def test(p):
'''Return quality control decisions.
'''
# The test is run on re-ordered data.
nlevels, z, t = ICDC.reordered_data(p)
# Define default QC.
defaultqc = np.zeros(p.n_levels(), dtype=bool)
# No check for the Caspian Sea or Great Lakes.
lat = p.latitude()
lon = p.longitude()
if ((lat >= 35.0 and lat <= 45.0 and lon >= 45.0 and lon <= 60.0) or
(lat >= 40.0 and lat <= 50.0 and lon >= -95.0 and lon <= -75.0)):
return defaultqc
# Get range.
ranges = get_climatology_range(nlevels, z, lat, lon, p.month())
if ranges is None:
return defaultqc
# Perform the QC.
tmin, tmax = ranges
qc = ((t < tmin) | (t > tmax)) & (tmin != fillValue) & (tmax != fillValue)
return ICDC.revert_qc_order(p, qc)
def test(p, parameters):
'''Return quality control decisions.
'''
# The test is run on re-ordered data.
nlevels, z, t = ICDC.reordered_data(p)
# Define default QC.
defaultqc = np.zeros(p.n_levels(), dtype=bool)
# No check for the Caspian Sea or Great Lakes.
lat = p.latitude()
lon = p.longitude()
if ((lat >= 35.0 and lat <= 45.0 and lon >= 45.0 and lon <= 60.0) or
(lat >= 40.0 and lat <= 50.0 and lon >= -95.0 and lon <= -75.0)):
return defaultqc
# parameters
nc = Dataset('data/climatological_t_median_and_amd_for_aqc.nc', 'r')
# Get range.
ranges = get_climatology_range(nlevels, z, lat, lon, p.month(), nc)
if ranges is None:
return defaultqc
# Perform the QC.
tmin, tmax = ranges
qc = ((t < tmin) | (t > tmax)) & (tmin != nc.fillValue) & (tmax != nc.fillValue)
return ICDC.revert_qc_order(p, qc)
def test(p, parameters):
'''Return quality control decisions.
'''
# The test is run on re-ordered data.
nlevels, z, t = ICDC.reordered_data(p)
qc = np.zeros(
nlevels,
dtype=bool) # Reordered data may be a subset of available levels.
defaultqc = np.zeros(
p.n_levels(), dtype=bool) # Default QC flags for full set of levels.
if nlevels < 3: return defaultqc # Not enough levels to check.
# Ignore any levels outside of limits.
parminover = -2.3
parmaxover = 33.0
use = (t > parminover) & (t < parmaxover)
nuse = np.count_nonzero(use)
if nuse < 3: return defaultqc
zuse = z[use]
tuse = t[use]
origlevels = (np.arange(nlevels))[use]
# Extract sections of the arrays. We are QCing the values
# in the z2 and v3 arrays.
z1 = zuse[0:-2]
z2 = zuse[1:-1]
z3 = zuse[2:]
v1 = tuse[0:-2]
v2 = tuse[1:-1]
v3 = tuse[2:]
ol = origlevels[1:-1]
# Calculate the level of 'spike'.
z13 = z3 - z1
z12 = z2 - z1
z23 = z3 - z2
a = 0.5 * (v1 + v3)
q1 = np.abs(v2 - a)
q2 = np.abs(0.5 * (v3 - v1))
spike = q1 - q2
# Define the threshold at each level.
spikemax = np.ndarray(nuse - 2)
spikemax[:] = 4.0
spikemax[z2 > 1000.0] = 3.0
spikemax[z2 > 2000.0] = 2.0
# Set QC flags.
qc[ol[spike > spikemax]] = True
return ICDC.revert_qc_order(p, qc)
def test(p, parameters):
'''Return quality control decisions.
'''
# The test is run on re-ordered data.
nlevels, z, t = ICDC.reordered_data(p)
qc = np.zeros(nlevels, dtype=bool) # Reordered data may be a subset of available levels.
defaultqc = np.zeros(p.n_levels(), dtype=bool) # Default QC flags for full set of levels.
if nlevels < 3: return defaultqc # Not enough levels to check.
# Ignore any levels outside of limits.
parminover = -2.3
parmaxover = 33.0
use = (t > parminover) & (t < parmaxover)
nuse = np.count_nonzero(use)
if nuse < 3: return defaultqc
zuse = z[use]
tuse = t[use]
origlevels = (np.arange(nlevels))[use]
# Extract sections of the arrays. We are QCing the values
# in the z2 and v3 arrays.
z1 = zuse[0:-2]
z2 = zuse[1:-1]
z3 = zuse[2:]
v1 = tuse[0:-2]
v2 = tuse[1:-1]
v3 = tuse[2:]
ol = origlevels[1:-1]
# Calculate the level of 'spike'.
z13 = z3 - z1
z12 = z2 - z1
z23 = z3 - z2
a = 0.5 * (v1 + v3)
q1 = np.abs(v2 - a)
q2 = np.abs(0.5 * (v3 - v1))
spike = q1 - q2
# Define the threshold at each level.
spikemax = np.ndarray(nuse - 2)
spikemax[:] = 4.0
spikemax[z2 > 1000.0] = 3.0
spikemax[z2 > 2000.0] = 2.0
# Set QC flags.
qc[ol[spike > spikemax]] = True
return ICDC.revert_qc_order(p, qc)
def test(p):
'''Return quality control decisions.
'''
# Default set of QC flags to return.
qc = np.zeros(p.n_levels(), dtype=bool)
# Set minimum allowed levels.
db = wod_database(p)
if db == 'OSD':
minlevs = 7
elif db == 'CTD':
minlevs = 50
elif db == 'PFL':
minlevs = 20
elif db == 'APB':
minlevs = 20
elif db == 'MBT':
minlevs = 7
elif db == 'XBT':
minlevs = 20
else:
return qc # Do not have the information to QC other types.
# Check that we have the levels we need.
nlevels, z, t = ICDC.reordered_data(p)
if nlevels <= minlevs: return qc
# Count stuck values.
n = np.ones(nlevels, dtype=int)
for i in range(nlevels - minlevs):
for j in range(i + 1, nlevels):
diff = np.abs(t[i] - t[j])
if diff > 0.0001: break
n[i] += 1
# Find the largest stuck value range.
i = np.argmax(n)
if n[i] < minlevs: return qc
thick = z[i + n[i] - 1] - z[i]
if thick >= 200.0:
# If setting the QC flags we need to be careful about level order.
qclo = qc[0:nlevels]
qclo[i:i + n[i]] = True
qc = ICDC.revert_qc_order(p, qclo)
return qc
def test(p, parameters):
'''Return quality control decisions.
'''
# Default set of QC flags to return.
qc = np.zeros(p.n_levels(), dtype=bool)
# Set minimum allowed levels.
db = wod_database(p)
if db == 'OSD':
minlevs = 7
elif db == 'CTD':
minlevs = 50
elif db == 'PFL':
minlevs = 20
elif db == 'APB':
minlevs = 20
elif db == 'MBT':
minlevs = 7
elif db == 'XBT':
minlevs = 20
else:
return qc # Do not have the information to QC other types.
# Check that we have the levels we need.
nlevels, z, t = ICDC.reordered_data(p)
if nlevels <= minlevs: return qc
# Count stuck values.
n = np.ones(nlevels, dtype=int)
for i in range(nlevels - minlevs):
for j in range(i + 1, nlevels):
diff = np.abs(t[i] - t[j])
if diff > 0.0001: break
n[i] += 1
# Find the largest stuck value range.
i = np.argmax(n)
if n[i] < minlevs: return qc
thick = z[i + n[i] - 1] - z[i]
if thick >= 200.0:
# If setting the QC flags we need to be careful about level order.
qclo = qc[0:nlevels]
qclo[i:i+n[i]] = True
qc = ICDC.revert_qc_order(p, qclo)
return qc
def test(p, parameters):
'''Return a set of QC decisions.
'''
nlevels, z, t = ICDC.reordered_data(p)
qc = (t < parminover) | (t > parmaxover)
for i, tval in enumerate(t):
if qc[i]: continue # Already rejected.
zval = z[i]
if np.any((tval >= tcrude1) & (tval <= tcrude2) &
(zval <= zcrude1) & (zval >= zcrude2)):
qc[i] = True
return ICDC.revert_qc_order(p, qc)
def test(p):
'''Return a set of QC decisions.
'''
nlevels, z, t = ICDC.reordered_data(p)
qc = (t < parminover) | (t > parmaxover)
for i, tval in enumerate(t):
if qc[i]: continue # Already rejected.
zval = z[i]
if np.any((tval >= tcrude1) & (tval <= tcrude2) &
(zval <= zcrude1) & (zval >= zcrude2)):
qc[i] = True
return ICDC.revert_qc_order(p, qc)
def test(p, parameters):
'''Return quality control decisions.
'''
# Global ranges - data outside these bounds are ignored.
parminover = -2.3
parmaxover = 33.0
# The test is run on re-ordered data.
nlevels, z, t = ICDC.reordered_data(p)
qc = np.zeros(nlevels, dtype=bool)
# Calculate gradients and thresholds.
z0 = z[0:-1]
z1 = z[1:]
t0 = t[0:-1]
t1 = t[1:]
gradients = (t1 - t0) / (z1 - z0)
zmean = 0.5 * (z0 + z1)
zmean[zmean < 1.0] = 1.0
gradmin = -150.0 / zmean - 0.010
gradmin[gradmin < -4.0] = -4.0
gradmax = 100.0 / zmean + 0.015
gradmax[gradmax > 1.5] = 1.5
# Find where the gradients and outside the thresholds.
result = np.where(((gradients < gradmin) | (gradients > gradmax)) &
(t0 > parminover) & (t1 > parminover) &
(t0 < parmaxover) & (t1 < parmaxover))[0]
# Both levels that form the gradient have to be rejected.
if len(result) > 0:
qc[result] = True
qc[result + 1] = True
return ICDC.revert_qc_order(p, qc)
def test(p, parameters):
'''Return quality control decisions.
'''
# Global ranges - data outside these bounds are ignored.
parminover = -2.3
parmaxover = 33.0
# The test is run on re-ordered data.
nlevels, z, t = ICDC.reordered_data(p)
qc = np.zeros(nlevels, dtype=bool)
# Calculate gradients and thresholds.
z0 = z[0:-1]
z1 = z[1:]
t0 = t[0:-1]
t1 = t[1:]
gradients = (t1 - t0) / (z1 - z0)
zmean = 0.5 * (z0 + z1)
zmean[zmean < 1.0] = 1.0
gradmin = -150.0 / zmean - 0.010
gradmin[gradmin < -4.0] = -4.0
gradmax = 100.0 / zmean + 0.015
gradmax[gradmax > 1.5] = 1.5
# Find where the gradients and outside the thresholds.
result = np.where(((gradients < gradmin) | (gradients > gradmax))
& (t0 > parminover) & (t1 > parminover)
& (t0 < parmaxover) & (t1 < parmaxover))[0]
# Both levels that form the gradient have to be rejected.
if len(result) > 0:
qc[result] = True
qc[result + 1] = True
return ICDC.revert_qc_order(p, qc)
def test(p, parameters):
'''Return quality control decisions.
'''
# Initialise data.
qc = np.zeros(p.n_levels(), dtype=bool)
parminover = -2.3
parmaxover = 33.0
levminext = 6
deltaext = 0.5
maxextre = 4
# Check that we have the levels we need.
nlevels, z, t = ICDC.reordered_data(p)
if nlevels <= levminext: return qc
# Exclude data outside allowed range.
use = (t > parminover) & (t <= parmaxover)
nuse = np.count_nonzero(use)
if nuse < levminext: return qc
z = z[use]
t = t[use]
# Find and count the extrema.
ima = 0
for i in range(1, nuse - 1):
pcent = t[i]
pa = np.abs(pcent - t[i - 1])
pb = np.abs(pcent - t[i + 1])
pmin = min(pa, pb)
if pcent > t[i - 1] and pcent > t[i + 1] and pmin > deltaext:
ima += 1
if pcent < t[i - 1] and pcent < t[i + 1] and pmin > deltaext:
ima += 1
if ima > maxextre:
qc[:] = True
return qc
def test(p):
'''Return quality control decisions.
'''
# Initialise data.
qc = np.zeros(p.n_levels(), dtype=bool)
parminover = -2.3
parmaxover = 33.0
levminext = 6
deltaext = 0.5
maxextre = 4
# Check that we have the levels we need.
nlevels, z, t = ICDC.reordered_data(p)
if nlevels <= levminext: return qc
# Exclude data outside allowed range.
use = (t > parminover) & (t <= parmaxover)
nuse = np.count_nonzero(use)
if nuse < levminext: return qc
z = z[use]
t = t[use]
# Find and count the extrema.
ima = 0
for i in range(1, nuse - 1):
pcent = t[i]
pa = np.abs(pcent - t[i - 1])
pb = np.abs(pcent - t[i + 1])
pmin = min(pa, pb)
if pcent > t[i - 1] and pcent > t[i + 1] and pmin > deltaext:
ima += 1
if pcent < t[i - 1] and pcent < t[i + 1] and pmin > deltaext:
ima += 1
if ima > maxextre:
qc[:] = True
return qc
