def test_z0_vs_dmax(self):
self.ambient = pyplume.Ambient(self.z_max,
self.salinity,
self.temperature,
pressure=self.pressure)
self.assertEqual(self.ambient.get_sal_z(0),
self.ambient.get_sal_d(self.z_max))
def test_density_from_depth(self):
self.ambient = pyplume.Ambient(self.z_max,
self.salinity,
self.temperature,
depth=self.depth)
self.assertEqual(
self.ambient.rho.tolist(),
gsw.rho(self.salinity, self.temperature, self.pressure).tolist())
def test_profile_interpolation(self):
self.ambient = pyplume.Ambient(self.z_max,
self.salinity,
self.temperature,
pressure=self.pressure)
mid_rho = (self.ambient.rho[0] + self.ambient.rho[1]) / 2.
mid_sal = (self.salinity[0] + self.salinity[1]) / 2.
mid_temp = (self.temperature[0] + self.temperature[1]) / 2.
mid_pressure = (self.pressure[0] + self.pressure[1]) / 2.
mid_depth = mid_pressure / (1027. * 9.81 * 1.e-4)
self.assertEqual(mid_sal, self.ambient.get_sal_d(mid_depth))
# Ambient T/S profile
# load a csv containing columns for
# z (depth - metres)
# SA (Absolute Salinity - Kg/m3)
# CT (Conservative temperature - C)
# This one was generated using the script in the ctdtools repo here:
# https://github.com/alistaireverett/ctdtools/blob/master/examples/proc_cnv_short.py
amb_df = pd.read_csv('ambient.csv', ';')
# create an ambient profile object from the temp/sal data
ambient = pyplume.Ambient(h_w,
amb_df['SA'],
amb_df['CT'],
depth=amb_df['depth'])
# Load discharges
# These are from a surface mass balance model, see Halbach et al. (submitted).
# Contains the following columns:
# Year, Month, Day, Hour, Discharge (m3/s)
disch_data = pd.read_csv('kronebreen_runoff.csv', ';')
def to_dt(x):
"""
Function to convert Y/M/D/H columns to python datetime object