{"id":13734471,"url":"https://github.com/CosmiQ/CometTS","last_synced_at":"2025-05-08T10:32:05.537Z","repository":{"id":62563919,"uuid":"117258195","full_name":"CosmiQ/CometTS","owner":"CosmiQ","description":"Comet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons","archived":false,"fork":false,"pushed_at":"2019-10-23T17:09:05.000Z","size":16564,"stargazers_count":63,"open_issues_count":1,"forks_count":16,"subscribers_count":8,"default_branch":"master","last_synced_at":"2024-10-12T19:48:45.666Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CosmiQ.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.MD","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-01-12T15:38:31.000Z","updated_at":"2024-04-13T10:08:17.000Z","dependencies_parsed_at":"2022-11-03T16:00:29.751Z","dependency_job_id":null,"html_url":"https://github.com/CosmiQ/CometTS","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CosmiQ%2FCometTS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CosmiQ%2FCometTS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CosmiQ%2FCometTS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CosmiQ%2FCometTS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CosmiQ","download_url":"https://codeload.github.com/CosmiQ/CometTS/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224720964,"owners_count":17358485,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T03:00:56.175Z","updated_at":"2024-11-15T02:32:44.682Z","avatar_url":"https://github.com/CosmiQ.png","language":"Jupyter Notebook","funding_links":[],"categories":["`Python` processing of optical imagery (non deep learning)","Visualization"],"sub_categories":["Processing imagery - post processing"],"readme":"\u003ch1 align=\"center\"\u003eComet Time Series (CometTS) Visualizer\u003c/h1\u003e\n\n![Niamey Time Series Plot](ExamplePlots/Niamey.png)\n\n\u003cp align=\"center\"\u003e\n\n\u003cimg align=\"center\" src=\"https://img.shields.io/pypi/v/cometts.svg\" alt=\"PyPI\"\u003e\n\u003cimg align=\"center\" src=\"https://joss.theoj.org/papers/10.21105/joss.01047/status.svg\" alt=\"DOI badge\" \u003e\n\u003cimg align=\"center\" src=\"https://travis-ci.com/jshermeyer/CometTS.svg?branch=master\" alt=\"build\"\u003e\n\u003cimg align=\"center\" src=\"https://img.shields.io/github/license/jshermeyer/cometts.svg\" alt=\"license\"\u003e\n\u003cimg align=\"center\" src=\"https://img.shields.io/docker/build/cosmiqworks/cw-eval.svg\" alt=\"docker\"\u003e\n\u003ca href=\"https://codecov.io/gh/cosmiq/cometts\"\u003e\u003cimg align=\"center\" src=\"https://codecov.io/gh/cosmiq/cometts/branch/master/graph/badge.svg\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n- [Installation Instructions](#installation-instructions)\n- [Dependencies](#dependencies)\n- [License](#license)\n---\n\n\n## Base Functionality\nComet Time Series (``CometTS``) is an open source tool coded in python including jupyter notebooks and command line utility that enables users to visualize or extract relevant statistics from [almost any](https://gdal.org/drivers/raster/index.html) format time series of overhead imagery within a specific region of interest (ROI). To use ``CometTS``, you must define your ROI, provide a CSV file documenting how your imagery is organized, and then run one of the ``CometTS`` analysis tools. This usually takes the following steps\n\n1. Outline and download your ROI with a service like [geojson.io](www.geojson.io)\n2. Organize your imagery and document it with the [CometTS.CSV_It tool](https://github.com/CosmiQ/CometTS/blob/master/CometTS/CSV_It.py)\n3. Analyze your data using:\n   - [CometTS](https://github.com/CosmiQ/CometTS/blob/master/CometTS/CometTS.py) for trend analysis  (optionally, mask unwanted clouds and other features with [`--maskit` option](https://github.com/CosmiQ/CometTS/search?l=Python\u0026q=--maskit))\n   - or [CometTS.ARIMA](https://github.com/CosmiQ/CometTS/blob/master/CometTS/ARIMA.py) for averaging and anomaly detection\n4. Plot the results using [the plotting notebook](Notebooks/Plot_Results.ipynb)\n\nA full walkthrough of this functionality with example data is included in two notebooks: [CSV_Creator](Notebooks/CSV_Creator.ipynb) and [CometTS_Visualizer](Notebooks/CometTS_Visualizer.ipynb)\n\n### File Formats:\n\n[Supported Raster Formats](https://gdal.org/drivers/raster/index.html)\n\n[Supported Vector Formats](https://gdal.org/drivers/vector/index.html)\n\n## Installation\nPython 2.7 or 3.6 are the base requirements plus several packages.  ``CometTS`` can be installed in multiple ways including conda, pip, docker, and cloning this repository. \n\n\n### Clone it\nWe recommend cloning to add all sample data and easier access to the jupyter notebooks that leverage our plotting functions.\n```\ngit clone https://github.com/CosmiQ/CometTS.git\n```\nIf you would like the full functionality of a python package we have several options.\n\n### pip\n```\npip install CometTS\n```\npip installs may fail on macs with python3 as GDAL is finicky.  Use some of the alternative approaches below.\n\n### Docker\n```\ndocker pull jss5102/cometts\ndocker run -it -v /nfs:/nfs --name cometts jss5102/cometts /bin/bash \n```\n\n### Conda\nCreate a conda environment!\n\n```\ngit clone https://github.com/CosmiQ/CometTS.git\ncd CometTS\nconda env create -f environment.yml\nsource activate CometTS\npip install CometTS\n```\n\n### Dependencies\nAll dependencies can be found in the docker file [Dockerfile](./Dockerfile) or\n[environment.yml](./environment.yml) or [requirements.txt](./requirements.txt).\n\n## Examples\n\n#### Agadez, Niger\n![Agadez Time Series Plot](ExamplePlots/Agadez.png)\nSeasonal variation in brightness that likely indicates seasonal migrations and population fluctuations in central Niger, Africa.\n\n\n#### Suruc Refugee Camp, Turkey\n![Suruc Time Series Plot](ExamplePlots/Suruc.png)\nIncrease in brightness coinciding with the establishment of a refugee camp in southern Turkey, north of Syria.\n\n\n#### Allepo, Syria\n![Allepo Time Series Plot](ExamplePlots/Allepo.png)\nBrightness declines (i.e., putative population decline) as a result of Syrian Civil War and military actions in Aleppo.\n\n\n#### NDVI Visualization north of Houston, Texas\n![Allepo Time Series Plot](ExamplePlots/NDVI_3.png)\nA visualization of the Normalized Difference Vegetation Index (NDVI) in a field north of Houston using a time-series of Landsat imagery.\n\n\n#### Landsat Multispectral Visualization\n![Landsat Time Series Plot](ExamplePlots/LandsatPlot.png)\nA visualization of three Landsat bands in New Orleans, Louisiana.  Note the effects of Katrina in 2005.\n\n\n## Contribute or debug?\nInterested in proposing a change, fixing a bug, or asking for help? Check out the [contributions](https://github.com/CosmiQ/CometTS/blob/master/CONTRIBUTING.MD) guidance.\n\n\n## License\nSee [LICENSE](./LICENSE).\n\n## Traffic\n![PyPI](https://img.shields.io/pypi/dm/cometts.svg)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCosmiQ%2FCometTS","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FCosmiQ%2FCometTS","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCosmiQ%2FCometTS/lists"}