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SO_logPlot.jl
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SO_logPlot.jl
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cd(@__DIR__)
using PyPlot
using CSV, DataFrames
using Polynomials
using LaTeXStrings
using Printf
function loglogplot(DF, size, title)
fig, ax = plt.subplots(figsize = (7, 7))
ax.set_xscale("log"); # Set logarithmic scale
ax.set_yscale("log");
ax.set_xlabel("N", fontsize = 14)
ax.set_ylabel("Time [sec]", fontsize = 14)
ax.set_title("Execution time as function of system size - "*title, fontweight ="bold")
ax.set_xlim([90,size+10])
ax.tick_params(labelsize = 12)
# Learning, direct
ax.scatter(DF[!, :N], DF[!, :L_direct], label="Learning, direct", s=10, marker="o",c="firebrick")
logX = log.(DF[!, :N])
logY = log.(DF[!, :L_direct])
f = fit(logX, logY, 1)
intercept, slope = f.coeffs
y_fit = exp(intercept) .* (DF[!, :N]).^ slope
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(DF[!, :N], y_fit, label=label_txt, color="firebrick")
# Learning, on-the-fly
ax.scatter(DF[!, :N], DF[!, :L_onthefly], label="Learning, on-the-fly", s=10, marker="s",c="darkblue")
logX = log.(DF[!, :N])
logY = log.(DF[!, :L_onthefly])
f = fit(logX, logY, 1) # log(y) = mlog(x) + n
intercept, slope = f.coeffs # n, m
y_fit = exp(intercept) .* (DF[!, :N]).^ slope # y(x)=exp(n)*x^m
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(DF[!, :N], y_fit, label=label_txt, color="darkblue")
# No Learning
ax.scatter(DF[!, :N], DF[!, :NL], label="No Learning", s=10, marker="^",c="grey")
logX = log.(DF[!, :N])
logY = log.(DF[!, :NL])
f = fit(logX, logY, 1) # log(y) = mlog(x) + n
intercept, slope = f.coeffs # n, m
y_fit = exp(intercept) .* (DF[!, :N]).^ slope # y(x)=exp(n)*x^m
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(DF[!, :N], y_fit, label=label_txt, color="grey")
legend()
ax.legend(loc="best", fontsize=12)
end
path = "./times/"
filename1 = "times_array_10resets.csv"
df1 = CSV.read(path*filename1, DataFrame, header=1)
loglogplot(df1, 10000, "10 resets")
fig_name = "logPlot_N10000"
savefig(path * fig_name * ".png")
# savefig(path * fig_name * ".svg")
filename2 = "times_array_1000resets.csv"
df2 = CSV.read(path*filename2, DataFrame, header=1)
loglogplot(df2, 2000, "1000 resets")
fig_name = "logPlot_N2000"
savefig(path * fig_name * ".png")
# savefig(path * fig_name * ".svg")
##. Separate fits by range - 10 resets
fig, ax = plt.subplots(figsize = (7, 7))
ax.set_xscale("log"); # Set logarithmic scale
ax.set_yscale("log");
ax.set_xlabel("N", fontsize = 14)
ax.set_ylabel("Time [sec]", fontsize = 14)
ax.set_title("Execution time as function of system size - 10 resets", fontweight ="bold")
ax.set_xlim([90,10010])
ax.tick_params(labelsize = 12)
# Learning, direct
ax.scatter(df1[!, :N], df1[!, :L_direct], label="Learning, direct", s=10, marker="o",c="firebrick")
N_max = 1100
logX = log.(df1[df1.N .<= N_max , :N])
logY = log.(df1[df1.N .<= N_max , :L_direct])
f = fit(logX, logY, 1)
intercept, slope = f.coeffs
y_fit = exp(intercept) .* (df1[df1.N .<= N_max, :N]).^ slope
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df1[df1.N .<= N_max, :N], y_fit, label=label_txt)
N_min = 1100
N_max = 10000
logX = log.(df1[N_min .< df1.N .<= N_max , :N])
logY = log.(df1[N_min .< df1.N .<= N_max , :L_direct])
f = fit(logX, logY, 1)
intercept, slope = f.coeffs
y_fit = exp(intercept) .* (df1[N_min .< df1.N .<= N_max, :N]).^ slope
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df1[N_min .< df1.N .<= N_max, :N], y_fit, label=label_txt)
# Learning, on-the-fly
ax.scatter(df1[!, :N], df1[!, :L_onthefly], label="Learning, on-the-fly", s=10, marker="s",c="darkblue")
logX = log.(df1[!, :N])
logY = log.(df1[!, :L_onthefly])
f = fit(logX, logY, 1) # log(y) = mlog(x) + n
intercept, slope = f.coeffs # n, m
y_fit = exp(intercept) .* (df1[!, :N]).^ slope # y(x)=exp(n)*x^m
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df1[!, :N], y_fit, label=label_txt, color="darkblue")
# No Learning
ax.scatter(df1[!, :N], df1[!, :NL], label="No Learning", s=10, marker="^",c="grey")
logX = log.(df1[!, :N])
logY = log.(df1[!, :NL])
f = fit(logX, logY, 1) # log(y) = mlog(x) + n
intercept, slope = f.coeffs # n, m
y_fit = exp(intercept) .* (df1[!, :N]).^ slope # y(x)=exp(n)*x^m
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df1[!, :N], y_fit, label=label_txt, color="grey")
legend()
ax.legend(loc="best", fontsize=12)
fig_name = "logPlot_N10000_fits"
savefig(path * fig_name * ".png")
savefig(path * fig_name * ".svg")
##. Separate fits by range - 1000 resets
fig, ax = plt.subplots(figsize = (7, 7))
ax.set_xscale("log"); # Set logarithmic scale
ax.set_yscale("log");
ax.set_xlabel("N", fontsize = 14)
ax.set_ylabel("Time [sec]", fontsize = 14)
ax.set_title("Execution time as function of system size - 1000 resets", fontweight ="bold")
ax.set_xlim([90,2010])
ax.tick_params(labelsize = 12)
# Learning, direct
ax.scatter(df2[!, :N], df2[!, :L_direct], label="Learning, direct", s=10, marker="o",c="firebrick")
N_max = 1200
logX = log.(df2[df2.N .<= N_max , :N])
logY = log.(df2[df2.N .<= N_max , :L_direct])
f = fit(logX, logY, 1)
intercept, slope = f.coeffs
y_fit = exp(intercept) .* (df2[df2.N .<= N_max, :N]).^ slope
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df2[df2.N .<= N_max, :N], y_fit, label=label_txt)
N_min = 1200
N_max = 2000
logX = log.(df2[N_min .< df2.N .<= N_max , :N])
logY = log.(df2[N_min .< df2.N .<= N_max , :L_direct])
f = fit(logX, logY, 1)
intercept, slope = f.coeffs
y_fit = exp(intercept) .* (df2[N_min .< df2.N .<= N_max, :N]).^ slope
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df2[N_min .< df2.N .<= N_max, :N], y_fit, label=label_txt)
# Learning, on-the-fly
ax.scatter(df2[!, :N], df2[!, :L_onthefly], label="Learning, on-the-fly", s=10, marker="s",c="darkblue")
logX = log.(df2[!, :N])
logY = log.(df2[!, :L_onthefly])
f = fit(logX, logY, 1) # log(y) = mlog(x) + n
intercept, slope = f.coeffs # n, m
y_fit = exp(intercept) .* (df2[!, :N]).^ slope # y(x)=exp(n)*x^m
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df2[!, :N], y_fit, label=label_txt, color="darkblue")
# No Learning
ax.scatter(df2[!, :N], df2[!, :NL], label="No Learning", s=10, marker="^",c="grey")
logX = log.(df2[!, :N])
logY = log.(df2[!, :NL])
f = fit(logX, logY, 1) # log(y) = mlog(x) + n
intercept, slope = f.coeffs # n, m
y_fit = exp(intercept) .* (df2[!, :N]).^ slope # y(x)=exp(n)*x^m
n = @sprintf("%.3E", exp(intercept))
m = @sprintf("%.3f", slope)
label_txt = raw"y(x) = " * n * raw"*" * raw"$x^{" * m * raw"}$"
plot(df2[!, :N], y_fit, label=label_txt, color="grey")
legend()
ax.legend(loc="best", fontsize=12)
fig_name = "logPlot_N2000_fits"
savefig(path * fig_name * ".png")
savefig(path * fig_name * ".svg")