Python pyplot.loglog方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_wcc_distribution(_g, _plot_img):
    """Plot weakly connected components size distributions
    :param _g: Transaction graph
    :param _plot_img: WCC size distribution image (log-log plot)
    :return:
    """
    all_wcc = nx.weakly_connected_components(_g)
    wcc_sizes = Counter([len(wcc) for wcc in all_wcc])
    size_seq = sorted(wcc_sizes.keys())
    size_hist = [wcc_sizes[x] for x in size_seq]

    plt.figure(figsize=(16, 12))
    plt.clf()
    plt.loglog(size_seq, size_hist, 'ro-')
    plt.title("WCC Size Distribution")
    plt.xlabel("Size")
    plt.ylabel("Number of WCCs")
    plt.savefig(_plot_img) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_distance_comparisons(self, distance1, distance2, logaxis=False,
        figure_size=(7, 5), filename=None, filetype="png", dpi=300):
        """
        Creates a plot comparing different distance metrics for the 
        specific rupture and target site combination
        """
        xdist = self._calculate_distance(distance1)       
        ydist = self._calculate_distance(distance2)       
        plt.figure(figsize=figure_size)

        if logaxis:
            plt.loglog(xdist, ydist, color='b', marker='o', linestyle='None')
        else:
            plt.plot(xdist, ydist, color='b', marker='o', linestyle='None')
        
        plt.xlabel("%s (km)" % distance1, size='medium')
        plt.ylabel("%s (km)" % distance2, size='medium')
        plt.title('Rupture: M=%6.1f, Dip=%3.0f, Ztor=%4.1f, Aspect=%5.2f'
                   % (self.magnitude, self.dip, self.ztor, self.aspect))
        _save_image(filename, filetype, dpi)
        plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def PlotOrthogonalResiduals(ModelX, ModelY, DataX, DataY):

    """
    """
    
    # setup the figure
    Fig = CreateFigure(AspectRatio=1.2)
    
    # Get residuals
    Residuals, OrthoX, OrthoY = OrthogonalResiduals(ModelX, ModelY, DataX, DataY)
    
    # plot model and data
    plt.loglog()
    plt.axis('equal')
    plt.plot(ModelX,ModelY,'k-', lw=1)
    plt.plot(DataX,DataY,'k.', ms=2)
    
    # plot orthogonals
    for i in range(0,len(DataX)):
        plt.plot([DataX[i],OrthoX[i]],[DataY[i],OrthoY[i]],'-',color=[0.5,0.5,0.5])
    
    plt.savefig(PlotDirectory+FilenamePrefix + "_ESRSOrthoResiduals.png", dpi=300) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def example2():
    """
    Compute the GRADEV of a nonstationary white phase noise.
    """
    N=1000 # number of samples
    f = 1 # data samples per second
    s=1+5/N*np.arange(0,N)
    y=s*np.random.randn(1,N)[0,:]
    x = [xx for xx in np.linspace(1,len(y),len(y))]
    x_ax, y_ax, (err_l, err_h) , ns = allan.gradev(y,data_type='phase',rate=f,taus=x)
    plt.loglog(x_ax, y_ax,'b.',label="No gaps")
    y[int(0.4*N):int(0.6*N,)] = np.NaN  # Simulate missing data
    x_ax, y_ax, (err_l, err_h), ns = allan.gradev(y,data_type='phase',rate=f,taus=x)
    plt.loglog(x_ax, y_ax,'g.',label="With gaps")
    plt.grid()
    plt.legend()
    plt.xlabel('Tau / s')
    plt.ylabel('Overlapping Allan deviation')
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_fee_rates(fee_rates):
    exponent_min = -6
    exponent_max = 0

    bin_factor = 10

    bins_log = 10**np.linspace(
        exponent_min, exponent_max, (exponent_max - exponent_min) * bin_factor + 1)
    print(bins_log)
    fig, ax = plt.subplots(figsize=standard_figsize, dpi=300)
    ax.axvline(x=1E-6, c='k', ls='--')
    ax.hist(fee_rates, bins=bins_log)
    plt.loglog()
    ax.set_xlabel("Fee rate bins [sat per sat]")
    ax.set_ylabel("Number of channels")
    plt.tight_layout()
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_base_fees(base_fees):
    exponent_min = 0
    exponent_max = 5

    bin_factor = 10

    bins_log = 10**np.linspace(
        exponent_min, exponent_max, (exponent_max - exponent_min) * bin_factor + 1)

    fig, ax = plt.subplots(figsize=standard_figsize, dpi=300)
    ax.hist(base_fees, bins=bins_log)
    ax.axvline(x=1E3, c='k', ls='--')
    plt.loglog()
    ax.set_xlabel("Base fee bins [msat]")
    ax.set_ylabel("Number of channels")
    plt.tight_layout()
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_cltv(time_locks):
    exponent_min = 0
    exponent_max = 3

    bin_factor = 10

    bins_log = 10**np.linspace(
        exponent_min, exponent_max, (exponent_max - exponent_min) * bin_factor + 1)

    fig, ax = plt.subplots(figsize=standard_figsize, dpi=300)

    ax.hist(time_locks, bins=bins_log)
    plt.loglog()
    ax.set_xlabel("CLTV bins [blocks]")
    ax.set_ylabel("Number of channels")
    plt.tight_layout()
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
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() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
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") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_losses(loss_vals, loss_names, filename, title, xlabel, ylabel, spacing=0):
    """
    Given a list of errors, plot the objectives of the training and show
    """
    plt.close('all')
    for li, lvals in enumerate(loss_vals):
        iterations = range(len(lvals))
        # lvals.insert(0, 0)
        if spacing == 0:
            plt.loglog(iterations, lvals, '-',label=loss_names[li])
            # plt.semilogx(iterations, lvals, 'x-')
        else:
            xvals = [ii*spacing for ii in iterations]
            plt.loglog( xvals, lvals, '-',label=loss_names[li])

    plt.grid()
    plt.legend(loc='upper left')
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.savefig(filename)
    plt.close('all') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def _scale_curve(self):
        """
        Puts the data-misfit and regularizing function values in the range
        [-10, 10].
        """
        if self.loglog:
            x, y = numpy.log(self.dnorm), numpy.log(self.mnorm)
        else:
            x, y = self.dnorm, self.mnorm

        def scale(a):
            vmin, vmax = a.min(), a.max()
            l, u = -10, 10
            return (((u - l) / (vmax - vmin)) *
                    (a - (u * vmin - l * vmax) / (u - l)))
        return scale(x), scale(y) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_powA(k,powNbody,powLPT,powRecon,LxN,RxN,label,c_i):
    c = plt.rcParams['axes.prop_cycle'].by_key()['color']

    ax1.loglog(k,powLPT,color = c[c_i],ls='--')
    ax1.loglog(k,powRecon,color=c[c_i],ls=':')
    l0=ax1.loglog(k,powNbody,color=c[c_i],ls='-',label=label)

    l1 = ax2.plot(k, powLPT/powNbody,color = c[c_i],ls='--')
    l2 = ax2.plot(k, powRecon/powNbody,label = label,color = c[c_i],ls=':')
    ax2.axhline(y=1, color='k', linestyle='--')


    ax3.loglog(k, 1-(LxN/np.sqrt(powLPT*powNbody))**2,color = c[c_i],ls='--')
    ax3.loglog(k, 1-(RxN/np.sqrt(powRecon*powNbody))**2,color = c[c_i],ls=':')
    return l0,l1,l2
#----------plot residual -----------------# 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def _fractal_correlation_plot(r_vals, corr, d2):
    fit = 2 ** np.polyval(d2, np.log2(r_vals))
    plt.loglog(r_vals, corr, "bo")
    plt.loglog(r_vals, fit, "r", label=r"$D2$ = %0.3f" % d2[0])
    plt.title("Correlation Dimension")
    plt.xlabel(r"$\log_{2}$(r)")
    plt.ylabel(r"$\log_{2}$(c)")
    plt.legend()
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def _fractal_dfa_plot(windows, fluctuations, dfa):
    fluctfit = 2 ** np.polyval(dfa, np.log2(windows))
    plt.loglog(windows, fluctuations, "bo")
    plt.loglog(windows, fluctfit, "r", label=r"$\alpha$ = %0.3f" % dfa[0])
    plt.title("DFA")
    plt.xlabel(r"$\log_{2}$(Window)")
    plt.ylabel(r"$\log_{2}$(Fluctuation)")
    plt.legend()
    plt.show()


# =============================================================================
#  Utils MDDFA
# ============================================================================= 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import loglog [as 別名]
def plot_fourier_spectrum(time_series, time_step, figure_size=(7, 5),
        filename=None, filetype="png", dpi=300):
    """
    Plots the Fourier spectrum of a time series 
    """
    freq, amplitude = get_fourier_spectrum(time_series, time_step)
    plt.figure(figsize=figure_size)
    plt.loglog(freq, amplitude, 'b-')
    plt.xlabel("Frequency (Hz)", fontsize=14)
    plt.ylabel("Fourier Amplitude", fontsize=14)
    _save_image(filename, filetype, dpi)
    plt.show()