semvec/pypi-packages huggingface dataset:
LPV 2.2
Fast Parser Library
Requires Python: >=3.6

# LPV
## Fast Python Parser Library

### About:
LPV/LexParserVisit is a Python parser library for parsing or lexing. LPV also has AST for executing node tree.

### Usage:
examples are [here](https://github.com/Xp-op/LPV/tree/main/example)

### Contributing:
Any contributions is greatly appreciated.

### Documentation:
The documentation is still in progress. You can learn it by 
[example](https://github.com/Xp-op/LPV/tree/main/example) for now.
nowem 0.0.9
API wrapper for princess connect re:dive(tw server)
Requires Python: >=3.6

API wrapper for [princess connect re:dive(tw server)](http://www.princessconnect.so-net.tw/)

originated from [cc004/pcrjjc2](https://github.com/cc004/pcrjjc2)

# usage

please read src in `example/`, major usages included

> REMINDER: THE EXAMPLES WERE WRITTEN WHEN PCR VERSION 2.8.1
>
> PLEASE CHECK `vesion` ARGUMENT IS UPDATED IN FUNCTION `PCRClient()` CALL WHEN USE

`load_index.py`: dump account data

`pkg_decoder.py`: decode raw encoded package, capture packages yourself
> meme: I use Android simulator + Android proxy + Fiddler to catch packages

`tutorial.py`: give it a newly created account playerprefs, it will pass the tutorial and some main quests
dcolor 0.1.0
decorate string library

# dcolor

![issues](https://img.shields.io/github/issues/tatsuya4649/dcolor)
![forks](https://img.shields.io/github/forks/tatsuya4649/dcolor)
![starts](https://img.shields.io/github/forks/tatsuya4649/dcolor)
![python](https://img.shields.io/pypi/pyversions/dcolor)
![pypi](https://badge.fury.io/py/dcolor.svg)

dcolor is open-source package for Python.

dcolor for easily modifying charactor.

## Install

```
$ pip install dcolor
```

## Usage

```python:normal.py
print("Hello World")
```

output:

![normal](https://raw.githubusercontent.com/tatsuya4649/dcolor/master/docs/assets/usage/normal.png)

```python:colors.py
for dcolor import colors

color_string = colors("Hello World", "red")

print(color_string)
```

output:

![colors](https://raw.githubusercontent.com/tatsuya4649/dcolor/master/docs/assets/usage/colors.png)

```python:backgrounds.py
for dcolor import backgrounds

back_string = backgrounds("Hello World", "red")

print(back_string)
```
output:

![backgrounds](https://raw.githubusercontent.com/tatsuya4649/dcolor/master/docs/assets/usage/backgrounds.png)

```python:attributes.py
for dcolor import attributes

attr_string = attributes("Hello World", "bold")

print(attr_string)
```

output:

![attributes](https://raw.githubusercontent.com/tatsuya4649/dcolor/master/docs/assets/usage/attributes.png)

## LICENSE

dcolor project is licensed under the terms of MIT license.
vicdyf-tiisaishima 0.0.2
Estimate cell state dynamics with fluctuation
Requires Python: >=3.7

# vicdyf: Variational Inference of Cell state Dynamics with fluctuation
vicdyf is intended to estimated cell state dynamics with fluctuation from spliced and unspliced transcript abundance.

## Instalation
You can install vicdyf using pip command from your shell.
```shell
pip install vicdyf
```

## Usage
You need to prepare an [`AnnData` object](https://anndata.readthedocs.io/en/latest/) which includes raw spliced and unspliced counts as `layers` named  as `spliced` and `unspliced` like a [scvelo data set](https://scvelo.readthedocs.io/Pancreas/). Apply `vicdyf` workflow on the object:

```python
import vicdyf
adata = vicdyf.workflow.estimate_dynamics(adata)
```

`vicdyf.workflow.estimate_dynamics` have optional parameters as below:
- `use_genes`: gene names for dynamics estimation (default: `None`)
- `first_epoch`: number of epochs for deriving latent representation (default: `500`)
- `second_epoch`: number of epochs for optimizing dynamics (default: `500`)
- `param_path`: a path where the optimized parameters of `vicdyf.modules.VicDyf`are stored (default: `.vicdyf_opt_pt`)
- `lr`: Learning rate for [Adam optimizer](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam) of [pytorch](https://pytorch.org)
- `batch_size`: Size of mini batches in the optimization procedure
- `num_workers`: Number of workers in [data loader](https://pytorch.org/docs/stable/data.html) of [pytorch](https://pytorch.org)
- `val_ratio`: proportion of validation data set
- `test_ratio`: proportion of test data set
- `model_params`: a dictionary which describe the configuration of `vicdyf.modules.VicDyf`. The keys of the dictionary is as below:
  - `z_dim`: dimension of latent representation (default `10`)
  - `enc_z_h_dim`: dimension of hidden units in encoder layers (default `50`)
  - `enc_d_h_dim`: dimension of hidden units in dynamics encoder layers (default `50`)
  - `dec_z_h_dim`: dimension of hidden units in encoder layers (default `50`)
  - `num_enc_z_layers`: the layer number of the encoder (default `2`)
  - `num_enc_z_layers`: the layer number of the dynamics encoder (default `2`)
  - `num_dec_z_layers`: the layer number of the decoder (default `2`)


Here, the `AnnData` object acuires sevral elements in `layers`, `obsm`, `obsp` and `obs`.
- `layers`: 
  - `vicdyf_expression`: Expected gene expression level
  - `vicdyf_mean_velocity`: Expected gene expression change
  - `vicdyf_velocity`: Stochasticaly sampled  gene expression change
  - `vicdyf_fluctuation`: Fluctuation level for each gene
- `obsm`:
  - `X_vicdyf_z`: Stochasticaly smapled latent representation
  - `X_vicdyf_zl`: Expected latent representation
  - `X_vicdyf_d`: Stochasticaly smapled changes of latent representation
  - `X_vicdyf_dl`: Expected changes of latent representation
  - `X_vicdyf_umap`: 2D UMAP embeddings of expected latent representation for visualization
  - `X_vicdyf_sdumap`: 2D UMAP embeddings of `X_vicdyf_d` for visualization
  - `X_vicdyf_mdumap`: 2D UMAP embeddings of `X_vicdyf_dl` for visualization
bt43 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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