python类show()的实例源码

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.time_array,self.delta)

#        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_log(self):
#        pl.subplot(121)
        pl.semilogy(self.time_array, self.delta, 'c')
        pl.xlabel('$time (s)$')
        pl.ylabel('$\\Delta\\theta$ (radians)')
        pl.xlim(0, self.T)
#        pl.ylim(1E-11, 0.01)
        pl.text(42, 1E-7, '$\\Delta\\theta$ versus time $F_D = 1.2$', fontsize = 'x-large')
        pl.title('Chaotic Regime')
        pl.show()

#    def show_log_sub122(self):
#        pl.subplot(122)
#        pl.semilogy(self.time_array, self.delta, 'g')
#        pl.xlabel('$time (s)$')
#        pl.ylabel('$\\Delta\\theta$ (radians)')
#        pl.xlim(0, self.T)
#        pl.ylim(1E-6, 100)
#        pl.text(20, 1E-5, '$\\Delta\\theta$ versus time $F_D = 1.2$', fontsize = 'x-large')
#        pl.title('Chaotic Regime')
#        pl.show()

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 double_scan(self):
        v_0 = 81.09589
        while 0 <= v_0:
            theta_0 = 7.1610
            while 7.1610 <= theta_0 < 7.1620:
                t = targeted_baseball(v_0, theta_0, 0.01, 10, 135)
                t.calculate()
                t.show()
                if a < 220:
                    theta_0 = theta_0 + 0.0001
                else:
                    print(a, '\n', v_0,'\n', theta_0)
                    break
            v_0 = v_0 + 0.00001
            if a >= 220:
                break

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 both air drag and \n reduced air density-adiabatic', fontdict = font)
        pl.show()

def DrawDvs(pl, closes, curve, sign, dvs, pandl, sh, title, leag=None, lad=None ):
    pl.figure
    pl.subplot(311)
    pl.title("id:%s Sharpe ratio: %.2f"%(str(title),sh))
    pl.plot(closes)
    DrawLine(pl, sign, closes)
    pl.subplot(312)
    pl.grid()
    if dvs != None:
        pl.plot(dvs)
    if isinstance(curve, np.ndarray):
        DrawZZ(pl, curve, 'r')
    if leag != None:
        pl.plot(leag, 'r')
    if lad != None:
        pl.plot(lad, 'b')
    #pl.plot(stock.GuiYiHua(closes[:i])[60:])
    pl.subplot(313)
    pl.plot(sign)
    pl.plot(pandl)
    pl.show()
    pl.close()

def DrawDvsAndZZ(pl, dvs, zz, closes=None):
    """dvs?zz??????; dvs : ????closes, """
    dvs = np.array(dvs)
    pl.figure
    if closes == None:
        pl.plot(dvs)
        pl.plot(zz[:,0], zz[:,1], 'r')
    else:
        pl.subplot(211)
        pl.plot(closes)
        pl.grid()
        pl.subplot(212)
        pl.grid()
        pl.plot(dvs)
        pl.plot(zz[:,0], zz[:,1], 'r')
    pl.show()
    pl.close()

def DrawHist(pl, shs):  
    """??????, shs: ??? array"""
    shs = np.array(shs, dtype=float)
    #print "mean: %.2f"%shs.mean()
    shs = shs[np.isnan(shs) == False]
    if len(shs)>0:
        pl.figure
        pl.hist(shs)
        def ShowHitCount(shs):
            #????
            go_count = len(shs) - len(shs[np.isnan(shs)])
            #???
            if len(shs) != 0:
                v = float(go_count)/ float(len(shs))
                #print("trade rato:%.2f%%"%(v*100))
            #?????
            if go_count>0:
                v = float(len(shs[shs>0]))/float(go_count)
                #print("win rato: %.2f%%"%(v*100))
        pl.show()
        #ShowHitCount(shs)

def _test_AsynDrawKline(self):
        code = '300033'
        start_day = '2017-8-25'
        #df = stock.getHisdatDataFrameFromRedis(code, start_day)
        df = stock.getFiveHisdatDf(code, start_day=start_day)
        import account
        account = account.LocalAcount(account.BackTesting())
        #????????
        indexs = agl.GenRandomArray(len(df), 3)
        trade_bSell = [0,1,0]
        df_trades = df[df.index.map(lambda x: x in df.index[indexs])]
        df_trades = df_trades.copy()
        df_trades[AsynDrawKline.enum.trade_bSell] = trade_bSell

        plt.ion()
        for i in range(10):
            AsynDrawKline.drawKline(df[i*10:], df_trades)

        plt.ioff()
        #plt.show()  #???????? ????????

def _test_boll(self):
        code = '300113'
        df_five_hisdat = getFiveHisdatDf(code,'2017-5-1')
        #print(closes)
        #upper, middle, lower = BOLL(df_five_hisdat['c'])
        #print(upper[-1], lower[-1])
        upper, middle, lower = TDX_BOLL(df_five_hisdat['c'])
        print upper[-10:]
        print middle[-10:]
        print lower[-10:]
        #df_five_hisdat['upper'] = upper
        #df_five_hisdat['lower'] = lower
        #df_five_hisdat['mid'] = middle
        #df = df_five_hisdat[['upper', 'c', 'mid', 'lower']]
        #df.plot()
        #pl.show()
        upper, middle, lower, boll_w = TDX_BOLL2(df_five_hisdat['c'])
        print boll_w

def plot(self):
        #??????????????????
        pl.figure
        #?????
        a = []
        for h in self.weituo_historys:
            a.append(h.price)
        a = GuiYiHua(a)
        pl.plot(a, 'b')
        #???
        a = np.array(self.total_moneys)
        a = GuiYiHua(a)
        pl.plot(a, 'r')
        pl.legend(['price list', 'money list'])
        pl.show()
        pl.close()

    #???????????????, ??????????

def Unittest_Kline():
    """"""
    kline = Guider("600100", "")
    print(kline.getData(0).date, kline.getLastData().date)

    #kline.myprint()
    obv = kline.OBV()

    pl.figure
    pl.subplot(2,1,1)
    pl.plot(kline.getCloses())
    pl.subplot(2,1,2)
    ma,m2,m3 = kline.MACD()
    pl.plot(ma)
    pl.plot(m2,'r')
    left = np.arange(0, len(m3))
    pl.bar(left,m3)
    #pl.plot(obv, 'y')
    pl.show()


#Unittest_Kstp()    
#
#???????????
#----------------------------------------------------------------------

def visualize_reconstruction(xp, model, x, visualization_dir, epoch, gpu=False):
    x_variable = chainer.Variable(xp.asarray(x))
    _x = model.decode(model.encode(x_variable), test=True)
    _x.to_cpu()
    _x = _x.data

    fig = pylab.gcf()
    fig.set_size_inches(8.0, 8.0)
    pylab.clf()
    pylab.gray()
    for m in range(50):
        i = m / 10
        j = m % 10
        pylab.subplot(10, 10, 20 * i + j + 1, xticks=[], yticks=[])
        pylab.imshow(x[m].reshape((28, 28)), interpolation="none")
        pylab.subplot(10, 10, 20 * i + j + 10 + 1, xticks=[], yticks=[])
        pylab.imshow(_x[m].reshape((28, 28)), interpolation="none")
        # pylab.imshow(np.clip((_x_batch.data[m] + 1.0) / 2.0, 0.0, 1.0).reshape(
        # (config.img_channel, config.img_width, config.img_width)), interpolation="none")
        pylab.axis("off")
    pylab.savefig("{}/reconstruction_{}.png".format(visualization_dir, epoch))
    # pylab.show()

def view_waveforms_clusters(data, halo, threshold, templates, amps_lim, n_curves=200, save=False):

    nb_templates = templates.shape[1]
    n_panels     = numpy.ceil(numpy.sqrt(nb_templates))
    mask         = numpy.where(halo > -1)[0]
    clust_idx    = numpy.unique(halo[mask])
    fig          = pylab.figure()    
    square       = True
    center       = len(data[0] - 1)//2
    for count, i in enumerate(xrange(nb_templates)):
        if square:
            pylab.subplot(n_panels, n_panels, count + 1)
            if (numpy.mod(count, n_panels) != 0):
                pylab.setp(pylab.gca(), yticks=[])
            if (count < n_panels*(n_panels - 1)):
                pylab.setp(pylab.gca(), xticks=[])

        subcurves = numpy.where(halo == clust_idx[count])[0]
        for k in numpy.random.permutation(subcurves)[:n_curves]:
            pylab.plot(data[k], '0.5')

        pylab.plot(templates[:, count], 'r')        
        pylab.plot(amps_lim[count][0]*templates[:, count], 'b', alpha=0.5)
        pylab.plot(amps_lim[count][1]*templates[:, count], 'b', alpha=0.5)

        xmin, xmax = pylab.xlim()
        pylab.plot([xmin, xmax], [-threshold, -threshold], 'k--')
        pylab.plot([xmin, xmax], [threshold, threshold], 'k--')
        #pylab.ylim(-1.5*threshold, 1.5*threshold)
        ymin, ymax = pylab.ylim()
        pylab.plot([center, center], [ymin, ymax], 'k--')
        pylab.title('Cluster %d' %i)

    if nb_templates > 0:
        pylab.tight_layout()
    if save:
        pylab.savefig(os.path.join(save[0], 'waveforms_%s' %save[1]))
        pylab.close()
    else:
        pylab.show()
    del fig

def view_artefact(data, save=False):

    fig          = pylab.figure()    
    pylab.plot(data.T)
    if save:
        pylab.savefig(os.path.join(save[0], 'artefact_%s' %save[1]))
        pylab.close()
    else:
        pylab.show()
    del fig

def view_trigger_snippets(trigger_snippets, chans, save=None):
    # Create output directory if necessary.
    if os.path.exists(save):
        for f in os.listdir(save):
            p = os.path.join(save, f)
            os.remove(p)
        os.removedirs(save)
    os.makedirs(save)
    # Plot figures.
    fig = pylab.figure()
    for (c, chan) in enumerate(chans):
        ax = fig.add_subplot(1, 1, 1)
        for n in xrange(0, trigger_snippets.shape[2]):
            y = trigger_snippets[:, c, n]
            x = numpy.arange(- (y.size - 1) / 2, (y.size - 1) / 2 + 1)
            b = 0.5 + 0.5 * numpy.random.rand()
            ax.plot(x, y, color=(0.0, 0.0, b), linestyle='solid')
        y = numpy.mean(trigger_snippets[:, c, :], axis=1)
        x = numpy.arange(- (y.size - 1) / 2, (y.size - 1) / 2 + 1)
        ax.plot(x, y, color=(1.0, 0.0, 0.0), linestyle='solid')
        ax.grid(True)
        ax.set_xlim([numpy.amin(x), numpy.amax(x)])
        ax.set_title("Channel %d" %chan)
        ax.set_xlabel("time")
        ax.set_ylabel("amplitude")
        if save is not None:
            # Save plot.
            filename = "channel-%d.png" %chan
            path = os.path.join(save, filename)
            pylab.savefig(path)
        fig.clf()
    if save is None:
        pylab.show()
    else:
        pylab.close(fig)
    return

def view_mahalanobis_distribution(data_1, data_2, save=None):
    '''Plot Mahalanobis distribution Before and After'''
    fig = pylab.figure()
    ax = fig.add_subplot(1,2,1)
    if len(data_1) == 3:
        d_gt, d_ngt, d_noi = data_1
    elif len(data_1) == 2:
        d_gt, d_ngt = data_1
    if len(data_1) == 3:
        ax.hist(d_noi, bins=50, color='k', alpha=0.5, label="Noise")
    ax.hist(d_ngt, bins=50, color='b', alpha=0.5, label="Non GT")
    ax.hist(d_gt, bins=75, color='r', alpha=0.5, label="GT")
    ax.grid(True)
    ax.set_title("Before")
    ax.set_ylabel("")
    ax.set_xlabel('# Samples')
    ax.set_xlabel('Distances')

    if len(data_2) == 3:
        d_gt, d_ngt, d_noi = data_2
    elif len(data_2) == 2:
        d_gt, d_ngt = data_2
    ax = fig.add_subplot(1,2,2)
    if len(data_2) == 3:
        ax.hist(d_noi, bins=50, color='k', alpha=0.5, label="Noise")
    ax.hist(d_ngt, bins=50, color='b', alpha=0.5, label="Non GT")
    ax.hist(d_gt, bins=75, color='r', alpha=0.5, label="GT")
    ax.grid(True)
    ax.set_title("After")
    ax.set_ylabel("")
    ax.set_xlabel('Distances')


    ax.legend()
    if save is None:
        pylab.show()
    else:
        pylab.savefig(save)
        pylab.close(fig)
    return