semvec/pypi-packages huggingface dataset:
gitcal-wilgysef 0.0.1
Visualize when git commits were made in a repository in a calendar-like format.
Requires Python: >=3.6

# Gitcal
[![Build Status](https://www.travis-ci.com/WiLGYSeF/gitcal.svg?branch=master)](https://www.travis-ci.com/WiLGYSeF/gitcal)
[![codecov](https://codecov.io/gh/WiLGYSeF/gitcal/branch/master/graph/badge.svg?token=R4FFV0ZCGB)](https://codecov.io/gh/WiLGYSeF/gitcal)

A python script to visualize when git commits were made in a repository in a calendar-like format.

# Examples

Display the number of commits in 2 hour blocks in 12 columns. A block with at least 4 commits is colored in green:

![sample gitcal usage](https://raw.githubusercontent.com/WiLGYSeF/gitcal/master/docs/images/sample-gitcal.png)

---

An example of displaying two tables side-by-side, one with a border and different date range:

![sample gitcal usage](https://raw.githubusercontent.com/WiLGYSeF/gitcal/master/docs/images/sample-gitcal-table-mix.png)

---

Compare commits with other members of a collaborative project:

![sample gitcal usage](https://raw.githubusercontent.com/WiLGYSeF/gitcal/master/docs/images/sample-gitcal-all-users.png)

# Installation

1. Clone this repository and place somewhere like `~/.local/bin/gitcal-py`
2. Create a symlink from `~/.local/bin/gitcal-py/gitcal.py` to `~/.local/bin/gitcal`

# Displaying Multiple tables

gitcal uses the `-T/--table` argument to display multiple tables.
A table is created based on the arguments on each side of `-T`, where the command below is used to create multiple tables:

```bash
gitcal -T -n "My Commits" --num --no-label -f "<my username>" -t 4 -T -n "Partner 1" -f "<partner 1>" -f "<also partner 1>"
#     ^   ^_______________________table 2________________________^    ^______________________table 3______________________^
#     |
# table 1
```

A sample result of the above command (below describes each table):

![sample gitcal usage](https://raw.githubusercontent.com/WiLGYSeF/gitcal/master/docs/images/sample-gitcal-multiple-tables.png)

### Table 1

Since there are no arguments to the left of the first `-T`, the default table with all the daily commits is displayed per week.

### Table 2

To the right side of the first `-T`, we:
- set the table name with `-n`
- turn on commit counts with `--num` (underlines are used to distinguish number entries since there is no spacing on borderless tables)
- turn off labels with `--no-label` since the labels from the first table can be used instead
- filter for one user with `-f`
- set the color threshold `-t` to 4 commits

### Table 3

The second `-T` separates the second and third table options.
Note how table 3 only explicitly sets the table name and user filter, but the settings from the second table are carried over to the third table as well.
Most, but not all, settings are carried over between tables so they do not have to be repeated.
emmaa 1.17.0
Ecosystem of Machine-maintained Models with Automated Analysis

# EMMAA
EMMAA is an Ecosystem of Machine-maintained Models with Automated Analysis.
The primary way users can interact with EMMAA is by using the EMMAA Dashboard
which can be accessed 
[here](http://emmaa.indra.bio).

## Documentation
For a detailed documentation of EMMA, visit http://emmaa.readthedocs.io.
The documentation contains three main sections:
- A conceptual description of the [EMMAA architecture and approach](https://emmaa.readthedocs.io/en/latest/architecture/index.html)
- An [introduction to the EMMAA Dashboard](https://emmaa.readthedocs.io/en/latest/dashboard/index.html)
- A [module-level documentation of all of EMMAA's code base](https://emmaa.readthedocs.io/en/latest/modules/index.html) linked directly to the source code on Github

## Concept
The main idea behind EMMAA is to create a set of computational models that
are kept up-to-date using automated machine reading, knowledge-assembly, and
model generation. Each model starts with a prior network of relevant concepts
connected through a set of known mechanisms. This set of mechanisms is then
extended by reading literature or other sources of information each day,
determining how new information relates to the existing model, and then
updating the model with the new information.

Models are also available for automated analysis in which relevant queries
that fall within the scope of each model can be automatically mapped
to structural and dynamical analysis procedures on the model. This allows
recognizing and reporting changes to the model that result in meaningful
changes to analysis results.

## Applications
The primary application area of EMMAA is the molecular biology of cancer,
however, it can be applied to other domains that the INDRA system and the
reading systems integrated with INDRA can handle.

## Installation
Users primarily interact with EMMAA via the
[Dashboard](http://emmaa.indra.bio), for which no dependencies need to be
installed.

To set up programmatic access to EMMAA's features locally, do the following:
```
git clone https://github.com/indralab/emmaa.git
cd emmaa
pip install git+https://github.com/sorgerlab/indra.git
pip install git+https://github.com/indralab/indra_db.git
pip install -e .
```

A Dockerized version of EMMAA is available at
https://hub.docker.com/r/labsyspharm/emmaa, which can be obtained as
```
docker pull labsyspharm/emmaa
```

## Funding
The development of EMMAA is funded under the DARPA Automating Scientific
Knowledge Extraction (ASKE) program under award HR00111990009.