mirror of
https://github.com/apache/superset.git
synced 2024-09-12 08:39:45 -04:00
663 lines
20 KiB
Python
663 lines
20 KiB
Python
from collections import OrderedDict, defaultdict
|
|
from datetime import datetime, timedelta
|
|
import json
|
|
import uuid
|
|
|
|
from flask import flash, request
|
|
from markdown import markdown
|
|
from pandas.io.json import dumps
|
|
from werkzeug.datastructures import ImmutableMultiDict
|
|
from werkzeug.urls import Href
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from panoramix import app, utils
|
|
from panoramix.forms import FormFactory
|
|
|
|
from six import string_types
|
|
|
|
config = app.config
|
|
|
|
|
|
class BaseViz(object):
|
|
viz_type = None
|
|
verbose_name = "Base Viz"
|
|
template = None
|
|
is_timeseries = False
|
|
form_fields = [
|
|
'viz_type',
|
|
'granularity',
|
|
('since', 'until'),
|
|
'metrics', 'groupby',
|
|
]
|
|
js_files = []
|
|
css_files = []
|
|
|
|
def __init__(self, datasource, form_data):
|
|
self.orig_form_data = form_data
|
|
self.datasource = datasource
|
|
self.request = request
|
|
self.viz_type = form_data.get("viz_type")
|
|
|
|
# TODO refactor all form related logic out of here and into forms.py
|
|
ff = FormFactory(self)
|
|
form_class = ff.get_form()
|
|
defaults = form_class().data.copy()
|
|
previous_viz_type = form_data.get('previous_viz_type')
|
|
if isinstance(form_data, ImmutableMultiDict):
|
|
form = form_class(form_data)
|
|
else:
|
|
form = form_class(**form_data)
|
|
data = form.data.copy()
|
|
|
|
if not form.validate():
|
|
for k, v in form.errors.items():
|
|
if not data.get('json') and not data.get('async'):
|
|
flash("{}: {}".format(k, " ".join(v)), 'danger')
|
|
if previous_viz_type != self.viz_type:
|
|
data = {
|
|
k: form.data[k]
|
|
for k in form_data.keys()
|
|
if k in form.data}
|
|
defaults.update(data)
|
|
self.form_data = defaults
|
|
|
|
self.form_data['previous_viz_type'] = self.viz_type
|
|
self.token = self.form_data.get(
|
|
'token', 'token_' + uuid.uuid4().hex[:8])
|
|
|
|
self.metrics = self.form_data.get('metrics') or []
|
|
self.groupby = self.form_data.get('groupby') or []
|
|
self.reassignments()
|
|
|
|
@classmethod
|
|
def flat_form_fields(cls):
|
|
l = []
|
|
for obj in cls.form_fields:
|
|
if isinstance(obj, (tuple, list)):
|
|
l += [a for a in obj]
|
|
else:
|
|
l.append(obj)
|
|
return l
|
|
|
|
def reassignments(self):
|
|
pass
|
|
|
|
def get_url(self, **kwargs):
|
|
d = self.orig_form_data.copy()
|
|
if 'action' in d:
|
|
del d['action']
|
|
d.update(kwargs)
|
|
# Remove unchecked checkboxes because HTML is weird like that
|
|
for key in d.keys():
|
|
if d[key] == False:
|
|
del d[key]
|
|
href = Href(
|
|
'/panoramix/datasource/{self.datasource.type}/'
|
|
'{self.datasource.id}/'.format(**locals()))
|
|
return href(d)
|
|
|
|
def get_df(self, query_obj=None):
|
|
if not query_obj:
|
|
query_obj = self.query_obj()
|
|
|
|
self.error_msg = ""
|
|
self.results = None
|
|
|
|
self.results = self.datasource.query(**query_obj)
|
|
df = self.results.df
|
|
if df is None or df.empty:
|
|
raise Exception("No data, review your incantations!")
|
|
else:
|
|
if 'timestamp' in df.columns:
|
|
df.timestamp = pd.to_datetime(df.timestamp, utc=False)
|
|
if self.datasource.offset:
|
|
df.timestamp += timedelta(hours=self.datasource.offset)
|
|
return df
|
|
|
|
@property
|
|
def form(self):
|
|
return self.form_class(**self.form_data)
|
|
|
|
@property
|
|
def form_class(self):
|
|
return FormFactory(self).get_form()
|
|
|
|
def query_filters(self):
|
|
form_data = self.form_data
|
|
# Building filters
|
|
filters = []
|
|
for i in range(1, 10):
|
|
col = form_data.get("flt_col_" + str(i))
|
|
op = form_data.get("flt_op_" + str(i))
|
|
eq = form_data.get("flt_eq_" + str(i))
|
|
if col and op and eq:
|
|
filters.append((col, op, eq))
|
|
return filters
|
|
|
|
def query_obj(self):
|
|
"""
|
|
Building a query object
|
|
"""
|
|
form_data = self.form_data
|
|
groupby = form_data.get("groupby") or []
|
|
metrics = form_data.get("metrics") or ['count']
|
|
granularity = form_data.get("granularity")
|
|
limit = int(form_data.get("limit", 0))
|
|
row_limit = int(
|
|
form_data.get("row_limit", config.get("ROW_LIMIT")))
|
|
since = form_data.get("since", "1 year ago")
|
|
from_dttm = utils.parse_human_datetime(since)
|
|
if from_dttm > datetime.now():
|
|
from_dttm = datetime.now() - (from_dttm-datetime.now())
|
|
until = form_data.get("until", "now")
|
|
to_dttm = utils.parse_human_datetime(until)
|
|
if from_dttm >= to_dttm:
|
|
flash("The date range doesn't seem right.", "danger")
|
|
from_dttm = to_dttm # Making them identical to not raise
|
|
|
|
# extras are used to query elements specific to a datasource type
|
|
# for instance the extra where clause that applies only to Tables
|
|
extras = {
|
|
'where': form_data.get("where", '')
|
|
}
|
|
d = {
|
|
'granularity': granularity,
|
|
'from_dttm': from_dttm,
|
|
'to_dttm': to_dttm,
|
|
'is_timeseries': self.is_timeseries,
|
|
'groupby': groupby,
|
|
'metrics': metrics,
|
|
'row_limit': row_limit,
|
|
'filter': self.query_filters(),
|
|
'timeseries_limit': limit,
|
|
'extras': extras,
|
|
}
|
|
return d
|
|
|
|
def get_json(self):
|
|
payload = {
|
|
'data': json.loads(self.get_json_data()),
|
|
'form_data': self.form_data,
|
|
}
|
|
return json.dumps(payload)
|
|
|
|
def get_json_data(self):
|
|
return json.dumps([])
|
|
|
|
def get_data_attribute(self):
|
|
content = {
|
|
'viz_name': self.viz_type,
|
|
'json_endpoint': self.get_url(json="true"),
|
|
'token': self.token,
|
|
}
|
|
return json.dumps(content)
|
|
|
|
class TableViz(BaseViz):
|
|
viz_type = "table"
|
|
verbose_name = "Table View"
|
|
template = 'panoramix/viz_table.html'
|
|
form_fields = BaseViz.form_fields + ['row_limit']
|
|
css_files = ['lib/dataTables/dataTables.bootstrap.css']
|
|
is_timeseries = False
|
|
js_files = [
|
|
'lib/dataTables/jquery.dataTables.min.js',
|
|
'lib/dataTables/dataTables.bootstrap.js']
|
|
|
|
def query_obj(self):
|
|
d = super(TableViz, self).query_obj()
|
|
d['is_timeseries'] = False
|
|
d['timeseries_limit'] = None
|
|
return d
|
|
|
|
def get_df(self):
|
|
df = super(TableViz, self).get_df()
|
|
if (
|
|
self.form_data.get("granularity") == "all" and
|
|
'timestamp' in df):
|
|
del df['timestamp']
|
|
for m in self.metrics:
|
|
df[m + '__perc'] = np.rint((df[m] / np.max(df[m])) * 100)
|
|
return df
|
|
|
|
|
|
class MarkupViz(BaseViz):
|
|
viz_type = "markup"
|
|
verbose_name = "Markup Widget"
|
|
template = 'panoramix/viz_markup.html'
|
|
form_fields = ['viz_type', 'markup_type', 'code']
|
|
is_timeseries = False
|
|
|
|
def rendered(self):
|
|
markup_type = self.form_data.get("markup_type")
|
|
code = self.form_data.get("code", '')
|
|
if markup_type == "markdown":
|
|
return markdown(code)
|
|
elif markup_type == "html":
|
|
return code
|
|
|
|
|
|
class WordCloudViz(BaseViz):
|
|
"""
|
|
Integration with the nice library at:
|
|
https://github.com/jasondavies/d3-cloud
|
|
"""
|
|
viz_type = "word_cloud"
|
|
verbose_name = "Word Cloud"
|
|
template = 'panoramix/viz_word_cloud.html'
|
|
is_timeseries = False
|
|
form_fields = [
|
|
'viz_type',
|
|
('since', 'until'),
|
|
'groupby', 'metric', 'limit',
|
|
('size_from', 'size_to'),
|
|
'rotation',
|
|
]
|
|
js_files = [
|
|
'lib/d3.min.js',
|
|
'lib/d3.layout.cloud.js',
|
|
'widgets/viz_wordcloud.js',
|
|
]
|
|
|
|
def query_obj(self):
|
|
d = super(WordCloudViz, self).query_obj()
|
|
metric = self.form_data.get('metric')
|
|
if not metric:
|
|
raise Exception("Pick a metric!")
|
|
d['metrics'] = [self.form_data.get('metric')]
|
|
d['groupby'] = [d['groupby'][0]]
|
|
return d
|
|
|
|
def get_json_data(self):
|
|
df = self.get_df()
|
|
df.columns = ['text', 'size']
|
|
return df.to_json(orient="records")
|
|
|
|
|
|
class NVD3Viz(BaseViz):
|
|
viz_type = None
|
|
verbose_name = "Base NVD3 Viz"
|
|
template = 'panoramix/viz_nvd3.html'
|
|
is_timeseries = False
|
|
js_files = [
|
|
'lib/d3.min.js',
|
|
'lib/nvd3/nv.d3.min.js',
|
|
'widgets/viz_nvd3.js',
|
|
]
|
|
css_files = [
|
|
'lib/nvd3/nv.d3.css',
|
|
'widgets/viz_nvd3.css',
|
|
]
|
|
|
|
|
|
class BubbleViz(NVD3Viz):
|
|
viz_type = "bubble"
|
|
verbose_name = "Bubble Chart"
|
|
is_timeseries = False
|
|
form_fields = [
|
|
'viz_type',
|
|
('since', 'until'),
|
|
('series', 'entity'),
|
|
('x', 'y'),
|
|
('size', 'limit'),
|
|
('x_log_scale', 'y_log_scale'),
|
|
('show_legend', None),
|
|
]
|
|
|
|
def query_obj(self):
|
|
form_data = self.form_data
|
|
d = super(BubbleViz, self).query_obj()
|
|
d['groupby'] = list({
|
|
form_data.get('series'),
|
|
form_data.get('entity')
|
|
})
|
|
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')
|
|
|
|
d['metrics'] = [
|
|
self.z_metric,
|
|
self.x_metric,
|
|
self.y_metric,
|
|
]
|
|
if not all(d['metrics'] + [self.entity, self.series]):
|
|
raise Exception("Pick a metric for x, y and size")
|
|
return d
|
|
|
|
def get_df(self):
|
|
df = super(BubbleViz, self).get_df()
|
|
df = df.fillna(0)
|
|
df['x'] = df[[self.x_metric]]
|
|
df['y'] = df[[self.y_metric]]
|
|
df['size'] = df[[self.z_metric]]
|
|
df['shape'] = 'circle'
|
|
df['group'] = df[[self.series]]
|
|
return df
|
|
|
|
def get_json_data(self):
|
|
df = self.get_df()
|
|
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,
|
|
"color": utils.color(k),
|
|
'values': v })
|
|
return dumps({
|
|
'chart_data': chart_data,
|
|
'query': self.results.query,
|
|
'duration': self.results.duration,
|
|
})
|
|
|
|
class BigNumberViz(BaseViz):
|
|
viz_type = "big_number"
|
|
verbose_name = "Big Number"
|
|
template = 'panoramix/viz_bignumber.html'
|
|
is_timeseries = True
|
|
js_files = [
|
|
'lib/d3.min.js',
|
|
'widgets/viz_bignumber.js',
|
|
]
|
|
css_files = [
|
|
'widgets/viz_bignumber.css',
|
|
]
|
|
form_fields = [
|
|
'viz_type',
|
|
'granularity',
|
|
('since', 'until'),
|
|
'metric',
|
|
'compare_lag',
|
|
'compare_suffix',
|
|
#('rolling_type', 'rolling_periods'),
|
|
]
|
|
|
|
def reassignments(self):
|
|
metric = self.form_data.get('metric')
|
|
if not metric:
|
|
self.form_data['metric'] = self.orig_form_data.get('metrics')
|
|
|
|
|
|
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_json_data(self):
|
|
form_data = self.form_data
|
|
df = self.get_df()
|
|
df = df.sort(columns=df.columns[0])
|
|
df['timestamp'] = df[[0]].astype(np.int64) // 10**9
|
|
compare_lag = form_data.get("compare_lag", "")
|
|
compare_lag = int(compare_lag) if compare_lag and compare_lag.isdigit() else 0
|
|
d = {
|
|
'data': df.values.tolist(),
|
|
'compare_lag': compare_lag,
|
|
'compare_suffix': form_data.get('compare_suffix', ''),
|
|
}
|
|
return json.dumps(d)
|
|
|
|
|
|
class NVD3TimeSeriesViz(NVD3Viz):
|
|
viz_type = "line"
|
|
verbose_name = "Time Series - Line Chart"
|
|
sort_series = False
|
|
is_timeseries = True
|
|
form_fields = [
|
|
'viz_type',
|
|
'granularity', ('since', 'until'),
|
|
'metrics',
|
|
'groupby', 'limit',
|
|
('rolling_type', 'rolling_periods'),
|
|
('time_compare', 'num_period_compare'),
|
|
('line_interpolation', None),
|
|
('show_brush', 'show_legend'),
|
|
('rich_tooltip', 'y_axis_zero'),
|
|
('y_log_scale', 'contribution'),
|
|
]
|
|
|
|
def get_df(self, query_obj=None):
|
|
form_data = self.form_data
|
|
df = super(NVD3TimeSeriesViz, self).get_df(query_obj)
|
|
|
|
df = df.fillna(0)
|
|
if form_data.get("granularity") == "all":
|
|
raise Exception("Pick a time granularity for your time series")
|
|
|
|
df = df.pivot_table(
|
|
index="timestamp",
|
|
columns=form_data.get('groupby'),
|
|
values=form_data.get('metrics'))
|
|
|
|
if self.sort_series:
|
|
dfs = df.sum()
|
|
dfs.sort(ascending=False)
|
|
df = df[dfs.index]
|
|
|
|
if form_data.get("contribution"):
|
|
dft = df.T
|
|
df = (dft / dft.sum()).T
|
|
|
|
num_period_compare = form_data.get("num_period_compare")
|
|
if num_period_compare:
|
|
num_period_compare = int(num_period_compare)
|
|
df = df / df.shift(num_period_compare)
|
|
df = df[num_period_compare:]
|
|
|
|
rolling_periods = form_data.get("rolling_periods")
|
|
rolling_type = form_data.get("rolling_type")
|
|
if rolling_periods and rolling_type:
|
|
if rolling_type == 'mean':
|
|
df = pd.rolling_mean(df, int(rolling_periods))
|
|
elif rolling_type == 'std':
|
|
df = pd.rolling_std(df, int(rolling_periods))
|
|
elif rolling_type == 'sum':
|
|
df = pd.rolling_sum(df, int(rolling_periods))
|
|
return df
|
|
|
|
def to_series(self, df, classed='', title_suffix=''):
|
|
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
|
|
df['timestamp'] = pd.to_datetime(df.index, utc=False)
|
|
if isinstance(name, string_types):
|
|
series_title = name
|
|
else:
|
|
name = ["{}".format(s) for s in name]
|
|
if len(self.form_data.get('metrics')) > 1:
|
|
series_title = ", ".join(name)
|
|
else:
|
|
series_title = ", ".join(name[1:])
|
|
color = utils.color(series_title)
|
|
if title_suffix:
|
|
series_title += title_suffix
|
|
|
|
d = {
|
|
"key": series_title,
|
|
"color": color,
|
|
"classed": classed,
|
|
"values": [
|
|
{'x': ds, 'y': ys[i]}
|
|
for i, ds in enumerate(df.timestamp)]
|
|
}
|
|
chart_data.append(d)
|
|
return chart_data
|
|
|
|
def get_json_data(self):
|
|
df = self.get_df()
|
|
chart_data = self.to_series(df)
|
|
|
|
time_compare = self.form_data.get('time_compare')
|
|
if time_compare:
|
|
query_object = self.query_obj()
|
|
delta = utils.parse_human_timedelta(time_compare)
|
|
query_object['inner_from_dttm'] = query_object['from_dttm']
|
|
query_object['inner_to_dttm'] = query_object['to_dttm']
|
|
query_object['from_dttm'] -= delta
|
|
query_object['to_dttm'] -= delta
|
|
|
|
df2 = self.get_df(query_object)
|
|
df2.index += delta
|
|
chart_data += self.to_series(
|
|
df2, classed='dashed', title_suffix="---")
|
|
chart_data = sorted(chart_data, key=lambda x: x['key'])
|
|
|
|
data = {
|
|
'chart_data': chart_data,
|
|
'query': self.results.query,
|
|
'duration': self.results.duration,
|
|
}
|
|
return dumps(data)
|
|
|
|
|
|
class NVD3TimeSeriesBarViz(NVD3TimeSeriesViz):
|
|
viz_type = "bar"
|
|
verbose_name = "Time Series - Bar Chart"
|
|
form_fields = [
|
|
'viz_type',
|
|
'granularity', ('since', 'until'),
|
|
'metrics',
|
|
'groupby', 'limit',
|
|
('rolling_type', 'rolling_periods'),
|
|
'show_legend',
|
|
]
|
|
|
|
|
|
class NVD3CompareTimeSeriesViz(NVD3TimeSeriesViz):
|
|
viz_type = 'compare'
|
|
verbose_name = "Time Series - Percent Change"
|
|
form_fields = [
|
|
'viz_type',
|
|
'granularity', ('since', 'until'),
|
|
'metrics',
|
|
'groupby', 'limit',
|
|
('rolling_type', 'rolling_periods'),
|
|
'show_legend',
|
|
]
|
|
|
|
|
|
class NVD3TimeSeriesStackedViz(NVD3TimeSeriesViz):
|
|
viz_type = "area"
|
|
verbose_name = "Time Series - Stacked"
|
|
sort_series = True
|
|
form_fields = [
|
|
'viz_type',
|
|
'granularity', ('since', 'until'),
|
|
'metrics',
|
|
'groupby', 'limit',
|
|
('rolling_type', 'rolling_periods'),
|
|
('rich_tooltip', 'show_legend'),
|
|
]
|
|
|
|
|
|
class DistributionPieViz(NVD3Viz):
|
|
viz_type = "pie"
|
|
verbose_name = "Distribution - NVD3 - Pie Chart"
|
|
is_timeseries = False
|
|
form_fields = [
|
|
'viz_type',
|
|
('since', 'until'),
|
|
'metrics', 'groupby',
|
|
'limit',
|
|
('donut', 'show_legend'),
|
|
]
|
|
|
|
def query_obj(self):
|
|
d = super(DistributionPieViz, self).query_obj()
|
|
d['is_timeseries'] = False
|
|
return d
|
|
|
|
def get_df(self):
|
|
df = super(DistributionPieViz, self).get_df()
|
|
df = df.pivot_table(
|
|
index=self.groupby,
|
|
values=[self.metrics[0]])
|
|
df = df.sort(self.metrics[0], ascending=False)
|
|
return df
|
|
|
|
def get_json_data(self):
|
|
df = self.get_df()
|
|
df = df.reset_index()
|
|
df.columns = ['x', 'y']
|
|
df['color'] = map(utils.color, df.x)
|
|
return dumps({
|
|
'chart_data': df.to_dict(orient="records"),
|
|
'query': self.results.query,
|
|
'duration': self.results.duration,
|
|
})
|
|
|
|
|
|
class DistributionBarViz(DistributionPieViz):
|
|
viz_type = "dist_bar"
|
|
verbose_name = "Distribution - Bar Chart"
|
|
is_timeseries = False
|
|
form_fields = [
|
|
'viz_type', 'metrics', 'groupby',
|
|
('since', 'until'),
|
|
'limit',
|
|
('show_legend', None),
|
|
]
|
|
|
|
def get_df(self):
|
|
df = super(DistributionPieViz, self).get_df()
|
|
df = df.pivot_table(
|
|
index=self.groupby,
|
|
values=self.metrics)
|
|
df = df.sort(self.metrics[0], ascending=False)
|
|
return df
|
|
|
|
def get_json_data(self):
|
|
df = self.get_df()
|
|
series = df.to_dict('series')
|
|
chart_data = []
|
|
for name, ys in series.items():
|
|
if df[name].dtype.kind not in "biufc":
|
|
continue
|
|
df['timestamp'] = pd.to_datetime(df.index, utc=False)
|
|
if isinstance(name, string_types):
|
|
series_title = name
|
|
elif len(self.metrics) > 1:
|
|
series_title = ", ".join(name)
|
|
else:
|
|
series_title = ", ".join(name[1:])
|
|
d = {
|
|
"key": series_title,
|
|
"color": utils.color(series_title),
|
|
"values": [
|
|
{'x': ds, 'y': ys[i]}
|
|
for i, ds in enumerate(df.timestamp)]
|
|
}
|
|
chart_data.append(d)
|
|
return dumps({
|
|
'chart_data': chart_data,
|
|
'query': self.results.query,
|
|
'duration': self.results.duration,
|
|
})
|
|
|
|
|
|
viz_types_list = [
|
|
TableViz,
|
|
NVD3TimeSeriesViz,
|
|
NVD3CompareTimeSeriesViz,
|
|
NVD3TimeSeriesStackedViz,
|
|
NVD3TimeSeriesBarViz,
|
|
DistributionBarViz,
|
|
DistributionPieViz,
|
|
BubbleViz,
|
|
MarkupViz,
|
|
WordCloudViz,
|
|
BigNumberViz,
|
|
]
|
|
# This dict is used to
|
|
viz_types = OrderedDict([(v.viz_type, v) for v in viz_types_list])
|