def get_measurement_list(site):
"""Extracts the measurement types that are available in the Hilltop WaterUse.hts file
for a given site"""
base_url = 'http://wateruse.ecan.govt.nz'
hts = 'WaterUse.hts'
raw_list = ws.measurement_list(base_url, hts, site)
raw_list2 = raw_list.reset_index()
filtered_list = raw_list2.loc[raw_list2['Measurement'].isin(['Compliance Volume','Water Meter','Volume','Volume [Flow]','Volume [Average Flow]'])]
return filtered_list
def test_site_mtypes(hts):
sites = site_list(base_url, hts)
site1 = sites.iloc[2].SiteName
mtype_df1 = measurement_list(base_url, hts, site1).reset_index().iloc[0]
tsdata1 = get_data(base_url,
hts,
site1,
mtype_df1.Measurement,
from_date=str(mtype_df1.From),
to_date=str(mtype_df1.From))
assert len(tsdata1) == 1
base_url = 'http://wateruse.ecan.govt.nz' hts = 'WQAll.hts' site = 'SQ31045' measurement = 'Total Phosphorus' from_date = '1983-11-22 10:50' to_date = '2018-04-13 14:05' dtl_method = 'trend' ########################################## ### Examples ## Get site list sites = site_list(base_url, hts) ## Get the measurement types for a specific site mtype_df1 = measurement_list(base_url, hts, site) ## Get the water quality parameter data (only applies to WQ data) mtype_df2 = wq_sample_parameter_list(base_url, hts, site) ## Get the time series data for a specific site and measurement type tsdata1 = get_data(base_url, hts, site, measurement, from_date=from_date, to_date=to_date) ## Get extra WQ time series data (only applies to WQ data) tsdata2, extra2 = get_data(base_url, hts, site, measurement, from_date=from_date, to_date=to_date, parameters=True) ## Get WQ sample data (only applies to WQ data) tsdata3 = get_data(base_url, hts, site, 'WQ Sample', from_date=from_date, to_date=to_date) ## Convert values under the detection limit to numeric values (only applies to WQ data) tsdata4, extra4 = get_data(base_url, hts, site, measurement, from_date=from_date, to_date=to_date, parameters=True, dtl_method=dtl_method)
def test_measurement_list():
mtype_df1 = measurement_list(base_url, hts, site)
assert len(mtype_df1) > 30
hts_sites_df = ws.site_list(base_url, hts, location=True)
# Export hts sites table to csv file
hts_sites_df.to_csv('Sites in hts file.csv', index=False)
# Generate a list of all sites in the server file
hts_sites_list = sorted(hts_sites_df.SiteName.tolist(), key=str.lower)
# Generate a measurement summary and sample parameter summary for all sites
# Initialise empty lists
site_measurement_summary = []
site_sample_parameter_summary = []
# Loop through all sites in the hts file
for site in hts_sites_list:
# Call site-specific measurement list
measurement_summary = ws.measurement_list(base_url, hts, site)
# Append list to measurement summary table
site_measurement_summary.append(measurement_summary)
# Try calling the site-specific sample parameter list
try:
sample_parameter_summary = ws.wq_sample_parameter_list(
base_url, hts, site)
# Append list to sample parameter summary table
site_sample_parameter_summary.append(sample_parameter_summary)
# Some sites have measurements but no sample parameters
except ValueError:
continue
# Concatenate summary lists into pandas dataframes
site_measurement_summary_df = pd.concat(site_measurement_summary, sort=False)
site_sample_parameter_summary_df = pd.concat(site_sample_parameter_summary,
param['Input']['hilltop_hts'])
ht_sites['Wap'] = convert_site_names(ht_sites.SiteName)
ht_sites1 = ht_sites[
ht_sites['Wap'].isin(wap_ratios.index)
& ~ht_sites['Wap'].isin(param['Input']['browns_rock_waps'])].copy(
)
ht_sites1.rename(columns={'SiteName': 'Site'}, inplace=True)
mtype_list = []
for site in ht_sites1.Site:
timer = 10
while timer > 0:
try:
m1 = ws.measurement_list(
param['Input']['hilltop_base_url'],
param['Input']['hilltop_hts'], site)
break
except Exception as err:
err1 = err
timer = timer - 1
if timer == 0:
raise ValueError(err1)
else:
print(err1)
sleep(3)
mtype_list.append(m1)
mtypes = pd.concat(mtype_list).reset_index()
mtypes1 = mtypes[mtypes.To >= from_date]
def test_measurement_list(data):
mtype_df1 = measurement_list(data['base_url'], data['hts'], data['site'])
assert len(mtype_df1) > 6
## Nasa data
ds1 = xr.open_dataset(nc1)
da1 = ds1[nasa_mtype].resample(time='D', closed='right',
label='left').sum('time') * 3
del ds1
## Met stations
sites = ws.site_list(ecan_base_url, hts_name, True)
sites.rename(columns={'SiteName': 'Site'}, inplace=True)
mtypes = []
for s in sites['Site'].values:
mtypes.append(ws.measurement_list(ecan_base_url, hts_name, s, mtype))
mtypes_df = pd.concat(mtypes).reset_index().drop('DataType', axis=1)
mtypes_df['n_days'] = (mtypes_df['To'] - mtypes_df['From']).dt.days
mtypes_df1 = mtypes_df[mtypes_df['n_days'] >= min_n_days].copy()
sites1 = pd.merge(sites, mtypes_df1, on='Site')
ts_data = []
for index, row in sites1.iterrows():
ts_data.append(
ws.get_data(ecan_base_url, hts_name, row['Site'], row['Measurement']))
ts_data_df = pd.concat(ts_data)
