pyspectral 0.13.0+ds-3 source package in Ubuntu
Changelog
pyspectral (0.13.0+ds-3) unstable; urgency=medium
* debian/patches:
- New 0003-Switch-to-platformdirs.patch (Closes: #1067988).
-- Antonio Valentino <email address hidden> Fri, 29 Mar 2024 18:05:06 +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 | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pyspectral_0.13.0+ds-3.dsc | 3.7 KiB | daa3a85d959513cbe03bd809d7c68080dc68f31484f2ee37d3210b99dd9765ab |
| pyspectral_0.13.0+ds.orig.tar.xz | 3.4 MiB | ca6159af2e494fbe20eb638a4c194e8264a7426e410f9d160fb7ee77ecb4e263 |
| pyspectral_0.13.0+ds-3.debian.tar.xz | 117.1 KiB | 5f39c0253f2db3501cd78508a0d1f6b15d4464332a1feeefe951c287cff013aa |
Available diffs
- diff from 0.13.0+ds-1 to 0.13.0+ds-3 (1.4 KiB)
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.
