def plot_roc(targetdb='iquod.db'):
# get qc tests
testNames = main.importQC('qctests')
# connect to database
conn = sqlite3.connect(targetdb, isolation_level=None)
cur = conn.cursor()
# extract matrix of test results and true flags into a dataframe
query = 'SELECT truth, raw, ' + ','.join(testNames) + ' FROM ' + sys.argv[1] + ' WHERE training=0;'
cur.execute(query)
rawresults = cur.fetchall()
df = pandas.DataFrame(rawresults).astype('str')
df.columns = ['Truth', 'raw'] + testNames
# unpack truth and qc data
truth = df[['Truth']].apply(unpack_truth).values.tolist()
df = df.assign(leveltruth=pandas.Series(truth))
df[['Truth']] = df[['Truth']].apply(dbutils.parse_truth)
for t in testNames:
df[[t]] = df[[t]].apply(dbutils.parse)
# prepare ROC function
assessROC = generateROC()
df['roc'] = df.apply(assessROC, axis=1)
# set up dirs for figures
os.makedirs(figdir)
os.makedirs(figdir + '/FP')
os.makedirs(figdir + '/FN')
os.makedirs(figdir + '/TP')
os.makedirs(figdir + '/TN')
df.apply(plotRow, axis=1)
def setUp(self):
filenames = main.readInput('datafiles.json')
profiles = main.extractProfiles(filenames)
# identify and import tests
testNames = main.importQC('qctests')
testNames.sort()
for testName in testNames:
exec('from qctests import ' + testName)
# Set up any keyword arguments needed by tests.
kwargs = {'profiles': profiles}
testResults = []
testVerbose = []
trueResults = []
trueVerbose = []
firstProfile = True
delete = []
currentFile = ''
self.profiles = []
for iprofile, pinfo in enumerate(profiles):
# Load the profile data.
if pinfo.file_name != currentFile:
if currentFile != '': f.close()
currentFile = pinfo.file_name
f = open(currentFile)
if f.tell() != pinfo.file_position: f.seek(pinfo.file_position)
self.profiles.append(wod.WodProfile(f))
def setUp(self):
filenames = main.readInput('datafiles.json')
profiles = main.extractProfiles(filenames)
# identify and import tests
testNames = main.importQC('qctests')
testNames.sort()
for testName in testNames:
exec('from qctests import ' + testName)
# Set up any keyword arguments needed by tests.
kwargs = {'profiles' : profiles}
testResults = []
testVerbose = []
trueResults = []
trueVerbose = []
firstProfile = True
delete = []
currentFile = ''
self.profiles = []
for iprofile, pinfo in enumerate(profiles):
# Load the profile data.
if pinfo.file_name != currentFile:
if currentFile != '': f.close()
currentFile = pinfo.file_name
f = open(currentFile)
if f.tell() != pinfo.file_position: f.seek(pinfo.file_position)
self.profiles.append(wod.WodProfile(f))
def plot_roc():
# get qc tests
testNames = main.importQC('qctests')
# connect to database
conn = sqlite3.connect('iquod.db', isolation_level=None)
cur = conn.cursor()
# extract matrix of test results and true flags into a dataframe
query = 'SELECT truth, raw, ' + ','.join(testNames) + ' FROM ' + sys.argv[1] + ' WHERE training=0;'
cur.execute(query)
rawresults = cur.fetchall()
df = pandas.DataFrame(rawresults).astype('str')
df.columns = ['Truth', 'raw'] + testNames
# unpack truth and qc data
truth = df[['Truth']].apply(unpack_truth).values.tolist()
df = df.assign(leveltruth=pandas.Series(truth))
df[['Truth']] = df[['Truth']].apply(dbutils.parse_truth)
for t in testNames:
df[[t]] = df[[t]].apply(dbutils.parse)
# prepare ROC function
assessROC = generateROC()
df['roc'] = df.apply(assessROC, axis=1)
# set up dirs for figures
os.makedirs(figdir)
os.makedirs(figdir + '/FP')
os.makedirs(figdir + '/FN')
os.makedirs(figdir + '/TP')
os.makedirs(figdir + '/TN')
df.apply(plotRow, axis=1)
def importQC_test(self):
'''
make sure main.importQC returns a valid list of tests that actually exist
'''
tests = main.importQC("qctests")
assert isinstance(tests, list), 'importQC did not return a list'
for test in tests:
assert os.path.isfile('qctests/'+test+'.py'), 'test ' + test + ' is not found.'
def importQC_test(self):
'''
make sure main.importQC returns a valid list of tests that actually exist
'''
tests = main.importQC("qctests")
assert isinstance(tests, list), 'importQC did not return a list'
for test in tests:
assert os.path.isfile('qctests/' + test +
'.py'), 'test ' + test + ' is not found.'
def query2df(meta, filter, tablename, database='iquod.db'):
'''
meta: list of strings of metadata to extract
filter: string describing WHERE filter for SQL query, such as:
'uid==1234'
'cruise!=99 and month==10' etc
tablename: sql table to extract from
database: filename of database file
return a dataframe with columns for every QC test plus specified metadata.
also parses out truth if requested in the metadata list
'''
# get qc tests
testNames = main.importQC('qctests')
# connect to database
conn = sqlite3.connect(database, isolation_level=None)
cur = conn.cursor()
# extract matrix of test results into a dataframe
query = 'SELECT '
if len(meta) > 0:
query += ','.join(meta) + ','
query += ','.join(testNames) + ' FROM ' + tablename
if filter:
query += ' WHERE ' + filter
cur.execute(query)
rawresults = cur.fetchall()
df = pandas.DataFrame(rawresults).astype('str')
df.columns = meta + testNames
for t in testNames:
df[[t]] = df[[t]].apply(dbutils.parse)
# deal with truth data if present
# column 'leveltruth' will persist per-level truth,
# while 'Truth' summarizes by or'ing all levels together
if 'truth' in meta:
def unpack_truth(results):
return results.apply(dbutils.unpack_qc)
truth = df[['truth']].apply(unpack_truth).values.tolist()
df = df.assign(leveltruth=pandas.Series(truth))
df[['truth']] = df[['truth']].apply(dbutils.parse_truth)
return df
def dump_row(uid, table, database='iquod.db'):
'''
print all database keys and values for uid
'''
# extract and parse row
conn = sqlite3.connect(database, isolation_level=None)
cur = conn.cursor()
query = 'SELECT * FROM ' + table + ' WHERE uid=' + str(uid)
cur.execute(query)
rawresults = cur.fetchall()
df = pandas.DataFrame(rawresults).astype('str')
df.columns = [description[0] for description in cur.description]
testNames = main.importQC('qctests')
testNames = [t.lower() for t in testNames]
for t in testNames:
df[[t]] = df[[t]].apply(dbutils.parse)
df[['truth']] = df[['truth']].apply(dbutils.parse_truth)
for col in list(df):
print col, ':', df.ix[0][col]
def plot_uid_pathology(uid, table, database='iquod.db'):
# extract and parse row
conn = sqlite3.connect(database, isolation_level=None)
cur = conn.cursor()
query = 'SELECT * FROM ' + table + ' WHERE uid=' + str(uid)
cur.execute(query)
rawresults = cur.fetchall()
df = pandas.DataFrame(rawresults).astype('str')
df.columns = [description[0] for description in cur.description]
testNames = main.importQC('qctests')
testNames = [t.lower() for t in testNames]
for t in testNames:
df[[t]] = df[[t]].apply(dbutils.parse)
def unpack_truth(results):
return results.apply(dbutils.unpack_qc)
truth = df[['truth']].apply(unpack_truth).values.tolist()
df = df.assign(leveltruth=pandas.Series(truth))
df[['truth']] = df[['truth']].apply(dbutils.parse_truth)
plotPathology(df.ix[0], '.')
query = "UPDATE " + sys.argv[
1] + " SET " + test + "=? WHERE uid=" + str(
profile.uid()) + ";"
main.dbinteract(query, [main.pack_array(result)])
except:
print 'db exception', sys.exc_info()
########################################
# main
########################################
if len(sys.argv) > 2:
# Identify and import tests
testNames = main.importQC('qctests')
testNames.sort()
print('{} quality control checks have been found'.format(len(testNames)))
testNames = main.checkQCTestRequirements(testNames)
print('{} quality control checks are able to be run:'.format(
len(testNames)))
for testName in testNames:
print(' {}'.format(testName))
# set up a directory for logging
logdir = "autoqc-logs-" + str(calendar.timegm(time.gmtime()))
os.makedirs(logdir)
# Parallel processing.
print('\nPlease wait while QC is performed\n')
def builddb(infile,
check_originator_flag_type=True,
months_to_use=range(1, 13),
outfile='iquod.db',
dbtable='iquod'):
conn = sqlite3.connect(outfile, isolation_level=None)
cur = conn.cursor()
# Identify tests
testNames = main.importQC('qctests')
testNames.sort()
# set up our table
query = "CREATE TABLE IF NOT EXISTS " + dbtable + """(
raw text,
truth BLOB,
uid integer PRIMARY KEY,
year integer,
month integer,
day integer,
time real,
lat real,
long real,
country text,
cruise integer,
ocruise text,
probe integer,
training integer,
flagged integer,
"""
for i in range(len(testNames)):
query += testNames[i].lower() + ' BLOB'
if i < len(testNames) - 1:
query += ','
else:
query += ');'
cur.execute(query)
# populate table from wod-ascii data
fid = open(infile)
uids = []
good = 0
bad = 0
while True:
# extract profile as wodpy object and raw text
start = fid.tell()
profile = wod.WodProfile(fid)
end = fid.tell()
fid.seek(start)
raw = fid.read(end - start)
fid.seek(end)
# set up dictionary for populating query string
p = profile.npdict()
p['raw'] = "'" + raw + "'"
# check for duplicate profiles in raw data
if p['uid'] in uids:
if profile.is_last_profile_in_file(fid) == True:
break
else:
continue
uids.append(p['uid'])
# skip pathological profiles
isgood = assessProfile(profile, check_originator_flag_type,
months_to_use)
if not isgood and profile.is_last_profile_in_file(fid) == True:
break
elif not isgood:
continue
# encode temperature error codes into truth array
truth = encodeTruth(profile)
p['truth'] = main.pack_array(truth)
# extract country code
country = profile.primary_header['Country code']
# originator cruise
orig_cruise = profile.originator_cruise()
# keep tabs on how many good and how many bad profiles have been added to db
# nowire == index of first wire break level
wireqc = qctests.CSIRO_wire_break.test(profile, {})
try:
nowire = list(wireqc).index(True)
except:
nowire = len(truth)
# flag only counts if its before the wire break:
flagged = dbutils.summarize_truth(truth[0:nowire])
if flagged:
bad += 1
else:
good += 1
query = "INSERT INTO " + dbtable + " (raw, truth, uid, year, month, day, time, lat, long, country, cruise, ocruise, probe, flagged) values (?,?,?,?,?,?,?,?,?,?,?,?,?,?);"
values = (p['raw'], p['truth'], p['uid'], p['year'], p['month'],
p['day'], p['time'], p['latitude'], p['longitude'], country,
p['cruise'], orig_cruise, p['probe_type'], int(flagged))
main.dbinteract(query, values, targetdb=outfile)
if profile.is_last_profile_in_file(fid) == True:
break
conn.commit()
print('number of clean profiles written:', good)
print('number of flagged profiles written:', bad)
print('total number of profiles written:', good + bad)
def db_to_df(table,
filter_on_wire_break_test=False,
filter_on_tests={},
n_to_extract=numpy.iinfo(numpy.int32).max,
applyparse=True,
targetdb='iquod.db'):
'''
Reads the table from targetdb into a pandas dataframe.
If filter_on_wire_break_test is True, the results from that test are used to exclude
levels below a wire break from the test results and the wire break test is not returned.
filter_on_tests is a generalised form of filter_on_wire_break and is used to exclude results; it takes a list of
[testname, action], where levels failing <testname> are excluded towards the surface (if action is 'up'), towards depth (if action is 'down') and the whole profile deleted (if action is 'remove').
Set n_to_extract to limit the number of rows extracted to the specified number.
'''
# what tests are available
testNames = main.importQC('qctests')
testNames.sort()
# connect to database
conn = sqlite3.connect(targetdb, isolation_level=None)
cur = conn.cursor()
# extract matrix of test results and true flags into a dataframe
query = 'SELECT uid, truth'
for test in testNames:
query += ', ' + test.lower()
query += ' FROM ' + table
query += ' WHERE uid IN (SELECT uid FROM ' + table + ' ORDER BY RANDOM() LIMIT ' + str(
n_to_extract) + ')'
cur.execute(query)
rawresults = cur.fetchall()
sub = 1000
df_final = None
for i in range(math.ceil(len(rawresults) / sub)):
df = pandas.DataFrame(rawresults[i * sub:(i + 1) *
sub]).astype('bytes')
df.columns = ['uid', 'Truth'] + testNames
df = df.astype({'uid': 'int'})
if filter_on_wire_break_test:
nlevels = get_n_levels_before_fail(df['CSIRO_wire_break'])
del df['CSIRO_wire_break'] # No use for this now.
testNames = df.columns[2:].values.tolist()
for i in range(len(df.index)):
for j in range(1, len(df.columns)):
qc = unpack_qc(df.iloc[i, j])
# Some QC tests may return only one value so check for this.
if len(qc) > 1:
qc = qc[:nlevels[i]]
df.iat[i, j] = main.pack_array(qc)
todrop = set()
for action in filter_on_tests:
# Check if the action is relevant.
if action == 'Optional' or action == 'At least one from group':
continue
# Initialise variables.
nlevels = -1
outcomes = False
qcresults = []
for testname in filter_on_tests[action]:
for i in range(0, len(df.index)):
if action == 'Remove above reject':
nlevels = get_reversed_n_levels_before_fail(
[df[testname][i]])[0]
elif action == 'Remove below reject':
nlevels = get_n_levels_before_fail([df[testname][i]
])[0]
elif action == 'Remove profile':
outcomes = check_for_fail([df[testname][i]])[0]
elif action == 'Remove rejected levels':
qcresults = unpack_qc_results([df[testname][i]])[0]
else:
raise NameError('Unrecognised action: ' + action)
if (((action == 'Remove above reject'
or action == 'Remove below reject') and nlevels == 0)
or
(action == 'Remove profile' and outcomes == True) or
(action == 'Remove rejected levels'
and numpy.count_nonzero(qcresults == False) == 0)):
# Completely remove a profile if it has no valid levels or if it
# has a fail and the action is to remove.
todrop.add(i)
elif (action != 'Remove profile'):
for j in range(1, len(df.columns)):
# Retain only the levels that passed testname.
# Some QC tests may return only one value so check for this.
qc = unpack_qc(df.iloc[i, j])
if len(qc) > 1:
if action == 'Remove above reject':
qc = qc[nlevels:]
elif action == 'Remove below reject':
qc = qc[:nlevels]
elif action == 'Remove rejected levels':
qc = qc[qcresults == False]
df.iat[i, j] = main.pack_array(qc)
del df[testname] # No need to keep this any longer.
df.reset_index(inplace=True, drop=True)
todrop = list(todrop)
if len(todrop) > 0:
df.drop(todrop, inplace=True)
df.reset_index(inplace=True, drop=True)
testNames = df.columns[2:].values.tolist()
if applyparse:
df[['Truth']] = df[['Truth']].apply(parse_truth)
df[testNames] = df[testNames].apply(parse)
if i == 0:
df_final = df
else:
df_final = pandas.concat([df_final, df])
return df_final.reset_index(drop=True)
try:
query = "UPDATE " + sys.argv[1] + " SET " + test + "=? WHERE uid=" + str(profile.uid()) + ";"
main.dbinteract(query, [main.pack_array(result)])
except:
print 'db exception', sys.exc_info()
########################################
# main
########################################
if len(sys.argv)>2:
# Identify and import tests
testNames = main.importQC('qctests')
testNames.sort()
print('{} quality control checks have been found'.format(len(testNames)))
testNames = main.checkQCTestRequirements(testNames)
print('{} quality control checks are able to be run:'.format(len(testNames)))
for testName in testNames:
print(' {}'.format(testName))
# set up a directory for logging
logdir = "autoqc-logs-" + str(calendar.timegm(time.gmtime()))
os.makedirs(logdir)
# Parallel processing.
print('\nPlease wait while QC is performed\n')
# set up global parmaeter store
import util.post as post
import util.main as main
import sys, sqlite3
tests = main.importQC(sys.argv[1])
tablename = 'quota'
def add_test(testname, tablename, database='iquod.db'):
conn = sqlite3.connect(database, isolation_level=None)
cur = conn.cursor()
query = 'ALTER TABLE ' + tablename + ' ADD ' + testname + ' BLOB;'
cur.execute(query)
for test in tests:
add_test(test.lower(), tablename)
def db_to_df(table,
filter_on_wire_break_test=False,
filter_on_tests={},
n_to_extract=numpy.iinfo(numpy.int32).max):
'''
Reads the table from iquod.db into a pandas dataframe.
If filter_on_wire_break_test is True, the results from that test are used to exclude
levels below a wire break from the test results and the wire break test is not returned.
filter_on_tests is a generalised form of filter_on_wire_break and is used to exclude results; it takes a list of
[testname, action], where levels failing <testname> are excluded towards the surface (if action is 'up'), towards depth (if action is 'down') and the whole profile deleted (if action is 'remove').
Set n_to_extract to limit the number of rows extracted to the specified number.
'''
# what tests are available
testNames = main.importQC('qctests')
testNames.sort()
# connect to database
conn = sqlite3.connect('iquod.db', isolation_level=None)
cur = conn.cursor()
# extract matrix of test results and true flags into a dataframe
query = 'SELECT uid, truth'
for test in testNames:
query += ', ' + test.lower()
query += ' FROM ' + table
query += ' WHERE uid IN (SELECT uid FROM ' + table + ' ORDER BY RANDOM() LIMIT ' + str(n_to_extract) + ')'
cur.execute(query)
rawresults = cur.fetchall()
df = pandas.DataFrame(rawresults).astype('str')
df.columns = ['uid', 'Truth'] + testNames
if filter_on_wire_break_test:
nlevels = get_n_levels_before_fail(df['CSIRO_wire_break'])
del df['CSIRO_wire_break'] # No use for this now.
testNames = df.columns[2:].values.tolist()
for i in range(len(df.index)):
for j in range(1, len(df.columns)):
qc = unpack_qc(df.iloc[i, j])
# Some QC tests may return only one value so check for this.
if len(qc) > 1:
qc = qc[:nlevels[i]]
df.iat[i, j] = main.pack_array(qc)
todrop = set()
for action in filter_on_tests:
# Check if the action is relevant.
if action == 'Optional' or action == 'At least one from group': continue
# Initialise variables.
nlevels = -1
outcomes = False
qcresults = []
for testname in filter_on_tests[action]:
for i in range(0, len(df.index)):
if action == 'Remove above reject':
nlevels = get_reversed_n_levels_before_fail([df[testname][i]])[0]
elif action == 'Remove below reject':
nlevels = get_n_levels_before_fail([df[testname][i]])[0]
elif action == 'Remove profile':
outcomes = check_for_fail([df[testname][i]])[0]
elif action == 'Remove rejected levels':
qcresults = unpack_qc_results([df[testname][i]])[0]
else:
raise NameError('Unrecognised action: ' + action)
if (((action == 'Remove above reject' or action == 'Remove below reject') and nlevels == 0) or
(action == 'Remove profile' and outcomes == True) or
(action == 'Remove rejected levels' and numpy.count_nonzero(qcresults == False) == 0)):
# Completely remove a profile if it has no valid levels or if it
# has a fail and the action is to remove.
todrop.add(i)
elif (action != 'Remove profile'):
for j in range(1, len(df.columns)):
# Retain only the levels that passed testname.
# Some QC tests may return only one value so check for this.
qc = unpack_qc(df.iloc[i, j])
if len(qc) > 1:
if action == 'Remove above reject':
qc = qc[nlevels:]
elif action == 'Remove below reject':
qc = qc[:nlevels]
elif action == 'Remove rejected levels':
qc = qc[qcresults == False]
df.iat[i, j] = main.pack_array(qc)
del df[testname] # No need to keep this any longer.
todrop = list(todrop)
if len(todrop) > 0:
df.drop(todrop, inplace=True)
df.reset_index(inplace=True, drop=True)
testNames = df.columns[2:].values.tolist()
df[['Truth']] = df[['Truth']].apply(parse_truth)
df[testNames] = df[testNames].apply(parse)
return df
