def plotting(sim_context1,sim_context2,diff,data_df,total_samples): plt.plot(sim_context1,label="Context 1") plt.plot(sim_context2,label="Context 2") x_labels_word1 = data_df["word1"] x_labels_word2 = data_df["word2"] xlabels = [0] * total_samples xticks_x = [0] * total_samples for wp in range (total_samples): xlabels[wp] = x_labels_word1[wp]+ "\n"+x_labels_word2[wp] xticks_x[wp] = wp+1 plt.plot(diff,label="Difference") plt.legend(loc='center right') # Add title and x, y labels plt.title("Elmo Embedding Model Results", fontsize=16, fontweight='bold') plt.xlabel("Word") plt.ylabel("Similarity") plt.xticks(xticks_x, xlabels) plt.show()
def plot_four(plot_data): fig = plt.figure(figsize=(20, 10)) # gs = gridspec.GridSpec(1, 2, height_ratios=[1, 2]) ax = fig.add_subplot(223, projection='3d') ax.scatter(plot_data['sx'], plot_data['sy'], plot_data['sz']) ax.plot(plot_data['sx'], plot_data['sy'], plot_data['sz'], color='b') ax.view_init(azim=0, elev=90) #xy plane plt.xticks(fontsize=10) ax.set_title('Displacement Projection in xy Plane',size=20) ax2 = fig.add_subplot(224, projection='3d') ax2.scatter(plot_data['sx'], plot_data['sy'], plot_data['sz']) ax2.plot(plot_data['sx'], plot_data['sy'], plot_data['sz'], color='b') ax2.view_init(azim=0, elev=45) ax2.set_title('Displacement',size=20) ax3 = fig.add_subplot(221) # 50 represents number of points to make between T.min and T.max xnew = np.linspace(0,8,50) spl = make_interp_spline(pd.Series(range(9)), plot_data['tilt1'], k=3) # type: BSpline x = spl(xnew) spl = make_interp_spline(pd.Series(range(9)), plot_data['tilt2'], k=3) # type: BSpline y = spl(xnew) spl = make_interp_spline(pd.Series(range(9)), plot_data['compass'], k=3) # type: BSpline z = spl(xnew) ax3.plot(x,"b-",label='tilt1') ax3.plot(y,"r-",label='tilt2') ax3.plot(z,"g-",label='compass') ax3.legend(loc="lower left",prop={'size': 20}) ax3.set_title('Orientation Plot (degree)',size=20) ax3.tick_params(labelsize=20) ax4 = fig.add_subplot(222) # x = gaussian_filter1d(plot_data['ax'], sigma=1) # y = gaussian_filter1d(plot_data['ay'], sigma=1) # z = gaussian_filter1d(plot_data['az'], sigma=1) # mag = gaussian_filter1d(plot_data['accelerometer'], sigma=1) spl = make_interp_spline(pd.Series(range(9)), plot_data['ax'], k=3) # type: BSpline x = spl(xnew) spl = make_interp_spline(pd.Series(range(9)), plot_data['ay'], k=3) # type: BSpline y = spl(xnew) spl = make_interp_spline(pd.Series(range(9)), plot_data['az'], k=3) # type: BSpline z = spl(xnew) spl = make_interp_spline(pd.Series(range(9)), plot_data['accelerometer'], k=3) # type: BSpline mag = spl(xnew) ax4.plot(x/1000,"c--",label='ax') ax4.plot(y/1000,"g--",label='ay') ax4.plot(z/1000,"b--",label='az') ax4.plot(mag,"r-",label='Acc') ax4.legend(loc="lower left",prop={'size': 20}) ax4.set_title('Acceleration Plot (g)',size=20) ax4.tick_params(labelsize=20) plt.tight_layout() plt.show() fig.savefig('FourInOne.png')
def bar_graph(category, age_grp, sex, x, y, year=None, country=None): plt.figure() plt.ylabel('ATE = Y1 - Y0') plt.xlabel('Years') plt.bar(range(len(x)), x, align='center') plt.xticks(range(len(x)), y, rotation='vertical') if country: plt.title("%s Suicide Rates for WC; %s ages %s" % (country, sex, age_grp)) name = country + sex + age_grp + '.png' # plt.show() plt.tight_layout() plt.savefig('./graphs/Countries' + '/' + sex + '/' + name.replace(' ', '_')) elif year: plt.title("Change in Suicide Rates per Country in %s; %s ages %s" % (year, sex, age_grp)) name = category + sex + str(year) + age_grp + '.png' # plt.show() plt.tight_layout() plt.savefig('./graphs/' + category + '/' + sex + '/' + str(year) + '/' + name.replace(' ', '')) else: plt.title("Change in Suicide Rates in %s Countries; %s ages %s" % (category, sex, age_grp)) name = category + sex + age_grp + '.png' # plt.show() plt.tight_layout() plt.savefig('./graphs/' + category + '/' + sex + '/' + name.replace(' ', ''))
def tmp(cm, acts): import matplotlib.pyplot as plt import numpy as np plt.imshow(cm, interpolation='nearest') plt.xticks(np.arange(0, len(acts)), acts) plt.yticks(np.arange(0, len(acts)), acts)
def plot(): plt.figure(figsize=(20, 10)) width = 0.5 index = np.arange(26) print 'SUM PLOT 1', sum(row[0] for row in data) print 'SUM PLOT 2', sum(row[1] for row in data) print 'SUM PLOT 3', sum(row[2] for row in data) print data[0] p0 = plt.bar(index, data[0], width, color='y') # people p1 = plt.bar(index, data[1], width, color='g') # nature p2 = plt.bar(index, data[2], width, color='r') # activity p3 = plt.bar(index, data[3], width, color='b') # food p4 = plt.bar(index, data[4], width, color='c') # symbols p5 = plt.bar(index, data[5], width, color='m') # objects p6 = plt.bar(index, data[6], width, color='k') # flags p7 = plt.bar(index, data[7], width, color='w') # uncategorized plt.ylabel('Usage') plt.title('Emoji category usage per city') plt.xticks(index + width/2.0, cities) plt.yticks(np.arange(0, 1, 0.1)) plt.legend((p0[0], p1[0], p2[0], p3[0], p4[0], p5[0], p6[0], p7[0]), categories_names) plt.show()
def getGraph(): for i, clf in enumerate((svm, rbf_svc, rbf_svc_tunning)): # Se grafican las fronteras plt.subplot(2, 2, i + 1) plt.subplots_adjust(wspace=0.4, hspace=0.4) Z = clf.predict(np.c_[x_matrizSetEntrenamientoVect, y_clases]) #Color en las gráficas Z = Z.reshape(x_matrizSetEntrenamientoVect.shape) plt.contourf(x_matrizSetEntrenamientoVect, y_clases, Z, cmap=plt.cm.Paired, alpha=0.8) #Puntos de entrenamiento plt.scatter(x_matrizSetEntrenamientoVect[:, 0], x_matrizSetEntrenamientoVect[:, 1], c=y_clases, cmap=plt.cm.Paired) plt.xlabel('Longitud Sepal') plt.ylabel('Peso Sepal') plt.xlim(x_matrizSetEntrenamientoVect.min(), x_matrizSetEntrenamientoVect.max()) plt.ylim(y_clases.min(), y_clases.max()) plt.xticks(()) plt.yticks(()) plt.title(titles[i]) plt.show()
def plot_gallery(images, titles, h, w, n_row=3,n_col=4): plt.figure(figsize=(1.8*n_col, 2.4*n_row)) plt.subplots_adjust(bottom=0,left=.01,right=.99,top=.90,hspace=.35) for i in range(n_row * n_col): plt.subplot(n_row,n_col,i+1) plt.imshow(images[i].reshape(h,w),cmap=plt.cm.gray) plt.title(titles[i],size=12) plt.xticks(()) plt.yticks(())
def multiplot_gen_property_type(): # # font = {'family': 'Liberation Serif', # 'weight': 'normal', # 'size': 15 # } # # # play around with the font size if it is too big or small # matplotlib.rcParams['axes.titlesize'] = 12 # matplotlib.rcParams['axes.labelsize'] = 12 # matplotlib.rc('font', **font) # # matplotlib.rcParams['text.usetex'] = True # matplotlib.rcParams['pdf.fonttype'] = 42 # matplotlib.rcParams['pdf.use14corefonts'] = True x = list(data.keys()) y1=[] y2=[] y3=[] y4=[] y5=[] y6=[] for year in data.keys(): for option_name, count in data[year].items(): if option_name == 'domain': y1.append(count) if option_name == 'sitekey': y2.append(count) if option_name == 'third-party': y3.append(count) if option_name == 'websocket': y4.append(count) if option_name == 'webrtc': y5.append(count) if option_name == 'csp': y6.append(count) plt.plot(x, y1,'-o',label='domain') plt.plot(x, y2,'-v',label='sitekey') plt.plot(x, y3,'-^',label='third-party') plt.plot(x, y4,'-<',label='websocket') plt.plot(x, y5,'->',label='webrtc') plt.plot(x, y6,'-1',label='csp') plt.xticks(rotation='vertical') plt.xlabel('Year') plt.ylabel('Count') plt.legend(ncol=2) plt.tight_layout() plt.savefig('easylist-property-type.pdf ', format='pdf', dpi=1200)
def plot_average(collected_results, versions, args, plot_std=True): test_type = args.test_type model_name = args.model means, stds = [], [] for version in versions: data = collected_results[version] if (plot_std): means.append(np.mean(data)) stds.append(np.std(data)) else: means.append(data) means = np.array(means) stds = np.array(stds) if (test_type == "size" or test_type == "allsize"): x = ["0%", "20%", "40%", "60%", "80%", "100%"] elif (test_type == "accdomain" or test_type == "moredomain"): x = [0, 1, 2, 3, 4] else: x = versions color = 'blue' plt.plot(x, means, color=color) if (plot_std): plt.fill_between(x, means - stds, means + stds, alpha=0.1, edgecolor=color, facecolor=color, linewidth=1, antialiased=True) plt.xticks(np.arange(len(x)), x, fontsize=18) plt.yticks(fontsize=18) plt.xlabel(XLABELS[test_type], fontsize=18) plt.ylabel('average absolute effect size', fontsize=18) plt.title("Influence of {} on bias removal \nfor {}".format( TITLES[test_type], MODEL_FORMAL_NAMES[model_name]), fontsize=18) plt.tight_layout() plot_path = os.path.join( args.eval_results_dir, "plots", "{}-{}-avg{}.png".format(model_name, test_type, "-std" if plot_std else "")) plt.savefig(plot_path)
def plot_score_dist(spacing, std_along, prob_miss, max_distance): from matplotlib.pylab import plt plt.close("Score Dist") plt.figure("Score Dist") d = np.linspace(0, max_distance, 500) plt.plot(d, [score_dist(di, spacing, std_along, prob_miss) for di in d]) plt.vlines(spacing, 0, 1) plt.vlines(spacing * 2, 0, 1, ls='--') plt.annotate("Miss-detect the next mine", (spacing * 2, 0.5), (12, 0), textcoords='offset points') plt.ylabel('$p(d)$') plt.xlabel('$d$') plt.grid() plt.xticks(np.arange(max_distance)) plt.xlim(0, max_distance) plt.savefig('score_dist.pdf')
def f3(): xs = [0, 1, 2, 5] ys = [0, 0, 1, 1] fig, ax = plt.subplots() ax.set_yticklabels([]) ax.set_xticklabels([]) plt.plot(xs, ys, label="$f_n(x)$") plt.xticks(xs, ['', r'$n-1$', r'$n$', '']) plt.yticks([1], ['$1$']) plt.legend() ax.spines['left'].set_position('zero') ax.spines['right'].set_color('none') ax.spines['bottom'].set_position('zero') ax.spines['top'].set_color('none') ax.axis('equal') plt.show()
def f4(): capped_xs = np.linspace(-0.94, 0.8, 1000) xs = np.linspace(-1, 1, 1000) ys = [1 / (1 - x) for x in capped_xs] def getN(N): def fN(x): total = 0.0 for i in range(N + 1): total += x**i return total return fN yss = [] for N in range(1, 4): fN = getN(N) yss.append([fN(x) for x in xs]) fig, ax = plt.subplots() ax.set_yticklabels([]) ax.set_xticklabels([]) plt.plot(capped_xs, ys, label=r"$\frac{1}{1-x}$") for i, ys in enumerate(yss): plt.plot(xs, ys, label="$N={}$".format(i + 1), alpha=0.3) plt.plot([1, 1], [0, 5], "k--", alpha=0.3) plt.plot(-1, 0.5, 'o', markerfacecolor='None', markeredgecolor='C0', markersize=5) plt.xticks([-1, 1], ['-1', '1']) plt.legend() ax.spines['left'].set_position('zero') ax.spines['right'].set_color('none') ax.spines['bottom'].set_position('zero') ax.spines['top'].set_color('none') ax.axis('equal') plt.show()
def print_all_results(collected_results, versions, args): test_type = args.test_type model_name = args.model for test_name in collected_results: test_results = collected_results[test_name] x, y = [], [] for version in versions: if (version in test_results): x.append(version) y.append(test_results[version]['mean']) plt.plot(x, y, label=test_name) plt.xticks(np.arange(len(x)), x) plt.xlabel(XLABELS[test_type]) plt.ylabel('average absolute effect size') plt.legend(loc='best') plt.title("SEAT effect sizes on {} with {}".format(model_name, TITLES[test_type])) plot_path = os.path.join(args.eval_results_dir, "plots", "{}-{}.png".format(model_name, test_type)) plt.savefig(plot_path)
def tmp2(cm, acts): import numpy as np import matplotlib.pyplot as plt conf_arr = cm norm_conf = [] for i in conf_arr: a = 0 tmp_arr = [] a = sum(i, 0) for j in i: tmp_arr.append(0 if a == 0 else float(j) / float(a)) norm_conf.append(tmp_arr) fig = plt.figure(figsize=(8, 8)) plt.clf() ax = fig.add_subplot(111) ax.set_aspect(1) res = ax.imshow(np.array(norm_conf), cmap=plt.cm.jet, interpolation='nearest') width, height = conf_arr.shape # for x in range(width): # for y in range(height): # ax.annotate(str(conf_arr[x][y]), xy=(y, x), # horizontalalignment='center', # verticalalignment='center') cb = fig.colorbar(res) ax.set_xlim(-.5, len(acts) - .5) ax.set_ylim(-.5, len(acts) - .5) # alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' plt.xticks(range(width), acts, rotation=-90) plt.yticks(range(height), acts)
import os import numpy as np from joblib import load from matplotlib.pylab import plt dataset = 'BANC_freesurf' tpot_dict = 'gpr' project_wd = os.getcwd() saved_tpot = load( os.path.join(project_wd, 'tpot_%s_%s.dump' % (dataset, tpot_dict))) # Get the internal cross validation scores cv_scores = [ saved_tpot['evaluated_individuals_'][model]['internal_cv_score'] for model in saved_tpot['evaluated_individuals_'].keys() ] model_names = [model for model in saved_tpot['evaluated_individuals_'].keys()] # Just a quick solution to plot something, but you should plot histograms ind = np.arange(len(cv_scores)) plt.bar(ind, cv_scores) plt.ylabel('MAE') plt.xticks(ind) plt.show() plt.figure() plt.scatter(cv_scores) plt.show()
# In[100]: get_ipython().run_cell_magic( 'latex', '', '$\\textbf{Visualize the Correlations}: $\n$\\text{Cor}(X_i,Y_j) = \\frac{\\text{Cov}(X_i,Y_j)}{\\sigma_{X_i}\\sigma_{Y_j}}$' ) # In[101]: R = np.corrcoef(data.T) plt.figure(figsize=(10, 8)) plt.pcolor(R) plt.colorbar() plt.xlim([0, len(headers)]) plt.ylim([0, len(headers)]) plt.xticks(np.arange(32) + 0.5, np.array(headers), rotation='vertical') plt.yticks(np.arange(32) + 0.5, np.array(headers)) plt.show() # In[108]: #Lets fit both the models using PCA/FA down to two dimensions. #construct a function implementing the factor analysis which returns a vector of n_components largest # variances and the corresponding components (as column vectors in a matrix). You can # check your work by using decomposition.FactorAnalysis from sklearn #### ~THIS FUNCTION IS WAS A STAB, NEW CODE HERE: ########### def FactorAnalysis(data, n_components): ni = 20
import pandas as pd import numpy as np from matplotlib.pylab import plt #load plot library df = pd.read_csv("../data/well_monthly_2017.csv", header=0) cumulative_months = [] months_label = [] for m in range(1, 13): if m < 10: pad = '0' + str(m) else: pad = str(m) year_month = "2017-" + pad month_data = df[df["month"] == year_month] month_data.drop(columns=["month", "well"], inplace=True) cumulative_month = sum(month_data.values.flatten()) cumulative_months.append(cumulative_month) months_label.append("m-" + str(m)) month_count = 12 y_pos = np.arange(month_count) plt.bar(y_pos, cumulative_months, align='center', alpha=0.5) plt.xticks(y_pos, months_label) plt.ylabel('Pumped water') plt.title('Well water pumped in one year 2017') plt.show()
import pandas as pd import numpy as np from matplotlib.pylab import plt #load plot library df = pd.read_csv("../data/well_monthly.csv", header=0) cum_pump = [] wells_label = [] for well_i in range(1, 18): data_i = df[df["well"] == well_i] data_i.drop(columns=["month", "well"], inplace=True) data = data_i.values.flatten() effective = data[0:len(data) - 1] my_cumulative_pump = sum(effective) cum_pump.append(my_cumulative_pump) print("well:", well_i, "; pump:", my_cumulative_pump) wells_label.append('W-' + str(well_i)) well_count = 17 y_pos = np.arange(well_count) plt.bar(y_pos, cum_pump, align='center', alpha=0.5) plt.xticks(y_pos, wells_label) plt.ylabel('Pumped water') plt.title('Well water pumped in one year 2017') print("Total water usage:", sum(cum_pump)) plt.show()
def multiplot_gen_content_type(): font = {'family': 'Liberation Serif', 'weight': 'normal', 'size': 15 } # play around with the font size if it is too big or small matplotlib.rcParams['axes.titlesize'] = 12 matplotlib.rcParams['axes.labelsize'] = 12 matplotlib.rc('font', **font) # matplotlib.rcParams['text.usetex'] = True matplotlib.rcParams['pdf.fonttype'] = 42 matplotlib.rcParams['pdf.use14corefonts'] = True x = list(data.keys()) y1 =[] y2 =[] y3 =[] y4 =[] y5 =[] y6 =[] y7 =[] y8 =[] y9 =[] y10 =[] y11 =[] y12 =[] y13 =[] y14 =[] y15 =[] print("---",y1) for year in data.keys(): for option_name, count in data[year].items(): if option_name == 'script': y1.append(count) if option_name == 'xmlhttprequest': y2.append(count) if option_name == 'document': y3.append(count) if option_name == 'elemhide': y4.append(count) if option_name == 'subdocument': y5.append(count) if option_name == 'image': y6.append(count) if option_name == 'popup': y7.append(count) if option_name == 'ping': y8.append(count) if option_name == 'stylesheet': y9.append(count) if option_name == 'object': y10.append(count) if option_name == 'generichide': y11.append(count) if option_name == 'font': y12.append(count) if option_name == 'media': y13.append(count) if option_name == 'genericblock': y14.append(count) if option_name == 'other': y15.append(count) print("x-->",x) print("y-->",y1) print("y-->",y2) plt.xticks(rotation='vertical') plt.xlabel('Year') plt.ylabel('Count') plt.legend(ncol=2) plt.tight_layout() plt.savefig('easylist-content-type.pdf ', format='pdf', dpi=1200)