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DM-28480: Update for new flake8 #72

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Jan 27, 2021
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DM-28480: Update for new flake8 #72

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59383fd
Change trigger condition for actions
timj Jan 26, 2021
12c1cb0
Update actions to py3.8
timj Jan 26, 2021
96b31d7
Fix flake8 f-string warning
timj Jan 26, 2021
ea5732f
Switch to ignoring W503
timj Jan 26, 2021
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8 changes: 5 additions & 3 deletions .github/workflows/lint.yaml
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Original file line number Diff line number Diff line change
@@ -1,8 +1,10 @@
name: lint

on:
- push
- pull_request
push:
branches:
- master
pull_request:

jobs:
lint:
Expand All @@ -13,7 +15,7 @@ jobs:
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.7
python-version: 3.8

- name: Install
run: pip install -r <(curl https://raw.githubusercontent.com/lsst/linting/master/requirements.txt)
Expand Down
52 changes: 26 additions & 26 deletions python/lsst/cp/pipe/makeBrighterFatterKernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@ class MakeBrighterFatterKernelTaskConfig(pexConfig.Config):
)
isrDesirableSteps = pexConfig.ListField(
dtype=str,
doc="isr operations that it is advisable to perform, but are not mission-critical." +
doc="isr operations that it is advisable to perform, but are not mission-critical."
" WARNs are logged for any of these found to be False.",
default=['doBias', 'doDark', 'doCrosstalk', 'doDefect', 'doLinearize']
)
Expand Down Expand Up @@ -142,7 +142,7 @@ class MakeBrighterFatterKernelTaskConfig(pexConfig.Config):
)
xcorrCheckRejectLevel = pexConfig.Field(
dtype=float,
doc="Sanity check level for the sum of the input cross-correlations. Arrays which " +
doc="Sanity check level for the sum of the input cross-correlations. Arrays which "
"sum to greater than this are discarded before the clipped mean is calculated.",
default=2.0
)
Expand All @@ -158,7 +158,7 @@ class MakeBrighterFatterKernelTaskConfig(pexConfig.Config):
)
nSigmaClipKernelGen = pexConfig.Field(
dtype=float,
doc="Number of sigma to clip to during pixel-wise clipping when generating the kernel. See " +
doc="Number of sigma to clip to during pixel-wise clipping when generating the kernel. See "
"the generateKernel docstring for more info.",
default=4
)
Expand All @@ -184,7 +184,7 @@ class MakeBrighterFatterKernelTaskConfig(pexConfig.Config):
)
biasCorr = pexConfig.Field(
dtype=float,
doc="An empirically determined correction factor, used to correct for the sigma-clipping of" +
doc="An empirically determined correction factor, used to correct for the sigma-clipping of"
" a non-Gaussian distribution. Post DM-15277, code will exist here to calculate appropriate values",
default=0.9241
)
Expand All @@ -195,7 +195,7 @@ class MakeBrighterFatterKernelTaskConfig(pexConfig.Config):
)
fixPtcThroughOrigin = pexConfig.Field(
dtype=bool,
doc="Constrain the fit of the photon transfer curve to go through the origin when measuring" +
doc="Constrain the fit of the photon transfer curve to go through the origin when measuring"
"the gain?",
default=True
)
Expand All @@ -210,13 +210,13 @@ class MakeBrighterFatterKernelTaskConfig(pexConfig.Config):
)
ignoreAmpsForAveraging = pexConfig.ListField(
dtype=str,
doc="List of amp names to ignore when averaging the amplifier kernels into the detector" +
doc="List of amp names to ignore when averaging the amplifier kernels into the detector"
" kernel. Only relevant for level = AMP",
default=[]
)
backgroundWarnLevel = pexConfig.Field(
dtype=float,
doc="Log warnings if the mean of the fitted background is found to be above this level after " +
doc="Log warnings if the mean of the fitted background is found to be above this level after "
"differencing image pair.",
default=0.1
)
Expand Down Expand Up @@ -435,22 +435,22 @@ def validateIsrConfig(self):

for configParam in self.config.isrMandatorySteps:
if configDict[configParam] is False:
raise RuntimeError('Must set config.isr.%s to True '
'for brighter-fatter kernel calculation' % configParam)
raise RuntimeError(f'Must set config.isr.{configParam} to True '
'for brighter-fatter kernel calculation')

for configParam in self.config.isrForbiddenSteps:
if configDict[configParam] is True:
raise RuntimeError('Must set config.isr.%s to False '
'for brighter-fatter kernel calculation' % configParam)
raise RuntimeError(f'Must set config.isr.{configParam} to False '
'for brighter-fatter kernel calculation')

for configParam in self.config.isrDesirableSteps:
if configParam not in configDict:
self.log.info('Failed to find key %s in the isr config dict. You probably want ' +
'to set the equivalent for your obs_package to True.' % configParam)
self.log.info('Failed to find key %s in the isr config dict. You probably want '
'to set the equivalent for your obs_package to True.', configParam)
continue
if configDict[configParam] is False:
self.log.warn('Found config.isr.%s set to False for brighter-fatter kernel calculation. '
'It is probably desirable to have this set to True' % configParam)
'It is probably desirable to have this set to True', configParam)

# subtask settings
if not self.config.isr.assembleCcd.doTrim:
Expand Down Expand Up @@ -1239,8 +1239,8 @@ def _iterativeRegression(self, x, y, fixThroughOrigin=False, nSigmaClip=None, ma
res = (y - slope * x) / x
resMean = afwMath.makeStatistics(res, afwMath.MEANCLIP, sctrl).getValue()
resStd = np.sqrt(afwMath.makeStatistics(res, afwMath.VARIANCECLIP, sctrl).getValue())
index = np.where((res > (resMean + nSigmaClip*resStd)) |
(res < (resMean - nSigmaClip*resStd)))
index = np.where((res > (resMean + nSigmaClip*resStd))
| (res < (resMean - nSigmaClip*resStd)))
self.log.debug("%.3f %.3f %.3f %.3f" % (resMean, resStd, np.max(res), nSigmaClip))
if np.shape(np.where(index))[1] == 0 or (nIter >= maxIter): # run out of points or iters
break
Expand Down Expand Up @@ -1439,8 +1439,8 @@ def successiveOverRelax(self, source, maxIter=None, eLevel=None):
# Calculate the initial error
for i in range(1, func.shape[0] - 1):
for j in range(1, func.shape[1] - 1):
resid[i, j] = (func[i, j - 1] + func[i, j + 1] + func[i - 1, j] +
func[i + 1, j] - 4*func[i, j] - source[i - 1, j - 1])
resid[i, j] = (func[i, j - 1] + func[i, j + 1] + func[i - 1, j]
+ func[i + 1, j] - 4*func[i, j] - source[i - 1, j - 1])
inError = np.sum(np.abs(resid))

# Iterate until convergence
Expand All @@ -1454,24 +1454,24 @@ def successiveOverRelax(self, source, maxIter=None, eLevel=None):
if nIter%2 == 0:
for i in range(1, func.shape[0] - 1, 2):
for j in range(1, func.shape[1] - 1, 2):
resid[i, j] = float(func[i, j-1] + func[i, j + 1] + func[i - 1, j] +
func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
resid[i, j] = float(func[i, j-1] + func[i, j + 1] + func[i - 1, j]
+ func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
func[i, j] += omega*resid[i, j]*.25
for i in range(2, func.shape[0] - 1, 2):
for j in range(2, func.shape[1] - 1, 2):
resid[i, j] = float(func[i, j - 1] + func[i, j + 1] + func[i - 1, j] +
func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
resid[i, j] = float(func[i, j - 1] + func[i, j + 1] + func[i - 1, j]
+ func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
func[i, j] += omega*resid[i, j]*.25
else:
for i in range(1, func.shape[0] - 1, 2):
for j in range(2, func.shape[1] - 1, 2):
resid[i, j] = float(func[i, j - 1] + func[i, j + 1] + func[i - 1, j] +
func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
resid[i, j] = float(func[i, j - 1] + func[i, j + 1] + func[i - 1, j]
+ func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
func[i, j] += omega*resid[i, j]*.25
for i in range(2, func.shape[0] - 1, 2):
for j in range(1, func.shape[1] - 1, 2):
resid[i, j] = float(func[i, j - 1] + func[i, j + 1] + func[i - 1, j] +
func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
resid[i, j] = float(func[i, j - 1] + func[i, j + 1] + func[i - 1, j]
+ func[i + 1, j] - 4.0*func[i, j] - dx*dx*source[i - 1, j - 1])
func[i, j] += omega*resid[i, j]*.25
outError = np.sum(np.abs(resid))
if outError < inError*eLevel:
Expand Down
10 changes: 5 additions & 5 deletions python/lsst/cp/pipe/measureCrosstalk.py
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Expand Up @@ -220,9 +220,9 @@ def run(self, inputExp, sourceExps=[]):
sourceAmpName = sourceAmp.getName()
sourceAmpImage = sourceIm[sourceAmp.getBBox()]
sourceMask = sourceAmpImage.mask.array
select = ((sourceMask & detected > 0) &
(sourceMask & bad == 0) &
np.isfinite(sourceAmpImage.image.array))
select = ((sourceMask & detected > 0)
& (sourceMask & bad == 0)
& np.isfinite(sourceAmpImage.image.array))
count = np.sum(select)
self.log.debug(" Source amplifier: %s", sourceAmpName)

Expand Down Expand Up @@ -606,8 +606,8 @@ def measureCrosstalkCoefficients(self, ratios, rejIter, rejSigma):
else:
correctionFactor = self.sigmaClipCorrection(rejSigma)
calib.coeffErr[ii][jj] = np.std(values) * correctionFactor
calib.coeffValid[ii][jj] = (np.abs(calib.coeffs[ii][jj]) >
calib.coeffErr[ii][jj] / np.sqrt(calib.coeffNum[ii][jj]))
calib.coeffValid[ii][jj] = (np.abs(calib.coeffs[ii][jj])
> calib.coeffErr[ii][jj] / np.sqrt(calib.coeffNum[ii][jj]))

if calib.coeffNum[ii][jj] > 1:
self.debugRatios('measure', ratios, ordering[ii], ordering[jj],
Expand Down
4 changes: 2 additions & 2 deletions python/lsst/cp/pipe/ptc/astierCovPtcFit.py
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Expand Up @@ -399,8 +399,8 @@ def evalCovModel(self, mu=None):
# assumes that mu is 1d
bigMu = mu[:, np.newaxis, np.newaxis]*gain
# c(=a*b in Astier+19) also has a contribution to the last term, that is absent for now.
covModel = (bigMu/(gain*gain)*(a1*bigMu+2./3.*(bigMu*bigMu)*(a2 + c1) +
(1./3.*a3 + 5./6.*ac)*(bigMu*bigMu*bigMu)) + noise[np.newaxis, :, :]/gain**2)
covModel = (bigMu/(gain*gain)*(a1*bigMu+2./3.*(bigMu*bigMu)*(a2 + c1)
+ (1./3.*a3 + 5./6.*ac)*(bigMu*bigMu*bigMu)) + noise[np.newaxis, :, :]/gain**2)
# add the Poisson term, and the read out noise (variance)
covModel[:, 0, 0] += mu/gain

Expand Down
4 changes: 2 additions & 2 deletions python/lsst/cp/pipe/ptc/cpSolvePtcTask.py
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Expand Up @@ -524,7 +524,7 @@ def fitPtc(self, dataset):
if dataset.ptcFitType:
ptcFitType = dataset.ptcFitType
else:
raise RuntimeError(f"ptcFitType is None of empty in PTC dataset.")
raise RuntimeError("ptcFitType is None of empty in PTC dataset.")
matrixSide = self.config.maximumRangeCovariancesAstier
nanMatrix = np.empty((matrixSide, matrixSide))
nanMatrix[:] = np.nan
Expand Down Expand Up @@ -615,7 +615,7 @@ def errFunc(p, x, y):
varVecFinal = varVecOriginal[mask]

if Counter(mask)[False] > 0:
self.log.info((f"Number of points discarded in PTC of amplifier {ampName}:" +
self.log.info((f"Number of points discarded in PTC of amplifier {ampName}:"
f" {Counter(mask)[False]} out of {len(meanVecOriginal)}"))

if (len(meanVecFinal) < len(parsIniPtc)):
Expand Down
18 changes: 9 additions & 9 deletions python/lsst/cp/pipe/ptc/plotPtc.py
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Expand Up @@ -126,7 +126,7 @@ def run(self, filenameFull, datasetPtc, linearizer=None, log=None):
elif ptcFitType in ["EXPAPPROXIMATION", "POLYNOMIAL"]:
self._plotStandardPtc(datasetPtc, ptcFitType, pdfPages)
else:
raise RuntimeError(f"The input dataset had an invalid dataset.ptcFitType: {ptcFitType}. \n" +
raise RuntimeError(f"The input dataset had an invalid dataset.ptcFitType: {ptcFitType}. \n"
"Options: 'FULLCOVARIANCE', EXPAPPROXIMATION, or 'POLYNOMIAL'.")
if linearizer:
self._plotLinearizer(datasetPtc, linearizer, pdfPages)
Expand Down Expand Up @@ -306,11 +306,11 @@ def plotCovariances(mu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, cov
chi2FullModelNoBVar = calculateWeightedReducedChi2(varVecFinalNoB, varVecModelFinalNoB,
varWeightsFinalNoB, len(meanVecFinalNoB),
3)
stringLegend = (f"Gain: {gain:.4} e/ADU \n" +
f"Noise: {noise:.4} e \n" +
r"$a_{00}$: %.3e 1/e"%aCoeffs[0, 0] +
"\n" + r"$b_{00}$: %.3e 1/e"%bCoeffs[0, 0] +
f"\nLast in fit: {meanVecFinal[-1]:.7} ADU ")
stringLegend = (f"Gain: {gain:.4} e/ADU \n"
f"Noise: {noise:.4} e \n"
+ r"$a_{00}$: %.3e 1/e"%aCoeffs[0, 0] + "\n"
+ r"$b_{00}$: %.3e 1/e"%bCoeffs[0, 0]
+ f"\nLast in fit: {meanVecFinal[-1]:.7} ADU ")
minMeanVecFinal = np.nanmin(meanVecFinal)
maxMeanVecFinal = np.nanmax(meanVecFinal)
deltaXlim = maxMeanVecFinal - minMeanVecFinal
Expand Down Expand Up @@ -390,10 +390,10 @@ def plotCovariances(mu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, cov
pdfPages.savefig(f2)
fResCov00.suptitle("Residuals (data-model) for Cov00 (Var)", fontsize=supTitleFontSize)
pdfPages.savefig(fResCov00)
fCov01.suptitle("Cov01 as in Astier+19 (nearest parallel neighbor covariance) \n" +
fCov01.suptitle("Cov01 as in Astier+19 (nearest parallel neighbor covariance) \n"
" Fit: Eq. 20, Astier+19", fontsize=supTitleFontSize)
pdfPages.savefig(fCov01)
fCov10.suptitle("Cov10 as in Astier+19 (nearest serial neighbor covariance) \n" +
fCov10.suptitle("Cov10 as in Astier+19 (nearest serial neighbor covariance) \n"
"Fit: Eq. 20, Astier+19", fontsize=supTitleFontSize)
pdfPages.savefig(fCov10)

Expand Down Expand Up @@ -1039,7 +1039,7 @@ def _plotLinearizer(self, dataset, linearizer, pdfPages):
f.suptitle("Linearity \n Fit: Polynomial (degree: %g)"
% (len(pars)-1),
fontsize=supTitleFontSize)
f2.suptitle(r"Fractional NL residual" + "\n" +
f2.suptitle(r"Fractional NL residual" "\n"
r"$100\times \frac{(k_0 + k_1*Time-\mu)}{k_0+k_1*Time}$",
fontsize=supTitleFontSize)
pdfPages.savefig(f)
Expand Down
18 changes: 9 additions & 9 deletions python/lsst/cp/pipe/ptc/plotPtcGen2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -153,7 +153,7 @@ def run(self, filenameFull, datasetPtc, linearizer=None, log=None):
elif ptcFitType in ["EXPAPPROXIMATION", "POLYNOMIAL"]:
self._plotStandardPtc(datasetPtc, ptcFitType, pdfPages)
else:
raise RuntimeError(f"The input dataset had an invalid dataset.ptcFitType: {ptcFitType}. \n" +
raise RuntimeError(f"The input dataset had an invalid dataset.ptcFitType: {ptcFitType}. \n"
"Options: 'FULLCOVARIANCE', EXPAPPROXIMATION, or 'POLYNOMIAL'.")
if linearizer:
self._plotLinearizer(datasetPtc, linearizer, pdfPages)
Expand Down Expand Up @@ -333,11 +333,11 @@ def plotCovariances(mu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, cov
chi2FullModelNoBVar = calculateWeightedReducedChi2(varVecFinalNoB, varVecModelFinalNoB,
varWeightsFinalNoB, len(meanVecFinalNoB),
3)
stringLegend = (f"Gain: {gain:.4} e/ADU \n" +
f"Noise: {noise:.4} e \n" +
r"$a_{00}$: %.3e 1/e"%aCoeffs[0, 0] +
"\n" + r"$b_{00}$: %.3e 1/e"%bCoeffs[0, 0] +
f"\nLast in fit: {meanVecFinal[-1]:.7} ADU ")
stringLegend = (f"Gain: {gain:.4} e/ADU \n"
f"Noise: {noise:.4} e \n"
+ r"$a_{00}$: %.3e 1/e"%aCoeffs[0, 0] + "\n"
+ r"$b_{00}$: %.3e 1/e"%bCoeffs[0, 0]
+ f"\nLast in fit: {meanVecFinal[-1]:.7} ADU ")
minMeanVecFinal = np.nanmin(meanVecFinal)
maxMeanVecFinal = np.nanmax(meanVecFinal)
deltaXlim = maxMeanVecFinal - minMeanVecFinal
Expand Down Expand Up @@ -417,10 +417,10 @@ def plotCovariances(mu, covs, covsModel, covsWeights, covsNoB, covsModelNoB, cov
pdfPages.savefig(f2)
fResCov00.suptitle("Residuals (data-model) for Cov00 (Var)", fontsize=supTitleFontSize)
pdfPages.savefig(fResCov00)
fCov01.suptitle("Cov01 as in Astier+19 (nearest parallel neighbor covariance) \n" +
fCov01.suptitle("Cov01 as in Astier+19 (nearest parallel neighbor covariance) \n"
" Fit: Eq. 20, Astier+19", fontsize=supTitleFontSize)
pdfPages.savefig(fCov01)
fCov10.suptitle("Cov10 as in Astier+19 (nearest serial neighbor covariance) \n" +
fCov10.suptitle("Cov10 as in Astier+19 (nearest serial neighbor covariance) \n"
"Fit: Eq. 20, Astier+19", fontsize=supTitleFontSize)
pdfPages.savefig(fCov10)

Expand Down Expand Up @@ -1066,7 +1066,7 @@ def _plotLinearizer(self, dataset, linearizer, pdfPages):
f.suptitle("Linearity \n Fit: Polynomial (degree: %g)"
% (len(pars)-1),
fontsize=supTitleFontSize)
f2.suptitle(r"Fractional NL residual" + "\n" +
f2.suptitle(r"Fractional NL residual" "\n"
r"$100\times \frac{(k_0 + k_1*Time-\mu)}{k_0+k_1*Time}$",
fontsize=supTitleFontSize)
pdfPages.savefig(f)
Expand Down