pip install pyfbad            # normal install
pip install --upgrade pyfbad  # or update if needed

# connet to mongodb
from pyfbad.data import database as db
database_obj = db.MongoDB('db_name', PORT, 'db_path')
database = database_obj.get_mongo_db()

# check the collections
collections = dataset_obj.get_collection_names(database)

# buil mongodb query
filter = dataset_obj.add_filter(
[],
'time',
{
    "column_name": "datetime",
    "date_type": "hourly",
    "start_time": "2019-02-06 00:00:00",
    "finish_time": "2019-10-06 00:00:00"
})

# get data from db as dataframe
data = dataset_obj.get_data_as_df(
    database=database,
    collection=collections[0],
    filter=filter
)

from pyfbad.features import create_feature as cf
cf_obj = cf.Features()
df_model = cf_obj.get_model_data(df=df, time_column_name="_id.datetime", value_column_name="_id.count", filter=['_id.country','TR'])

from pyfbad.models import models as md
models=md.Model_Prophet()
model_result = models.train_model(df_model)
anomaly_result = models.train_forecast(model_result)

from pyfbad.notification import notifications as nt
gmail_obj = nt.Email()
if 1 or -1 in anomaly_result['anomaly']:
    gmail_obj.send_gmail('[email protected]','password','[email protected]')

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   └── pyfbad
│      ├── __init__.py    <- Makes pyfbad a Python module
│      │
│      ├── data           <- Scripts to read raw data
│      │   └── database.py
│      │   └── __init__.py
│      │
│      ├── features       <- Scripts to turn raw data into features for modeling
│      │   └── create_feature.py
│      │   └── __init__.py
│      │
│      ├── models         <- Scripts to train models and then use trained models to make
│      │   │                 predictions
│      │   └── models.py
│      │   └── __init__.py
│      │
│      └── notification  <- Scripts for setting up notification systems.
│          └── notification.py
│          └── __init__.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io