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better logging control
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Jingang Liang
parent
cd41186a25
commit
1bb0aab643
1 changed files with 37 additions and 20 deletions
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@ -35,6 +35,9 @@ _FIT_F = 2 # Fission
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# Upper temperature limit
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TEMPERATURE_LIMIT = 3000
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# Logging control
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DETAILED_LOGGING = 1
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def _faddeeva(z):
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r"""Evaluate the complex Faddeeva function.
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@ -158,7 +161,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
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A list of orders (number of poles) to be searched
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n_vf_iter : int, optional
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Number of maximum VF iterations
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log : bool, optional
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log : bool or int, optional
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Whether to print running logs
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path_out : str, optional
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Path to save the figures
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@ -228,7 +231,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
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best_quality = best_ratio = -np.inf
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for i, order in enumerate(orders):
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if log:
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print("Order={} {}/{}".format(order, i, len(orders)))
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print("Order={}({}/{})".format(order, i, len(orders)))
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# initial guessed poles
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poles = np.linspace(s[0], s[-1], order/2)
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poles = poles + poles*0.01j
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@ -237,7 +240,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
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found_better = False
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# fitting iteration
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for i_vf in range(n_vf_iter):
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if log:
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if log > DETAILED_LOGGING:
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print("VF iteration {}/{}".format(i_vf+1, n_vf_iter))
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# call vf
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@ -256,7 +259,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
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new_poles = np.array(new_poles)
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# re-calculate residues if poles changed
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if n_real_poles > 0:
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if log:
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if log > DETAILED_LOGGING:
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print(" # real poles: {}".format(n_real_poles))
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new_poles, residues, cf, f_fit, rms = \
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m.vectfit(f, s, new_poles, weight, skip_pole=True)
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@ -279,14 +282,14 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
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if np.any(test_xs < -atol):
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quality = -np.inf
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if log:
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if log > DETAILED_LOGGING:
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print(" # poles: {}".format(new_poles.size))
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print(" Max relative error: {:.3f}%".format(maxre*100))
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print(" Satisfaction: {:.1f}%, {:.1f}%".format(ratio*100, ratio2*100))
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print(" Quality: {:.2f}".format(quality))
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if quality > best_quality:
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if log:
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if log > DETAILED_LOGGING:
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print(" Best by far!")
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found_better = True
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best_quality, best_ratio = quality, ratio
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@ -298,7 +301,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
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found_ideal = True
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break
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else:
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if log:
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if log > DETAILED_LOGGING:
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print(" Discarded!")
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if found_ideal:
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@ -311,7 +314,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
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if order > max(2*n_peaks, 50) and best_ratio > 0.7:
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n_discarded += 1
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if n_discarded >= 10 or (n_discarded >= 5 and best_ratio > 0.9):
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if log:
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if log > DETAILED_LOGGING:
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print("Couldn't get better results. Stop!")
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break
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@ -387,7 +390,7 @@ def _vectfit_nuclide(endf_file, njoy_error=5e-4, vf_error=1e-3, vf_pieces=None,
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Fractional error tolerance for data fitting
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vf_pieces : integer, optional
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Number of equal-in-mementum spaced energy pieces for data fitting
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log : bool, optional
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log : bool or int, optional
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Whether to print running logs
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path_out : str, optional
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Path to write out data files
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@ -551,7 +554,7 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
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Curve fitting order
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spacing : float, optional
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Inner window spacing (sqrt energy space)
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log : bool, optional
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log : bool or int, optional
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Whether to print running logs
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Returns
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@ -597,6 +600,10 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
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warnings.warn("Curvefit order not specified.")
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n_cf = 5
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if log:
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print("# windows: {}, spacing: {}, CF order: {}".format(
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n_win, spacing, n_cf))
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# sort poles (and residues) by the real component of the pole
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for ip in range(n_pieces):
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indices = mp_poles[ip].argsort()
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@ -610,7 +617,7 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
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# consecutive poles and curve fit coefficients to reproduce cross section
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win_data = []
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for iw in range(n_win):
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if log:
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if log > DETAILED_LOGGING:
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print("Processing window {}/{}...".format(iw+1, n_win))
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# inner window boundaries
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@ -643,7 +650,7 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
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center_pole_ind = np.argmin((np.fabs(poles.real - incenter)))
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lp, rp = center_pole_ind, center_pole_ind
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while True:
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if log:
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if log > DETAILED_LOGGING:
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print("Trying poles {} to {}".format(lp, rp))
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# calculate the cross sections contributed by the windowed poles
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if rp > lp:
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@ -664,13 +671,14 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
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if re.size == 0 or np.all(re <= rtol) or \
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(re.max() < 2*rtol and (re>rtol).sum() < 0.01*relerr.size):
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# meet tolerances
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if log:
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if log > DETAILED_LOGGING:
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print("Accuracy satisfied.")
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break
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# we expect curvefit succeeds for the first window
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# TODO: find the energy boundary below which no poles are allowed
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if iw == 0:
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raise RuntimeError('Curvefit failed for the first window!')
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raise RuntimeError('Pure curvefit failed for the first window!')
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# try to include one more (center nearest) pole
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if rp >= n_poles:
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@ -1002,11 +1010,11 @@ class WindowedMultipole(EqualityMixin):
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endf_file : str
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Path to ENDF evaluation
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vf_options : dict
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Dictionary of keyword arguments passed to
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:func:`openmc.data.multipole._vectfit_nuclide`
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Dictionary of keyword arguments, e.g. {'rtol':0.001, 'log':True},
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passed to :func:`openmc.data.multipole._vectfit_nuclide`
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wmp_options : dict
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Dictionary of keyword arguments passed to
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:func:`openmc.data.WindowedMultipole.from_multipole`
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Dictionary of keyword arguments, e.g. {'search': True, 'log': True},
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passed to :func:`openmc.data.WindowedMultipole.from_multipole`
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Returns
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-------
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@ -1032,7 +1040,7 @@ class WindowedMultipole(EqualityMixin):
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Dictionary or Path to the multipole data
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search : bool, optional
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Whether to search for optimal window size and curvefit order
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log : bool, optional
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log : bool or int, optional
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Whether to print running logs
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**kwargs
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Keyword arguments passed to :func:`openmc.data.multipole._windowing`
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@ -1073,7 +1081,9 @@ class WindowedMultipole(EqualityMixin):
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wmp = _windowing(mp_data, log=log, **kwargs)
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except:
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continue
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# wmp library metric: less poles, less CF order and less windows
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# wmp library metric:
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# - performance (average # used poles per window and CF order)
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# - memory (# windows)
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metric = -(wmp.poles_per_window*10 + wmp.fit_order +
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0.01*wmp.n_windows)
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if best_wmp is None or metric > best_metric:
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@ -1081,6 +1091,13 @@ class WindowedMultipole(EqualityMixin):
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print("Best library by far.")
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best_wmp = deepcopy(wmp)
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best_metric = metric
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# return the best wmp library
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if log:
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print("Best WMP library: {} poles, {} windows, {} used poles per "
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"window, {} CF order".format(best_wmp.n_poles, best_wmp.n_windows,
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best_wmp.poles_per_window, best_wmp.fit_order))
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return best_wmp
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def _evaluate(self, E, T):
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