uncertainties =============== uncertainties refactor ----------------------- :: (chroma_env)delta:chroma blyth$ python -m uncertainties.1to2 -w chroma RefactoringTool: Refactored chroma/benchmark.py --- chroma/benchmark.py (original) +++ chroma/benchmark.py (refactored) @@ -45,7 +45,7 @@ # first kernel call incurs some driver overhead run_times.append(elapsed) - return nphotons/ufloat((np.mean(run_times),np.std(run_times))) + return nphotons/ufloat(np.mean(run_times),np.std(run_times)) def load_photons(number=100, nphotons=500000): """Returns the average number of photons moved to the GPU device memory @@ -67,7 +67,7 @@ # first kernel call incurs some driver overhead run_times.append(elapsed) - return nphotons/ufloat((np.mean(run_times),np.std(run_times))) + return nphotons/ufloat(np.mean(run_times),np.std(run_times)) def propagate(gpu_detector, number=10, nphotons=500000, nthreads_per_block=64, max_blocks=1024): @@ -95,7 +95,7 @@ # first kernel call incurs some driver overhead run_times.append(elapsed) - return nphotons/ufloat((np.mean(run_times),np.std(run_times))) + return nphotons/ufloat(np.mean(run_times),np.std(run_times)) @tools.profile_if_possible def pdf(gpu_detector, npdfs=10, nevents=100, nreps=16, ndaq=1, @@ -153,7 +153,7 @@ # first kernel call incurs some driver overhead run_times.append(elapsed) - return nevents*nreps*ndaq/ufloat((np.mean(run_times),np.std(run_times))) + return nevents*nreps*ndaq/ufloat(np.mean(run_times),np.std(run_times)) @tools.profile_if_possible def pdf_eval(gpu_detector, npdfs=10, nevents=25, nreps=16, ndaq=128, @@ -238,7 +238,7 @@ # first kernel call incurs some driver overhead run_times.append(elapsed) - return nevents*nreps*ndaq/ufloat((np.mean(run_times),np.std(run_times))) + return nevents*nreps*ndaq/ufloat(np.mean(run_times),np.std(run_times)) if __name__ == '__main__': RefactoringTool: Refactored chroma/likelihood.py --- chroma/likelihood.py (original) +++ chroma/likelihood.py (refactored) @@ -101,12 +101,12 @@ hit_prob = np.maximum(hit_prob, 0.5 / ntotal) hit_channel_prob = np.log(hit_prob).sum() - log_likelihood = ufloat((hit_channel_prob, 0.0)) + log_likelihood = ufloat(hit_channel_prob, 0.0) # Then include the probability densities of the observed # charges and times. - log_likelihood += ufloat((np.log(pdf_prob[self.event.channels.hit]).sum(), - 0.0)) + log_likelihood += ufloat(np.log(pdf_prob[self.event.channels.hit]).sum(), + 0.0) return -log_likelihood @@ -178,7 +178,7 @@ avg_like = mom1 / mom0 rms_like = (mom2 / mom0 - avg_like**2)**0.5 # Don't forget to return a negative log likelihood - return ufloat((-avg_like, rms_like/sqrt(mom0))) + return ufloat(-avg_like, rms_like/sqrt(mom0)) if __name__ == '__main__': from chroma.demo import detector as build_detector @@ -212,7 +212,7 @@ import matplotlib.pyplot as plt - plt.errorbar(x, [v.nominal_value for v in l], [v.std_dev() for v in l]) + plt.errorbar(x, [v.nominal_value for v in l], [v.std_dev for v in l]) plt.xlabel('X Position (m)') plt.ylabel('Negative Log Likelihood') plt.show() RefactoringTool: Refactored chroma/tools.py --- chroma/tools.py (original) +++ chroma/tools.py (refactored) @@ -16,9 +16,9 @@ return a def ufloat_to_str(x): - msd = -int(math.floor(math.log10(x.std_dev()))) + msd = -int(math.floor(math.log10(x.std_dev))) return '%.*f +/- %.*f' % (msd, round(x.nominal_value, msd), - msd, round(x.std_dev(), msd)) + msd, round(x.std_dev, msd)) def progress(seq): "Print progress while iterating over `seq`." RefactoringTool: Refactored chroma/histogram/histogram.py --- chroma/histogram/histogram.py (original) +++ chroma/histogram/histogram.py (refactored) @@ -82,10 +82,10 @@ ma.masked_where(mbins.mask, self.errs[mbins.filled(0)]) if np.iterable(x): - return uarray((value.filled(fill_value), err.filled(fill_err))) - else: - return ufloat((value.filled(fill_value).item(), \ - err.filled(fill_err).item())) + return uarray(value.filled(fill_value), err.filled(fill_err)) + else: + return ufloat(value.filled(fill_value).item(), \ + err.filled(fill_err).item()) def interp(self, x): """ @@ -127,9 +127,9 @@ See sum(). """ if width: - return np.dot(np.diff(self.bins), uarray((self.hist, self.errs))) - else: - return np.sum(uarray((self.hist, self.errs))) + return np.dot(np.diff(self.bins), uarray(self.hist, self.errs)) + else: + return np.sum(uarray(self.hist, self.errs)) def integrate(self, x1, x2, width=False): """ @@ -157,9 +157,9 @@ if width: return np.dot(np.diff(self.bins[i1:i2+2]), - uarray((self.hist[i1:i2+1], self.errs[i1:i2+1]))) - else: - return np.sum(uarray((self.hist[i1:i2+1], self.errs[i1:i2+1]))) + uarray(self.hist[i1:i2+1], self.errs[i1:i2+1])) + else: + return np.sum(uarray(self.hist[i1:i2+1], self.errs[i1:i2+1])) def scale(self, c): """Scale bin contents and errors by `c`.""" RefactoringTool: Refactored chroma/histogram/histogramdd.py --- chroma/histogram/histogramdd.py (original) +++ chroma/histogram/histogramdd.py (refactored) @@ -158,7 +158,7 @@ value, err = ma.array(self.hist[filledbins], mask=valuemask), \ ma.array(self.errs[filledbins], mask=valuemask) - return uarray((value.filled(fill_value), err.filled(fill_err))) + return uarray(value.filled(fill_value), err.filled(fill_err)) def reset(self): """Reset all bin contents/errors to zero.""" @@ -173,7 +173,7 @@ def usum(self): """Return the sum of the bin contents and uncertainty.""" - return np.sum(uarray((self.hist, self.errs))) + return np.sum(uarray(self.hist, self.errs)) def scale(self, c): """Scale bin values and errors by `c`.""" RefactoringTool: Files that were modified: RefactoringTool: chroma/benchmark.py RefactoringTool: chroma/likelihood.py RefactoringTool: chroma/tools.py RefactoringTool: chroma/histogram/histogram.py RefactoringTool: chroma/histogram/histogramdd.py (chroma_env)delta:chroma blyth$