semvec/pypi-packages huggingface dataset:
chronobiology 0.0.5
A python package to calculate and plot circadian cycles data.
Requires Python: >=3.6
Keywords: chronobiology,actogram,periodogram,sleep,analysis

Circadian rhythms are ~24 hour cycles of physiology and behaviour that occur in virtually all organisms from bacteria to man. These rhythms are generated by an internal biological clock and persist even in isolation from any external environmental cues. In humans, circadian rhythms in activity are typically measured using calibrated wrist-worn accelerometers. By contrast, the activity of laboratory animals is typically measured using home cage running wheels. Circadian data are typically double plotted as actograms, showing activity across multiple days. The circadian field has developed standard methods for analysing circadian rhythms. This primarily includes methods to detect recurring features in the data, enabling the period length of activity cycles to be determined. Under entrained conditions, this period will normally be determined by environmental zeitgebers A range of different methods are used to determine the underlying period in biological time series. Three of the most commonly used are the Enright periodogram, Fourier analysis and the Lomb-Scargle periodogram. In addition, activity onset is also frequently used to characterise phase shifts in rhythms in response to environmental zeitgebers. Circadian disruption may occur as a result of environmental conditions. This includes misalignment (when two or more rhythms adopt an abnormal phase relationship) and desynchrony (when two or more rhythms exhibit a different period). A range of approaches have been used to assess circadian disruption. These methods range from simple visual inspection of actograms to metrics such as periodogram power, variability in activity onset, light phase activity, activity bouts, interdaily stability, intradaily variability and relative amplitude. This package provides a set of tools to calculate and plot these parameters based on activity measurements for further inspection and analysis.
osu-sr-calculator 1.1.0
MrHelix's osu! star rating calculator rewritten for your Python needs
Requires Python: >=3.7
Keywords: osu!,osu,star rating,calculator,sr

# osu_sr_calculator

Package to calculate star rating of any osu beatmap with any mod combination.

Updated on: 19.02.2021

## Disclaimer
calculated star ratings may be varying slightly from original values (margin of 0.01)

## Usage
```python
from osu_sr_calculator import calculateStarRating
starRating = calculateStarRating(returnAllDifficultyValues, filepath, map_id, mods, allCombinations)
```
calculateStarRating method accepts these parameters:

- returnAllDifficultyValues: returns total star rating value if False. when set to True, method will also return aim and speed difficulty
- filepath (optional if map_id is set): Path to .osu file
- map_id (optional if filepath is set): BeatmapID number of a beatmap
- mods (optional): Specify which mods to include during star rating calculation
- allCombinations (optional): when set to True, will return star rating of every possible mod combination

## Examples
- local file nomod star rating:
```python
starRating = calculateStarRating(filepath='path/to/file.osu')
# Response: { 'nomod': x.xxxx }
```

- BeatmapID DT star rating:
```python
starRating = calculateStarRating(map_id=123456, mods=['DT'])
# Response: { 'DT': x.xxxx }
```

- All possible star ratings:
```python
starRatings = calculateStarRating(filepath='path/to/file.osu', allCombinations=True)
# Response:
# {
#     nomod: x.xxxx,
#     DT: x.xxxx,
#     HT: x.xxxx,
#     HR: x.xxxx,
#     HRDT: x.xxxx,
#     HRHT: x.xxxx,
#     EZ: x.xxxx,
#     EZDT: x.xxxx,
#     EZHT: x.xxxx,
# }
```

- Aim and Speed ratings:
```python
starRatings = calculateStarRating(filepath='path/to/file.osu', returnAllDifficultyValues=True)
# Response:
# {
#     nomod: {
#         aim: x.xxxx,
#         speed: x.xxxx,
#         total: x.xxxx,
#     }
# }
```