semvec/pypi-packages huggingface dataset:
MDMPyClient 1.0.2
Cliente para el Metadatamanager
License: MIT

# MDMPyClient

Python client for [MetadataManager](https://github.com/SDMXISTATTOOLKIT/META-DATA.MANAGER). It allows you to create codelist, concept schemes and more artefacts from simple code in python.

# Installation

You can install this package with pip.

    pip install mdmpyclient

# Documentation

[Documentation](https://mdmpyclient.readthedocs.io/en/latest/)
weenspace-django-jwt 1.0.1
JSON Web Token for Django REST and GraphQL.
License: MIT
Requires Python: >=3.10.0,<4.0.0
Keywords: django,jwt,API,REST,GraphQL

<p align="center">
  <a href="https://github.com/WeenSpace/weenspace-django-jwt/"><img width="420px" src="https://github.com/WeenSpace/weenspace-django-jwt/_static/logo.png" alt='Django GraphQL JWT'></a>
</p>

<p align="center">
    JSON Web Token authentication for Django GraphQL.
    <br>Fantastic <strong>documentation</strong> is available at <a href="https://github.com/WeenSpace/weenspace-django-jwt">https://github.com/WeenSpace/weenspace-django-jwt</a>.
</p>
<p align="center">
    <a href="https://github.com/WeenSpace/weenspace-django-jwt/actions">
        <img src="https://github.com/WeenSpace/weenspace-django-jwt/actions/workflows/test-suite.yml/badge.svg" alt="Test">
    </a>
    <a href="https://codecov.io/gh/flavors/weenspace-django-jwt">
        <img src="https://img.shields.io/codecov/c/github/flavors/weenspace-django-jwt?color=%2334D058" alt="Coverage">
    </a>
    <a href="https://www.codacy.com/gh/flavors/weenspace-django-jwt/dashboard">
        <img src="https://app.codacy.com/project/badge/Grade/4f9fd439fbc74be88a215b9ed2abfcf9" alt="Codacy">
    </a>
    <a href="https://pypi.python.org/pypi/weenspace-django-jwt">
        <img src="https://img.shields.io/pypi/v/weenspace-django-jwt.svg" alt="Package version">
    </a>
</p>

## Installation

Install last stable version from Pypi:

```sh
pip install weenspace-django-jwt
```

Add `AuthenticationMiddleware` middleware to your *MIDDLEWARE* settings:


```py
MIDDLEWARE = [
    # ...
    "django.contrib.auth.middleware.AuthenticationMiddleware",
    # ...
]
```

Add `JWTMiddleware` middleware to your *GRAPHENE* settings:

```py
GRAPHENE = {
    "SCHEMA": "mysite.myschema.schema",
    "MIDDLEWARE": [
        "django_jwt.middleware.JWTMiddleware",
    ],
}
```

Add `JWTBackend` backend to your *AUTHENTICATION_BACKENDS*:

```py
AUTHENTICATION_BACKENDS = [
    "django_jwt.backends.JWTBackend",
    "django.contrib.auth.backends.ModelBackend",
]
```

## Schema

Add *weenspace-django-jwt* mutations to the root schema:

```py
import graphene
import django_jwt


class Mutation(graphene.ObjectType):
    create_token = django_jwt.Create.Field()
    verify_token = django_jwt.Verify.Field()
    refresh_token = django_jwt.Refresh.Field()


schema = graphene.Schema(mutation=Mutation)
```
seglines 0.0.5
Segmented Least Squares
License: MIT
Keywords: seglines

# seglines

Compute segmented least squares for a dataset.  Probably assumes that `X = 1...N` or something.

```
$ seglines 10 data.csv --plot
opt = 70.19
segment  1:   0 (1.330)      13 (13.880)    f(x) = 1.009·x + 1.693
segment  2:  14 (1.340)      27 (14.190)    f(x) = 0.997·x + -11.887
segment  3:  28 (3.350)      41 (15.760)    f(x) = 0.987·x + -25.083
segment  4:  42 (3.420)      55 (17.630)    f(x) = 0.991·x + -37.058
segment  5:  56 (5.160)      69 (18.600)    f(x) = 0.949·x + -46.523
segment  6:  70 (3.870)      83 (17.660)    f(x) = 0.973·x + -63.211
segment  7:  84 (5.930)      97 (17.490)    f(x) = 0.977·x + -76.830
segment  8:  98 (5.210)     111 (17.590)    f(x) = 0.897·x + -82.366
segment  9: 112 (3.810)     125 (16.410)    f(x) = 0.963·x + -102.544
segment 10: 126 (16.780)    139 (3.190)     f(x) = -1.036·x + 147.696
```


## Install

`pip install seglines`

Depends only on `numpy`.  When using `--plot`, we also need `matplotlib`.


## Usage

There is a `--help` option: `seglines --help`

Use `seglines L data.csv` where `L` is the number of segments you want to segmentize into.

In case you want to generate an `L`-segmented linear dataset, use

`seglines --generate 5 10 > data.csv`

and then

`seglines 5 data.csv`

This will output the segments, e.g.

```
opt = 16.49
segment  1:  0 (0.410)      9 (10.330)    f(x) = 0.961·x + 1.355
segment  2: 10 (3.750)     19 (13.260)    f(x) = 0.987·x + -5.741
segment  3: 20 (13.530)    29 (4.210)     f(x) = -1.031·x + 33.960
segment  4: 30 (13.880)    37 (7.420)     f(x) = -0.913·x + 41.254
segment  5: 38 (5.190)     49 (14.720)    f(x) = 0.904·x + -29.629
```

To create a plot of the dataset, add `--plot`:

`seglines 5 data.csv --plot`

![plot of seglines](https://raw.githubusercontent.com/pgdr/seglines/master/assets/plot.png)