semvec/pypi-packages huggingface dataset:
DLex-DeaL 0.0.3
DL's Lex
Requires Python: >=3.6

# DLex
DL‘s Lexer

DLex可以使用正规文法和正则表达式两种格式进行分析。由NFA到DFA的过程，默认对DFA进行最小化。

## 使用方法

### 正规文法

#### DFA画图

##### 执行语句

```powershell
python .\cli.py -g .\test\rule.txt -d
```

##### 运行结果

[![bvJmlj.png](https://s1.ax1x.com/2022/03/15/bvJmlj.png)](https://imgtu.com/i/bvJmlj)

#### 分析代码获取token流

##### 执行语句

```powershell
python .\cli.py -g .\test\rule.txt -p -codefile .\test\codeGr
```

`.\test\codeGr`中的内容：

```
import A
int main(){
    string s = "helloWorld";
    return 0;
}
```

##### 运行结果

```
row:1,col:0,value:import
row:1,col:6,value:A
row:2,col:0,value:int
row:2,col:3,value:main
row:2,col:8,value:(
row:2,col:9,value:)
row:2,col:10,value:{
row:3,col:4,value:string
row:3,col:10,value:s
row:3,col:12,value:=
row:3,col:14,value:"helloWorld"
row:3,col:27,value:;
row:4,col:4,value:return
row:4,col:10,value:0
row:4,col:12,value:;
row:5,col:0,value:}
```

#### 对DFA进行持久化

##### 执行语句

```powershell
python .\cli.py -g .\test\rule.txt -pk
```

##### 运行结果

获得一个名为`DFAresult`的pickle文件。

### 正则表达式

#### DFA画图

为了便于得到token的类型，对于正则表达式采用每一种模式分别生成一个DFA的办法，所以需要有选择的进行画图。

比如，查看一个类型为`COMPLEX`的DFA，则：

##### 执行语句

```powershell
python .\cli.py -r .\config.json -d -type 'COMPLEX'
```

##### 运行结果

[![bvNY9S.png](https://s1.ax1x.com/2022/03/15/bvNY9S.png)](https://imgtu.com/i/bvNY9S)

#### 分析代码获取token流

##### 执行语句

```powershell
python .\cli.py -r .\config.json -p -codefile .\test\codeRe
```

`.\test\codeRe`中的内容：

```
import A
int main(){
    string s = "I want to say :\"helloWorld\"";
    cp = 4+8i
    se = 5E+9
    int a=10
    return 0;
}
"""sdfas
```

##### 运行结果

```
LexerError: Lexer error on 's' line: 9 column: 8
Token(IMPORT, 'import', position=1:6)
Token(ID, 'A', position=1:8)
Token(INT, 'int', position=2:3)
Token(MAIN, 'main', position=2:8)
Token(LP, '(', position=2:9)
Token(RP, ')', position=2:10)
Token(CLP, '{', position=2:11)
Token(STRING, 'string', position=3:10)
Token(ID, 's', position=3:12)
Token(ASSIGN, '=', position=3:14)
Token(STRINGVALUE, '"I want to say :\\"helloWorld\\""', position=3:46)
Token(SEMI, ';', position=3:47)
Token(ID, 'cp', position=4:6)
Token(ASSIGN, '=', position=4:8)
Token(COMPLEX, '4+8i', position=4:13)
Token(ID, 'se', position=5:6)
Token(ASSIGN, '=', position=5:8)
Token(SCIENCE, '5E+9', position=5:13)
Token(INT, 'int', position=6:7)
Token(ID, 'a', position=6:9)
Token(ASSIGN, '=', position=6:10)
Token(INTEGER, '10', position=6:12)
Token(RETURN, 'return', position=7:10)
Token(INTEGER, '0', position=7:12)
Token(SEMI, ';', position=7:13)
Token(CRP, '}', position=8:1)
Token(STRINGVALUE, '""', position=9:2)
```

#### 对DFA进行持久化

##### 执行语句

```powershell
python .\cli.py -r .\config.json -pk
```

##### 运行结果

获得一个名为`DFAresult`的pickle文件。
sgRNAble 1.0.12
CRISPR-Cas9 single guide RNA generation tool
Requires Python: ==3.7.*

# sgRNAble

__sgRNAble__ is a tool for high-throughput design of sgRNA libraries targeting selected genes or whole genomes, while considering both on-target binding potential and off-target effects of a given sgRNA in a user-defined genome.

## Basic Usage

```
 sgrnable -t TARGET_SEQUENCE.fasta -g GENOME_SEQUENCE.fasta
          [GENOME_SEQUENCE.fasta ...] [-a AZIMUTH_CUTOFF]
          [-c COPY_NUMBER [COPY_NUMBER ...]]
          [-o OUTPUT_DIR] [-p PURPOSE]
          [-th NUM_THREADS] [-m MAX_MEMORY] [-v]
```

## Documentation

All documentation for the current release is accessible through the [sgRNAble wiki](https://github.com/hallamlab/sgRNAble/wiki).

## Authors
* [Siddarth Raghuvanshi](https://github.com/Siddarth-Raghuvanshi)
* [Ahmed Abdelmoneim](https://github.com/AhmedAbdelmoneim)
* [Avery Noonan](https://github.com/Noonanav)

## Contact

Need something? Please contact Professor Steven Hallam and Avery Noonan at: shallam@mail.ubc.ca and avery.noonan@ubc.ca

## References

Farasat, I., & Salis, H. M. (2016). A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation. _PLOS Computational Biology_, __12__(1), e1004724. doi:10.1371/journal.pcbi.1004724

Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., . . . Root, D. E. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. _Nature biotechnology_, __34__(184). doi:10.1038/nbt.3437
fixedincome-analytics 0.1.3
Fixed Income Securities and Derivatives analytics package
Requires Python: >=3.7

# FixedIncome
Python Fixed Income Securities &amp; Derivatives analytics package, originally written for [15.438 Fixed Income Securities &amp; Derivatives](http://student.mit.edu/catalog/m15b.html#15.438) at the MIT Sloan School of Management.

## Installation
Installation is done through PIP; enter the following on the command line:
```bash
$ pip3 install fixedincome-analytics
```

### Updating
Updating is also done through PIP; enter the following on the command line:
```bash
$ pip3 install fixedincome-analytics -U
```

## Usage
This package is named `fixedincome`. We recommend using it in Python via
```python3
import fixedincome as fi
```

### Documentation
Documentation is available through Python's built-in `help()` function. As an example, we can see package-level documentation using the following Python code:
```python3
import fixedincome as fi
help(fi)
```
num2rank 1.0.3
Converts integers (1, 2, 3) into ranks (1st, 2nd, 3rd)
Keywords: python,number,1st,2nd,3rd,1 to 1st,number converter,number suffix converter,number suffix

Description:

A package that allows the conversion between integers, for example 1, 2 or 3, to ranks, for example 1st, 2nd or 3rd.

Example Usage:

 `print(num2rank(5))`