hughhhh closed pull request #5322: Deconstuct utils in viz.py
URL: https://github.com/apache/incubator-superset/pull/5322
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diff --git a/superset/viz.py b/superset/viz.py
index a460b9f416..64bb420456 100644
--- a/superset/viz.py
+++ b/superset/viz.py
@@ -37,9 +37,22 @@
from six import string_types, text_type
from six.moves import cPickle as pkl, reduce
-from superset import app, cache, get_manifest_file, utils
+from superset import app, cache, get_manifest_file
from superset.exceptions import NullValueException
-from superset.utils import DTTM_ALIAS, JS_MAX_INTEGER, merge_extra_filters
+from superset.utils import (
+ DTTM_ALIAS,
+ error_msg_from_exception,
+ get_metric_name,
+ get_metric_names,
+ JS_MAX_INTEGER,
+ json_int_dttm_ser,
+ json_iso_dttm_ser,
+ merge_extra_filters,
+ parse_human_datetime,
+ parse_human_timedelta,
+ QueryStatus,
+ split_adhoc_filters_into_base_filters,
+)
config = app.config
@@ -243,7 +256,7 @@ def query_obj(self):
# 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)
+ split_adhoc_filters_into_base_filters(form_data)
merge_extra_filters(form_data)
@@ -269,10 +282,10 @@ def query_obj(self):
if (len(since_words) == 2 and since_words[1] in grains):
since += ' ago'
- self.time_shift = utils.parse_human_timedelta(time_shift)
+ self.time_shift = parse_human_timedelta(time_shift)
- since = utils.parse_human_datetime(since)
- until = utils.parse_human_datetime(until)
+ since = parse_human_datetime(since)
+ until = parse_human_datetime(until)
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:
@@ -324,7 +337,7 @@ def cache_timeout(self):
def get_json(self):
return json.dumps(
self.get_payload(),
- default=utils.json_int_dttm_ser, ignore_nan=True)
+ default=json_int_dttm_ser, ignore_nan=True)
def cache_key(self, query_obj):
"""
@@ -352,7 +365,7 @@ def get_payload(self, query_obj=None):
payload = self.get_df_payload(query_obj)
df = payload.get('df')
- if self.status != utils.QueryStatus.FAILED:
+ if self.status != QueryStatus.FAILED:
if df is not None and df.empty:
payload['error'] = 'No data'
else:
@@ -381,32 +394,32 @@ def get_df_payload(self, query_obj=None):
self.query = cache_value['query']
self._any_cached_dttm = cache_value['dttm']
self._any_cache_key = cache_key
- self.status = utils.QueryStatus.SUCCESS
+ self.status = QueryStatus.SUCCESS
is_loaded = True
except Exception as e:
logging.exception(e)
logging.error('Error reading cache: ' +
- utils.error_msg_from_exception(e))
+ 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:
+ if self.status != 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
+ self.status = QueryStatus.FAILED
stacktrace = traceback.format_exc()
if (
is_loaded and
cache_key and
cache and
- self.status != utils.QueryStatus.FAILED):
+ self.status != QueryStatus.FAILED):
try:
cache_value = dict(
dttm=cached_dttm,
@@ -448,7 +461,7 @@ def get_df_payload(self, query_obj=None):
def json_dumps(self, obj, sort_keys=False):
return json.dumps(
obj,
- default=utils.json_int_dttm_ser,
+ default=json_int_dttm_ser,
ignore_nan=True,
sort_keys=sort_keys,
)
@@ -516,8 +529,8 @@ def query_obj(self):
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']):
+ sort_by_label = get_metric_name(sort_by)
+ if sort_by_label not in get_metric_names(d['metrics']):
d['metrics'] += [sort_by]
d['orderby'] = [(sort_by, not fd.get('order_desc', True))]
@@ -578,7 +591,7 @@ def get_data(self, df):
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)
+ obj, default=json_iso_dttm_ser, sort_keys=sort_keys)
else:
return super(TableViz, self).json_dumps(obj)
@@ -780,8 +793,8 @@ def get_data(self, df):
for obj in records
}
- start = utils.parse_human_datetime(form_data.get('since'))
- end = utils.parse_human_datetime(form_data.get('until'))
+ start = parse_human_datetime(form_data.get('since'))
+ end = parse_human_datetime(form_data.get('until'))
if not start or not end:
raise Exception('Please provide both time bounds (Since and
Until)')
domain = form_data.get('domain_granularity')
@@ -946,9 +959,9 @@ def query_obj(self):
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['x'] = df[[get_metric_name(self.x_metric)]]
+ df['y'] = df[[get_metric_name(self.y_metric)]]
+ df['size'] = df[[get_metric_name(self.z_metric)]]
df['shape'] = 'circle'
df['group'] = df[[self.series]]
@@ -1141,12 +1154,12 @@ def process_data(self, df, aggregate=False):
df = df.pivot_table(
index=DTTM_ALIAS,
columns=fd.get('groupby'),
- values=utils.get_metric_names(fd.get('metrics')))
+ values=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')),
+ values=get_metric_names(fd.get('metrics')),
fill_value=0,
aggfunc=sum)
@@ -1215,7 +1228,7 @@ def run_extra_queries(self):
i = 0
for option in time_compare:
query_object = self.query_obj()
- delta = utils.parse_human_timedelta(option)
+ delta = parse_human_timedelta(option)
query_object['inner_from_dttm'] = query_object['from_dttm']
query_object['inner_to_dttm'] = query_object['to_dttm']
@@ -1316,7 +1329,7 @@ def to_series(self, df, classed=''):
self.form_data.get('metric_2'),
]
for i, m in enumerate(metrics):
- m = utils.get_metric_name(m)
+ m = get_metric_name(m)
ys = series[m]
if df[m].dtype.kind not in 'biufc':
continue
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