# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=too-many-lines """This module contains the 'Viz' objects These objects represent the backend of all the visualizations that Superset can render. """ from __future__ import annotations import copy import dataclasses import logging import math import re from collections import defaultdict, OrderedDict from datetime import date, datetime, timedelta from itertools import product from typing import ( Any, Callable, cast, Dict, List, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, ) import geohash import numpy as np import pandas as pd import polyline import simplejson as json from dateutil import relativedelta as rdelta from flask import request from flask_babel import lazy_gettext as _ from geopy.point import Point from pandas.tseries.frequencies import to_offset from superset import app from superset.common.db_query_status import QueryStatus from superset.constants import NULL_STRING from superset.errors import ErrorLevel, SupersetError, SupersetErrorType from superset.exceptions import ( CacheLoadError, NullValueException, QueryClauseValidationException, QueryObjectValidationError, SpatialException, SupersetSecurityException, ) from superset.extensions import cache_manager, security_manager from superset.models.helpers import QueryResult from superset.sql_parse import validate_filter_clause from superset.typing import Column, Metric, QueryObjectDict, VizData, VizPayload from superset.utils import core as utils, csv from superset.utils.cache import set_and_log_cache from superset.utils.core import ( apply_max_row_limit, DTTM_ALIAS, ExtraFiltersReasonType, get_column_name, get_column_names, get_column_names_from_columns, get_metric_names, is_adhoc_column, JS_MAX_INTEGER, merge_extra_filters, QueryMode, simple_filter_to_adhoc, ) from superset.utils.date_parser import get_since_until, parse_past_timedelta from superset.utils.dates import datetime_to_epoch from superset.utils.hashing import md5_sha_from_str if TYPE_CHECKING: from superset.common.query_context_factory import QueryContextFactory from superset.connectors.base.models import BaseDatasource config = app.config stats_logger = config["STATS_LOGGER"] relative_start = config["DEFAULT_RELATIVE_START_TIME"] relative_end = config["DEFAULT_RELATIVE_END_TIME"] logger = logging.getLogger(__name__) METRIC_KEYS = [ "metric", "metrics", "percent_metrics", "metric_2", "secondary_metric", "x", "y", "size", ] class BaseViz: # pylint: disable=too-many-public-methods """All visualizations derive this base class""" viz_type: Optional[str] = None verbose_name = "Base Viz" credits = "" is_timeseries = False cache_type = "df" enforce_numerical_metrics = True def __init__( self, datasource: "BaseDatasource", form_data: Dict[str, Any], force: bool = False, force_cached: bool = False, ) -> None: if not datasource: raise QueryObjectValidationError(_("Viz is missing a datasource")) self.datasource = datasource self.request = request self.viz_type = form_data.get("viz_type") self.form_data = form_data self.query = "" self.token = utils.get_form_data_token(form_data) self.groupby: List[Column] = self.form_data.get("groupby") or [] self.time_shift = timedelta() self.status: Optional[str] = None self.error_msg = "" self.results: Optional[QueryResult] = None self.applied_template_filters: List[str] = [] self.errors: List[Dict[str, Any]] = [] self.force = force self._force_cached = force_cached self.from_dttm: Optional[datetime] = None self.to_dttm: Optional[datetime] = None self._extra_chart_data: List[Tuple[str, pd.DataFrame]] = [] self.process_metrics() self.applied_filters: List[Dict[str, str]] = [] self.rejected_filters: List[Dict[str, str]] = [] @property def force_cached(self) -> bool: return self._force_cached def process_metrics(self) -> None: # metrics in Viz is order sensitive, so metric_dict should be # OrderedDict self.metric_dict = OrderedDict() for mkey in METRIC_KEYS: val = self.form_data.get(mkey) if val: if not isinstance(val, list): val = [val] for o in val: label = utils.get_metric_name(o) self.metric_dict[label] = o # Cast to list needed to return serializable object in py3 self.all_metrics = list(self.metric_dict.values()) self.metric_labels = list(self.metric_dict.keys()) @staticmethod def handle_js_int_overflow( data: Dict[str, List[Dict[str, Any]]] ) -> Dict[str, List[Dict[str, Any]]]: for record in data.get("records", {}): for k, v in list(record.items()): if isinstance(v, int): # if an int is too big for Java Script to handle # convert it to a string if abs(v) > JS_MAX_INTEGER: record[k] = str(v) return data def run_extra_queries(self) -> None: """Lifecycle method to use when more than one query is needed In rare-ish cases, a visualization may need to execute multiple queries. That is the case for FilterBox or for time comparison in Line chart for instance. In those cases, we need to make sure these queries run before the main `get_payload` method gets called, so that the overall caching metadata can be right. The way it works here is that if any of the previous `get_df_payload` calls hit the cache, the main payload's metadata will reflect that. The multi-query support may need more work to become a first class use case in the framework, and for the UI to reflect the subtleties (show that only some of the queries were served from cache for instance). In the meantime, since multi-query is rare, we treat it with a bit of a hack. Note that the hack became necessary when moving from caching the visualization's data itself, to caching the underlying query(ies). """ def apply_rolling(self, df: pd.DataFrame) -> pd.DataFrame: rolling_type = self.form_data.get("rolling_type") rolling_periods = int(self.form_data.get("rolling_periods") or 0) min_periods = int(self.form_data.get("min_periods") or 0) if rolling_type in ("mean", "std", "sum") and rolling_periods: kwargs = dict(window=rolling_periods, min_periods=min_periods) if rolling_type == "mean": df = df.rolling(**kwargs).mean() elif rolling_type == "std": df = df.rolling(**kwargs).std() elif rolling_type == "sum": df = df.rolling(**kwargs).sum() elif rolling_type == "cumsum": df = df.cumsum() if min_periods: df = df[min_periods:] if df.empty: raise QueryObjectValidationError( _( "Applied rolling window did not return any data. Please make sure " "the source query satisfies the minimum periods defined in the " "rolling window." ) ) return df def get_samples(self) -> Dict[str, Any]: query_obj = self.query_obj() query_obj.update( { "is_timeseries": False, "groupby": [], "metrics": [], "orderby": [], "row_limit": config["SAMPLES_ROW_LIMIT"], "columns": [o.column_name for o in self.datasource.columns], "from_dttm": None, "to_dttm": None, } ) payload = self.get_df_payload(query_obj) # leverage caching logic return { "data": payload["df"].to_dict(orient="records"), "colnames": payload.get("colnames"), "coltypes": payload.get("coltypes"), } def get_df(self, query_obj: Optional[QueryObjectDict] = None) -> pd.DataFrame: """Returns a pandas dataframe based on the query object""" if not query_obj: query_obj = self.query_obj() if not query_obj: return pd.DataFrame() self.error_msg = "" timestamp_format = None if self.datasource.type == "table": granularity_col = self.datasource.get_column(query_obj["granularity"]) if granularity_col: timestamp_format = granularity_col.python_date_format # The datasource here can be different backend but the interface is common self.results = self.datasource.query(query_obj) self.applied_template_filters = self.results.applied_template_filters or [] self.query = self.results.query self.status = self.results.status self.errors = self.results.errors df = self.results.df # Transform the timestamp we received from database to pandas supported # datetime format. If no python_date_format is specified, the pattern will # be considered as the default ISO date format # If the datetime format is unix, the parse will use the corresponding # parsing logic. if not df.empty: utils.normalize_dttm_col( df=df, timestamp_format=timestamp_format, offset=self.datasource.offset, time_shift=self.time_shift, ) if self.enforce_numerical_metrics: self.df_metrics_to_num(df) df.replace([np.inf, -np.inf], np.nan, inplace=True) return df def df_metrics_to_num(self, df: pd.DataFrame) -> None: """Converting metrics to numeric when pandas.read_sql cannot""" metrics = self.metric_labels for col, dtype in df.dtypes.items(): if dtype.type == np.object_ and col in metrics: df[col] = pd.to_numeric(df[col], errors="coerce") def process_query_filters(self) -> None: utils.convert_legacy_filters_into_adhoc(self.form_data) merge_extra_filters(self.form_data) utils.split_adhoc_filters_into_base_filters(self.form_data) @staticmethod def dedup_columns(*columns_args: Optional[List[Column]]) -> List[Column]: # dedup groupby and columns while preserving order labels: List[str] = [] deduped_columns: List[Column] = [] for columns in columns_args: for column in columns or []: label = get_column_name(column) if label not in labels: deduped_columns.append(column) return deduped_columns def query_obj(self) -> QueryObjectDict: # pylint: disable=too-many-locals """Building a query object""" self.process_query_filters() metrics = self.all_metrics or [] groupby = self.dedup_columns(self.groupby, self.form_data.get("columns")) groupby_labels = get_column_names(groupby) is_timeseries = self.is_timeseries if DTTM_ALIAS in groupby_labels: del groupby[groupby_labels.index(DTTM_ALIAS)] is_timeseries = True granularity = self.form_data.get("granularity") or self.form_data.get( "granularity_sqla" ) limit = int(self.form_data.get("limit") or 0) timeseries_limit_metric = self.form_data.get("timeseries_limit_metric") # apply row limit to query row_limit = int(self.form_data.get("row_limit") or config["ROW_LIMIT"]) row_limit = apply_max_row_limit(row_limit) # default order direction order_desc = self.form_data.get("order_desc", True) try: since, until = get_since_until( relative_start=relative_start, relative_end=relative_end, time_range=self.form_data.get("time_range"), since=self.form_data.get("since"), until=self.form_data.get("until"), ) except ValueError as ex: raise QueryObjectValidationError(str(ex)) from ex time_shift = self.form_data.get("time_shift", "") self.time_shift = parse_past_timedelta(time_shift) from_dttm = None if since is None else (since - self.time_shift) to_dttm = None if until is None else (until - self.time_shift) if from_dttm and to_dttm and from_dttm > to_dttm: raise QueryObjectValidationError( _("From date cannot be larger than to date") ) self.from_dttm = from_dttm self.to_dttm = to_dttm # validate sql filters for param in ("where", "having"): clause = self.form_data.get(param) if clause: try: validate_filter_clause(clause) except QueryClauseValidationException as ex: raise QueryObjectValidationError(ex.message) from ex # extras are used to query elements specific to a datasource type # for instance the extra where clause that applies only to Tables extras = { "druid_time_origin": self.form_data.get("druid_time_origin", ""), "having": self.form_data.get("having", ""), "having_druid": self.form_data.get("having_filters", []), "time_grain_sqla": self.form_data.get("time_grain_sqla"), "where": self.form_data.get("where", ""), } return { "granularity": granularity, "from_dttm": from_dttm, "to_dttm": to_dttm, "is_timeseries": is_timeseries, "groupby": groupby, "metrics": metrics, "row_limit": row_limit, "filter": self.form_data.get("filters", []), "timeseries_limit": limit, "extras": extras, "timeseries_limit_metric": timeseries_limit_metric, "order_desc": order_desc, } @property def cache_timeout(self) -> int: if self.form_data.get("cache_timeout") is not None: return int(self.form_data["cache_timeout"]) if self.datasource.cache_timeout is not None: return self.datasource.cache_timeout if ( hasattr(self.datasource, "database") and self.datasource.database.cache_timeout ) is not None: return self.datasource.database.cache_timeout if config["DATA_CACHE_CONFIG"].get("CACHE_DEFAULT_TIMEOUT") is not None: return config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"] return config["CACHE_DEFAULT_TIMEOUT"] def get_json(self) -> str: return json.dumps( self.get_payload(), default=utils.json_int_dttm_ser, ignore_nan=True ) def cache_key(self, query_obj: QueryObjectDict, **extra: Any) -> str: """ The cache key is made out of the key/values in `query_obj`, plus any other key/values in `extra`. We remove datetime bounds that are hard values, and replace them with the use-provided inputs to bounds, which may be time-relative (as in "5 days ago" or "now"). The `extra` arguments are currently used by time shift queries, since different time shifts wil differ only in the `from_dttm`, `to_dttm`, `inner_from_dttm`, and `inner_to_dttm` values which are stripped. """ cache_dict = copy.copy(query_obj) cache_dict.update(extra) for k in ["from_dttm", "to_dttm", "inner_from_dttm", "inner_to_dttm"]: if k in cache_dict: del cache_dict[k] cache_dict["time_range"] = self.form_data.get("time_range") cache_dict["datasource"] = self.datasource.uid cache_dict["extra_cache_keys"] = self.datasource.get_extra_cache_keys(query_obj) cache_dict["rls"] = security_manager.get_rls_cache_key(self.datasource) cache_dict["changed_on"] = self.datasource.changed_on json_data = self.json_dumps(cache_dict, sort_keys=True) return md5_sha_from_str(json_data) def get_payload(self, query_obj: Optional[QueryObjectDict] = None) -> VizPayload: """Returns a payload of metadata and data""" try: self.run_extra_queries() except SupersetSecurityException as ex: error = dataclasses.asdict(ex.error) self.errors.append(error) self.status = QueryStatus.FAILED payload = self.get_df_payload(query_obj) # if payload does not have a df, we are raising an error here. df = cast(Optional[pd.DataFrame], payload["df"]) if self.status != QueryStatus.FAILED: payload["data"] = self.get_data(df) if "df" in payload: del payload["df"] filters = self.form_data.get("filters", []) filter_columns = [flt.get("col") for flt in filters] columns = set(self.datasource.column_names) applied_template_filters = self.applied_template_filters or [] applied_time_extras = self.form_data.get("applied_time_extras", {}) applied_time_columns, rejected_time_columns = utils.get_time_filter_status( self.datasource, applied_time_extras ) payload["applied_filters"] = [ {"column": get_column_name(col)} for col in filter_columns if is_adhoc_column(col) or col in columns or col in applied_template_filters ] + applied_time_columns payload["rejected_filters"] = [ {"reason": ExtraFiltersReasonType.COL_NOT_IN_DATASOURCE, "column": col} for col in filter_columns if not is_adhoc_column(col) and col not in columns and col not in applied_template_filters ] + rejected_time_columns if df is not None: payload["colnames"] = list(df.columns) return payload def get_df_payload( # pylint: disable=too-many-statements self, query_obj: Optional[QueryObjectDict] = None, **kwargs: Any ) -> Dict[str, Any]: """Handles caching around the df payload retrieval""" if not query_obj: query_obj = self.query_obj() cache_key = self.cache_key(query_obj, **kwargs) if query_obj else None cache_value = None logger.info("Cache key: %s", cache_key) is_loaded = False stacktrace = None df = None if cache_key and cache_manager.data_cache and not self.force: cache_value = cache_manager.data_cache.get(cache_key) if cache_value: stats_logger.incr("loading_from_cache") try: df = cache_value["df"] self.query = cache_value["query"] self.applied_template_filters = cache_value.get( "applied_template_filters", [] ) self.status = QueryStatus.SUCCESS is_loaded = True stats_logger.incr("loaded_from_cache") except Exception as ex: # pylint: disable=broad-except logger.exception(ex) logger.error( "Error reading cache: %s", utils.error_msg_from_exception(ex), exc_info=True, ) logger.info("Serving from cache") if query_obj and not is_loaded: if self.force_cached: logger.warning( "force_cached (viz.py): value not found for cache key %s", cache_key, ) raise CacheLoadError(_("Cached value not found")) try: invalid_columns = [ col for col in get_column_names_from_columns( query_obj.get("columns") or [] ) + get_column_names_from_columns(query_obj.get("groupby") or []) + utils.get_column_names_from_metrics( cast(List[Metric], query_obj.get("metrics") or []) ) if col not in self.datasource.column_names ] if invalid_columns: raise QueryObjectValidationError( _( "Columns missing in datasource: %(invalid_columns)s", invalid_columns=invalid_columns, ) ) df = self.get_df(query_obj) if self.status != QueryStatus.FAILED: stats_logger.incr("loaded_from_source") if not self.force: stats_logger.incr("loaded_from_source_without_force") is_loaded = True except QueryObjectValidationError as ex: error = dataclasses.asdict( SupersetError( message=str(ex), level=ErrorLevel.ERROR, error_type=SupersetErrorType.VIZ_GET_DF_ERROR, ) ) self.errors.append(error) self.status = QueryStatus.FAILED except Exception as ex: # pylint: disable=broad-except logger.exception(ex) error = dataclasses.asdict( SupersetError( message=str(ex), level=ErrorLevel.ERROR, error_type=SupersetErrorType.VIZ_GET_DF_ERROR, ) ) self.errors.append(error) self.status = QueryStatus.FAILED stacktrace = utils.get_stacktrace() if is_loaded and cache_key and self.status != QueryStatus.FAILED: set_and_log_cache( cache_manager.data_cache, cache_key, {"df": df, "query": self.query}, self.cache_timeout, self.datasource.uid, ) return { "cache_key": cache_key, "cached_dttm": cache_value["dttm"] if cache_value is not None else None, "cache_timeout": self.cache_timeout, "df": df, "errors": self.errors, "form_data": self.form_data, "is_cached": cache_value is not None, "query": self.query, "from_dttm": self.from_dttm, "to_dttm": self.to_dttm, "status": self.status, "stacktrace": stacktrace, "rowcount": len(df.index) if df is not None else 0, "colnames": list(df.columns) if df is not None else None, "coltypes": utils.extract_dataframe_dtypes(df, self.datasource) if df is not None else None, } @staticmethod def json_dumps(query_obj: Any, sort_keys: bool = False) -> str: return json.dumps( query_obj, default=utils.json_int_dttm_ser, ignore_nan=True, sort_keys=sort_keys, ) @staticmethod def has_error(payload: VizPayload) -> bool: return ( payload.get("status") == QueryStatus.FAILED or payload.get("error") is not None or bool(payload.get("errors")) ) def payload_json_and_has_error(self, payload: VizPayload) -> Tuple[str, bool]: return self.json_dumps(payload), self.has_error(payload) @property def data(self) -> Dict[str, Any]: """This is the data object serialized to the js layer""" content = { "form_data": self.form_data, "token": self.token, "viz_name": self.viz_type, "filter_select_enabled": self.datasource.filter_select_enabled, } return content def get_csv(self) -> Optional[str]: df = self.get_df_payload()["df"] # leverage caching logic include_index = not isinstance(df.index, pd.RangeIndex) return csv.df_to_escaped_csv(df, index=include_index, **config["CSV_EXPORT"]) def get_data(self, df: pd.DataFrame) -> VizData: # pylint: disable=no-self-use return df.to_dict(orient="records") @property def json_data(self) -> str: return json.dumps(self.data) def raise_for_access(self) -> None: """ Raise an exception if the user cannot access the resource. :raises SupersetSecurityException: If the user cannot access the resource """ security_manager.raise_for_access(viz=self) class TableViz(BaseViz): """A basic html table that is sortable and searchable""" viz_type = "table" verbose_name = _("Table View") credits = 'a Superset original' is_timeseries = False enforce_numerical_metrics = False def process_metrics(self) -> None: """Process form data and store parsed column configs. 1. Determine query mode based on form_data params. - Use `query_mode` if it has a valid value - Set as RAW mode if `all_columns` is set - Otherwise defaults to AGG mode 2. Determine output columns based on query mode. """ # Verify form data first: if not specifying query mode, then cannot have both # GROUP BY and RAW COLUMNS. if ( not self.form_data.get("query_mode") and self.form_data.get("all_columns") and ( self.form_data.get("groupby") or self.form_data.get("metrics") or self.form_data.get("percent_metrics") ) ): raise QueryObjectValidationError( _( "You cannot use [Columns] in combination with " "[Group By]/[Metrics]/[Percentage Metrics]. " "Please choose one or the other." ) ) super().process_metrics() self.query_mode: QueryMode = QueryMode.get( self.form_data.get("query_mode") ) or ( # infer query mode from the presence of other fields QueryMode.RAW if len(self.form_data.get("all_columns") or []) > 0 else QueryMode.AGGREGATE ) columns: List[str] # output columns sans time and percent_metric column percent_columns: List[str] = [] # percent columns that needs extra computation if self.query_mode == QueryMode.RAW: columns = get_metric_names(self.form_data.get("all_columns")) else: columns = get_column_names(self.groupby) + get_metric_names( self.form_data.get("metrics") ) percent_columns = get_metric_names( self.form_data.get("percent_metrics") or [] ) self.columns = columns self.percent_columns = percent_columns self.is_timeseries = self.should_be_timeseries() def should_be_timeseries(self) -> bool: # TODO handle datasource-type-specific code in datasource conditions_met = ( self.form_data.get("granularity") and self.form_data.get("granularity") != "all" ) or ( self.form_data.get("granularity_sqla") and self.form_data.get("time_grain_sqla") ) if self.form_data.get("include_time") and not conditions_met: raise QueryObjectValidationError( _("Pick a granularity in the Time section or " "uncheck 'Include Time'") ) return bool(self.form_data.get("include_time")) def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() if self.query_mode == QueryMode.RAW: query_obj["columns"] = self.form_data.get("all_columns") order_by_cols = self.form_data.get("order_by_cols") or [] query_obj["orderby"] = [json.loads(t) for t in order_by_cols] # must disable groupby and metrics in raw mode query_obj["groupby"] = [] query_obj["metrics"] = [] # raw mode does not support timeseries queries query_obj["timeseries_limit_metric"] = None query_obj["timeseries_limit"] = None query_obj["is_timeseries"] = None else: sort_by = self.form_data.get("timeseries_limit_metric") if sort_by: sort_by_label = utils.get_metric_name(sort_by) if sort_by_label not in utils.get_metric_names(query_obj["metrics"]): query_obj["metrics"].append(sort_by) query_obj["orderby"] = [ (sort_by, not self.form_data.get("order_desc", True)) ] elif query_obj["metrics"]: # Legacy behavior of sorting by first metric by default first_metric = query_obj["metrics"][0] query_obj["orderby"] = [ (first_metric, not self.form_data.get("order_desc", True)) ] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: """ Transform the query result to the table representation. :param df: The interim dataframe :returns: The table visualization data The interim dataframe comprises of the group-by and non-group-by columns and the union of the metrics representing the non-percent and percent metrics. Note the percent metrics have yet to be transformed. """ # Transform the data frame to adhere to the UI ordering of the columns and # metrics whilst simultaneously computing the percentages (via normalization) # for the percent metrics. if df.empty: return None columns, percent_columns = self.columns, self.percent_columns if DTTM_ALIAS in df and self.is_timeseries: columns = [DTTM_ALIAS] + columns df = pd.concat( [ df[columns], (df[percent_columns].div(df[percent_columns].sum()).add_prefix("%")), ], axis=1, ) return self.handle_js_int_overflow( dict(records=df.to_dict(orient="records"), columns=list(df.columns)) ) @staticmethod def json_dumps(query_obj: Any, sort_keys: bool = False) -> str: return json.dumps( query_obj, default=utils.json_iso_dttm_ser, sort_keys=sort_keys, ignore_nan=True, ) class TimeTableViz(BaseViz): """A data table with rich time-series related columns""" viz_type = "time_table" verbose_name = _("Time Table View") credits = 'a Superset original' is_timeseries = True def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() if not self.form_data.get("metrics"): raise QueryObjectValidationError(_("Pick at least one metric")) if self.form_data.get("groupby") and len(self.form_data["metrics"]) > 1: raise QueryObjectValidationError( _("When using 'Group By' you are limited to use a single metric") ) sort_by = utils.get_first_metric_name(query_obj["metrics"]) is_asc = not query_obj.get("order_desc") query_obj["orderby"] = [(sort_by, is_asc)] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None columns = None values: Union[List[str], str] = self.metric_labels if self.form_data.get("groupby"): values = self.metric_labels[0] columns = get_column_names(self.form_data.get("groupby")) pt = df.pivot_table(index=DTTM_ALIAS, columns=columns, values=values) pt.index = pt.index.map(str) pt = pt.sort_index() return dict( records=pt.to_dict(orient="index"), columns=list(pt.columns), is_group_by=bool(self.form_data.get("groupby")), ) class PivotTableViz(BaseViz): """A pivot table view, define your rows, columns and metrics""" viz_type = "pivot_table" verbose_name = _("Pivot Table") credits = 'a Superset original' is_timeseries = False enforce_numerical_metrics = False def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() groupby = self.form_data.get("groupby") columns = self.form_data.get("columns") metrics = self.form_data.get("metrics") transpose = self.form_data.get("transpose_pivot") if not columns: columns = [] if not groupby: groupby = [] if not groupby: raise QueryObjectValidationError( _("Please choose at least one 'Group by' field ") ) if transpose and not columns: raise QueryObjectValidationError( _( ( "Please choose at least one 'Columns' field when " "select 'Transpose Pivot' option" ) ) ) if not metrics: raise QueryObjectValidationError(_("Please choose at least one metric")) deduped_cols = self.dedup_columns(groupby, columns) if len(deduped_cols) < (len(groupby) + len(columns)): raise QueryObjectValidationError(_("Group By' and 'Columns' can't overlap")) sort_by = self.form_data.get("timeseries_limit_metric") if sort_by: sort_by_label = utils.get_metric_name(sort_by) if sort_by_label not in utils.get_metric_names(query_obj["metrics"]): query_obj["metrics"].append(sort_by) if self.form_data.get("order_desc"): query_obj["orderby"] = [ (sort_by, not self.form_data.get("order_desc", True)) ] return query_obj @staticmethod def get_aggfunc( metric: str, df: pd.DataFrame, form_data: Dict[str, Any] ) -> Union[str, Callable[[Any], Any]]: aggfunc = form_data.get("pandas_aggfunc") or "sum" if pd.api.types.is_numeric_dtype(df[metric]): # Ensure that Pandas's sum function mimics that of SQL. if aggfunc == "sum": return lambda x: x.sum(min_count=1) # only min and max work properly for non-numerics return aggfunc if aggfunc in ("min", "max") else "max" @staticmethod def _format_datetime(value: Union[pd.Timestamp, datetime, date, str]) -> str: """ Format a timestamp in such a way that the viz will be able to apply the correct formatting in the frontend. :param value: the value of a temporal column :return: formatted timestamp if it is a valid timestamp, otherwise the original value """ tstamp: Optional[pd.Timestamp] = None if isinstance(value, pd.Timestamp): tstamp = value if isinstance(value, (date, datetime)): tstamp = pd.Timestamp(value) if isinstance(value, str): try: tstamp = pd.Timestamp(value) except ValueError: pass if tstamp: return f"__timestamp:{datetime_to_epoch(tstamp)}" # fallback in case something incompatible is returned return cast(str, value) def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None if self.form_data.get("granularity") == "all" and DTTM_ALIAS in df: del df[DTTM_ALIAS] metrics = [utils.get_metric_name(m) for m in self.form_data["metrics"]] aggfuncs: Dict[str, Union[str, Callable[[Any], Any]]] = {} for metric in metrics: aggfuncs[metric] = self.get_aggfunc(metric, df, self.form_data) groupby = self.form_data.get("groupby") or [] columns = self.form_data.get("columns") or [] for column in groupby + columns: if is_adhoc_column(column): # TODO: check data type pass else: column_obj = self.datasource.get_column(column) if column_obj and column_obj.is_temporal: ts = df[column].apply(self._format_datetime) df[column] = ts if self.form_data.get("transpose_pivot"): groupby, columns = columns, groupby df = df.pivot_table( index=get_column_names(groupby), columns=get_column_names(columns), values=metrics, aggfunc=aggfuncs, margins=self.form_data.get("pivot_margins"), ) # Re-order the columns adhering to the metric ordering. df = df[metrics] # Display metrics side by side with each column if self.form_data.get("combine_metric"): df = df.stack(0).unstack().reindex(level=-1, columns=metrics) return dict( columns=list(df.columns), html=df.to_html( na_rep="null", classes=( "dataframe table table-striped table-bordered " "table-condensed table-hover" ).split(" "), ), ) class TreemapViz(BaseViz): """Tree map visualisation for hierarchical data.""" viz_type = "treemap" verbose_name = _("Treemap") credits = 'd3.js' is_timeseries = False def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() sort_by = self.form_data.get("timeseries_limit_metric") if sort_by: sort_by_label = utils.get_metric_name(sort_by) if sort_by_label not in utils.get_metric_names(query_obj["metrics"]): query_obj["metrics"].append(sort_by) if self.form_data.get("order_desc"): query_obj["orderby"] = [ (sort_by, not self.form_data.get("order_desc", True)) ] return query_obj def _nest(self, metric: str, df: pd.DataFrame) -> List[Dict[str, Any]]: nlevels = df.index.nlevels if nlevels == 1: result = [{"name": n, "value": v} for n, v in zip(df.index, df[metric])] else: result = [ {"name": l, "children": self._nest(metric, df.loc[l])} for l in df.index.levels[0] ] return result def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None df = df.set_index(get_column_names(self.form_data.get("groupby"))) chart_data = [ {"name": metric, "children": self._nest(metric, df)} for metric in df.columns ] return chart_data class CalHeatmapViz(BaseViz): """Calendar heatmap.""" viz_type = "cal_heatmap" verbose_name = _("Calendar Heatmap") credits = "cal-heatmap" is_timeseries = True def get_data(self, df: pd.DataFrame) -> VizData: # pylint: disable=too-many-locals if df.empty: return None form_data = self.form_data data = {} records = df.to_dict("records") for metric in self.metric_labels: values = {} for query_obj in records: v = query_obj[DTTM_ALIAS] if hasattr(v, "value"): v = v.value values[str(v / 10 ** 9)] = query_obj.get(metric) data[metric] = values try: start, end = get_since_until( relative_start=relative_start, relative_end=relative_end, time_range=form_data.get("time_range"), since=form_data.get("since"), until=form_data.get("until"), ) except ValueError as ex: raise QueryObjectValidationError(str(ex)) from ex if not start or not end: raise QueryObjectValidationError( "Please provide both time bounds (Since and Until)" ) domain = form_data.get("domain_granularity") diff_delta = rdelta.relativedelta(end, start) diff_secs = (end - start).total_seconds() if domain == "year": range_ = end.year - start.year + 1 elif domain == "month": range_ = diff_delta.years * 12 + diff_delta.months + 1 elif domain == "week": range_ = diff_delta.years * 53 + diff_delta.weeks + 1 elif domain == "day": range_ = diff_secs // (24 * 60 * 60) + 1 # type: ignore else: range_ = diff_secs // (60 * 60) + 1 # type: ignore return { "data": data, "start": start, "domain": domain, "subdomain": form_data.get("subdomain_granularity"), "range": range_, } def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["metrics"] = self.form_data.get("metrics") mapping = { "min": "PT1M", "hour": "PT1H", "day": "P1D", "week": "P1W", "month": "P1M", "year": "P1Y", } time_grain = mapping[self.form_data.get("subdomain_granularity", "min")] if self.datasource.type == "druid": query_obj["granularity"] = time_grain else: query_obj["extras"]["time_grain_sqla"] = time_grain return query_obj class NVD3Viz(BaseViz): """Base class for all nvd3 vizs""" credits = 'NVD3.org' viz_type: Optional[str] = None verbose_name = "Base NVD3 Viz" is_timeseries = False class BubbleViz(NVD3Viz): """Based on the NVD3 bubble chart""" viz_type = "bubble" verbose_name = _("Bubble Chart") is_timeseries = False def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["groupby"] = [self.form_data.get("entity")] if self.form_data.get("series"): query_obj["groupby"].append(self.form_data.get("series")) # dedup groupby if it happens to be the same query_obj["groupby"] = self.dedup_columns(query_obj["groupby"]) # pylint: disable=attribute-defined-outside-init self.x_metric = self.form_data["x"] self.y_metric = self.form_data["y"] self.z_metric = self.form_data["size"] self.entity = self.form_data.get("entity") self.series = self.form_data.get("series") or self.entity query_obj["row_limit"] = self.form_data.get("limit") query_obj["metrics"] = [self.z_metric, self.x_metric, self.y_metric] if len(set(self.metric_labels)) < 3: raise QueryObjectValidationError(_("Please use 3 different metric labels")) if not all(query_obj["metrics"] + [self.entity]): raise QueryObjectValidationError(_("Pick a metric for x, y and size")) return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None df["x"] = df[[utils.get_metric_name(self.x_metric)]] df["y"] = df[[utils.get_metric_name(self.y_metric)]] df["size"] = df[[utils.get_metric_name(self.z_metric)]] df["shape"] = "circle" df["group"] = df[[get_column_name(self.series)]] # type: ignore series: Dict[Any, List[Any]] = defaultdict(list) for row in df.to_dict(orient="records"): series[row["group"]].append(row) chart_data = [] for k, v in series.items(): chart_data.append({"key": k, "values": v}) return chart_data class BulletViz(NVD3Viz): """Based on the NVD3 bullet chart""" viz_type = "bullet" verbose_name = _("Bullet Chart") is_timeseries = False def query_obj(self) -> QueryObjectDict: form_data = self.form_data query_obj = super().query_obj() self.metric = form_data[ # pylint: disable=attribute-defined-outside-init "metric" ] query_obj["metrics"] = [self.metric] if not self.metric: raise QueryObjectValidationError(_("Pick a metric to display")) return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None df["metric"] = df[[utils.get_metric_name(self.metric)]] values = df["metric"].values return { "measures": values.tolist(), } class BigNumberViz(BaseViz): """Put emphasis on a single metric with this big number viz""" viz_type = "big_number" verbose_name = _("Big Number with Trendline") credits = 'a Superset original' is_timeseries = True def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() metric = self.form_data.get("metric") if not metric: raise QueryObjectValidationError(_("Pick a metric!")) query_obj["metrics"] = [self.form_data.get("metric")] self.form_data["metric"] = metric return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None df = df.pivot_table( index=DTTM_ALIAS, columns=[], values=self.metric_labels, dropna=False, aggfunc=np.min, # looking for any (only) value, preserving `None` ) df = self.apply_rolling(df) df[DTTM_ALIAS] = df.index return super().get_data(df) class BigNumberTotalViz(BaseViz): """Put emphasis on a single metric with this big number viz""" viz_type = "big_number_total" verbose_name = _("Big Number") credits = 'a Superset original' is_timeseries = False def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() metric = self.form_data.get("metric") if not metric: raise QueryObjectValidationError(_("Pick a metric!")) query_obj["metrics"] = [self.form_data.get("metric")] self.form_data["metric"] = metric # Limiting rows is not required as only one cell is returned query_obj["row_limit"] = None return query_obj class NVD3TimeSeriesViz(NVD3Viz): """A rich line chart component with tons of options""" viz_type = "line" verbose_name = _("Time Series - Line Chart") sort_series = False is_timeseries = True pivot_fill_value: Optional[int] = None def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() sort_by = self.form_data.get( "timeseries_limit_metric" ) or utils.get_first_metric_name(query_obj.get("metrics") or []) is_asc = not self.form_data.get("order_desc") if sort_by: sort_by_label = utils.get_metric_name(sort_by) if sort_by_label not in utils.get_metric_names(query_obj["metrics"]): query_obj["metrics"].append(sort_by) query_obj["orderby"] = [(sort_by, is_asc)] return query_obj def to_series( # pylint: disable=too-many-branches self, df: pd.DataFrame, classed: str = "", title_suffix: str = "" ) -> List[Dict[str, Any]]: cols = [] for col in df.columns: if col == "": cols.append("N/A") elif col is None: cols.append("NULL") else: cols.append(col) df.columns = cols series = df.to_dict("series") chart_data = [] for name in df.T.index.tolist(): ys = series[name] if df[name].dtype.kind not in "biufc": continue series_title: Union[List[str], str, Tuple[str, ...]] if isinstance(name, list): series_title = [str(title) for title in name] elif isinstance(name, tuple): series_title = tuple(str(title) for title in name) else: series_title = str(name) if ( isinstance(series_title, (list, tuple)) and len(series_title) > 1 and len(self.metric_labels) == 1 ): # Removing metric from series name if only one metric series_title = series_title[1:] if title_suffix: if isinstance(series_title, str): series_title = (series_title, title_suffix) elif isinstance(series_title, list): series_title = series_title + [title_suffix] elif isinstance(series_title, tuple): series_title = series_title + (title_suffix,) values = [] non_nan_cnt = 0 for ds in df.index: if ds in ys: data = {"x": ds, "y": ys[ds]} if not np.isnan(ys[ds]): non_nan_cnt += 1 else: data = {} values.append(data) if non_nan_cnt == 0: continue data = {"key": series_title, "values": values} if classed: data["classed"] = classed chart_data.append(data) return chart_data def process_data(self, df: pd.DataFrame, aggregate: bool = False) -> VizData: if self.form_data.get("granularity") == "all": raise QueryObjectValidationError( _("Pick a time granularity for your time series") ) if df.empty: return df if aggregate: df = df.pivot_table( index=DTTM_ALIAS, columns=get_column_names(self.form_data.get("groupby")), values=self.metric_labels, fill_value=0, aggfunc=sum, ) else: df = df.pivot_table( index=DTTM_ALIAS, columns=get_column_names(self.form_data.get("groupby")), values=self.metric_labels, fill_value=self.pivot_fill_value, ) rule = self.form_data.get("resample_rule") method = self.form_data.get("resample_method") if rule and method: df = getattr(df.resample(rule), method)() if self.sort_series: dfs = df.sum() dfs.sort_values(ascending=False, inplace=True) df = df[dfs.index] df = self.apply_rolling(df) if self.form_data.get("contribution"): dft = df.T df = (dft / dft.sum()).T return df def run_extra_queries(self) -> None: time_compare = self.form_data.get("time_compare") or [] # backwards compatibility if not isinstance(time_compare, list): time_compare = [time_compare] for option in time_compare: query_object = self.query_obj() try: delta = parse_past_timedelta(option) except ValueError as ex: raise QueryObjectValidationError(str(ex)) from ex query_object["inner_from_dttm"] = query_object["from_dttm"] query_object["inner_to_dttm"] = query_object["to_dttm"] if not query_object["from_dttm"] or not query_object["to_dttm"]: raise QueryObjectValidationError( _( "An enclosed time range (both start and end) must be specified " "when using a Time Comparison." ) ) query_object["from_dttm"] -= delta query_object["to_dttm"] -= delta df2 = self.get_df_payload(query_object, time_compare=option).get("df") if df2 is not None and DTTM_ALIAS in df2: dttm_series = df2[DTTM_ALIAS] + delta df2 = df2.drop(DTTM_ALIAS, axis=1) df2 = pd.concat([dttm_series, df2], axis=1) label = "{} offset".format(option) df2 = self.process_data(df2) self._extra_chart_data.append((label, df2)) def get_data(self, df: pd.DataFrame) -> VizData: comparison_type = self.form_data.get("comparison_type") or "values" df = self.process_data(df) if comparison_type == "values": # Filter out series with all NaN chart_data = self.to_series(df.dropna(axis=1, how="all")) for i, (label, df2) in enumerate(self._extra_chart_data): chart_data.extend( self.to_series( df2, classed="time-shift-{}".format(i), title_suffix=label ) ) else: chart_data = [] for i, (label, df2) in enumerate(self._extra_chart_data): # reindex df2 into the df2 index combined_index = df.index.union(df2.index) df2 = ( df2.reindex(combined_index) .interpolate(method="time") .reindex(df.index) ) if comparison_type == "absolute": diff = df - df2 elif comparison_type == "percentage": diff = (df - df2) / df2 elif comparison_type == "ratio": diff = df / df2 else: raise QueryObjectValidationError( "Invalid `comparison_type`: {0}".format(comparison_type) ) # remove leading/trailing NaNs from the time shift difference diff = diff[diff.first_valid_index() : diff.last_valid_index()] chart_data.extend( self.to_series( diff, classed="time-shift-{}".format(i), title_suffix=label ) ) if not self.sort_series: chart_data = sorted(chart_data, key=lambda x: tuple(x["key"])) return chart_data class MultiLineViz(NVD3Viz): """Pile on multiple line charts""" viz_type = "line_multi" verbose_name = _("Time Series - Multiple Line Charts") is_timeseries = True def query_obj(self) -> QueryObjectDict: return {} def get_data(self, df: pd.DataFrame) -> VizData: # pylint: disable=import-outside-toplevel,too-many-locals multiline_fd = self.form_data # Late import to avoid circular import issues from superset.charts.dao import ChartDAO axis1_chart_ids = multiline_fd.get("line_charts", []) axis2_chart_ids = multiline_fd.get("line_charts_2", []) all_charts = { chart.id: chart for chart in ChartDAO.find_by_ids(axis1_chart_ids + axis2_chart_ids) } axis1_charts = [all_charts[chart_id] for chart_id in axis1_chart_ids] axis2_charts = [all_charts[chart_id] for chart_id in axis2_chart_ids] filters = multiline_fd.get("filters", []) add_prefix = multiline_fd.get("prefix_metric_with_slice_name", False) data = [] min_x, max_x = None, None for chart, y_axis in [(chart, 1) for chart in axis1_charts] + [ (chart, 2) for chart in axis2_charts ]: prefix = f"{chart.chart}: " if add_prefix else "" chart_fd = chart.form_data chart_fd["filters"] = chart_fd.get("filters", []) + filters if "extra_filters" in multiline_fd: chart_fd["extra_filters"] = multiline_fd["extra_filters"] if "time_range" in multiline_fd: chart_fd["time_range"] = multiline_fd["time_range"] viz_obj = viz_types[chart.viz_type]( chart.datasource, form_data=chart_fd, force=self.force, force_cached=self.force_cached, ) df = viz_obj.get_df_payload()["df"] chart_series = viz_obj.get_data(df) or [] for series in chart_series: x_values = [value["x"] for value in series["values"]] min_x = min(x_values + ([min_x] if min_x is not None else [])) max_x = max(x_values + ([max_x] if max_x is not None else [])) series_key = ( series["key"] if isinstance(series["key"], (list, tuple)) else [series["key"]] ) data.append( { "key": prefix + ", ".join(series_key), "type": "line", "values": series["values"], "yAxis": y_axis, } ) bounds = [] if min_x is not None: bounds.append({"x": min_x, "y": None}) if max_x is not None: bounds.append({"x": max_x, "y": None}) for series in data: series["values"].extend(bounds) return data class NVD3DualLineViz(NVD3Viz): """A rich line chart with dual axis""" viz_type = "dual_line" verbose_name = _("Time Series - Dual Axis Line Chart") sort_series = False is_timeseries = True def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() m1 = self.form_data.get("metric") m2 = self.form_data.get("metric_2") if not m1: raise QueryObjectValidationError(_("Pick a metric for left axis!")) if not m2: raise QueryObjectValidationError(_("Pick a metric for right axis!")) if m1 == m2: raise QueryObjectValidationError( _("Please choose different metrics" " on left and right axis") ) query_obj["metrics"] = [m1, m2] return query_obj def to_series(self, df: pd.DataFrame, classed: str = "") -> List[Dict[str, Any]]: cols = [] for col in df.columns: if col == "": cols.append("N/A") elif col is None: cols.append("NULL") else: cols.append(col) df.columns = cols series = df.to_dict("series") chart_data = [] metrics = [self.form_data["metric"], self.form_data["metric_2"]] for i, metric in enumerate(metrics): metric_name = utils.get_metric_name(metric) ys = series[metric_name] if df[metric_name].dtype.kind not in "biufc": continue series_title = metric_name chart_data.append( { "key": series_title, "classed": classed, "values": [ {"x": ds, "y": ys[ds] if ds in ys else None} for ds in df.index ], "yAxis": i + 1, "type": "line", } ) return chart_data def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None if self.form_data.get("granularity") == "all": raise QueryObjectValidationError( _("Pick a time granularity for your time series") ) metric = utils.get_metric_name(self.form_data["metric"]) metric_2 = utils.get_metric_name(self.form_data["metric_2"]) df = df.pivot_table(index=DTTM_ALIAS, values=[metric, metric_2]) chart_data = self.to_series(df) return chart_data class NVD3TimeSeriesBarViz(NVD3TimeSeriesViz): """A bar chart where the x axis is time""" viz_type = "bar" sort_series = True verbose_name = _("Time Series - Bar Chart") class NVD3TimePivotViz(NVD3TimeSeriesViz): """Time Series - Periodicity Pivot""" viz_type = "time_pivot" sort_series = True verbose_name = _("Time Series - Period Pivot") def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["metrics"] = [self.form_data.get("metric")] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None df = self.process_data(df) freq = to_offset(self.form_data.get("freq")) try: freq = type(freq)(freq.n, normalize=True, **freq.kwds) except ValueError: freq = type(freq)(freq.n, **freq.kwds) df.index.name = None df[DTTM_ALIAS] = df.index.map(freq.rollback) df["ranked"] = df[DTTM_ALIAS].rank(method="dense", ascending=False) - 1 df.ranked = df.ranked.map(int) df["series"] = "-" + df.ranked.map(str) df["series"] = df["series"].str.replace("-0", "current") rank_lookup = { row["series"]: row["ranked"] for row in df.to_dict(orient="records") } max_ts = df[DTTM_ALIAS].max() max_rank = df["ranked"].max() df[DTTM_ALIAS] = df.index + (max_ts - df[DTTM_ALIAS]) df = df.pivot_table( index=DTTM_ALIAS, columns="series", values=utils.get_metric_name(self.form_data["metric"]), ) chart_data = self.to_series(df) for serie in chart_data: serie["rank"] = rank_lookup[serie["key"]] serie["perc"] = 1 - (serie["rank"] / (max_rank + 1)) return chart_data class NVD3CompareTimeSeriesViz(NVD3TimeSeriesViz): """A line chart component where you can compare the % change over time""" viz_type = "compare" verbose_name = _("Time Series - Percent Change") class NVD3TimeSeriesStackedViz(NVD3TimeSeriesViz): """A rich stack area chart""" viz_type = "area" verbose_name = _("Time Series - Stacked") sort_series = True pivot_fill_value = 0 class HistogramViz(BaseViz): """Histogram""" viz_type = "histogram" verbose_name = _("Histogram") is_timeseries = False def query_obj(self) -> QueryObjectDict: """Returns the query object for this visualization""" query_obj = super().query_obj() numeric_columns = self.form_data.get("all_columns_x") if numeric_columns is None: raise QueryObjectValidationError( _("Must have at least one numeric column specified") ) self.columns = ( # pylint: disable=attribute-defined-outside-init numeric_columns ) query_obj["columns"] = numeric_columns + self.groupby # override groupby entry to avoid aggregation query_obj["groupby"] = None query_obj["metrics"] = None return query_obj def labelify(self, keys: Union[List[str], str], column: str) -> str: if isinstance(keys, str): keys = [keys] # removing undesirable characters labels = [re.sub(r"\W+", r"_", k) for k in keys] if len(self.columns) > 1 or not self.groupby: # Only show numeric column in label if there are many labels = [column] + labels return "__".join(labels) def get_data(self, df: pd.DataFrame) -> VizData: """Returns the chart data""" if df.empty: return None chart_data = [] if len(self.groupby) > 0: groups = df.groupby(get_column_names(self.groupby)) else: groups = [((), df)] for keys, data in groups: chart_data.extend( [ { "key": self.labelify(keys, get_column_name(column)), "values": data[get_column_name(column)].tolist(), } for column in self.columns ] ) return chart_data class DistributionBarViz(BaseViz): """A good old bar chart""" viz_type = "dist_bar" verbose_name = _("Distribution - Bar Chart") is_timeseries = False def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() if len(query_obj["groupby"]) < len(self.form_data.get("groupby") or []) + len( self.form_data.get("columns") or [] ): raise QueryObjectValidationError( _("Can't have overlap between Series and Breakdowns") ) if not self.form_data.get("metrics"): raise QueryObjectValidationError(_("Pick at least one metric")) if not self.form_data.get("groupby"): raise QueryObjectValidationError(_("Pick at least one field for [Series]")) sort_by = self.form_data.get("timeseries_limit_metric") if sort_by: sort_by_label = utils.get_metric_name(sort_by) if sort_by_label not in utils.get_metric_names(query_obj["metrics"]): query_obj["metrics"].append(sort_by) query_obj["orderby"] = [ (sort_by, not self.form_data.get("order_desc", True)) ] elif query_obj["metrics"]: # Legacy behavior of sorting by first metric by default first_metric = query_obj["metrics"][0] query_obj["orderby"] = [ (first_metric, not self.form_data.get("order_desc", True)) ] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: # pylint: disable=too-many-locals if df.empty: return None metrics = self.metric_labels columns = get_column_names(self.form_data.get("columns")) groupby = get_column_names(self.groupby) # pandas will throw away nulls when grouping/pivoting, # so we substitute NULL_STRING for any nulls in the necessary columns filled_cols = groupby + columns df = df.copy() df[filled_cols] = df[filled_cols].fillna(value=NULL_STRING) sortby = utils.get_metric_name( self.form_data.get("timeseries_limit_metric") or metrics[0] ) row = df.groupby(groupby).sum()[sortby].copy() is_asc = not self.form_data.get("order_desc") row.sort_values(ascending=is_asc, inplace=True) pt = df.pivot_table(index=groupby, columns=columns, values=metrics) if self.form_data.get("contribution"): pt = pt.T pt = (pt / pt.sum()).T pt = pt.reindex(row.index) # Re-order the columns adhering to the metric ordering. pt = pt[metrics] chart_data = [] for name, ys in pt.items(): if pt[name].dtype.kind not in "biufc" or name in groupby: continue if isinstance(name, str): series_title = name else: offset = 0 if len(metrics) > 1 else 1 series_title = ", ".join([str(s) for s in name[offset:]]) values = [] for i, v in ys.items(): x = i if isinstance(x, (tuple, list)): x = ", ".join([str(s) for s in x]) else: x = str(x) values.append({"x": x, "y": v}) chart_data.append({"key": series_title, "values": values}) return chart_data class SunburstViz(BaseViz): """A multi level sunburst chart""" viz_type = "sunburst" verbose_name = _("Sunburst") is_timeseries = False credits = ( "Kerry Rodden " '@bl.ocks.org' ) def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None form_data = copy.deepcopy(self.form_data) cols = get_column_names(form_data.get("groupby")) cols.extend(["m1", "m2"]) metric = utils.get_metric_name(form_data["metric"]) secondary_metric = ( utils.get_metric_name(form_data["secondary_metric"]) if "secondary_metric" in form_data else None ) if metric == secondary_metric or secondary_metric is None: df.rename(columns={df.columns[-1]: "m1"}, inplace=True) df["m2"] = df["m1"] else: df.rename(columns={df.columns[-2]: "m1"}, inplace=True) df.rename(columns={df.columns[-1]: "m2"}, inplace=True) # Re-order the columns as the query result set column ordering may differ from # that listed in the hierarchy. df = df[cols] return df.to_numpy().tolist() def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["metrics"] = [self.form_data["metric"]] secondary_metric = self.form_data.get("secondary_metric") if secondary_metric and secondary_metric != self.form_data["metric"]: query_obj["metrics"].append(secondary_metric) if self.form_data.get("sort_by_metric", False): query_obj["orderby"] = [(query_obj["metrics"][0], False)] return query_obj class SankeyViz(BaseViz): """A Sankey diagram that requires a parent-child dataset""" viz_type = "sankey" verbose_name = _("Sankey") is_timeseries = False credits = 'd3-sankey on npm' def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() if len(query_obj["groupby"]) != 2: raise QueryObjectValidationError( _("Pick exactly 2 columns as [Source / Target]") ) query_obj["metrics"] = [self.form_data["metric"]] if self.form_data.get("sort_by_metric", False): query_obj["orderby"] = [(query_obj["metrics"][0], False)] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None source, target = get_column_names(self.groupby) (value,) = self.metric_labels df.rename( columns={source: "source", target: "target", value: "value",}, inplace=True, ) df["source"] = df["source"].astype(str) df["target"] = df["target"].astype(str) recs = df.to_dict(orient="records") hierarchy: Dict[str, Set[str]] = defaultdict(set) for row in recs: hierarchy[row["source"]].add(row["target"]) def find_cycle(graph: Dict[str, Set[str]]) -> Optional[Tuple[str, str]]: """Whether there's a cycle in a directed graph""" path = set() def visit(vertex: str) -> Optional[Tuple[str, str]]: path.add(vertex) for neighbour in graph.get(vertex, ()): if neighbour in path or visit(neighbour): return (vertex, neighbour) path.remove(vertex) return None for vertex in graph: cycle = visit(vertex) if cycle: return cycle return None cycle = find_cycle(hierarchy) if cycle: raise QueryObjectValidationError( _( "There's a loop in your Sankey, please provide a tree. " "Here's a faulty link: {}" ).format(cycle) ) return recs class ChordViz(BaseViz): """A Chord diagram""" viz_type = "chord" verbose_name = _("Directed Force Layout") credits = 'Bostock' is_timeseries = False def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["groupby"] = [ self.form_data.get("groupby"), self.form_data.get("columns"), ] query_obj["metrics"] = [self.form_data.get("metric")] if self.form_data.get("sort_by_metric", False): query_obj["orderby"] = [(query_obj["metrics"][0], False)] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None df.columns = ["source", "target", "value"] # Preparing a symetrical matrix like d3.chords calls for nodes = list(set(df["source"]) | set(df["target"])) matrix = {} for source, target in product(nodes, nodes): matrix[(source, target)] = 0 for source, target, value in df.to_records(index=False): matrix[(source, target)] = value return { "nodes": list(nodes), "matrix": [[matrix[(n1, n2)] for n1 in nodes] for n2 in nodes], } class CountryMapViz(BaseViz): """A country centric""" viz_type = "country_map" verbose_name = _("Country Map") is_timeseries = False credits = "From bl.ocks.org By john-guerra" def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() metric = self.form_data.get("metric") entity = self.form_data.get("entity") if not self.form_data.get("select_country"): raise QueryObjectValidationError("Must specify a country") if not metric: raise QueryObjectValidationError("Must specify a metric") if not entity: raise QueryObjectValidationError("Must provide ISO codes") query_obj["metrics"] = [metric] query_obj["groupby"] = [entity] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None cols = get_column_names([self.form_data.get("entity")]) # type: ignore metric = self.metric_labels[0] cols += [metric] ndf = df[cols] df = ndf df.columns = ["country_id", "metric"] return df.to_dict(orient="records") class WorldMapViz(BaseViz): """A country centric world map""" viz_type = "world_map" verbose_name = _("World Map") is_timeseries = False credits = 'datamaps on npm' def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["groupby"] = [self.form_data["entity"]] if self.form_data.get("sort_by_metric", False): query_obj["orderby"] = [(query_obj["metrics"][0], False)] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None # pylint: disable=import-outside-toplevel from superset.examples import countries cols = get_column_names([self.form_data.get("entity")]) # type: ignore metric = utils.get_metric_name(self.form_data["metric"]) secondary_metric = ( utils.get_metric_name(self.form_data["secondary_metric"]) if "secondary_metric" in self.form_data else None ) columns = ["country", "m1", "m2"] if metric == secondary_metric: ndf = df[cols] ndf["m1"] = df[metric] ndf["m2"] = ndf["m1"] else: if secondary_metric: cols += [metric, secondary_metric] else: cols += [metric] columns = ["country", "m1"] ndf = df[cols] df = ndf df.columns = columns data = df.to_dict(orient="records") for row in data: country = None if isinstance(row["country"], str): if "country_fieldtype" in self.form_data: country = countries.get( self.form_data["country_fieldtype"], row["country"] ) if country: row["country"] = country["cca3"] row["latitude"] = country["lat"] row["longitude"] = country["lng"] row["name"] = country["name"] else: row["country"] = "XXX" return data class FilterBoxViz(BaseViz): """A multi filter, multi-choice filter box to make dashboards interactive""" query_context_factory: Optional[QueryContextFactory] = None viz_type = "filter_box" verbose_name = _("Filters") is_timeseries = False credits = 'a Superset original' cache_type = "get_data" filter_row_limit = 1000 def query_obj(self) -> QueryObjectDict: return {} def run_extra_queries(self) -> None: query_obj = super().query_obj() filters = self.form_data.get("filter_configs") or [] query_obj["row_limit"] = self.filter_row_limit self.dataframes = {} # pylint: disable=attribute-defined-outside-init for flt in filters: col = flt.get("column") if not col: raise QueryObjectValidationError( _("Invalid filter configuration, please select a column") ) query_obj["groupby"] = [col] metric = flt.get("metric") query_obj["metrics"] = [metric] if metric else [] asc = flt.get("asc") if metric and asc is not None: query_obj["orderby"] = [(metric, asc)] self.get_query_context_factory().create( datasource={"id": self.datasource.id, "type": self.datasource.type}, queries=[query_obj], ).raise_for_access() df = self.get_df_payload(query_obj=query_obj).get("df") self.dataframes[col] = df def get_data(self, df: pd.DataFrame) -> VizData: filters = self.form_data.get("filter_configs") or [] data = {} for flt in filters: col = flt.get("column") metric = flt.get("metric") df = self.dataframes.get(col) if df is not None and not df.empty: if metric: df = df.sort_values( utils.get_metric_name(metric), ascending=flt.get("asc") ) data[col] = [ {"id": row[0], "text": row[0], "metric": row[1]} for row in df.itertuples(index=False) ] else: df = df.sort_values(col, ascending=flt.get("asc")) data[col] = [ {"id": row[0], "text": row[0]} for row in df.itertuples(index=False) ] else: data[col] = [] return data def get_query_context_factory(self) -> QueryContextFactory: if self.query_context_factory is None: # pylint: disable=import-outside-toplevel from superset.common.query_context_factory import QueryContextFactory self.query_context_factory = QueryContextFactory() return self.query_context_factory class ParallelCoordinatesViz(BaseViz): """Interactive parallel coordinate implementation Uses this amazing javascript library https://github.com/syntagmatic/parallel-coordinates """ viz_type = "para" verbose_name = _("Parallel Coordinates") credits = ( '' "Syntagmatic's library" ) is_timeseries = False def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["groupby"] = [self.form_data.get("series")] sort_by = self.form_data.get("timeseries_limit_metric") if sort_by: sort_by_label = utils.get_metric_name(sort_by) if sort_by_label not in utils.get_metric_names(query_obj["metrics"]): query_obj["metrics"].append(sort_by) if self.form_data.get("order_desc"): query_obj["orderby"] = [ (sort_by, not self.form_data.get("order_desc", True)) ] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: return df.to_dict(orient="records") class HeatmapViz(BaseViz): """A nice heatmap visualization that support high density through canvas""" viz_type = "heatmap" verbose_name = _("Heatmap") is_timeseries = False credits = ( 'inspired from mbostock @' "bl.ocks.org" ) def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["metrics"] = [self.form_data.get("metric")] query_obj["groupby"] = [ self.form_data.get("all_columns_x"), self.form_data.get("all_columns_y"), ] if self.form_data.get("sort_by_metric", False): query_obj["orderby"] = [(query_obj["metrics"][0], False)] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None x = get_column_name(self.form_data.get("all_columns_x")) # type: ignore y = get_column_name(self.form_data.get("all_columns_y")) # type: ignore v = self.metric_labels[0] if x == y: df.columns = ["x", "y", "v"] else: df = df[[x, y, v]] df.columns = ["x", "y", "v"] norm = self.form_data.get("normalize_across") overall = False max_ = df.v.max() min_ = df.v.min() if norm == "heatmap": overall = True else: gb = df.groupby(norm, group_keys=False) if len(gb) <= 1: overall = True else: df["perc"] = gb.apply( lambda x: (x.v - x.v.min()) / (x.v.max() - x.v.min()) ) df["rank"] = gb.apply(lambda x: x.v.rank(pct=True)) if overall: df["perc"] = (df.v - min_) / (max_ - min_) df["rank"] = df.v.rank(pct=True) return {"records": df.to_dict(orient="records"), "extents": [min_, max_]} class HorizonViz(NVD3TimeSeriesViz): """Horizon chart https://www.npmjs.com/package/d3-horizon-chart """ viz_type = "horizon" verbose_name = _("Horizon Charts") credits = ( '' "d3-horizon-chart" ) class MapboxViz(BaseViz): """Rich maps made with Mapbox""" viz_type = "mapbox" verbose_name = _("Mapbox") is_timeseries = False credits = "Mapbox GL JS" def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() label_col = self.form_data.get("mapbox_label") if not self.form_data.get("groupby"): if ( self.form_data.get("all_columns_x") is None or self.form_data.get("all_columns_y") is None ): raise QueryObjectValidationError( _("[Longitude] and [Latitude] must be set") ) query_obj["columns"] = [ self.form_data.get("all_columns_x"), self.form_data.get("all_columns_y"), ] if label_col and len(label_col) >= 1: if label_col[0] == "count": raise QueryObjectValidationError( _( "Must have a [Group By] column to have 'count' as the " + "[Label]" ) ) query_obj["columns"].append(label_col[0]) if self.form_data.get("point_radius") != "Auto": query_obj["columns"].append(self.form_data.get("point_radius")) # Ensure this value is sorted so that it does not # cause the cache key generation (which hashes the # query object) to generate different keys for values # that should be considered the same. query_obj["columns"] = sorted(set(query_obj["columns"])) else: # Ensuring columns chosen are all in group by if ( label_col and len(label_col) >= 1 and label_col[0] != "count" and label_col[0] not in self.form_data["groupby"] ): raise QueryObjectValidationError( _("Choice of [Label] must be present in [Group By]") ) if ( self.form_data.get("point_radius") != "Auto" and self.form_data.get("point_radius") not in self.form_data["groupby"] ): raise QueryObjectValidationError( _("Choice of [Point Radius] must be present in [Group By]") ) if ( self.form_data.get("all_columns_x") not in self.form_data["groupby"] or self.form_data.get("all_columns_y") not in self.form_data["groupby"] ): raise QueryObjectValidationError( _( "[Longitude] and [Latitude] columns must be present in " + "[Group By]" ) ) return query_obj def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None label_col = self.form_data.get("mapbox_label") has_custom_metric = label_col is not None and len(label_col) > 0 metric_col = [None] * len(df.index) if has_custom_metric: if label_col[0] == self.form_data.get("all_columns_x"): # type: ignore metric_col = df[self.form_data.get("all_columns_x")] elif label_col[0] == self.form_data.get("all_columns_y"): # type: ignore metric_col = df[self.form_data.get("all_columns_y")] else: metric_col = df[label_col[0]] # type: ignore point_radius_col = ( [None] * len(df.index) if self.form_data.get("point_radius") == "Auto" else df[self.form_data.get("point_radius")] ) # limiting geo precision as long decimal values trigger issues # around json-bignumber in Mapbox geo_precision = 10 # using geoJSON formatting geo_json = { "type": "FeatureCollection", "features": [ { "type": "Feature", "properties": {"metric": metric, "radius": point_radius}, "geometry": { "type": "Point", "coordinates": [ round(lon, geo_precision), round(lat, geo_precision), ], }, } for lon, lat, metric, point_radius in zip( df[self.form_data.get("all_columns_x")], df[self.form_data.get("all_columns_y")], metric_col, point_radius_col, ) ], } x_series, y_series = ( df[self.form_data.get("all_columns_x")], df[self.form_data.get("all_columns_y")], ) south_west = [x_series.min(), y_series.min()] north_east = [x_series.max(), y_series.max()] return { "geoJSON": geo_json, "hasCustomMetric": has_custom_metric, "mapboxApiKey": config["MAPBOX_API_KEY"], "mapStyle": self.form_data.get("mapbox_style"), "aggregatorName": self.form_data.get("pandas_aggfunc"), "clusteringRadius": self.form_data.get("clustering_radius"), "pointRadiusUnit": self.form_data.get("point_radius_unit"), "globalOpacity": self.form_data.get("global_opacity"), "bounds": [south_west, north_east], "renderWhileDragging": self.form_data.get("render_while_dragging"), "tooltip": self.form_data.get("rich_tooltip"), "color": self.form_data.get("mapbox_color"), } class DeckGLMultiLayer(BaseViz): """Pile on multiple DeckGL layers""" viz_type = "deck_multi" verbose_name = _("Deck.gl - Multiple Layers") is_timeseries = False credits = 'deck.gl' def query_obj(self) -> QueryObjectDict: return {} def get_data(self, df: pd.DataFrame) -> VizData: # Late imports to avoid circular import issues # pylint: disable=import-outside-toplevel from superset import db from superset.models.slice import Slice slice_ids = self.form_data.get("deck_slices") slices = db.session.query(Slice).filter(Slice.id.in_(slice_ids)).all() return { "mapboxApiKey": config["MAPBOX_API_KEY"], "slices": [slc.data for slc in slices], } class BaseDeckGLViz(BaseViz): """Base class for deck.gl visualizations""" is_timeseries = False credits = 'deck.gl' spatial_control_keys: List[str] = [] def get_metrics(self) -> List[str]: # pylint: disable=attribute-defined-outside-init self.metric = self.form_data.get("size") return [self.metric] if self.metric else [] def process_spatial_query_obj(self, key: str, group_by: List[str]) -> None: group_by.extend(self.get_spatial_columns(key)) def get_spatial_columns(self, key: str) -> List[str]: spatial = self.form_data.get(key) if spatial is None: raise ValueError(_("Bad spatial key")) if spatial.get("type") == "latlong": return [spatial.get("lonCol"), spatial.get("latCol")] if spatial.get("type") == "delimited": return [spatial.get("lonlatCol")] if spatial.get("type") == "geohash": return [spatial.get("geohashCol")] return [] @staticmethod def parse_coordinates(latlog: Any) -> Optional[Tuple[float, float]]: if not latlog: return None try: point = Point(latlog) return (point.latitude, point.longitude) except Exception as ex: raise SpatialException( _("Invalid spatial point encountered: %s" % latlog) ) from ex @staticmethod def reverse_geohash_decode(geohash_code: str) -> Tuple[str, str]: lat, lng = geohash.decode(geohash_code) return (lng, lat) @staticmethod def reverse_latlong(df: pd.DataFrame, key: str) -> None: df[key] = [tuple(reversed(o)) for o in df[key] if isinstance(o, (list, tuple))] def process_spatial_data_obj(self, key: str, df: pd.DataFrame) -> pd.DataFrame: spatial = self.form_data.get(key) if spatial is None: raise ValueError(_("Bad spatial key")) if spatial.get("type") == "latlong": df[key] = list( zip( pd.to_numeric(df[spatial.get("lonCol")], errors="coerce"), pd.to_numeric(df[spatial.get("latCol")], errors="coerce"), ) ) elif spatial.get("type") == "delimited": lon_lat_col = spatial.get("lonlatCol") df[key] = df[lon_lat_col].apply(self.parse_coordinates) del df[lon_lat_col] elif spatial.get("type") == "geohash": df[key] = df[spatial.get("geohashCol")].map(self.reverse_geohash_decode) del df[spatial.get("geohashCol")] if spatial.get("reverseCheckbox"): self.reverse_latlong(df, key) if df.get(key) is None: raise NullValueException( _( "Encountered invalid NULL spatial entry, \ please consider filtering those out" ) ) return df def add_null_filters(self) -> None: spatial_columns = set() for key in self.spatial_control_keys: for column in self.get_spatial_columns(key): spatial_columns.add(column) if self.form_data.get("adhoc_filters") is None: self.form_data["adhoc_filters"] = [] line_column = self.form_data.get("line_column") if line_column: spatial_columns.add(line_column) for column in sorted(spatial_columns): filter_ = simple_filter_to_adhoc( {"col": column, "op": "IS NOT NULL", "val": ""} ) self.form_data["adhoc_filters"].append(filter_) def query_obj(self) -> QueryObjectDict: # add NULL filters if self.form_data.get("filter_nulls", True): self.add_null_filters() query_obj = super().query_obj() group_by: List[str] = [] for key in self.spatial_control_keys: self.process_spatial_query_obj(key, group_by) if self.form_data.get("dimension"): group_by += [self.form_data["dimension"]] if self.form_data.get("js_columns"): group_by += self.form_data.get("js_columns") or [] metrics = self.get_metrics() # Ensure this value is sorted so that it does not # cause the cache key generation (which hashes the # query object) to generate different keys for values # that should be considered the same. group_by = sorted(set(group_by)) if metrics: query_obj["groupby"] = group_by query_obj["metrics"] = metrics query_obj["columns"] = [] first_metric = query_obj["metrics"][0] query_obj["orderby"] = [ (first_metric, not self.form_data.get("order_desc", True)) ] else: query_obj["columns"] = group_by return query_obj def get_js_columns(self, data: Dict[str, Any]) -> Dict[str, Any]: cols = self.form_data.get("js_columns") or [] return {col: data.get(col) for col in cols} def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None # Processing spatial info for key in self.spatial_control_keys: df = self.process_spatial_data_obj(key, df) features = [] for data in df.to_dict(orient="records"): feature = self.get_properties(data) extra_props = self.get_js_columns(data) if extra_props: feature["extraProps"] = extra_props features.append(feature) return { "features": features, "mapboxApiKey": config["MAPBOX_API_KEY"], "metricLabels": self.metric_labels, } def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: raise NotImplementedError() class DeckScatterViz(BaseDeckGLViz): """deck.gl's ScatterLayer""" viz_type = "deck_scatter" verbose_name = _("Deck.gl - Scatter plot") spatial_control_keys = ["spatial"] is_timeseries = True def query_obj(self) -> QueryObjectDict: # pylint: disable=attribute-defined-outside-init self.is_timeseries = bool( self.form_data.get("time_grain_sqla") or self.form_data.get("granularity") ) self.point_radius_fixed = self.form_data.get("point_radius_fixed") or { "type": "fix", "value": 500, } return super().query_obj() def get_metrics(self) -> List[str]: # pylint: disable=attribute-defined-outside-init self.metric = None if self.point_radius_fixed.get("type") == "metric": self.metric = self.point_radius_fixed["value"] return [self.metric] return [] def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: return { "metric": data.get(self.metric_label) if self.metric_label else None, "radius": self.fixed_value if self.fixed_value else data.get(self.metric_label) if self.metric_label else None, "cat_color": data.get(self.dim) if self.dim else None, "position": data.get("spatial"), DTTM_ALIAS: data.get(DTTM_ALIAS), } def get_data(self, df: pd.DataFrame) -> VizData: # pylint: disable=attribute-defined-outside-init self.metric_label = utils.get_metric_name(self.metric) if self.metric else None self.point_radius_fixed = self.form_data.get("point_radius_fixed") self.fixed_value = None self.dim = self.form_data.get("dimension") if self.point_radius_fixed and self.point_radius_fixed.get("type") != "metric": self.fixed_value = self.point_radius_fixed.get("value") return super().get_data(df) class DeckScreengrid(BaseDeckGLViz): """deck.gl's ScreenGridLayer""" viz_type = "deck_screengrid" verbose_name = _("Deck.gl - Screen Grid") spatial_control_keys = ["spatial"] is_timeseries = True def query_obj(self) -> QueryObjectDict: self.is_timeseries = bool( self.form_data.get("time_grain_sqla") or self.form_data.get("granularity") ) return super().query_obj() def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: return { "position": data.get("spatial"), "weight": (data.get(self.metric_label) if self.metric_label else None) or 1, "__timestamp": data.get(DTTM_ALIAS) or data.get("__time"), } def get_data(self, df: pd.DataFrame) -> VizData: self.metric_label = ( # pylint: disable=attribute-defined-outside-init utils.get_metric_name(self.metric) if self.metric else None ) return super().get_data(df) class DeckGrid(BaseDeckGLViz): """deck.gl's DeckLayer""" viz_type = "deck_grid" verbose_name = _("Deck.gl - 3D Grid") spatial_control_keys = ["spatial"] def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: return { "position": data.get("spatial"), "weight": (data.get(self.metric_label) if self.metric_label else None) or 1, } def get_data(self, df: pd.DataFrame) -> VizData: self.metric_label = ( # pylint: disable=attribute-defined-outside-init utils.get_metric_name(self.metric) if self.metric else None ) return super().get_data(df) def geohash_to_json(geohash_code: str) -> List[List[float]]: bbox = geohash.bbox(geohash_code) return [ [bbox.get("w"), bbox.get("n")], [bbox.get("e"), bbox.get("n")], [bbox.get("e"), bbox.get("s")], [bbox.get("w"), bbox.get("s")], [bbox.get("w"), bbox.get("n")], ] class DeckPathViz(BaseDeckGLViz): """deck.gl's PathLayer""" viz_type = "deck_path" verbose_name = _("Deck.gl - Paths") deck_viz_key = "path" is_timeseries = True deser_map = { "json": json.loads, "polyline": polyline.decode, "geohash": geohash_to_json, } def query_obj(self) -> QueryObjectDict: # pylint: disable=attribute-defined-outside-init self.is_timeseries = bool( self.form_data.get("time_grain_sqla") or self.form_data.get("granularity") ) query_obj = super().query_obj() self.metric = self.form_data.get("metric") line_col = self.form_data.get("line_column") if query_obj["metrics"]: self.has_metrics = True query_obj["groupby"].append(line_col) else: self.has_metrics = False query_obj["columns"].append(line_col) return query_obj def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: line_type = self.form_data["line_type"] deser = self.deser_map[line_type] line_column = self.form_data["line_column"] path = deser(data[line_column]) if self.form_data.get("reverse_long_lat"): path = [(o[1], o[0]) for o in path] data[self.deck_viz_key] = path if line_type != "geohash": del data[line_column] data["__timestamp"] = data.get(DTTM_ALIAS) or data.get("__time") return data def get_data(self, df: pd.DataFrame) -> VizData: self.metric_label = ( # pylint: disable=attribute-defined-outside-init utils.get_metric_name(self.metric) if self.metric else None ) return super().get_data(df) class DeckPolygon(DeckPathViz): """deck.gl's Polygon Layer""" viz_type = "deck_polygon" deck_viz_key = "polygon" verbose_name = _("Deck.gl - Polygon") def query_obj(self) -> QueryObjectDict: # pylint: disable=attribute-defined-outside-init self.elevation = self.form_data.get("point_radius_fixed") or { "type": "fix", "value": 500, } return super().query_obj() def get_metrics(self) -> List[str]: metrics = [self.form_data.get("metric")] if self.elevation.get("type") == "metric": metrics.append(self.elevation.get("value")) return [metric for metric in metrics if metric] def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: super().get_properties(data) elevation = self.form_data["point_radius_fixed"]["value"] type_ = self.form_data["point_radius_fixed"]["type"] data["elevation"] = ( data.get(utils.get_metric_name(elevation)) if type_ == "metric" else elevation ) return data class DeckHex(BaseDeckGLViz): """deck.gl's DeckLayer""" viz_type = "deck_hex" verbose_name = _("Deck.gl - 3D HEX") spatial_control_keys = ["spatial"] def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: return { "position": data.get("spatial"), "weight": (data.get(self.metric_label) if self.metric_label else None) or 1, } def get_data(self, df: pd.DataFrame) -> VizData: self.metric_label = ( # pylint: disable=attribute-defined-outside-init utils.get_metric_name(self.metric) if self.metric else None ) return super().get_data(df) class DeckGeoJson(BaseDeckGLViz): """deck.gl's GeoJSONLayer""" viz_type = "deck_geojson" verbose_name = _("Deck.gl - GeoJSON") def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() query_obj["columns"] += [self.form_data.get("geojson")] query_obj["metrics"] = [] query_obj["groupby"] = [] return query_obj def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: geojson = data[get_column_name(self.form_data["geojson"])] return json.loads(geojson) class DeckArc(BaseDeckGLViz): """deck.gl's Arc Layer""" viz_type = "deck_arc" verbose_name = _("Deck.gl - Arc") spatial_control_keys = ["start_spatial", "end_spatial"] is_timeseries = True def query_obj(self) -> QueryObjectDict: self.is_timeseries = bool( self.form_data.get("time_grain_sqla") or self.form_data.get("granularity") ) return super().query_obj() def get_properties(self, data: Dict[str, Any]) -> Dict[str, Any]: dim = self.form_data.get("dimension") return { "sourcePosition": data.get("start_spatial"), "targetPosition": data.get("end_spatial"), "cat_color": data.get(dim) if dim else None, DTTM_ALIAS: data.get(DTTM_ALIAS), } def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None return { "features": super().get_data(df)["features"], # type: ignore "mapboxApiKey": config["MAPBOX_API_KEY"], } class EventFlowViz(BaseViz): """A visualization to explore patterns in event sequences""" viz_type = "event_flow" verbose_name = _("Event flow") credits = 'from @data-ui' is_timeseries = True def query_obj(self) -> QueryObjectDict: query = super().query_obj() form_data = self.form_data event_key = form_data["all_columns_x"] entity_key = form_data["entity"] meta_keys = [ col for col in form_data["all_columns"] or [] if col not in (event_key, entity_key) ] query["columns"] = [event_key, entity_key] + meta_keys if form_data["order_by_entity"]: query["orderby"] = [(entity_key, True)] return query def get_data(self, df: pd.DataFrame) -> VizData: return df.to_dict(orient="records") class PairedTTestViz(BaseViz): """A table displaying paired t-test values""" viz_type = "paired_ttest" verbose_name = _("Time Series - Paired t-test") sort_series = False is_timeseries = True def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() sort_by = self.form_data.get("timeseries_limit_metric") if sort_by: sort_by_label = utils.get_metric_name(sort_by) if sort_by_label not in utils.get_metric_names(query_obj["metrics"]): query_obj["metrics"].append(sort_by) if self.form_data.get("order_desc"): query_obj["orderby"] = [ (sort_by, not self.form_data.get("order_desc", True)) ] return query_obj def get_data(self, df: pd.DataFrame) -> VizData: """ Transform received data frame into an object of the form: { 'metric1': [ { groups: ('groupA', ... ), values: [ {x, y}, ... ], }, ... ], ... } """ if df.empty: return None groups = get_column_names(self.form_data.get("groupby")) metrics = self.metric_labels df = df.pivot_table(index=DTTM_ALIAS, columns=groups, values=metrics) cols = [] # Be rid of falsey keys for col in df.columns: if col == "": cols.append("N/A") elif col is None: cols.append("NULL") else: cols.append(col) df.columns = cols data: Dict[str, List[Dict[str, Any]]] = {} series = df.to_dict("series") for name_set in df.columns: # If no groups are defined, nameSet will be the metric name has_group = not isinstance(name_set, str) data_ = { "group": name_set[1:] if has_group else "All", "values": [ { "x": t, "y": series[name_set][t] if t in series[name_set] else None, } for t in df.index ], } key = name_set[0] if has_group else name_set if key in data: data[key].append(data_) else: data[key] = [data_] return data class RoseViz(NVD3TimeSeriesViz): viz_type = "rose" verbose_name = _("Time Series - Nightingale Rose Chart") sort_series = False is_timeseries = True def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None data = super().get_data(df) result: Dict[str, List[Dict[str, str]]] = {} for datum in data: # type: ignore key = datum["key"] for val in datum["values"]: timestamp = val["x"].value if not result.get(timestamp): result[timestamp] = [] value = 0 if math.isnan(val["y"]) else val["y"] result[timestamp].append( { "key": key, "value": value, "name": ", ".join(key) if isinstance(key, list) else key, "time": val["x"], } ) return result class PartitionViz(NVD3TimeSeriesViz): """ A hierarchical data visualization with support for time series. """ viz_type = "partition" verbose_name = _("Partition Diagram") def query_obj(self) -> QueryObjectDict: query_obj = super().query_obj() time_op = self.form_data.get("time_series_option", "not_time") # Return time series data if the user specifies so query_obj["is_timeseries"] = time_op != "not_time" return query_obj @staticmethod def levels_for( time_op: str, groups: List[str], df: pd.DataFrame ) -> Dict[int, pd.Series]: """ Compute the partition at each `level` from the dataframe. """ levels = {} for i in range(0, len(groups) + 1): agg_df = df.groupby(groups[:i]) if i else df levels[i] = ( agg_df.mean() if time_op == "agg_mean" else agg_df.sum(numeric_only=True) ) return levels @staticmethod def levels_for_diff( time_op: str, groups: List[str], df: pd.DataFrame ) -> Dict[int, pd.DataFrame]: # Obtain a unique list of the time grains times = list(set(df[DTTM_ALIAS])) times.sort() until = times[len(times) - 1] since = times[0] # Function describing how to calculate the difference func = { "point_diff": [pd.Series.sub, lambda a, b, fill_value: a - b], "point_factor": [pd.Series.div, lambda a, b, fill_value: a / float(b)], "point_percent": [ lambda a, b, fill_value=0: a.div(b, fill_value=fill_value) - 1, lambda a, b, fill_value: a / float(b) - 1, ], }[time_op] agg_df = df.groupby(DTTM_ALIAS).sum() levels = { 0: pd.Series( { m: func[1](agg_df[m][until], agg_df[m][since], 0) for m in agg_df.columns } ) } for i in range(1, len(groups) + 1): agg_df = df.groupby([DTTM_ALIAS] + groups[:i]).sum() levels[i] = pd.DataFrame( { m: func[0](agg_df[m][until], agg_df[m][since], fill_value=0) for m in agg_df.columns } ) return levels def levels_for_time( self, groups: List[str], df: pd.DataFrame ) -> Dict[int, VizData]: procs = {} for i in range(0, len(groups) + 1): self.form_data["groupby"] = groups[:i] df_drop = df.drop(groups[i:], 1) procs[i] = self.process_data(df_drop, aggregate=True) self.form_data["groupby"] = groups return procs def nest_values( self, levels: Dict[int, pd.DataFrame], level: int = 0, metric: Optional[str] = None, dims: Optional[List[str]] = None, ) -> List[Dict[str, Any]]: """ Nest values at each level on the back-end with access and setting, instead of summing from the bottom. """ if dims is None: dims = [] if not level: return [ { "name": m, "val": levels[0][m], "children": self.nest_values(levels, 1, m), } for m in levels[0].index ] if level == 1: metric_level = levels[1][metric] return [ { "name": i, "val": metric_level[i], "children": self.nest_values(levels, 2, metric, [i]), } for i in metric_level.index ] if level >= len(levels): return [] dim_level = levels[level][metric][[dims[0]]] return [ { "name": i, "val": dim_level[i], "children": self.nest_values(levels, level + 1, metric, dims + [i]), } for i in dim_level.index ] def nest_procs( self, procs: Dict[int, pd.DataFrame], level: int = -1, dims: Optional[Tuple[str, ...]] = None, time: Any = None, ) -> List[Dict[str, Any]]: if dims is None: dims = () if level == -1: return [ {"name": m, "children": self.nest_procs(procs, 0, (m,))} for m in procs[0].columns ] if not level: return [ { "name": t, "val": procs[0][dims[0]][t], "children": self.nest_procs(procs, 1, dims, t), } for t in procs[0].index ] if level >= len(procs): return [] return [ { "name": i, "val": procs[level][dims][i][time], "children": self.nest_procs(procs, level + 1, dims + (i,), time), } for i in procs[level][dims].columns ] def get_data(self, df: pd.DataFrame) -> VizData: if df.empty: return None groups = get_column_names(self.form_data.get("groupby")) time_op = self.form_data.get("time_series_option", "not_time") if not groups: raise ValueError("Please choose at least one groupby") if time_op == "not_time": levels = self.levels_for("agg_sum", groups, df) elif time_op in ["agg_sum", "agg_mean"]: levels = self.levels_for(time_op, groups, df) elif time_op in ["point_diff", "point_factor", "point_percent"]: levels = self.levels_for_diff(time_op, groups, df) elif time_op == "adv_anal": procs = self.levels_for_time(groups, df) return self.nest_procs(procs) else: levels = self.levels_for("agg_sum", [DTTM_ALIAS] + groups, df) return self.nest_values(levels) def get_subclasses(cls: Type[BaseViz]) -> Set[Type[BaseViz]]: return set(cls.__subclasses__()).union( [sc for c in cls.__subclasses__() for sc in get_subclasses(c)] ) viz_types = { o.viz_type: o for o in get_subclasses(BaseViz) if o.viz_type not in config["VIZ_TYPE_DENYLIST"] }