semvec/pypi-packages huggingface dataset:
aditya 0.1
My personal tools
License: MIT

# Streaming statistics calculator

This tool allows users to calculate statistical features such as mean, median, variance, standard deviation, and skewness, along with the minimum and maximum of a series of numbers in real time without storing them. It can calculate all these features in O(1) space complexity. 

This is very useful when dealing with large series of numbers on a resource contrained system. It uses different mathematical logic to keep track of certain variables for its functioning. 

# Installation 

    pip install streaming-stats

# Execution

```Python
from streaming_stats import StreamingStats

example_series = [4.45, -2, 7.1, -8.7, 3, -2, 45, ,6, -12.3, 53.6, 2.7, 0, 3.6]

object = StreamingStats()

for number in example_series:
  object.update(number)

print(object.get_mean())
print(object.get_median())
print(object.get_std())
print(object.get_variance())
print(object.get_min())
print(object.get_max())
print(object.get_skewness())
```
Other features of the tool includes merging of two different `StreamingStats` objects. There can be situation when users would need to find variance (suppose) of the combined series. The `merge()` method helps in achieving the same.

```Python
object1 = StreamingStats()
object2 = StreamingStats()

for number in series1:
  object1.update(number)

for number in series2:
  object.update(number)

merged_object = object1.merge(object2)

print(merged_object.get_mean())
print(merged_object.get_median())
print(merged_object.get_std())
print(merged_object.get_variance())
print(merged_object.get_min())
print(merged_object.get_max())
print(merged_object.get_skewness())
```

> Note that the `example_series` list was created just for demonstration. Users don't need to have the numbers of the series stored anywhere. The numbers can be added to the `StreamingStats` object as soon as it is recieved.
scHyper 1.0.14
scHyper: Reconstructing cell-cell communication through hypergraph neural networks
License: MIT
Requires Python: >=3.9

# scHyper: Reconstructing cell-cell communication through hypergraph neural networks 

## What is scHyper?

![workflow](../../桌面/研/workflow.jpg)

We developed scHyper, which aims to decode context-driven intercellular communication from scRNA-seq data by constructing hypergraph networks and applying hypergraph neural network models . (i) scHyper integrates the Cellinker and CellChatDB to obtain rich L-R interaction relationships and signaling pathway annotations. (ii) scHyper constructs intercellular communication networks in the form of hypergraphs, analyzing intercellular communication from a global perspective. (iii) scHyper uses hypergraph neural network models to learn intercellular communication patterns and capture nonlinear relationships and complex interaction patterns. (iv) scHyper implements nonparametric tests to evaluate the statistical significance of interactions between cell types and thus identifies intercellular communication relationships that are biologically significant.

## How to use scHyper?

First, we construct the intercellular communication tensor and hypergraph network. The relevant code can be found in the dataprocess.py. Through data processing, we generated the following three files，and the user needs to specify the save path, such as save_path='to/path/data/demo'.

------test_data.npz;

------train_data.npz;

------use_to_predict.npz.

Second, we train model by graph neural networks. To run the code:

```
cd model
python main_torch.py --data demo 
```

Users can experiment by modifying the following parameters:

The **--data** argument can take a folder that contains the three files from the first step. 

The **--save-path** argument specifies the path where the model will be saved.

The **--load-path** argument specifies the loading paths for the training set, test set and prediction arrays (from Step 1).

Finally, we used nonparametric tests to identify significant intercellular communications. The relevant code can be found in the dataprocess.py. 

## Tutorial

To facilitate understanding and use of scHyper, we provide a simple demo, including count and meta, which provides tutorials on data processing, results and visualization.
tuic-tunnel 0.1.6
TUIC tunnel plugin for turbo-tunnel.
License: MIT
Requires Python: >=3.5

# TUIC plugin for turbo-tunnel

## WHAT IS TUIC

[TUIC](https://github.com/EAimTY/tuic) is a high performance proxy based on QUIC protocol.

## INSTALL

```bash
$ pip3 install tuic-tunnel
```

## HOW TO USE

### Simple Usage

```bash
$ turbo-tunnel -l http://127.0.0.1:8888 -t tuic://token@proxy.com:8443 -p tuic_tunnel
```

`proxy.com:8443` is the `TUIC` server domain and udp port, `token` is the `TUIC` token.

### Use With Config File

```yaml
version: 1.0

listen: 
  - http://127.0.0.1:8888 # 配置监听地址

plugins:
  - tuic_tunnel

tunnels:
  - id: tuic
    url: tuic://token@proxy.com:8443/

rules:
  - id: tuic
    priority: 99
    addr: "*.baidu.com"
    port: 80;443;
    tunnel: tuic
```

```bash
$ turbo-tunnel -c config.yaml
```