def test_AlloUsage1():
a1 = AlloUsage(from_date, to_date, site_filter=site_filter)
combo_ts1 = a1.get_ts(datasets, 'A-JUN', cols)
combo_ts2 = a1.get_ts(datasets, 'M', cols)
a1.plot_group(freq, export_path=base_dir)
a1.plot_stacked(freq, export_path=base_dir)
assert (len(combo_ts1) == 73) & (len(combo_ts2) == 869)
### Test 4
sites1 = mssql.rd_sql(server,
database,
sites_table,
['ExtSiteID', 'CatchmentGroupName', summ_col],
where_in={'CatchmentGroupName': catch_group})
site_filter = {'SwazName': sites1.SwazName.unique().tolist()}
a1 = AlloUsage(from_date, to_date, site_filter=site_filter)
ts1 = a1.get_ts(datasets, freq, cols, irr_season=True)
a1.plot_group(
'A-JUN',
val='total',
with_restr=True,
export_path=r'E:\ecan\local\Projects\requests\suz\2018-12-17\plots')
a1.plot_stacked(
'A-JUN',
val='total',
export_path=r'E:\ecan\local\Projects\requests\suz\2018-12-17\plots')
### Test 5
a1 = AlloUsage(from_date, to_date, site_filter=waitaki)
a1.allo.to_csv(os.path.join(export_path, 'waitaki_allo_2019-02-27.csv'))
a1.allo_wap.to_csv(os.path.join(export_path,
'waitaki_allo_wap_2019-02-27.csv'))
site_filter=site_filter,
crc_filter=crc_filter)
combo_ts = a1.get_ts(datasets,
freq, ['SwazName', 'use_type', 'date'],
irr_season=True)
combo_ts.to_csv(os.path.join(export_dir, export2))
#########################################
### Plotting
### Grouped
## Lumped
a1.plot_group('A-JUN',
group='SwazGroupName',
export_path=os.path.join(export_dir, plot_dir),
irr_season=True)
## broken up
a1.plot_group('A-JUN',
group='SwazName',
export_path=os.path.join(export_dir, plot_dir),
irr_season=True)
### Stacked
## lumped
a1.plot_stacked('A-JUN',
group='SwazGroupName',
export_path=os.path.join(export_dir, plot_dir),
irr_season=True)
site_filter=site_filter,
crc_filter=crc_filter)
combo_ts = a1.get_ts(datasets,
freq, ['SwazName', 'use_type', 'date'],
irr_season=True)
combo_ts.to_csv(os.path.join(py_path, export2))
#########################################
### Plotting
### Grouped
## Lumped
a1.plot_group('A-JUN',
group='SwazGroupName',
export_path=plot_path,
irr_season=True)
## broken up
a1.plot_group('A-JUN',
group='SwazName',
export_path=plot_path,
irr_season=True)
### Stacked
## lumped
a1.plot_stacked('A-JUN',
group='SwazGroupName',
export_path=plot_path,
irr_season=True)
########################################
### Read in sites
test_crc = pd.read_csv(test_sites_csv).crc.unique().tolist()
########################################
### generate!
#sites1 = mssql.rd_sql(server, database, sites_table, ['ExtSiteID', 'CatchmentGroupName', summ_col], where_in={'CatchmentGroupName': catch_group})
#
#site_filter = {'SwazName': sites1.SwazName.unique().tolist()}
a1 = AlloUsage()
a1 = AlloUsage(from_date, to_date, crc_filter={'crc': test_crc})
ts1 = a1.get_ts(datasets, freq, cols, usage_allo_ratio=2)
ts1.to_csv(os.path.join(export_path, export1))
a1.plot_group('A-JUN',
val='total',
group='crc',
with_restr=False,
export_path=export_path)
# a1.plot_stacked('A-JUN', val='total', export_path=r'E:\ecan\local\Projects\requests\suz\2018-12-17\plots')
self = AlloUsage()