semvec/pypi-packages huggingface dataset:
SNP1 0.0.1
Returns AP,GP sum and progression series
Requires Python: >=3.7

## SNP1 

this package can be installed by going to command prompt and type the following command:
             pip install SNP1
             
this package can be used to find the AP and GP progression series and sum.
the package has 4 modules namely:-
        AP()
        APsum()
        GP()
        GPsum()

AP(a,d,n)

this module provides the AP series , the required inputs are 
a- starting term
d- common difference
n- number of terms

the output will be in print form

APsum(a,d,n)

this module provides the AP sum , the required inputs are
a- starting term
d- common difference
n- number of terms

the output will be in print form

GP(a,r,n)

this module provides the GP series, the required inputs are 
a- starting term
r- common ratio
n- number of terms

the output will be in print form

GPsum(a,r,n)

this module provides the GP sum , the required inputs are

a- starting term
r- common ratio
n- number of terms

the output is in print form
django-nestedquery 0.2.0
Django Nested Query
Requires Python: >=3.8

# django-nestedquery
Given a django model such as 

```python
class SensorReading(models.Model):
    sensor=models.PositiveIntegerField()
    timestamp=models.DatetimeField()
    reading=models.IntegerField()
```

This allows the construction of queries such as 

```python
from nestedquery import NestedQuery

qs=NestedQuery(SensorReading.objects.filter(sensor=1)).filter(reading__gte=10)
```

resulting in  SQL looking something like

```SQL
SELECT * FROM (
    SELECT * FROM SensorReading
    WHERE sensor = 1
) as VirtualTable
WHERE VirtualTable.reading >= 10;
```

While this example is obviously simplistic, This Pattern becomes more useful when dealing with more complex queries, for example when dealing with aggregates

```python
readings = (
    NestedQuery(
        SensorReading.objects.filter(**filters)
        .annotate(
            previous_read=Window(
                expression=window.Lead("timestamp"),
                partition_by=[F("sensor"),],
                order_by=[
                    "timestamp",
                ],
                frame=RowRange(start=-1, end=0),
            )
        )
        .annotate(delta=Abs(Extract(F("timestamp") - F("previous_read"), "epoch")))
    )
    .values("sensor")
    .annotate(min=Min("delta"), max=Max("delta"))
)
```
musicron 0.1.14
A cron for music!
Requires Python: >=3.7

# Welcome to Musicron #
Do you have a lot of radio shows you enjoy, but forget to tune in? Maybe you like anonradio! Or tilderadio! But miss shows?

## Miss shows no more with Musicron! ##

Here are some of the features of Musicron!

* Easy YAML based configuration
* Trigger shows hourly or daily
* Plugin based architecture! Create your own plugins with an easy to use API
* Supports a variety of services

** How to Use **

Simply populate the shows you want in your config.yml file like so:

```
---
- name: "HBR-1"
  time: "13:20"
  service:
    provider: "VLC"
    url: "http://www.hbr1.com/playlist/trance.aac.m3u"
    plugin: "vlc"
```

```time``` can be in the format of:
* *H:M* for play every day
* *H:M Day* for play on a specified day
* *TEST* for plugin testing

-or-

You can add the prefix *U* to any time for UTC