semvec/pypi-packages huggingface dataset:
StringToInteger 1.0.1
Convert your string to integer
Requires Python: >=2.3,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*

# String-to-Integer-for-Python

Example code:
```
import StrToInt as sti

sti1 = sti.StrToInt("1")
sti2 = sti.StrToInt("1132")
sti3 = sti.StrToInt("nine")
print(sti1)
print(sti2)
print(sti3)
```

Output:
```
1
1132
9
```

Library can be used with Python 2 and 3
fizzbuzz-co 0.0.5
FizzBuzz For Corporate Practices

# FizzBuzz Co.

This is a module that helps perform the FizzBuzz excercise with minimal effort. In addition, it also gives an ability for one party to test the output of another.

I wanted to learn how publishing of python modules for wider distribution can be performed. Therefore, I chose to do an experiment of that with one of the most famous examples of excercises given in companies, when testing a new programmer. And since Python essentially has a package for everthing - why not make it for FizzBuzz. Hence, FizzBuzz Co. (FizzBuzz + Companies).

## Installation

Run (approximatelly, depending on the current release version and your version of pip/pip3) the following to install:

```python
pip install fizzbuzz-co
```

## Usage

```python
from FizzBuzz import FizzBuzz
```

### Print FizzBuzz values from 5 to 20:

```python
# Input:
fizzbuzz=FizzBuzz()
fizzbuzz.fizzbuzz(amount=15, start=5)
```

```
# Output
Buzz
Fizz
7
8
Fizz
Buzz
11
Fizz
13
14
FizzBuzz
16
17
Fizz
19
Buzz
```

### Perform a check if a list of information fits the FizzBuzz criteria

```python
# Input (Positive outcome)
fizzbuzz=FizzBuzz()
sequence=[1, 2, "Fizz", 4, "Buzz"]
answer = fizzbuzz.compliance(sequence=sequence, start=1)
print(answer)
```

```
# Output:
The candidate has passed.
```

```python
# Input (Negative outcome)
fizzbuzz=FizzBuzz()
sequence=[1, "Fizz", 4, "Buzz"]
answer=fizzbuzz.compliance(sequence=sequence, start=1)
print(answer)
```

```
# Output:
The candidate has not passed.
```

### Report generation for the candidates performance

```python
# Input
fizzbuzz=FizzBuzz()
sequence=[1, "Banana", "Fizz", 4, "Buzz"]
fizzbuzz.compliance_report(sequence=sequence, start=1)

# Output in a generated file: FizzBuzz_Report_YYYYMMDD_HHMM (year-month-day_hour_minute)
```

Real Answer | Candidate's Answer | Status
:------------: | :------------: | :------------: |
1 | 1 | :heavy_check_mark:
2 | Banana | :x:
Fizz | Fizz | :heavy_check_mark:
4 | 4 | :heavy_check_mark:
Buzz | Buzz | :heavy_check_mark:
pandoc-svgbob-filter 0.0.3
pandoc filters to render svgbob diagrams
Keywords: pandoc filter svgbob

# pandoc-svgbob-filter
Pandoc filter to render svgbob

# Requirements

This filter requires `svgbob` and `rsvg-convert` in `$PATH`

# Install

install by pip-git:

`$ pip3 install git+https://github.com/pandocker/pandoc-svgbob-filter.git`

# Options

Reference svgbob help (`svgbob --help`)

```
svgbob 0.3.2
SvgBobRus is an ascii to svg converter

USAGE:
    svgbob [FLAGS] [OPTIONS] [input] [SUBCOMMAND]

FLAGS:
    -h, --help       Prints help information
    -s               parse an inline string
    -V, --version    Prints version information

OPTIONS:
        --font-family <font-family>      text will be rendered with this font (default: 'arial')
        --font-size <font-size>          text will be rendered with this font size (default: 14)
    -o, --output <output>                where to write svg output [default: STDOUT]
        --scale <scale>                  scale the entire svg (dimensions, font size, stroke width) by this factor
                                         (default: 1)
        --stroke-width <stroke-width>    stroke width for all lines (default: 2)

ARGS:
    <input>    svgbob text file or inline string to parse [default: STDIN]

SUBCOMMANDS:
    build    Batch convert files to svg.
    help     Prints this message or the help of the given subcommand(s)
```

Filter options inherits options for svgbob itself.
They are applied as attributes or preset by setting metadata

| Filter option  | yaml metadata         | Description                                                               | default value |
|:---------------|:----------------------|:--------------------------------------------------------------------------|:-------------:|
| `font-family`  | `svgbob.font-family`  | Text will be rendered with this font                                      |    "Arial"    |
| `font-size`    | `svgbob.size`         | text will be rendered with this font size                                 |      14       |
| `scale`        | `svgbob.scale`        | scale the entire svg (dimensions, font size, stroke width) by this factor |       1       |
| `stroke-width` | `svgbob.stroke-width` | stroke width for all lines                                                |       2       |

# Sample Markdown

```markdown
---
# YAML frontmatter
svgbob:
  font-family: "Source Code Pro"
#  font-size: 12
#  scale: 2
#  stroke-width: 3
...

[Example](bob.bob){.svgbob font-size=14 stroke-width=2 scale=3}
```

[Example](bob.bob){.svgbob font-size=14 stroke-width=2 scale=3}

# Usage example

```bash
pandoc README.md -F pandoc-svgbob-filter -o README.html
```

# Build `svgbob` binary (when updated)

```shell
$ git clone https://github.com/pandocker/pandoc-svgbob-filter.git
$ cd pandoc-svgbob-filter
$ make svgbob
# builds binary under "svgbob" directory
```
raptorarima 0.0.3
Implementation of ARIMA model (Auto Regressive Integrated Moving Average)

# Raptor ARIMA Model Python API

* This API Supports to model time series data using box-jenkins model.

# Python Installation:
* Command for installation: 'pip3 install raptorarima'

## Usage

The ARIMA Model is an implementation from scratch which using MA, AR and differencing to model the Data.
``` python
Define the p,d,q parameters.
q = 1
d = 0
p = 3

m = ARIMA(q, d, p)
pred = m.fit_predict(x)
```


## Installation

``` bash
git clone https://github.com/swaroop9ai9/Fintech/raptor_arima_api.git
cd raptor arima
python setup.py install
```

### Requirements

- pandas
- numpy
- random

### Note

Make sure to use Time series Data Values.

### Steps to create pip package
1) Prepare directory with setup.py, packagename(apiname(__init__.py[with main code]),other py files which supports the __init__.py file.)
2) Upload the package to Git
3) cd packagename
4) chmod +x apiname #makes it an executable
5) python3 setup.py bdist_wheel
  [
  * build: build package information.
  * dist: Contains your .whl file. A WHL file is a package saved in the Wheel format, which is the standard built-package format used for Python distributions. You    can directly install a .whl file using pip install some_package.whl on your system
  * project.egg.info: An egg package contains compiled bytecode, package information, dependency links, and captures the info used by the setup.py test command when running tests.]

6) [Optional] to check install on local machine 
  python -m pip install dist/packagename-0.1-py3-none-any.whl

7) python3 -m twine upload dist/* [in order to upload the package] (Will ask your username and password in pypi)

8) Login to pypi account to view the uploaded package
xiyang-morph-pkg 1.0.5
morph package

Fuzhou Xiyang network technology company, face morph project.

# install:

pip install xiyang-morph-pkg

# install relation packages
dlib >= 19.9.0
numpy >= 1.13.1
scipy >= 0.18.0
opencv-contrib-python>=4.2.0.34


# import package
from xiyang_morph import morph
# param 1: str_img_path 
# param 2: dist_img_path
# param 3：result img save path
morph.morph("../../imgs/test6.jpg", "../../imgs/test7.jpg", "../img/result.jpg")
fizzbuzzco 0.0.3
FizzBuzz For Corporate Practices

# FizzBuzz Co.

This is a module that helps perform the FizzBuzz excercise with minimal effort. In addition, it also gives an ability for one party to test the output of another.

I wanted to learn how publishing of python modules for wider distribution can be performed. Therefore, I chose to do an experiment of that with one of the most famous examples of excercises given in companies, when testing a new programmer. And since Python essentially has a package for everthing - why not make it for FizzBuzz. Hence, FizzBuzz Co. (FizzBuzz + Companies).

## Installation

Run the following to install:

```python
pip install fizzbuzz_co
```

## Usage

```python
from fizzbuzz_co import FizzBuzz
```

### Print FizzBuzz values from 5 to 20:

```python
# Input:
fizzbuzz=FizzBuzz()
fizzbuzz.fizzbuzz(amount=15, start=5)
```

```
# Output
Buzz
Fizz
7
8
Fizz
Buzz
11
Fizz
13
14
FizzBuzz
16
17
Fizz
19
Buzz
```

### Perform a check if a list of information fits the FizzBuzz criteria

```python
# Input (Positive outcome)
fizzbuzz=FizzBuzz()
sequence=[1, 2, "Fizz", 4, "Buzz"]
answer = fizzbuzz.compliance(sequence=sequence, start=1)
print(answer)
```

```
# Output:
The candidate has passed.
```

```python
# Input (Negative outcome)
fizzbuzz=FizzBuzz()
sequence=[1, "Fizz", 4, "Buzz"]
answer=fizzbuzz.compliance(sequence=sequence, start=1)
print(answer)
```

```
# Output:
The candidate has not passed.
```

### Report generation for the candidates performance

```python
# Input
fizzbuzz=FizzBuzz()
sequence=[1, "Banana", "Fizz", 4, "Buzz"]
fizzbuzz.compliance_report(sequence=sequence, start=1)

# Output in a generated file: FizzBuzz_Report_YYYYMMDD_HHMM (year-month-day_hour_minute)
```

Real Answer | Candidate's Answer | Status
:------------: | :------------: | :------------: |
1 | 1 | :heavy_check_mark:
2 | Banana | :x:
Fizz | Fizz | :heavy_check_mark:
4 | 4 | :heavy_check_mark:
Buzz | Buzz | :heavy_check_mark: