def test_io_netcdf():
## Read
h4 = hydro().rd_netcdf(path.join(py_dir, netcdf1))
h4._base_stats_fun()
assert (len(h4._base_stats) > 4)
## Write
h4.to_netcdf(path.join(py_dir, netcdf1))
## Read
h4 = hydro().rd_netcdf(path.join(py_dir, netcdf1))
h4._base_stats_fun()
assert (len(h4._base_stats) > 4)
def test_io_csv(csv):
tparam = param[csv].copy()
## Read
h1 = hydro().rd_csv(path.join(py_dir, csv), **tparam)
h1._base_stats_fun()
assert (len(h1._base_stats) > 4)
## Write
dformat = tparam['dformat']
out_param = {}
if dformat == 'long':
out_param.update({'pivot': False})
else:
out_param.update({'pivot': True})
h1.to_csv(path.join(py_dir, csv), **out_param)
## Read
h1 = hydro().rd_csv(path.join(py_dir, csv), **tparam)
h1._base_stats_fun()
assert (len(h1._base_stats) > 4)
Created on Thu Oct 26 15:34:32 2017
@author: MichaelEK
"""
from core.classes.hydro import hydro
import pytest
from os import path, getcwd
from geopandas import read_file
py_dir = path.realpath(path.join(getcwd(), path.dirname(__file__)))
netcdf1 = 'test_netcdf1.nc'
poly_shp = 'test_poly.shp'
## Read in data
h1 = hydro().rd_netcdf(path.join(py_dir, netcdf1))
h1._base_stats_fun()
stats = h1._base_stats
## Test selection options
poly = read_file(path.join(py_dir, poly_shp)).set_index('site')
@pytest.mark.parametrize('poly_in', [path.join(py_dir, poly_shp), poly])
def test_sel_by_poly(poly_in):
h2 = h1.sel_ts_by_poly(poly_in, 100, pivot=True)
assert (len(h2.columns) == 4)
h3 = h1.sel_by_poly(poly_in, 100)
h3._base_stats_fun()
assert (len(h3._base_stats) == 4)
h4 = h1.sel_ts_by_poly(poly_in,
malf_csv = 'malf.csv' alf_csv = 'alf.csv' days_mis_csv = 'alf_days_mis.csv' ## Plotting start = '1986-07-01' end = '1987-06-30' x_period = 'month' time_format = '%d-%m-%Y' flow_sites = 70105 ################################################ #### Import data h1 = hydro().get_data(mtypes=mtypes1, sites=sites1, qual_codes=qual_codes) ################################################ #### Tools ### Flow tools ## MALF and flow stats fstats = h1.stats(mtypes=mtypes1) fstats malf1 = h1.malf7d() malf1 malf3 = h1.malf7d(intervals=intervals) malf3
mtypes4 = 'gwl'
mtypes5 = 'gwl_m'
mtypes6 = 'usage'
mtypes7 = 'flow_tel'
sites1 = [70105, 69607, 69602, 65101, 69505]
sites2 = [66, 137]
sites3 = ['BT27/5020']
sites4 = ['J38/0774', 'J38/0874', 'J38/0811', 'I39/0033']
qual_codes = [10, 18, 20, 30, 50]
from_date = '2015-01-01'
to_date = '2017-06-30'
poly = r'S:\Surface Water\backups\MichaelE\Projects\otop\GIS\vector\min_flow\catch1.shp'
### From the MSSQL server (the easy way) - Loads in both the time series data and the geo locations
h1 = hydro().get_data(mtypes=mtypes1, sites=sites1, qual_codes=qual_codes)
h2 = h1.get_data(mtypes=mtypes2, sites=sites2, qual_codes=qual_codes)
h3 = h2.get_data(mtypes=mtypes3, sites=sites1, qual_codes=qual_codes)
gwl1 = hydro().get_data(mtypes=mtypes4, sites=sites3, qual_codes=qual_codes)
gwl2 = hydro().get_data(mtypes=mtypes5, sites=sites3)
use1 = hydro().get_data(mtypes=mtypes6, sites=sites4)
tel1 = hydro().get_data(mtypes=mtypes7,
sites=sites1,
from_date=from_date,
to_date=to_date)
## Find sites based on a polygon shapefile with a 100 m buffer distance (for m_flow)
h4 = hydro().get_data(mtypes=[mtypes1, mtypes2],
#### Load data
### Parameters
mtypes1 = 'gwl_m'
mtypes2 = 'gwl'
sites1 = ['K37/3556']
sites2 = ['L36/0633']
qual_codes = [10, 18, 20, 50]
from_date = '2015-01-01'
to_date = '2017-06-30'
poly = r'P:\examples\regression_tests\ashburton.shp'
### From the MSSQL server (the easy way) - Loads in both the time series data and the geo locations
h1 = hydro().get_data(mtypes=mtypes1, sites=sites2)
h2 = h1.get_data(mtypes=mtypes2, sites=sites2, qual_codes=qual_codes)
## Find sites based on a polygon shapefile with a 10 m buffer distance
h4 = h1.get_data(mtypes=mtypes2,
sites=poly,
buffer_dis=10,
qual_codes=qual_codes)
##################################################
#### Look at the attributes and data contained in the new object
## Look at the general stats of the imported data
print(h1)
print(h2)
h2
out_param = {}
if dformat == 'long':
out_param.update({'pivot': False})
else:
out_param.update({'pivot': True})
h1.to_csv(path.join(py_dir, csv), **out_param)
## Read
h1 = hydro().rd_csv(path.join(py_dir, csv), **tparam)
h1._base_stats_fun()
assert (len(h1._base_stats) > 4)
## Base import
tparam = param[csv_files[0]]
h1 = hydro().rd_csv(path.join(py_dir, csv_files[0]), **tparam)
h1._base_stats_fun()
h1_len = len(h1._base_stats)
## Combine test
h2 = hydro().rd_csv(path.join(py_dir, extra_csv), **tparam)
h2._base_stats_fun()
h2_len = len(h2._base_stats)
def test_combine():
h3 = h1.combine(h2)
h3._base_stats_fun()
assert (len(h3._base_stats) == (h1_len + h2_len))
def test_ecan_get_data_atmos(mtypes):
h1 = hydro().get_data(mtypes=mtypes, sites=sites4, from_date=from_date, to_date=to_date, qual_codes=qual_codes, min_count=min_count, resample_code=resample_code)
h1._base_stats_fun()
assert (len(h1._base_stats) == 1)
def test_ecan_get_data_usage():
h1 = hydro().get_data(mtypes=mtypes3, sites=sites3, to_date=to_date, qual_codes=qual_codes)
h1._base_stats_fun()
assert (len(h1._base_stats) == 1)
def test_ecan_get_data_aq(mtypes):
h1 = hydro().get_data(mtypes=mtypes, sites=sites2, from_date=from_date, to_date=to_date, qual_codes=qual_codes)
h1._base_stats_fun()
assert (len(h1._base_stats) == 1)