def granularity(period_name, timezone=None, origin=None):
if not period_name or period_name == 'all':
return 'all'
iso_8601_dict = {
'5 seconds': 'PT5S',
'30 seconds': 'PT30S',
'1 minute': 'PT1M',
'5 minutes': 'PT5M',
'1 hour': 'PT1H',
'6 hour': 'PT6H',
'one day': 'P1D',
'1 day': 'P1D',
'7 days': 'P7D',
'week': 'P1W',
'week_starting_sunday': 'P1W',
'week_ending_saturday': 'P1W',
'month': 'P1M',
}
granularity = {'type': 'period'}
# if timezone:
# granularity['timeZone'] = timezone
granularity['timeZone'] = "Asia/Shanghai"
if origin:
dttm = utils.parse_human_datetime(origin)
granularity['origin'] = dttm.isoformat()
if period_name in iso_8601_dict:
granularity['period'] = iso_8601_dict[period_name]
if period_name in ('week_ending_saturday', 'week_starting_sunday'):
# use Sunday as start of the week
granularity['origin'] = '2016-01-03T00:00:00'
elif not isinstance(period_name, string_types):
granularity['type'] = 'duration'
granularity['duration'] = period_name
elif period_name.startswith('P'):
# identify if the string is the iso_8601 period
granularity['period'] = period_name
else:
granularity['type'] = 'duration'
granularity['duration'] = utils.parse_human_timedelta(
period_name).total_seconds() * 1000
return granularity
def granularity(period_name, timezone=None, origin=None):
if not period_name or period_name == 'all':
return 'all'
iso_8601_dict = {
'5 seconds': 'PT5S',
'30 seconds': 'PT30S',
'1 minute': 'PT1M',
'5 minutes': 'PT5M',
'1 hour': 'PT1H',
'6 hour': 'PT6H',
'one day': 'P1D',
'1 day': 'P1D',
'7 days': 'P7D',
'week': 'P1W',
'week_starting_sunday': 'P1W',
'week_ending_saturday': 'P1W',
'month': 'P1M',
}
granularity = {'type': 'period'}
if timezone:
granularity['timeZone'] = timezone
if origin:
dttm = utils.parse_human_datetime(origin)
granularity['origin'] = dttm.isoformat()
if period_name in iso_8601_dict:
granularity['period'] = iso_8601_dict[period_name]
if period_name in ('week_ending_saturday', 'week_starting_sunday'):
# use Sunday as start of the week
granularity['origin'] = '2016-01-03T00:00:00'
elif not isinstance(period_name, string_types):
granularity['type'] = 'duration'
granularity['duration'] = period_name
elif period_name.startswith('P'):
# identify if the string is the iso_8601 period
granularity['period'] = period_name
else:
granularity['type'] = 'duration'
granularity['duration'] = utils.parse_human_timedelta(
period_name).total_seconds() * 1000
return granularity
def compute_time_compare(granularity, periods):
if not granularity:
return None
# convert old db_engine_spec granularity to ISO duration
if granularity in db_engine_specs_map:
granularity = db_engine_specs_map[granularity]
try:
obj = isodate.parse_duration(granularity) * periods
except isodate.isoerror.ISO8601Error:
# if parse_human_timedelta can parse it, return it directly
delta = '{0} {1}{2}'.format(periods, granularity, 's' if periods > 1 else '')
obj = parse_human_timedelta(delta)
if obj:
return delta
raise Exception('Unable to parse: {0}'.format(granularity))
if isinstance(obj, isodate.duration.Duration):
return isodate_duration_to_string(obj)
elif isinstance(obj, datetime.timedelta):
return timedelta_to_string(obj)
def test_parse_human_timedelta(self, mock_now):
mock_now.return_value = datetime(2016, 12, 1)
self.assertEquals(parse_human_timedelta('now'), timedelta(0))
def get_data(self, df):
fd = self.form_data
df = df.fillna(0)
if fd.get("granularity") == "all":
raise Exception("Pick a time granularity for your time series")
df = df.pivot_table(index=DTTM_ALIAS,
columns=fd.get('groupby'),
values=fd.get('metrics'))
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_periods = fd.get("rolling_periods")
rolling_type = fd.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()
num_period_compare = fd.get("num_period_compare")
if num_period_compare:
num_period_compare = int(num_period_compare)
prt = fd.get('period_ratio_type')
if prt and prt == 'growth':
df = (df / df.shift(num_period_compare)) - 1
elif prt and prt == 'value':
df = df - df.shift(num_period_compare)
else:
df = df / df.shift(num_period_compare)
df = df[num_period_compare:]
chart_data = self.to_series(df)
time_compare = fd.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[DTTM_ALIAS] += delta
df2 = df2.pivot_table(index=DTTM_ALIAS,
columns=fd.get('groupby'),
values=fd.get('metrics'))
chart_data += self.to_series(df2,
classed='superset',
title_suffix="---")
chart_data = sorted(chart_data, key=lambda x: x['key'])
return chart_data
def test_parse_human_timedelta(self, mock_now):
mock_now.return_value = datetime(2016, 12, 1)
self.assertEquals(parse_human_timedelta('now'), timedelta(0))
def get_data(self, df):
fd = self.form_data
df = df.fillna(0)
if fd.get("granularity") == "all":
raise Exception("Pick a time granularity for your time series")
df = df.pivot_table(
index=DTTM_ALIAS,
columns=fd.get('groupby'),
values=fd.get('metrics'))
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_periods = fd.get("rolling_periods")
rolling_type = fd.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()
num_period_compare = fd.get("num_period_compare")
if num_period_compare:
num_period_compare = int(num_period_compare)
prt = fd.get('period_ratio_type')
if prt and prt == 'growth':
df = (df / df.shift(num_period_compare)) - 1
elif prt and prt == 'value':
df = df - df.shift(num_period_compare)
else:
df = df / df.shift(num_period_compare)
df = df[num_period_compare:]
chart_data = self.to_series(df)
time_compare = fd.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[DTTM_ALIAS] += delta
df2 = df2.pivot_table(
index=DTTM_ALIAS,
columns=fd.get('groupby'),
values=fd.get('metrics'))
chart_data += self.to_series(
df2, classed='superset', title_suffix="---")
chart_data = sorted(chart_data, key=lambda x: x['key'])
return chart_data
