semvec/pypi-packages huggingface dataset:
kdump-kritanta 0.0.1
Static mach-o binary analysis tool.
Requires Python: >=3.6

# kdump
Static Mach-O binary metadata analysis tool / information dumper
---

### Installation

```shell
pip3 install kdump
```

### Usage

```shell
usage: kdump [-h] [--map] [--classes] [--binding] [--class GETCLASS] [--headers] [--out OUTDIR] filename

```

`--headers` Dumps headers to directory specified by `--out`  
`--out <directory>` Directory to dump headers for class  
`--map` Prints a map of segments/sections, and their respective VM/File offsets  
`--classes` Dumps classnames  
`--binding` Prints binding info actions  
`--class` Get info about a specific class  

---

written in python for the sake of platform independence when operating on static binaries and libraries

#### Special thanks to

IDA for making it possible to write the code without actually understanding full internals  
JLevin and *OS Internals Vol 1 for actually understanding the internals and specifics + writing documentation  
arandomdev for guidance + code
emmv 0.0.4
Metrics for unsupervised anomaly detection models
Requires Python: >=3.6

# EMMV

Implementation of EM/MV metrics based on N. Goix et al.

This is a means of evaluating anomaly detection models without anomaly labels

## Installation

```shell
pip install emmv
```

## Example Use

```python
from emmv import emmv_scores

test_scores = emmv_scores(model, features)
```

- Where 'model' is your **trained** scikit-learn, PyOD, or PyCaret model
- Where 'features' is a 2D DataFrame of features (the *X* matrix)

Example resulting object:

```json
{ 
    "em": 0.77586,
    "mv": 0.25367
}
```

If you are using models without a built-in *decision_function* (e.g. Keras or ADTK models), then you need to specify an anomaly scoring function. Please see examples in the examples folder.

## Running Examples

```shell
pip install .
python ./examples/sklearn_example.py
```

## Interpreting scores

- The best model should have the **highest** Excess Mass score
- The best model should have the **lowest** Mass Volume score
- Probably easiest to just use one of the metrics
- Extreme values are possible

## Contact

Please feel free to get in touch at christian.oleary@mtu.ie

## Citation

```latex
@Misc{emmv,
author =   {Christian O'Leary},
title =    {EMMV library},
howpublished = {\url{https://pypi.org/project/emmv/}},
year = {2021--2021}
}
```
py-dsa 0.0.3
The py_dsa package contains all the data structures and algorithms implementations
Requires Python: >=3

## py-dsa

![GitHub followers](https://img.shields.io/github/followers/smv1999?style=for-the-badge)
![GitHub forks](https://img.shields.io/github/forks/smv1999/py_dsa?style=for-the-badge)
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![Lines of code](https://img.shields.io/tokei/lines/github/smv1999/py_dsa?style=for-the-badge)
![GitHub](https://img.shields.io/github/license/smv1999/py_dsa?color=blue&style=for-the-badge)
![PyPI](https://img.shields.io/pypi/v/py_dsa?color=blue&style=for-the-badge)

### Introduction 

The py-dsa module contains all the data structures and algorithms implementations.  

### Installation 

You can install the module using pip as shown below.

```bash
pip install py-dsa
```
### Usage

Consider the following examples :

```python
from py_dsa.data_structures import *

test_linkedlist = LinkedList()
test_linkedlist.add_first(10)
test_linkedlist.add_first(20)
test_linkedlist.add_first(30)
test_linkedlist.remove_last()
test_linkedlist.reverse_list()
test_linkedlist.print_list()
"""
Output :
20
30
"""
```

```python
from py_dsa.data_structures import *

test_tree = Tree()
test_tree.add(10)
test_tree.add(5)
test_tree.add(30)
print(test_tree.height())
test_tree.invert_tree()
test_tree.print_tree(traversal='postorder')
"""
Output:
2
30
5
10
"""
```

```python
from py_dsa.algorithms import *

a = [1, 2, 3, 4.5]
s = Searching()
print(s.linear_search(a, 3))
"""
Output : 
2
"""
```
### Testing 

To install py-dsa, along with the tools you need to develop and run tests. Run the following command :
```bash
$ pip install -e .[dev]
```

For running the tests, type the following command :

```bash
py.test
```

### Bugs/Requests 

Please use the [GitHub issue tracker](https://github.com/smv1999/py_dsa/issues) to submit bugs or request features.

### License 

Copyright Vaidhyanathan S M, 2021

Distributed under the terms of the [MIT](https://github.com/smv1999/py_dsa/blob/main/LICENSE) license, py-dsa is free and open source software.