semvec/pypi-packages huggingface dataset:
r2analyze 0.1.0
r2pipe script for Intezer Analyze

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/r2analyze)
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# r2analyze - Radare2 integration with Intezer Analyze

Execute the plugin via `#!pipe` and save yourself time while reversing. The plugin helps you focus on the malicious and unique functions.

## How to use

1. Run `pip install r2analyze` to download and install.
2. Add your API key as a shell environment variable `INTEZER_API_KEY`.
3. Upload the sample to [Analyze](https://analyze.intezer.com).
3. Open the file with radare2 and analyze the file with for example `aaa`.
4. Run `#!pipe r2analyze`.
5. Flag starting with `gene_` has been added to all functions with code-reuse.

## Example

After we have submitted the file to Intezer Analyze, we open the file
with radare2:

```bash
$ r2 7c82689142a415b0a34553478e445988980f48705735939d6d33c17e4e8dac94
 -- *(ut64*)buffer ought to be illegal
[0x004028e3]> aaa
[x] Analyze all flags starting with sym. and entry0 (aa)
[x] Analyze function calls (aac)
[x] Analyze len bytes of instructions for references (aar)
[x] Finding and parsing C++ vtables (avrr)
[x] Type matching analysis for all functions (aaft)
[x] Propagate noreturn information (aanr)
[x] Use -AA or aaaa to perform additional experimental analysis.
```

When we run `r2analyze`, it will query Analyze for code reuse. Functions
that share code already malware will be marked with a gene `flag`. All
the flags are added to a new flag space called "gene" for easier
filtering.

```bash
[0x004028e3]> #!pipe r2analyze
Analyzing 7c82689142a415b0a34553478e445988980f48705735939d6d33c17e4e8dac94
Functions found 194.
[0x004028e3]> fs gene
[0x004028e3]> f
0x00401000 1 gene_malware_ScarCruft_4198400
0x004013e0 1 gene_malware_ScarCruft_4199392
0x00401f20 1 gene_malware_ScarCruft_4202272
0x00402090 1 gene_malware_ScarCruft_4202640
```

Function identified as sharing code with ScarCruft:

```bash
[0x004028e3]> pdfs @ 4202272
;-- gene_malware_ScarCruft_4202272:
0x00401f4a call dword [sym.imp.KERNEL32.dll_CreateFileA]
0x00401f5f call dword [sym.imp.KERNEL32.dll_GetFileSizeEx]
0x00401f7a call dword [sym.imp.KERNEL32.dll_CloseHandle]
0x00401f98:
0x00401fb0:
0x00401fc7 fcn.00401f20+0xb0 fcn.00401f20+0xb0
0x00401fd0:
0x00401fd3:
0x00401ff0:
0x00401ff5:
0x00402003:
0x0040200c int32_t arg_1ch
0x0040200d int32_t arg_18h
0x0040200e uint32_t arg_14h
0x0040200f int32_t arg_10h
0x00402010 int32_t arg_ch
0x00402014 int32_t arg_8h
0x00402018 call fcn.00402090 fcn.00402090
```
sparkcleaner 1.1.1
cleaning functions for pyspark df
Requires Python: >=3.6

# pyspark-df-cleaner
Making life easier. This package is used for cleaning Pyspark dataframes. The module will be extended in the future.

It currently consists of three main features:
- Removing leading zeros from column -> e.g. Turns "0000F45" into "F45"
- Casting int/long column to date -> e.g. Turns int/long column into Pyspark DateType()
- keep_alphanumeric_string -> e.g. Turns "444-555-666" into "444555666"

Should you have any suggestions for additional features, just let me know.
amlpp 0.1.9.7
Wrapper for ml library
Requires Python: >=3.0

# Description

library wrapper for ml libraries.

### Сapabilities
The main class is a conveyor as an analogue of a pipeline. There is a partial interchangeability. The focus is on expanding built-in capabilities such as auto-typing predictive models, feature importance inference, scores, and various future features. There are also adapted transformers, classes for customizing the structure of projects.

### Status
At the moment, the functionality is not wide, designed for expansion in the future
valvetraces 0.0.5
Interact with Valve's GFX trace system
Requires Python: >=3.6

# Valve Traces

Access the Valve traces service through a simple command line interface. This
tool makes sure traces and frames are tagged with information coming from the
machine that captured/ran the trace to avoid any confusion and guesswork as to
what environment is needed to reproduce an issue.

## Installing the tool

This should be as simple as:

    $ pip install --user valvetraces
    $ export PATH=~/.local/bin:$PATH

    $ valvetraces
    usage: valvetraces [-h] [--username USERNAME] [-u URL] {login,list_apps,create_app,list,download,upload_trace} ...

    positional arguments:
      {login,list_apps,create_app,list,download,upload_trace}
        login               Log in the valve traces service
        list_apps           List the applications defined in the service
        create_app          Create a new application in the service
        list                List the traces available in the service
        download            Download a trace from the service
        upload_trace        Upload a trace

    optional arguments:
      -h, --help            show this help message and exit
      --username USERNAME   Username you want to use in the service
      -u URL, --url URL     URL to the service

## Authenticate to the service

First, you need to create an [account on the service](https://linux-perf.steamos.cloud/), then
set a username and password.

You can then check that everything is working by doing:

    $ VALVETRACESPASSWORD=$PASSWORD valvetraces --username $username login

## Interact with traces

Check out `valvetraces --help` for more information.