# HoLcStore

HoLcStore is a Django app for creating a simple TimeSeries store in your database.

## Getting Started

1. Add "holcstore" to your INSTALLED_APPS setting like this ```python

INSTALLED_APPS = [

..., "holcstore",

]

```

2. Start using the abstract model Store by importing it ```python

from hostore.models import Store

class YourStore(Store):

# add new fields

class Meta(Store.Meta):

abstract = False # add your meta

```

## Basic Usage : Store class

#### Saving a timeserie to database

```python

from path.to.YourStore import YourStore import pandas as pd

key = "3000014324556" client_id = 0 idx = pd.date_range('2024-01-01 00:00:00+00:00', '2024-01-02 00:00:00+00:00', freq='30min') ds = pd.Series(data=[1]*len(idx), index=idx) # Put the load curve to the store without versionning YourStore.set_lc(key, ds, client_id) # If you want to activate the versionning YourStore.set_lc(key, ds, client_id, versionning=True) # Each time you call set_lc with your id, a new version will be saved in the database

```

#### Saving multiple timeseries to database

```python

from path.to.YourStore import YourStore import pandas as pd

key = "3000014324556" client_id = 0 idx = pd.date_range('2024-01-01 00:00:00+00:00', '2024-01-02 00:00:00+00:00', freq='30min') df = pd.Series(data={'key1': [1]*len(idx), 'key2': [2]*len(idx), }, index=idx) # Put the load curve to the store without versionning YourStore.set_many_lc(df, client_id) # If you want to activate the versionning YourStore.set_many_lc(df, client_id, versionning=True)

```

#### Getting a load curve from the database

```python

from path.to.YourStore import YourStore

key = "3000014324556" client_id = 0 # Get the load curve from the database # I multiple versions exists, they will be combined beginning with the version 0 and using # https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.combine_first.html datas = YourStore.get_lc(key, client_id)

if not datas:

my_timeserie = datas[0]['data'] last_updated = datas[0]['last_modified']

# If you want to retreive all versions datas = YourStore.get_lc(key, client_id, combined_versions=False) # datas contains all timeseries linked to key

# If you want to retreive a specific version datas = YourStore.get_lc(key, client_id, version=1)

```

## Basic Usage : TimeseriesStore class

### Define your class in models.py ```python class MyTimeseriesStore(TimeseriesStore):

year = models.IntegerField() kind = models.CharField(max_length=100)

class Meta(TimeseriesStore.Meta):

unique_together = ('year', 'kind')

```

### Usage samples ```python # specify attributes to set ts_attrs = dict(year=2020, kind='a')

# build a timeserie ds_ts = pd.Series(...)

# set timeserie to db MyTimeseriesStore.set_ts(ts_attrs, ds_ts)

# update existing timeserie in db (will combine ds_ts + ds_ts_v2, giving priority to ds_ts_v2 datas) MyTimeseriesStore.set_ts(ts_attrs, ds_ts_v2, update=True)

# get timeserie from db if unique match ds_ts = MyTimeseriesStore.get_ts(ts_attrs, flat=True)

# get timeseries from db if multiple match datas = MyTimeseriesStore.get_ts(ts_attrs) ds_ts1 = datas[0]['data'] ```

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