def residualFunc(pars, hPocket, ePocket, rzz_0, rzz_15, rzz_30, rzz_45):
h_gamma_0 = pars["h_gamma_0"].value
e_gamma_0 = pars["e_gamma_0"].value
gamma_dos = pars["gamma_dos"].value
gamma_k = pars["gamma_k"].value
power = pars["power"].value
mu = pars["mu"].value
M = pars["M"].value
print("h_gamma_0 = ", h_gamma_0)
print("e_gamma_0 = ", e_gamma_0)
print("gamma_dos = ", gamma_dos)
print("gamma_k = ", gamma_k)
print("power = ", power)
print("mu = ", mu)
print("M = ", M)
power = int(power)
if power % 2 == 1:
power += 1
start_total_time = time.time()
# hPocket
hPocket.mu = mu
hPocket.M = M
hPocket.discretize_FS(mesh_xy_rough=501)
hPocket.dos_k_func()
hPocket.doping()
hPocketCondObject = Conductivity(hPocket, Bamp=45, gamma_0=h_gamma_0,
gamma_k=gamma_k, power=power, gamma_dos=gamma_dos)
# ePocket
ePocket.mu = mu
ePocket.M = M
ePocket.discretize_FS(mesh_xy_rough=501)
ePocket.dos_k_func()
ePocket.doping()
ePocketCondObject = Conductivity(ePocket, Bamp=45, gamma_0=e_gamma_0,
gamma_k=gamma_k, power=power, gamma_dos=gamma_dos)
ADMRObject = ADMR([hPocketCondObject, ePocketCondObject])
ADMRObject.Btheta_array = Btheta_array
ADMRObject.runADMR()
print("ADMR time : %.6s seconds \n" % (time.time() - start_total_time))
diff_0 = rzz_0 - ADMRObject.rzz_array[0, :]
diff_15 = rzz_15 - ADMRObject.rzz_array[1, :]
diff_30 = rzz_30 - ADMRObject.rzz_array[2, :]
diff_45 = rzz_45 - ADMRObject.rzz_array[3, :]
return np.concatenate((diff_0, diff_15, diff_30, diff_45))
def residualFunc(pars, bandObject, rzz_0, rzz_15, rzz_30, rzz_45):
gamma_0 = pars["gamma_0"].value
gamma_dos = pars["gamma_dos"].value
gamma_k = pars["gamma_k"].value
power = pars["power"].value
mu = pars["mu"].value
M = pars["M"].value
print("gamma_0 = ", gamma_0)
print("gamma_dos = ", gamma_dos)
print("gamma_k = ", gamma_k)
print("power = ", power)
print("mu = ", mu)
print("M = ", M)
power = int(power)
if power % 2 == 1:
power += 1
start_total_time = time.time()
bandObject.mu = mu
bandObject.M = M
bandObject.discretize_FS()
bandObject.dos_k_func()
bandObject.doping()
condObject = Conductivity(bandObject,
Bamp=45,
gamma_0=gamma_0,
gamma_k=gamma_k,
power=power,
gamma_dos=gamma_dos)
ADMRObject = ADMR([condObject])
ADMRObject.Btheta_array = Btheta_array
ADMRObject.runADMR()
print("ADMR time : %.6s seconds" % (time.time() - start_total_time))
diff_0 = rzz_0 - ADMRObject.rzz_array[0, :]
diff_15 = rzz_15 - ADMRObject.rzz_array[1, :]
diff_30 = rzz_30 - ADMRObject.rzz_array[2, :]
diff_45 = rzz_45 - ADMRObject.rzz_array[3, :]
return np.concatenate((diff_0, diff_15, diff_30, diff_45))
mu = out.params["mu"].value
## Compute ADMR with final parameters from the fit
bandObject.mu = mu
bandObject.discretize_FS()
bandObject.dos_k_func()
bandObject.doping()
condObject = Conductivity(bandObject,
Bamp=45,
gamma_0=gamma_0,
gamma_k=gamma_k,
power=power,
gamma_dos=gamma_dos)
ADMRObject = ADMR([condObject])
ADMRObject.Btheta_array = Btheta_array
ADMRObject.runADMR()
ADMRObject.fileADMR(folder="data_NdLSCO_0p21")
#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<#
## Figures ////////////////////////////////////////////////////////////////#
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>#
#///// RC Parameters //////#
mpl.rcdefaults()
mpl.rcParams['font.size'] = 24. # change the size of the font in every figure
mpl.rcParams['font.family'] = 'Arial' # font Arial in every figure
mpl.rcParams['axes.labelsize'] = 24.
mpl.rcParams['xtick.labelsize'] = 24
mpl.rcParams['ytick.labelsize'] = 24
mpl.rcParams['xtick.direction'] = "in"
mpl.rcParams['ytick.direction'] = "in"
")",
}
## ONE BAND Horio et al. /////////////////////////////////////////////////////////
bandObject = BandStructure(**params)
## Discretize
bandObject.setMuToDoping(0.30)
bandObject.runBandStructure(printDoping=True)
# bandObject.mc_func()
# print("mc = " + "{:.3f}".format(bandObject.mc))
# bandObject.figDiscretizeFS2D()
# bandObject.figMultipleFS2D()
## Conductivity
condObject = Conductivity(bandObject, **params)
# condObject.figdfdE()
# condObject.runTransport()
# condObject.omegac_tau_func()
# print("omega_c * tau = " + "{:.3f}".format(condObject.omegac_tau))
# condObject.figScatteringPhi(kz=0)
# condObject.solveMovementFunc()
# condObject.figCumulativevft()
## ADMR
amro1band = ADMR([condObject], **params)
amro1band.runADMR()
amro1band.fileADMR(folder="sim/Tl2201_Tc_20K/")
amro1band.figADMR(folder="sim/Tl2201_Tc_20K/")
class FittingADMR:
def __init__(self,
init_member,
ranges_dict,
data_dict,
pipi_FSR=False,
folder="",
method="differential_evolution",
population=100,
N_generation=20,
mutation_s=0.1,
crossing_p=0.9,
normalized_data=True,
**trash):
## Initialize
self.init_member = deepcopy(init_member)
self.member = deepcopy(init_member)
self.ranges_dict = ranges_dict
self.data_dict = data_dict
self.folder = folder
self.normalized_data = normalized_data
self.pipi_FSR = pipi_FSR
self.pars = Parameters()
for param_name, param_range in self.ranges_dict.items():
if param_name in self.init_member.keys():
self.pars.add(param_name,
value=self.init_member[param_name],
min=param_range[0],
max=param_range[-1])
elif param_name in self.init_member["band_params"].keys():
self.pars.add(
param_name,
value=self.init_member["band_params"][param_name],
min=param_range[0],
max=param_range[-1])
self.method = method # "shgo", "differential_evolution", "leastsq"
## Differential evolution
self.population = population
self.N_generation = N_generation
self.mutation_s = mutation_s
self.crossing_p = crossing_p
## Objects
if pipi_FSR == False:
self.bandObject = BandStructure(**self.member)
else:
self.bandObject = Pocket(**self.member)
self.condObject = None
self.admrObject = None
## Empty spaces
self.nb_calls = 0
self.json_name = None
self.rhozz_data_matrix = None
self.rzz_data_matrix = None
self.Bphi_array = None
self.Btheta_array = None
self.Btheta_data_dict = {}
self.rhozz_data_dict = {}
self.rzz_data_dict = {}
def produce_ADMR_object(self):
## Update bandObject
for param_name in self.ranges_dict.keys():
if hasattr(self.bandObject, param_name):
setattr(self.bandObject, param_name, self.member[param_name])
if param_name in self.bandObject._band_params.keys():
self.bandObject[param_name] = self.member["band_params"][
param_name]
## Adjust the doping if need be
if self.member["fixdoping"] >= -1 and self.member["fixdoping"] <= 1:
self.bandObject.setMuToDoping(self.member["fixdoping"])
self.member["band_params"]["mu"] = self.bandObject["mu"]
self.bandObject.runBandStructure()
self.condObject = Conductivity(self.bandObject, **self.member)
self.admrObject = ADMR([self.condObject], **self.member)
self.admrObject.Btheta_array = self.Btheta_array
self.admrObject.Bphi_array = self.Bphi_array
def load_Bphi_data(self):
## Create array of phi at the selected temperature
Bphi_array = []
for t, phi in self.data_dict.keys():
if (self.member["data_T"] == t) * np.isin(
phi, np.array(self.member["Bphi_array"])):
Bphi_array.append(float(phi)) # put float for JSON
Bphi_array.sort()
self.Bphi_array = np.array(Bphi_array)
def load_Btheta_data(self):
## Create Initial Btheta
Btheta_array = np.arange(
self.member["Btheta_min"],
self.member["Btheta_max"] + self.member["Btheta_step"],
self.member["Btheta_step"])
## Create Bphi
self.load_Bphi_data()
# Cut Btheta_array to theta_cut
Btheta_cut_array = np.zeros(len(self.Bphi_array))
for i, phi in enumerate(self.Bphi_array):
Btheta_cut_array[i] = self.data_dict[self.member["data_T"], phi][3]
Btheta_cut_min = np.min(
Btheta_cut_array) # minimum cut for Btheta_array
# New Btheta_array with cut off if necessary
self.Btheta_array = Btheta_array[Btheta_array <= Btheta_cut_min]
def load_and_interp_data(self):
"""
data_dict[data_T,phi] = [filename, col_theta, col_rzz, theta_cut, rhozz_0]
"""
## Create Btheta array
self.load_Btheta_data()
## Create array of phi at the selected temperature
self.load_Bphi_data()
## Interpolate data over theta of simulation
self.rzz_data_matrix = np.zeros(
(len(self.Bphi_array), len(self.Btheta_array)))
self.rhozz_data_matrix = np.zeros(
(len(self.Bphi_array), len(self.Btheta_array)))
for i, phi in enumerate(self.Bphi_array):
filename = self.data_dict[self.member["data_T"], phi][0]
col_theta = self.data_dict[self.member["data_T"], phi][1]
col_rzz = self.data_dict[self.member["data_T"], phi][2]
rhozz_0 = self.data_dict[self.member["data_T"], phi][4]
data = np.loadtxt(filename, dtype="float", comments="#")
theta = data[:, col_theta]
rzz = data[:, col_rzz]
## Order data
index_order = np.argsort(theta)
theta = theta[index_order]
rzz = rzz[index_order]
rzz /= np.interp(0, theta, rzz)
rzz_i = np.interp(self.Btheta_array, theta,
rzz) # "i" is for interpolated
self.rhozz_data_matrix[i, :] = rzz_i * rhozz_0
self.rzz_data_matrix[i, :] = rzz_i
def compute_diff(self, pars):
"""Compute diff = sim - data matrix"""
self.pars = pars
start_total_time = time.time()
## Load data
self.load_and_interp_data()
## Update Btheta & Bphi function of the data
self.member["Bphi_array"] = list(self.Bphi_array)
self.member["Btheta_min"] = float(np.min(
self.Btheta_array)) # float need for JSON
self.member["Btheta_max"] = float(np.max(self.Btheta_array))
self.member["Btheta_step"] = float(self.Btheta_array[1] -
self.Btheta_array[0])
## Update member with fit parameters
for param_name in self.ranges_dict.keys():
if param_name in self.init_member.keys():
self.member[param_name] = self.pars[param_name].value
elif param_name in self.init_member["band_params"].keys():
self.member["band_params"][param_name] = self.pars[
param_name].value
print(param_name + " : " +
"{0:g}".format(self.pars[param_name].value))
## Compute ADMR ------------------------------------------------------------
self.produce_ADMR_object()
self.admrObject.runADMR()
self.nb_calls += 1
print("---- call #" + str(self.nb_calls) + " in %.6s seconds ----" %
(time.time() - start_total_time))
## Compute diff
diff_matrix = np.zeros_like(self.rzz_data_matrix)
for i in range(self.Bphi_array.size):
if self.normalized_data == True:
diff_matrix[i, :] = self.rzz_data_matrix[
i, :] - self.admrObject.rzz_array[i, :]
else:
diff_matrix[i, :] = (self.rhozz_data_matrix[i, :] -
self.admrObject.rhozz_array[i, :]) * 1e5
return diff_matrix.flatten()
def runFit(self):
## Initialize parameters
self.nb_calls = 0
## Run fit algorithm
if self.method == "least_square":
out = minimize(self.compute_diff, self.pars)
if self.method == "shgo":
out = minimize(self.compute_diff,
self.pars,
method='shgo',
sampling_method='sobol',
options={"f_tol": 1e-16},
n=100,
iters=20)
if self.method == "differential_evolution":
out = minimize(self.compute_diff,
self.pars,
method='differential_evolution')
if self.method == "ampgo":
out = minimize(self.compute_diff, self.pars, method='ampgo')
else:
print("This method does not exist in the class")
## Display fit report
report_fit(out)
## Export final parameters from the fit
for param_name in self.ranges_dict.keys():
if param_name in self.init_member.keys():
self.member[param_name] = out.params[param_name].value
elif param_name in self.init_member["band_params"].keys():
self.member["band_params"][param_name] = out.params[
param_name].value
## Save BEST member to JSON
self.save_member_to_json()
## Compute the FINAL member
self.fig_compare(fig_save=True)
def load_member_from_json(self):
with open(self.folder + "/" + self.json_name, "r") as f:
self.member = json.load(f)
def save_member_to_json(self):
self.produce_ADMR_object()
path = self.folder + "/data_" + \
"p" + "{0:.2f}".format(self.member["data_p"]) + "_" + \
"T" + "{0:.1f}".format(self.member["data_T"]) + "_fit_" + self.admrObject.fileNameFunc() + ".json"
with open(path, 'w') as f:
json.dump(self.member, f, indent=4)
def fig_compare(self, fig_show=True, fig_save=False):
## Create Btheta array
self.load_Btheta_data()
## Create array of phi at the selected temperature
self.load_Bphi_data()
## Load non-interpolated data ------------------------------------------
Btheta_cut = np.max(self.Btheta_array)
for i, phi in enumerate(self.Bphi_array):
filename = self.data_dict[self.member["data_T"], phi][0]
col_theta = self.data_dict[self.member["data_T"], phi][1]
col_rzz = self.data_dict[self.member["data_T"], phi][2]
rhozz_0 = self.data_dict[self.member["data_T"], phi][4]
data = np.loadtxt(filename, dtype="float", comments="#")
theta = data[:, col_theta]
rzz = data[:, col_rzz]
## Order data
index_order = np.argsort(theta)
theta = theta[index_order]
rzz = rzz[index_order]
rzz = rzz / np.interp(0, theta, rzz)
rhozz = rzz * rhozz_0
self.Btheta_data_dict[phi] = theta[theta <= Btheta_cut]
self.rhozz_data_dict[phi] = rhozz[theta <= Btheta_cut]
self.rzz_data_dict[phi] = rzz[theta <= Btheta_cut]
## Run ADMR
self.produce_ADMR_object()
self.admrObject.runADMR()
## Plot >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
fig_list = []
## Plot Parameters
fig, axes = plt.subplots(1, 1, figsize=(10.5, 5.8))
fig.subplots_adjust(left=0.18, right=0.82, bottom=0.18, top=0.95)
if self.normalized_data == True:
axes.axhline(y=1, ls="--", c="k", linewidth=0.6)
#############################################
fig.text(0.84,
0.89,
r"$B$ = " + str(self.member["Bamp"]) + " T",
fontsize=14)
fig.text(0.84,
0.84,
r"$T$ (data) = " + str(self.member["data_T"]) + " K",
fontsize=14)
fig.text(0.84,
0.79,
r"$T$ (sim) = " + str(self.member["T"]) + " K",
fontsize=14)
fig.text(0.84,
0.74,
r"$p$ (data) = " + "{0:.2f}".format(self.member["data_p"]),
fontsize=14)
fig.text(0.84,
0.69,
r"$p$ (sim) = " +
"{0:.3f}".format(self.admrObject.totalHoleDoping),
fontsize=14)
#############################################
#############################################
axes.set_xlim(0, 90)
# axes.set_ylim(1+1.2*(min_y-1),1.2*(max_y-1)+1)
axes.tick_params(axis='x', which='major', pad=7)
axes.tick_params(axis='y', which='major', pad=8)
axes.set_xlabel(r"$\theta$ ( $^{\circ}$ )", labelpad=8)
axes.set_ylabel(r"$\rho_{\rm zz}$ / $\rho_{\rm zz}$ ( 0 )", labelpad=8)
#############################################
colors = [
'#000000', '#3B528B', '#FF0000', '#C7E500', '#ff0080', '#dfdf00'
]
if self.normalized_data == True:
axes.set_ylabel(r"$\rho_{\rm zz}$ / $\rho_{\rm zz}$ ( 0 )",
labelpad=8)
for i, phi in enumerate(self.Bphi_array):
line = axes.plot(self.Btheta_data_dict[phi],
self.rzz_data_dict[phi],
label=r"$\phi$ = " + str(phi))
plt.setp(line,
ls="-",
c=colors[i],
lw=2,
marker="",
mfc=colors[i],
ms=7,
mec=colors[i],
mew=0)
for i, phi in enumerate(self.Bphi_array):
line = axes.plot(self.Btheta_array,
self.admrObject.rzz_array[i, :])
plt.setp(line,
ls="--",
c=colors[i],
lw=2,
marker="",
mfc=colors[i],
ms=5,
mec=colors[i],
mew=0)
else:
axes.set_ylabel(r"$\rho_{\rm zz}$ ( m$\Omega$ cm )", labelpad=8)
for i, phi in enumerate(self.Bphi_array):
line = axes.plot(self.Btheta_data_dict[phi],
self.rhozz_data_dict[phi] * 1e5,
label=r"$\phi$ = " + str(phi))
plt.setp(line,
ls="-",
c=colors[i],
lw=2,
marker="",
mfc=colors[i],
ms=7,
mec=colors[i],
mew=0)
for i, phi in enumerate(self.Bphi_array):
line = axes.plot(self.Btheta_array,
self.admrObject.rhozz_array[i, :] * 1e5)
plt.setp(line,
ls="--",
c=colors[i],
lw=2,
marker="",
mfc=colors[i],
ms=5,
mec=colors[i],
mew=0)
######################################################
plt.legend(loc=0,
fontsize=14,
frameon=False,
numpoints=1,
markerscale=1,
handletextpad=0.5)
######################################################
##///Set ticks space and minor ticks space ///#
xtics = 30 # space between two ticks
mxtics = xtics / 2. # space between two minor ticks
majorFormatter = FormatStrFormatter(
'%g') # put the format of the number of ticks
axes.xaxis.set_major_locator(MultipleLocator(xtics))
axes.xaxis.set_major_formatter(majorFormatter)
axes.xaxis.set_minor_locator(MultipleLocator(mxtics))
if self.normalized_data == True:
axes.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
else:
axes.yaxis.set_major_formatter(FormatStrFormatter('%g'))
fig_list.append(fig)
#///////////////////////////////////////////////////////////////////////////////
if fig_show == True:
plt.show()
else:
plt.close(fig)
## Figure Parameters //////////////////////////////////////////////////////#
for iniCondObject in self.admrObject.initialCondObjectDict.values():
fig_list.append(iniCondObject.figParameters(fig_show=fig_show))
## Save figures list --------------
if fig_save == True:
file_figures = PdfPages(self.folder + "/data_" + \
"p" + "{0:.2f}".format(self.member["data_p"]) + "_" + \
"T" + "{0:.1f}".format(self.member["data_T"]) + "_fit_" + self.admrObject.fileNameFunc() + ".pdf")
for fig in fig_list:
file_figures.savefig(fig)
file_figures.close()