arcsi | Software to automate the production of optical analysis

by remotesensinginfo Python Version: v3.8.1 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | arcsi Summary

arcsi is a Python library typically used in Manufacturing, Utilities, Aerospace, Defense, Internet of Things (IoT) applications. arcsi has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Software to automate the production of optical analysis ready data (ARD) from Landsat, Sentinel-2 and others.

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Quality

Security

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Support

arcsi has a low active ecosystem.

It has 13 star(s) with 7 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 32 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of arcsi is v3.8.1

Quality

arcsi has 0 bugs and 0 code smells.

Security

arcsi has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

arcsi code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

arcsi is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

arcsi releases are available to install and integrate.

Build file is available. You can build the component from source.

It has 21856 lines of code, 740 functions and 53 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed arcsi and discovered the below as its top functions. This is intended to give you an instant insight into arcsi implemented functionality, and help decide if they suit your requirements.

Extract header information from input header
Build a dictionary of the viewing angle array
Return a numpy array from the values list
Resample a spectral response function
Extracts the header values from the header file
Gets the coordinates of the corners of the crop
Extract the coordinates from the GeoJSON header
Get a list of band filenames
Convert an image to a surface reflection
Runs through the input directory tree
Generate ARCSI files
Build ARCSI files
Run ARCSIMulti
This function finds the dark target offsets of the dark target
Convert image to surface reflectance
Performs local DOS on a single band
Convert an image to a surface
Resample image resampling to given path
Setup sen2 database
Setup Landsat database
Convert an image to a surface Reflectance
Build a Lookup from the input directory
Perform ARCSIP1
Generate cloud mask
Extracts header parameters from the header file
Run ARCSI

Get all kandi verified functions for this library.

arcsi Key Features

No Key Features are available at this moment for arcsi.

arcsi Examples and Code Snippets

No Code Snippets are available at this moment for arcsi.

Community Discussions

Trending Discussions on arcsi

JSON metedata file writing nestled floats to .txt or .csv

QUESTION

JSON metedata file writing nestled floats to .txt or .csv

Asked 2018-Jul-12 at 19:44

Very new to python so please forgive me if this is a silly question but I have been attempting to loop an extraction of certain information within a .json file (specifically the date and one value in particular) in order to create a time series. Due to me having over 300 files I would like this to be done automatically, in order to easily create a time series of certain values. I have managed to print the data, however have failed to extract this information to a text file that would be readable in something like excel.

Please find attached both the example .json file I am trying to extract and my code so far. Thanks!

{ "AcquasitionInfo": { "Date": { "Day": 27, "Month": 3, "Year": 2011 }, "EarthSunDistance": 0.9977766, "SolarAzimuth": 154.94013617, "SolarZenith": 53.1387049, "Time": { "Hour": 11, "Minute": 0, "Second": 21 }, "sensorAzimuth": 0.0, "sensorZenith": 0.0 }, "FileInfo": { "CLOUD_MASK": "LS5TM_20110327_lat53lon354_r23p204_clouds.kea", "FileBaseName": "LS5TM_20110327_lat53lon354_r23p204", "IMAGE_DEM": "LS5TM_20110327_lat53lon354_r23p204_dem.kea", "METADATA": "LS5TM_20110327_lat53lon354_r23p204_meta.json", "ProviderMetadata": "LT05_L1TP_204023_20110327_20161208_01_T1_MTL.txt", "RADIANCE": "LS5TM_20110327_lat53lon354_r23p204_vmsk_mclds_rad.kea", "RADIANCE_WHOLE": "LS5TM_20110327_lat53lon354_r23p204_vmsk_rad.kea", "SREF_6S_IMG": "LS5TM_20110327_lat53lon354_r23p204_vmsk_mclds_topshad_rad_srefdem.kea", "STD_SREF_IMG": "LS5TM_20110327_lat53lon354_r23p204_vmsk_mclds_topshad_rad_srefdem_stdsref.kea", "THERMAL_BRIGHT": "LS5TM_20110327_lat53lon354_r23p204_vmsk_thrad_thermbright.kea", "THERMAL_BRIGHT_WHOLE": "LS5TM_20110327_lat53lon354_r23p204_vmsk_thrad_thermbright.kea", "THERM_RADIANCE_WHOLE": "LS5TM_20110327_lat53lon354_r23p204_vmsk_thermrad.kea", "TOA": "LS5TM_20110327_lat53lon354_r23p204_vmsk_mclds_rad_toa.kea", "TOA_WHOLE": "LS5TM_20110327_lat53lon354_r23p204_vmsk_rad_toa.kea", "TOPO_SHADOW_MASK": "LS5TM_20110327_lat53lon354_r23p204_toposhad.kea", "VALID_MASK": "LS5TM_20110327_lat53lon354_r23p204_valid.kea", "VIEW_ANGLE": "LS5TM_20110327_lat53lon354_r23p204_viewangle.kea" }, "ImageInfo": { "CellSizeRefl": 30.0, "CellSizeTherm": 30.0, "CloudCover": 52.0, "CloudCoverLand": 79.0 }, "LocationInfo": { "Geographical": { "BBOX": { "BLLat": 52.06993, "BLLon": -5.34028, "BRLat": 52.08621, "BRLon": -1.72003, "TLLat": 54.09075, "TLLon": -5.45257, "TRLat": 54.10827, "TRLon": -1.65856 }, "CentreLat": 53.10330325240661, "CentreLon": -3.5429440927905724 }, "Projected": { "BBOX": { "BLX": 354735.0, "BLY": 5776815.0, "BRX": 572985.0, "BRY": 5776815.0, "TLX": 354735.0, "TLY": 5992035.0, "TRX": 572985.0, "TRY": 5992035.0 }, "CentreX": 463860.0, "CentreY": 5884425.0, "VPOLY": { "MaxXX": 572985.0, "MaxXY": 5950185.0, "MaxYX": 405795.0, "MaxYY": 5992035.0, "MinXX": 354735.0, "MinXY": 5819025.0, "MinYX": 521775.0, "MinYY": 5776815.0 } } }, "ProductsInfo": { "ARCSIProducts": [ "CLOUDS", "DOSAOTSGL", "STDSREF", "METADATA" ], "ARCSI_AOT_RANGE_MAX": 0.5, "ARCSI_AOT_RANGE_MIN": 0.05, "ARCSI_AOT_VALUE": 0.5, "ARCSI_CLOUD_COVER": 0.627807080745697, "ARCSI_LUT_ELEVATION_MAX": 1100, "ARCSI_LUT_ELEVATION_MIN": -100, "ProcessDate": { "Day": 11, "Month": 7, "Year": 2018 }, "ProcessTime": { "Hour": 7, "Minute": 24, "Second": 55 } }, "SensorInfo": { "ARCSISensorName": "LS5TM", "Path": 204, "Row": 23, "SensorID": "TM", "SpacecraftID": "LANDSAT_5" }, "SoftwareInfo": { "Name": "ARCSI", "URL": "http://www.rsgislib.org/arcsi", "Version": "3.1.4" } }

...

ANSWER

Answered 2018-Jul-12 at 19:44

You forgot the data in the last line :

Source https://stackoverflow.com/questions/51308285

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Vulnerabilities

No vulnerabilities reported

Install arcsi

You can download it from GitHub.
You can use arcsi like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.