def plot(x, y, z, save):
"""Plots a test image of the Bivariate Gaussian
Required Inputs
x,y,z :: arrays :: must all be same length
save :: string / bool :: save location or '' / False
"""
pp = Pretty_Plotter()
pp.s = 1.0
pp._teXify() # LaTeX
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)
# create contour plot
c = ax.contourf(x, y, z, 200, cmap=magma)
# axis labels
ax.set_xlabel(r'$\phi_1$', fontsize=pp.axfont)
ax.set_ylabel(r'$\phi_2$', fontsize=pp.axfont)
ax.grid(False) # remove grid
pp.save_or_show(save, PLOT_LOC)
pass
def plot(lines, save=False):
"""Plots the two-point correlation function
Required Inputs
itau :: {(x,y,e)} :: plots (x,y,e) as error bars
pacc :: {(x,y,e)} :: plots (x,y,e) as error bars
# subtitle :: str :: subtitle for the plot
# op_name :: str :: the name of the operator for the title
save :: bool :: True saves the plot, False prints to the screen
"""
pp = Pretty_Plotter()
pp.s = 1.5
pp._teXify() # LaTeX
fig, ax = plt.subplots(1, figsize = (10, 8))
ax = [ax]
fig.suptitle(r"Showing that $n$ has little effect on $\tau_{\text{int}}$",
fontsize=pp.ttfont+2)
ax[0].set_title(r"HMC; lattice: $(100,)$; $m=0.01$; $M=10^5$; $\vartheta=\frac{\pi}{2}$", fontsize=pp.ttfont)
ax[-1].set_xlabel(r'$\delta\tau$')
ax[0].set_ylabel(r'$\tau_{\text{int}}$')
for line in lines:
m = next(markers)
c = next(measured_colours)
label = line['n_steps']
x = line['step_size']
y = line['itau']
ax[0].scatter(x, y, alpha=0.5, c=c, marker=m, label=int(label))
for a in ax:
a.legend(loc='best', shadow=True, fontsize = pp.axfont)
xi,xf = a.get_xlim()
a.set_xlim(xmin=xi-0.01*(xf-xi), xmax=xf+0.01*(xf-xi)) # give a decent view of the first point
yi,yf = a.get_ylim()
a.set_ylim(ymax=yf + .05*(yf-yi), ymin=yi-.05*(yf-yi)) # give 5% extra room at top
ax[0].legend(bbox_to_anchor=(0., -0.3, 1., .102), loc=9,
ncol=6, mode="expand", borderaxespad=0.)
fig.subplots_adjust(bottom=0.3)
pp.save_or_show(save, PLOT_LOC)
pass
