/
viz.py
1199 lines (1077 loc) · 34.2 KB
/
viz.py
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from collections import OrderedDict, defaultdict
from datetime import datetime, timedelta
import json
import uuid
from flask import flash, request, Markup
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"
is_timeseries = False
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('since', 'until'),
'metrics', 'groupby',
)
},)
js_files = []
css_files = []
form_overrides = {}
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.query = ""
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()
def get_form_override(self, fieldname, attr):
if (
fieldname in self.form_overrides and
attr in self.form_overrides[fieldname]):
s = self.form_overrides[fieldname][attr]
if attr == 'label':
s = '<label for="{fieldname}">{s}</label>'.format(**locals())
s = Markup(s)
return s
def fieldsetizer(self):
"""
Makes form_fields support either a list approach or a fieldsets
approach
"""
return self.fieldsets
@classmethod
def flat_form_fields(cls):
l = set()
for d in cls.fieldsets:
for obj in d['fields']:
if isinstance(obj, (tuple, list)):
l |= {a for a in obj}
elif obj:
l.add(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/explore/{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)
self.query = self.results.query
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))
# Extra filters (coming from dashboard)
extra_filters = form_data.get('extra_filters', [])
if extra_filters:
extra_filters = json.loads(extra_filters)
for slice_id, slice_filters in extra_filters.items():
if slice_filters:
for col, vals in slice_filters:
if col and vals:
filters += [(col, 'in', ",".join(vals))]
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", ''),
'having': form_data.get("having", ''),
}
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()),
'query': self.query,
'form_data': self.form_data,
'json_endpoint': self.json_endpoint,
'csv_endpoint': self.csv_endpoint,
'standalone_endpoint': self.standalone_endpoint,
}
return json.dumps(payload)
def get_csv(self):
df = self.get_df()
return df.to_csv(index=False)
def get_json_data(self):
return json.dumps([])
@property
def json_endpoint(self):
return self.get_url(json="true")
@property
def csv_endpoint(self):
return self.get_url(csv="true")
@property
def standalone_endpoint(self):
return self.get_url(standalone="true")
@property
def data(self):
content = {
'viz_name': self.viz_type,
'json_endpoint': self.json_endpoint,
'token': self.token,
'form_data': self.form_data,
}
return content
@property
def json_data(self):
return dumps(self.data)
class TableViz(BaseViz):
viz_type = "table"
verbose_name = "Table View"
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('since', 'until'),
'row_limit',
('include_search', None),
)
},
{
'label': "GROUP BY",
'fields': (
'groupby',
'metrics',
)
},
{
'label': "NOT GROUPED BY",
'fields': (
'all_columns',
)
},)
css_files = ['lib/dataTables/dataTables.bootstrap.css']
is_timeseries = False
js_files = [
'lib/d3.min.js',
'lib/dataTables/jquery.dataTables.min.js',
'lib/dataTables/dataTables.bootstrap.js',
'widgets/viz_table.js',
]
css_files = ['widgets/viz_table.css']
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")
if fd.get('all_columns'):
d['columns'] = fd.get('all_columns')
d['groupby'] = []
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']
return df
def get_json_data(self):
df = self.get_df()
return json.dumps(
dict(
records=df.to_dict(orient="records"),
columns=list(df.columns),
),
default=utils.json_iso_dttm_ser,
)
class PivotTableViz(BaseViz):
viz_type = "pivot_table"
verbose_name = "Pivot Table"
css_files = [
'lib/dataTables/dataTables.bootstrap.css',
'widgets/viz_pivot_table.css']
is_timeseries = False
js_files = [
'lib/dataTables/jquery.dataTables.min.js',
'lib/dataTables/dataTables.bootstrap.js',
'widgets/viz_pivot_table.js']
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('since', 'until'),
'groupby',
'columns',
'metrics',
'pandas_aggfunc',
)
},)
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("groupby and columns can't overlap")
d['groupby'] = list(set(groupby) | set(columns))
d['is_timeseries'] = False
d['timeseries_limit'] = None
return d
def get_df(self):
df = super(PivotTableViz, self).get_df()
if (
self.form_data.get("granularity") == "all" and
'timestamp' in df):
del df['timestamp']
df = df.pivot_table(
index=self.form_data.get('groupby'),
columns=self.form_data.get('columns'),
values=self.form_data.get('metrics'),
aggfunc=self.form_data.get('pandas_aggfunc'),
margins=True,
)
return df
def get_json_data(self):
return dumps(self.get_df().to_html(
na_rep='',
classes=(
"dataframe table table-striped table-bordered "
"table-condensed table-hover")))
class MarkupViz(BaseViz):
viz_type = "markup"
verbose_name = "Markup Widget"
js_files = ['widgets/viz_markup.js']
fieldsets = (
{
'label': None,
'fields': ('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
def get_json_data(self):
return dumps(dict(html=self.rendered()))
class WordCloudViz(BaseViz):
"""
Integration with the nice library at:
https://github.com/jasondavies/d3-cloud
"""
viz_type = "word_cloud"
verbose_name = "Word Cloud"
is_timeseries = False
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('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"
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
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('since', 'until'),
'series', 'entity',
'x', 'y',
'size', 'limit',
)
},
{
'label': 'Chart Options',
'fields': (
('x_log_scale', 'y_log_scale'),
('show_legend', None),
'max_bubble_size',
)
},)
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"
is_timeseries = True
js_files = [
'lib/d3.min.js',
'widgets/viz_bignumber.js',
]
css_files = [
'widgets/viz_bignumber.css',
]
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('since', 'until'),
'metric',
'compare_lag',
'compare_suffix',
)
},)
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
fieldsets = (
{
'label': None,
'fields': (
'granularity', ('since', 'until'),
'metrics',
'groupby', 'limit',
),
}, {
'label': 'Chart Options',
'fields': (
('show_brush', 'show_legend'),
('rich_tooltip', 'y_axis_zero'),
('y_log_scale', 'contribution'),
('y_axis_format', 'x_axis_showminmax'),
('line_interpolation', None),
),
}, {
'label': 'Advanced Analytics',
'description': (
"This section contains options "
"that allow for advanced analytical post processing "
"of query reults"),
'fields': (
('rolling_type', 'rolling_periods'),
'time_compare',
'num_period_compare',
None,
('resample_how', 'resample_rule',), 'resample_fillmethod'
),
},
)
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'))
fm = form_data.get("resample_fillmethod")
if not fm:
fm = None
how = form_data.get("resample_how")
rule = form_data.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(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_type in ('mean', 'std', 'sum') and rolling_periods:
if rolling_type == 'mean':
df = pd.rolling_mean(df, int(rolling_periods), min_periods=0)
elif rolling_type == 'std':
df = pd.rolling_std(df, int(rolling_periods), min_periods=0)
elif rolling_type == 'sum':
df = pd.rolling_sum(df, int(rolling_periods), min_periods=0)
elif rolling_type == 'cumsum':
df = df.cumsum()
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[ds]}
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"
fieldsets = (
{
'label': None,
'fields': (
'granularity', ('since', 'until'),
'metrics',
'groupby', 'limit',
('rolling_type', 'rolling_periods'),
'show_legend',
)
},)
class NVD3CompareTimeSeriesViz(NVD3TimeSeriesViz):
viz_type = 'compare'
verbose_name = "Time Series - Percent Change"
fieldsets = (
{
'label': None,
'fields': (
'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
fieldsets = (
{
'label': None,
'fields': (
'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
fieldsets = (
{
'label': None,
'fields': (
('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
fieldsets = (
{
'label': None,
'fields': (
'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,
})
class SunburstViz(BaseViz):
viz_type = "sunburst"
verbose_name = "Sunburst"
is_timeseries = False
js_files = [
'lib/d3.min.js',
'widgets/viz_sunburst.js']
css_files = ['widgets/viz_sunburst.css']
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('since', 'until'),
'groupby',
'metric', 'secondary_metric',
'row_limit',
)
},)
form_overrides = {
'metric': {
'label': 'Primary Metric',
'description': (
"The primary metric is used to "
"define the arc segment sizes"),
},
'secondary_metric': {
'label': 'Secondary Metric',
'description': (
"This secondary metric is used to "
"define the color as a ratio against the primary metric"),
},
'groupby': {
'label': 'Hierarchy',
'description': "This defines the level of the hierarchy",
},
}
def get_df(self):
df = super(SunburstViz, self).get_df()
return df
def get_json_data(self):
df = self.get_df()
# if m1 == m2 duplicate the metric column
cols = self.form_data.get('groupby')
metric = self.form_data.get('metric')
secondary_metric = self.form_data.get('secondary_metric')
if metric == secondary_metric:
ndf = df[cols]
ndf['m1'] = df[metric]
ndf['m2'] = df[metric]
else:
cols += [
self.form_data['metric'], self.form_data['secondary_metric']]
ndf = df[cols]
return ndf.to_json(orient="values")
def query_obj(self):
qry = super(SunburstViz, self).query_obj()
qry['metrics'] = [
self.form_data['metric'], self.form_data['secondary_metric']]
return qry
class SankeyViz(BaseViz):
viz_type = "sankey"
verbose_name = "Sankey"
is_timeseries = False
js_files = [
'lib/d3.min.js',
'lib/d3-sankey.js',
'widgets/viz_sankey.js']
css_files = ['widgets/viz_sankey.css']
fieldsets = (
{
'label': None,
'fields': (
'granularity',
('since', 'until'),
'groupby',
'metric',
'row_limit',
)
},)
form_overrides = {
'groupby': {
'label': 'Source / Target',
'description': "Choose a source and a target",
},
}
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_json_data(self):
df = self.get_df()
df.columns = ['source', 'target', 'value']
d = df.to_dict(orient='records')
return dumps(d)
class DirectedForceViz(BaseViz):
viz_type = "directed_force"