mirror of
https://github.com/apache/superset.git
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016f202423
* refactor dashboard and slice models * appease various linters * remove shortcuts & import indirection * appease mypy * fix bad imports * lint * address various issues * ignore type issue * remove unused imports * lint
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import pandas as pd
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from sqlalchemy import BigInteger, Date, DateTime, String
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from superset import db
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from superset.models.slice import Slice
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from superset.utils.core import get_example_database
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from .helpers import (
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config,
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get_example_data,
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get_slice_json,
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merge_slice,
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misc_dash_slices,
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TBL,
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)
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def load_multiformat_time_series(only_metadata=False, force=False):
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"""Loading time series data from a zip file in the repo"""
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tbl_name = "multiformat_time_series"
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database = get_example_database()
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table_exists = database.has_table_by_name(tbl_name)
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if not only_metadata and (not table_exists or force):
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data = get_example_data("multiformat_time_series.json.gz")
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pdf = pd.read_json(data)
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pdf.ds = pd.to_datetime(pdf.ds, unit="s")
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pdf.ds2 = pd.to_datetime(pdf.ds2, unit="s")
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pdf.to_sql(
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tbl_name,
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database.get_sqla_engine(),
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if_exists="replace",
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chunksize=500,
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dtype={
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"ds": Date,
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"ds2": DateTime,
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"epoch_s": BigInteger,
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"epoch_ms": BigInteger,
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"string0": String(100),
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"string1": String(100),
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"string2": String(100),
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"string3": String(100),
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},
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index=False,
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)
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print("Done loading table!")
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print("-" * 80)
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print(f"Creating table [{tbl_name}] reference")
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obj = db.session.query(TBL).filter_by(table_name=tbl_name).first()
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if not obj:
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obj = TBL(table_name=tbl_name)
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obj.main_dttm_col = "ds"
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obj.database = database
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dttm_and_expr_dict = {
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"ds": [None, None],
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"ds2": [None, None],
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"epoch_s": ["epoch_s", None],
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"epoch_ms": ["epoch_ms", None],
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"string2": ["%Y%m%d-%H%M%S", None],
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"string1": ["%Y-%m-%d^%H:%M:%S", None],
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"string0": ["%Y-%m-%d %H:%M:%S.%f", None],
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"string3": ["%Y/%m/%d%H:%M:%S.%f", None],
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}
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for col in obj.columns:
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dttm_and_expr = dttm_and_expr_dict[col.column_name]
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col.python_date_format = dttm_and_expr[0]
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col.dbatabase_expr = dttm_and_expr[1]
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col.is_dttm = True
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db.session.merge(obj)
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db.session.commit()
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obj.fetch_metadata()
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tbl = obj
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print("Creating Heatmap charts")
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for i, col in enumerate(tbl.columns):
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slice_data = {
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"metrics": ["count"],
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"granularity_sqla": col.column_name,
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"row_limit": config["ROW_LIMIT"],
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"since": "2015",
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"until": "2016",
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"viz_type": "cal_heatmap",
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"domain_granularity": "month",
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"subdomain_granularity": "day",
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}
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slc = Slice(
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slice_name=f"Calendar Heatmap multiformat {i}",
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viz_type="cal_heatmap",
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datasource_type="table",
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datasource_id=tbl.id,
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params=get_slice_json(slice_data),
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)
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merge_slice(slc)
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misc_dash_slices.add("Calendar Heatmap multiformat 0")
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