semvec/pypi-packages huggingface dataset:
heathcliff 0.1.0
Pull features from ArcGIS Feature Servers
Requires Python: >=3.13

# Heathcliff
A Command line Interface for pulling data from ArcGIS REST servers concurrently

# Installation
```bash
$ pip install heathcliff
```

TODO: Aliasing the command

# Usage
```bash
$ heathcliff 'https://<server>/MapServer/0' './out/*'

Using service name <Service Name>
Extracting Features:  100%|████████████████████████████████████████| 7/7 [00:14<00:36,  7.34s/it]
Building geojson: 100%|████████████████████████████████████████| 338/338 [00:13<00:00, 24.28it/s]

$ head ./out/<Service Name>.geojson
{"type": "FeatureCollection", "features": [{"type": "Feature", "id": 1, "geometry": {"type": "Polygon", "coordinates": [...
...
```
rpa-sap-mock-bk 0.1.3
Robot Framework library for SAP Cloud SDK APIs

hư viện Robot Framework để tích hợp SAP Cloud SDK.

## Cài đặt

```bash
pip install rpa-sap-mock-bk
```

## Sử dụng trong Robot

```robot
*** Settings ***
Library    rpa_sap

*** Variables ***
${BASE_URL}      https://your-sap-url.com
${TOKEN}         your-access-token

*** Test Cases ***
Test SAP API
    Connect To SAP System    ${BASE_URL}    ${TOKEN}
    ${json}=    Set Variable    {"houseNumber": "123"}
    ${result}=    Update Business Partner Address    1003764    28238    ${json}
    Log    ${result}
```

## License

MIT
pyqcoda 1.0.3
Temporal disaggregation of daily precipitation into hourly using Q-CODA.
Requires Python: >=3.7

# pyqcoda

**pyqcoda** is a Python library for temporal disaggregation of daily precipitation into hourly time series using **Q-CODA** (Quasi-Comonotonicity-based Disaggregation Algorithm), a combination of **comonotonicity transformation** and an **iterative adjusted k-nearest neighbors (KNN)** algorithm. It is tailored for hydrological and climate data processing tasks where hourly data is required but only daily observations are available.

---

## 🌧️ Overview

- **Input:**
  - `train_data.csv`: Hourly precipitation data with columns `datetime` (hourly resolution) and `precipitation` (mm).
  - `test_data.csv`: Daily precipitation data with the same column names but daily resolution (`datetime` at 00:00:00 for each day).

- **Output:**
  - A pandas DataFrame (or CSV) with hourly precipitation disaggregated from the daily values in `test_data`, using statistical patterns learned from `train_data`.

---

## ✨ Features

- Disaggregates daily totals into 24-hour precipitation series.
- Preserves sub-daily maxima in reconstructed data.
- Season-aware (DJF, MAM, JJA, SON) to capture seasonal variability.
- Combines **comonotonicity** with **KNN-based iterative adjustments**.
- Suitable for hydrological modeling and climate studies.

---

## 📦 Installation

### From PyPI (recommended)

```bash
pip install pyqcoda
```
### From Github

```bash
git clone https://github.com/carloscorreag/pyqcoda.git
cd pyqcoda
pip install .
```

---

## 🚀 Usage example

```python
import pandas as pd
from pyqcoda import pyqcoda

# 1. Load your training (hourly) and testing (daily) datasets
df_train = pd.read_csv("train_data.csv", index_col=0, parse_dates=True)
df_test = pd.read_csv("test_data.csv", index_col=0, parse_dates=True)

# 2. Instantiate pyqcoda and disaggregate
qc = pyqcoda()
simulated_series = qc.disaggregate(df_train, df_test)

# 3. Convert results to hourly DataFrame
df_hourly = qc.get_hourly_dataframe(simulated_series)

# 4. Save output
df_hourly.to_csv("disaggregated_output.csv")
print("Hourly disaggregated precipitation saved to disaggregated_output.csv")
```

---

## 🔧 Requirements

- Python 3.7+
- pandas ≥ 1.2.4
- numpy ≥ 1.21.6
- scikit-learn ≥ 1.0.2

---

## 📄 License
This project is licensed under the MIT License — see the LICENSE file for details.

---

## 📖 Citation
Correa, C. (2025). pyqcoda: Temporal disaggregation of daily precipitation into hourly using Q-CODA. [![DOI](https://zenodo.org/badge/1024745705.svg)](https://doi.org/10.5281/zenodo.16364100)
limedev 0.7.2
Toolkit for Python development, especially packaging 
Requires Python: >=3.11

[![PyPI Package latest release](https://img.shields.io/pypi/v/limedev.svg)][1]
[![PyPI Wheel](https://img.shields.io/pypi/wheel/limedev.svg)][1]
[![Supported versions](https://img.shields.io/pypi/pyversions/limedev.svg)][1]
[![Supported implementations](https://img.shields.io/pypi/implementation/limedev.svg)][1]

# LimeDev <!-- omit in toc -->

LimeDev is collection tools for Python development. These tools are more or less thin wrappers around other packages.

## Table of Contents <!-- omit in toc -->

- [Quick start guide](#quick-start-guide)
    - [The first steps](#the-first-steps)
        - [Installing](#installing)
        - [Importing](#importing)

# Quick start guide

Here's how you can start

## The first steps

### Installing

Install LimeDev with pip

```
pip install limedev
```

### Importing

Import name is the same as install name, `limedev`.

```python
import limedev
```

# Changelog <!-- omit in toc -->

## 0.7.2 2025-07-24 <!-- omit in toc -->

- Readme encoding now utf8

## 0.7.1 2025-06-28 <!-- omit in toc -->

- Testing toolkit updated
- Command `test` replaced with `limedev`

## 0.6.2 2025-01-14 <!-- omit in toc -->

- Changed signature of the benchmarking function
- Best-of sampling on benchmarking function

## 0.6.1 2024-07-17 <!-- omit in toc -->

- Running the benchmakable function once in setup to better accompany numba
- Expanding benchmarking prefixes

## 0.6.0 2024-07-17 <!-- omit in toc -->

### Features <!-- omit in toc -->

#### Readme toolkit <!-- omit in toc -->

- Readme tool now gives the full pyproject dictionary
- readme make tool allows abbreviation

#### Testing toolkit <!-- omit in toc -->

- Benchmarking tools added

## 0.5.0 2024-07-16 <!-- omit in toc -->

- Updated CLI framework
- Support for python 3.9 dropped
- Support for python 3.13 added

## 0.4.1 2023-11-06 <!-- omit in toc -->

- Fixed configs

## 0.4.0 2023-11-06 <!-- omit in toc -->

- Updated profiling structure
- function_cli
- Python version range moved from 3.9 -3.11 to 3.10-3.12

## 0.3.0 2023-08-27 <!-- omit in toc -->

- Change to testing interface

## 0.2.2 2023-08-27 <!-- omit in toc -->

- Fix to performance test deleting previous performance data

## 0.2.1 2023-08-06 <!-- omit in toc -->

- Performance results dump sorting fix

## 0.2.0 2023-08-06 <!-- omit in toc -->

- Bugfixes
- Cleaner structure
- Reworked readme build

## 0.1.0 2023-05-04 <!-- omit in toc -->

- Initial assembly of the tools

[1]: <https://pypi.org/project/limedev> "Project PyPI page"
bridgeobjects 0.1.32
A collection of modules that allows the user to utilise objects used in bridge games, for example, a board or a contract.
Requires Python: ==3.13.*

# bridgeobjects
A collection of modules that allows the user to utilise objects used in bridge games, for example, a board or a contract.

The classes form a natural hierarchy:

    event
        └── board
            ├── auction
            │   └── call
            │       └── denomination
            ├── contract
            ├── hand
            │   └── card
            │       └── suit
            └── trick

(However, some classes are natural subsidiaries not represented in this classification, for example, there are four cards in a trick.)


The objects can be created using a simple, self explanatory, human readable set of definitions
by using Portable Bridge Notation (PBN) (http://www.tistis.nl/pbn/) or
Richard’s Bridge Notation (RBN) (http://www.rpbridge.net/7a12.htm).

## Installation
```bash
pip install bridgeobjects
```
modelstack 1.0.1
ModelStack - Lightweight Local Model Registry
Requires Python: >=3.7

# ModelStack

ModelStack is a lightweight, local-first model registry designed for data scientists and machine learning engineers. It allows efficient storage, versioning, and retrieval of ML models along with their associated metadata, without requiring any cloud services or external infrastructure.

## Features

- Local registry to manage models directly on your machine  
- Command Line Interface (CLI) for intuitive interaction  
- Version control for models  
- Support for PyTorch, Joblib, and Pickle formats  
- Metadata tracking (accuracy, metrics, timestamp, etc.)

## Installation

To install ModelStack locally, run:

``` pip install modelstack ```

## Usage
After installation, you can use the modelstack CLI.

To initailize a  db

``` modelstack init ```

To register a model in db

``` modelstack register "name_of_model metadata" ```

To list all models in db 

``` modelstack list ```

To delete a model 

``` modelstack delete "name_of_model" ```

To delete all 

``` modelstack delete --all ```