from fireant.dataset import *
from fireant.database import VerticaDatabase
from pypika import Tables, functions as fn

vertica_database = VerticaDatabase(user='myuser', password='mypassword')
analytics, customers = Tables('analytics', 'customers')

my_dataset = DataSet(
    database=vertica_database,
    table=analytics,
    fields=[
        Field(
            # Non-aggregate definition
            alias='customer',
            definition=customers.id,
            label='Customer'
        ),
        Field(
            # Date/Time type, also non-aggregate
            alias='date',
            definition=analytics.timestamp,
            type=DataType.date,
            label='Date'
        ),
        Field(
            # Text type, also non-aggregate
            alias='device_type',
            definition=analytics.device_type,
            type=DataType.text,
            label='Device_type'
        ),
        Field(
            # Aggregate definition (The SUM function aggregates a group of values into a single value)
            alias='clicks',
            definition=fn.Sum(analytics.clicks),
            label='Clicks'
        ),
        Field(
            # Aggregate definition (The SUM function aggregates a group of values into a single value)
            alias='customer-spend-per-clicks',
            definition=fn.Sum(analytics.customer_spend / analytics.clicks),
            type=DataType.number,
            label='Spend / Clicks'
        )
    ],
    joins=[
        Join(customers, analytics.customer_id == customers.id),
    ],

from fireant import Matplotlib, Pandas, day

 matplotlib_chart, pandas_df = my_dataset.data \
      .dimension(
         # Select the date dimension with a daily interval to group the data by the day applies to
         # dimensions are referenced by `dataset.fields.{alias}`
         day(my_dataset.fields.date),

         # Select the device_type dimension to break the data down further by which device it applies to
         my_dataset.fields.device_type,
      ) \
      .filter(
         # Filter the result set to data to the year of 2018
         my_dataset.fields.date.between(date(2018, 1, 1), date(2018, 12, 31))
      ) \
      # Add a week over week reference to compare data to values from the week prior
      .reference(WeekOverWeek(dataset.fields.date))
      .widget(
         # Add a matpotlib chart widget
         Matplotlib()
            # Add axes with series to the chart
            .axis(Matplotlib.LineSeries(dataset.fields.clicks))

            # metrics are referenced by `dataset.metrics.{alias}`
            .axis(Matplotlib.ColumnSeries(
                my_dataset.fields['customer-spend-per-clicks']
            ))
      ) \
      .widget(
         # Add a pandas data frame table widget
         Pandas(
             my_dataset.fields.clicks,
             my_dataset.fields['customer-spend-per-clicks']
         )
      ) \
      .fetch()

 # Display the chart
 matplotlib_chart.plot()

 # Display the chart
 print(pandas_df)