pyspectral 0.8.9+ds-1 source package in Ubuntu

Changelog

pyspectral (0.8.9+ds-1) unstable; urgency=medium

  [ Bas Couwenberg ]
  * Update gbp.conf to use --source-only-changes by default.

  [ Antonio Valentino ]
  * New upstream release.
  * Set distribution to unstable.

 -- Antonio Valentino <email address hidden>  Mon, 08 Jul 2019 05:47:10 +0000

Upload details

Uploaded by:
Debian GIS Project on 2019-07-09
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
Eoan release on 2019-07-10 universe misc

Builds

Eoan: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
pyspectral_0.8.9+ds-1.dsc 2.4 KiB 3cf5f7b700e17891db87533d2417b1d24475fcbf79a900234b6f458d912b69a2
pyspectral_0.8.9+ds.orig.tar.xz 3.4 MiB 031dd1b25750042aa42f0f1f5f8d9b4e21edbb0502b14d7e1c474fd3a636583f
pyspectral_0.8.9+ds-1.debian.tar.xz 115.7 KiB 8a14c4e0a63b87d19f42b9bb427ca57ee1a94055204b640a1c00ea8e8a933a4a

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.