semvec/pypi-packages huggingface dataset:
evt 0.0.2
Estimators and analysis for extreme value theory (EVT)
Requires Python: >=3.7

Estimators and analysis for extreme value theory (EVT).
The package is structured as follows.
Example notebooks are provided as links.

* The `Dataset` [object](https://github.com/spmvg/evt/blob/master/tutorial/0_the_dataset_object.ipynb) performs sanity-checks and contains plotting routines.
  * Generic plots of the dataset.
  * Maximum-to-sum plot.
  * Mean excess function.
* The [peaks over threshold](https://github.com/spmvg/evt/blob/master/tutorial/1_peaks_over_threshold.ipynb) method.
  * Plot the tail.
  * QQ-plot against exponential.
  * Zipf-plot.
* The [block maxima](https://github.com/spmvg/evt/blob/master/tutorial/2_block_maxima.ipynb) method.
  * Plot block maxima against the dataset.
* Estimators: calculate estimates and [confidence intervals](https://github.com/spmvg/evt/blob/master/tutorial/3_hill_estimator_and_the_estimate_object.ipynb). Plotting routines for analysis.
   * [Hill estimator](https://github.com/spmvg/evt/blob/master/tutorial/3_hill_estimator_and_the_estimate_object.ipynb).
     Plot against order statistics.
   * [Moment estimator](https://github.com/spmvg/evt/blob/master/tutorial/4_moment_estimator.ipynb) (Dekkers-Einmahl-De Haan).
     Plot against order statistics.
   * [Maximum likelihood for the generalized Pareto distribution](https://github.com/spmvg/evt/blob/master/tutorial/5_maximum_likelihood_generalized_pareto.ipynb).
     Plot fit quality.
   * [Maximum likelihood for the generalized extreme value distribution](https://github.com/spmvg/evt/blob/master/tutorial/6_maximum_likelihood_generalized_extreme_value.ipynb).
     Plot fit quality.

Documentation
-------------
Documentation is provided [here](https://evt.readthedocs.io/en/latest/).
Example notebooks are provided [here](https://github.com/spmvg/evt/blob/master/tutorial/).

Installation
------------
Releases are made available on PyPi.
The recommended installation method is via `pip`:

```python
pip install evt
```

For a development setup, the requirements are in `dev-requirements.txt`.
Subsequently, the repo can be locally `pip`-installed.
qt-widgets 0.0.1.dev7
Qt widgets library
Requires Python: >=3.7

# qt-widgets

Reusable Qt widgets library.

## Browser Widget

Automatic layout for similar objects.

![](docs/img/browser.jpg)

```python
model: List[str] = [f'data {_}' for _ in range(10_000)]

browser = BrowserWidget(
    config=BrowserConfig(
        item=Item(
            # width=200,
        ),
        page=Page(
            index=4,
            size=25
        ),
    ),
    builder=lambda item: QPushButton(item),
    model=model,
)
```

# Gallery Widget

Based on Browser Widget with auto-resize behavior.

![](docs/img/gallery.jpg)

```python
def builder(path: str):
    def reader() -> numpy.ndarray:
        return cv2.imread(path)

    return reader


widget = GalleryWidget(
    images=[
        builder('image1.jpg'),
        builder('image2.jpg')
    ],
    config=BrowserConfig(
        page=Page(size=20)
    )
)
```

# Install

```
pip install qt-widgets
```

## Requirements

PyQt5

```
pip install pyqt5
pip install pyqt5-tools
```

## Usage

Run any script from the [test folder](tests).