semvec/pypi-packages huggingface dataset:
linefinder 0.9.3
A tool for finding and classifying the worldlines of Lagrangian parcels of mass, in the context of hydrodynamic simulations of galaxy formation.

[![Build Status](https://travis-ci.com/zhafen/linefinder.svg?branch=master)](https://travis-ci.com/zhafen/linefinder)

# linefinder
A tool for finding the worldlines of Lagrangian parcels of mass, in the context of hydrodynamic simulations of galaxy formation.

Fundamental parts of this code are based on previous work by Daniel Anglés-Alcázar.

## Installation

`pip install linefinder`

## Using Data Products

Please [see the wiki for information on how to work with data products produced by the code](https://github.com/zhafen/linefinder/wiki).
pyemblib 0.1.2
Lightweight package for reading/writing pre-trained word embedding files

# pyemblib

A module for reading, writing, and using trained word embeddings.

## Installation

Install with pip!

```bash
pip install pyemblib
```

## Usage

This package currently supports word embeddings trained by the following packages:

- [word2vec](https://code.google.com/archive/p/word2vec/)
- [GloVe](https://nlp.stanford.edu/projects/glove/)

### Reading

Both text-format and binary embedding files are supported.

The example below shows reading each format of embedding:
```python
## import text embeddings
text_embs = pyemblib.read('/tmp/text_embeddings.txt', mode=pyemblib.Mode.Text)
## import binary embeddings
bin_embs = pyemblib.read('/tmp/bin_embeddings.bin', mode=pyemblib.Mode.Binary)
```

Embeddings are read as a `pyemblib.Embeddings` object, which inherits from Python's dictionary class; keys are words, and values are the embedding arrays.

To get the word vector for "python", just use dictionary access:
```python
vec = embs['python']
print(vec)
# [ 0.001 -0.237 ... ]
```

### Writing

The same text and binary modes can be used for writing out embedding files as for reading.

```python
embs = { 'a' : np.array([0.3 0.1 -0.2]), 'b' : np.array([-0.9, -0.2, -0.2]) }
## write as text
pyemblib.write(embs, '/tmp/text_embeddings.txt', mode=pyemblib.Mode.Text)
## write as binary
pyemblib.write(embs, '/tmp/bin_embeddings.bin', mode=pyemblib.Mode.Binary)
```

## Feedback
Please report any issues you encounter to the [Github Issues page](https://github.com/drgriffis/pyemblib/issues)!
ptoclockplot 0.0.1
A package for plotting clusters in a simple "pseudo-temporal-ordering", like a clock face.

# What is a "clockplot?"


The clockplot is designed to help visualize how subgroups of a population are arranged over time, while maintaining a metric of for the heterogeneity or purity of that subgroup. 

<div style="text-align:center;">
    <img src="img/populations.PNG" alt="defining populations and subpopulations"/>
    <p><em>
        How are populations and subpopulations defined for a clockplot?
    </p></em>
</div>

This visualization was designed with biology in mind. For example, given a cell population defined by a particular CD marker, we might be want to define subpopulations by the different combinations of other markers expressed by those cells, and see *when* those phenotypes might appear. The clockplot lets you arrange the stages or time points like hours on a clock, and then the subpopulations are "pulled" towards each time point based on their percentage component of that timepoint (this algorithm is partially inspired by tracking how the center of mass along a frequency wave moves during a Fourier transform). 

Essentially, the angle starting from 12 o'clock indicates how much time has passed in the experiment before a given population appeared. The population's proximity to the center of the clockplot indicates the *heterogeneity* of the population with respect to time. For instance, a population in the center of the plot indicates that the population is equally likely to appear at all time points of the experiment, while a population near the periphery has a stronger association to a given time point.  

That said, this way of visualizing data could be applied to other data driven fields as well. Consider examining how the content of different stock portfolios changes over time, or looking at which times during the year we see more action versus cin&eacute; art movies are released?

Example with the included `lineardemo` data, which is simulated data made by "rolling" a population bolus across a series of time points. We can then order the populations based on their angle from 12 o'clock: 2, 4, 3, 8, 9, 12, 5, 7, 6, 11, 10. 

![A sample clockplot using the built-in lineardemo data](img/demo.png)

Please see `demo.ipynb` for a demonstration of the usage.