python类plot()的实例源码

def generate_inverted_sinewave_dataset(N = 1000, f = 1.0, p = 0.0, a1 = 1.0, a2 = 0.3):
    """models_actinf.generate_inverted_sinewave_dataset

    Generate the inverted sine dataset used in Bishop's (Bishop96)
    mixture density paper

    Returns:
    - matrices X, Y
    """
    X = np.linspace(0,1,N)
    # FIXME: include phase p
    Y = a1 * X + a2 * np.sin(f * (2 * 3.1415926) * X) + np.random.uniform(-0.1, 0.1, N)
    X,Y = Y[:,np.newaxis],X[:,np.newaxis]

    # pl.subplot(211)
    # pl.plot(Y, X, "ko", alpha=0.25)
    # pl.subplot(212)
    # pl.plot(X, Y, "ko", alpha=0.25)
    # pl.show()

    return X,Y

def plot_word_frequencies(freq, user):
        samples = [item for item, _ in freq.most_common(50)]

        freqs = np.array([float(freq[sample]) for sample in samples])
        freqs /= np.max(freqs)

        ylabel = "Normalized word count"

        pylab.grid(True, color="silver")
        kwargs = dict()
        kwargs["linewidth"] = 2
        kwargs["label"] = user
        pylab.plot(freqs, **kwargs)
        pylab.xticks(range(len(samples)), [nltk.compat.text_type(s) for s in samples], rotation=90)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.gca().set_yscale('log', basey=2)

def predicted_vs_actual_sale_price(self, x_train, y_train, title_name):
        # Split the training data into an extra set of test
        x_train_split, x_test_split, y_train_split, y_test_split = train_test_split(x_train, y_train)
        print(np.shape(x_train_split), np.shape(x_test_split), np.shape(y_train_split), np.shape(y_test_split))
        lasso = LassoCV(alphas=[0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1,
                                0.3, 0.6, 1],
                        max_iter=50000, cv=10)
        # lasso = RidgeCV(alphas=[0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1,
        #                         0.3, 0.6, 1], cv=10)

        lasso.fit(x_train_split, y_train_split)
        y_predicted = lasso.predict(X=x_test_split)
        plt.figure(figsize=(10, 5))
        plt.scatter(y_test_split, y_predicted, s=20)
        rmse_pred_vs_actual = self.rmse(y_predicted, y_test_split)
        plt.title(''.join([title_name, ', Predicted vs. Actual.', ' rmse = ', str(rmse_pred_vs_actual)]))
        plt.xlabel('Actual Sale Price')
        plt.ylabel('Predicted Sale Price')
        plt.plot([min(y_test_split), max(y_test_split)], [min(y_test_split), max(y_test_split)])
        plt.tight_layout()

def predicted_vs_actual_sale_price_xgb(self, xgb_params, x_train, y_train, seed, title_name):
        # Split the training data into an extra set of test
        x_train_split, x_test_split, y_train_split, y_test_split = train_test_split(x_train, y_train)
        dtrain_split = xgb.DMatrix(x_train_split, label=y_train_split)
        dtest_split = xgb.DMatrix(x_test_split)

        res = xgb.cv(xgb_params, dtrain_split, num_boost_round=1000, nfold=4, seed=seed, stratified=False,
                     early_stopping_rounds=25, verbose_eval=10, show_stdv=True)

        best_nrounds = res.shape[0] - 1
        print(np.shape(x_train_split), np.shape(x_test_split), np.shape(y_train_split), np.shape(y_test_split))
        gbdt = xgb.train(xgb_params, dtrain_split, best_nrounds)
        y_predicted = gbdt.predict(dtest_split)
        plt.figure(figsize=(10, 5))
        plt.scatter(y_test_split, y_predicted, s=20)
        rmse_pred_vs_actual = self.rmse(y_predicted, y_test_split)
        plt.title(''.join([title_name, ', Predicted vs. Actual.', ' rmse = ', str(rmse_pred_vs_actual)]))
        plt.xlabel('Actual Sale Price')
        plt.ylabel('Predicted Sale Price')
        plt.plot([min(y_test_split), max(y_test_split)], [min(y_test_split), max(y_test_split)])
        plt.tight_layout()

def ex2():
    x = np.linspace(-10, 10)

    # "--" = dashed line
    plt.plot(x, np.sin(x), "--", label="sinus")
    plt.plot(x, np.cos(x), label="cosinus")

    # Show the legend using the labels above
    plt.legend()

    # The trick here is we have to make another plot on top of the two others.
    pi2 = np.pi/2

    # Find B such that (-B * pi/2) >= -10 > ((-B-1) * pi/2), i.e. the
    # first multiple of pi/2 higher than -10.
    b = pi2*int(-10.0/pi2)

    # x2 is all multiples of pi/2 between -10 and 10.
    x2 = np.arange(b, 10, pi2)

    # "b." = blue dots
    plt.plot(x2, np.sin(x2), "b.")
    plt.show()

def plot_inf_res(df, symbols=[], plot_top=0, time_shift=0):

    if len(symbols) > 0:
        df = df.loc[symbols]

    if plot_top > 0:
        idx = df.groupby(level=0)['INFORMATION_SURPLUS_PCT'].max().sort_values(ascending=False).index
        df = df.reindex(index=idx, level=0)[0:(time_shift+1)*plot_top]

    grouped = df.groupby(level=0)
    ax = plt.figure()
    first = True
    for i, group in grouped:
        if first:
            ax = group.plot(x='SHIFT', y='INFORMATION_SURPLUS_PCT', label=str(i))
            first = False
        else:
            group.plot(ax=ax, x='SHIFT', y='INFORMATION_SURPLUS_PCT', label=str(i))

    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=1.0)
    ax.set_xlabel('Time-shift of sentiment data (days) with financial data')
    ax.set_ylabel('Information Surplus %')

def visualize_bin_list(self, bin_list, path):
        """
        Will create a histogram of all bin_list entries and save it to the specified path
        """
        # TODO use savelogic here
        for jj, bin_entry in enumerate(bin_list):
            hist_x, hist_y = self._traceanalysis_logic.calculate_histogram(bin_entry, num_bins=50)
            pb.plot(hist_x[0:len(hist_y)], hist_y)
            fname = 'bin_' + str(jj) + '.png'
            savepath = os.path.join(path, fname)
            pb.savefig(savepath)
            pb.close()

    # =========================================================================
    #                           Connecting to GUI
    # =========================================================================

    # absolutely not working at the moment.

def stat_personal(self):
        if not os.path.exists(self.file_path + self.ip.ip):
            os.mkdir(self.file_path + self.ip.ip)
            print('make dir %s' % self.ip.ip)
        try:
            items = self.ip.info_set.count()
        except:
            return 0
        my_info = Info.objects.filter(ip = self.ip).order_by('date')
        dates = list(range(len(my_info)))
        bmis = [info.get_bmi() for info in my_info]
        pl.figure('my', figsize = (5.2, 2.8), dpi = 100)
        pl.plot(dates, bmis, '*-', color = '#20b2aa', linewidth = 1.5)
        pl.ylabel(u'BMI?', fontproperties = zhfont)
        pl.ylim(0.0, 50.0)
        pl.savefig(self.file_path + self.ip.ip + '/my.jpg')
        pl.cla()
        return items

def _plotFMeasures(fstepsize=.1,  stepsize=0.0005, start = 0.0, end = 1.0):
    """Plots 10 fmeasure Curves into the current canvas."""
    p = sc.arange(start, end, stepsize)[1:]
    for f in sc.arange(0., 1., fstepsize)[1:]:
        points = [(x, _fmeasureCurve(f, x)) for x in p
                  if 0 < _fmeasureCurve(f, x) <= 1.5]
        try:
            xs, ys = zip(*points)
            curve, = pl.plot(xs, ys, "--", color="gray", linewidth=0.8)  # , label=r"$f=%.1f$"%f) # exclude labels, for legend
            # bad hack:
            # gets the 10th last datapoint, from that goes a bit to the left, and a bit down
            datapoint_x_loc = int(len(xs)/2)
            datapoint_y_loc = int(len(ys)/2)
            # x_left = 0.05
            # y_left = 0.035
            x_left = 0.035
            y_left = -0.02
            pl.annotate(r"$f=%.1f$" % f, xy=(xs[datapoint_x_loc], ys[datapoint_y_loc]), xytext=(xs[datapoint_x_loc] - x_left, ys[datapoint_y_loc] - y_left), size="small", color="gray")
        except Exception as e:
            print e 

#colors = "gcmbbbrrryk"
#colors = "yyybbbrrrckgm"  # 7 is a prime, so we'll loop over all combinations of colors and markers, when zipping their cycles

def show_results(self):
        pl.plot(self.t1, self.n_A1, 'b--', label='A1: Time Step = 0.05')
        pl.plot(self.t1, self.n_B1, 'b', label='B1: Time Step = 0.05')
        pl.plot(self.t2, self.n_A2, 'g--', label='A2: Time Step = 0.1')
        pl.plot(self.t2, self.n_B2, 'g', label='B2: Time Step = 0.1')
        pl.plot(self.t1, self.n_A1_true, 'r--', label='True A1: Time Step = 0.05')
        pl.plot(self.t1, self.n_B1_true, 'r', label='True B1: Time Step = 0.05')
        pl.plot(self.t2, self.n_A2_true, 'c--', label='True A2: Time Step = 0.1')
        pl.plot(self.t2, self.n_B2_true, 'c', label='True B2: Time Step = 0.1')
        pl.title('Double Decay Probelm-Approximation Compared with True in Defferent Time Steps')
        pl.xlim(0.0, 0.1)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True, fontsize='small')
        pl.grid(True)
        pl.savefig("computational_physics homework 4(improved-7).png")

def show(self):
#        pl.semilogy(self.theta, self.omega)
#                , label = '$L =%.1f m, $'%self.l + '$dt = %.2f s, $'%self.dt + '$\\theta_0 = %.2f radians, $'%self.theta[0] + '$q = %i, $'%self.q + '$F_D = %.2f, $'%self.F_D + '$\\Omega_D = %.1f$'%self.Omega_D)
        pl.plot(self.theta_phase ,self.omega_phase, '.', label = '$t \\approx 2\\pi n / \\Omega_D$')
        pl.xlabel('$\\theta$ (radians)')
        pl.ylabel('$\\omega$ (radians/s)')
        pl.legend()
#        pl.text(-1.4, 0.3, '$\\omega$ versus $\\theta$ $F_D = 1.2$', fontsize = 'x-large')
        pl.title('Chaotic Regime')
#        pl.show()
#        pl.semilogy(self.time_array, self.delta)
#        pl.legend(loc = 'upper center', fontsize = 'small')
#        pl.xlabel('$time (s)$')
#        pl.ylabel('$\\Delta\\theta (radians)$')
#        pl.xlim(0, self.T)
#        pl.ylim(float(input('ylim-: ')),float(input('ylim+: ')))
#        pl.ylim(1E-11, 0.01)
#        pl.text(4, -0.15, 'nonlinear pendulum - Euler-Cromer method')
#        pl.text(10, 1E-3, '$\\Delta\\theta versus time F_D = 0.5$')
#        pl.title('Simple Harmonic Motion')
        pl.title('Chaotic Regime')

def show_complex(self):
        font = {'family': 'serif',
                'color':  'k',
                'weight': 'normal',
                'size': 16,
        }
        pl.title('The Trajectory of Tageted Baseball\n with air flow in adiabatic model', fontdict = font)
        pl.plot(self.x, self.y, label = '$v_0 = %.5f m/s$'%self.v0 + ', ' + '$\\theta = %.4f \degree$'%self.theta)
        pl.xlabel('x $m$')
        pl.ylabel('y $m$')
        pl.xlim(0, 300)
        pl.ylim(-100, 20)
        pl.grid()
        pl.legend(loc = 'upper right', shadow = True, fontsize = 'small')
        pl.text(15, -90, 'scan to approach the minimum velocity and corresponding launching angle', fontdict = font)
        pl.show()

def show_simple(self):
        font = {'family': 'serif',
                'color':  'k',
                'weight': 'normal',
                'size': 16,
        }
        pl.title('The Trajectory of Tageted Baseball\n with air flow in adiabatic model', fontdict = font)
        pl.plot(self.x, self.y, label ='$\\alpha = %.0f \degree$'%self.alpha)
        pl.xlabel('x $m$')
        pl.ylabel('y $m$')
        pl.xlim(0, 400)
        pl.ylim(-100, 200)
        pl.grid()
        pl.legend(loc = 'upper right', shadow = True, fontsize = 'medium')
        pl.text(5, -80, 'trojectories varing with angles of wind', fontdict = font)
        pl.show()

def show_results(self):
        font = {'family': 'serif',
                'color':  'k',
                'weight': 'normal',
                'size': 14,
        }
        pl.plot(self.x, self.y, 'c', label='firing angle = 45°')
        pl.title('The Trajectory of a Cannon Shell', fontdict = font)
        pl.xlabel('x (k$m$)')
        pl.ylabel('y ($km$)')
        pl.xlim(0, 60)
        pl.ylim(0, 20)
        pl.grid(True)
        pl.legend(loc='upper right', shadow=True, fontsize='large')
        pl.text(41, 16, 'Only with air drag', fontdict = font)
        pl.show()

def show_results(self):
        font = {'family': 'serif',
                'color':  'k',
                'weight': 'normal',
                'size': 12,
        }
        pl.plot(self.x, self.y, 'c', label='firing angle = 45°')
        pl.title('The Trajectory of a Cannon Shell', fontdict = font)
        pl.xlabel('x (k$m$)')
        pl.ylabel('y ($km$)')
        pl.xlim(0, 60)
        pl.ylim(0, 20)
        pl.grid(True)
        pl.legend(loc='upper right', shadow=True, fontsize='large')
        pl.text(34, 16, '       With both air drag and \n reduced air density-isothermal', fontdict = font)
        pl.show()

def show_results(self):
        font = {'family': 'serif',
                'color':  'k',
                'weight': 'normal',
                'size': 12,
        }
        pl.plot(self.x, self.y, 'c', label='firing angle = 45°')
        pl.title('The Trajectory of a Cannon Shell', fontdict = font)
        pl.xlabel('x (k$m$)')
        pl.ylabel('y ($km$)')
        pl.xlim(0, 60)
        pl.ylim(0, 20)
        pl.grid(True)
        pl.legend(loc='upper right', shadow=True, fontsize='large')
        pl.text(34.5, 16, '      With air drag and the \n dependence of g on altitude', fontdict = font)
        pl.show()

def plot1(self):
#        fig = pl.figure(figsize=(8,8))
        ax1 = fig.add_subplot(111, projection='3d')
        ax1.scatter(self.x,self.y,self.z,c='k',s=10,marker='.')
#        pl.plot(self.x, self.y,'ok')
#        pl.plot(self.x, self.y,'c')
#        ???
        ax1.set_zlabel('$z$') 
        ax1.set_ylabel('$y$')
        ax1.set_xlabel('$x$')
        ax1.set_title('Random walk in three dimensions')

#    def plot2(self):
##        fig = pl.figure(figsize=(8,8))
#        ax = fig.add_subplot(111, projection='3d')
#        ax1.scatter(self.x,self.y,self.z,c='r',s=100,marker='o')

def plot(self):
        pl.plot(self.n,self.r2ave,'.c')
        pl.plot(self.n,self.r2ave_fit,'k')
#        pl.ylim(0,100)
        pl.ylim(0,40)
#        for i in range(self.M):
#            self.x = 0
#            for j in range(self.N):
#                for k in range(j):
#                    rnd = random.random()        

#                rnd = random.random()
#                if rnd > 0.5:
#                    self.x +=1
#                else:
#                    self.x -=1
##            print(self.x)
#            self.x2 += math.pow(self.x,2)
##            print(self.x2)
#        self.x2ave = self.x2/self.M
#        print(self.x2ave)
##        return self.x2ave

def plot(self):
        pl.plot(self.n,self.r2ave,'.c')
        pl.plot(self.n,self.r2ave_fit,'k')
        pl.ylim(0,100)
#        for i in range(self.M):
#            self.x = 0
#            for j in range(self.N):
#                for k in range(j):
#                    rnd = random.random()        

#                rnd = random.random()
#                if rnd > 0.5:
#                    self.x +=1
#                else:
#                    self.x -=1
##            print(self.x)
#            self.x2 += math.pow(self.x,2)
##            print(self.x2)
#        self.x2ave = self.x2/self.M
#        print(self.x2ave)
##        return self.x2ave

def plot(self):
        pl.plot(self.n,self.r2ave,'.c')
        pl.plot(self.n,self.r2ave_fit,'k')
        pl.ylim(0,100)
#        for i in range(self.M):
#            self.x = 0
#            for j in range(self.N):
#                for k in range(j):
#                    rnd = random.random()        

#                rnd = random.random()
#                if rnd > 0.5:
#                    self.x +=1
#                else:
#                    self.x -=1
##            print(self.x)
#            self.x2 += math.pow(self.x,2)
##            print(self.x2)
#        self.x2ave = self.x2/self.M
#        print(self.x2ave)
##        return self.x2ave