Saravanan666/pypi-org huggingface dataset:
Package: rio-chopt
rio-cliThis utility helps you deploy your code as an API locally on your machine
usingDocker. It is a python based package that can be installed via
pip.QuickstartInstall rio:pip install rio-choptDeploy your package :rio deploy -l path/to/package/foldereg:rio deploy -l /Users/abcdef@ghi.com/Documents/git/myProjectNote: you will be prompted to enter yourdocker hub username, password, and emailYou can find a sample projecthere.- Deploy Package Arguments and Optionsrio deploy -l path/to/package/folder-lis for local deployment (only local is available for now).-nis for specifying a package name. If left out, the folder name is chosen as package name.-pis a port you specify for it to be spun up on(The valid range is 1024-65535). If left out, a port will be
assigned.-qis to deploy with no extra prompts to affirm redeployments and no webpage opening when finishing the deployment.- Re-deploying a package.rio deploy -l path/to/package/folderIf you used a custom name for your package, you will have to specify it with the package name (-n) flag just like you did initially.e.g.rio deploy -l /Users/abcdef@ghi.com/Documents/git/myUpdatedProjectorrio deploy -l -n myCustomName /Users/abcdef@ghi.com/Documents/git/myUpdatedProjectNote: It will re-use the port from the first deployment.Other commands:DescriptionCommandList packages deployedrio list -lStop a package ¹rio stop -l myProjectStop all packagesrio stop -l --allStart a packagerio start -l myProjectStart all packagesrio start -l --allUndeploy a packagerio undeploy -l myProjectUndeploy all packagesrio undeploy -l --allBegin RIO session ² ³rio begin -lEnd RIO sessionrio end -l¹ If a model API associated with this package is running, you will be asked to enter 'Y' to stop it and proceed with deleting the package.² A RIO Session begins automatically upon runningrio deployorrio list.³ You can also point to a yaml file like this:rio begin -l -f /Users/myUser/Documents/docker-creds.yaml. Download a sample credential filehere.You can use the--helpcommand in front of any command from within the CLI for help with options and arguments.Eg:rio deploy --help
Package: rio-client
rio-clientExample:In [1]: from rio_client import *

In [2]: client = Client('http://mysender:replace-token@rio.intra.yourcorp.com/1/myproject')

In [3]: client.emit('post-comment-published', {'content':'Spam','content_id':1})
Out[3]:
{'event_uuid': u'966812c3-6f2f-4155-a733-5054f75265f1',
 'is_success': True,
  'message': u'ok',
   'task_id': u'81ca44c4-cbf6-43eb-ab39-c26159d7379f'}HISTORY0.2.2typofix for flask extentions.0.2.1bugfix for wrong flask conf item.0.2.0add flask contribadd scrapy contrib0.1.2bugfix for rio dsn parser0.1.1add extra option for client0.1.0initial release
Package: rio-cogeo
rio-cogeoCloud Optimized GeoTIFF (COG) creation and validation plugin for Rasterio.Documentation:https://cogeotiff.github.io/rio-cogeo/Source Code:https://github.com/cogeotiff/rio-cogeoCloud Optimized GeoTIFFThis plugin aims to facilitate the creation and validation of Cloud Optimized
GeoTIFF (COG or COGEO). While it respects theCOG specifications, this plugin also
enforces several features:Internal overviews(User can remove overview with option--overview-level 0)Internal tiles(default profiles have 512x512 internal tiles)Important: in GDAL 3.1 a new COG driver has been added (doc,discussion), starting withrio-cogeoversion 2.2,--use-cog-driveroption was added to create COG using theCOGdriver.Install$pipinstall-Upip
$pipinstallrio-cogeoOr install from source:$pipinstall-Upip
$pipinstallgit+https://github.com/cogeotiff/rio-cogeo.gitGDAL VersionIt is recommended to use GDAL > 2.3.2. Previous versions might not be able to
create proper COGs (ref:https://github.com/OSGeo/gdal/issues/754).More info inhttps://github.com/cogeotiff/rio-cogeo/issues/55MoreBlog post on good and bad COG formats:https://medium.com/@_VincentS_/do-you-really-want-people-using-your-data-ec94cd94dc3fCheckoutrio-gluiorrio-vizrasterio plugins to explore COG locally in your web browser.Contribution & DevelopmentSeeCONTRIBUTING.mdChangesSeeCHANGES.md.LicenseSeeLICENSE
Package: rio-colormap
A Rasterio CLI command plugin example.UsageGiven a path to a raster dataset, this project’srio metasaycommand prints
a picture of a cow saying one random or optionally specified (see--item)
metadata item from the dataset.$riometasaytests/data/RGB.byte.tif____________
< Width: 791 >
 ------------
  \
   \   \_\_    _/_/
    \      \__/
           (oo)\_______
           (__)\       )\/\
               ||----w |
               ||     ||This project is a recipe for new, actually useful plugins, and hopefully
generates a few nostalgic chuckles or groans.InstallationIf you’ve alreadyinstalled Rasterio,
installation is justpip installrio-metasay.TestingSee.travis.ymlfor an example of how to use Rasterio releases to speed up
your testing builds.
Package: rio-ds-mask
rio-ds-maskARasterioplugin for extracting
an image’s dataset-level mask.UsageUsage: rio ds-mask [OPTIONS] INPUT OUTPUT

  Extract an image's dataset-level mask.

  Both output driver and datatype are derived from the input image if not
  given.

  In some cases this plugin alters GDAL's returned mask values.  When
  writing masks GDAL uses 0's for opaque and 255's for transparent, but when
  reading masks the returned value differs based on the image's datatype.  8
  bit images produce 8 bit masks where 0's are opaque and 255's are
  transparent, however 16 bit images use 0's for opaque and 1's for
  transparent, still stored as 8 bit.  If the image's datatype is 'int16' or
  'uint16' and the mask's maximum value is 1, then all 1's are translated to
  255's.  The mask's datatype is preserved.  I have not fully investigated
  all of GDAL's masking options to determine if the behavior is consistent.
  If it is found to be a deliberate choice then the normalization will be
  removed.

Options:
  -f, --format, --driver TEXT     Output format driver
  -t, --dtype [ubyte|uint8|uint16|int16|uint32|int32|float32|float64]
                                  Output data type.
  --co, --profile NAME=VALUE      Driver specific creation options.See the
                                  documentation for the selected output driver
                                  for more information.
  --help                          Show this message and exit.This example command creates a singlebanduint8image that is acceptable
to use as a GDAL mask band, meaning that pixels with a value of255are
transparent and pixels with a vaue of0are opaque.  The image is
losslessly compressed and internally tiled.$riods-mask\--driverGTiff\tests/data/alpha.tif\mask.tif\--coCOMPRESS=DEFLATE\--coTILED=YESInstallingFirstinstall Rasterio,
then:$pipinstallrio-ds-mask--userDeveloping$gitclonehttps://github.com/geowurster/rio-ds-mask.git$cdrio-ds-mask$pipinstall-e.\[all\]$pytest--covrio-ds-mask--cov-reportterm-missingLicenseSeeLICENSE.txtChangelogSeeCHANGES.md
Package: rio-faux
rio-fauxNow you can share your dataset!Documentation:https://cogeotiff.github.io/rio-faux/Source Code:https://github.com/cogeotiff/rio-fauxCreate a copy of your dataset without copying the data.Install$pipinstall-Upip
$pipinstallrio-fauxOr install from source:$pipinstall-Upip
$pipinstallgit+https://github.com/cogeotiff/rio-faux.gitUsage$ rio faux --help
Usage: rio faux [OPTIONS] INPUT OUTPUT

  Create empty copy.

Options:
  --forward-band-tags         Forward band tags to output bands.
  --forward-dataset-tags      Forward dataset tags to output image.
  --co, --profile NAME=VALUE  Driver specific creation options. See the documentation for the selected output driver for more information.
  --config NAME=VALUE         GDAL configuration options.
  --help                      Show this message and exit.$ rio faux tests/fixtures/cog_band_tags.tif out.tifContribution & DevelopmentSeeCONTRIBUTING.mdChangesSeeCHANGES.md.LicenseSeeLICENSE
Package: rio-glui
Explore CloudOptimized geotiff on your browser using Mapbox GL JS.InstallYou can install rio-glui using pip$pipinstall-Upip$pipinstallrio-gluior install from source:$gitclonehttps://github.com/mapbox/rio-glui.git$cdrio-glui$pipinstall-e.UsageUsage: rio glui [OPTIONS] PATH

  Rasterio glui cli.

Options:
-b, --bidx BIDX                   Raster band index
--scale INTEGER Min Max           Min and Max data bounds to rescale data from.
--colormap [cfastie|schwarzwald]  Rio-tiler compatible colormap name ('cfastie' or 'schwarzwald')
--tiles-format [png|jpg|webp]     Tile image format (default: png)
--tiles-dimensions INTEGER        Dimension of images being served (default: 512)
--nodata INTEGER                  Force mask creation from a given nodata value
--gl-tile-size INTEGER            mapbox-gl tileSize (default is the same as `tiles-dimensions`)
--port INTEGER                    Webserver port (default: 8080)
--playground                      Launch playground app
--mapbox-token TOKEN              Pass Mapbox token
--help                            Show this message and exit.Explore COG hosted on aws$riogluihttps://oin-hotosm.s3.amazonaws.com/5ac626e091b5310010e0d482/0/5ac626e091b5310010e0d483.tifPlaygroundThe–playgroundoption opens aplaygroundtemplate where you an interact with the data to applyrio-color formula.Creating CloudOptimized GeotiffTo create rio-glui friendly files (CloudOptimized Geotiff) you can use another rasterio plugin: rio-cogeo (https://github.com/cogeotiff/rio-cogeo.git).ExtrasThis plugin also enable raster visualisation in Jupyter Notebook using [mapboxgl-jupyter](https://github.com/mapbox/mapboxgl-jupyter)Contribution & DevellopementIssues and pull requests are more than welcome.Dev install & Pull-Request$gitclonehttps://github.com/mapbox/rio-glui.git$cdrio-glui$pipinstall-e.[dev]Python3.6 onlyThis repo is set to usepre-committo runflake8,pydocstringandblack(“uncompromising Python code formatter”) when commiting new code.$pre-commitinstall$gitadd.$gitcommit-m'my change'black....................................................................Passed
Flake8...................................................................Passed
Verifying PEP257 Compliance..............................................Passed$gitpushorigin
Package: rio-joydivision
imageStreams out moody post-punk geojson w/ y-offsets derived from raster
dataUsage: rio joydivision [OPTIONS] INPUTFILE

  LOVE

      WILL

          TEAR

              US

                  APART

Options:
  -r, --row-interval INTEGER  Row interval to sample on [default = 1]
  -c, --col-interval INTEGER  Column interval to sample on [default = 1]
  -f, --scaling-factor FLOAT  Value to scale y-offset by [default = 1.0]
  -n, --nodata-set TEXT       Value to set nodata to [default = None]
  --version                   Show the version and exit.
  --help                      Show this message and exit.
Package: rio-merge-rgba
A `rio merge` alternative optimized for large RGBA scenetifs`rio merge-rgba` is a CLI with nearly identical arguments to `rio merge`. They accomplish the same task, merging many rasters into one. The differences are in the implementation:`rio merge-rgba`::1. only accepts 4-band RGBA rasters2. writes the destination data to disk rather than an in-memory array3. reads/writes in windows corresponding to the destination block layout4. once a window is filled with data values, the rest of the source files are skipped for that windowThis is both faster and more memory efficient but limited to RGBA rasters.
Package: rio-metasay
A Rasterio CLI command plugin example.UsageGiven a path to a raster dataset, this project’srio metasaycommand prints
a picture of a cow saying one random or optionally specified (see--item)
metadata item from the dataset.$riometasaytests/data/RGB.byte.tif____________
< Width: 791 >
 ------------
  \
   \   \_\_    _/_/
    \      \__/
           (oo)\_______
           (__)\       )\/\
               ||----w |
               ||     ||This project is a recipe for new, actually useful plugins, and hopefully
generates a few nostalgic chuckles or groans.InstallationIf you’ve alreadyinstalled Rasterio,
installation is justpip installrio-metasay.TestingSee.travis.ymlfor an example of how to use Rasterio releases to speed up
your testing builds.
Package: rio-pansharpen
pansharpens Landsat 8 scenes.What is pansharpening?Pansharpening is a process of using the spatial information in the high-resolution grayscale band (panchromatic, or pan-band) and color information in the multispectral bands to create a single high-resolution color image.P pan-pixel cluster + M single multispectral pixel = M pan-sharpened pixelFind more examples and information in theMapbox pansharpening blog post.InstallWe highly recommend installing in a virtualenv. Once activated,pip install -U pip
pip install rio-pansharpenOr install from sourcegit checkout https://github.com/mapbox/rio-pansharpen.git
cd rio-pansharpen
pip install -U pip
pip install -r requirements.txt
pip install -e .Python APIpansharpen.workerTheworkermodule pansharpens Landsat 8. Visit theUSGS Landsat pagepage for more information on Landsat 8 band designations.1.worker.pansharpenTheworker.pansharpenfunction accepts the following as inputs:numpy 3D array with shape == (3, vis_height, vis_width)affine transform defining the georeferencing of the vis arraynumpy 2D array with shape == (pan_height, pan_width)affine transform defining the georeferencing of the pan arraypansharpening methodand outputs:numpy 3D array with shape == (3, pan_height, pan_width)>>> from pansharpen import worker
>>> from pansharpen.methods import Brovey
...
>>> pansharpened = worker.pansharpen(vis, vis_transform, pan, pan_transform,
                       pan_dtype, r_crs, dst_crs, weight,
                       method="Brovey", src_nodata=0)2.worker.calculate_landsat_pansharpen>>> from pansharpen import worker
>>> from pansharpen.utils import _calc_windows
>>> import riomucho
...
>>> worker.calculate_landsat_pansharpen(src_paths, dst_path, dst_dtype,
        weight, verbosity, jobs, half_window,
        customwindow)CLIpansharpenUsage: rio pansharpen [OPTIONS] [SRC_PATHS]... DST_PATH

  Pansharpens a landsat scene. Input is a panchromatic band (B8), plus 3 color
  bands (B4, B3, B2)

     rio pansharpen B8.tif B4.tif B3.tif B2.tif out.tif

  Or with shell expansion

     rio pansharpen LC80410332015283LGN00_B{8,4,3,2}.tif out.tif

Options:
  --dst-dtype [uint16|uint8]
  -w, --weight FLOAT          Weight of blue band [default = 0.2]
  -v, --verbosity
  -j, --jobs INTEGER          Number of processes [default = 1]
  --half-window               Use a half window assuming pan in aligned with
                              rgb bands, default: False
  -c, --customwindow INTEGER  Specify blocksize for custom windows >
                              150[default=src_blockswindows]
  --help                      Show this message and exit.
  --help                 Show this message and exit.Comparison of Different Pansharpening MethodsWe’ve implemented the Weighted Brovey Transform for pansharpening, which is appropriate for data like Landsat where the panchromatic band is relatively similar in resolution to the color bands.For more information on other pansharpening methods such as IHS, PCA, P+XS, Wavelet, VWP, Wavelet with Canny Edge Detector etc, please read our noteshere.
Package: riordinato
RiordinatoRiordinato is a python library for organizing files with prefixes.InstallationUse the package managerpipto install riordinato.$pipinstallriordinatoUse the cliRiordinato includes a cli so you can organize all your files from the terminal.First you must go to the directory where you want riordinato to organize your files, then you must start the database with:$riordinatoinitNow you have to add a prefix and a directory path.$riordinatoadd<prefix><path>Now to organize your files put:$riordinatoorganizeIf you want to know more about the cli read the documentationhere.UsageRiordinato is used to organize files by prefixes. For example, we want to move files that have the prefixes python and work./home/user/documents
    ├── pythonWork.py
    ├── python_examples.txt
    ├── family.jpg
    ├── dog.png
    ├── index.html
    ├── work_list.txt
    ├── any_work.docx
    ├── python_exercise.pdf
    ├── work_for_later.docx
    │
    ├── python/
    └── work/First import riordinatofromriordinatoimportRiordinatoDefine a directory where we have the files we want to move.path='/home/user/documents'NOTE:You can also put a windows path.Create the instance.organize=Riordinato(path)If you want to see the files that are in the path you can print the files attribute.>>>print(organize.files)['pythonWork.py','python_examples.txt','family.jpg','dog.png','index.html','work_list.txt','any_work.docx','work_for_later.docx','python_exercise.pdf']Now you have to create a prefix. to do it is the same when you create a new item for a dictionary, the key is the prefix and the value is the destinationorganize.prefixes['python']='./python'organize.prefixes['work']='./work'NOTE:Riordinato by default transforms all paths into an absolute path. This allows your program to run from any path.To organize our files we use the movefiles methodorganize.movefiles()And our directory would look like this./home/user/documents
    ├── family.jpg
    ├── dog.png
    ├── index.html
    ├── any_work.docx          
    │
    ├── python/
    │   ├── python_exercise.pdf
    │   ├── pythonWork.py
    │   └── python_examples.txt
    └── work/
        ├── work_for_later.docx
        └── work_list.txtIf we want to move files with a specific prefix, use the "specific" parameter of the method.organize.movefiles(specific='python')/home/user/documents
    ├── family.jpg
    ├── dog.png
    ├── index.html
    ├── work_list.txt
    ├── work_for_later.docx
    ├── any_work.docx
    │
    ├── python/
    │   ├── python_exercise.pdf
    │   ├── pythonWork.py
    │   └── python_examples.txt
    └── work/You can also ignore files that contain a certain prefix. In this case we will ignore the files that contain the python prefix.organize.movefile(ignore='python')/home/user/documents
    ├── pythonWork.py
    ├── python_examples.txt
    ├── family.jpg
    ├── dog.png
    ├── index.html
    ├── any_work.docx
    ├── python_exercise.pdf
    │
    ├── python/
    └── work/
        ├── work_for_later.docx
        └── work_list.txtNOTE:the specific and ignore parameters are also compatible with lists.ContributingA contributing.md will be added soon.LicenseMIT
Package: rios.conversion
RIOS.CONVERSION is aPythonpackage that supports
converting research instruments in various formats
to and fromRIOSdata structures.The following APIs have been implemented:redcap_to_riosConverts a REDCap Data Dictionary format to
the RIOS Instrument, Form, and CalculationSet
format.rios_to_redcapConverts a RIOS Instrument, Form, and CalculationSet
to the REDCap Data Dictionary format.qualtrics_to_riosConverts a Qualtrics data dictionary to the RIOS
Instrument and Form format.rios_to_qualtricsConverts a RIOS Instrument, Form, and CalculationSet
to the Qualtrics format.Import these functions for use:>>> from rios.conversion import (
>>>     redcap_to_rios,
>>>     qualtrics_to_rios,
>>>     rios_to_redcap,
>>>     rios_to_qualtrics,
>>> )Notes:The question order, text, and associated enumerations,
are all converted correctly; however the converted expressions
used for “calculated fields” and “skip logic”, as well as the display
niceties of section breaks and separators will most likely require
some “tweaking” because the various systems model pages, events and actions
differently.For example a RIOS calculation is an expression applied to an assessment,
independently of the data collection, while a REDCap “calculated field”
is a read-only field which evaluates its expression and displays the result
during data collection.Installationpip install rios.conversionCopyright (c) 2015, Prometheus Research, LLC
Package: rios.core
RIOS.CORE OverviewRIOS.CORE is aPythonpackage that provides basic validation and
formatting functionality for data structures that adhere to theRIOSspecifications (formally known as PRISMH).Example UsageThis package exposes a handful of simple functions for validating and
formatting the standard RIOS data structures:>>> from rios.core import validate_instrument, get_instrument_json

>>> instrument = {"foo": "bar", "id": "urn:my-instrument", "title": "An Instrument Title", "record": [{"id": "field1","type": "text"}], "version": "1.0"}
>>> validate_instrument(instrument)
Traceback (most recent call last):
    ...
colander.Invalid: {'': u'Unrecognized keys in mapping: "{\'foo\': \'bar\'}"'}

>>> del instrument['foo']
>>> validate_instrument(instrument)

>>> print get_instrument_json(instrument)
{
  "id": "urn:my-instrument",
  "version": "1.0",
  "title": "An Instrument Title",
  "record": [
    {
      "id": "field1",
      "type": "text"
    }
  ]
}For more information on the available functionality, please read the API
documentation.ContributingContributions and/or fixes to this package are more than welcome. Please submit
them by forking this repository and creating a Pull Request that includes your
changes. We ask that you please include unit tests and any appropriate
documentation updates along with your code changes.This project will adhere to theSemantic Versioningmethodology as much as
possible, so when building dependent projects, please use appropriate version
restrictions.A development environment can be set up to work on this package by doing the
following:$ python -m venv rios
$ cd rios
$ . bin/activate
$ git clone git@github.com:prometheusresearch/rios.core.git
$ cd rios.core
$ pip install -e .
$ pip install -r requirements.txt
$ pytestLicense/CopyrightThis project is licensed under the Apache v2 license. See the accompanyingLICENSE.rstfile for details.Copyright (c) 2015, Prometheus Research, LLC
Package: rios_preprocessor
HomepagesVisit theViridian Logichomepage atviridianlogic.comVisit the home ofRIOSon the web atnaturalcapitalproject.orgDiscussTalk to us at Viridian Logic throughour websiteLooking for a place to ask questions about RIOS? Check out theNatural
Capital Project forums!Documentationrios_preprocessoris meant as a replacement for the ArcGIS python
code that currently produces the inputs for the main RIOS program.rios_preprocessorproduces rasters of:Downslope retention indexUpslope sourceRiparian indexSlope indexthat are used as input for the following RIOS objectives:Erosion ControlNitrogen RetentionPhosphorusFlood MitigationGroundwater Recharge/BaseflowAlong the way, there are some additional tricksrios_preprocessoruses that may be useful in other contexts:rasterizes an input shapefile river network and matches it to a
(raster) DEM/DTMidentifies discontinuous river banks along a rasterized riveridentifies the end points of rasterized riversDependenciesnumpypandasgeopandasfionarasterio > 1.0shapelypygeoprocessing ~= 0.3.3Installationpip install rios_preprocessorChangelogVersion 0.2.3Reference stream input (vector) projection verified against raster projection. Vector reprojected if necessary.Minor PEP8 changesVersion 0.2.2Began implementing routine to recover normalisation factors (flood objective only so far)Minor bug fixesVersion 0.2.1Refactored stream rasterization to use geopandasBegan introducing logging features (unfinished)Permitted a riparian zone of zero widthMinor bug fixesVersion 0.2.0Refactored and modularised code for increased usabilityRaster projection checks addedSet deprecation warning for get_objectives_listMinor bug fixesVersion 0.1.9Fixed upslope source and riparian index getting affected by integer bugVersion 0.1.8Deprecated GDAL style transforms in line with rasterio > 0.36Set upper limit on pygeoprocessing to retain flow algorithmsMinor bug fixesVersion 0.1.7Minor bug fixesVersion 0.1.6Implemented loggingPEP8 fixesVersion 0.1.5Python 3 style print statement fixingCleaned up documentation