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replace bare except: with specific exception types #167

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26 changes: 13 additions & 13 deletions src/original/fitting/OGC_AmsterdamUMC/LSQ_fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,7 +94,7 @@ def parfun(i):

output = Parallel(n_jobs=njobs)(delayed(parfun)(i) for i in tqdm(range(len(dw_data)), position=0, leave=True))
Dt, Fp, Dp = np.transpose(output)
except:
except Exception:
# if fails, retry using single core
single = True
else:
Expand Down Expand Up @@ -147,7 +147,7 @@ def fit_segmented(bvalues, dw_data, bounds=([0, 0, 0.005],[0.005, 0.7, 0.2]), cu
params, _ = curve_fit(lambda b, Dp: Fp * np.exp(-b * Dp), bvalues, dw_data_remaining, p0=(p0[2]), bounds=bounds2)
Dp = params[0]
return Dt, Fp, Dp
except:
except (RuntimeError, ValueError):
# if fit fails, return zeros
# print('segmented fit failed')
return 0., 0., 0.
Expand Down Expand Up @@ -183,7 +183,7 @@ def parfun(i):

output = Parallel(n_jobs=njobs)(delayed(parfun)(i) for i in tqdm(range(len(dw_data)), position=0, leave=True))
Dt, Fp, Dp, S0 = np.transpose(output)
except:
except Exception:
single = True
else:
single = True
Expand All @@ -207,7 +207,7 @@ def parfun(i):

output = Parallel(n_jobs=njobs)(delayed(parfun)(i) for i in tqdm(range(len(dw_data)), position=0, leave=True))
Dt, Fp, Dp = np.transpose(output)
except:
except Exception:
single = True
else:
single = True
Expand Down Expand Up @@ -257,7 +257,7 @@ def fit_least_squares(bvalues, dw_data, S0_output=False, fitS0=True,
return order(Dt, Fp, Dp, S0)
else:
return order(Dt, Fp, Dp)
except:
except (RuntimeError, ValueError):
# if fit fails, then do a segmented fit instead
# print('lsq fit failed, trying segmented')
if S0_output:
Expand Down Expand Up @@ -297,7 +297,7 @@ def parfun(i):

output = Parallel(n_jobs=njobs)(delayed(parfun)(i) for i in tqdm(range(len(dw_data)), position=0, leave=True))
Fp0, Dt, Fp1, Dp1, Fp2, Dp2 = np.transpose(output)
except:
except Exception:
single = True
else:
single = True
Expand Down Expand Up @@ -357,7 +357,7 @@ def fit_least_squares_tri_exp(bvalues, dw_data, S0_output=False, fitS0=True,
return Fp0, Dt, Fp1, Dp1, Fp2, Dp2
else:
return Dt, Fp1, Dp1, Fp2, Dp2
except:
except (RuntimeError, ValueError):
# if fit fails, then do a segmented fit instead
# print('lsq fit failed, trying segmented')
if S0_output:
Expand Down Expand Up @@ -396,7 +396,7 @@ def parfun(i):

output = Parallel(n_jobs=njobs)(delayed(parfun)(i) for i in tqdm(range(len(dw_data)), position=0, leave=True))
Dt, Fp, Dp, Fp0, Fp2, Dp2 = np.transpose(output)
except:
except Exception:
# if fails, retry using single core
single = True
else:
Expand Down Expand Up @@ -463,7 +463,7 @@ def fit_segmented_tri_exp(bvalues, dw_data, bounds=([0, 0, 0, 0.005, 0, 0.06], [
params, _ = curve_fit(lambda b, Dp: Fp2 * np.exp(-b * Dp), bvalueslow, dw_data, p0=(0.1), bounds=bounds1)
Dp2 = params[0]
return Fp0, Dt, Fp, Dp, Fp2, Dp2
except:
except (RuntimeError, ValueError):
# if fit fails, return zeros
# print('segnetned fit failed')
return 0., 0., 0., 0., 0., 0.
Expand Down Expand Up @@ -619,7 +619,7 @@ def parfun(i):
output = Parallel(n_jobs=arg.jobs)(delayed(parfun)(i) for i in tqdm(range(len(dw_data)), position=0,
leave=True))
Dt_pred, Fp_pred, Dp_pred, S0_pred = np.transpose(output)
except:
except Exception:
single = True
else:
single = True
Expand Down Expand Up @@ -665,14 +665,14 @@ def fit_bayesian(bvalues, dw_data, neg_log_prior, x0=[0.001, 0.2, 0.05, 1], fitS
else:
params = minimize(neg_log_posterior, x0=x0[:3], args=(bvalues, dw_data, neg_log_prior), bounds=bounds[:3])
if not params.success:
raise (params.message)
raise RuntimeError(params.message)
if fitS0:
Dt, Fp, Dp, S0 = params.x[0], params.x[1], params.x[2], params.x[3]
else:
Dt, Fp, Dp = params.x[0], params.x[1], params.x[2]
S0 = 1
return order(Dt, Fp, Dp, S0)
except:
except (RuntimeError, ValueError, TypeError):
# if fit fails, return regular lsq-fit result
# print('a bayes fit fialed')
return fit_least_squares(bvalues, dw_data, S0_output=True)
Expand Down Expand Up @@ -717,7 +717,7 @@ def goodness_of_fit(bvalues, Dt, Fp, Dp, S0, dw_data, Fp2=None, Dp2=None):
adjusted_R2 = 1 - ((1 - R2) * (len(bvalues)) / (len(bvalues) - 6 - 1))
R2[R2 < 0] = 0
adjusted_R2[adjusted_R2 < 0] = 0
except:
except (IndexError, ValueError):
if Fp2 is None:
datasim = ivim(bvalues, Dt, Fp, Dp, S0)
else:
Expand Down
2 changes: 1 addition & 1 deletion src/original/fitting/PV_MUMC/triexp_fitting_algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,7 +196,7 @@ def find_idx_nearest(array, value):

return Dpar, Fmv, Dmv, Dint, Fint, S0

except:
except Exception:
return 0, 0, 0, 0, 0, 0


Expand Down
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