# 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=C,R,W
"""This module contains the 'Viz' objects
These objects represent the backend of all the visualizations that
Superset can render.
"""
import copy
import dataclasses
import inspect
import logging
import math
import re
import uuid
from collections import defaultdict, OrderedDict
from datetime import datetime, timedelta
from itertools import product
from typing import (
Any,
Callable,
cast,
Dict,
List,
Optional,
Set,
Tuple,
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, cache, security_manager
from superset.constants import NULL_STRING
from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
from superset.exceptions import (
NullValueException,
QueryObjectValidationError,
SpatialException,
)
from superset.models.helpers import QueryResult
from superset.typing import QueryObjectDict, VizData, VizPayload
from superset.utils import core as utils
from superset.utils.core import (
DTTM_ALIAS,
JS_MAX_INTEGER,
merge_extra_filters,
QueryMode,
to_adhoc,
)
from superset.utils.dates import datetime_to_epoch
from superset.utils.hashing import md5_sha_from_str
if TYPE_CHECKING:
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:
"""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,
) -> None:
if not datasource:
raise Exception(_("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[str] = self.form_data.get("groupby") or []
self.time_shift = timedelta()
self.status: Optional[str] = None
self.error_msg = ""
self.results: Optional[QueryResult] = None
self.errors: List[Dict[str, Any]] = []
self.force = force
self.from_dttm: Optional[datetime] = None
self.to_dttm: Optional[datetime] = None
# Keeping track of whether some data came from cache
# this is useful to trigger the when
# in the cases where visualization have many queries
# (FilterBox for instance)
self._any_cache_key: Optional[str] = None
self._any_cached_dttm: Optional[str] = None
self._extra_chart_data: List[Tuple[str, pd.DataFrame]] = []
self.process_metrics()
def process_metrics(self) -> None:
# metrics in TableViz is order sensitive, so metric_dict should be
# OrderedDict
self.metric_dict = OrderedDict()
fd = self.form_data
for mkey in METRIC_KEYS:
val = fd.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 d in data.get("records", {}):
for k, v in list(d.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:
d[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).
"""
pass
def apply_rolling(self, df: pd.DataFrame) -> pd.DataFrame:
fd = self.form_data
rolling_type = fd.get("rolling_type")
rolling_periods = int(fd.get("rolling_periods") or 0)
min_periods = int(fd.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) -> List[Dict[str, Any]]:
query_obj = self.query_obj()
query_obj.update(
{
"groupby": [],
"metrics": [],
"row_limit": config["SAMPLES_ROW_LIMIT"],
"columns": [o.column_name for o in self.datasource.columns],
}
)
df = self.get_df(query_obj)
return df.to_dict(orient="records")
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.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:
if DTTM_ALIAS in df.columns:
if timestamp_format in ("epoch_s", "epoch_ms"):
# Column has already been formatted as a timestamp.
dttm_col = df[DTTM_ALIAS]
one_ts_val = dttm_col[0]
# convert time column to pandas Timestamp, but different
# ways to convert depending on string or int types
try:
int(one_ts_val)
is_integral = True
except (ValueError, TypeError):
is_integral = False
if is_integral:
unit = "s" if timestamp_format == "epoch_s" else "ms"
df[DTTM_ALIAS] = pd.to_datetime(
dttm_col, utc=False, unit=unit, origin="unix"
)
else:
df[DTTM_ALIAS] = dttm_col.apply(pd.Timestamp)
else:
df[DTTM_ALIAS] = pd.to_datetime(
df[DTTM_ALIAS], utc=False, format=timestamp_format
)
if self.datasource.offset:
df[DTTM_ALIAS] += timedelta(hours=self.datasource.offset)
df[DTTM_ALIAS] += 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)
def query_obj(self) -> QueryObjectDict:
"""Building a query object"""
form_data = self.form_data
self.process_query_filters()
gb = self.groupby
metrics = self.all_metrics or []
columns = form_data.get("columns") or []
# merge list and dedup while preserving order
groupby = list(OrderedDict.fromkeys(gb + columns))
is_timeseries = self.is_timeseries
if DTTM_ALIAS in groupby:
groupby.remove(DTTM_ALIAS)
is_timeseries = True
granularity = form_data.get("granularity") or form_data.get("granularity_sqla")
limit = int(form_data.get("limit") or 0)
timeseries_limit_metric = form_data.get("timeseries_limit_metric")
row_limit = int(form_data.get("row_limit") or config["ROW_LIMIT"])
# default order direction
order_desc = form_data.get("order_desc", True)
try:
since, until = utils.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))
time_shift = form_data.get("time_shift", "")
self.time_shift = utils.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
# 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": form_data.get("druid_time_origin", ""),
"having": form_data.get("having", ""),
"having_druid": form_data.get("having_filters", []),
"time_grain_sqla": form_data.get("time_grain_sqla"),
"time_range_endpoints": form_data.get("time_range_endpoints"),
"where": 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
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_ids(self.datasource)
if config["ENABLE_ROW_LEVEL_SECURITY"] and self.datasource.is_rls_supported
else []
)
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"""
self.run_extra_queries()
payload = self.get_df_payload(query_obj)
df = payload.get("df")
if self.status != utils.QueryStatus.FAILED:
payload["data"] = self.get_data(df)
if "df" in payload:
del payload["df"]
return payload
def get_df_payload(
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
logger.info("Cache key: {}".format(cache_key))
is_loaded = False
stacktrace = None
df = None
cached_dttm = datetime.utcnow().isoformat().split(".")[0]
if cache_key and cache and not self.force:
cache_value = cache.get(cache_key)
if cache_value:
stats_logger.incr("loading_from_cache")
try:
df = cache_value["df"]
self.query = cache_value["query"]
self._any_cached_dttm = cache_value["dttm"]
self._any_cache_key = cache_key
self.status = utils.QueryStatus.SUCCESS
is_loaded = True
stats_logger.incr("loaded_from_cache")
except Exception as ex:
logger.exception(ex)
logger.error(
"Error reading cache: " + utils.error_msg_from_exception(ex)
)
logger.info("Serving from cache")
if query_obj and not is_loaded:
try:
invalid_columns = [
col
for col in (query_obj.get("columns") or [])
+ (query_obj.get("groupby") or [])
+ utils.get_column_names_from_metrics(
cast(
List[Union[str, Dict[str, Any]]], query_obj.get("metrics"),
)
)
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 != utils.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 = utils.QueryStatus.FAILED
except Exception as ex:
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 = utils.QueryStatus.FAILED
stacktrace = utils.get_stacktrace()
if (
is_loaded
and cache_key
and cache
and self.status != utils.QueryStatus.FAILED
):
try:
cache_value = dict(dttm=cached_dttm, df=df, query=self.query)
stats_logger.incr("set_cache_key")
cache.set(cache_key, cache_value, timeout=self.cache_timeout)
except Exception as ex:
# cache.set call can fail if the backend is down or if
# the key is too large or whatever other reasons
logger.warning("Could not cache key {}".format(cache_key))
logger.exception(ex)
cache.delete(cache_key)
return {
"cache_key": self._any_cache_key,
"cached_dttm": self._any_cached_dttm,
"cache_timeout": self.cache_timeout,
"df": df,
"errors": self.errors,
"form_data": self.form_data,
"is_cached": self._any_cache_key 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,
}
def json_dumps(self, obj: Any, sort_keys: bool = False) -> str:
return json.dumps(
obj, default=utils.json_int_dttm_ser, ignore_nan=True, sort_keys=sort_keys
)
def payload_json_and_has_error(self, payload: VizPayload) -> Tuple[str, bool]:
has_error = (
payload.get("status") == utils.QueryStatus.FAILED
or payload.get("error") is not None
or bool(payload.get("errors"))
)
return self.json_dumps(payload), has_error
@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()
include_index = not isinstance(df.index, pd.RangeIndex)
return df.to_csv(index=include_index, **config["CSV_EXPORT"])
def get_data(self, df: pd.DataFrame) -> VizData:
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.
fd = self.form_data
if (
not fd.get("query_mode")
and fd.get("all_columns")
and (fd.get("groupby") or fd.get("metrics") or fd.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(fd.get("query_mode")) or (
# infer query mode from the presence of other fields
QueryMode.RAW
if len(fd.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 = utils.get_metric_names(fd.get("all_columns") or [])
else:
columns = utils.get_metric_names(self.groupby + (fd.get("metrics") or []))
percent_columns = utils.get_metric_names(fd.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:
fd = self.form_data
# TODO handle datasource-type-specific code in datasource
conditions_met = (fd.get("granularity") and fd.get("granularity") != "all") or (
fd.get("granularity_sqla") and fd.get("time_grain_sqla")
)
if fd.get("include_time") and not conditions_met:
raise QueryObjectValidationError(
_("Pick a granularity in the Time section or " "uncheck 'Include Time'")
)
return bool(fd.get("include_time"))
def query_obj(self) -> QueryObjectDict:
d = super().query_obj()
fd = self.form_data
if self.query_mode == QueryMode.RAW:
d["columns"] = fd.get("all_columns")
order_by_cols = fd.get("order_by_cols") or []
d["orderby"] = [json.loads(t) for t in order_by_cols]
# must disable groupby and metrics in raw mode
d["groupby"] = []
d["metrics"] = []
# raw mode does not support timeseries queries
d["timeseries_limit_metric"] = None
d["timeseries_limit"] = None
d["is_timeseries"] = None
else:
sort_by = fd.get("timeseries_limit_metric")
if sort_by:
sort_by_label = utils.get_metric_name(sort_by)
if sort_by_label not in d["metrics"]:
d["metrics"].append(sort_by)
d["orderby"] = [(sort_by, not fd.get("order_desc", True))]
return d
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))
)
def json_dumps(self, obj: Any, sort_keys: bool = False) -> str:
return json.dumps(
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:
d = super().query_obj()
fd = self.form_data
if not fd.get("metrics"):
raise QueryObjectValidationError(_("Pick at least one metric"))
if fd.get("groupby") and len(fd["metrics"]) > 1:
raise QueryObjectValidationError(
_("When using 'Group By' you are limited to use a single metric")
)
return d
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
fd = self.form_data
columns = None
values: Union[List[str], str] = self.metric_labels
if fd.get("groupby"):
values = self.metric_labels[0]
columns = fd.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=len(fd.get("groupby", [])) > 0,
)
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:
d = 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"))
if set(groupby) & set(columns):
raise QueryObjectValidationError(_("Group By' and 'Columns' can't overlap"))
return d
@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"
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 []
def _format_datetime(value: Any) -> Optional[str]:
if isinstance(value, str):
return f"__timestamp:{datetime_to_epoch(pd.Timestamp(value))}"
return None
for column_name in groupby + columns:
column = self.datasource.get_column(column_name)
if column and column.type in ("DATE", "DATETIME", "TIMESTAMP"):
ts = df[column_name].apply(_format_datetime)
df[column_name] = ts
if self.form_data.get("transpose_pivot"):
groupby, columns = columns, groupby
df = df.pivot_table(
index=groupby,
columns=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()
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 _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(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:
if df.empty:
return None
form_data = self.form_data
data = {}
records = df.to_dict("records")
for metric in self.metric_labels:
values = {}
for obj in records:
v = obj[DTTM_ALIAS]
if hasattr(v, "value"):
v = v.value
values[str(v / 10 ** 9)] = obj.get(metric)
data[metric] = values
try:
start, end = utils.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))
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_ = diff_delta.years + 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:
d = super().query_obj()
fd = self.form_data
d["metrics"] = fd.get("metrics")
return d
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 BoxPlotViz(NVD3Viz):
"""Box plot viz from ND3"""
viz_type = "box_plot"
verbose_name = _("Box Plot")
sort_series = False
is_timeseries = True
def to_series(
self, df: pd.DataFrame, classed: str = "", title_suffix: str = ""
) -> List[Dict[str, Any]]:
label_sep = " - "
chart_data = []
for index_value, row in zip(df.index, df.to_dict(orient="records")):
if isinstance(index_value, tuple):
index_value = label_sep.join(index_value)
boxes: Dict[str, Dict[str, Any]] = defaultdict(dict)
for (label, key), value in row.items():
if key == "nanmedian":
key = "Q2"
boxes[label][key] = value
for label, box in boxes.items():
if len(self.form_data["metrics"]) > 1:
# need to render data labels with metrics
chart_label = label_sep.join([index_value, label])
else:
chart_label = index_value
chart_data.append({"label": chart_label, "values": box})
return chart_data
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
form_data = self.form_data
# conform to NVD3 names
def Q1(series: pd.Series) -> float:
# need to be named functions - can't use lambdas
return np.nanpercentile(series, 25)
def Q3(series: pd.Series) -> float:
return np.nanpercentile(series, 75)
whisker_type = form_data.get("whisker_options")
if whisker_type == "Tukey":
def whisker_high(series: pd.Series) -> float:
upper_outer_lim = Q3(series) + 1.5 * (Q3(series) - Q1(series))
return series[series <= upper_outer_lim].max()
def whisker_low(series: pd.Series) -> float:
lower_outer_lim = Q1(series) - 1.5 * (Q3(series) - Q1(series))
return series[series >= lower_outer_lim].min()
elif whisker_type == "Min/max (no outliers)":
def whisker_high(series: pd.Series) -> float:
return series.max()
def whisker_low(series: pd.Series) -> float:
return series.min()
elif " percentiles" in whisker_type: # type: ignore
low, high = cast(str, whisker_type).replace(" percentiles", "").split("/")
def whisker_high(series: pd.Series) -> float:
return np.nanpercentile(series, int(high))
def whisker_low(series: pd.Series) -> float:
return np.nanpercentile(series, int(low))
else:
raise ValueError("Unknown whisker type: {}".format(whisker_type))
def outliers(series: pd.Series) -> Set[float]:
above = series[series > whisker_high(series)]
below = series[series < whisker_low(series)]
# pandas sometimes doesn't like getting lists back here
return set(above.tolist() + below.tolist())
aggregate = [Q1, np.nanmedian, Q3, whisker_high, whisker_low, outliers]
df = df.groupby(form_data.get("groupby")).agg(aggregate)
chart_data = self.to_series(df)
return chart_data
class BubbleViz(NVD3Viz):
"""Based on the NVD3 bubble chart"""
viz_type = "bubble"
verbose_name = _("Bubble Chart")
is_timeseries = False
def query_obj(self) -> QueryObjectDict:
form_data = self.form_data
d = super().query_obj()
d["groupby"] = [form_data.get("entity")]
if form_data.get("series"):
d["groupby"].append(form_data.get("series"))
# dedup groupby if it happens to be the same
d["groupby"] = list(dict.fromkeys(d["groupby"]))
self.x_metric = form_data["x"]
self.y_metric = form_data["y"]
self.z_metric = form_data["size"]
self.entity = form_data.get("entity")
self.series = form_data.get("series") or self.entity
d["row_limit"] = form_data.get("limit")
d["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(d["metrics"] + [self.entity]):
raise QueryObjectValidationError(_("Pick a metric for x, y and size"))
return d
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[[self.series]]
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
d = super().query_obj()
self.metric = form_data["metric"]
d["metrics"] = [self.metric]
if not self.metric:
raise QueryObjectValidationError(_("Pick a metric to display"))
return d
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:
d = super().query_obj()
metric = self.form_data.get("metric")
if not metric:
raise QueryObjectValidationError(_("Pick a metric!"))
d["metrics"] = [self.form_data.get("metric")]
self.form_data["metric"] = metric
return d
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:
d = super().query_obj()
metric = self.form_data.get("metric")
if not metric:
raise QueryObjectValidationError(_("Pick a metric!"))
d["metrics"] = [self.form_data.get("metric")]
self.form_data["metric"] = metric
# Limiting rows is not required as only one cell is returned
d["row_limit"] = None
return d
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 to_series(
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:
d = {"x": ds, "y": ys[ds]}
if not np.isnan(ys[ds]):
non_nan_cnt += 1
else:
d = {}
values.append(d)
if non_nan_cnt == 0:
continue
d = {"key": series_title, "values": values}
if classed:
d["classed"] = classed
chart_data.append(d)
return chart_data
def process_data(self, df: pd.DataFrame, aggregate: bool = False) -> VizData:
fd = self.form_data
if fd.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=fd.get("groupby"),
values=self.metric_labels,
fill_value=0,
aggfunc=sum,
)
else:
df = df.pivot_table(
index=DTTM_ALIAS,
columns=fd.get("groupby"),
values=self.metric_labels,
fill_value=self.pivot_fill_value,
)
rule = fd.get("resample_rule")
method = fd.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 fd.get("contribution"):
dft = df.T
df = (dft / dft.sum()).T
return df
def run_extra_queries(self) -> None:
fd = self.form_data
time_compare = fd.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 = utils.parse_past_timedelta(option)
except ValueError as ex:
raise QueryObjectValidationError(str(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(
_(
"`Since` and `Until` time bounds should be specified "
"when using the `Time Shift` feature."
)
)
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:
label = "{} offset".format(option)
df2[DTTM_ALIAS] += delta
df2 = self.process_data(df2)
self._extra_chart_data.append((label, df2))
def get_data(self, df: pd.DataFrame) -> VizData:
fd = self.form_data
comparison_type = fd.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:
fd = self.form_data
# Late imports to avoid circular import issues
from superset import db
from superset.models.slice import Slice
slice_ids1 = fd.get("line_charts")
slices1 = db.session.query(Slice).filter(Slice.id.in_(slice_ids1)).all()
slice_ids2 = fd.get("line_charts_2")
slices2 = db.session.query(Slice).filter(Slice.id.in_(slice_ids2)).all()
return {
"slices": {
"axis1": [slc.data for slc in slices1],
"axis2": [slc.data for slc in slices2],
}
}
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:
d = super().query_obj()
m1 = self.form_data.get("metric")
m2 = self.form_data.get("metric_2")
d["metrics"] = [m1, m2]
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")
)
return d
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, m in enumerate(metrics):
m = utils.get_metric_name(m)
ys = series[m]
if df[m].dtype.kind not in "biufc":
continue
series_title = m
d = {
"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",
}
chart_data.append(d)
return chart_data
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
fd = self.form_data
if self.form_data.get("granularity") == "all":
raise QueryObjectValidationError(
_("Pick a time granularity for your time series")
)
metric = utils.get_metric_name(fd["metric"])
metric_2 = utils.get_metric_name(fd["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:
d = super().query_obj()
d["metrics"] = [self.form_data.get("metric")]
return d
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
fd = self.form_data
df = self.process_data(df)
freq = to_offset(fd.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(fd["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 DistributionPieViz(NVD3Viz):
"""Annoy visualization snobs with this controversial pie chart"""
viz_type = "pie"
verbose_name = _("Distribution - NVD3 - Pie Chart")
is_timeseries = False
def get_data(self, df: pd.DataFrame) -> VizData:
def _label_aggfunc(labels: pd.Series) -> str:
"""
Convert a single or multi column label into a single label, replacing
null values with `NULL_STRING` and joining multiple columns together
with a comma. Examples:
>>> _label_aggfunc(pd.Series(["abc"]))
'abc'
>>> _label_aggfunc(pd.Series([1]))
'1'
>>> _label_aggfunc(pd.Series(["abc", "def"]))
'abc, def'
>>> # note: integer floats are stripped of decimal digits
>>> _label_aggfunc(pd.Series([0.1, 2.0, 0.3]))
'0.1, 2, 0.3'
>>> _label_aggfunc(pd.Series([1, None, "abc", 0.8], dtype="object"))
'1, , abc, 0.8'
"""
label_list: List[str] = []
for label in labels:
if isinstance(label, str):
label_recast = label
elif label is None or isinstance(label, float) and math.isnan(label):
label_recast = NULL_STRING
elif isinstance(label, float) and label.is_integer():
label_recast = str(int(label))
else:
label_recast = str(label)
label_list.append(label_recast)
return ", ".join(label_list)
if df.empty:
return None
metric = self.metric_labels[0]
df = pd.DataFrame(
{"x": df[self.groupby].agg(func=_label_aggfunc, axis=1), "y": df[metric]}
)
df.sort_values(by="y", ascending=False, inplace=True)
return df.to_dict(orient="records")
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"""
d = super().query_obj()
d["row_limit"] = self.form_data.get("row_limit", int(config["VIZ_ROW_LIMIT"]))
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 = numeric_columns
d["columns"] = numeric_columns + self.groupby
# override groupby entry to avoid aggregation
d["groupby"] = []
return d
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(self.groupby)
else:
groups = [((), df)]
for keys, data in groups:
chart_data.extend(
[
{
"key": self.labelify(keys, column),
"values": data[column].tolist(),
}
for column in self.columns
]
)
return chart_data
class DistributionBarViz(DistributionPieViz):
"""A good old bar chart"""
viz_type = "dist_bar"
verbose_name = _("Distribution - Bar Chart")
is_timeseries = False
def query_obj(self) -> QueryObjectDict:
d = super().query_obj()
fd = self.form_data
if len(d["groupby"]) < len(fd.get("groupby") or []) + len(
fd.get("columns") or []
):
raise QueryObjectValidationError(
_("Can't have overlap between Series and Breakdowns")
)
if not fd.get("metrics"):
raise QueryObjectValidationError(_("Pick at least one metric"))
if not fd.get("groupby"):
raise QueryObjectValidationError(_("Pick at least one field for [Series]"))
return d
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
fd = self.form_data
metrics = self.metric_labels
columns = fd.get("columns") or []
# pandas will throw away nulls when grouping/pivoting,
# so we substitute NULL_STRING for any nulls in the necessary columns
filled_cols = self.groupby + columns
df[filled_cols] = df[filled_cols].fillna(value=NULL_STRING)
row = df.groupby(self.groupby).sum()[metrics[0]].copy()
row.sort_values(ascending=False, inplace=True)
pt = df.pivot_table(index=self.groupby, columns=columns, values=metrics)
if fd.get("contribution"):
pt = pt.T
pt = (pt / pt.sum()).T
pt = pt.reindex(row.index)
chart_data = []
for name, ys in pt.items():
if pt[name].dtype.kind not in "biufc" or name in self.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})
d = {"key": series_title, "values": values}
chart_data.append(d)
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
fd = self.form_data
cols = fd.get("groupby") or []
cols.extend(["m1", "m2"])
metric = utils.get_metric_name(fd["metric"])
secondary_metric = (
utils.get_metric_name(fd["secondary_metric"])
if "secondary_metric" in fd
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:
qry = super().query_obj()
fd = self.form_data
qry["metrics"] = [fd["metric"]]
secondary_metric = fd.get("secondary_metric")
if secondary_metric and secondary_metric != fd["metric"]:
qry["metrics"].append(secondary_metric)
return qry
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:
qry = super().query_obj()
if len(qry["groupby"]) != 2:
raise QueryObjectValidationError(
_("Pick exactly 2 columns as [Source / Target]")
)
qry["metrics"] = [self.form_data["metric"]]
return qry
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
source, target = 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(g: 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 g.get(vertex, ()):
if neighbour in path or visit(neighbour):
return (vertex, neighbour)
path.remove(vertex)
return None
for v in g:
cycle = visit(v)
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 DirectedForceViz(BaseViz):
"""An animated directed force layout graph visualization"""
viz_type = "directed_force"
verbose_name = _("Directed Force Layout")
credits = 'd3noob @bl.ocks.org'
is_timeseries = False
def query_obj(self) -> QueryObjectDict:
qry = super().query_obj()
if len(self.form_data["groupby"]) != 2:
raise QueryObjectValidationError(_("Pick exactly 2 columns to 'Group By'"))
qry["metrics"] = [self.form_data["metric"]]
return qry
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
df.columns = ["source", "target", "value"]
return df.to_dict(orient="records")
class ChordViz(BaseViz):
"""A Chord diagram"""
viz_type = "chord"
verbose_name = _("Directed Force Layout")
credits = 'Bostock'
is_timeseries = False
def query_obj(self) -> QueryObjectDict:
qry = super().query_obj()
fd = self.form_data
qry["groupby"] = [fd.get("groupby"), fd.get("columns")]
qry["metrics"] = [fd.get("metric")]
return qry
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
m = [[matrix[(n1, n2)] for n1 in nodes] for n2 in nodes]
return {"nodes": list(nodes), "matrix": m}
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:
qry = super().query_obj()
qry["metrics"] = [self.form_data["metric"]]
qry["groupby"] = [self.form_data["entity"]]
return qry
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
fd = self.form_data
cols = [fd.get("entity")]
metric = self.metric_labels[0]
cols += [metric]
ndf = df[cols]
df = ndf
df.columns = ["country_id", "metric"]
d = df.to_dict(orient="records")
return d
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:
qry = super().query_obj()
qry["groupby"] = [self.form_data["entity"]]
return qry
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
from superset.examples import countries
fd = self.form_data
cols = [fd.get("entity")]
metric = utils.get_metric_name(fd["metric"])
secondary_metric = (
utils.get_metric_name(fd["secondary_metric"])
if "secondary_metric" in fd
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
d = df.to_dict(orient="records")
for row in d:
country = None
if isinstance(row["country"], str):
if "country_fieldtype" in fd:
country = countries.get(fd["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 d
class FilterBoxViz(BaseViz):
"""A multi filter, multi-choice filter box to make dashboards interactive"""
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:
qry = super().query_obj()
filters = self.form_data.get("filter_configs") or []
qry["row_limit"] = self.filter_row_limit
self.dataframes = {}
for flt in filters:
col = flt.get("column")
if not col:
raise QueryObjectValidationError(
_("Invalid filter configuration, please select a column")
)
qry["groupby"] = [col]
metric = flt.get("metric")
qry["metrics"] = [metric] if metric else []
df = self.get_df_payload(query_obj=qry).get("df")
self.dataframes[col] = df
def get_data(self, df: pd.DataFrame) -> VizData:
filters = self.form_data.get("filter_configs") or []
d = {}
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")
)
d[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"))
d[col] = [
{"id": row[0], "text": row[0]}
for row in df.itertuples(index=False)
]
else:
df[col] = []
return d
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:
d = super().query_obj()
fd = self.form_data
d["groupby"] = [fd.get("series")]
return d
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:
d = super().query_obj()
fd = self.form_data
d["metrics"] = [fd.get("metric")]
d["groupby"] = [fd.get("all_columns_x"), fd.get("all_columns_y")]
return d
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
fd = self.form_data
x = fd.get("all_columns_x")
y = fd.get("all_columns_y")
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 = fd.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:
d = super().query_obj()
fd = self.form_data
label_col = fd.get("mapbox_label")
if not fd.get("groupby"):
if fd.get("all_columns_x") is None or fd.get("all_columns_y") is None:
raise QueryObjectValidationError(
_("[Longitude] and [Latitude] must be set")
)
d["columns"] = [fd.get("all_columns_x"), fd.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]"
)
)
d["columns"].append(label_col[0])
if fd.get("point_radius") != "Auto":
d["columns"].append(fd.get("point_radius"))
d["columns"] = list(set(d["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 fd["groupby"]
):
raise QueryObjectValidationError(
_("Choice of [Label] must be present in [Group By]")
)
if (
fd.get("point_radius") != "Auto"
and fd.get("point_radius") not in fd["groupby"]
):
raise QueryObjectValidationError(
_("Choice of [Point Radius] must be present in [Group By]")
)
if (
fd.get("all_columns_x") not in fd["groupby"]
or fd.get("all_columns_y") not in fd["groupby"]
):
raise QueryObjectValidationError(
_(
"[Longitude] and [Latitude] columns must be present in "
+ "[Group By]"
)
)
return d
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
fd = self.form_data
label_col = fd.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] == fd.get("all_columns_x"): # type: ignore
metric_col = df[fd.get("all_columns_x")]
elif label_col[0] == fd.get("all_columns_y"): # type: ignore
metric_col = df[fd.get("all_columns_y")]
else:
metric_col = df[label_col[0]] # type: ignore
point_radius_col = (
[None] * len(df.index)
if fd.get("point_radius") == "Auto"
else df[fd.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[fd.get("all_columns_x")],
df[fd.get("all_columns_y")],
metric_col,
point_radius_col,
)
],
}
x_series, y_series = df[fd.get("all_columns_x")], df[fd.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": fd.get("mapbox_style"),
"aggregatorName": fd.get("pandas_aggfunc"),
"clusteringRadius": fd.get("clustering_radius"),
"pointRadiusUnit": fd.get("point_radius_unit"),
"globalOpacity": fd.get("global_opacity"),
"bounds": [south_west, north_east],
"renderWhileDragging": fd.get("render_while_dragging"),
"tooltip": fd.get("rich_tooltip"),
"color": fd.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:
fd = self.form_data
# Late imports to avoid circular import issues
from superset import db
from superset.models.slice import Slice
slice_ids = fd.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]:
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")]
elif spatial.get("type") == "delimited":
return [spatial.get("lonlatCol")]
elif spatial.get("type") == "geohash":
return [spatial.get("geohashCol")]
return []
@staticmethod
def parse_coordinates(s: Any) -> Optional[Tuple[float, float]]:
if not s:
return None
try:
p = Point(s)
return (p.latitude, p.longitude) # pylint: disable=no-member
except Exception:
raise SpatialException(_("Invalid spatial point encountered: %s" % s))
@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:
fd = self.form_data
spatial_columns = set()
for key in self.spatial_control_keys:
for column in self.get_spatial_columns(key):
spatial_columns.add(column)
if fd.get("adhoc_filters") is None:
fd["adhoc_filters"] = []
line_column = fd.get("line_column")
if line_column:
spatial_columns.add(line_column)
for column in sorted(spatial_columns):
filter_ = to_adhoc({"col": column, "op": "IS NOT NULL", "val": ""})
fd["adhoc_filters"].append(filter_)
def query_obj(self) -> QueryObjectDict:
fd = self.form_data
# add NULL filters
if fd.get("filter_nulls", True):
self.add_null_filters()
d = super().query_obj()
gb: List[str] = []
for key in self.spatial_control_keys:
self.process_spatial_query_obj(key, gb)
if fd.get("dimension"):
gb += [fd["dimension"]]
if fd.get("js_columns"):
gb += fd.get("js_columns") or []
metrics = self.get_metrics()
gb = list(set(gb))
if metrics:
d["groupby"] = gb
d["metrics"] = metrics
d["columns"] = []
else:
d["columns"] = gb
return d
def get_js_columns(self, d: Dict[str, Any]) -> Dict[str, Any]:
cols = self.form_data.get("js_columns") or []
return {col: d.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 d in df.to_dict(orient="records"):
feature = self.get_properties(d)
extra_props = self.get_js_columns(d)
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, d: 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:
fd = self.form_data
self.is_timeseries = bool(fd.get("time_grain_sqla") or fd.get("granularity"))
self.point_radius_fixed = fd.get("point_radius_fixed") or {
"type": "fix",
"value": 500,
}
return super().query_obj()
def get_metrics(self) -> List[str]:
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, d: Dict[str, Any]) -> Dict[str, Any]:
return {
"metric": d.get(self.metric_label) if self.metric_label else None,
"radius": self.fixed_value
if self.fixed_value
else d.get(self.metric_label)
if self.metric_label
else None,
"cat_color": d.get(self.dim) if self.dim else None,
"position": d.get("spatial"),
DTTM_ALIAS: d.get(DTTM_ALIAS),
}
def get_data(self, df: pd.DataFrame) -> VizData:
fd = self.form_data
self.metric_label = utils.get_metric_name(self.metric) if self.metric else None
self.point_radius_fixed = fd.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:
fd = self.form_data
self.is_timeseries = bool(fd.get("time_grain_sqla") or fd.get("granularity"))
return super().query_obj()
def get_properties(self, d: Dict[str, Any]) -> Dict[str, Any]:
return {
"position": d.get("spatial"),
"weight": (d.get(self.metric_label) if self.metric_label else None) or 1,
"__timestamp": d.get(DTTM_ALIAS) or d.get("__time"),
}
def get_data(self, df: pd.DataFrame) -> VizData:
self.metric_label = 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, d: Dict[str, Any]) -> Dict[str, Any]:
return {
"position": d.get("spatial"),
"weight": (d.get(self.metric_label) if self.metric_label else None) or 1,
}
def get_data(self, df: pd.DataFrame) -> VizData:
self.metric_label = 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]]:
p = geohash.bbox(geohash_code)
return [
[p.get("w"), p.get("n")],
[p.get("e"), p.get("n")],
[p.get("e"), p.get("s")],
[p.get("w"), p.get("s")],
[p.get("w"), p.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:
fd = self.form_data
self.is_timeseries = bool(fd.get("time_grain_sqla") or fd.get("granularity"))
d = super().query_obj()
self.metric = fd.get("metric")
line_col = fd.get("line_column")
if d["metrics"]:
self.has_metrics = True
d["groupby"].append(line_col)
else:
self.has_metrics = False
d["columns"].append(line_col)
return d
def get_properties(self, d: Dict[str, Any]) -> Dict[str, Any]:
fd = self.form_data
line_type = fd["line_type"]
deser = self.deser_map[line_type]
line_column = fd["line_column"]
path = deser(d[line_column])
if fd.get("reverse_long_lat"):
path = [(o[1], o[0]) for o in path]
d[self.deck_viz_key] = path
if line_type != "geohash":
del d[line_column]
d["__timestamp"] = d.get(DTTM_ALIAS) or d.get("__time")
return d
def get_data(self, df: pd.DataFrame) -> VizData:
self.metric_label = 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:
fd = self.form_data
self.elevation = fd.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, d: Dict[str, Any]) -> Dict[str, Any]:
super().get_properties(d)
fd = self.form_data
elevation = fd["point_radius_fixed"]["value"]
type_ = fd["point_radius_fixed"]["type"]
d["elevation"] = (
d.get(utils.get_metric_name(elevation)) if type_ == "metric" else elevation
)
return d
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, d: Dict[str, Any]) -> Dict[str, Any]:
return {
"position": d.get("spatial"),
"weight": (d.get(self.metric_label) if self.metric_label else None) or 1,
}
def get_data(self, df: pd.DataFrame) -> VizData:
self.metric_label = utils.get_metric_name(self.metric) if self.metric else None
return super(DeckHex, self).get_data(df)
class DeckGeoJson(BaseDeckGLViz):
"""deck.gl's GeoJSONLayer"""
viz_type = "deck_geojson"
verbose_name = _("Deck.gl - GeoJSON")
def query_obj(self) -> QueryObjectDict:
d = super().query_obj()
d["columns"] += [self.form_data.get("geojson")]
d["metrics"] = []
d["groupby"] = []
return d
def get_properties(self, d: Dict[str, Any]) -> Dict[str, Any]:
geojson = d[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:
fd = self.form_data
self.is_timeseries = bool(fd.get("time_grain_sqla") or fd.get("granularity"))
return super().query_obj()
def get_properties(self, d: Dict[str, Any]) -> Dict[str, Any]:
dim = self.form_data.get("dimension")
return {
"sourcePosition": d.get("start_spatial"),
"targetPosition": d.get("end_spatial"),
"cat_color": d.get(dim) if dim else None,
DTTM_ALIAS: d.get(DTTM_ALIAS),
}
def get_data(self, df: pd.DataFrame) -> VizData:
if df.empty:
return None
d = super().get_data(df)
return {
"features": d["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"]
if col != event_key and col != 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 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
fd = self.form_data
groups = fd.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 nameSet in df.columns:
# If no groups are defined, nameSet will be the metric name
hasGroup = not isinstance(nameSet, str)
Y = series[nameSet]
d = {
"group": nameSet[1:] if hasGroup else "All",
"values": [{"x": t, "y": Y[t] if t in Y else None} for t in df.index],
}
key = nameSet[0] if hasGroup else nameSet
if key in data:
data[key].append(d)
else:
data[key] = [d]
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
def levels_for(
self, 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
def levels_for_diff(
self, 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:
return [
{
"name": i,
"val": levels[1][metric][i],
"children": self.nest_values(levels, 2, metric, [i]),
}
for i in levels[1][metric].index
]
if level >= len(levels):
return []
return [
{
"name": i,
"val": levels[level][metric][dims][i],
"children": self.nest_values(levels, level + 1, metric, dims + [i]),
}
for i in levels[level][metric][dims].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
fd = self.form_data
groups = fd.get("groupby", [])
time_op = fd.get("time_series_option", "not_time")
if not len(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)
viz_types = {
o.viz_type: o
for o in globals().values()
if (
inspect.isclass(o)
and issubclass(o, BaseViz)
and o.viz_type not in config["VIZ_TYPE_DENYLIST"]
)
}