diff --git a/experiments.py b/experiments.py
index 0ac3fd0ec76cd6193c37fd372ee67531cf74cf60..9df517b0ddcf7ba58dba5558b6140fb5843fcc26 100644
--- a/experiments.py
+++ b/experiments.py
@@ -274,31 +274,33 @@ def kogan04_calibrate(dm, **kwargs):
#omega = round(omega, 4)
colors = ("red", "green", "blue", "orange", "violet")
#omega = 7.1271
- omega = 3.5
- fig, ax = adjust_background(
+ #omega = 3.5
+ omega = 3.62
+ fig1, ax1 = adjust_background(
dm,
omega = omega,
- vac_arr = np.array([0.72881, 1.13091, 1.50788, 1.6838, 3.61891])*omega,
+ #vac_arr = np.array([0.72881, 1.13091, 1.50788, 1.6838, 3.61891])*omega,
+ vac_arr = np.array([0.73922, 1.14706, 1.52942, 1.70785, 3.6706])*omega,
xscale = 1/(omega * sc.eV/(13.47e12*sc.h)),
colors = colors,
include_Ga = True,
integral_method = -15,
**kwargs,
)
- #fig, ax = plot_calibration_lines(
- # dm,
- # omega = omega,
- # #omega = 7.418,
- # vac = np.array([29, 45, 60, 67, 144]) * omega * sc.eV/(13.47e15*sc.h),
- # colors = colors,
- # xscale = 1/(omega * sc.eV/(13.47e12*sc.h)),
- # yscale = np.pi,
- # calibrate_min = 1,
- # calibrate_max = 1.6,
- # include_Ga = True,
- # integral_method = -15,
- # **kwargs,
- # )
+ fig2, ax2 = plot_calibration_lines(
+ dm,
+ omega = omega,
+ #omega = 7.418,
+ vac = np.array([29, 45, 60, 67, 144]) * omega * sc.eV/(13.47e15*sc.h),
+ colors = colors,
+ xscale = 1/(omega * sc.eV/(13.47e12*sc.h)),
+ yscale = np.pi,
+ calibrate_min = 1,
+ calibrate_max = 1.6,
+ include_Ga = True,
+ integral_method = -15,
+ **kwargs,
+ )
shift_Vdc = 0.017
for i, trace in enumerate((0, 10, 21, 26, 40)):
@@ -306,7 +308,8 @@ def kogan04_calibrate(dm, **kwargs):
vdc_exp = data[:,1] # in mV
g_exp = data[:,0] / 2 # in 2e²/h = 1/π
# Plot original data
- ax.plot(vdc_exp, g_exp, '.', markersize=1, color=colors[i])
+ ax1.plot(vdc_exp, g_exp, '.', markersize=1, color=colors[i])
+ ax2.plot(vdc_exp, g_exp, '.', markersize=1, color=colors[i])
# Plot symmetrized data
sorting = np.argsort(vdc_exp)
vdc_exp = vdc_exp[sorting]
@@ -316,9 +319,10 @@ def kogan04_calibrate(dm, **kwargs):
assert truncate > 0
g_mean = (g_exp[truncate:] + g_exp[:truncate-1:-1]) / 2
spline = BSpline(*splrep(vdc_exp[truncate:], g_mean, k=3, s=2e-4 if trace==40 else 6e-5), extrapolate=False)
- ax.plot(vdc_exp[truncate:], spline(vdc_exp[truncate:]), color=colors[i], alpha=0.5, zorder=10)
+ ax1.plot(vdc_exp[truncate:], spline(vdc_exp[truncate:]), color=colors[i], alpha=0.5, zorder=10)
+ ax2.plot(vdc_exp[truncate:], spline(vdc_exp[truncate:]), color=colors[i], alpha=0.5, zorder=10)
- return fig, ax
+ return (fig1, fig2), (ax1, ax2)
def bruhat18_calibrate(dm, frequency_GHz=12, **kwargs):
"""
@@ -640,7 +644,8 @@ def kogan04(dm, **parameters):
#omega = round(omega, 5)
#omega = 7.1271
#omega = 7
- omega = 3.5
+ omega = 3.62
+ correction = 1.42
vac_mueV_arr = np.array([29, 45, 60, 67, 144])
vac_omega_arr = vac_mueV_arr / omega_mueV
vac_arr = vac_mueV_arr / tkrg_mueV
@@ -648,8 +653,8 @@ def kogan04(dm, **parameters):
print(f"omega = {omega}")
print(f"Vac = {vac_arr}")
print(f"Vac/omega = {vac_omega_arr}")
- print(f"corrected Vac = {1.4*vac_arr}")
- print(f"corrected Vac = {(1.4*vac_omega_arr).round(5)} Ω")
+ print(f"corrected Vac = {correction*vac_arr}")
+ print(f"corrected Vac = {(correction*vac_omega_arr).round(5)} Ω")
vdc_mueV_max = 400
vdc_max = vdc_mueV_max / omega_mueV * omega
print(f"Vdc max. = {vdc_max}")
diff --git a/final_plots.py b/final_plots.py
index ccb8c0c540741b9c1ffee7b10f6c63a71c47d7d2..2f1fba2a0171344e08954fdb7e277af81d552850 100644
--- a/final_plots.py
+++ b/final_plots.py
@@ -29,7 +29,7 @@ from plot_pulses import load_all_pulses_full, integrate_ft
from kondo import solveTV0_scalar
# color maps with better sampling
-viridis = plt.cm.viridis.resampled(0x10000)
+viridis = mplcolors.LinearSegmentedColormap.from_list('viridis', plt.cm.viridis.colors, 0x10000)
seismic = plt.cm.seismic.resampled(0x10000)
# In this program all energies are given in units of the RTRG Kondo
@@ -235,6 +235,7 @@ def export_interp(
o3r_scale_x = TK_VOLTAGE_O3/TK_VOLTAGE,
o3r_scale_y = TK_VOLTAGE_O3/TK_VOLTAGE,
o3r_scale_fixed = TK_VOLTAGE_O3/TK_VOLTAGE,
+ g_flags_bad = DataManager.SOLVER_FLAGS["improved_initial_conditions"],
**parameters
):
"""
@@ -319,7 +320,7 @@ def export_interp(
o3p = DataManager.SOLVER_FLAGS["second_order_rg_equations"],
)
i_flags_good = DataManager.SOLVER_FLAGS["improved_initial_conditions"]
- g_flags_bad = DataManager.SOLVER_FLAGS["improved_initial_conditions"]
+ #g_flags_bad = DataManager.SOLVER_FLAGS["improved_initial_conditions"]
o2_data = dm.list(**{fixed_parameter:fixed_value*o2_scale_fixed}, **parameters, min_version=(14,15,-1,-1))
o2_data = o2_data.loc[o2_data.solver_flags & (global_bad_flags | bad_flags["o2"] | required_flags["o2"]) == required_flags["o2"]]
@@ -905,11 +906,11 @@ def export_omega5_interp_deviation(
def export_vdc0_interp(
filename = "figdata/vdc0_interp.npz",
omega_min = 0.1,
- omega_max = 40,
- vac_omega_min = 0.01,
+ omega_max = 16.5372,
+ vac_omega_min = 0,
vac_omega_max = 10,
- omega_res = 201,
- vac_res = 201,
+ omega_res = 401,
+ vac_res = 301,
korder = 2,
):
"""
@@ -922,10 +923,12 @@ def export_vdc0_interp(
x_parameter = "omega",
y_parameter = "vac_omega",
y_sort_parameter = "vac",
+ g_flags_bad = 0,
x_arr = np.linspace(omega_min, omega_max, omega_res),
y_arr = np.linspace(vac_omega_min, vac_omega_max, vac_res),
y_func = (lambda data: data["vac"]/data["omega"]),
- korder = korder,
+ kxorder = korder,
+ kyorder = korder,
special_selection = (lambda data, *args, **kwargs: data["omega"] > 0),
**parameters)
@@ -1325,13 +1328,17 @@ def export_kogan04_pgfplots():
"""
#omega = 3.1
#xL = 0.05
- omega = 3.5
- xL = 0.166667
+ #omega = 3.5
+ #xL = 0.166667
+ omega = 3.62
+ xL = 0.2
#omega = 4
#xL = 0.2
dm = DataManager()
# adjusted by factor 1.4:
- vac_omega = np.array([0.72881, 1.13091, 1.50788, 1.6838, 3.61891])
+ #vac_omega = np.array([0.72881, 1.13091, 1.50788, 1.6838, 3.61891])
+ # adjusted by factor 1.42:
+ vac_omega = np.array([0.73922, 1.14706, 1.52942, 1.70785, 3.6706])
parameters = dict(
d = 1e9,
method = "mu",
@@ -1357,7 +1364,7 @@ def export_kogan04_pgfplots():
#g_shift_sym = 0.015
# for omega=3.5, xL=0.166667:
prefactor_sym = 0.096*np.pi
- g_shift_sym = 0.02
+ g_shift_sym = 0.021
# for omega=4, xL=0.2:
#prefactor_sym = 0.093*np.pi
#g_shift_sym = 0.024
@@ -1376,7 +1383,7 @@ def export_kogan04_pgfplots():
gdc_funcs_asym.append(interp1d(data_sel.vdc, g_shift_asym + prefactor_asym*data_sel.dc_conductance, kind="cubic", bounds_error=False))
except ValueError:
gdc_funcs_asym.append(lambda x: np.nan*np.empty_like(x))
- vdc, *arrays = adjust_argument_array(0, 25.5, *gdc_funcs, *gdc_funcs_asym, s1=5e-4, maxit=4, initial_resolution=51)
+ vdc, *arrays = adjust_argument_array(0, 26.5, *gdc_funcs, *gdc_funcs_asym, s1=5e-4, maxit=4, initial_resolution=54)
vdc = np.append(-vdc[:0:-1], vdc)
interp = interp_vac0(dm, **parameters)
@@ -1392,7 +1399,7 @@ def export_kogan04_pgfplots():
def export_bruhat18_pgfplots(
plot_s = 2e-3,
- init_res = 41,
+ init_res = 101,
):
dm = DataManager()
omega1 = 4.14
@@ -1447,18 +1454,20 @@ def export_bruhat18_pgfplots(
g1_pat_funcs = [get_pat(omega1, omega1*vac) for vac in vac_omega1]
g2_pat_funcs = [get_pat(omega2, omega2*vac) for vac in vac_omega2]
- vdc1, *arrays1 = adjust_argument_array(0, 21.5, *gdc_funcs1, *g1_pat_funcs, s1=plot_s, maxit=4, initial_resolution=init_res)
+ vdc1, *arrays1 = adjust_argument_array(0, 21.5, *gdc_funcs1, s1=plot_s, maxit=3, initial_resolution=init_res)
+ vdc1_pat, *arrays1_pat = adjust_argument_array(0, 21.5, *g1_pat_funcs, s1=1.2*plot_s, maxit=3, initial_resolution=init_res)
vdc1 = np.append(-vdc1[:0:-1], vdc1) / omega1
- n1 = len(vac_omega1)
- g1 = np.array([np.append(a[:0:-1], a) for a in arrays1[:n1]])
- g1_pat = np.array([np.append(a[:0:-1], a) for a in arrays1[n1:]])
- vdc2, *arrays2 = adjust_argument_array(0, 21.5, *gdc_funcs2, *g2_pat_funcs, s1=plot_s, maxit=4, initial_resolution=init_res)
+ vdc1_pat = np.append(-vdc1_pat[:0:-1], vdc1_pat) / omega1
+ g1 = np.array([np.append(a[:0:-1], a) for a in arrays1])
+ g1_pat = np.array([np.append(a[:0:-1], a) for a in arrays1_pat])
+ vdc2, *arrays2 = adjust_argument_array(0, 21.5, *gdc_funcs2, s1=plot_s, maxit=4, initial_resolution=init_res)
+ vdc2_pat, *arrays2_pat = adjust_argument_array(0, 21.5, *g2_pat_funcs, s1=1.2*plot_s, maxit=3, initial_resolution=init_res)
vdc2 = np.append(-vdc2[:0:-1], vdc2) / omega2
- n2 = len(vac_omega2)
- g2 = np.array([np.append(a[:0:-1], a) for a in arrays2[:n2]])
- g2_pat = np.array([np.append(a[:0:-1], a) for a in arrays2[n2:]])
+ vdc2_pat = np.append(-vdc2_pat[:0:-1], vdc2_pat) / omega2
+ g2 = np.array([np.append(a[:0:-1], a) for a in arrays2])
+ g2_pat = np.array([np.append(a[:0:-1], a) for a in arrays2_pat])
- vdc1_asym, *g1_asym = adjust_argument_array(0, 21.5, *gdc_funcs1_asym, s1=plot_s, maxit=4, initial_resolution=init_res)
+ vdc1_asym, *g1_asym = adjust_argument_array(0, 21.5, *gdc_funcs1_asym, s1=plot_s, maxit=3, initial_resolution=init_res)
vdc1_asym = np.append(-vdc1_asym[:0:-1], vdc1_asym) / omega1
g1_asym = np.array([np.append(a[:0:-1], a) for a in g1_asym])
@@ -1471,27 +1480,43 @@ def export_bruhat18_pgfplots(
sel = ~np.isnan(g_exp2_bg)
g_exp2_bg_spl = splrep(vdc_omega_exp2[sel], g_exp2_bg[sel], s=1.1e-2)
bg2 = np.array([quad_vec((lambda t: splev(vdc2+vac*np.cos(t), g_exp2_bg_spl, ext=3)), 0, np.pi)[0]/np.pi for vac in vac_omega2])
+ bg2_pat = np.array([quad_vec((lambda t: splev(vdc2_pat+vac*np.cos(t), g_exp2_bg_spl, ext=3)), 0, np.pi)[0]/np.pi for vac in vac_omega2])
bg1 = np.array([quad_vec((lambda t: splev((vdc1+vac*np.cos(t))*omega1/omega2, g_exp2_bg_spl, ext=3)), 0, np.pi)[0]/np.pi for vac in vac_omega1])
+ bg1_pat = np.array([quad_vec((lambda t: splev((vdc1_pat+vac*np.cos(t))*omega1/omega2, g_exp2_bg_spl, ext=3)), 0, np.pi)[0]/np.pi for vac in vac_omega1])
np.savetxt("figdata/bruhat18_19GHz.dat",
- np.array([vdc1, *g1, *bg1, *(g1 + bg1), *g1_pat, *(g1_pat + bg1)]).T,
+ np.array([vdc1, *g1, *bg1, *(g1 + bg1)]).T,
header = " ".join((
"vdc",
" ".join(f"g{v:.3g}" for v in vac_omega1),
" ".join(f"bg{v:.3g}" for v in vac_omega1),
" ".join(f"gbg{v:.3g}" for v in vac_omega1),
+ )),
+ fmt = "%.6g",
+ comments = "")
+ np.savetxt("figdata/bruhat18_19GHz_pat.dat",
+ np.array([vdc1_pat, *g1_pat, *(g1_pat + bg1_pat)]).T,
+ header = " ".join((
+ "vdc",
" ".join(f"gpat{v:.3g}" for v in vac_omega1),
" ".join(f"gpatbg{v:.3g}" for v in vac_omega1),
)),
fmt = "%.6g",
comments = "")
np.savetxt("figdata/bruhat18_12GHz.dat",
- np.array([vdc2, *g2, *bg2, *(g2 + bg2), *g2_pat, *(g2_pat + bg2)]).T,
+ np.array([vdc2, *g2, *bg2, *(g2 + bg2)]).T,
header = " ".join((
"vdc",
" ".join(f"g{v:.3g}" for v in vac_omega2),
" ".join(f"bg{v:.3g}" for v in vac_omega2),
" ".join(f"gbg{v:.3g}" for v in vac_omega2),
+ )),
+ fmt = "%.6g",
+ comments = "")
+ np.savetxt("figdata/bruhat18_12GHz_pat.dat",
+ np.array([vdc2_pat, *g2_pat, *(g2_pat + bg2_pat)]).T,
+ header = " ".join((
+ "vdc",
" ".join(f"gpat{v:.3g}" for v in vac_omega2),
" ".join(f"gpatbg{v:.3g}" for v in vac_omega2),
)),
diff --git a/plot.py b/plot.py
index 07529c85c5e831f11ac3b04e63d03ec95bee9e62..af28de0ef82a738273ce4320cdb9003c750b1d2d 100644
--- a/plot.py
+++ b/plot.py
@@ -7,7 +7,7 @@ Kondo FRTRG, generate plots based on save data
"""
import matplotlib.pyplot as plt
-from matplotlib.colors import LogNorm, FuncNorm
+from matplotlib.colors import LogNorm, FuncNorm, LinearSegmentedColormap
import argparse
import pandas as pd
import numpy as np
@@ -25,6 +25,9 @@ from data_management import DataManager, KondoImport
from kondo import solveTV0_scalar
from scipy.integrate import quad, quad_vec
+# color maps with better sampling
+viridis = LinearSegmentedColormap.from_list('viridis', plt.cm.viridis.colors, 0x10000)
+
# reference_values maps (omega, vdc, vac) to reference values
REFERENCE_VALUES = {
(10, 24, 22) : dict(
@@ -2348,10 +2351,25 @@ def overview_3d_contours(dm, show_contours=False, show_surfaces=True, save_image
fill_levels = 1.1/np.linspace(1, 6.12, 256)[::-1] - 0.1
colors = plt.cm.viridis(np.linspace(0, 1, 256))
levels = 1/np.arange(1, 50, 0.5)[::-1]
- real_cmap = lambda x: plt.cm.viridis(1 - (1.1/(x+0.1)-1)/5.12)[...,:3]
- vdc_resolution = 400
- omega_resolution = 400
+ real_cmap = lambda x: viridis(1 - (1.1/(x+0.1)-1)/5.12)
omega_max = 16.5372
+ vdc = data_omega5["vdc"][0]
+ #vdc_resolution = data_omega5["vdc"].shape[1]
+ omega = data_vdc0["omega"][0]
+ #omega_resolution = data_vdc0["omega"].shape[1]
+ vac = data_omega5["vac"][:,0]
+ #assert np.allclose(vac, omega_max*data_vdc0["vac_omega"][:,0])
+
+ data = dm.list(omega=16.5372, good_flags=0x1000, bad_flags=0x2c8c, voltage_branches=4, method="mu", d=1e9, solver_tol_rel=1e-8, solver_tol_abs=1e-10, padding=0, xL=0.5)
+ #data = data.loc[(data.vdc < 165.38) & (data.vac < 165.38)]
+ x_arr, y_arr = np.meshgrid(np.linspace(-165.372, 165.372, 601), np.linspace(0, 330.744, 601))
+ gdc_omega5_img_arr = np.pi*griddata(
+ (data.vdc-data.vac, data.vdc+data.vac),
+ data.dc_conductance,
+ (x_arr, y_arr),
+ method="cubic")
+ array = np.array(0xffff*real_cmap(gdc_omega5_img_arr[::-1]), dtype=np.uint16)
+ imwrite(f"figdata/overview_z_rotated.png", array, format="PNG-FI")
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
@@ -2365,11 +2383,10 @@ def overview_3d_contours(dm, show_contours=False, show_surfaces=True, save_image
cmap_ax.set_xlim(0, 1)
cmap_ax.set_ylim(min_m, 1)
- vdc0_selection = data_vdc0["omega"][0] <= omega_max + 2e-3
- vdc0_y = data_vdc0["vac_omega"][:,vdc0_selection]
+ vdc0_y = data_vdc0["vac_omega"]
vdc0_x = np.zeros_like(vdc0_y)
- vdc0_z = data_vdc0["omega"][:,vdc0_selection]
- vdc0_m = np.pi*data_vdc0["gdc_J_o3a_p"][:,vdc0_selection]
+ vdc0_z = data_vdc0["omega"]
+ vdc0_m = np.pi*data_vdc0["gdc_J_o3a_p"]
if show_surfaces:
ax.contourf(vdc0_m, vdc0_y, vdc0_z/TK_VOLTAGE, zdir="x", levels=fill_levels, offset=0, zorder=-10, colors=colors)
@@ -2377,9 +2394,19 @@ def overview_3d_contours(dm, show_contours=False, show_surfaces=True, save_image
contour = ax.contour(vdc0_m, vdc0_y, vdc0_z/TK_VOLTAGE, zdir="x", levels=levels, offset=-offset, zorder=10, colors="black")
save_contour_coordinates(contour, "figdata/overview_contour_x.dat", "vdc", "omega", "gdc")
if save_images:
- array = np.array(255*real_cmap(vdc0_m.T[::-1,::-1]), dtype=np.ubyte)
- img = Image.fromarray(array)
- img.save("figdata/overview_x.png")
+ array = np.array(0xffff*real_cmap(vdc0_m.T[::-1,::-1]), dtype=np.uint16)
+ imwrite(f"figdata/overview_x.png", array, format="PNG-FI")
+ array_new = np.empty((array.shape[1], array.shape[0]+array.shape[1], 4), dtype=np.uint16)
+ array_new[:,array.shape[0]:].fill(0xffff)
+ array_new[:,array.shape[0]:,3].fill(0)
+ array_new[:,:array.shape[0]] = array.transpose((1,0,2))
+ sheared_array = array_new.flat[:array_new.size - 4*array_new.shape[0]].reshape(array.shape[1], array.shape[0]+array.shape[1]-1, 4).transpose((1,0,2))
+ imwrite(f"figdata/overview_x_sheared.png", sheared_array, format="PNG-FI")
+ #array_new = np.empty((array.shape[0], array.shape[0]+array.shape[1], 4), dtype=np.uint16)
+ #array_new[:,array.shape[1]:].fill(0xffff)
+ #array_new[:,:array.shape[1]] = array
+ #sheared_array = array_new.flat[:array_new.size - 4*array_new.shape[0]].reshape(array.shape[0], array.shape[0]+array.shape[1]-1, 4)
+ #imwrite(f"figdata/overview_x_sheared.png", sheared_array, format="PNG-FI")
omega5 = data_omega5["omega"]
omega5_x = data_omega5["vdc"]/omega5
@@ -2391,12 +2418,10 @@ def overview_3d_contours(dm, show_contours=False, show_surfaces=True, save_image
contour = ax.contour(omega5_x, omega5_y, omega5_m, zdir="z", levels=levels, offset=omega5/TK_VOLTAGE+offset, colors="black")
save_contour_coordinates(contour, "figdata/overview_contour_z.dat", "vac", "vdc", "gdc")
if save_images:
- array = np.array(255*real_cmap(omega5_m[::-1]), dtype=np.ubyte)
- img = Image.fromarray(array)
- img.save("figdata/overview_z.png")
+ imwrite(f"figdata/overview_z.png", np.array(0xffff*real_cmap(omega5_m[::-1]), dtype=np.uint16), format="PNG-FI")
- vac0_x = np.linspace(0, 10, vdc_resolution)
- vac0_z = np.linspace(1e-3, omega_max, omega_resolution)
+ vac0_x = vdc/omega_max
+ vac0_z = omega
vac0_vdc = vac0_x.reshape((-1,1)) * vac0_z.reshape((1,-1))
vac0_m = np.pi*interp(vac0_vdc)
if show_surfaces:
@@ -2405,9 +2430,14 @@ def overview_3d_contours(dm, show_contours=False, show_surfaces=True, save_image
contour = ax.contour(vac0_x, vac0_m, vac0_z/TK_VOLTAGE, zdir="y", levels=levels, offset=-offset, zorder=10, colors="black")
save_contour_coordinates(contour, "figdata/overview_contour_y.dat", "omega", "vac", "gdc")
if save_images:
- array = np.array(255*real_cmap(vac0_m.T[::-1]), dtype=np.ubyte)
- img = Image.fromarray(array)
- img.save("figdata/overview_y.png")
+ array = np.array(0xffff*real_cmap(vac0_m.T[::-1]), dtype=np.uint16)
+ imwrite(f"figdata/overview_y.png", array, format="PNG-FI")
+ array_new = np.empty((array.shape[1], array.shape[0]+array.shape[1], 4), dtype=np.uint16)
+ array_new[:,array.shape[0]:].fill(0xffff)
+ array_new[:,array.shape[0]:,3].fill(0)
+ array_new[:,:array.shape[0]] = array[::-1].transpose((1,0,2))
+ sheared_array = array_new.flat[:array_new.size - 4*array_new.shape[0]].reshape(array.shape[1], array.shape[0]+array.shape[1]-1, 4).transpose((1,0,2))[::-1]
+ imwrite(f"figdata/overview_y_sheared.png", sheared_array, format="PNG-FI")
#ax.plot_surface(vdc0_x, vdc0_y, vdc0_z, rstride=1, cstride=1, facecolors=plt.cm.viridis(vdc0_m), shade=False, zorder=-10)
#ax.set_axis_off()