,0.19,0.08,0.02,0.60,0.25,0.91,0.92,0.42,0.26,0.59,0.54,0.85,0.96,0.48,0.58,0.29,0.19,0.82,0.19,NA +SHAPE_urb,urb_distr,OMN,Population|Urban|Share,%,0.05,0.00,0.74,0.95,0.92,0.15,0.37,0.20,0.94,0.62,0.60,0.29,0.18,0.35,0.87,0.42,0.18,0.83,0.57,NA +SHAPE_urb,urb_distr,ROU,Population|Urban|Share,%,0.20,0.16,0.76,0.48,0.21,0.97,0.68,0.48,0.54,0.81,0.73,0.94,0.66,0.27,0.49,0.65,0.71,0.96,0.71,NA +SHAPE_urb,urb_green,SEN,Population|Urban|Share,%,0.74,0.75,0.15,0.12,0.75,0.11,0.28,0.34,0.71,0.96,0.55,0.36,0.86,0.24,0.73,0.31,0.36,0.37,0.36,NA +SHAPE_urb,urb_distr,AGO,Population|Urban|Share,%,0.73,0.19,0.96,0.49,0.24,0.83,1.00,0.73,0.59,0.68,0.36,0.75,0.46,0.33,0.34,0.82,0.26,0.50,0.26,NA +SHAPE_urb,urb_tech,FJI,Population|Urban|Share,%,0.06,0.86,0.93,0.45,0.16,0.16,0.50,0.97,0.40,0.49,0.00,0.29,0.75,0.71,0.49,0.30,0.14,0.50,0.10,NA +SHAPE_urb,urb_green,TLS,Population|Urban|Share,%,0.63,0.11,0.77,0.68,0.07,0.23,0.82,0.55,0.08,0.02,0.11,0.26,0.35,0.64,0.54,0.18,0.34,0.07,0.46,NA +SHAPE_urb,urb_distr,COM,Population|Urban|Share,%,0.00,0.16,0.48,0.62,0.57,0.32,0.44,0.91,0.83,0.06,0.87,0.11,0.74,0.49,0.78,0.04,0.39,0.40,0.26,NA +SHAPE_urb,urb_distr,COL,Population|Urban|Share,%,0.48,0.01,0.20,0.76,0.58,0.94,0.44,0.23,0.67,0.03,0.50,0.42,0.85,0.46,0.46,0.31,0.55,0.44,0.96,NA +SHAPE_urb,urb_tech,HUN,Population|Urban|Share,%,0.25,0.02,0.44,0.02,0.37,0.82,0.60,0.40,0.95,0.98,0.53,0.96,0.37,0.65,0.88,0.44,0.67,0.38,0.76,NA +SHAPE_urb,urb_distr,LUX,Population|Urban|Share,%,0.02,0.79,0.13,0.84,0.09,0.10,0.60,0.78,0.88,0.29,0.04,0.84,0.83,0.28,0.03,0.90,0.38,0.18,0.93,NA +SHAPE_urb,urb_green,DNK,Population|Urban|Share,%,0.35,0.39,0.48,0.66,0.50,0.08,0.88,0.30,0.81,0.38,0.97,0.74,0.15,0.96,0.17,0.27,0.64,0.11,0.87,NA +SHAPE_urb,urb_tech,MRT,Population|Urban|Share,%,0.82,0.97,0.55,0.70,0.14,0.77,0.41,0.22,0.91,1.00,0.49,0.78,0.96,0.75,0.86,0.55,0.30,0.83,0.80,NA +SHAPE_urb,urb_distr,LSO,Population|Urban|Share,%,0.35,0.43,0.17,0.71,0.93,0.33,0.39,0.56,0.79,0.20,0.39,0.18,0.22,0.81,0.73,0.55,0.45,0.84,0.24,NA +SHAPE_urb,urb_tech,HTI,Population|Urban|Share,%,0.81,0.02,0.40,0.54,0.99,0.54,0.74,0.84,0.11,0.98,0.54,0.33,0.49,0.32,0.86,0.45,0.26,0.16,0.73,NA +SHAPE_urb,urb_green,PAK,Population|Urban|Share,%,0.29,0.65,0.45,0.94,0.54,0.85,0.15,0.80,0.86,0.89,0.06,0.94,0.26,0.09,0.75,0.13,0.49,0.88,0.47,NA +SHAPE_urb,urb_distr,SAU,Population|Urban|Share,%,0.38,0.63,0.11,0.79,0.47,0.29,0.99,0.33,0.88,0.06,0.53,0.15,0.31,0.87,0.15,0.04,0.18,0.33,1.00,NA +SHAPE_urb,urb_distr,LBY,Population|Urban|Share,%,0.23,0.22,0.09,0.46,0.03,0.17,0.03,0.32,0.28,0.61,0.49,0.87,0.91,0.20,0.33,0.17,0.68,0.44,0.23,NA +SHAPE_urb,urb_tech,GUF,Population|Urban|Share,%,0.11,0.08,0.72,0.40,0.49,0.34,0.26,0.80,0.61,0.31,0.66,0.95,0.46,0.30,0.71,0.65,0.80,0.92,0.84,NA +SHAPE_urb,urb_tech,MDV,Population|Urban|Share,%,0.94,0.98,0.48,0.16,0.18,0.63,0.08,0.50,0.40,0.65,0.83,0.19,0.36,0.94,0.49,0.14,0.97,0.16,0.93,NA +SHAPE_urb,urb_distr,COG,Population|Urban|Share,%,0.15,0.97,0.87,0.35,0.09,0.30,0.70,0.26,0.53,0.52,0.38,0.52,0.73,0.69,0.32,0.19,0.94,0.83,0.68,NA +SHAPE_urb,urb_tech,GHA,Population|Urban|Share,%,0.43,0.85,0.50,0.19,0.67,0.10,0.77,0.33,0.90,0.98,0.35,0.50,0.99,0.58,0.72,0.34,0.04,0.08,0.04,NA +SHAPE_urb,urb_tech,GBR,Population|Urban|Share,%,0.11,0.91,0.14,0.28,0.36,0.15,0.10,0.15,0.90,0.35,0.04,0.93,0.85,0.48,0.44,0.58,0.14,0.94,0.44,NA +SHAPE_urb,urb_distr,HUN,Population|Urban|Share,%,0.86,0.19,0.11,0.99,0.97,0.58,0.42,0.68,0.65,0.94,0.29,0.35,0.09,0.56,0.33,0.43,0.34,0.80,0.03,NA +SHAPE_urb,urb_green,BWA,Population|Urban|Share,%,0.27,0.98,0.54,0.16,0.33,0.38,0.93,0.45,0.69,0.21,0.75,0.81,0.50,0.10,0.33,0.65,0.84,0.47,0.35,NA +SHAPE_urb,urb_tech,NCL,Population|Urban|Share,%,0.92,0.53,0.84,0.79,0.01,0.17,0.96,0.92,0.36,0.87,0.06,0.63,0.92,0.67,0.58,0.53,0.05,0.98,0.41,NA +SHAPE_urb,urb_distr,BOL,Population|Urban|Share,%,0.68,0.96,0.93,0.93,0.35,0.66,0.75,0.82,0.31,0.31,0.28,0.33,0.60,0.19,0.18,0.44,0.35,0.08,0.40,NA +SHAPE_urb,urb_distr,NZL,Population|Urban|Share,%,0.98,0.85,0.70,0.16,0.41,0.08,0.71,0.41,0.63,0.55,0.76,0.93,0.23,0.74,0.11,0.48,0.30,0.26,0.97,NA +SHAPE_urb,urb_tech,DOM,Population|Urban|Share,%,0.42,0.89,0.86,0.36,0.63,0.22,0.31,0.27,0.49,0.46,0.66,0.46,0.18,0.47,0.28,0.18,0.53,0.65,0.15,NA +SHAPE_urb,urb_distr,QAT,Population|Urban|Share,%,0.40,0.26,0.81,0.07,0.10,0.84,0.21,0.30,0.92,0.96,0.09,0.11,0.82,0.92,0.28,0.50,0.96,0.79,0.58,NA +SHAPE_urb,urb_tech,NIC,Population|Urban|Share,%,0.35,0.66,0.44,1.00,0.26,0.52,0.33,0.83,0.43,0.17,0.36,0.09,0.07,0.48,0.89,0.68,0.20,0.93,0.21,NA +SHAPE_urb,urb_green,SRB,Population|Urban|Share,%,0.01,0.62,0.47,0.17,0.45,0.77,0.92,0.32,0.64,0.07,0.57,0.24,0.94,0.61,0.93,0.62,0.31,0.98,0.10,NA +SHAPE_urb,urb_green,CAF,Population|Urban|Share,%,0.43,0.08,0.11,0.07,0.93,0.63,0.62,0.21,0.21,0.33,0.87,0.71,0.85,0.33,0.21,0.50,0.40,0.54,0.02,NA +SHAPE_urb,urb_tech,FSM,Population|Urban|Share,%,0.31,0.57,0.11,0.64,0.90,0.73,0.31,0.25,0.54,0.59,0.32,0.27,0.13,0.30,0.01,0.21,0.88,0.73,0.90,NA +SHAPE_urb,urb_green,BTN,Population|Urban|Share,%,1.00,0.35,0.55,0.89,0.23,0.04,0.98,0.15,0.55,0.93,0.71,0.37,0.38,0.43,0.22,0.79,0.89,0.56,0.61,NA +SHAPE_urb,urb_green,ABW,Population|Urban|Share,%,0.49,0.22,0.79,0.49,0.51,0.70,0.70,0.74,0.17,0.12,0.40,0.00,0.44,0.92,0.63,0.63,0.99,0.86,0.51,NA +SHAPE_urb,urb_tech,NOR,Population|Urban|Share,%,0.85,0.57,0.07,0.53,0.70,0.25,0.92,0.78,0.85,0.22,0.72,0.33,0.44,0.57,0.61,0.18,0.01,0.67,0.49,NA +SHAPE_urb,urb_green,JPN,Population|Urban|Share,%,0.49,0.19,0.76,0.79,0.98,0.33,0.20,0.33,0.10,0.02,0.97,0.41,0.74,0.15,0.11,0.63,0.43,0.89,0.45,NA +SHAPE_urb,urb_green,TTO,Population|Urban|Share,%,0.39,0.11,0.83,0.95,0.35,0.46,0.09,0.65,0.45,1.00,0.73,0.10,0.49,0.15,0.30,0.96,0.77,0.34,0.60,NA +SHAPE_urb,urb_tech,RUS,Population|Urban|Share,%,0.60,0.29,0.09,0.71,0.27,0.65,0.72,0.72,0.86,0.49,0.83,0.83,0.99,0.96,0.31,0.90,0.26,0.02,0.01,NA +SHAPE_urb,urb_green,BOL,Population|Urban|Share,%,0.91,0.67,0.68,0.53,0.88,0.88,0.55,0.71,0.83,0.25,0.36,0.59,0.73,0.91,0.86,0.32,0.30,0.60,0.23,NA +SHAPE_urb,urb_green,MUS,Population|Urban|Share,%,0.38,0.68,0.16,0.00,0.75,0.47,0.40,0.52,0.33,0.16,0.02,0.44,0.24,0.25,0.42,0.41,0.32,0.61,0.47,NA +SHAPE_urb,urb_green,TKM,Population|Urban|Share,%,0.91,0.45,0.82,0.66,0.10,0.62,0.93,0.56,0.27,0.83,0.70,0.45,0.11,0.35,0.47,0.76,0.37,0.67,0.02,NA diff --git a/message_ix_models/data/test/snapshot-0/MESSAGEix-GLOBIOM_1.1_R11_no-policy_baseline.xlsx b/message_ix_models/data/test/snapshot-0/MESSAGEix-GLOBIOM_1.1_R11_no-policy_baseline.xlsx deleted file mode 100644 index 396e9413bf..0000000000 --- a/message_ix_models/data/test/snapshot-0/MESSAGEix-GLOBIOM_1.1_R11_no-policy_baseline.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c17310de79765a517f8835ae4df731b0bb89f73971daa5aa15acf5d8da3bdc40 -size 135453950 diff --git a/message_ix_models/data/test/snapshot-1/MESSAGEix-GLOBIOM_1.1_R11_no-policy_baseline.xlsx b/message_ix_models/data/test/snapshot-1/MESSAGEix-GLOBIOM_1.1_R11_no-policy_baseline.xlsx deleted file mode 100644 index af23859e58..0000000000 --- a/message_ix_models/data/test/snapshot-1/MESSAGEix-GLOBIOM_1.1_R11_no-policy_baseline.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:741494c45f75af68fc384317b6937ed16b58b564f35a60ffb43a5ff827f92d1c -size 137692828 diff --git a/message_ix_models/data/test/transport/GFEI_FE_by_Powertrain_2017.csv b/message_ix_models/data/test/transport/GFEI_FE_by_Powertrain_2017.csv new file mode 100644 index 0000000000..705d9f8a5e --- /dev/null +++ b/message_ix_models/data/test/transport/GFEI_FE_by_Powertrain_2017.csv @@ -0,0 +1,330 @@ +# Fuel consumption +# +# Source: +# - Fuzzed version of same file in message_data. +# - Data underlies "Figure 37. Fuel consumption range by type of powertrain +# and vehicle size, 2017"` in +# https://theicct.org/publications/gfei-tech-policy-drivers-2005-2017. +# Private correspondence from the report authors. +# - Values correspond exclusively to new vehicle registrations in year 2017. +# +# Units: 0.01 Lge / vkm +# +Country, FuelTypeReduced, value +Turkey, Diesel,0.16 +Romania, Diesel,0.70 +Japan, Unspec.,0.22 +Belgium, Plug-in,0.10 +Greece, Plug-in,0.43 +Greece, Electric,0.15 +Croatia, LPG,0.38 +United Kingdom, Electric,0.44 +Mexico, Diesel,0.79 +Germany, CNG,0.05 +Spain, CNG,0.80 +USA, CNG,0.44 +South Korea, Unspec.,0.09 +Finland, Petrol,0.84 +Netherlands, Petrol,0.09 +India, Diesel,0.87 +Australia, LPG,0.68 +Austria, Electric,0.72 +Germany, Petrol,0.03 +Thailand, CNG,0.81 +Hungary, Petrol,0.49 +Belgium, Hybrid,0.30 +Bulgaria, Plug-in,0.93 +South Africa, Plug-in,0.10 +Greece, Diesel,0.97 +Greece, Petrol,0.71 +Austria, CNG,0.87 +Argentina, Hybrid,0.41 +Thailand, Petrol,0.25 +Denmark, Petrol,0.29 +Ukraine, Hybrid,0.31 +Australia, Electric,0.31 +Malta, Diesel,0.86 +Croatia, CNG,0.93 +Slovakia, CNG,0.39 +Poland, LPG,0.49 +Belgium, LPG,0.73 +Spain, LPG,0.61 +Ireland, Hybrid,0.63 +Austria, Hybrid,0.79 +Austria, Flexfuel,0.04 +Spain, Hybrid,0.11 +Latvia, CNG,0.43 +Netherlands, LPG,0.07 +Bulgaria, Electric,0.02 +Romania, Hybrid,0.88 +Iceland, Petrol,0.99 +Mexico, Petrol,0.23 +Ukraine, Petrol,0.65 +Sweden, Plug-in,0.20 +South Korea, Petrol,0.33 +Estonia, CNG,0.53 +Germany, LPG,0.51 +Philippines, Unspec.,0.15 +Brazil, Electric,1.00 +Italy, Electric,0.96 +Denmark, LPG,0.88 +Greece, CNG,0.25 +Turkey, Petrol,0.60 +Latvia, Plug-in,0.56 +Ireland, Petrol,0.91 +Turkey, Hybrid,0.50 +Croatia, Electric,0.19 +Portugal, LPG,0.56 +India, CNG,0.37 +Brazil, Hybrid,0.20 +Russia, Diesel,0.64 +Slovenia, Plug-in,0.16 +Czech Republic, Electric,0.61 +France, Diesel,0.57 +Australia, Plug-in,0.19 +United Kingdom, Plug-in,0.12 +Estonia, Hybrid,0.64 +Romania, LPG,0.06 +Ukraine, Diesel,0.78 +Argentina, Diesel,0.77 +Croatia, Plug-in,0.12 +Japan, Hydrogen,0.65 +France, Flexfuel,0.55 +Indonesia, Petrol,0.80 +Estonia, Diesel,0.75 +Indonesia, Diesel,0.61 +Croatia, Diesel,0.84 +Slovakia, Diesel,0.30 +South Korea, Electric,0.98 +Czech Republic, LPG,0.83 +Portugal, Hybrid,0.50 +Lithuania, Petrol,0.07 +Canada, Diesel,0.05 +Germany, Hybrid,0.87 +Japan, Diesel,0.44 +Mexico, Plug-in,0.70 +Iceland, Electric,0.68 +Sweden, Diesel,0.39 +Ukraine, CNG,0.44 +South Africa, Electric,0.08 +USA, Hybrid,0.44 +Sweden, Petrol,0.36 +Hungary, CNG,0.18 +USA, Hydrogen,0.71 +Poland, Plug-in,0.55 +Sweden, CNG,0.06 +USA, Electric,0.23 +Italy, Diesel,0.67 +Chile, Electric,0.63 +Lithuania, Diesel,0.31 +South Korea, Plug-in,0.51 +Austria, Diesel,0.45 +Argentina, Petrol,0.21 +Slovakia, Flexfuel,0.30 +USA, Diesel,0.89 +Egypt, Diesel,0.63 +Cyprus, Plug-in,0.11 +Luxembourg, LPG,0.94 +Italy, Hybrid,0.80 +Cyprus, Diesel,0.27 +Sweden, LPG,0.57 +Netherlands, Electric,0.39 +South Korea, Hybrid,0.31 +South Korea, Diesel,0.44 +Slovakia, Petrol,0.03 +United Kingdom, Diesel,0.52 +Thailand, Plug-in,0.76 +Brazil, Unspec.,0.83 +Luxembourg, Diesel,0.23 +Brazil, Petrol,0.09 +South Korea, LPG,0.07 +Spain, Petrol,0.21 +Iceland, Plug-in,0.21 +Slovakia, Plug-in,0.23 +Latvia, Petrol,0.34 +Denmark, Electric,0.79 +China, Plug-in,0.43 +India, LPG,0.01 +Finland, Electric,0.54 +Australia, Diesel,0.10 +USA, Petrol,0.14 +Lithuania, Electric,0.94 +Sweden, Hybrid,0.21 +India, Hybrid,0.08 +China, Hybrid,0.07 +China, Diesel,0.75 +Portugal, CNG,0.45 +Austria, LPG,0.41 +Romania, CNG,0.22 +Romania, Plug-in,0.02 +Chile, Diesel,0.91 +Slovakia, Electric,0.05 +Chile, Plug-in,0.18 +Greece, LPG,0.10 +Netherlands, Diesel,0.70 +India, Electric,0.98 +United Kingdom, Hybrid,0.26 +Spain, Electric,0.73 +Hungary, Plug-in,0.29 +France, Electric,0.42 +Malta, Petrol,0.73 +Portugal, Plug-in,0.62 +Denmark, Diesel,0.80 +Spain, Plug-in,0.61 +China, Petrol,0.60 +Denmark, Plug-in,0.43 +Finland, CNG,0.74 +Poland, Hybrid,0.48 +United Kingdom, CNG,0.98 +Ireland, Diesel,0.41 +Lithuania, LPG,0.79 +Bulgaria, CNG,0.61 +Germany, Diesel,0.65 +Luxembourg, Petrol,0.87 +Czech Republic, Hybrid,0.70 +Slovenia, Electric,0.86 +Latvia, Diesel,0.34 +Italy, Plug-in,0.84 +Hungary, Diesel,0.10 +Czech Republic, Diesel,0.18 +Italy, Petrol,0.59 +Chile, Hybrid,0.84 +Argentina, Flexfuel,0.57 +Indonesia, Plug-in,0.05 +Denmark, Hybrid,0.69 +Hungary, LPG,0.39 +Russia, Petrol,0.11 +Malaysia, Unspec.,0.78 +Romania, Electric,0.47 +Brazil, Diesel,0.18 +Czech Republic, Plug-in,0.38 +Czech Republic, Petrol,0.40 +Bulgaria, LPG,0.23 +Latvia, LPG,0.58 +Estonia, Electric,0.93 +Iceland, Diesel,0.34 +Canada, Plug-in,0.31 +Turkey, Electric,0.29 +Bulgaria, Petrol,0.46 +USA, Plug-in,0.47 +Belgium, Electric,0.46 +Ukraine, Plug-in,0.08 +Peru, Unspec.,0.81 +Belgium, Petrol,0.10 +Poland, Flexfuel,0.43 +Estonia, Petrol,0.28 +Lithuania, Plug-in,0.43 +France, LPG,0.68 +Belgium, CNG,0.31 +Canada, Hybrid,0.21 +Japan, Electric,0.50 +Finland, LPG,0.82 +USA, Unspec.,0.10 +Japan, Plug-in,0.14 +Estonia, LPG,0.06 +South Africa, Hybrid,0.57 +Egypt, Petrol,0.89 +Egypt, Unspec.,0.18 +Thailand, Electric,0.96 +Thailand, Diesel,0.30 +Canada, CNG,0.73 +Poland, Diesel,0.44 +Chile, Petrol,0.59 +Iceland, CNG,0.00 +Canada, Flexfuel,0.16 +Indonesia, Hybrid,0.42 +Germany, Electric,0.90 +Netherlands, CNG,0.56 +Belgium, Diesel,0.30 +Finland, Flexfuel,0.72 +Iceland, Hybrid,0.42 +Canada, Petrol,0.03 +Slovenia, Petrol,0.37 +Turkey, Plug-in,0.71 +Bulgaria, Hybrid,0.81 +Luxembourg, Electric,0.15 +Sweden, Electric,0.44 +Poland, CNG,0.36 +Czech Republic, Flexfuel,0.76 +Latvia, Electric,0.27 +Spain, Diesel,0.87 +Ireland, Electric,0.94 +Poland, Electric,0.21 +Portugal, Electric,0.76 +Greece, Hybrid,0.67 +Italy, LPG,0.94 +Italy, Flexfuel,0.05 +Mexico, Electric,0.66 +India, Petrol,0.73 +Mexico, Hybrid,0.55 +Hungary, Hybrid,0.31 +Lithuania, Hybrid,0.92 +Ukraine, LPG,0.28 +Netherlands, Plug-in,0.02 +United Kingdom, Flexfuel,0.98 +Germany, Flexfuel,0.97 +China, CNG,0.23 +Austria, Plug-in,0.03 +Netherlands, Hybrid,0.97 +Russia, Plug-in,0.91 +Japan, Petrol,0.18 +Croatia, Petrol,0.45 +Slovenia, LPG,0.04 +France, Hybrid,0.73 +Thailand, LPG,0.44 +Hungary, Electric,0.59 +Australia, Petrol,0.93 +Latvia, Hybrid,0.38 +Brazil, Plug-in,0.34 +South Korea, Hydrogen,0.93 +Luxembourg, Plug-in,0.05 +Ireland, LPG,0.62 +Russia, Hybrid,0.48 +Slovakia, LPG,0.47 +France, Plug-in,0.52 +Russia, Electric,0.90 +Malta, Electric,0.66 +United Kingdom, Petrol,0.55 +Ukraine, Electric,0.06 +Malta, Plug-in,0.15 +South Africa, Petrol,0.92 +Australia, Hybrid,0.62 +Lithuania, CNG,0.57 +Estonia, Plug-in,0.79 +Bulgaria, Diesel,0.60 +Germany, Plug-in,0.25 +Slovenia, CNG,0.12 +Finland, Diesel,0.19 +Austria, Petrol,0.38 +Romania, Petrol,0.97 +Mexico, Unspec.,0.91 +China, Electric,0.02 +France, CNG,0.54 +Portugal, Diesel,0.08 +Japan, Hybrid,0.04 +Czech Republic, CNG,0.87 +Thailand, Hybrid,0.11 +Denmark, CNG,0.80 +Indonesia, Unspec.,0.91 +Slovenia, Hybrid,0.31 +Luxembourg, Hybrid,0.14 +Finland, Plug-in,0.35 +Finland, Hybrid,0.02 +Cyprus, Petrol,0.91 +Croatia, Hybrid,0.20 +Portugal, Petrol,0.37 +Luxembourg, CNG,0.05 +Brazil, Flexfuel,0.33 +France, Petrol,0.37 +USA, Flexfuel,0.22 +Ireland, Plug-in,0.08 +India, Plug-in,0.28 +Italy, CNG,0.72 +Poland, Petrol,0.49 +South Africa, Diesel,0.37 +Cyprus, Hybrid,0.38 +Malta, Hybrid,0.53 +Canada, Electric,0.83 +Slovenia, Diesel,0.25 +Romania, Flexfuel,0.21 +Slovakia, Hybrid,0.46 diff --git a/message_ix_models/model/bare.py b/message_ix_models/model/bare.py index 9da3d4c348..7928ae104f 100644 --- a/message_ix_models/model/bare.py +++ b/message_ix_models/model/bare.py @@ -35,13 +35,13 @@ def create_res(context, quiet=True): Parameters ---------- - context : Context - :attr:`.Context.scenario_info` determines the model name and scenario name of - the created Scenario. If not provided, the defaults are: + context : .Context + :attr:`.model.Config.scenario_info` determines the model name and scenario name + of the created Scenario. If not provided, the defaults are: - Model name generated by :func:`name`. - Scenario name "baseline". - quiet : bool, *optional* + quiet : bool, optional Passed to `quiet` argument of :func:`.build.apply_spec`. Returns diff --git a/message_ix_models/model/build.py b/message_ix_models/model/build.py index ce660bb2e1..e590604245 100644 --- a/message_ix_models/model/build.py +++ b/message_ix_models/model/build.py @@ -39,7 +39,7 @@ def apply_spec( # noqa: C901 ---------- spec : .Spec Specification of changes to make to `scenario`. - data : callable, *optional* + data : callable, optional Function to add data to `scenario`. `data` can either manipulate the scenario directly, or return a :class:`dict` compatible with :func:`.add_par_data`. diff --git a/message_ix_models/model/disutility.py b/message_ix_models/model/disutility.py index f4e8d2a267..20bbb19ae8 100644 --- a/message_ix_models/model/disutility.py +++ b/message_ix_models/model/disutility.py @@ -48,11 +48,11 @@ def get_spec( Parameters ---------- - groups : list of |Code| + groups : list of .Code Identities of the consumer groups with distinct disutilities. - technologies : list of |Code| + technologies : list of .Code The technologies to which the disutilities are applied. - template : |Code| + template : .Code """ s = Spec() diff --git a/message_ix_models/model/emissions.py b/message_ix_models/model/emissions.py index da40dea732..d668b77929 100644 --- a/message_ix_models/model/emissions.py +++ b/message_ix_models/model/emissions.py @@ -31,7 +31,7 @@ def get_emission_factors(units: Optional[str] = None) -> Quantity: Parameters ---------- - unit : str, *optional* + unit : str, optional Expression for units of the returned quantity. Tested values include: - “tC / TJ”, source units (default), @@ -106,7 +106,7 @@ def add_tax_emission( scen : :class:`message_ix.Scenario` price : float Price in the first model year, in USD / tonne CO₂. - conversion_factor : float, *optional* + conversion_factor : float, optional Factor for converting `price` into the model's internal emissions units, currently USD / tonne carbon. Optional: a default value is retrieved from :mod:`iam_units`. diff --git a/message_ix_models/model/macro.py b/message_ix_models/model/macro.py index 37ac1177f9..036368e316 100644 --- a/message_ix_models/model/macro.py +++ b/message_ix_models/model/macro.py @@ -44,8 +44,8 @@ def generate( node list indicated by :attr:`.model.Config.regions`. - "year": All periods from the period *before* the first model year. - "commodity": The elements of `commodities`. - - "sector": If each entry of `commodities` is a |Code| and has an annotation with - id="macro-sector", the value of that annotation. Otherwise, the same as + - "sector": If each entry of `commodities` is a :class:`.Code` and has an annotation + with id="macro-sector", the value of that annotation. Otherwise, the same as `commodity`. `value` supplies the parameter value, which is the same for all observations. @@ -57,7 +57,7 @@ def generate( MACRO parameter for which to generate data. context Used with :func:`.bare.get_spec`. - commodities : list of str or |Code| + commodities : list of str or .Code Commodities to include in the MESSAGE-MACRO linkage. value : float Parameter value. diff --git a/message_ix_models/model/structure.py b/message_ix_models/model/structure.py index 20d9d492f9..d694748350 100644 --- a/message_ix_models/model/structure.py +++ b/message_ix_models/model/structure.py @@ -94,7 +94,7 @@ def get_codes(name: str) -> List[Code]: @lru_cache() def get_codelist(name: str) -> Codelist: - """Return a |Codelist| for the dimension/set `name` in MESSAGE-GLOBIOM scenarios.""" + """Return a :class:`.Codelist` for `name` in MESSAGEix-GLOBIOM scenarios.""" cl = Codelist(id=name.replace("/", "_").upper()) cl.extend(get_codes(name)) return cl @@ -227,7 +227,7 @@ def process_units_anno(set_name: str, code: Code, quiet: bool = False) -> None: ---------- set_name : str Used in logged messages when `quiet` is :data:`False`. - quiet : bool, *optional* + quiet : bool, optional If :data:`False` (the default), log on level :ref:`WARNING ` if: - the annotation is missing, or diff --git a/message_ix_models/project/gea/data.py b/message_ix_models/project/gea/data.py new file mode 100644 index 0000000000..ed4fdb0d01 --- /dev/null +++ b/message_ix_models/project/gea/data.py @@ -0,0 +1,118 @@ +"""Handle data from the Global Energy Assessment (GEA).""" + +import logging +from functools import lru_cache +from typing import TYPE_CHECKING, Dict, List, Set, Tuple + +import genno + +from message_ix_models.tools.exo_data import ( + ExoDataSource, + iamc_like_data_for_query, + register_source, +) +from message_ix_models.util import package_data_path, path_fallback + +if TYPE_CHECKING: + from sdmx.model.common import Code + + from message_ix_models import Computer + +log = logging.getLogger(__name__) + + +@register_source +class GEA(ExoDataSource): + """Provider of exogenous data from the GEA data source. + + To use data from this source, call :func:`.exo_data.prepare_computer` with the + arguments: + + - `source`: "GEA". + - `source_kw` including: + + - `model`, `scenario`: model name and scenario name. See + :func:`.get_model_scenario`. + - `measure`: See the source data for details. + - `aggregate`, `interpolate`: see :meth:`.ExoDataSource.transform`. + """ + + id = "GEA" + + #: By default, do not aggregate. + aggregate = False + + #: By default, do not interpolate. + interpolate = False + + def __init__(self, source, source_kw): + if source != self.id: + raise ValueError(source) + + # Pieces for query + model = source_kw.pop("model", None) + scenario = source_kw.pop("scenario", None) + variable = source_kw.pop("measure") + + # Check for a valid (model, scenario) combination + check = (model, scenario) + if check not in get_model_scenario(): + log.error(f"No data for (model, scenario) = {check!r}") + raise ValueError(check) + + self.raise_on_extra_kw(source_kw) + + # Identify input data path + self.path = path_fallback( + "gea", "GEADB_ARCHIVE_20171108.zip", where="private test" + ) + if "test" in self.path.parts: + log.warning(f"Reading random data from {self.path}") + + # Assemble query + self.query = " and ".join( + [ + f"MODEL == {model!r}" if model else "True", + f"SCENARIO == {scenario!r}", + f"VARIABLE == {variable!r}", + ] + ) + + def __call__(self): + return iamc_like_data_for_query( + self.path, + self.query, + archive_member=self.path.with_suffix(".csv").name, + non_iso_3166="keep", + ) + + def transform(self, c: "Computer", base_key: genno.Key) -> genno.Key: + """Prepare `c` to transform raw data from `base_key`. + + Compared to :meth:`.ExoDataSource.transform`, this version: + + - Does not perform interpolation. + """ + k = super().transform(c, base_key) + + # TODO Incorporate the following + def adapt_nodes(nodes: List["Code"]) -> Dict[str, str]: + """Convert `nodes` with IDs e.g. “R11_AFR” to a mapping. + + From e.g. “AFR” to “R11_AFR”. + """ + return {r.split("_")[-1]: r for r in map(str, nodes)} + + return k + + +@lru_cache +def get_model_scenario() -> Set[Tuple[str, str]]: + """Return a set of valid GEA (model name, scenario name) combinations. + + These are read from :file:`data/gea/model-scenario.json`. + """ + import json + + with open(package_data_path("gea", "model-scenario.json")) as f: + return set((v["model"], v["scenario"]) for v in json.load(f)) diff --git a/message_ix_models/project/shape/data.py b/message_ix_models/project/shape/data.py new file mode 100644 index 0000000000..65cf5c998f --- /dev/null +++ b/message_ix_models/project/shape/data.py @@ -0,0 +1,132 @@ +"""Handle data from the SHAPE project.""" + +import logging + +from message_ix_models.tools.exo_data import ( + ExoDataSource, + iamc_like_data_for_query, + register_source, +) +from message_ix_models.util import path_fallback + +log = logging.getLogger(__name__) + +#: Information about data file version, suffixes, "variable" codes, and extra columns to +#: drop. +INFO = { + "gdp": dict( + latest="1.2", + suffix=".mif", + variable="GDP|PPP", + ), + "gini": dict( + drop=[ + "tgt.achieved", + "Base gini imputed", + "Share of final consumption among GDP imputed", + ], + latest="1.1", + suffix=".csv", + variable="Gini", + ), + "population": dict( + latest="1.2", + suffix=".mif", + variable="Population", + ), + "urbanisation": dict( + drop=["Notes"], + latest="1.0", + suffix=".csv", + variable="Population|Urban|Share", + ), +} + +#: Convert unit forms appearing in files to pint-compatible expressions. +UNITS = { + "%": "", # urbanisation + "billion $2005/yr": "GUSD_2005 / year", # gdp + "NA": "dimensionless", # gini +} + + +@register_source +class SHAPE(ExoDataSource): + """Provider of exogenous data from the SHAPE project data source. + + To use data from this source, call :func:`.exo_data.prepare_computer` with the + arguments: + + - `source`: "SHAPE". + - `source_kw` including: + + - `measure`: one of the keys of :data:`.INFO`. + - `version` (optional): "latest" (default) or a version string like "1.2". + - `scenario`: one of the SHAPE "SDP" scenario names. + - `aggregate`, `interpolate`: see :meth:`.ExoDataSource.transform`. + """ + + id = "SHAPE" + + def __init__(self, source, source_kw): + if source != self.id: + raise ValueError(source) + + measure = source_kw.pop("measure", None) + version = source_kw.pop("version", "latest") + scenario = source_kw.pop("scenario", None) + + try: + # Retrieve information about the `quantity` + info = INFO[measure] + except KeyError: + raise ValueError(f"quantity must be one of {sorted(INFO.keys())}") + + self.raise_on_extra_kw(source_kw) + + # Choose the version: replace "latest" with the actual version + version = version.replace("latest", info["latest"]) + + # Construct path to data file + self.path = path_fallback( + "shape", + f"{measure}_v{version.replace('.', 'p')}{info['suffix']}", + where="private test", + ) + if "test" in self.path.parts: + log.warning(f"Reading random data from {self.path}") + + variable = info.get("variable", measure) + self.query = " and ".join( + [ + f"Scenario == {scenario!r}" if scenario else "True", + f"Variable == {variable!r}", + ] + ) + + self.to_drop = info.get("drop", []) + if scenario: + self.unique = "MODEL SCENARIO VARIABLE UNIT" + else: + self.unique = "MODEL VARIABLE UNIT" + self.extra_dims = ("SCENARIO",) + + def __call__(self): + # - Read the file. Use ";" for .mif files; set columns as index on load. + # - Drop columns "Model" (meaningless); others from `info`. + # - Drop empty columns (final column in .mif files). + # - Convert column labels to integer. + # - Stack to long format. + # - Apply final column names. + # data = shape_data_from_file(self.path, self.drop) + return iamc_like_data_for_query( + self.path, + self.query, + drop=self.to_drop, + replace={"Unit": UNITS}, + unique=self.unique, + # For pd.DataFrame.read_csv() + na_values=[""], + keep_default_na=False, + sep=";" if self.path.suffix == ".mif" else ",", + ) diff --git a/message_ix_models/report/__init__.py b/message_ix_models/report/__init__.py index 0be8fed763..fd01006481 100644 --- a/message_ix_models/report/__init__.py +++ b/message_ix_models/report/__init__.py @@ -30,20 +30,22 @@ log = logging.getLogger(__name__) -# Add to the configuration keys stored by Reporter.configure(). -genno.config.STORE.add("output_path") -genno.config.STORE.add("output_dir") +# Ignore a section in global.yaml used to define YAML anchors +try: + # genno ≥ 1.25 + genno.config.handles("_iamc formats", False, False)(genno.config.store) +except AttributeError: + # genno < 1.25 + # TODO Remove once the minimum supported version in message-ix-models is ≥ 1.25 + @genno.config.handles("_iamc formats") + def _(c: Reporter, info): + pass + #: List of callbacks for preparing the Reporter. CALLBACKS: List[Callable] = [] -# Ignore a section in global.yaml used to define YAML anchors -@genno.config.handles("_iamc formats") -def _(c: Reporter, info): - pass - - @genno.config.handles("iamc") def iamc(c: Reporter, info): """Handle one entry from the ``iamc:`` config section. @@ -192,7 +194,7 @@ def log_before(context, rep, key) -> None: def report(context: Context, *args, **kwargs): - """Report (post-process) solution data in a |Scenario| and store time series data. + """Report (post-process) solution data in a :class:`.Scenario`. This function provides a single, common interface to call both the :mod:`genno` -based (:mod:`message_ix_models.report`) and ‘legacy’ ( @@ -297,17 +299,17 @@ def prepare_reporter( scenario: Optional[Scenario] = None, reporter: Optional[Reporter] = None, ) -> Tuple[Reporter, Key]: - """Return a :class:`message_ix.Reporter` and `key` prepared to report a |Scenario|. + """Return a :class:`.Reporter` and `key` prepared to report a :class:`.Scenario`. Parameters ---------- - context : Context + context : .Context The code responds to :py:`context.report`, which is an instance of :class:`.report.Config`. - scenario : Scenario, *optional* + scenario : .Scenario, optional Scenario to report. If not given, :meth:`.Context.get_scenario` is used to retrieve a Scenario. - reporter : message_ix.Reporter, *optional* + reporter : .Reporter, optional Existing reporter to extend with computations. If not given, it is created using :meth:`message_ix.Reporter.from_scenario`. diff --git a/message_ix_models/report/compat.py b/message_ix_models/report/compat.py index c64dc38a2d..9e0d198b1b 100644 --- a/message_ix_models/report/compat.py +++ b/message_ix_models/report/compat.py @@ -102,14 +102,14 @@ def func( ---------- technologies : List of technology IDs to include. - name : str, *optional* + name : str, optional If given, the name of the resulting key. Default: a name like "_123" generated with :func:`anon`. - filters : dict, *optional* + filters : dict, optional Additional filters for selecting data from "{base_name}:*". Keys are short dimension names (for instance, "c" for "commodity"); values are lists of IDs. - unit_key : str, *optional* + unit_key : str, optional Key for units to apply to the result. Must appear in :attr:`.Config.units`. """ base = single_key(c.full_key(base_name)) @@ -153,9 +153,9 @@ def eff( Parameters ---------- - filters_in : dict, *optional* + filters_in : dict, optional Passed as the `filters` parameter to :func:`inp`. - filters_out : dict, *optional* + filters_out : dict, optional Passed as the `filters` parameter to :func:`out`. """ # TODO Check whether append / drop "t" is necessary diff --git a/message_ix_models/report/config.py b/message_ix_models/report/config.py index 19af1a5c01..46eec60601 100644 --- a/message_ix_models/report/config.py +++ b/message_ix_models/report/config.py @@ -72,7 +72,7 @@ def use_file(self, file_path: Union[str, Path, None]) -> None: Parameters ---------- - file_path : PathLike, *optional* + file_path : PathLike, optional This may be: 1. The complete path to any existing file. diff --git a/message_ix_models/report/operator.py b/message_ix_models/report/operator.py index 65f993bad1..0777b700d8 100644 --- a/message_ix_models/report/operator.py +++ b/message_ix_models/report/operator.py @@ -219,12 +219,12 @@ def remove_ts( def from_url(url: str, cls=ixmp.TimeSeries) -> ixmp.TimeSeries: """Return a :class:`ixmp.TimeSeries` or subclass instance, given its `url`. - .. todo:: Move upstream, to :mod:`ixmp.reporting`. + .. todo:: Move upstream, to :mod:`ixmp.report`. Parameters ---------- - cls : type, *optional* - Subclass to instantiate and return; for instance, |Scenario|. + cls : type, optional + Subclass to instantiate and return; for instance, :class:`.Scenario`. """ ts, mp = cls.from_url(url) assert ts is not None diff --git a/message_ix_models/report/plot.py b/message_ix_models/report/plot.py index 0dabcff1c8..51d405fb17 100644 --- a/message_ix_models/report/plot.py +++ b/message_ix_models/report/plot.py @@ -47,8 +47,8 @@ class MyPlot(Plot): c.add("plot myplot", MyPlot, "scenario") - …that is, giving "scenario" or another key that points to a |Scenario| object with - stored time series data. See the examples in this file. + …that is, giving "scenario" or another key that points to a :class:`.Scenario` + object with stored time series data. See the examples in this file. """ #: 'Static' geoms: list of plotnine objects that are not dynamic. @@ -71,7 +71,7 @@ class MyPlot(Plot): inputs: Sequence[str] = [] #: List of regular expressions corresponding to :attr:`inputs`. These are passed as - #: the `expr` argument to :func:`filter_ts` to filter the entire set of time series + #: the `expr` argument to :func:`.filter_ts` to filter the entire set of time series #: data. inputs_regex: List[re.Pattern] = [] diff --git a/message_ix_models/report/sim.py b/message_ix_models/report/sim.py index f8f8a3e63a..087362cef6 100644 --- a/message_ix_models/report/sim.py +++ b/message_ix_models/report/sim.py @@ -148,10 +148,10 @@ def add_simulated_solution( Parameters ---------- - data : dict or pandas.DataFrame, *optional* + data : dict or pandas.DataFrame, optional If given, a mapping from MESSAGE item (set, parameter, or variable) names to inputs that are passed to :func:`simulate_qty`. - path : Path, *optional* + path : Path, optional If given, a path to a directory containing one or more files with names like :file:`ACT.csv.gz`. These files are taken as containing "simulated" model solution data for the MESSAGE variable with the same name. See diff --git a/message_ix_models/report/util.py b/message_ix_models/report/util.py index 39496a4d4c..0826822679 100644 --- a/message_ix_models/report/util.py +++ b/message_ix_models/report/util.py @@ -100,7 +100,7 @@ def collapse(df: pd.DataFrame, var=[]) -> pd.DataFrame: Parameters ---------- - var : list of str, *optional* + var : list of str, optional Strings or dimensions to concatenate to the 'Variable' column. The first of these is usually a string value used to populate the column. These are joined using the pipe ('|') character. @@ -175,7 +175,7 @@ def copy_ts(rep: Reporter, other: str, filters: Optional[dict]) -> Key: ---------- other_url : str URL of the other scenario from which to copy time series data. - filters : dict, *optional* + filters : dict, optional Filters; passed via :func:`.store_ts` to :meth:`ixmp.TimeSeries.timeseries`. Returns diff --git a/message_ix_models/testing/__init__.py b/message_ix_models/testing/__init__.py index 54e0e38702..74d3edaab2 100644 --- a/message_ix_models/testing/__init__.py +++ b/message_ix_models/testing/__init__.py @@ -60,7 +60,7 @@ def session_context(pytestconfig, tmp_env): Uses the :func:`.tmp_env` fixture from ixmp. This fixture also sets: - - :attr:`.Context.cache_path`, depending on whether the :program:`--local-cache` CLI + - :attr:`.Config.cache_path`, depending on whether the :program:`--local-cache` CLI option was given: - If not given: pytest's :doc:`standard cache directory `. @@ -167,7 +167,7 @@ def mix_models_cli(session_context, tmp_env): def bare_res(request, context: Context, solved: bool = False) -> message_ix.Scenario: - """Return or create a |Scenario| containing the bare RES, for use in testing. + """Return or create a :class:`.Scenario` containing the bare RES for use in testing. The Scenario has a model name like "MESSAGEix-GLOBIOM [regions] Y[years]", for instance "MESSAGEix-GLOBIOM R14 YB" (see :func:`.bare.name`) and a scenario name @@ -181,12 +181,12 @@ def bare_res(request, context: Context, solved: bool = False) -> message_ix.Scen Parameters ---------- - request : .Request or None + request : .FixtureRequest or None The pytest :fixture:`pytest:request` fixture. If provided the pytest test node name is used for the scenario name of the returned Scenario. - context : Context + context : .Context Passed to :func:`.testing.bare_res`. - solved : bool, *optional* + solved : bool, optional Return a solved Scenario. Returns diff --git a/message_ix_models/testing/cli.py b/message_ix_models/testing/cli.py index ef97bb5f57..119f518f37 100644 --- a/message_ix_models/testing/cli.py +++ b/message_ix_models/testing/cli.py @@ -11,11 +11,22 @@ def cli(): FILENAMES = [ "advance/advance_compare_20171018-134445.csv.zip", + "gea/GEADB_ARCHIVE_20171108.zip", "iea/372f7e29-en.zip", "iea/8624f431-en.zip", "iea/cac5fa90-en.zip", + "shape/gdp_v1p0.mif", + "shape/gdp_v1p1.mif", + "shape/gdp_v1p2.mif", + "shape/gini_v1p0.csv", + "shape/gini_v1p1.csv", + "shape/population_v1p0.mif", + "shape/population_v1p1.mif", + "shape/population_v1p2.mif", + "shape/urbanisation_v1p0.csv", "ssp/SSP-Review-Phase-1.csv.gz", "ssp/SspDb_country_data_2013-06-12.csv.zip", + "transport/GFEI_FE_by_Powertrain_2017.csv", ] @@ -50,6 +61,13 @@ def fuzz_private_data(filename, frac: float): # pragma: no cover path_in = private_data_path(p) path_out = package_data_path("test", p) + # Shared arguments for read_csv() and to_csv() + comment, engine, sep = None, "pyarrow", "," + if "GFEI" in str(p): + comment, engine = "#", "c" + if p.suffix == ".mif": + sep = ";" + # Read the data with TemporaryDirectory() as td: td_path = Path(td) @@ -66,10 +84,23 @@ def fuzz_private_data(filename, frac: float): # pragma: no cover else: target = path_in - # - Read the data using dask & pyarrow. + # - Read the data + # - Use dask & pyarrow. + # - Prevent values like "NA" being auto-transformed to np.nan. # - Subset the data if `frac` < 1.0. - # - Compute the resulting pandas.DataFrame - df = dd.read_csv(target, engine="pyarrow").sample(frac=frac).compute() + # - Compute the resulting pandas.DataFrame. + df = ( + dd.read_csv( + target, + comment=comment, + engine=engine, + keep_default_na=False, + na_values=[], + sep=sep, + ) + .sample(frac=frac) + .compute() + ) # Determine columns in which to replace numerical data if "iea" in filename: @@ -77,7 +108,7 @@ def fuzz_private_data(filename, frac: float): # pragma: no cover cols = ["Value"] else: # All columns with numeric names, for instance 2000, 2001, etc. - cols = list(filter(char.isnumeric, df.columns)) + cols = list(filter(lambda c: char.isnumeric(c) or c.lower() == c, df.columns)) # Shape of random data size = (df.shape[0], len(cols)) @@ -96,4 +127,4 @@ def fuzz_private_data(filename, frac: float): # pragma: no cover else: target = path_out - df.to_csv(target, index=False, float_format="%.2f") + df.to_csv(target, float_format="%.2f", index=False, sep=sep) diff --git a/message_ix_models/tests/model/test_disutility.py b/message_ix_models/tests/model/test_disutility.py index 83aaa41fe0..27c6455410 100644 --- a/message_ix_models/tests/model/test_disutility.py +++ b/message_ix_models/tests/model/test_disutility.py @@ -74,7 +74,7 @@ def spec(groups, techs, template): @pytest.fixture def scenario(request, test_context, techs): - """Fixture: a |Scenario| with technologies given by :func:`techs`.""" + """Fixture: a :class:`.Scenario` with technologies given by :func:`techs`.""" test_context.regions = "R14" s = testing.bare_res(request, test_context, solved=False) s.check_out() diff --git a/message_ix_models/tests/model/test_structure.py b/message_ix_models/tests/model/test_structure.py index dcc0ce2f21..71732e908a 100644 --- a/message_ix_models/tests/model/test_structure.py +++ b/message_ix_models/tests/model/test_structure.py @@ -42,12 +42,12 @@ ], ) def test_codelists(kind, exp): - """:func:`codelists` returns the expected IDs.""" + """:func:`.codelists` returns the expected IDs.""" assert exp == set(codelists(kind)) class TestGetCodes: - """Test :func:`get_codes` for different code lists.""" + """Test :func:`.get_codes` for different code lists.""" @pytest.mark.parametrize( "name", diff --git a/message_ix_models/tests/project/test_gea.py b/message_ix_models/tests/project/test_gea.py new file mode 100644 index 0000000000..9ca47e0d22 --- /dev/null +++ b/message_ix_models/tests/project/test_gea.py @@ -0,0 +1,57 @@ +import pytest +from genno import Computer + +from message_ix_models.project.gea.data import GEA # noqa: F401 +from message_ix_models.tools.exo_data import prepare_computer +from message_ix_models.util import HAS_MESSAGE_DATA as FULL + +M = "Final Energy|Transportation|Total" +S = "geama_450_btr_nsink" + + +class TestGEA: + @pytest.mark.parametrize( + "source_kw", + ( + (dict(measure=M, model="GEA", scenario=S)), + pytest.param( + dict(measure=M, model="IMAGE", scenario=S), + marks=pytest.mark.xfail( + raises=ValueError, + reason="Non-existent (model, scenario) combination", + ), + ), + ), + ) + @pytest.mark.parametrize( + "regions, aggregate, N_n, size", + ( + # Some values are different if testing without MESSAGE_DATA, using the + # reduced/ fuzzed data + ("R12", False, 18 if FULL else 4, 198 if FULL else 44), + # No mapping from GEA to MESSAGE code lists + ("R12", True, 2 if FULL else 0, 22 if FULL else 0), + ), + ) + def test_prepare_computer( + self, test_context, source_kw, regions, aggregate, N_n, size + ): + test_context.model.regions = regions + + c = Computer() + + source = "GEA" + source_kw.update(aggregate=aggregate) + + keys = prepare_computer(test_context, c, source, source_kw) + + # Keys have expected names + assert source_kw["measure"].lower() == keys[0].name + + # Preparation of data runs successfully + result = c.get(keys[0]) + + # Data have expected size, dimensions, and coords + assert size == result.size + assert {"n", "y"} == set(result.dims) + assert N_n == len(result.coords["n"]) diff --git a/message_ix_models/tests/project/test_shape.py b/message_ix_models/tests/project/test_shape.py new file mode 100644 index 0000000000..1f251141a8 --- /dev/null +++ b/message_ix_models/tests/project/test_shape.py @@ -0,0 +1,95 @@ +import genno +import pytest + +from message_ix_models.project.shape.data import SHAPE # noqa: F401 +from message_ix_models.tools.exo_data import prepare_computer + + +class TestSHAPE: + @pytest.mark.parametrize( + "source_kw, dimensionality", + ( + (dict(measure="gdp", scenario="society"), {}), + (dict(measure="gini", scenario="society"), {}), + (dict(measure="population", scenario="all_SHAPE_SDPs"), {}), + (dict(measure="urbanisation", scenario="urb_tech"), {}), + pytest.param( + dict(measure="foo", scenario="urb_tech"), + {}, + marks=pytest.mark.xfail(raises=ValueError), + ), + ), + ) + @pytest.mark.parametrize( + "regions, aggregate, N_n", + ( + ("R12", True, 12), + ("R12", False, 183), + ), + ) + def test_prepare_computer( + self, test_context, source_kw, dimensionality, regions, aggregate, N_n + ): + test_context.model.regions = regions + + source = "SHAPE" + source_kw.update(aggregate=aggregate) + + c = genno.Computer() + + keys = prepare_computer(test_context, c, source, source_kw) + + # Key has an informative name + assert source_kw["measure"] == keys[0].name + + # Preparation of data runs successfully + result = c.get(keys[0]) + + # Data have the expected dimensions and size + assert {"n", "y"} == set(result.dims) + assert N_n <= len(result.coords["n"]) + assert N_n * 14 <= result.size + + +@pytest.mark.parametrize( + "args, size", + [ + (("gdp", None), 23904), + (("gdp", "1.2"), 23904), + (("gdp", "1.1"), 14193), + (("gdp", "1.0"), 14193), + (("gini", None), 9333), + (("gini", "1.1"), 9333), + pytest.param( + ("gini", "1.0"), + 9333, + marks=pytest.mark.xfail( + raises=genno.ComputationError, + reason="File format differs, lacks 'tgt.achieved' column", + ), + ), + (("population", None), 7968), + (("population", "1.2"), 7968), + (("population", "1.1"), 4731), + (("population", "1.0"), 4731), + (("urbanisation", None), 11001), + ], +) +def test_get_shape_data(test_context, args, size): + test_context.model.regions = "R12" + + source = "SHAPE" + source_kw = dict(measure=args[0], aggregate=False, interpolate=False) + if args[1]: + source_kw.update(version=args[1]) + + c = genno.Computer() + + keys = prepare_computer(test_context, c, source, source_kw) + + # Preparation of data runs successfully + result = c.get(keys[0]) + + # Data have the expected size + assert size == result.size + assert {"n", "y", "SCENARIO"} == set(result.dims) diff --git a/message_ix_models/tests/test_report.py b/message_ix_models/tests/test_report.py index 51bd254957..483000f07d 100644 --- a/message_ix_models/tests/test_report.py +++ b/message_ix_models/tests/test_report.py @@ -229,7 +229,7 @@ def test_cli(mix_models_cli): ), ) def test_collapse(input, exp): - """Test :meth:`.report.util.collapse` and use of :data:`.REPLACE_VARS`. + """Test :func:`.report.util.collapse` and use of :data:`.REPLACE_VARS`. This test is parametrized with example input and expected output strings for the ``variable`` IAMC column. There should be ≥1 example for each pattern in diff --git a/message_ix_models/tests/test_testing.py b/message_ix_models/tests/test_testing.py index b7e5b6e5d5..26b19d08cc 100644 --- a/message_ix_models/tests/test_testing.py +++ b/message_ix_models/tests/test_testing.py @@ -4,12 +4,12 @@ def test_bare_res_no_request(test_context): - """:func:`bare_res` works with `request` = :obj:`None`.""" + """:func:`.bare_res` works with `request` = :obj:`None`.""" bare_res(None, test_context, solved=False) def test_bare_res_solved(request, test_context): - """:func:`bare_res` works with `solve` = :obj:`True`. + """:func:`.bare_res` works with `solve` = :obj:`True`. This test can be removed once this feature of the test function is used by another test. diff --git a/message_ix_models/tests/test_util.py b/message_ix_models/tests/test_util.py index cf0d43bd59..43bde462be 100644 --- a/message_ix_models/tests/test_util.py +++ b/message_ix_models/tests/test_util.py @@ -32,6 +32,7 @@ make_source_tech, maybe_query, package_data_path, + path_fallback, private_data_path, replace_par_data, same_node, @@ -334,6 +335,31 @@ def test_package_data_path(): ) +def test_path_fallback(caplog): + # Can be called with where=list() including both strings and paths + result = path_fallback( + "test", "macro", "kgdp.csv", where=["private", package_data_path()] + ) + assert package_data_path("test", "macro", "kgdp.csv") == result + + assert 1 <= len(caplog.messages) + assert caplog.messages[-1].startswith("Not found: ") + caplog.clear() + + # "package", "private", and "test" each expanded to a path + with pytest.raises(ValueError, match=r"'foo.bar' not found in any of \["): + path_fallback("foo", "bar", where="cache package private test") + + assert 4 <= len(caplog.messages) + assert caplog.messages[-1].startswith("Not found: ") + caplog.clear() + + # Empty argument raises an exception + with pytest.raises(ValueError, match="No directories identified among ''"): + path_fallback("foo", "bar") + assert 0 == len(caplog.messages) + + @pytest.mark.xfail( condition=MESSAGE_DATA_PATH is None, reason="Requires message_data to be installed." ) diff --git a/message_ix_models/tests/tools/iea/test_eei.py b/message_ix_models/tests/tools/iea/test_eei.py new file mode 100644 index 0000000000..25d9c204fd --- /dev/null +++ b/message_ix_models/tests/tools/iea/test_eei.py @@ -0,0 +1,76 @@ +import genno +import pandas as pd +import pytest + +from message_ix_models.tools.exo_data import prepare_computer +from message_ix_models.tools.iea.eei import IEA_EEI # noqa: F401 +from message_ix_models.util import HAS_MESSAGE_DATA + +pytestmark = pytest.mark.skipif( + condition=not HAS_MESSAGE_DATA, reason="No fuzzed/random test data for this source." +) + +# Infill data for R12 nodes not present in the IEA data +# NB these are hand-picked as of 2022-07-20 so that the ratio of freight activity / GDP +# is roughly consistent across regions +# FIXME replace with actual data +R12_MAP = [ + ("R12_EEU", "R12_AFR"), + ("R12_NAM", "R12_CHN"), + ("R12_EEU", "R12_EEU"), + ("R12_FSU", "R12_FSU"), + ("R12_LAM", "R12_LAM"), + ("R12_PAO", "R12_MEA"), + ("R12_NAM", "R12_NAM"), + ("R12_PAO", "R12_PAO"), + ("R12_LAM", "R12_PAS"), + ("R12_LAM", "R12_RCPA"), + ("R12_WEU", "R12_SAS"), + ("R12_WEU", "R12_WEU"), +] + + +class TestIEA_EEI: + @pytest.mark.parametrize( + "source_kw, dimensionality", + ( + ( + dict( + indicator="Passenger load factor", + # broadcast_map="bc:n-n2", + ), + {"Mode/vehicle type", "SECTOR"}, + ), + ), + ) + @pytest.mark.parametrize( + "regions, aggregate, N_n", + ( + ("R12", False, 28), + ("R12", True, 5), + ), + ) + def test_prepare_computer( # pragma: no cover cf. iiasa/message-ix-models#164 + self, test_context, source_kw, dimensionality, regions, aggregate, N_n + ): + test_context.model.regions = regions + + source = "IEA EEI" + source_kw.update(aggregate=aggregate) + + c = genno.Computer() + s = pd.Series(1.0, index=pd.MultiIndex.from_tuples(R12_MAP, names=["n", "n2"])) + c.add("bc:n-n2", genno.Quantity(s)) + + keys = prepare_computer(test_context, c, source, source_kw) + + # Keys have informative names + assert "passenger load factor" == keys[0].name + + # Preparation of data runs successfully + result = c.get(keys[0]) + + # assert 1394 == result.size + assert 400 <= result.size + assert {"n", "y"} | dimensionality == set(result.dims) + assert N_n == len(result.coords["n"]) diff --git a/message_ix_models/tests/tools/test_gfei.py b/message_ix_models/tests/tools/test_gfei.py new file mode 100644 index 0000000000..d84c2db291 --- /dev/null +++ b/message_ix_models/tests/tools/test_gfei.py @@ -0,0 +1,34 @@ +import pytest +from genno import Computer + +from message_ix_models.tools.exo_data import prepare_computer +from message_ix_models.tools.gfei import GFEI # noqa: F401 + + +class TestGFEI: + @pytest.mark.parametrize( + "regions, aggregate, N_n, size", + ( + ("R12", False, 50, 317), + ("R12", True, 11, 77), + ), + ) + def test_prepare_computer(self, test_context, regions, aggregate, N_n, size): + test_context.model.regions = regions + + source = "GFEI" + source_kw = dict(aggregate=aggregate, plot=True) + + c = Computer() + + keys = prepare_computer(test_context, c, source, source_kw) + + # Preparation of data and plotting runs successfully + c.add("tmp", [keys[0], "plot GFEI debug"]) + result, *_ = c.get("tmp") + + # Data have the expected dimensions, coords, and size + assert {"n", "t", "y"} == set(result.dims) + assert N_n == len(result.coords["n"]) + assert {2017} == set(result.coords["y"].data) + assert size == result.size diff --git a/message_ix_models/tests/util/test_cache.py b/message_ix_models/tests/util/test_cache.py index 94317aac5b..5e1b97a520 100644 --- a/message_ix_models/tests/util/test_cache.py +++ b/message_ix_models/tests/util/test_cache.py @@ -16,7 +16,7 @@ class TestEncoder: def test_sdmx(self): - """:mod:`message_ix_models` configures :class:`.Encoder` for |Code|.""" + """:mod:`message_ix_models` configures :class:`.Encoder` for :class:`.Code`.""" codes0 = [sdmx_model.Code(id=f"FOO{i}", name="foo") for i in range(5)] codes1 = [f"FOO{i}" for i in range(5)] diff --git a/message_ix_models/tools/exo_data.py b/message_ix_models/tools/exo_data.py index 78ff487abf..bdfbcc8a14 100644 --- a/message_ix_models/tools/exo_data.py +++ b/message_ix_models/tools/exo_data.py @@ -5,7 +5,7 @@ from copy import deepcopy from operator import itemgetter from pathlib import Path -from typing import Any, Dict, Literal, Mapping, Optional, Tuple, Type +from typing import Any, Dict, List, Literal, Mapping, Optional, Tuple, Type from genno import Computer, Key, Quantity, quote @@ -49,6 +49,12 @@ class ExoDataSource(ABC): #: If not set by :meth:`.__init__`, the dimensions are :math:`(n, y)`. extra_dims: Tuple[str, ...] = () + #: :any:`True` if :meth:`.transform` should aggregate data on the |n| dimension. + aggregate: bool = True + + #: :any:`True` if :meth:`.transform` should interpolate data on the |y| dimension. + interpolate: bool = True + @abstractmethod def __init__(self, source: str, source_kw: Mapping) -> None: """Handle `source` and `source_kw`. @@ -98,23 +104,35 @@ def transform(self, c: "Computer", base_key: Key) -> Key: The default implementation: - 1. Aggregates the data (:func:`.genno.operator.aggregate`) on the |n| dimension - using "n::groups". - 2. Interpolates the data (:func:`.genno.operator.interpolate`) on the |y| - dimension using "y::coords". + 1. If :attr:`.aggregate` is :any:`True`, aggregates the data ( + :func:`.genno.operator.aggregate`) on the |n| dimension using the key + "n::groups". + 2. If :attr:`.interpolate` is :any:`True`, interpolates the data ( + :func:`.genno.operator.interpolate`) on the |y| dimension using "y::coords". """ + k = base_key # Aggregate - c.add(base_key + "1", "aggregate", base_key, "n::groups", keep=False) + if self.aggregate: + k = c.add(k + "1", "aggregate", k, "n::groups", keep=False) # Interpolate to the desired set of periods - kw = dict(fill_value="extrapolate") - k2 = base_key + "2" - c.add(k2, "interpolate", base_key + "1", "y::coords", kwargs=kw) + if self.interpolate: + kw = dict(fill_value="extrapolate") + k = c.add(k + "2", "interpolate", k, "y::coords", kwargs=kw) - return k2 + return k def raise_on_extra_kw(self, kwargs) -> None: - """Helper for subclasses.""" + """Helper for subclasses to handle the `source_kw` argument. + + 1. Store :attr:`.aggregate` and :attr:`.interpolate`, if they remain in + `kwargs`. + 2. Raise :class:`ValueError` if there are any other, unhandled keyword arguments + in `kwargs`. + """ + self.aggregate = kwargs.pop("aggregate", self.aggregate) + self.interpolate = kwargs.pop("interpolate", self.interpolate) + if len(kwargs): log.error( f"Unhandled extra keyword arguments for {type(self).__name__}: " @@ -143,13 +161,13 @@ def prepare_computer( source : str Identifier of the source, possibly with other information to be handled by a :class:`ExoDataSource`. - source_kw : dict, *optional* + source_kw : dict, optional Keyword arguments for a Source class. These can include indexers, selectors, or other information needed by the source class to identify the data to be returned. If the key "measure" is present, it **should** be one of :data:`MEASURES`. - strict : bool, *optional* + strict : bool, optional Raise an exception if any of the keys to be added already exist. Returns @@ -165,7 +183,7 @@ def prepare_computer( source_kw = source_kw or dict() if measure := source_kw.get("measure"): if measure not in MEASURES: - log.warning(f"source keyword {measure = } not in recognized {MEASURES}") + log.debug(f"source keyword {measure = } not in recognized {MEASURES}") else: measure = "UNKNOWN" @@ -175,6 +193,8 @@ def prepare_computer( try: # Instantiate a Source object to provide this data source_obj = cls(source, deepcopy(source_kw or dict())) + # except Exception as e: # For debugging + # log.debug(repr(e)) except Exception: pass # Class does not recognize the arguments @@ -183,6 +203,7 @@ def prepare_computer( # Add structural information to the Computer c.require_compat("message_ix_models.report.operator") + c.graph.setdefault("context", context) # Retrieve the node codelist c.add("n::codes", quote(get_codes(f"node/{context.model.regions}")), strict=strict) @@ -292,8 +313,11 @@ def iamc_like_data_for_query( query: str, *, archive_member: Optional[str] = None, + drop: Optional[List[str]] = None, non_iso_3166: Literal["keep", "discard"] = "discard", replace: Optional[dict] = None, + unique: str = "MODEL SCENARIO VARIABLE UNIT", + **kwargs, ) -> Quantity: """Load data from `path` in IAMC-like format and transform to :class:`.Quantity`. @@ -326,7 +350,7 @@ def iamc_like_data_for_query( from message_ix_models.util.pycountry import iso_3166_alpha_3 - unique = dict() + unique_values = dict() def drop_unique(df, names) -> pd.DataFrame: if len(df) == 0: @@ -337,7 +361,7 @@ def drop_unique(df, names) -> pd.DataFrame: values = df[name].unique() if len(values) > 1: raise RuntimeError(f"Not unique {name!r}: {values}") - unique[name] = values[0] + unique_values[name] = values[0] return df.drop(names_list, axis=1) def assign_n(df: pd.DataFrame) -> pd.DataFrame: @@ -357,19 +381,26 @@ def assign_n(df: pd.DataFrame) -> pd.DataFrame: # A direct path, possibly compressed source = path + kwargs.setdefault("engine", "pyarrow") + set_index = ["n"] + sorted( + set(["MODEL", "SCENARIO", "VARIABLE", "UNIT"]) - set(unique.split()) + ) + tmp = ( - pd.read_csv(source, engine="pyarrow") + pd.read_csv(source, **kwargs) + .drop(columns=drop or []) .query(query) .replace(replace or {}) + .dropna(how="all", axis=1) .rename(columns=lambda c: c.upper()) - .pipe(drop_unique, "MODEL SCENARIO VARIABLE UNIT") + .pipe(drop_unique, unique) .pipe(assign_n) .dropna(subset=["n"]) .drop("REGION", axis=1) - .set_index("n") + .set_index(set_index) .rename(columns=lambda y: int(y)) .rename_axis(columns="y") .stack() .dropna() ) - return Quantity(tmp, units=unique["UNIT"]) + return Quantity(tmp, units=unique_values["UNIT"]) diff --git a/message_ix_models/tools/gfei.py b/message_ix_models/tools/gfei.py new file mode 100644 index 0000000000..f1882477d7 --- /dev/null +++ b/message_ix_models/tools/gfei.py @@ -0,0 +1,127 @@ +"""Handle data from the Global Fuel Economy Initiative (GFEI).""" + +import logging +from typing import TYPE_CHECKING + +import genno +import plotnine as p9 + +from message_ix_models.tools.exo_data import ExoDataSource, register_source +from message_ix_models.util import path_fallback + +if TYPE_CHECKING: + from genno import Computer, Quantity + + from message_ix_models import Context + +log = logging.getLogger(__name__) + + +@register_source +class GFEI(ExoDataSource): + """Provider of exogenous data from the GFEI 2017 data source. + + To use data from this source, call :func:`.exo_data.prepare_computer` with the + arguments: + + - `source`: "GFEI". + - `source_kw` including: + + - `plot` (optional, default :any:`False`): add a task with the key + "plot GFEI debug" to generate diagnostic plot using :class:`.Plot`. + - `aggregate`, `interpolate`: see :meth:`.ExoDataSource.transform`. + + The source data: + + - is derived from + https://theicct.org/publications/gfei-tech-policy-drivers-2005-2017, specifically + the data underlying “Figure 37. Fuel consumption range by type of powertrain and + vehicle size, 2017”. + - has resolution of individual countries. + - corresponds to new vehicle registrations in 2017. + - has units of megajoule / kilometre, converted from original litres of gasoline + equivalent per 100 km. + + .. note:: if py:`source_kw["aggregate"] is True`, the aggregation performed is an + unweighted :func:`sum`. To produce meaningful values for multi-country regions, + instead perform perform a weighted mean using appropriate weights; for instance + the vehicle activity for each country. The class currently **does not** do this + automatically. + """ + + id = "GFEI" + + #: By default, do not aggregate. + aggregate = False + + #: By default, do not interpolate. + interpolate = False + + def __init__(self, source, source_kw): + if source != self.id: + raise ValueError(source) + + self.plot = source_kw.pop("plot", False) + + self.raise_on_extra_kw(source_kw) + + # Set the name of the returned quantity + self.name = "fuel economy" + + self.path = path_fallback( + "transport", "GFEI_FE_by_Powertrain_2017.csv", where="private test" + ) + if "test" in self.path.parts: + log.warning(f"Reading random data from {self.path}") + + def __call__(self): + import genno.operator + + from message_ix_models.util.pycountry import iso_3166_alpha_3 + + def relabel_n(qty: "Quantity") -> "Quantity": + labels = {n: iso_3166_alpha_3(n) for n in qty.coords["n"].data} + return genno.operator.relabel(qty, {"n": labels}) + + # - Read the CSV file, rename columns. + # - Assign the y value. + # - Convert units. + return ( + genno.operator.load_file( + self.path, dims={"Country": "n", "FuelTypeReduced": "t"} + ) + .pipe(relabel_n) + .pipe(lambda qty: qty.expand_dims(y=[2017])) + .pipe(genno.operator.convert_units, "MJ / (vehicle km)") + ) + + def transform(self, c: "Computer", base_key: genno.Key) -> genno.Key: + """Prepare `c` to transform raw data from `base_key`.""" + ks = genno.KeySeq(super().transform(c, base_key)) + + if self.plot: + # Path for debug output + context: "Context" = c.graph["context"] + debug_path = context.get_local_path("debug") + debug_path.mkdir(parents=True, exist_ok=True) + c.configure(output_dir=debug_path) + + c.add(f"plot {self.id} debug", Plot, ks.base) + + return ks.base + + +class Plot(genno.compat.plotnine.Plot): + """Diagnostic plot of processed data.""" + + basename = "GFEI-fuel-economy-t" + + static = [ + p9.aes(x="n", y="value"), + p9.geom_col(stat="identity", position="dodge"), + p9.theme(axis_text_x=p9.element_text(rotation=90, hjust=1)), + ] + + def generate(self, data): + for technology, group_df in data.groupby("t"): + yield p9.ggplot(group_df) + self.static + p9.ggtitle(technology) diff --git a/message_ix_models/tools/iea/eei.py b/message_ix_models/tools/iea/eei.py new file mode 100644 index 0000000000..20e49a0322 --- /dev/null +++ b/message_ix_models/tools/iea/eei.py @@ -0,0 +1,308 @@ +"""Handle data from the IEA Energy Efficiency Indicators (EEI).""" +# FIXME This file is currently excluded from coverage measurement. See +# iiasa/message-ix-models#164 + +import logging +import re +from typing import TYPE_CHECKING, Literal + +import genno +import numpy as np +import pandas as pd +import plotnine as p9 + +from message_ix_models import Context +from message_ix_models.tools.exo_data import ExoDataSource, register_source +from message_ix_models.util import cached, path_fallback + +if TYPE_CHECKING: + from genno import Computer + +log = logging.getLogger(__name__) + +#: Mapping of weights to variables used as weights for weighted averaging. +#: +#: .. todo:: Replace with tests showing usage of :func:`wavg`. +WAVG_MAP = { + "Fuel intensity": "vehicle-kilometres", + "Passenger load factor": "vehicle-kilometres", + "Vehicle use": "vehicle stock", + "Vehicle-kilometres energy intensity": "vehicle-kilometres", + "Freight load factor": "vehicle-kilometres", + # Rest of variables are Weighted with population: + # "Passenger-kilometres per capita" + # "Per capita energy intensity" + # "Vehicle-kilometres per capita" + # "Tonne-kilometres per capita" + # TODO The following should not be weighted with population + # "Tonne-kilometres energy intensity": np.average, + # "Passenger-kilometres energy intensity": np.average, +} + + +@register_source +class IEA_EEI(ExoDataSource): + """Provider of exogenous data from the IEA Energy Efficiency Indicators data source. + + To use data from this source, call :func:`.exo_data.prepare_computer` with the + arguments: + + - `source`: "IEA_EEI". + - `source_kw` including: + + - `measure`: name of a measure or indicator in the data. + - `broadcast_map` (optional): name of a :class:`.Key` containing a mapping for + :func:`genno.operator.broadcast_map`. + - `plot` (optional, default :any:`False`): add a task with the key + "plot IEA_EEI debug" to generate diagnostic plot using :class:`.Plot`. + - `aggregate`, `interpolate`: see :meth:`.ExoDataSource.transform`. + """ + + id = "IEA EEI" + + #: By default, do not aggregate. + aggregate = False + + #: By default, do not interpolate. + interpolate = False + + def __init__(self, source, source_kw): + if source != self.id: + raise ValueError(source) + + measure = source_kw.pop("measure", None) + self.broadcast_map = source_kw.pop("broadcast_map", None) + self.plot = source_kw.pop("plot", False) + + self.raise_on_extra_kw(source_kw) + + self.path = path_fallback( + "transport", + "Energyefficiencyindicators_2020-extended.xlsx", + where="private", + ) + + # Prepare query + self.query = f"INDICATOR == {measure!r}" + self.measure = "INDICATOR" + self.name = measure.lower() + + # Determine whether to perform a weighted average operation + self.weights = None + if False: # pragma: no cover + # TODO This code never executes; update and reactivate + pass + + def __call__(self): + from genno.operator import unique_units_from_dim + + tmp = ( + iea_eei_data_raw(self.path) + .query(self.query) + .rename(columns={"TIME_PERIOD": "y"}) + ) + + # Identify dimensions + # - Not the "value" or measure columns. + # - Not columns filled entirely with "__NA". + dims = [ + c + for c, s in tmp.items() + if (c not in {"value", self.measure} and set(s.unique()) != {"__NA"}) + ] + + return genno.Quantity(tmp.set_index(dims)["value"]).pipe( + unique_units_from_dim, dim="UNIT_MEASURE" + ) + + def transform(self, c: "Computer", base_key: genno.Key) -> genno.Key: + ks = genno.KeySeq(super().transform(c, base_key)) + k = ks.base + + if self.broadcast_map: + k_map = genno.Key(self.broadcast_map) + rename = {k_map.dims[1]: k_map.dims[0]} + k = c.add( + ks[0], "broadcast_map", ks.base, self.broadcast_map, rename=rename + ) + + if self.weights: + # TODO Add operations for computing a weighted mean + pass + + if self.plot: + # Path for debug output + context: "Context" = c.graph["context"] + debug_path = context.get_local_path("debug") + debug_path.mkdir(parents=True, exist_ok=True) + c.configure(output_dir=debug_path) + + c.add(f"plot {self.id} debug", Plot, k) + + return k + + +class Plot(genno.compat.plotnine.Plot): + """Diagnostic plot of processed data.""" + + basename = "IEA_EEI-data" + + static = [ + p9.aes(x="Year", y="Value", color="region"), + p9.geom_line(), + p9.facet_wrap("Variable", scales="free_y"), + p9.labs(x="Year", y="mode"), + p9.theme(subplots_adjust={"wspace": 0.15}, figure_size=(11, 9)), + ] + + def generate(self, data): + for mode, group_df in data.groupby("Mode/vehicle type"): + yield p9.ggplot(group_df) + self.static + p9.ggtitle(mode) + + +SECTOR_MEASURE_EXPR = re.compile(r"(?P[^ -]+)[ -](?P.+)") +MEASURE_UNIT_EXPR = re.compile(r"(?P.+) \((?P.+)\)") + + +def extract_measure_and_units(df: pd.DataFrame) -> pd.DataFrame: + # Identify the column containing a units expression: either "Indicator" or "Product" + measure_unit_col = ({"Indicator", "Product"} & set(df.columns)).pop() + # - Split the identified column to UNIT_MEASURE and either INDICATOR or PRODUCT. + # - Concatenate with the other columns. + return pd.concat( + [ + df.drop(measure_unit_col, axis=1), + df[measure_unit_col] + .str.extract(MEASURE_UNIT_EXPR) + .rename(columns={"MEASURE1": measure_unit_col.upper()}), + ], + axis=1, + ) + + +def melt(df: pd.DataFrame) -> pd.DataFrame: + """Melt on any dimensions.""" + index_cols = set(df.columns) & { + "Activity", + "Country", + "End use", + "INDICATOR", + "MEASURE", + "Mode/vehicle type", + "PRODUCT", + "SECTOR", + "Subsector", + "UNIT_MEASURE", + } + return df.melt(id_vars=sorted(index_cols), var_name="TIME_PERIOD") + + +@cached +def iea_eei_data_raw(path, non_iso_3166: Literal["keep", "discard"] = "discard"): + from message_ix_models.util.pycountry import iso_3166_alpha_3 + + xf = pd.ExcelFile(path) + + dfs = [] + for sheet_name in xf.sheet_names: + # Parse the sheet name + match = SECTOR_MEASURE_EXPR.fullmatch(sheet_name) + if match is None: + continue + + # Preserve the sector and/or measure ID from the sheet name + s, m = match.groups() + assign = dict() + if s not in ("Activity",): + assign.update(SECTOR=s.lower()) + if m in ("Energy", "Emissions"): + assign.update(MEASURE=m.lower()) + + # - Read the sheet. + # - Drop rows containing only null values. + # - Right-strip whitespaces from columns containing strings. + # - Assign sector and/or measure ID. + # - Extract units. + # - Melt from wide to long layout. + # - Drop null values. + df = ( + xf.parse(sheet_name, header=1, na_values="..") + .dropna(how="all") + .apply(lambda col: col.str.rstrip() if col.dtype == object else col) + .assign(**assign) + .pipe(extract_measure_and_units) + .pipe(melt) + .dropna(subset="value") + ) + assert not df.isna().any(axis=None) + dfs.append(df) + + return ( + pd.concat(dfs) + .fillna("__NA") + .assign(n=lambda df: df["Country"].apply(iso_3166_alpha_3)) + .drop("Country", axis=1) + ) + + +def wavg(measure: str, df: pd.DataFrame, weight_data: pd.DataFrame) -> pd.DataFrame: + """Perform masked & weighted average for `measure` in `df`, using `weight_data`. + + .. todo:: Replace this with usage of genno; add tests. + + :data:`.WAVG_MAP` is used to select a data from `weight_data` appropriate for + weighting `measure`: either "population", "vehicle stock" or "vehicle-kilometres*. + If the measure to be used for weights is all NaNs, then "population" is used as a + fallback as weight. + + The weighted average is performed by grouping `df` on the "region", "year", and + "Mode/vehicle type" dimensions, i.e. the values returned are averages weighted + within these groups. + + Parameters + ---------- + measure : str + Name of measure contained in `df`. + df : pandas.DataFrame + Data to be aggregated. + weight_data : pandas.DataFrame. + Data source for weights. + + Returns + ------- + pandas.DataFrame + """ + # Choose the measure for weights using `WAVG_MAP`. + weights = WAVG_MAP.get(measure, "population") + if weight_data[weights]["value"].isna().all(): + # If variable to be used for weights is all NaNs, then use population as weights + # since pop data is available in all cases + weights = "population" + + # Align the data and the weights into a single data frame + id_cols = ["region", "year", "Mode/vehicle type"] + data = df.merge( + weight_data[weights], + on=list(filter(lambda c: c in weight_data[weights].columns, id_cols)), + ) + + units = data["units_x"].unique() + assert 1 == len(units), units + + def _wavg(group): + # Create masked arrays, masking NaNs from the weighted average computation: + d = np.ma.masked_invalid(group["value_x"].values) + w = np.ma.masked_invalid(group["value_y"].values) + # Compute weighted average + return np.ma.average(d, weights=w) + + # - Apply _wavg() to groups by `id_cols`. + # - Return to a data frame. + # - Re-insert "units" and "variable" columns. + return ( + data.groupby(id_cols) + .apply(_wavg) + .rename("value") + .reset_index() + .assign(units=units[0], variable=measure) + ) diff --git a/message_ix_models/tools/wb.py b/message_ix_models/tools/wb.py index 3376288f5d..e76f449f6e 100644 --- a/message_ix_models/tools/wb.py +++ b/message_ix_models/tools/wb.py @@ -22,9 +22,9 @@ def assign_income_groups( # noqa: C901 ) -> None: """Annotate `cl_node` with income groups. - Each node is assigned an |Annotation| with :py:`id="wb-income-group"`, according to - the income groups of its children (countries), as reflected in `cl_income_group` - (see :func:`.get_income_group_codelist`). + Each node is assigned an :class:`.Annotation` with :py:`id="wb-income-group"`, + according to the income groups of its children (countries), as reflected in + `cl_income_group` (see :func:`.get_income_group_codelist`). Parameters ---------- @@ -164,7 +164,7 @@ def fetch_codelist(id: str) -> "sdmx.model.common.Codelist": @lru_cache() def get_income_group_codelist() -> "sdmx.model.common.Codelist": - """Return a |Codelist| with World Bank income group information. + """Return a :class:`.Codelist` with World Bank income group information. The returned code list is a modified version of the one with URN ``…Codelist=WB:CL_REF_AREA_WDI(1.0)``, via :func:`.fetch_codelist`. diff --git a/message_ix_models/util/__init__.py b/message_ix_models/util/__init__.py index 2b6fcc3941..cb546373a2 100644 --- a/message_ix_models/util/__init__.py +++ b/message_ix_models/util/__init__.py @@ -4,11 +4,13 @@ from functools import partial, update_wrapper from importlib.metadata import version from itertools import count +from pathlib import Path from typing import ( TYPE_CHECKING, Callable, Collection, Dict, + List, Mapping, MutableMapping, Optional, @@ -20,6 +22,7 @@ import message_ix import pandas as pd import pint +from platformdirs import user_cache_path from ._convert_units import convert_units, series_of_pint_quantity from ._logging import mark_time, preserve_log_level, silence_log @@ -75,6 +78,7 @@ "merge_data", "minimum_version", "package_data_path", + "path_fallback", "preserve_log_level", "private_data_path", "replace_par_data", @@ -101,7 +105,7 @@ def add_par_data( data Dict with keys that are parameter names, and values are pd.DataFrame or other arguments - dry_run : bool, *optional* + dry_run : optional Only show what would be done. See also @@ -306,7 +310,7 @@ def ffill( Dimension to fill along. Must be a column in `df`. values : list of str Labels along `dim` that must be present in the returned data frame. - expr : str, *optional* + expr : str, optional If provided, :meth:`.DataFrame.eval` is called. This can be used to assign one column to another. For instance, if `dim` == "year_vtg" and `expr` is "year_act = year_vtg", then forward filling is performed along the "year_vtg" dimension/ @@ -382,17 +386,17 @@ def make_io(src, dest, efficiency, on="input", **kwargs): Parameters ---------- - src : tuple (str, str, str) - Input commodity, level, unit. - dest : tuple (str, str, str) - Output commodity, level, unit. + src : tuple of str + Input (commodity, level, unit) + dest : tuple of str + Output (commodity, level, unit) efficiency : float Conversion efficiency. on : 'input' or 'output' If 'input', `efficiency` applies to the input, and the output, thus the activity level of the technology, is in dest[2] units. If 'output', the opposite. kwargs - Passed to :func:`~message_ix.make_df`. + Passed to :func:`.make_df`. Returns ------- @@ -432,10 +436,10 @@ def make_matched_dfs( Used to populate other columns of each data frame. Duplicates—which occur when the target parameter has fewer dimensions than `base`—are dropped. par_values : - Argument names (e.g. ‘fix_cost’) are passed to :func:`~.message_ix.make_df`. - If the value is :class:`float`, it overwrites the "value" column; if - :class:`pint.Quantity`, its magnitude overwrites "value" and its units the - "units" column, as a formatted string. + Argument names (e.g. ‘fix_cost’) are passed to :func:`.make_df`. If the value is + :class:`float`, it overwrites the "value" column; if :class:`pint.Quantity`, its + magnitude overwrites "value" and its units the "units" column, as a formatted + string. Returns ------- @@ -476,9 +480,9 @@ def make_source_tech( Parameters ---------- - info : Scenario or ScenarioInfo + info : .Scenario or .ScenarioInfo common : dict - Passed to :func:`~message_ix.make_df`. + Passed to :func:`.make_df`. **values Values for 'capacity_factor' (optional; default 1.0), 'output', 'var_cost', and optionally 'technical_lifetime'. @@ -598,6 +602,71 @@ def marker(test_func): return decorator +def path_fallback( + *parts: Union[str, Path], + where: Union[str, List[Union[str, Path]]] = "", +) -> Path: + """Locate a path constructed from `parts` found in the first of several directories. + + This allows to implement ‘fallback’ behaviour in which files or directories in + certain locations are used preferentially. + + Parameters + ---------- + parts : + Path parts or fragments such as directory names and a final file name. + where : + Either: + + - :class:`str` containing one or more of: + + - "cache": locate `parts` in the :mod:`message_ix_models` cache directory. + - "package": locate `parts` in :mod:`message_ix_models` package data (same + as :func:`.package_data_path`). + - "private": locate `parts` in the :mod:`message_data` :file:`/data/` + directory (same as :func:`.private_data_path`). + - "test": locate test data in :py:`package_data_path("test", ...)` + + - :class:`list` where each element is :class:`str` (one of the above) or a + :class:`pathlib.Path`. + + Returns + ------- + pathlib.Path + The first of the locations indicated by `where` in which the file or directory + `parts` exists. + + Raises + ------ + ValueError + If `where` is empty or `parts` are not found in any of the indicated locations. + """ + dirs = [] + for item in where.split() if isinstance(where, str) else where: + if isinstance(item, str): + if item == "cache": + dirs.append(user_cache_path("message-ix-models")) + elif item == "package": + dirs.append(package_data_path()) + elif item == "private": + dirs.append(private_data_path()) + elif item == "test": + dirs.append(package_data_path("test")) + else: + dirs.append(item) + + for path in [d.joinpath(*parts) for d in dirs]: + if not path.exists(): + log.debug(f"Not found: {path}") + continue + return path + + if not dirs: + raise ValueError(f"No directories identified among {where!r}") + else: + raise ValueError(f"'{Path(*parts)!s}' not found in any of {dirs}") + + def replace_par_data( scenario: message_ix.Scenario, parameters: Union[str, Sequence[str]], @@ -708,9 +777,9 @@ def strip_par_data( # noqa: C901 Parameters ---------- - dry_run : bool, *optional* + dry_run : bool, optional If :data:`True`, only show what would be done. - dump : dict, *optional* + dump : dict, optional If provided, stripped data are stored in this dictionary. Otherwise, they are discarded. diff --git a/message_ix_models/util/_logging.py b/message_ix_models/util/_logging.py index 7dc575d128..49f5a2cf6e 100644 --- a/message_ix_models/util/_logging.py +++ b/message_ix_models/util/_logging.py @@ -37,7 +37,7 @@ class Formatter(logging.Formatter): Parameters ---------- - use_color : bool, *optional* + use_color : bool, optional If :any:`True`, :mod:`colorama` is used to colour log messages. """ @@ -228,6 +228,7 @@ def configure(): # Hide lower-level messages for some upstream packages from the file log ("graphviz._tools", logging.WARNING), ("matplotlib", logging.WARNING), + ("PIL", logging.INFO), ("pycountry.db", logging.WARNING), ): logging.getLogger(name).setLevel(level) @@ -243,11 +244,11 @@ def setup( Parameters ---------- - level : str, *optional* + level : str, optional Log level for the console log handler. - console : bool, *optional* + console : bool, optional If :obj:`False`, do not print any messages to console. - file : bool, *optional* + file : bool, optional If :obj:`False`, do not print any messages to file. """ @@ -279,9 +280,9 @@ def silence_log(names: Optional[str] = None, level: int = logging.ERROR): Parameters ---------- - names : str, *optional* + names : str, optional Space-separated names of loggers to quiet. - level : int, *optional* + level : int, optional Minimum level of log messages to allow. Examples diff --git a/message_ix_models/util/cache.py b/message_ix_models/util/cache.py index 389d2dec5d..5509d2350c 100644 --- a/message_ix_models/util/cache.py +++ b/message_ix_models/util/cache.py @@ -7,7 +7,7 @@ - :class:`sdmx.model.IdentifiableArtefact`, including :class:`.Code`: hashed as their string representation / ID. - :class:`ixmp.Platform`, :class:`xarray.Dataset`: ignored, with a warning logged. -- :class:`ScenarioInfo`: only the :attr:`~ScenarioInfo.set` entries are hashed. +- :class:`.ScenarioInfo`: only the :attr:`~ScenarioInfo.set` entries are hashed. """ import json diff --git a/message_ix_models/util/click.py b/message_ix_models/util/click.py index 830c3247cc..567851aada 100644 --- a/message_ix_models/util/click.py +++ b/message_ix_models/util/click.py @@ -52,7 +52,7 @@ def decorator(f): def default_path_cb(*default_parts): """Return a callback function for click.Option handling. - If no option value is given, the callback uses :meth:`Context.get_local_path` and + If no option value is given, the callback uses :meth:`.Context.get_local_path` and `default_parts` to provide a path that is relative to local data directory, e.g. the current working directory (see :doc:`/data`). """ diff --git a/message_ix_models/util/common.py b/message_ix_models/util/common.py index 3c2a12bf31..0d0275be21 100644 --- a/message_ix_models/util/common.py +++ b/message_ix_models/util/common.py @@ -87,7 +87,7 @@ class MappingAdapter(Adapter): Parameters ---------- - maps : dict of sequence of (str, str) + maps : dict of sequence of tuple Keys are names of dimensions. Values are sequences of 2-tuples; each tuple consists of an original label and a target label. @@ -174,7 +174,7 @@ def load_package_data(*parts: str, suffix: Optional[str] = ".yaml") -> Any: ---------- parts : iterable of str Used to construct a path under :file:`message_ix_models/data/`. - suffix : str, *optional* + suffix : str, optional File name suffix, including, the ".", e.g. :file:`.yaml`. Returns diff --git a/message_ix_models/util/config.py b/message_ix_models/util/config.py index d6bf3299df..7b784edf59 100644 --- a/message_ix_models/util/config.py +++ b/message_ix_models/util/config.py @@ -170,7 +170,8 @@ class Config: #: given by :func:`.platformdirs.user_cache_path`. cache_path: Optional[str] = None - #: Paths of files containing debug outputs. See :meth:`Context.write_debug_archive`. + #: Paths of files containing debug outputs. See + #: :meth:`.Context.write_debug_archive`. debug_paths: Sequence[str] = field(default_factory=list) #: Whether an operation should be carried out, or only previewed. Different modules diff --git a/message_ix_models/util/context.py b/message_ix_models/util/context.py index b5c2b3e84d..d384cba4f1 100644 --- a/message_ix_models/util/context.py +++ b/message_ix_models/util/context.py @@ -53,7 +53,7 @@ def get_instance(cls, index=0) -> "Context": Parameters ---------- - index : int, *optional* + index : int, optional Index of the Context instance to return, e.g. ``-1`` for the most recently created. """ @@ -198,7 +198,7 @@ def clone_to_dest(self, create=True) -> message_ix.Scenario: Parameters ---------- - create : bool, *optional* + create : bool, optional If :obj:`True` (the default) and the base scenario does not exist, a bare RES scenario is created. Otherwise, an exception is raised. @@ -297,7 +297,7 @@ def get_local_path(self, *parts: str, suffix=None) -> Path: return result.with_suffix(suffix) if suffix else result def get_platform(self, reload=False) -> ixmp.Platform: - """Return a |Platform| from :attr:`.Config.platform_info`. + """Return a :class:`.Platform` from :attr:`.Config.platform_info`. When used through the CLI, :attr:`.Config.platform_info` is a 'base' platform as indicated by the --url or --platform options. @@ -324,7 +324,7 @@ def get_platform(self, reload=False) -> ixmp.Platform: return self["_mp"] def get_scenario(self) -> message_ix.Scenario: - """Return a |Scenario| from :attr:`~.Config.scenario_info`. + """Return a :class:`.Scenario` from :attr:`~.Config.scenario_info`. When used through the CLI, :attr:`~.Config.scenario_info` is a ‘base’ scenario for an operation, indicated by the ``--url`` or @@ -333,10 +333,19 @@ def get_scenario(self) -> message_ix.Scenario: return message_ix.Scenario(self.get_platform(), **self.core.scenario_info) def set_scenario(self, scenario: message_ix.Scenario) -> None: - """Update :attr:`.Config.scenario_info` to match an existing `scenario`.""" + """Update :attr:`.Config.scenario_info` to match an existing `scenario`. + + :attr:`.Config.url` is also updated. + """ self.core.scenario_info.update( model=scenario.model, scenario=scenario.scenario, version=scenario.version ) + try: + url = scenario.url + except AttributeError: + # Compatibility with ixmp <3.5 + url = f"{scenario.model}/{scenario.scenario}/{scenario.version}" + self.core.url = f"ixmp://{scenario.platform.name}/{url}" def handle_cli_args( self, diff --git a/message_ix_models/util/pycountry.py b/message_ix_models/util/pycountry.py index f2b4968f22..6373ad1ea4 100644 --- a/message_ix_models/util/pycountry.py +++ b/message_ix_models/util/pycountry.py @@ -7,8 +7,9 @@ COUNTRY_NAME = { "Korea": "Korea, Republic of", "Republic of Korea": "Korea, Republic of", - "South Korea": "Korea, Republic of", "Russia": "Russian Federation", + "South Korea": "Korea, Republic of", + "Turkey": "Türkiye", } diff --git a/message_ix_models/util/scenarioinfo.py b/message_ix_models/util/scenarioinfo.py index 8a51f676c3..c31e9ca8e2 100644 --- a/message_ix_models/util/scenarioinfo.py +++ b/message_ix_models/util/scenarioinfo.py @@ -1,4 +1,4 @@ -""":class:`ScenarioInfo` class.""" +""":class:`.ScenarioInfo` class.""" import logging import re @@ -22,7 +22,7 @@ # TODO: use kw_only=True once python 3.10 is oldest supported version @dataclass() class ScenarioInfo: - """Information about a |Scenario| object. + """Information about a :class:`.Scenario` object. Code that prepares data for a target Scenario can accept a ScenarioInfo instance. This avoids the need to create or load an actual Scenario, which can be slow under @@ -367,7 +367,7 @@ class Spec: A Spec collects 3 :class:`.ScenarioInfo` instances at the attributes :attr:`.add`, :attr:`.remove`, and :attr:`.require`. This is the type that is accepted by :func:`.apply_spec`; :doc:`model-build` describes how a Spec is used to modify a - |Scenario|. A Spec may also be used to express information about the target + :class:`.Scenario`. A Spec may also be used to express information about the target structure of data to be prepared; like ScenarioInfo, this can happen before the target Scenario exists. diff --git a/message_ix_models/util/sdmx.py b/message_ix_models/util/sdmx.py index 1e7176826f..acfbb84538 100644 --- a/message_ix_models/util/sdmx.py +++ b/message_ix_models/util/sdmx.py @@ -29,7 +29,7 @@ def as_codes( # noqa: C901 data: Union[List[str], Dict[str, CodeLike]], ) -> List[Code]: - """Convert `data` to a :class:`list` of |Code| objects. + """Convert `data` to a :class:`list` of :class:`.Code` objects. Various inputs are accepted: diff --git a/message_ix_models/workflow.py b/message_ix_models/workflow.py index 0443529a47..a884cec9a3 100644 --- a/message_ix_models/workflow.py +++ b/message_ix_models/workflow.py @@ -37,12 +37,12 @@ class WorkflowStep: Parameters ---------- name : str - ``"model name/scenario name"`` for the |Scenario| produced by the step. - action : CallbackType, *optional* + ``"model name/scenario name"`` for the :class:`.Scenario` produced by the step. + action : CallbackType, optional Function to be executed to modify the base into the target Scenario. - clone : bool, *optional* + clone : bool, optional :obj:`True` to clone the base scenario the target. - target : str, *optional* + target : str, optional URL for the scenario produced by the workflow step. Parsed to :attr:`scenario_info` and :attr:`platform_info`. kwargs @@ -221,7 +221,7 @@ def truncate(self, name: str): """Truncate the workflow at the step `name`. The step `name` is replaced with a new :class:`WorkflowStep` that simply loads - the target |Scenario| that would be produced by the original step. + the target :class:`.Scenario` that would be produced by the original step. Raises ------ @@ -299,7 +299,7 @@ def make_click_command(wf_callback: str, name: str, slug: str, **kwargs) -> "Com slug : str File name fragment for writing the workflow diagram; the path :file:`{slug}-workflow.svg` is used. - kwargs : *optional* + kwargs : optional Passed to :func:`click.command`, for instance to define additional parameters for the command. """ diff --git a/pyproject.toml b/pyproject.toml index f4211f2275..29f6555482 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -87,6 +87,8 @@ exclude_also = [ omit = [ # The single existing test is far from enough "message_ix_models/report/legacy/*", + # See https://github.com/iiasa/message-ix-models/issues/164 + "message_ix_models/tools/iea/eei.py", ] [tool.mypy]