pollination-imageless-annual-glare


Namepollination-imageless-annual-glare JSON
Version 0.1.7 PyPI version JSON
download
home_pagehttps://github.com/pollination/imageless-annual-glare
SummaryImageless annual glare recipe for Pollination.
upload_time2023-11-29 20:45:01
maintainermostapha, ladybug-tools
docs_urlNone
authorladybug-tools
requires_python
licensePolyForm Shield License 1.0.0, https://polyformproject.org/wp-content/uploads/2020/06/PolyForm-Shield-1.0.0.txt
keywords honeybee radiance ladybug-tools daylight imageless-annual-glare
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # imageless-annual-glare

Run an annual glare study for a Honeybee model to compute hourly Daylight Glare
Probability (DGP) for each sensor in a model's sensor grids.

This recipe uses the image-less glare method developed by Nathaniel Jones to
estimate glare at each sensor. [More information on this method can be found here](https://github.com/nljones/Accelerad/wiki/The-Imageless-Method-for-Spatial-and-Annual-Glare-Analysis).

The resulting DGP is used to compute Glare Autonomy (GA), which is the percentage
of occupied time that a view is free of glare.

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/pollination/imageless-annual-glare",
    "name": "pollination-imageless-annual-glare",
    "maintainer": "mostapha, ladybug-tools",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "mostapha@ladybug.tools, info@ladybug.tools",
    "keywords": "honeybee,radiance,ladybug-tools,daylight,imageless-annual-glare",
    "author": "ladybug-tools",
    "author_email": "info@ladybug.tools",
    "download_url": "https://files.pythonhosted.org/packages/3e/b5/c75949d414fab677d7c5cb1a15f97b4383fbec08337f30143b7ebfd37f16/pollination-imageless-annual-glare-0.1.7.tar.gz",
    "platform": null,
    "description": "# imageless-annual-glare\n\nRun an annual glare study for a Honeybee model to compute hourly Daylight Glare\nProbability (DGP) for each sensor in a model's sensor grids.\n\nThis recipe uses the image-less glare method developed by Nathaniel Jones to\nestimate glare at each sensor. [More information on this method can be found here](https://github.com/nljones/Accelerad/wiki/The-Imageless-Method-for-Spatial-and-Annual-Glare-Analysis).\n\nThe resulting DGP is used to compute Glare Autonomy (GA), which is the percentage\nof occupied time that a view is free of glare.\n",
    "bugtrack_url": null,
    "license": "PolyForm Shield License 1.0.0, https://polyformproject.org/wp-content/uploads/2020/06/PolyForm-Shield-1.0.0.txt",
    "summary": "Imageless annual glare recipe for Pollination.",
    "version": "0.1.7",
    "project_urls": {
        "Homepage": "https://github.com/pollination/imageless-annual-glare",
        "docker": "https://hub.docker.com/r/ladybugtools/honeybee-radiance",
        "icon": "https://raw.githubusercontent.com/ladybug-tools/artwork/master/icons_components/honeybee/png/annualglare.png"
    },
    "split_keywords": [
        "honeybee",
        "radiance",
        "ladybug-tools",
        "daylight",
        "imageless-annual-glare"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "77339a1afd916ee09716e33b654262d9b1cfa2e9dde28571adf0678a6588f13c",
                "md5": "8fd07fc17135da9e886007679d07af4e",
                "sha256": "f1037f91789a12ccda8562161318948803f8553b48e2ad40a0b58015885a3d06"
            },
            "downloads": -1,
            "filename": "pollination_imageless_annual_glare-0.1.7-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "8fd07fc17135da9e886007679d07af4e",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": null,
            "size": 11352,
            "upload_time": "2023-11-29T20:44:58",
            "upload_time_iso_8601": "2023-11-29T20:44:58.736499Z",
            "url": "https://files.pythonhosted.org/packages/77/33/9a1afd916ee09716e33b654262d9b1cfa2e9dde28571adf0678a6588f13c/pollination_imageless_annual_glare-0.1.7-py2.py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "3eb5c75949d414fab677d7c5cb1a15f97b4383fbec08337f30143b7ebfd37f16",
                "md5": "d6e19a2dcc60dec7e4df3779899c1302",
                "sha256": "92c1630b0297c336d0b3e378cd8eae8fc208833b58c998fc37dfcf9adee3b839"
            },
            "downloads": -1,
            "filename": "pollination-imageless-annual-glare-0.1.7.tar.gz",
            "has_sig": false,
            "md5_digest": "d6e19a2dcc60dec7e4df3779899c1302",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 10212,
            "upload_time": "2023-11-29T20:45:01",
            "upload_time_iso_8601": "2023-11-29T20:45:01.468902Z",
            "url": "https://files.pythonhosted.org/packages/3e/b5/c75949d414fab677d7c5cb1a15f97b4383fbec08337f30143b7ebfd37f16/pollination-imageless-annual-glare-0.1.7.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-11-29 20:45:01",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "pollination",
    "github_project": "imageless-annual-glare",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "requirements": [],
    "lcname": "pollination-imageless-annual-glare"
}
        
Elapsed time: 0.16767s