semvec/pypi-packages huggingface dataset:
philoseismos 0.0.32a0
Engineering seismologist's toolbox
Requires Python: >=3.6

# philoseismos: engineering seismologist's toolbox.


<p align="center">
  <i>
    The Classical Greeks had a love for wisdom —  <br>
    It came down to us as <b>philo·sophia</b>.  <br>
    And I have a passion for the seismic method —  <br>
    Let this be an ode to <b>philo·seismos</b>.  <br>
    O how sweet it is —  <br>
    Listening to the echos from the earth. <br>
  </i>
</p>
<p align="right">
  Öz Yilmaz <br>
  Seismic Data Analysis
 </p>

 ## Features
 * Working with SEG-Y files
 * Dispersion image calculation from seismograms (phase-shift method)
 * Dispersion curve calculation for Rayleigh waves for horizontally layered media
py2asm 0.3
A package for converting python to asm compiled code
Requires Python: >=3.6

![py2nasm](https://storage.googleapis.com/replit/images/1589798461984_ad504d1dfa0cfb79bc9f7b8915cf3911.png)


> # **Py2asm**
> #### *py2asm is a package to convert python code to .so basically compiled assembly code it is simple and fast *

> #### *Convert to .so code using py2asm*
``` code
py2asm path/to/yourfile.py
```

> ### *Then run*
``` code1
python3 setup.py build_ext --inplace
```
voice-analyzer 1.0.0
Human voice analyzer

# Voice-analyzer
Human voice analyzer for determining human parameters.

## Requirements
- python 3.7 x64 
- pip (for installation)

## Installation
```
$ pip install voice-analyzer
```

## Usage
### Import
```python
from voice_analyzer import *
```

### Sex
To determine sex, a ml model trained on the basis of voice frequency is used. 
The success rate is ~87%.
```python
va_sex = analyzer.Sex()
```
You can use your own model.
```python
va_sex = analyzer.Sex("path\\to\\model.h5")
```

#### Prediction
```python
va_sex.predict("path\\to\\file.mp3")
```
The function returns the gender as a string:
- male (0)
- female (1)

#### Train
You can train the model based on your own dataset.
```python
from voice_analyzer import *
import pandas as pd

va_sex = analyzer.Sex()
data = [
    {'sex': 0, 'frequency': 180},
    {'sex': 1, 'frequency': 220}
]
df = pd.DataFrame(data)

va_sex.train(df, "path\\to\\save\\model.h5")
```
#### Tests
You can check the percent accuracy of the model on your dataset.
```python
from voice_analyzer import *
import pandas as pd

va_sex = analyzer.Sex()
data = [
    {'sex': 0, 'frequency': 180},
    {'sex': 1, 'frequency': 220}
]
df = pd.DataFrame(data)

va_sex.tests(df)
```