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https://github.com/alkasm/timingdiagram
Temporal boolean algebra
https://github.com/alkasm/timingdiagram
data-structures
Last synced: 3 days ago
JSON representation
Temporal boolean algebra
- Host: GitHub
- URL: https://github.com/alkasm/timingdiagram
- Owner: alkasm
- License: mit
- Created: 2019-08-18T07:34:40.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-30T01:34:27.000Z (almost 5 years ago)
- Last Synced: 2024-12-14T00:48:29.903Z (about 1 month ago)
- Topics: data-structures
- Language: Python
- Homepage:
- Size: 38.1 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# timingdiagram
Work with discrete state changes over time.
* Reduce data to state changes
* Compare diagrams over time
* Query state by time
* Use any ordered index
## Install
From PyPI with `pip`:
```sh
pip install timingdiagram-alkasm
```
From source:
```sh
git clone https://github.com/alkasm/timingdiagram
cd timingdiagram
pip install .
```
Note that you must have `pip >= 19.0` installed in your environment to install from source, since this project uses the `pyproject.toml` file defined in [PEP-0517](https://www.python.org/dev/peps/pep-0517/) instead of `setup.py`. More from PyPA [here](https://pip.pypa.io/en/stable/reference/pip/#pep-517-and-518-support).
## Try it out
```python
>>> from timingdiagram import TimingDiagram
>>> d1 = TimingDiagram(enumerate([False, False, False, True, True, False, True]))
>>> d2 = TimingDiagram(enumerate([False, True, False, False, True, False, False]))
>>> d1 | d2
TimingDiagram([(0, False), (1, True), (2, False), (3, True), (5, False), (6, True)])
```
## Example
Suppose you had a log of users signing in and out of a service, and the log included the time, user id, and action the user took. We can view each user's login/logout history as a timing diagram, and simply `&` them all together to see when all users were logged in at the same time:
```python
log = """\
2019-08-27T19:38:50 001768bf-af44-46a6-890d-048f2c50aa29 login
2019-08-27T19:51:11 084c07f0-dd0d-46a3-8eb5-1d4cb13756a4 logout
2019-08-27T19:55:25 001768bf-af44-46a6-890d-048f2c50aa29 logout
2019-08-27T19:58:37 001768bf-af44-46a6-890d-048f2c50aa29 login
2019-08-27T20:17:21 a8118353-eb81-4ce0-8d10-6f3f9de6d7ca login
2019-08-27T20:45:19 001768bf-af44-46a6-890d-048f2c50aa29 logout
2019-08-27T21:01:45 001768bf-af44-46a6-890d-048f2c50aa29 login
2019-08-27T21:18:09 001768bf-af44-46a6-890d-048f2c50aa29 logout
2019-08-27T22:02:37 084c07f0-dd0d-46a3-8eb5-1d4cb13756a4 login
2019-08-27T22:55:54 001768bf-af44-46a6-890d-048f2c50aa29 login
2019-08-27T23:08:07 001768bf-af44-46a6-890d-048f2c50aa29 logout
2019-08-27T23:23:04 a8118353-eb81-4ce0-8d10-6f3f9de6d7ca logout
2019-08-27T23:47:50 001768bf-af44-46a6-890d-048f2c50aa29 login
2019-08-27T23:55:10 084c07f0-dd0d-46a3-8eb5-1d4cb13756a4 logout
2019-08-27T23:56:33 001768bf-af44-46a6-890d-048f2c50aa29 logout\
""".split("\n")
from collections import defaultdict
from functools import reduce
from timingdiagram import TimingDiagram
sessions = defaultdict(list)
for row in log:
ts, userid, action = row.split()
sessions[userid].append((ts, action == "login"))
all_logged_in = reduce(lambda d1, d2: d1 & d2, map(TimingDiagram, sessions.values()))
```
From just a few lines of code, we get a timing diagram corresponding to when all the users were logged in:
```python
TimingDiagram([
('2019-08-27T19:38:50', False),
('2019-08-27T22:55:54', True),
('2019-08-27T23:08:07', False),
('2019-08-27T23:56:33', False)
])
```
So all users were logged in between `22:55:54` and `23:08:07` on `2019-08-27`. The additional states at the beginning and end signify the start and end times of the logs.