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# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional
import pandas as pd
from sqlalchemy import BigInteger, Date, DateTime, inspect, String
from superset import app, db
from superset.models.slice import Slice
from superset.sql_parse import Table
from superset.utils.core import DatasourceType
from ..utils.database import get_example_database
from .helpers import (
get_example_url,
get_slice_json,
get_table_connector_registry,
merge_slice,
misc_dash_slices,
)
def load_multiformat_time_series( # pylint: disable=too-many-locals
only_metadata: bool = False, force: bool = False
) -> None:
"""Loading time series data from a zip file in the repo"""
tbl_name = "multiformat_time_series"
database = get_example_database()
with database.get_sqla_engine() as engine:
schema = inspect(engine).default_schema_name
table_exists = database.has_table(Table(tbl_name, schema))
if not only_metadata and (not table_exists or force):
url = get_example_url("multiformat_time_series.json.gz")
pdf = pd.read_json(url, compression="gzip")
# TODO(bkyryliuk): move load examples data into the pytest fixture
if database.backend == "presto":
pdf.ds = pd.to_datetime(pdf.ds, unit="s")
pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d")
pdf.ds2 = pd.to_datetime(pdf.ds2, unit="s")
pdf.ds2 = pdf.ds2.dt.strftime("%Y-%m-%d %H:%M%:%S")
else:
pdf.ds = pd.to_datetime(pdf.ds, unit="s")
pdf.ds2 = pd.to_datetime(pdf.ds2, unit="s")
pdf.to_sql(
tbl_name,
engine,
schema=schema,
if_exists="replace",
chunksize=500,
dtype={
"ds": String(255) if database.backend == "presto" else Date,
"ds2": String(255) if database.backend == "presto" else DateTime,
"epoch_s": BigInteger,
"epoch_ms": BigInteger,
"string0": String(100),
"string1": String(100),
"string2": String(100),
"string3": String(100),
},
index=False,
)
print("Done loading table!")
print("-" * 80)
print(f"Creating table [{tbl_name}] reference")
table = get_table_connector_registry()
obj = db.session.query(table).filter_by(table_name=tbl_name).first()
if not obj:
obj = table(table_name=tbl_name, schema=schema)
db.session.add(obj)
obj.main_dttm_col = "ds"
obj.database = database
obj.filter_select_enabled = True
dttm_and_expr_dict: dict[str, tuple[Optional[str], None]] = {
"ds": (None, None),
"ds2": (None, None),
"epoch_s": ("epoch_s", None),
"epoch_ms": ("epoch_ms", None),
"string2": ("%Y%m%d-%H%M%S", None),
"string1": ("%Y-%m-%d^%H:%M:%S", None),
"string0": ("%Y-%m-%d %H:%M:%S.%f", None),
"string3": ("%Y/%m/%d%H:%M:%S.%f", None),
}
for col in obj.columns:
dttm_and_expr = dttm_and_expr_dict[col.column_name]
col.python_date_format = dttm_and_expr[0]
col.database_expression = dttm_and_expr[1]
col.is_dttm = True
db.session.commit()
obj.fetch_metadata()
tbl = obj
print("Creating Heatmap charts")
for i, col in enumerate(tbl.columns):
slice_data = {
"metrics": ["count"],
"granularity_sqla": col.column_name,
"row_limit": app.config["ROW_LIMIT"],
"since": "2015",
"until": "2016",
"viz_type": "cal_heatmap",
"domain_granularity": "month",
"subdomain_granularity": "day",
}
slc = Slice(
slice_name=f"Calendar Heatmap multiformat {i}",
viz_type="cal_heatmap",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(slice_data),
)
merge_slice(slc)
misc_dash_slices.add("Calendar Heatmap multiformat 0")