diff --git a/Additional/SpiralLoop (version 1).xls b/Additional/SpiralLoop (version 1).xls new file mode 100644 index 0000000..c88efbc Binary files /dev/null and b/Additional/SpiralLoop (version 1).xls differ diff --git a/Benchmarking/.idea/.gitignore b/Benchmarking/.idea/.gitignore new file mode 100644 index 0000000..26d3352 --- /dev/null +++ b/Benchmarking/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/Benchmarking/.idea/Spyder.iml b/Benchmarking/.idea/Spyder.iml new file mode 100644 index 0000000..8b8c395 --- /dev/null +++ b/Benchmarking/.idea/Spyder.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/Benchmarking/.idea/inspectionProfiles/profiles_settings.xml b/Benchmarking/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/Benchmarking/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/Benchmarking/.idea/misc.xml b/Benchmarking/.idea/misc.xml new file mode 100644 index 0000000..d1e22ec --- /dev/null +++ b/Benchmarking/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/Benchmarking/.idea/modules.xml b/Benchmarking/.idea/modules.xml new file mode 100644 index 0000000..55f3dcd --- /dev/null +++ b/Benchmarking/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/Benchmarking/.spyproject/config/codestyle.ini b/Benchmarking/.spyproject/config/codestyle.ini new file mode 100644 index 0000000..0f54b4c --- /dev/null +++ b/Benchmarking/.spyproject/config/codestyle.ini @@ -0,0 +1,8 @@ +[codestyle] +indentation = True +edge_line = True +edge_line_columns = 79 + +[main] +version = 0.2.0 + diff --git a/Benchmarking/.spyproject/config/defaults/defaults-codestyle-0.2.0.ini b/Benchmarking/.spyproject/config/defaults/defaults-codestyle-0.2.0.ini new file mode 100644 index 0000000..0b95e5c --- /dev/null +++ b/Benchmarking/.spyproject/config/defaults/defaults-codestyle-0.2.0.ini @@ -0,0 +1,5 @@ +[codestyle] +indentation = True +edge_line = True +edge_line_columns = 79 + diff --git a/Benchmarking/.spyproject/config/defaults/defaults-encoding-0.2.0.ini b/Benchmarking/.spyproject/config/defaults/defaults-encoding-0.2.0.ini new file mode 100644 index 0000000..0ce193c --- /dev/null +++ b/Benchmarking/.spyproject/config/defaults/defaults-encoding-0.2.0.ini @@ -0,0 +1,3 @@ +[encoding] +text_encoding = utf-8 + diff --git a/Benchmarking/.spyproject/config/defaults/defaults-vcs-0.2.0.ini b/Benchmarking/.spyproject/config/defaults/defaults-vcs-0.2.0.ini new file mode 100644 index 0000000..ee25483 --- /dev/null +++ b/Benchmarking/.spyproject/config/defaults/defaults-vcs-0.2.0.ini @@ -0,0 +1,4 @@ +[vcs] +use_version_control = False +version_control_system = + diff --git a/Benchmarking/.spyproject/config/defaults/defaults-workspace-0.2.0.ini b/Benchmarking/.spyproject/config/defaults/defaults-workspace-0.2.0.ini new file mode 100644 index 0000000..2a73ab7 --- /dev/null +++ b/Benchmarking/.spyproject/config/defaults/defaults-workspace-0.2.0.ini @@ -0,0 +1,6 @@ +[workspace] +restore_data_on_startup = True +save_data_on_exit = True +save_history = True +save_non_project_files = False + diff --git a/Benchmarking/.spyproject/config/encoding.ini b/Benchmarking/.spyproject/config/encoding.ini new file mode 100644 index 0000000..a17aced --- /dev/null +++ b/Benchmarking/.spyproject/config/encoding.ini @@ -0,0 +1,6 @@ +[encoding] +text_encoding = utf-8 + +[main] +version = 0.2.0 + diff --git a/Benchmarking/.spyproject/config/vcs.ini b/Benchmarking/.spyproject/config/vcs.ini new file mode 100644 index 0000000..fd66eae --- /dev/null +++ b/Benchmarking/.spyproject/config/vcs.ini @@ -0,0 +1,7 @@ +[vcs] +use_version_control = False +version_control_system = + +[main] +version = 0.2.0 + diff --git a/Benchmarking/.spyproject/config/workspace.ini b/Benchmarking/.spyproject/config/workspace.ini new file mode 100644 index 0000000..e09a825 --- /dev/null +++ b/Benchmarking/.spyproject/config/workspace.ini @@ -0,0 +1,10 @@ +[workspace] +restore_data_on_startup = True +save_data_on_exit = True +save_history = True +save_non_project_files = False + +[main] +version = 0.2.0 +recent_files = ['comparison_HH_increased_resolution.py'] + diff --git a/Benchmarking/Simple_testing.py b/Benchmarking/Simple_testing.py new file mode 100644 index 0000000..1da81f3 --- /dev/null +++ b/Benchmarking/Simple_testing.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 13:45:56 2021 + +@author: Joschka +""" + +import numpy as np + +import matplotlib_inline as plt + +x = \ No newline at end of file diff --git a/Benchmarking/comparison_HH_increased_resolution.py b/Benchmarking/comparison_HH_increased_resolution.py new file mode 100644 index 0000000..7c282d6 --- /dev/null +++ b/Benchmarking/comparison_HH_increased_resolution.py @@ -0,0 +1,182 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 11:49:41 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x_m = np.linspace(-0.05, 0.05, 51) +z_m = np.linspace(-0.05, 0.05, 201) + + +z = z_m*1e3 +x = x_m*1e3 #for plotting in mm + +#Import Values from simulation +B_z_sim = np.loadtxt('data/B_z_HH2.txt') +B_x_sim = np.loadtxt('data/B_x_HH2.txt') + + +################# My simulation ######################### +I = 5 +HH = 1 +d_coils = 44 +R_inner = 44-3*1.7 + +layers = 6 +windings = 2 +wire_width = 1.7 +wire_height = 2.6 + + + + +B_z, B_x = bf.B_multiple_raster_test(I,HH,R_inner,d_coils,layers,windings,wire_width, wire_height, x_m,z_m) +#B_test = B_field_ideal_AHH(layers*windings,I,R_inner*1e-3,d_coils*1e-3,z_m) + +#B_x = np.concatenate((-np.flip(B_r),B_r[1:len(B_r)])) + + + + + + + + + +#Calculate gradients/curvature +B_z_sim_grad = np.gradient(np.gradient(B_z_sim,z_m),z_m)/1e4 +B_x_sim_grad = np.gradient(B_x_sim,x_m)/100 +B_z_grad = np.gradient(np.gradient(B_z,z_m),z_m)/1e4 +B_x_grad = np.gradient(B_x,x_m)/100 + + +#Calculate relative differences in permille +rel_diff_Bz = (B_z-B_z_sim)/B_z +rel_diff_Bx = (B_x-B_x_sim)/B_x +rel_diff_Bz_grad = (B_z_grad-B_z_sim_grad)/B_z_grad + +rel_diff_Bz_grad_mean = (B_z_grad-B_z_sim_grad)/np.mean(B_z_grad) +rel_diff_Bx_grad = (B_x_grad-B_x_sim_grad)/B_x_grad + +#Plotting +plt.figure(1,figsize=(20,18)) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_z along z-axis, comparison of simulations", fontsize=30) + + +#Field plot +########################## +plt.subplot(3,2,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim,linestyle = "dashdot", label = r"$B_{z, sim}$: Numerical Matlab simulation") +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +############################# +plt.subplot(3,2,3) +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +plt.ylabel("absolute deviation [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + +############################# +plt.subplot(3,2,5) +plt.plot(z,1000*rel_diff_Bz, label = "$(B_z - B_{z, sim}) / B_z$") +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +######################Gradient plot############################ + +################ +plt.subplot(3,2,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z^2 B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim_grad,linestyle = "dashdot", label = r"$\nabla_z^2 B_{z, sim}$: Numerical Matlab sim.") +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.title("Curvature of B-field",fontsize = 30) +plt.legend(loc='lower right') + + +################# + +plt.subplot(3,2,4) +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") +plt.ylabel(r"absolute deviation $[G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.legend() + +##################### +plt.subplot(3,2,6) +plt.plot(z,1000*rel_diff_Bz_grad, label = r"$(\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}) / \nabla_z^2 B_z$") +#plt.ylim(-57,10) +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +plt.savefig("output/HH_benchmark_5A_6x2.pdf") +plt.show() + +############### relative deviation with averaging by the mean not the individual value ######################################## +plt.figure(2) + +plt.plot(z,1000*rel_diff_Bz_grad_mean, label = r"$(\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}) / mean(\nabla_z^2 B_z)$") +#plt.ylim(-57,10) +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() +plt.savefig("output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf") +plt.show() + +##################### x-Axis ######################################################### + +plt.figure(3) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_x along x-axis, comparison of simulations", fontsize=30) + + +#Field plot +########################## + +plt.plot(x,B_x,linestyle = "solid", label = r"$B_x$: Result via elliptic integrals") +plt.plot(x,B_x_sim,linestyle = "dashdot", label = r"$B_{x, sim}$: Numerical Matlab simulation") +plt.plot(x,(B_x-B_x_sim), label = r"$B_x - B_{x, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_x$ [G]") +plt.xlabel("x-axis [mm]") +plt.legend() + + + + +################# + + +plt.savefig("output/HH_benchmark_5A_6x2_x-axis.pdf") +plt.show() \ No newline at end of file diff --git a/Benchmarking/comparison_HH_increased_resolution_different_z_dimensions.py b/Benchmarking/comparison_HH_increased_resolution_different_z_dimensions.py new file mode 100644 index 0000000..76eb620 --- /dev/null +++ b/Benchmarking/comparison_HH_increased_resolution_different_z_dimensions.py @@ -0,0 +1,153 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 11:49:41 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +import B_field_calculation as bf + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x_m = np.linspace(-0.05,0.05,51) +z_m = np.linspace(-0.05,0.05,201) + +z_m_2 = np.linspace(-0.05,0.05,1001) + +z_2 = z_m_2*1e3 + +z = z_m*1e3 +x = x_m*1e3 #for plotting in mm + +#Import Values from simulation +B_z_sim = np.loadtxt('data/B_z_HH2.txt') +B_x_sim = np.loadtxt('data/B_x_HH2.txt') + + +################# My simulation ######################### +I = 5 +HH = 1 +d_coils = 78 +R_inner = 44-3*1.7 + +layers = 6 +windings = 2 +wire_width = 1.7 +wire_height = 2.6 + + +B_z, B_x = bf.B_multiple_raster_test(I,HH,R_inner,d_coils,layers,windings,wire_width, wire_height, x_m,z_m_2) +#B_test = B_field_ideal_AHH(layers*windings,I,R_inner*1e-3,d_coils*1e-3,z_m) + +#B_x = np.concatenate((-np.flip(B_r),B_r[1:len(B_r)])) + + +#Calculate gradients/curvature +B_z_sim_grad = np.gradient(np.gradient(B_z_sim,z_m),z_m)/1e4 +B_x_sim_grad = np.gradient(B_x_sim,x_m)/100 +B_z_grad = np.gradient(np.gradient(B_z,z_m_2),z_m_2)/1e4 +B_x_grad = np.gradient(B_x,x_m)/100 + + +#try plot +plt.figure(1) +plt.plot(z_2,B_z,linestyle = "solid", label = r"$B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim,linestyle = "dashdot", label = r"$B_{z, sim}$: Numerical Matlab simulation") +#plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() +plt.show() + +plt.figure(2) +plt.plot(z_2,B_z_grad,linestyle = "solid", label = r"$\nabla_z^2 B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim_grad,linestyle = "dashdot", label = r"$\nabla_z^2 B_{z, sim}$: Numerical Matlab sim.") +#plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.title("Curvature of B-field",fontsize = 30) +plt.legend(loc='lower right') +plt.show() + + +#Calculate relative differences in permille +rel_diff_Bz = (B_z-B_z_sim)/B_z +rel_diff_Bx = (B_x-B_x_sim)/B_x +rel_diff_Bz_grad = (B_z_grad-B_z_sim_grad)/B_z_grad +rel_diff_Bx_grad = (B_x_grad-B_x_sim_grad)/B_x_grad + +#Plotting +plt.figure(figsize=(20,18)) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_z along z-axis, comparison of simulations", fontsize=30) + + +#Field plot +########################## +plt.subplot(3,2,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim,linestyle = "dashdot", label = r"$B_{z, sim}$: Numerical Matlab simulation") +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +############################# +plt.subplot(3,2,3) +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +plt.ylabel("absolute deviation [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + +############################# +plt.subplot(3,2,5) +plt.plot(z,1000*rel_diff_Bz, label = "$(B_z - B_{z, sim}) / B_z$") +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +######################Gradient plot############################ + +################ +plt.subplot(3,2,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z^2 B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim_grad,linestyle = "dashdot", label = r"$\nabla_z^2 B_{z, sim}$: Numerical Matlab sim.") +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.title("Curvature of B-field",fontsize = 30) +plt.legend(loc='lower right') + + +################# + +plt.subplot(3,2,4) +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") +plt.ylabel(r"absolute deviation $[G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.legend() + +##################### +plt.subplot(3,2,6) +plt.plot(z,1000*rel_diff_Bz_grad, label = r"$(\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}) / \nabla_z^2 B_z$") +plt.ylim(-57,10) +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + +plt.show() \ No newline at end of file diff --git a/Benchmarking/comparison_HH_increased_resolution_different_z_dimensions1.py b/Benchmarking/comparison_HH_increased_resolution_different_z_dimensions1.py new file mode 100644 index 0000000..b33a61b --- /dev/null +++ b/Benchmarking/comparison_HH_increased_resolution_different_z_dimensions1.py @@ -0,0 +1,153 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 11:49:41 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +import B_field_calculation as bf + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x_m = np.linspace(-0.05,0.05,51) +z_m = np.linspace(-0.05,0.05,201) + +z_m_2 = np.linspace(-0.05,0.05,1001) + +z_2 = z_m_2*1e3 + +z = z_m*1e3 +x = x_m*1e3 #for plotting in mm + +#Import Values from simulation +B_z_sim = np.loadtxt('data/B_z_HH2.txt') +B_x_sim = np.loadtxt('data/B_x_HH2.txt') + + +################# My simulation ######################### +I = 5 +HH = 1 +d_coils = 44 +R_inner = 44-3*1.7 + +layers = 6 +windings = 2 +wire_width = 1.7 +wire_height = 2.6 + + +B_z, B_x = bf.B_multiple_raster_test(I,HH,R_inner,d_coils,layers,windings,wire_width, wire_height, x_m,z_m_2) +#B_test = B_field_ideal_AHH(layers*windings,I,R_inner*1e-3,d_coils*1e-3,z_m) + +#B_x = np.concatenate((-np.flip(B_r),B_r[1:len(B_r)])) + + +#Calculate gradients/curvature +B_z_sim_grad = np.gradient(np.gradient(B_z_sim,z_m),z_m)/1e4 +B_x_sim_grad = np.gradient(B_x_sim,x_m)/100 +B_z_grad = np.gradient(np.gradient(B_z,z_m_2),z_m_2)/1e4 +B_x_grad = np.gradient(B_x,x_m)/100 + + +#try plot +plt.figure(1) +plt.plot(z_2,B_z,linestyle = "solid", label = r"$B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim,linestyle = "dashdot", label = r"$B_{z, sim}$: Numerical Matlab simulation") +#plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() +plt.show() + +plt.figure(2) +plt.plot(z_2,B_z_grad,linestyle = "solid", label = r"$\nabla_z^2 B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim_grad,linestyle = "dashdot", label = r"$\nabla_z^2 B_{z, sim}$: Numerical Matlab sim.") +#plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.title("Curvature of B-field",fontsize = 30) +plt.legend(loc='lower right') +plt.show() + + +#Calculate relative differences in permille +rel_diff_Bz = (B_z-B_z_sim)/B_z +rel_diff_Bx = (B_x-B_x_sim)/B_x +rel_diff_Bz_grad = (B_z_grad-B_z_sim_grad)/B_z_grad +rel_diff_Bx_grad = (B_x_grad-B_x_sim_grad)/B_x_grad + +#Plotting +plt.figure(figsize=(20,18)) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_z along z-axis, comparison of simulations", fontsize=30) + + +#Field plot +########################## +plt.subplot(3,2,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim,linestyle = "dashdot", label = r"$B_{z, sim}$: Numerical Matlab simulation") +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +############################# +plt.subplot(3,2,3) +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +plt.ylabel("absolute deviation [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + +############################# +plt.subplot(3,2,5) +plt.plot(z,1000*rel_diff_Bz, label = "$(B_z - B_{z, sim}) / B_z$") +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +######################Gradient plot############################ + +################ +plt.subplot(3,2,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z^2 B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim_grad,linestyle = "dashdot", label = r"$\nabla_z^2 B_{z, sim}$: Numerical Matlab sim.") +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.title("Curvature of B-field",fontsize = 30) +plt.legend(loc='lower right') + + +################# + +plt.subplot(3,2,4) +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") +plt.ylabel(r"absolute deviation $[G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.legend() + +##################### +plt.subplot(3,2,6) +plt.plot(z,1000*rel_diff_Bz_grad, label = r"$(\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}) / \nabla_z^2 B_z$") +plt.ylim(-57,10) +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + +plt.show() \ No newline at end of file diff --git a/Benchmarking/comparison_HH_normal.py b/Benchmarking/comparison_HH_normal.py new file mode 100644 index 0000000..06f2ab9 --- /dev/null +++ b/Benchmarking/comparison_HH_normal.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 11:49:41 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +import B_field_calculation as bf + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x_m = np.linspace(-0.05,0.05,51) +z_m = np.linspace(-0.05,0.05,51) + + + +z = z_m*1e3 +x = x_m*1e3 #for plotting in mm + +#Import Values from simulation +B_z_sim = np.loadtxt('data/B_z_HH.txt') +B_x_sim = np.loadtxt('data/B_x_HH.txt') + + +################# My simulation ######################### +I = 5 +HH = 1 +d_coils = 44 +R_inner = 44 + +layers = 10 +windings = 2 +wire_width = 1 +wire_height = 2.6 + + +B_z, B_x = bf.B_multiple(I,HH,R_inner,d_coils,layers,windings,wire_width, wire_height, x_m,z_m) +#B_test = B_field_ideal_AHH(layers*windings,I,R_inner*1e-3,d_coils*1e-3,z_m) + +#B_x = np.concatenate((-np.flip(B_r),B_r[1:len(B_r)])) + + +#Calculate gradients/curvature +B_z_sim_grad = np.gradient(np.gradient(B_z_sim,z_m),z_m)/1e4 +B_x_sim_grad = np.gradient(B_x_sim,x_m)/100 +B_z_grad = np.gradient(np.gradient(B_z,z_m),z_m)/1e4 +B_x_grad = np.gradient(B_x,x_m)/100 + + +#Calculate relative differences in permille +rel_diff_Bz = (B_z-B_z_sim)/B_z +rel_diff_Bx = (B_x-B_x_sim)/B_x +rel_diff_Bz_grad = (B_z_grad-B_z_sim_grad)/B_z_grad +rel_diff_Bx_grad = (B_x_grad-B_x_sim_grad)/B_x_grad + +#Plotting +plt.figure(figsize=(20,18)) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_z along z-axis, comparison of simulations", fontsize=30) + + +#Field plot +########################## +plt.subplot(3,2,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim,linestyle = "dashdot", label = r"$B_{z, sim}$: Numerical Matlab simulation") +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +############################# +plt.subplot(3,2,3) +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +plt.ylabel("absolute deviation [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + +############################# +plt.subplot(3,2,5) +plt.plot(z,1000*rel_diff_Bz, label = "$(B_z - B_{z, sim}) / B_z$") +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +######################Gradient plot############################ + +################ +plt.subplot(3,2,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z^2 B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim_grad,linestyle = "dashdot", label = r"$\nabla_z^2 B_{z, sim}$: Numerical Matlab sim.") +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.title("Curvature of B-field",fontsize = 30) +plt.legend(loc='lower right') + + +################# + +plt.subplot(3,2,4) +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") +plt.ylabel(r"absolute deviation $[G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.legend() + +##################### +plt.subplot(3,2,6) +plt.plot(z,1000*rel_diff_Bz_grad, label = r"$(\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}) / \nabla_z^2 B_z$") +plt.ylim(-57,10) +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + +plt.show() \ No newline at end of file diff --git a/Benchmarking/data/B_x.txt b/Benchmarking/data/B_x.txt new file mode 100644 index 0000000..e68a577 --- /dev/null +++ b/Benchmarking/data/B_x.txt @@ -0,0 +1,51 @@ +2.58228909714096 +2.62974538222051 +2.65944955784724 +2.67018023408114 +2.66129310777269 +2.63274636464909 +2.58507635240118 +2.51932891811042 +2.43695775772549 +2.33970435103465 +2.22947425589503 +2.10822224575473 +1.97785501967328 +1.84015609055015 +1.69673381785331 +1.5489908693351 +1.39811178932252 +1.24506469044685 +1.09061312356843 +0.935334651061495 +0.779643326178502 +0.623814004093807 +0.468007079187523 +0.312292808736715 +0.156674829998973 +0.00111281003184087 +-0.154455601231865 +-0.310093199262038 +-0.465841648637876 +-0.621699513490636 +-0.777599570919756 +-0.933385222755551 +-1.08878604652763 +-1.24339285381046 +-1.39663306776149 +-1.54774779802038 +-1.69577266767607 +-1.83952518780893 +-1.97760218416845 +-2.1083912972814 +-2.23010067296318 +-2.34081035784062 +-2.43854734635146 +-2.52138353563691 +-2.58755212369357 +-2.63557369709183 +-2.66437927197459 +-2.67341503397356 +-2.66271358255533 +-2.63291973763518 +-2.58526513884324 diff --git a/Benchmarking/data/B_x_HH.txt b/Benchmarking/data/B_x_HH.txt new file mode 100644 index 0000000..15fd767 --- /dev/null +++ b/Benchmarking/data/B_x_HH.txt @@ -0,0 +1,51 @@ +-3.16996429106098e-16 +2.12281581202234e-16 +2.90868024110935e-16 +-1.52549847753924e-16 +-4.48460713009524e-17 +1.78201203793193e-16 +-1.79897763352699e-16 +-2.26367535827165e-16 +4.43187153642555e-17 +-2.69312350198447e-16 +2.23189522419176e-17 +-1.08999614889527e-16 +-9.00043928275807e-17 +-3.54577478489659e-17 +-2.1298587893348e-16 +-3.95274091236075e-17 +-7.6369466306403e-17 +4.99322805325164e-17 +-8.50014503228636e-18 +-3.11972669919669e-17 +-1.72431513512095e-17 +-1.660477311205e-17 +-2.46330733588707e-18 +2.02615701994091e-18 +-1.6153745008296e-17 +8.46545056276682e-19 +2.2065682614425e-17 +2.901845430614e-17 +6.85562717706034e-18 +-1.47798440153224e-17 +4.8919202022546e-17 +9.30089338879725e-17 +-8.02691246803988e-17 +3.22797344409764e-17 +-5.15143483426073e-17 +1.49547041417009e-16 +-3.49720252756924e-17 +1.31672450720544e-16 +9.14823772291129e-17 +-5.1680881796301e-17 +4.92383911421257e-17 +2.1582735598713e-16 +8.57092175010621e-17 +2.96096480667529e-16 +8.26005930321116e-17 +1.85185200507476e-16 +2.05613304160579e-16 +-1.51212375953946e-16 +-1.57651669496772e-16 +-2.41140440948584e-16 +-1.97619698383278e-17 diff --git a/Benchmarking/data/B_x_HH1.txt b/Benchmarking/data/B_x_HH1.txt new file mode 100644 index 0000000..1079f04 --- /dev/null +++ b/Benchmarking/data/B_x_HH1.txt @@ -0,0 +1,51 @@ +-1.77967016123937e-16 +2.64285121565067e-16 +2.19350579366839e-16 +2.34326447134947e-16 +3.81500386836819e-17 +-2.26797747249208e-17 +-3.0069696732582e-17 +8.47620584831788e-17 +2.58543186859583e-17 +-3.27862736959617e-18 +-2.45480719085478e-16 +-3.4198685550102e-16 +4.26082780169423e-17 +5.10598507919013e-17 +-1.19383669616724e-17 +-4.75106065600528e-17 +-4.11268241684581e-17 +-2.01873240346373e-16 +-2.31967223207619e-17 +4.16680578929629e-17 +-3.89480114826313e-17 +-1.30312427515378e-17 +5.24996712769621e-17 +3.12250225675825e-19 +-1.79023462720806e-18 +-6.9111383282916e-18 +6.35602681597902e-18 +-3.19744231092045e-17 +3.92741394961149e-17 +4.92661467177413e-18 +-2.87270207621759e-17 +1.2490009027033e-18 +-7.52176099183544e-18 +-8.22675261247241e-17 +7.24420523567915e-18 +-1.27509114378199e-16 +-1.56485935320916e-16 +-1.87849735766576e-16 +7.32747196252603e-17 +1.85962356624714e-17 +4.34097202628436e-17 +-6.32827124036339e-18 +1.46660461552983e-16 +-4.40758540776187e-17 +2.40030217923959e-16 +4.31876756579186e-17 +7.31636973227978e-16 +-1.86517468137026e-16 +1.27453603226968e-16 +-1.07469588783715e-16 +6.30606677987089e-17 diff --git a/Benchmarking/data/B_x_HH2.txt b/Benchmarking/data/B_x_HH2.txt new file mode 100644 index 0000000..44c1602 --- /dev/null +++ b/Benchmarking/data/B_x_HH2.txt @@ -0,0 +1,51 @@ +-1.24750904051396e-16 +2.93279289742543e-16 +2.26136817604861e-16 +3.56503021547994e-17 +-1.22107185474007e-16 +-2.02709377061794e-16 +-2.22426244089746e-17 +2.23689122780257e-16 +-1.11324144347336e-16 +-1.87964227515991e-16 +9.9704966505243e-17 +5.02584085460001e-17 +-1.34052491329584e-16 +9.60863333343553e-17 +1.57263091438153e-16 +5.76830250231808e-17 +7.78335729201274e-17 +5.30478438953708e-17 +-3.31262794972531e-17 +-7.1352646013878e-17 +3.41671135828392e-17 +-2.4980018054066e-19 +-1.02834407655905e-17 +5.13478148889135e-19 +3.49442697000768e-17 +-2.0122792321331e-18 +-1.21430643318376e-17 +-5.13478148889135e-19 +8.451572774959e-18 +1.85407245112401e-17 +2.6256774532385e-17 +4.37705427458468e-17 +2.32869279415127e-17 +-5.21527265817667e-17 +-9.9253938401489e-17 +8.98170426921752e-17 +1.01585406753202e-17 +-1.25455201782643e-17 +-9.28701560098943e-17 +-1.48658862997308e-16 +-9.35918009759007e-17 +-7.8381745538536e-17 +1.90625293328139e-16 +-2.665645482125e-16 +1.59205981731247e-16 +-1.37223565843669e-16 +2.35367281220533e-16 +1.01918473660589e-16 +9.99200722162641e-17 +7.39408534400354e-17 +-8.88178419700125e-17 diff --git a/Benchmarking/data/B_x_x10.txt b/Benchmarking/data/B_x_x10.txt new file mode 100644 index 0000000..82f3b43 --- /dev/null +++ b/Benchmarking/data/B_x_x10.txt @@ -0,0 +1,51 @@ +0.315870252483617 +0.255751600481404 +0.186566907419062 +0.109579894595394 +0.0265188340157648 +-0.0605131846806727 +-0.149172561144372 +-0.237039624364386 +-0.321792991214021 +-0.401371661996773 +-0.474105051893869 +-0.538797228405318 +-0.594759769626575 +-0.641795860096254 +-0.680144730243721 +-0.710399274104225 +-0.733410476873435 +-0.750190699147325 +-0.761824833391987 +-0.769394839059607 +-0.773919932063492 +-0.776312195414932 +-0.777345741324315 +-0.777636734525558 +-0.7776314177094 +-0.777599570919756 +-0.777631417709401 +-0.777636734525557 +-0.777345741324316 +-0.776312195414932 +-0.773919932063492 +-0.769394839059607 +-0.761824833391987 +-0.750190699147325 +-0.733410476873435 +-0.710399274104225 +-0.68014473024372 +-0.641795860096254 +-0.594759769626576 +-0.538797228405317 +-0.474105051893868 +-0.401371661996773 +-0.32179299121402 +-0.237039624364387 +-0.149172561144371 +-0.0605131846806727 +0.0265188340157647 +0.109579894595394 +0.186566907419063 +0.255751600481404 +0.315870252483617 diff --git a/Benchmarking/data/B_x_x20.txt b/Benchmarking/data/B_x_x20.txt new file mode 100644 index 0000000..d7f5520 --- /dev/null +++ b/Benchmarking/data/B_x_x20.txt @@ -0,0 +1,51 @@ +0.838336941886738 +0.704528329101358 +0.540869156075738 +0.35046355927954 +0.138717140535653 +-0.0869617178249859 +-0.317812922539589 +-0.544633260253625 +-0.758773254273728 +-0.953026552475999 +-1.12224822987221 +-1.26361615688701 +-1.37654277003895 +-1.46232024454193 +-1.52361931467383 +-1.56395876595001 +-1.58723193850584 +-1.59733657523226 +-1.59791936470777 +-1.59222336048851 +-1.58301538123973 +-1.57256834273773 +-1.56267637311523 +-1.55468543867366 +-1.5495271744476 +-1.54774779802038 +-1.5495271744476 +-1.55468543867366 +-1.56267637311523 +-1.57256834273773 +-1.58301538123973 +-1.59222336048851 +-1.59791936470777 +-1.59733657523226 +-1.58723193850584 +-1.56395876595 +-1.52361931467383 +-1.46232024454193 +-1.37654277003895 +-1.26361615688701 +-1.1222482298722 +-0.953026552475999 +-0.758773254273728 +-0.544633260253624 +-0.31781292253959 +-0.0869617178249862 +0.138717140535653 +0.350463559279539 +0.54086915607574 +0.704528329101358 +0.838336941886739 diff --git a/Benchmarking/data/B_z.txt b/Benchmarking/data/B_z.txt new file mode 100644 index 0000000..f9adda9 --- /dev/null +++ b/Benchmarking/data/B_z.txt @@ -0,0 +1,51 @@ +-4.48637597842307 +-4.59077060562329 +-4.67041322168728 +-4.72240325284354 +-4.74436993663005 +-4.73459770750829 +-4.69211662587652 +-4.61674838668992 +-4.50910292358247 +-4.37052591912355 +-4.20300278459966 +-4.00902903659917 +-3.79145982624433 +-3.55335235560578 +-3.29781410027413 +-3.02786751635909 +-2.74633880333963 +-2.45577492365425 +-2.15838996506787 +-1.85603942975685 +-1.55021931502741 +-1.24208591874369 +-0.932492042829694 +-0.622035500630058 +-0.311116360833961 +2.24015250793741e-16 +0.311116360833964 +0.622035500630061 +0.932492042829691 +1.24208591874369 +1.55021931502741 +1.85603942975685 +2.15838996506787 +2.45577492365425 +2.74633880333963 +3.02786751635908 +3.29781410027412 +3.55335235560578 +3.79145982624433 +4.00902903659918 +4.20300278459966 +4.37052591912355 +4.50910292358246 +4.61674838668993 +4.69211662587652 +4.73459770750829 +4.74436993663004 +4.72240325284354 +4.67041322168726 +4.59077060562329 +4.48637597842307 diff --git a/Benchmarking/data/B_z_HH.txt b/Benchmarking/data/B_z_HH.txt new file mode 100644 index 0000000..4bad2a5 --- /dev/null +++ b/Benchmarking/data/B_z_HH.txt @@ -0,0 +1,51 @@ +10.6536698262602 +11.2355834431335 +11.8308215916294 +12.4354708668342 +13.0449371679322 +13.6539956497498 +14.256886633848 +14.8474615439352 +15.4193788084015 +15.9663442568647 +16.4823843046589 +16.9621339792356 +17.4011166569334 +17.7959893766068 +18.1447277134393 +18.446727900502 +18.7028109839925 +18.9151233996802 +19.0869390335573 +19.2223779319734 +19.3260648763817 +19.4027560509914 +19.4569636870496 +19.4926071771896 +19.5127154666301 +19.5192004530879 +19.5127154666301 +19.4926071771896 +19.4569636870496 +19.4027560509914 +19.3260648763817 +19.2223779319735 +19.0869390335574 +18.9151233996802 +18.7028109839925 +18.446727900502 +18.1447277134393 +17.7959893766068 +17.4011166569334 +16.9621339792356 +16.4823843046588 +15.9663442568647 +15.4193788084015 +14.8474615439352 +14.256886633848 +13.6539956497498 +13.0449371679322 +12.4354708668342 +11.8308215916294 +11.2355834431335 +10.6536698262602 diff --git a/Benchmarking/data/B_z_HH1.txt b/Benchmarking/data/B_z_HH1.txt new file mode 100644 index 0000000..e251ceb --- /dev/null +++ b/Benchmarking/data/B_z_HH1.txt @@ -0,0 +1,51 @@ +10.6538828816951 +11.2391166045421 +11.8379555109104 +12.4464506538621 +13.0599583946471 +13.6731901710357 +14.2803092432835 +14.8750786818901 +15.4510606469831 +16.0018614545581 +16.5214105175275 +17.0042548219564 +17.4458452446815 +17.842787903183 +18.1930338192903 +18.4959839616112 +18.7524940110866 +18.9647730529274 +19.1361813633915 +19.2709428334617 +19.3737958295774 +19.4496114219081 +19.503009586584 +19.5380025365883 +19.5576905341118 +19.5640303243012 +19.5576905341118 +19.5380025365883 +19.503009586584 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+1.87436037319072 +2.18580745356457 +2.49472321949396 +2.7992906009947 +3.09717195315329 +3.38550680956306 +3.66094785676015 +3.91974320619202 +4.15786985177971 +4.37121773974612 +4.55581648023133 +4.7080884377943 +4.8251044395172 +4.90481363458011 +4.94621885573329 +4.94947398459438 +4.91588977139334 +4.84784741265535 +4.74863218780255 +4.62220979364239 diff --git a/Benchmarking/data/B_z_x20.txt b/Benchmarking/data/B_z_x20.txt new file mode 100644 index 0000000..936f2e4 --- /dev/null +++ b/Benchmarking/data/B_z_x20.txt @@ -0,0 +1,51 @@ +-5.05333952991992 +-5.2710697587335 +-5.4538224324177 +-5.59176900495866 +-5.67627228666901 +-5.70088154813024 +-5.66214168699473 +-5.56003717220652 +-5.39796618146884 +-5.18225073408282 +-4.92129699201981 +-4.62458945768248 +-4.30171384112198 +-3.96156065061021 +-3.61178911392315 +-3.2585583414038 +-2.90648100201777 +-2.55873130299066 +-2.21723908837497 +-1.88291554820721 +-1.55587406801886 +-1.2356261677816 +-0.921244732301142 +-0.611494601598914 +-0.304934960273855 +1.29882216093336e-16 +0.304934960273854 +0.611494601598912 +0.921244732301141 +1.23562616778161 +1.55587406801886 +1.88291554820721 +2.21723908837497 +2.55873130299066 +2.90648100201777 +3.2585583414038 +3.61178911392315 +3.96156065061021 +4.30171384112198 +4.62458945768248 +4.92129699201981 +5.18225073408281 +5.39796618146885 +5.56003717220652 +5.66214168699473 +5.70088154813023 +5.67627228666901 +5.59176900495865 +5.4538224324177 +5.27106975873349 +5.05333952991991 diff --git a/Benchmarking/output/HH_benchmark_5A_6x2.pdf b/Benchmarking/output/HH_benchmark_5A_6x2.pdf new file mode 100644 index 0000000..eb1531d Binary files /dev/null and b/Benchmarking/output/HH_benchmark_5A_6x2.pdf differ diff --git a/Benchmarking/output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf b/Benchmarking/output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf new file mode 100644 index 0000000..d97294c Binary files /dev/null and b/Benchmarking/output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf differ diff --git a/Benchmarking/output/HH_benchmark_5A_6x2_x-axis.pdf b/Benchmarking/output/HH_benchmark_5A_6x2_x-axis.pdf new file mode 100644 index 0000000..7d7f1b1 Binary files /dev/null and b/Benchmarking/output/HH_benchmark_5A_6x2_x-axis.pdf differ diff --git a/Coil_geometry/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/Coil_geometry/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/Coil_geometry/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Coil_geometry/00_Simple_testing.py b/Coil_geometry/00_Simple_testing.py new file mode 100644 index 0000000..35f0ab9 --- /dev/null +++ b/Coil_geometry/00_Simple_testing.py @@ -0,0 +1,45 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 31 09:28:25 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + + +x = np.linspace(-10, 10, 3001) +z = np.linspace(-10, 10, 3001) +print(3001//2) + +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) + +percentage = 0.05 +absolut = 5 +diff = percentage*0.01*5+ absolut *1e-3 +print(diff) +Bz1, Bx = HH_Coil.B_multiple(5, x, z) + + +Bz2, Bx = HH_Coil.B_multiple(5+ diff, x, z) +print(Bz2[1500]-Bz1[1500]) +print(" ") + +percentage = 0 #.02 +absolut = 2 +diff = percentage*0.01*5+ absolut *1e-3 +print(diff) + + +Bz2, Bx = HH_Coil.B_multiple(5+ diff, x, z) +print(Bz2[1500]-Bz1[1500]) +print((Bz2[1500]-Bz1[1500])/Bz2[1500]) + +#Power = cs.rho_copper_20 *wire_length* I_current**2 /(self.get_wire_area()) \ No newline at end of file diff --git a/Coil_geometry/01_geometry_HH.py b/Coil_geometry/01_geometry_HH.py new file mode 100644 index 0000000..577639b --- /dev/null +++ b/Coil_geometry/01_geometry_HH.py @@ -0,0 +1,151 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-50, 50, 301) +z = np.linspace(-50, 50, 301) + + + +I = 5 +HH = 1 +d_coils = 44 +R_mid = 44 + + +layers = 6 +windings = 2 +wire_width = 1.7 +wire_height = 2.6 + + +#reference coil +HH_Coil_44 = BC.BCoil(HH, 44 ,44, 6, 2, wire_width = 1.7, wire_height= 2.6) + + +#Coil from first sketch +HH_Coil_y = BC.BCoil(HH, 55.2 ,44, 6, 2, wire_width = 1.7, wire_height= 2.6) +B_z_y, B_x_y = HH_Coil_y.B_multiple(6.5,x,z) +B_z_y_curv = BC.BCoil.curv(B_z_y, z) + +d_coils_2 = 55.2 + +#New coil +HH_Coil_54 = BC.BCoil(HH, 54 ,48.8, 4, 4, 1,1) + +HH_Coil_54.cooling(5) + +#Compensation Coil +HH_Coil_78 = BC.BCoil(1,54,37,4, 4, 1,1) + +#HH_Coil_44.Bz_plot_HH(I,x,z) + + +#HH_Coil_44.Bz_plot_HH_comp(HH_Coil_54,I,x,z) + +B_z, B_x = HH_Coil_44.B_multiple(I,x,z) +B_z_2, B_x_2 = HH_Coil_54.B_multiple(I,x,z) + +B_z_3,B_x_3 = HH_Coil_78.B_multiple(-0.72,x,z) + + + +B_z_curvature = np.gradient(np.gradient(B_z,z),z)*1e2 +B_z_curvature_2 = BC.BCoil.curv(B_z_2, z) + +B_z_curv_3 = BC.BCoil.curv(B_z_3, z) + +B_tot = B_z_2 + B_z_3 + +B_tot_curv = BC.BCoil.curv(B_tot, z) +plt.figure(300) + + +plt.suptitle("Helmholtz coil field B_z along z-axis, comparison to field yesterday") + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z_y,linestyle = "solid", label = r"$B_{sketch}$, B-field according to current solidworks sketch, d = 55.2 mm, R = 44 mm, 6 x 2") +plt.plot(z,B_z_2,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_y_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{sketch}$, d = 55.2 mm, R = 44 mm, 6 x 2") +plt.plot(z,B_z_curvature_2,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") +#plt.plot(z,B_z_curv_3,linestyle = "solid", label = r"$\nabla_z^2 B_{z,2}$, d = 54 mm, R = 37 mm, I = -0.7 A") +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend(loc='lower right') + +plt.show() + + +plt.figure(200,figsize=(15,13)) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_z along z-axis") + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_{ref}$, reference, optimal HH-configuration d = 44 mm, R = 44 mm") +plt.plot(z,B_z_2,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") +plt.plot(z,B_z_3,linestyle = "solid", label = r"$B_{z,2}$, d = 54 mm, R = 37 mm, I = -0.7 A, 4 x 4") +plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,B_z_curvature,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +plt.plot(z,B_z_curvature_2,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") +plt.plot(z,B_z_curv_3,linestyle = "solid", label = r"$\nabla_z^2 B_{z,2}$, d = 54 mm, R = 37 mm, I = -0.7 A") +plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() +plt.close() + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/02_geometry_AHH_Try_inside_of_HH_for_compensation.py b/Coil_geometry/02_geometry_AHH_Try_inside_of_HH_for_compensation.py new file mode 100644 index 0000000..a59880d --- /dev/null +++ b/Coil_geometry/02_geometry_AHH_Try_inside_of_HH_for_compensation.py @@ -0,0 +1,63 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-50, 50, 300) +z = np.linspace(-50, 50, 301) + + + +HH_Coil_78 = BC.BCoil(-1,54,37,4, 4, 1,1) + + + +B_z,B_x = HH_Coil_78.B_multiple(1,x,z) + +#B_x = np.concatenate((-np.flip(B_x),B_x)) +#x = np.concatenate((-np.flip(r),r)) + + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +#plt.rcParams.update({'font.size': 15}) +plt.suptitle("Anti Helmholtz coil field, I = 1 A, d = 54 mm, R = 37 mm ", fontsize = 30) + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_z$, d = 54 mm, R = 37 mm") +plt.plot(x,B_x, label = r"$B_x$, d = 54 mm, R = 37 mm") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B$ [G]") +plt.xlabel("z-axis / x-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z B_z$, d = 54 mm, R = 37 mm") +plt.plot(x,B_x_grad,linestyle = "solid", label = r"$\nabla_x B_x$, d = 54 mm, R = 37 mm") + +plt.ylabel(r"$\nabla_i B_i [G/cm]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +plt.title("Gradient of B-field") +plt.legend() + +plt.show() diff --git a/Coil_geometry/04_Iterative_Testing.py b/Coil_geometry/04_Iterative_Testing.py new file mode 100644 index 0000000..87b0799 --- /dev/null +++ b/Coil_geometry/04_Iterative_Testing.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 24 16:24:52 2021 + +@author: Joschka +""" + +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +x = np.linspace(-1, 1, 11) +z = np.linspace(-1, 1, 11) + +I_current = 5*16 + +HH_Coil = HH_Coil_comp = BC.BCoil(HH = 1, distance = 54 ,radius = 37,layers = 1, windings = 1,wire_width = 8, wire_height = 8) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) +HH_Coil.print_info() + +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 50) +Bz_curv = BC.BCoil.curv(Bz, z) +HH_Coil.cooling(I_current,30) +print(f"B_z(0) = {Bz[1]:.2f} G") +print(f"B_z_curvature(0) = {Bz_curv[1]:.4f} G/cm^2") + +B = [] +Curv = [] +array_width = np.arange(0.2,11,0.1) +#array_width = [5.7] +for width in array_width: + height = 20/width + HH_Coil = HH_Coil_comp = BC.BCoil(HH = 1, distance = 54 ,radius = 37,layers = 1, windings = 1,wire_width = width, wire_height = height) + HH_Coil.set_R_outer(49.3) + HH_Coil.set_d_min(49.8) + #HH_Coil.print_info() + + Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 30) + Bz_curv = BC.BCoil.curv(Bz, z) + HH_Coil.cooling(I_current,30) + B.append(Bz[5]) + Curv.append(Bz_curv[5]) + print(f"width = {width}mm, height = {height}mm") + print(f"B_z(0) = {Bz[5]:.2f} G") + print(f"B_z_curvature(0) = {Bz_curv[5]:.4f} G/cm^2") + +plt.plot(array_width,Curv) +#plt.plot(array_width,B) +plt.ylabel("curvature") +plt.xlabel("total width [mm]") +plt.show() + diff --git a/Coil_geometry/05_try_diff_geometry_HH1.py b/Coil_geometry/05_try_diff_geometry_HH1.py new file mode 100644 index 0000000..8f964af --- /dev/null +++ b/Coil_geometry/05_try_diff_geometry_HH1.py @@ -0,0 +1,120 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-15, 15, 30000) +z = np.linspace(-15, 15, 30000) + +#New coil +I_current = 5 +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) + +HH_Coil.print_info() +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) +Bz_curv = BC.BCoil.curv(Bz, z) +HH_Coil.cooling(I_current) + +print(f"B_z(0) = {Bz[150]:.2f} G") +print(f"B_z_curvature(0) = {Bz_curv[150]:.4f} G/cm^2") + + +print(x[500]) + +# I_current = 5*16 +# HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 1, windings = 1, wire_height = 10, wire_width = 6) +# HH_Coil.set_R_outer(49.3) +# HH_Coil.set_d_min(49.8) + +# HH_Coil.print_info() +# Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 50) +# Bz_curv = BC.BCoil.curv(Bz, z) +# HH_Coil.cooling(I_current) + +# print(f"B_z(0) = {Bz[150]:.2f} G") +# print(f"B_z_curvature(0) = {Bz_curv[150]:.4f} G/cm^2") + + + +#Compensation Coil +HH_Coil_comp = BC.BCoil(HH = 1, distance = 54 ,radius = 37, layers = 4, windings = 4,wire_height = 1, wire_width = 1) + + + +#HH_Coil_44.Bz_plot_HH(I,x,z) + + +#HH_Coil_44.Bz_plot_HH_comp(HH_Coil_54,I,x,z) +I_HH = 5 +I_comp = -1.4 + +#calculate field +B_z, B_x = HH_Coil.B_multiple(I_HH,x,z) +B_z_comp,B_x_comp = HH_Coil_comp.B_multiple(I_comp,x,z) + +#Calculate curvature +B_z_curv = BC.BCoil.curv(B_z, z) +B_z_comp_curv = BC.BCoil.curv(B_z_comp, z) + + +B_tot = B_z + B_z_comp + +B_tot_curv = BC.BCoil.curv(B_tot, z) +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_{ref}$, reference, optimal HH-configuration d = 44 mm, R = 44 mm") +plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,B_z_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/06_only_geometry_AHH.py b/Coil_geometry/06_only_geometry_AHH.py new file mode 100644 index 0000000..8dd9799 --- /dev/null +++ b/Coil_geometry/06_only_geometry_AHH.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-50, 50, 301) +z = np.linspace(-50, 50, 301) + + + +AHH_Coil = BC.BCoil(-1,54,37,4, 4, 1,1) + +AHH_Coil.set_R_outer(49.3) + +#AHH_Coil.print_info() + +#B_z,B_x = AHH_Coil.B_multiple(1,x,z) + +#B_z_grad = BC.BCoil.Bgrad(B_z, z) +#B_x_grad = BC.BCoil.Bgrad(B_x,x) + +plt.figure(1,figsize=(10,13)) +#plt.rcParams.update({'font.size': 15}) +plt.suptitle("Anti Helmholtz coil field, I = 2 A, d = 82 mm, R_inner = 46.3 mm ", fontsize = 13) + +#Field plot +########################## + +d=82 +AHH_Coil = BC.BCoil(-1,d,47.3,4, 4, 1,1) + +#AHH_Coil.set_R_outer(49.3) + +AHH_Coil.print_info() +#B = AHH_Coil.B_multiple_3d(10, x,z,raster=2) +AHH_Coil.cooling(10) + +B_z,B_x = AHH_Coil.B_multiple(10,x,z) +#B_z = B[:,150,1] +#B_x = B[150,:,0] + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +plt.subplot(2,1,1) + +plt.plot(z,B_z,linestyle = "solid", label = f"$B_z$, d = {d} mm") +plt.plot(x,B_x, label = f"$B_x$, d = {d} mm") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) +#plt.ylim(-0.5,0.4) +plt.ylabel(r"$B$ [G]") +plt.xlabel("z-axis / x-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z B_z$") +plt.plot(x,B_x_grad,linestyle = "solid", label = r"$\nabla_x B_x$") + +plt.ylabel(r"$\nabla_i B_i [G/cm]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +plt.title("Gradient of B-field") +plt.legend() +plt.savefig("output/AHH_field.pdf") +plt.show() + +#AHH_Coil.plot_3d(2, 80, 80) + +""" +print(" ") +print(f"B_grad_z(0) = {B_z_grad[1500]} G/cm") +print(f"B_grad_z(10 mm) = {B_z_grad[1800]} G/cm") +print(f"Diff B_grad z 10mm - 0 mm, {-(B_z_grad[1800]-B_z_grad[1500])} G/cm, relative: {(B_z_grad[1800]-B_z_grad[1500])/-B_z_grad[1500]}") +print(" ") +print(f"B_grad_x(0) = {B_x_grad[1500]} G/cm") +print(f"B_grad_x(10 mm) = {B_x_grad[1800]} G/cm") +print(f"Diff B_grad x 10mm - 0 mm, {B_x_grad[1800]-B_x_grad[1500]} G/cm, relative: {(B_x_grad[1800]-B_x_grad[1500])/-B_x_grad[1500]}") +""" + + diff --git a/Coil_geometry/07_02_testing B_tot.py b/Coil_geometry/07_02_testing B_tot.py new file mode 100644 index 0000000..14e0e5c --- /dev/null +++ b/Coil_geometry/07_02_testing B_tot.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis + +axis = 30001 #30001 for -15 to 15 = 1μm +x = np.linspace(-15, 15, axis) +z = np.linspace(-15, 15, axis) + + +#New coil +I_current = 5 +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1, windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) + +HH_Coil.print_info() +#Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) +B_tot_z, B_tot_x = HH_Coil.B_tot_along_axis(I_current, x, z,raster = 8) + +Bz_curv = BC.BCoil.curv(B_tot_z, z) +Bx_curv = BC.BCoil.curv(B_tot_x, x) +HH_Coil.cooling(I_current,25) + +B_0 = B_tot_z[axis//2] +print(f"B_tot(0,0) = {B_0} G") +print(f"B_tot_x = {B_tot_x[15000]}") +print(f"B_z_curvature(0) = {Bz_curv[axis//2]:.5f} G/cm^2") +print(f"B_x_curvature(0) = {Bx_curv[axis//2]:.5f} G/cm^2") +print("") +print("Differences along z-axis:") + +print(f"B_tot_z(1 μm) = {B_tot_z[15001]}") +print(f"B_tot_z(1 mm) = {B_tot_z[16000]}") + +print(f"Diff B 1 μm: {B_tot_z[15001] - B_0}, relative: {(B_tot_z[15001] - B_0)/B_0}") + +print(f"Diff B 1 mm: {B_tot_z[16000] - B_0}, relative: {(B_tot_z[16000] - B_0)/B_0}") + +print(f"Diff B 0.5 mm: {B_tot_z[15500] - B_0}, relative: {(B_tot_z[15500] - B_0)/B_0}") +print(" ") + +print("Differences along x-axis:") +print(f"B_tot_x(1 μm) = {B_tot_x[15001]}") +print(f"B_tot_x(1 mm) = {B_tot_x[16000]}") + +print(f"Diff B 1 μm: {B_tot_x[15001] - B_0}, relative: {(B_tot_x[15001] - B_0)/B_0}") + +print(f"Diff B 1 mm: {B_tot_x[16000] - B_0}, relative: {(B_tot_x[16000] - B_0)/B_0}") + +print(f"Diff B 0.5 mm: {B_tot_x[15500] - B_0}, relative: {(B_tot_x[15500] - B_0)/B_0}") + + +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +#plt.plot(z,B_totz,linestyle = "solid", label = r"$B_z along z-axis") +#plt.plot(x,Bx,label = "B_x along x") +plt.plot(z,B_tot_z, label = "New B_tot along z-axis") +plt.plot(x,B_tot_x, label = "B_tot along x-axis") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,Bz_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +plt.plot(x,Bx_curv,label = "B_x_curv") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/08_plotting_07_HH_without_comp1.py b/Coil_geometry/08_plotting_07_HH_without_comp1.py new file mode 100644 index 0000000..5fae7ac --- /dev/null +++ b/Coil_geometry/08_plotting_07_HH_without_comp1.py @@ -0,0 +1,86 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-50, 50, 3001) +z = np.linspace(-50, 50, 3001) + +#New coil +I_current = 5 +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) + +HH_Coil.print_info() +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) +Bz_curv = BC.BCoil.curv(Bz, z) +HH_Coil.cooling(I_current) + + +I_HH = 5 + +#calculate field +B_z, B_x = HH_Coil.B_multiple(I_HH,x,z) + +#Calculate curvature +B_z_curv = BC.BCoil.curv(B_z, z) + + + +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_{ref}$, reference, optimal HH-configuration d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,B_z_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/09_geometry_HH_check_other_axis.py b/Coil_geometry/09_geometry_HH_check_other_axis.py new file mode 100644 index 0000000..b494990 --- /dev/null +++ b/Coil_geometry/09_geometry_HH_check_other_axis.py @@ -0,0 +1,108 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.arange(-50, 50, 0.5) +print(len(x)//2) +z = np.arange(-50, 50, 0.5) + +#New coil +I_current = 5 +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) + +HH_Coil.print_info() +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 4) +Bz_curv = BC.BCoil.curv(Bz, z) + +B = HH_Coil.B_multiple_3d(I_current, x, z,raster = 2) +B_tot = BC.BCoil.B_tot_3d(B) + +HH_Coil.cooling(I_current) + +HH_Coil.plot_3d(I_current, 80, 80) +""" +print(f"B_z(0) = {Bz[15000]} G") +print(f"B_z_curvature(0) = {Bz_curv[15000]:.4f} G/cm^2") + + +print(f"B_z(1 μm) = {Bz[15001]}") +print(f"B_z(1 mm) = {Bz[16000]}") + +print(f"Diff B 1 μm: {Bz[15001] - Bz[15000]}, relative: {(Bz[15001] - Bz[15000])/Bz[15000]}") + + +print(f"Diff B 1 mm: {Bz[16000] - Bz[15000]}, relative: {(Bz[16000] - Bz[15000])/Bz[15000]}") +""" + +I_HH = 5 + +#calculate field +B_z, B_x = HH_Coil.B_multiple(I_HH,x,z) + +#Calculate curvature +B_z_curv = BC.BCoil.curv(B_z, z) + + + +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_{ref}$, reference, optimal HH-configuration d = 44 mm, R = 44 mm") +plt.plot(z,B_tot[:,len(x)//2], label = "B_tot_z") +plt.plot(x,B_x,label = "B_x") +plt.plot(x,B_tot[len(z)//2,:],label = "B_tot_x") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,B_z_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/10_comparison_Ilzh_small-bias.py b/Coil_geometry/10_comparison_Ilzh_small-bias.py new file mode 100644 index 0000000..93d566d --- /dev/null +++ b/Coil_geometry/10_comparison_Ilzh_small-bias.py @@ -0,0 +1,102 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-15, 15, 30001) +z = np.linspace(-15, 15, 30001) + + +#New coil +I_current = 10 +HH_Coil = BC.BCoil(HH = 1, distance = 70 ,radius = 40.5 , layers = 1, windings = 1, wire_height = 1, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_inner(40.5) + + +HH_Coil.print_info() +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) +Bz_curv = BC.BCoil.curv(Bz, z) +HH_Coil.cooling(I_current) + +print(f"B_z(0) = {Bz[15000]} G") +print(f"B_z_curvature(0) = {Bz_curv[15000]:.4f} G/cm^2") + + +print(f"B_z(1 μm) = {Bz[15001]}") +print(f"B_z(1 mm) = {Bz[16000]}") + +print(f"Diff B 1 μm: {Bz[15001] - Bz[15000]}, relative: {(Bz[15001] - Bz[15000])/Bz[15000]}") + + + +print(f"Diff B 1 mm: {Bz[16000] - Bz[15000]}, relative: {(Bz[16000] - Bz[15000])/Bz[15000]}") + +print(f"Diff B 0.5 mm: {Bz[15500] - Bz[15000]}, relative: {(Bz[15500] - Bz[15000])/Bz[15000]}") + + +I_HH = 5 + +#calculate field +B_z, B_x = HH_Coil.B_multiple(I_HH,x,z) + +#Calculate curvature +B_z_curv = BC.BCoil.curv(B_z, z) + + + +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_{ref}$, reference, optimal HH-configuration d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,B_z_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/11_Final_HH.py b/Coil_geometry/11_Final_HH.py new file mode 100644 index 0000000..029f226 --- /dev/null +++ b/Coil_geometry/11_Final_HH.py @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-15, 15, 30001) +z = np.linspace(-15, 15, 30001) + + +#New coil +I_current = 10 +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 2, wire_height = 2, wire_width = 1, windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_inner(44.5) +HH_Coil.set_d_min(48.8) +print(f"height = {HH_Coil.get_coil_height()}") +HH_Coil.print_info() + +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) + +B_tot_z, B_tot_x = HH_Coil.B_multiple(I_current, x, z,raster = 10) + +Bz_curv = BC.BCoil.curv(Bz, z) +HH_Coil.cooling(I_current,28) + +print(f"B_z(0) = {Bz[15000]} G") +print(f"B_z_curvature(0) = {Bz_curv[15000]:.10f} G/cm^2") + + +print(f"B_z(1 μm) = {Bz[15001]}") +print(f"B_z(1 mm) = {Bz[16000]}") + +print(f"Diff B 1 μm: {Bz[15001] - Bz[15000]}, relative: {(Bz[15001] - Bz[15000])/Bz[15000]}") + + +print(f"Diff B 1 mm: {Bz[16000] - Bz[15000]}, relative: {(Bz[16000] - Bz[15000])/Bz[15000]}") + +print(f"Diff B 0.5 mm: {Bz[15500] - Bz[15000]}, relative: {(Bz[15500] - Bz[15000])/Bz[15000]}") + + + + +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,Bz,linestyle = "solid", label = r"$B_z along z-axis") +plt.plot(z,B_tot_z, linestyle = "dashed", label = "New B_tot along z-axis") +#plt.plot(x,B_tot_x, label = "B_tot along x-axis") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,Bz_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/12_Final_Plotting.py b/Coil_geometry/12_Final_Plotting.py new file mode 100644 index 0000000..ab4916c --- /dev/null +++ b/Coil_geometry/12_Final_Plotting.py @@ -0,0 +1,89 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis + +axis = 3001 #30001 for -15 to 15 = 1μm +x = np.linspace(-5, 5, axis) +z = np.linspace(-5, 5, axis) + + +#New coil +I_current = 10 +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 2, wire_height = 2, wire_width = 1, windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_inner(44.5) +HH_Coil.set_d_min(48.8) + +print(HH_Coil.resistance(22)) +print(HH_Coil.induct_perry()) + +HH_Coil.print_info() +#Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) +B_tot_z, B_tot_x = HH_Coil.B_tot_along_axis(I_current, x, z,raster = 8) + +Bz_curv = BC.BCoil.curv(B_tot_z, z) +Bx_curv = BC.BCoil.curv(B_tot_x, x) + +B_0 = B_tot_z[axis//2] + +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +#plt.plot(z,B_totz,linestyle = "solid", label = r"$B_z along z-axis") +#plt.plot(x,Bx,label = "B_x along x") +plt.plot(z,B_tot_z, label = r"$B_{{tot}}$ along z-axis") +plt.plot(x,B_tot_x, label = r"$B_{{tot}}$ along x-axis") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B$ [G]") +plt.xlabel("z / x -axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,Bz_curv,linestyle = "solid", label = r"$B_{curvature}$ along z-axis") +plt.plot(x,Bx_curv,label = r"$B_{curvature}$ along x-axis") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_{z,x}^2 B_tot [G/cm^2]$") +plt.xlabel("z / x -axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry/Untitled.ipynb b/Coil_geometry/Untitled.ipynb new file mode 100644 index 0000000..2235b31 --- /dev/null +++ b/Coil_geometry/Untitled.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "id": "672091c7", + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Aug 24 16:24:52 2021\n", + "\n", + "@author: Joschka\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0,'..\\src')\n", + "\n", + "import coil_class_jupyter as BC\n", + "\n", + "#from IPython import get_ipython\n", + "#get_ipython().run_line_magic('matplotlib', 'qt')\n", + "#get_ipython().run_line_magic('matplotlib', 'inline')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d3a46f0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HH = 1, Distance = 57.8 mm, z_min = 24.9 mm, z_max = 32.9 mm\n", + "Radius = 45.29999999999999 mm, Radius_inner = 41.29999999999999 mm, Radius_outer = 49.30 mm\n", + "layers = 1, windings = 1, wire_width = 8.0, wire_height = 8.0 mm \n", + "current density = 1.25 A/mm^2\n", + "Power = 0.47817553461759527 W\n", + "B_z(0) = 13.37 G\n", + "B_z_curvature(0) = 0.3775 G/cm^2\n", + "current density = 1.2499999999999998 A/mm^2\n", + "Power = 0.5193429647502357 W\n", + "width = 0.2mm, height = 320.0mm\n", + "B_z(0) = 1.81 G\n", + "B_z_curvature(0) = 0.1083 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5188151771844329 W\n", + "width = 0.30000000000000004mm, height = 213.33333333333331mm\n", + "B_z(0) = 2.59 G\n", + "B_z_curvature(0) = 0.1629 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5182873896186297 W\n", + "width = 0.4000000000000001mm, height = 159.99999999999997mm\n", + "B_z(0) = 3.32 G\n", + "B_z_curvature(0) = 0.2160 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5177596020528267 W\n", + "width = 0.5000000000000001mm, height = 127.99999999999997mm\n", + "B_z(0) = 4.00 G\n", + "B_z_curvature(0) = 0.2669 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5172318144870235 W\n", + "width = 0.6000000000000001mm, height = 106.66666666666666mm\n", + "B_z(0) = 4.63 G\n", + "B_z_curvature(0) = 0.3147 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5167040269212204 W\n", + "width = 0.7000000000000002mm, height = 91.4285714285714mm\n", + "B_z(0) = 5.20 G\n", + "B_z_curvature(0) = 0.3588 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5161762393554175 W\n", + "width = 0.8000000000000003mm, height = 79.99999999999997mm\n", + "B_z(0) = 5.74 G\n", + "B_z_curvature(0) = 0.3987 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5156484517896143 W\n", + "width = 0.9000000000000001mm, height = 71.1111111111111mm\n", + "B_z(0) = 6.22 G\n", + "B_z_curvature(0) = 0.4344 G/cm^2\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#set up axis\n", + "x = np.linspace(-50, 50, 301)\n", + "z = np.linspace(-50, 50, 301)\n", + "\n", + "#New coil\n", + "HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1)\n", + "\n", + "#Compensation Coil\n", + "HH_Coil_comp = BC.BCoil(HH = 1, distance = 54 ,radius = 37, layers = 4, windings = 4,wire_height = 1, wire_width = 1)\n", + "\n", + "\n", + "\n", + "#HH_Coil_44.Bz_plot_HH(I,x,z)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eef49ab", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Coil_geometry/output/AHH_field.pdf b/Coil_geometry/output/AHH_field.pdf new file mode 100644 index 0000000..492e461 Binary files /dev/null and b/Coil_geometry/output/AHH_field.pdf differ diff --git a/Coil_geometry/output/first_compensation_idea.png b/Coil_geometry/output/first_compensation_idea.png new file mode 100644 index 0000000..1a2cf96 Binary files /dev/null and b/Coil_geometry/output/first_compensation_idea.png differ diff --git a/Coil_geometry/output/gradient_field_of_compensation_coil.pdf b/Coil_geometry/output/gradient_field_of_compensation_coil.pdf new file mode 100644 index 0000000..cc2d628 Binary files /dev/null and b/Coil_geometry/output/gradient_field_of_compensation_coil.pdf differ diff --git a/Coil_geometry_AHH/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/Coil_geometry_AHH/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/Coil_geometry_AHH/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Coil_geometry_AHH/00_Simple_testing.py b/Coil_geometry_AHH/00_Simple_testing.py new file mode 100644 index 0000000..70c8cd0 --- /dev/null +++ b/Coil_geometry_AHH/00_Simple_testing.py @@ -0,0 +1,45 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 31 09:28:25 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') +print(10/2.7) + +x = np.linspace(-10, 10, 3001) +z = np.linspace(-10, 10, 3001) +print(3001//2) + +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) + +percentage = 0.05 +absolut = 5 +diff = percentage*0.01*5+ absolut *1e-3 +print(diff) +Bz1, Bx = HH_Coil.B_multiple(5, x, z) + + +Bz2, Bx = HH_Coil.B_multiple(5+ diff, x, z) +print(Bz2[1500]-Bz1[1500]) +print(" ") + +percentage = 0 #.02 +absolut = 2 +diff = percentage*0.01*5+ absolut *1e-3 +print(diff) + + +Bz2, Bx = HH_Coil.B_multiple(5+ diff, x, z) +print(Bz2[1500]-Bz1[1500]) +print((Bz2[1500]-Bz1[1500])/Bz2[1500]) + +#Power = cs.rho_copper_20 *wire_length* I_current**2 /(self.get_wire_area()) \ No newline at end of file diff --git a/Coil_geometry_AHH/01_geometry_fixed_AHH.py b/Coil_geometry_AHH/01_geometry_fixed_AHH.py new file mode 100644 index 0000000..c1886e1 --- /dev/null +++ b/Coil_geometry_AHH/01_geometry_fixed_AHH.py @@ -0,0 +1,92 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-50, 50, 10001) +z = np.linspace(-50, 50, 10001) + + + +d=82 + +AHH_Coil = BC.BCoil(HH = -1, distance = d ,radius = 47.3 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) +AHH_Coil.set_R_inner(44.5) +print(f"height = {AHH_Coil.get_coil_height()*1e3}mm") +#AHH_Coil.set_R_outer(49.3) +I = 10 +AHH_Coil.print_info() +R = AHH_Coil.resistance(30) +print(f"R = {R} ") +#B = AHH_Coil.B_multiple_3d(10, x,z,raster=2) +AHH_Coil.cooling(I,30) + +B_z,B_x = AHH_Coil.B_multiple(I,x,z) +#B_z = B[:,150,1] +#B_x = B[150,:,0] + +B_tot_z, B_tot_x = AHH_Coil.B_tot_along_axis(I, x, z) + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +lim = 7000 +B_0 = B_z_grad[5000] +print((B_0- B_z_grad[6700])) +print((B_0- B_z_grad[6700])/B_0) + + +plt.subplot(2,1,1) + +plt.plot(z,B_z,linestyle = "solid", label = f"$B_z$, d = {d} mm") +plt.plot(z,B_tot_z, label = "B_tot_z") +plt.plot(x,B_x, label = f"$B_x$, d = {d} mm") +plt.plot(z,B_tot_x, label = "B_tot_x") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) +#plt.ylim(-0.5,0.4) +plt.ylabel(r"$B$ [G]") +plt.xlabel("z-axis / x-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z B_z$") +plt.plot(x,B_x_grad,linestyle = "solid", label = r"$\nabla_x B_x$") + +plt.ylabel(r"$\nabla_i B_i [G/cm]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +plt.title("Gradient of B-field") +plt.legend() +plt.savefig("output/AHH_field.pdf") +plt.show() + + +#AHH_Coil.plot_3d(I, 80, 80) +#print(B_z_grad[1500]) +#print(2*B_x_grad[1500]) +""" +print(" ") +print(f"B_grad_z(0) = {B_z_grad[1500]} G/cm") +print(f"B_grad_z(10 mm) = {B_z_grad[1800]} G/cm") +print(f"Diff B_grad z 10mm - 0 mm, {-(B_z_grad[1800]-B_z_grad[1500])} G/cm, relative: {(B_z_grad[1800]-B_z_grad[1500])/-B_z_grad[1500]}") +print(" ") +print(f"B_grad_x(0) = {B_x_grad[1500]} G/cm") +print(f"B_grad_x(10 mm) = {B_x_grad[1800]} G/cm") +print(f"Diff B_grad x 10mm - 0 mm, {B_x_grad[1800]-B_x_grad[1500]} G/cm, relative: {(B_x_grad[1800]-B_x_grad[1500])/-B_x_grad[1500]}") +""" + + diff --git a/Coil_geometry_AHH/02_geometry_fixed_search_distance_plots.py b/Coil_geometry_AHH/02_geometry_fixed_search_distance_plots.py new file mode 100644 index 0000000..62ead08 --- /dev/null +++ b/Coil_geometry_AHH/02_geometry_fixed_search_distance_plots.py @@ -0,0 +1,130 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-50, 50, 1001) +z = np.linspace(-50, 50, 1001) + + + + +#Field plot +########################## + +d=82 + +AHH_Coil = BC.BCoil(HH = -1, distance = d ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) +AHH_Coil.set_R_inner(44.5) + +I = 10 +AHH_Coil.print_info() +R = AHH_Coil.resistance(30) +print(f"R = {R} ") +#B = AHH_Coil.B_multiple_3d(10, x,z,raster=2) +AHH_Coil.cooling(I,30) + +B_z,B_x = AHH_Coil.B_multiple(I,x,z) +#B_z = B[:,150,1] +#B_x = B[150,:,0] + +B_tot_z, B_tot_x = AHH_Coil.B_tot_along_axis(I, x, z) + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +# lim = 7000 +# B_0 = B_z_grad[5000] +# print((B_0- B_z_grad[6700])) +# print((B_0- B_z_grad[6700])/B_0) + +distance = np.arange(80,95,2) + +for d in distance: + print(d) + AHH_Coil = BC.BCoil(HH = -1, distance = d ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) + B_z,B_x = AHH_Coil.B_multiple(I,x,z) + B_z_grad = BC.BCoil.Bgrad(B_z, z) + B_x_grad = BC.BCoil.Bgrad(B_x,x) + + B_z_curv = BC.BCoil.Bgrad(B_z_grad, z) + B_x_curv = BC.BCoil.Bgrad(B_z_grad, z) + + B_z_3rd = BC.BCoil.Bgrad(B_z_curv, z) + B_x_3rd = BC.BCoil.Bgrad(B_z_curv, z) + + + plt.subplot(2,2,1) + plt.plot(z,B_z,linestyle = "solid", label = f"d = {d} mm") + #plt.plot(x,B_x, label = f"$B_x$, d = {d} mm") + + plt.title("B-field" ) + plt.ylabel(r"$B$ [G]") + plt.xlabel("z-axis / x-axis [mm]") + plt.legend() + + plt.subplot(2,2,2) + plt.plot(z,B_z_grad,linestyle = "solid", label = f"$d = {d} mm") + #plt.plot(x,B_x_grad,linestyle = "solid", label = f"$Grad_x B_x$, d = {d} mm") + + plt.ylabel(r"$\nabla_i B_i [G/cm]$") + plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) + plt.title("Gradient of B-field") + plt.legend() + + plt.subplot(2,2,3) + plt.title("Curvature") + plt.plot(z,B_z_curv,linestyle = "solid", label = f"$ d = {d} mm") + #plt.plot(x,B_x_curv, label = f"Curv. $B_x$, d = {d} mm") + + plt.title("B-field" ) + plt.ylabel(r"$B$ [G/cm^2]") + plt.xlabel("z-axis / x-axis [mm]") + plt.legend() + + plt.subplot(2,2,4) + plt.title("3rd derivative") + plt.plot(z,B_z_3rd,linestyle = "dashed", label = f"d = {d} mm") + plt.plot(x,B_x_3rd, label = f"3rd der. $B_x$, d = {d} mm") + + plt.ylabel(r"$B$ [G/cm^3]") + plt.xlabel("z-axis / x-axis [mm]") + plt.legend() + + + + + + #plt.savefig("output/AHH_field.pdf") +plt.show() + + +#AHH_Coil.plot_3d(I, 80, 80) +#print(B_z_grad[1500]) +#print(2*B_x_grad[1500]) +""" +print(" ") +print(f"B_grad_z(0) = {B_z_grad[1500]} G/cm") +print(f"B_grad_z(10 mm) = {B_z_grad[1800]} G/cm") +print(f"Diff B_grad z 10mm - 0 mm, {-(B_z_grad[1800]-B_z_grad[1500])} G/cm, relative: {(B_z_grad[1800]-B_z_grad[1500])/-B_z_grad[1500]}") +print(" ") +print(f"B_grad_x(0) = {B_x_grad[1500]} G/cm") +print(f"B_grad_x(10 mm) = {B_x_grad[1800]} G/cm") +print(f"Diff B_grad x 10mm - 0 mm, {B_x_grad[1800]-B_x_grad[1500]} G/cm, relative: {(B_x_grad[1800]-B_x_grad[1500])/-B_x_grad[1500]}") +""" + + diff --git a/Coil_geometry_AHH/03_iteratively_find_exact_d_3rd_derivative.py b/Coil_geometry_AHH/03_iteratively_find_exact_d_3rd_derivative.py new file mode 100644 index 0000000..9296f26 --- /dev/null +++ b/Coil_geometry_AHH/03_iteratively_find_exact_d_3rd_derivative.py @@ -0,0 +1,66 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis + +res = 1001 +zr = res//2 +x = np.linspace(-5, 5, res) +z = np.linspace(-5, 5, res) + + +#Field plot +########################## + + +I = 10 + + +AHH_Coil = BC.BCoil(HH = -1, distance = 81.8 ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) +AHH_Coil.print_info() +AHH_Coil.cooling(I, 30) +print(f"R (30 degree C)= {AHH_Coil.resistance(30)}") + +B_z,B_x = AHH_Coil.B_multiple(I,x,z) + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +B_z_curv = BC.BCoil.Bgrad(B_z_grad, z) +B_x_curv = BC.BCoil.Bgrad(B_z_grad, z) + +B_z_3rd = BC.BCoil.Bgrad(B_z_curv, z) +B_x_3rd = BC.BCoil.Bgrad(B_z_curv, z) + + + + + +""" +print(" ") +print(f"B_grad_z(0) = {B_z_grad[1500]} G/cm") +print(f"B_grad_z(10 mm) = {B_z_grad[1800]} G/cm") +print(f"Diff B_grad z 10mm - 0 mm, {-(B_z_grad[1800]-B_z_grad[1500])} G/cm, relative: {(B_z_grad[1800]-B_z_grad[1500])/-B_z_grad[1500]}") + +print(" ") +print(f"B_grad_x(0) = {B_x_grad[1500]} G/cm") +print(f"B_grad_x(10 mm) = {B_x_grad[1800]} G/cm") +print(f"Diff B_grad x 10mm - 0 mm, {B_x_grad[1800]-B_x_grad[1500]} G/cm, relative: {(B_x_grad[1800]-B_x_grad[1500])/-B_x_grad[1500]}") +""" + + diff --git a/Coil_geometry_AHH/04_final_AHH_coil.py b/Coil_geometry_AHH/04_final_AHH_coil.py new file mode 100644 index 0000000..4016e02 --- /dev/null +++ b/Coil_geometry_AHH/04_final_AHH_coil.py @@ -0,0 +1,103 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis + +#set up axis +x = np.linspace(-50, 50, 10001) +z = np.linspace(-50, 50, 10001) + + + +d=81.8 + +AHH_Coil = BC.BCoil(HH = -1, distance = d ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) +print(AHH_Coil.power(10, 25)) + +h =AHH_Coil.get_coil_height() +w = AHH_Coil.get_coil_width() + +vert_surf = h * 46.875*1e-3 *2 *np.pi +hor_surf = np.pi*(AHH_Coil.get_R_outer()**2-AHH_Coil.get_R_inner()**2) + +tot = 2*vert_surf + 2*hor_surf +print(f"Surface area = {tot}") + +print(AHH_Coil.get_coil_height()) +print(AHH_Coil.get_coil_width()) + +I = 10 +AHH_Coil.print_info() +R = AHH_Coil.resistance(30) +print(f"R = {R} ") +#B = AHH_Coil.B_multiple_3d(10, x,z,raster=2) +AHH_Coil.cooling(I,30) + +B_z,B_x = AHH_Coil.B_multiple(I,x,z) +#B_z = B[:,150,1] +#B_x = B[150,:,0] + +B_tot_z, B_tot_x = AHH_Coil.B_tot_along_axis(I, x, z) + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +lim = 7000 +B_0 = B_z_grad[5000] +print((B_0- B_z_grad[6700])) +print((B_0- B_z_grad[6700])/B_0) + + +plt.subplot(2,1,1) + +plt.plot(z,B_z,linestyle = "solid", label = f"$B_z$, d = {d} mm") +#plt.plot(z,B_tot_z, label = "B_tot_z") +plt.plot(x,B_x, label = f"$B_x$, d = {d} mm") +#plt.plot(z,B_tot_x, label = "B_tot_x") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) +#plt.ylim(-0.5,0.4) +plt.ylabel(r"$B$ [G]") +plt.xlabel("z-axis / x-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z B_z$") +plt.plot(x,B_x_grad,linestyle = "solid", label = r"$\nabla_x B_x$") + +plt.ylabel(r"$\nabla_i B_i [G/cm]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +plt.title("Gradient of B-field") +plt.legend() +plt.savefig("output/AHH_field.pdf") +plt.show() + + +#AHH_Coil.plot_3d(I, 80, 80) +#print(B_z_grad[1500]) +#print(2*B_x_grad[1500]) + +print(" ") +print(f"B_grad_z(0) = {B_z_grad[1500]} G/cm") +print(f"B_grad_z(10 mm) = {B_z_grad[1800]} G/cm") +print(f"Diff B_grad z 10mm - 0 mm, {-(B_z_grad[1800]-B_z_grad[1500])} G/cm, relative: {(B_z_grad[1800]-B_z_grad[1500])/-B_z_grad[1500]}") +print(" ") +print(f"B_grad_x(0) = {B_x_grad[1500]} G/cm") +print(f"B_grad_x(10 mm) = {B_x_grad[1800]} G/cm") +print(f"Diff B_grad x 10mm - 0 mm, {B_x_grad[1800]-B_x_grad[1500]} G/cm, relative: {(B_x_grad[1800]-B_x_grad[1500])/-B_x_grad[1500]}") + diff --git a/Coil_geometry_AHH/05_comparison_opt_AHH_vs_not_cutting_optical_ax.py b/Coil_geometry_AHH/05_comparison_opt_AHH_vs_not_cutting_optical_ax.py new file mode 100644 index 0000000..525798f --- /dev/null +++ b/Coil_geometry_AHH/05_comparison_opt_AHH_vs_not_cutting_optical_ax.py @@ -0,0 +1,133 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis + +#set up axis +lim = 10001 +zr = lim//2 +x = np.linspace(-50, 50, lim) +z = np.linspace(-50, 50, lim) + + + +d_opt=81.8 +d_comp = 69.4 + +AHH_opt = BC.BCoil(HH = -1, distance = d_opt ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) +AHH_opt = BC.BCoil(HH = -1, distance = d_opt ,radius = 46.875 ,layers = 8, windings = 2 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) +AHH_opt.set_R_outer(49.25) + + +AHH_comp = BC.BCoil(HH = -1, distance = d_comp ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) + + +I = 10 +print("Optimum configuration:") +AHH_opt.print_info() + +print("Not cutting optical axis:") +AHH_comp.print_info() + + +Bz_opt, Bx_opt = AHH_opt.B_multiple(I,x,z) +Bz_comp, Bx_comp = AHH_comp.B_multiple(I, x, z) + + +#B_z = B[:,150,1] +#B_x = B[150,:,0] +#B_tot_z, B_tot_x = AHH_Coil.B_tot_along_axis(I, x, z) + +Bz_grad_opt = BC.BCoil.Bgrad(Bz_opt, z) +Bx_grad_opt = BC.BCoil.Bgrad(Bx_opt,x) + +Bz_grad_comp = BC.BCoil.Bgrad(Bz_comp, z) +Bx_grad_comp = BC.BCoil.Bgrad(Bx_comp,x) + +Bz_rel_opt = (Bz_grad_opt[zr]- Bz_grad_opt)/Bz_grad_opt[zr]*100 +Bx_rel_opt = (Bx_grad_opt[zr]- Bx_grad_opt)/Bx_grad_opt[zr]*100 + +Bz_rel_comp = (Bz_grad_comp[zr]- Bz_grad_comp)/Bz_grad_comp[zr]*100 +Bx_rel_comp = (Bx_grad_comp[zr]- Bx_grad_comp)/Bx_grad_comp[zr]*100 + +plt.figure(figsize = (10,30)) +plt.tight_layout() +plt.subplot(3,1,1) + +plt.plot(z,Bz_opt,linestyle = "solid", color = "orange", label = f"$B_z$, d = {d_opt} mm") +plt.plot(z,Bz_comp,linestyle = "solid",color = "blue", label = f"$B_z$, d = {d_comp} mm") + +plt.plot(x,Bx_opt, linestyle = "dashed", color = "orange", label = f"$B_x$, d = {d_opt} mm") +plt.plot(x,Bx_comp,linestyle = "dashed", color = "blue", label = f"$B_x$, d = {d_comp} mm") +#plt.plot(z,B_tot_x, label = "B_tot_x") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B$ [G]") +plt.xlabel("z-axis / x-axis [mm]") +plt.legend() + + + +plt.subplot(3,1,2) +plt.plot(z,Bz_grad_opt,linestyle = "solid", color = "orange",label = f"$B_z$, d = {d_opt} mm") +plt.plot(z,Bz_grad_comp,linestyle = "solid",color = "blue", label = f"$B_z$, d = {d_comp} mm") + +plt.plot(x,Bx_grad_opt,linestyle = "dashed",color = "orange", label = f"$B_x$, d = {d_opt} mm") +plt.plot(x,Bx_grad_comp, linestyle = "dashed",color = "blue", label = f"$B_x$, d = {d_comp} mm") + +plt.ylabel(r"$\nabla_i B_i [G/cm]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +#plt.title("Gradient of B-field") +plt.legend() + +plt.subplot(3,1,3) +plt.plot(z,Bz_rel_opt,linestyle = "solid", color = "orange",label = f"$B_z$, d = {d_opt} mm") +plt.plot(z,Bz_rel_comp,linestyle = "solid",color = "blue", label = f"$B_z$, d = {d_comp} mm") + +plt.plot(x,Bx_rel_opt,linestyle = "dashed",color = "orange", label = f"$B_x$, d = {d_opt} mm") +plt.plot(x,Bx_rel_comp, linestyle = "dashed",color = "blue", label = f"$B_x$, d = {d_comp} mm") + +plt.ylabel(r"rel. Deviation from Grad to center [%]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +#plt.title(r"$\nabla_i B_i") +#plt.ylim(-0.05,0.05) +plt.xlim(-10,10) +plt.legend() + +plt.savefig("output/AHH_field.pdf") + +plt.show() +print("") +print(" 10 μm") +print(f"Optimum: Dev. of gradient (z) +- 10μm to center: {Bz_rel_opt[zr+1]:.8f} %") +print(f"Not cutting opt. axis: Dev. of gradient (z) +- 10μm to center: {Bz_rel_comp[zr+1]:.8f} %") +print("") +print(" 1mm ") +print(f"Optimum: Dev. of gradient (z) +- 1 mm to center: {Bz_rel_opt[zr+100]:.6f} %") +print(f"Not cutting opt. axis: Dev. of gradient (z) +- 1 mm to center: {Bz_rel_comp[zr+100]:.3f} %") + +print("") +print(" 10mm ") +print(f"Optimum: Dev. of gradient (z) +- to center: {Bz_rel_opt[zr+1000]:.2f} %") +print(f"Not cutting opt. axis: Dev. of gradient (z) to center: {Bz_rel_comp[zr+1000]:.2f} %") + +print("") +print(" 17mm ") +print(f"Optimum: Dev. of gradient (z) +- 1 mm to center: {Bz_rel_opt[zr+1700]:.2f} %") +print(f"Not cutting opt. axis: Dev. of gradient (z) +- 1 mm to center: {Bz_rel_comp[zr+1700]:.2f} %") diff --git a/Coil_geometry_AHH/06_surface_calculation_AHH.py b/Coil_geometry_AHH/06_surface_calculation_AHH.py new file mode 100644 index 0000000..49a669c --- /dev/null +++ b/Coil_geometry_AHH/06_surface_calculation_AHH.py @@ -0,0 +1,103 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis + +#set up axis +x = np.linspace(-50, 50, 10001) +z = np.linspace(-50, 50, 10001) + + + +d=81.8 + +AHH_Coil = BC.BCoil(HH = -1, distance = d ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) + + +h =AHH_Coil.get_coil_height() +w = AHH_Coil.get_coil_width() + +vert_surf = h * 46.875*1e-3 *2 *np.pi +hor_surf = np.pi*(AHH_Coil.get_R_outer()**2-AHH_Coil.get_R_inner()**2) + +tot = 2*vert_surf + 2*hor_surf +print(f"Surface area = {tot}") + +print(AHH_Coil.get_coil_height()) +print(AHH_Coil.get_coil_width()) + +I = 10 +AHH_Coil.print_info() +R = AHH_Coil.resistance(30) +print(f"R = {R} ") +#B = AHH_Coil.B_multiple_3d(10, x,z,raster=2) +AHH_Coil.cooling(I,30) + +B_z,B_x = AHH_Coil.B_multiple(I,x,z) +#B_z = B[:,150,1] +#B_x = B[150,:,0] + +B_tot_z, B_tot_x = AHH_Coil.B_tot_along_axis(I, x, z) + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +lim = 7000 +B_0 = B_z_grad[5000] +print((B_0- B_z_grad[6700])) +print((B_0- B_z_grad[6700])/B_0) + + +plt.subplot(2,1,1) + +plt.plot(z,B_z,linestyle = "solid", label = f"$B_z$, d = {d} mm") +#plt.plot(z,B_tot_z, label = "B_tot_z") +plt.plot(x,B_x, label = f"$B_x$, d = {d} mm") +#plt.plot(z,B_tot_x, label = "B_tot_x") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) +#plt.ylim(-0.5,0.4) +plt.ylabel(r"$B$ [G]") +plt.xlabel("z-axis / x-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z B_z$") +plt.plot(x,B_x_grad,linestyle = "solid", label = r"$\nabla_x B_x$") + +plt.ylabel(r"$\nabla_i B_i [G/cm]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +plt.title("Gradient of B-field") +plt.legend() +plt.savefig("output/AHH_field.pdf") +plt.show() + + +#AHH_Coil.plot_3d(I, 80, 80) +#print(B_z_grad[1500]) +#print(2*B_x_grad[1500]) +""" +print(" ") +print(f"B_grad_z(0) = {B_z_grad[1500]} G/cm") +print(f"B_grad_z(10 mm) = {B_z_grad[1800]} G/cm") +print(f"Diff B_grad z 10mm - 0 mm, {-(B_z_grad[1800]-B_z_grad[1500])} G/cm, relative: {(B_z_grad[1800]-B_z_grad[1500])/-B_z_grad[1500]}") +print(" ") +print(f"B_grad_x(0) = {B_x_grad[1500]} G/cm") +print(f"B_grad_x(10 mm) = {B_x_grad[1800]} G/cm") +print(f"Diff B_grad x 10mm - 0 mm, {B_x_grad[1800]-B_x_grad[1500]} G/cm, relative: {(B_x_grad[1800]-B_x_grad[1500])/-B_x_grad[1500]}") +""" diff --git a/Coil_geometry_AHH/07_final_AHH_lowered height.py b/Coil_geometry_AHH/07_final_AHH_lowered height.py new file mode 100644 index 0000000..405f69a --- /dev/null +++ b/Coil_geometry_AHH/07_final_AHH_lowered height.py @@ -0,0 +1,107 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis + +#set up axis +x = np.linspace(-5, 5, 10001) +z = np.linspace(-5, 5, 10001) + + + +d=69.4 + +AHH_Coil = BC.BCoil(HH = -1, distance = d ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) +print(AHH_Coil.power(10, 25)) + +h =AHH_Coil.get_coil_height() +w = AHH_Coil.get_coil_width() + +vert_surf = h * 46.875*1e-3 *2 *np.pi +hor_surf = np.pi*(AHH_Coil.get_R_outer()**2-AHH_Coil.get_R_inner()**2) + +tot = 2*vert_surf + 2*hor_surf +print(f"Surface area = {tot}") + +print(AHH_Coil.get_coil_height()) +print(AHH_Coil.get_coil_width()) + +I = 10 +AHH_Coil.print_info() +R = AHH_Coil.resistance(30) +print(f"R = {R} ") +#B = AHH_Coil.B_multiple_3d(10, x,z,raster=2) +AHH_Coil.cooling(I,30) + +B_z,B_x = AHH_Coil.B_multiple(I,x,z) +#B_z = B[:,150,1] +#B_x = B[150,:,0] + +B_tot_z, B_tot_x = AHH_Coil.B_tot_along_axis(I, x, z) + +B_z_grad = BC.BCoil.Bgrad(B_z, z) +B_x_grad = BC.BCoil.Bgrad(B_x,x) + +lim = 7000 +B_0 = B_z_grad[5000] +print((B_0- B_z_grad[6700])) +print((B_0- B_z_grad[6700])/B_0) + + +plt.subplot(2,1,1) + +plt.plot(z,B_z,linestyle = "solid", label = r"$B_{{tot}}$ along z-axis") +#plt.plot(z,B_tot_z, label = "B_tot_z") +plt.plot(x,B_x, label = r"$B_{{tot}}$ along x-axis") +#plt.plot(z,B_tot_x, label = "B_tot_x") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) +#plt.ylim(-0.5,0.4) +plt.ylabel(r"$B$ [G]") +plt.xlabel("z-axis / x-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z B_{tot}$ along z-axis") +plt.plot(x,B_x_grad,linestyle = "solid", label = r"$\nabla_x B_{tot}$ along x-axis") + +plt.ylabel(r"$\nabla_i B_i [G/cm]$") +plt.xlabel("z-axis /x-axis [mm]")#plt.xlim(-10,10) +plt.title("Gradient of B-field") +plt.legend() +plt.savefig("output/AHH_field.pdf") +plt.show() + + +#AHH_Coil.plot_3d(I, 80, 80) +#print(B_z_grad[1500]) +#print(2*B_x_grad[1500]) +""" +print(" ") +print(f"B_grad_z(0) = {B_z_grad[1500]} G/cm") +print(f"B_grad_z(10 mm) = {B_z_grad[1800]} G/cm") +print(f"Diff B_grad z 10mm - 0 mm, {-(B_z_grad[1800]-B_z_grad[1500])} G/cm, relative: {(B_z_grad[1800]-B_z_grad[1500])/-B_z_grad[1500]}") +print(" ") +print(f"B_grad_x(0) = {B_x_grad[1500]} G/cm") +print(f"B_grad_x(10 mm) = {B_x_grad[1800]} G/cm") +print(f"Diff B_grad x 10mm - 0 mm, {B_x_grad[1800]-B_x_grad[1500]} G/cm, relative: {(B_x_grad[1800]-B_x_grad[1500])/-B_x_grad[1500]}") +""" + +print(AHH_Coil.resistance(22)) +print(AHH_Coil.induct_perry()) +print(AHH_Coil.power(10, 22)) diff --git a/Coil_geometry_AHH/11_Final_HH.py b/Coil_geometry_AHH/11_Final_HH.py new file mode 100644 index 0000000..51d7c74 --- /dev/null +++ b/Coil_geometry_AHH/11_Final_HH.py @@ -0,0 +1,103 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-20, 20, 40001) +z = np.linspace(-20, 20, 40001) + + +#New coil +I_current = 10 +d=69.4 + +HH_Coil = BC.BCoil(HH = -1, distance = d ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) + +HH_Coil.print_info() + +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) + +B_tot_z, B_tot_x = HH_Coil.B_multiple(I_current, x, z,raster = 10) + +Bz = BC.BCoil.Bgrad(Bz, z) +HH_Coil.cooling(I_current,28) + +print(f"B_z(0) = {Bz[15000]} G") +#print(f"B_z_curvature(0) = {Bz_curv[15000]:.10f} G/cm^2") + + +print(f"B_z(1 μm) = {Bz[15001]}") +print(f"B_z(1 mm) = {Bz[16000]}") + +print(f"Diff B 1 μm: {Bz[15001] - Bz[15000]}, relative: {(Bz[15001] - Bz[15000])/Bz[15000]}") + +print(f"Diff B 0.5 mm: {Bz[15500] - Bz[15000]}, relative: {(Bz[15500] - Bz[15000])/Bz[15000]}") + +print(f"Diff B 1 mm: {Bz[25000] - Bz[15000]}, relative: {(Bz[25000] - Bz[15000])/Bz[15000]}") + +print(f"Diff B 1 mm: {Bz[32000] - Bz[15000]}, relative: {(Bz[32000] - Bz[15000])/Bz[15000]}") + + +print(z[32000]) +print(z[15000]) + + + +plt.figure(300) + + + +""" +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,Bz,linestyle = "solid", label = r"$B_z along z-axis") +plt.plot(z,B_tot_z, linestyle = "dashed", label = "New B_tot along z-axis") +#plt.plot(x,B_tot_x, label = "B_tot along x-axis") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,Bz_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() +""" + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Coil_geometry_AHH/Untitled.ipynb b/Coil_geometry_AHH/Untitled.ipynb new file mode 100644 index 0000000..2235b31 --- /dev/null +++ b/Coil_geometry_AHH/Untitled.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "id": "672091c7", + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Aug 24 16:24:52 2021\n", + "\n", + "@author: Joschka\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0,'..\\src')\n", + "\n", + "import coil_class_jupyter as BC\n", + "\n", + "#from IPython import get_ipython\n", + "#get_ipython().run_line_magic('matplotlib', 'qt')\n", + "#get_ipython().run_line_magic('matplotlib', 'inline')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d3a46f0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HH = 1, Distance = 57.8 mm, z_min = 24.9 mm, z_max = 32.9 mm\n", + "Radius = 45.29999999999999 mm, Radius_inner = 41.29999999999999 mm, Radius_outer = 49.30 mm\n", + "layers = 1, windings = 1, wire_width = 8.0, wire_height = 8.0 mm \n", + "current density = 1.25 A/mm^2\n", + "Power = 0.47817553461759527 W\n", + "B_z(0) = 13.37 G\n", + "B_z_curvature(0) = 0.3775 G/cm^2\n", + "current density = 1.2499999999999998 A/mm^2\n", + "Power = 0.5193429647502357 W\n", + "width = 0.2mm, height = 320.0mm\n", + "B_z(0) = 1.81 G\n", + "B_z_curvature(0) = 0.1083 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5188151771844329 W\n", + "width = 0.30000000000000004mm, height = 213.33333333333331mm\n", + "B_z(0) = 2.59 G\n", + "B_z_curvature(0) = 0.1629 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5182873896186297 W\n", + "width = 0.4000000000000001mm, height = 159.99999999999997mm\n", + "B_z(0) = 3.32 G\n", + "B_z_curvature(0) = 0.2160 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5177596020528267 W\n", + "width = 0.5000000000000001mm, height = 127.99999999999997mm\n", + "B_z(0) = 4.00 G\n", + "B_z_curvature(0) = 0.2669 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5172318144870235 W\n", + "width = 0.6000000000000001mm, height = 106.66666666666666mm\n", + "B_z(0) = 4.63 G\n", + "B_z_curvature(0) = 0.3147 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5167040269212204 W\n", + "width = 0.7000000000000002mm, height = 91.4285714285714mm\n", + "B_z(0) = 5.20 G\n", + "B_z_curvature(0) = 0.3588 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5161762393554175 W\n", + "width = 0.8000000000000003mm, height = 79.99999999999997mm\n", + "B_z(0) = 5.74 G\n", + "B_z_curvature(0) = 0.3987 G/cm^2\n", + "current density = 1.25 A/mm^2\n", + "Power = 0.5156484517896143 W\n", + "width = 0.9000000000000001mm, height = 71.1111111111111mm\n", + "B_z(0) = 6.22 G\n", + "B_z_curvature(0) = 0.4344 G/cm^2\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#set up axis\n", + "x = np.linspace(-50, 50, 301)\n", + "z = np.linspace(-50, 50, 301)\n", + "\n", + "#New coil\n", + "HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 4, wire_height = 1, wire_width = 1)\n", + "\n", + "#Compensation Coil\n", + "HH_Coil_comp = BC.BCoil(HH = 1, distance = 54 ,radius = 37, layers = 4, windings = 4,wire_height = 1, wire_width = 1)\n", + "\n", + "\n", + "\n", + "#HH_Coil_44.Bz_plot_HH(I,x,z)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eef49ab", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Coil_geometry_AHH/output/AHH_field.pdf b/Coil_geometry_AHH/output/AHH_field.pdf new file mode 100644 index 0000000..e18e3e3 Binary files /dev/null and b/Coil_geometry_AHH/output/AHH_field.pdf differ diff --git a/Coil_geometry_AHH/output/first_compensation_idea.png b/Coil_geometry_AHH/output/first_compensation_idea.png new file mode 100644 index 0000000..1a2cf96 Binary files /dev/null and b/Coil_geometry_AHH/output/first_compensation_idea.png differ diff --git a/Coil_geometry_AHH/output/gradient_field_of_compensation_coil.pdf b/Coil_geometry_AHH/output/gradient_field_of_compensation_coil.pdf new file mode 100644 index 0000000..cc2d628 Binary files /dev/null and b/Coil_geometry_AHH/output/gradient_field_of_compensation_coil.pdf differ diff --git a/Cooling/02_implement_power_equality.py b/Cooling/02_implement_power_equality.py new file mode 100644 index 0000000..1140015 --- /dev/null +++ b/Cooling/02_implement_power_equality.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Sep 20 11:41:53 2021 + +@author: Joschka +""" + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC +from src import physical_constants as cs + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up constants +#h_air =10 #Heat transfer with air W/m^2 K +e_cu = 3e-2 #emissivity copper, polished + +rho_cu = 1.7*1e-8 + +I = 10 #A + + +#set up axis +x = np.linspace(-50, 50, 10001) +z = np.linspace(-50, 50, 10001) + + + +AHH_opt = BC.BCoil(HH = -1, distance = 81.8 ,radius = 46.875 ,layers = 4, windings = 4 , wire_width= 1, wire_height= 2 ,layers_spacing = 0.25, windings_spacing= 0.25) + +h = AHH_opt.get_coil_height() + +w = AHH_opt.get_coil_width() +print(h) +print(w) + +vert_surf = h * AHH_opt.radius * 2 *np.pi +hor_surf = np.pi*(AHH_opt.get_R_outer()**2-AHH_opt.get_R_inner()**2) + +S_coil = 2*vert_surf + 2*hor_surf +#S_coil = S_coil/2 +print(f"Surface area = {S_coil}") + +def power_bal(T,h_air): + T_0 = 22.5 + + f = h_air * S_coil *(T-T_0) - 0.5*AHH_opt.power(I, T) + return f +print(e_cu * S_coil * cs.sigma_B**4 * (50**4 - 22.5**4)) +T = np.linspace(20,120,500) +T_calc = np.linspace(20,2200,1000) + +for h_air in [2.5,10,25]: + pos_min = np.argmin(np.abs(power_bal(T_calc,h_air))) + T_SS = T_calc[pos_min] + print(f"T_ss = {T_SS} °C") + plt.plot(T,power_bal(T,h_air),label = f"$h_{{air}} = {h_air} \; W/m^2 K$ , $T_{{SS}}$ = {T_SS:.2f}°C") +plt.ylabel("Power balance [W]") +plt.xlabel("temparature [°C]") +plt.title(f"Power balance, free convection, AHH coil, I = {I} A, windings: 4 x 4") +plt.legend() +plt.show() + +print(AHH_opt.power(I, 25)/2) diff --git a/Cooling/03_Weidemueller_coil.py b/Cooling/03_Weidemueller_coil.py new file mode 100644 index 0000000..1e981c8 --- /dev/null +++ b/Cooling/03_Weidemueller_coil.py @@ -0,0 +1,53 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Sep 20 18:01:04 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC +from src import physical_constants as cs + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +AHH_opt = BC.BCoil(HH = 1, distance = 70, radius = 60, layers = 2, windings = 10, wire_height = 1, wire_width = np.pi/4) +I = 5 +AHH_opt.cooling(I, 36) +print(f"res = {AHH_opt.resistance(36)/2:.2f} Ohm") +h = AHH_opt.get_coil_height() +w = AHH_opt.get_coil_width() + +vert_surf = h * AHH_opt.radius * 2 *np.pi +hor_surf = np.pi*(AHH_opt.get_R_outer()**2-AHH_opt.get_R_inner()**2) + +S_coil = 2*vert_surf + 2*hor_surf #+5e-3 +#S_coil = S_coil/2 +print(f"Surface area = {S_coil}") + +def power_bal(T,h_air): + T_0 = 22.5 + + f = h_air * S_coil *(T-T_0) - 0.5*AHH_opt.power(I, T) + return f + +#print(e_cu * S_coil * cs.sigma_B**4 * (50**4 - 22.5**4)) +T = np.linspace(20,120,500) +T_calc = np.linspace(20,2200,1000) + +for h_air in [2.5,10,25]: + pos_min = np.argmin(np.abs(power_bal(T_calc,h_air))) + T_SS = T_calc[pos_min] + print(f"T_ss = {T_SS} °C") + plt.plot(T,power_bal(T,h_air),label = f"$h_{{air}} = {h_air} \; W/m^2 K$ , $T_{{SS}}$ = {T_SS:.2f}°C") +plt.ylabel("Power balance [W]") +plt.xlabel("temparature [°C]") +plt.title(f"Power balance, free convection, Weidemüller Coil, I = {I} A, windings: 2 x 10") +plt.legend() +plt.show() + +print(AHH_opt.power(I, 940)/2) diff --git a/Cooling/First_power_current_dens_estimations.py b/Cooling/First_power_current_dens_estimations.py new file mode 100644 index 0000000..1371fd9 --- /dev/null +++ b/Cooling/First_power_current_dens_estimations.py @@ -0,0 +1,55 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 11:49:41 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf +from src import physical_constants as cs + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x_m = np.linspace(-0.05, 0.05, 51) +z_m = np.linspace(-0.05, 0.05, 201) + + +z = z_m*1e3 +x = x_m*1e3 #for plotting in mm + + +################# My simulation ######################### +I = 5 +HH = 1 +d_coils = 54 +R_radius = 48.8 +R_inner = R_radius-3*1.7 + +layers = 4 +windings = 4 +wire_width = 1 +wire_height = 1 + + +B_z, B_x = bf.B_multiple_raster(I,HH,R_inner,d_coils,layers,windings,wire_width, wire_height, x_m,z_m) + +#Calculate gradients/curvature +B_z_grad = np.gradient(np.gradient(B_z,z_m),z_m)/1e4 +B_x_grad = np.gradient(B_x,x_m)/100 + + +wire_area = wire_height * wire_width + +wire_length = layers*windings*2*R_radius*np.pi + + +j_dens = I/wire_area #[A/mm^2] +Power = cs.rho_copper_20 *wire_length*1e-3* I**2 /(wire_area* 1e-6) + + +print(f"current density = {j_dens} A/mm^2") +print(f"Power = {Power} W") diff --git a/Magnetic_magnification/.ipynb_checkpoints/Calc_Trap_frequency_displacement-checkpoint.ipynb b/Magnetic_magnification/.ipynb_checkpoints/Calc_Trap_frequency_displacement-checkpoint.ipynb new file mode 100644 index 0000000..e40a346 --- /dev/null +++ b/Magnetic_magnification/.ipynb_checkpoints/Calc_Trap_frequency_displacement-checkpoint.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 109, + "id": "f8d06107", + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Aug 24 16:24:52 2021\n", + "\n", + "@author: Joschka\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0,'..')\n", + "\n", + "from src import coil_class as BC\n", + "from src import physical_constants as cs\n", + "\n", + "#from IPython import get_ipython\n", + "#get_ipython().run_line_magic('matplotlib', 'qt')\n", + "#get_ipython().run_line_magic('matplotlib', 'inline')\n", + "\n", + "\n", + "from scipy.optimize import curve_fit\n", + "plt.rcParams['axes.grid'] = True\n", + "plt.rcParams['grid.alpha'] = 0.5" + ] + }, + { + "cell_type": "markdown", + "id": "99eb6ef1-8920-4f7d-9b58-fd00c731d2bc", + "metadata": {}, + "source": [ + "## Set up coils" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "199e2602-bec6-429a-a639-6ea35ababb86", + "metadata": {}, + "outputs": [], + "source": [ + "HH = 1\n", + "I = 5\n", + "\n", + "d_coils = 50\n", + "Radius = 30\n", + "\n", + "layers = 4\n", + "windings = 4\n", + "wire_width = 1\n", + "wire_height = 1\n", + "\n", + "Coil = BC.BCoil(HH,d_coils,Radius, layers, windings, wire_width, wire_height)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "40cee79d-d6ef-4df4-ae03-5e82785433e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z = np.arange(-100,100,10)\n", + "print(np.size(z))\n", + "r = np.arange(1e-15,100,5)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 4)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)\n", + "\n", + "#plt.figure(figsize=(16,10))\n", + "plt.quiver(r_m,z_m,B[:,:,1],B[:,:,0])\n", + "plt.xlabel(\"radius r [m]\")\n", + "plt.ylabel(\"z-axis [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "28dcdd21-f4b6-49e6-a935-4012a941f186", + "metadata": {}, + "outputs": [], + "source": [ + "z = np.arange(-100,100,1)\n", + "r = np.arange(1e-3,100,1)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 2)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "ad02e8cd-e92a-4d27-845d-cc7e22d6e7e5", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.concatenate((-np.flip(r),r))\n", + "B_tot_x = np.concatenate((np.flip(B_tot[:,len(z)//2]),B_tot[:,len(z)//2]))\n", + "B_tot_z = B_tot[0,:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "52475c2c-e2f0-4615-a5f4-9f0a86155a3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.plot(x,np.gradient(B_tot_x,x))\\nplt.xlabel(\"radius r [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\n#plt.xlim(0,0.01)\\nplt.show()\\nplt.plot(z,np.gradient(B_tot[0,:],z))\\nplt.xlabel(\"z_axis [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\nplt.show()\\n'" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.plot(x,B_tot_x)\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(-0.1,0.1)\n", + "plt.show()\n", + "plt.plot(z,B_tot_z)\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\n", + "\"\"\"\n", + "plt.plot(x,np.gradient(B_tot_x,x))\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(0,0.01)\n", + "plt.show()\n", + "plt.plot(z,np.gradient(B_tot[0,:],z))\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "37c71478-cae2-40ea-8712-b2f751c6c54d", + "metadata": {}, + "source": [ + "## Fit harmonic function" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "443f51a2-a502-4d4a-8692-15610c193cc7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(x,a,b):\n", + " return a*x**2+b\n", + "\n", + "x_SI = 1e-3 * x\n", + "z_SI = 1e-3 * z\n", + "\n", + "B_tot_x_SI = 1e-4*B_tot_x\n", + "B_tot_z_SI = 1e-4*B_tot_z\n" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "id": "59145c9b-aac3-4bea-ba79-b2a3992d05f6", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_x, pcov = curve_fit(func,x_SI[a:b],B_tot_x_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "49c6fb78-ba74-41e3-9a2a-c4dca7109643", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DFEUhJyeHH374gejoaGbNmlVqMU+ePJkpU6aUWGbFihW0b9++wLmnnnqKjRs3sn79eu6++26uXLlCpUqVioxr3759AHzwwQe89957REREcP78eRYuXEjDhg3zh6B88803NGvWzKS4TVmocNSoUTzxxBP5r9955x1WrlzJgw8+yObNmwu9/w8//EBUVJRJ71vU72hzfq7LTQI0d+5cPD09i+0tuqFOnTrMmTOHqKgoUlNT+fTTT2nbti0HDhwgMjKyyDpTpkxh4sSJhc6fOnUKDw+PUonfGoxGI8nJycTExJi0JoSwHGkL2yFtUbaMRmP+o5bi6PX6MozIdLt27SIvL49KlSoBkJmZSeXKlVm+fDkNGjQotbGlQ4cOpV+/fiWWCQkJKfL9evbsyeLFi2nXrh3Lli2ja9euRZbbs2cP7u7u/Pjjj4SFhQFQtWrVQj1Zt2qrG4xGI3l5ebf8N3B0dMzvkXrvvfdYsWIFq1evxt/fH39//yJ70m51z5ycHPR6PXFxcYV+htPT028Z+w0axUbSbo1Gw5IlS+jfv3+R1+vUqUO3bt34/PPPzbqv0WikadOmdOjQgc8++6zIMkX1AIWGhpKcnIyXl5dZ72dLDAYDMTEx1KxZE51OZ+1w7Jq0he2Qtihb2dnZxMXFERERoY7FVBTIy8y/fqNnxdnZGY1GY9lgHN3AjPfo2rUrAQEBTJo0CYBLly7x+uuvc/nyZfbv349Op+P06dMcOXKEvn37lngvU8uZKy0tjebNm3PixAkmTJjAhAkTiiw3YMAAXF1d+emnn4q9V0lt8d9eqqysLBwdHXFwuNmPUlQv1Q0TJ05kzpw5rF+/Pj8Bu13Z2dnExsYSFhZWaHxvamoqfn5+pKSk3PL3d7noAdq0aRMnTpxg4cKFZtfVarW0aNGC6OjoYss4Ozvj7Oxc6LxOpyv3/4PUarUV4nNUBNIWtkPaouzodLoCYz/Iy4QpVfKvawDXsgrm9QvgZPoie/v27WPSpEn5Tw8iIyN55ZVX6Nu3L/Hx8VSvXp2//vqLzMxM7r333hLvVVK5yZMnM3ny5BLrr1y5ssjkwsvLi+rVq7Nz505cXV2LTSIPHDjAa6+9ZlKSmd9W//Lcc88xcODA/NePPfYY999/f4GnMlWqVCny/jeSnw0bNtxx8vPv+Ir6GTbnZ7pcJEDfffcdzZo1o1GjRmbXvTGK/1bPFIUQQogbTp8+zbVr12jatGmh8zqdDj8/PzZs2MCbb75J5cqVmT9/Plu3biUmJoZnnnmGtLQ0ateuzc8//8y2bdsKlXN1vZn2DR8+nIceeqjEeKpUqVLstXvvvZcxY8YwderUIq+npqZy5syZEsfC3oqfnx9+fn75r11dXQkICKBmzZol1ps4cSKzZ88uteSnNFk1AUpPTycmJib/dWxsLPv378fPz49q1aoBasP9+uuvxTbs4MGDqVKlSn7X3MSJE7nrrruIjIwkNTWVzz77jP379/PFF19Y/gMJIYQomaOb2hNznaIoZGdn4+LiUjaPwEy0Z88eNBoNgYGBJCYmkpGRwaZNm3j77bcZPnw4Pj4+dOzYkQYNGjB//nxCQ0PJzs7mkUceYfHixdSqVYtnn32WBQsWMHjw4ALl/uu/yYW57r33XiZNmkSrVq2KvH7gwAG0Wm2ZdwS8++67zJgxgz/++ANnZ2cSExMB8PX1LfKpS1mzagK0e/duOnfunP96zJgxAAwZMiR/fYEFCxagKAqPPPJIkfeIj48vMAjq2rVrPPPMMyQmJuLt7U2TJk3YuHEjLVu2tNwHEUIIYRqNpuBjKEUBow6cXMwan2Npe/fuRVGU/B4OX19fIiMjmTZtGkOGDMkvd+7cufykZunSpfTs2ZNatWoB6tjVGwsm/rtcaatatSpff/11iY+/6tSpU6br4SmKwkcffURqaip33XVXgWvbt28vNlkrSzYzCNqWpKam4u3tbdIgKltmMBiIjo4mMjJSxjpYmbSF7ZC2KFs3BqzmD4L+jzLtASpl586d4+GHH2bz5s0AvP3221SrVo0nn3wSUKep33///URFRRUoZ6vKS1uU9D1lzu9vmQMqhBBC3IbY2FhCQkLyXwcHB3P8+HFA7UHauXMn3bt3L1RO2AZJgIQQQojb0KBBA6Kjo4mKiuL48eMMGjSIo0ePEhUVxejRo/nll1/Q6XSFygnbUC5mgQkhhBC2xtfXN3+F5Rv+/PNPk8oJ65MeICGEEELYHUmAhBBCCGF3JAESQgghhN2RBEgIIYQQdkcSICGEEELYHUmAhBBCWJysuStKS2l9L8k0eCGEEBbj6OiIRqPh0qVLVK5cudAKw4qikJOTA2DTqw/bg/LQFoqicOnSJTQaDY6Ojnd0L0mAhBBCWIxOp6Nq1aqcO3eOM2fOFLquKAp6vR4HBweb/aVrL8pLW2g0GqpWrXrHW9lIAiSEEMKiPDw8iIyMJC8vr9A1g8FAXFwcYWFhsjeblZWXtnB0dCyV+CQBEkIIYXE6na7IX1oGgwGtVouLi4tN/9K1B/bWFjIIWgghhBB2RxIgIYQQQtgdSYCEEEIIYXckARJCCCGE3ZEESAghhBB2RxIgIYQQQtgdSYCEEEIIYXckARJCCCGE3ZEESAghhBB2RxIgIYQQQtgdSYCEEEIIYXckARJCCCGE3ZEESAghhBB2RxIgIYQQQtgdSYCEEEIIYXckARJCCCGE3ZEESAghhBB2RxIgIYQQQtgdSYCEEEIIYXckARJCCCGE3ZEESAghhBB2x8HaAQghhKUoikJWrp7s7Gxyc7LIzc4iOzuD82fj0GkVXNzccXJ2w8nZBVcXF1yc5H+JQtgLq/60b9y4kY8++og9e/aQkJDAkiVL6N+/f/71oUOHMnfu3AJ1WrVqxfbt20u876JFixg/fjynTp2iRo0avPfee9x3332W+AhCCCvS5+WREB/NpbijZCWcgKuxOGZdxiX3Ch76q/gYr+FDOm4apUC9WgBb/nMvRcslvEjR+ZDh4EuWUyUMbpXRVKqBe0htKofXJygkAq1OOs6FqAismgBlZGTQqFEjnnjiCe6///4iy/Ts2ZPZs2fnv3Zycirxntu2bWPgwIFMmjSJ++67jyVLlvDQQw+xefNmWrVqVarxCyHKTm5WBnFHtnM1ejvahH34px8nxJBAqEZPaHGVNEXcBwf06HBEjyOG/PMOGiOVuUZl4zXIPQO5QDqQBBxTy2QqzlxwqEqyd100VZpSuXZbQms3RedY8v+XhBC2x6oJUK9evejVq1eJZZydnQkKCjL5ntOnT6dbt26MGzcOgHHjxrFhwwamT5/Ozz//fEfxCiHKTl52Oqf3rCHtyBr8krYTmneGSI2hYCEN5CiOJOiCueZajTzvcDRewTh6BeLqG4SnXzBe/kG4uHqgc3QBnRM6ReF0dDSRkZGg0YAhB31OFlmZaaReSSD9SgLZ1xLJS7mIknYBl5Qz+GbHE2S8iJsmh5qGU5B8CpL/gEOQrTgS41yLtOA2VGrUg7CojmglIRLC5tn8A+/169cTEBCAj48PHTt25L333iMgIKDY8tu2beOll14qcK5Hjx5Mnz7dwpEKIe7UlbgjxG/5BZf4DdTIOkRtjf7mRQ1cwZt4l7pkVW6Ie0QLAms0IqBKDcIdzPhfmeFfSZRWC1pXHBxd8fTwwzMgrNhq+twczsad4PLpfWSd2Y3nlYOE5ZzES5NJ7dwjEHcE4r4hfbkrZzyaYIjoQvUOA/GsXO02/iWEEJZm0wlQr169ePDBBwkLCyM2Npbx48fTpUsX9uzZg7Ozc5F1EhMTCQwMLHAuMDCQxMTEYt8nJyeHnJyc/NepqakAGAwGDAZDcdVsnsFgwGg0luvPUFFIWxTv6tmjnN38M75nVhKuP02lGxc0kEAlYr1aQvVOhDToSNWwmjTUFhyDo4BZ/6632xYanQMh1esTUr0+8DgAer2e6JgjXDy8Dse4jdTK2IOvJo0G6Vvh0FaMB9/jpEsDsmr1JaL9I7hXqmLWe9oD+dmwHRWhLcyJ3aYToIEDB+YfN2jQgObNmxMWFsaff/7JgAEDiq2n0RR88K8oSqFz/zZlyhQmTpxY6PypU6fw8PC4jchtg9FoJDk5mZiYGLRaGbhpTdIWBemzUrm6fxlVzv1JdUMs/tfP5yk6DjhEcdG/DS7VWxMcWhO/6/9e2XqIOXXqjt+71NtC54Z/oz7QqA/n9AZ2nT6MPm4boclbieIEtXIOwaFDGA9O4YRTfZIj7sU3qgdaR5c7f+8KQH42bEdFaIv09HSTy9p0AvRfwcHBhIWFER0dXWyZoKCgQr09SUlJhXqF/m3cuHGMGTMm/3VqaiqhoaHUqFEDLy+vOw/cSgwGAzExMdSsWROdTmftcOyatAWgKJw/vJHkTd9Q+8paXMkF1NlXB5wak1r9Hmq2f5AmwZbtJbF0W9SrWwd4AEVROHnqJOe2LiTg7EoaGE9QN+8wnDxMysnPOBVyL1W7Pkul8KhSj6E8kZ8N21ER2uLGExxTlKsE6MqVK5w9e5bg4OBiy7Ru3Zo1a9YUGAe0evVq2rRpU2wdZ2fnIh+p6XS6cvtNcINWq60Qn6MisNe2MOblcmTN93junUm4PpYbI2JOaaoRFzGQyC5DaFa12HlcFlFWbVGrdj1q1Z6IokzgxMljXNjwPXUuLCaYKzS9MB9+nM8Jl0Zo2o2mVtv71EHZdshefzZsUXlvC3PitmoClJ6eTkxMTP7r2NhY9u/fj5+fH35+fkyYMIH777+f4OBgzpw5w+uvv46/v3+BNX0GDx5MlSpVmDJlCgAvvvgiHTp04IMPPqBfv34sW7aMtWvXsnnz5jL/fELYs+zMNI788QVVjn1HlJKknlMc2ePZGee7nqRJ6+7UsJM1dTQaDbVr16N27Y/JzpnM1nW/4bhvLk2zd1A7+wCsfYLY9ZNIbTaKqG6D0To4WjtkISo8qyZAu3fvpnPnzvmvbzyGGjJkCF999RWHDh3ihx9+4Nq1awQHB9O5c2cWLlyIp6dnfp34+PgCzyrbtGnDggULePPNNxk/fjw1atRg4cKFsgaQEGUkPe0ahxd/SK3YH2mG2h19BW8OhT5GnXteoG1g8T249sDF2Yk2PR+Fno8Se+oEZ1dOo9mlJUToT8OOMZzf+SHn6z9Lk3tH4uhU9GQPIcSd0yiKoty6mH1JTU3F29ublJSUcj8GKPr6eifltTuzorCHtsjOzmbPkunUPvEV/lwD4AIBxNZ+kkZ9R+Lh4VnyDcqILbbFpaQETiyfSv1zC/AlDYB4TQgJzV+jec9B6CpwT5kttoe9qghtYc7v73I1BkgIYXvy9Aa2/TmXsH0f0ZYLAJzXBHG+8Wia9H6SEFkU8JYqBwRT+amPSU8bz7bln1EnehbVlAtU2/UCR/Z+RXqH8bTs0LvE2axCCPNIAiSEuC2KorB9yzo8/hlHB+NxAK7ixen6I2nUbzRVnGSat7k8PL1p/dh4MlJHsuu3d2kQP4/6hmOw7lG2bWmDR/+PiKrXwNphClEhVNx+VSGExcScTeCvqcNouWYAUcbjZOHM/ohncH3lIM0efA0HSX7uiLuXHy2GTSPvud0cCOiHQdHQOncrNRZ2YfmXr3Hxapq1QxSi3JMESAhhspTMPBb88CXu37ahV/pidBqFY5XuRhm1h8ZDPsLFw9faIVYoXoHVaDTiB64NXc9pt4a4aXK4N+krrk1vw6JlS8jRl98Ve4WwNnkEJoS4JUVRWLXjAC5/vczD7AYNJDkEo/T+mLpN77F2eBVepYjGVHplA2fXfYPP5knUJp7IvU+w/HBPgu//iFZ1ynYdJSEqAukBEkKU6Py1LGZ8+QktV/ahE7vJw4H4+s8R8Oo+AiX5KTtaLaFdn8V9zD7iQvuh1Sj0z1tJ5fl388W8BaRk5Vk7QiHKFUmAhBBFMhoVftp4hG2fPMLzlybip0nnknsteGYD1R58HxxdrR2iXdJ6VibsyR/IeHgx1xwDqK5N5Nno51jw0UhWHzpn7fCEKDckARJCFBJ3JYM3P/+edmv784BmHUY0XG0ygsovbcYxRGYh2QL3Ol3xGbOTy+F9cdAYeda4EP9f+zFh9u9czci1dnhC2DxJgIQQ+RRF4Zed8Sz89FXeSX6FMG0S6S7BMPh3fPtNAQdZmdimuPriP3Qeuf1mka3zoKk2hpfPPM37n3zIpuhL1o5OCJsmCZAQAoCrGbmM/mELrr8/zVjtjzhojGTWug+P0TvQVm9v7fBECZyaDMTl+e1kBLXCU5PFB/qPODz3JSYtP0h2nswUE6IokgAJIdgUfYmnP1nAiFPP0le3HYPGAWPPD3F7ZDa4eFs7PGEKn1Dcn/6TvJYjAHjO4Xc67xrO4M9WcCwh1crBCWF7JAESwo4ZjAofrzrBD7O/5Pu8sdTWniPPLQDdE3+ivetZkK0XyhedI469p8AD36N3cKOd7gifpL7IG1/8wMJd8daOTgibIgmQEHbqcnoOg77dTs7G6XzjNA0vTRaGqnfh+NwmqHaXtcMTd6LB/Tg8/TcG3+pU0Vxhvm4C/yz5jv/9eoCsXHkkJgRIAiSEXdp1Jpm+n66nR/xU3nCcr55s8RS6J/4AzyDrBidKR2A9dM+uR6nZHRdNHl85forn/m+478stxF7OsHZ0QlidJEBC2BFFUfh202memLWed7LfZ4jDGhQ00GMy9P4YdI7WDlGUJhdvNI/8DM2HodUovOX4Iw9d/oJ+n2/kr8MJ1o5OCKuSrTCEsBPZeQZeX3yIjfuO8pPTRzTSnkZxcEEzYBbU62ft8ISl6BygzzTwCYO1bzPM4S9CDFcYPW8Ew+9uwAtdItFqZayXsD/SAySEHUhKy+bhWdvZuX8fi53fVpMfVz80Q36X5MceaDTQbjQ88D2Kzomeul385DSZ79fuZ9TPe8nM1Vs7QiHKnCRAQlRwh8+n0G/GFlLOHWOR8ztU0ySBbwSap9ZCaEtrhyfKUoP70QxeBi4+NNNGM995MlsPRfPgzG1cuJZl7eiEKFOSAAlRga04lMADM7fikRrDIpdJBJIMlevAsL+gUg1rhyesIawNDP0D3PxpoInlV5f3uHjhLPfO2MLe+KvWjk6IMiMJkBAVkKIofLPxNCN+2kt1/WkWub6Hn3INAqNg6J8y08veBV3/PvAIIpJ4Fru9hy49gUdmbWfVkURrRydEmZAESIgKxmhUeOePo7y34hiNNDEscpuClzEFQprCkOXg7m/tEIUtCKgDT6wAr6pUM55jucdk/PUXeW7eHn7cdsba0QlhcZIACVGBZOcZeP7nfczecoYGmtP86vYhroY0CG0Fg5eCm5+1QxS2pFINNQnyCSNQf4HfPd8nQLnC+GVH+OCv4yiKYu0IhbAYSYCEqCBSMvMY/P1O/jyUQF3dORZ5foyTIR3C2sLji2VPL1E03zB4YiX4VccvL4EVvlPxI5Wv1p/i5V8OkKs3WjtCISxCEiAhKoCLqdk8+PVWdsYmU9f5Csu8PsY59xpUaQaPLgRnD2uHKGyZdxUYvAy8quCXdYa/Az7FR5vJ4n3neXLuLtk+Q1RIkgAJUc6dTc7koa+3cfJiOg0801nm9SFOWUkQUA8e+w2cPa0doigPfKrB4OXgXhnf1GNsCPkSP8c8NkVfZvD3O0jNzrN2hEKUKkmAhCjHTl1K56GvtxF3JZMo31yWeHyIU9pZ8KsOg5bKmB9hHv+aMGgJuHjjfXkv60O/wc9FYdeZqzz2zQ6uZuRaO0IhSo0kQEKUU8cSUhn49TYSUrJp4K9hsec0HK/GgNf1xxmegdYOUZRHQVFqz6GjO14XNrMufB7+bjoOnU9h4KxtJKVmWztCIUqFJEBClEP7z17j4VnbuZyeS4MgdxZX/g7HpIPg5q8mPz7VrB2iKM9CW8Ij80HnhPeZlayJ+ptAL2dOXlR7HM9dzbR2hELcMUmAhChn9sZf5fFvd5CSlUfTUG8WRSzFKXYtOLioA579I60doqgIqneC/l8B4HtgFitbn6CqrytnrmQy8OvtnE2WJEiUb5IACVGO7D97jSHf7SQ9R89d1f34OWoPzvtmAxoY8A1UbW7tEEVFEvUAdBkPgN/GN/m9ezoR/u6cv5bFI99s57zsHybKMUmAhCgnDp67xqDvdpCWo6dVhB9z70rA+Z+31Yvd34V691o3QFExtX8ZmgwCxYjviuH8eq8b4ZXcOHc1i0dmbZdNVEW5JQmQEOXA4fMpPP7tDtKy9bQM92NONw3Oy58DFGjxFLQeae0QRUWl0cA9n6iPxPIy8F8+iIWPhFLNz4345Ewe+WY7iSkyMFqUP5IACWHjDp9P4bFvd5Carad5mC+z7w/BddHjoM+GyO7Q8wP1l5QQlqJzhId+gMp1IT2RwN+HsuCJhoT6uRJ3RU2CLsrsMFHOSAIkhA2LSUpj0HfXBzxX82HO4Ia4L30CMi5BYAN4YDboHKwdprAHLt7w2C/qTMOLhwjZ+Co/P9WKqr6uxF7O4NFvtpMs6wSJckQSICFs1NnkTB7/didXM/NoVNWbuU+0wGPta3B+D7j4wMB5ssWFKFs+1eChuaDRwaFfqXpiDj8/fRfB3i6cupTBkO93kiYrRotyQhIgIWxQUlo2g77bQWJqNpEBHsx5oiWeR+bBvh9Bo4UHvge/CGuHKexReDvoMVk9Xj2e0JTd/PhkK/zcnTh0PoWn5u4mO0/2DhO2z6oJ0MaNG+nbty8hISFoNBqWLl2afy0vL49XX32VqKgo3N3dCQkJYfDgwVy4cKHEe86ZMweNRlPoKztbnk+L8iElK4/B3+3kzJVMqvq68uOTrfC9sg9WjFULdH0Lana1bpDCvrV6Fho+DIoBfh1KTaerzH2iJR7ODuyITWbU/L3kGWQXeWHbrJoAZWRk0KhRI2bMmFHoWmZmJnv37mX8+PHs3buXxYsXc/LkSe6999ZTfb28vEhISCjw5eLiYomPIESpyszVM2zOLo4nplHZ05mfnmpFkPYa/DIIjHlQrx+0HW3tMIW902ig73QIagiZV2Dh40QFOvHdkOY4O2hZeyyJsb8dxGhUrB2pEMWy6ujJXr160atXryKveXt7s2bNmgLnPv/8c1q2bEl8fDzVqhW/1L9GoyEoKKhUYxXC0vIMRp6bt5c9cVfxcnHgh2EtCfNxgjlDIP2iOgOn35cy40vYBkdXePgn+LojJOyHFa/Qqt8XfPV4U575YQ9L9p3H29WRt/vWQyPfs8IGlavpIykpKWg0Gnx8fEosl56eTlhYGAaDgcaNGzNp0iSaNGlSbPmcnBxycnLyX6empgJgMBgwGMrvs2yDwYDRaCzXn6GiuFVbKIrCuMWH2XDyEq6OOr4d3IxaAe4Y/5mE9ux2FGdPjA/+AA6uIO15R+TnohR5VoH7v0f70wA0++ZhDGtPx6gH+eiBKF765SBztp4hyMuZp9sXP15N2sN2VIS2MCf2cpMAZWdn89prr/Hoo4/i5eVVbLk6deowZ84coqKiSE1N5dNPP6Vt27YcOHCAyMii90iaMmUKEydOLHT+1KlTeHiU31k2RqOR5ORkYmJi0GplvLs13aotftyXzG8HrqLVwGsdKuOVe5nzm/4kdMt0AC40G0dashGSo8s48opHfi5KWzCV6j9J5cPfoPzxEmfy/KjrWY2nW1Tim11XeP+vE5B5lU7VPYusLe1hOypCW6Snp5tcVqMoik08pNVoNCxZsoT+/fsXupaXl8eDDz5IfHw869evLzEB+i+j0UjTpk3p0KEDn332WZFliuoBCg0NJTk52az3sjUGg4GYmBhq1qyJTqezdjh2raS2+GX3OcYtOQzApH71eLRlNUi/iHZWBzQZlzA2HYrSZ5o1wq6Q5OfCAowGtPPuQxO3GSWoIcYnVqHonHh3xXHmbI3DSadhzhMtaBXhV6iqtIftqAhtkZqaip+fHykpKbf8/W3zPUB5eXk89NBDxMbG8s8//5idkGi1Wlq0aEF0dPF/OTs7O+Ps7FzovE6nK7ffBDdotdoK8TkqgqLaYt2JJN5cdgSAUZ1rMqh1BBiNsGyEuthhQH20vd4Hab9SJT8XpUyng/u/hZlt0SQeRPfPROj1AePvqc/F1BxWHk5k+Ly9/PZcG2oFFu4JkvawHeW9LcyJ26b7uG4kP9HR0axdu5ZKlSqZfQ9FUdi/fz/BwcEWiFCI23foXAojf9qLwagwoGkVXu5eS72w5RM4vQ4c3eDB2epgUyFsnVcw9J+pHu+YCcf/RKfV8MnAxjQP8yU1W8/Q73fKlhnCZlg1AUpPT2f//v3s378fgNjYWPbv3098fDx6vZ4HHniA3bt389NPP2EwGEhMTCQxMZHc3JvLrQ8ePJhx48blv544cSKrVq3i9OnT7N+/nyeffJL9+/czfPjwsv54QhQrISWLYXN3kZlroF1Nf94f0FCdKRO/A/55Ty3U+yOoXNu6gQphjlrdofUo9XjpCEg5h4ujjm8GN6d6ZXcupGQzbM4uMnP11o1TCKycAO3evZsmTZrkz9AaM2YMTZo04a233uLcuXMsX76cc+fO0bhxY4KDg/O/tm7dmn+P+Ph4EhIS8l9fu3aNZ555hrp169K9e3fOnz/Pxo0badmyZZl/PiGKkpmr56m5u7mUlkOtQA++fLwpTg5ayE6FRU+pi8tFPQiNH7N2qEKYr+vbENIUsq/B4mfAaMDX3Ym5T7SkkrsTRy6k8vIvB2SNIGF1Vh0D1KlTJ0oag23K+Oz169cXeP3JJ5/wySef3GloQliE0agwZuEBjlxIpZK7E98NaYGXi6N68a9xkBIPPmFwzyey3o8onxyc1K1aZraDuC2w/Uto8zyhfm7MHNSMx77ZwcrDiXyy9iQvd5ceTmE9Nj0GSIiKZtraaP46koiTTsvXg5oR6uemXji+AvbPAzRw39fgXPSUYSHKBb+Im/uF/T0Jko4B0CLcjykDogD4/J8Ylu47b60IhZAESIiy8vepNL7acBqAKQOiaB5+fUpwxmX4/QX1uM3zENbaShEKUYqaDobIHmDIUR+F6dWxm/c3q8pznWoAMHbRQfbGX7VmlMKOSQIkRBnYG3+V6VuSAHiuUw3ub1ZVvaAo8PuL16e814POb1gxSiFKkUYD934Grr6QeBA2fpR/6X/da9O9XiC5eiPD5+3jYnqeFQMV9koSICEsLDElm+d+2keeEbrVDeB//x73cHAhHP8DtA5w30xwlE17RQXiGaSOZwPYNBXO7QFAe316fN1gL65k5PLOP4lk5Zbf7RdE+SQJkBAWlKM3MHzeHi6n5xLu68TUBxui1V4f3JxyDlaMVY87vQbBjawXqBCWUv8+dVajYoAlz0JuJgDuzg58O6Q5fu5OnErO5c1lR0ya+CJEaZEESAgLmrD8KPvPXsPb1ZG3Ogfh7nx94qWiwLJRkJMCVZpD25esG6gQltT7I/AMhivR8Pc7+aer+Ljy+cON0Gpg6f4LzNl6xnoxCrsjCZAQFjJ/Rzw/74xHo4HpAxsS4uV48+L++epqzw4u6qwvnc3vSiPE7XP1hX4z1OMdM+HsrvxLd1WvxDMt1FX+3/3zGNtPX7FGhMIOSQIkhAXsibvK28vVDU7/16M2HSIr37yYngSrXlePO40D/5pWiFCIMlbzbmj0KKDA8ufzZ4UB9KvrTf/GIRiMCiN/2suFa1nWi1PYDUmAhChlSanZPDdvD3kGhV4NgniuY42CBVb8T10lN6jhzW0DhLAHPd4DN3+4dAw2T8s/rdFoeLdffepdHxQ9fN4esvNkULSwLJMSoNTUVLO/hLBHeoORUfP3kZSWQ2SABx892Ejd4+uGEyvg6FLQ6NRHAvLoS9gTNz/o/aF6vPFjSDqef8nVScfXg5rh4+bIwXMpvL3siJWCFPbCpATIx8cHX19fk7/8/Pw4ffq0pWMXwuZ8vPokO88k4+HswNeDmuHhfDPB0eamo135P/VFm+dl1pewT/UHQK2eYMxTH4UZb/b0hPq5MeORpmg0sHD3WRbtOWfFQEVFZ/Kfn7/99ht+fn63LKcoCr17976joIQoj/4+dpGZG04B8NEDDale2aPA9coHZqBJSwC/6uq0dyHskUYDfabBmS1wbiea3d+Bb+f8y+0i/RndtRafrD3Jm0sPE1XVm1qBsjWMKH0mJUBhYWF06NCBSpUqmXTT6tWr4+joeOuCQlQQZ5MzGfPLAQCeaBtOr6jgggXituJ7aol63PczcHQt4wiFsCHeVeDut2HFK2j+mYRDz7pAZP7l57vUZHdcMpuiL/PcvD0sH9Xu5hISQpQSkx6BxcbGmpz8ABw+fJjQ0NDbDkqI8iRXb2TU/L2kZOXRKNSHcb3qFiygz0X7p7rOj7HJYIhob4UohbAxzZ+Eaq3R5GUQtOejApe0Wg3TBzYmyMuFU5cyeH3JIVkkUZQ6mQUmxB2avOIYB86l4O3qyBePNsHJ4T8/Vts+R3MlGr2zL8rdE60TpBC2RquFvp+iaB3xuLAZTqwscLmShzOfP9oEnVbDsv0X+HnnWSsFKioqk/sUs7Ky+Pvvv7nnnnsAGDduHDk5OfnXdTodkyZNwsVF9jIS9mPFoYT81WunPdSIqr5uBQtci4cN6l+3SU1eJNDFu4wjFMKGVa6NctdINFuno131GtTsAk43f4ZahPsxtkdtpqw8zoTfj9CwqjcNqsjPkCgdJvcA/fDDD3z99df5r2fMmMHWrVvZt28f+/btY968eXz11VcWCVIIW3Q2OZNXfzsIwPCONehaN7BwoZWvgT4LJawtqWE9yzhCIWyf0v5l8tyC0KSchU0fF7r+dPvq3F03gFy9ked/3kdGjt4KUYqKyOQE6KeffmLYsGEFzs2fP59169axbt06PvroI3755ZdSD1AIW6Q3GHlxwT7ScvQ0rebDy91rFS50chWc+BO0Dhh7fqjOfhFCFOTkzsWm1/fC2/IZXI4ucFmr1fDxg40I9nYh9nIGE5bL+kCidJicAJ08eZJatW7+T97FxQWt9mb1li1bcvTo0dKNTggb9enf0eyNv4answOfPtwER91/fpTystQVnwHueg4C6ha+iRACgPQqHVFqdlPXBlrxirpZ8L/4uDnxycDGaDXw655zLNt/3kqRiorE5AQoJSUFB4ebQ4YuXbpEeHh4/muj0VhgTJAQFdW2U1eYsS4GgMkDogj1cytcaPMncC0OPEOgo6z5I0SJNBqMPd4HnTOcXg9HlhQqclf1Sozqok6Vf3PJYc4mZ5ZxkKKiMTkBqlq1KocPHy72+sGDB6latWqpBCWErbqakctLC/ejKPBQ86r0bRRSuNCVU7B5unrcczI4exQuI4QoyC8C2o9Rj1e9DjlphYq80KUmzcN8ScvR88KCfeQZjGUcpKhITE6AevfuzVtvvUV2dnaha1lZWUycOJE+ffqUanBC2BJFURi76CCJqdlUr+zOhHvrF11w5atgyIHqnaFe/zKNUYhyre1o8I2AtATY8EGhyw46LdMfboyniwP74q8xfe3Jso9RVBgmJ0Cvv/46ycnJ1K5dm48++ohly5axfPlyPvzwQ2rXrs3Vq1d5/fXXLRmrEFY1b3sca45exEmn5bOHm+DmVMQqEidXQ8wa0DpC749l4LMQ5nB0gV7XN0vdPlPtTf2Pqr5uvD+gIQBfrj/F1lOXyzJCUYGYnAAFBgaydetW6taty2uvvcZ9991H//79GTduHPXq1WPz5s0EBhYxDViICuDUpXTeW3EMgLE9axe9FokhT+26B2j1LPjXLMMIhagganWHGwOiV71RZJE+DYN5uEUoigIv/3KAlKy8Mg5SVARmrQQdERHBX3/9xaVLl9i+fTvbt2/n0qVL/PXXX1SvXt1SMQphVXkGI2MW7ic7z0j7SH+GtY0ouuDOb+BKNLj5Q8exZRukEBVJj8mgdYCTKyHm7yKLjL+nHuGV3EhIyZap8eK23NZWGH5+frRs2ZKWLVuatEO8EOXZjH9i8re6+OiBRmi1RTzWyrgCG95Xj7uOB1nxWYjbV7kWtHxGPV71OhgKL37o7uzAtOtT45fsO88fBy+UcZCivDMpARowYACpqakm3/Sxxx4jKSnptoMSwlbsi7+aP+V9Uv8GBHkXs9XLuvcgOwUCo6DJoDKMUIgKquNYcPWDS8dh9/dFFmlazZeRndVHzW8sOUxiSuFJOkIUx6QEaNmyZVy6dInU1NRbfqWkpPD777+Tnp5u6diFsKjMXD1jfjmAwahwb6MQ7i1qyjvAxSOwZ7Z63Ot90OrKLkghKipXX+hyfQzQ+smQmVxksRe6RhJVxZuUrDz+99sB2TVemMykBEhRFGrVqoWvr+8tv/z8/MjIyLB03EJY3OQVx4i9nEGQlwuT+jUoupCiwF+vgWKEuvdCeLuyDVKIiqzpUAioB1lXYf37RRZx1Gn5ZGAjnB20bIq+zI/b48o2RlFumbQb/Lp168y+cZUqVcyuI4StWH8iiXnb4wH4+MFGeLs5Fl3w+J8Qu1Fdwbb7pDKMUAg7oHOAnlPgh36w61to/kSR28rUDPBkXK86TPj9KJNXHKNtTX9qVJYFSEXJTEqAOnbsaOk4hLAZKVl5vLpI3eV9aJtw2kX6F11QnwtrxqvHrUeCb3jZBCiEPaneCWr3UTcWXj0eHv+tyGKDW4fz9/EkNkVf5pVfD/Db8DboipqwIMR1tzULTIiKbNIfR7mYmkOEvzuv9qxTfME9cyD5NLhXvrmEvxCi9HWfpE6Lj1mj7hVWBK1Wwwf3N8TTWV0l+rvNp8s2RlHuSAIkxL+sO57Eb3vOodHARw80xNWpmAHN2Sk3p713eg2cPcsuSCHsTaUa0HyYerx6PBiL3gMsxMeVN+9RH5F9vPokMUkyGUcUTxIgIa5Lycpj3OJDAAxrG0Hz8BLWuNryKWRegUqR0HRIGUUohB3r+Co4eULiQThc9GMwgIeah9KhVmVy9UbG/qbO4hSiKJIACXHdu38cJTE1mwh/d17pXrv4ginnYdsX6nG3iaArZoC0EKL0uPtDu9Hq8d/vQF7Ra/5oNBreHxCFh7MDe+Ov8f3m2LKLUZQrZidAXbp04dq1a4XOp6am0qVLF7PutXHjRvr27UtISAgajYalS5cWuK4oChMmTCAkJARXV1c6derEkSO3XvJ80aJF1KtXD2dnZ+rVq8eSJUvMikvYn3UnkvjVlEdfAOsmgz4bqrWG2r3LLkgh7N1dI8AzBFLOws5ZxRYL8XHlzT43HoWd4NQleRQmCjM7AVq/fj25ubmFzmdnZ7Np0yaz7pWRkUGjRo2YMWNGkdc//PBDpk2bxowZM9i1axdBQUF069aNtLS0Yu+5bds2Bg4cyKBBgzhw4ACDBg3ioYceYseOHWbFJuxHSlYe4xaZ+Ojr4hHY/5N63P1d2e1diLLk5HZzccRNHxe7OCLAwBahtI/0J0dv5H+/yqMwUZjJCdDBgwc5eFCdGnz06NH81wcPHmTfvn189913Zq/906tXL959910GDBhQ6JqiKEyfPp033niDAQMG0KBBA+bOnUtmZibz588v9p7Tp0+nW7dujBs3jjp16jBu3Di6du3K9OnTzYpN2I8pK46Z9ugLYM1bgAL1+kPV5mURnhDi3xo9AgH11YkIm6YWW0yj0fD+/Q3zH4XN3iKPwkRBJq0DBNC4cWM0Gg0ajabIR12urq58/vnnpRZYbGwsiYmJdO/ePf+cs7MzHTt2ZOvWrTz77LNF1tu2bRsvvfRSgXM9evQoMQHKyckhJycn//WNfc8MBgMGg+EOPoV1GQwGjEZjuf4Mlrbt9BUW7DoLwJT76uOko/h/r9Pr0cWsRdE6Yuw8Hsz4d5W2sB3SFrblttqj69vofn4IZecsjM2eBN+wIosFeTrxWs/avLnsCFNXn+TuOpUJ9XMrpcgrnorws2FO7CYnQLGxsSiKQvXq1dm5cyeVK1fOv+bk5ERAQAA6XentgZSYmAhAYGBggfOBgYHExRW/1HliYmKRdW7cryhTpkxh4sSJhc6fOnUKD4/yu5qo0WgkOTmZmJgYtFoZ7/5fOXojY5edA+Ce2l745F0hOvpK0YUVI+Grx6EDrta8j6QrergSbfJ7SVvYDmkL23Jb7aFUIzSwJe4Xd5L++zgSWhf+//cNTX0UogJdOHQxmzE/7+K9bsFo5NF1kSrCz4Y5+5CanACFhakZtrGY9Rcs5b/fqIqi3PKb19w648aNY8yYmwvZpaamEhoaSo0aNfDy8rqNqG2DwWAgJiaGmjVrlmpyWlF8tOoEF9LyCPJy5t2HWuDpUsJsrqNL0V09geLkgfc97+LtXszq0MWQtrAd0ha25bbbw/MD+LYzXnGr8OjxJgTWK7boJ35V6P35FvZeyOJIhjv3NZGtmopSEX42bjzBMYXJCdC/nTp1iunTp3Ps2DE0Gg1169blxRdfpEaNGrdzuyIFBQUBao9OcHBw/vmkpKRCPTz/rfff3p5b1XF2dsbZ2bnQeZ1OV26/CW7QarUV4nOUtiMXUvhm8xkAJvWPwsfdpfjCBj2snwKAps3z6LyK/14qibSF7ZC2sC231R5Vm0K9/miOLkW3/j14dEGxRWsGevFi10g+WnWC91Ycp3OdQCp5FP5/vij/PxvmxG12H9eqVauoV68eO3fupGHDhjRo0IAdO3ZQv3591qxZY+7tihUREUFQUFCBe+bm5rJhwwbatGlTbL3WrVsXimP16tUl1hH2RW8w8tqiQxiMCr2jguhW7xYJzYH56uMut0rqnl9CCNvQ5U3Q6ODkSogveabvMx2qUyfIk6uZeUz642gZBShsmdk9QK+99hovvfQS77//fqHzr776Kt26dTP5Xunp6cTExOS/jo2NZf/+/fj5+VGtWjVGjx7N5MmTiYyMJDIyksmTJ+Pm5sajjz6aX2fw4MFUqVKFKVPUv9BffPFFOnTowAcffEC/fv1YtmwZa9euZfPmzeZ+VFFBzd5yhkPnU/BycWDCvfVLLpyXDeuvf6+3f1m2vBDClvhHQuNHYd+P6uKIQ/8odmkKR52W9+9vyIAvt7B0/wX6NalC59oBZRywsCVm9wAdO3aMJ598stD5YcOGcfSoeVn17t27adKkCU2aNAFgzJgxNGnShLfeeguAsWPHMnr0aEaMGEHz5s05f/48q1evxtPz5i+h+Ph4EhIS8l+3adOGBQsWMHv2bBo2bMicOXNYuHAhrVq1Mvejigoo/komU9ecAOCNPnUJ8Czh0RfA7u8g9Tx4VYXmhb/vhRBW1uk10DlD3GY49XeJRRuH+vBE2wgA3lxymIwcfVlEKGyU2T1AlStXZv/+/URGRhY4v3//fgICzMumO3XqhKIUvziVRqNhwoQJTJgwodgy69evL3TugQce4IEHHjArFlHxKYrCm8sOk51npHX1SjzUPLTkCtmpsPFj9bjTq+B4i2RJCFH2vKtCi6dg+xdqL1D1LlDCDKaXu9di1ZFEzl3NYtqak4y/p/jB06JiM7sH6Omnn+aZZ57hgw8+YNOmTWzevJn333+fZ599lmeeecYSMQpRKv48lMDGk5dw0ml5774Gt54Ku+0LyEpWNzxt9GjJZYUQ1tN+DDh5QMIBOLasxKJuTg5M6t8AgDlbz3D0gumzhkTFYnYCNH78eN566y0+//xzOnbsSIcOHZgxYwYTJkzgjTfesESMQtyxtOw83vldfUT7XKcaVK98i/WdMi7DtutbtHR5A3S3NWFSCFEW3P2h9Sj1+J/31JmbJehcO4DeUUEYjApvLD2EUbbJsEtmJ0AajYaXXnqJc+fOkZKSQkpKCufOnePFF1+UxaWEzZq6+iRJaTmEV3LjuU4mLNew+RPITYfgRlC3n+UDFELcmdYjwdVPnbF5oPjtkm546576uDvp2Bd/LX81eGFf7mg3eE9Pz/wBybezG7wQZeHw+RR+2HYGgEn9G+DieIt1IlITYNe36nGXt0ocTyCEsBEuXupMTYANH4G+8Kbd/xbk7cLL1/f+++Cv41xOzymxvKh4rLobvBCWZjAqvLHkEEYF+jYKoX1k5VtX2jwN9NkQehfU7Gr5IIUQpaPFk+ARBCnxsO+HWxYf3DqMesFepGTlMXnFsTIIUNgSq+4GL4Slzd8Rx4FzKXg6OzC+T91bV7h2FvbMUY+7vFHsmiJCCBvk6HqzF2jjVHUdrxI45E+IgMV7z7PtVDF7AYoKyWZ3gxfiTiWlZfPhKnXNn1d61CbAy4Rp7Js+BkMuhLeHiA4WjlAIUeqaDYEt09X1u/bMgbuGl1i8STVfHmtVjXnb43lz6SFWvtgBJwd57G0PTG7l2NhYTp06haIo7Ny5k9jY2Pyv8+fPk5qayrBhwywZqxBmeX/FcdKy9URV8ebxu8JuXSE5FvbNU487y4xGIcolB2fo8Ip6vGkq5Gbessr/etTB38OJU5cy+HbzaQsHKGyFyQlQWFgY4eHhGI1GmjdvTlhYWP5XcHBwoQ3I+vTpU2CFZiHK0q4zySzedx6NBt7t3wCd1oRHWRs/AqMeanSBsNaWD1IIYRmNHwefapCRdHNCQwm8XR15vbf6iHzGPzEkpGRZOkJhAyzWz7dx40aysuSbSJQ9g1HhrWVHABjYPJRGoT63rnTlFBz4WT3u/KblghNCWJ6DE3R8VT3eMh1y0m5Z5b4mVWge5ktmroHJK45bNj5hE+RBp6hw5u+I41hCKl4uDvyvR23TKq1/HxQj1OoJVZtZNkAhhOU1fBj8qkPmFdjx9S2LazQaJvarj1YDvx+4IAOi7YAkQKJCuZKew0f/GvhcycP51pWSjsOhX9XjTuMsGJ0QoszoHKDja+rx1s8hO+WWVeqHePNYK3W84ITlR8gzGC0ZobAySYBEhfLx6hOkZuupG+zFoy2rmVZpwweAAnXugZDGlgxPCFGWoh4A/9qQfQ22zzSpysvda+Hr5siJi2n8uC3OsvEJq5IESFQYB87eXNL+nX71cdCZ8O196SQcWaIed3rNgtEJIcqcVgedro8F2v4FZN9641MfNyf+16MOAJ+sOcmlNFkhuqKSBEhUCEajwlvLj6Ao6mDGFuF+plXcNBVQoHYfCIqyaIxCCCuo1x/8a6mPwHbeeiwQwMAWoURV8SYtR88Hf8mA6IrKYgnQ66+/jp+fib+EhLhDv+09x4Gz13B30jGuVx3TKl05dXPsT8f/WS44IYT1aHXQYax6vO0Lk2aE6bTqgGiA3/acY2/8VUtGKKzE7AToypWbI+PPnj3LW2+9xf/+979C+4CNGzcOHx+fOw5QiFtJz9Hz4V/qwOcXukaatuIzqHt+KQao2Q1CmlgwQiGEVTUYAJVqQtZVk9YFAmhazZcHmlUF4J3fj2I0KpaMUFiByQnQoUOHCA8PJyAggDp16rB//35atGjBJ598wqxZs+jcuTNLly61YKhCFO2LdTFcTs8hvJIbT7SNMK3S1Tg4sEA97jjWcsEJIaxPq4P211eH3vo55KSbVG1sj9q4O+nYf/Yayw6ct2CAwhpMToDGjh1LVFQUGzZsoFOnTtxzzz307t2blJQUrl69yrPPPsv7779vyViFKCT+SibfbYoF4I0+9Uzfw2fLdHXV5+qdILSlxeITQtiIqAfBN0JdF2j39yZVCfByYUTnmgB8sPIEmbl6S0YoypjJCdCuXbt47733aNeuHR9//DEXLlxgxIgRaLVatFotzz//PMePy2AxUbamrDxGrsFI25qVuLtugGmVUi/c3POrg/T+CGEXdA439wjb+plJe4QBPNkugqq+riSmZjNzg+wTVpGYnAAlJycTFBQEgIeHB+7u7gUGOfv6+pKWduvBZUKUlu2nr7DycCJaDYy/px4ajQn7fQFs+VTd8T2sLYS3tWyQQgjb0XAg+IRBxiXYM9ukKi6Ouvx9wr7ecIrz12SLp4rCrEHQ//0FY/IvHCFKmcGo8M7vRwF4tFU16gR5mVYx7SLsmaMed5CZX0LYFZ0jtH9ZPd7yKeSZlsz0ahBEywg/cvRGPlgpTzoqCgdzCg8dOhRnZ3VrgezsbIYPH467uzsAOTmyWJQoO7/uPsvRhFQ8XRx46e5aplfc9jnos6FqC3X8jxDCvjR6BDZ+BClnYc9cuGv4LatoNBreuqcefWdsZvmBCwxpE0azMFnmpbwzuQdoyJAhBAQE4O3tjbe3N48//jghISH5rwMCAhg8eLAlYxUCgLTsPD5erU57f7FrpGn7fQFkXIFd1wc/dhgL0oMphP1xcIL2Y9TjLdMhL9ukag2qePNQs1AAJsq0+ArB5B6g2bNNe14qhKV9uf4Ul9Nzqe7vzuDW4aZX3P4F5GVAcGOI7Gap8IQQtq7xY7DxY0g9D/t+hJZPm1TtlR61+fNQAgfPpbB0/3kGNK1q4UCFJclWGKJcOX8ti+82q9Pex/Wua/q098xk2DFLPe7wP+n9EcKeOThDu5fU482fgN60IRyVPZ0Z0bkGAB+vOkF2nsFSEYoyIAmQKFemrjpBrt5Iqwg/06e9A+z4GnLTIKA+1O5tuQCFEOVDk0HgGaz2Au3/yeRqw9pGEOLtwoWUbGZvOWO5+ITFSQIkyo3D51NYvE9djfWNPnVNn4WYnQo7vlKPO7wCWvm2F8LuObpA29Hq8aZpoM81qZqLo45XetQG4Mt1MVxJlwlA5ZX8JhDlgqIovPfnMQD6Nw6hYVUf0yvvnKXuBO1fC+r1s0yAQojyp9kQ8AhUZ4Qd+Nnkav0bV6F+iBdpOXo++zvaggEKS5IESJQL604kse30FZwctPl/fZkkJ13dARrUsT9anWUCFEKUP46u0PZF9XjTVDDkmVRNq9XwxvXFEX/aEc/pS6btLSZsiyRAwubpDUYmr1AXH3uibThVfd1Mr7z7e8hKBr/qUH+AhSIUQpRbzZ4A98pwLQ4OLjS5Wpua/nSpE4DeqPDBX7I4YnkkCZCweb/sPkdMUjq+bo6M6FTT9Ip5WerOz6Cu/qoza91PIYQ9cHKDNi+oxxs/BoPpG56O61UHrQZWHbnIrjPJFgpQWIokQMKmpefombbmJAAvdI3E29XR9Mp75kJGEvhUU/cAEkKIojQfBm6V4GosHP7N5GqRgZ4MbFENgPf+PIaiyOKI5YkkQMKmfbPxNJfTcwiv5MZjrcJMr6jPUff6AXW9D50ZiZMQwr44e0DrUerxxo/BaPr6Pi91i8TNScf+s9dYcSjRQgEKS5AESNisy+k5fLvpNAD/61HH9EUPAfbNg7QL4FVFXfVVCCFK0uIpcPGBK9FwdKnJ1QI8XXi6fXUAPl59gjyD0TLxiVJn8wlQeHg4Go2m0NfIkSOLLL9+/foiyx8/LoPUypsZ/8SQkWugYVVvekcFmV5Rn6uu7grqDA8HE/cKE0LYLxcvuOs59Xjjx2A0PZF5ukN1Krk7EXs5g192n7VQgKK02XwCtGvXLhISEvK/1qxZA8CDDz5YYr0TJ04UqBcZGVkW4YpSEn8lk592xAHwas86pi96CHBwgbquh3sANJUNeoUQJmr1LDh5QtJROPGnydU8nB0Y1UWdoPHp2miycmWLjPLA5hOgypUrExQUlP/1xx9/UKNGDTp27FhivYCAgAL1dDpZ/6U8mbbmBHkGhfaR/rSt6W96RYNeXc8DoO0L6jofQghhCldfaPWMerzxIzBjUPOjrapR1deVpLQcvt8Sa6EARWkqV/OCc3NzmTdvHmPGjLllj0CTJk3Izs6mXr16vPnmm3Tu3LnYsjk5OeTk3FzOPDU1FQCDwYDBUH4zeYPBgNFoLHef4VhCKssOXADglW6RZsWvOfgL2qtnUNwqYWwyBGzks5fXtqiIpC1si821R8vhaLfPRJNwAMOJVRDZzaRqDhp46e5IXv71IDM3nOLh5lXwcXOycLCly+ba4jaYE3u5SoCWLl3KtWvXGDp0aLFlgoODmTVrFs2aNSMnJ4cff/yRrl27sn79ejp06FBknSlTpjBx4sRC50+dOoWHh0dphV/mjEYjycnJxMTEoC1H+19NXJOAokDHcA+cM5OIjk4yraLRQMQ/7+MMXKr5EMlxFywapznKa1tURNIWtsUW26Nyjf5UOv4TuWsmEaeEgYmP4Ou6KUT4OhF7NZfJS/fwdAszeq9tgC22hbnS001flVujlKOFC3r06IGTkxO///67WfX69u2LRqNh+fLlRV4vqgcoNDSU5ORkvLy87ihmazIYDMTExFCzZs1y8whwR2wyj367EwethlWj2xFeyd3kupoji9EufgrFxQfjC/vB2Xbarjy2RUUlbWFbbLI90i+i/bwJGn02hscWQ/VOJlddd+IST/2wBycHLX+/1J4Qn/LzGN4m28JMqamp+Pn5kZKScsvf3+WmByguLo61a9eyePFis+veddddzJs3r9jrzs7OODsXnimk0+nK7TfBDVqtttx8DkVR+HCVuujhwy1DqRFgRgJjNMLmaQBo7hqBzs3XEiHekfLUFhWdtIVtsbn28A6BpkNg59foNk+FyK4mV+1aN5CWEX7sjE3m83Wn+PCBRhYMtPTZXFuYyZy4y00f1+zZswkICKBPnz5m1923bx/BwcEWiEqUptVHL7L/7DVcHXW80NXMWXsn/lRnbjh7qTM5hBDiTrR9EXROELcFzmwxuZpGo+G1XnUA+G3POWKS0iwVobhD5SIBMhqNzJ49myFDhuDgULDTaty4cQwefHOq8/Tp01m6dCnR0dEcOXKEcePGsWjRIkaNGlXWYQszGIwK01arvT/D2oUT4OliemVFgQ0fqsctnwFXn9IPUAhhX7z/tYjqxo/Mqtq0mi/d6gViVOCTNdEWCE6UhnKRAK1du5b4+HiGDRtW6FpCQgLx8fH5r3Nzc3nllVdo2LAh7du3Z/Pmzfz5558MGCA7gduyPw5e4MTFNDxdHHimfQ3zKkevhsSD4OgOd42wTIBCCPvT7iXQOsDpdXBut1lVX+5eC40G/jyUwJELKRYKUNyJcpEAde/eHUVRqFWrVqFrc+bMYf369fmvx44dS0xMDFlZWSQnJ7Np0yZ69+5dhtEKc+kNRqavVf9KerZDdbzdzNi3S1FgwwfqcYsnwb2SBSIUQtgl3zBo+LB6fKOX2UR1grzo2zAEIL93W9iWcpEAiYpt0d5zxF7OwM/diSfaRphX+dQ/cH4POLhCm+ctE6AQwn61HwMaLUSvggv7zao6+u5IdFoNfx9PYm/8VcvEJ26bJEDCqnL0Bj77OwaAEZ1q4O5sxsRERbn5bL7ZUPAIKP0AhRD2rVINaHC/emzmWKDqlT24v2kVAKauPlHakYk7JAmQsKoFO89y/loWgV7OPH5XmHmVz2yG+G3qTI22L1gmQCGEaP8KoIHjf8DFo2ZVfaFrJI46DVtirrD11GXLxCduiyRAwmqycg3MWKf2/ozqEomLo5nrTmy8/ky+ySDwCinl6IQQ4rqAOlDvXvV408dmVa3q68YjLasBMHX1ScrR2sMVniRAwmp+2HaGS2k5VPV1ZWDzUPMqx++A2I2gdVRnagghhCV1+J/638OL4bJ5U9tHda6Ji6OWPXFXWX/ikgWCE7dDEiBhFWnZeczccAqA0XfXwsnBzG/FG70/jR8BHzOTJyGEMFdQFNTqBSiwaapZVQO8XBjSOhyAj1efkF4gGyEJkLCKOVvOcDUzj+qV3enf2MzHV+f3QMxa0Oig3RjLBCiEEP/V8Xov0MFfIDnWrKrPdqyBh7MDRy6ksurIRQsEJ8wlCZAoc6nZeXyz6TSg9v446Mzt/bn+DL7hQ+Bn5rR5IYS4XVWaQY2uoBjy9x40lbrMRzgA09eexGiUXiBrkwRIlLk5W86Qmq2nZoAHfaLM3KMt4SCcWAFooP3LFolPCCGK1XGs+t/9P8O1s2ZVfbJdBJ7ODhxPTGP10UQLBCfMIQmQKFMpWXl8e73358Wu6iJhZrmxDkeDAeBv5oapQghxp6rdBeHtwZgHWz41q6qP2797gaKlF8jKJAESZepG709kgAe9ze39SToGx5arx+1fKf3ghBDCFDd6gfb+AGnm9eQ82a56fi/QqiPSC2RNkgCJMpOSlce3m6/3/tx9O70/18f+1O0LgfVKOTohhDBReHsIbQWGHNjymVlVvd0ceaKdOnZReoGsSxIgUWZmb4klLVtPrUAPejcws/fncgwcWawe31iPQwghrEGjgQ7Xe4F2fw/p5q3t82TbCDxdHDhxMY2/pBfIaiQBEmUiJSuP7zar00Zf7FoLrbm9P5umgmKEWj0huJEFIhRCCDPU7AohTUCfBdu/MKuqt5sjw65v/Pyp9AJZjSRAokx8v1nt/akd6EmvBkHmVU6OhYML1eMbf3UJIYQ1/bsXaOc3kJlsVvVh7W72Aq08LL1A1iAJkLC4lKw8vt9yvffn7kjze382f6Kuu1GjK1RtZoEIhRDiNtTuBYFRkJsOO2aaVdXb1ZEnr48F+vRvWRfIGiQBEhZ3Y+xP7UBPetY3s/fn2lnYP1897ii9P0IIG6LRQIfr65FtnwnZKWZVf+L6WKCTF9NlLJAVSAIkLCotO4/ZW84A8HzXmub3/myZrq63Ed5eXX9DCCFsSd1+4F8bclJg5yyzqnq7OvLE9bFAn/8TI3uElTFJgIRF/bg9jpSsPGpUdqeXuTO/Us6p62wAdHy19IMTQog7pdVCh+vrkm37EnLSzao+rG047k46jiWk8vexJAsEKIojCZCwmMxcPd9uUsf+jOxc0/x1fzZ/AoZctfcnor0FIhRCiFJQfwD4VYesZHVavBl83JwYdH2n+M//iZZeoDIkCZCwmPk74knOyCWskhv3NjJzx3fp/RFClBc6h5t7E279HPKyzKr+VPsIXBy1HDiXwsboyxYIUBRFEiBhEdl5BmZtVFd9HtGphvk7vkvvjxCiPGk4ELyrQUYS7JlrVlV/D2ceaxUGwOd/Sy9QWZEESFjEL7vPkpSWQxUfV+5rUtW8ytL7I4Qob3SO0G60erzlU9DnmFX9mQ7VcXLQsjvuKttOXyn9+EQhkgCJUperNzJz/SkAhndUf6jNIr0/QojyqMnj4BkCaRdg3zyzqgZ6uTCweSgAM/6JsUR04j8kARKlbvHec1xIySbA05kHr/9Am0x6f4QQ5ZWDM7R9UT3ePB0MeWZVH96pBo46DVtPXWFPnHkrSwvzSQIkSpXeYOTL670/z3asgYujzrwbSO+PEKI8azYE3AMgJf7mFj4mquLjyv1N1SEDn/0tvUCWJgmQKFW/H7xAfHImldydeLRlNfMqS++PEKK8c3SFNs+rx5umgkFvVvURndQlQzacvMShc+atLC3MIwmQKDVGo8JX13t/hrWLwNVJen+EEHao+TBw9YPk03D4N7OqVqvkRt+G6qKxX22QXiBLkgRIlJq/jydx8mI6ns4ODGodZl5l6f0RQlQUzh43e4E2fGB2L9BznWoCsPJwIqcumbeytDCdJECiVCiKwhfr1L9WBrUOw8vF0bwbSO+PEKIiafkMuFVSe4HMHAtUO8iTu+sGoijw9YZTFgpQSAIkSsW201fYf/Yazg5ahrWLMK+y9P4IISoaZ4+bM8I2fmj2jLARnWsAsHjveS5cM29laWEaSYBEqbgx9mdgi1D8PZzNqyy9P0KIiqjFU+BeGa6egQM/m1W1aTVf7qruh96o8M2m05aJz85JAiTu2KFzKWyKvoxOq+Hp9tXNqyy9P0KIisrJHdqOVo83fgT6XLOqj7g+FmjBzrMkZ5hXV9yaJEDijn25Xh37069RCKF+buZVlt4fIURF1nyYui7QtXjY/5NZVdtH+tOgihdZeQbmbIm1UID2SxIgcUdiktL560gioK5iahbp/RFCVHRObtB+jHq8aapZe4RpNJr8XqA5W8+QnmPebDJRMptPgCZMmIBGoynwFRQUVGKdDRs20KxZM1xcXKhevTozZ84so2jtz9cbTqEo0K1eILUCPc2rLL0/Qgh70GwoeARBylnY96NZVXvUD6K6vzup2Xrm74izTHx2yuYTIID69euTkJCQ/3Xo0KFiy8bGxtK7d2/at2/Pvn37eP3113nhhRdYtGhRGUZsHy5cy2LJvvMAjJDeHyGEKJqj681eoI1TIS/b5Ko6rYbhHdX/v367KZYcvcESEdqlcpEAOTg4EBQUlP9VuXLlYsvOnDmTatWqMX36dOrWrctTTz3FsGHD+Pjjj8swYvvw/eZY9EaFu6r70aSar3mVN36k9v6EtZPeHyFExdd0yM2d4vfMNqtq/yZVCPJyISkth6XX/+gUd87B2gGYIjo6mpCQEJydnWnVqhWTJ0+mevWiZxtt27aN7t27FzjXo0cPvvvuO/Ly8nB0LLxAX05ODjk5N5/LpqamAmAwGDAYym+2bTAYMBqNFvkMqVl5/LwzHoBn2keY9x7Jp9Hu/RENYOj0OpTjf2NTWbIthHmkLWyL3bSH1hFN+1fQrhiDsmkqxkaPgpOHSVV1GniibRhTVp5g1sbTDGgcglarKfUQK0JbmBO7zSdArVq14ocffqBWrVpcvHiRd999lzZt2nDkyBEqVapUqHxiYiKBgYEFzgUGBqLX67l8+TLBwcGF6kyZMoWJEycWOn/q1Ck8PEz7BrVFRqOR5ORkYmJi0GpLt7Nv4cGrZOQaCPd1IpirREdfM7lu8La38VYMpAe35lxOJYiOLtXYbJEl20KYR9rCtthVe3i0orp7FZwyzpO8YgpX6g81uWpzXyNujlpOXcpg3roDtK7mXurhVYS2SE83fesQm0+AevXqlX8cFRVF69atqVGjBnPnzmXMmDFF1tFoCmbGiqIUef6GcePGFbhXamoqoaGh1KhRAy8vrzv9CFZjMBiIiYmhZs2a6HRmbkxaghy9kT9+2wDAqK61qVWriumVk46ijVsFgGufyUQGR5ZaXLbMUm0hzCdtYVvsrT00uW/B0mfxj56PX4//gauPyXUHnYOvN8byx6lsBndtXOqxVYS2uPEExxQ2nwD9l7u7O1FRUUQX02sQFBREYmJigXNJSUk4ODgU2WME4OzsjLNz4dWLdTpduf0muEGr1Zb65/h973kupecQ4u1CvyZV0enM+Ethw/uAAvX6oavatNRiKg8s0Rbi9khb2Ba7ao+GD8KW6WguHUO34wvo+pbJVYe1q87sLXHsibvG/nMpNAvzK/XwyntbmBN3uevjysnJ4dixY0U+ygJo3bo1a9asKXBu9erVNG/evMjxP8I8RqPC1xvVZdmHtYvA0Zzk59weOP4HaLTQ+Q0LRSiEEDZMq4Mub6rH22dCepLJVQO9XLividrj/vUG2R7jTtl8AvTKK6+wYcMGYmNj2bFjBw888ACpqakMGTIEUB9fDR48OL/88OHDiYuLY8yYMRw7dozvv/+e7777jldeecVaH6FCWXvsIqcvZeDp4sDDLauZV/mfd9T/NnwYKtcu/eCEEKI8qNMHQppCXgZsmmZW1ac7qJtNrzl2kZgk08e7iMJsPgE6d+4cjzzyCLVr12bAgAE4OTmxfft2wsLCAEhISCA+Pj6/fEREBCtWrGD9+vU0btyYSZMm8dlnn3H//fdb6yNUKDd6fwbdFYaHsxlPUGM3wun1oHWETrLujxDCjmk00HW8erz7O7h21uSqNQM8ubtuIIoC38omqXfE5scALViwoMTrc+bMKXSuY8eO7N2710IR2a/dZ5LZE3cVJ52WoW3DTa+oKPD3JPW42RDwNaOuEEJURNU7q+ugxW2GjR/CvZ+bXHV4x+qsPXaRxXvPM6Z7LQI8XSwYaMVl8z1Awnbc6P0Z0LSKeT9wJ/+CczvBwRU6/M9C0QkhRDny716gfT/BZdOXA2ke7kfTaj7kGozM2XLGMvHZAUmAhElOXUpnzdGLaDTwdIeiF6EsktEAayeox62eAc+S93ETQgi7Ue0uqNUTFAP8/Y5ZVZ+9vj3Gj9vjyJBNUm+LJEDCJN9tjgWga51AalQ2Y3HI/fPh0nFw8YF2L1kmOCGEKK+6vq3OjD22HM7uMrlat7qBRPi7k5at55fdpo8hEjdJAiRuKTkjl0V7zgHwdPsI0yvmZsK6yepxh1fA1cz9woQQoqILrAeNHlWP17yljpk0gVarYVg79f/H32+JxWA0rZ64SRIgcUvztseRozfSsKo3LSPMWHhrx0x14z/vUGjxtOUCFEKI8qzz6+DgAvFb1TGTJnqgaVV83Bw5m5zFmqOJt64gCpAESJQoO8/AD9vOAPBku4hitxMpJDMZNk9Xj7u8CY4yS0EIIYrkXQVaDVeP105Qx06awNVJx+Ot1CVhvtkUa6HgKi5JgESJlh+4wOX0XIK9XegdVfTq20XaNBVyUiAwCqIeslyAQghREbR7SR0mcOm4OnbSRIPbhOGk07In7ip7469aMMCKRxIgUSxFUfju+l8VT7QNN33bi6txsHOWetxtApTTXYWFEKLMuPpA++s7FqybrI6hNEGApwv3Ng4Bbk5WEaaR30yiWJuiL3PiYhruTjoGtjBj24t174EhFyI6QI2ulgtQCCEqkhZPqWMm0y6oYyhN9NT1ySkrDyVwNtm0xElIAiRK8M31ZdYHtqiGt6uJG8kmHICDv6jH3d5RF/sSQghxa44uNzdK3TwdMq6YVK1OkBftI/0xKjBn6xmLhVfRSAIkinQiMY1N0ZfRatTHXyZRFPjrdUCBBg9ASBNLhiiEEBVP1EMQFKWOoVw/xeRqT7VXF6hduOssqdl5loquQpEESBTpxiZ7PRsEEernZlql43+o+9o4uMDdEywXnBBCVFRaLfS4nvjs/h6SjplUrUOkP7UCPUjP0bNwpyyMaApJgEQhl9JyWLb/AnDzr4pb0ufA6utdt22eB59QC0UnhBAVXER7qHOPukXGqjdMqqLRaHiqnfr/69lbYtEbjJaMsEKQBEgUMm97HLkGI02q+dC0momrN+/4Gq6eAY8gaDvakuEJIUTF1+0d0DrCqb8heo1JVe5tHIK/hxMXUrJZdeSihQMs/yQBEgXk6A38tCMOgGFtTdz2Iv0SbPxIPe46HpzN2CtMCCFEYZVqwF3XF0dc9ToYbj2ux8VRx6PXF0acvUWmxN+KJECigN8PJHA5PZcgLxd6NjBx5/b1kyEnFYIa3tzTRgghxJ3p8D9w84fLJ2H3bJOqPH5XNRx1GnbHXeXguWuWja+ckwRI5FMUJf+vhsFtwkxb+PDiEdgzRz3u+b4seiiEEKXFxVvdJwzUPzSzbr3Sc4CnC/c0VBdGnL3ljAWDK//kt5XIt+vMVY5cSMXFUcsjpix8qChq16xihLr3QnhbywcphBD2pOkQCKinJj8bPjSpyo3hC38cvEBSarYloyvXJAES+b6/voz6fU2q4uvudOsKJ1bA6fWgc1IH7AkhhChdOgfo8Z56vHMWJB2/ZZWoqt40D/Mlz6Awb3uchQMsvyQBEgCcTc5k9dFEwMSFD3MzYeVr6nHrkeBn4oBpIYQQ5qnRBWr3BqMeVv5P7X2/hSeu9wL9tCOe7DzTdpe3N5IACQB+2HYGowLtavpTK9Dz1hU2fwIp8eBVVR2oJ4QQwnJ6TlEXmY3dCEcW37J4j/qBhHi7cCUjl+UHLpRBgOWPJECCjBw9C3apK4cOaxd+6wpXTsGW6epxz8ng5G6x2IQQQgC+4dD+ZfV41RuQk1ZicQedlsFtwgF1MLRiQq+RvZEESLBo7znSsvVE+LvTqVZAyYUVBVa+qu72XqOLOvhZCCGE5bV5AXwjIC3BpAHRD7cIxcVRy7GEVLafTi6DAMsXSYDsnNGoMOf6VMmhbcLRam+xe/uJFRCzRl2htNdHstu7EEKUFUcX6PWBerz9y1sOiPZxc+L+plUBWRixKJIA2bmN0Zc4fTkDT2cH7m9WteTC/x743OZ58K9p+QCFEELcVKsH1O6jDohe8cotB0TfmNSy9thFziZnlkGA5YckQHbuh23qFMkHmlfFw9mh5MIFBj6/UgbRCSGEKOTGgOgzm245ILpmgCftavpjVGDeDpkS/2+SANmxuCsZrDuRBMDg1uElF74c86+Bz1Nk4LMQQliLb1jBAdHZKSUWH3J9MPTCXWdlSvy/SAJkx37cFoeiQMdalYnwLyGhMRrh9xfVgc8174a6fcsuSCGEEIW1eQH8qqsDotdOLLFolzoBVPV15VpmHsv3y5T4GyQBslOZuXp+2a1OfR96/a+DYu37EeI2g6Mb9JkmA5+FEMLaHF2g76fq8e7vIH57sUV1Wg2D7lJ3iZ+zVabE3yAJkJ1auu8Cqdl6wiq50bFW5eILpiXC6vHqcZc31a5XIYQQ1hfRAZoMUo+XPw/6nGKLPtQ8FGcHLUcTUtkTd+tNVe2BJEB2SFEU5m49A8Cgu8JKnvq+4n+QkwIhTaDV8LIJUAghhGm6TwL3ALh8EjZNLbaYr7sT/RtXAdReICEJkF3aEZvMiYtpuDrqeLB5aPEFj/0Bx5aDRgf3fg5aXdkFKYQQ4tZcfaH39UURN02DpGPFFh3cRu3B/+twIhdll3hJgOzRjd6f+5pWwdvVsehC2SnqGhMAbV+AoKiyCU4IIYR56vWHWr3AmAfLX1AnrhShfog3LcJ90RsVftoRX7Yx2iBJgOzMhWtZrD56EYAhJU19XztRnV3gVx06vlo2wQkhhDCfRgN9poKTJ5zbqQ6KLsaNKfHzd8STqy86UbIXkgDZmZ92xGEwKtxV3Y/aQcXs+h678eYPUN9PwdG17AIUQghhPu8qcPfb6vHaCXD1TJHFetQPItDLmcvpOaw8nFBm4dkim0+ApkyZQosWLfD09CQgIID+/ftz4sSJEuusX78ejUZT6Ov48ZL3TanocvQGFuy8xdT37FRYOlI9bjpEnWUghBDC9jV/Eqq1gdx0WDqiyEdhjjotj7W6OSXentl8ArRhwwZGjhzJ9u3bWbNmDXq9nu7du5ORkXHLuidOnCAhISH/KzIysgwitl0rDyVyJSOXYG8X7q4bWHShVePU7S58wqDHe2UboBBCiNun1UL/L8HRHeK2qBumFuHhlqE46jTsi7/G4fMlryJdkdl8AvTXX38xdOhQ6tevT6NGjZg9ezbx8fHs2bPnlnUDAgIICgrK/9Lp7HsW04/b1X1gHm1ZDQddEU1/YiXsmwdo4L6Z4FzMIzIhhBC2yS/i5h+vf79T5KywAE8XejYIBtRhEfbK5hOg/0pJUbNVPz+/W5Zt0qQJwcHBdO3alXXr1lk6NJt29IK6+JWDVsPAlkVMfc+4os4eAGg9EsLalG2AQgghSkezoVCzGxhyYMmzYMgrVOTGytBL910gJavwdXtwi+2/bYuiKIwZM4Z27drRoEGDYssFBwcza9YsmjVrRk5ODj/++CNdu3Zl/fr1dOhQeExLTk4OOTk3V9BMTU0FwGAwYDCU343jDAYDRqMRg8HAj9vOANC9fiCV3BwLfi5FQfvHaDQZSSj+tTF2eh3K8ee2Rf9uC2Fd0ha2RdrDQu6ZjnZmWzQJBzBu+BCl42sFLjcN9SIywIPopHR+2x3P0DbhFaItzIldo5SjTUFGjhzJn3/+yebNm6latapZdfv27YtGo2H58uWFrk2YMIGJEwtvJrdr1y48PDxuO15rMxqNJCcn4+zhw6Df4snWK3zYM4SGQQVndXmdWUXI9rdQNDrOdPueHL86Voq44rrRFn5+fmi15a7jtUKRtrAt0h6W4xm3mirbxqNodMTd/S3ZleoVuP778RS+2H6ZUG9HZvUPRVGUct8W6enptGjRgpSUFLy8vEosW256gJ5//nmWL1/Oxo0bzU5+AO666y7mzZtX5LVx48YxZsyY/NepqamEhoZSo0aNW/4D2jKDwUBMTAzbrjiRrVeIDPBgQLsoNP/ezDTlHNql6vLpSoexVGslO71bwo22qFmzpt2PRbM2aQvbIu1hQZGRGFP2oj26hLC9kzE+9Q84uedffjpUz5y96zibkscVh0q0DPMp921x4wmOKWw+AVIUheeff54lS5awfv16IiIibus++/btIzg4uMhrzs7OODs7Fzqv0+nK7TfBDRqNhp93ngNgUOswHBz+1eSGPFjytLrqc5VmaDu8AuX889oyrVZbIb6nKgJpC9si7WFB90yDs9vRXIlGt+o1dZbYdT7uOu5rWoV52+OZv/MsratXKvdtYU7cNt/HNXLkSObNm8f8+fPx9PQkMTGRxMREsrKy8suMGzeOwYMH57+ePn06S5cuJTo6miNHjjBu3DgWLVrEqFGjrPERrOrQxWxiLmXg5qTjviZVCl5cNxnO7gBnb3jge9DZfD4shBDCHG5+cP+3oNHC/p/gwMIClx+/Phh61ZGLdrc/mM0nQF999RUpKSl06tSJ4ODg/K+FC282YkJCAvHxN/c1yc3N5ZVXXqFhw4a0b9+ezZs38+effzJgwABrfASr+uO4Omuuf5MqeLr8a9+vmL9h8yfq8b2fgW942QcnhBDC8sLb3dzS6I+X4HJM/qU6QV60CPfFYFRYuOuclQK0Dpv/k9+UMdpz5swp8Hrs2LGMHTvWQhGVH0mp2WyJUxeMfPz6yp8ApF1Up0aiQPNhUL+/VeITQghRRjr8D85shjOb4Leh8ORacHQB1F6gXWeusmDXWbpVrVLyfSoQm+8BErfvlz3nMSjQLMyHeiHXB3MbDbD4aci4BIENoMdk6wYphBDC8rQ6GPANuFWCxEOwZnz+pZ4NgvD3cOJiWg7bz956l4WKQhKgCkpvMPLzLnXfr8daVrt5YfM0iN0Ajm7wwGzZ6FQIIeyFVzDc97V6vHMWHFWXhXF20PFQc3WB3D+Omz6LqryTBKiCWnfiEokp2Xg7a+nZIEg9eeofdeAzQJ+pULmW9QIUQghR9iK7QZvrq/4vGwmXowF4tFU1NBrYn5BF7GX76AWSBKiCmn99f5dukV44O2ghORZ+fQIUIzR+HBo/auUIhRBCWEXXtyD0LshJhQWPQnYqVX3d6BhZGYAF158eVHSSAFVA565msv7kJQB61/KC3AxY8BhkX4MqzdTeHyGEEPZJ5wgP/QCeIXD5JCwZDkYjj1zfJ3Lx3vPk6MvvdhimkgSoAlq46yyKAm1qVCLE0wHN8lGQdATcA2DgvPyR/0IIIeyUZyA8PA90znDiT9j4IZ1q+ePvpiM5M4+/DidaO0KLkwSogskzGFl4vfvykRah+B37Ae2xZaB1hIE/gleIlSMUQghhE6o0g3uurwe3fgoOMX/RI1KdMTx/R3wJFSsGSYAqmL+PXSQpLQd/Dye6Ox2k8sGv1Au9P4Rqd1k3OCGEELalyWPQajgA2qXDGRB8Ba0GdsQmE5OUbuXgLEsSoArmp+tZ+3P18nBa9gwaFIxNh6gLHgohhBD/1f1dCG+PJjedqN2vcW9NJwB+3lmxe4EkAapA4q9ksin6MgFcZcjpl9HkpJLp3wilx/vWDk0IIYSt0jnCg3NQfKrhlH6OiRmTcCGHRXvPkZ1XcQdDSwJUgfy8Kx4PMvnVcyoO6RdQKkVyrv1H4FB4p3shhBAin7s/xkd+xeDkhXfyAWa5fUVqZg4rDydYOzKLkQSogsjVG1m8K5YvHT8lLO80uAdgfOQXjM7e1g5NCCFEeeAfybn2H6PonOlg3MlbDj8wf3uctaOyGEmAKog1RxJ5JedLOugOoTi6waMLwTfs1hWFEEKI67IqN8LYfyYKGoY6rKbxuXmcvJhm7bAsQhKgCiJ77Xs86LARI1o0D86BKk2tHZIQQojyqF4/ND3eA+ANx/kcXPmtlQOyDEmAKoDkNVO5P20eANe6vA+1elg5IiGEEOVa65Gcqz0UgH5nJpF75E/rxmMBkgCVdzu/wW/LOwAs8h6CX4dnrRyQEEKIiiDkwams0nbAEQO6RUMgZq21QypVkgCVZ3vmwopXAJih74dL13FWDkgIIURFoXVw4GSbD1lhaInOmKfuKRm70dphlRpJgMqrAwvg9xcB+Ebfm++dHufuegFWDkoIIURF8kCLcF7Sj2KNoSnos2H+QIjbZu2wSoUkQOXR4cWw9DlAYZ3Xvbynf4z7mlbF2UFn7ciEEEJUIMHerrSrHcKovBc47dUK8jLhpwfh3G5rh3bHJAEqb/b9BIueBMVIVoNHefryQEDDwBah1o5MCCFEBTSwRSg5ODEo/QWMYe0gNw1+6A+xm6wd2h2RBKg82fYlLBsBihGaPM4P/i+hN2poUs2HWoGe1o5OCCFEBdSlTgABns6cz9SwtvGnEN5eTYLm3Q8nVlo7vNsmCVB5oCiwbjKsuj7IufUolL6fs3D3BQAelt4fIYQQFuKg0/Jg86oAzNuXDI/9BrV7gyFHHRh9YKGVI7w9kgDZOqMRVr4KGz5QX3d5E7q/y664a5y+nIGbk44+DUOsG6MQQogKbWDzagBsir7E2TQjPPQjNHwYFAMseQZ2fmPlCM0nCZAt0+fAkmdh59fq694fQ4f/gUbDgl3xAPRtGIKHs4MVgxRCCFHRVavkRtualVAU+HXPOdA5QP+voOX1tedWvKI+qVAU6wZqBkmAbFX6JZjbFw79AhodDPgGWj4NQGp2HisOqTv0PiSPv4QQQpSBh1uovUC/7j6LwaiAVgu9PoCOr6kFNnwAvw2DvCwrRmk6SYBsUeJh+KYLnN0BLt7w+CJo+FD+5eX7L5CdZyQywIOm1XysF6cQQgi70b1+IL5ujiSkZLPhZJJ6UqOBzuPg3s9B6wBHFsPs3pCWaN1gTSAJkK05sRK+7wEp8eBXA576G2p0LlDkl91nAXVqokajsUaUQggh7Iyzg44BTdXB0D/vPFvwYtPBMHgZuPrChb0wqzMkHLBClKaTBMhWGI2w+RP4+RHITVenGT61FvwjCxQ7eiGVg+dScNRpuK9JFSsFK4QQwh7dmHW87ngSl9NzCl4Mb6f+0e5fC9IuwPc91YV7bZQkQLYg4wr8PBDWTgAUaDYUBi0BN79CRW/0/nSrF0glD+cyDVMIIYR9iwz0pFGoD3qjwtJ95wsXqFQDnlwDNbqoq0b/9gT8MQbysss+2FuQBMja4rbBzHYQvRp0znDPJ3DPdNA5FiqanWdgyfVvuIHXB6MJIYQQZenBZupjsF93n0MpataXqw88+iu0e0l9vfs7+O5uuHKq7II0gSRA1mI0wqapMKeP2lVYqSY8/Tc0H6YOKivCqiOJpGTlEeLtQrua/mUcsBBCCAF9G4Xg7KDlxMU0Dp1PKbqQzgHungCPLQK3SpB4CL7uAId+K9NYSyIJkDVcjYMf+8Pf76iLSDUcCM9sgKCoEqvdePz1YPNQdFoZ/CyEEKLsebs60rNBEKD2ApUo8m4YvhnC2qrjWxc9CUueg6yrZRBpySQBKktGI+z6Fr5qA7EbwMEV7p0B930Nzh4lVo2/ksmWmCtoNOQvSS6EEEJYw4PN1MHQy/afJzvPUHJhrxAYvBw6jAU0cGA+fHGX1fcRkwSoJKX5vDI5Fn64F/58Wc2Cq7WG57ZA00HFPvL6t1/3qL0/7Wr6U9XXrfTiEkIIIczUpkYlqvi4kpqtZ/XRi7euoHOALm/AsFXqkI/0RPj5YVj8DGQml05QigLn95pcXBKgkszqCN/1gH3zICf99u5hyIOtM9RenzObwNENen4AQ1eoo+VNuYVRye9mHCgrPwshhLAyrVbD/U3VpVh+3X32FqX/pVor9ZFYmxdAo4WDC+GLVurYoNvdRiPjsvp79svWakeDiSQBKpEWzm6HZSNham1Y/oK6SrOpYv6Gr9rC6jfU6YBh7dRen7uGq0uIm2jjyUskpmbj6+ZIt3qBt/E5hBBCiNL1wPXHYJtjLnPhmhnbXzi6QvdJ6nR5/9qQkaSODZrdGxIOmnYPRYEzm+GXwTC1jvp79tIxdTa1icpFAvTll18SERGBi4sLzZo1Y9OmTSWW37BhA82aNcPFxYXq1aszc+bM23vjkTuh69vgV119bLV3LsxsCz/0h+i1xWerybHw86MwbwBcPqGOgO/7GQz5Xb2XmW5sfHpfk6o4O+hu77MIIYQQpahaJTdaRfihKLB47y0GQxelanN4diN0fkMdExu/VX3y8sdL6vp4RTHkwcFf1HJz+sDRZWDMg5Am0GcavGD6IzCb30Z84cKFjB49mi+//JK2bdvy9ddf06tXL44ePUq1aoXXwomNjaV37948/fTTzJs3jy1btjBixAgqV67M/fffb96bewVB+zHqWgZxW2HXN+o/9ul16lflOtD4MTDkQsYlSL+obmJ6bhcYctRNTFs9Cx1fVddFuA2X0nL4+5i654o8/hJCCGFLHmweyo7YZH7bc46RnWuavz2Towt0HAuNHoE1b6l7ie3+Hg4vguDG4BEAHoHgXhn0ObBnjrp0DKhJU+NHoPmTENRAPZeaavJb23wCNG3aNJ588kmeeuopAKZPn86qVav46quvmDJlSqHyM2fOpFq1akyfPh2AunXrsnv3bj7++GPzE6AbNBoIb6t+XY2DnbNgz1y4dBzWjC+6TvXO0PN9CKhze+953eK959AbFRqH+lA7yPOO7iWEEEKUpt5RQby97DBnrmSy68xVWkYU3sHAJD6h8OBsaPEUrHwVLh5SZ0sXxSMQWj6tJj5F7JhgKptOgHJzc9mzZw+vvfZagfPdu3dn69atRdbZtm0b3bt3L3CuR48efPfdd+Tl5eHoWHiF5ZycHHJybu5pkpKiLux09epVDIb/Tu/zgpavQMNn0BxaiObsLhQXL3CrDB6VwK0yim8YBDVSE6ert7/WgaIozN98HGNOJn3qVOOqmfcyGAykpqZy9epVdDp5dGZN0ha2Q9rCtkh72I7bbYu7a3iyZP8FftxwlEif+ncWhHc9eGgpnNuFJvUCZF5Wn7BkXEaTm4kS2Q2lbl9wcIYcIKfg78XU6z1ARa5Q/R82nQBdvnwZg8FAYGDBgb+BgYEkJiYWWScxMbHI8nq9nsuXLxMcHFyozpQpU5g4cWKh8+Hh4bcffCl7Zjo8Y+0ghBBCiGLMuP5lWb+aVCotLQ1vb+8Sy9h0AnTDf58pKopS4nPGosoXdf6GcePGMWbMmPzXRqOR5ORkKlWqZP7zTBuSmppKaGgoZ8+excvLy9rh2DVpC9shbWFbpD1sR0VoC0VRSEtLIyQk5JZlbToB8vf3R6fTFertSUpKKtTLc0NQUFCR5R0cHKhUqVKRdZydnXF2Ljh1zsfH5/YDtzFeXl7l9pu5opG2sB3SFrZF2sN2lPe2uFXPzw02PQ3eycmJZs2asWbNmgLn16xZQ5s2bYqs07p160LlV69eTfPmzYsc/yOEEEII+2PTCRDAmDFj+Pbbb/n+++85duwYL730EvHx8QwfPhxQH18NHjw4v/zw4cOJi4tjzJgxHDt2jO+//57vvvuOV155xVofQQghhBA2xqYfgQEMHDiQK1eu8M4775CQkECDBg1YsWIFYWFhACQkJBAfH59fPiIighUrVvDSSy/xxRdfEBISwmeffXb7U+DLMWdnZ95+++1Cj/dE2ZO2sB3SFrZF2sN22FtbaBRT5ooJIYQQQlQgNv8ITAghhBCitEkCJIQQQgi7IwmQEEIIIeyOJEBCCCGEsDuSAFVwOTk5NG7cGI1Gw/79+wtci4+Pp2/fvri7u+Pv788LL7xAbm6udQKtoM6cOcOTTz5JREQErq6u1KhRg7fffrvQv7O0Rdn58ssviYiIwMXFhWbNmrFp0yZrh1ThTZkyhRYtWuDp6UlAQAD9+/fnxIkTBcooisKECRMICQnB1dWVTp06ceTIEStFbD+mTJmCRqNh9OjR+efspS0kAargxo4dW+SS4AaDgT59+pCRkcHmzZtZsGABixYt4uWXX7ZClBXX8ePHMRqNfP311xw5coRPPvmEmTNn8vrrr+eXkbYoOwsXLmT06NG88cYb7Nu3j/bt29OrV68CS2mI0rdhwwZGjhzJ9u3bWbNmDXq9nu7du5ORkZFf5sMPP2TatGnMmDGDXbt2ERQURLdu3UhLS7Ni5BXbrl27mDVrFg0bNixw3m7aQhEV1ooVK5Q6deooR44cUQBl3759Ba5ptVrl/Pnz+ed+/vlnxdnZWUlJSbFCtPbjww8/VCIiIvJfS1uUnZYtWyrDhw8vcK5OnTrKa6+9ZqWI7FNSUpICKBs2bFAURVGMRqMSFBSkvP/++/llsrOzFW9vb2XmzJnWCrNCS0tLUyIjI5U1a9YoHTt2VF588UVFUeyrLaQHqIK6ePEiTz/9ND/++CNubm6Frm/bto0GDRoU6B3q0aMHOTk57NmzpyxDtTspKSn4+fnlv5a2KBu5ubns2bOH7t27FzjfvXt3tm7daqWo7FNKSgpA/s9BbGwsiYmJBdrG2dmZjh07SttYyMiRI+nTpw933313gfP21BY2vxK0MJ+iKAwdOpThw4fTvHlzzpw5U6hMYmJioQ1lfX19cXJyKrSZrCg9p06d4vPPP2fq1Kn556Qtysbly5cxGAyF/q0DAwPl37kMKYrCmDFjaNeuHQ0aNADI//cvqm3i4uLKPMaKbsGCBezZs4fdu3cXumZPbSE9QOXIhAkT0Gg0JX7t3r2bzz//nNTUVMaNG1fi/TQaTaFziqIUeV4UZGpb/NuFCxfo2bMnDz74IE899VSBa9IWZee//6by71y2Ro0axcGDB/n5558LXZO2sbyzZ8/y4osv8tNPP+Hi4lJsOXtoC+kBKkdGjRrFww8/XGKZ8PBw3n33XbZv315oP5fmzZvz2GOPMXfuXIKCgtixY0eB61evXiUvL69Q5i8KM7Utbrhw4QKdO3emdevWzJo1q0A5aYuy4e/vj06nK9Tbk5SUJP/OZeT5559n+fLlbNy4kapVq+afDwoKAtTeh+Dg4Pzz0jalb8+ePSQlJdGsWbP8cwaDgY0bNzJjxoz82Xl20RZWHH8kLCQuLk45dOhQ/teqVasUQPntt9+Us2fPKopyc+DthQsX8ustWLBABt5awLlz55TIyEjl4YcfVvR6faHr0hZlp2XLlspzzz1X4FzdunVlELSFGY1GZeTIkUpISIhy8uTJIq8HBQUpH3zwQf65nJycCjnw1tpSU1ML/H44dOiQ0rx5c+Xxxx9XDh06ZFdtIQmQHYiNjS00C0yv1ysNGjRQunbtquzdu1dZu3atUrVqVWXUqFHWC7QCOn/+vFKzZk2lS5cuyrlz55SEhIT8rxukLcrOggULFEdHR+W7775Tjh49qowePVpxd3dXzpw5Y+3QKrTnnntO8fb2VtavX1/gZyAzMzO/zPvvv694e3srixcvVg4dOqQ88sgjSnBwsJKammrFyO3Dv2eBKYr9tIUkQHagqARIUdSeoj59+iiurq6Kn5+fMmrUKCU7O9s6QVZQs2fPVoAiv/5N2qLsfPHFF0pYWJji5OSkNG3aNH8qtrCc4n4GZs+enV/GaDQqb7/9thIUFKQ4OzsrHTp0UA4dOmS9oO3IfxMge2kLjaIoihWevAkhhBBCWI3MAhNCCCGE3ZEESAghhBB2RxIgIYQQQtgdSYCEEEIIYXckARJCCCGE3ZEESAghhBB2RxIgIYQQQtgdSYCEEEIIYXckARJCVBidOnVi9OjRt1V3zpw5aDQaNBrNbd/jTnXq1Ck/hv3791slBiHshewGL4SoMBYvXoyjo+Nt1/fy8uLEiRO4u7uXYlSmW7x4MadOnaJly5ZWeX8h7IkkQEKICsPPz++O6ms0GoKCgkopGvP5+fmRmppqtfcXwp7IIzAhRKm6dOkSQUFBTJ48Of/cjh07cHJyYvXq1cXW27VrF926dcPf3x9vb286duzI3r1786+vX78eJycnNm3alH9u6tSp+Pv7k5CQABR+BPbll18SGRmJi4sLgYGBPPDAA2Z/nvDwcN59910GDx6Mh4cHYWFhLFu2jEuXLtGvXz88PDyIiopi9+7d+XXmzJmDj48Pf/zxB7Vr18bNzY0HHniAjIwM5s6dS3h4OL6+vjz//PMYDAazYxJC3DlJgIQQpapy5cp8//33TJgwgd27d5Oens7jjz/OiBEj6N69e7H10tLSGDJkCJs2bWL79u1ERkbSu3dv0tLSgJvJzaBBg0hJSeHAgQO88cYbfPPNNwQHBxe63+7du3nhhRd45513OHHiBH/99RcdOnS4rc/0ySef0LZtW/bt20efPn0YNGgQgwcP5vHHH2fv3r3UrFmTwYMH8++9pTMzM/nss89YsGABf/31F+vXr2fAgAGsWLGCFStW8OOPPzJr1ix+++2324pJCHGHrLwbvRCighoxYoRSq1Yt5bHHHlMaNGigZGVlmVVfr9crnp6eyu+//55/LicnR2nSpIny0EMPKfXr11eeeuqpAnU6duyovPjii4qiKMqiRYsULy8vJTU11aT3mz17tuLt7V3ofFhYmPL444/nv05ISFAAZfz48fnntm3bpgBKQkJC/r0AJSYmJr/Ms88+q7i5uSlpaWn553r06KE8++yzBd4vNjZWAZR9+/aZFLcQ4vZID5AQwiI+/vhj9Ho9v/zyCz/99BMuLi4AxMfH4+Hhkf9141FZUlISw4cPp1atWnh7e+Pt7U16ejrx8fH593RycmLevHksWrSIrKwspk+fXuz7d+vWjbCwMKpXr86gQYP46aefyMzMvK3P0rBhw/zjwMBAAKKiogqdS0pKyj/n5uZGjRo1CpQJDw/Hw8OjwLl/1xFClB0ZBC2EsIjTp09z4cIFjEYjcXFx+UlESEhIgSne/2/njl2Si+Iwjj+GJIHFDSKaikCIkCChoZYGieD6DzSEUrhLDdJQREMtQiAtRUkEd3BrkoSGEBKXwKC9uEtDFJYtgZA1vK9RvPrCC2b43u8H7nLuPeeesz387jm3tnF5YWFB9/f3SiaTGhoaksfj0dTUlCqVypdxC4WCJKlUKqlUKjU8sdXd3a1isahcLqfT01Otr69rY2NDFxcXMgzjn9by+WSZy+Vq2FatVuv2qT1Tr+1zHwCtQwUIQNNVKhXNz89rbm5Om5ubikajuru7kyS53W75fL6PqxaAzs/PFYvFFAqF5Pf75fF49PDw8GXc6+trLS8v6+DgQJOTk4pEIn8NEG63WzMzM0okErq6upJt2zo7O/u+hQNoG1SAADTd6uqqyuWydnZ25PV6lc1mFY1GlclkGvbx+XyyLEsTExN6fn5WPB5XV1fXx/3X11eFw2HNzs5qcXFRpmlqbGxM29vbisfjf4yXyWR0c3Oj6elp9fb26uTkRNVqVSMjI9+yZgDthQoQgKbK5XJKJpOyLEs9PT3q6OiQZVnK5/Pa3d1t2O/w8FCPj48KBAIKh8OKxWLq7+//uL+1tSXbtrW/vy9JGhgYUCqV0traWt2/JhuGoePjYwWDQY2Ojmpvb0/pdFp+v7/pawbQflxvb5/ObQKAQx0dHWlpaUlPT08/Og/btjU8PKzLy0uNj4//6FyA/xkVIAD4rVwuy+v1amVl5Ufeb5omFSqgRagAAYB+/YixtlHbMAz19fW1fA63t7d6eXmRJA0ODqqzs7PlcwCcggAEAAAch09gAADAcQhAAADAcQhAAADAcQhAAADAcQhAAADAcQhAAADAcQhAAADAcQhAAADAcd4BHKzbVDwe4QkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*x_SI,1e4*func(x_SI,popt_x[0],popt_x[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*x_SI,1e4*B_tot_x_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\" )\n", + "#plt.ylim(0,8)\n", + "#plt.xlim(-0.01,0.01)\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,20)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "77c981bb-02be-4de9-8990-849b6a7fbee1", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_z, pcov = curve_fit(func,z_SI[a:b],B_tot_z_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "f1f7cdd7-f13c-41e2-be5e-a375dc5e4d06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*z_SI,1e4*func(z_SI,popt_z[0],popt_z[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*z_SI,1e4*B_tot_z_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\")\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,50)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "id": "624fbda5-4658-4f9e-ba0f-33168c27e5c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T_expansion = T/4 = 66.2961644033217 ms\n", + "omega_x = 23.69362301623883\n", + "Magnification for t_tof = 20.0 ms: M = 0.47387246032477665\n", + "for z_start = 1 μm after T_expansion z_end = 1.4110642480981759 μm\n" + ] + } + ], + "source": [ + "m = 2.69e-25\n", + "\n", + "k_x = -2*popt_x[0]*9.9*cs.mu_B\n", + "\n", + "omega = np.sqrt(k_x/m)\n", + "f = omega/(2*np.pi)\n", + "\n", + "T = 1/f\n", + "T_exp = T/4\n", + "print(f\"T_expansion = T/4 = {T_exp*1e3} ms\" )\n", + "print(f\"omega_x = {omega}\") \n", + "\n", + "t_tof = 20e-3\n", + "M = omega * t_tof\n", + "\n", + "print(f\"Magnification for t_tof = {t_tof*1e3} ms: M = {M}\")\n", + "\n", + "\n", + "start_z = 1e-6\n", + "d_t = 1e-3\n", + "def force(z):\n", + " return 2*0.248*z*9.9*cs.mu_B\n", + "z = start_z\n", + "v = 0\n", + "for t in np.arange(0,T_exp,d_t):\n", + " v = v + force(z)/m * d_t\n", + " #print(v)\n", + " z = z + v * d_t\n", + "print(f\"for z_start = 1 μm after T_expansion z_end = {z*1e6} μm\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1561200c-5630-4c8a-b0b5-b6a535805eef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Magnetic_magnification/.ipynb_checkpoints/untitled1-checkpoint.py b/Magnetic_magnification/.ipynb_checkpoints/untitled1-checkpoint.py new file mode 100644 index 0000000..e7ccada --- /dev/null +++ b/Magnetic_magnification/.ipynb_checkpoints/untitled1-checkpoint.py @@ -0,0 +1,34 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Aug 27 15:14:48 2021 + +@author: Joschka +""" +from src import physical_constants as cs +import numpy as np + +m = 2.69e-25 +k = 2*0.2097*9.9*cs.mu_B + +omega = np.sqrt(k/m) +f = omega/(2*np.pi) + +T = 1/f +T_exp = T/4 +#print(T_exp) + + +start_z = 1e-6 +d_t = 1e-3 +def force(z): + return 2*0.248*z*9.9*cs.mu_B +z = start_z +v = 0 +for t in np.arange(0,T_exp,d_t): + v = v + force(z)/m * d_t + #print(v) + z = z + v * d_t +print(z) +print(omega) +print(omega*1000e-3) +print(700*20e-3*2*np.pi) \ No newline at end of file diff --git a/Magnetic_magnification/01_Calculate_trap_frequency.py b/Magnetic_magnification/01_Calculate_trap_frequency.py new file mode 100644 index 0000000..e7ccada --- /dev/null +++ b/Magnetic_magnification/01_Calculate_trap_frequency.py @@ -0,0 +1,34 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Aug 27 15:14:48 2021 + +@author: Joschka +""" +from src import physical_constants as cs +import numpy as np + +m = 2.69e-25 +k = 2*0.2097*9.9*cs.mu_B + +omega = np.sqrt(k/m) +f = omega/(2*np.pi) + +T = 1/f +T_exp = T/4 +#print(T_exp) + + +start_z = 1e-6 +d_t = 1e-3 +def force(z): + return 2*0.248*z*9.9*cs.mu_B +z = start_z +v = 0 +for t in np.arange(0,T_exp,d_t): + v = v + force(z)/m * d_t + #print(v) + z = z + v * d_t +print(z) +print(omega) +print(omega*1000e-3) +print(700*20e-3*2*np.pi) \ No newline at end of file diff --git a/Magnetic_magnification/Calc_Trap_frequency_displacement.ipynb b/Magnetic_magnification/Calc_Trap_frequency_displacement.ipynb new file mode 100644 index 0000000..e40a346 --- /dev/null +++ b/Magnetic_magnification/Calc_Trap_frequency_displacement.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 109, + "id": "f8d06107", + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Aug 24 16:24:52 2021\n", + "\n", + "@author: Joschka\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0,'..')\n", + "\n", + "from src import coil_class as BC\n", + "from src import physical_constants as cs\n", + "\n", + "#from IPython import get_ipython\n", + "#get_ipython().run_line_magic('matplotlib', 'qt')\n", + "#get_ipython().run_line_magic('matplotlib', 'inline')\n", + "\n", + "\n", + "from scipy.optimize import curve_fit\n", + "plt.rcParams['axes.grid'] = True\n", + "plt.rcParams['grid.alpha'] = 0.5" + ] + }, + { + "cell_type": "markdown", + "id": "99eb6ef1-8920-4f7d-9b58-fd00c731d2bc", + "metadata": {}, + "source": [ + "## Set up coils" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "199e2602-bec6-429a-a639-6ea35ababb86", + "metadata": {}, + "outputs": [], + "source": [ + "HH = 1\n", + "I = 5\n", + "\n", + "d_coils = 50\n", + "Radius = 30\n", + "\n", + "layers = 4\n", + "windings = 4\n", + "wire_width = 1\n", + "wire_height = 1\n", + "\n", + "Coil = BC.BCoil(HH,d_coils,Radius, layers, windings, wire_width, wire_height)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "40cee79d-d6ef-4df4-ae03-5e82785433e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z = np.arange(-100,100,10)\n", + "print(np.size(z))\n", + "r = np.arange(1e-15,100,5)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 4)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)\n", + "\n", + "#plt.figure(figsize=(16,10))\n", + "plt.quiver(r_m,z_m,B[:,:,1],B[:,:,0])\n", + "plt.xlabel(\"radius r [m]\")\n", + "plt.ylabel(\"z-axis [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "28dcdd21-f4b6-49e6-a935-4012a941f186", + "metadata": {}, + "outputs": [], + "source": [ + "z = np.arange(-100,100,1)\n", + "r = np.arange(1e-3,100,1)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 2)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "ad02e8cd-e92a-4d27-845d-cc7e22d6e7e5", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.concatenate((-np.flip(r),r))\n", + "B_tot_x = np.concatenate((np.flip(B_tot[:,len(z)//2]),B_tot[:,len(z)//2]))\n", + "B_tot_z = B_tot[0,:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "52475c2c-e2f0-4615-a5f4-9f0a86155a3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.plot(x,np.gradient(B_tot_x,x))\\nplt.xlabel(\"radius r [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\n#plt.xlim(0,0.01)\\nplt.show()\\nplt.plot(z,np.gradient(B_tot[0,:],z))\\nplt.xlabel(\"z_axis [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\nplt.show()\\n'" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.plot(x,B_tot_x)\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(-0.1,0.1)\n", + "plt.show()\n", + "plt.plot(z,B_tot_z)\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\n", + "\"\"\"\n", + "plt.plot(x,np.gradient(B_tot_x,x))\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(0,0.01)\n", + "plt.show()\n", + "plt.plot(z,np.gradient(B_tot[0,:],z))\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "37c71478-cae2-40ea-8712-b2f751c6c54d", + "metadata": {}, + "source": [ + "## Fit harmonic function" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "443f51a2-a502-4d4a-8692-15610c193cc7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(x,a,b):\n", + " return a*x**2+b\n", + "\n", + "x_SI = 1e-3 * x\n", + "z_SI = 1e-3 * z\n", + "\n", + "B_tot_x_SI = 1e-4*B_tot_x\n", + "B_tot_z_SI = 1e-4*B_tot_z\n" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "id": "59145c9b-aac3-4bea-ba79-b2a3992d05f6", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_x, pcov = curve_fit(func,x_SI[a:b],B_tot_x_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "49c6fb78-ba74-41e3-9a2a-c4dca7109643", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*x_SI,1e4*func(x_SI,popt_x[0],popt_x[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*x_SI,1e4*B_tot_x_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\" )\n", + "#plt.ylim(0,8)\n", + "#plt.xlim(-0.01,0.01)\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,20)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "77c981bb-02be-4de9-8990-849b6a7fbee1", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_z, pcov = curve_fit(func,z_SI[a:b],B_tot_z_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "f1f7cdd7-f13c-41e2-be5e-a375dc5e4d06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*z_SI,1e4*func(z_SI,popt_z[0],popt_z[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*z_SI,1e4*B_tot_z_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\")\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,50)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "id": "624fbda5-4658-4f9e-ba0f-33168c27e5c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T_expansion = T/4 = 66.2961644033217 ms\n", + "omega_x = 23.69362301623883\n", + "Magnification for t_tof = 20.0 ms: M = 0.47387246032477665\n", + "for z_start = 1 μm after T_expansion z_end = 1.4110642480981759 μm\n" + ] + } + ], + "source": [ + "m = 2.69e-25\n", + "\n", + "k_x = -2*popt_x[0]*9.9*cs.mu_B\n", + "\n", + "omega = np.sqrt(k_x/m)\n", + "f = omega/(2*np.pi)\n", + "\n", + "T = 1/f\n", + "T_exp = T/4\n", + "print(f\"T_expansion = T/4 = {T_exp*1e3} ms\" )\n", + "print(f\"omega_x = {omega}\") \n", + "\n", + "t_tof = 20e-3\n", + "M = omega * t_tof\n", + "\n", + "print(f\"Magnification for t_tof = {t_tof*1e3} ms: M = {M}\")\n", + "\n", + "\n", + "start_z = 1e-6\n", + "d_t = 1e-3\n", + "def force(z):\n", + " return 2*0.248*z*9.9*cs.mu_B\n", + "z = start_z\n", + "v = 0\n", + "for t in np.arange(0,T_exp,d_t):\n", + " v = v + force(z)/m * d_t\n", + " #print(v)\n", + " z = z + v * d_t\n", + "print(f\"for z_start = 1 μm after T_expansion z_end = {z*1e6} μm\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1561200c-5630-4c8a-b0b5-b6a535805eef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Noise/00_Simple_testing.py b/Noise/00_Simple_testing.py new file mode 100644 index 0000000..5f51dae --- /dev/null +++ b/Noise/00_Simple_testing.py @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 31 09:28:25 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + + +x = np.linspace(-10, 10, 3001) +z = np.linspace(-10, 10, 3001) + +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 6, windings = 4, wire_height = 1, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +I = 5 + +""" +percentage = 0.05 +absolut = 5 +diff = percentage*0.01*5+ absolut *1e-3 +print(diff) + + + +Bz2, Bx = HH_Coil.B_multiple(I+ diff, x, z) +print(Bz2[1500]-Bz1[1500]) +print(" ") +""" +percentage = 0 #.02 +absolut = 0.125 +diff = percentage*0.01*5+ absolut *1e-3 +print(diff) + +Bz1, Bx = HH_Coil.B_multiple(I, x, z) +Bz2, Bx = HH_Coil.B_multiple(I+ diff, x, z) +print(Bz2[1500]-Bz1[1500]) +print((Bz2[1500]-Bz1[1500])/Bz2[1500]) +#print(100e-6/10) +#Power = cs.rho_copper_20 *wire_length* I_current**2 /(self.get_wire_area()) + diff --git a/Noise/01_HH_noise.py b/Noise/01_HH_noise.py new file mode 100644 index 0000000..1ff4282 --- /dev/null +++ b/Noise/01_HH_noise.py @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 23 17:40:37 2021 + +@author: Joschka +""" + + +import matplotlib.pyplot as plt +import numpy as np +#from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-15, 15, 30001) +z = np.linspace(-15, 15, 30001) + + +#New coil +I_current = 10 +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 2, windings = 6, wire_height = 2, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) + +HH_Coil.print_info() +Bz, Bx = HH_Coil.B_multiple(I_current,x,z,raster = 10) +Bz_curv = BC.BCoil.curv(Bz, z) +HH_Coil.cooling(I_current) + +print(f"B_z(0) = {Bz[15000]} G") +print(f"B_z_curvature(0) = {Bz_curv[15000]:.4f} G/cm^2") + + +print(f"B_z(1 μm) = {Bz[15001]}") +print(f"B_z(1 mm) = {Bz[16000]}") + +print(f"Diff B 1 μm: {Bz[15001] - Bz[15000]}, relative: {(Bz[15001] - Bz[15000])/Bz[15000]}") + + +print(f"Diff B 1 mm: {Bz[16000] - Bz[15000]}, relative: {(Bz[16000] - Bz[15000])/Bz[15000]}") + +print(f"Diff B 0.5 mm: {Bz[15500] - Bz[15000]}, relative: {(Bz[15500] - Bz[15000])/Bz[15000]}") +I_HH = I_current + +#calculate field +B_z, B_x = HH_Coil.B_multiple(I_HH,x,z) + +#Calculate curvature +B_z_curv = BC.BCoil.curv(B_z, z) + + + +plt.figure(300) + + + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_{ref}$, reference, optimal HH-configuration d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp,linestyle = "solid", label = r"$B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A, 4 x 4") + +#plt.plot(z,B_tot,linestyle = "solid", label = r"$B_{z,1} + B_{z,2}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +plt.subplot(2,1,2) +plt.plot(z,B_z_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{ref}$, d = 44 mm, R = 44 mm") +#plt.plot(z,B_z_comp_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1}$, d = 54 mm, R = 48.8 mm, I = 5 A") + +#plt.plot(z,B_tot_curv,linestyle = "solid", label = r"$\nabla_z^2 B_{z,1} + B_{z,2}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') + +#plt.savefig("output/first_compensation_idea.png") + +plt.show() + + + + +""" +AHH ############################################################################ +############################################################################### +############################################################################### +""" diff --git a/Stern_gerlach_separation/.ipynb_checkpoints/Calc_Trap_frequency_displacement-checkpoint.ipynb b/Stern_gerlach_separation/.ipynb_checkpoints/Calc_Trap_frequency_displacement-checkpoint.ipynb new file mode 100644 index 0000000..e40a346 --- /dev/null +++ b/Stern_gerlach_separation/.ipynb_checkpoints/Calc_Trap_frequency_displacement-checkpoint.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 109, + "id": "f8d06107", + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Aug 24 16:24:52 2021\n", + "\n", + "@author: Joschka\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0,'..')\n", + "\n", + "from src import coil_class as BC\n", + "from src import physical_constants as cs\n", + "\n", + "#from IPython import get_ipython\n", + "#get_ipython().run_line_magic('matplotlib', 'qt')\n", + "#get_ipython().run_line_magic('matplotlib', 'inline')\n", + "\n", + "\n", + "from scipy.optimize import curve_fit\n", + "plt.rcParams['axes.grid'] = True\n", + "plt.rcParams['grid.alpha'] = 0.5" + ] + }, + { + "cell_type": "markdown", + "id": "99eb6ef1-8920-4f7d-9b58-fd00c731d2bc", + "metadata": {}, + "source": [ + "## Set up coils" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "199e2602-bec6-429a-a639-6ea35ababb86", + "metadata": {}, + "outputs": [], + "source": [ + "HH = 1\n", + "I = 5\n", + "\n", + "d_coils = 50\n", + "Radius = 30\n", + "\n", + "layers = 4\n", + "windings = 4\n", + "wire_width = 1\n", + "wire_height = 1\n", + "\n", + "Coil = BC.BCoil(HH,d_coils,Radius, layers, windings, wire_width, wire_height)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "40cee79d-d6ef-4df4-ae03-5e82785433e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z = np.arange(-100,100,10)\n", + "print(np.size(z))\n", + "r = np.arange(1e-15,100,5)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 4)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)\n", + "\n", + "#plt.figure(figsize=(16,10))\n", + "plt.quiver(r_m,z_m,B[:,:,1],B[:,:,0])\n", + "plt.xlabel(\"radius r [m]\")\n", + "plt.ylabel(\"z-axis [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "28dcdd21-f4b6-49e6-a935-4012a941f186", + "metadata": {}, + "outputs": [], + "source": [ + "z = np.arange(-100,100,1)\n", + "r = np.arange(1e-3,100,1)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 2)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "ad02e8cd-e92a-4d27-845d-cc7e22d6e7e5", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.concatenate((-np.flip(r),r))\n", + "B_tot_x = np.concatenate((np.flip(B_tot[:,len(z)//2]),B_tot[:,len(z)//2]))\n", + "B_tot_z = B_tot[0,:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "52475c2c-e2f0-4615-a5f4-9f0a86155a3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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EATe2doN9d4LdYCR/NlK++JQpUzBlypRLPu/n59fu8bfffovx48ejV6/Lf2sSBKHDsZejVquhVqt1j6uqqgAAGo0GGo1x74jQaDTQarVGP685kXuOlpKfVitix5mW7q/rY3wMirc7cnzgmjD8eKoQ3yblYfGEcAS4O5jstTojxXVsaNJg/YFMAMADo0Oh1Zpucdiu5Delny/e/i0Ve1NKUFWnhpNK0o+IK7KU38Wuknt+gPFzNOQ85v3u/ouLFy/ihx9+wMaNG6+4b01NDUJCQqDRaDBw4EA8//zziIuLu+T+a9aswapVqzpsT0tLg7Oz81XF/XdarRZlZWVITU2FQmE+38iNSe45Wkp+Z4saUFLTCEdbBbw15UhJqdD72O7I0QFArJ89ThQ24I0fk/Dg0O5tDZHiOv5wvhJltY3wcbJBlH0NUlJSTPZaXclPEEX4u9igoLoZX+47hWtCjPv3z9gs5Xexq+SeH2D8HGtqavTe12IKoI0bN8LFxQUzZsy47H59+vTBhg0b0L9/f1RVVeHNN9/EqFGjcOLECURGdr7K8vLly7FkyRLd46qqKgQFBSE8PByurq5GzUOj0SA1NRURERFQKqW/I8cU5J6jpeT3dXoyAGB8Hx9E94ky6NjuyvFx0R33bTyKX1Jq8PQtQ+Dm0D0r1APdfx01WhHfbt8HAHhoXAT69A417et1Mb8pAzT45PcsnKlQ4t7rumdl+q6ylN/FrpJ7foDxc2zrwdGHxRRAn3zyCe66664rjuUZMWIERowYoXs8atQoDBo0CG+//TbeeuutTo9RqVRQqVQdtiuVSpO86RQKhcnObS7knqMl5LerdbmJSf38uhRnd+Q4vo8v+vi54HxhNb44kouF4yNM9lqd6c7r+MvZAmSX1cHd0Razh4d0y2t2Jb9J/fzxye9Z2J1cDBECbJTm3fJgCb+LV0Pu+QHGzdGQc5j3O7vVvn37kJycjPvvv9/gYxUKBYYOHWrSpmYic5NeXIO04lrYKASM6+0jdTiXJAiCbg2s9b9noKFJnmMdRFHEh3talr2YMyIEjnbm+91zSIgH3B1tUVHXhMQs65yniayDRRRA69atw+DBgxEbG2vwsaIoIikpCf7+/iaIjMg87TzbchvziF5e3dqt1BU3DPBHoLsDSmoasU2ma1EdSi/DidxKqGwUmBMfKnU4l2WjVODaPi1Fc9v7iEiOJC2AampqkJSUhKSkJABARkYGkpKSkJ2drdunqqoKX3311SVbf+bMmYPly5frHq9atQq//PIL0tPTkZSUhPnz5yMpKQkLFiwwaS5E5qTtg2titK/EkVyZrVKB+deEAQA+2psGjVZ+i6R+uLel9ef2IT3h7dyxu93cTGp93+w8e5GL1pJsSVoAJSYmIi4uTneH1pIlSxAXF4dnnnlGt8/mzZshiiJmz57d6Tmys7NRUFCge1xRUYEHH3wQffv2xaRJk5CXl4e9e/di2LBhpk2GyEyU1TbiaOsSE9dZQAEEALOGBsHNwRaZpXW6W/fl4nxhFRKSi6EQgPuvMZ+JDy9nTFQP2NkokF1Wh9Qi/e+qIbIkknZEjxs37orfLh588EE8+OCDl3w+ISGh3eM33ngDb7zxhjHCI7JI+1KKIYpAHz8XBHbz3Dpd5aSywdyRIXjrt1R8sCcN18f4QRAEqcMyiraJHqfE+CPU20niaPTjaGeDEb28sPdCMRKSixHp6yJ1SERGZxFjgIhIfwnJxQCAsb2lXfndUHPiQ6GyUeBEbiUOpZdJHY5R5FfUY3tSy6zKD5rBoqeGGBfV8v5JuMDV4UmeWAARyYhWK2LvhZYCaFyU+d791RlvZxVuH9ITwJ9jZizdJ/sz0KwVMbKXF2KD3KUOxyDjWgvoIxnlqOXiqCRDLICIZORUXiVKaxvhrLLBkFAPqcMx2AOje0EhtLRinSvQf0Izc1RZ14Qv/mi5ocOcFj3VV5i3E4I9HdGo0eJAWqnU4RAZHQsgIhlp6/4aFeEFWzOfwK4zIV5OmNK/ZcoKS18kddPhLNQ2atDHzwVjoyyrOxJomaOpLe6EZHaDkfxY3l9IIrqkPa3jNcx58sMrWTCmZWLE7SfykVteJ3E0XVPX2Ix1+zMAtLT+WOqA7rZusITkYt4OT7LDAohIJirqGpGUUwHgzw8uS9S/pxtGRXhBoxV1RYSl+b9D2SirbUSIlyOmDQiQOpwuGxnuBTulAnkV9Ugr5u3wJC8sgIhkYn9qCbQiEOXrDH83y7j9/VIeam0F2vxHDsprGyWOxjANTRp82Np9t3BchNmvpXU5jnY2GN7LEwCw50KJxNEQGZfl/mYSUTu/p7YMVL0mwnJbf9qMjvRGtL8r6ps02HQoS+pwDLLlSA5KatQIdHfA9LhAqcO5aqMjvQEAv6eyACJ5YQFEJBMH01o+oEZFeEkcydUTBEF359T6A5kWcxu2ulmDD1oXPV0wLhx2Npb/J3ZUREsBdCi9FI3NWomjITIey//tJCLkVdQjs7QOSoWAYWGeUodjFDf090eIlyPKahstphVo69E8FFQ2wNdVhdsH95Q6HKPo6+cKLyc71DVqdGPMiOSABRCRDLR1Twzo6QYXe/Ne/V1fNkoFFo2PAAB8uDcddY3m3QrUpNHivYRUAC1jmOxtlRJHZBwKhYD41lag/ewGIxlhAUQkAwdaP5hGhXtLHIlx3RIXqGsF+uygebcCfZuUj9zyeng722H2sGCpwzGq0W0FUEqxxJEQGQ8LICILJ4oifm+dqTdeBuN//urvrUDmOhaoWaPFe7tbWn/uH90LDnbyaP1pM6p1IPSJ3EpUNTRJHA2RcbAAIrJwqUU1KK5WQ2WjwKBgy1v+4kr+2gq0/nfznBdo27E8pJfUwt3RFnePCJE6HKMLdHdAL28naLQiDnFZDJIJFkBEFq5tnaYhoR6yGXfyVzZKBZZMjAIAfLgn3ezmBWpo0uCNXRcAtMz746yykTgi02i7G4y3w5NcsAAisnBtH0jxMhv/81fTBgQg2t8V1epm3UBjc/HZwSwUVDbA380e94yUX+tPm7bpFQ6ll0kcCZFxsAAismAarYhD6S0tQG3f0OVIoRCw7PreAICNB7OQV1EvcUQtqhqa8G5rQfbEdVGybIFrMyyspQBKvliN0hq1xNEQXT0WQEQW7HReJaoamuFib4OYAFepwzGpsVE9MKKXJxqbtXj15/NShwMAeOe3VFTUNSHCxxkzBln+rM+X4+lkhz5+LgCAPzLYCkSWjwUQkQX7vXX25+FhXha95pQ+BEHA0zdEQxCAb5LykZgp7YdwWnGNblD2v6f2lf3/PwCM6NXWDcaB0GT55P8bSyRjB9Paur/kdfv7pcQEumHWkCAAwMrvzkCjFSWL5fnvz6JJI2J87x4Y38dHsji604jWhVE5DojkgAUQkYVSN2twpLUVRM7jf/5u6eTecLG3wem8Kmw5kiNJDL+dv4iE5GLYKgWsuDFakhikwHFAJCcsgIgs1LGsCjQ0aeHtrEKkj7PU4XQbb2cVFl/Xclv8Sz+dQ1FVQ7e+fo26GSu+OQMAuG9UGHr1sJ7/e44DIjlhAURkoQ6ktd3+7gVBECSOpnvNHRmC/oFuqGpoxopvT0MUu68r7JWfzyOvoh49PRzw2ITIbntdczE8rK0bjOOAyLKxACKyUAesbPzPX9koFXj51gGwUQj45cxF/HS6sFte94+MMnzauibZSzMGwEmmkx5ezp8DodkCRJaNBRCRBWpo0uBkbgWAPz+QrE10gCseGRcOAFjxzWlcNHFXWHVDE/7xvxMAgDuGBuGaSOsZd/VXw1pbgDgOiCwdCyAiC3QipwJNGhE9XFQI9nSUOhzJLLw2An38XFBa24jHvjiOZo3WJK8jiiL+ufUUskrrEOjugOVT+5rkdSyBl7MKvX05DogsHwsgIguUmFUOABga6mF143/+SmWjxHt3DYKTnRKHM8rw5q8pJnmdTYey8MOpAtgoBLx9ZxzcHGxN8jqW4s/b4TkOiCwXCyAiC9R2+/vQUE+JI5Ferx7OWHPrAADAO7tT8dOpAqOe/1B6KZ7//hwA4J9T+mBQsIdRz2+JOA6I5IAFEJGF0WhFHM1sawFiAQQAN8UGYO7IEIgi8PjmJKO1TJwrqMIDGxPRqNFiSowf5l8TZpTzWjqOAyI5YAFEZGGSC6tRrW6Gk51SNycLAc9M64dJ0b5o1GjxwMZEnMqtvKrzZZXWYt76P1CtbsbQUA+8MWugVXc3/hXHAZEcsAAisjCJWS0fOINCPKxi/Sl9KRUC3podh2GhnqhWN+OOjw5iz4XiLp3reHY5Zrx3ABer1IjydcbHc4bKeqX3ruA4ILJ0/OtJZGGOsPvrkuxtlfh43hDEh3uhtlGD+zYcwfrfM6DVc80wURTx9fFczP7vIZTWNiIm0BWb7h8ON0frHvTcGY4DIkvHAojIgoiiiCOtXQ5DQjkYtzOu9rbYcO8wzIgLhEYrYtV3Z3H7hwdxrqDqsse1dHkdwRNbTqChSYvxvXtgy4Mj4eNi302RWxaOAyJLZ33TmBJZsNzyehRWNcBGISAuiAXQpdjZKPCfmbGIDXLHKz+fx9Gsckx5cx+GhHjg5rhARPk4w9vZFmcu1iOpMhc/nC7E/tQSiCJgp1Rg0bUReGRcOLsYL8OrdQ26lKIaJGaVY3I/P6lDIjIICyAiC9I2/icm0A0OdhyTcjmCIGBufCgmRvvixR/O4afTBUjMKtfNofSnfN2/xkb1wDPTohFuRQucXo2hYZ4tBVBmGQsgsjiSfr3Zu3cvpk2bhoCAAAiCgG+++abd8/PmzYMgCO1+RowYccXzbt26FdHR0VCpVIiOjsbXX39togyIutef43/Y+qOvAHcHvHvXIBxcPgH/mNwboyK8EOzpCJWNAj5ONhge5onHro3A3n+Mx8b7hrH4MUDb+/CPzL8XlUTmT9IWoNraWsTGxuLee+/Frbfe2uk+119/PdavX697bGdnd9lzHjx4ELNmzcLzzz+PW265BV9//TVmzpyJ/fv3Y/jw4UaNn6i7/Tn+hwOgDeXrao+F4yOwcHwEAECj0SAlJQWRkZFQKtma1hVDQlreh2fyKlHX2AxHO3YqkOWQ9N06ZcoUTJky5bL7qFQq+Pnp37S6du1aTJw4EcuXLwcALF++HHv27MHatWvxxRdfXFW8RFIqr21ESlENAGBICFuASHo9PRzg52qPwqoGJOVUID7cOheIJctk9uV6QkICfHx84O7ujrFjx+LFF1+Ej4/PJfc/ePAgnnjiiXbbJk+ejLVr117yGLVaDbX6z7sYqqpa7hbRaDTQaDRXl8DfaDQaaLVao5/XnMg9R6nyO5LRMt9KeA8nuDvYmPT15X4NAfnn2F35DQ5xxw+nCvFHeimGd3PXLK+h5TN2joacx6wLoClTpuD2229HSEgIMjIysGLFClx77bU4evQoVCpVp8cUFhbC19e33TZfX18UFhZe8nXWrFmDVatWddielpYGZ2fjjgfQarUoKytDamoqFAp53mEi9xylym9nUksBFOmuQEqKaRb9bCP3awjIP8fuyi/EsRkAsPdcHq4P0m++JWPhNbR8xs6xpqZG733NugCaNWuW7t8xMTEYMmQIQkJC8MMPP2DGjBmXPO7v09WLonjZKeyXL1+OJUuW6B5XVVUhKCgI4eHhcHV1vYoMOtJoNEhNTUVERIRsxx3IPUep8kv9raUAunZAKCIjA036WnK/hoD8c+yu/KY6V+G9wyVILmlEWK/unTqA19DyGTvHth4cfZh1AfR3/v7+CAkJuey3Xz8/vw6tPUVFRR1ahf5KpVJ12qKkVCpN8qZTKBQmO7e5kHuO3Z1fQ5MGp/Na1rYa3surW15X7tcQkH+O3ZFf3wB3uKhsUK1uRkpxHWIC3Uz2Wp3hNbR8xszRkHNYVJtaaWkpcnJy4O/vf8l9Ro4ciZ07d7bbtmPHDsTHx5s6PCKTOZFTgSaNCB8XFYI9HaUOh0hHqRAwqHVQfmIml8UgyyFpAVRTU4OkpCQkJSUBADIyMpCUlITs7GzU1NRg6dKlOHjwIDIzM5GQkIBp06bB29sbt9xyi+4cc+bM0d3xBQCPP/44duzYgZdffhnnz5/Hyy+/jF27dmHx4sXdnB2R8bRN3jc01JMrkpPZaZsP6EiHSSaJzJekXWCJiYkYP3687nHbOJy5c+fi/fffx6lTp/Dpp5+ioqIC/v7+GD9+PLZs2QIXFxfdMdnZ2e0GTsXHx2Pz5s14+umnsWLFCoSHh2PLli2cA4gs2h9c/4vMWNu8VImZZVccc0lkLiQtgMaNGwdRvPRdA7/88ssVz5GQkNBh22233YbbbrvtakIjMhsarYhjWVwBnsxXbE932CoFXKxSI7e8HkHspiULYFFjgIisUXJhNarVzXBW2aCPn8uVDyDqZg52St3g57bWSiJzxwKIyMy1LYAaF+zO1cnJbLW1Tra9X4nMHf+aEpm5tm/U7P4ic9b2/jzChVHJQrAAIjJjoijiSCYHQJP5G9x6K3xqUQ3KahsljoboylgAEZmx3PJ6XKxSw0YhIC6IBRCZL08nO0T4tCwddJS3w5MFYAFEZMbaxlPEBLrBwU6+M8GSPLTNB8QJEckSsAAiMmN/ZLTd/s7WHzJ/Q0LaxgGxACLzxwKIyIwl6sb/cAA0mb+2gdCn8irR0KSROBqiy2MBRGSmymsbkVJUAwAYEsIWIDJ/QZ4O8HFRoUkj4kROhdThEF0WCyAiM9U2kDS8hxO8nFUSR0N0ZYIg/GU+IA6EJvPGAojITLWNo+D8P2RJ2sarcUZoMncsgIjM1BGO/yEL1PZ+PZZVDo320ms9EkmNBRCRGWpo0uBUXiUAYBgLILIgffxc4KyyQbW6GcmF1VKHQ3RJLICIzNCJnAo0aUT4uKgQ5OkgdThEerNRKhAX7A6A64KReWMBRGSG/jr+RxAEiaMhMgzXBSNLwAKIyAy1fXBw/S+yRG3v2yMZZRBFjgMi88QCiMjMaLQijmW1zQDN8T9keeKCPGCjEFBY1YC8inqpwyHqFAsgIjOTXFiNanUznFU26OPnInU4RAZzsFOiX6AbACCR3WBkplgAEZmZtvE/ccHusFHyV5Qs09DW2cu5LhiZK/51JTIznACR5KBtPiC2AJG5stFnJ09Pw/4QC4KAY8eOISQkpEtBEVkrURRZAJEstA2ETr5Yjcq6Jrg52kocEVF7ehVAFRUVWLt2Ldzc3K64ryiKeOSRR6DRcCVgIkPlltfjYpUaNgoBA4PcpQ6HqMu8nVXo5e2E9JJaHM0uw7V9fKUOiagdvQogALjjjjvg4+Oj176PPvpolwMismZtrT8xgW5wsFNKHA3R1RkS6oH0klocySxnAURmR68CSKvVGnTS6mpOf07UFW3z/wwLY/cXWb4hoZ74MjEXiRwITWaIg6CJzEjbB8WQEE6ASJavbRzbidxKqJs5LILMi95dYFqtFmfOnEH//v0BAB988AEaGxt1zyuVSjz88MNQKFhTEXVFeW0jUopqAACDWQCRDIR6OcLb2Q4lNY04nVeJwSFs2STzoXcBtHnzZnz44YfYs2cPAOAf//gH3N3dYWPTcoqSkhLY29tj/vz5pomUSOYSW2d/Du/hBC9nlcTREF09QRAwJMQTP58pxJHMchZAZFb0bq5Zv349FixY0G7bnj17kJGRgYyMDLz66qvYtGmT0QMkshZt3V8c/0Ny0nY7PMcBkbnRuwA6d+4coqOjL/n82LFjceLECaMERWSNjujG/7AAIvloGweUmFUOrZYLo5L50LsAKikpgbOzs+5xeno6QkNDdY9tbW1RW1tr1OCIrEVDkwan8ioBcAJEkpfoAFc42CpRUdeEtOIaqcMh0tG7APL19UVycrLucY8ePdoNeD537hz8/PyMGx2RlUjKqUCTRoSPiwpBng5Sh0NkNLZKBeKC3QH8Oc0DkTnQuwCaMGECXnzxxU6fE0URa9aswYQJE4wWGJE1aRsfMTTME4IgSBwNkXH9uS4YxwGR+dD7LrB///vfGDRoEIYPH46lS5ciKioKgiDg/PnzeO2115CcnIxPP/3UlLESyVbbN+OhvP2dZGho60DoI1ksgMh86F0AhYeHY+fOnZg3bx5mzZql+5YqiiL69OmDHTt2ICIiwmSBEsmVRiviWOst8EM4/odkKC7YAwoByCmrR2FlA/zc7KUOiUj/AggAhg0bhrNnzyIpKQkXLlwAAERGRiIuLs4kwRFZg/OFVahWN8NZZYO+/q5Sh0NkdM4qG0QHuOJ0XhUSs8pw44AAqUMiMqwAajNw4EAMHDjQyKEQWafE1u6vQSEeUCo4/ofkaUiIZ0sBlFnOAojMgl6DoJcsWWLQLe7Lly9HWdmV+3r37t2LadOmISAgAIIg4JtvvtE919TUhKeeegr9+/eHk5MTAgICMGfOHOTn51/2nBs2bIAgCB1+Ghoa9I6fqDu1zf/D8T8kZ20TIh7hQGgyE3oVQG+++Sbq6ur0Pum7776LioqKK+5XW1uL2NhYvPPOOx2eq6urw7Fjx7BixQocO3YM27Ztw4ULF3DTTTdd8byurq4oKCho92Nvzz5nMj+iKP45ASLH/5CMtU3wea6gCtUNTRJHQ6RnF5goirq7vvShb2vRlClTMGXKlE6fc3Nzw86dO9tte/vttzFs2DBkZ2cjODj4kucVBMGgOYnUajXUarXucVVVFQBAo9FAozHuCsYajQZardbo5zUncs/RmPnllNXhYpUatkoB/QNczOb/TO7XEJB/juaWXw9nWwR5OCCnvB5HM8swOtL7qs9pbjkam9zzA4yfoyHn0asAWr9+vcFB+Pr6GnzMlVRWVkIQBLi7u192v5qaGoSEhECj0WDgwIF4/vnnLztQe82aNVi1alWH7Wlpae1mvzYGrVaLsrIypKamtptIUk7knqMx89uVVg0ACPe0Q25WujHCMwq5X0NA/jmaY35RnkrklAM7jqfBD1c/KaI55mhMcs8PMH6ONTX6zzauVwE0d+7cLgdjLA0NDfjnP/+JO++8E66ul75Tpk+fPtiwYQP69++PqqoqvPnmmxg1ahROnDiByMjITo9Zvnw5lixZontcVVWFoKAghIeHX/a1ukKj0SA1NRURERFQKpVGPbe5kHuOxsxvw5nTAIBrevtf8v0pBblfQ0D+OZpjfuPL7fFr2hlkVAtGeb+bY47GJPf8AOPn2NaDo48u3QXW3ZqamnDHHXdAq9Xivffeu+y+I0aMwIgRI3SPR40ahUGDBuHtt9/GW2+91ekxKpUKKpWqw3alUmmSN51CoTDZuc2F3HM0Vn5HsyoAAMPCvMzu/0ru1xCQf47mlt/wXl4AgKScSmghwFZ59d/4zS1HY5N7foBxczTkHGbfptbU1ISZM2ciIyMDO3fuNLhFRqFQYOjQoUhJSTFRhERdU17biJSiluZaDoAmaxDewxnujraob9LgbL7+39SJTMGsC6C24iclJQW7du2Cl5eXwecQRRFJSUnw9/c3QYREXZfYOvtzhI8zPJ3sJI6GyPQUCgFDQng7PJkHSQugmpoaJCUlISkpCQCQkZGBpKQkZGdno7m5GbfddhsSExPxf//3f9BoNCgsLERhYSEaGxt155gzZw6WL1+ue7xq1Sr88ssvSE9PR1JSEubPn4+kpCQsWLCgu9MjuizdAqihnP+HrMefC6NyZXiSlsEF0H333Yfq6uoO22tra3HfffcZdK7ExETExcXp7tBasmQJ4uLi8MwzzyA3Nxfbt29Hbm4uBg4cCH9/f93PgQMHdOfIzs5GQUGB7nFFRQUefPBB9O3bF5MmTUJeXh727t2LYcOGGZoqkUn90Tb/Twi7v8h6tBX8iVllEEVR4mjImhk8CHrjxo146aWX4OLi0m57fX09Pv30U3zyySd6n2vcuHGX/QXQ55cjISGh3eM33ngDb7zxht4xEEmhrrEZp3IrAQDDwlgAkfWICXSDnY0CJTWNyCytQ5i3k9QhkZXSuwCqqqqCKIoQRRHV1dXtZlbWaDT48ccf4ePjY5IgieTmeHYFmrUiAtzs0dPDQepwiLqNykaJgT3d8UdmGY5klrEAIsnoXQC5u7vr1tWKiorq8LwgCJ1OJkhEHf2R0Tr+J8xT7xnWieRiSKgH/sgsQ2JmGWYOCZI6HLJSehdAu3fvhiiKuPbaa7F161Z4ev7ZbG9nZ4eQkBAEBHCFXyJ9tBVA7P4iazQ01BNAGgdCk6T0LoDGjh0LoOVOraCgINlOy01kao3NWhzPafnDP4zz/5AVGhTsAUEA0ktqUVKjhrdzx4loiUzN4EHQISEhqKiowLp163Du3DkIgoDo6Gjcd999cHNzM0WMRLJyKq8SDU1aeDrZIcLHuGvNEVkCN0db9PZ1wfnCaiRmluP6GP0XryYyFoObcRITExEeHo433ngDZWVlKCkpweuvv47w8HAcO3bMFDESyYpu/E+oB8f/kNUa0nY7PCdEJIkYXAA98cQTuOmmm5CZmYlt27bh66+/RkZGBm688UYsXrzYBCESycsR3QSI7P4i69X2/j+SxXFAJA2Du8ASExPx3//+FzY2fx5qY2ODZcuWYciQIUYNjkhuNFpRVwANDzN8aRciuWibEfpMXiXqGpvhaGcRa3OTjBjcAuTq6ors7OwO23NycjpMjkhE7Z0vrEJ1QzOcVTbo68/fF7Jege4OCHR3QLNWxLGsCqnDIStkcAE0a9YszJ8/H1u2bEFOTg5yc3OxefNm3H///Zg9e7YpYiSSjSOt438GhXjARsk7Kcm6DW+dBuJwRqnEkZA1MrjN8bXXXoMgCJgzZw6am5sBALa2tnj44Yfx0ksvGT1AIjn5Q9f9xfE/RMN7eWLb8TwcSmcBRN3P4ALIzs4Ob775JtasWYO0tDSIooiIiAg4OjqaIj4i2RBFkRMgEv1F2zi4EzmVaGjSwN5WKXFEZE26vBq8o6Mj+vfvjwEDBsDR0bFLq8ETWZOMklqU1DTCzkaBAT05ZxZRiJcjfF1VaNRocSybd4NR9zK4ANq4cSPq6+s7bG9bDZ6IOtfW+jMwyB0qG37TJRIEQdcKdDid8wFR9+Jq8ETdRNf9xfl/iHSG9/LE9hP5HAhN3Y6rwRN1k7YB0Bz/Q/Snthag49kVUDdr2DpK3YarwRN1g/yKeuSW10OpEDAoxEPqcIjMRngPJ3g7q1BSo8aJnEp+QaBu06XV4IODg6+4htEjjzyC5557Dt7e3lcXIZEMtN3mGxPgCmcVZ7wlatMyDsgTP5wqwOH0UhZA1G0MHgQdEhKi1wKOmzZtQlVVVZeCIpKbg2ktBdCIcC5/QfR3w3u1TYjIgdDUfUw2Fa0oiqY6NZHFOdQ6wHNELxZARH/XNg7oaFY5mjRaiaMha8G5+IlMLLe8DjllLeN/uAI8UUeRPs7wcLRFfZMGJ3MrpQ6HrAQLICITa+v+GtDTjeN/iDqhUAi6sT+8HZ66CwsgIhM71DrB20h2fxFdEidEpO7GAojIhERR1N0BxvE/RJfWNhA6MbMMzRwHRN3AZAXQ3XffDVdXV1Odnsgi5JTVI6+iHrZKAUNCOf8P0aX08XOFq70Nahs1OJPPO4jJ9K6qAKqtrcUnn3yCd999FykpKe2ee//99zkHEFm9ttaf2J7ucLTj+B+iS1FyHBB1M70LoOzsbIwdOxYuLi6YOHEisrOzMWjQINx///149NFHMXDgQOzdu9eUsRJZnIPs/iLSW9vvyYE0FkBkenoXQEuXLkVjYyPef/99ODo6YvLkyYiMjERBQQEuXryIqVOnYuXKlSYMlciyiKKouwNsJCdAJLqi+PCWXoM/Mso4HxCZnN5t8nv37sX27dsxbNgwTJ06Fd7e3vjkk0/g6+sLAHj66acxYcIEkwVKZGmySutQWNUAO6UCg4I5/ofoSvr4ucDTyQ5ltY04kVOBIZw3i0xI7xag4uJihISEAAA8PT3h6OioK34AwM/PD+Xl5caPkMhCtXV/DQxyh4MdV7gmuhKFQtBNF/F7KrvByLT0LoBEUWy3Bpg+64ERWTOu/0VkuPiItnFAJRJHQnJn0G0pzzzzDBwdHQEAjY2NePHFF+Hm5gYAqKurM350RBbqr/P/cAJEIv21jQM6nl2B+kYNW0/JZPQugMaMGYPk5GTd4/j4eKSnp3fYh4iA9JJaFFWrYWejQFywu9ThEFmMUC9HBLjZI7+yAUcyyzAmqofUIZFM6V0AJSQkmDAMInlp6/4aFOwOe1t+gyXSlyAIiI/wxv+O5uJAWikLIDIZLoVBZAIHdd1fnAyUyFDx4RwHRKYnaQG0d+9eTJs2DQEBARAEAd98802750VRxMqVKxEQEAAHBweMGzcOZ86cueJ5t27diujoaKhUKkRHR+Prr782UQZEHWm1f87/0zagk4j01zYO6FReJSrrmiSOhuRK0gKotrYWsbGxeOeddzp9/pVXXsHrr7+Od955B0eOHIGfnx8mTpyI6urqS57z4MGDmDVrFu655x6cOHEC99xzD2bOnInDhw+bKg2ids4WVKGsthHOKhsMDHKXOhwii+PnZo9ePZwgisAhLotBJiJpATRlyhS88MILmDFjRofnRFHE2rVr8e9//xszZsxATEwMNm7ciLq6Onz++eeXPOfatWsxceJELF++HH369MHy5csxYcIErF271oSZEP1pX0pLs/2IXp6wVbKXmagrRrW2Ah1IZTcYmYbZrs6YkZGBwsJCTJo0SbdNpVJh7NixOHDgAB566KFOjzt48CCeeOKJdtsmT5582QJIrVZDrVbrHldVtaxErNFooNForiKLjjQaDbRardHPa07knuOV8tuXUgwAGNnL02L/D+R+DQH552jp+Y3s5YHPDmXh99SSS+Zg6TleidzzA4yfoyHn0asAOnnypN4nHDBggN77Xk5hYSEAtJttuu1xVlbWZY/r7Ji283VmzZo1WLVqVYftaWlpcHZ2NiTsK9JqtSgrK0NqaioUCnm2Dsg9x8vlp27W4khGGQAgyLYWKSkpUoR41eR+DQH552jp+fUQNRAApBbX4tCJc/By7PhxZek5Xonc8wOMn2NNTY3e++pVAA0cOBCCIHSYDbozxq5U//56+sRg6DHLly/HkiVLdI+rqqoQFBSE8PBwuLq6diHqS9NoNEhNTUVERASUSnneHi33HC+X376UEjRpM+DnqsK1Q6ItdsZ0uV9DQP45yiG/6IAynMmvwkXRDSMiAzo8L4ccL0fu+QHGz7GtB0cfehVAGRkZun8fP34cS5cuxT/+8Q+MHDkSQEu303/+8x+88sorBoZ6aX5+fgBaWnT8/f1124uKijq08Pz9uL+39lzpGJVKBZVK1WG7Uqk0yZtOoVCY7NzmQu45Xiq/A+ktrT/XRPaAjY3Z9jDrRe7XEJB/jpae3zUR3jiTX4WD6WWYMTio030sPccrkXt+gHFzNOQcerU3hYSE6H5Wr16Nt956Cw899BAGDBiAAQMG4KGHHsLatWvx/PPPdznovwsLC4Ofnx927typ29bY2Ig9e/YgPj7+kseNHDmy3TEAsGPHjsseQ2Qs+1sHQI+O5Pw/RFdrpG4+oFKIoihxNCQ3Bn9FPXXqFMLCwjpsDwsLw9mzZw06V01NDVJTU3WPMzIykJSUBE9PTwQHB2Px4sVYvXo1IiMjERkZidWrV8PR0RF33nmn7pg5c+YgMDAQa9asAQA8/vjjGDNmDF5++WXcfPPN+Pbbb7Fr1y7s37/f0FSJDFJSo8bZgpbm11ERLICIrtawME/YKgXkVdQjs7QOYd5OUodEMmLwiKO+ffvihRdeQENDg26bWq3GCy+8gL59+xp0rsTERMTFxSEuLg4AsGTJEsTFxeGZZ54BACxbtgyLFy/GI488giFDhiAvLw87duyAi4uL7hzZ2dkoKCjQPY6Pj8fmzZuxfv16DBgwABs2bMCWLVswfPhwQ1MlMsjvrbfr9vV3hbdzxy5VIjKMo50NBod4AAD2XiiWOBqSG4NbgD744ANMmzYNQUFBiI2NBQCcOHECgiDg+++/N+hc48aNu2yzpiAIWLlyJVauXHnJfTpbo+y2227DbbfdZlAsRFeL3V9Exjc2ygeH0suw90Ix5saHSh0OyYjBBdCwYcOQkZGBTZs24fz58xBFEbNmzcKdd94JJyc2T5J1EkUR+1tbgK5h9xeR0YyJ8sbLP7eMA1I3a6Cyke9gYOpeXbpNxdHREQ8++KCxYyGyWGnFtSiobICdUoGhoZ5Sh0MkG9H+rujhokJxtRpHM8sRzy8YZCR6FUDbt2/X+4Q33XRTl4MhslT7W2d/HhLqAQc7fkMlMhZBEDA60hvbjuVhT0oxCyAyGr0KoOnTp+t1MkEQZD1lN9Gl6Lq/OP6HyOjGRvVoKYCSi7F8imE32xBdil4FkFarNXUcRBarSaPFodYJEEdH9JA4GiL5uSbCG4IAnC+sRlFVA3xc7aUOiWTgqhbe+Out8ETWKimnAjXqZng42qJfgHGXTiEiwMtZhf6BbgCAvSlcHZ6Mw+ACSKPR4Pnnn0dgYCCcnZ2Rnp4OAFixYgXWrVtn9ACJzN3u80UAWpa/UCgsc+0vInM3NqqldXUP5wMiIzG4AHrxxRexYcMGvPLKK7Czs9Nt79+/Pz7++GOjBkdkCXYnt/xBvrYPu7+ITGVMawG0P6UYGi2XxaCrZ3AB9Omnn+Kjjz7CXXfd1W7RsQEDBuD8+fNGDY7I3BVWNuBcQRUEARgTyQKIyFTigtzhYm+D8romnMqrlDockgGDC6C8vDxERER02K7VatHU1GSUoIgsRUJyS/fXwCB3eHH5CyKTsVEqMCq85S5LLotBxmBwAdSvXz/s27evw/avvvpKt6YXkbX4rXX8z/jePhJHQiR/bd1gLIDIGAyeCfrZZ5/FPffcg7y8PGi1Wmzbtg3Jycn49NNPDV4LjMiSqZu1ugVQWQARmd6YqJYWoOM5Faisb4Kz3VXdyExWzuB3z7Rp07Blyxb8+OOPEAQBzzzzDM6dO4fvvvsOEydONEWMRGYpMasctY0a9HBR8fZ3om7Q08MR4T2coNGKOJDK2+Hp6nRpLbDJkydj8uTJxo6FyKLsab37a1wUb38n6i5jo3yQVpyBhORiTIpmyyt1HdsPibqo7fb38X34R5iou4xvnW7it+QiaHk7PF0FvVqAPD09ceHCBXh7e8PDwwOCcOlvu2VlZUYLjshc5Vc1Ib2kFjYKget/EXWj4WFecFbZoLhajVN5lXCUOiCyWHoVQG+88QZcXFwAAGvXrjVlPEQWITGvDkDL6u+u9rYSR0NkPexsFBgb1QM/nCrAr+eLMS1U6ojIUulVAJ04cQK33XYbVCoVwsLCEB8fDxubLg0fIpKFI7ktBRDv/iLqfhP6+rQWQEWYFsrfQeoavcYAvf3226ipqQEAjB8/nt1cZNXqGzU4UVgPgON/iKQwvrcPFK2rw1+s4QS81DV6NeOEhobirbfewqRJkyCKIg4ePAgPD49O9x0zZoxRAyQyN4fSS9GoERHgbo9IH2epwyGyOh5OdhgS4ok/MstwOKcO13AOXuoCvQqgV199FQsWLMCaNWsgCAJuueWWTvcTBAEajcaoARKZm4QLrZMfRvW47A0BRGQ6E/r64I/MMhzKqZU6FLJQenWBTZ8+HYWFhaiqqoIoikhOTkZ5eXmHH3aNkdyJoojfWtf/Gtebi58SSeW6aF8AwMnCelQ3NEscDVkig0YyOzs7Y/fu3QgLC+MgaLJKp/OqkF/RAHsbAfHhXlKHQ2S1wns4I9TLEZmlddifWoIbYwOlDoksjMETIY4dO5bFD1mtX84UAgCGBDrC3lYpcTRE1m1C600Iv7YuSkxkCM4ETWSAHWdbCqD4YCeJIyGiCX1bCqCE5GJoOCs0GYgFEJGeMkpqceFiDWwUAob15PyzRFIbHOwOZzsFyuuacCy7XOpwyMKwACLSU1v318hennBWsfuLSGo2SgWGtn4Z2XXuosTRkKVhAUSkp7YCaGLr3SdEJL0RQS3d0bvOsgAiw+g1mnnGjBl6n3Dbtm1dDobIXBVVNeB4dgUA4Lq+Pqi6mCNtQEQEABgS6AAbhYC04lqkF9egVw9OTkr60asAcnNzM3UcRGZtR+u3y7hgd/i62qOKXzaJzIKTnRIje3liX2opfjpdiIXjI6QOiSyEXgXQ+vXrTR0HkVlr6/6a3M9P4kiI6O+mxPhhX2opfjxVwAKI9MYxQERXUFnfhINppQCASRz/Q2R2Jkb7QqkQcCa/ClmlXBqD9NOlGQ3/97//4csvv0R2djYaGxvbPXfs2DGjBEZkLhKSi9CsFRHp44xePZy53h2RmfF0ssOIXp74PbUUP54qxMPjwqUOiSyAwS1Ab731Fu699174+Pjg+PHjGDZsGLy8vJCeno4pU6aYIkYiSbH7i8j8Te3vDwD46XSBxJGQpTC4AHrvvffw0Ucf4Z133oGdnR2WLVuGnTt34rHHHkNlZaUpYiSSTEOTBgnJxQBYABGZs8n9/KAQgJO5lcgpq5M6HLIABhdA2dnZiI+PBwA4ODiguroaAHDPPffgiy++MG50RBLbl1KCukYNAtzsERPoKnU4RHQJ3s4qDA9rWaCYrUCkD4MLID8/P5SWtgwIDQkJwaFDhwAAGRkZEEXjr8USGhoKQRA6/CxcuLDT/RMSEjrd//z580aPjeTvuxP5AIDrY/whCILE0RDR5Uzt39JK+8OpQokjIUtgcAF07bXX4rvvvgMAzJ8/H0888QQmTpyIWbNm4ZZbbjF6gEeOHEFBQYHuZ+fOnQCA22+//bLHJScntzsuMjLS6LGRvNU1NmNn6/w/Nw0MkDgaIrqSyTF+EATgRE4FcsvZDUaXZ/BdYB999BG0Wi0AYMGCBfD09MT+/fsxbdo0LFiwwOgB9ujRo93jl156CeHh4Rg7duxlj/Px8YG7u7ter6FWq6FWq3WPq6qqAAAajcbod/xoNBpotVpZ30kklxx3nilEfZMGwZ4OiPH/8+4vueR3OczR8sk9P6Bjjl6Othga4oE/Msvx48l8zL8mTOIIr441XkNjnE9fBhdAubm5CAoK0j2eOXMmZs6cCVEUkZOTg+DgYENPqbfGxkZs2rQJS5YsuWJ3RFxcHBoaGhAdHY2nn34a48ePv+S+a9aswapVqzpsT0tLg7OzcadV12q1KCsrQ2pqKhQKeU7DJJcctxxsaUaP76lCamqqbrtc8rsc5mj55J4f0HmOg32V+CMT+DoxE2N8m6UN8CpZ6zW8GjU1NXrvK4gGDtxRKpUoKCiAj49Pu+2lpaXw8fExaaX65Zdf4s4770R2djYCAjrvkkhOTsbevXsxePBgqNVqfPbZZ/jggw+QkJCAMWPGdHpMZy1AQUFBKCsrg6urcQe+ajQapKamIiIiAkqlPFcUl0OOVfVNGL7mNzRqRPz42Cj09nXRPSeH/K6EOVo+uecHdJ7jxaoGxL+cAADYv2wc/N3sJYzw6ljrNbwaVVVV8PT0RGVl5RU/vw1uARJFsdPWl5qaGtjbm/aNtm7dOkyZMuWSxQ8A9O7dG71799Y9HjlyJHJycvDaa69dsgBSqVRQqVQdtiuVSpO86RQKhcnObS4sPcdd5/PRqBER5euM6AD3Ds9ben76YI6WT+75AR1zDPBwwpAQDyRmlWPH2SLcZ+HdYNZ4Da+GIefQuwBasmQJAEAQBKxYsQKOjo665zQaDQ4fPoyBAwfqH6WBsrKysGvXri6tNj9ixAhs2rTJBFGRXG1vvftr2gAOfiayNFP7+yMxqxzfncy3+AKITEfvAuj48eMAWlqATp06BTs7O91zdnZ2iI2NxdKlS40fYav169fDx8cHN9xwg8HHHj9+HP7+/iaIiuSopEaNA61rf02LZQFEZGluHOCPF344i+PZFcgoqUWYt5PUIZEZ0rsA2r17NwDg3nvvxZtvvmn0sTGXo9VqsX79esydOxc2Nu1DXr58OfLy8vDpp58CANauXYvQ0FD069dPN2h669at2Lp1a7fFS5btp1MF0GhFDOjphlD+4SSyOD6u9rgmsgf2XijGN8fz8MTEKKlDIjNk8JDr9evX64qf3Nxc5OXlGT2ov9u1axeys7Nx3333dXiuoKAA2dnZuseNjY1YunQpBgwYgNGjR2P//v344YcfMGPGDJPHSfLw3YmWWWRvYusPkcW6Ja7l9/ebpDyTTNJLls/gAkir1eK5556Dm5sbQkJCEBwcDHd3dzz//PO6+YGMbdKkSRBFEVFRHav4DRs2ICEhQfd42bJlSE1NRX19PcrKyrBv3z5MnTrVJHGR/ORX1OOPzDIIAnDDAHabElmqyf384GinRFZpHY5ll0sdDpkhgwugf//733jnnXfw0ksv4fjx4zh27BhWr16Nt99+GytWrDBFjETd5oeTLa0/Q0M94e/mIHE0RNRVjnY2uL51AeNtx0zfU0GWx+ACaOPGjfj444/x8MMPY8CAAYiNjcUjjzyC//73v9iwYYMJQiTqPt+eaPlDycHPRJbvlkGBAIDvTxagsdk0PRRkuQwugMrKytCnT58O2/v06YOysjKjBEUkhXMFVTidVwVbpYAb+rP7i8jSxYd7w8dFhcr6JuxOLpI6HDIzBhdAsbGxeOeddzpsf+eddxAbG2uUoIik8FViLgDgur6+8HSyu8LeRGTulAoBN7cuZPw1u8HobwyeCfqVV17BDTfcgF27dmHkyJEQBAEHDhxATk4OfvzxR1PESGRyjc1afJPU8gfy9iE9JY6GiIzllrie+O++DPx2vgiVdU1wc7SVOiQyEwa3AI0dOxYXLlzALbfcgoqKCpSVlWHGjBlITk7G6NGjTREjkcn9dr4IZbWN8HFRYUxkD6nDISIjiQ5wRR8/FzRqtPjhVIHU4ZAZMbgFKDs7G0FBQXjxxRc7fc6Uq8ETmcpXiTkAgBmDesJGKc9Vl4ms1fS4QLz003l8fTwXdw7nZxS1MPgvfVhYGIqLiztsLy0tRVgY11why1NU3YCECy3vaXZ/EcnPzQMDIAjAkcxyZJfWSR0OmQmDCyApV4MnMoWvj+VBoxUxOMQD4T2cpQ6HiIzM380B10R4AwC2JGZfYW+yFhazGjyRKYiiiC9bu79uH8zWHyK5mj0sGPtSSvBlYi4WXxcFW3Z1Wz2LWQ2eyBSO51QgrbgW9rYKLn1BJGMTo33h7axCcbUav567iOtj+Ptu7SxiNXgiU2kb/Dy1vz9c7Hl7LJFc2SoVuH1IT7yfkIbP/8hhAURXtxo8kSWrb9ToVn6/fXCQxNEQkanNHtpyB9i+lGLklHEwtLVjJyhZrR9PFaBG3YxgT0cMD/OUOhwiMrFgL0eMjvSGKAKbj3AwtLVjAURW69NDWQCAWUODoFB0vLORiORn9rCWVqAvE3PRpOECqdaMBRBZpaScCpzIqYCdUoE7hrL7i8ha/H0wNFkvFkBklT49mAkAuHGAP7ycVdIGQ0Tdpm0wNAB8/keOxNGQlFgAkdUprVHj+5Mtg5/nxIdKGwwRdTsOhiaABRBZoS2JOWhs1iK2pxsGBrlLHQ4RdbO/Dob+4g8OhrZWLIDIqmi0Iv7vUMsfvHtGhkobDBFJ5s7WwdCbj+SgoUkjcTQkBRZAZFV+PXcReRX18HC0xY2c+ZnIak2M9kWguwPKahvxzfE8qcMhCbAAIqvy6cGWW9/vGBYMe1ulxNEQkVRslArMax0DuG5/BkRRlDYg6nYsgMhqpBbVYH9qCRQCcNfwYKnDISKJzRoWBCc7JVKKarA3pUTqcKibsQAiq7GpdeLDCX190dPDUeJoiEhqrva2mNk6D9i6/RkSR0PdjQUQWYXKuibdwqdzRoZIHA0RmYt748MgCMDeC8W4cLFa6nCoG7EAIquw6XAWahs16OPngmsivKUOh4jMRLCXIyZF+wIAPmErkFVhAUSy19Ck0f1hWzA2HILAdb+I6E/3j+4FANh2PA+lNWqJo6HuwgKIZO+ro7korW1ETw8H3vpORB0MCfHAgJ5uaGzWYtMhToxoLVgAkaw1a7T4aG8aAOCB0b1go+RbnojaEwQB868JAwB8digL6mZOjGgN+GlAsvbj6ULklNXD08kOM4dw1Xci6tzU/v7wd7NHSY0aW49yYkRrwAKIZEsURbyf0NL6My8+FA52nPiQiDpnq1ToxgK9l5CKJo1W4ojI1FgAkWztTSnBuYIqONopees7EV3RncOC4e1sh9zyenzN5TFkjwUQydb7CakAgDuGBsPd0U7iaIjI3DnYKfHgmJZWoHd3p6KZrUCyxgKIZOl4djkOpZfBRiHg/tFhUodDRBbiruEh8HSyQ1ZpHbafyJc6HDIhFkAkS2t3pQAAbh4YiAB3B4mjISJL4aSy0X1peue3VGi0XCRVrsy+AFq5ciUEQWj34+fnd9lj9uzZg8GDB8Pe3h69evXCBx980E3Rkjk4klmGPReKYaMQ8NiECKnDISILM2dkKNwcbJFeUovvT7IVSK7MvgACgH79+qGgoED3c+rUqUvum5GRgalTp2L06NE4fvw4/vWvf+Gxxx7D1q1buzFikoooinj1l2QAwO1DghDi5SRxRERkaZxVNrp5gd75LRVatgLJko3UAejDxsbmiq0+bT744AMEBwdj7dq1AIC+ffsiMTERr732Gm699VYTRknmYH9qCf7IKIOdUoFHr2XrDxF1zdz4UPx3XzpSimrw85lCTO3PWeTlxiIKoJSUFAQEBEClUmH48OFYvXo1evXq1em+Bw8exKRJk9ptmzx5MtatW4empibY2tp2OEatVkOt/nP9l6qqKgCARqOBRmPcGUE1Gg20Wq3Rz2tOpMpRFEW8+nNL68+dw4Pg62Jnkhh4DeVB7jnKPT/AtDk62ykwb2QI3t6dhjd2XsB1fXpAqejedQR5Dbt2Pn2ZfQE0fPhwfPrpp4iKisLFixfxwgsvID4+HmfOnIGXl1eH/QsLC+Hr69tum6+vL5qbm1FSUgJ//45V/Jo1a7Bq1aoO29PS0uDs7Gy8ZABotVqUlZUhNTUVCoVF9EAaTKocD2bX4mReJVQ2AiYHtRTOpsBrKA9yz1Hu+QGmz3GMnwbr7RRIKarB+z8fw+RIV6O/xuXwGhqupqZG733NvgCaMmWK7t/9+/fHyJEjER4ejo0bN2LJkiWdHvP31b5FUex0e5vly5e3O1dVVRWCgoIQHh4OV1fjvuE1Gg1SU1MREREBpVKeMxNLkaNWK2LxzwcAAPeNCsOwAVEmey1eQ3mQe45yzw/onhwfrVBhzU/J+OJUFeZfN7BbZ5TnNTRcWw+OPsy+APo7Jycn9O/f/5Lf7v38/FBYWNhuW1FREWxsbDptMQIAlUoFlUrVYbtSqTTJm06hUJjs3Oaiu3P84XQ+zhdWw0Vlg4fGhpv8dXkN5UHuOco9P8D0Oc6ND8OnB7ORV1GPjYeysXB8944t5DU0jCHnsLg2NbVajXPnznXalQUAI0eOxM6dO9tt27FjB4YMGdLp+B+yfE0aLd7YeQEA8MCYXpz1mYiMxt5WiX9M7g0AeD8hDaU16iscQZbC7AugpUuXYs+ePcjIyMDhw4dx2223oaqqCnPnzgXQ0n01Z84c3f4LFixAVlYWlixZgnPnzuGTTz7BunXrsHTpUqlSIBP79GAWMkpq4eVkh/uu4azPRGRcN8UGoF+AK2rUzXj7t1SpwyEjMfsCKDc3F7Nnz0bv3r0xY8YM2NnZ4dChQwgJaVncsqCgANnZ2br9w8LC8OOPPyIhIQEDBw7E888/j7feeou3wMtUaY0aa3e1tP78Y3JvOKssrleXiMycQiHgX1P7AgA2HcpCZkmtxBGRMZj9p8XmzZsv+/yGDRs6bBs7diyOHTtmoojInLy+8wKqG5oR7e+K24cESR0OEcnUqAhvjI3qgT0XivHqjmS8e+cgqUOiq2T2LUBEl3I2vwpf/NHS+vfstOhun6ODiKzLP6f0gSAAP5wswNGscqnDoavEAogskiiKeO77M9CKwA39/TG8V+d3+BERGUtff1fcNqgnAOCZb09zoVQLxwKILNIvZwpxKL0MKhsF/jmlj9ThEJGVeGpKH7ja2+BMfhU2HcqSOhy6CiyAyOI0NGnwwg/nAAAPjumFIE9HiSMiImvh7azCP65v+dL12i/JKKpukDgi6ioWQGRxPtyTjtzyevi52uPhceFSh0NEVubOYcEY0NMN1epmrG79MkaWhwUQWZTUomq8u7tlHo7lU/vA0c7sb2QkIplRKgS8MD0GggB8k5SPA2klUodEXcACiCyGVivin1tPoVGjxfjePXBTbIDUIRGRlRrQ0x13D2+Zj+6Zb8+gsVkrcURkKBZAZDH+749sJGaVw9FOiRdu6X/JxW2JiLrD0km94eVkh9SiGny8P13qcMhALIDIIhRU1uPln84DAJZN7o1AdweJIyIia+fmaKubIfrNXSlIK66ROCIyBAsgMnuiKOLpr0+jRt2MQcHuuGdkqNQhEREBAGYMCsToSG+om7V48ssTaNawK8xSsAAis/fDqQL8er4ItkoBL906gDM+E5HZEAQBL986AC4qGyTlVOCjfewKsxQsgMisldSosXL7GQDAI+MiEOXrInFERETtBbg74Jlp0QCAtTtTkFxYLXFEpA8WQGS2RFHEP746gZKaRvT2dcEj4znnDxGZp9sG98R1fX3QqNFiyZdJaGJXmNljAURm67NDWdidXAw7GwXenD0QKhul1CEREXVKEASsntEf7o62OJNfpZuvjMwXCyAySxcuVuPF1hlWl0/pgz5+rhJHRER0eT4u9nju5hgAwDu/peJkboW0AdFlsQAis9PQpMFjXxyHulmLcb17YF58qNQhERHpZdoAf9zQ3x/NWhGLPj+OqoYmqUOiS2ABRGbn5Z/P43xhNbyd7fDqbbGc8JCILIYgCFh9S3/09HBAdlkdln11EqIoSh0WdYIFEJmV3clFWP97JgDg1dti0cNFJW1AREQGcnO0xbt3DoKtUsDPZwqx4UCm1CFRJ1gAkdnILq3D4s1JAIC5I0Mwvo+PtAEREXVRbJA7/t06S/TqH88hKadC2oCoAxZAZBbqGpvx4GeJqKxvwsAgd/zrhr5Sh0REdFXmxodian8/NGlELPy/Y6is43ggc8ICiCQniiKe2nqqddyPCh/cPZi3vBORxROEltnrgz0dkVdRjye/SoJWy/FA5oIFEElu3f4MfHciHzYKAe/dNQh+bvZSh0REZBSu9rZ4765BsLNRYNe5Iry6I1nqkKgVCyCS1IHUEqz+sWW+nxU3RmNYmKfEERERGVdMoBteuXUAAOD9hDT872iuxBERwAKIJJRZUouFnx+DVmxZUXnOyBCpQyIiMonpcYFYND4CALB820kcySyTOCJiAUSSKK1RY+76P1Be14T+gW5YfUt/zvdDRLK2ZGIUpsS0DIp+6LOjyCmrkzokq8YCiLpdXWMz7tuYiKzSOvT0cMC6eUNgb8tBz0QkbwqFgP/MjEVMoCvKahtx34YjnClaQiyAqFs1a7R49PPjOJFTAXdHW2y8bxh8XDjomYisg6OdDT6eMxQ+LiqkFNXggY2JaGjSSB2WVWIBRN1GFEU8s/0Mfj1fBJWNAuvmDkF4D2epwyIi6lZ+bvb4ZN5QOKtscDijDIs+P4YmjVbqsKwOCyDqNm/+moLPD2dDEIA37xiIwSG844uIrFNMoBs+njtEd3v8sv+d5BxB3YwFEHWLd3enYu2uFADAszdG4/oYf4kjIiKS1oheXnjvzkFQKgR8fTwPz31/lgundiMWQGRyH+1Nw6u/tEz+9Y/JvTFvVJjEERERmYfron3xn9tjAQAbDmTijZ0XJI7IerAAIpNatz8Dq388D6DlFtCFrfNgEBFRi+lxgXju5n4AgLd+S8VrvySzJagbsAAik/nsUBae//4sAOCxayPw2IRIiSMiIjJPc0aG4l9T+wAA3tmditU/nmMRZGI2UgdA8vTVqXKsO9oy0+nD48LxxMQoiSMiIjJvD44Jh8pGiWe3n8F/92WgoVGD2X34MW0qbAEioxJFEWt+Ot+u+Fk2uTdneSYi0sPc+FC8NKM/BAH47HA23jxQDA3vDjMJFkBkNM0aLZZ+dRIf788EAPzz+t546vo+LH6IiAxwx7BgvD4zFgoB+CWlGo9tTuJkiSZg9gXQmjVrMHToULi4uMDHxwfTp09HcnLyZY9JSEiAIAgdfs6fP99NUVufhiYNFmw6iq3HcqFUCFgyqgceGM27vYiIuuKWuJ54646BsFUAP5+5iDv/ewilNWqpw5IVsy+A9uzZg4ULF+LQoUPYuXMnmpubMWnSJNTW1l7x2OTkZBQUFOh+IiM5CNcUiqobMPu/h7DrXMsMz+/dGYdJka5Sh0VEZNGmxPhh9aQAuNrb4Fh2BW59/wAySq782Uf6MfvRVT///HO7x+vXr4ePjw+OHj2KMWPGXPZYHx8fuLu7X/E11Go11Oo/K+uqqioAgEajgUZj3GZHjUYDrVZr9PNK5Ux+FR7cdAyFlQ1wtbfBh3cPwuBgN6Smlssmx7+T2zXsDHO0fHLPD5B/jhqNBv18VNj8wFA8uCkJmaV1mPHe7y1/Z0M8pA7PKIx9DQ05j9kXQH9XWVkJAPD0vPIyCnFxcWhoaEB0dDSefvppjB8/vtP91qxZg1WrVnXYnpaWBmdn465VpdVqUVZWhtTUVCgUZt8Ad1n7Mmvw2v4iqJtF9HS1xarr/ODRXIrU1GLZ5NgZOV3DS2GOlk/u+QHyz7EtP08Ar07ywTO7CpFSqsZdHx/GIyN6YEqU5be0G/sa1tTU6L2vIFrQRAOiKOLmm29GeXk59u3bd8n9kpOTsXfvXgwePBhqtRqfffYZPvjgAyQkJHTaatRZC1BQUBDKysrg6mrcN5hGo0FqaioiIiKgVCqNeu7uotWKeGd3Gt78LRUAMDrSG2/NioWrgy0AeeR4OXLPD2COciD3/AD55/j3/Ooam/HkV6ew4+xFAMCsIT3x7LRoqGwst/gz9jWsqqqCp6cnKisrr/j5bVEtQIsWLcLJkyexf//+y+7Xu3dv9O7dW/d45MiRyMnJwWuvvdZpAaRSqaBSqTpsVyqVJvmlUigUJju3qZXWqLHkyxPYc6EYADD/mjAsn9IHNsr2v4CWnKM+5J4fwBzlQO75AfLP8a/5uTgo8cHdg/H+njS8tiMZWxJzcb6wGu/fPRgB7g5Sh9plxryGhpzDYsrGRx99FNu3b8fu3bvRs2dPg48fMWIEUlJSTBCZ9fgjowxT39qHPReKobJR4JXbBmDFjdEdih8iIjINhULAwvER2HDvMLg72uJEbiVufHs/9rZ+KSX9mf0nlyiKWLRoEbZt24bffvsNYWFdu7X6+PHj8PfnCuRdodWKeHd3Ku746CAuVqkR3sMJ3y4ahZlDgqQOjYjIKo2N6oHvFl2DaH9XlNU2Ys4nf+D5789yviADmH0X2MKFC/H555/j22+/hYuLCwoLCwEAbm5ucHBoafJbvnw58vLy8OmnnwIA1q5di9DQUPTr1w+NjY3YtGkTtm7diq1bt0qWh6XKLa/Dsv+dxIG0UgDALXGBeGF6DJxUZv/WISKStSBPR2x7JB4v/nAOnx3Kwrr9Gfg9tQRvzY5DlK+L1OGZPbP/FHv//fcBAOPGjWu3ff369Zg3bx4AoKCgANnZ2brnGhsbsXTpUuTl5cHBwQH9+vXDDz/8gKlTp3ZX2BZPFEV8mZiD578/hxp1M+xtFXjuphjcPqQnZ3YmIjIT9rZKPD89BuN698Cy/53E+cJqTHt7P5ZP6YM5I0OhUPDv9aWYfQGkz01qGzZsaPd42bJlWLZsmYkikr+LVQ3459aT2J3c0qc8KNgd/5k5EGHeThJHRkREnZnQ1xc/LR6Nf3x1EnsuFGPld2fx46lCrLm1P8J7GHc6F7kw+zFA1H20WhGfH87GxNf3YHdyMexsFFg+pQ++WhDP4oeIyMz5uNhjw71DseqmfnC0U+KPzDJMeXMf3t2diiaNVurwzI7ZtwBR9ziTX4mnvzmN49kVAIABPd3wn9tjEcl+ZCIiiyEIAubGh2JCXx/86+vT2HuhGK/+kozvTxZg9S0xiAuWxwzSxsACyMrVqJvx+o4L2HAgA1oRcFbZYMnEKMwZGcLb24mILFRPD0dsvHcovknKw3PfncW5girc8t4B3DqoJ56a0hs+LvZShyg5FkBWqlmjxZeJuXh95wWUtK4wfMMAf6y4IRp+bvzFICKydIIg4Ja4nhgd2QNrfjyPrcdysfVYLn45U4jHJkRgXnwY7Cx4FumrxQLIyoiiiIQLxVjz4zlcuNiyZkqolyNW3RyDsVE9JI6OiIiMzdtZhf/MjMXdI4KxcvsZnMitxOofz+OLP3KwdFJvTO3vZ5V397IAsiIncirw2o5k7EspAQC4O9ri8QmRuGt4iFV/CyAisgZxwR74+pFR2HosFy//nIyMklos/PwY+ge6Ydn1vXFNhLdVFUIsgKzA6bxKvLHzAn49XwQAsFMqMDc+BIvGR8LN0Vbi6IiIqLsoFAJuHxKEKf398fG+dPx3bzpO5VXinnV/ID7cC09OisLgEE+pw+wWLIBk7HReJd78NQU7W1cOVgjALXE98fiESAR7OUocHRERScVZZYPF10XhnhEheHd3GjYdysKBtFIceP8gRvbywqPXRmBkuJesW4RYAMmMKIrYn1qCD/ekY39qS1eXIADTBwbi0Wsj0IsTYhERUSsvZxWemRaN+64Jxdu/pmLb8VwcTC/FwfRSxAW7Y9H4CIzv7SPLGaVZAMlEk0aLH08V4MM96ThbUAUAUCoE3DjAH49eG4EIH87nQ0REnevp4YiXbxuAx66LxEd70rD5SA6OZ1dg/sZE9OrhhPtGhWHGoEA42smnbJBPJlaqqKoBn/+Rjc8PZ6OouuV2dgdbJWYNDcL8a8IQ5MmuLiIi0k+guwNW3RyDhddGYN2+DHx+OBvpxbV4+pvTePWXZNw5PBj3jAhBgLuD1KFeNRZAFkgURfyRUYZNh7Px06kCNGtb1kvzdlZhzsgQ3DMiBB5OdhJHSURElsrHxR7Lp/bFoxMi8VViDtb/nonssjq8n5CGD/ekYXxvH9w5PBjjevtAaaHdYyyALEhhZQO2HsvFV4k5yCyt020fEuKBOfGhuL6fH29nJyIio3FW2eDeUWGYMzIUu85dxPrfM3AovQy/ni/Cr+eL4O9mj5lDgnDroJ4Wd3MNCyAzV6tuxq5zF/FtUj4SkovQ2tgDJzslpsUG4J6RIegX4CZtkEREJGtKhYDJ/fwwuZ8f0opr8MXhbPzvWC4KKhvw5q8pePPXFAwO8cD0uEDc2N/fInohWACZoYYmDRKSi/HdyXz8eu4iGpr+XMV3aKgHZg4JwtT+/nBS8fIREVH3Cu/hjKdvjMbSyb3xy5lC/O9oLn5PLcHRrHIczSrHc9+dwdgoH9wSF4gJfX1gb6uUOuRO8RPUTDQ0aXAwrRTfnczHjjMXUaNu1j0X6uWIGwcEYMagQN7GTkREZsHeVombBwbi5oGBuFjVgO9O5OPr43k4k1+FXecuYte5i3CyU2Jcbx9M6ueL8X184GpvPpPvsgCSUEmNGr+dL8Kv5y5iX0oJ6ho1uucC3OxxY2wApg0IQEygq6wnoyIiIsvm62qP+0f3wv2je+HCxWp8czwP3yblI6+iHj+cKsAPpwpgqxQQH+6NSf18MTHaV/IV6VkAdSNRFJF8sRpfnizHid8OISmnAqL45/N+rvaY1M8XN8UGYFCwhywnniIiInmL8nXBsuv7YOmk3jiZV4lfzhTilzOFSC+uxZ4LxdhzoRhPf3MacUHu+GTuYMniZAHUjU7nVWHaO7+32xYT6Irr+vriur6+6BfAlh4iIpIHhULAwCB3DAxyx1PX90FqUQ1+OVOIHWcv4kROBSrqmuAs4VhWFkDdqF+AK3p6OMDfScBNg0MxMdoffm7SNgESERF1hwgfZ0T4RGDh+AgUVNYjv6JB0i/9LIC6kUIh4LclY5CelorIyGAoleY5Mp6IiMiU/N0c4O/mAI1Gc+WdTYSz5nUzS50xk4iISE5YABEREZHVYQFEREREVocFEBEREVkdFkBERERkdVgAERERkdVhAURERERWhwUQERERWR0WQERERGR1WAARERGR1WEBRERERFaHBRARERFZHRZAREREZHVYABEREZHVsZE6AHMkiiIAoKqqyujn1mg0qKmpQVVVFZRKpdHPbw7knqPc8wOYoxzIPT9A/jnKPT/A+Dm2fW63fY5fDgugTlRXVwMAgoKCJI6EiIiIDFVdXQ03N7fL7iOI+pRJVkar1SI/Px8uLi4QBMGo566qqkJQUBBycnLg6upq1HObC7nnKPf8AOYoB3LPD5B/jnLPDzB+jqIoorq6GgEBAVAoLj/Khy1AnVAoFOjZs6dJX8PV1VW2b+g2cs9R7vkBzFEO5J4fIP8c5Z4fYNwcr9Ty04aDoImIiMjqsAAiIiIiq8MCqJupVCo8++yzUKlUUodiMnLPUe75AcxRDuSeHyD/HOWeHyBtjhwETURERFaHLUBERERkdVgAERERkdVhAURERERWhwUQERERWR0WQCby4osvIj4+Ho6OjnB3d+90n+zsbEybNg1OTk7w9vbGY489hsbGxnb7nDp1CmPHjoWDgwMCAwPx3HPP6bXGSXdLSEiAIAid/hw5ckS3X2fPf/DBBxJGbpjQ0NAO8f/zn/9st48+19VcZWZmYv78+QgLC4ODgwPCw8Px7LPPdojf0q/je++9h7CwMNjb22Pw4MHYt2+f1CF1yZo1azB06FC4uLjAx8cH06dPR3Jycrt95s2b1+FajRgxQqKIDbdy5coO8fv5+emeF0URK1euREBAABwcHDBu3DicOXNGwogN19nfFUEQsHDhQgCWdw337t2LadOmISAgAIIg4Jtvvmn3vD7XTK1W49FHH4W3tzecnJxw0003ITc316hxciZoE2lsbMTtt9+OkSNHYt26dR2e12g0uOGGG9CjRw/s378fpaWlmDt3LkRRxNtvvw2gZYrwiRMnYvz48Thy5AguXLiAefPmwcnJCU8++WR3p3RZ8fHxKCgoaLdtxYoV2LVrF4YMGdJu+/r163H99dfrHus7a6e5eO655/DAAw/oHjs7O+v+rc91NWfnz5+HVqvFhx9+iIiICJw+fRoPPPAAamtr8dprr7Xb11Kv45YtW7B48WK89957GDVqFD788ENMmTIFZ8+eRXBwsNThGWTPnj1YuHAhhg4diubmZvz73//GpEmTcPbsWTg5Oen2u/7667F+/XrdYzs7OynC7bJ+/fph165dusd/XTTzlVdeweuvv44NGzYgKioKL7zwAiZOnIjk5GS4uLhIEa7Bjhw5Ao1Go3t8+vRpTJw4EbfffrtumyVdw9raWsTGxuLee+/Frbfe2uF5fa7Z4sWL8d1332Hz5s3w8vLCk08+iRtvvBFHjx413sKwIpnU+vXrRTc3tw7bf/zxR1GhUIh5eXm6bV988YWoUqnEyspKURRF8b333hPd3NzEhoYG3T5r1qwRAwICRK1Wa/LYr0ZjY6Po4+MjPvfcc+22AxC//vpraYIygpCQEPGNN9645PP6XFdL88orr4hhYWHttlnydRw2bJi4YMGCdtv69Okj/vOf/5QoIuMpKioSAYh79uzRbZs7d6548803SxfUVXr22WfF2NjYTp/TarWin5+f+NJLL+m2NTQ0iG5ubuIHH3zQTREa3+OPPy6Gh4fr/s5b8jX8+98Kfa5ZRUWFaGtrK27evFm3T15enqhQKMSff/7ZaLGxC0wiBw8eRExMDAICAnTbJk+eDLVajaNHj+r2GTt2bLsJoiZPnoz8/HxkZmZ2d8gG2b59O0pKSjBv3rwOzy1atAje3t4YOnQoPvjgA2i12u4P8Cq8/PLL8PLywsCBA/Hiiy+26x7S57pamsrKSnh6enbYbonXsbGxEUePHsWkSZPabZ80aRIOHDggUVTGU1lZCQAdrldCQgJ8fHwQFRWFBx54AEVFRVKE12UpKSkICAhAWFgY7rjjDqSnpwMAMjIyUFhY2O56qlQqjB071mKvZ2NjIzZt2oT77ruv3WLcln4N2+hzzY4ePYqmpqZ2+wQEBCAmJsao15VdYBIpLCyEr69vu20eHh6ws7NDYWGhbp/Q0NB2+7QdU1hYiLCwsG6JtSvWrVuHyZMnIygoqN32559/HhMmTICDgwN+/fVXPPnkkygpKcHTTz8tUaSGefzxxzFo0CB4eHjgjz/+wPLly5GRkYGPP/4YgH7X1ZKkpaXh7bffxn/+85922y31OpaUlECj0XS4Rr6+vhZ5ff5KFEUsWbIE11xzDWJiYnTbp0yZgttvvx0hISHIyMjAihUrcO211+Lo0aMWMcPw8OHD8emnnyIqKgoXL17ECy+8gPj4eJw5c0Z3zTq7nllZWVKEe9W++eYbVFRUtPvyaOnX8K/0uWaFhYWws7ODh4dHh32M+XvKAsgAK1euxKpVqy67z5EjRzqMebmUv1b3bURRbLf97/uIrQOgOzvWFLqSc25uLn755Rd8+eWXHfb96wfkwIEDAbSMqZHyg9OQHJ944gndtgEDBsDDwwO33XabrlUI0O+6dreuXMf8/Hxcf/31uP3223H//fe329ccr6MhOvu9kvL6GMOiRYtw8uRJ7N+/v932WbNm6f4dExODIUOGICQkBD/88ANmzJjR3WEabMqUKbp/9+/fHyNHjkR4eDg2btyoGwgsp+u5bt06TJkypV0rsqVfw8505ZoZ+7qyADLAokWLcMcdd1x2n7+32FyKn58fDh8+3G5beXk5mpqadJWxn59fh2q3rdnz79WzqXQl5/Xr18PLyws33XTTFc8/YsQIVFVV4eLFi92W099dzXVt+wOcmpoKLy8vva6rFAzNMT8/H+PHj8fIkSPx0UcfXfH85nAd9eHt7Q2lUtnp75U5x30ljz76KLZv3469e/eiZ8+el93X398fISEhSElJ6abojMvJyQn9+/dHSkoKpk+fDqClxcDf31+3j6Vez6ysLOzatQvbtm277H6WfA3b7uC73DXz8/NDY2MjysvL27UCFRUVIT4+3njBGG00EXXqSoOg8/Pzdds2b97cYRC0u7u7qFardfu89NJLZj0IWqvVimFhYeKTTz6p1/5vv/22aG9v326gtyX57rvvRABiVlaWKIr6XVdzl5ubK0ZGRop33HGH2NzcrNcxlnQdhw0bJj788MPttvXt29ciB0FrtVpx4cKFYkBAgHjhwgW9jikpKRFVKpW4ceNGE0dnGg0NDWJgYKC4atUq3YDal19+Wfe8Wq222EHQzz77rOjn5yc2NTVddj9Luoa4xCDoy12ztkHQW7Zs0e2Tn59v9EHQLIBMJCsrSzx+/Li4atUq0dnZWTx+/Lh4/Phxsbq6WhRFUWxubhZjYmLECRMmiMeOHRN37dol9uzZU1y0aJHuHBUVFaKvr684e/Zs8dSpU+K2bdtEV1dX8bXXXpMqrSvatWuXCEA8e/Zsh+e2b98ufvTRR+KpU6fE1NRU8b///a/o6uoqPvbYYxJEargDBw6Ir7/+unj8+HExPT1d3LJlixgQECDedNNNun30ua7mLC8vT4yIiBCvvfZaMTc3VywoKND9tLH067h582bR1tZWXLdunXj27Flx8eLFopOTk5iZmSl1aAZ7+OGHRTc3NzEhIaHdtaqrqxNFURSrq6vFJ598Ujxw4ICYkZEh7t69Wxw5cqQYGBgoVlVVSRy9fp588kkxISFBTE9PFw8dOiTeeOONoouLi+56vfTSS6Kbm5u4bds28dSpU+Ls2bNFf39/i8mvjUajEYODg8Wnnnqq3XZLvIbV1dW6zzwAur+bbV8U9blmCxYsEHv27Cnu2rVLPHbsmHjttdeKsbGxen8p0wcLIBOZO3euCKDDz+7du3X7ZGVliTfccIPo4OAgenp6iosWLerwDfrkyZPi6NGjRZVKJfr5+YkrV64029YfURTF2bNni/Hx8Z0+99NPP4kDBw4UnZ2dRUdHRzEmJkZcu3btFb/tmIujR4+Kw4cPF93c3ER7e3uxd+/e4rPPPivW1ta220+f62qu1q9f3+n79q+NxZZ+HUVRFN99910xJCREtLOzEwcNGtTutnFLcqlrtX79elEURbGurk6cNGmS2KNHD9HW1lYMDg4W586dK2ZnZ0sbuAFmzZol+vv7i7a2tmJAQIA4Y8YM8cyZM7rntVqtruVEpVKJY8aMEU+dOiVhxF3zyy+/iADE5OTkdtst8Rru3r270/fl3LlzRVHU75rV19eLixYtEj09PUUHBwfxxhtvNHrOgiia4bTCRERERCbEeYCIiIjI6rAAIiIiIqvDAoiIiIisDgsgIiIisjosgIiIiMjqsAAiIiIiq8MCiIiIiKwOCyAiIiKyOiyAiEg2Vq5cqVud3lCZmZkQBAGCIHT5HFdr5cqVuhjWrl0rSQxE1oIFEBHJxtKlS/Hrr79e1Tl27dp11efoqqVLl6KgoOCKK7oT0dWzkToAIiJjcXZ2hrOz81Wdw8vLC15eXkaKyDBt8SuVSklen8iasAWIiIzmr91If/0ZN27cFY8tLS3F7Nmz0bNnTzg6OqJ///744osvdM8XFxfDz88Pq1ev1m07fPgw7OzssGPHDgAdu8ASEhIwbNgwODk5wd3dHaNGjUJWVpZBOc2bNw/Tp0/H6tWr4evrC3d3d6xatQrNzc34xz/+AU9PT/Ts2ROffPJJh/+HL7/8EqNHj4aDgwOGDh2KCxcu4MiRIxgyZAicnZ1x/fXXo7i42KB4iMg4WAARkdEEBQWhoKBA93P8+HF4eXlhzJgxVzy2oaEBgwcPxvfff4/Tp0/jwQcfxD333IPDhw8DAHr06IFPPvkEK1euRGJiImpqanD33XfjkUcewaRJkzqcr7m5GdOnT8fYsWNx8uRJHDx4EA8++CAEQTA4r99++w35+fnYu3cvXn/9daxcuRI33ngjPDw8cPjwYSxYsAALFixATk5Ou+OeffZZPP300zh27BhsbGwwe/ZsLFu2DG+++Sb27duHtLQ0PPPMMwbHQ0RGYNS15YmIWtXX14vDhw8Xb7zxRlGj0XTpHFOnThWffPLJdtseeeQRMSoqSrzrrrvEmJgYsb6+Xvfcs88+K8bGxoqiKIqlpaUiADEhIUGv18rIyBABiMePH2+3fe7cuWJISEi7HHr37i2OHj1a97i5uVl0cnISv/jii3bn+vjjj3X7fPHFFyIA8ddff9VtW7Nmjdi7d+8OsYSEhIhvvPGGXnETUddwDBARmcT8+fNRXV2NnTt3QqG4cmOzRqPBSy+9hC1btiAvLw9qtRpqtRpOTk7t9nvttdcQExODL7/8EomJibC3t+/0fJ6enpg3bx4mT56MiRMn4rrrrsPMmTPh7+9vcC79+vVrl4Ovry9iYmJ0j5VKJby8vFBUVNTuuAEDBrQ7BgD69+/fbtvfjyGi7sEuMCIyuhdeeAE///wztm/fDhcXF72O+c9//oM33ngDy5Ytw2+//YakpCRMnjwZjY2N7fZLT09Hfn4+tFrtFcfzrF+/HgcPHkR8fDy2bNmCqKgoHDp0yOB8bG1t2z0WBKHTbVqt9pLHtXW9/X3b348hou7BFiAiMqqtW7fiueeew08//YTw8HC9j9u3bx9uvvlm3H333QAArVaLlJQU9O3bV7dPY2Mj7rrrLsyaNQt9+vTB/PnzcerUKV3rSmfi4uIQFxeH5cuXY+TIkfj8888xYsSIridIRLLAFiAiMprTp09jzpw5eOqpp9CvXz8UFhaisLAQZWVlVzw2IiICO3fuxIEDB3Du3Dk89NBDKCwsbLfPv//9b1RWVuKtt97CsmXL0LdvX8yfP7/T82VkZGD58uU4ePAgsrKysGPHDly4cKFdQUVE1osFEBEZTWJiIurq6vDCCy/A399f9zNjxowrHrtixQoMGjQIkydPxrhx4+Dn54fp06frnk9ISMDatWvx2WefwdXVFQqFAp999hn279+P999/v8P5HB0dcf78edx6662IiorCgw8+iEWLFuGhhx4yZspEZKEEURRFqYMgIpJaZmYmwsLCcPz4ccmWwmgTGhqKxYsXY/HixZLGQSRnbAEiIvqL+Ph4xMfHS/Laq1evhrOzM7KzsyV5fSJrwhYgIuoWU6ZMwb59+zp97l//+hf+9a9/dXNE7TU3NyMzMxMAoFKpEBQU1O0xlJWV6cZL9ejRA25ubt0eA5G1YAFERN0iLy8P9fX1nT7n6ekJT0/Pbo6IiKwZCyAiIiKyOhwDRERERFaHBRARERFZHRZAREREZHVYABEREZHVYQFEREREVocFEBEREVkdFkBERERkdf4f/yHtmQ3wh+sAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.plot(x,np.gradient(B_tot_x,x))\\nplt.xlabel(\"radius r [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\n#plt.xlim(0,0.01)\\nplt.show()\\nplt.plot(z,np.gradient(B_tot[0,:],z))\\nplt.xlabel(\"z_axis [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\nplt.show()\\n'" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.plot(x,B_tot_x)\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(-0.1,0.1)\n", + "plt.show()\n", + "plt.plot(z,B_tot_z)\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\n", + "\"\"\"\n", + "plt.plot(x,np.gradient(B_tot_x,x))\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(0,0.01)\n", + "plt.show()\n", + "plt.plot(z,np.gradient(B_tot[0,:],z))\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "37c71478-cae2-40ea-8712-b2f751c6c54d", + "metadata": {}, + "source": [ + "## Fit harmonic function" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "443f51a2-a502-4d4a-8692-15610c193cc7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(x,a,b):\n", + " return a*x**2+b\n", + "\n", + "x_SI = 1e-3 * x\n", + "z_SI = 1e-3 * z\n", + "\n", + "B_tot_x_SI = 1e-4*B_tot_x\n", + "B_tot_z_SI = 1e-4*B_tot_z\n" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "id": "59145c9b-aac3-4bea-ba79-b2a3992d05f6", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_x, pcov = curve_fit(func,x_SI[a:b],B_tot_x_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "49c6fb78-ba74-41e3-9a2a-c4dca7109643", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*x_SI,1e4*func(x_SI,popt_x[0],popt_x[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*x_SI,1e4*B_tot_x_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\" )\n", + "#plt.ylim(0,8)\n", + "#plt.xlim(-0.01,0.01)\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,20)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "77c981bb-02be-4de9-8990-849b6a7fbee1", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_z, pcov = curve_fit(func,z_SI[a:b],B_tot_z_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "f1f7cdd7-f13c-41e2-be5e-a375dc5e4d06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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58mSysrKYOnWqhYlLj6+XB8P7mBPpvTdnOykZORYnEhGRymDM9M3kOZx0blCNHm60bMGZWH6C7MEHH6Rfv3707NmTl156qXD7zp07SU5Opnfv3oXbfHx86Nq1KwsXLmTQoEGnPV5ubi65ubmF99PT0wFwOBw4HK53Kqdvswg+rhnC2n1pjJu5hZeviTvtfg6HA6fT6ZLvobJRW7gOtYXrUFu4lrO1x+Idh5mxIRm7DUZc3gin02lBwnMryc+SpcXMtGnTWLFiBcuXLz/lseTkZAAiIiKKbI+IiGDXrl1nPOaYMWN4/vnnT9m+fft2AgMDLzBx2RjQIoAn9qXx1fK9xEdBTJVTL41zOp2kpqaybds27HbLO9QqNbWF61BbuA61hWs5U3s4nAbP/Gyuv9S3UTD29GQS0pOtinlWmZnFn1jWsmJmz549PPLII8ycORNfX98z7vfvha4Mwzjr4lcjRozgscceK7yfnp5O7dq1qV+/PsHBwRcevAw0aACzdjv4fcMBpm7MYeLAU3tnHA4H27ZtIzY2Fg8PDwtSyglqC9ehtnAdagvXcqb2+HLZHnYcySPY15PnrmtLWIC3hSnP7sSZleKwrJhZsWIFKSkptGnTpnCbw+Fg3rx5TJgwgS1btgBmD02NGjUK90lJSTmlt+ZkPj4++Pic2rPh4eHh0h+wEZc3YfbmFOYlHOLv7al0aVj9lH3sdrvLv4/KQm3hOtQWrkNt4Vr+3R4ZOfmM+8O8FPuRng2pHuxnZbxzKsnPkWV9gT169GDdunWsXr268Na2bVtuv/12Vq9eTb169YiMjGTWrFmFz8nLy2Pu3Ll07NjRqthlJqZaAP0viQFg9PRNODSRnoiIlKIJf23jUGYe9aoF0P+SaKvjlCrLemaCgoKIiyt6OiUgIICqVasWbh82bBijR4+mQYMGNGjQgNGjR+Pv789tt91mReQy93CPWL5duZfNyRl8s2IPN19cx+pIIiJSAew6fIyJCxIB+O8VTfD2rFjjmlz63QwfPpxhw4YxZMgQ2rZty759+5g5cyZBQUFWRysTof7ePNQ9FoDXft9KRk6+xYlERKQiGD19U+Gl2N0auf+l2P/mUsXMnDlzGD9+fOF9m83GqFGjSEpKIicnh7lz557Sm1PRDOgQQ71qARzKzOXt2dusjiMiIm5u4fZD/L7hAB52G89c0fSsF9G4K5cqZgS8Pe08e2VTAD5ZsJNtKcW/NE1ERORkDqfBCz9vBOD29nVoGFExz2yomHFB8Y3C6dkknAKnwQu/bMQwNBhYRERK7qvl5jjMYF9PHu3Z0Oo4ZUbFjIv6b7+meHvYmbf1IH9sSrE6joiIuJljeY7CS7GH9WxIFReeU+ZCqZhxUTHVAri3c10AXvxlI7n5miJcRESK74u1R0k9lke96gH071CxLsX+NxUzLuzBbrFEBPuwOzWLj/9OtDqOiIi4icTDx/hh41EAnunXFC83XxX7XCr2u3NzAT6ePN23CQDvztnBwWMFFicSERF38MpvWyhwQucG1YhvdOqM8hWNihkXd1XLKNpGVyE738HHyw9bHUdERFzc39sOMWtTCnYbjOzbuEJeiv1vKmZcnM1mY9RVzbDZYM7OTJYlplodSUREXFRegZPnftoAwBWNgmkQHmhxovKhYsYNxNUM4daLawPw/M9at0lERE5v4t/m/GRhAd70bx1mdZxyo2LGTTzaswGB3nY2JWcwdeluq+OIiIiLSUrL5v/+NC/FfrJPQ4J8Ks/q5Spm3ERYgDcDjlfZb8zcwpFjeRYnEhERV/LSr5vIynPQJroK17WqaXWccqVixo30axRMo4hAjmblM27WVqvjiIiIi1iQcIhf1yZht8ELVzfDbq/4g35PpmLGjXjYbTx7hXmp9udLdrFxf7rFiURExGp5BU6e/Wk9YC5W3CwqxOJE5U/FjJu5pF5V+rWogdOAUT9v0LpNIiKV3McLdrLj4DGqBfrwaK+Ku/7S2aiYcUNP922Cr5edpTtT+XltktVxRETEIvuPZvPW8UG/T/dtTIifl8WJrKFixg3VDPVjSHwsAC//upGMnHyLE4mIiBVe+nUj2fkOLo6pwrWtK9eg35OpmHFT93epR0xVfw6k52owsIhIJTRv60Gmr0vGw27jhavjKsVMv2eiYsZN+Xp58MLVcQBMXpjI+n1pFicSEZHyklvgYNTxmX7v7BBDkxrBFieylooZN9alYXWubBmF04CR36/TzMAiIpXE/+bvZMehY1QP8mFYrwZWx7Gcihk390y/JgT5eLJmbxpTl+yyOo6IiJSxfUezeXu2Oeh3ZN8mBPtWzkG/J1Mx4+bCg335T59GAIydsYWUjByLE4mISFl68eeN5OQ7aVc3jKtbRVkdxyWomKkAbm8fTYtaIWTkFvDiL5usjiMiImVkzpYUZmwwB/2+WMkH/Z5MxUwF4GG38fI1zbHb4Oc1+5m39aDVkUREpJSdPOj3ro4xNIoMsjiR61AxU0E0rxXCgA4xADzz43py8h3WBhIRkVL1wdwdJB7OIjzIh0d6atDvyVTMVCCP925IeJAPuw5n8e6c7VbHERGRUrLjYCYT/toGwH+vaEqQBv0WoWKmAgny9eK5K5sB8P6c7Ww/mGlxIhERuVCGYTDy+/XkFTjp2rA6V7aoYXUkl6NipoLp2zySrg2rk+dw8swP67UQpYiIm/tu5T4W7TiMr5edl67RoN/TUTFTwdhsNl64uhk+nnYWbj/Mj6v3Wx1JRETOU+qxPF76dSMAj/RoSO0wf4sTuSYVMxVQdNUAHupuLkT50q8bScvSQpQiIu5ozPRNHMnKp3FkEPd2rmt1HJelYqaCuq9LPepXD+BQZh5jf99sdRwRESmhRdsP8/WKvdhs8PK1zfHy0J/sM9G/TAXl4+nBS9c0B2Dq0t2s3H3E4kQiIlJcuQUORv6wDoDb29ehTXQVixO5NhUzFViH+lW57qKaGAY8/d068gqcVkcSEZFieOev7ew4aC4k+Z/LGlsdx+WpmKngRvZtQhV/LzYnZ/DhPM09IyLi6rYkZ/DeHHNOmVFXNiPET3PKnIuKmQquaqAPz17ZFIC3/tzGthTNPSMi4qocToMnv11LvsOgV9MI+jaPtDqSW1AxUwlc06pm4dwzI75bi9OpuWdERFzR5IWJrN5zlCAfTy0kWQIqZioBm83Gy9fG4e/twbLEI3y+dLfVkURE5F/2pGbx+swtADzVtzGRIb4WJ3IfKmYqiVpV/PnPZY0AePW3zSSlZVucSERETjAMg5E/rCcrz0G7umHcenEdqyO5FRUzlciADjG0qh1KZm6BljoQEXEh36/ax7ytB/H2tDPmuubY7Tq9VBIqZioRD7uNsTe0wMvDxh+bUvhlbZLVkUREKr1Dmbm88MuJJQsaUL96oMWJ3I+KmUqmYUQQD8SbSx2M+mkDhzNzLU4kIlK5PffTBo4eX7Lg/i71rI7jllTMVEJDu8XSKCKIw8fyeO6nDVbHERGptH5bl8Sva5PwsNt4/caWWrLgPOlfrRLy9rTz2o0t8LDb+GVtEjPWJ1sdSUSk0kk9lsczP64HYHDXesTVDLE4kftSMVNJtagVWtid+d8f1nPkWJ7FiUREKpfnf97Aocw8GoQH8nCPBlbHcWsqZiqxR3o0IDY8sMjgMxERKXuzNh7gx9X7sdvgtRtb4uPpYXUkt6ZiphLz9fLgtRtaYLeZlwX+uemA1ZFERCq8tKx8Rn5vroh9X5d6tKodam2gCkDFTCXXuk4V7u1snm56+vt1pGXnW5xIRKRie+GXjaRk5FKvegCP9mxodZwKQcWM8FivhtSrFsCB9Fxe+Fmnm0REysrszQf4duVebDZ47YYW+Hrp9FJpUDEj5ummG83TTd+u3MvMDbq6SUSktB05lseT35qnl+7pVJc20WEWJ6o4VMwIAG2iw7ivyz+nm1J1dZOISKl69qcNHMzIJTY8kCeOr5UnpUPFjBR6tGdDGkYEcigzj//+sE5rN4mIlJJf1u7n5zX78bDbeOPGljq9VMpUzEghXy8P3rixFZ52G9PXJfOz1m4SEblgKRk5PPODOTneg/H1aamrl0qdihkponmtEIZ2N9dueuaH9aSk51icSETEfRmGwdPfredIVj5NawQztLsmxysLKmbkFA92iyWuZjBp2fk89Z1ON4mInK9vV+7jj00H8PKwMe7mlnh76s9uWdC/qpzCy8POuJta4e1hZ/bmFL5ctsfqSCIibmff0WyeP76Y76O9GtI4MtjiRBWXihk5rYYRQTze25zM6YVfNrLr8DGLE4mIuA+n0+Dxr1aTkVtA6zqh3H98clIpGypm5Izu7VyP9nXDyMpz8OiXqylwOK2OJCLiFv63YAeLd6Ti7+3Bmze1wtNDf27Lkv515Yw87DbeuKklQT6erNx9lHfnbLc6koiIy9u4P53Xf98KwLNXNCWmWoDFiSo+FTNyVrWq+PPCNc0A+L8/E1iz56i1gUREXFhOvoNhX64iz+GkZ5MIbr64ttWRKgUVM3JO17SqSb8WNXA4DR79cjVZeQVWRxIRcUmv/b6FrQcyqRbozSvXN8dms1kdqVJQMSPnZLPZePmaOCKDfdlx6Bijp2+yOpKIiMv5e9shPl6wE4CxN7SgWqCPxYkqDxUzUiyh/t68cVNLAD5bvJvZmw9YnEhExHUczcrjia/XAHBb+zp0bxxhcaLKRcWMFFun2Grcc2ldAP7z9VrNDiwigjnL71PfriMpLYe61QL4b78mVkeqdFTMSIkM79OIJjWCOXwsj8e/XoPTqdmBRaRy+2LpHmZsSMbLw8Zbt7TG39vT6kiVjqXFzHvvvUeLFi0IDg4mODiYDh068NtvvxU+bhgGo0aNIioqCj8/P+Lj49mwYYOFicXH04O3b22Fr5ed+QmH+N+CHVZHEhGxzLaUDF74xfy79J/LGtG8VojFiSonS4uZWrVq8corr7B8+XKWL19O9+7dufrqqwsLlrFjxzJu3DgmTJjAsmXLiIyMpFevXmRkZFgZu9KLDQ/i2SvMy7Vf+30L6/amWZxIRKT85eQ7eOiL1eTkO+ncoBr3XqpZfq1iaTFz5ZVX0rdvXxo2bEjDhg15+eWXCQwMZPHixRiGwfjx4xk5ciTXXXcdcXFxTJ48maysLKZOnWplbAFubVebPs0iyXcYPDxtFcdydbm2iFQur87YzKakdKoGmBdI2O26DNsqLnNiz+Fw8PXXX3Ps2DE6dOjAzp07SU5Opnfv3oX7+Pj40LVrVxYuXMigQYNOe5zc3Fxyc3ML76enpxce3+FwlO2bKEMOhwOn0+lS7+Hla5qyeu9Rdh46xnM/rufV65tbHalcuGJbVFZqC9dR2driry0Hmfh3IgCvXhdHVX8vl3rvFaE9SpLd8mJm3bp1dOjQgZycHAIDA/n+++9p2rQpCxcuBCAioujlbREREezateuMxxszZgzPP//8Kdu3b99OYGBg6YYvR06nk9TUVLZt24bd7jrjth/vEMbwGfv5ZuU+YgPzia8XZHWkMueqbVEZqS1cR2Vqi8NZBTz+0x4Arm4SQi2PNBISXOt0e0Voj8zMzGLva3kx06hRI1avXs3Ro0f59ttvufPOO5k7d27h4/+ePdEwjLPOqDhixAgee+yxwvvp6enUrl2b+vXrExzsvsuvOxwOtm3bRmxsLB4eHlbHKdSgAezK9WPCX9uZsOQwvdo2IqZqxV6HxFXbojJSW7iOytIWDqfBqInLSMtx0iQyiDE3t8PHy/Xeb0VojxNnVorD8mLG29ub2NhYANq2bcuyZcv4v//7P5588kkAkpOTqVGjRuH+KSkpp/TWnMzHxwcfn1NnXfTw8HDbBj3Bbre75PsY1rMhS3ceYWliKg9PW8N3Qzri4+laGUubq7ZFZaS2cB2VoS3e/mtr4WrYE26/CH9fb6sjnZG7t0dJcrtc35NhGOTm5lK3bl0iIyOZNWtW4WN5eXnMnTuXjh07WphQ/s3Tw85bt7amir8XG/anM/pXLXcgIhXPwu2H+L8/EwAYfW1z6ld336ELFY2lxczTTz/N/PnzSUxMZN26dYwcOZI5c+Zw++23Y7PZGDZsGKNHj+b7779n/fr1DBw4EH9/f2677TYrY8tpRIb4Mu7mVgBMXrSLGeuTrA0kIlKKDmXm8si01RgG3NS2Fte0rml1JDmJpaeZDhw4QP/+/UlKSiIkJIQWLVowY8YMevXqBcDw4cPJzs5myJAhHDlyhPbt2zNz5kyCgir+IFN31K1ROIO61uODuTv4zzdraRYVQu0wf6tjiYhcEKfT4NEvV3MwI5cG4YE8f1Wc1ZHkXywtZj7++OOzPm6z2Rg1ahSjRo0qn0BywZ7o3YhlO1NZufsoQ6eu5OvBHfH2dLmzmSIixfbe3O3MTziEr5edd26/CD9v9xyDUpEV669Menp6iW9SOXkdHz8T4ufFmr1pjPlN42dExH0t3nGYcbO2AvDC1XE0jNCZAVdUrJ6Z0NDQs14O/W82m42tW7dSr56mdq6MalXx540bW3Lvp8uZ+HcibaKrcEWLKKtjiYiUSEp6DkOnrsLhNLiudU1ubFPL6khyBsU+zfTNN98QFhZ2zv0Mw6Bv374XFErcX8+mEQzuWp/3527nyW/W0jgymNhwjfwXEfdQ4HAy9ItVHMrMpVFEEC9f27xEX+qlfBWrmImOjqZLly5UrVq1WAetV68eXl5eFxRM3N8TvRuyes8RFu9IZcjnK/jhwU74e1s+tZGIyDm99vsWlu5MJdDHk/fu0DgZV1esMTM7d+4sdiEDsH79emrXrn3eoaRiODH/THiQD1sPZPL0d+swDMPqWCIiZ/X7hmQ+mLcDgLE3tKCe5pNxebrMRMpUeJAvE267CA+7jR9W7+ezJbutjiQickaJh47xxFdrALjn0rr0bV7jHM8QV1DsPv/s7Gz+/PNPrrjiCsBcA+nk1ak9PDx48cUX8fX1Lf2U4tba1Q3jyT6NGD19My/+vJHmNUNoVTvU6lgiIkVk5zl44POVZOQW0Da6Ck9d3tjqSFJMxe6Z+fTTT/nggw8K70+YMIGFCxeyatUqVq1axWeffcZ7771XJiHF/d3XuR6XNYsgz+Fk8JQVHMzIPfeTRETKiWEYjPhuLZuS0qka4M2E2y7Cy0MnL9xFsVvq888/5+677y6yberUqfz111/89ddfvPbaa3z11VelHlAqBpvNxus3tqRe9QCS03N4cOpK8h1Oq2OJiADwyd+J/LB6Px52G+/cfhGRITrL4E6KXcxs3bqVhg0bFt739fXFbv/n6e3atWPjxo2lm04qlCBfLz7s35ZAH0+W7kxl9HRNqCci1lu0/XDh76ORfZtwSb3iX/AirqHYxUxaWhqenv8MsTl48CAxMTGF951OZ5ExNCKnExseyBs3tQRg4t+JfL9qr8WJRKQy2380m6FTV+JwGlzbuiZ3dYqxOpKch2IXM7Vq1WL9+vVnfHzt2rXUqqXZEeXcLmsWycPdYwF46tt1rN+XZnEiEamMcvIdDP5sBYeP5dEsKpjRmhjPbRW7mOnbty/PPvssOTk5pzyWnZ3N888/T79+/Uo1nFRcw3o2pFuj6uQWOBk0ZQWpx/KsjiQilYhhGDzzw3rW7k2jir8X79/RRhPjubFiFzNPP/00qampNGrUiNdee40ff/yRn376ibFjx9KoUSOOHDnC008/XZZZpQKx222Mv6U1MVX92Xc0myGfr9CAYBEpNxP/TuTrFXux2+DtWy+idpi/1ZHkAhS7mImIiGDhwoU0adKEp556imuvvZZrrrmGESNG0LRpUxYsWEBERERZZpUKJsTPiw8HmAOCF+9IZdRPG6yOJCKVwLytB3npV/OClaf7NuHSBtUsTiQXqkQL5dStW5cZM2aQmprKtm3bAIiNjS3WApQip9MwIoj/u6UV9366nM+X7KZxZBD9O8RYHUtEKqgdBzMZOnUlTgNuaFOLey6ta3UkKQXnNSNQWFgY7dq1o127dipk5IL1aBLB8MvMmTZH/byRhdsOWZxIRCqitOx87p28nPScAtpEV+Hla+M04LeCKFYxc91115Genl7sg95+++2kpKScdyipfAZ3rcc1raJwOA2GTF3JrsPHrI4kIhWIw2nw0Ber2HHoGDVCfHn/jjb4eGrAb0VRrGLmxx9/5ODBg6Snp5/zlpaWxs8//0xmZmZZZ5cKxGaz8cr1LWhZK4SjWea3p4ycfKtjiUgFMWb6JuZtPYivl52PBrSlepCP1ZGkFBVrzIxhGEVm/xUpC75eHnw4oC1Xvr2AhJRMhk5dxcd3tsVT66OIyAWYumQ3/1uwE4A3bmxFXM0QixNJaStWMfPXX3+V+MA1a9Ys8XNEIoJ9+d+dbbnpg0XM3XqQ53/eyAtXN9N5bRE5L/MTDvLMj+aEr8N6NqBfixoWJ5KyUKxipmvXrmWdQ6RQi1qhjL+5NQ98voIpi3dRt1oAd+uKAxEpoYQDGQz57J+lCh7p0cDqSFJG1H8vLqlPXCQjLjevcHrx1438sfGAxYlExJ0czMjlrknLyMgt4OKYKrxyvZYqqMhUzIjLuq9zPW5tVxvDgIenrdIaTiJSLDn5Du6fspy9R7KJrurPB/3b6sqlCk7FjLgsm83GC1fHcWlsNbLyHNwzeRlJadlWxxIRF+Z0Gjz+9RpW7T5KiJ8Xnwy8mLAAb6tjSRlTMSMuzcvDzju3X0RseCAH0nO5a+Iy0nXJtoicwZjfNvHr2iQ87Tbev6MN9asHWh1JykGJi5nu3btz9OjRU7anp6fTvXv30sgkUkSInxcTB15M9SAfNidnMOjTFeQVaFFKESnqkwU7+Wi+eQn2aze2oEP9qhYnkvJS4mJmzpw55OXlnbI9JyeH+fPnl0ookX+rHebPxIEXE+DtwaIdh/nPN2twOg2rY4mIi/htXRIvHl88cnifRlzbupbFiaQ8FXuhybVr1xb+/8aNG0lOTi6873A4mDFjhuaWkTIVVzOE9+5ow92TlvHj6v3UCPHjqeNXPIlI5bUsMZVHvlyNYcAdl9Thga71rY4k5azYxUyrVq2w2WzYbLbTnk7y8/Pj7bffLtVwIv/WpWF1Xrm+BU98vYb3524nKtSXAVplW6TS2paSwb2Tl5NX4KRX0wiev0qLR1ZGxS5mdu7ciWEY1KtXj6VLl1K9evXCx7y9vQkPD8fDQ5e+Sdm7oU0tko5m88asrTz30waqBfrQt7lm9RSpbJLSsrnzk2WkZefTuk4ob93SGg+7CpnKqNjFTHR0NABOpwZeivWGdo8lKT2HqUt2M2zaaoJ9vbi0QTWrY4lIOTlyLI8BHy9l39Fs6lUL4OM7L8bPW1+oK6vzujR7+/btPPTQQ/Ts2ZNevXrx8MMPs3379tLOJnJGNpuNF6+Oo2/zSPIcTu6fspw1e45aHUtEysGx3ALumrSMhJRMIoN9+fSedppLppIrcTHz+++/07RpU5YuXUqLFi2Ii4tjyZIlNGvWjFmzZpVFRpHT8rDbePPmVoWT6g2cuJRtKZlWxxKRMpRX4GTwZytYvecoof5eTLmnHbWq+FsdSyxW4mLmqaee4tFHH2XJkiWMGzeON998kyVLljBs2DCefPLJssgockY+nh68378NLWuFcCQrn/4fL2HfUc0SLFIROZwGj321mvkJh/Dz8uCTgRfTICLI6ljiAkpczGzatIl77rnnlO133303GzduLJVQIiUR6OPJxLvaUb96AElpOfT/eAmHMnOtjiUipcgwDJ77aT2/rE3Cy8PG+/3bcFGdKlbHEhdR4mKmevXqrF69+pTtq1evJjw8vDQyiZRYWIA3U+5pT1SILzsOHqP/x0tJy9KyByIVgWEYvPLbZj5bvBubDcbd1IquDauf+4lSaRT7aqYT7rvvPu6//3527NhBx44dsdlsLFiwgFdffZXHH3+8LDKKFEtUqB+f3duemz5YzKakdAZMXMpn97QjyNfL6mgicgH+788EPpi3A4DR1zbnypZRFicSV1PiYuaZZ54hKCiIN954gxEjRgAQFRXFqFGjePjhh0s9oEhJ1KseyOf3tufmDxexZs9R7pm0nMl3t9MlmyJu6oO52xn/RwIAz17RlFvb1bE4kbiiEp9mstlsPProo+zdu5e0tDTS0tLYu3cvjzzyiGZdFJfQKDKIKXe3J8jHk6WJqdw/ZTm5BQ6rY4lICX26KJExv20G4D+XNeLuS+tanEhc1QWtmh0UFERQkDmSXKtmiytpXiuEiXddjJ+XB/MTDvHg56vId2jCRxF38dXyPTz74wYAhnaL5cFusRYnElemVbOlwmobE8b/7myLt6edPzYdYOjUlSpoRNzANyv28uS35uLGd3eqy+O9G1qcSFydVs2WCq1TbDU+7N+G+z9dwe8bzIJmwm0X4eVxXpNfi0gZ+2bFXv7zzZrCFbCfuaKJhjDIOWnVbKnw4huF88GANgw6XtA8NHUVb9/WWgWNiIv5dyHz4tVaAVuKR6tmS6XQ7aSCZsaGZB7+YhVv3aqCRsRVfKtCRi5AsX+TR0dHExMTg9PppG3btkRHRxfeatSocUoh069fP5KSkko9sMj56tYonA/6t8Hbw85v682CJq9AY2hErPbtir08cVIh88JVKmSkZMrsa+m8efPIztYaOeJaujUuWtA88NkKcvJ12baIVT5fsovHvy5ayNjtKmSkZNTHLpVOt8bhfDigDT6edv7cnMK9k5eTlVdgdSyRSufjBTsZ+f16AO7sEK1CRs6bihmplOIbhTPprnb4e3uwYNshBn6yjIwcreUkUl4mzE7gxV/MxYkHd63PqKuaqZCR86ZiRiqtDvWrMuWe9gT5mjMF3/HxUo5mnTqHkoiUHsMweO33zbw+cysAj/VqyJN9GmmMjFwQFTNSqbWJrsIX911CFX8v1uw5yi0fLiYlI8fqWCIVktNp8PzPG3nnr+0AjOzbhId7NFAhIxdMxYxUenE1Q5h2fweqBfqwOTmDG95bxK7Dx6yOJVKh5BU4efSr1UxamAjAi1c3474u9awNJRVGmRUzTz/9NGFhYWV1eJFS1SgyiG8f6ECdMH92p2Zx/XuL2Lg/3epYIhVCVl4B9326nB9X78fTbmP8za3o3yHG6lhSgZS4mDl8+HDh/+/Zs4dnn32W//znP6esyzRixAhCQ0MvOKBIeYmuGsA3gzvQODKIQ5m53PzhIpbuTLU6lohbO5qVxx3/W8LcrQfx9bLz0Z1tuaa1lr6R0lXsYmbdunXExMQQHh5O48aNWb16NRdffDFvvvkmH374Id26deOHH34ow6giZS882JcvB3WgXUwYGTkF9P94CbM2HrA6lohbSkrL5sb3F7Fy91FC/Lz4/N5L6NYo3OpYUgEVu5gZPnw4zZs3Z+7cucTHx3PFFVfQt29f0tLSOHLkCIMGDeKVV14py6wi5SLEz4tP72lHzybh5BY4GTRlOZ8t3mV1LBG3sjk5neveXUhCSiaRwb58PbgDbaKrWB1LKqhiFzPLli3j5Zdf5tJLL+X1119n//79DBkyBLvdjt1u56GHHmLz5s1lmVWk3Ph6efD+HW24qW0tnAb894f1vPLbZpxOw+poIi7v722HuPG9RSSl5VC/egDfPNCBhhFBVseSCqzYxUxqaiqRkZEABAYGEhAQUGSAb5UqVcjIyCj9hCIW8fSw8+r1LXisV0MA3p+7nUe+XE2u1nMSOaNvV+7jzk+WkpFbQLu6YXz3QCdqVfG3OpZUcMVeNRs4ZS4AzQ0gFZ3NZuPhHg2oGerHk9+u5ec1+0lOy2Z4hxCro4m4FMMw+Gx1Kp+tNueQuaplFK/d2AIfT49zPFPkwpWomBk4cCA+Pj4A5OTkMHjwYAICAgDIzc0t/XQiLuL6NrWIDPFl8JQVLEs8wrDUTCZH1iY2ItjqaCKWyy1w8NS36/h+9REAhsTX54nejbQ8gZSbYhczd955Z5H7d9xxxyn7DBgw4MITibioTrHV+OaBjgycuJR9aTlc//5i3r39IjrFVrM6mohlDmbkMmjKclbuPordBs9f1ZT+HepaHUsqmWIXMxMnTizLHCJuoVFkEN8/0IGBHy9k88FcBnyylFFXNtUEYFIpbdyfzn2fLmff0WyCfT15qkt1bm5Xx+pYUglpOQOREqoe5MPYy6K4plUUDqfBMz9u4Jkf1pPv0MBgqTx+35DMDe8vZN/RbOpVC+C7BzpwUZQG+oo1LC1mxowZw8UXX0xQUBDh4eFcc801bNmypcg+hmEwatQooqKi8PPzIz4+ng0bNliUWMTk7Wnn9RuaM7xPI2w2mLJ4F3d+spTUY1p1Wyo2p9Pg7T8TGDRlBVl5Di6Nrcb3QzpRt1qA1dGkErO0mJk7dy4PPvggixcvZtasWRQUFNC7d2+OHftnkb+xY8cybtw4JkyYwLJly4iMjKRXr166DFwsZ7PZGBIfywd3tMHf24OF2w9z5dsLWLv3qNXRRMpEek4+909ZwRuztgIwoEM0k+66mBB/L4uTSWVnaTEzY8YMBg4cSLNmzWjZsiUTJ05k9+7drFixAjB7ZcaPH8/IkSO57rrriIuLY/LkyWRlZTF16lQro4sU6t0skh8eNL+Z7juazQ3vL+KrZXusjiVSqrYeyODqCX/zx6YDeHvaGXt9C164Og5PD41WEOuV6NLsspaWlgZQOBnfzp07SU5Opnfv3oX7+Pj40LVrVxYuXMigQYNOOUZubm6Ry8TT082Vjx0OBw6HoyzjlymHw4HT6XTr91BRnK4t6lfz57vBl/DEN2v5c/NBhn+7llW7j/DMFU3w8dQv+7Kiz0X5+HVdEk99t56sPAc1Qnx597bWtKgVUuTfXW3hWipCe5Qku8sUM4Zh8Nhjj3HppZcSFxcHQHJyMgARERFF9o2IiGDXrtOvlTNmzBief/75U7Zv376dwMDAUk5dfpxOJ6mpqWzbtg27XX8crXS2tni8fRBRfgV8tuoIXyzbw8qdKYzoGkFkkLrhy4I+F2Ur32HwyYrDfL/R/KLZqoYfT3WNwC87hYSElCL7qi1cS0Voj8zMzGLv6zLFzNChQ1m7di0LFiw45bF/zzRsGMYZZx8eMWIEjz32WOH99PR0ateuTf369QkOdt8JzhwOB9u2bSM2NhYPD82oaaVztcWohhDf/CCPfrWGLYdyefjX/bx6fXN6N404zdHkQuhzUXb2pGYxfNoa1u4zC5l7L43hP70bnvG0ktrCtVSE9jhxZqU4XKKYeeihh/jpp5+YN28etWrVKtx+Yi2o5ORkatSoUbg9JSXllN6aE3x8fApnKT6Zh4eH2zboCXa7vUK8j4rgXG3Ro2kkvz4czENfrGL1nqM88PkqBnaMYUTfxprevZTpc1H6ZqxP5j/frCEjp4BgX09ev7ElvZtFnvN5agvX4u7tUZLclvY9GYbB0KFD+e6775g9ezZ16xadNbJu3bpERkYya9aswm15eXnMnTuXjh07lndckRKpHebPV4M6cF9n8+d60sJEbnhvEbsOHzvHM0WskVvgYNRPGxj82QoycgpoXSeU6Y90LlYhI2IlS4uZBx98kM8++4ypU6cSFBREcnIyycnJZGdnA+bppWHDhjF69Gi+//571q9fz8CBA/H39+e2226zMrpIsXh72hnZrykf39mWUH8v1u1Lo99bC/hmxV4Mw7A6nkihrQcyuOadhUxamAjAoC71+GpQB614LW7B0tNM7733HgDx8fFFtk+cOJGBAwcCMHz4cLKzsxkyZAhHjhyhffv2zJw5k6CgoHJOK27DUQCZyZC2D9L3mv/NOgz52ZCfdfy/2WA4wdMHPH3/+a9vMARGQFANCIo0b4GR4HFhH5UeTSKY/nBnHpm2imWJR3ji6zXM3nyAl69pTpUA71J64yIl53QaTF6UyJjfNpNX4CQswJvXb2xB98alMMbLMCArFTKSzM9kxvFb1mEoyIGC3H/+6ywALz/w8j/+Xz/wCYbgKAiuCSG1zP96q7iSU9mMCv71MD09nZCQENLS0tx+AHBCQgINGjRw2/Ofpc4wIPMAJK+D5LXH/7sOUneCUYqXI9q9oGp9qN4IqjXCWbUBiTmBRLfphYdnya5ScjgN3p+7nTdnbaXAaRAR7MPrN7akc4PqpZe3EtHn4sIcSM/hia/XMD/hEADdGlXn1RtaEB7kW+JjOdL2s3/Fb9T0zsB+OAEOboGDWyE3rXRDB4RDZBxENofIFhARB1VjL/gLR0VTET4bJfn7rdYX95JxAHbOgx1zYOdcSDvD5HR2LwiuAcG1IKQmBFQ/6Rvf8f/abFCQd9I3xGzIPmoWSBlJ5mtlHgBnPhzcbN4wz83WA4zZQVCzDdS6+J+bf9hZ43vYbTzYLZYuDarzyJer2HHwGP0/XsrAjjEM79MIf299JKXsGYbBL2uTeObH9RzNysfH085/+zXhjkuiz3ilaBGOfNi/GvYuM2/7luNxdDe1z7S/f1WztzMwwuztDKhmfg5P7hm1eZifxZN7T7OPQPo+SN9v9rDmZcCxFNg+27yd4B0I0Z2gXleo2xUimpmfb6k09JtTXJthQNJq2PADbP0dDm4q+rjNDtUaHv+WdvxWvYn5S7M05lZwOs1TVQe3msXMoS0YKZsxktZiz80wi6odc06EgahWUK8b1IuHOpeYv6RPo3mtEH59qDMvT9/IZ4t3M2lhIn9tSeHV61twSb2qF55b5AxSMnJ45of1/L7hAABxNYMZf3MrYsPPcureMMyf/x1zYPtfsOtvyCs6B4iBjbzgGLxqtcRevbHZk1m9EYTVB6+S9/ScVk4aHNoGB9b90xObvN7MkvC7eQPzy0u9btDsGqjfo/ReX1yWTjO5iYrQZVhshmGeNtrwvXk7knjSgzazYKnXFerGmwWDT/lOhuhwOEjYsokGIQV47F8Be5eb304PJxTd0dMP6naGRn2h0eXmN9LTmLMlhRHfrSMpLQeA/pdE89TljQnw0XeNc6lUn4sLZBgG36/ax/M/byQtOx/P472ED3aLxft0s1TnZ8OOubDlV0iYZfZWnswvDGq3h1ptoVZbHJEtSdh9oPzbwumAA+vNrDvmwO5FZu/OCd5B0LgvNL0GYnuc8QtGRVMRPhsl+futYsZNVIQfzHPKSYM1X8KKiZCy8Z/tnn7Q8DJoepX5bescp3LK2hnbIn3/P79Qd8wxBzyerGZb85dqk6ugWoMiD2Xk5DN6+ma+WLrb3DXUj1eub66xNOdQKT4XpSApLZv/fr+ePzebs/Y2iwrmtRta0jTqX78Ts4/Clt9g8y/maZyTiwJPX4juaPY61utmjlU5qffTZdqiIM/8crH5V9j4g3ma6gTfUGh1O7S965TPYEXjMu1xAVTMnETFjBvYtxKWfwLrv/3nl6enLzToDc2uNQsZ7wBrM56kWG1hGJCyCbb+Bpunw77lRR+PbAFx15vvr0p04eYFCYd48tu17DtqTk9wVcso/ntFk/MakFkZVOjPRSkocDiZtDCRN2dt5VieA28POw/3iGVQ1/p4nZjJNzfTLGA2fGf2wDjz/zlAcM1/ehajO531dI1LtoXTaRY2G743C5uTe5diOptFTeMrwbPiXVHoku1RQipmTqJixkU5neb57QVvwp4l/2yv3hja3g0tbga/UMvinc15tUV60vHC5lez18ZZ8M9jtS6G5jdB8xvAP4zM3AJe/30Lny5KxGlAkK8nw/s05rZ2dfCwa1DjySrc56IUrdp9hKe/X8+mJHNK+DbRVRhzXXMaRgSZp2a2/QlrpsKWGebg9xOqNzF7QRv1hRotiz2Q1uXb4sR7Xv6J+bvHcJrbAyOhwxDz945PxZnyw+XboxhUzJxExYyLcRSY3wAXvPnPqSQPb/N8dtu7zTEwLn4VwgW3RVYqbPrJ7InaOR84/hH08Db/gLS6Hep3Z13SMUb+sI61e81LW1vWDuXla+KIqxlSem/GzVWYz0UpOpqVx+szt/D5kt0YBoT4eTHi8sbc1LY29rRdsOozWD216OmXsHrHewqvg4im5/W6btUWR/fAyk/N24nTwb4h0O5+aD/YvNrKzblVe5yBipmTqJhxEY4C81vgvNfh6PEVz72D4OK74ZIhZxwc64pKtS0yDphd4Ks/Nwc9nxAYCS1vwdHyNj7b5sNrv28hM7cAmw1ualObJy5rRPWgyjGQ8Wzc/nNRigocTqYu3c24WVs5mmWeKrr+olo83TuGqntmmX+4d8795wl+VaDFLdDyZqjR6oK/RLhlWxTkwbqvYMH4fwbwe/pBm4HQ+XEIdN8xa27ZHv+iYuYkKmYsZhhmL8SfL/7zy8K/KlzyAFx8n8ueSjqbMmuL5HWw6nPzl2vW4X+217qY9MY38fKuJny5zjxlEOjjydDusdzVKaZSL1zptp+LUjY/4SAv/rKRrQfMy6UbRQQxtrOdlgd/hjXTIOfo8T1t5gDeiwZA436lemWPW7eF02EOep4/zpwKAsy5azoMhQ4PmjODuxm3bo/jVMycRMWMhXbMhT9Gwf6V5n2/MPPbTtu73XpK8jJvi4I885z+qs8hYeY/sxl7+nK4zmW8frgDXxyoDdiIrurPU30a0ycusniTnVUwbvm5KEXbUjJ45bct/LHJnDOmll8+rzfdTvsjv2A78bkDc/LI1rebpzBPGnBemipEWxiGeRXXny/8U9T4V4Uu/zF/b7nRZd0VoT00A7BY6/B2mDHinwmsvAKg41DzW44bfsMpd57e0ORK85ZxANZ+aZ6GOriZqjt+ZAw/8nTVaCZmd+HTwx154PMsWtYO5ck+jehY3/3P9cu57T+azf/9kcDXK/bgNAzaeSQwInIprdLnYNtw/IpAu5c5FUDrAVC/G9jd8w9aubLZzLlo6neHjT/C7Bfh8DaY8RQsfg8uG232aFXCLw6uTsWMlJ7cTJj/Oix6Bxx55i/TtndDlycgMNzqdO4pKAI6PQwdHzIvYV85GdZ/S9CxXTzMFB70m8qfzjZM3RfPHR+l0qlBOE/2aaxBwhXUkWN5vDd3O5MWJhJUcIR7POZzt/98auTvgRNnJqs1Mk8jtbylQgxktYTNZs4e3PgKWP0ZzHnFHOv3pTk4nz6vmLMbi8tQMSMXzjBg3Tcw65l/5nGI7Wl+4Cv4xFTlxmaDWm3M22WjzSvCVkzGY99yetuW0tt7KfuNqny1syv3vx1Py7g4hnaPpVmUipqK4GhWHp8s2Mnkv3fQOn8l/+fxF718V+KJA/Ix1zmKu87shandTj0HpcXD0xwM3PxGmP8GLHzbPA31Xkfzqqeuw82roMRyKmbkwhzeDj8/AonzzftVYswipmEf/UItKz6B5jfviwbAgQ2wcgqs+YKonMMM8/yOhz2+Z96WFry1sRs0vJwHezamRa1Qq1PLeTicmctH83cye9FS+jln85vHXKK8U//ZoWZb8+cg7roKNUeKy/EOgB7PQus7YMbT5pxRiyaYX+L6jjVn9dbvO0upmJHz48iHhW/BnFfBkWteztjlCXNcjBZ1Kz8RzeDyV6DnKPNqjBWTsCfOJ95jDfEeazi48xO+fb8rn9W+jut7daVd3bBKOVDY3ew/ms3keVs4uPw7rjX+5CmP9eZy7YDhVwVby1uhdf/znhNGzlNYPbhtmjlT8m9PQup2+GoANOoH/V6H4CirE1ZaKmak5PaugJ8fNhd3A3OdlivehLC61uaqzLx8zRmEm99g9patmkLBis+onn2QwZ4/Q9LPLJ7UhHHB/WjY7Tb6tKr7z3T24jLW7TnK77N+I3zndwy2L6SK3bzU2jh+SbXtogHYSvmSajkPDXqZyyHMew3+Hm8uxrlzHvR8DtreU2TNKikfKmak+PJzzNH9i94BDPNS6z5jzKUH9G3fdVStDz1H4dltJGz9nawlE/FNnM0l9k1ckrmJtJ/e5Yfp8djaDKBXfE9C/L2sTlypOZwGC1auZc/cybRPm8ET9n1w/MKjHP8a+LTtj631HWV2SbWcJy9f6PGMeYrvp4fN9demP2HO7H31O+bnUMqNihkpnn0r4PsH4NAW836Lm82BqLpawnV5eEGTK/BvcgWk7SVr6acULP+UkNwkbnRMh6XT2bgkhvmRlxPdtT9xTZrqFFQ5OnD4CKtmfkbo1m+41LkGD5sBdsizeZNVry+hHQbgWy9el1S7uohmcM9MWPYx/Pk87F4E718KvV5QL005UjEjZ1eQB/PGmjNjGg4IjIAr34JGfaxOJiURUgv/Xk9DjyfJS5jNgb8+JDJ5Nk1tiTQ98B589R7rPOPIangtTXr2JzgswurEFZLD4WDt4llkLv6UVumz6WM7vsCjDfYEtSSo/QBC296It66QcS92D2h/PzS8DH580LwgYvoTsOlns5cmtLbVCSs8FTNyZimb4Nv74MA6837c9dD3dfAPszaXnD+7B96NelG7US+MY4fZ9fc08ld/RWzWapoXrIeN68nfMJp1ge2g+Y006nIT3v66SuaCGAY71szj4OIviE6eRWsOmdttkGIPJzX2eur2uIfaEZrGwO1ViYYBP8Gyj2DWc+ZaWO91NK/wbHWbTseXIRUzcirDgOWfwO9PQ0GOOTbminHQ7Fqrk0kpsgVUJbr3g9D7QdKSd7L5z8mEbf+RBs4dND+2CBYvInvxCNZU6YJfi2up3+FqPHwDrY7tHpxOkjYuIHnpN0TunUE95wHqHX8oEz8SwuKpdulAarfqTbhOQ1Qsdju0H2TOtfX9YNi7FH4cYi5NcuV4c4FPKXUqZqSorFT46SHzMl8wP5DXvKcZfCu4kMi6tL99FIbxHAkbVpD892fEJP1GbZJpeWQWzJ1F7txH2Bp0MbYm/ajb8Xp8QmtYHdulGPk57Fn5O0dXfk+tlDnUMI5w4l8oy/BhY1BHPFtcT9Mu19Pa133XJpNiqlof7p5hXu3012jY+APsXQbXfgB1O1udrsJRMSP/SPwbvrsP0veZSxH0eh7aP6ABbJWIzWajQVxbGsS1paDgDVYt/Yu05V9SP3UutTlAk4yFsHQhziX/JdG3IRk1uxB+UT8imlxqDjiuZDKTtrBn6c/Ytv9JnfSV1CGHOscfyzD8WO/fDkejK2gefxNtQ0OtjCpWsHuYi+vWizdP2aduh8lXwqXDIP5pcx02KRUqZgQcBeYg33mvgeGEsPpwwycQ1crqZGIhT08PWnfsCR17kl/gYNXqxRxe/j01D8ymCduJyd0CO7bAjo/IxJ/dIW0xYi6lRvPuhNW7qEJehZN9aDd7V/9Bzrb5VD+4iEhHEk1OevyAUYVNwZfi0fQKmnfqR4dgjTcSoGYbGDQPfh8BKz+FBW+a89LcMFGX3JcSFTOV3dHd5jeGPYvN+61uh8vHmlPmixzn5elB67adoG0nnE6D9Vu3krTyV/x2z6Fp9grCbJk0TZsHa+bBmtFmcRPYgrya7Qmp356oJpfgE1TV6rdRIkZBLskJqziUsATnrsVEHFlJpDOZk4fp5hkebPBowqGISwmK60PzNp2I99W3bTkNn0C46m3z1P1PD5vTXXzQGa5+F5pcYXU6t6dipjLb+KM5PiYnDbyDzFl8W9xodSpxcXa7jbjGjYhr3Ah4jPSsHBYun8exTX8QenA5jfM3EmTLomnmYtiyGLYA02G/vQYpgY3JD2+Bb1QTqsW0ILxOIzw8rf01ZDidHD2UxIEdazi2byNG0jqqpG2gdv5OalDAySODHIaNLfZ6JIVehL1uZ2Iv7kvrGtUtyy5uqOnVENUavr7LnGjvy9vhkgfNJUl02um8qZipjPKzYcZTsGKSeb9mW7j+f1qOQM5LsL8vHbv0hi69AUjPymb5msVkbJmL/4Hl1MzeQi1SiHImEZWeBOl/wTZgHuQaXuzyiCLVN5rcwJo4g6LwrFIH/+p1CKpeh8DQ6gQHBeLrdX6nrAocTjKycslMP0z6of1kpiSSn7ob0vbhlbmPoKw9RBXspgqZnO4akzQjgJ3esRyp0gLv+pcS3aIbTSLDaapLbOVChNaBu34zJ9lbNAEWv2P2juu003lTMVPZHD6+MNqB9YANLn0Uuj1dKQdvStkI9vejbYdu0KEbAIZhsP9AEvs2LiZn13J8Dm+kalYiNR178bXlU8+5i3pZuyALSDn1eMcMH5IIIMMWRI7dnwKbNw6bJw67F06bJwUOJ0fsBh5GAR5GPh7OAnyMbAKcmQSTSQhZVLEZZ83sNGwk2cM56BNNZkgDPGq2plrDS4iJbUorz4o39kdcgKc3XPYyRHeCHx7457TTNe9B435Wp3M7KmYqk40/mbNT5qZDQHW47iOo383qVFLB2Ww2oiKjiIq8DriucLujoID9exI4nLienOQtkL4Pr2NJBOYkEZqfQphxBDsGAbZcAsilBqngLMkLF72bgT+pnuGk+0SS618DI7gmnlXrElonjhr14qjpH0jNUnnHIiXQuC8Mnv/Paadpt+m003lQMVMZOPLhj1FmdyZAnQ5md2aw5gkR63h4ehJVtwlRdZucfgenE2d2GlkZhzh29CA56YfJz0rDWZCHsyAPoyAXZ34uaUePEhJWHbuXNx6ePti9vLF7B+AXXBW/kOoEhFTFKyCMIE9vdG2RuCSddrpgKmYquvQk+OYuc/EzgI4PQY/ndFpJXJ/djj2gCoEBVQiMPP1U/w6Hg4SEBBo0aICHh04HiRs742mn983eGzkrzYZWke2cZ34Ydi8Cn2C4+TPo/ZIKGRERV3XitFPNtuaVptNuNXvWHQVWJ3NpKmYqIqcT5r0On14Nxw5CRHO4fw40udLqZCIici4nTju1f8C8v+BNmHINZJ5mhLwAKmYqnqxU+OIWmP2iOZtvqzvg3lnmOiEiIuIePL3h8lfMcTPegZA4H97vDLsWWZ3MJamYqUj2r4YPu0LC7+DpC1dNgGveAS8/q5OJiMj5iLsO7vsLqjeGzGSY1A8WvQPG2acbqGxUzFQUK6fAx73N5Qmq1IV7ZsFF/a1OJSIiF6p6Q7j3T4i7AQwH/P40fH0n5KRbncxlqJhxd/k55jofPw0FRy406muOj6nRwupkIiJSWnwCzZnaL38N7F7mcjQfdYMDG61O5hJUzLizo7vhk8tg5WSw2aHHs3Dz5+AXanUyEREpbTYbtL/fHBwcXBMOb4P/9YC1X1mdzHIqZtzVtj/hgy6QtBr8wuCOb6Hz42BXk4qIVGi1L4ZB86BeN8jPgu/ug18fh4Jcq5NZRn/53I3hhLmvwWfXQ/YRc/XVQfOgfnerk4mISHkJqGZ+ie0y3Ly/7H8w8XI4usfaXBZRMeNG7HkZ2L+8Hf56CTCgzUC4awaE1rY6moiIlDe7B3QfCbd9Db6hx2cN7mL23FcyKmbcRfJ6YmYOxJbwO3j4wNXvwJX/B16+VicTERErNext9tDXaAXZqfDZ9djmjTV78isJFTPuYM007BMvwztzL0ZoHbhnJrS+w+pUIiLiKqpEw92/Q5u7AAP73FeoNe8xcyLVSkDFjCsryDMHdX0/CFtBNpk1OuC89y+IamV1MhERcTVevnDleLjmPQxPXwKTFmH/X7x5+qmCUzHjqtL2waS+5qAubDi7DGdvl3HgV8XqZCIi4spa3Ybz7lnkBdbClrYXPukDyz6u0LMGq5hxRTvnmYO49i4D3xC47SuMrk+Zc8mIiIicS0QzEntPxmjUDxx58Otj8P1gyMuyOlmZ0F9HV2IYsGC8udp11iGIbA73zzUHd4mIiJSA0zsQ542fQq8XweYBa6eZk+wd2mZ1tFKnYsZV5KTDV/3hj+fMEegtbzPXVwqra3UyERFxVzYbdHoY7vwJAsIhZSN8GA8bf7I6WalSMeMKUjaZa2xs+hk8vOGKN+Gad7XatYiIlI6YS2HwfKjTEfIyzC/Pv48ER77VyUqFihmrrfsGPupurrERXAvungFt7zaraRERkdISFGn20HR8yLy/aAJMvgoykq3NVQpUzFilIBd+fQK+vcdcW6NevDnpUc02VicTEZGKysMLer8EN00B7yDYvRDe7wyJC6xOdkFUzFghdSd83AuWfWTe7/w43PEdBFS1NpeIiFQOTa+C++dAeFM4lmL20Pz9f257+baKmfK28Sf4oCskrTFXu779G+jxrLnGhoiISHmpFgv3/gEtbgHDAbOehS/vgJw0q5OVmIqZ8lKQB789ZQ66yk2D2u3NwVgNelmdTEREKivvALj2ffPCEw9v2PyLebVT8jqrk5WIipnycGQXTOwDS94z73d8GAb+CiG1rM0lIiJis5kXntw9A0JqQ+oO+F9PWD3V6mTFpmKmrG3+FT7obK6N4RsKt34JvV80B2GJiIi4ipptzAtRYntCQQ788AD8/Ajk51id7JxUzJQVR755Df+028zzjzXbmqeVGvWxOpmIiMjp+YfBbV9D/NOADVZMgk96w5FEi4OdnYqZsnAkESZebl7DD9BhKNz1G4TWsTSWiIjIOdntEP8k3PGNeaFK0hrzwpVNv1id7IxUzJS2tV+b1+zvXQY+IXDz53DZy+DpbXUyERGR4ovt+c/8ZzlH4cvb4ZdHXXKxShUzpSUnHb4bBN/dC7npUPsS87RSkyusTiYiInJ+QmvDXTPMC1cAln/iklc7qZgpDXuXm4N8104Dmx3iR5hXK1WJtjqZiIjIhfH0Ni9c6f89BEbCoS3mMjyL3gWn0+p0gIqZC+N0wLzX4OPjg6NC6phjY+KfAg9Pq9OJiIiUnvrd4YG/oeHl4MiD30fA1BshM8XqZCpmzlvaXph8Jcx+yZw5Me764yuSXmJ1MhERkbIRUA1u/QL6vQGevrDtD3ivI2ydaWksS4uZefPmceWVVxIVFYXNZuOHH34o8rhhGIwaNYqoqCj8/PyIj49nw4YN1oQ92YYfzMbb9Td4B8I178P1H4NfqNXJREREypbNBhffe3xtp2Zw7KDZQ/Pbk5bNSWNpMXPs2DFatmzJhAkTTvv42LFjGTduHBMmTGDZsmVERkbSq1cvMjIyyjnpcTnp8OOD8PWdx+eOaWP2xrS61WxcERGRyiK8Cdw3G9oPNu8ved8cS5O8vtyjWDqw4/LLL+fyyy8/7WOGYTB+/HhGjhzJddddB8DkyZOJiIhg6tSpDBo0qDyjwo458ONQSNsD2KDzY+ZAX83kKyIilZWXL1z+KtTvAT8OgZQN5tVO8U9Cp0fLbfyoy45S3blzJ8nJyfTu3btwm4+PD127dmXhwoVnLGZyc3PJzc0tvJ+eng6Aw+HA4XCUPEheJrY/RmFf8QkARmg0zqvegeiOHD9wyY95HhwOB06n8/zeg5QqtYXrUFu4DrWFayn39qjfA+6fj33649i2/AqzX8LY9CvOq9+B6o3P65Alye6yxUxycjIAERERRbZHRESwa9euMz5vzJgxPP/886ds3759O4GBgSXK4JeyihpLXsT72D4AjsReT0rLoRh5/pCQUKJjXSin00lqairbtm3Dbte4bSupLVyH2sJ1qC1ci2Xt0eoZgqtcTMSKN/BIWoXtw64caj6I1Ea3gd2jRIfKzMws9r4uW8ycYPvXWBTDME7ZdrIRI0bw2GOPFd5PT0+ndu3a1K9fn+Dg4OK9aH4WttkvYlv6ITYMjJBaOK94i+B68RTzCKXO4XCwbds2YmNj8fAo2Q+ElC61hetQW7gOtYVrsbQ9GjaES27E+GUY9m2zCF8zgeqHl5hnNarGFvswJ86sFIfLFjORkZGA2UNTo0aNwu0pKSmn9NaczMfHBx8fn1O2e3h4FK9Bdy8xVwpN3W7ev2gAtt4v4+FrVRnzD7vdXvz3IWVKbeE61BauQ23hWixtj9BacPvXsOozmDEC295leHzYBXo8Zw4YLkZvUUlyu2xfYN26dYmMjGTWrFmF2/Ly8pg7dy4dO3Ys/RfMz4GZz8DEPmYhE1QDbv8GrnobXKCQERERcSs2G1zUH4YsgnrxUJBjTrQ3qR+k7ijVl7K0ZyYzM5Nt27YV3t+5cyerV68mLCyMOnXqMGzYMEaPHk2DBg1o0KABo0ePxt/fn9tuu610gyT+Db8Mg0Nbzfstb4U+Y8CvSum+joiISGUTWhv6/2Cu6zTzGdi9EN7rBN2ehvYPlMoVT5YWM8uXL6dbt26F90+MdbnzzjuZNGkSw4cPJzs7myFDhnDkyBHat2/PzJkzCQoKKp0AWakw6xmzGwwgIByuHA+N+5XO8UVEROT4RHv3QGwPc5qTxPkw87+w9ku44v+gVpsLOrylxUx8fDyGYZzxcZvNxqhRoxg1alTpvrBhmP+Avz8NWYfNbW3ugp7PqTdGRESkrFSJgQE/werPzF6a5HXwvx7Q7n7o/t/zHtbhsmNmyszh7fDpVfD9ILOQqd4E7v7d7JFRISMiIlK27Ha4aAAMXQ4tbgYMWPoBvNMONv5kdjiU9JCln9JFFeTC3LHwbgfYOc9cIKvHczBonhaHFBERKW+B1eG6D83xNFXqQkYSfNUfvrgVju4p0aFc9tLsUvdxHzh2fLBx/e7mip9h9Qofdjgc5OfnWxTu3E7M5piTk6PLHi12trbw8vJS+4iIlET9buYVT/PfgAXjYetvZqdD+8fO+dQTKk8xk5oAYeHQ5xWIu75wYUjDMEhOTubo0aPW5jsHwzAoKChg165dZ500UMreudoiNDSUyMhItZOISHF5+ZljZuJuMK8u3r0I/nyh2E+vPMVMq9vgqlMvtz5RyISHh+Pv7++yf4AMwyA3NxcfHx+XzVhZnKktDMMgKyuLlJQUgCKTPYqISDGEN4aB080Bwj+NBDKK9bTKU8xcPhb8io6SdjgchYVM1apVLQpWPCeu+vL19VUxY7GztYWfnx9gzlQdHh6uU04iIiV1YoBw5KXwfP3iPaWMI7m0E2Nk/P39LU4iFcmJnydXHoMlIuLyAqsVe9dKXcycoJ4OKU36eRIRKV8qZkRERMStqZhxU/Hx8QwbNszqGKUqMTERm83G6tWrz/sYf//9N82bN8fLy4trrrmGOXPmYLPZXP5qNREROX+VZwCwuLzatWuTlJREtWrFP0/6b4899hitWrXit99+IzAwEH9/f5KSkggJCQFg0qRJDBs2TMWNiEgFop4ZKXRiMjireHh4EBkZiafn+dfY27dvp3v37tSqVYvQ0FC8vb0154uISAWnYsaNOZ1Ohg8fTlhYGJGRkacsyDlu3DiaN29OQEAAtWvXZsiQIWRmZhY+PmnSJEJDQ/nll19o2rQpPj4+7Nq1i5iYGF566SUGDBhAYGAg0dHR/Pjjjxw8eJCrr76awMBAmjdvzvLly4u83rfffkuzZs3w8fEhJiaGN954o8jjMTExjB49mrvvvpugoCDq1KnDhx9+WPj46U4zbdiwgX79+hEcHExQUBCdO3dm+/btp/xbnHju4cOHufvuu7HZbEyaNKnIaaY5c+Zw1113kZaWhs1mK1zIVERE3JuKmX8xDIOsvIJyv51t9fAzmTx5MgEBASxZsoSxY8fywgsvMGvWrMLH7XY7b731FuvXr2fy5MnMnj2b4cOHFzlGVlYWY8aM4X//+x8bNmwgPDwcgDfffJNOnTqxatUq+vXrR//+/RkwYAB33HEHK1euJDY2lgEDBhTmXrFiBTfddBO33HIL69atY9SoUTzzzDNMmjSpyOu98cYbtG3bllWrVjFkyBAeeOABNm/efNr3t2/fPrp06YKvry+zZ89mxYoV3H333RQUFJyy74lTVMHBwYwfP56kpCRuvvnmIvt07NiR8ePHExwcTFJSEklJSTzxxBMl/ncXERHXojEz/5Kd76Dps7+X++tufOEy/L1L1hwtWrTgueeeA6BBgwZMmDCBP//8k169egEUGSBct25dXnzxRR544AHefffdwu35+fm8++67tGzZssix+/bty6BBgwB49tlnee+997j44ou58cYbAXjyySfp0KEDBw4cIDIyknHjxtGjRw+eeeYZABo2bMjGjRt57bXXGDhwYJHjDhkypPAYb775JnPmzKFx48anvL933nmHkJAQpk2bhpeXV+FxT+fEKSqbzUZISAiRkZGn7OPt7U1ISAg2m+20j4uIiHtSz4wba9GiRZH7NWrUKJxKH+Cvv/6iV69e1KxZk6CgIAYMGMDhw4c5duxY4T7e3t6nHOffx46IiACgefPmp2w78XqbNm2iU6dORY7RqVMnEhIScDgcpz3uiaLi5MwnW716NZ07dy4sZERERE5HPTP/4uflwcYXLrPkdUvq33/kbTZb4QDeXbt20bdvXwYPHsyLL75IWFgYCxYs4J577ikyM62fn99pB8eefOwTj59u24nXMwzjlOOc7tTZ2TL/24mlAURERM5Gxcy/2Gy2Ep/ucUXLly+noKCAN954A7vd7ID76quvyuz1mjZtyoIFC4psW7hwIQ0bNjzv9YlatGjB5MmTyc/PL7XeGW9v7yI9RSIi4v50mqmCql+/PgUFBbz99tvs2LGDKVOm8P7775fZ6z3++OP8+eefvPjii2zdupXJkyczYcKECxpgO3ToUNLT07nllltYvnw5CQkJTJkyhS1btpz3MWNiYsjMzOTPP//k0KFDZGVlnfexRETENaiYqaBatWrFuHHjePXVV4mLi+Pzzz9nzJgxZfZ6F110EV999RXTpk0jLi6OZ599lhdeeKHI4N+Sqlq1KrNnzyYzM5OuXbvSpk0bPvroowvqpenYsSODBw/m5ptvpnr16owdO/a8jyUiIq7BZpzPNcFuJD09nZCQENLS0ggODi7yWE5ODjt37qRu3br4+vpalLB4DMMgJycHX19fTQBnsXO1hTv9XLk7h8NBQkICDRo0OO/TmVI61BaupSK0x9n+fv+bemZERETEramYEREREbemYkZERETcmooZERERcWsqZkRERMStqZgRERERt6ZiRkRERNyaihkRERFxaypmRERExK2pmBERERG3pmJGRERE3Jqn1QGkcsrMzCQoKMjqGKWigi9vJiLi8tQzI5aYOXMma9aswTAMt7s5nU6ysrJwOp0qZEREXICKGbHEunXraNGihdUxRESkAlAxI+XO6XRit5/7R2/Pnj3Ex8fTtGlTWrRowddff10O6Vzn9UVEpHg0ZkbK3cKFC+nQocM59/P09GT8+PG0atWKlJQULrroIvr27UtAQEA5pDzz6/v7+5fL64uISPGoZ0bK3bx58+jates596tRowatWrUCIDw8nLCwMFJTU8s4neu8voiIFI+KGSl3eXl5eHl5leg5y5cvx+l0Urt27TJK5dqvLyIiZ6Zixs116dIFm81WeAsLC+Oaa67h4MGDVkc7rYSEBBo1alSi5xw+fJgBAwbw4YcfllEq1359ERE5OxUzbswwDFavXs3rr79OUlIS+/bt44svvmD27Nm89NJLVscjIyODzz77rMi26dOnc/nllxfr+QUFBeTm5nLttdcyYsQIOnbsWOzXjo+PZ9KkSSWJe1rn+/oiIlJ+VMy4sYSEBDIyMoiPjycyMpKoqCguu+wy6tevz7Fjx6yOx7Rp0xg+fDj5+fmF244cOUJoaOgp+yYmJmKz2fjmm2/o0qULPj4+fP/99wwcOJDu3bvTv3//Msk4evToIj1bJ27jxo3DMIwyf30REblwKmbc2IoVK/D29qZ58+aA2Yvw0UcfkZCQwODBg0vtdUaPHk1gYOBZb/Pnzz/leffddx89evRg7ty5gHm6plq1aqd9jdWrVwPw6quv8swzz7BhwwbCw8P58ssv+eGHH2jVqhWtWrVi3bp1pfa+AB566CGSkpIKbw888ADR0dHcdNNN/P3332X++iIicuF0afa/GQbkZ5X/63r5g81WoqesXLmS/Px8wsLCAMjKyiI8PJzff/+dtm3bllq0wYMHc9NNN511n5o1a552+1VXXcWPP/5Iz549mT59On379j3tfmvWrCEgIICvv/6amJgYAGJjY3E6nReU/VyCgoIKl1V4/vnnmT59OnPnzqVWrVrUqlXrtK+vWX9FRFyLipl/y8+C0VHl/7pP7wfvks2fsmLFCm666abC8TEHDx5kxIgRDBo0iDVr1uDh4cGOHTvYsGEDV1555VmPdbb9wsLCCgumkurTpw8jR44EYPv27Wc8XbN69WquuuqqwkKmpEaPHs3o0aML72dnZ7N48WKGDh1auO23336jc+fOp33+888/z8SJE5k7dy7R0dHnlUFERKyh00xubNWqVVx66aXExsYSGxtLhw4deOKJJ9iwYQO7du0CzD/gmzdvPuexzrbf+Z5mArPno169eixZsgRfX98zvv6aNWuIj48/95s+g8GDB7N69erCW9u2bXnhhRdO2XY6KmRERNybemb+zcvf7CWx4nVLYMeOHRw9epSLLrrolO0eHh6EhYUxd+5c/vvf/1K9enWmTp3KwoUL2bZtG/fffz8ZGRk0atSIL774gkWLFp2yn5+fX+ExL+Q0E5inmh577DHeeOON0z6enp5OYmIirVu3LsG/QFH/7j3y8/MjPDyc2NjYsz5PhYyIiPtTMfNvNluJT/dYYcWKFdhsNiIiIkhOTubYsWPMnz+f5557jsGDBxMaGkrXrl2Ji4tj6tSp1K5dm5ycHG699Va+++47GjZsyKBBg5g2bRoDBgwost+/XchpJjCLmRdffJH27duf9vE1a9Zgt9sLBzKXl5deeokJEybwyy+/4OPjQ3JyMgBVqlTBx8enXLOIiMj5UzHjplauXIlhGIU9D1WqVKFBgwaMGzeOO++8s3C/vXv3FhYoP/zwA3369KFhw4YANG7cuHByvZP3K221atXigw8+wHaGAc5r1qyhcePGZz0NVdoMw+C1114jPT2dSy65pMhjixcvPmPhJSIirkfFjJsaM2YMY8aMOes+e/fuLXL6Z9OmTTRp0qTw/oYNG7j++utP2a8sXHXVVWd8bOjQoUUG6paGOXPmnPVxm81GWlpaqb6miIhYQwOAK7CdO3cSFfXPlVk1atQoHOS7cuVKli5dSu/evU/ZT0RExJ2omKnA4uLiSEhIoHnz5mzevJn+/fuzceNGmjdvzrBhw/jqq6/w8PA4ZT8RERF3otNMFViVKlVYtWpVkW2//vprsfYTERFxF+qZEREREbemYkZERETcmooZERERcWsqZkRERMStqZhBqyBL6dLPk4hI+arUxYyXlxcAWVlZFieRiuTEz9OJny8RESlblfrSbA8PD0JDQ0lJSQHA39//jFPuW80wDHJzcwFcNmNlcaa2MAyDrKwsUlJSCA0NxcPDw6qIIiKVSqUuZgAiIyMBCgsaV2UYBgUFBXh6eqqYsdi52iI0NLTw50pERMpepS9mbDYbNWrUIDw8nPz8fKvjnJHD4WDXrl1ER0frG7/FztYWXl5eah8RkXJW6YuZEzw8PFz6j5DD4cBut+Pr6+vSOSsDtYWIiGtxiwHA7777LnXr1sXX15c2bdowf/58qyOJiIiIi3D5YubLL79k2LBhjBw5klWrVtG5c2cuv/xydu/ebXU0ERERcQEuX8yMGzeOe+65h3vvvZcmTZowfvx4ateuzXvvvWd1NBEREXEBLj1mJi8vjxUrVvDUU08V2d67d28WLlx42ufk5uYWXjYLkJaWBsCRI0dwOBxlF7aMORwO0tPTOXLkiMZpWExt4TrUFq5DbeFaKkJ7pKenA8WbiNSli5lDhw7hcDiIiIgosj0iIoLk5OTTPmfMmDE8//zzp2yPiYkpi4giIiJShjIyMggJCTnrPi5dzJzw77k8DMM441wrI0aM4LHHHiu873Q6SU1NpWrVqm49P0t6ejq1a9dmz549BAcHWx2nUlNbuA61hetQW7iWitAehmGQkZFBVFTUOfd16WKmWrVqeHh4nNILk5KSckpvzQk+Pj74+PgU2RYaGlpWEctdcHCw2/5gVjRqC9ehtnAdagvX4u7tca4emRNcegCwt7c3bdq0YdasWUW2z5o1i44dO1qUSkRERFyJS/fMADz22GP079+ftm3b0qFDBz788EN2797N4MGDrY4mIiIiLsDli5mbb76Zw4cP88ILL5CUlERcXBzTp08nOjra6mjlysfHh+eee+6UU2hS/tQWrkNt4TrUFq6lsrWHzSjONU8iIiIiLsqlx8yIiIiInIuKGREREXFrKmZERETEramYEREREbemYsaN5Obm0qpVK2w2G6tXry7y2O7du7nyyisJCAigWrVqPPzww+Tl5VkTtIJKTEzknnvuoW7duvj5+VG/fn2ee+65U/6d1Rbl591336Vu3br4+vrSpk0b5s+fb3WkCm3MmDFcfPHFBAUFER4ezjXXXMOWLVuK7GMYBqNGjSIqKgo/Pz/i4+PZsGGDRYkrjzFjxmCz2Rg2bFjhtsrUFipm3Mjw4cNPO62zw+GgX79+HDt2jAULFjBt2jS+/fZbHn/8cQtSVlybN2/G6XTywQcfsGHDBt58803ef/99nn766cJ91Bbl58svv2TYsGGMHDmSVatW0blzZy6//HJ2795tdbQKa+7cuTz44IMsXryYWbNmUVBQQO/evTl27FjhPmPHjmXcuHFMmDCBZcuWERkZSa9evcjIyLAwecW2bNkyPvzwQ1q0aFFke6VqC0PcwvTp043GjRsbGzZsMABj1apVRR6z2+3Gvn37Crd98cUXho+Pj5GWlmZB2spj7NixRt26dQvvqy3KT7t27YzBgwcX2da4cWPjqaeesihR5ZOSkmIAxty5cw3DMAyn02lERkYar7zySuE+OTk5RkhIiPH+++9bFbNCy8jIMBo0aGDMmjXL6Nq1q/HII48YhlH52kI9M27gwIED3HfffUyZMgV/f/9THl+0aBFxcXFFem0uu+wycnNzWbFiRXlGrXTS0tIICwsrvK+2KB95eXmsWLGC3r17F9neu3dvFi5caFGqyictLQ2g8DOwc+dOkpOTi7SLj48PXbt2VbuUkQcffJB+/frRs2fPItsrW1u4/AzAlZ1hGAwcOJDBgwfTtm1bEhMTT9knOTn5lIU3q1Spgre39ymLdErp2b59O2+//TZvvPFG4Ta1Rfk4dOgQDofjlH/riIgI/TuXE8MweOyxx7j00kuJi4sDKPy3P1277Nq1q9wzVnTTpk1jxYoVLF++/JTHKltbqGfGIqNGjcJms531tnz5ct5++23S09MZMWLEWY9ns9lO2WYYxmm3S1HFbYuT7d+/nz59+nDjjTdy7733FnlMbVF+/v1vqn/n8jN06FDWrl3LF198ccpjapeyt2fPHh555BE+//xzfH19z7hfZWkL9cxYZOjQodxyyy1n3ScmJoaXXnqJxYsXn7K+Rtu2bbn99tuZPHkykZGRLFmypMjjR44cIT8//5SqXE5V3LY4Yf/+/XTr1q1w4dOTqS3KR7Vq1fDw8DilFyYlJUX/zuXgoYce4qeffmLevHnUqlWrcHtkZCRg9grUqFGjcLvapfStWLGClJQU2rRpU7jN4XAwb948JkyYUHiVWaVpCwvH60gx7Nq1y1i3bl3h7ffffzcA45tvvjH27NljGMY/g073799f+Lxp06Zp0GkZ2Lt3r9GgQQPjlltuMQoKCk55XG1Rftq1a2c88MADRbY1adJEA4DLkNPpNB588EEjKirK2Lp162kfj4yMNF599dXCbbm5uRV20KmV0tPTi/xtWLdundG2bVvjjjvuMNatW1fp2kLFjJvZuXPnKVczFRQUGHFxcUaPHj2MlStXGn/88YdRq1YtY+jQodYFrYD27dtnxMbGGt27dzf27t1rJCUlFd5OUFuUn2nTphleXl7Gxx9/bGzcuNEYNmyYERAQYCQmJlodrcJ64IEHjJCQEGPOnDlFfv6zsrIK93nllVeMkJAQ47vvvjPWrVtn3HrrrUaNGjWM9PR0C5NXDidfzWQYlastVMy4mdMVM4Zh9uD069fP8PPzM8LCwoyhQ4caOTk51oSsoCZOnGgAp72dTG1Rft555x0jOjra8Pb2Ni666KLCS4SlbJzp53/ixImF+zidTuO5554zIiMjDR8fH6NLly7GunXrrAtdify7mKlMbWEzDMOw4OyWiIiISKnQ1UwiIiLi1lTMiIiIiFtTMSMiIiJuTcWMiIiIuDUVMyIiIuLWVMyIiIiIW1MxIyIiIm5NxYyIiIi4NRUzIuJy4uPjGTZs2Hk9d9KkSYWrnZ/vMS5UfHx8YYbVq1dbkkGkMtGq2SLicr777ju8vLzO+/nBwcFs2bKFgICAUkxVfN999x3bt2+nXbt2lry+SGWjYkZEXE5YWNgFPd9msxEZGVlKaUouLCyM9PR0y15fpLLRaSYROa2DBw8SGRnJ6NGjC7ctWbIEb29vZs6cecbnLVu2jF69elGtWjVCQkLo2rUrK1euLHx8zpw5eHt7M3/+/MJtb7zxBtWqVSMpKQk49TTTu+++S4MGDfD19SUiIoIbbrihxO8nJiaGl156iQEDBhAYGEh0dDQ//vgjBw8e5OqrryYwMJDmzZuzfPnywudMmjSJ0NBQfvnlFxo1aoS/vz833HADx44dY/LkycTExFClShUeeughHA5HiTOJSOlQMSMip1W9enU++eQTRo0axfLly8nMzOSOO+5gyJAh9O7d+4zPy8jI4M4772T+/PksXryYBg0a0LdvXzIyMoB/CpX+/fuTlpbGmjVrGDlyJB999BE1atQ45XjLly/n4Ycf5oUXXmDLli3MmDGDLl26nNd7evPNN+nUqROrVq2iX79+9O/fnwEDBnDHHXewcuVKYmNjGTBgACevv5uVlcVbb73FtGnTmDFjBnPmzOG6665j+vTpTJ8+nSlTpvDhhx/yzTffnFcmESkFFq/aLSIubsiQIUbDhg2N22+/3YiLizOys7NL9PyCggIjKCjI+Pnnnwu35ebmGq1btzZuuukmo1mzZsa9995b5Dldu3Y1HnnkEcMwDOPbb781goODjfT09GK93sSJE42QkJBTtkdHRxt33HFH4f2kpCQDMJ555pnCbYsWLTIAIykpqfBYgLFt27bCfQYNGmT4+/sbGRkZhdsuu+wyY9CgQUVeb+fOnQZgrFq1qli5ReT8qWdGRM7q9ddfp6CggK+++orPP/8cX19fAHbv3k1gYGDh7cTpqJSUFAYPHkzDhg0JCQkhJCSEzMxMdu/eXXhMb29vPvvsM7799luys7MZP378GV+/V69eREdHU69ePfr378/nn39OVlbWeb2XFi1aFP5/REQEAM2bNz9lW0pKSuE2f39/6tevX2SfmJgYAgMDi2w7+TkiUr40AFhEzmrHjh3s378fp9PJrl27CguCqKioIpcdnxi0O3DgQA4ePMj48eOJjo7Gx8eHDh06kJeXV+S4CxcuBCA1NZXU1NQzXnkUFBTEypUrmTNnDjNnzuTZZ59l1KhRLFu2jNDQ0BK9l5OvkLLZbGfc5nQ6T/ucE/ucbtvJzxGR8qWeGRE5o7y8PG6//XZuvvlmXnrpJe655x4OHDgAgKenJ7GxsYW3E8XM/Pnzefjhh+nbty/NmjXDx8eHQ4cOFTnu9u3befTRR/noo4+45JJLGDBgwFmLAU9PT3r27MnYsWNZu3YtiYmJzJ49u+zeuIi4FfXMiMgZjRw5krS0NN566y0CAwP57bffuOeee/jll1/O+JzY2FimTJlC27ZtSU9P5z//+Q9+fn6FjzscDvr370/v3r256667uPzyy2nevDlvvPEG//nPf0453i+//MKOHTvo0qULVapUYfr06TidTho1alQm71lE3I96ZkTktObMmcP48eOZMmUKwcHB2O12pkyZwoIFC3jvvffO+LxPPvmEI0eO0Lp1a/r378/DDz9MeHh44eMvv/wyiYmJfPjhhwBERkbyv//9j//+97+nnS03NDSU7777ju7du9OkSRPef/99vvjiC5o1a1bq71lE3JPNME66BlFExM1NmjSJYcOGcfToUUtzJCYmUrduXVatWkWrVq0szSJS0alnRkQqnLS0NAIDA3nyySctef3LL79cPUci5Ug9MyJSoWRkZBQOUg4NDaVatWrlnmHfvn1kZ2cDUKdOHby9vcs9g0hlomJGRERE3JpOM4mIiIhbUzEjIiIibk3FjIiIiLg1FTMiIiLi1lTMiIiIiFtTMSMiIiJuTcWMiIiIuDUVMyIiIuLW/h+xFXt4bx3BngAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*z_SI,1e4*func(z_SI,popt_z[0],popt_z[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*z_SI,1e4*B_tot_z_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\")\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,50)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "id": "624fbda5-4658-4f9e-ba0f-33168c27e5c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T_expansion = T/4 = 66.2961644033217 ms\n", + "omega_x = 23.69362301623883\n", + "Magnification for t_tof = 20.0 ms: M = 0.47387246032477665\n", + "for z_start = 1 μm after T_expansion z_end = 1.4110642480981759 μm\n" + ] + } + ], + "source": [ + "m = 2.69e-25\n", + "\n", + "k_x = -2*popt_x[0]*9.9*cs.mu_B\n", + "\n", + "omega = np.sqrt(k_x/m)\n", + "f = omega/(2*np.pi)\n", + "\n", + "T = 1/f\n", + "T_exp = T/4\n", + "print(f\"T_expansion = T/4 = {T_exp*1e3} ms\" )\n", + "print(f\"omega_x = {omega}\") \n", + "\n", + "t_tof = 20e-3\n", + "M = omega * t_tof\n", + "\n", + "print(f\"Magnification for t_tof = {t_tof*1e3} ms: M = {M}\")\n", + "\n", + "\n", + "start_z = 1e-6\n", + "d_t = 1e-3\n", + "def force(z):\n", + " return 2*0.248*z*9.9*cs.mu_B\n", + "z = start_z\n", + "v = 0\n", + "for t in np.arange(0,T_exp,d_t):\n", + " v = v + force(z)/m * d_t\n", + " #print(v)\n", + " z = z + v * d_t\n", + "print(f\"for z_start = 1 μm after T_expansion z_end = {z*1e6} μm\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1561200c-5630-4c8a-b0b5-b6a535805eef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Stern_gerlach_separation/.ipynb_checkpoints/untitled1-checkpoint.py b/Stern_gerlach_separation/.ipynb_checkpoints/untitled1-checkpoint.py new file mode 100644 index 0000000..e7ccada --- /dev/null +++ b/Stern_gerlach_separation/.ipynb_checkpoints/untitled1-checkpoint.py @@ -0,0 +1,34 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Aug 27 15:14:48 2021 + +@author: Joschka +""" +from src import physical_constants as cs +import numpy as np + +m = 2.69e-25 +k = 2*0.2097*9.9*cs.mu_B + +omega = np.sqrt(k/m) +f = omega/(2*np.pi) + +T = 1/f +T_exp = T/4 +#print(T_exp) + + +start_z = 1e-6 +d_t = 1e-3 +def force(z): + return 2*0.248*z*9.9*cs.mu_B +z = start_z +v = 0 +for t in np.arange(0,T_exp,d_t): + v = v + force(z)/m * d_t + #print(v) + z = z + v * d_t +print(z) +print(omega) +print(omega*1000e-3) +print(700*20e-3*2*np.pi) \ No newline at end of file diff --git a/Stern_gerlach_separation/01_Calculate_trap_frequency.py b/Stern_gerlach_separation/01_Calculate_trap_frequency.py new file mode 100644 index 0000000..0539ed7 --- /dev/null +++ b/Stern_gerlach_separation/01_Calculate_trap_frequency.py @@ -0,0 +1,43 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Aug 27 15:14:48 2021 + +@author: Joschka +""" +from src import physical_constants as cs +import numpy as np + + +mu = 9.9* cs.mu_B + +Grad_Bz = cs.m_Dy_164 * 9.81/(8*mu) + +print("For levitation:") +print(f"dBz/dz = {Grad_Bz*1e4*1e-2:.4f} G/cm") +print("") + +T = 10e-6 +sigma = np.sqrt(cs.k_B*T/cs.m_Dy_164) +dz = 2*sigma * 10e-3 +print(sigma*10e-3) + +#dz = 250e-6 +dt = 10e-3 + +Grad_Bz = 2 * dz * cs.m_Dy_164/(dt**2 * mu) + + +print(" ") +print("For Stern-Gerlach separation:") +print(f"dBz/dz = {Grad_Bz*1e4*1e-2:.4f} G/cm") +print(" ") + + +a = 8*mu*2.67*1e-2/cs.m_Dy_164 + 9.81 +s = 0.5 * a * dt**2 +print(s) + +print(0.5*9.81*dt**2) + +print((2.8778-2.8775)/2.8778) +print(16*dz) \ No newline at end of file diff --git a/Stern_gerlach_separation/Calc_Trap_frequency_displacement.ipynb b/Stern_gerlach_separation/Calc_Trap_frequency_displacement.ipynb new file mode 100644 index 0000000..e40a346 --- /dev/null +++ b/Stern_gerlach_separation/Calc_Trap_frequency_displacement.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 109, + "id": "f8d06107", + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Aug 24 16:24:52 2021\n", + "\n", + "@author: Joschka\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sys\n", + "sys.path.insert(0,'..')\n", + "\n", + "from src import coil_class as BC\n", + "from src import physical_constants as cs\n", + "\n", + "#from IPython import get_ipython\n", + "#get_ipython().run_line_magic('matplotlib', 'qt')\n", + "#get_ipython().run_line_magic('matplotlib', 'inline')\n", + "\n", + "\n", + "from scipy.optimize import curve_fit\n", + "plt.rcParams['axes.grid'] = True\n", + "plt.rcParams['grid.alpha'] = 0.5" + ] + }, + { + "cell_type": "markdown", + "id": "99eb6ef1-8920-4f7d-9b58-fd00c731d2bc", + "metadata": {}, + "source": [ + "## Set up coils" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "199e2602-bec6-429a-a639-6ea35ababb86", + "metadata": {}, + "outputs": [], + "source": [ + "HH = 1\n", + "I = 5\n", + "\n", + "d_coils = 50\n", + "Radius = 30\n", + "\n", + "layers = 4\n", + "windings = 4\n", + "wire_width = 1\n", + "wire_height = 1\n", + "\n", + "Coil = BC.BCoil(HH,d_coils,Radius, layers, windings, wire_width, wire_height)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "40cee79d-d6ef-4df4-ae03-5e82785433e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + }, + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z = np.arange(-100,100,10)\n", + "print(np.size(z))\n", + "r = np.arange(1e-15,100,5)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 4)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)\n", + "\n", + "#plt.figure(figsize=(16,10))\n", + "plt.quiver(r_m,z_m,B[:,:,1],B[:,:,0])\n", + "plt.xlabel(\"radius r [m]\")\n", + "plt.ylabel(\"z-axis [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "28dcdd21-f4b6-49e6-a935-4012a941f186", + "metadata": {}, + "outputs": [], + "source": [ + "z = np.arange(-100,100,1)\n", + "r = np.arange(1e-3,100,1)\n", + "\n", + "B, B_tot = Coil.B_multiple_3d(I, r,z,raster = 2)\n", + "\n", + "z_m, r_m = np.meshgrid(z,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "ad02e8cd-e92a-4d27-845d-cc7e22d6e7e5", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.concatenate((-np.flip(r),r))\n", + "B_tot_x = np.concatenate((np.flip(B_tot[:,len(z)//2]),B_tot[:,len(z)//2]))\n", + "B_tot_z = B_tot[0,:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "52475c2c-e2f0-4615-a5f4-9f0a86155a3e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.plot(x,np.gradient(B_tot_x,x))\\nplt.xlabel(\"radius r [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\n#plt.xlim(0,0.01)\\nplt.show()\\nplt.plot(z,np.gradient(B_tot[0,:],z))\\nplt.xlabel(\"z_axis [mm]\")\\nplt.ylabel(\"total field B_tot [G]\")\\nplt.show()\\n'" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.plot(x,B_tot_x)\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(-0.1,0.1)\n", + "plt.show()\n", + "plt.plot(z,B_tot_z)\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\n", + "\"\"\"\n", + "plt.plot(x,np.gradient(B_tot_x,x))\n", + "plt.xlabel(\"radius r [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "#plt.xlim(0,0.01)\n", + "plt.show()\n", + "plt.plot(z,np.gradient(B_tot[0,:],z))\n", + "plt.xlabel(\"z_axis [mm]\")\n", + "plt.ylabel(\"total field B_tot [G]\")\n", + "plt.show()\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "37c71478-cae2-40ea-8712-b2f751c6c54d", + "metadata": {}, + "source": [ + "## Fit harmonic function" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "443f51a2-a502-4d4a-8692-15610c193cc7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(x,a,b):\n", + " return a*x**2+b\n", + "\n", + "x_SI = 1e-3 * x\n", + "z_SI = 1e-3 * z\n", + "\n", + "B_tot_x_SI = 1e-4*B_tot_x\n", + "B_tot_z_SI = 1e-4*B_tot_z\n" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "id": "59145c9b-aac3-4bea-ba79-b2a3992d05f6", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_x, pcov = curve_fit(func,x_SI[a:b],B_tot_x_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "49c6fb78-ba74-41e3-9a2a-c4dca7109643", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*x_SI,1e4*func(x_SI,popt_x[0],popt_x[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*x_SI,1e4*B_tot_x_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\" )\n", + "#plt.ylim(0,8)\n", + "#plt.xlim(-0.01,0.01)\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,20)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "77c981bb-02be-4de9-8990-849b6a7fbee1", + "metadata": {}, + "outputs": [], + "source": [ + "nr_points = 20\n", + "a = 100-nr_points//2\n", + "b = 100+ nr_points//2\n", + "popt_z, pcov = curve_fit(func,z_SI[a:b],B_tot_z_SI[a:b])" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "f1f7cdd7-f13c-41e2-be5e-a375dc5e4d06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(1e3*z_SI,1e4*func(z_SI,popt_z[0],popt_z[1]),label = \"harmonic fit\")\n", + "plt.plot(1e3*z_SI,1e4*B_tot_z_SI, label = r\"$ B_{tot} = \\sqrt{r^2 + z^2} $\")\n", + "plt.xlabel(\"x-axis [mm]\")\n", + "plt.xlim(-50,50)\n", + "plt.ylim(0,50)\n", + "plt.ylabel(\"B_tot [G]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "id": "624fbda5-4658-4f9e-ba0f-33168c27e5c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T_expansion = T/4 = 66.2961644033217 ms\n", + "omega_x = 23.69362301623883\n", + "Magnification for t_tof = 20.0 ms: M = 0.47387246032477665\n", + "for z_start = 1 μm after T_expansion z_end = 1.4110642480981759 μm\n" + ] + } + ], + "source": [ + "m = 2.69e-25\n", + "\n", + "k_x = -2*popt_x[0]*9.9*cs.mu_B\n", + "\n", + "omega = np.sqrt(k_x/m)\n", + "f = omega/(2*np.pi)\n", + "\n", + "T = 1/f\n", + "T_exp = T/4\n", + "print(f\"T_expansion = T/4 = {T_exp*1e3} ms\" )\n", + "print(f\"omega_x = {omega}\") \n", + "\n", + "t_tof = 20e-3\n", + "M = omega * t_tof\n", + "\n", + "print(f\"Magnification for t_tof = {t_tof*1e3} ms: M = {M}\")\n", + "\n", + "\n", + "start_z = 1e-6\n", + "d_t = 1e-3\n", + "def force(z):\n", + " return 2*0.248*z*9.9*cs.mu_B\n", + "z = start_z\n", + "v = 0\n", + "for t in np.arange(0,T_exp,d_t):\n", + " v = v + force(z)/m * d_t\n", + " #print(v)\n", + " z = z + v * d_t\n", + "print(f\"for z_start = 1 μm after T_expansion z_end = {z*1e6} μm\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1561200c-5630-4c8a-b0b5-b6a535805eef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Test_class.py b/Test_class.py new file mode 100644 index 0000000..5d21e54 --- /dev/null +++ b/Test_class.py @@ -0,0 +1,186 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 11:49:41 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x_m = np.linspace(-0.05, 0.05, 101) +z_m = np.linspace(-0.05, 0.05, 101) + + +z = z_m*1e3 +x = x_m*1e3 #for plotting in mm + +#Import Values from simulation + +################# My simulation ######################### +I = 5 +HH = 1 +d_coils = 44 +R_mid = 44 +R_inner = 44-3*1.7 + +layers = 6 +windings = 2 +wire_width = 1.7 +wire_height = 2.6 + + +HH_Coil1 = BC.BCoil(HH, d_coils ,R_mid, layers, windings, wire_width, wire_height) +#HH_Coil1.print_info() +#B_z_sim, B_x_sim = HH_Coil1.B_multiple(5, x, z) + +#B_z, B_x = bf.B_multiple_raster(I,HH,R_inner,d_coils,layers,windings,wire_width, wire_height, x_m,z_m) + +#B_test = B_field_ideal_AHH(layers*windings,I,R_inner*1e-3,d_coils*1e-3,z_m) + +#B_x = np.concatenate((-np.flip(B_r),B_r[1:len(B_r)])) + + +HH_Coil1.Bz_plot_HH(I,x,z) + + + + + +#Calculate gradients/curvature +B_z_sim_grad = np.gradient(np.gradient(B_z_sim,z_m),z_m)/1e4 +B_x_sim_grad = np.gradient(B_x_sim,x_m)/100 +#B_z_grad = np.gradient(np.gradient(B_z,z_m),z_m)/1e4 +B_z_grad = np.gradient(B_z,z_m)/100 + +B_z_sim_grad = np.gradient(B_z_grad,z_m)/100 + +B_x_grad = np.gradient(B_x,x_m)/100 + + +#Calculate relative differences in permille +rel_diff_Bz = (B_z-B_z_sim)/np.mean(B_z) +#rel_diff_Bx = (B_x-B_x_sim)/np.mean(B_x) +rel_diff_Bz_grad = (B_z_grad-B_z_sim_grad)/np.mean(B_z_grad) + +rel_diff_Bz_grad_mean = (B_z_grad-B_z_sim_grad)/np.mean(B_z_grad) +#rel_diff_Bx_grad = (B_x_grad-B_x_sim_grad)/np.mean(B_x_grad) + +#Plotting +plt.figure(1,figsize=(20,18)) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_z along z-axis, comparison of simulations", fontsize=30) + + +#Field plot +########################## +plt.subplot(3,2,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim,linestyle = "dashdot", label = r"$B_{z, sim}$: Numerical Matlab simulation") +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +############################# +plt.subplot(3,2,3) +plt.plot(z,(B_z-B_z_sim), label = r"$B_z - B_{z, sim}$") +plt.ylabel("absolute deviation [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + +############################# +plt.subplot(3,2,5) +plt.plot(z,1000*rel_diff_Bz, label = "$(B_z - B_{z, sim}) / B_z$") +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +######################Gradient plot############################ + +################ +plt.subplot(3,2,2) +plt.plot(z,B_z_grad,linestyle = "solid", label = r"$\nabla_z^2 B_z$: Result via elliptic integrals") +plt.plot(z,B_z_sim_grad,linestyle = "dashdot", label = r"$\nabla_z^2 B_{z, sim}$: Numerical Matlab sim.") +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.title("Curvature of B-field",fontsize = 30) +plt.legend(loc='lower right') + + +################# + +plt.subplot(3,2,4) +plt.plot(z,(B_z_grad-B_z_sim_grad), label = r"$\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}$") +plt.ylabel(r"absolute deviation $[G/cm^2]$") +plt.xlabel("z-axis [mm]") +plt.legend() + +##################### +plt.subplot(3,2,6) +plt.plot(z,1000*rel_diff_Bz_grad, label = r"$(\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}) / \nabla_z^2 B_z$") +#plt.ylim(-57,10) +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() + + +plt.savefig("output/HH_benchmark_5A_6x2.pdf") +plt.show() + +############### relative deviation with averaging by the mean not the individual value ######################################## +plt.figure(2) + +plt.plot(z,1000*rel_diff_Bz_grad_mean, label = r"$(\nabla_z^2 B_z - \nabla_z^2 B_{z, sim}) / mean(\nabla_z^2 B_z)$") +#plt.ylim(-57,10) +plt.ylabel("relative deviation [‰]") +plt.xlabel("z-axis [mm]") +plt.legend() +plt.savefig("output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf") +plt.show() + +##################### x-Axis ######################################################### + +plt.figure(3) + +plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_x along x-axis, comparison of simulations", fontsize=30) + + +#Field plot +########################## + +plt.plot(x,B_x,linestyle = "solid", label = r"$B_x$: Result via elliptic integrals") +plt.plot(x,B_x_sim,linestyle = "dashdot", label = r"$B_{x, sim}$: Numerical Matlab simulation") +plt.plot(x,(B_x-B_x_sim), label = r"$B_x - B_{x, sim}$") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ,fontsize = 30) + +plt.ylabel(r"$B_x$ [G]") +plt.xlabel("x-axis [mm]") +plt.legend() + + + + +################# + + +plt.savefig("output/HH_benchmark_5A_6x2_x-axis.pdf") +plt.show() + diff --git a/Test_class1.py b/Test_class1.py new file mode 100644 index 0000000..29e24c2 --- /dev/null +++ b/Test_class1.py @@ -0,0 +1,81 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Aug 16 11:49:41 2021 + +@author: Joschka +""" +import matplotlib.pyplot as plt +import numpy as np +from src import B_field_calculation as bf + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') +#get_ipython().run_line_magic('matplotlib', 'inline') + +#set up axis +x = np.linspace(-50, 50, 101) +z = np.linspace(-50, 50, 101) + + + +################# My simulation ######################### +I = 5 +HH = 1 +d_coils = 44 +R_mid = 44 + + +layers = 6 +windings = 2 +wire_width = 1.7 +wire_height = 2.6 + + +HH_Coil_44 = BC.BCoil(HH, d_coils ,R_mid, layers, windings, wire_width, wire_height) + +d_coils_2 = 55.2 +HH_Coil_54 = BC.BCoil(HH, d_coils_2 ,R_mid, layers, windings, wire_width, wire_height) + +#HH_Coil_44.Bz_plot_HH(I,x,z) + + +#HH_Coil_44.Bz_plot_HH_comp(HH_Coil_54,I,x,z) + +B_z, B_x = HH_Coil_44.B_multiple(I,x,z) +B_z_2, B_x_2 = HH_Coil_54.B_multiple(I,x,z) + +B_z_curvature = np.gradient(np.gradient(B_z,z),z)*1e2 +B_z_curvature_2 = np.gradient(np.gradient(B_z_2,z),z)*1e2 + + +plt.figure(100,figsize=(13,10)) + +#plt.rcParams.update({'font.size': 15}) +plt.suptitle("Helmholtz coil field B_z along z-axis") + + +#Field plot +########################## +plt.subplot(2,1,1) +plt.plot(z,B_z,linestyle = "solid", label = r"$B_z$, d = 44 mm") +plt.plot(z,B_z_2,linestyle = "solid", label = r"$B_{z,2}$, d = 55.2 mm") +#plt.xlim(-0.01,0.01) +plt.title("B-field" ) + +plt.ylabel(r"$B_z$ [G]") +plt.xlabel("z-axis [mm]") +plt.legend() + +plt.subplot(2,1,2) +plt.plot(z,B_z_curvature,linestyle = "solid", label = r"$\nabla_z^2 B_z$, d = 44 mm") +plt.plot(z,B_z_curvature_2,linestyle = "solid", label = r"$\nabla_z^2 B_{z,2}, d = 55.2 mm$") + + +plt.ylabel(r"$\nabla_z^2 B_z [G/cm^2]$") +plt.xlabel("z-axis [mm]")#plt.xlim(-10,10) +plt.title("Curvature of B-field") +plt.legend(loc='lower right') + +plt.show() diff --git a/output/AHH_field.pdf b/output/AHH_field.pdf new file mode 100644 index 0000000..a4a70e9 Binary files /dev/null and b/output/AHH_field.pdf differ diff --git a/output/HH_benchmark_5A_6x2.pdf b/output/HH_benchmark_5A_6x2.pdf new file mode 100644 index 0000000..4be788d Binary files /dev/null and b/output/HH_benchmark_5A_6x2.pdf differ diff --git a/output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf b/output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf new file mode 100644 index 0000000..6c23f40 Binary files /dev/null and b/output/HH_benchmark_5A_6x2_rel_deviation_via_mean.pdf differ diff --git a/output/HH_benchmark_5A_6x2_x-axis.pdf b/output/HH_benchmark_5A_6x2_x-axis.pdf new file mode 100644 index 0000000..95029f0 Binary files /dev/null and b/output/HH_benchmark_5A_6x2_x-axis.pdf differ diff --git a/src/coil_class.py b/src/coil_class.py index ea18cbf..a0817f6 100644 --- a/src/coil_class.py +++ b/src/coil_class.py @@ -97,8 +97,8 @@ class BCoil: N_windings = self.layers * self.windings B = cs.mu_0 * N_windings * I_current / 2 * self.radius ** 2 * ( - 1 / (self.radius ** 2 + (z_SI - self.distance / 2) ** 2) ** (3 / 2) + self.HH * 1 / ( - self.radius ** 2 + (z_arg + self.distance / 2) ** 2) ** (3 / 2)) + 1 / (self.radius ** 2 + (z_SI - self.distance / 2) ** 2) ** (3 / 2) + self.HH * 1 / ( + self.radius ** 2 + (z_arg + self.distance / 2) ** 2) ** (3 / 2)) B *= 1e4 # conversion Gauss return B @@ -109,16 +109,16 @@ class BCoil: def B_z_loop(I_current, R_loop, z_loc, r, z): """calculate z-component of B-field at position r and z for each individual loop""" B_z = 2e-7 * I_current * 1 / ((R_loop + r) ** 2 + (z - z_loc) ** 2) ** (1 / 2) * ( - sp.ellipk(BCoil.k_sq(R_loop, z_loc, r, z)) + sp.ellipe(BCoil.k_sq(R_loop, z_loc, r, z)) * ( - R_loop ** 2 - r ** 2 - (z - z_loc) ** 2) / ((R_loop - r) ** 2 + (z - z_loc) ** 2)) + sp.ellipk(BCoil.k_sq(R_loop, z_loc, r, z)) + sp.ellipe(BCoil.k_sq(R_loop, z_loc, r, z)) * ( + R_loop ** 2 - r ** 2 - (z - z_loc) ** 2) / ((R_loop - r) ** 2 + (z - z_loc) ** 2)) B_z *= 1e4 # conversion to gauss return B_z def B_r_loop(I_current, R_loop, z_loc, r, z): """calculate r-component of B-field at position r and z for each individual loop""" B_r = 2e-7 * I_current / r * (z - z_loc) / ((R_loop + r) ** 2 + (z - z_loc) ** 2) ** (1 / 2) * ( - -sp.ellipk(BCoil.k_sq(R_loop, z_loc, r, z)) + sp.ellipe(BCoil.k_sq(R_loop, z_loc, r, z)) * ( - R_loop ** 2 + r ** 2 + (z - z_loc) ** 2) / ((R_loop - r) ** 2 + (z - z_loc) ** 2)) + -sp.ellipk(BCoil.k_sq(R_loop, z_loc, r, z)) + sp.ellipe(BCoil.k_sq(R_loop, z_loc, r, z)) * ( + R_loop ** 2 + r ** 2 + (z - z_loc) ** 2) / ((R_loop - r) ** 2 + (z - z_loc) ** 2)) B_r *= 1e4 # conversion to gauss return B_r @@ -154,11 +154,11 @@ class BCoil: for xx_in in range(0, raster): for zz_in in range(0, raster): z_pos = z_start + zz * ( - self.wire_height + self.windings_spacing) - self.wire_height / 2 + zz_in * self.wire_height / ( - raster - 1) + self.wire_height + self.windings_spacing) - self.wire_height / 2 + zz_in * self.wire_height / ( + raster - 1) R_pos = R_start + xx * ( - self.wire_width + self.layers_spacing) - self.wire_width / 2 + xx_in * self.wire_width / ( - raster - 1) + self.wire_width + self.layers_spacing) - self.wire_width / 2 + xx_in * self.wire_width / ( + raster - 1) B_z += BCoil.B_z_loop(I_current, R_pos, z_pos, 0, z_SI) + BCoil.B_z_loop(self.HH * I_current, R_pos, -z_pos, 0, z_SI) @@ -206,11 +206,11 @@ class BCoil: for xx_in in range(0, raster): for zz_in in range(0, raster): z_pos = z_start + zz * ( - self.wire_height + self.windings_spacing) - self.wire_height / 2 + zz_in * self.wire_height / ( - raster - 1) + self.wire_height + self.windings_spacing) - self.wire_height / 2 + zz_in * self.wire_height / ( + raster - 1) R_pos = R_start + xx * ( - self.wire_width + self.layers_spacing) - self.wire_width / 2 + xx_in * self.wire_width / ( - raster - 1) + self.wire_width + self.layers_spacing) - self.wire_width / 2 + xx_in * self.wire_width / ( + raster - 1) # z-field along z-axis (x-Field always zero) B_tot_z += BCoil.B_z_loop(I_current, R_pos, z_pos, 0, z_SI) + BCoil.B_z_loop( @@ -252,11 +252,11 @@ class BCoil: for xx_in in range(0, raster): for zz_in in range(0, raster): z_pos = z_start + zz * ( - self.wire_height + self.windings_spacing) - self.wire_height / 2 + zz_in * self.wire_height / ( - raster - 1) + self.wire_height + self.windings_spacing) - self.wire_height / 2 + zz_in * self.wire_height / ( + raster - 1) R_pos = R_start + xx * ( - self.wire_width + self.layers_spacing) - self.wire_width / 2 + xx_in * self.wire_width / ( - raster - 1) + self.wire_width + self.layers_spacing) - self.wire_width / 2 + xx_in * self.wire_width / ( + raster - 1) # compute z-value of field B[:, el, 1] += BCoil.B_z_loop(I_current, R_pos, z_pos, np.abs(x_SI[el]), @@ -278,6 +278,7 @@ class BCoil: return B + @staticmethod def B_tot_3d(B): return np.sqrt(B[:, :, 0] ** 2 + B[:, :, 1] ** 2) @@ -300,7 +301,7 @@ class BCoil: plt.ylabel("z-axis [mm]") plt.show() - def Bcurv(B_field, z): + def curv(B_field, z): return np.gradient(np.gradient(B_field, z), z) * 1e2 def Bgrad(B_field, z): @@ -410,7 +411,7 @@ class BCoil: """ L = 4 * np.pi * (self.windings * self.layers) ** 2 * (1e2 * self.radius) ** 2 / ( - 0.2317 * 100 * self.radius + 0.44 * 100 * self.get_coil_height() + 0.39 * 100 * self.get_coil_width()) + 0.2317 * 100 * self.radius + 0.44 * 100 * self.get_coil_height() + 0.39 * 100 * self.get_coil_width()) return L * 1e-9 def resistance(self, T): diff --git a/time_response/01_independence_of_N.py b/time_response/01_independence_of_N.py new file mode 100644 index 0000000..3e177ae --- /dev/null +++ b/time_response/01_independence_of_N.py @@ -0,0 +1,75 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 7 13:18:18 2021 + +@author: Joschka +""" + +import matplotlib.pyplot as plt +import numpy as np + +from src import coil_class as BC + +from IPython import get_ipython +get_ipython().run_line_magic('matplotlib', 'qt') + +I = 10 + +HH_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 4, windings = 2, wire_height = 2, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) +HH_Coil.print_info() +HH_Coil.cooling(I,20) +print(f"length = {HH_Coil.get_wire_length()}") + + +Fast_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 8, windings = 8, wire_height =0.5, wire_width = 0.5,windings_spacing=0, layers_spacing = 0) +Fast_Coil.set_R_outer(49.3) +Fast_Coil.set_d_min(49.8) + +Ilz_Coil = BC.BCoil(HH = 1, distance = 70 ,radius = 40.5 , layers = 6, windings = 1, wire_height = 2.7, wire_width = 1,windings_spacing=0.25, layers_spacing = 0.25) +Ilz_Coil.set_R_inner(40.5) +Ilz_Coil.print_info() + +L = HH_Coil.inductivity() + +R = HH_Coil.resistance(20) + +HH_Coil.cooling(I,22) + +#AHH_Coil = BC.BCoil(-1, 82 , 47.3 , 4, 6, wire_width= 1, wire_height= 1.5 ,layers_spacing = 0.25, windings_spacing= 0.25) + +def I_current(Coil, I_0, t): + L = Coil.induct_perry() + + #L = Coil.inductivity() + R = Coil.resistance(22.5) + print(f"L={L}") + print(f" R= {R}") + tau = L/R + print(f" τ = {tau}") + I = I_0 * (1-np.exp(-R/L * t)) + return I + +def I_current_exp(I_0,R,L,t): + print("") + print(L/R) + I = I_0* (1-np.exp(-R/L * t)) + return I + +t = np.linspace(0,0.005,1000) +plt.title("time response") +plt.plot(t*1e3,I_current(HH_Coil,I,t),label = "I_max = 10 A, 2 x 4") +plt.plot(t*1e3,8*I_current(Fast_Coil,10/8,t),label = "I_max = 10/8 A, 8 x 8") +#plt.plot(t*1e3,I_current(Ilz_Coil,I,t),label = "Ilz theo") +#plt.plot(t*1e3,I_current_exp(I,42e-3,14e-6,t),label = "Ilz exp") +#plt.plot(t*1e3,I_current_exp(I,0.85,3.75e-3,t),label = "Paper: Fast switching") + +plt.xlabel("time [ms]") +plt.ylabel("current I [A]") +plt.legend() +plt.show() + +print(Fast_Coil.power(10/8,22)) + +print(Fast_Coil.resistance(22)) \ No newline at end of file diff --git a/time_response/R_test.py b/time_response/R_test.py new file mode 100644 index 0000000..3d95363 --- /dev/null +++ b/time_response/R_test.py @@ -0,0 +1,128 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 7 13:18:18 2021 + +@author: Joschka +""" + +import matplotlib.pyplot as plt +import numpy as np + +from src import coil_class as BC + +from IPython import get_ipython + +# get_ipython().run_line_magic('matplotlib', 'qt') + +I = 1.25 + +HH_Coil = BC.BCoil(HH=1, distance=54, radius=48, layers=8, windings=8, wire_height=0.5, wire_width=0.5, + windings_spacing=0.25, layers_spacing=0.25) +HH_Coil.set_R_outer(49.3) +HH_Coil.set_d_min(49.8) +HH_Coil.print_info() +# todo: asdkjflö + +# Fast_Coil = BC.BCoil(HH = 1, distance = 54 ,radius = 48 , layers = 8, windings = 8, wire_height =0.5, wire_width = 0.5,windings_spacing=0, layers_spacing = 0) +# Fast_Coil.set_R_outer(49.3) +# Fast_Coil.set_d_min(49.8) + + +# AHH_Coil = BC.BCoil(-1, 82 , 47.3 , 4, 6, wire_width= 1, wire_height= 1.5 ,layers_spacing = 0.25, windings_spacing= 0.25) + +def I_t(Coil, I_0, t): + L = Coil.induct_perry() + + # L = Coil.inductivity() + R = Coil.resistance(22.5) + print(f"L={L}") + print(f" R= {R}") + tau = L / R + print(f" τ = {tau}") + I = I_0 * (1 - np.exp(-R / L * t)) + return I + + +def I_t_2(Coil, I_0, t): + L = Coil.induct_perry() + + # L = Coil.inductivity() + R = 2 * Coil.resistance(22.5) + print(f"L={L}") + print(f" R= {R}") + tau = L / R + print(f" τ = {tau}") + I = I_0 * (1 - np.exp(-R / L * t)) + return I + + +def U_t(t, U_0, t_f): + if t < t_f: + U = 2 * U_0 - U_0 / t_f * t + else: + U = U_0 + return U + + +test = np.vectorize(U_t) + + +def I_t_3(Coil, I_0, t_f, t): + L = Coil.induct_perry() + + # L = Coil.inductivity() + R = Coil.resistance(22.5) + # print(f"L={L}") + # print(f" R= {R}") + # tau = L/R + # print(f" τ = {tau}") + # print(R*I_0) + I = test(t, R * I_0, t_f * 1e-3) / R * (1 - np.exp(-R / L * t)) + return I + + +def I_current_exp(I_0, R, L, t): + print("") + print(L / R) + I = I_0 * (1 - np.exp(-R / L * t)) + return I + + +def main(): + # execute some code here + t = np.linspace(0, 0.002, 1000) + + # set up color + color = iter(plt.cm.rainbow(np.linspace(0, 1, 5))) + + plt.figure(1) + plt.subplot(2, 1, 1) + plt.title("time response") + plt.plot(t * 1e3, I_t(HH_Coil, I, t), label="R = R_coil, U = const. = 10 V ") + plt.plot(t * 1e3, I_t_2(HH_Coil, I, t), label="R = 2 * R_coil, U = const. = 20 V") + + for t_f in np.arange(0.2, 1.2, 0.3): + print(t_f) + plt.plot(t * 1e3, I_t_3(HH_Coil, I, t_f, t), c=next(color), label=f"U overshoot, t_f = {t_f:.1f} ms") + + plt.xlabel("time [ms]") + plt.ylabel("current I [A]") + plt.legend() + plt.show() + + color = iter(plt.cm.rainbow(np.linspace(0, 1, 5))) + + plt.subplot(2, 1, 2) + for t_f in np.arange(0.2, 1.2, 0.3): + plt.plot(t * 1e3, test(t, 10, t_f * 1e-3), c=next(color), label=f"U overshoot, t_f = {t_f:.1f} ms") + + plt.xlabel("time [ms]") + plt.ylabel("voltage U [V]") + plt.legend() + + plt.show() + + +if __name__ == "__main__": + print("g") + main() diff --git a/time_response/test.py b/time_response/test.py new file mode 100644 index 0000000..8adc732 --- /dev/null +++ b/time_response/test.py @@ -0,0 +1,5 @@ +import time_response.R_test as R + +print("hi") +print(R.I_current_exp(1, 1, 1, 4)) +print(R.HH_Coil) diff --git a/time_response/untitled3.py b/time_response/untitled3.py new file mode 100644 index 0000000..764da43 --- /dev/null +++ b/time_response/untitled3.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 7 13:26:18 2021 + +@author: Joschka +""" + +import numpy as np + +from src import physical_constants as cs + +L = 4*np.pi*1e-7*16**2 *0.046925**2 * np.pi/4.75e-3 +r = 0.046925 +R = cs.rho_copper_20 * 16* 2*r * np.pi/1e-6 + +print(R) diff --git a/untitled0.py b/untitled0.py new file mode 100644 index 0000000..5165424 --- /dev/null +++ b/untitled0.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 1 10:42:13 2021 + +@author: Joschka +""" + +for t \ No newline at end of file