| Name | hazelbean JSON |
| Version |
1.6.9
JSON |
| download |
| home_page | https://github.com/jandrewjohnson/hazelbean |
| Summary | Collection of geospatial algorithms, parallel computation utilities and project management tools. |
| upload_time | 2024-12-16 18:35:31 |
| maintainer | Justin Andrew Johnson |
| docs_url | None |
| author | Justin Andrew Johnson |
| requires_python | >=3.6 |
| license | https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. Section 1 – Definitions. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. BY-NC-SA Compatible License means a license listed at creativecommons.org/compatiblelicenses, approved by Creative Commons as essentially the equivalent of this Public License. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. License Elements means the license attributes listed in the name of a Creative Commons Public License. The License Elements of this Public License are Attribution, NonCommercial, and ShareAlike. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. Licensor means the individual(s) or entity(ies) granting rights under this Public License. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. Section 2 – Scope. License grant. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and produce, reproduce, and Share Adapted Material for NonCommercial purposes only. Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. Term. The term of this Public License is specified in Section 6(a). Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. 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You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i). Other rights. Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise. Patent and trademark rights are not licensed under this Public License. To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes. Section 3 – License Conditions. Your exercise of the Licensed Rights is expressly made subject to the following conditions. Attribution. 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You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, Adapted Material that restrict exercise of the rights granted under the Adapter's License You apply. Section 4 – Sui Generis Database Rights. 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| keywords |
gis
economics
land use
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
anytree
certifi
cftime
Click
click-plugins
cligj
cycler
Cython
descartes
entrypoints
future
GDAL
geopandas
keyring
kiwisolver
Markdown
matplotlib
mkl-fft
mkl-rando
munc
netCDF4
numpy
pandas
psycopg2
pygeoprocessing
pyparsing
pyproj
PySAL
pyshp
python-dateutil
pytz
pywin32-ctypes
QtAwesome
QtPy
Rtree
scipy
Shapely
six
SQLAlchem
taskgrap
tornado
wincertstore
xlrd
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Hazelbean
Hazelbean is a collection of geospatial processing tools based on gdal, numpy, scipy, cython, pygeoprocessing, taskgraph, natcap.invest, geopandas and many others to assist in common spatial analysis tasks in sustainability science, ecosystem service assessment, global integrated modelling assessment, natural capital accounting, and/or calculable general equilibrium modelling.
Hazelbean started as a personal research package of scripts for Justin Johnson and is was not originally intended for broad release. However, hazelbean is transitioning towards having full-support, primarily because it underlies several important software releases, including some from the Natural Capital Project. Thus, even in this transitory state, it is available via "pip install hazelbean". Note that hazelbean only provides a Python 3+, 64 bit, Windows version, however with the exception of precompiled cython files, it should be cross-platform and cross-version. The precompiled files are only loaded as needed.
See the Hazelbean documentation in the docs folder of this repository.
## Installation
- Install Mambaforge from https://github.com/conda-forge/miniforge#mambaforge
- For convenience, during installation, I select yes for "Add Mambaforge to my PATH environment Variable"
- (PC) Open the Miniforge Prompt (search for it in the start menu) or (Mac) just type "mamba init"
- Create a new mamba environment with the following commands (here it is named hazelbean_env):
`mamba create -n hazelbean_env -c conda-forge`
- Activate the environment
`mamba activate hazelbean_env`
- Install libraries using conda command:
`mamba install -c conda-forge natcap.invest geopandas rasterstats netCDF4 cartopy xlrd markdown qtpy qtawesome plotly descartes pygeoprocessing taskgraph cython rioxarray dask google-cloud-datastore google-cloud-storage aenum anytree statsmodels openpyxl seaborn twine pyqt ipykernel imageio pandoc conda`
- And then finally, install non-conda distributions via pip:
`pip install mglearn pandoc datascience hazelbean`
## Numpy errors
If numpy throws "wrong size or changes size binary": upgrade numpy at the end of the installation process. See for details: https://stackoverflow.com/questions/66060487/valueerror-numpy-ndarray-size-changed-may-indicate-binary-incompatibility-exp
# Mac specific errors
Your python environment has to have permissions to access and write to the base data folder.
## More information
See the author's personal webpage, https://justinandrewjohnson.com/ for more details about the underlying research.
## Project Flow
One key component of Hazelbean is that it manages directories, base_data, etc. using a concept called ProjectFlow. ProjectFlow defines a tree of tasks that can easily be run in parallel where needed and keeping track of task-dependencies. ProjectFlow borrows heavily in concept (though not in code) from the task_graph library produced by Rich Sharp but adds a predefined file structure suited to research and exploration tasks.
### Project Flow notes
Project Flow is intended to flow easily into the situation where you have coded a script that grows and grows until you think "oops, I should really make this modular." Thus, it has several modalities useful to researchers ranging from simple drop-in solution to complex scripting framework.
#### Notes
In run.py, initialize the project flow object. This is the only place where user supplied (possibly absolute but can be relative) path is stated. The p ProjectFlow object is the one global variable used throughout all parts of hazelbean.
```python
import hazelbean as hb
if __name__ == '__main__':
p = hb.ProjectFlow(r'C:\Files\Research\cge\gtap_invest\projects\feedback_policies_and_tipping_points')
```
In a multi-file setup, in the run.py you will need to import different scripts, such as main.py i.e.:
```python
import visualizations.main
```
The script file mainpy can have whatever code, but in particular can include "task" functions. A task function, shown below, takes only p as an agrument and returns p (potentially modified). It also must have a conditional (if p.run_this:) to specify what always runs (and is assumed to run trivially fast, i.e., to specify file paths) just by nature of having it in the task tree and what is run only conditionally (based on the task.run attribute, or optionally based on satisfying a completed function.)
```python
def example_task_function(p):
"""Fast function that creates several tiny geotiffs of gaussian-like kernels for later use in ffn_convolve."""
if p.run_this:
for i in computationally_intensive_loop:
print(i)
```
**Important Non-Obvious Note**
Importing the script will define function(s) to add "tasks", which take the ProjectFlow object as an argument and returns it after potential modification.
```python
def add_all_tasks_to_task_tree(p):
p.generated_kernels_task = p.add_task(example_task_function)
```
## Creating a new release
Github Actions will now generate a new set of binaries for each release, upload them to PyPI and then trigger a condaforge build. All you need to do is make and tag the release.
## Manually builds to PyPI via Twine
To upload built packages to PyPI, you will need an API key from your PyPI
account, and you will need a local install of the `twine` utility. To install
`twine`, you can use either `pip` or `mamba`. For example:
```bash
pip install twine
```
Once you have built the package for your target platform(s), you can upload the
file to PyPI with twine via the `twine` command. For example, if you have all
of your target distributions in the `dist/` directory, you can upload them all
with:
```bash
twine upload --username=__token__ --password="$PYPI_API_TOKEN" dist/*
```
Raw data
{
"_id": null,
"home_page": "https://github.com/jandrewjohnson/hazelbean",
"name": "hazelbean",
"maintainer": "Justin Andrew Johnson",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "gis, economics, land use",
"author": "Justin Andrew Johnson",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/79/37/d7cee4611c8917e15e29cb40384c752c80e086e9566f6e039903d0b95a94/hazelbean-1.6.9.tar.gz",
"platform": null,
"description": "# Hazelbean\nHazelbean is a collection of geospatial processing tools based on gdal, numpy, scipy, cython, pygeoprocessing, taskgraph, natcap.invest, geopandas and many others to assist in common spatial analysis tasks in sustainability science, ecosystem service assessment, global integrated modelling assessment, natural capital accounting, and/or calculable general equilibrium modelling.\n\nHazelbean started as a personal research package of scripts for Justin Johnson and is was not originally intended for broad release. However, hazelbean is transitioning towards having full-support, primarily because it underlies several important software releases, including some from the Natural Capital Project. Thus, even in this transitory state, it is available via \"pip install hazelbean\". Note that hazelbean only provides a Python 3+, 64 bit, Windows version, however with the exception of precompiled cython files, it should be cross-platform and cross-version. The precompiled files are only loaded as needed.\n\nSee the Hazelbean documentation in the docs folder of this repository.\n\n\n## Installation\n\n- Install Mambaforge from https://github.com/conda-forge/miniforge#mambaforge\n- For convenience, during installation, I select yes for \"Add Mambaforge to my PATH environment Variable\"\n- (PC) Open the Miniforge Prompt (search for it in the start menu) or (Mac) just type \"mamba init\"\n- Create a new mamba environment with the following commands (here it is named hazelbean_env):\n\n`mamba create -n hazelbean_env -c conda-forge`\n\n- Activate the environment\n\n`mamba activate hazelbean_env`\n\n- Install libraries using conda command:\n\n`mamba install -c conda-forge natcap.invest geopandas rasterstats netCDF4 cartopy xlrd markdown qtpy qtawesome plotly descartes pygeoprocessing taskgraph cython rioxarray dask google-cloud-datastore google-cloud-storage aenum anytree statsmodels openpyxl seaborn twine pyqt ipykernel imageio pandoc conda`\n\n- And then finally, install non-conda distributions via pip:\n\n`pip install mglearn pandoc datascience hazelbean`\n\n\n## Numpy errors\n\nIf numpy throws \"wrong size or changes size binary\": upgrade numpy at the end of the installation process. See for details: https://stackoverflow.com/questions/66060487/valueerror-numpy-ndarray-size-changed-may-indicate-binary-incompatibility-exp\n\n# Mac specific errors\n\nYour python environment has to have permissions to access and write to the base data folder.\n\n## More information\nSee the author's personal webpage, https://justinandrewjohnson.com/ for more details about the underlying research.\n\n## Project Flow\n\nOne key component of Hazelbean is that it manages directories, base_data, etc. using a concept called ProjectFlow. ProjectFlow defines a tree of tasks that can easily be run in parallel where needed and keeping track of task-dependencies. ProjectFlow borrows heavily in concept (though not in code) from the task_graph library produced by Rich Sharp but adds a predefined file structure suited to research and exploration tasks.\n\n### Project Flow notes\n\nProject Flow is intended to flow easily into the situation where you have coded a script that grows and grows until you think \"oops, I should really make this modular.\" Thus, it has several modalities useful to researchers ranging from simple drop-in solution to complex scripting framework.\n\n#### Notes\n\nIn run.py, initialize the project flow object. This is the only place where user supplied (possibly absolute but can be relative) path is stated. The p ProjectFlow object is the one global variable used throughout all parts of hazelbean.\n\n```python\nimport hazelbean as hb\n\nif __name__ == '__main__':\n p = hb.ProjectFlow(r'C:\\Files\\Research\\cge\\gtap_invest\\projects\\feedback_policies_and_tipping_points')\n```\n\nIn a multi-file setup, in the run.py you will need to import different scripts, such as main.py i.e.:\n```python\nimport visualizations.main\n```\n\nThe script file mainpy can have whatever code, but in particular can include \"task\" functions. A task function, shown below, takes only p as an agrument and returns p (potentially modified). It also must have a conditional (if p.run_this:) to specify what always runs (and is assumed to run trivially fast, i.e., to specify file paths) just by nature of having it in the task tree and what is run only conditionally (based on the task.run attribute, or optionally based on satisfying a completed function.)\n```python\ndef example_task_function(p):\n \"\"\"Fast function that creates several tiny geotiffs of gaussian-like kernels for later use in ffn_convolve.\"\"\"\n\n if p.run_this:\n for i in computationally_intensive_loop:\n print(i)\n```\n**Important Non-Obvious Note**\n\nImporting the script will define function(s) to add \"tasks\", which take the ProjectFlow object as an argument and returns it after potential modification.\n\n```python\ndef add_all_tasks_to_task_tree(p):\n p.generated_kernels_task = p.add_task(example_task_function)\n```\n\n## Creating a new release\n\nGithub Actions will now generate a new set of binaries for each release, upload them to PyPI and then trigger a condaforge build. All you need to do is make and tag the release.\n\n## Manually builds to PyPI via Twine\n\nTo upload built packages to PyPI, you will need an API key from your PyPI\naccount, and you will need a local install of the `twine` utility. To install\n`twine`, you can use either `pip` or `mamba`. For example:\n\n```bash\npip install twine\n```\n\nOnce you have built the package for your target platform(s), you can upload the\nfile to PyPI with twine via the `twine` command. For example, if you have all\nof your target distributions in the `dist/` directory, you can upload them all\nwith:\n\n```bash\ntwine upload --username=__token__ --password=\"$PYPI_API_TOKEN\" dist/*\n``` \n",
"bugtrack_url": null,
"license": "https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (\"Public License\"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. Section 1 \u2013 Definitions. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. BY-NC-SA Compatible License means a license listed at creativecommons.org/compatiblelicenses, approved by Creative Commons as essentially the equivalent of this Public License. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. License Elements means the license attributes listed in the name of a Creative Commons Public License. The License Elements of this Public License are Attribution, NonCommercial, and ShareAlike. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. Licensor means the individual(s) or entity(ies) granting rights under this Public License. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. Section 2 \u2013 Scope. License grant. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and produce, reproduce, and Share Adapted Material for NonCommercial purposes only. Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. Term. The term of this Public License is specified in Section 6(a). Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material. Downstream recipients. Offer from the Licensor \u2013 Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. Additional offer from the Licensor \u2013 Adapted Material. Every recipient of Adapted Material from You automatically receives an offer from the Licensor to exercise the Licensed Rights in the Adapted Material under the conditions of the Adapter\u2019s License You apply. No downstream restrictions. 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