我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用pylab.legend()。
def display_results_figure(results, METRIC): import pylab as pb color = iter(pb.cm.rainbow(np.linspace(0, 1, len(results)))) plots = [] for method in results.keys(): x = [] y = [] for train_perc in sorted(results[method].keys()): x.append(train_perc) y.append(results[method][train_perc][0]) c = next(color) (pi, ) = pb.plot(x, y, color=c) plots.append(pi) from matplotlib.font_manager import FontProperties fontP = FontProperties() fontP.set_size('small') pb.legend(plots, map(method_name_mapper, results.keys()), prop=fontP, bbox_to_anchor=(0.6, .65)) pb.xlabel('#Tweets from target rumour for training') pb.ylabel('Accuracy') pb.title(METRIC.__name__) pb.savefig('incrementing_training_size.png')
def display_pr_curve(precision, recall): # following examples from sklearn # TODO: f1 operating point import pylab as plt # Plot Precision-Recall curve plt.clf() plt.plot(recall, precision, label='Precision-Recall curve') plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([0.0, 1.05]) plt.xlim([0.0, 1.0]) plt.title('Precision-Recall example: Max f1={0:0.2f}'.format(max_f1)) plt.legend(loc="lower left") plt.show()
def ex2(): x = np.linspace(-10, 10) # "--" = dashed line plt.plot(x, np.sin(x), "--", label="sinus") plt.plot(x, np.cos(x), label="cosinus") # Show the legend using the labels above plt.legend() # The trick here is we have to make another plot on top of the two others. pi2 = np.pi/2 # Find B such that (-B * pi/2) >= -10 > ((-B-1) * pi/2), i.e. the # first multiple of pi/2 higher than -10. b = pi2*int(-10.0/pi2) # x2 is all multiples of pi/2 between -10 and 10. x2 = np.arange(b, 10, pi2) # "b." = blue dots plt.plot(x2, np.sin(x2), "b.") plt.show()
def _plotFMeasures(fstepsize=.1, stepsize=0.0005, start = 0.0, end = 1.0): """Plots 10 fmeasure Curves into the current canvas.""" p = sc.arange(start, end, stepsize)[1:] for f in sc.arange(0., 1., fstepsize)[1:]: points = [(x, _fmeasureCurve(f, x)) for x in p if 0 < _fmeasureCurve(f, x) <= 1.5] try: xs, ys = zip(*points) curve, = pl.plot(xs, ys, "--", color="gray", linewidth=0.8) # , label=r"$f=%.1f$"%f) # exclude labels, for legend # bad hack: # gets the 10th last datapoint, from that goes a bit to the left, and a bit down datapoint_x_loc = int(len(xs)/2) datapoint_y_loc = int(len(ys)/2) # x_left = 0.05 # y_left = 0.035 x_left = 0.035 y_left = -0.02 pl.annotate(r"$f=%.1f$" % f, xy=(xs[datapoint_x_loc], ys[datapoint_y_loc]), xytext=(xs[datapoint_x_loc] - x_left, ys[datapoint_y_loc] - y_left), size="small", color="gray") except Exception as e: print e #colors = "gcmbbbrrryk" #colors = "yyybbbrrrckgm" # 7 is a prime, so we'll loop over all combinations of colors and markers, when zipping their cycles
def show_results(self): pl.plot(self.t1, self.n_A1, 'b--', label='A1: Time Step = 0.05') pl.plot(self.t1, self.n_B1, 'b', label='B1: Time Step = 0.05') pl.plot(self.t2, self.n_A2, 'g--', label='A2: Time Step = 0.1') pl.plot(self.t2, self.n_B2, 'g', label='B2: Time Step = 0.1') pl.plot(self.t1, self.n_A1_true, 'r--', label='True A1: Time Step = 0.05') pl.plot(self.t1, self.n_B1_true, 'r', label='True B1: Time Step = 0.05') pl.plot(self.t2, self.n_A2_true, 'c--', label='True A2: Time Step = 0.1') pl.plot(self.t2, self.n_B2_true, 'c', label='True B2: Time Step = 0.1') pl.title('Double Decay Probelm-Approximation Compared with True in Defferent Time Steps') pl.xlim(0.0, 0.1) pl.ylim(0.0, 100.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='best', shadow=True, fontsize='small') pl.grid(True) pl.savefig("computational_physics homework 4(improved-7).png")
def show(self): # pl.semilogy(self.theta, self.omega) # , label = '$L =%.1f m, $'%self.l + '$dt = %.2f s, $'%self.dt + '$\\theta_0 = %.2f radians, $'%self.theta[0] + '$q = %i, $'%self.q + '$F_D = %.2f, $'%self.F_D + '$\\Omega_D = %.1f$'%self.Omega_D) pl.plot(self.theta_phase ,self.omega_phase, '.', label = '$t \\approx 2\\pi n / \\Omega_D$') pl.xlabel('$\\theta$ (radians)') pl.ylabel('$\\omega$ (radians/s)') pl.legend() # pl.text(-1.4, 0.3, '$\\omega$ versus $\\theta$ $F_D = 1.2$', fontsize = 'x-large') pl.title('Chaotic Regime') # pl.show() # pl.semilogy(self.time_array, self.delta) # pl.legend(loc = 'upper center', fontsize = 'small') # pl.xlabel('$time (s)$') # pl.ylabel('$\\Delta\\theta (radians)$') # pl.xlim(0, self.T) # pl.ylim(float(input('ylim-: ')),float(input('ylim+: '))) # pl.ylim(1E-11, 0.01) # pl.text(4, -0.15, 'nonlinear pendulum - Euler-Cromer method') # pl.text(10, 1E-3, '$\\Delta\\theta versus time F_D = 0.5$') # pl.title('Simple Harmonic Motion') pl.title('Chaotic Regime')
def show(self): # pl.semilogy(self.theta, self.omega) # , label = '$L =%.1f m, $'%self.l + '$dt = %.2f s, $'%self.dt + '$\\theta_0 = %.2f radians, $'%self.theta[0] + '$q = %i, $'%self.q + '$F_D = %.2f, $'%self.F_D + '$\\Omega_D = %.1f$'%self.Omega_D) pl.plot(self.time_array,self.delta) # pl.show() # pl.semilogy(self.time_array, self.delta) # pl.legend(loc = 'upper center', fontsize = 'small') # pl.xlabel('$time (s)$') # pl.ylabel('$\\Delta\\theta (radians)$') # pl.xlim(0, self.T) # pl.ylim(float(input('ylim-: ')),float(input('ylim+: '))) # pl.ylim(1E-11, 0.01) # pl.text(4, -0.15, 'nonlinear pendulum - Euler-Cromer method') # pl.text(10, 1E-3, '$\\Delta\\theta versus time F_D = 0.5$') # pl.title('Simple Harmonic Motion') # pl.title('Chaotic Regime')
def show_simple(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 16, } pl.title('The Trajectory of Tageted Baseball\n with air flow in adiabatic model', fontdict = font) pl.plot(self.x, self.y, label ='$\\alpha = %.0f \degree$'%self.alpha) pl.xlabel('x $m$') pl.ylabel('y $m$') pl.xlim(0, 400) pl.ylim(-100, 200) pl.grid() pl.legend(loc = 'upper right', shadow = True, fontsize = 'medium') pl.text(5, -80, 'trojectories varing with angles of wind', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 14, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(41, 16, 'Only with air drag', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 12, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(34, 16, ' With both air drag and \n reduced air density-isothermal', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 12, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(34.5, 16, ' With air drag and the \n dependence of g on altitude', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 12, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(34.5, 16, ' With both air drag and \n reduced air density-adiabatic', fontdict = font) pl.show()
def main(): data = pd.read_table('../Real_Values.txt').get_values() x = [float(d) for d in data] test = np.array([669, 592, 664, 1005, 699, 401, 646, 472, 598, 681, 1126, 1260, 562, 491, 714, 530, 521, 687, 776, 802, 499, 536, 871, 801, 965, 768, 381, 497, 458, 699, 549, 427, 358, 219, 635, 756, 775, 969, 598, 630, 649, 722, 835, 812, 724, 966, 778, 584, 697, 737, 777, 1059, 1218, 848, 713, 884, 879, 1056, 1273, 1848, 780, 1206, 1404, 1444, 1412, 1493, 1576, 1178, 836, 1087, 1101, 1082, 775, 698, 620, 651, 731, 906, 958, 1039, 1105, 620, 576, 707, 888, 1052, 1072, 1357, 768, 986, 816, 889, 973, 983, 1351, 1266, 1053, 1879, 2085, 2419, 1880, 2045, 2212, 1491, 1378, 1524, 1231, 1577, 2459, 1848, 1506, 1589, 1386, 1111, 1180, 1075, 1595, 1309, 2092, 1846, 2321, 2036, 3587, 1637, 1416, 1432, 1110, 1135, 1233, 1439, 894, 628, 967, 1176, 1069, 1193, 1771, 1199, 888, 1155, 1254, 1403, 1502, 1692, 1187, 1110, 1382, 1808, 2039, 1810, 1819, 1408, 803, 1568, 1227, 1270, 1268, 1535, 873, 1006, 1328, 1733, 1352, 1906, 2029, 1734, 1314, 1810, 1540, 1958, 1420, 1530, 1126, 721, 771, 874, 997, 1186, 1415, 973, 1146, 1147, 1079, 3854, 3407, 2257, 1200, 734, 1051, 1030, 1370, 2422, 1531, 1062, 530, 1030, 1061, 1249, 2080, 2251, 1190, 756, 1161, 1053, 1063, 932, 1604, 1130, 744, 930, 948, 1107, 1161, 1194, 1366, 1155, 785, 602, 903, 1142, 1410, 1256, 742, 985, 1037, 1067, 1196, 1412, 1127, 779, 911, 989, 946, 888, 1349, 1124, 761, 994, 1068, 971, 1157, 1558, 1223, 782, 2790, 1835, 1444, 1098, 1399, 1255, 950, 1110, 1345, 1224, 1092, 1446, 1210, 1122, 1259, 1181, 1035, 1325, 1481, 1278, 769, 911, 876, 877, 950, 1383, 980, 705, 888, 877, 638, 1065, 1142, 1090, 1316, 1270, 1048, 1256, 1009, 1175, 1176, 870, 856, 860]) n_predict = 100 extrapolation = fourierExtrapolation(x, n_predict) pl.figure() pl.plot(np.arange(len(x), len(extrapolation) + len(x)), extrapolation, 'r', label = 'extrapolation') pl.plot(x, 'b', label = 'Given Data', linewidth = 3) pl.legend() pl.ylabel('BPM') pl.xlabel('Sample') pl.title('Fourier Extrapolation') pl.savefig('FourierExtrapolation.png') #pl.show() with open('Fourier_PredValues.txt', 'w') as out: out.write(str([e for e in extrapolation]).strip('[]'))
def plot_clusters_pca(responsibilities, color_groups): from sklearn.decomposition import RandomizedPCA import pylab as pl from random import shuffle colors = list(colors_dict.values()) shuffle(colors) pca = RandomizedPCA(n_components=2) X = pca.fit_transform(responsibilities) # print >>stderr, pca.explained_variance_ratio_ pl.figure() pl.scatter(X[:, 0], X[:, 1], c="grey", label="unknown") for c, sub, i in zip(colors, color_groups, count(0)): pl.scatter(X[sub, 0], X[sub, 1], c=c, label=str(i)) pl.legend() pl.title("PCA responsibility matrix") pl.show()
def plot(func): random_state = check_random_state(0) one_core = [] multi_core = [] sample_sizes = range(1000, 6000, 1000) for n_samples in sample_sizes: X = random_state.rand(n_samples, 300) start = time.time() func(X, n_jobs=1) one_core.append(time.time() - start) start = time.time() func(X, n_jobs=-1) multi_core.append(time.time() - start) pl.figure('scikit-learn parallel %s benchmark results' % func.__name__) pl.plot(sample_sizes, one_core, label="one core") pl.plot(sample_sizes, multi_core, label="multi core") pl.xlabel('n_samples') pl.ylabel('Time (s)') pl.title('Parallel %s' % func.__name__) pl.legend()
def _auto_plots(self,mode,filebasename,figdir,plotargs): """Generate standard plots and write png and and pdf. Chooses filename and plot title.""" import pylab try: os.makedirs(figdir) except OSError,err: if err.errno != errno.EEXIST: raise def figs(*args): return os.path.join(figdir,*args) modefilebasename = filebasename + self._suffix[mode] _plotargs = plotargs.copy() # need a copy because of changing 'title' if plotargs.get('title') is None: # None --> set automatic title _plotargs['title'] = self._title[mode]+' '+self.legend pylab.clf() self.plot(**_plotargs) pylab.savefig(figs(modefilebasename + '.png')) # png pylab.savefig(figs(modefilebasename + '.pdf')) # pdf print "--- Plotted %(modefilebasename)r (png,pdf)." % vars()
def plot(self,direction,legend=True,**kwargs): """Plot all distributions; colors are set automatically to kwargs[cmap].""" import pylab pnormalize = pylab.normalize(vmin=1,vmax=len(self.distribution[direction])) kwargs.setdefault('alpha',1.0) kwargs.setdefault('linewidth',4) fmt = kwargs.pop('fmt','-') cmap = kwargs.pop('cmap',pylab.cm.jet) count = 0 for target,(hist,edges) in self.distribution[direction].items(): count += 1 midpoints = 0.5*(edges[1:] + edges[:-1]) kwargs['color'] = cmap(pnormalize(count)) pylab.plot(midpoints,hist,fmt,label="%s" % target,**kwargs) if legend: pylab.legend(loc='best', prop=pylab.matplotlib.font_manager.FontProperties(size=6))
def plot(self, outpath=''): pylab.figure(figsize = (17,10)) diff = self.f2-self.f3 pylab.subplot(2,1,1) pylab.plot(range(self.lengthSeq), self.f2, 'r-', label = "f2") pylab.plot(range(self.lengthSeq), self.f3, 'g-', label = "f3") pylab.xlim([0., self.lengthSeq]) pylab.tick_params(axis='both', which='major', labelsize=25) pylab.subplot(2,1,2) diff2 = diff/self.f3 diff2 /= np.max(diff2) pylab.plot(range(self.lengthSeq), diff2, 'b-', label = "Rescaled (by max) difference / f3") pylab.xlabel("Temps (en images)", fontsize = 25) pylab.tick_params(axis='both', which='major', labelsize=25) pylab.xlim([0., self.lengthSeq]) #pylab.legend(loc= 2, prop = {'size':15}) pylab.savefig(outpath) pylab.close()
def bootstrap_extradata(self, nBoot, extradataA, nbins = 20): pops =[] meanpop = [[] for i in data.cat] pylab.figure(figsize = (14,14)) for i in xrange(min(4, len(extradataA))): #pylab.subplot(2,2,i+1) if i ==0: pylab.title("Bootstrap on means", fontsize = 20.) pop = extradataA[i]# & (self.GFP > 2000)]# for index in xrange(nBoot): newpop = np.random.choice(pop, size=len(pop), replace=True) #meanpop[i].append(np.mean(newpop)) pops.append(newpop) pylab.legend() #pylab.title(cat[i]) pylab.xlabel("Angle(degree)", fontsize = 15) pylab.xlim([0., 90.]) for i in xrange(len(extradataA)): for j in xrange(i+1, len(extradataA)): statT, pvalue = scipy.stats.ttest_ind(pops[i], pops[j], equal_var=False) print "cat{0} & cat{1} get {2} ({3})".format(i,j, pvalue,statT) pylab.savefig("/users/biocomp/frose/frose/Graphics/FINALRESULTS-diff-f3/mean_nBootstrap{0}_bins{1}_GFPsup{2}_FLO_{3}.png".format(nBoot, nbins, 'all', randint(0,999)))
def main(): # Read the example RT structure and RT dose files # The testdata was downloaded from the dicompyler website as testdata.zip # Obtain the structures and DVHs from the DICOM data rtssfile = 'testdata/rtss.dcm' rtdosefile = 'testdata/rtdose.dcm' RTss = dicomparser.DicomParser(rtssfile) #RTdose = dicomparser.DicomParser("testdata/rtdose.dcm") RTstructures = RTss.GetStructures() # Generate the calculated DVHs calcdvhs = {} for key, structure in RTstructures.iteritems(): calcdvhs[key] = dvhcalc.get_dvh(rtssfile, rtdosefile, key) if (key in calcdvhs) and (len(calcdvhs[key].counts) and calcdvhs[key].counts[0]!=0): print ('DVH found for ' + structure['name']) pl.plot(calcdvhs[key].counts * 100/calcdvhs[key].counts[0], color=dvhcalc.np.array(structure['color'], dtype=float) / 255, label=structure['name'], linestyle='dashed') #else: # print("%d: no DVH"%key) pl.xlabel('Distance (cm)') pl.ylabel('Percentage Volume') pl.legend(loc=7, borderaxespad=-5) pl.setp(pl.gca().get_legend().get_texts(), fontsize='x-small') pl.savefig('testdata/dvh.png', dpi = 75)
def filters_legend(lp, filters): #, **kwa): I = argsort_filters(filters) #plt.legend([lp[i] for i in I], [filters[i] for i in I], **kwa) return [lp[i] for i in I], [filters[i] for i in I]
def plotfitquality(H, xe, ye, A): ''' H,xe,ye from plotalignment() ''' import pylab as plt xe /= 1000. ye /= 1000. xx = (xe[:-1] + xe[1:])/2. yy = (ye[:-1] + ye[1:])/2. XX,YY = np.meshgrid(xx, yy) XX = XX.ravel() YY = YY.ravel() XY = np.vstack((XX,YY)).T Mdist = np.sqrt(mahalanobis_distsq(XY, A.mu, A.C)) assert(len(H.ravel()) == len(Mdist)) mod = A.getModel(XX, YY) R2 = XX**2 + YY**2 mod[R2 > (A.match.rad)**2] = 0. mod *= (H.sum() / mod.sum()) plt.clf() rng = (0, 7) plt.hist(Mdist, 100, weights=H.ravel(), histtype='step', color='b', label='data', range=rng) plt.hist(Mdist, 100, weights=mod, histtype='step', color='r', label='model', range=rng) plt.xlabel('| Chi |') plt.ylabel('Number of matches') plt.title('Gaussian peak fit quality') plt.legend(loc='upper right')
def plot_com(self): pylab.plot( [-p[1] for p in self.com_real], [p[0] for p in self.com_real], 'g-', lw=2) pylab.plot( [-p[1] for p in self.com_ref], [p[0] for p in self.com_ref], 'k--', lw=1) pylab.legend(('$p_G$', '$p_G^{ref}$'), loc='upper right') pylab.grid(False) pylab.xlim(self.xlim) pylab.ylim(self.ylim) pylab.xlabel(self.xlabel) pylab.ylabel(self.ylabel) pylab.title("COM trajectory")
def plot_zmp(self): pylab.plot( [-p[1] for p in self.zmp_real], [p[0] for p in self.zmp_real], 'r-', lw=2) pylab.plot( [-p[1] for p in self.zmp_ref], [p[0] for p in self.zmp_ref], 'k--', lw=1) pylab.legend(('$p_Z$', '$p_Z^{ref}$'), loc='upper right') pylab.grid(False) pylab.xlim(self.xlim) pylab.ylim(self.ylim) pylab.xlabel(self.xlabel) pylab.ylabel(self.ylabel) pylab.title("ZMP trajectory")
def displayRetirementWithMonthlies(monthlies, rate, terms): plt.figure('retireMonth') plt.clf() for monthly in monthlies: xvals, yvals = retire(monthly, rate, terms) plt.plot(xvals, yvals, label='retire:' + str(monthly)) plt.legend(loc='upper left')
def displayRetirementWithRates(monthly, rates, terms): plt.figure('retireRate') plt.clf() for rate in rates: xvals, yvals = retire(monthly, rate, terms) plt.plot(xvals, yvals, label='retire:' + str(monthly) + ':' + str(int(rate * 100))) plt.legend(loc='upper left')
def displayRetirementWithMonthsAndRates(monthlies, rates, terms): plt.figure('retireBoth') plt.clf() plt.xlim(30 * 12, 40 * 12) for monthly in monthlies: for rate in rates: xvals, yvals = retire(monthly, rate, terms) plt.plot(xvals, yvals, label='retire:' + str(monthly) + ':' + str(int(rate * 100))) plt.legend(loc='upper left')
def zipf(self, message, users): source_user = message.author.name source_user = source_user.strip('@').split('#')[0] target_users = [user.strip('@').split('#')[0] for user in users.split()] if len(users) == 0: target_users = [source_user] if users == '*': if message.server is not None: target_users = [member.name for member in message.server.members] target_users = [user for user in target_users if self.check_nickname_valid(user.lower()) is None] image_file_name = self.quotes_file_name(source_user.lower())[:-4] + '.png' pylab.title('Word frequencies') for user in target_users: quotes_file = codecs.open(self.quotes_file_name(user.lower()), 'r', encoding='utf-8') lines = quotes_file.readlines() quotes_file.close() if len(lines) < 20: continue tokenizer = nltk.tokenize.RegexpTokenizer(r'\w+') tokens = self.filter_to_english_words(tokenizer.tokenize(str(lines))) if len(tokens) < 200: continue freq = nltk.FreqDist(tokens) self.plot_word_frequencies(freq, user) pylab.legend() pylab.savefig(image_file_name) pylab.gcf().clear() await self.client.send_file(message.channel, image_file_name)
def create_plot_file(trajFile, plot_filename, plot_others=False, verbose = False): """ Creates plot to demonstrate performance of smarty or smirky trajFile - csv file generated by smarty, smarty_elemental, or smirky plot_filename - pdf to save plot file to plot_others - if True plots data for all reftypes separately, optional """ data = pd.read_csv(trajFile, quotechar="'") numerator = data.columns[-2].lower() timeseries = load_trajectory(trajFile) time_fractions = scores_vs_time(timeseries, numerator) max_score = max(time_fractions['all']) *100.0 if verbose: print("Maximum score was %.1f %%" % max_score) # plot overall score pl.plot( time_fractions['all'], 'k-', linewidth = 2.0) if plot_others: reftypes = [k for k in time_fractions] reftypes.remove('all') # Plot scors for individual types for reftype in reftypes: pl.plot(time_fractions[reftype]) pl.legend(['all']+reftypes, loc='lower right') pl.xlabel('Iterations') pl.ylabel('Fraction of reference type found') pl.ylim(-0.1, 1.1) pl.savefig(plot_filename)
def smooth_curve(curve_data,N_smooth,exp_max=-1,shift_0=0,fix_first_nonzero=False,plotit=False,t='x for plot'): """ smoothens the curve data for plotting as good as possible while maintaining last and first value curve data => np.array, 1D that should be smoothened out N_smooth => number of points to smooth over (float) exp_max => adjust exponential behavior for average (0='normal' moving average) shift_0 => manually fix from where on smoothing is active, e.g. up till where no smootthing is applied fix_first_nonzero => if set to true, then automatically determines shift_0 to be where the first nonzero entry is plotit => plot results t => x-cooordinate for plot """ a=curve_data N=N_smooth v=np.exp(np.linspace(exp_max, 0., N)) v=v/v.sum() a_v=np.convolve(a,v,'same') if fix_first_nonzero==True: shift_0=np.nonzero(a != 0)[0][0] for n in range(0,len(v)): if n!=0: v=np.exp(np.linspace(exp_max, 0., n)) v=v/v.sum() a_v[n+shift_0]=np.convolve(a,v,'same')[n+shift_0] a_v[len(a)-n-1]=np.convolve(a,v,'same')[len(a)-n-1] else: a_v[n+shift_0]=a[n+shift_0] for i in range(0,n+shift_0): a_v[i]=a[i] a_v[len(a)-n-1]=a[len(a)-n-1] if plotit: try: np.sin(t) except: t=np.linspace(0,len(curve_data),len(curve_data)) import pylab as plt plt.plot(t,a,label='original data') plt.plot(t,a_v,label='smoothened') plt.legend(loc='best',fancybox=True) plt.title('curve smoothing') plt.show() return a_v
def plot_measurements_and_simulation_results(time_points, ydata, y_sim): pl.subplot2grid((4, 2), (0, 0)) pl.plot(time_points, ydata[:,0], label = "measurements") pl.plot(time_points, y_sim[:,0], label = "simulation") pl.title("Measurement data compared to simulation results") pl.xlabel("t") pl.ylabel("X", rotation = 0, labelpad = 20) pl.legend(loc = "lower left") pl.subplot2grid((4, 2), (1, 0)) pl.plot(time_points, ydata[:,1], label = "measurements") pl.plot(time_points, y_sim[:,1], label = "simulation") pl.xlabel("t") pl.ylabel("Y", rotation = 0, labelpad = 15) pl.legend("lower right") pl.subplot2grid((4, 2), (2, 0)) pl.plot(time_points, ydata[:,2], label = "measurements") pl.plot(time_points, y_sim[:,2], label = "simulation") pl.xlabel("t") pl.ylabel(r"\phi", rotation = 0, labelpad = 15) pl.legend("lower left") pl.subplot2grid((4, 2), (3, 0)) pl.plot(time_points, ydata[:,3], label = "measurements") pl.plot(time_points, y_sim[:,3], label = "simulation") pl.xlabel("t") pl.ylabel("v", rotation = 0, labelpad = 20) pl.legend("upperleft") pl.subplot2grid((4, 2), (0, 1), rowspan = 4) pl.plot(ydata[:,0], ydata[:, 1], label = "measurements") pl.plot(y_sim[:,0], y_sim[:,1], label = "estimations") pl.title("Measured race track compared to simulated results") pl.xlabel("X") pl.ylabel("Y", rotation = 0, labelpad = 20) pl.legend(loc = "upper left") pl.show()
def show_results(self): pl.plot(self.t, self.n_A1, 'b--', label='A1: Time Constant = 1') pl.plot(self.t, self.n_B1, 'g', label='B1: Time Constant = 2') pl.plot(self.t, self.n_A2, 'k--', label='A2: Time Constant = 1') pl.plot(self.t, self.n_B2, 'c', label='B2: Time Constant = 2') pl.plot(self.t, self.n_A3, 'm--', label='A3: Time Constant = 1') pl.plot(self.t, self.n_B3, 'y', label='B3: Time Constant = 2') pl.title('Double Decay Probelm - Nuclei with Different Time Constans') pl.xlim(0.0, 5.0) pl.ylim(0.0, 100.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='upper right', shadow=True, fontsize='small')
def show_results(self): pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A: Time Constant = 1') pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B: Time Constant = 2') pl.title('Double Decay Probelm - Nuclei with Different Time Constans') pl.xlim(0.0, 5.0) pl.ylim(0.0, 100.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='best', shadow=True)
def show_results(self): pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A') pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B') pl.title('Double Decay Probelm-Situation 1') pl.xlim(0.0, 5.0) pl.ylim(0.0, 100.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='best', shadow=True)
def show_results(self): pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A: Time Constant = 2') pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B: Time Constant = 1') pl.title('Double Decay Probelm - Nuclei with Different Time Constans') pl.xlim(0.0, 5.0) pl.ylim(0.0, 100.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='best', shadow=True)
def show_results(self): pl.plot(self.t, self.n_A, 'b', label='Number of Nuclei A') pl.plot(self.t, self.n_B, 'g+', label='Number of Nuclei B') pl.title('Double Decay Probelm-Situation 3') pl.xlim(0.0, 5.0) pl.ylim(25.0, 75.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='best', shadow=True)
def show_results(self): pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A') pl.plot(self.t, self.n_B, 'b', label='Number of Nuclei B') pl.plot(self.t, self.n_A_true, 'g--', label='True Number of Nuclei A') pl.plot(self.t, self.n_B_true, 'g', label='True Number of Nuclei B') pl.title('Double Decay Probelm-Approximation Compared with True') pl.xlim(0.0, 2.5) pl.ylim(0.0, 100.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='best', shadow=True) pl.grid(True)
def show_results(self): pl.plot(self.t, self.n_A1, 'b--', label='A1') pl.plot(self.t, self.n_B1, 'g', label='B1') pl.plot(self.t, self.n_A2, 'r--', label='A2') pl.plot(self.t, self.n_B2, 'c', label='B2') pl.plot(self.t, self.n_A3, 'm', label='A3') pl.plot(self.t, self.n_B3, 'k+', label='B3') pl.title('Double Decay Probelm-Three Situations') pl.xlim(0.0, 5.0) pl.ylim(0.0, 100.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc="best", shadow=True, fontsize='small')
def show_results(self): pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A') pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B') pl.title('Double Decay Probelm-Situation 2') pl.xlim(0.0, 5.0) pl.ylim(10.0, 90.0) pl.xlabel('time ($s$)') pl.ylabel('Number of Nuclei') pl.legend(loc='best', shadow=True)
def show(self): pl.plot(self.t, self.theta, label = '$F_D =$' + str(self.F_D)) pl.xlim(0, 100) pl.ylim(-4, 4) pl.xlabel('time ($s$)') pl.ylabel('$\\theta$ (radians)') pl.legend() # pl.text(32, 2, '$\\theta$ versus time $F_D =$' + str(self.F_D)) #pl.subplot(311) #r1 = routes_to_chaos(amplitude = 1.35) #r1.calculate() #r1.show() #pl.subplot(312) #r2 = routes_to_chaos(amplitude = 1.44) #r2.calculate() #r2.show() #pl.subplot(313) #r3 = routes_to_chaos(amplitude = 1.465) #r3.calculate() #r3.show() #pl.show() #r= routes_to_chaos(amplitude = 1.465) #r.calculate() #r.show()
def DrawTs(pl, ts=[], lines = None, title="", high=[], low=[],mid=[], save_file=False,legends=None): """?????, ts: closes, save_file: ?????????""" pl.figure legend = [] if len(ts)>0: pl.plot(ts) legend.append('ts') if len(high)>0: pl.plot(high) legend.append('high') if len(low)>0: pl.plot(low) legend.append('low') if len(mid)>0: pl.plot(mid) legend.append('mid') prop = fm.FontProperties(fname="c:/windows/fonts/simsun.ttc") if title != "": pl.title(title, fontproperties=prop) if lines != None: i = lines if i>=len(ts): i = len(ts)-1 pl.plot([i,i], [ts[i]-ts[i]*0.1, ts[i]+ts[i]*0.1], 'g') legend.append('lines') if legends is not None: legend = legends pl.legend(legend, loc='upper left') if save_file: fname = 't3.png' pl.savefig(fname) return fname pl.show() pl.close()
def DrawClosesAndVolumes(pl, closes, volumes, zz=None, avg=None, trade_index=None,\ title=None, closes_dp=None, closes_bankuai=None): """?closes??df??? closes_dp: ?? closes_bankuai: ?? """ legend = [] pl.figure pl.subplot(211) if title != None: pl.title(title, fontproperties=getFont()) pl.plot(closes) legend.append('close') if zz != None: DrawZZ(pl, zz, c='r') if avg != None: pl.plot(avg) if not agl.IsNone(closes_dp): pl.plot(closes_dp) legend.append('dapan') if not agl.IsNone(closes_bankuai): pl.plot(closes_bankuai) legend.append('bankuai') if trade_index != None: pl, index, ts = pl, trade_index, closes _DrawVLine(pl, index, ts) pl.legend(legend, loc='upper left') pl.subplot(212) pl.plot(volumes) pl.show() pl.close()
def legend(self, *args, **kwargs): pl.legend(*args, **kwargs)
def plot_checked(self): import pylab as pl pl.ioff() from statistics import expectation exp = [] # The expectation plotter if len(self._stored_t) != 0: pl.figure(2) pl.title(" Method %s"%("OFSP")) pl.xlabel("Time, t") pl.ylabel("Expectation") for i in range(len(self._stored_t)): exp.append(expectation((self._stored_domain_states[i],self._stored_p[i]))) EXP = np.array(exp).T for i in range(EXP.shape[0]): pl.plot(self._stored_t,EXP[i,:],'x-',label=self.model.species[i]) pl.legend() # The probability plotter if len(self._probed_t) != 0: pl.figure(3) pl.title(" Method %s | Probing States over Time "%("OFSP")) pl.xlabel("Time, t") pl.ylabel("Probability") probs = np.array(self._probed_probs).T for i in range(probs.shape[0]): pl.plot(self._probed_t,probs[i,:],'x-',label=str(self._probed_states[0][:,i])) pl.legend() pl.show()
def plot_checked(self): """ plot_checked plots the expectations of the data check pointed. """ import pylab as pl pl.ioff() if len(self._stored_t) != 0: pl.figure(2) pl.title(" Method %s"%(self.model_name)) pl.xlabel("Time,t") pl.ylabel("Expectation") exp = [] for i in range(len(self._stored_t)): exp.append(np.sum(np.multiply(self._stored_X[i],self._stored_w[i]),axis=1)) EXP = np.array(exp).T for i in range(EXP.shape[0]): pl.plot(self._stored_t,EXP[i,:],'x-',label=self.model.species[i]) pl.legend() # The probability plotter if len(self._probed_t) != 0: pl.figure(3) pl.title(" Method %s | Probing States over Time "%(self.model_name)) pl.xlabel("Time, t") pl.ylabel("Marginal Probability") probs = np.array(self._probed_probs).T for i in range(probs.shape[0]): pl.plot(self._probed_t,probs[i,:],'x-',label=str(self._probed_states[0][self.stoc_vector,i])) pl.legend() pl.show()
