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Removing redundancy
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@castelao
castelao committed Jan 14, 2020
1 parent c6d6011 commit 92027ba
Showing 1 changed file with 21 additions and 147 deletions.
168 changes: 21 additions & 147 deletions cotede/qc.py
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Expand Up @@ -193,15 +193,6 @@ def evaluate(self, v, cfg):
except:
module_logger.warning("Fail on valid_speed")

if 'global_range' in cfg:
y = GlobalRange(self.input, v, cfg['global_range'], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'regional_range' in cfg:
module_logger.warning(
"Sorry, I'm no ready to evaluate regional_range()")
Expand All @@ -210,76 +201,6 @@ def evaluate(self, v, cfg):
module_logger.warning(
"Sorry, I'm no ready to evaluate pressure_increasing()")

if 'profile_envelop' in cfg:
y = ProfileEnvelop(
self.input, v, cfg['profile_envelop'], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'constant_cluster_size' in cfg:
y = ConstantClusterSize(
self.input, v, cfg['constant_cluster_size'], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'gradient' in cfg:
y = Gradient(self.input, v, cfg['gradient'], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'gradient_depthconditional' in cfg:
y = GradientDepthConditional(self.input,
v,
cfg['gradient_depthconditional'],
autoflag=True)
if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'spike' in cfg:
y = Spike(self.input, v, cfg['spike'], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]


if 'spike_depthconditional' in cfg:
y = SpikeDepthConditional(self.input,
v,
cfg['spike_depthconditional'],
autoflag=True)
if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'stuck_value' in cfg:
y = StuckValue(self.input, v, cfg['stuck_value'], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'grey_list' in cfg:
module_logger.warning("Sorry I'm not ready to evaluate grey_list()")

Expand All @@ -295,8 +216,27 @@ def evaluate(self, v, cfg):
module_logger.warning(
"Sorry I'm not ready to evaluate deepest_pressure()")

if 'tukey53H_norm' in cfg:
y = Tukey53H(self.input, v, cfg['tukey53H_norm'], autoflag=True)
catalog = {
'bin_spike': Bin_Spike,
'cars_normbias': CARS_NormBias,
'constant_cluster_size': ConstantClusterSize,
'cum_rate_of_change': CumRateOfChange,
'digit_roll_over': DigitRollOver,
'global_range': GlobalRange,
'gradient': Gradient,
'gradient_depthconditional': GradientDepthConditional,
'profile_envelop': ProfileEnvelop,
'rate_of_change': RateOfChange,
'spike': Spike,
'spike_depthconditional': SpikeDepthConditional,
'stuck_value': StuckValue,
'tukey53H_norm': Tukey53H,
'woa_normbias': WOA_NormBias,
}

for criterion in [c for c in catalog if c in cfg]:
Procedure = catalog[criterion]
y = Procedure(self.input, v, cfg[criterion], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
Expand All @@ -316,27 +256,6 @@ def evaluate(self, v, cfg):
# w = wfunc(z[ind]-z[i], cfg_tmp['dzwindow'])
# smooth[i] = (T[ind]*w).sum()/w.sum()

if 'digit_roll_over' in cfg:
y = DigitRollOver(self.input,
v,
cfg['digit_roll_over'],
autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'bin_spike' in cfg:
y = Bin_Spike(self.input, v, cfg['bin_spike'], autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'density_inversion' in cfg:
try:
y = DensityInversion(self.input,
Expand All @@ -351,27 +270,6 @@ def evaluate(self, v, cfg):
except:
module_logger.warning("Fail on density_inversion")

if 'woa_normbias' in cfg:
y = WOA_NormBias(self.input, v, cfg['woa_normbias'], autoflag=True)

if self.saveauxiliary:
for f in y.features:
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'cars_normbias' in cfg:
y = CARS_NormBias(self.input,
v,
cfg['cars_normbias'],
autoflag=True)

if self.saveauxiliary:
for f in y.features:
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

#if 'pstep' in cfg:
# ind = np.isfinite(self.input[v])
# ind = ma.getmaskarray(self.input[v])
Expand All @@ -380,30 +278,6 @@ def evaluate(self, v, cfg):
# [ma.masked_all(1),
# np.diff(self.input['PRES'][ind])])

if 'rate_of_change' in cfg:
y = RateOfChange(self.input,
v,
cfg['rate_of_change'],
autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

if 'cum_rate_of_change' in cfg:
y = CumRateOfChange(self.input,
v,
cfg['cum_rate_of_change'],
autoflag=True)

if self.saveauxiliary:
for f in y.features.keys():
self.features[v][f] = y.features[f]
for f in y.flags:
self.flags[v][f] = y.flags[f]

# FIXME: the Anomaly Detection and Fuzzy require some features
# to be estimated previously. Generalize this.
if 'anomaly_detection' in cfg:
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