def plot(fast_fit, x): ''' Plots the allowed regions in the C9-C10 plane for imaginary Wilson coefficients ''' import texfig import flavio.plots import matplotlib.pyplot as plt fig = texfig.figure() opt = dict(x_min=-2, x_max=2, y_min=-2, y_max=2, n_sigma=(1, 2), interpolation_factor=5) flavio.plots.likelihood_contour(fast_fit.log_likelihood, col=0, **opt, threads=2) #flavio.plots.flavio_branding(y=0.07, x=0.05) #crashes LaTeX plt.gca().set_aspect(1) plt.axhline(0, c='k', lw=0.2) plt.axvline(0, c='k', lw=0.2) plt.plot(x[0], x[1], marker='x') #compute best fit first! plt.xlabel(r'$\mathrm{Im}\ C_9$') plt.ylabel(r'$\mathrm{Im}\ C_{10}$') texfig.savefig('fitIm_C9C10')
def drawplot(filein, fileout, model, offtot=0, offBs=0, offRK=0): ''' Draw the plot using shaded areas for the global fit and contour lines for Bs-only and RK-only fits ''' fig = texfig.figure() trgtot, ztot = triang(readfile(filein, -1, 9, offtot)) plt.tricontourf(trgtot, ztot, levels=[0.0, 1.0, 4.0, 9.0], colors=('#00B400', '#00FF00', '#BFFF80')) trgBs, zBs = triang(readfile(filein, -2, 6, offBs)) plt.tricontour(trgBs, zBs, levels=[1.0, 4.0], colors='b', linestyles=('-', '--')) trgRK, zRK = triang(readfile(filein, -3, 6, offRK)) plt.tricontour(trgRK, zRK, levels=[1.0, 4.0], colors='#800000', linestyles=('-.', ':')) if model == 'LQ': plt.xlabel(r"$M_{S_3} [\mathrm{TeV}]$") plt.ylabel(r'$\mathrm{Im}\ y^{QL}_{32}y^{QL*}_{32}$') if model == 'Z': plt.xlabel(r"$M_{Z'} [\mathrm{TeV}]$") plt.ylabel(r'$\mathrm{Im}\ \lambda^Q_{23}$') texfig.savefig(fileout)
def page_plot(p, f, pol, p_front=0): pphi = p phi = pphi * f plt.clf() fig = texfig.figure(width=1.5, ratio=1) ax = fig.add_subplot(111, aspect='equal') plot_particles(phi=phi, ax=ax, pol=pol) ax.axis('off') if pol == '+': prefix = 'plus_' else: prefix = 'cross_' texfig.savefig('flipbook_frames/' + prefix + str(p + 1 + p_front)) plt.close(fig)
def page_plot(p, f, pol, p_front=0): pphi = p phi = pphi * f plt.clf() fig = texfig.figure(width=1.5, ratio=1) ax = fig.add_subplot(111, aspect='equal') plot_particles(phi=phi, ax=ax, pol=pol) ax.axis('off') if pol == '+': prefix = 'plus_' else: prefix = 'cross_' texfig.savefig('flipbook_frames/' + prefix + str(p+1+p_front)) plt.close(fig)
def single_plot(files_prefix, bg_parameters, *args, **kwargs): plt.clf() fig = texfig.figure(width=tex_width) plot_parametric_evolution(files_prefix=files_prefix, *args, **kwargs) plt.xscale('log') plt.yscale('log') plt.xlabel(r'scale factor \(a\)') plt.ylabel(r'tensor perturbations \(|h|\)') handles, labels = plt.gca().get_legend_handles_labels() plt.legend(handles + [ bg_legend_handle ], labels + [ "for " + bg_parameters ], loc='lower left') texfig.savefig('plots/' + files_prefix)
color='darkblue', xy=(v * t_medio - 0.8, t_medio + 0.0), size=fontsize) # vdt label = pylab.annotate(r"$v\Delta t$", color='darkblue', xy=(v * t_medio - 0.1, -0.45), size=fontsize) # cdt' label = pylab.annotate(r"$c\Delta t^\prime$", color='darkblue', xy=(s0 + 0.1, t_medio * 0.8), size=fontsize) # # Legenda Luz 2 # x2, y2 = [s0+v*t_0, t_0] # label = pylab.annotate( # r"$ t_0+dt$", color='darkblue', # xy=(x2, y2), xytext=(+70,+20), size=fontsize, # textcoords='offset points', ha='right', va='bottom', # arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0', color='darkblue') # ) plt.axis('off') ax.set_aspect('equal') fig.set_size_inches(4, 4) # plt.show() texfig.savefig('Pitagoras', transparent=True)
a = np.logspace(-5, 0, 100) plt.plot(a, slope_analytic(a, alpha_M=-2, h_0=10**-4.977515978085766), ls='dotted', c=plots[1][0].get_color()) plt.plot(a, slope_analytic(a, alpha_M=-3, h_0=10**0.02414219792993043), ls='dotted', c=plots[0][0].get_color()) plt.xscale('log') plt.yscale('log') plt.xlabel(r'scale factor \(a\)') plt.ylabel(r'tensor perturbations \(|h|\)') handles, labels = plt.gca().get_legend_handles_labels() slope_handle = plt.Line2D((0,1),(0,0), c='black', ls='dotted') plt.legend(handles + [ slope_handle, bg_legend_handle ], labels + [ r"analytic slope $h(a) \propto a^{-\left(1+\frac{\alphaM}{2}\right)}$", "for " + r"$k=0.01$, $\cT=1$" ], loc='lower left') texfig.savefig('plots/growing_aM') # varying both alphaM and beta plt.clf() fig, axes = texfig.subplots(width=tex_width, nrows=2, ncols=2, sharex=True, sharey=True) for (i, row) in enumerate([["0", "0.1"], ["0.4", "1"]]): for (j, beta_str) in enumerate(row): ax = axes[i][j] plt.sca(ax) plot_parametric_evolution('varying_aM0_beta_' + beta_str, ur'\alphaMnot', LCDM_pvalue=0) ax.set_title(ur'$\beta=' + beta_str + '$')
# #4 agents big range # initial = [(33,0),(41,0),(7,0),(80,0)] # targets = [[0,9],[60,69],[20,39],[69,95]] # public_targets = [[0,9],[60,69],[20,39],[55,95]] # obs_range = 4 time_i = 10 # con_dict = con_ar = con_init() # bel_lines = belief_chart_init() # belief_update(10) # belief_update(20) # belief_update(40) ax_ar = grid_init(nrows, ncols, obs_range) # grid_update(50) # plt.show() texfig.savefig("likelihood"+str(time_i)) # update() # plt.show() # ani = FuncAnimation(fig, update_all, frames=frames, interval=500, blit=True,repeat=False) # plt.show() # ani.save('8_agents-3range-wheel.mp4',writer = writer) # ani.save('decen.gif',dpi=80,writer='imagemagick') # fig = plt.figure(figsize=(4,4)) # ax = fig.add_subplot(111, projection='polar') # ax.set_ylim(0,100) # # data = np.random.rand(50)*6+2 # theta = np.linspace(0,2.*np.pi, num=50) # l, = ax.plot([],[])
all_std_devs = np.array(all_std_devs) #print ('all std shape',all_std_devs.shape) # assemble x coordinates with correct number of image sets x_coordinates = sorted(list(set(number_of_images_in_sets)))[:min_num_sets] #print (x_coordinates) for alpha_idx in range(5): y_coordinates = np.mean(all_std_devs[:, :, alpha_idx], axis=0) # [i[alpha_idx] for i in all_std_devs] y_deviation = np.std(all_std_devs[:, :, alpha_idx], axis=0) #plt.plot(x_coordinates, y_coordinates, label="alpha "+str(alpha_idx) , marker="s") plt.errorbar(x_coordinates, y_coordinates, y_deviation, label="alpha " + str(alpha_idx), marker="s") plt.xlabel("Number of images") plt.ylabel("Standard deviation of alphas") #plt.ylim([0,1]) #plt.xlim([0,x_max]) plt.legend(loc=1) #plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #place to the right plt.grid(True) plt.show() if SAVE: texfig.savefig(SAVE)
# Luz 2 tt_max = tt_max+v*dt_0 tt = np.linspace(0.0, tt_max, N) t_0 = t_0 +dt_0 dist_0 = s0 + v*t_0 t_prime = np.linspace(t_0, tt_max+t_0, N) li_1 = dist_0 - tt ax.plot(li_1, t_prime, color='gold', zorder=0) # Legenda Luz 2 x2, y2 = [s0+v*t_0, t_0] label = pylab.annotate( r"$ t_0+dt$", color='darkblue', xy=(x2, y2), xytext=(+70,+20), size=fontsize, textcoords='offset points', ha='right', va='bottom', arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0', color='darkblue') ) step_arrow = 0.05 plt.arrow(0, t_max, 0, step_arrow, shape='full', length_includes_head=True, head_width=.15, color='k', zorder=10) plt.arrow(s0+v*t_max, t_max, v*step_arrow, 0.05, shape='full', length_includes_head=True, head_width=.15, color='k', zorder=10) # ax.set(xlabel='x', ylabel='ct ', # title='About as simple as it gets, folks') # ax.grid() plt.axis('off') ax.set_aspect('equal') fig.set_size_inches(3, 3) texfig.savefig('doppler', transparent=True)
# #4 agents big range # initial = [(33,0),(41,0),(7,0),(80,0)] # targets = [[0,9],[60,69],[20,39],[69,95]] # public_targets = [[0,9],[60,69],[20,39],[55,95]] # obs_range = 4 time_i = 10 # con_dict = con_ar = con_init() # bel_lines = belief_chart_init() # belief_update(10) # belief_update(20) # belief_update(40) ax_ar = grid_init(nrows, ncols, obs_range) # grid_update(50) # plt.show() texfig.savefig("observation" + str(time_i)) # update() # plt.show() # ani = FuncAnimation(fig, update_all, frames=frames, interval=500, blit=True,repeat=False) # plt.show() # ani.save('8_agents-3range-wheel.mp4',writer = writer) # ani.save('decen.gif',dpi=80,writer='imagemagick') # fig = plt.figure(figsize=(4,4)) # ax = fig.add_subplot(111, projection='polar') # ax.set_ylim(0,100) # # data = np.random.rand(50)*6+2 # theta = np.linspace(0,2.*np.pi, num=50) # l, = ax.plot([],[]) #
step_arrow = 0.05 plt.arrow(0.3 * w, 0.0, step_arrow, 0.0, shape='full', length_includes_head=True, head_width=.45, color='gold', zorder=9, alpha=1.0) plt.arrow(-0.3 * w, 0.0, -step_arrow, 0.0, shape='full', length_includes_head=True, head_width=.45, color='gold', zorder=9, alpha=1.0) # ax.set(xlabel='x', ylabel='ct ', # title='About as simple as it gets, folks') # ax.grid() plt.axis('off') ax.set_aspect('equal') plt.tight_layout() fig.set_size_inches(4, 2) texfig.savefig('trem', transparent=True)
ax.text(A[0] + 0.1, A[1] - 0.15, r"$A$", color='darkblue', size=fontsize) # A ax.text(B[0] - 0.15, B[1] + 0.1, r"$B$", color='darkblue', size=fontsize) # B ax.text(0.5 * np.exp(t_max), 0.0, r"$x$", color='k', size=12) # x ax.text(0.0, 0.5 * np.exp(t_max), r"$y$", color='k', size=12) # y ax.text(0.5 * np.exp(t_max), 0.5 * np.exp(t_max) * np.tanh(beta), r"$x^\prime$", color='k', size=12) # x' ax.text(0.5 * np.exp(t_max) * np.tanh(beta), 0.5 * np.exp(t_max), r"$y^\prime$", color='k', size=12) # y' ax.text(0.5 * A[0] - 0.13, 0.5 * A[1], r"$\beta$", color='darkblue', size=fontsize) # Beta A ax.text(0.5 * B[0], 0.5 * B[1] - 0.13, r"$\beta$", color='darkblue', size=fontsize) # Beta B plt.axis('off') ax.set_aspect('equal') fig.set_size_inches(4, 4) # plt.show() texfig.savefig('Hiperbole', transparent=True)
global con_dict, df change_array = [] for id_no in categories: for id_other in categories: if int(id_other) in df[str(i)][id_no]['Visible']: if con_dict[(id_no, id_other)]._visible != True: con_dict[(id_no, id_other)].set(visible=True, zorder=0) change_array.append(con_dict[(id_no, id_other)]) else: if con_dict[(id_no, id_other)]._visible == True: con_dict[(id_no, id_other)].set(visible=False, zorder=0) change_array.append(con_dict[(id_no, id_other)]) return change_array def coords(s, ncols): return (int(s / ncols), int(s % ncols)) # ---------- PART 2: nrows = 10 ncols = 10 obs_range = 3 con_dict = con_ar = con_init() t_i = 40 for s_i in range(0, t_i): update_all(s_i) texfig.savefig("images/Connections_a" + str(s_i))
cat_range = range(N) value_dict = dict([[c_r, 0.0] for c_r in cat_range]) for v_d in value_dict.keys(): for k_i in values.keys(): if literal_eval(k_i)[v_d] == 1: value_dict[v_d] += 100 * values[k_i] val = list(value_dict.values()) val += val[:1] l.set_data(angles, val) l_f.set_xy(np.array([angles, val]).T) # plot_data = [l_d,f_d] return l_d + f_d def coords(s, ncols): return (int(s / ncols), int(s % ncols)) # ---------- PART 2: nrows = 10 ncols = 10 obs_range = 3 update_all(1) texfig.savefig("images/initial_belief") # t_i = 40 # for s_i in range(1,50): # update_all(s_i) # texfig.savefig("images/Belief_simplex_async"+str(s_i))
size=fontsize) # gamma til t_leg = 1.02 * t_max ax.text(t_leg, f1(t_leg) - 0.01, r"$x^a(u)$", color='darkblue', size=fontsize) # x^a ax.text(t_leg, f2(t_leg) - 0.01, r"$\tilde{x}^a(u)$", color='darkblue', size=fontsize) # x^a til # Xi x2, y2 = [t0, f1(t0) + (t_medio - 0.05) * (f2(t0) - f1(t0))] label = pylab.annotate(r"$ \xi^a(u) $", color='darkblue', xy=(x2, y2), xytext=(60, 5.0), size=fontsize, textcoords='offset points', ha='right', va='bottom', arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='darkblue')) plt.axis('off') ax.set_aspect('equal') fig.set_size_inches(4, 4) plt.tight_layout() texfig.savefig('DesvioGeodesico', transparent=True) # plt.show()
light = (0.1+0.2*t)*w ax.plot(light, 0*t, color='gold', lw=2) ax.plot(-light, 0*t, color='gold', lw=2) # setas step_arrow = 0.05 plt.arrow(0.3*w, 0.0, step_arrow, 0.0, shape='full', length_includes_head=True, head_width=.45, color='gold', zorder=9, alpha=1.0) plt.arrow(-0.3*w, 0.0, -step_arrow, 0.0, shape='full', length_includes_head=True, head_width=.45, color='gold', zorder=9, alpha=1.0) # Velocidade x2, y2 = [(0.3+0.1*t)*w, 0.65*h + 0*t] ax.plot(x2, y2, color='darkblue', lw=2) plt.arrow(0.42*w, 0.65*h, step_arrow, 0.0, shape='full', length_includes_head=True, head_width=.35, color='darkblue', zorder=9, alpha=1.0) label = pylab.annotate( r"$\mathbf{v}$", color='darkblue', xy=(0.3*w, 0.65*h), xytext=(0.25*w,0.63*h), size=fontsize ) # ax.set(xlabel='x', ylabel='ct ', # title='About as simple as it gets, folks') # ax.grid() plt.axis('off') ax.set_aspect('equal') plt.tight_layout() fig.set_size_inches(4, 2) # plt.show() texfig.savefig('TremMovimento', transparent=True)
plt.plot(angulo[0], angulo[1], angulo[2], color="red", alpha=0.9, lw=1.2, zorder=5) #Legenda Vetores for i in range(0, 3): ax.text(P_0[0] + base[i][0] + 0.05, P_0[1] + base[i][1] - 0.04, P_0[2] + base[i][2] - 0.15, base_label[i], color='k', zorder=10, size=14) ax.text(P_0[0] + 0.43 * (base[0][0] + 0.23 * base[2][0]), P_0[1] + 0.43 * (base[0][1] + 0.23 * base[2][1]), P_0[2] + 0.43 * (base[0][2] + 0.23 * base[2][2]), r'$\alpha$', color='r', zorder=10, alpha=0.7, size=14) plt.tight_layout() fig.set_size_inches(4, 4, 4) plt.axis('off') texfig.savefig('EsferaTransporteParalelo', transparent=True)
# #4 agents big range # initial = [(33,0),(41,0),(7,0),(80,0)] # targets = [[0,9],[60,69],[20,39],[69,95]] # public_targets = [[0,9],[60,69],[20,39],[55,95]] # obs_range = 4 # con_dict = con_ar = con_init() # bel_lines = belief_chart_init() ax_ar = grid_init(nrows, ncols, obs_range) # update_all(50) # texfig.savefig("test") # update() # plt.show() # ani = FuncAnimation(fig, update_all, frames=frames, interval=1000, blit=True,repeat=False) # plt.show() texfig.savefig("case_environment_5") # ani.save('decen.gif',dpi=80,writer='imagemagick') # fig = plt.figure(figsize=(4,4)) # ax = fig.add_subplot(111, projection='polar') # ax.set_ylim(0,100) # # data = np.random.rand(50)*6+2 # theta = np.linspace(0,2.*np.pi, num=50) # l, = ax.plot([],[]) # # def update(i): # global data # data += (np.random.rand(50)+np.cos(i*2.*np.pi/50.))*2 # data[-1] = data[0] # l.set_data(theta, data )
def errorplot(data, smdata, expdata, obslabels, leglabels, fout): ''' Plots the model predictions as dots+errorbars, and SM predictions and experimental values as shaded rectangles ''' fig = texfig.figure() nobs = len(obslabels) nhyp = len(leglabels) ax = plt.gca() plt.xlim([0, nobs + 0.7]) #plt.ylim([-0.055, 0.015]) markers = ['o', '^', 's', 'o', '^', 's'] colors = ['b', 'b', 'b', 'r', 'r', 'r'] for o in range(0, nobs): for i in range(0, nhyp): if o == 0: plt.plot(o + (i + 1) / (nhyp + 1), data[o][i][0], marker=markers[i], color=colors[i], label=leglabels[i]) else: plt.plot(o + (i + 1) / (nhyp + 1), data[o][i][0], marker=markers[i], color=colors[i]) plt.errorbar(o + (i + 1) / (nhyp + 1), data[o][i][0], yerr=data[o][i][1], color=colors[i]) if o == 0: ax.add_patch( Rectangle((o, smdata[o][0] - smdata[o][1]), 1, 2 * smdata[o][1], color='orange', alpha=0.7, label='SM')) ax.add_patch( Rectangle((o, expdata[o][0] - expdata[o][1]), 1, 2 * expdata[o][1], color='green', alpha=0.7, label='Experimental')) else: ax.add_patch( Rectangle((o, expdata[o][0] - expdata[o][1]), 1, 2 * expdata[o][1], color='green', alpha=0.7)) ax.add_patch( Rectangle((o, smdata[o][0] - smdata[o][1]), 1, 2 * smdata[o][1], color='orange', alpha=0.7)) ax.set_xticks(np.linspace(0.5, nobs + 0.5, nobs + 1)) ax.set_xticklabels(obslabels + ['']) plt.legend() texfig.savefig(fout)
obs_range = 3 # #4 agents big range # initial = [(33,0),(41,0),(7,0),(80,0)] # targets = [[0,9],[60,69],[20,39],[69,95]] # public_targets = [[0,9],[60,69],[20,39],[55,95]] # obs_range = 4 # con_dict = con_ar = con_init()'874' (140647750019760) = {dict} <class 'dict'>: {'BeliefCalls': 736, 'BadTrace': {'37': [85, 1], '43': [78, 0], '27': [76, 0], '11': [85, 0], '49': [76, 1], '20': [66, 1], '9': [85, 1], '26': [86, 0], '5': [67, 1], '31': [69, 0], '1': [78, 0], '25': [85, 0], '41': [76, 0], '33': [67, 1], '23': [85, 1], '2': [79, 0], '6': [66, 1], '4': [68, 1], '3': [69, 0], '24': [95, 1], '7': [76, 1], '21': [76, 1], '14': [77, 0], '13': [76, 0], '38': [95, 1], '29': [78, 0], '46': [68, 1], '34': [66, 1], '22': [75, 1], '19': [67, 1], '45': [69, 0], '15': [78, 0], '40': [86, 0], '16': [79, 0], '39': [85, 0], '47': [67, 1], '12': [86, 0], '10': [95, 1], '30': [79, 0], '8': [75, 1], '18': [68, 1], '17': [69, 0], '48': [66, 1], '35': [76, 1], '28': [77, 0], '0': [77, 0], '44': [79, 0], '32': [68, 1], '50': [75, 1], '42': [77, 0], '36': [75, 1]}, 'LastSeen': {'37': [[67, 0], 0], '874': [[77, 0], 0], '122': [[34, 0], 0], '684': [[33, 0], 50], '168': [[27, 0], 8]}, 'ActBelief': {'(0, 0, 1, 0, 0)': 0.0042983371692197755, '(1, 1, 1, 0, 1)': ...… View time_i = 50 bel_lines = belief_chart_init() for j in range(time_i+1): belief_update(j) # ax_ar = grid_init(nrows, ncols, obs_range) # update_all(50) texfig.savefig("Call_results_"+str(time_i)) # update() # plt.show() # ani = FuncAnimation(fig, update_all, frames=frames, interval=500, blit=True,repeat=False) # plt.show() # ani.save('8_agents-3range-wheel.mp4',writer = writer) # ani.save('decen.gif',dpi=80,writer='imagemagick') # fig = plt.figure(figsize=(4,4)) # ax = fig.add_subplot(111, projection='polar') # ax.set_ylim(0,100) # # data = np.random.rand(50)*6+2 # theta = np.linspace(0,2.*np.pi, num=50) # l, = ax.plot([],[])
#plt.ylim([0,1]) x_coordinates = x_coordinates + bar_width / 2 plt.xlim([ np.min(x_coordinates) - x_coordinates.shape[0] / (len(years)), np.max(x_coordinates) + x_coordinates.shape[0] / (len(years)) ]) #plt.legend(loc=9, ncol=NUMBER_OF_ALPHAS_TO_PLOT, numpoints=1, handletextpad=0, columnspacing=0.5) #plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #place to the right #plt.grid(True) traditional_patch = mpatches.Patch(color=(242 / 255, 242 / 255, 242 / 255), label='Traditional') deep_learning_patch = mpatches.Patch(color=(000 / 255, 70 / 255, 160 / 255), label='Deep Learning') human_patch = mpatches.Patch(color=(201 / 255, 169 / 255, 000 / 255), label='Human') plt.legend(loc=1, handles=[traditional_patch, deep_learning_patch, human_patch]) #plt.title("Top-5 Error at ImageNet Classification Challenge") plt.gca().yaxis.grid(True) plt.gca().set_axisbelow(True) if SAVE4PRES: plt.savefig(SAVE4PRES + ".png") if SAVE4LATEX: texfig.savefig(SAVE4LATEX) plt.show()
# import texfig first to configure Matplotlib's backend import texfig # then, import PyPlot import matplotlib.pyplot as plt # obtain a nicely configured figure from texfig (or make your own) fig = texfig.figure() # plot as usual plt.title(r'Title with $\vect{x}$') plt.plot(range(10)) # save your plot as both a PDF and a PGF file with texfig (or save a '.pfg' file on your own) texfig.savefig("example_plot") # Now \usepackage{pgf} and \input the .pgf file in your LaTeX document. Admire the beauty of LaTeX vector plots.
ax.scatter(xx, yy, color='darkblue', s=20, zorder=5) # pontos ax.plot(xi_1, xi_2, color='darkblue', lw=1.5, zorder=0) # vetor step_arrow = 0.05 # seta vetor plt.arrow(P[0] + (1 - step_arrow) * (x_1 - P[0]), P[1] + (1 - step_arrow) * (y_1 - P[1]), (x_1 - P[0]) * step_arrow, (y_1 - P[1]) * step_arrow, shape='full', length_includes_head=True, head_width=.15, color='darkblue', zorder=10) # Legendas ax.text(P[0] - 0.5, P[1] - 0.5, r"$P$", color='darkblue', size=fontsize) # P ax.text(O[0] + 0.1, O[1] - 0.1, r"$O$", color='darkblue', size=fontsize) # O G = [a * np.cos(0.7 * np.pi), b * np.sin(0.7 * np.pi)] ax.text(G[0] - 0.5, G[1] + 0.3, r"$\gamma$", color='darkblue', size=fontsize) # gamma XI = [(0.5) * (O[0] + P[0]), (0.5) * (O[1] + P[1])] ax.text(XI[0] - 0.3, XI[1] + 0.7, r"$\xi^a$", color='darkblue', size=fontsize) # xi plt.axis('off') ax.set_aspect('equal') fig.set_size_inches(4, 4) # plt.show() texfig.savefig('SmallLoop', transparent=True)
values = df[str(i)][id_no]['ActBelief'] cat_range = range(N) value_dict = dict([[c_r, 0.0] for c_r in cat_range]) for v_d in value_dict.keys(): for k_i in values.keys(): if literal_eval(k_i)[v_d] == 1: value_dict[v_d] += 100 * values[k_i] val = list(value_dict.values()) val += val[:1] l.set_data(angles, val) l_f.set_xy(np.array([angles, val]).T) # plot_data = [l_d,f_d] return l_d + f_d def coords(s, ncols): return (int(s / ncols), int(s % ncols)) # ---------- PART 2: nrows = 10 ncols = 10 obs_range = 3 t_i = 40 for s_i in range(0, 1): update_all(s_i) texfig.savefig("images/Belief_simplex_sync_short" + str(s_i))
obs_range = 3 # #4 agents big range # initial = [(33,0),(41,0),(7,0),(80,0)] # targets = [[0,9],[60,69],[20,39],[69,95]] # public_targets = [[0,9],[60,69],[20,39],[55,95]] # obs_range = 4 # con_dict = con_ar = con_init() time_i = 75 bel_lines = belief_chart_init() for j in range(time_i + 1): belief_update(j) # ax_ar = grid_init(nrows, ncols, obs_range) # update_all(50) texfig.savefig("Avg_thin_results_lv_" + str(time_i)) # update() # plt.show() # ani = FuncAnimation(fig, update_all, frames=frames, interval=500, blit=True,repeat=False) # plt.show() # ani.save('8_agents-3range-wheel.mp4',writer = writer) # ani.save('decen.gif',dpi=80,writer='imagemagick') # fig = plt.figure(figsize=(4,4)) # ax = fig.add_subplot(111, projection='polar') # ax.set_ylim(0,100) # # data = np.random.rand(50)*6+2 # theta = np.linspace(0,2.*np.pi, num=50) # l, = ax.plot([],[]) #
ha='right', va='bottom', arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='darkblue')) # theta-menos phi_leg = phi_0 - epsilon theta_leg = 0.1 * pi x_leg = r * sin(phi_leg) * cos(theta_leg) y_leg = r * sin(phi_leg) * sin(theta_leg) z_leg = r * cos(phi_leg) x2, y2, _ = proj3d.proj_transform(x_leg, y_leg, z_leg, ax.get_proj()) label = pylab.annotate(r"$ \theta=\theta_0-\varepsilon$", color='darkblue', xy=(x2, y2), xytext=(-5, +35), size=fontsize, textcoords='offset points', ha='right', va='bottom', arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0', color='darkblue')) # Plotar plt.tight_layout() fig.set_size_inches(4, 4, 4) plt.axis('off') texfig.savefig('EsferaLoop', transparent=True)
def plot(fin, fout, x0=None): ''' Read data from file and plot it in flavio-style ''' f = open(fin, 'rt') _x = [] _y = [] for l in f.readlines(): ls = l.split('\t') _x.append(float(ls[1])) _y.append(float(ls[2])) f.close() stepx = float('Inf') stepy = float('Inf') minx = min(_x) miny = min(_y) maxx = max(_x) maxy = max(_y) for i in range(0, len(_x)): if _x[i] != minx: stepx = min(stepx, _x[i] - minx) for i in range(0, len(_y)): if _y[i] != miny: stepy = min(stepy, _y[i] - miny) x, y = np.meshgrid(np.arange(minx, maxx, stepx), np.arange(miny, maxy, stepy)) shape1, shape2 = x.shape f = open(fin, 'rt') i = 0 zbs = np.zeros(x.shape) zDMs = np.zeros(x.shape) zACP = np.zeros(x.shape) zglob = np.zeros(x.shape) for l in f.readlines(): i1 = i % shape1 i2 = i // shape1 i += 1 ls = l.split('\t') zbs[i1, i2] = float(ls[-4]) zACP[i1, i2] = float(ls[-2]) zDMs[i1, i2] = float(ls[-3]) zglob[i1, i2] = float(ls[-1]) f.close() zbs = zbs - np.min(zbs) zDMs = zDMs - np.min(zDMs) zACP = zACP - np.min(zACP) zglob = zglob - np.min(zglob) levels = [delta_chi2(n, dof=2) for n in (1, 2)] plotbs = { 'x': x, 'y': y, 'z': zbs, 'levels': levels, 'interpolation_factor': 5, 'col': 0, 'label': r'$b \to s \mu^+ \mu^-$' } plotDMs = { 'x': x, 'y': y, 'z': zDMs, 'levels': levels, 'interpolation_factor': 5, 'col': 1, 'label': r'$\Delta B_s$' } plotACP = { 'x': x, 'y': y, 'z': zACP, 'levels': levels, 'interpolation_factor': 5, 'col': 2, 'label': r'$A_{CP}^{\mathrm{mix}}$' } plotglob = { 'x': x, 'y': y, 'z': zglob, 'levels': levels, 'interpolation_factor': 5, 'col': 3, 'label': 'Global' } fig = texfig.figure() #fig = plt.figure() plt.xlim([-0.15, 0.15]) plt.ylim([-0.15, 0.15]) flavio.plots.contour(**plotbs) flavio.plots.contour(**plotDMs) flavio.plots.contour(**plotACP) flavio.plots.contour(**plotglob) plt.axhline(0, c='k', lw=0.2) plt.axvline(0, c='k', lw=0.2) if x0 is not None: plt.plot(x0[0], x0[1], marker='x', c='k') plt.xlabel(r'$\mathrm{Re}\ y^{QL}_{32} y^{QL*}_{22}$') plt.ylabel(r'$\mathrm{Im}\ y^{QL}_{32} y^{QL*}_{22}$') #plt.xlabel(r'$\mathrm{Re}\ \lambda^Q_{23}$') #plt.ylabel(r'$\mathrm{Im}\ \lambda^Q_{23}$') plt.legend(loc=2, bbox_to_anchor=(1.05, 1)) texfig.savefig(fout)