semvec/pypi-packages huggingface dataset:
CTDbscan 1.0.0
Spatial clustering of continuous trajectories

This package can be used for spatial clustering of continuous trajectories. It only needs to provide the latitude and longitude information sorted by positioning time and positioning time to find the dwell point of the trajectory.

The input data is in DataFrame format, and the column names must be ['longitude', 'latitude', 'positioning_time'].

eps is the minimum number of clusters, and min_time is the clustering interval time.
satseg 0.1.1
A python package to assist in multispectral satellite image segmentation.
Requires Python: >=3.8,<4.0

# SatSeg (WIP)
A python package to assist in multispectral satellite image segmentation.

## Installation
```
pip install satseg
```

## Usage

### Training
```python
from satseg.dataset import create_datasets
from satseg.model import save_model, train_model

# A list of paths to the satellite image tif files (.TIF)
tif_paths = ["01.tif". "02.tif"]
# A list of paths to the shapefiles (.SHP) of the masks
mask_paths = ["mask.shp"]

# Create the train and validation datasets
# The intermediate image files will be saved at ./temp/labeled/
train_set, val_set = create_datasets(tif_paths, mask_paths, "temp/labeled")

# Train the model
model, metrics = train_model(train_set, val_set, "unet")

# Save the model at a specified path
model_path = "model.pt"
save_model(model, model_path)
```


### Inference
```python
from satseg.dataset import create_inference_dataset
from satseg.model import load_model, run_inference


tif_paths = ["03.tif", "04.tif"]
# The intermediate image files will be saved at ./temp/unlabeled/
dataset = create_inference_dataset(tif_paths, "temp/unlabeled/", 256)

# Load a previously saved model
model_path = "model.pt"
model = load_model(model_path)

# Run inference on the dataset using the loaded model
# Results will be saved ./temp/infer/
run_inference(dataset, model, "temp/infer/")

```
sensenova 1.0.6
Python client library for the Sensenova API
Requires Python: >=3.7.1

# Sensenova Python Library

The Sensenova Python library provides convenient access to the Sensenova API from applications written in the Python
language.

## Installation

You don't need this source code unless you want to modify the package. If you just
want to use the package, just run:

```sh
pip install --upgrade sensenova
```


## Usage

### Configure API KEY

Set environment variable:

```shell
export SENSENOVA_ACCESS_KEY_ID=
export SENSENOVA_SECRET_ACCESS_KEY=
```

Or set sensenova.access_key_id and sensenova.secret_access_key to its value:

```python
import sensenova

sensenova.access_key_id = "..."
sensenova.secret_access_key = "..."
```

### API

#### cli

```shell
sensenova api chat_completions.create -m xxx -g user "Say this is a test!" --stream
```

#### python

```python
import sensenova

# create a chat completion
chat_completion = sensenova.ChatCompletion.create(
    model="xxx",
    messages=[{"role": "user", "content": "Say this is a test!"}]
)

# print the chat completion
print(chat_completion.data.choices[0].message)
```

#### python async

Async support is available in the API by prepending a to a network-bound method:

```python
import sensenova
import asyncio


async def create_chat_completion():
    chat_completion_resp = await sensenova.ChatCompletion.acreate(
        model="xxx",
        messages=[{"role": "user", "content": "Say this is a test!"}]
    )
    print(chat_completion_resp.data.choices[0].message)


asyncio.run(create_chat_completion())
```

To make async requests more efficient, you can pass in your own aiohttp.ClientSession, but you must manually close the
client session at the end of your program/event loop:

```python
import sensenova
from aiohttp import ClientSession

sensenova.aiosession.set(ClientSession())
# At the end of your program, close the http session
await sensenova.aiosession.get().close()
```

### Tools

Run the following command on data.json to get the cleaned_data.json file

```sh
sensenova tools fine_tunes.prepare_data -f data.json
```

## Requirements

- Python 3.7.1+
AllGoGet 0.0.1
The AllGo package is a powerful tool that provides a seamless integration between the advanced features of MetaTrader5 and the analytical flexibility of pandas. It allows users to easily access and manipulate financial data directly from MetaTrader5 for detailed analysis within the familiar environment of pandas.

# AllGo

The AllGo package is a powerful tool that provides a seamless integration between the advanced features of MetaTrader5 and the analytical flexibility of pandas. It allows users to easily access and manipulate financial data directly from MetaTrader5 for detailed analysis within the familiar environment.
khmeri 0.0.1
A small example package
Requires Python: >=3.8

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Khmeri is a lightweight ORM for SQLite3 written in Python

## Installation

The ORM can be installed through the Python Package Index with the commmand:
```python
pip install khmeri
```

## Usage 

If you have ever used another ORM, such as Django's ORM, you will see that the syntax is almost the same, with Khmeri focusing on being lightweight and having simple syntax, for quick writing of models in small to medium-sized databases. 

Let's define a model as an example:

```python
import khmeri  

class object_one(khmeri.Model):  

    id = khmeri.Int(primary_key=True) # auto-increment
    task = khmeri.Str()
    interval = khmeri.Int()
```
now, we need to connect to a SQLite3 database:

```python
db = khmeri.Database('sample_database.db')
```
To perform basic CRUD operations, set the database as the backend of your object:

```python
object_one.objects.backend = db
```
## Instance Attributes

The following instance attributes can be applied to each module:

### Database
```python
- connected
- cursor
- connection
- connect()
- close()
- select(sql, *args, size=None)
- execute(sql, *args, autocommit=True)
- commit()
```
### Str
```python
- name
- type
- default
- primary_key
```
### Int
```python
- name
- type
- default
- primary_key
```
### Float
```python
- name
- type
- default
- primary_key 
```
### Model
```python
- __table__
- __mappings__
- __fields__
- __primary_key__
- objects
- exists()
- create()
- drop()
- save()
- update()
- delete()
- keys(), values(), items(), copy() and other methods imported from dict
```

## Contributing

This project is still very much in its infant stage and any help is appreciated. The next steps I would like to take would be implementing tests to evaluate how reliable the ORM is and then updating the models to handle more complex tasks than simple CRUD operations. You can open an issue or PR and I will make sure to see to it promptly.
bvqpy 0.0.1.2
Barcelona Vocabulary Questonnaire
Requires Python: >=3.7
Keywords: python,vocabulary

# bvqpy

## Overview

This package contains data from a Barcelona Vocabulary Questionnaire (BVQ), a vocabulary checklist aimed at to assessing lexical development in toddlers growing up in Catalan-Spanish bilingual environments. The questionnaire was deployed using [formr](https://formr.org/), and the materials used to generate the items are available in the GitHub repository.

This package also contains multiple functions that extract the data and generate many metrics used frequently in the language acquisition literature to examine participants’ vocabulary or to consult acquisition norms of particular words. Documentation and working examples are available in the [package website](https://gongcastro.github.io/bvq).


## Installation

Using pip:

```python
pip install bvqpy
```

## Data accesibility

BVQ is currently a developing package for internal use at the [Center for Brain and Cognititon](https://www.upf.edu/web/cbc) (CBC). For this reason, logging in requires a password that can be required by writing to the maintainer of this package ([gongarciacastro@gmail.com](mailto:gongarciacastro@gmail.com)).

## Code of Conduct

Please note that the bvq project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.