def sortHeaders_test(self):
'''
check basic behavior of header sorting
'''
p = []
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2004, 3, 17, 11.3],
cruise=1))
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2004, 3, 17, 22.6],
cruise=1))
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2004, 3, 17, 1.1],
cruise=1))
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2004, 3, 17, 15.9],
cruise=1))
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2005, 1, 11, 11.5],
cruise=2))
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2005, 1, 11, 22.2],
cruise=2))
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2005, 1, 11, 1.0],
cruise=2))
p.append(
util.testingProfile.fakeProfile([0], [0],
date=[2005, 1, 11, 16.0],
cruise=2))
sortedProfiles = main.sort_headers(p)
truth = {
1: [p[2], p[0], p[3], p[1]],
2: [p[6], p[4], p[7], p[5]],
}
assert sortedProfiles == truth, 'incorrectly sorted profiles'
def sortHeaders_test(self):
'''
check basic behavior of header sorting
'''
p = []
p.append(util.testingProfile.fakeProfile([0], [0], date=[2004, 3, 17, 11.3], cruise=1))
p.append(util.testingProfile.fakeProfile([0], [0], date=[2004, 3, 17, 22.6], cruise=1))
p.append(util.testingProfile.fakeProfile([0], [0], date=[2004, 3, 17, 1.1], cruise=1))
p.append(util.testingProfile.fakeProfile([0], [0], date=[2004, 3, 17, 15.9], cruise=1))
p.append(util.testingProfile.fakeProfile([0], [0], date=[2005, 1, 11, 11.5], cruise=2))
p.append(util.testingProfile.fakeProfile([0], [0], date=[2005, 1, 11, 22.2], cruise=2))
p.append(util.testingProfile.fakeProfile([0], [0], date=[2005, 1, 11, 1.0], cruise=2))
p.append(util.testingProfile.fakeProfile([0], [0], date=[2005, 1, 11, 16.0], cruise=2))
sortedProfiles = main.sort_headers(p)
truth = {1: [p[2], p[0], p[3], p[1]],
2: [p[6], p[4], p[7], p[5]],
}
assert sortedProfiles == truth, 'incorrectly sorted profiles'
def test(p):
"""
Runs the quality control check on profile p and returns a numpy array
of quality control decisions with False where the data value has
passed the check and True where it failed.
"""
global EN_track_headers
global EN_track_results
global threadFile
cruise = p.cruise()
uid = p.uid()
# don't bother if cruise == 0 or None
if cruise in [0, None]:
return np.zeros(1, dtype=bool);
# The headers from an entire cruise must be analyzed all at once;
# we'll write the results to the global data store, in a dictionary
# with ntuple keys (cruise, uid), and values as single element
# numpy arrays, containing either a true or a false (per all the other
# qc return objects)
# check if this profile has been examined already
if (cruise, uid) in EN_track_results.keys():
return EN_track_results[(cruise, uid)]
# some detector types cannot be assessed by this test; do not raise flag.
if p.probe_type in [None]:
return np.zeros(1, dtype=bool)
# the first time this test is run, sort ds.threadProfiles into a cruise-keyed dictionary:
if ds.threadFile != threadFile:
EN_track_headers = main.sort_headers(ds.threadProfiles)
threadFile = ds.threadFile
# since we didn't find an answer already calculated,
# we still need to do the calculation for this cruise;
# all the relevant headers are sitting in the EN_track_headers list.
headers = EN_track_headers[cruise]
# start all as passing by default:
for i in range(len(headers)):
EN_track_results[(headers[i].cruise(), headers[i].uid())] = np.zeros(1, dtype=bool)
# copy the list of headers;
# remove entries as they are flagged.
passedHeaders = copy.deepcopy(headers)
rejects = findOutlier(passedHeaders)
while rejects != []:
passedIndex = [x for x in range(len(passedHeaders)) if x not in rejects ]
passedHeaders = [passedHeaders[index] for index in passedIndex ]
rejects = findOutlier(passedHeaders)
# if more than half got rejected, reject everyone
if len(passedHeaders) < len(headers) / 2:
for i in range(len(headers)):
EN_track_results[(headers[i].cruise(), headers[i].uid())][0] = True
return EN_track_results[(cruise, uid)]
