PrivacyScanData/BI/examples/random_time_series.py

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import pandas as pd
from sqlalchemy import DateTime

from superset import db
from superset.models.slice import Slice
from superset.utils import core as utils

from .helpers import config, get_example_data, get_slice_json, merge_slice, TBL


def load_random_time_series_data(
    only_metadata: bool = False, force: bool = False
) -> None:
    """Loading random time series data from a zip file in the repo"""
    tbl_name = "random_time_series"
    database = utils.get_example_database()
    table_exists = database.has_table_by_name(tbl_name)

    if not only_metadata and (not table_exists or force):
        data = get_example_data("random_time_series.json.gz")
        pdf = pd.read_json(data)
        pdf.ds = pd.to_datetime(pdf.ds, unit="s")
        pdf.to_sql(
            tbl_name,
            database.get_sqla_engine(),
            if_exists="replace",
            chunksize=500,
            dtype={"ds": DateTime},
            index=False,
        )
        print("Done loading table!")
        print("-" * 80)

    print(f"Creating table [{tbl_name}] reference")
    obj = db.session.query(TBL).filter_by(table_name=tbl_name).first()
    if not obj:
        obj = TBL(table_name=tbl_name)
    obj.main_dttm_col = "ds"
    obj.database = database
    db.session.merge(obj)
    db.session.commit()
    obj.fetch_metadata()
    tbl = obj

    slice_data = {
        "granularity_sqla": "day",
        "row_limit": config["ROW_LIMIT"],
        "since": "2019-01-01",
        "until": "2019-02-01",
        "metric": "count",
        "viz_type": "cal_heatmap",
        "domain_granularity": "month",
        "subdomain_granularity": "day",
    }

    print("Creating a slice")
    slc = Slice(
        slice_name="Calendar Heatmap",
        viz_type="cal_heatmap",
        datasource_type="table",
        datasource_id=tbl.id,
        params=get_slice_json(slice_data),
    )
    merge_slice(slc)