我们从Python开源项目中，提取了以下20个代码示例，用于说明如何使用matplotlib.pyplot.loglog()。
def radLossPlot(self, title=0): ''' to plot the radiative losses vs temperature ''' fontsize = 16 temp = self.RadLoss['temperature'] rate = self.RadLoss['rate'] plt.loglog(temp, rate) # plt.ylabel(r'erg s$^{-1}$ ($\int\,$ N$_e\,$N$_H\,$d${\it l}$)$^{-1}$',fontsize=fontsize) plt.xlabel(self.RadLoss['xlabel'],fontsize=fontsize) plt.ylabel(self.RadLoss['ylabel'],fontsize=fontsize) if title: title = 'Radiative loss rate, minAbund = %10.2e'%(self.MinAbund) if self.EDensity.size == 1: title += ', density = %10.2e'%(self.EDensity) plt.title(title, fontsize=fontsize)
def plot(self, filename=None, marker='+', color='blue'): """Plot Power Law picture. filename: if assgin, picture will write to file, not show on screen, else show on screen. marker : see http://matplotlib.org/api/markers_api.html color : point color """ plt.loglog(*self.points, linestyle='None', marker=marker, markeredgecolor=color) if self.line_ready: x = self.points[0] x = np.linspace(x[1], max(x), 10) y = [self.prob(i) for i in x] plt.loglog(x, y, linestyle='-') if filename is not None: plt.savefig(filename) log.debug("plot %s ok." % filename) else: plt.show()
def fig_memory_usage(): # FAST memory x = [1,3,7,14,30,90,180] y_fast = [0.653,1.44,2.94,4.97,9.05,19.9,35.2] # ConvNetQuake y_convnet = [6.8*1e-5]*7 # Create figure plt.loglog(x,y_fast,"o-") plt.hold('on') plt.loglog(x,y_convnet,"o-") # plot markers plt.loglog(x,[1e-5,1e-5,1e-5,1e-5,1e-5,1e-5,1e-5],'o') plt.ylabel("Memory usage (GB)") plt.xlabel("Continous data duration (days)") plt.xlim(1,180) plt.grid("on") plt.savefig("./figures/memoryusage.eps") plt.close()
def fig_run_time(): # fast run time x_fast = [1,3,7,14,30,90,180] y_fast = [289,1.13*1e3,2.48*1e3,5.41*1e3,1.56*1e4, 6.61*1e4,1.98*1e5] x_auto = [1,3] y_auto = [1.54*1e4, 8.06*1e5] x_convnet = [1,3,7,14,30] y_convnet = [9,27,61,144,291] # create figure plt.loglog(x_auto,y_auto,"o-") plt.hold('on') plt.loglog(x_fast[0:5],y_fast[0:5],"o-") plt.loglog(x_convnet,y_convnet,"o-") # plot x markers plt.loglog(x_convnet,[1e0]*len(x_convnet),'o') # plot y markers y_markers = [1,60,3600,3600*24] plt.plot([1]*4,y_markers,'ko') plt.ylabel("run time (s)") plt.xlabel("continous data duration (days)") plt.xlim(1,35) plt.grid("on") plt.savefig("./figures/runtimes.eps")
def degree_histogram(pedgraph): """Return a list of the frequency of each degree value. Parameters ---------- pedgraph : Networkx graph A graph Notes ----- Note: the bins are width one, hence len(list) can be large (Order(number_of_edges)) """ degree_sequence = sorted(nx.degree(pedgraph).values(), reverse=True) # degree sequence # print "Degree sequence", degree_sequence dmax = max(degree_sequence) plt.loglog(degree_sequence, 'b-', marker='o', markersize=5, markerfacecolor='#FF8C00', antialiased=True, color='#000000') plt.title("(out)Degree Rank Plot") plt.ylabel("(out)Degree") plt.xlabel("Rank") print "\t > Degree histogram plot created in ~/dgRankPlot.png" plt.savefig("dgRankPlot.png") #plt.show()
def plotseds_impl(sedfiles, plotfile, labels=None, fluxlabel="Flux", figsize=(10,6), xlim=None, ylim=None): # Initialize figure with the appropriate size plt.figure(figsize=figsize) plt.clf() if labels == None: labels = sedfiles # setup the figure plt.grid(True) # loop over sed files and labels for sedfile,label in zip(sedfiles,labels): data = np.loadtxt(arch.opentext(sedfile)) if len(data.shape)==2: plt.loglog(data[:,0], data[:,1], label=label) else: return False # there is just one data point # set axis limits if requested if xlim != None: plt.xlim(xlim) if ylim != None: plt.ylim(ylim) # add axis labels and a legend plt.gca().xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter("%g")) plt.xlabel(r"$\lambda\,(\mu \mathrm{m})$", fontsize='large') plt.ylabel(fluxlabel, fontsize='large') plt.legend() # Save the figure plt.savefig(plotfile, bbox_inches='tight', pad_inches=0.25) plt.close() return True # -----------------------------------------------------------------
def degree_distribution(DG): in_degree = DG.in_degree() out_degree = DG.out_degree() # print(in_degree) in_histogram = list([0]) * (in_degree[max(in_degree, key=in_degree.get)] + 1) out_histogram = list([0]) * (out_degree[max(out_degree, key=out_degree.get)] + 1) for k, v in in_degree.items(): in_histogram[v] += 1 # in_histogram = list([0]) # in_degree_count = Counter(in_degree_sequence) # print(sum(in_histogram)) in_pk = [z/sum(in_histogram) for z in in_histogram] # print(in_histogram) # print(in_pk) plt.title('in_degree') plt.xlabel('K+1') plt.ylabel('Pk') plt.loglog(range(len(in_histogram)), in_pk, 'bo') plt.savefig('c-elegans_in_degree_distribution.svg') # plt.show() for k, v in out_degree.items(): out_histogram[v] += 1 # print(len(out_degree_sequence)) out_pk = [z/sum(out_histogram) for z in out_histogram] plt.title('out_degree_distribution') plt.xlabel('K+1') plt.ylabel('Pk') plt.loglog(range(len(out_histogram)), out_pk, 'bo') plt.savefig('c-elegans_out_degree_distribution.svg')
def tfst(beta=0.6, ca=1., de2=0.000545, theta=0., kmin=1e-2, kmax=10., npoints=200,wrt='n'): """ beta: Total plasma beta, ca: Alfven speed based on mean field, de2: me/mi, theta: Angle of propagation in degrees kkmin: Minimum Wavenumber of interest in units of kdi kkmax: Maximum wavenumber of interest in units of kdi Output is an array with 4 columns, k, w-fast, w-alf, w-slow """ import matplotlib.pyplot as plt kmmn=np.log10(kmin) kmmx=np.log10(kmax) kk=np.logspace(kmmn,kmmx,npoints) warray=np.zeros((3,npoints)) for i in range(0,npoints): f,s = tfps(beta, ca, de2, theta, kk[i]) warray[:,i]=f plt.loglog(kk,warray[0,:], label='Fast/Magnetosonic') plt.loglog(kk,warray[1,:], label='Alfven/KAW') plt.loglog(kk,warray[2,:], label='Slow') plt.xlabel('$kd_i$') plt.ylabel('$\omega/\omega_{ci}$') plt.legend(loc='best',fancybox=True,framealpha=0.2) plt.title('Dispersion Relation for beta='+str(beta)+' and me/mi='+str(de2)) plt.show() if wrt == 'y': ofile=open('disp'+str(theta)+'.dat','w') print>> ofile,'# k', 'Acoustic', 'Alfven', 'Fast' for i in range(npoints): print>> ofile, kk[i],warray[2,i],warray[1,i],warray[0,i] ofile.close()
def tfst(beta=0.6, ca=1., de2=0.000545, theta=0., kmin=1e-2, kmax=10., npoints=200,wrt='n'): """ beta: Total plasma beta, ca: Alfven speed based on mean field, de2: me/mi, theta: Angle of propagation as fraction of pi/2), kkmin: Minimum Wavenumber of interest in units of kdi kkmax: Maximum wavenumber of interest in units of kdi Output is an array with 4 columns, k, w-fast, w-alf, w-slow """ kmmn=np.log10(kmin) kmmx=np.log10(kmax) kk=np.logspace(kmmn,kmmx,npoints) warray=np.zeros((3,npoints)) for i in range(0,npoints): f,s = tfps(beta, ca, de2, theta, kk[i]) warray[:,i]=f plt.loglog(kk,warray[0,:], label='Fast/Magnetosonic') plt.loglog(kk,warray[1,:], label='Alfven/KAW') plt.loglog(kk,warray[2,:], label='Slow') plt.xlabel('$kd_i$') plt.ylabel('$\omega/\omega_{ci}$') plt.legend(loc='best',fancybox=True,framealpha=0.2) plt.title('Dispersion Relation for beta='+str(beta)+' and me/mi='+str(de2)) plt.show() if wrt == 'y': ofile=open('disp'+str(theta*pi/2.)+'.dat','w') print>> ofile,'# k', 'Acoustic', 'Alfven', 'Fast' for i in range(npoints): print>> ofile, kk[i],warray[2,i],warray[1,i],warray[0,i] ofile.close()
def log_binning(counter_dict,bin_count=35): max_x = np.log10(max(counter_dict.keys())) max_y = np.log10(max(counter_dict.values())) max_base = max([max_x,max_y]) min_x = np.log10(min(drop_zeros(counter_dict.keys()))) bins = np.logspace(min_x,max_base,num=bin_count) # Based off of: http://stackoverflow.com/questions/6163334/binning-data-in-python-with-scipy-numpy #bin_means_y = (np.histogram(counter_dict.keys(),bins,weights=counter_dict.values())[0] / np.histogram(counter_dict.keys(),bins)[0]) #bin_means_x = (np.histogram(counter_dict.keys(),bins,weights=counter_dict.keys())[0] / np.histogram(counter_dict.keys(),bins)[0]) bin_means_y = np.histogram(counter_dict.keys(),bins,weights=counter_dict.values())[0] bin_means_x = np.histogram(counter_dict.keys(),bins,weights=counter_dict.keys())[0] return bin_means_x,bin_means_y #def plot_degree(y, title=None, noplot=False): # if len(y) > 6000: # return # G = nxG(y) # degree = sorted(nx.degree(G).values(), reverse=True) # if noplot: # return degree # #plt.plot(degree) # x = np.arange(1, y.shape[0] + 1) # fig = plt.figure() # plt.loglog(x, degree) # if title: # plt.title(title) # plt.draw() # #def plot_degree_(y, title=None): # if len(y) > 6000: # return # G = nxG(y) # degree = sorted(nx.degree(G).values(), reverse=True) # x = np.arange(1, y.shape[0] + 1) # plt.loglog(x, degree) # if title: # plt.title(title)
def draw_degree(G): degree = nx.degree_histogram(G) x = range(len(degree)) y = [z/float(sum(degree)) for z in degree] plt.loglog(x,y,color='blue',linewidth=2)
def plot(): # Plot the error against the grid spacing dx. Lx = 2*pi degrees = range(2, 13, 2) errors = [] dxs = [] for d in range(len(degrees)): dx = [] error = [] for simulation_index in range(0, 5): number_of_points = 4*(2**simulation_index) dx.append(Lx/number_of_points) error.append(compute_error(degrees[d], simulation_index, number_of_points)) errors.append(error) dxs.append(dx) print "Errors in the L2 norm: ", errors plt.clf() colours_expected = ["-r", "-g", "-b", "-y", "-c", "-k"] colours = ["o--r", "o--g", "o--b", "o--y", "o--c", "o--k"] for d in range(0, len(degrees)): # Plot the errors. plt.loglog(dxs[d], errors[d], colours[d], label=r"Order = %d" % degrees[d]) # Plot the expected convergence line for comparison. expected_convergence = (numpy.array([0.8*max(errors[d])*(1.0/2**degrees[d])**i for i in range(len(errors[d]))])) plt.loglog(dxs[d], expected_convergence, colours_expected[d], label=r"") plt.xlabel(r"Grid spacing $\Delta x$ (m)") plt.ylabel(r"Solution error in the L2 norm") plt.legend(loc='best', numpoints=1) plt.savefig("mms_convergence_analysis.pdf", bbox_inches='tight')
def test_errors(): orders = [4*(i+1) for i in range(6)] l1_errors = [None for o in orders] l2_errors = [None for o in orders] for i,o in enumerate(orders): s = PyballdDiscretization(o,r_h, o, THETA_MIN, THETA_MAX, L=1*r_h) R,THETA = s.get_coords_2d() X,THETA = s.get_x2d() f_ana = f(R,THETA) dfdr_ana = dfdr(R,THETA) dfdr_ana[-1] = 0 dfdr_num = s.differentiate_wrt_R(f_ana,1,0) dfdr_num[-1] = 0 delta = dfdr_num - dfdr_ana if 2 <= i <= 4: plt.plot(X[:,-1],delta[:,-1],lw=3, label="order = {}".format(o)) l1_errors[i] = np.max(np.abs(delta[1:])) l2_errors[i] = s.l2_norm_to_infty(delta) plt.xlabel(r'$X$',fontsize=16) plt.ylabel('error',fontsize=16) plt.legend() for postfix in ['.png','.pdf']: name = 'domain_pointwise_errors_alg'+postfix if USE_FIGS_DIR: name = 'figs/' + name plt.savefig(name, bbox_inches='tight') plt.clf() plt.semilogy(orders,l1_errors,'bo--',lw=3,ms=12) plt.xlabel('order',fontsize=16) plt.ylabel(r'|error|$_\infty$',fontsize=16) for postfix in ['.png','.pdf']: name = 'domain_l1_errors_alg'+postfix if USE_FIGS_DIR: name = 'figs/' + name plt.savefig(name, bbox_inches='tight') plt.clf() plt.loglog(orders,l1_errors,'bo--',lw=3,ms=12) plt.xlabel('order',fontsize=16) plt.ylabel(r'|error|$_\infty$',fontsize=16) for postfix in ['.png','.pdf']: name = 'domain_l1_errors_alg_loglog'+postfix if USE_FIGS_DIR: name = 'figs/' + name plt.savefig(name, bbox_inches='tight') plt.clf()
def test_errors(): orders = [4*(i+1) for i in range(10)] l1_errors = [None for o in orders] l2_errors = [None for o in orders] for i,o in enumerate(orders): s = PyballdDiscretization(o,r_h, o, THETA_MIN, THETA_MAX, L=25*r_h) R,THETA = s.get_coords_2d() X,THETA = s.get_x2d() f_ana = f(R,THETA) dfdr_ana = dfdr(R,THETA) dfdr_ana[-1] = 0 dfdr_num = s.differentiate_wrt_R(f_ana,1,0) dfdr_num[-1] = 0 delta = dfdr_num - dfdr_ana if 2 <= i <= 4: plt.plot(X[:,-1],delta[:,-1],lw=3, label="order = {}".format(o)) l1_errors[i] = np.max(np.abs(delta[1:])) l2_errors[i] = s.l2_norm_to_infty(delta) plt.xlabel(r'$X$',fontsize=16) plt.ylabel('error',fontsize=16) plt.legend() for postfix in ['.png','.pdf']: name = 'domain_pointwise_errors_exp'+postfix if USE_FIGS_DIR: name = 'figs/' + name plt.savefig(name, bbox_inches='tight') plt.clf() plt.semilogy(orders,l1_errors,'bo--',lw=3,ms=12) plt.xlabel('order',fontsize=16) plt.ylabel(r'|error|$_\infty$',fontsize=16) for postfix in ['.png','.pdf']: name = 'domain_l1_errors_exp'+postfix if USE_FIGS_DIR: name = 'figs/' + name plt.savefig(name, bbox_inches='tight') plt.clf() plt.loglog(orders,l1_errors,'bo--',lw=3,ms=12) plt.xlabel('order',fontsize=16) plt.ylabel(r'|error|$_\infty$',fontsize=16) for postfix in ['.png','.pdf']: name = 'domain_l1_errors_loglog_exp'+postfix if USE_FIGS_DIR: name = 'figs/' + name plt.savefig(name, bbox_inches='tight') plt.clf()
def make_map(self, method, plot=False): """ This function ... :return: """ data = np.genfromtxt("./eg_user_files/observations_stack.dat") dataID = np.genfromtxt("./eg_user_files/observations_stack.dat", usecols=0, dtype='string') wa = np.array([24, 100, 160, 250., 350., 500.]) # wavelengths in um # Loop over all pixels for i in range(len(data[:, 0])): ydata = [data[i, 30], data[i, 34], data[i, 36], data[i, 38], data[i, 40], data[i, 42]] yerr = [data[i, 31], data[i, 35], data[i, 37], data[i, 39], data[i, 41], data[i, 43]] D = data[i, 1] * 3 * 10 ** 5 / 67.30 # Do the fit for this pixel if method == "grid": t1, t2, mdust, ratio = self.fit_grid(wa, ydata, yerr, D) elif method == "genetic": t1, t2, mdust, ratio = self.fit_genetic(wa, ydata, yerr, D) if plot: plt.plot(Mddist, Mdprob) plt.title(dataID[i]) plt.xlabel('T_cold (K)') plt.show() print(Md_sav, T1_sav, T2_sav) plt.errorbar(wa, ydata, yerr=yerr, fmt='bo') x2 = np.arange(10., 600., 0.1) plt.loglog() plt.title(dataID[i]) plt.ylim(0.001, 1) plt.plot(x2, two_blackbodies(x2, D, np.log10(Mdratio_sav) + Md_sav, T1_sav, np.log10(Mdratio_sav) + Md_sav, T2_sav), 'r', label="best fit") plt.xlabel('Wavelength (microns)') plt.ylabel('Flux (Jy)') plt.plot(x2, blackbody(x2, D, np.log10(Mdratio_sav) + Md_sav, T1_sav), ':', lw=2, label="cold dust: logMd = %s, Tc= %s K " % (np.log10(Mdratio_sav) + Md_sav, T1_sav)) plt.plot(x2, blackbody(x2, D, np.log10(Mdratio_sav) + Md_sav, T2_sav), ':', lw=2, label="warm dust: logMd = %s, Tc= %s K " % (np.log10(Mdratio_sav) + Md_sav, T2_sav)) plt.legend(loc=4) plt.show() plt.legend(frameon=False) plt.savefig('fit_bb.png') # -----------------------------------------------------------------
def psplot(pslist, nbins = 0, filename=None, figsize=(12, 8), showlegend=True): """ Plots a list of PS objects. If the PS has a slope, it is plotted as well. if nbins > 0, I bin the spectra. add option for linear plot ? """ plt.figure(figsize=figsize) for ps in pslist: if not np.all(np.isfinite(np.log10(ps.p))): print "No power to plot (probably flat curve !), skipping this one." continue # We bin the points if nbins > 0: logf = np.log10(ps.f[1:]) # we remove the first one logbins = np.linspace(np.min(logf), np.max(logf), nbins+1) # So nbins +1 numbers here. bins = 10**logbins bincenters = 0.5*(bins[:-1] + bins[1:]) # nbins centers logbins[0] -= 1.0 logbins[-1] += 1.0 binindexes = np.digitize(logf, logbins) # binindexes go from 1 to nbins+1 binvals = [] binstds = [] for i in range(1, nbins+1): vals = ps.p[1:][binindexes == i] binvals.append(np.mean(vals)) binstds.append(np.std(vals)/np.sqrt(vals.size)) bincenters = np.array(bincenters) binvals = np.array(binvals) binstds = np.array(binstds) plt.loglog(bincenters, binvals, marker=".", linestyle="-", color=ps.plotcolour, label = "%s" % (ps)) else: plt.loglog(ps.f, ps.p, marker=".", linestyle="None", color=ps.plotcolour, label = "%s" % (ps)) if ps.slope != None: plt.loglog(ps.slope["f"], ps.slope["p"], marker="None", color=ps.plotcolour, label = "Slope %s = %.3f" % (ps, ps.slope["slope"])) plt.axvline(ps.slope["fmin"], color = ps.plotcolour, dashes = (5,5)) plt.axvline(ps.slope["fmax"], color = ps.plotcolour, dashes = (5,5)) plt.xlabel("Frequency [1/days]") plt.ylabel("Power") if showlegend: plt.legend() #plt.text(np.min(10**fitx), np.max(10**pfit), "Log slope : %.2f" % (popt[0]), color="red") if filename: plt.save(filename) else: plt.show()
def plot_distribution(xs, ys, title, xlabel, ylabel, log_axis=None, save_fig=False, legend=None, legend_loc=1, font_size=20, y_sup_lim=None): """ Plots a set of values (xs, ys) with matplotlib. :param xs: either a list with x values or a list of lists, representing different sample sets to be plotted in the same figure. :param ys: either a list with y values or a list of lists, representing different sample sets to be plotted in the same figure. :param title: String, plot title :param xlabel: String, label on the x axis :param ylabel: String, label on the y axis :param log_axis: String (accepted values are False, "x", "y" or "xy"), determines which axis are plotted using logarithmic scale :param save_fig: String, figure's filename or False (to show the interactive plot) :param legend: list of strings with legend entries or None (if no legend is needed) :param legend_loc: integer, indicates the location of the legend (if present) :param font_size: integer, title, xlabel and ylabel font size :param y_sup_lim: float, y axis superior limit (if None or not present, use default matplotlib value) :return: None :type: None """ plt.figure() ax = plt.subplot(111) # Plot data if not (isinstance(xs[0], list) or isinstance(xs[0], np.ndarray)): plt.plot(xs, ys) # marker='o' else: for i in range(len(xs)): plt.plot(xs[i], ys[i], ' ', linestyle='solid') # marker='o' # Plot title and xy labels plt.title(title, {'color': 'k', 'fontsize': font_size}) plt.ylabel(ylabel, {'color': 'k', 'fontsize': font_size}) plt.xlabel(xlabel, {'color': 'k', 'fontsize': font_size}) # Change axis to log scale if log_axis == "y": plt.yscale('log') elif log_axis == "x": plt.xscale('log') elif log_axis == "xy": plt.loglog() # Include legend if legend: lgd = ax.legend(legend, loc=legend_loc) # Force y limit if y_sup_lim: ymin, ymax = plt.ylim() plt.ylim(ymin, y_sup_lim) # Output result if save_fig: plt.savefig(CFG.figs_path + save_fig + '.pdf', format='pdf', dpi=600) plt.close() else: plt.show()
def intensityRatioInterpolate(self,data, scale = 'lin', plot=0, verbose=0): ''' to take a set of date and interpolate against the IntensityRatio the scale can be one of 'lin'/'linear' [default], 'loglog', 'logx', 'logy', ''' # first, what variable to use if self.IntensityRatio['temperature'].max() > self.IntensityRatio['temperature'].min(): x = self.IntensityRatio['ratio'] y = self.IntensityRatio['temperature'] if verbose: print('using temperature with %i5 values'%(len(x))) print(' number of values') else: x = self.IntensityRatio['ratio'] y = self.IntensityRatio['eDensity'] # if x[0] > x[-1]: x = sorted(x) sy = [] for idx in range(len(y) -1, -1, -1): sy.append(y[idx]) else: sy = y # if 'lin' in scale: y2 = interpolate.splrep(x, sy, s=0) interpolatedData = interpolate.splev(data,y2) if plot: plt.plot(sy, x) plt.plot(interpolatedData, data, 'bD') elif scale == 'loglog': y2 = interpolate.splrep(np.log(x), np.log(sy), s=0) interpolatedData = np.exp(interpolate.splev(np.log(data),y2)) if plot: plt.loglog(sy, x) plt.loglog(interpolatedData, data, 'bD') elif scale == 'logx': y2 = interpolate.splrep(x, np.log(sy), s=0) interpolatedData = np.exp(interpolate.splev(data,y2)) if plot: plt.semilogx(sy, x) plt.semilogx(interpolatedData, data, 'bD') elif scale == 'logy': y2 = interpolate.splrep(np.log(x), sy, s=0) interpolatedData = interpolate.splev(np.log(data),y2) if plot: plt.semilogy(sy, x) plt.semilogy(interpolatedData, data, 'bD') else: print(' scale not understood = %s'%(scale)) for i, avalue in enumerate(interpolatedData): print(' data, value = %12.3e %12.3e'%(data[i], avalue)) self.IntensityRatioInterpolated = {'data':data, 'value':interpolatedData}
