semvec/pypi-packages huggingface dataset:
dependency_inspector 0.2.2
A common dependencies checker/resolver.
Requires Python: >=3.8,<4.0

# dependency-inspector

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A common dependencies checker/resolver.

- **Github repository**: <https://github.com/MrLYC/DependencyInspector/>
- **Documentation** <https://mrlyc.github.io/DependencyInspector/>

## Installation

```bash
pip install dependency-inspector
```

## Getting started

Assuming you have two interdependent services, write the dependencies into *artifact.yaml* in the following format:

```yaml
name: app-frontend
version: "1.1.2"
dependencies:
  - name: app-backend
    version: "1.x.x"
---
name: app-backend
version: "1.0.1"
dependencies:
  - name: app-frontend
    version: ">1.0"
---
name: app-backend
version: "1.0.10"
dependencies:
  - name: app-frontend
    version: ">1.1"
```

Run this command to resolve the dependencies:

```bash
dependency_inspector --artifacts artifact.yaml      
```

The output is:

```
--- Dependency Graph ---
* --> app-frontend, app-backend
app-frontend --> app-backend
app-backend --> app-frontend

--- Resolution ---
app-frontend==1.1.2
app-backend==1.0.10

--- Artifacts ---
dependencies:
- enabled: true
  name: app-backend
  version: ~=1.0.10
name: app-frontend
version: 1.1.2
---
dependencies:
- enabled: true
  name: app-frontend
  version: ~=1.1.2
name: app-backend
version: 1.0.10
```
autodistill-gpt-4v 0.1.5
GPT-4V model for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill GPT-4V Module

This repository contains the code supporting the GPT-4V base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[GPT-4V](https://openai.com/research/gpt-4v-system-card), developed by OpenAI, is a multi-modal language model. With GPT-4V, you can ask questions about images in natural language. The `autodistill-gpt4v` module enables you to classify images using GPT-4V.

This model uses the [gpt-4-vision-preview API](https://openai.com/blog/new-models-and-developer-products-announced-at-devday) announced by OpenAI on November 6th, 2023.

> [!NOTE]  
> Using this project will incur billing charges for API calls to the OpenAI GPT-4 Vision API.
> Refer to the [OpenAI pricing](https://openai.com/pricing) page for more information and to calculate your expected pricing. This package makes one API call per image you want to label.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [GPT-4V Autodistill documentation](https://autodistill.github.io/autodistill/base_models/gpt_4v/).

## Installation

To use GPT-4V with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-gpt-4v
```

## Quickstart

```python
from autodistill_gpt_4v import GPT4V

# define an ontology to map class names to our GPT-4V prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = GPT4V(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
base_model.label("./context_images", extension=".jpeg")
```

## License

This project is licensed under an [MIT license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
squic 1.0.4
SQUIC is a second-order, L1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation. This repository contains the source code for the Python interface of SQUIC.
Requires Python: >=3.6

# SQUIC for Python
SQUIC is a second-order, L1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation. This repository contains the source code for the Python(v3) interface of SQUIC. 

**For an interactive session using SQUIC for Python, see Google Colab:**

[![SQUIC for Python](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1iQB5hz07UMd5C1PR3w3xM3306BVcFGiO?usp=sharing)

## Installation

#### Step 1:
Download the library $\mathtt{libSQUIC}$ from [here](www.gitlab.ci.inf.usi.ch/SQUIC/lib), and follow its README instructions. The default and recommended location for $\mathtt{libSQUIC}$ is the home directory, i.e., ``~/``. Note that precompiled versions are available.

#### Step 2:
Run the following command to install the library:
```angular2
pip3 install squic
```
Use ```export SQUIC_LIB_PATH=/path/to/squic/```, replacing ```/path/to/squic/``` with the path of where $\mathtt{libSQUIC}$ can be found.
If this variable is not set, the default location of $\mathtt{libSQUIC}$ is ```$HOME```.
This should be done before calling Python.

## Example
In this example, we will use SQUIC to estimate the precision matrix of a synthetically generated dataset with correlated random variables, where the true precision matrix is tridiagonal.

```angular2
import squic
import numpy as np

# generate sample from tridiagonal precision matrix
p = 1024
n = 100
l = .4

# generate a tridiagonal matrix
np.random.seed(1)
a = -0.5 * np.ones(p-1)
b = 1.25 * np.ones(p)
iC_star = np.diag(a,-1) + np.diag(b,0) + np.diag(a,1)

# generate the data
L = np.linalg.cholesky(iC_star)
Y = np.linalg.solve(L.T,np.random.randn(p,n))

[X,W,info_times,info_objective,info_logdetX,info_trSX] = squic.run(Y,l)
```
For further details type ``help(SQUIC)`` or check the [User Manual](https://www.gitlab.ci.inf.usi.ch/SQUIC/gitlab-profile/-/raw/main/SQUIC_User_Manual.pdf).