semvec/pypi-packages huggingface dataset:
usbmon-exporter 0.1.0
Prometheus exporter for usbmon
Requires Python: >=3.10
Keywords: prometheus, grafana, usbmon

# usbmon Exporter

`usbmon_exporter` is a Prometheus exporter, that captures USB device metrics
using the Linux Kernel usbmon interface.

## Installation

Install using `pipx`:

```console
$ sudo PIPX_HOME=/opt/pipx PIPX_BIN_DIR=/usr/local/bin pipx install usbmon-exporter
```

## Usage

Make sure the `usbmon` Kernel module is loaded:

```console
$ sudo modprobe usbmon
```

Run `usbmon_exporter` as root:

```console
$ sudo usbmon_exporter
```

Metrics should now be readable at http://localhost:10040/metrics.

Options:
- `-l`, `--listen-address`: Address to listen on for metrics exposition.
  Default: `0.0.0.0:10040` (listen on all interfaces on port 10040).
- `-d`, `--device`: Path to the usbmon device. Default: `/dev/usbmon0`.

## Exposed metrics

Here is a summary of all exposed metrics.

### Counter `usbmon_urbs_by_usb_id_total{usb_id}`

Total number of URBs by USB ID.

### Counter `usbmon_urbs_by_bus_total{busnum, direction, xfer_type}`

Total number of URBs by bus number.

### Counter `usbmon_urb_errors_total{busnum, xfer_type}`

Total number of URB errors.

### Counter `usbmon_urb_submit_errors_total{busnum, xfer_type}`

Total number of URB submission errors.

### Gauge `usbmon_devices{busnum}`

Current number of USB devices.

### Histogram `usbmon_urb_size_bytes{busnum, xfer_type}`

Size of URBs in bytes.

### Gauge `usbmon_stats_queued`

Number of URBs currently queued in the usbmon buffer.

### Counter `usbmon_stats_dropped_total`

Total number of URBs dropped due to usbmon buffer overflow.

### Gauge `usbmon_pending_usb_id_assignment`

Current number of URBs pending USB ID assignment. This metric is mostly for
debugging `usbmon_exporter`. Counts the number of URBs, that are recorded during
enumeration, that cannot be assigned to a USB ID, before the Kernel emits a
uevent for the new device. The value should be `0` most of the time.

## Labels

### `usb_id`

Identifies the host USB port. Either `<busnum>-<devpath>` or `bus<busnum>`. For
traffic before enumeration `devpath` is set to `0`.

Example: `1-4.2`.

### `busnum`

Bus number.

### `direction`

URB direction, either `in` or `out`.

### `xfer_type`

URB type, one of `Isochronous`, `Interrupt`, `Control` or `Bulk`.
aditya-portfolio-cli 0.0.3
A portfolio that will is not even readable
Requires Python: >=3.7

**HELLO THERE**  


okay hear me out, hear me out 

I thought this would be a good idea,   


okay i don't even know what to make in this "AI" world and also I don't know how to write markdown file 


I somehow messed up bad 

well i have fixed now 


0.0.3 is fixed i hoped so
bounded-models 0.0.5
A framework to check if Pydantic models are bounded
Requires Python: >=3.13

# Bounded Models

[![PyPI](https://img.shields.io/pypi/v/bounded-models)](https://pypi.org/project/bounded-models/)
![PyPI - License](https://img.shields.io/pypi/l/bounded-models)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bounded-models)
[![Test Status](https://github.com/shunichironomura/bounded-models/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunichironomura/bounded-models/actions)
[![Documentation](https://img.shields.io/badge/docs-zensical-blue)](https://shunichironomura.github.io/bounded-models/)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

> [!WARNING]
> This project is in early development. The API may change significantly.

A framework to check if Pydantic models are "bounded" and enable uniform sampling from constrained spaces.

## Installation

```bash
pip install bounded-models
# or
uv add bounded-models
```

## Quick Start

```python
from typing import Literal
from bounded_models import FieldHandlerRegistry
from pydantic import BaseModel, Field

class Config(BaseModel):
    mode: Literal["fast", "slow"]
    threshold: float = Field(ge=0.0, le=1.0)
    count: int = Field(ge=1, le=10)

registry = FieldHandlerRegistry.default()

registry.check_model_boundedness(Config)  # True
registry.model_dimensions(Config)  # 3

# Sample from unit hypercube [0, 1]^dim
registry.sample_model([0.5, 0.5, 0.5], Config)
# Config(mode='slow', threshold=0.5, count=5)
```

## Supported Field Types

| Type | Example |
|------|---------|
| Numeric (`int`, `float`) | `Field(ge=0, le=10)` |
| `Literal` | `Literal["a", "b", "c"]` |
| `Enum` | `class Color(Enum): ...` |
| Nested `BaseModel` | `BoundedModel` subclasses |

## Documentation

For detailed usage, see the [documentation](https://shunichironomura.github.io/bounded-models/).

## License

MIT
Topsis-Ansh-102303380 1.0.3
A Python implementation of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Requires Python: >=3.7

# TOPSIS Python Package

### Overview

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis (MCDA) method for ranking and selecting alternatives based on multiple criteria.

## Features

- Ranking of options based on multiple criteria  
- Flexible weight and impact specification  
- Ideal solution distance calculation  
- CSV input and output support  

## Installation

```pip install Topsis-Ansh-102303380```

## Usage

### Command-Line Syntax
 
```bash
python -m Topsis inputFileName "weights(comma separated)" "impacts(comma separated)" outputFileName
```
### Example

```bash
python3 -m Topsis Topsis/data.csv "1,1,2,2,1" "+,+,-,-,-" my-result.csv
```

## Input Output Format

### Input File

| Fund Name | P1  | P2  | P3  | P4   | P5   |
|----------|-----|-----|-----|------|------|
| M1 | 0.67 | 0.45 | 6.5 | 42.6 | 12.56 |
| M2 | 0.60 | 0.36 | 3.6 | 53.3 | 14.47 |
| M3 | 0.82 | 0.67 | 3.8 | 63.1 | 17.10 |
| M4 | 0.60 | 0.36 | 3.5 | 69.2 | 18.42 |
| M5 | 0.76 | 0.58 | 4.8 | 43.0 | 12.29 |
| M6 | 0.69 | 0.48 | 6.6 | 48.7 | 14.12 |
| M7 | 0.79 | 0.62 | 4.8 | 59.2 | 16.35 |
| M8 | 0.84 | 0.71 | 6.5 | 34.5 | 10.64 |

---

### Output File

| Fund Name | P1  | P2  | P3  | P4   | P5   | Topsis Score | Rank |
|----------|-----|-----|-----|------|------|--------------|------|
| M1 | 0.67 | 0.45 | 6.5 | 42.6 | 12.56 | 20.58 | 2 |
| M2 | 0.60 | 0.36 | 3.6 | 53.3 | 14.47 | 40.83 | 4 |
| M3 | 0.82 | 0.67 | 3.8 | 63.1 | 17.10 | 30.07 | 3 |
| M4 | 0.60 | 0.36 | 3.5 | 69.2 | 18.42 | 50.22 | 5 |
| M5 | 0.76 | 0.58 | 4.8 | 43.0 | 12.29 | 10.41 | 1 |
| M6 | 0.69 | 0.48 | 6.6 | 48.7 | 14.12 | 80.51 | 8 |
| M7 | 0.79 | 0.62 | 4.8 | 59.2 | 16.35 | 70.74 | 7 |
| M8 | 0.84 | 0.71 | 6.5 | 34.5 | 10.64 | 60.33 | 6 |

> **Note:** Your input data and output result may be different.

## License
 © 2026 Ansh Mahajan MIT License