semvec/pypi-packages huggingface dataset:
pik-address-formatter 1.1
Address formatter for address components from housing
Requires Python: ~=3.6
Keywords: apiqa

# pik-address-formatter #

This project aim is to provide address formatter from address components of housing building

Example:
```python
from address_formatter import all_formats
address_components = {
    "region": "Курганская", "region_type_full": "область",
    "area": "Катайский", "area_type_full": "район",
    "city": "Серов", "city_type_full": "город",
    "city_district": "Кировский", "city_district_type_full": "округ",
    "settlement": "Дрянное", "settlement_type_full": "село",
    "street": "Майская", "street_type_full": "улица",
    "house": "5", "house_type_full": "дом",
    "section": "6", "building": "7",
}
print(all_formats("plain address ", address_components, "5", 7)['all'])
```
Курганская обл., Катайский р⁠-⁠н, г. Серов, Кировский окр., с. Дрянное, ул. Майская, д. 5, корп. 6, стр. 7, м. 45

```text
    all_formats return dict of address formats
        all - full address with region, district, city, township, etc
        street_only - street or village
        finishing_with_village - region, district, city, township and village
        starting_with_street - street, building, section, construction, premise
        finishing_with_street - region, district, city, township, village, street
```

## HowToUse ##

* Add pik-address-formatter to requirements.txt
```
pik-address-formatter>=1.0,<2.0
```

* Add address_formats method to target model
```python
from django.db import models
from address_formatter import all_formats

class Account(models.Model):
    ...
    @property
    def address_formats(self):
        return all_formats(
            self.premise.address,
            self.building.user_address_components,
            self.premise.user_number,
            self.building.type,
    )
```
* If you dosn't have a premise model, just use without premise data
```python
from django.db import models
from address_formatter import all_formats

class Account(models.Model):
    ...
    @property
    def address_formats(self):
        return all_formats(
            self.building.address,
            self.building.user_address_components,
            building_type=self.building.type,
    )
```

* For details see docstring of all_formats
trend-alert 0.0.2
Stock or Futures donchian breakout screener
Requires Python: >=3.6

This is screener that uses donchian channels, Average True Range, and other inputs to determine if a stock, future or other financial instrument with timeseries (OHLC) is may breakout out of its defined channel.

This is not advice to trade, it is only information to use at your own risk.

Required data:
- Time series in pandas dataframe (columns need to be titled like: High, Low, Open, Close)
- Define the number of periods (default: 55 periods)
- Average True Range (ATR) window(default: 14 periods)
- ATR multiple (default: 1.5)- used to determine how far from breakout price before positive screen

The response returned is a tuple of the alert and plot. Plot can be set to false if chart is not desired. The alert is one of three options: 1, -1, 0
*  1 - Possbile breakout to go long
* -1 - Possible breakuot to go short
*  0 - Too far from either price channel

Dependencies:
- pandas
- ta
- plotly

Output is a tuple of the alert and plot. Plot can be set to false if chart is not desired.