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Copy pathASW___4_Diffuse_Interface.py
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ASW___4_Diffuse_Interface.py
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import os
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import matplotlib as mpl
from matplotlib.colors import *
import time
from copy import *
from ASW___analysis_input_file_handling import *
from ASW___plot_python_analysis_results import *
'''
##########################################
## ##
## FIT FUNCTIONS ##
## ##
##########################################
'''
############# FUNCTION diffuse_interface ############
def diffuse_interface_function(x, SSA, thickness, s_l_d_d):
func = np.zeros(np.size(x))
for i in range(len(x)):
func[i] = 2*np.pi*s_l_d_d**2*SSA*x[i]**(-4)*np.exp(-x[i]**2*thickness**2)
return func
############# FUNCTION diffuse_interface_fit ############
def diffuse_interface_fit_function(x, SSA, thickness, s_l_d_d):
if (SSA <= 0) or (thickness <= 0):
return np.zeros(np.size(x)) + 1e10
fitfunc = np.zeros(np.size(x))
for i in range(len(x)):
fitfunc[i] = np.log(2*np.pi*s_l_d_d**2*SSA*x[i]**(-4)*np.exp(-x[i]**2*thickness**2))
return fitfunc
############# FUNCTION diffuse_interface_uncertainty ############
def diffuse_interface_uncertainty_function(x, SSA, thickness, s_l_d_d, SSA_err, thickness_err):
delta = np.zeros(np.size(x))
for i in range(len(x)):
delta[i] = 2*np.pi*s_l_d_d**2*x[i]**(-4)*np.exp(-x[i]**2*thickness**2)*np.sqrt(SSA_err**2 + SSA**2*x[i]**4*4*thickness**2*thickness_err**2)
return delta
############################################
'''
##########################################
## ##
## DATA & PARAMETER HANDLING ##
## ##
##########################################
'''
########### FUNCTION to set_diffuse_interface_fit_ranges ##########
def set_diffuse_interface_fit_ranges(sample_analysis_directories, sample_plot_details, sample_diffuse_interface_input, sample_gudrun_results, sample_diffuse_interface_results):
os.chdir(sample_analysis_directories.Python_directory)
Temperature, Porod_range_left, Porod_range_right = np.loadtxt("GP_analysis_Porod_range_%s_%s_%s.txt" %(sample_plot_details.sample, sample_plot_details.action, sample_plot_details.analysis), unpack=True)
for i in range(sample_diffuse_interface_results.number_of_files):
file = sample_gudrun_results.file_start[i]
if file in sample_diffuse_interface_input.files_manual_fit_range:
pass
else:
try:
sample_diffuse_interface_input.q_fit_range_min[file] = Porod_range_left[i]
sample_diffuse_interface_input.q_fit_range_max[file] = Porod_range_right[i]
except(IndexError):
sample_diffuse_interface_input.q_fit_range_min[file] = Porod_range_left
sample_diffuse_interface_input.q_fit_range_max[file] = Porod_range_right
return
########### FUNCTION to assign manual fit parameters ##########
def assign_manual_diffuse_interface_fit_parameters(i, sample_gudrun_results, sample_diffuse_interface_input, sample_diffuse_interface_results):
file_start = sample_gudrun_results.file_start[i]
uncertainty = sample_diffuse_interface_input.manual_fit_uncertainty[file_start]/100.
sample_diffuse_interface_results.fit_tag[i] = 0
sample_diffuse_interface_results.SSA[i] = sample_diffuse_interface_input.manual_fit_SSA[file_start]
sample_diffuse_interface_results.SSA_err[i] = uncertainty * sample_diffuse_interface_input.manual_fit_SSA[file_start]
sample_diffuse_interface_results.thickness[i] = sample_diffuse_interface_input.manual_fit_DI_thickness[file_start]
sample_diffuse_interface_results.thickness_err[i] = np.max([uncertainty * sample_diffuse_interface_input.manual_fit_DI_thickness[file_start], 0.2])
return
############################################
'''
##########################################
## ##
## PLOTTING ##
## ##
##########################################
'''
########### FUNCTION to plot_individual_diffuse_interface_fits ##########
def plot_individual_diffuse_interface_fits(i, sample_plot_details, sample_material_properties, sample_diffuse_interface_input, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_results, sample_gudrun_results):
# get_short_names_of_diffuse_interface_parameters
file_start = sample_gudrun_results.file_start[i]
file_end = sample_gudrun_results.file_end[i]
T = sample_gudrun_results.temperatures[i]
color = sample_plot_details.color_scale[i]
q_min = sample_diffuse_interface_input.q_plot_min
q_max = sample_diffuse_interface_input.q_plot_max
s_l_d_d = sample_material_properties.scattering_length_density_difference
SSA = sample_diffuse_interface_results.SSA[i]
SSA_err = sample_diffuse_interface_results.SSA_err[i]
thickness = sample_diffuse_interface_results.thickness[i]
thickness_err = sample_diffuse_interface_results.thickness_err[i]
# Convert DCS (barn/sr/atom) to I(q) (m^2/cm^3) (barn & angstrom conversion: 1e-28*1e24=1e-4 )
I = (sample_diffuse_interface_temporary_fit_parameters.y*sample_material_properties.atomic_number_density*1e-4)
I_err = (sample_diffuse_interface_temporary_fit_parameters.yerr*sample_material_properties.atomic_number_density*1e-4)
# get q data
q = sample_diffuse_interface_temporary_fit_parameters.q
q_fit = sample_diffuse_interface_temporary_fit_parameters.q_fit
# save I(q) and q for later use:
sample_diffuse_interface_results.I_data[file_start] = I
sample_diffuse_interface_results.q_data[file_start] = q
sample_diffuse_interface_results.q_fit[file_start] = q_fit
# Labels
xlabel = "Q ($\AA^{-1}$)"
ylabel = "I(Q) (m$^2$/cm$^3$)"
if file_start == file_end:
plot_title = "Scan %i, %.1f K" %(file_start, T)
else:
plot_title = "Scans %i - %i, %.1f K" %(file_start, file_end, T)
# Margins & positions
top = 0.92
bottom = 0.14
left = 0.18
right = 0.83
# Initialise Plot:
fig = plt.figure(figsize=(9,6))
fig.patch.set_facecolor("w") # colour of outer box
plt.subplots_adjust(left=left, right=right, top=top, bottom=bottom) # margins
mpl.rcParams["lines.linewidth"] = 2 # linewidth of plots
plt.rcParams["mathtext.default"] = "regular" # mathtext same font as regular text
rc('axes', linewidth=2) # linewidth of axes
ax = plt.subplot(1,1,1)
# plot fit
ax.plot(q_fit, diffuse_interface_function(q_fit, SSA, thickness, s_l_d_d), color="m", linestyle="-", zorder=3)
ax.plot(q_fit, diffuse_interface_function(q_fit, SSA, thickness, s_l_d_d) - diffuse_interface_uncertainty_function(q_fit, SSA, thickness, s_l_d_d, SSA_err, thickness_err), color="m", linestyle="--", zorder=4)
ax.plot(q_fit, diffuse_interface_function(q_fit, SSA, thickness, s_l_d_d) + diffuse_interface_uncertainty_function(q_fit, SSA, thickness, s_l_d_d, SSA_err, thickness_err), color="m", linestyle="--", zorder=5)
result_parameters_1 = "SSA\n\n\nThickness"
result_parameters_2 = " = %.2f\n $\pm$ %.2f\n\n\n = %.3f\n $\pm$ %.3f" %(SSA, SSA_err, thickness, thickness_err)
# plot data
ax.plot(q, I, color=color, linewidth=2, zorder=2)
ax.fill_between(q, I-I_err, I+I_err, color=color, alpha=0.3, zorder=0)
# ranges
ymin = np.min(I) / (np.max(I) / np.min(I))**0.2
ymax = np.max(I) * (np.max(I) / np.min(I))**0.2
ax.set_xscale('log')
ax.set_yscale('log')
# ticks
x_ticks = [0.02, 0.04, 0.07, 0.1, 0.2]
x_ticklabels = ["0.02", "0.04", "0.07", "0.1", "0.2"]
ax.xaxis.set_ticks(x_ticks)
ax.xaxis.set_ticklabels(x_ticklabels)
y_ticks, y_ticklabels = plot_python_analysis_determine_ticks(ymin, ymax, scale="log")
ax.yaxis.set_ticks(y_ticks)
ax.yaxis.set_ticklabels(y_ticklabels)
# axes
ax.set_xlim([q_min, q_max])
ax.set_ylim([ymin, ymax])
# labels
# title
ax.text(0.5, 1.0, plot_title, transform=fig.transFigure, horizontalalignment='center', verticalalignment='top', multialignment="center", color="k", fontsize=sample_plot_details.label_fontsize)
# xlabel
ax.text(left+(right-left)/2.,0., xlabel, transform=fig.transFigure, horizontalalignment='center', verticalalignment='bottom', multialignment="center", color="k", fontsize=sample_plot_details.label_fontsize)
# ylabel
ax.text(0.0,bottom+(top-bottom)/2., ylabel, transform=fig.transFigure, horizontalalignment='left', verticalalignment='center', multialignment="center", color="k", fontsize=sample_plot_details.label_fontsize, rotation=90)
# annotations
# time
ax.text(0.97, 0.97, time.strftime("%H:%M:%S %d.%m.%Y"), transform=ax.transAxes, horizontalalignment="right", verticalalignment="top", fontsize=sample_plot_details.annotation_fontsize)
# result_parameters
ax.text(right*1.01, bottom+(top-bottom)*0.97, result_parameters_1, transform=fig.transFigure, horizontalalignment="left", verticalalignment="top", fontsize=sample_plot_details.annotation_fontsize)
ax.text(right*1.01, bottom+(top-bottom)*0.97, result_parameters_2, transform=fig.transFigure, horizontalalignment="left", verticalalignment="top", fontsize=sample_plot_details.annotation_fontsize)
if sample_diffuse_interface_results.fit_tag[i] == 0:
ax.text(0.97, 0.9, "manual fit", transform=ax.transAxes, horizontalalignment="right", verticalalignment="top", fontsize=sample_plot_details.annotation_fontsize)
if sample_diffuse_interface_results.forced_fit[i] == 1:
ax.text(0.97, 0.9, "forced fit", transform=ax.transAxes, horizontalalignment="right", verticalalignment="top", fontsize=sample_plot_details.annotation_fontsize)
# tick appearances
ax.tick_params(which="major", labelsize=sample_plot_details.tick_fontsize, width=sample_plot_details.tick_width, length=sample_plot_details.tick_length, direction="out")
ax.tick_params(which="minor", labelsize=sample_plot_details.minor_tick_fontsize, width=sample_plot_details.minor_tick_width, length=sample_plot_details.minor_tick_length, direction="out", labelleft=False)
# save plot
plt.savefig("diffuse_interface_fit_NIMROD000%i.png" %(file_start))
if file_start in sample_diffuse_interface_input.files_to_fit_live:
plt.show()
plt.close()
return
########### FUNCTION to plot diffuse interface thickness ##########
def plot_diffuse_interface_thickness(sample_plot_details, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results):
# set parameters for plot
filename = "%s_%s_%s__thickness_vs_" %(sample_plot_details.sample, sample_plot_details.action, sample_plot_details.analysis)
ylabel = "Thickness of Diffuse Layer ($\AA$)"
# color [color, alpha]
color = ["k", 0.5]
# data
thickness = sample_diffuse_interface_results.thickness
thickness_err = sample_diffuse_interface_results.thickness_err
# plot settings and y-data
plot = "scatter_errorbar"
# [[y values, y errors], [ , ], ...]
y = [thickness, thickness_err]
# axes [ymin, ymax, logscale?, tickpositions?, ticklables?]
ymin = (np.min(thickness) - np.min(thickness_err)) / 1.1
ymax = (np.max(thickness) + np.max(thickness_err)) * 1.1
yscale = [0.5, ymax] #originally [0., ymax]
plot_python_analysis_results(sample_plot_details, sample_gudrun_results, y, yscale, ylabel, filename, plot, color)
# logscale
if ymin <= 0:
ymin = np.max([np.min(thickness)/10., ymax/100.])
yscale[0] = ymin
yscale.append("log")
y_ticks, y_ticklabels = plot_python_analysis_determine_ticks(ymin, ymax, scale="log")
yscale.append(y_ticks)
yscale.append(y_ticklabels)
filename = filename.split("_vs_")[0] + "_logscale_vs_"
plot_python_analysis_results(sample_plot_details, sample_gudrun_results, y, yscale, ylabel, filename, plot, color)
return
########### FUNCTION to plot diffuse interface SSA ##########
def plot_diffuse_interface_SSA(sample_plot_details, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results):
# set parameters for plot
filename = "%s_%s_%s__DI_SSA_vs_" %(sample_plot_details.sample, sample_plot_details.action, sample_plot_details.analysis)
ylabel = "Specific Surface Area (m$^2$/cm$^3$)"
# color [color, alpha]
color = ["k", 0.5]
# data
SSA = sample_diffuse_interface_results.SSA
SSA_err = sample_diffuse_interface_results.SSA_err
# plot settings and y-data
plot = "scatter_errorbar"
# [[y values, y errors], [ , ], ...]
y = [SSA, SSA_err]
# axes [ymin, ymax, logscale?, tickpositions?, ticklables?]
ymin = (np.min(SSA) - np.min(SSA_err)) / 1.1
ymax = (np.max(SSA) + np.max(SSA_err)) * 1.1
yscale = [120., ymax]
plot_python_analysis_results(sample_plot_details, sample_gudrun_results, y, yscale, ylabel, filename, plot, color)
# logscale
if ymin <= 0:
ymin = np.max([np.min(SSA)/10., ymax/100.])
yscale[0] = ymin
yscale.append("log")
y_ticks, y_ticklabels = plot_python_analysis_determine_ticks(ymin, ymax, scale="log")
yscale.append(y_ticks)
yscale.append(y_ticklabels)
filename = filename.split("_vs_")[0] + "_logscale_vs_"
plot_python_analysis_results(sample_plot_details, sample_gudrun_results, y, yscale, ylabel, filename, plot, color)
return
########### FUNCTION to plot_all_diffuse_interface_fits ##########
def plot_all_diffuse_interface_fits(sample_plot_details, sample_material_properties, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results):
# set parameters for plot
filename = "%s_%s_%s__diffuse_interface_fits" %(sample_plot_details.sample, sample_plot_details.action, sample_plot_details.analysis)
xlabel = "Q ($\AA^{-1}$)"
ylabel = "I(Q) (m$^2$/cm$^3$)"
xmin = sample_diffuse_interface_input.q_plot_min
xmax = sample_diffuse_interface_input.q_plot_max
# Margins & positions
top = 0.92
bottom = 0.14
left = 0.15
right = 0.88
hspace = 0.02
cbarwidth = 0.03
# Initialise Plot:
fig = plt.figure(figsize=(9,6))
fig.patch.set_facecolor("w") # colour of outer box
plt.subplots_adjust(left=left, right=right, top=top, bottom=bottom) # margins
mpl.rcParams["lines.linewidth"] = 2 # linewidth of plots
plt.rcParams["mathtext.default"] = "regular" # mathtext same font as regular text
rc('axes', linewidth=2) # linewidth of axes
# Subplot positions
# [x position, y position, rel width, rel heigth]
ax_DCS = plt.axes([left, bottom, right-(cbarwidth+hspace+left), top-bottom])
# colorbar
cbar = plt.axes([right-cbarwidth, bottom, cbarwidth, top-bottom])
# Plot Gudrun results:
for i in range (sample_diffuse_interface_results.number_of_files):
if sample_gudrun_results.file_start[i] in sample_plot_details.exceptions:
print ("NIMROD000%i.mint01 (%i K) not plotted" %(sample_gudrun_results.file_start[i], sample_gudrun_results.temperatures[i]))
else:
file_start = sample_gudrun_results.file_start[i]
color = sample_plot_details.color_scale[i]
q_fit = sample_diffuse_interface_results.q_fit[file_start]
q_data = sample_diffuse_interface_results.q_data[file_start]
I_data = sample_diffuse_interface_results.I_data[file_start]
s_l_d_d = sample_material_properties.scattering_length_density_difference
SSA = sample_diffuse_interface_results.SSA[i]
thickness = sample_diffuse_interface_results.thickness[i]
ax_DCS.plot(q_data, I_data, color=color)
ax_DCS.plot(q_fit, diffuse_interface_function(q_fit, SSA, thickness, s_l_d_d), color=color, linestyle="--")
# annotations
if sample_diffuse_interface_results.fit_tag[i] == 0:
ax_DCS.text(0.97, 0.97-i*0.05, "manual fit", transform=ax_DCS.transAxes, horizontalalignment="right", verticalalignment="top", fontsize=sample_plot_details.annotation_fontsize, color=color)
if sample_diffuse_interface_results.forced_fit[i] == 1:
ax_DCS.text(0.97, 0.97-i*0.05, "forced fit", transform=ax_DCS.transAxes, horizontalalignment="right", verticalalignment="top", fontsize=sample_plot_details.annotation_fontsize, color=color)
# colorbar
cb = mpl.colorbar.ColorbarBase(
cbar ,
cmap = sample_plot_details.color_map ,
norm = sample_plot_details.color_norm ,
boundaries = sample_plot_details.color_bounds ,
ticks = sample_plot_details.color_plot_ticks ,
spacing = 'proportional' ,
orientation = 'vertical'
)
# Tick appearances
# DCS
ax_DCS.tick_params(which="major", labelsize=sample_plot_details.tick_fontsize, width=sample_plot_details.tick_width, length=sample_plot_details.tick_length, direction="out")
ax_DCS.tick_params(which="minor", labelsize=sample_plot_details.minor_tick_fontsize, width=sample_plot_details.minor_tick_width, length=sample_plot_details.minor_tick_length, direction="out", labelleft=False)
# colorbar
cbar.tick_params(which="major", labelsize=sample_plot_details.tick_fontsize, width=sample_plot_details.tick_width, length=sample_plot_details.tick_length, direction="out")
# labels
# title
ax_DCS.text(0.5, 1.0, sample_plot_details.plot_title, transform=fig.transFigure, horizontalalignment='center', verticalalignment='top', multialignment="center", color="k", fontsize=sample_plot_details.label_fontsize)
# xlabel
ax_DCS.text(left+(right-left)/2., 0., xlabel, transform=fig.transFigure, horizontalalignment='center', verticalalignment='bottom', multialignment="center", color="k", fontsize=sample_plot_details.label_fontsize)
# ylabel
ax_DCS.text(0.0, bottom+(top-bottom)/2., ylabel, transform=fig.transFigure, horizontalalignment='left', verticalalignment='center', multialignment="center", color="k", fontsize=sample_plot_details.label_fontsize, rotation=90)
# colorbar
cbar.text(1.0, bottom+(top-bottom)/2., sample_plot_details.color_bar_label, transform=fig.transFigure, horizontalalignment='right', verticalalignment='center', multialignment="center", color="k", fontsize=sample_plot_details.label_fontsize, rotation=90)
# axes
ax_DCS.set_xscale("log")
ax_DCS.set_yscale("log")
ax_DCS.set_xlim(xmin, xmax)
# Ticks
x_ticks = [0.02, 0.04, 0.07, 0.1, 0.2]
x_ticklabels = ["0.02", "0.04", "0.07", "0.1", "0.2"]
ax_DCS.xaxis.set_ticks(x_ticks)
ax_DCS.xaxis.set_ticklabels(x_ticklabels)
# save plot
plt.savefig(filename + ".pdf")
plt.savefig(filename + ".png")
#~ plt.show()
plt.close()
return
############################################
'''
##########################################
## ##
## RUNNING FITS ##
## ##
##########################################
'''
########### FUNCTION to manually_fit_diffuse_interface_live ##########
def manually_fit_diffuse_interface_live(i, sample_plot_details, sample_material_properties, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results):
continue_fitting = True
uncertainty = sample_diffuse_interface_input.manual_fit_uncertainty[sample_gudrun_results.file_start[i]]/100.
while continue_fitting:
plot_individual_diffuse_interface_fits(i, sample_plot_details, sample_material_properties, sample_diffuse_interface_input, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_results, sample_gudrun_results)
modify_fit = input("Modify fit parameters and plot again?\n(y/n)\n")
if "y" in modify_fit:
print("\nCurrent parameters:")
print("SSA\t= %.2f" %(sample_diffuse_interface_results.SSA[i]))
print("thickness\t= %.2f" %(sample_diffuse_interface_results.thickness[i]))
parameter_to_modify = input("\nWhich fit parameter is to be changed?\n(s/t)\n")
new_value = float(input("New value?\n"))
if ("s" in parameter_to_modify):
sample_diffuse_interface_results.SSA[i] = new_value
sample_diffuse_interface_results.SSA_err[i] = uncertainty * new_value
if ("t" in parameter_to_modify):
print("modified thickness")
sample_diffuse_interface_results.thickness[i] = new_value
sample_diffuse_interface_results.thickness_err[i] = uncertainty * new_value
elif "n" in modify_fit:
continue_fitting = False
print("\nFinal parameters:")
print("SSA\t= %.2f" %(sample_diffuse_interface_results.SSA[i]))
print("thickness\t= %.2f" %(sample_diffuse_interface_results.thickness[i]))
else:
print("Invalid input. Try again.")
return
########### FUNCTION to run_diffuse_interface_fit ##########
def run_diffuse_interface_fit(i, sample_material_properties, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results):
print("Diffuse Interface fit")
file_start = sample_gudrun_results.file_start[i]
# Convert DCS (barn/sr/atom) to I(q) (m^2/cm^3) (barn & angstrom conversion: 1e-28*1e24=1e-4 )
I = (sample_diffuse_interface_temporary_fit_parameters.y_fit*sample_material_properties.atomic_number_density*1e-4)
I_err = (sample_diffuse_interface_temporary_fit_parameters.yerr_fit*sample_material_properties.atomic_number_density*1e-4)
# set fit parameters
q = sample_diffuse_interface_temporary_fit_parameters.q_fit
weight = I_err/I
s_l_d_d = sample_material_properties.scattering_length_density_difference
# start parameters
SSA = 1000.
thickness = 2.
# run fit
popt, pcov = curve_fit(lambda q, SSA, thickness: diffuse_interface_fit_function(q, SSA, thickness, s_l_d_d), q, np.log(I), p0=[SSA, thickness], sigma=weight, absolute_sigma=True, maxfev=10000)
perr = np.sqrt(np.diag(pcov))
if np.inf not in popt:
# write results to lists
sample_diffuse_interface_results.fit_tag[i] = 1
sample_diffuse_interface_results.SSA[i] = popt[0]
sample_diffuse_interface_results.thickness[i] = popt[1]
if np.inf not in pcov:
sample_diffuse_interface_results.SSA_err[i] = perr[0]
sample_diffuse_interface_results.thickness_err[i] = perr[1]
else:
sample_diffuse_interface_results.SSA_err[i] = popt[0]*0.05
sample_diffuse_interface_results.thickness_err[i] = popt[1]*0.05
if sample_diffuse_interface_results.thickness_err[i] < 0.5:
sample_diffuse_interface_results.thickness_err[i] = 0.5
elif sample_diffuse_interface_results.thickness_err[i] > np.max([1.5*sample_diffuse_interface_results.thickness[i], 0.5]):
sample_diffuse_interface_results.thickness_err[i] = np.max([1.5*sample_diffuse_interface_results.thickness[i], 0.5])
elif i>0:
# force fit
sample_diffuse_interface_results.forced_fit[i] = 1
print("\nForcing fit\n")
sample_diffuse_interface_results.SSA[i] = sample_diffuse_interface_results.SSA[i-1]
sample_diffuse_interface_results.thickness[i] = sample_diffuse_interface_results.thickness[i-1]
sample_diffuse_interface_results.SSA_err[i] = sample_diffuse_interface_results.SSA_err[i-1]
sample_diffuse_interface_results.thickness_err[i] = sample_diffuse_interface_results.thickness_err[i-1]
else:
print("\n\n++++++++++\n\nFit unsuccessfull\n\nManual fit required\n\n++++++++++\n\n")
return
########### FUNCTION to call_diffuse_interface_analysis_loop ##########
def call_diffuse_interface_analysis_loop(i, sample_material_properties, sample_analysis_directories, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_input, sample_gudrun_results, sample_diffuse_interface_results, sample_plot_details):
file_start = sample_gudrun_results.file_start[i]
T = sample_gudrun_results.temperatures[i]
q_fit_min = sample_diffuse_interface_input.q_fit_range_min[file_start]
q_fit_max = sample_diffuse_interface_input.q_fit_range_max[file_start]
print("----------------------------------\nFile:%i\t\t\t%i of %i" %(file_start, (i+1), sample_diffuse_interface_results.number_of_files))
# read neutron data from file
python_fits_read_neutron_data_from_file(i, [q_fit_min, q_fit_max], sample_analysis_directories, sample_diffuse_interface_input, sample_gudrun_results, sample_plot_details, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_results)
if file_start in sample_plot_details.exceptions:
return
if file_start in sample_diffuse_interface_input.files_to_fit_manually:
# take manual fit
assign_manual_diffuse_interface_fit_parameters(i, sample_gudrun_results, sample_diffuse_interface_input, sample_diffuse_interface_results)
if file_start in sample_diffuse_interface_input.files_to_fit_live:
manually_fit_diffuse_interface_live(i, sample_plot_details, sample_material_properties, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results)
else:
# run fit
run_diffuse_interface_fit(i, sample_material_properties, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results)
# print results
print("\nFit result:\n SSA\t\t= %.1e +/- %.1e" %(sample_diffuse_interface_results.SSA[i], sample_diffuse_interface_results.SSA_err[i]))
print(" thickness\t= %.1e +/- %.1e" %(sample_diffuse_interface_results.thickness[i], sample_diffuse_interface_results.thickness_err[i]))
# Plot fit:
plot_individual_diffuse_interface_fits(i, sample_plot_details, sample_material_properties, sample_diffuse_interface_input, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_results, sample_gudrun_results)
return
############################################
'''
##########################################
## ##
## SAVING RESULTS ##
## ##
##########################################
'''
############# FUNCTION to save results to file ############
def save_diffuse_interface_results_to_file(input, sample_diffuse_interface_input, sample_gudrun_results, sample_diffuse_interface_results):
# set up variables to save results to file
Save_Header = ""
Save_Data = []
Save_Format = []
# sort results into variables
Save_Header += "temperatures, "
Save_Data.append(sample_gudrun_results.temperatures)
Save_Format.append('%1.4e')
Save_Header += "temp_err_neg, "
Save_Data.append(sample_gudrun_results.temp_err_neg)
Save_Format.append('%1.4e')
Save_Header += "temp_err_pos, "
Save_Data.append(sample_gudrun_results.temp_err_pos)
Save_Format.append('%1.4e')
Save_Header += "times, "
Save_Data.append(sample_gudrun_results.times)
Save_Format.append('%1.4e')
Save_Header += "time_err_neg, "
Save_Data.append(sample_gudrun_results.time_err_neg)
Save_Format.append('%1.4e')
Save_Header += "time_err_pos, "
Save_Data.append(sample_gudrun_results.time_err_pos)
Save_Format.append('%1.4e')
Save_Header += "SSA, "
Save_Data.append(sample_diffuse_interface_results.SSA)
Save_Format.append('%1.4e')
Save_Header += "SSA_err, "
Save_Data.append(sample_diffuse_interface_results.SSA_err)
Save_Format.append('%1.4e')
Save_Header += "thickness, "
Save_Data.append(sample_diffuse_interface_results.thickness)
Save_Format.append('%1.4e')
Save_Header += "thickness_err, "
Save_Data.append(sample_diffuse_interface_results.thickness_err)
Save_Format.append('%1.4e')
Save_Header += "fitted_by_python"
Save_Data.append(sample_diffuse_interface_results.fit_tag)
Save_Format.append('%i')
# save results to file
np.savetxt("diffuse_interface_fit_%s_%s_%s.txt" %(input["sample"], input["action"], input["analysis"]), np.transpose(Save_Data), fmt=Save_Format, header=Save_Header)
return
############################################
'''
##########################################
## ##
## MAIN FUNCTION ##
## ##
##########################################
'''
########### FUNCTION to run_diffuse_interface_analysis ##########
def run_diffuse_interface_analysis(input, sample_material_properties, sample_analysis_directories, sample_plot_details, sample_gudrun_results, sample_diffuse_interface_input):
print("\n\n-------------------\n\nRunning Diffuse Interface analysis.\n\n")
# Initialise arrays for fit results
try:
number_of_files = len(sample_gudrun_results.file_start)
except(TypeError):
number_of_files = 1
sample_diffuse_interface_results = diffuse_interface_results(number_of_files)
# Initialise temporary storage for data and start parameters
sample_diffuse_interface_temporary_fit_parameters = diffuse_interface_temporary_fit_parameters()
# Set q fit range (based on double Guinier-Porod fit):
set_diffuse_interface_fit_ranges(sample_analysis_directories, sample_plot_details, sample_diffuse_interface_input, sample_gudrun_results, sample_diffuse_interface_results)
# Fits
for i in range(sample_diffuse_interface_results.number_of_files):
call_diffuse_interface_analysis_loop(i, sample_material_properties, sample_analysis_directories, sample_diffuse_interface_temporary_fit_parameters, sample_diffuse_interface_input, sample_gudrun_results, sample_diffuse_interface_results, sample_plot_details)
print("----------------------------------\n----------------------------------\n\nFits complete\n")
print("\n\n------------------------------------\n\nDiffuse Interface Summary\n")
print("%i Python fits\n" %(np.count_nonzero(sample_diffuse_interface_results.fit_tag==1)))
for i in range(sample_diffuse_interface_results.number_of_files):
if sample_diffuse_interface_results.forced_fit[i] == 1:
print("%i\tforced fit" %(sample_gudrun_results.file_start[i]))
if sample_diffuse_interface_results.fit_tag[i] == 0:
print("%i\tmanual fit" %(sample_gudrun_results.file_start[i]))
if sample_diffuse_interface_results.fit_tag[i] == 2:
print("%i\tnot analysed" %(sample_gudrun_results.file_start[i]))
print("\n\n")
# Save results to file
save_diffuse_interface_results_to_file(input, sample_diffuse_interface_input, sample_gudrun_results, sample_diffuse_interface_results)
# Plot results
plot_diffuse_interface_thickness(sample_plot_details, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results)
plot_diffuse_interface_SSA(sample_plot_details, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results)
plot_all_diffuse_interface_fits(sample_plot_details, sample_material_properties, sample_diffuse_interface_input, sample_diffuse_interface_results, sample_gudrun_results)