def process_local_range_test(self, coverage, parameter, input_name, datlim, datlimz, dims):
qc_array = coverage.get_parameter_values(parameter.name)
indexes = np.where(qc_array == -88)[0]
from ion_functions.qc.qc_functions import dataqc_localrangetest_wrapper
# dat
value_array = coverage.get_parameter_values(input_name)
time_array = coverage.get_parameter_values(coverage.temporal_parameter_name)
# datlim is an argument and comes from the lookup table
# datlimz is an argument and comes from the lookup table
# dims is an argument and is created using the column headings
# pval_callback, well as for that...
# TODO: slice_ is the window of the site data product, but for
# now we'll just use a global slice
slice_ = slice(None)
def parameter_callback(param_name):
return coverage.get_parameter_values(param_name, slice_)
qc_array = dataqc_localrangetest_wrapper(value_array, datlim, datlimz, dims, parameter_callback)
return_dictionary = {
coverage.temporal_parameter_name : time_array[indexes],
parameter.name : qc_array[indexes]
}
log.error("Here's what it would look like\n%s", return_dictionary)
def process_local_range_test(self, coverage, parameter, input_name, datlim,
datlimz, dims):
return # Not ready
qc_array = self.get_parameter_values(coverage, parameter.name)
indexes = np.where(qc_array == -88)[0]
from ion_functions.qc.qc_functions import dataqc_localrangetest_wrapper
# dat
value_array = self.get_parameter_values(coverage, input_name)[indexes]
time_array = self.get_parameter_values(
coverage, coverage.temporal_parameter_name)[indexes]
# datlim is an argument and comes from the lookup table
# datlimz is an argument and comes from the lookup table
# dims is an argument and is created using the column headings
# pval_callback, well as for that...
# TODO: slice_ is the window of the site data product, but for
# now we'll just use a global slice
slice_ = slice(None)
def parameter_callback(param_name):
return coverage.get_parameter_values(param_name, slice_)
qc_array = dataqc_localrangetest_wrapper(value_array, datlim, datlimz,
dims, parameter_callback)
return_dictionary = {
coverage.temporal_parameter_name: time_array[indexes],
parameter.name: qc_array[indexes]
}
log.error("Here's what it would look like\n%s", return_dictionary)
def test_lrt_wrapper(self):
t = np.array(
[
3580144703.7555027,
3580144704.7555027,
3580144705.7555027,
3580144706.7555027,
3580144707.7555027,
3580144708.7555027,
3580144709.7555027,
3580144710.7555027,
3580144711.7555027,
3580144712.7555027,
]
)
pressure = np.random.rand(10) * 2 + 33.0
t_v = ntp_to_month(t)
dat = t_v + pressure + np.arange(16, 26)
def lim1(p, m):
return p + m + 10
def lim2(p, m):
return p + m + 20
def callback(f):
if f == "time":
return t
if f == "pressure":
return pressure
pressure_grid, month_grid = np.meshgrid(np.arange(0, 150, 10), np.arange(11))
points = np.column_stack([pressure_grid.flatten(), month_grid.flatten()])
datlim_0 = lim1(points[:, 0], points[:, 1])
datlim_1 = lim2(points[:, 0], points[:, 1])
datlim = np.column_stack([datlim_0, datlim_1])
datlimz = points
qc_out = qcfunc.dataqc_localrangetest_wrapper(dat, datlim, datlimz, ["pressure", "month"], callback)
np.testing.assert_array_equal(qc_out, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
def test_lrt_wrapper(self):
t = np.array([3580144703.7555027, 3580144704.7555027, 3580144705.7555027, 3580144706.7555027, 3580144707.7555027, 3580144708.7555027, 3580144709.7555027, 3580144710.7555027, 3580144711.7555027, 3580144712.7555027])
pressure = np.random.rand(10) * 2 + 33.0
t_v = ntp_to_month(t)
dat = t_v + pressure + np.arange(16,26)
def lim1(p,m):
return p+m+10
def lim2(p,m):
return p+m+20
def callback(f):
if f=='time':
return t
if f=='pressure':
return pressure
pressure_grid, month_grid = np.meshgrid(np.arange(0,150,10), np.arange(11))
points = np.column_stack([pressure_grid.flatten(), month_grid.flatten()])
datlim_0 = lim1(points[:,0], points[:,1])
datlim_1 = lim2(points[:,0], points[:,1])
datlim = np.column_stack([datlim_0, datlim_1])
datlimz = points
qc_out = qcfunc.dataqc_localrangetest_wrapper(dat, datlim, datlimz, ['pressure', 'month'], callback)
np.testing.assert_array_equal(qc_out, [1 ,1 ,1 ,1 ,1 ,0 ,0 ,0 ,0 ,0])
