Ubuntu
pyspectral package

pyspectral 0.13.1+ds-1 source package in Ubuntu

Changelog

pyspectral (0.13.1+ds-1) unstable; urgency=medium

  * New upstream release.
  * Update dates in d/copyright.
  * debian/patches:
    - Drop 0003-Switch-to-platformdirs.patch, applied upstream.
    - Refresh remaining patches.

 -- Antonio Valentino <email address hidden>  Wed, 08 May 2024 05:54:21 +0000

Upload details

Uploaded by:
Debian GIS Project
Uploaded to:
Sid
Original maintainer:
Debian GIS Project
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release universe misc

Builds

Oracular: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
pyspectral_0.13.1+ds-1.dsc 3.7 KiB b0964c4ad743b1a76b5e0b950be86cef7f968903692ed3cc9ca5ef0fa5e7d7fb
pyspectral_0.13.1+ds.orig.tar.xz 3.5 MiB a394db9eda3a746651e715ac565ed052cf0bc7c811d586792289ae5acd57eb92
pyspectral_0.13.1+ds-1.debian.tar.xz 116.8 KiB 8cca84962eb32c1131a3d25d54233f6ff772dfd32edbe061d989802240f5c5ed

Available diffs

No changes file available.

Binary packages built by this source

pyspectral-bin: Reading and manipulaing satellite sensor spectral responses -- scripts

 Reading and manipulaing satellite sensor spectral responses and the
 solar spectrum, to perform various corrections to VIS and NIR band data.
 .
 Given a passive sensor on a meteorological satellite PySpectral
 provides the relative spectral response (rsr) function(s) and offer
 some basic operations like convolution with the solar spectrum to
 derive the in band solar flux, for instance.
 .
 The focus is on imaging sensors like AVHRR, VIIRS, MODIS, ABI, AHI,
 OLCI and SEVIRI. But more sensors are included and if others are
 needed they can be easily added. With PySpectral it is possible to
 derive the reflective and emissive parts of the signal observed in any
 NIR band around 3-4 microns where both passive terrestrial emission
 and solar backscatter mix the information received by the satellite.
 Furthermore PySpectral allows correcting true color imagery for the
 background (climatological) atmospheric signal due to Rayleigh
 scattering of molecules, absorption by atmospheric gases and aerosols,
 and Mie scattering of aerosols.
 .
 This package provides utilities and executable scripts.

python3-pyspectral: Reading and manipulaing satellite sensor spectral responses

 Reading and manipulaing satellite sensor spectral responses and the
 solar spectrum, to perform various corrections to VIS and NIR band data.
 .
 Given a passive sensor on a meteorological satellite PySpectral
 provides the relative spectral response (rsr) function(s) and offer
 some basic operations like convolution with the solar spectrum to
 derive the in band solar flux, for instance.
 .
 The focus is on imaging sensors like AVHRR, VIIRS, MODIS, ABI, AHI,
 OLCI and SEVIRI. But more sensors are included and if others are
 needed they can be easily added. With PySpectral it is possible to
 derive the reflective and emissive parts of the signal observed in any
 NIR band around 3-4 microns where both passive terrestrial emission
 and solar backscatter mix the information received by the satellite.
 Furthermore PySpectral allows correcting true color imagery for the
 background (climatological) atmospheric signal due to Rayleigh
 scattering of molecules, absorption by atmospheric gases and aerosols,
 and Mie scattering of aerosols.

python3-pyspectral-doc: Reading and manipulaing satellite sensor spectral responses -- documentation

 Reading and manipulaing satellite sensor spectral responses and the
 solar spectrum, to perform various corrections to VIS and NIR band data.
 .
 Given a passive sensor on a meteorological satellite PySpectral
 provides the relative spectral response (rsr) function(s) and offer
 some basic operations like convolution with the solar spectrum to
 derive the in band solar flux, for instance.
 .
 The focus is on imaging sensors like AVHRR, VIIRS, MODIS, ABI, AHI,
 OLCI and SEVIRI. But more sensors are included and if others are
 needed they can be easily added. With PySpectral it is possible to
 derive the reflective and emissive parts of the signal observed in any
 NIR band around 3-4 microns where both passive terrestrial emission
 and solar backscatter mix the information received by the satellite.
 Furthermore PySpectral allows correcting true color imagery for the
 background (climatological) atmospheric signal due to Rayleigh
 scattering of molecules, absorption by atmospheric gases and aerosols,
 and Mie scattering of aerosols.
 .
 This package includes the PySpectral documentation in HTML format.