mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-29 06:35:48 -04:00
f strings instead of .format for string editing (#2987)
This commit is contained in:
Jonathan Shimwell
GitHub
parent
e8ae7063af
commit
5d2b352025
38 changed files with 197 additions and 229 deletions
|
|
@ -194,9 +194,8 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
test_xs_ref[i] = np.interp(test_energy, energy, ce_xs[i])
|
||||
|
||||
if log:
|
||||
print(" energy: {:.3e} to {:.3e} eV ({} points)".format(
|
||||
energy[0], energy[-1], ne))
|
||||
print(" error tolerance: rtol={}, atol={}".format(rtol, atol))
|
||||
print(f" energy: {energy[0]:.3e} to {energy[-1]:.3e} eV ({ne} points)")
|
||||
print(f" error tolerance: rtol={rtol}, atol={atol}")
|
||||
|
||||
# transform xs (sigma) and energy (E) to f (sigma*E) and s (sqrt(E)) to be
|
||||
# compatible with the multipole representation
|
||||
|
|
@ -230,8 +229,8 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
orders = list(range(lowest_order, highest_order + 1, 2))
|
||||
|
||||
if log:
|
||||
print("Found {} peaks".format(n_peaks))
|
||||
print("Fitting orders from {} to {}".format(orders[0], orders[-1]))
|
||||
print(f"Found {n_peaks} peaks")
|
||||
print(f"Fitting orders from {orders[0]} to {orders[-1]}")
|
||||
|
||||
# perform VF with increasing orders
|
||||
found_ideal = False
|
||||
|
|
@ -239,7 +238,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
best_quality = best_ratio = -np.inf
|
||||
for i, order in enumerate(orders):
|
||||
if log:
|
||||
print("Order={}({}/{})".format(order, i, len(orders)))
|
||||
print(f"Order={order}({i}/{len(orders)})")
|
||||
# initial guessed poles
|
||||
poles_r = np.linspace(s[0], s[-1], order//2)
|
||||
poles = poles_r + poles_r*0.01j
|
||||
|
|
@ -249,7 +248,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
# fitting iteration
|
||||
for i_vf in range(n_vf_iter):
|
||||
if log >= DETAILED_LOGGING:
|
||||
print("VF iteration {}/{}".format(i_vf + 1, n_vf_iter))
|
||||
print(f"VF iteration {i_vf + 1}/{n_vf_iter}")
|
||||
|
||||
# call vf
|
||||
poles, residues, cf, f_fit, rms = vf.vectfit(f, s, poles, weight)
|
||||
|
|
@ -268,7 +267,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
# re-calculate residues if poles changed
|
||||
if n_real_poles > 0:
|
||||
if log >= DETAILED_LOGGING:
|
||||
print(" # real poles: {}".format(n_real_poles))
|
||||
print(f" # real poles: {n_real_poles}")
|
||||
new_poles, residues, cf, f_fit, rms = \
|
||||
vf.vectfit(f, s, new_poles, weight, skip_pole=True)
|
||||
|
||||
|
|
@ -296,10 +295,10 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
quality = -np.inf
|
||||
|
||||
if log >= DETAILED_LOGGING:
|
||||
print(" # poles: {}".format(new_poles.size))
|
||||
print(" Max relative error: {:.3f}%".format(maxre*100))
|
||||
print(" Satisfaction: {:.1f}%, {:.1f}%".format(ratio*100, ratio2*100))
|
||||
print(" Quality: {:.2f}".format(quality))
|
||||
print(f" # poles: {new_poles.size}")
|
||||
print(f" Max relative error: {maxre * 100:.3f}%")
|
||||
print(f" Satisfaction: {ratio * 100:.1f}%, {ratio2 * 100:.1f}%")
|
||||
print(f" Quality: {quality:.2f}")
|
||||
|
||||
if quality > best_quality:
|
||||
if log >= DETAILED_LOGGING:
|
||||
|
|
@ -354,7 +353,7 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
mp_residues = np.concatenate((best_residues[:, real_idx],
|
||||
best_residues[:, conj_idx]*2), axis=1)/1j
|
||||
if log:
|
||||
print("Final number of poles: {}".format(mp_poles.size))
|
||||
print(f"Final number of poles: {mp_poles.size}")
|
||||
|
||||
if path_out:
|
||||
if not os.path.exists(path_out):
|
||||
|
|
@ -378,14 +377,14 @@ def _vectfit_xs(energy, ce_xs, mts, rtol=1e-3, atol=1e-5, orders=None,
|
|||
ax2.set_ylabel('relative error', color='r')
|
||||
ax2.tick_params('y', colors='r')
|
||||
|
||||
plt.title("MT {} vector fitted with {} poles".format(mt, mp_poles.size))
|
||||
plt.title(f"MT {mt} vector fitted with {mp_poles.size} poles")
|
||||
fig.tight_layout()
|
||||
fig_file = os.path.join(path_out, "{:.0f}-{:.0f}_MT{}.png".format(
|
||||
energy[0], energy[-1], mt))
|
||||
plt.savefig(fig_file)
|
||||
plt.close()
|
||||
if log:
|
||||
print("Saved figure: {}".format(fig_file))
|
||||
print(f"Saved figure: {fig_file}")
|
||||
|
||||
return (mp_poles, mp_residues)
|
||||
|
||||
|
|
@ -423,7 +422,7 @@ def vectfit_nuclide(endf_file, njoy_error=5e-4, vf_pieces=None,
|
|||
|
||||
# make 0K ACE data using njoy
|
||||
if log:
|
||||
print("Running NJOY to get 0K point-wise data (error={})...".format(njoy_error))
|
||||
print(f"Running NJOY to get 0K point-wise data (error={njoy_error})...")
|
||||
|
||||
nuc_ce = IncidentNeutron.from_njoy(endf_file, temperatures=[0.0],
|
||||
error=njoy_error, broadr=False, heatr=False, purr=False)
|
||||
|
|
@ -477,9 +476,8 @@ def vectfit_nuclide(endf_file, njoy_error=5e-4, vf_pieces=None,
|
|||
mts = [2, 27]
|
||||
|
||||
if log:
|
||||
print(" MTs: {}".format(mts))
|
||||
print(" Energy range: {:.3e} to {:.3e} eV ({} points)".format(
|
||||
E_min, E_max, n_points))
|
||||
print(f" MTs: {mts}")
|
||||
print(f" Energy range: {E_min:.3e} to {E_max:.3e} eV ({n_points} points)")
|
||||
|
||||
# ======================================================================
|
||||
# PERFORM VECTOR FITTING
|
||||
|
|
@ -500,7 +498,7 @@ def vectfit_nuclide(endf_file, njoy_error=5e-4, vf_pieces=None,
|
|||
# VF piece by piece
|
||||
for i_piece in range(vf_pieces):
|
||||
if log:
|
||||
print("Vector fitting piece {}/{}...".format(i_piece + 1, vf_pieces))
|
||||
print(f"Vector fitting piece {i_piece + 1}/{vf_pieces}...")
|
||||
# start E of this piece
|
||||
e_bound = (sqrt(E_min) + piece_width*(i_piece-0.5))**2
|
||||
if i_piece == 0 or sqrt(alpha*e_bound) < 4.0:
|
||||
|
|
@ -534,12 +532,12 @@ def vectfit_nuclide(endf_file, njoy_error=5e-4, vf_pieces=None,
|
|||
if not os.path.exists(path_out):
|
||||
os.makedirs(path_out)
|
||||
if not mp_filename:
|
||||
mp_filename = "{}_mp.pickle".format(nuc_ce.name)
|
||||
mp_filename = f"{nuc_ce.name}_mp.pickle"
|
||||
mp_filename = os.path.join(path_out, mp_filename)
|
||||
with open(mp_filename, 'wb') as f:
|
||||
pickle.dump(mp_data, f)
|
||||
if log:
|
||||
print("Dumped multipole data to file: {}".format(mp_filename))
|
||||
print(f"Dumped multipole data to file: {mp_filename}")
|
||||
|
||||
return mp_data
|
||||
|
||||
|
|
@ -605,9 +603,8 @@ def _windowing(mp_data, n_cf, rtol=1e-3, atol=1e-5, n_win=None, spacing=None,
|
|||
|
||||
if log:
|
||||
print("Windowing:")
|
||||
print(" config: # windows={}, spacing={}, CF order={}".format(
|
||||
n_win, spacing, n_cf))
|
||||
print(" error tolerance: rtol={}, atol={}".format(rtol, atol))
|
||||
print(f" config: # windows={n_win}, spacing={spacing}, CF order={n_cf}")
|
||||
print(f" error tolerance: rtol={rtol}, atol={atol}")
|
||||
|
||||
# sort poles (and residues) by the real component of the pole
|
||||
for ip in range(n_pieces):
|
||||
|
|
@ -623,7 +620,7 @@ def _windowing(mp_data, n_cf, rtol=1e-3, atol=1e-5, n_win=None, spacing=None,
|
|||
win_data = []
|
||||
for iw in range(n_win):
|
||||
if log >= DETAILED_LOGGING:
|
||||
print("Processing window {}/{}...".format(iw + 1, n_win))
|
||||
print(f"Processing window {iw + 1}/{n_win}...")
|
||||
|
||||
# inner window boundaries
|
||||
inbegin = sqrt(E_min) + spacing * iw
|
||||
|
|
@ -658,7 +655,7 @@ def _windowing(mp_data, n_cf, rtol=1e-3, atol=1e-5, n_win=None, spacing=None,
|
|||
lp = rp = center_pole_ind
|
||||
while True:
|
||||
if log >= DETAILED_LOGGING:
|
||||
print("Trying poles {} to {}".format(lp, rp))
|
||||
print(f"Trying poles {lp} to {rp}")
|
||||
|
||||
# calculate the cross sections contributed by the windowed poles
|
||||
if rp > lp:
|
||||
|
|
@ -1108,7 +1105,7 @@ class WindowedMultipole(EqualityMixin):
|
|||
for n_w in np.unique(np.linspace(n_win_min, n_win_max, 20, dtype=int)):
|
||||
for n_cf in range(10, 1, -1):
|
||||
if log:
|
||||
print("Testing N_win={} N_cf={}".format(n_w, n_cf))
|
||||
print(f"Testing N_win={n_w} N_cf={n_cf}")
|
||||
|
||||
# update arguments dictionary
|
||||
kwargs.update(n_win=n_w, n_cf=n_cf)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue