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 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 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)
z_21 = r * cos(phi_2[0]) x_12 = r * sin(phi_1) * cos(theta_1[1]) y_12 = r * sin(phi_1) * sin(theta_1[1]) z_12 = r * cos(phi_1) x_22 = r * sin(phi_2[1]) * cos(theta_2) y_22 = r * sin(phi_2[1]) * sin(theta_2) z_22 = r * cos(phi_2[1]) xx_21 = r * sin(phi_2[0]) * cos(theta_22) yy_21 = r * sin(phi_2[0]) * sin(theta_22) zz_21 = r * cos(phi_2[0]) xx_22 = r * sin(phi_2[1]) * cos(theta_22) yy_22 = r * sin(phi_2[1]) * sin(theta_22) zz_22 = r * cos(phi_2[1]) #Ajustes da Imagem fig = texfig.figure() ax = fig.add_subplot(111, projection='3d') ax.view_init(azim=-azim_init, elev=elev_init) # vista da imagem fig.set_size_inches(4, 4, 4) cut = 0.65 ax.set_xlim([-r * cut, r * cut]) ax.set_ylim([-r * cut, r * cut]) ax.set_zlim([-r * cut, r * cut]) #Renderizar ax.plot_surface(x, y, z, rstride=1, cstride=1, color='c',
import obj_analysis_lib as oal OUTPUTPATH = "/user/HS204/m09113/my_project_folder/Results/imageNet_performance" SAVE4PRES = None SAVE4LATEX = None SAVE4LATEX = OUTPUTPATH #SAVE4PRES =OUTPUTPATH if SAVE4LATEX: import texfig import matplotlib.pyplot as plt import matplotlib.patches as mpatches if SAVE4LATEX: fig = texfig.figure(width=5.8) #entire page if SAVE4PRES: #plt.rcParams["font.family"] ="monospace" plt.figure(figsize=(10, 5)) plt.rc('axes', prop_cycle=(cycler( 'color', ['r', 'g', 'b', 'y', 'c', 'gold', 'm', 'k', 'slategray', 'peru']))) years = ["2010", "2011", "2012", "2013", "2014", "Human", "2015", "2016"] bar_width = 0.6 x_coordinates = np.arange(len(years)) y_coordinates = [28, 25.7, 16.4, 11.7, 7.4, 5.0, 3.6, 2.9]
from matplotlib.animation import FuncAnimation from matplotlib.offsetbox import (DrawingArea, OffsetImage, AnnotationBbox) import numpy as np from gridworld import * import texfig # ---------- PART 1: Globals with open('5agents_4range_async.json') as json_file: data = json.load(json_file) df = pd.DataFrame(data) my_dpi = 100 scale_factor = 0.33 # Writer = matplotlib.animation.writers['ffmpeg'] # writer = Writer(fps=2.5, metadata=dict(artist='Me'), bitrate=1800) fig = texfig.figure(width=3.3 * scale_factor, ratio=2, dpi=my_dpi) my_palette = plt.cm.get_cmap("tab10", len(df.index)) cat = [str(d_i) for d_i in df['0'][0]['Id_no']] # categories = [cat[0],cat[3],cat[1],cat[4],cat[2]] # sync categories = [cat[0], cat[3], cat[1], cat[2], cat[4]] # async belief_good = df['0'][0]['GoodBelief'] belief_bad = df['0'][0]['BadBelief'] N = len(categories) categories = [categories[0], categories[2], categories[3]] angles = [n / float(N) * 2 * pi for n in range(N)] angles += angles[:1] axis_array = [] l_data = [] f_data = [] belief_x_good = [] belief_x_bad = []
import numpy as np from cycler import cycler import glob import obj_analysis_lib as oal #SAVE="/user/HS204/m09113/my_project_folder/Results/alpha_deviations_KF-ITW_mix_of_expressions" SAVE = "/user/HS204/m09113/my_project_folder/Results/alpha_deviations_KF-ITW_all_videos_thesis" #SAVE=None if SAVE: import texfig import matplotlib.pyplot as plt if SAVE: #fig = texfig.figure(width=8.268) #entire page fig = texfig.figure(width=5.8) plt.rc('axes', prop_cycle=(cycler( 'color', ['r', 'g', 'b', 'y', 'c', 'gold', 'm', 'k', 'slategray', 'peru']))) #id_and_expression_dirs = glob.glob('/user/HS204/m09113/my_project_folder/KF-ITW-prerelease_alpha_experiments/*/expression_mix') #id_and_expression_dirs = glob.glob('/user/HS204/m09113/my_project_folder/KF-ITW-prerelease_alpha_experiments/02/happy_only') id_and_expression_dirs = glob.glob( '/user/HS204/m09113/my_project_folder/KF-ITW-prerelease_alpha_experiments/multi_iter75_reg30/*/*_only' ) if len(id_and_expression_dirs) == 0: print("ERROR: no videos found!!") exit(0)
# 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.
#!/usr/bin/env python3.5 import sys, os import numpy as np from cycler import cycler import glob import obj_analysis_lib as oal SAVE = "/user/HS204/m09113/my_project_folder/Results/alpha_deviations_KF-ITW_over_pose_prepared" #SAVE=None if SAVE: import texfig import matplotlib.pyplot as plt if SAVE: fig = texfig.figure(width=8.268) #entire page fig = texfig.figure(width=4.8) #entire page plt.rc('axes', prop_cycle=(cycler( 'color', ['r', 'g', 'b', 'y', 'c', 'gold', 'm', 'k', 'slategray', 'peru']))) NUMBER_OF_ALPHAS_TO_PLOT = 10 #id_and_expression_dirs = glob.glob('/user/HS204/m09113/my_project_folder/KF-ITW-prerelease_alpha_experiments/*/expression_mix') id_and_expression_dirs = glob.glob( '/user/HS204/m09113/my_project_folder/KF-ITW-prerelease_alpha_experiments/*/*_only' ) if len(id_and_expression_dirs) == 0: print("ERROR: no videos found!!") exit(0)
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) single_plot(files_prefix="varying_aM", bg_parameters=r"$k=0.01$, $\cT=1$", plabel=r'\alphaM', LCDM_pvalue=0) single_plot(files_prefix="varying_k", bg_parameters=r"$\alphaM=0$, $\cT=1$", plabel='k') single_plot(files_prefix="varying_cT", bg_parameters=r"$k=0.01$, $\alphaM=0$", plabel=r'\cT', LCDM_pvalue=1) single_plot(files_prefix="varying_beta", bg_parameters=r"$k=0.01$, $\alphaMnot=-1$, $\cT=1$", plabel=r'\betaexp') # show growing modes and slope of the amplitude plt.clf() fig = texfig.figure(width=tex_width) plots = plot_parametric_evolution(files_prefix="growing_aM", plabel=r'\alphaM', LCDM_pvalue=0) def slope_analytic(a, alpha_M, h_0): return a**(-1 - alpha_M/2.) * h_0 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 \(|h|\)')
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)
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)
OUTPUTPATH = "/user/HS204/m09113/my_project_folder/Results/mesh_distances_KF-ITW_patrik_thesis" #OUTPUTPATH="/user/HS204/m09113/my_project_folder/Results/mesh_distances_KF-ITW_iterations_thesis" #OUTPUTPATH='mesh_distances_KF-ITW_fitting_types_BMVC' SAVE4PRES = None SAVE4LATEX = None SAVE4LATEX = OUTPUTPATH #SAVE4PRES =OUTPUTPATH if SAVE4LATEX: import texfig import matplotlib.pyplot as plt if SAVE4LATEX: #fig = texfig.figure(width=8.268) #entire page fig = texfig.figure(width=4.8) #philipp thesis 5.8 if SAVE4PRES: #plt.rcParams["font.family"] ="monospace" plt.figure(figsize=(10, 8 / 3 * 2)) # each curve has: label, marker, [log files] #comparing every multifit #distance_files = [ ['02 neutral','*',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/02/neutral/distances.log']], # ['02 surprised','o',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/02/surprised/distances.log']], # ['02 happy','+',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/02/happy/distances.log']], # ['08 neutral','*',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/08/neutral/distances.log']], # ['08 surprised','o',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/08/surprised/distances.log']], # ['11 neutral','*',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/11/neutral/distances.log']], # ['11 surprised','o',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/11/surprised/distances.log']], # ['11 happy','+',['/user/HS204/m09113/my_project_folder/KF-ITW-prerelease/11/happy/distances.log']],
#else: #f, axarr = plt.subplots(len(plots), sharex=True, squeeze=False) # #for plot_idx, plot in enumerate(plots): #alphas = load_alphas( plot[1]) #x_coordinates = range(len(alphas)) # #for num_alpha in range(len(alphas[0])): #axarr[plot_idx,0].plot(x_coordinates, alphas[:,num_alpha]) #axarr[plot_idx,0].set_title(plot[0]) #axarr[plot_idx,0].set_ylim([-1.2,1]) ##### Plot without subfigures: if SAVE: #fig = texfig.figure(width=8.268) #entire page fig = texfig.figure(width=4.8) alphas = load_alphas(plots[0][1]) x_coordinates = range(len(alphas)) for alpha_idx in range(len(alphas[0])): plt.plot(x_coordinates, alphas[:, alpha_idx], label="alpha " + str(alpha_idx + 1)) #axarr[plot_idx,0].set_title(plot[0]) #axarr[plot_idx,0].set_ylim([-1.2,1]) plt.xlabel("Iterations") plt.ylabel("Coefficient value") plt.ylim([-1.0, 0.6]) plt.xlim([0, 100])