semvec/pypi-packages huggingface dataset:
bai-stats 0.1.0
Package for statistical analysis

# bai_stats

bai_stats is a Python library for statistical analysis.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install bai_stats.

```bash
pip install bai_stats
```

## Usage

```python
import bai_stats
import scipy.stats as stats

data=stats.norm.rvs(size=100)

best_fit_distribution_table=bai_stats.best_fit_distributions(data,max_shape_par=0,plot_dist=True) 
# returns best_fit_distribution_table

```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
fame 1.2.0
A little language for schema-enforced models.

A little language for schema-enforced models
============================================

Fame is a framework for metamodelling in Python.

-  Entities are described by models
-  Models are described by metamodels
-  Models have fields, derived fields and constraints
-  Entities can have custom fields
-  Entities can be created even if data is invalid
-  There are functions to validate and get error messages
-  Validation checks presence and type of fields
-  Validation checks all constraints
-  Derived fields are memoized

Example

::

    class Experiment(Model):

        @schema
        def metamodel(self, m):
            m.field('name', str)
            m.field('subject', options(
                'user',
                'visitor',
                'email',
                'listing',
                'market'
            ))
            m.field('treatments', array(str))
            m.field('percent_exposed', int, default=100)
            m.field('design', nullable(regexp('^https?')))

        @derived_field
        def is_miscellanous(self):
            return self.subject not in ['user', 'visitor']

        @constraint("expected percent_exposed to not exceed 100, got {}")
        def constraint(self):
            if self.percent_exposed > 100:
                return self.percent_exposed

Conventions

-  Define metamodel function first
-  Then define derived field functions
-  Then define all the constraint functions
-  Constraint functions are all named “constraint”, that is intentional

Installation
------------

To install this package, run

::

    pip install fame

Contributing
------------

Bug reports and pull requests are welcome on github at,
https://github.com/akuhn/py-fame
toro-mkdocs-togglers 1.0.4
A Python markdown extension for wrapping texts and images in a special tag for toggling

## TORO Toggler Markdown Extension

Content Toggler Markdown extension for TORO Documentation.


#### Installation
```
pip install toro-mkdocs-togglers
```

#### Usage

To properly use the toggler extension use the following markdown:
```
||| "TabName1"
    Tab1 content.
||| "TabName2"
    Tab2 content.
```

The produced HTML from the markdown will be:
```
<div class="toro-toggler" data-names="TabName1,TabName2">
    <div data-related-divider="TabName1">
        <p>Tab1 content.</p>
    </div>
    <div data-related-divider="TabName2">
        <p>Tab2 content.</p>
    </div>
</div>
```

To use the toggler extension with a nested toggler use the following markdown:
```
||| "TabName1"
    /// "NestedTabName1"
        Nested Tab1 content.
    /// "NestedTabName2"
        Nested Tab2 content.
||| "TabName2"
    Tab2 content.
```

The produced HTML from the markdown will be:
```
<div class="toro-toggler" data-names="TabName1,TabName2">
    <div data-related-divider="TabName1">
        <div class="toro-toggler" data-names="NestedTabName1,NestedTabName2">
            <div data-related-divider="NestedTabName1">
                <p>Nested Tab1 content.</p>
            </div>
            <div data-related-divider="NestedTabName2">
                <p>Nested Tab2 content.</p>
            </div>
        </div>
    </div>
    <div data-related-divider="TabName2">
        <p>Tab2 content.</p>
    </div>
</div>
```