Python pylab.plot方法代码示例

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def error_bar_plot(experiment_data, results, title="", ylabel=""):

    true_effect = experiment_data.true_effects.mean()
    estimators = list(results.keys())

    x = list(estimators)
    y = [results[estimator].ate for estimator in estimators]

    cis = [
        np.array(results[estimator].ci) - results[estimator].ate
        if results[estimator].ci is not None
        else [0, 0]
        for estimator in estimators
    ]
    err = [[abs(ci[0]) for ci in cis], [abs(ci[1]) for ci in cis]]

    plt.figure(figsize=(12, 5))
    (_, caps, _) = plt.errorbar(x, y, yerr=err, fmt="o", markersize=8, capsize=5)
    for cap in caps:
        cap.set_markeredgewidth(2)
    plt.plot(x, [true_effect] * len(x), label="True Effect")
    plt.legend(fontsize=12, loc="lower right")
    plt.ylabel(ylabel)
    plt.title(title) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_entropy():
    pylab.clf()
    pylab.figure(num=None, figsize=(5, 4))

    title = "Entropy $H(X)$"
    pylab.title(title)
    pylab.xlabel("$P(X=$coin will show heads up$)$")
    pylab.ylabel("$H(X)$")

    pylab.xlim(xmin=0, xmax=1.1)
    x = np.arange(0.001, 1, 0.001)
    y = -x * np.log2(x) - (1 - x) * np.log2(1 - x)
    pylab.plot(x, y)
    # pylab.xticks([w*7*24 for w in [0,1,2,3,4]], ['week %i'%(w+1) for w in
    # [0,1,2,3,4]])

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "entropy_demo.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight") 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_roc(auc_score, name, tpr, fpr, label=None):
    pylab.clf()
    pylab.figure(num=None, figsize=(5, 4))
    pylab.grid(True)
    pylab.plot([0, 1], [0, 1], 'k--')
    pylab.plot(fpr, tpr)
    pylab.fill_between(fpr, tpr, alpha=0.5)
    pylab.xlim([0.0, 1.0])
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('False Positive Rate')
    pylab.ylabel('True Positive Rate')
    pylab.title('ROC curve (AUC = %0.2f) / %s' %
                (auc_score, label), verticalalignment="bottom")
    pylab.legend(loc="lower right")
    filename = name.replace(" ", "_")
    pylab.savefig(
        os.path.join(CHART_DIR, "roc_" + filename + ".png"), bbox_inches="tight") 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_fermi_dirac(self):
        """
        Plots the obtained eigenvalue vs occupation plot

        """
        try:
            import matplotlib.pylab as plt
        except ModuleNotFoundError:
            import matplotlib.pyplot as plt
        arg = np.argsort(self.eigenvalues)
        plt.plot(
            self.eigenvalues[arg], self.occupancies[arg], linewidth=2.0, color="blue"
        )
        plt.axvline(self.efermi, linewidth=2.0, linestyle="dashed", color="black")
        plt.xlabel("Energies (eV)")
        plt.ylabel("Occupancy")
        return plt 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_total_dos(self, **kwargs):
        """
        Plots the total DOS

        Args:
            **kwargs: Variables for matplotlib.pylab.plot customization (linewidth, linestyle, etc.)

        Returns:
            matplotlib.pylab.plot
        """
        try:
            import matplotlib.pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        fig = plt.figure(1, figsize=(6, 4))
        ax1 = fig.add_subplot(111)
        ax1.set_xlabel("E (eV)", fontsize=14)
        ax1.set_ylabel("DOS", fontsize=14)
        plt.fill_between(self.energies, self.t_dos, **kwargs)
        return plt 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_orbital_resolved_dos(self, **kwargs):
        """
        Plots the orbital resolved DOS

        Args:
            **kwargs: Variable for matplotlib.pylab.plot customization (linewidth, linestyle, etc.)

        Returns:
            matplotlib.pylab.plot
        """
        try:
            import matplotlib.pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        if not (self.es_obj.grand_dos_matrix is not None):
            raise NoResolvedDosError(
                "Can not plot the orbital resolved dos since resolved dos values are not"
                " available"
            )
        plot = self.plot_total_dos()
        for key, val in self.orbital_dict.items():
            r_dos = self.get_orbital_resolved_dos(val)
            plt.plot(self.energies, r_dos, label=key, **kwargs)
        plot.legend()
        return plot 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_equilibration(temperature_next, strain_lst, nve_run_time_steps, project_parameter, debug_plot=True):
    if debug_plot:
        for strain in strain_lst:
            job_name = get_nve_job_name(
                temperature_next=temperature_next,
                strain=strain,
                steps_lst=project_parameter['nve_run_time_steps_lst'],
                nve_run_time_steps=nve_run_time_steps
            )
            ham_nve = project_parameter['project'].load(job_name)
            plt.plot(ham_nve['output/generic/temperature'], label='strain: ' + str(strain))
            plt.axhline(np.mean(ham_nve['output/generic/temperature'][-20:]), linestyle='--', color='red')
            plt.axvline(range(len(ham_nve['output/generic/temperature']))[-20], linestyle='--', color='black')
            plt.legend()
            plt.xlabel('timestep')
            plt.ylabel('Temperature K')
            plt.legend()
            plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def check_for_holes(temperature_next, strain_value_lst, nve_run_time_steps, project_parameter, debug_plot=True):
    max_lst, mean_lst = get_voronoi_volume(
        temperature_next=temperature_next,
        strain_lst=strain_value_lst,
        nve_run_time_steps=nve_run_time_steps,
        project_parameter=project_parameter
    )
    if debug_plot:
        plt.plot(strain_value_lst, mean_lst, label='mean')
        plt.plot(strain_value_lst, max_lst, label='max')
        plt.axhline(np.mean(mean_lst) * 2, color='black', linestyle='--')
        plt.legend()
        plt.xlabel('Strain')
        plt.ylabel('Voronoi Volume')
        plt.show()
    return np.array(max_lst) < np.mean(mean_lst) * 2 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_efrontier(self):
        """Plots the Efficient Frontier."""
        if self.efrontier is None:
            # compute efficient frontier first
            self.efficient_frontier()
        plt.plot(
            self.efrontier[:, 0],
            self.efrontier[:, 1],
            linestyle="-.",
            color="black",
            lw=2,
            label="Efficient Frontier",
        )
        plt.title("Efficient Frontier")
        plt.xlabel("Volatility")
        plt.ylabel("Expected Return")
        plt.legend() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_pr(auc_score, precision, recall, label=None, figure_path=None):
    """绘制R/P曲线"""
    try:
        from matplotlib import pylab
        pylab.figure(num=None, figsize=(6, 5))
        pylab.xlim([0.0, 1.0])
        pylab.ylim([0.0, 1.0])
        pylab.xlabel('Recall')
        pylab.ylabel('Precision')
        pylab.title('P/R (AUC=%0.2f) / %s' % (auc_score, label))
        pylab.fill_between(recall, precision, alpha=0.5)
        pylab.grid(True, linestyle='-', color='0.75')
        pylab.plot(recall, precision, lw=1)
        pylab.savefig(figure_path)
    except Exception as e:
        print("save image error with matplotlib")
        pass 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_pr_curve(pr_curve_dml, pr_curve_base, title):
    """
      Function that plots the PR-curve.

      Args:
        pr_curve: the values of precision for each recall value
        title: the title of the plot
    """
    plt.figure(figsize=(16, 9))
    plt.plot(np.arange(0.0, 1.05, 0.05),
             pr_curve_base, color='r', marker='o', linewidth=3, markersize=10)
    plt.plot(np.arange(0.0, 1.05, 0.05),
             pr_curve_dml, color='b', marker='o', linewidth=3, markersize=10)
    plt.grid(True, linestyle='dotted')
    plt.xlabel('Recall', color='k', fontsize=27)
    plt.ylabel('Precision', color='k', fontsize=27)
    plt.yticks(color='k', fontsize=20)
    plt.xticks(color='k', fontsize=20)
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title(title, color='k', fontsize=27)
    plt.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plotKChart(self, misClassDict, saveFigPath):
        kList = []
        misRateList = []
        for k, misClassNum in misClassDict.iteritems():
            kList.append(k)
            misRateList.append(1.0 - 1.0/k*misClassNum)

        fig = plt.figure(saveFigPath)
        plt.plot(kList, misRateList, 'r--')
        plt.title(saveFigPath)
        plt.xlabel('k Num.')
        plt.ylabel('Misclassified Rate')
        plt.legend(saveFigPath)
        plt.grid(True)
        plt.savefig(saveFigPath)
        plt.show()

################################### PART3 TEST ########################################
# 例子 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_xz_landscape(self):
        """
        plots the xz landscape, i.e., how your vna frequency span changes with respect to the x vector
        :return: None
        """
        if not qkit.module_available("matplotlib"):
            raise ImportError("matplotlib not found.")

        if self.xzlandscape_func:
            y_values = self.xzlandscape_func(self.spec.x_vec)
            plt.plot(self.spec.x_vec, y_values, 'C1')
            plt.fill_between(self.spec.x_vec, y_values+self.z_span/2., y_values-self.z_span/2., color='C0', alpha=0.5)
            plt.xlim((self.spec.x_vec[0], self.spec.x_vec[-1]))
            plt.ylim((self.xz_freqpoints[0], self.xz_freqpoints[-1]))
            plt.show()
        else:
            print('No xz funcion generated. Use landscape.generate_xz_function') 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def plot_fit_function(self, num_points=100):
        '''
        try:
            x_coords = np.linspace(self.x_vec[0], self.x_vec[-1], num_points)
        except Exception as message:
            print 'no x axis information specified', message
            return
        '''
        if not qkit.module_available("matplotlib"):
            raise ImportError("matplotlib not found.")
        if self.landscape:
            for trace in self.landscape:
                try:
                    # plt.clear()
                    plt.plot(self.x_vec, trace)
                    plt.fill_between(self.x_vec, trace + float(self.span) / 2, trace - float(self.span) / 2, alpha=0.5)
                except Exception:
                    print('invalid trace...skip')
            plt.axhspan(self.y_vec[0], self.y_vec[-1], facecolor='0.5', alpha=0.5)
            plt.show()
        else:
            print('No trace generated.') 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import plot [as 别名]
def process_degree_distribution(N, Pk, color, Psi, DPsi, symbol, label, count):
    report_times = scipy.linspace(0,30,3000)
    sums = 0*report_times
    for cnt in range(count):
        G = generate_network(Pk, N)
        t, S, I, R = EoN.fast_SIR(G, tau, gamma, rho=rho)
        plt.plot(t, I*1./N, '-', color = color, 
                                alpha = 0.1, linewidth=1)
        subsampled_I = EoN.subsample(report_times, t, I)
        sums += subsampled_I*1./N
    ave = sums/count
    plt.plot(report_times, ave, color = 'k')
    
    #Do EBCM    
    N= G.order()#N is arbitrary, but included because our implementation of EBCM assumes N is given.
    t, S, I, R = EoN.EBCM_uniform_introduction(N, Psi, DPsi, tau, gamma, rho, tmin=0, tmax=10, tcount = 41)
    plt.plot(t, I/N, symbol, color = color, markeredgecolor='k', label=label)

    for cnt in range(3):  #do 3 highlighted simulations
        G = generate_network(Pk, N)
        t, S, I, R = EoN.fast_SIR(G, tau, gamma, rho=rho)
        plt.plot(t, I*1./N, '-', color = 'k', linewidth=0.1)