pointpats 2.4.0-1 source package in Ubuntu
Changelog
pointpats (2.4.0-1) unstable; urgency=medium * Initial release. (Closes: #1058897) -- Josenilson Ferreira da Silva <email address hidden> Thu, 14 Dec 2023 21:51:09 -0300
Upload details
- Uploaded by:
- Josenilson Ferreira da Silva
- Uploaded to:
- Sid
- Original maintainer:
- Josenilson Ferreira da Silva
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Noble | release | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pointpats_2.4.0-1.dsc | 2.5 KiB | d6687a5455ae01c60de969b69341f9e9b58ab2a5815bba613f13a0563aa05854 |
| pointpats_2.4.0.orig.tar.gz | 4.1 MiB | 558604082c27c1bd4d05c1b8271a92c6c2ee4503fbc176d0f3c88c7094a99012 |
| pointpats_2.4.0-1.debian.tar.xz | 4.7 KiB | 48e366ec057ed1c7af53f34a6843fbac342080e99bd41f48526fe271676d8dbd |
No changes file available.
Binary packages built by this source
- python-pointpats-doc: statistical analysis of planar point patterns (common documentation)
The main objective of this module is to provide methods and functions for
analyzing spatial patterns in point data. This includes the detection and
characterization of different types of patterns, such as clusters, scatters
or random patterns.
.
The project is integrated with the PySAL library, which is a broader library
for spatial analysis in Python. This means that PointPatterns can be used in
conjunction with other tools available in the PySAL ecosystem.
.
One of the main features of the module is to provide methods to calculate
descriptive statistics, detect spatial clusters, perform orbit analysis
and even perform statistical tests to evaluate the significance of observed
patterns.
.
This package installs the common documentation package.
- python3-pointpats: statistical analysis of planar point patterns
The main objective of this module is to provide methods and functions for
analyzing spatial patterns in point data. This includes the detection and
characterization of different types of patterns, such as clusters, scatters
or random patterns.
.
The project is integrated with the PySAL library, which is a broader library
for spatial analysis in Python. This means that PointPatterns can be used in
conjunction with other tools available in the PySAL ecosystem.
.
One of the main features of the module is to provide methods to calculate
descriptive statistics, detect spatial clusters, perform orbit analysis
and even perform statistical tests to evaluate the significance of observed
patterns.
