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superset2/superset/viz.py
Vishal Jagtap 6441f69271 Typo fixes in viz.py and CONTRIBUTING.md (#5432)
2018-07-19 16:45:56 -07:00

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# -*- coding: utf-8 -*-
# pylint: disable=C,R,W
"""This module contains the 'Viz' objects
These objects represent the backend of all the visualizations that
Superset can render.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from collections import defaultdict
import copy
from datetime import datetime, timedelta
import hashlib
import inspect
from itertools import product
import logging
import math
import re
import traceback
import uuid
from dateutil import relativedelta as rdelta
from flask import request
from flask_babel import lazy_gettext as _
import geohash
from geopy.point import Point
from markdown import markdown
import numpy as np
import pandas as pd
from pandas.tseries.frequencies import to_offset
from past.builtins import basestring
import polyline
import simplejson as json
from six import string_types, text_type
from six.moves import cPickle as pkl, reduce
from superset import app, cache, get_css_manifest_files, utils
from superset.exceptions import NullValueException, SpatialException
from superset.utils import DTTM_ALIAS, JS_MAX_INTEGER, merge_extra_filters
config = app.config
stats_logger = config.get('STATS_LOGGER')
METRIC_KEYS = [
'metric', 'metrics', 'percent_metrics', 'metric_2', 'secondary_metric',
'x', 'y', 'size',
]
class BaseViz(object):
"""All visualizations derive this base class"""
viz_type = None
verbose_name = 'Base Viz'
credits = ''
is_timeseries = False
default_fillna = 0
cache_type = 'df'
enforce_numerical_metrics = True
def __init__(self, datasource, form_data, force=False):
if not datasource:
raise Exception(_('Viz is missing a datasource'))
utils.since_until_to_time_range(form_data)
self.datasource = datasource
self.request = request
self.viz_type = form_data.get('viz_type')
self.form_data = form_data
self.query = ''
self.token = self.form_data.get(
'token', 'token_' + uuid.uuid4().hex[:8])
self.groupby = self.form_data.get('groupby') or []
self.time_shift = timedelta()
self.status = None
self.error_message = None
self.force = force
# Keeping track of whether some data came from cache
# this is useful to trigger the <CachedLabel /> when
# in the cases where visualization have many queries
# (FilterBox for instance)
self._some_from_cache = False
self._any_cache_key = None
self._any_cached_dttm = None
self._extra_chart_data = []
self.process_metrics()
def process_metrics(self):
self.metric_dict = {}
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:
self.metric_dict[self.get_metric_label(o)] = 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())
def get_metric_label(self, metric):
if isinstance(metric, string_types):
return metric
if isinstance(metric, dict):
return metric.get('label')
@staticmethod
def handle_js_int_overflow(data):
for d in data.get('records', dict()):
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):
"""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 handle_nulls(self, df):
fillna = self.get_fillna_for_columns(df.columns)
df = df.fillna(fillna)
def get_fillna_for_col(self, col):
"""Returns the value to use as filler for a specific Column.type"""
if col:
if col.is_string:
return ' NULL'
return self.default_fillna
def get_fillna_for_columns(self, columns=None):
"""Returns a dict or scalar that can be passed to DataFrame.fillna"""
if columns is None:
return self.default_fillna
columns_dict = {col.column_name: col for col in self.datasource.columns}
fillna = {
c: self.get_fillna_for_col(columns_dict.get(c))
for c in columns
}
return fillna
def get_df(self, query_obj=None):
"""Returns a pandas dataframe based on the query object"""
if not query_obj:
query_obj = self.query_obj()
if not query_obj:
return None
self.error_msg = ''
self.results = None
timestamp_format = None
if self.datasource.type == 'table':
dttm_col = self.datasource.get_col(query_obj['granularity'])
if dttm_col:
timestamp_format = dttm_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.error_message = self.results.error_message
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.
df[DTTM_ALIAS] = df[DTTM_ALIAS].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, query_obj.get('metrics') or [])
df.replace([np.inf, -np.inf], np.nan)
self.handle_nulls(df)
return df
@staticmethod
def df_metrics_to_num(df, metrics):
"""Converting metrics to numeric when pandas.read_sql cannot"""
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 query_obj(self):
"""Building a query object"""
form_data = self.form_data
gb = form_data.get('groupby') or []
metrics = self.all_metrics or []
columns = form_data.get('columns') or []
groupby = []
for o in gb + columns:
if o not in groupby:
groupby.append(o)
is_timeseries = self.is_timeseries
if DTTM_ALIAS in groupby:
groupby.remove(DTTM_ALIAS)
is_timeseries = True
# extras are used to query elements specific to a datasource type
# for instance the extra where clause that applies only to Tables
utils.split_adhoc_filters_into_base_filters(form_data)
merge_extra_filters(form_data)
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.get('ROW_LIMIT'))
# default order direction
order_desc = form_data.get('order_desc', True)
since, until = utils.get_since_until(form_data)
time_shift = form_data.get('time_shift', '')
self.time_shift = utils.parse_human_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 Exception(_('From date cannot be larger than to date'))
self.from_dttm = from_dttm
self.to_dttm = to_dttm
filters = form_data.get('filters', [])
extras = {
'where': form_data.get('where', ''),
'having': form_data.get('having', ''),
'having_druid': form_data.get('having_filters', []),
'time_grain_sqla': form_data.get('time_grain_sqla', ''),
'druid_time_origin': form_data.get('druid_time_origin', ''),
}
d = {
'granularity': granularity,
'from_dttm': from_dttm,
'to_dttm': to_dttm,
'is_timeseries': is_timeseries,
'groupby': groupby,
'metrics': metrics,
'row_limit': row_limit,
'filter': filters,
'timeseries_limit': limit,
'extras': extras,
'timeseries_limit_metric': timeseries_limit_metric,
'order_desc': order_desc,
'prequeries': [],
'is_prequery': False,
}
return d
@property
def cache_timeout(self):
if self.form_data.get('cache_timeout') is not None:
return int(self.form_data.get('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.get('CACHE_DEFAULT_TIMEOUT')
def get_json(self):
return json.dumps(
self.get_payload(),
default=utils.json_int_dttm_ser, ignore_nan=True)
def cache_key(self, query_obj):
"""
The cache key is made out of the key/values in `query_obj`
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").
"""
cache_dict = copy.copy(query_obj)
for k in ['from_dttm', 'to_dttm']:
del cache_dict[k]
for k in ['since', 'until']:
cache_dict[k] = self.form_data.get(k)
cache_dict['datasource'] = self.datasource.uid
json_data = self.json_dumps(cache_dict, sort_keys=True)
return hashlib.md5(json_data.encode('utf-8')).hexdigest()
def get_payload(self, query_obj=None):
"""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:
if df is not None and df.empty:
payload['error'] = 'No data'
else:
payload['data'] = self.get_data(df)
if 'df' in payload:
del payload['df']
return payload
def get_df_payload(self, query_obj=None):
"""Handles caching around the df payload retrieval"""
if not query_obj:
query_obj = self.query_obj()
cache_key = self.cache_key(query_obj) if query_obj else None
logging.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('loaded_from_cache')
try:
cache_value = pkl.loads(cache_value)
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
except Exception as e:
logging.exception(e)
logging.error('Error reading cache: ' +
utils.error_msg_from_exception(e))
logging.info('Serving from cache')
if query_obj and not is_loaded:
try:
df = self.get_df(query_obj)
if self.status != utils.QueryStatus.FAILED:
stats_logger.incr('loaded_from_source')
is_loaded = True
except Exception as e:
logging.exception(e)
if not self.error_message:
self.error_message = '{}'.format(e)
self.status = utils.QueryStatus.FAILED
stacktrace = traceback.format_exc()
if (
is_loaded and
cache_key and
cache and
self.status != utils.QueryStatus.FAILED):
try:
cache_value = dict(
dttm=cached_dttm,
df=df if df is not None else None,
query=self.query,
)
cache_value = pkl.dumps(
cache_value, protocol=pkl.HIGHEST_PROTOCOL)
logging.info('Caching {} chars at key {}'.format(
len(cache_value), cache_key))
stats_logger.incr('set_cache_key')
cache.set(
cache_key,
cache_value,
timeout=self.cache_timeout)
except Exception as e:
# cache.set call can fail if the backend is down or if
# the key is too large or whatever other reasons
logging.warning('Could not cache key {}'.format(cache_key))
logging.exception(e)
cache.delete(cache_key)
return {
'cache_key': self._any_cache_key,
'cached_dttm': self._any_cached_dttm,
'cache_timeout': self.cache_timeout,
'df': df,
'error': self.error_message,
'form_data': self.form_data,
'is_cached': self._any_cache_key is not None,
'query': self.query,
'status': self.status,
'stacktrace': stacktrace,
'rowcount': len(df.index) if df is not None else 0,
}
def json_dumps(self, obj, sort_keys=False):
return json.dumps(
obj,
default=utils.json_int_dttm_ser,
ignore_nan=True,
sort_keys=sort_keys,
)
@property
def data(self):
"""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):
df = self.get_df()
include_index = not isinstance(df.index, pd.RangeIndex)
return df.to_csv(index=include_index, **config.get('CSV_EXPORT'))
def get_data(self, df):
return []
@property
def json_data(self):
return json.dumps(self.data)
class TableViz(BaseViz):
"""A basic html table that is sortable and searchable"""
viz_type = 'table'
verbose_name = _('Table View')
credits = 'a <a href="https://github.com/airbnb/superset">Superset</a> original'
is_timeseries = False
enforce_numerical_metrics = False
def should_be_timeseries(self):
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 Exception(_(
'Pick a granularity in the Time section or '
"uncheck 'Include Time'"))
return fd.get('include_time')
def query_obj(self):
d = super(TableViz, self).query_obj()
fd = self.form_data
if fd.get('all_columns') and (fd.get('groupby') or fd.get('metrics')):
raise Exception(_(
'Choose either fields to [Group By] and [Metrics] or '
'[Columns], not both'))
sort_by = fd.get('timeseries_limit_metric')
if fd.get('all_columns'):
d['columns'] = fd.get('all_columns')
d['groupby'] = []
order_by_cols = fd.get('order_by_cols') or []
d['orderby'] = [json.loads(t) for t in order_by_cols]
elif sort_by:
sort_by_label = utils.get_metric_name(sort_by)
if sort_by_label not in utils.get_metric_names(d['metrics']):
d['metrics'] += [sort_by]
d['orderby'] = [(sort_by, not fd.get('order_desc', True))]
# Add all percent metrics that are not already in the list
if 'percent_metrics' in fd:
d['metrics'] = d['metrics'] + list(filter(
lambda m: m not in d['metrics'],
fd['percent_metrics'] or [],
))
d['is_timeseries'] = self.should_be_timeseries()
return d
def get_data(self, df):
fd = self.form_data
if (
not self.should_be_timeseries() and
df is not None and
DTTM_ALIAS in df
):
del df[DTTM_ALIAS]
# Sum up and compute percentages for all percent metrics
percent_metrics = fd.get('percent_metrics') or []
percent_metrics = [self.get_metric_label(m) for m in percent_metrics]
if len(percent_metrics):
percent_metrics = list(filter(lambda m: m in df, percent_metrics))
metric_sums = {
m: reduce(lambda a, b: a + b, df[m])
for m in percent_metrics
}
metric_percents = {
m: list(map(
lambda a: None if metric_sums[m] == 0 else a / metric_sums[m], df[m]))
for m in percent_metrics
}
for m in percent_metrics:
m_name = '%' + m
df[m_name] = pd.Series(metric_percents[m], name=m_name)
# Remove metrics that are not in the main metrics list
metrics = fd.get('metrics') or []
metrics = [self.get_metric_label(m) for m in metrics]
for m in filter(
lambda m: m not in metrics and m in df.columns,
percent_metrics,
):
del df[m]
data = self.handle_js_int_overflow(
dict(
records=df.to_dict(orient='records'),
columns=list(df.columns),
))
return data
def json_dumps(self, obj, sort_keys=False):
if self.form_data.get('all_columns'):
return json.dumps(
obj,
default=utils.json_iso_dttm_ser,
sort_keys=sort_keys,
ignore_nan=True)
else:
return super(TableViz, self).json_dumps(obj)
class TimeTableViz(BaseViz):
"""A data table with rich time-series related columns"""
viz_type = 'time_table'
verbose_name = _('Time Table View')
credits = 'a <a href="https://github.com/airbnb/superset">Superset</a> original'
is_timeseries = True
def query_obj(self):
d = super(TimeTableViz, self).query_obj()
fd = self.form_data
if not fd.get('metrics'):
raise Exception(_('Pick at least one metric'))
if fd.get('groupby') and len(fd.get('metrics')) > 1:
raise Exception(_(
"When using 'Group By' you are limited to use a single metric"))
return d
def get_data(self, df):
fd = self.form_data
columns = None
values = 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 <a href="https://github.com/airbnb/superset">Superset</a> original'
is_timeseries = False
def query_obj(self):
d = super(PivotTableViz, self).query_obj()
groupby = self.form_data.get('groupby')
columns = self.form_data.get('columns')
metrics = self.form_data.get('metrics')
if not columns:
columns = []
if not groupby:
groupby = []
if not groupby:
raise Exception(_("Please choose at least one 'Group by' field "))
if not metrics:
raise Exception(_('Please choose at least one metric'))
if (
any(v in groupby for v in columns) or
any(v in columns for v in groupby)):
raise Exception(_("Group By' and 'Columns' can't overlap"))
return d
def get_data(self, df):
if (
self.form_data.get('granularity') == 'all' and
DTTM_ALIAS in df):
del df[DTTM_ALIAS]
df = df.pivot_table(
index=self.form_data.get('groupby'),
columns=self.form_data.get('columns'),
values=[self.get_metric_label(m) for m in self.form_data.get('metrics')],
aggfunc=self.form_data.get('pandas_aggfunc'),
margins=self.form_data.get('pivot_margins'),
)
# 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='',
classes=(
'dataframe table table-striped table-bordered '
'table-condensed table-hover').split(' ')),
)
class MarkupViz(BaseViz):
"""Use html or markdown to create a free form widget"""
viz_type = 'markup'
verbose_name = _('Markup')
is_timeseries = False
def query_obj(self):
return None
def get_df(self, query_obj=None):
return None
def get_data(self, df):
markup_type = self.form_data.get('markup_type')
code = self.form_data.get('code', '')
if markup_type == 'markdown':
code = markdown(code)
return dict(html=code, theme_css=get_css_manifest_files('theme'))
class SeparatorViz(MarkupViz):
"""Use to create section headers in a dashboard, similar to `Markup`"""
viz_type = 'separator'
verbose_name = _('Separator')
class WordCloudViz(BaseViz):
"""Build a colorful word cloud
Uses the nice library at:
https://github.com/jasondavies/d3-cloud
"""
viz_type = 'word_cloud'
verbose_name = _('Word Cloud')
is_timeseries = False
def query_obj(self):
d = super(WordCloudViz, self).query_obj()
d['groupby'] = [self.form_data.get('series')]
return d
def get_data(self, df):
fd = self.form_data
# Ordering the columns
df = df[[fd.get('series'), self.metric_labels[0]]]
# Labeling the columns for uniform json schema
df.columns = ['text', 'size']
return df.to_dict(orient='records')
class TreemapViz(BaseViz):
"""Tree map visualisation for hierarchical data."""
viz_type = 'treemap'
verbose_name = _('Treemap')
credits = '<a href="https://d3js.org">d3.js</a>'
is_timeseries = False
def _nest(self, metric, df):
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):
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 = (
'<a href=https://github.com/wa0x6e/cal-heatmap>cal-heatmap</a>')
is_timeseries = True
def get_data(self, df):
form_data = self.form_data
data = {}
records = df.to_dict('records')
for metric in self.metric_labels:
data[metric] = {
str(obj[DTTM_ALIAS].value / 10**9): obj.get(metric)
for obj in records
}
start, end = utils.get_since_until(form_data)
if not start or not end:
raise Exception('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
else:
range_ = diff_secs // (60 * 60) + 1
return {
'data': data,
'start': start,
'domain': domain,
'subdomain': form_data.get('subdomain_granularity'),
'range': range_,
}
def query_obj(self):
d = super(CalHeatmapViz, self).query_obj()
fd = self.form_data
d['metrics'] = fd.get('metrics')
return d
class NVD3Viz(BaseViz):
"""Base class for all nvd3 vizs"""
credits = '<a href="http://nvd3.org/">NVD3.org</a>'
viz_type = 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, classed='', title_suffix=''):
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 = defaultdict(dict)
for (label, key), value in row.items():
if key == 'median':
key = 'Q2'
boxes[label][key] = value
for label, box in boxes.items():
if len(self.form_data.get('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):
form_data = self.form_data
df = df.fillna(0)
# conform to NVD3 names
def Q1(series): # need to be named functions - can't use lambdas
return np.percentile(series, 25)
def Q3(series):
return np.percentile(series, 75)
whisker_type = form_data.get('whisker_options')
if whisker_type == 'Tukey':
def whisker_high(series):
upper_outer_lim = Q3(series) + 1.5 * (Q3(series) - Q1(series))
series = series[series <= upper_outer_lim]
return series[np.abs(series - upper_outer_lim).argmin()]
def whisker_low(series):
lower_outer_lim = Q1(series) - 1.5 * (Q3(series) - Q1(series))
# find the closest value above the lower outer limit
series = series[series >= lower_outer_lim]
return series[np.abs(series - lower_outer_lim).argmin()]
elif whisker_type == 'Min/max (no outliers)':
def whisker_high(series):
return series.max()
def whisker_low(series):
return series.min()
elif ' percentiles' in whisker_type:
low, high = whisker_type.replace(' percentiles', '').split('/')
def whisker_high(series):
return np.percentile(series, int(high))
def whisker_low(series):
return np.percentile(series, int(low))
else:
raise ValueError('Unknown whisker type: {}'.format(whisker_type))
def outliers(series):
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.median, 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):
form_data = self.form_data
d = super(BubbleViz, self).query_obj()
d['groupby'] = [
form_data.get('entity'),
]
if form_data.get('series'):
d['groupby'].append(form_data.get('series'))
self.x_metric = form_data.get('x')
self.y_metric = form_data.get('y')
self.z_metric = form_data.get('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 not all(d['metrics'] + [self.entity]):
raise Exception(_('Pick a metric for x, y and size'))
return d
def get_data(self, df):
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 = 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):
form_data = self.form_data
d = super(BulletViz, self).query_obj()
self.metric = form_data.get('metric')
def as_strings(field):
value = form_data.get(field)
return value.split(',') if value else []
def as_floats(field):
return [float(x) for x in as_strings(field)]
self.ranges = as_floats('ranges')
self.range_labels = as_strings('range_labels')
self.markers = as_floats('markers')
self.marker_labels = as_strings('marker_labels')
self.marker_lines = as_floats('marker_lines')
self.marker_line_labels = as_strings('marker_line_labels')
d['metrics'] = [
self.metric,
]
if not self.metric:
raise Exception(_('Pick a metric to display'))
return d
def get_data(self, df):
df = df.fillna(0)
df['metric'] = df[[self.get_metric_label(self.metric)]]
values = df['metric'].values
return {
'measures': values.tolist(),
'ranges': self.ranges or [0, values.max() * 1.1],
'rangeLabels': self.range_labels or None,
'markers': self.markers or None,
'markerLabels': self.marker_labels or None,
'markerLines': self.marker_lines or None,
'markerLineLabels': self.marker_line_labels or None,
}
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 <a href="https://github.com/airbnb/superset">Superset</a> original'
is_timeseries = True
def query_obj(self):
d = super(BigNumberViz, self).query_obj()
metric = self.form_data.get('metric')
if not metric:
raise Exception(_('Pick a metric!'))
d['metrics'] = [self.form_data.get('metric')]
self.form_data['metric'] = metric
return d
def get_data(self, df):
form_data = self.form_data
df.sort_values(by=df.columns[0], inplace=True)
compare_lag = form_data.get('compare_lag')
return {
'data': df.values.tolist(),
'compare_lag': compare_lag,
'compare_suffix': form_data.get('compare_suffix', ''),
}
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 <a href="https://github.com/airbnb/superset">Superset</a> original'
is_timeseries = False
def query_obj(self):
d = super(BigNumberTotalViz, self).query_obj()
metric = self.form_data.get('metric')
if not metric:
raise Exception(_('Pick a metric!'))
d['metrics'] = [self.form_data.get('metric')]
self.form_data['metric'] = metric
return d
def get_data(self, df):
form_data = self.form_data
df.sort_values(by=df.columns[0], inplace=True)
return {
'data': df.values.tolist(),
'subheader': form_data.get('subheader', ''),
}
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
def to_series(self, df, classed='', title_suffix=''):
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
if isinstance(name, list):
series_title = [text_type(title) for title in name]
elif isinstance(name, tuple):
series_title = tuple(text_type(title) for title in name)
else:
series_title = text_type(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, string_types):
series_title = (series_title, title_suffix)
elif isinstance(series_title, (list, tuple)):
series_title = series_title + (title_suffix,)
values = []
for ds in df.index:
if ds in ys:
d = {
'x': ds,
'y': ys[ds],
}
else:
d = {}
values.append(d)
d = {
'key': series_title,
'values': values,
}
if classed:
d['classed'] = classed
chart_data.append(d)
return chart_data
def process_data(self, df, aggregate=False):
fd = self.form_data
df = df.fillna(0)
if fd.get('granularity') == 'all':
raise Exception(_('Pick a time granularity for your time series'))
if not aggregate:
df = df.pivot_table(
index=DTTM_ALIAS,
columns=fd.get('groupby'),
values=utils.get_metric_names(fd.get('metrics')))
else:
df = df.pivot_table(
index=DTTM_ALIAS,
columns=fd.get('groupby'),
values=utils.get_metric_names(fd.get('metrics')),
fill_value=0,
aggfunc=sum)
fm = fd.get('resample_fillmethod')
if not fm:
fm = None
how = fd.get('resample_how')
rule = fd.get('resample_rule')
if how and rule:
df = df.resample(rule, how=how, fill_method=fm)
if not fm:
df = df.fillna(0)
if self.sort_series:
dfs = df.sum()
dfs.sort_values(ascending=False, inplace=True)
df = df[dfs.index]
if fd.get('contribution'):
dft = df.T
df = (dft / dft.sum()).T
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:]
return df
def run_extra_queries(self):
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()
delta = utils.parse_human_timedelta(option)
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 Exception(_(
'`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).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):
fd = self.form_data
comparison_type = fd.get('comparison_type') or 'values'
df = self.process_data(df)
if comparison_type == 'values':
chart_data = self.to_series(df)
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 Exception(
'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))
return sorted(chart_data, key=lambda x: tuple(x['key']))
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):
return None
def get_data(self, df):
fd = self.form_data
# Late imports to avoid circular import issues
from superset.models.core import Slice
from superset import db
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):
d = super(NVD3DualLineViz, self).query_obj()
m1 = self.form_data.get('metric')
m2 = self.form_data.get('metric_2')
d['metrics'] = [m1, m2]
if not m1:
raise Exception(_('Pick a metric for left axis!'))
if not m2:
raise Exception(_('Pick a metric for right axis!'))
if m1 == m2:
raise Exception(_('Please choose different metrics'
' on left and right axis'))
return d
def to_series(self, df, classed=''):
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.get('metric'),
self.form_data.get('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):
fd = self.form_data
df = df.fillna(0)
if self.form_data.get('granularity') == 'all':
raise Exception(_('Pick a time granularity for your time series'))
metric = self.get_metric_label(fd.get('metric'))
metric_2 = self.get_metric_label(fd.get('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):
d = super(NVD3TimePivotViz, self).query_obj()
d['metrics'] = [self.form_data.get('metric')]
return d
def get_data(self, df):
fd = self.form_data
df = self.process_data(df)
freq = to_offset(fd.get('freq'))
freq.normalize = True
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=self.get_metric_label(fd.get('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
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):
metric = self.metric_labels[0]
df = df.pivot_table(
index=self.groupby,
values=[metric])
df.sort_values(by=metric, ascending=False, inplace=True)
df = df.reset_index()
df.columns = ['x', 'y']
return df.to_dict(orient='records')
class HistogramViz(BaseViz):
"""Histogram"""
viz_type = 'histogram'
verbose_name = _('Histogram')
is_timeseries = False
def query_obj(self):
"""Returns the query object for this visualization"""
d = super(HistogramViz, self).query_obj()
d['row_limit'] = self.form_data.get(
'row_limit', int(config.get('VIZ_ROW_LIMIT')))
numeric_columns = self.form_data.get('all_columns_x')
if numeric_columns is None:
raise Exception(_('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 get_data(self, df):
"""Returns the chart data"""
chart_data = []
if len(self.groupby) > 0:
groups = df.groupby(self.groupby)
else:
groups = [((), df)]
for keys, data in groups:
if isinstance(keys, str):
keys = (keys,)
# removing undesirable characters
keys = [re.sub(r'\W+', r'_', k) for k in keys]
chart_data.extend([{
'key': '__'.join([c] + keys),
'values': data[c].tolist()}
for c 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):
d = super(DistributionBarViz, self).query_obj() # noqa
fd = self.form_data
if (
len(d['groupby']) <
len(fd.get('groupby') or []) + len(fd.get('columns') or [])
):
raise Exception(
_("Can't have overlap between Series and Breakdowns"))
if not fd.get('metrics'):
raise Exception(_('Pick at least one metric'))
if not fd.get('groupby'):
raise Exception(_('Pick at least one field for [Series]'))
return d
def get_data(self, df):
fd = self.form_data
metrics = self.metric_labels
row = df.groupby(self.groupby).sum()[metrics[0]].copy()
row.sort_values(ascending=False, inplace=True)
columns = fd.get('columns') or []
pt = df.pivot_table(
index=self.groupby,
columns=columns,
values=metrics)
if fd.get('contribution'):
pt = pt.fillna(0)
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, string_types):
series_title = name
elif len(metrics) > 1:
series_title = ', '.join(name)
else:
l = [str(s) for s in name[1:]] # noqa: E741
series_title = ', '.join(l)
values = []
for i, v in ys.items():
x = i
if isinstance(x, (tuple, list)):
x = ', '.join([text_type(s) for s in x])
else:
x = text_type(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 '
'@<a href="https://bl.ocks.org/kerryrodden/7090426">bl.ocks.org</a>')
def get_data(self, df):
fd = self.form_data
cols = fd.get('groupby')
metric = self.get_metric_label(fd.get('metric'))
secondary_metric = self.get_metric_label(fd.get('secondary_metric'))
if metric == secondary_metric or secondary_metric is None:
df.columns = cols + ['m1']
df['m2'] = df['m1']
return json.loads(df.to_json(orient='values'))
def query_obj(self):
qry = super(SunburstViz, self).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 = '<a href="https://www.npmjs.com/package/d3-sankey">d3-sankey on npm</a>'
def query_obj(self):
qry = super(SankeyViz, self).query_obj()
if len(qry['groupby']) != 2:
raise Exception(_('Pick exactly 2 columns as [Source / Target]'))
qry['metrics'] = [
self.form_data['metric']]
return qry
def get_data(self, df):
df.columns = ['source', 'target', 'value']
df['source'] = df['source'].astype(basestring)
df['target'] = df['target'].astype(basestring)
recs = df.to_dict(orient='records')
hierarchy = defaultdict(set)
for row in recs:
hierarchy[row['source']].add(row['target'])
def find_cycle(g):
"""Whether there's a cycle in a directed graph"""
path = set()
def visit(vertex):
path.add(vertex)
for neighbour in g.get(vertex, ()):
if neighbour in path or visit(neighbour):
return (vertex, neighbour)
path.remove(vertex)
for v in g:
cycle = visit(v)
if cycle:
return cycle
cycle = find_cycle(hierarchy)
if cycle:
raise Exception(_(
"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 @<a href="http://bl.ocks.org/d3noob/5141278">bl.ocks.org</a>'
is_timeseries = False
def query_obj(self):
qry = super(DirectedForceViz, self).query_obj()
if len(self.form_data['groupby']) != 2:
raise Exception(_("Pick exactly 2 columns to 'Group By'"))
qry['metrics'] = [self.form_data['metric']]
return qry
def get_data(self, df):
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 = '<a href="https://github.com/d3/d3-chord">Bostock</a>'
is_timeseries = False
def query_obj(self):
qry = super(ChordViz, self).query_obj()
fd = self.form_data
qry['groupby'] = [fd.get('groupby'), fd.get('columns')]
qry['metrics'] = [self.get_metric_label(fd.get('metric'))]
return qry
def get_data(self, df):
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):
qry = super(CountryMapViz, self).query_obj()
qry['metrics'] = [
self.form_data['metric']]
qry['groupby'] = [self.form_data['entity']]
return qry
def get_data(self, df):
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 <a href="https://www.npmjs.com/package/datamaps">npm</a>'
def query_obj(self):
qry = super(WorldMapViz, self).query_obj()
qry['metrics'] = [
self.form_data['metric'], self.form_data['secondary_metric']]
qry['groupby'] = [self.form_data['entity']]
return qry
def get_data(self, df):
from superset.data import countries
fd = self.form_data
cols = [fd.get('entity')]
metric = self.get_metric_label(fd.get('metric'))
secondary_metric = self.get_metric_label(fd.get('secondary_metric'))
if metric == secondary_metric:
ndf = df[cols]
# df[metric] will be a DataFrame
# because there are duplicate column names
ndf['m1'] = df[metric].iloc[:, 0]
ndf['m2'] = ndf['m1']
else:
cols += [metric, secondary_metric]
ndf = df[cols]
df = ndf
df.columns = ['country', 'm1', 'm2']
d = df.to_dict(orient='records')
for row in d:
country = None
if isinstance(row['country'], string_types):
country = countries.get(
fd.get('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 <a href="https://github.com/airbnb/superset">Superset</a> original'
cache_type = 'get_data'
def query_obj(self):
return None
def run_extra_queries(self):
qry = self.filter_query_obj()
filters = [g for g in self.form_data['groupby']]
self.dataframes = {}
for flt in filters:
qry['groupby'] = [flt]
df = self.get_df_payload(query_obj=qry).get('df')
self.dataframes[flt] = df
def filter_query_obj(self):
qry = super(FilterBoxViz, self).query_obj()
groupby = self.form_data.get('groupby')
if len(groupby) < 1 and not self.form_data.get('date_filter'):
raise Exception(_('Pick at least one filter field'))
qry['metrics'] = [
self.form_data['metric']]
return qry
def get_data(self, df):
d = {}
filters = [g for g in self.form_data['groupby']]
for flt in filters:
df = self.dataframes[flt]
d[flt] = [{
'id': row[0],
'text': row[0],
'filter': flt,
'metric': row[1]}
for row in df.itertuples(index=False)
]
return d
class IFrameViz(BaseViz):
"""You can squeeze just about anything in this iFrame component"""
viz_type = 'iframe'
verbose_name = _('iFrame')
credits = 'a <a href="https://github.com/airbnb/superset">Superset</a> original'
is_timeseries = False
def query_obj(self):
return None
def get_df(self, query_obj=None):
return None
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 = (
'<a href="https://syntagmatic.github.io/parallel-coordinates/">'
"Syntagmatic's library</a>")
is_timeseries = False
def query_obj(self):
d = super(ParallelCoordinatesViz, self).query_obj()
fd = self.form_data
d['metrics'] = copy.copy(fd.get('metrics'))
second = fd.get('secondary_metric')
if second not in d['metrics']:
d['metrics'] += [second]
d['groupby'] = [fd.get('series')]
return d
def get_data(self, df):
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 @<a href="http://bl.ocks.org/mbostock/3074470">'
'bl.ocks.org</a>')
def query_obj(self):
d = super(HeatmapViz, self).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):
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 = (
'<a href="https://www.npmjs.com/package/d3-horizon-chart">'
'd3-horizon-chart</a>')
class MapboxViz(BaseViz):
"""Rich maps made with Mapbox"""
viz_type = 'mapbox'
verbose_name = _('Mapbox')
is_timeseries = False
credits = (
'<a href=https://www.mapbox.com/mapbox-gl-js/api/>Mapbox GL JS</a>')
def query_obj(self):
d = super(MapboxViz, self).query_obj()
fd = self.form_data
label_col = fd.get('mapbox_label')
if not fd.get('groupby'):
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 Exception(_(
"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.get('groupby')):
raise Exception(_(
'Choice of [Label] must be present in [Group By]'))
if (fd.get('point_radius') != 'Auto' and
fd.get('point_radius') not in fd.get('groupby')):
raise Exception(_(
'Choice of [Point Radius] must be present in [Group By]'))
if (fd.get('all_columns_x') not in fd.get('groupby') or
fd.get('all_columns_y') not in fd.get('groupby')):
raise Exception(_(
'[Longitude] and [Latitude] columns must be present in [Group By]'))
return d
def get_data(self, df):
if df is None:
return None
fd = self.form_data
label_col = fd.get('mapbox_label')
custom_metric = label_col and len(label_col) >= 1
metric_col = [None] * len(df.index)
if custom_metric:
if label_col[0] == fd.get('all_columns_x'):
metric_col = df[fd.get('all_columns_x')]
elif label_col[0] == fd.get('all_columns_y'):
metric_col = df[fd.get('all_columns_y')]
else:
metric_col = df[label_col[0]]
point_radius_col = (
[None] * len(df.index)
if fd.get('point_radius') == 'Auto'
else df[fd.get('point_radius')])
# using geoJSON formatting
geo_json = {
'type': 'FeatureCollection',
'features': [
{
'type': 'Feature',
'properties': {
'metric': metric,
'radius': point_radius,
},
'geometry': {
'type': 'Point',
'coordinates': [lon, lat],
},
}
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)
],
}
return {
'geoJSON': geo_json,
'customMetric': custom_metric,
'mapboxApiKey': config.get('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'),
'viewportLongitude': fd.get('viewport_longitude'),
'viewportLatitude': fd.get('viewport_latitude'),
'viewportZoom': fd.get('viewport_zoom'),
'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 = '<a href="https://uber.github.io/deck.gl/">deck.gl</a>'
def query_obj(self):
return None
def get_data(self, df):
fd = self.form_data
# Late imports to avoid circular import issues
from superset.models.core import Slice
from superset import db
slice_ids = fd.get('deck_slices')
slices = db.session.query(Slice).filter(Slice.id.in_(slice_ids)).all()
return {
'mapboxApiKey': config.get('MAPBOX_API_KEY'),
'slices': [slc.data for slc in slices],
}
class BaseDeckGLViz(BaseViz):
"""Base class for deck.gl visualizations"""
is_timeseries = False
credits = '<a href="https://uber.github.io/deck.gl/">deck.gl</a>'
spatial_control_keys = []
def handle_nulls(self, df):
pass
def get_metrics(self):
self.metric = self.form_data.get('size')
return [self.metric] if self.metric else []
def process_spatial_query_obj(self, key, group_by):
spatial = self.form_data.get(key)
if spatial is None:
raise ValueError(_('Bad spatial key'))
if spatial.get('type') == 'latlong':
group_by += [spatial.get('lonCol')]
group_by += [spatial.get('latCol')]
elif spatial.get('type') == 'delimited':
group_by += [spatial.get('lonlatCol')]
elif spatial.get('type') == 'geohash':
group_by += [spatial.get('geohashCol')]
@staticmethod
def parse_coordinates(s):
if not s:
return None
try:
p = Point(s)
except Exception:
raise SpatialException(
_('Invalid spatial point encountered: %s' % s))
return (p.latitude, p.longitude)
def process_spatial_data_obj(self, key, df):
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)
if spatial.get('reverseCheckbox'):
df[key] = [
tuple(reversed(o)) if isinstance(o, (list, tuple)) else (0, 0)
for o in df[key]
]
del df[lon_lat_col]
elif spatial.get('type') == 'geohash':
latlong = df[spatial.get('geohashCol')].map(geohash.decode)
df[key] = list(zip(latlong.apply(lambda x: x[0]),
latlong.apply(lambda x: x[1])))
del df[spatial.get('geohashCol')]
if df.get(key) is None:
raise NullValueException(_('Encountered invalid NULL spatial entry, \
please consider filtering those out'))
return df
def query_obj(self):
d = super(BaseDeckGLViz, self).query_obj()
fd = self.form_data
gb = []
for key in self.spatial_control_keys:
self.process_spatial_query_obj(key, gb)
if fd.get('dimension'):
gb += [fd.get('dimension')]
if fd.get('js_columns'):
gb += fd.get('js_columns')
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):
cols = self.form_data.get('js_columns') or []
return {col: d.get(col) for col in cols}
def get_data(self, df):
if df is None:
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.get('MAPBOX_API_KEY'),
}
def get_properties(self, d):
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):
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(DeckScatterViz, self).query_obj()
def get_metrics(self):
self.metric = None
if self.point_radius_fixed.get('type') == 'metric':
self.metric = self.point_radius_fixed.get('value')
return [self.metric]
return None
def get_properties(self, d):
return {
'metric': d.get(self.metric_label),
'radius': self.fixed_value if self.fixed_value else d.get(self.metric_label),
'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):
fd = self.form_data
self.metric_label = \
self.get_metric_label(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.get('type') != 'metric':
self.fixed_value = self.point_radius_fixed.get('value')
return super(DeckScatterViz, self).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):
fd = self.form_data
self.is_timeseries = fd.get('time_grain_sqla') or fd.get('granularity')
return super(DeckScreengrid, self).query_obj()
def get_properties(self, d):
return {
'position': d.get('spatial'),
'weight': d.get(self.metric_label) or 1,
'__timestamp': d.get(DTTM_ALIAS) or d.get('__time'),
}
def get_data(self, df):
self.metric_label = self.get_metric_label(self.metric)
return super(DeckScreengrid, self).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):
return {
'position': d.get('spatial'),
'weight': d.get(self.metric_label) or 1,
}
def get_data(self, df):
self.metric_label = self.get_metric_label(self.metric)
return super(DeckGrid, self).get_data(df)
class DeckPathViz(BaseDeckGLViz):
"""deck.gl's PathLayer"""
viz_type = 'deck_path'
verbose_name = _('Deck.gl - Paths')
deck_viz_key = 'path'
deser_map = {
'json': json.loads,
'polyline': polyline.decode,
}
def query_obj(self):
d = super(DeckPathViz, self).query_obj()
line_col = self.form_data.get('line_column')
if d['metrics']:
d['groupby'].append(line_col)
else:
d['columns'].append(line_col)
return d
def get_properties(self, d):
fd = self.form_data
deser = self.deser_map[fd.get('line_type')]
path = deser(d[fd.get('line_column')])
if fd.get('reverse_long_lat'):
path = (path[1], path[0])
return {
self.deck_viz_key: path,
}
class DeckPolygon(DeckPathViz):
"""deck.gl's Polygon Layer"""
viz_type = 'deck_polygon'
deck_viz_key = 'polygon'
verbose_name = _('Deck.gl - Polygon')
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):
return {
'position': d.get('spatial'),
'weight': d.get(self.metric_label) or 1,
}
def get_data(self, df):
self.metric_label = self.get_metric_label(self.metric)
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):
d = super(DeckGeoJson, self).query_obj()
d['columns'] += [self.form_data.get('geojson')]
d['metrics'] = []
d['groupby'] = []
return d
def get_properties(self, d):
geojson = d.get(self.form_data.get('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']
def get_properties(self, d):
return {
'sourcePosition': d.get('start_spatial'),
'targetPosition': d.get('end_spatial'),
}
def get_data(self, df):
d = super(DeckArc, self).get_data(df)
arcs = d['features']
return {
'arcs': arcs,
'mapboxApiKey': config.get('MAPBOX_API_KEY'),
}
class EventFlowViz(BaseViz):
"""A visualization to explore patterns in event sequences"""
viz_type = 'event_flow'
verbose_name = _('Event flow')
credits = 'from <a href="https://github.com/williaster/data-ui">@data-ui</a>'
is_timeseries = True
def query_obj(self):
query = super(EventFlowViz, self).query_obj()
form_data = self.form_data
event_key = form_data.get('all_columns_x')
entity_key = form_data.get('entity')
meta_keys = [
col for col in form_data.get('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):
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):
"""
Transform received data frame into an object of the form:
{
'metric1': [
{
groups: ('groupA', ... ),
values: [ {x, y}, ... ],
}, ...
], ...
}
"""
fd = self.form_data
groups = fd.get('groupby')
metrics = fd.get('metrics')
df.fillna(0)
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 = {}
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, string_types)
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):
data = super(RoseViz, self).get_data(df)
result = {}
for datum in data:
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):
query_obj = super(PartitionViz, self).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, groups, df):
"""
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, groups, df):
# 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, df):
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).fillna(0)
self.form_data['groupby'] = groups
return procs
def nest_values(self, levels, level=0, metric=None, dims=()):
"""
Nest values at each level on the back-end with
access and setting, instead of summing from the bottom.
"""
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, level=-1, dims=(), time=None):
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):
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.get('VIZ_TYPE_BLACKLIST'))}
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