Summary:

A major refactor of the build infrastructure.

Summary: Python bindings to Charlie Conroy's Flexible Stellar Population Synthesis (FSPS) Fortran code

Summary:

A release candidate for v0.4. Major refactor of build infrastructure and some bugfixes.

Summary: What's Changed

• Hot Stars by @bd-j in https://github.com/dfm/python-fsps/pull/167
• Zsol and docs by @bd-j in https://github.com/dfm/python-fsps/pull/182
• Move parameter check by @bd-j in https://github.com/dfm/python-fsps/pull/187
• Less hacky check for raises by @dfm in https://github.com/dfm/python-fsps/pull/188

Full Changelog: https://github.com/dfm/python-fsps/compare/v0.4.1...v0.4.2rc1

Summary:

Release candidate for python-fsps with compatibility updates for FSPS v3.2

Summary:

A release candidate for v0.3

Summary: Part 1 of a 2 part manipulative experiment to investigate the existence of cooperative synergy in defensive behaviors of ‘guard’ crustaceans at Gump Research Station, Moorea, French Polynesia from July 2006 (CDD_in_Reef_Fish project). For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/727093

Summary: Part 2 of a 2 part manipulative experiment to investigate the existence of cooperative synergy in defensive behaviors of ‘guard’ crustaceans at Gump Research Station, Moorea, French Polynesia from July 2006 (CDD_in_Reef_Fish project). For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/727125

Summary:

Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This version is an early release so please submit a pull request or email achael@cfa.harvard.edu if you have trouble or need help for your application.

!Minor Python 3 bug alert! -- this static version on zenodo is  missing parentheses in a print statement on line 51 of parloop.py. If you plan to run this version with python 3, you will need to add in these parentheses. This bug is fixed in the next release on github (v1.1.1). Our apologies for the error!

The package contains several primary classes for loading, simulating, and manipulating VLBI data. The main classes are the Image, Array, Obsdata, Imager, and Caltable classes, which provide tools for loading images and data, producing simulated data from realistic u-v tracks, calibrating, inspecting, and plotting data, and producing images from data sets in various polariazations using various data terms and regularizers.

This version represents an incremental update to v0.1.2: However, since the original, very early eht-imaging released on zenodo with Chael et. al 2018 (10.5281/zenodo.1173414) was tagged as v1.0 before the software was tagged & released consistently in github, we have jumped forward to v1.1.0 for this release. 

Summary: The friendly PIL fork

Summary:

Colour Science for Python

Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science.
It is freely available under the New BSD License terms.

Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States.

Draft Release Notes

The draft release notes of the develop branch are available at this url.

Sponsors

We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.

Features

Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.

User Guide

Installation

Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:

$ pip install --user colour-science

The detailed installation procedure for the secondary dependencies is described in the Installation Guide.

Colour is also available for Anaconda from Continuum Analytics via conda-forge:

$ conda install -c conda-forge colour-science

Tutorial

The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.

How-To

The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases.

Contributing

If you would like to contribute to Colour, please refer to the following Contributing guide.

Changes

The changes are viewable on the Releases page.

Bibliography

The bibliography is available on the Bibliography page.

It is also viewable directly from the repository in BibTeX format.

API Reference

The main technical reference for Colour is the API Reference.

Code of Conduct

The Code of Conduct, adapted from the Contributor Covenant 1.4, is available on the Code of Conduct page.

About

Colour by Colour Developers
Copyright 2013 Colour Developers – colour-developers@colour-science.org
This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause
https://github.com/colour-science/colour

 

Summary:

Outputs from the probabilistic projection methods developed or assessed in the European Climate Projection system (EUCP) Horizon2020 project. The data can be previewed through our interactive atlas.

For more information, see the atlas about page, or the corresponding storyboard.

 

Preprocessed data of Atlas in EUCP-WP2

We provide some notebooks that check the original/raw data, fix/add the metadata using CF-conventions https://cfconventions.org/Data/cf-conventions/cf-conventions-1.9/cf-conventions.html and save data in a NetCDF format. See https://github.com/eucp-project/atlas/blob/main/python/README.md.

For two of the methods, REA and ClimWIP, pre-calculated weights have also been included. Note that these weights are only valid in the context of this specific model ensemble. Therefore, the original (pre-processed) model data is published together with the weights.

The pre-processed data follows the following standards:

coordinates

• climatology_bounds (climatology_bounds) datetime64[ns] ['2050-06-01', '2050-09-01', '2050-12-01', '2051-03-01']
• time (time) (datetime64[ns]) [2050-07-16 2051-01-16] # "JJA", "DJF"
• latitude (lat) (float64) [30, ..., 75]
• longitude (lon) (float64) [-10, ..., 40]
• percentile (percentile) (int64) [10, 25, 50, 75, 90]

variables

• tas (time, latitude, longitude, percentile) (float64)
• pr (time, latitude, longitude, percentile) (float64)

attributes

The attributes of variables and coordinates are defined as:

• "tas": {
  "description": "Change in Air Temperature",
  "standard_name": "Change in Air Temperature",
  "long_name": "Change in Near-Surface Air Temperature",
  "units": "K",
  "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",
  },
• "pr": {
  "description": "Relative precipitation",
  "standard_name": "Relative precipitation",
  "long_name": "Relative precipitation",
  "units": "%",
  "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",
  },
• "latitude": {"units": "degrees_north", "long_name": "latitude", "axis": "Y"},
• "longitude": {"units": "degrees_east", "long_name": "longitude", "axis": "X"},
• "time": {
  "climatology": "climatology_bounds",
  "long_name": "time",
  "axis": "T",
  "climatology_bounds": ["2050-6-1", "2050-9-1", "2050-12-1", "2051-3-1"],
  "description": "mean changes over 20 years 2041-2060 vs 1995-2014. The mid point 2050 is chosen as the representative time.",
  },
• "percentile": {"units": "%", "long_name": "percentile", "axis": "Z"},

The attributes of the data is defined as:

• "description": "Contains modified institute method data used for Atlas in EUCP project.",
• "history": "original institute method data files ...",

output file names

output_file_name = prefix_activity_institution-id_source_method_sub-method_cmor-var

example: atlas_EUCP_CNRM_CMIP6_KCC_cons_tas.nc

Reference:

https://eucp-project.github.io/atlas/about

Summary:

Preprocessed data of Atlas in EUCP-WP2

We provide some notebooks that check the original/raw data, fix/add the metadata using CF-conventions https://cfconventions.org/Data/cf-conventions/cf-conventions-1.9/cf-conventions.html and save data in a NetCDF format. See https://github.com/eucp-project/atlas/blob/main/python/README.md.

The pre-processed data follows the following standards:

coordinates

• climatology_bounds (climatology_bounds) datetime64[ns] ['2050-06-01', '2050-09-01', '2050-12-01', '2051-03-01']
• time (time) (datetime64[ns]) [2050-07-16 2051-01-16] # "JJA", "DJF"
• latitude (lat) (float64) [30, ..., 75]
• longitude (lon) (float64) [-10, ..., 40]
• percentile (percentile) (int64) [10, 25, 50, 75, 90]

variables

• tas (time, latitude, longitude, percentile) (float64)
• pr (time, latitude, longitude, percentile) (float64)

attributes

The attributes of variables and coordinates are defined as:

• "tas": {
  "description": "Change in Air Temperature",
  "standard_name": "Change in Air Temperature",
  "long_name": "Change in Near-Surface Air Temperature",
  "units": "K",
  "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",
  },
• "pr": {
  "description": "Relative precipitation",
  "standard_name": "Relative precipitation",
  "long_name": "Relative precipitation",
  "units": "%",
  "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",
  },
• "latitude": {"units": "degrees_north", "long_name": "latitude", "axis": "Y"},
• "longitude": {"units": "degrees_east", "long_name": "longitude", "axis": "X"},
• "time": {
  "climatology": "climatology_bounds",
  "long_name": "time",
  "axis": "T",
  "climatology_bounds": ["2050-6-1", "2050-9-1", "2050-12-1", "2051-3-1"],
  "description": "mean changes over 20 years 2041-2060 vs 1995-2014. The mid point 2050 is chosen as the representative time.",
  },
• "percentile": {"units": "%", "long_name": "percentile", "axis": "Z"},

The attributes of the data is defined as:

• "description": "Contains modified institute method data used for Atlas in EUCP project.",
• "history": "original institute method data files ...",

output file names

output_file_name = prefix_activity_institution-id_source_method_sub-method_cmor-var

example: atlas_EUCP_CNRM_CMIP6_KCC_cons_tas.nc

Reference:

https://eucp-project.github.io/atlas/about

Summary:

https://pillow.readthedocs.io/en/stable/releasenotes/7.1.2.html

Summary:

2.0 codebase, but with fixed setup.py

Summary:

Data from: Range-dependent flexibility in the acoustic field of view of echolocating porpoises (Phocoena phocoena). eLife 2015;10.7554/eLife.05651

Abstract:

Toothed whales use sonar to detect, locate, and track prey. They adjust emitted sound intensity, auditory sensitivity and click rate to target range, and terminate prey pursuits with high-repetition-rate, low-intensity buzzes. However, their narrow acoustic field of view (FOV) is considered stable throughout target approach, which could facilitate prey escape at close-range. Here we show that, like some bats, harbour porpoises can broaden their biosonar beam during the terminal phase of attack but, unlike bats, maintain the ability to change beamwidth within this phase. Based on video, MRI, and acoustic-tag recordings, we propose this flexibility is modulated by the melon and implemented to accommodate dynamic spatial relationships with prey and acoustic complexity of surroundings. Despite independent evolution and different means of sound generation and transmission, whales and bats adaptively change their FOV, suggesting that beamwidth flexibility has been an important driver in the evolution of echolocation for prey tracking.

The data set contains all the audio and video data from the trials with the 48-hydrophone array used in the final analysis.

Summary: The DPLA ingestion system

Summary: The DPLA ingestion system

Summary:

An open source iOS framework for GPU-based image and video processing

Summary:

Outputs from the probabilistic projection methods developed or assessed in the European Climate Projection system (EUCP) Horizon2020 project. The data can be previewed through our interactive atlas.

For more information, see the atlas about page, or the corresponding storyboard.

 

Preprocessed data of Atlas in EUCP-WP2

We provide some notebooks that check the original/raw data, fix/add the metadata using CF-conventions https://cfconventions.org/Data/cf-conventions/cf-conventions-1.9/cf-conventions.html and save data in a NetCDF format. See https://github.com/eucp-project/atlas/blob/main/python/README.md.

For two of the methods, REA and ClimWIP, pre-calculated weights have also been included. Note that these weights are only valid in the context of this specific model ensemble. Therefore, the original (pre-processed) model data is published together with the weights.

The pre-processed data follows the following standards:

coordinates

• climatology_bounds (climatology_bounds) datetime64[ns] ['2050-06-01', '2050-09-01', '2050-12-01', '2051-03-01']
• time (time) (datetime64[ns]) [2050-07-16 2051-01-16] # "JJA", "DJF"
• latitude (lat) (float64) [30, ..., 75]
• longitude (lon) (float64) [-10, ..., 40]
• percentile (percentile) (int64) [10, 25, 50, 75, 90]

variables

• tas (time, latitude, longitude, percentile) (float64)
• pr (time, latitude, longitude, percentile) (float64)

attributes

The attributes of variables and coordinates are defined as:

• "tas": {
  "description": "Change in Air Temperature",
  "standard_name": "Change in Air Temperature",
  "long_name": "Change in Near-Surface Air Temperature",
  "units": "K",
  "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",
  },
• "pr": {
  "description": "Relative precipitation",
  "standard_name": "Relative precipitation",
  "long_name": "Relative precipitation",
  "units": "%",
  "cell_methods": "time: mean changes over 20 years 2041-2060 vs 1995-2014",
  },
• "latitude": {"units": "degrees_north", "long_name": "latitude", "axis": "Y"},
• "longitude": {"units": "degrees_east", "long_name": "longitude", "axis": "X"},
• "time": {
  "climatology": "climatology_bounds",
  "long_name": "time",
  "axis": "T",
  "climatology_bounds": ["2050-6-1", "2050-9-1", "2050-12-1", "2051-3-1"],
  "description": "mean changes over 20 years 2041-2060 vs 1995-2014. The mid point 2050 is chosen as the representative time.",
  },
• "percentile": {"units": "%", "long_name": "percentile", "axis": "Z"},

The attributes of the data is defined as:

• "description": "Contains modified institute method data used for Atlas in EUCP project.",
• "history": "original institute method data files ...",

output file names

output_file_name = prefix_activity_institution-id_source_method_sub-method_cmor-var

example: atlas_EUCP_CNRM_CMIP6_KCC_cons_tas.nc

Reference:

https://eucp-project.github.io/atlas/about