Python pylab.plot方法代碼示例

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def test_lines_dists():
    import pylab
    ax = pylab.gca()

    xs, ys = (0,30), (20,150)
    pylab.plot(xs, ys)
    points = list(zip(xs, ys))
    p0, p1 = points

    xs, ys = (0,0,20,30), (100,150,30,200)
    pylab.scatter(xs, ys)

    dist = line2d_seg_dist(p0, p1, (xs[0], ys[0]))
    dist = line2d_seg_dist(p0, p1, np.array((xs, ys)))
    for x, y, d in zip(xs, ys, dist):
        c = Circle((x, y), d, fill=0)
        ax.add_patch(c)

    pylab.xlim(-200, 200)
    pylab.ylim(-200, 200)
    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def test_proj():
    import pylab
    M = test_proj_make_M()

    ts = ['%d' % i for i in [0,1,2,3,0,4,5,6,7,4]]
    xs, ys, zs = [0,1,1,0,0, 0,1,1,0,0], [0,0,1,1,0, 0,0,1,1,0], \
            [0,0,0,0,0, 1,1,1,1,1]
    xs, ys, zs = [np.array(v)*300 for v in (xs, ys, zs)]
    #
    test_proj_draw_axes(M, s=400)
    txs, tys, tzs = proj_transform(xs, ys, zs, M)
    ixs, iys, izs = inv_transform(txs, tys, tzs, M)

    pylab.scatter(txs, tys, c=tzs)
    pylab.plot(txs, tys, c='r')
    for x, y, t in zip(txs, tys, ts):
        pylab.text(x, y, t)

    pylab.xlim(-0.2, 0.2)
    pylab.ylim(-0.2, 0.2)

    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_roc_curve(y_true, y_score, size=None):
    """plot_roc_curve."""
    false_positive_rate, true_positive_rate, thresholds = roc_curve(
        y_true, y_score)
    if size is not None:
        plt.figure(figsize=(size, size))
        plt.axis('equal')
    plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy')
    plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
    plt.xlabel('False positive rate')
    plt.ylabel('True positive rate')
    plt.ylim([-0.05, 1.05])
    plt.xlim([-0.05, 1.05])
    plt.grid()
    plt.title('Receiver operating characteristic AUC={0:0.2f}'.format(
        roc_auc_score(y_true, y_score))) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_stats(x=None, y=None, label=None, color='navy'):
    """plot_stats."""
    y = np.array(y)
    y0 = y[0]
    y1 = y[1]
    y2 = y[2]
    y3 = y[3]
    y4 = y[4]
    plt.fill_between(x, y3, y4, color=color, alpha=0.08)
    plt.fill_between(x, y1, y2, color=color, alpha=0.08)
    plt.plot(x, y0, '-', lw=2, color=color, label=label)
    plt.plot(x, y0,
             linestyle='None',
             markerfacecolor='white',
             markeredgecolor=color,
             marker='o',
             markeredgewidth=2,
             markersize=8) 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def train(self):
        # self.load_model('./save_model/cartpole_a3c.h5')
        agents = [Agent(i, self.actor, self.critic, self.optimizer, self.env_name, self.discount_factor,
                        self.action_size, self.state_size) for i in range(self.threads)]

        for agent in agents:
            agent.start()

        while True:
            time.sleep(20)

            plot = scores[:]
            pylab.plot(range(len(plot)), plot, 'b')
            pylab.savefig("./save_graph/cartpole_a3c.png")

            self.save_model('./save_model/cartpole_a3c.h5') 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def test_lines_dists():
    import pylab
    ax = pylab.gca()

    xs, ys = (0,30), (20,150)
    pylab.plot(xs, ys)
    points = zip(xs, ys)
    p0, p1 = points

    xs, ys = (0,0,20,30), (100,150,30,200)
    pylab.scatter(xs, ys)

    dist = line2d_seg_dist(p0, p1, (xs[0], ys[0]))
    dist = line2d_seg_dist(p0, p1, np.array((xs, ys)))
    for x, y, d in zip(xs, ys, dist):
        c = Circle((x, y), d, fill=0)
        ax.add_patch(c)

    pylab.xlim(-200, 200)
    pylab.ylim(-200, 200)
    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_it():
    '''
    helper function to gain insight on provided data sets background,
    using pylab
    '''
    data1 = [[1.0, 1], [2.25, 3.5], [3.58333333333, 7.5], [4.95833333333, 13.0], [6.35833333333, 20.0], [7.775, 28.5], [9.20357142857, 38.5], [10.6410714286, 50.0], [12.085515873, 63.0], [13.535515873, 77.5]]
    data2 = [[1.0, 1], [1.75, 2.5], [2.41666666667, 4.5], [3.04166666667, 7.0], [3.64166666667, 10.0], [4.225, 13.5], [4.79642857143, 17.5], [5.35892857143, 22.0], [5.91448412698, 27.0], [6.46448412698, 32.5], [7.00993867244, 38.5], [7.55160533911, 45.0], [8.09006687757, 52.0], [8.62578116328, 59.5], [9.15911449661, 67.5], [9.69036449661, 76.0], [10.2197762613, 85.0], [10.7475540391, 94.5], [11.2738698286, 104.5], [11.7988698286, 115.0]]
    time1 = [item[0] for item in data1]
    resource1 = [item[1] for item in data1]
    time2 = [item[0] for item in data2]
    resource2 = [item[1] for item in data2]
    
    # plot in pylab (total resources over time)
    pylab.plot(time1, resource1, 'o')
    pylab.plot(time2, resource2, 'o')
    pylab.title('Silly Homework')
    pylab.legend(('Data Set no.1', 'Data Set no.2'))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.show()

#plot_it() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_question2():
    '''
    graph of total resources generated as a function of time,
    for four various upgrade_cost_increment values
    '''
    for upgrade_cost_increment in [0.0, 0.5, 1.0, 2.0]:
        data = resources_vs_time(upgrade_cost_increment, 5)
        time = [item[0] for item in data]
        resource = [item[1] for item in data]
    
        # plot in pylab (total resources over time for each constant)
        pylab.plot(time, resource, 'o')
        
    pylab.title('Silly Homework')
    pylab.legend(('0.0', '0.5', '1.0', '2.0'))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.show()

#plot_question2()   


# Question 3 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_question3():
    '''
    graph of total resources generated as a function of time;
    for upgrade_cost_increment == 0
    '''
    data = resources_vs_time(0.0, 100)
    time = [item[0] for item in data]
    resource = [item[1] for item in data]

    # plot in pylab on logarithmic scale (total resources over time for upgrade growth 0.0)
    pylab.loglog(time, resource)
        
    pylab.title('Silly Homework')
    pylab.legend('0.0')
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.show()

#plot_question3()


# Question 4 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def polyfitting():
    '''
    helper function to play around with polyfit from:
    http://www.wired.com/2011/01/linear-regression-with-pylab/
    '''
    x = [0.2, 1.3, 2.1, 2.9, 3.3]
    y = [3.3, 3.9, 4.8, 5.5, 6.9]
    slope, intercept = pylab.polyfit(x, y, 1)
    print 'slope:', slope, 'intercept:', intercept

    yp = pylab.polyval([slope, intercept], x)
    pylab.plot(x, yp)
    pylab.scatter(x, y)
    pylab.show()

#polyfitting() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_question7():
    '''
    graph of total resources generated as a function of time,
    for upgrade_cost_increment == 1
    '''
    data = resources_vs_time(1.0, 50)
    time = [item[0] for item in data]
    resource = [item[1] for item in data]
    a, b, c = pylab.polyfit(time, resource, 2)
    print 'polyfit with argument \'2\' fits the data, thus the degree of the polynomial is 2 (quadratic)'

    # plot in pylab on logarithmic scale (total resources over time for upgrade growth 0.0)
    #pylab.loglog(time, resource, 'o')

    # plot fitting function
    yp = pylab.polyval([a, b, c], time)
    pylab.plot(time, yp)
    pylab.scatter(time, resource)
    pylab.title('Silly Homework, Question 7')
    pylab.legend(('Resources for increment 1', 'Fitting function' + ', slope: ' + str(a)))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.grid()
    pylab.show() 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_entropy(self):
        """

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        plt.plot(
            self.temperatures,
            self.eV_to_J_per_mol / self.num_atoms * self.get_entropy_p(),
            label="S$_p$",
        )
        plt.plot(
            self.temperatures,
            self.eV_to_J_per_mol / self.num_atoms * self.get_entropy_v(),
            label="S$_V$",
        )
        plt.legend()
        plt.xlabel("Temperature [K]")
        plt.ylabel("Entropy [J K$^{-1}$ mol-atoms$^{-1}$]") 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def contour_pressure(self):
        """

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        x, y = self.meshgrid()
        p_coeff = np.polyfit(self.volumes, self.pressure.T, deg=self._fit_order)
        p_grid = np.array([np.polyval(p_coeff, v) for v in self._volumes]).T
        plt.contourf(x, y, p_grid)
        plt.plot(self.get_minimum_energy_path(), self.temperatures)
        plt.xlabel("Volume [$\AA^3$]")
        plt.ylabel("Temperature [K]") 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def contour_entropy(self):
        """

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        s_coeff = np.polyfit(self.volumes, self.entropy.T, deg=self._fit_order)
        s_grid = np.array([np.polyval(s_coeff, v) for v in self.volumes]).T
        x, y = self.meshgrid()
        plt.contourf(x, y, s_grid)
        plt.plot(self.get_minimum_energy_path(), self.temperatures)
        plt.xlabel("Volume [$\AA^3$]")
        plt.ylabel("Temperature [K]") 

# 需要導入模塊: import pylab [as 別名]
# 或者: from pylab import plot [as 別名]
def plot_contourf(self, ax=None, show_min_erg_path=False):
        """

        Args:
            ax:
            show_min_erg_path:

        Returns:

        """
        try:
            import pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        x, y = self.meshgrid()
        if ax is None:
            fig, ax = plt.subplots(1, 1)
        ax.contourf(x, y, self.energies)
        if show_min_erg_path:
            plt.plot(self.get_minimum_energy_path(), self.temperatures, "w--")
        plt.xlabel("Volume [$\AA^3$]")
        plt.ylabel("Temperature [K]")
        return ax