Python pyplot.semilogx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 semilogx [as 別名]
def test_mimo(self):
      # MIMO
      B = np.matrix('1,0;0,1')
      D = np.matrix('0,0')
      sysMIMO = ss(self.A,B,self.C,D)

      frqMIMO = sysMIMO.freqresp(self.omega)
      tfMIMO = tf(sysMIMO)

      #bode(sysMIMO) # - should throw not implemented exception
      #bode(tfMIMO) # - should throw not implemented exception

      #plt.figure(3)
      #plt.semilogx(self.omega,20*np.log10(np.squeeze(frq[0])))

      #plt.figure(4)
      #bode(sysMIMO,self.omega) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def db_magnitude_distance_by_trt(db1, dist_type,
        figure_size=(7, 5), filename=None, filetype="png", dpi=300):
    """
    Plot magnitude-distance comparison by tectonic region
    """
    trts=[]
    for i in db1.records:
        trts.append(i.event.tectonic_region)
    trt_types=list(set(trts))
    selector = SMRecordSelector(db1)
    plt.figure(figsize=figure_size)
    for trt in trt_types:
        subdb = selector.select_trt_type(trt, as_db=True)
        mag, dists = get_magnitude_distances(subdb, dist_type)
        plt.semilogx(dists, mag, "o", mec='k', mew=0.5, label=trt)
    plt.xlabel(DISTANCE_LABEL[dist_type], fontsize=14)
    plt.ylabel("Magnitude", fontsize=14)
    plt.title("Magnitude vs Distance by Tectonic Region", fontsize=18)
    plt.legend(loc='lower right', numpoints=1)
    plt.grid()
    _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 semilogx [as 別名]
def plot_summarize(prefix, trial, title=None):
    res = summarize(prefix, trial)
    err = res[:, 1:, 0]
    num = res[:, 1:, 3]
    marker = ('bo', 'r^', 'gs', 'mp', 'c*')
    for i in range(err.shape[1]):
        plt.semilogx(num[:, i], err[:, i], marker[i], ms=12)
    plt.legend(['Proposed', 'inTrees', 'NH', 'BATree', 'DTree2'], numpoints=1)
    plt.xlabel('# of Rules', fontsize=20)
    plt.ylabel('Test Error', fontsize=20)
    if title is None:
        title = prefix
    plt.title(title, fontsize=24)
    plt.show()
    if not os.path.exists('./result/fig/'):
        os.mkdir('./result/fig/')
    plt.savefig('./result/fig/compare_%s.pdf' % (prefix,), format="pdf", bbox_inches="tight")
    plt.close() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def sep_2D_plot(x, y, x_name, y_name, molecules, semilogx=False, savePic=False):
    """2D plot to compare all the molecules
    :param x: data for x-axis
    :param y: data for y-axis, y[i,j], where i is for a molecule, j is for a data point corresponding to a x value
    :return: if savePic is True, then .png image, otherwise a 'matplotlib.figure.Figure' object
    """
    fig = plt.figure()
    plt.xlabel(x_name)
    plt.ylabel(y_name)
    lineList = []
    labelList = []
    for i in range(len(molecules)):
        if semilogx:
            line, = plt.semilogx(x, y[i], 'o-')
        else:
            line, = plt.plot(x, y[i], 'o-')
        lineList.append(line)
        lineLabel = 'Comp {0}'.format(molecules[i])
        labelList.append(lineLabel)
        lgd = fig.legend(lineList, labelList, loc='upper center', bbox_to_anchor=(0.5, 1), ncol=len(molecules) / 2)
    if savePic:
        file_name = y_name.replace(' ', '_')
        fig.savefig('{}.png'.format(file_name), bbox_extra_artists=(lgd,), bbox_inches='tight')
    return fig 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def test_k_coeffs_binned(self):
        wavelengths = np.exp(np.arange(np.log(0.31e-6), np.log(29e-6), 1./20))
        wavelength_bins = np.array([wavelengths[0:-1], wavelengths[1:]]).T
        
        xsec_calc = TransitDepthCalculator(method="xsec")
        xsec_calc.change_wavelength_bins(wavelength_bins)
        ktab_calc = TransitDepthCalculator(method="ktables")
        ktab_calc.change_wavelength_bins(wavelength_bins)
                
        wavelengths, xsec_depths = xsec_calc.compute_depths(R_sun, M_jup, R_jup, 300, logZ=1, CO_ratio=1.5)
        wavelengths, ktab_depths = ktab_calc.compute_depths(R_sun, M_jup, R_jup, 300, logZ=1, CO_ratio=1.5)
        
        diffs = np.abs(ktab_depths - xsec_depths)

        '''plt.semilogx(wavelengths, xsec_depths)
        plt.semilogx(wavelengths, ktab_depths)
        plt.figure()
        plt.semilogx(wavelengths, 1e6 * diffs)
        plt.show()'''
        
        self.assertTrue(np.median(diffs) < 10e-6)
        self.assertTrue(np.percentile(diffs, 95) < 20e-6)
        self.assertTrue(np.max(diffs) < 30e-6) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [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 semilogx [as 別名]
def plot_series(series):
    plt.figure(1)
    # colors = [np.array([1, 0.1, 0.1]), np.array([0.1, 1, 0.1]), np.array([0.1, 0.1, 1])]
    colors = ['m', 'g', 'r', 'b', 'y']
    for i, s in enumerate(series):
        print(s['x'], s['y'], s['std'], s['label'])
        small_number = np.ones_like(s['x']) * (s['x'][1]*0.1)
        x_axis = np.where(s['x'] == 0, small_number, s['x'])
        plt.plot(x_axis, s['y'], color=colors[i], label=s['label'])
        plt.fill_between(x_axis, s['y'] - s['std'], s['y'] + s['std'], color=colors[i], alpha=0.2)
    plt.semilogx()
    plt.xlabel('MI reward bonus')
    plt.ylabel('Final intrinsic reward')
    plt.title('Final intrinsic reward in pointMDP with 10 good modes')
    plt.legend(loc='best')
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def db_magnitude_distance(db1, dist_type, figure_size=(7, 5),
        figure_title=None,filename=None, filetype="png", dpi=300):
    """
    Creates a plot of magnitude verses distance for a strong motion database
    """
    plt.figure(figsize=figure_size)
    mags, dists = get_magnitude_distances(db1, dist_type)
    plt.semilogx(np.array(dists), np.array(mags), "o", mec='k', mew=0.5)
    plt.xlabel(DISTANCE_LABEL[dist_type], fontsize=14)
    plt.ylabel("Magnitude", fontsize=14)
    if figure_title:
        plt.title(figure_title, fontsize=18)
    _save_image(filename, filetype, dpi)
    plt.grid()
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def db_magnitude_distance_by_site(db1, dist_type, classification="NEHRP",
        figure_size=(7, 5), filename=None, filetype="png", dpi=300):
    """
    Plot magnitude-distance comparison by site NEHRP or Eurocode 8 Site class   
    """ 
    if classification == "NEHRP":
        site_bounds = NEHRP_BOUNDS
    elif classification == "EC8":
        site_bounds = EC8_BOUNDS
    else:
        raise ValueError("Unrecognised Site Classifier!")
    selector = SMRecordSelector(db1)
    plt.figure(figsize=figure_size)
    total_idx = []
    for site_class in site_bounds.keys():
        site_idx = _site_selection(db1, site_class, classification)
        if site_idx:
            site_db = selector.select_records(site_idx, as_db=True)
            mags, dists = get_magnitude_distances(site_db, dist_type)
            plt.plot(np.array(dists), np.array(mags), "o", mec='k',
                     mew=0.5, label="Site Class %s" % site_class)
            total_idx.extend(site_idx)
    unc_idx = set(range(db1.number_records())).difference(set(total_idx))
    unc_db = selector.select_records(unc_idx, as_db=True)
    mag, dists = get_magnitude_distances(site_db, dist_type)
    plt.semilogx(np.array(dists), np.array(mags), "o", mfc="None", mec='k',
                 mew=0.5, label="Unclassified", zorder=0)
    plt.xlabel(DISTANCE_LABEL[dist_type], fontsize=14)
    plt.ylabel("Magnitude", fontsize=14)
    plt.grid()
    plt.legend(ncol=2,loc="lower right", numpoints=1)
    plt.title("Magnitude vs Distance (by %s Site Class)" % classification,
              fontsize=18)
    _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 semilogx [as 別名]
def plot_t(EM, HS, title, i):
    plt.figure(title, figsize=(10, 8))
    plt.subplot(i)
    plt.semilogx(time, EM)
    plt.semilogx(time, HS, '--')


###############################################################################
# Impulse HS 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def plot_f(EM, HS, title, i):
    plt.figure(title, figsize=(10, 8))
    plt.subplot(i)
    plt.semilogx(1/time, EM.real)
    plt.semilogx(1/time, HS.real, '--')
    plt.semilogx(1/time, EM.imag)
    plt.semilogx(1/time, HS.imag, '--')


###############################################################################
# Halfspace 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def plot_roc_curve(fpr, tpr, name='model', fig=None):
        if fig is None:
            fig = plt.figure()
            plt.semilogx(np.arange(0, 1, 0.01), np.arange(0, 1, 0.01), 'r', linestyle='--', label='Random guess')
        plt.semilogx(fpr, tpr, color=(random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)),
                     label='ROC curve with {}'.format(name))
        plt.title('Receiver Operating Characteristic')
        plt.xlabel('False Positive Rate')
        plt.ylabel('True Positive Rate')
        plt.legend(loc='best')
        return fig 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def plot(self, confidence_level: float=None, show: bool=True, file_name: str=None):
        """
        Plot the linear plot line.

        :confidence_level: the desired confidence level
        :show: True if the plot is to be shown
        :file_name: Save the plot as "file_name"
        """
        if confidence_level:
            self._set_confidence_level(confidence_level)

        plt.semilogx(self.cdf_x, _ftolnln(self.cdf[0]))
        axis = plt.gca()

        axis.grid(True, which='both')

        formatter = mpl.ticker.FuncFormatter(_weibull_ticks)
        axis.yaxis.set_major_formatter(formatter)
        yt_F = np.array([0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5,
                         0.6, 0.7, 0.8, 0.9, 0.95, 0.99])
        yt_lnF = _ftolnln(yt_F)
        plt.yticks(yt_lnF)

        plt.ylim(yt_lnF[1], yt_lnF[-1])
        plt.xlim(self.cdf_x.min(), self.cdf_x.max())

        self._plot_annotate()

        plt.ylabel('failure rate')
        plt.xlabel('cycles')

        if file_name:
            plt.savefig(file_name)
        if show:
            plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def test_isothermal(self):
        Ts = 5700
        Tp = 1500
        p = Profile()
        p.set_isothermal(Tp)
        calc = EclipseDepthCalculator()
        wavelengths, depths, info_dict = calc.compute_depths(p, R_sun, M_jup, R_jup, Ts, full_output=True)
                
        blackbody = np.pi * 2*h*c**2/wavelengths**5/(np.exp(h*c/wavelengths/k_B/Tp) - 1)

        rel_diffs = (info_dict["planet_spectrum"] - blackbody)/blackbody

        plt.loglog(1e6 * wavelengths, 1e-3 * blackbody, label="Blackbody")
        plt.loglog(1e6 * wavelengths, 1e-3 * info_dict["planet_spectrum"], label="PLATON")
        plt.xlabel("Wavelength (micron)", fontsize=12)
        plt.ylabel("Planet flux (erg/s/cm$^2$/micron)", fontsize=12)
        plt.legend()
        plt.tight_layout()
        plt.figure()
        plt.semilogx(1e6 * wavelengths, 100 * rel_diffs)
        plt.xlabel("Wavelength (micron)", fontsize=12)
        plt.ylabel("Relative difference (%)", fontsize=12)
        plt.tight_layout()
        plt.show()
        
        # Should be exact, but in practice isn't, due to our discretization
        self.assertLess(np.percentile(np.abs(rel_diffs), 50), 0.02)
        self.assertLess(np.percentile(np.abs(rel_diffs), 99), 0.05)
        self.assertLess(np.max(np.abs(rel_diffs)), 0.1)

        blackbody_star = np.pi * 2*h*c**2/wavelengths**5/(np.exp(h*c/wavelengths/k_B/Ts) - 1)        
        approximate_depths = blackbody / blackbody_star * (R_jup/R_sun)**2
        # Not expected to be very accurate because the star is not a blackbody
        self.assertLess(np.median(np.abs(approximate_depths - depths)/approximate_depths), 0.2) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import semilogx [as 別名]
def test_ktables_binned(self):
        wavelengths = np.exp(np.arange(np.log(0.31e-6), np.log(29e-6), 1./20))
        wavelengths = np.append(wavelengths[0:20], wavelengths[50:90])
        wavelength_bins = np.array([wavelengths[0:-1], wavelengths[1:]]).T

        profile = Profile()
        profile.set_from_radiative_solution(
            5052, 0.75 * R_sun, 0.03142 * AU, 1.129 * M_jup, 1.115 * R_jup,
            0.983, -1.77, -0.44, -0.56, 0.23)
        xsec_calc = EclipseDepthCalculator(method="xsec")
        xsec_calc.change_wavelength_bins(wavelength_bins)
        ktab_calc = EclipseDepthCalculator(method="ktables")
        ktab_calc.change_wavelength_bins(wavelength_bins)
        xsec_wavelengths, xsec_depths = xsec_calc.compute_depths(
            profile, 0.75 * R_sun, 1.129 * M_jup, 1.115 * R_jup,
            5052)
        ktab_wavelengths, ktab_depths = ktab_calc.compute_depths(
            profile, 0.75 * R_sun, 1.129 * M_jup, 1.115 * R_jup,
            5052)
        rel_diffs = np.abs(ktab_depths - xsec_depths)/ ktab_depths
        self.assertTrue(np.median(rel_diffs) < 0.03)
        self.assertTrue(np.percentile(rel_diffs, 95) < 0.15)
        self.assertTrue(np.max(rel_diffs) < 0.2)
        
        '''print(np.median(rel_diffs), np.percentile(rel_diffs, 95), np.max(rel_diffs))
        plt.loglog(xsec_wavelengths, xsec_depths)
        plt.loglog(ktab_wavelengths, ktab_depths)
        plt.figure()
        plt.semilogx(ktab_wavelengths, rel_diffs)
        plt.show()'''