From c53315b949aeabd386260b48a9d1e68105095afd Mon Sep 17 00:00:00 2001
From: Valentin Bruch <valentin.bruch@rwth-aachen.de>
Date: Mon, 20 Feb 2023 14:03:55 +0100
Subject: [PATCH] small changes in plots
---
final_plots.py | 122 ++++++++++++-
plot.py | 352 ++++++++++++++++++++++----------------
plot_pyqtgraph.py | 4 +
tikz/commutator_RG2.tex | 2 +-
tikz/contour.tex | 4 +-
tikz/floquet_matrices.tex | 4 +-
tikz/ga.tex | 5 +-
tikz/preamble.tex | 31 +++-
tikz/pulse_charge.tex | 14 +-
9 files changed, 371 insertions(+), 167 deletions(-)
diff --git a/final_plots.py b/final_plots.py
index 94533c7..ccb8c0c 100644
--- a/final_plots.py
+++ b/final_plots.py
@@ -226,7 +226,8 @@ def export_interp(
y_func = None,
kxorder = 2,
kyorder = 2,
- spline_s = 2e-4,
+ spline_s = 1e-4,
+ spline_s_ac = 2e-4,
special_selection = None,
o2_scale_x = TK_VOLTAGE_ORDER2/TK_VOLTAGE,
o2_scale_y = TK_VOLTAGE_ORDER2/TK_VOLTAGE,
@@ -344,7 +345,7 @@ def export_interp(
selected &= special_selection(thedata, order, method, padding, **kwargs)
return thedata.loc[selected]
- def gen_data(order, method, padding=False, s=spline_s, extend_vdc=10, extend_vac=10, **kwargs):
+ def gen_data(order, method, padding=False, s=spline_s, s_ac=spline_s_ac, extend_vdc=10, extend_vac=10, **kwargs):
suffix = "_p" if padding else ""
reduced_data = selection(order, method, padding, **kwargs)
results = {}
@@ -361,7 +362,7 @@ def export_interp(
if extend_vac:
ac_mirrored = reduced_data[reduced_data.vac<extend_vac].copy()
ac_mirrored.vac *= -1
- ac_mirrored.ac_current_phase += np.pi
+ ac_mirrored.ac_current_abs *= -1
extended_data.append(ac_mirrored)
if extend_vdc:
acdc_mirrored = ac_mirrored[ac_mirrored.vdc<extend_vdc].copy()
@@ -441,7 +442,7 @@ def export_interp(
except:
settings.logger.exception(f"in griddata interpolation of Iac for {order}{suffix} {method}")
try:
- iac_tck = bisplrep(*xy_data_i[::-1], 2*i_data.ac_current_abs, s=s, kx=kxorder, ky=kyorder)
+ iac_tck = bisplrep(*xy_data_i[::-1], 2*i_data.ac_current_abs, s=s_ac, kx=kxorder, ky=kyorder)
results[f"iac_{method}_{order}{suffix}_spline"] = bisplev(y_arr, x_arr, iac_tck)
results[f"gac_{method}_{order}{suffix}_ispline"] = bisplev(y_arr, x_arr, iac_tck, dx=1)
except:
@@ -615,6 +616,78 @@ def export_interp_relative_o3a(
selected &= special_selection(merged, order, method, padding, **kwargs)
return merged.loc[selected]
+ def gen_data_cmp_init(order, method, padding=False, s=spline_s, extend_vdc=10, extend_vac=10, **kwargs):
+ suffix = "_p" if padding else ""
+ reduced_data = selection(order, method, padding, voltage_branches[order], **kwargs)
+ reduced_data = reduced_data[(reduced_data.solver_flags_ref & i_flags_good == i_flags_good) & (reduced_data.solver_flags & g_flags_bad == 0)]
+ results = {}
+ if reduced_data.shape[0] < 100:
+ return results
+ if extend_vdc or extend_vac:
+ extended_data = [reduced_data]
+ if extend_vdc:
+ dc_mirrored = reduced_data[reduced_data.vdc<extend_vdc].copy()
+ dc_mirrored.vdc *= -1
+ dc_mirrored.dc_current *= -1
+ dc_mirrored.dc_current_ref *= -1
+ extended_data.append(dc_mirrored)
+ del dc_mirrored
+ if extend_vac:
+ ac_mirrored = reduced_data[reduced_data.vac<extend_vac].copy()
+ ac_mirrored.vac *= -1
+ ac_mirrored.ac_current_abs *= -1
+ ac_mirrored.ac_current_abs_ref *= -1
+ extended_data.append(ac_mirrored)
+ if extend_vdc:
+ acdc_mirrored = ac_mirrored[ac_mirrored.vdc<extend_vdc].copy()
+ acdc_mirrored.vdc *= -1
+ acdc_mirrored.dc_current *= -1
+ acdc_mirrored.dc_current_ref *= -1
+ extended_data.append(acdc_mirrored)
+ del acdc_mirrored
+ del ac_mirrored
+ reduced_data = pd.concat(extended_data)
+ settings.logger.info(f"Starting with init cmp. {order}{suffix} {method}, using {reduced_data.shape[0]} data points")
+ xy_data = (x_func(reduced_data), y_func(reduced_data))
+ try:
+ idc_tck = bisplrep(*xy_data[::-1], reduced_data.dc_current - reduced_data.dc_current_ref, s=s, kx=kxorder, ky=kyorder)
+ results[f"idc_{method}_{order}{suffix}_spline_cmp_init"] = bisplev(y_arr, x_arr, idc_tck)
+ results[f"gdc_{method}_{order}{suffix}_ispline_cmp_init"] = bisplev(y_arr, x_arr, idc_tck, dy=1)
+ except:
+ settings.logger.exception(f"in spline interpolation of Idc for {order}{suffix} {method} (cmp init)")
+ try:
+ results[f"idc_{method}_{order}{suffix}_cmp_init"] = griddata(
+ xy_data,
+ reduced_data.dc_current - reduced_data.dc_current_ref,
+ mesh,
+ method="cubic")
+ except:
+ settings.logger.exception(f"in griddata interpolation of Idc for {order}{suffix} {method} (cmp init)")
+ try:
+ results[f"iac_{method}_{order}{suffix}_cmp_init"] = griddata(
+ xy_data,
+ 2*(reduced_data.ac_current_abs - reduced_data.ac_current_abs_ref),
+ mesh,
+ method="cubic")
+ except:
+ settings.logger.exception(f"in griddata interpolation of Iac for {order}{suffix} {method} (cmp init)")
+ try:
+ iac_tck = bisplrep(*xy_data[::-1], 2*(reduced_data.ac_current_abs - reduced_data.ac_current_abs_ref), s=s, kx=kxorder, ky=kyorder)
+ results[f"iac_{method}_{order}{suffix}_spline_cmp_init"] = bisplev(y_arr, x_arr, iac_tck)
+ results[f"gac_{method}_{order}{suffix}_ispline_cmp_init"] = bisplev(y_arr, x_arr, iac_tck, dx=1)
+ except:
+ settings.logger.exception(f"in spline interpolation of Iac for {order}{suffix} {method} (cmp init)")
+ try:
+ sel = reduced_data.vac > 0
+ results[f"phase_{method}_{order}{suffix}_cmp_init"] = griddata(
+ (xy_data[0][sel], xy_data[1][sel]),
+ reduced_data.ac_current_phase[sel] - reduced_data.ac_current_phase_ref[sel],
+ mesh,
+ method="cubic")
+ except:
+ settings.logger.exception(f"in griddata interpolation of phase for {order}{suffix} {method} (cmp init)")
+ return results
+
def gen_data(order, method, padding=False, s=spline_s, extend_vdc=10, extend_vac=10, **kwargs):
suffix = "_p" if padding else ""
reduced_data = selection(order, method, padding, voltage_branches[order], **kwargs)
@@ -646,12 +719,19 @@ def export_interp_relative_o3a(
del ac_mirrored
reduced_data = pd.concat(extended_data)
g_data = reduced_data.loc[(reduced_data.solver_flags_ref | reduced_data.solver_flags) & g_flags_bad == 0]
+ if order == "o3a_vb7":
+ g_data = reduced_data
i_data = reduced_data.loc[reduced_data.solver_flags_ref & reduced_data.solver_flags & i_flags_good == i_flags_good]
settings.logger.info(f"Starting with {order}{suffix} {method}, using {g_data.shape[0]}/{i_data.shape[0]} data points")
xy_data = (x_func(g_data), y_func(g_data))
has_current = i_data.shape[0] >= 100
if has_current:
xy_data_i = (x_func(i_data), y_func(i_data))
+ try:
+ idc_tck_old = bisplrep(*xy_data[::-1], g_data.dc_current - g_data.dc_current_ref, s=s, kx=kxorder, ky=kyorder)
+ results[f"gdc_{method}_{order}{suffix}_ispline_old"] = bisplev(y_arr, x_arr, idc_tck_old, dy=1)
+ except:
+ settings.logger.exception(f"in spline interpolation of Idc for {order}{suffix} {method} (old)")
try:
results[f"gdc_{method}_{order}{suffix}"] = griddata(
xy_data,
@@ -660,6 +740,22 @@ def export_interp_relative_o3a(
method="cubic")
except:
settings.logger.exception(f"in griddata interpolation of Gdc for {order}{suffix} {method}")
+ try:
+ results[f"idc_{method}_{order}{suffix}_old"] = griddata(
+ xy_data,
+ g_data.dc_current - g_data.dc_current_ref,
+ mesh,
+ method="cubic")
+ except:
+ settings.logger.exception(f"in griddata interpolation of Idc for {order}{suffix} {method} (old)")
+ try:
+ results[f"iac_{method}_{order}{suffix}_old"] = griddata(
+ xy_data,
+ 2*(g_data.ac_current_abs - g_data.ac_current_abs_ref),
+ mesh,
+ method="cubic")
+ except:
+ settings.logger.exception(f"in griddata interpolation of Iac for {order}{suffix} {method} (old)")
if has_current:
try:
results[f"idc_{method}_{order}{suffix}"] = griddata(
@@ -711,6 +807,7 @@ def export_interp_relative_o3a(
np.savez(
filename,
**{fixed_parameter:fixed_value, x_parameter:x_mesh, y_parameter:y_mesh},
+ **gen_data_cmp_init("o3a", "mu"),
**gen_data("o3", "mu"),
**gen_data("o3a_vb7", "mu"),
**gen_data("o3p", "mu", integral_method=-1),
@@ -755,7 +852,8 @@ def export_omega5_interp(
y_arr = yarr,
kyorder = 3,
special_selection = special_selection,
- spline_s = 2e-5,
+ spline_s = 5e-6,
+ spline_s_ac = 1e-5,
d = 1e9,
solver_tol_rel = 1e-8,
solver_tol_abs = 1e-10,
@@ -797,7 +895,7 @@ def export_omega5_interp_deviation(
y_arr = yarr,
kyorder = 3,
special_selection = special_selection,
- spline_s = 2e-5,
+ spline_s = 1e-5,
d = 1e9,
solver_tol_rel = 1e-8,
solver_tol_abs = 1e-10,
@@ -1423,6 +1521,7 @@ def export_pulse_current_pgfplots(omega=1.5, pulse_duration=0.01):
solver_tol_abs = 1e-10,
has_fourier_coef = True,
bad_flags = DataManager.SOLVER_FLAGS["simplified_initial_conditions"] \
+ | DataManager.SOLVER_FLAGS["improved_initial_conditions"] \
| DataManager.SOLVER_FLAGS["second_order_rg_equations"] \
| DataManager.SOLVER_FLAGS["solve_integral_exactly"] \
| DataManager.SOLVER_FLAGS["extrapolate_voltage"] \
@@ -1483,6 +1582,7 @@ def export_pulse_charge_pgfplots(omega=1.5):
solver_tol_rel = 1e-8,
solver_tol_abs = 1e-10,
bad_flags = DataManager.SOLVER_FLAGS["simplified_initial_conditions"] \
+ | DataManager.SOLVER_FLAGS["improved_initial_conditions"] \
| DataManager.SOLVER_FLAGS["second_order_rg_equations"] \
| DataManager.SOLVER_FLAGS["solve_integral_exactly"] \
| DataManager.SOLVER_FLAGS["extrapolate_voltage"] \
@@ -1584,13 +1684,13 @@ def export_harmonic_modes(
n_phase_crossings = 10,
):
label = "-omega20"
+ #label = "-omega1"
vac_omega = np.array([5, 10, 20, 40, 80])
dm = DataManager()
data = dm.list(
omega = omega,
d = 1e9,
vdc = 0,
- method = "J",
voltage_branches = 0,
include_Ga = True,
integral_method = -15,
@@ -1601,7 +1701,9 @@ def export_harmonic_modes(
| DataManager.SOLVER_FLAGS["solve_integral_exactly"] \
| DataManager.SOLVER_FLAGS["extrapolate_voltage"] \
| DataManager.SOLVER_FLAGS["deleted"],
+ good_flags = DataManager.SOLVER_FLAGS["improved_initial_conditions"]
)
+ data = data.loc[data.method != "mu"]
current = []
t = np.linspace(-0.5, 0.5, 201)
current = []
@@ -1611,8 +1713,12 @@ def export_harmonic_modes(
phase_crossing_currents = []
for vac in vac_omega:
sel = np.abs(data.vac-vac*omega) < 1e-6
- if sel.sum() != 1:
+ if sel.sum() == 0:
+ settings.logger.warning(f"No results found for vac={vac}")
continue
+ if sel.sum() > 1:
+ settings.logger.warning(f"Unexpected number of results for vac={vac}: {sel.sum()}")
+ sel *= data.nmax >= data[sel].nmax.max()
row = data[sel].iloc[-1]
filename = os.path.join(row.dirname, row.basename)
kondo, = KondoImport.read_from_h5(filename, row.hash)
diff --git a/plot.py b/plot.py
index c6b6287..07529c8 100644
--- a/plot.py
+++ b/plot.py
@@ -912,9 +912,9 @@ def check_convergence(dm, **parameters):
c_j = drtol[j]
c_mu = drtol[mu]
c_mod = drtol[mod]
- s_j = 0.05 * table.nmax[j]**2
- s_mu = 0.08 * table.nmax[mu]**2
- s_mod = 0.08 * table.nmax[mod]**2
+ s_j = 0.12 * table.nmax[j]**2
+ s_mu = 0.15 * table.nmax[mu]**2
+ s_mod = 0.15 * table.nmax[mod]**2
lw_j = 0.3 * table.voltage_branches[j]
lw_mu = 0.3 * table.voltage_branches[mu]
lw_mod = 0.3 * table.voltage_branches[mod]
@@ -1061,57 +1061,71 @@ def check_convergence_fit(dm, **parameters):
settings.logger.warning(
"check_convergence expects specification of all physical parameters")
table = dm.list(**parameters)
- mod = (table.solver_flags & DataManager.SOLVER_FLAGS["simplified_initial_conditions"]) != 0
+ flags = DataManager.SOLVER_FLAGS["simplified_initial_conditions"] | DataManager.SOLVER_FLAGS["improved_initial_conditions"]
+ mod_bad = table.solver_flags & flags == DataManager.SOLVER_FLAGS["simplified_initial_conditions"]
+ mod_old = table.solver_flags & flags == 0
+ mod_good = table.solver_flags & flags == DataManager.SOLVER_FLAGS["improved_initial_conditions"]
d_fit_max = 9e11
d_fit_max_j_nopadding = 9e11
d_fit_max_j_shifted_nopadding = 2e9
- j = (~mod) & (table.method == "J")
- mu = (~mod) & (table.method == "mu")
+ j = mod_old & (table.method == "J")
+ mu_good = mod_good & (table.method == "mu")
+ mu_old = mod_old & (table.method == "mu")
+ mu_bad = mod_bad & (table.method == "mu")
logd_inv_arr = np.linspace(0, 1/np.log10(table.d.min()), 200)
logd_inv = 1/np.log10(table.d)
x = 1/np.log10(table.d)
- x3 = 1/np.log10(table.d)**3
x_j = x[j]
- x_mu = x[mu]
- x_mod = x[mod]
+ x_good = x[mu_good]
+ x_old = x[mu_old]
+ x_bad = x[mu_bad]
drtol = table.d*table.solver_tol_rel
norm = LogNorm(max(drtol.min(), 0.05), min(drtol.max(), 500))
c_j = drtol[j]
- c_mu = drtol[mu]
- c_mod = drtol[mod]
+ c_good = drtol[mu_good]
+ c_old = drtol[mu_old]
+ c_bad = drtol[mu_bad]
s_j = 0.05 * table.nmax[j]**2
- s_mu = 0.08 * table.nmax[mu]**2
- s_mod = 0.08 * table.nmax[mod]**2
+ s_good = 0.08 * table.nmax[mu_good]**2
+ s_old = 0.08 * table.nmax[mu_old]**2
+ s_bad = 0.08 * table.nmax[mu_bad]**2
lw_j = 0.3 * table.voltage_branches[j]
- lw_mu = 0.3 * table.voltage_branches[mu]
- lw_mod = 0.3 * table.voltage_branches[mod]
+ lw_good = 0.3 * table.voltage_branches[mu_good]
+ lw_old = 0.3 * table.voltage_branches[mu_old]
+ lw_bad = 0.3 * table.voltage_branches[mu_bad]
selections = {}
for r in range(10):
suffix = '_%d'%r if r else ''
if r in table.resonant_dc_shift.values:
- mu_shift = (~mod) \
+ mu_good_shift = mod_good \
+ & (table.method == "mu") \
+ & (table.d <= d_fit_max) \
+ & (table.resonant_dc_shift == r)
+ mu_old_shift = mod_old \
& (table.method == "mu") \
& (table.d <= d_fit_max) \
& (table.resonant_dc_shift == r)
- mu_mod_shift = mod \
+ mu_bad_shift = mod_bad \
& (table.method == "mu") \
& (table.d <= d_fit_max) \
& (table.resonant_dc_shift == r)
- j_shift = (~mod) \
+ j_shift = mod_old \
& (table.method == "J") \
& ( ((table.padding > 0) & (table.d <= d_fit_max)) \
| (table.d <= (d_fit_max_j_nopadding if r == 0 else d_fit_max_j_shifted_nopadding)) ) \
& (table.resonant_dc_shift == r)
- j_mod_shift = mod \
+ j_mod_shift = mod_bad \
& (table.method == "J") \
& ( ((table.padding > 0) & (table.d <= d_fit_max)) \
| (table.d <= (d_fit_max_j_nopadding if r == 0 else d_fit_max_j_shifted_nopadding)) ) \
& (table.resonant_dc_shift == r)
- if mu_shift.any():
- selections["mu" + suffix] = mu_shift
- if mu_mod_shift.any():
- selections["mu_mod" + suffix] = mu_mod_shift
+ if mu_good_shift.any():
+ selections["mu_good" + suffix] = mu_good_shift
+ if mu_old_shift.any():
+ selections["mu_old" + suffix] = mu_old_shift
+ if mu_bad_shift.any():
+ selections["mu_bad" + suffix] = mu_bad_shift
if j_shift.any():
selections["J" + suffix] = j_shift
if j_mod_shift.any():
@@ -1119,17 +1133,23 @@ def check_convergence_fit(dm, **parameters):
fit_func = lambda logd_inv, a, b, c: a + b * logd_inv**c
- x_mean = x3[~mod].mean()
- x_max = x3[~mod].max()
- x_shifted = x3[~mod] - x_mean
- s = (~mod).sum()
- s_xx = (x_shifted**2).sum()
+ x_bad_mean = (x[mod_bad]**2).mean()
+ x_bad_max = (x[mod_bad]**2).max()
+ x_bad_shifted = x[mod_bad]**2 - x_bad_mean
+ s_bad = (mod_bad).sum()
+ s_bad_xx = (x_bad_shifted**2).sum()
- xmod_mean = x3[mod].mean()
- xmod_max = x3[mod].max()
- xmod_shifted = x3[mod] - xmod_mean
- smod = mod.sum()
- smod_xx = (xmod_shifted**2).sum()
+ x_old_mean = (x[mod_old]**3).mean()
+ x_old_max = (x[mod_old]**3).max()
+ x_old_shifted = x[mod_old]**3 - x_old_mean
+ s_old = (mod_old).sum()
+ s_old_xx = (x_old_shifted**2).sum()
+
+ x_good_mean = (x[mod_good]**4).mean()
+ x_good_max = (x[mod_good]**4).max()
+ x_good_shifted = x[mod_good]**4 - x_good_mean
+ s_good = (mod_good).sum()
+ s_good_xx = (x_good_shifted**2).sum()
# Create figure
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True, sharey=False)
@@ -1188,171 +1208,215 @@ def check_convergence_fit(dm, **parameters):
ax1.scatter(
x_j,
table.dc_current[j],
- marker = 'x',
+ marker = 'o',
s = s_j,
c = c_j,
linewidths = lw_j,
norm = norm)
ax1.scatter(
- x_mu,
- table.dc_current[mu],
+ x_good,
+ table.dc_current[mu_good],
+ marker = '*',
+ s = s_good,
+ c = c_good,
+ linewidths = lw_good,
+ norm = norm)
+ ax1.scatter(
+ x_bad,
+ table.dc_current[mu_bad],
marker = '+',
- s = s_mu,
- c = c_mu,
- linewidths = lw_mu,
+ s = s_bad,
+ c = c_bad,
+ linewidths = lw_bad,
norm = norm)
ax1.scatter(
- x_mod,
- table.dc_current[mod],
- marker = '*',
- s = s_mod,
- c = c_mod,
- linewidths = lw_mod,
+ x_old,
+ table.dc_current[mu_old],
+ marker = 'x',
+ s = s_old,
+ c = c_old,
+ linewidths = lw_old,
norm = norm)
- bb = table.dc_current[~mod].mean()
- aa = (x_shifted*table.dc_current[~mod]).sum()/s_xx
- #ax1.plot(logd_inv_arr, aa*(logd_inv_arr**3 - x_mean) + bb, linewidth=0.5)
-
- bbmod = table.dc_current[mod].mean()
- aamod = (xmod_shifted*table.dc_current[mod]).sum()/smod_xx
- #ax1.plot(logd_inv_arr, aamod*(logd_inv_arr**3 - xmod_mean) + bbmod, linewidth=0.5)
-
- for name, selection in selections.items():
- try:
- (a, b, c), covar = curve_fit(
- fit_func,
- logd_inv[selection],
- table.dc_current[selection],
- (bb-aa*x_mean, aa, 3))
- aerr, berr, cerr = covar.diagonal()**0.5
- print(f"DC current ({name:9}): a={a:.9}±{aerr:.9}, b={b:.9}±{berr:.9}, c={c:.9}±{cerr:.9}")
- ax1.plot(logd_inv_arr, fit_func(logd_inv_arr, a, b, c), label=name)
- ax1.plot((0,logd_inv_arr[-1]), (a,a), ':', label=name)
- except:
- pass
+ bb_good = table.dc_current[mod_good].mean()
+ aa_good = (x_good_shifted*table.dc_current[mod_good]).sum()/s_good_xx
+ ax1.plot(logd_inv_arr, aa_good*(logd_inv_arr**4 - x_good_mean) + bb_good, linewidth=0.5)
+
+ bb_old = table.dc_current[mod_old].mean()
+ aa_old = (x_old_shifted*table.dc_current[mod_old]).sum()/s_old_xx
+ ax1.plot(logd_inv_arr, aa_old*(logd_inv_arr**3 - x_old_mean) + bb_old, linewidth=0.5)
+
+ bb_bad = table.dc_current[mod_bad].mean()
+ aa_bad = (x_bad_shifted*table.dc_current[mod_bad]).sum()/s_bad_xx
+ ax1.plot(logd_inv_arr, aa_bad*(logd_inv_arr**2 - x_bad_mean) + bb_bad, linewidth=0.5)
+
+ #for name, selection in selections.items():
+ # try:
+ # (a, b, c), covar = curve_fit(
+ # fit_func,
+ # logd_inv[selection],
+ # table.dc_current[selection],
+ # (bb-aa*x_mean, aa, 3))
+ # aerr, berr, cerr = covar.diagonal()**0.5
+ # print(f"DC current ({name:9}): a={a:.9}±{aerr:.9}, b={b:.9}±{berr:.9}, c={c:.9}±{cerr:.9}")
+ # ax1.plot(logd_inv_arr, fit_func(logd_inv_arr, a, b, c), label=name)
+ # ax1.plot((0,logd_inv_arr[-1]), (a,a), ':', label=name)
+ # except:
+ # pass
ax2.set_title("DC conductance")
ax2.scatter(
x_j,
table.dc_conductance[j],
- marker = 'x',
+ marker = 'o',
s = s_j,
c = c_j,
linewidths = lw_j,
norm = norm)
ax2.scatter(
- x_mu,
- table.dc_conductance[mu],
+ x_good,
+ table.dc_conductance[mu_good],
+ marker = '*',
+ s = s_good,
+ c = c_good,
+ linewidths = lw_good,
+ norm = norm)
+ ax2.scatter(
+ x_old,
+ table.dc_conductance[mu_old],
marker = '+',
- s = s_mu,
- c = c_mu,
- linewidths = lw_mu,
+ s = s_old,
+ c = c_old,
+ linewidths = lw_old,
norm = norm)
ax2.scatter(
- x_mod,
- table.dc_conductance[mod],
- marker = '*',
- s = s_mod,
- c = c_mod,
- linewidths = lw_mod,
+ x_bad,
+ table.dc_conductance[mu_bad],
+ marker = 'x',
+ s = s_bad,
+ c = c_bad,
+ linewidths = lw_bad,
norm = norm)
ax3.set_title("AC current")
ax3.scatter(
x_j,
table.ac_current_abs[j],
- marker = 'x',
+ marker = 'o',
s = s_j,
c = c_j,
linewidths = lw_j,
norm = norm)
ax3.scatter(
- x_mu,
- table.ac_current_abs[mu],
+ x_good,
+ table.ac_current_abs[mu_good],
+ marker = '*',
+ s = s_good,
+ c = c_good,
+ linewidths = lw_good,
+ norm = norm)
+ ax3.scatter(
+ x_old,
+ table.ac_current_abs[mu_old],
marker = '+',
- s = s_mu,
- c = c_mu,
- linewidths = lw_mu,
+ s = s_old,
+ c = c_old,
+ linewidths = lw_old,
norm = norm)
ax3.scatter(
- x_mod,
- table.ac_current_abs[mod],
- marker = '*',
- s = s_mod,
- c = c_mod,
- linewidths = lw_mod,
+ x_bad,
+ table.ac_current_abs[mu_bad],
+ marker = 'x',
+ s = s_bad,
+ c = c_bad,
+ linewidths = lw_bad,
norm = norm)
- b = table.ac_current_abs[~mod].mean()
- a = (x_shifted*table.ac_current_abs[~mod]).sum()/s_xx
- #ax3.plot(logd_inv_arr, a*(logd_inv_arr**3 - x_mean) + b, linewidth=0.5)
-
- bmod = table.ac_current_abs[mod].mean()
- amod = (xmod_shifted*table.ac_current_abs[mod]).sum()/smod_xx
- #ax3.plot(logd_inv_arr, amod*(logd_inv_arr**3 - xmod_mean) + bmod, linewidth=0.5)
-
- for name, selection in selections.items():
- try:
- (a, b, c), covar = curve_fit(
- fit_func,
- logd_inv[selection],
- table.ac_current_abs[selection],
- (bb-aa*x_mean, aa, 3))
- aerr, berr, cerr = covar.diagonal()**0.5
- print(f"AC current ({name:9}): a={a:.9}±{aerr:.9}, b={b:.9}±{berr:.9}, c={c:.9}±{cerr:.9}")
- ax3.plot(logd_inv_arr, fit_func(logd_inv_arr, a, b, c), label=name)
- ax3.plot((0,logd_inv_arr[-1]), (a,a), ':', label=name)
- except:
- pass
+ b = table.ac_current_abs[mu_good].mean()
+ a = (x_good_shifted*table.ac_current_abs[mu_good]).sum()/s_good_xx
+ ax3.plot(logd_inv_arr, a*(logd_inv_arr**4 - x_good_mean) + b, linewidth=0.5)
+
+ b = table.ac_current_abs[mu_old].mean()
+ a = (x_old_shifted*table.ac_current_abs[mu_old]).sum()/s_old_xx
+ ax3.plot(logd_inv_arr, a*(logd_inv_arr**3 - x_old_mean) + b, linewidth=0.5)
+
+ b = table.ac_current_abs[mu_bad].mean()
+ a = (x_bad_shifted*table.ac_current_abs[mu_bad]).sum()/s_bad_xx
+ ax3.plot(logd_inv_arr, a*(logd_inv_arr**2 - x_bad_mean) + b, linewidth=0.5)
+
+ #for name, selection in selections.items():
+ # try:
+ # (a, b, c), covar = curve_fit(
+ # fit_func,
+ # logd_inv[selection],
+ # table.ac_current_abs[selection],
+ # (bb-aa*x_mean, aa, 3))
+ # aerr, berr, cerr = covar.diagonal()**0.5
+ # print(f"AC current ({name:9}): a={a:.9}±{aerr:.9}, b={b:.9}±{berr:.9}, c={c:.9}±{cerr:.9}")
+ # ax3.plot(logd_inv_arr, fit_func(logd_inv_arr, a, b, c), label=name)
+ # ax3.plot((0,logd_inv_arr[-1]), (a,a), ':', label=name)
+ # except:
+ # pass
ax4.set_title("AC phase")
ax4.scatter(
x_j,
table.ac_current_phase[j],
- marker='x',
+ marker='o',
s=s_j,
c=c_j,
linewidths=lw_j,
norm=norm)
ax4.scatter(
- x_mu,
- table.ac_current_phase[mu],
+ x_good,
+ table.ac_current_phase[mu_good],
+ marker='*',
+ s=s_good,
+ c=c_good,
+ linewidths=lw_good,
+ norm=norm)
+ ax4.scatter(
+ x_old,
+ table.ac_current_phase[mu_old],
marker='+',
- s=s_mu,
- c=c_mu,
- linewidths=lw_mu,
+ s=s_old,
+ c=c_old,
+ linewidths=lw_old,
norm=norm)
ax4.scatter(
- x_mod,
- table.ac_current_phase[mod],
- marker='*',
- s=s_mod,
- c=c_mod,
- linewidths=lw_mod,
+ x_bad,
+ table.ac_current_phase[mu_bad],
+ marker='x',
+ s=s_bad,
+ c=c_bad,
+ linewidths=lw_bad,
norm=norm)
- b = table.ac_current_phase[~mod].mean()
- a = (x_shifted*table.ac_current_phase[~mod]).sum()/s_xx
- #ax4.plot(logd_inv_arr, a*(logd_inv_arr**3 - x_mean) + b, linewidth=0.5)
-
- bmod = table.ac_current_phase[mod].mean()
- amod = (xmod_shifted*table.ac_current_phase[mod]).sum()/smod_xx
- #ax4.plot(logd_inv_arr, amod*(logd_inv_arr**3 - xmod_mean) + bmod, linewidth=0.5)
-
- for name, selection in selections.items():
- try:
- (a, b, c), covar = curve_fit(
- fit_func,
- logd_inv[selection],
- table.ac_current_phase[selection],
- (bb-aa*x_mean, aa, 3))
- aerr, berr, cerr = covar.diagonal()**0.5
- print(f"AC phase ({name:9}): a={a:.9}±{aerr:.9}, b={b:.9}±{berr:.9}, c={c:.9}±{cerr:.9}")
- ax4.plot(logd_inv_arr, fit_func(logd_inv_arr, a, b, c), label=name)
- ax4.plot((0,logd_inv_arr[-1]), (a,a), ':', label=name)
- except:
- pass
+ b = table.ac_current_phase[mu_good].mean()
+ a = (x_good_shifted*table.ac_current_phase[mu_good]).sum()/s_good_xx
+ ax4.plot(logd_inv_arr, a*(logd_inv_arr**4 - x_good_mean) + b, linewidth=0.5)
+
+ b = table.ac_current_phase[mu_old].mean()
+ a = (x_old_shifted*table.ac_current_phase[mu_old]).sum()/s_old_xx
+ ax4.plot(logd_inv_arr, a*(logd_inv_arr**3 - x_old_mean) + b, linewidth=0.5)
+
+ b = table.ac_current_phase[mu_bad].mean()
+ a = (x_bad_shifted*table.ac_current_phase[mu_bad]).sum()/s_bad_xx
+ ax4.plot(logd_inv_arr, a*(logd_inv_arr**2 - x_bad_mean) + b, linewidth=0.5)
+
+ #for name, selection in selections.items():
+ # try:
+ # (a, b, c), covar = curve_fit(
+ # fit_func,
+ # logd_inv[selection],
+ # table.ac_current_phase[selection],
+ # (bb-aa*x_mean, aa, 3))
+ # aerr, berr, cerr = covar.diagonal()**0.5
+ # print(f"AC phase ({name:9}): a={a:.9}±{aerr:.9}, b={b:.9}±{berr:.9}, c={c:.9}±{cerr:.9}")
+ # ax4.plot(logd_inv_arr, fit_func(logd_inv_arr, a, b, c), label=name)
+ # ax4.plot((0,logd_inv_arr[-1]), (a,a), ':', label=name)
+ # except:
+ # pass
def convergence_overview(dm, **parameters):
diff --git a/plot_pyqtgraph.py b/plot_pyqtgraph.py
index 258c62f..9fd52da 100644
--- a/plot_pyqtgraph.py
+++ b/plot_pyqtgraph.py
@@ -803,6 +803,10 @@ def parse():
help="Minimal major version")
parser.add_argument("--min_version_minor", metavar="int", type=int, default=-1,
help="Minimal minor version")
+ parser.add_argument("--good_flags", metavar="int", type=int, default=0,
+ help="Required solver flags")
+ parser.add_argument("--bad_flags", metavar="int", type=int, default=0,
+ help="Forbidden solver flags")
args = parser.parse_args()
options = args.__dict__
diff --git a/tikz/commutator_RG2.tex b/tikz/commutator_RG2.tex
index 1d7cbe9..7cfb798 100644
--- a/tikz/commutator_RG2.tex
+++ b/tikz/commutator_RG2.tex
@@ -33,7 +33,7 @@
ytick = {0, 0.01, 0.02},
yticklabels = {$0$, $0.01$, $0.02$},
scaled ticks = false,
- ylabel = {$\|[\chi^{-1},ZG^2_{LR}]\|_\infty\,/\,J$},
+ ylabel = {$\|[\hat\chi^{-1},\hat{Z}\Gtwo_{LR}]\|_\infty\,/\,J$},
]
\addplot graphics[xmin=0, xmax=1, ymin=0, ymax=0.0288735933] {../figdata/colorbar_viridis.png};
\end{axis}
diff --git a/tikz/contour.tex b/tikz/contour.tex
index 7cdc29e..9e002cc 100644
--- a/tikz/contour.tex
+++ b/tikz/contour.tex
@@ -34,11 +34,11 @@
\legend{
%$O(J^2)$,
- %{$O(J^3)$, $G^a=0$},
+ %{$O(J^3)$, $\Ga=0$},
%{$O(J^3)$, approx.~$I[X]$},
%$O(J^3)$,
leading order,
- without $G^a$,
+ without $\Ga$,
approx.~integral,
full RG equations
}
diff --git a/tikz/floquet_matrices.tex b/tikz/floquet_matrices.tex
index b6b1c3a..603ee16 100644
--- a/tikz/floquet_matrices.tex
+++ b/tikz/floquet_matrices.tex
@@ -70,7 +70,7 @@
\addplot graphics[xmin=-9.5, xmax=9.5, ymin=-9.5, ymax=9.5] {../figdata/floquet_matrix_z_mu_diff_p.png};
\end{axis}
%
- \node[xshift=2mm, yshift=8mm] at (z_mu_small_p.north east) {$Z$};
+ \node[xshift=2mm, yshift=8mm] at (z_mu_small_p.north east) {$\hat{Z}$};
%
\begin{axis}[
name = z_J_small,
@@ -217,7 +217,7 @@
\addplot graphics[xmin=-9.5, xmax=9.5, ymin=-9.5, ymax=9.5] {../figdata/floquet_matrix_g201_mu_diff_p.png};
\end{axis}
%
- \node[xshift=2mm, yshift=8mm] at (g201_mu_small_p.north east) {$G^2_{LR}$};
+ \node[xshift=2mm, yshift=8mm] at (g201_mu_small_p.north east) {$\Gtwo_{LR}$};
%
\begin{axis}[
name = g201_J_small,
diff --git a/tikz/ga.tex b/tikz/ga.tex
index 99d19af..e90d31c 100644
--- a/tikz/ga.tex
+++ b/tikz/ga.tex
@@ -7,7 +7,7 @@
width = 4cm,
height = 4cm,
scale only axis,
- title = {absolute value $\displaystyle\|G^a\|_\infty$},
+ title = {absolute value $\displaystyle\|\Ga\|_\infty$},
xmin = 0, xmax = 10,
ymin = 0, ymax = 10,
xlabel = {$\vdc~(\Omega)$},
@@ -48,7 +48,8 @@
xshift = 28mm,
width = 4cm,
height = 4cm,
- title = {relative to $J^2$: $\displaystyle\frac{\|G^a\|_\infty}{\|G^2\|^2_\infty}$},
+ title = {relative to $J^2$: $\displaystyle\frac{\|\Ga\|_\infty}{\|\Gtwo\|^2_\infty}$},
+ title style = {yshift = -2.72mm},
scale only axis,
xmin = 0, xmax = 10,
ymin = 0, ymax = 10,
diff --git a/tikz/preamble.tex b/tikz/preamble.tex
index e3508d4..8959bd8 100644
--- a/tikz/preamble.tex
+++ b/tikz/preamble.tex
@@ -1,12 +1,37 @@
\newcommand\tikzsetnextfilename[1]{}
-\newcommand\tkv{T_\mathrm{K}}
-\newcommand\tkrg{T_\mathrm{K}'}
+\newcommand\tkv{T_K}
+\newcommand\tkrg{T_K'}
\newcommand\vdc{V_\mathrm{avg}}
\newcommand\vac{V_\mathrm{osc}}
-\newcommand\gdc{G_\mathrm{avg}}
+\newcommand\gdc{G}
\newcommand\gac{G_\mathrm{osc}}
\newcommand\idc{I_\mathrm{avg}}
\newcommand\iac{I_\mathrm{osc}}
+
+% Coupling vertex parametrization
+\newcommand\Ga{\hat{G}^a}
+\newcommand\Gb{\hat{G}^b}
+\newcommand\Gone{\hat{G}^1}
+\newcommand\Gtwo{\hat{G}^2}
+\newcommand\Gthree{\hat{G}^3}
+\newcommand\Gna{G^a}
+\newcommand\Gnb{G^b}
+\newcommand\Gnone{G^1}
+\newcommand\Gntwo{G^2}
+\newcommand\Gnthree{G^3}
+
+% Superoperator parametrization
+\newcommand\Lbar{\bar{L}}
+\newcommand\Labar{\Lbar^a}
+\newcommand\Lbbar{\Lbar^b}
+\newcommand\Lalgebra{L}
+\newcommand\La{\Lalgebra^a}
+\newcommand\Lb{\Lalgebra^b}
+\newcommand\Lone{\Lalgebra^1}
+\newcommand\Ltwo{\Lalgebra^2}
+\newcommand\Lthree{\Lalgebra^3}
+
+
\usepackage{pgfplots}
\pgfplotsset{compat=1.18}
\pgfplotsset{surf shading/precision=pdf}
diff --git a/tikz/pulse_charge.tex b/tikz/pulse_charge.tex
index 2147a79..3cb5c3b 100644
--- a/tikz/pulse_charge.tex
+++ b/tikz/pulse_charge.tex
@@ -13,13 +13,13 @@
mark size = 1pt,
]
\node[anchor=north west] at (axis description cs:0.02,0.97) {(a)};
- \draw[blue] (0.25, -0.02) -- ++(0, 5pt);
+ \draw[blue] (0.25, -0.02) -- ++(0, 4.5pt);
\draw[blue] (0.25, 0.83) -- ++(0, -7.5pt);
- \draw[green] (0.5, -0.02) -- ++(0, 5pt);
+ \draw[green] (0.5, -0.02) -- ++(0, 4.5pt);
\draw[green] (0.5, 0.83) -- ++(0, -7.5pt);
- \draw[orange] (0.75, -0.02) -- ++(0, 5pt);
+ \draw[orange] (0.75, -0.02) -- ++(0, 4.5pt);
\draw[orange] (0.75, 0.83) -- ++(0, -7.5pt);
- \draw[magenta] (1, -0.02) -- ++(0, 5pt);
+ \draw[magenta] (1, -0.02) -- ++(0, 4.5pt);
\draw[magenta] (1, 0.83) -- ++(0, -7.5pt);
%
\addplot[no marks, cyan] table [x=phase, y=q] {../figdata/pulse_charge_phase_interp.dat};
@@ -42,7 +42,9 @@
mark size = 1pt,
]
\node[anchor=north west] at (axis description cs:0.02,0.97) {(b)};
- \draw[cyan] (0.137701404, -0.02) -- ++(0, 5pt);
+ \draw[->,black] (0.034425351, -0.02) -- ++(0, 4.5pt);
+ \draw[->,black] (0.034425351, 0.83) -- ++(0, -7.5pt);
+ \draw[cyan] (0.137701404, -0.02) -- ++(0, 4.5pt);
\draw[cyan] (0.137701404, 0.83) -- ++(0, -7.5pt);
%
\addplot[no marks, blue] table [x=t, y=q1] {../figdata/pulse_charge_duration_interp.dat};
@@ -130,5 +132,7 @@
%
\draw[cyan] (0.137701404, 0) -- ++(0, 5pt);
\draw[cyan] (0.137701404, 3.3) -- ++(0, -5pt);
+ \draw[->,black] (0.034425351, 0) -- ++(0, 5pt);
+ \draw[->,black] (0.034425351, 3.3) -- ++(0, -5pt);
\end{axis}
\end{tikzpicture}%
--
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