python类legend()的实例源码

def smooth_curve(curve_data,N_smooth,exp_max=-1,shift_0=0,fix_first_nonzero=False,plotit=False,t='x for plot'):
    """
    smoothens the curve data for plotting as good as possible while maintaining last and first value
    curve data => np.array, 1D that should be smoothened out
    N_smooth => number of points to smooth over (float)
    exp_max => adjust exponential behavior for average (0='normal' moving average)
    shift_0 => manually fix from where on smoothing is active, e.g. up till where no smootthing is applied
    fix_first_nonzero => if set to true, then automatically determines shift_0 to be where the first nonzero entry is
    plotit => plot results
    t => x-cooordinate for plot
    """
    a=curve_data
    N=N_smooth
    v=np.exp(np.linspace(exp_max, 0., N))
    v=v/v.sum()
    a_v=np.convolve(a,v,'same')
    if fix_first_nonzero==True:
        shift_0=np.nonzero(a != 0)[0][0]
    for n in range(0,len(v)):
        if n!=0:
            v=np.exp(np.linspace(exp_max, 0., n))
            v=v/v.sum()
            a_v[n+shift_0]=np.convolve(a,v,'same')[n+shift_0]
            a_v[len(a)-n-1]=np.convolve(a,v,'same')[len(a)-n-1]
        else:
            a_v[n+shift_0]=a[n+shift_0]
            for i in range(0,n+shift_0):
                a_v[i]=a[i]
            a_v[len(a)-n-1]=a[len(a)-n-1]
    if plotit:
        try:
            np.sin(t)
        except:
            t=np.linspace(0,len(curve_data),len(curve_data))
        import pylab as plt
        plt.plot(t,a,label='original data')
        plt.plot(t,a_v,label='smoothened')
        plt.legend(loc='best',fancybox=True)
        plt.title('curve smoothing')
        plt.show()
    return a_v

def plot_measurements_and_simulation_results(time_points, ydata, y_sim):

    pl.subplot2grid((4, 2), (0, 0))
    pl.plot(time_points, ydata[:,0], label = "measurements")
    pl.plot(time_points, y_sim[:,0], label = "simulation")
    pl.title("Measurement data compared to simulation results")
    pl.xlabel("t")
    pl.ylabel("X", rotation = 0, labelpad = 20)
    pl.legend(loc = "lower left")

    pl.subplot2grid((4, 2), (1, 0))
    pl.plot(time_points, ydata[:,1], label = "measurements")
    pl.plot(time_points, y_sim[:,1], label = "simulation")
    pl.xlabel("t")
    pl.ylabel("Y", rotation = 0, labelpad = 15)
    pl.legend("lower right")

    pl.subplot2grid((4, 2), (2, 0))
    pl.plot(time_points, ydata[:,2], label = "measurements")
    pl.plot(time_points, y_sim[:,2], label = "simulation")
    pl.xlabel("t")
    pl.ylabel(r"\phi", rotation = 0, labelpad = 15)
    pl.legend("lower left")

    pl.subplot2grid((4, 2), (3, 0))
    pl.plot(time_points, ydata[:,3], label = "measurements")
    pl.plot(time_points, y_sim[:,3], label = "simulation")
    pl.xlabel("t")
    pl.ylabel("v", rotation = 0, labelpad = 20)
    pl.legend("upperleft")

    pl.subplot2grid((4, 2), (0, 1), rowspan = 4)
    pl.plot(ydata[:,0], ydata[:, 1], label = "measurements")
    pl.plot(y_sim[:,0], y_sim[:,1], label = "estimations")
    pl.title("Measured race track compared to simulated results")
    pl.xlabel("X")
    pl.ylabel("Y", rotation = 0, labelpad = 20)
    pl.legend(loc = "upper left")
    pl.show()

def show_results(self):
        pl.plot(self.t, self.n_A1, 'b--', label='A1: Time Constant = 1')
        pl.plot(self.t, self.n_B1, 'g', label='B1: Time Constant = 2')
        pl.plot(self.t, self.n_A2, 'k--', label='A2: Time Constant = 1')
        pl.plot(self.t, self.n_B2, 'c', label='B2: Time Constant = 2')
        pl.plot(self.t, self.n_A3, 'm--', label='A3: Time Constant = 1')
        pl.plot(self.t, self.n_B3, 'y', label='B3: Time Constant = 2')
        pl.title('Double Decay Probelm - Nuclei with Different Time Constans')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='upper right', shadow=True, fontsize='small')

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A: Time Constant = 1')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B: Time Constant = 2')
        pl.title('Double Decay Probelm - Nuclei with Different Time Constans')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B')
        pl.title('Double Decay Probelm-Situation 1')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A: Time Constant = 2')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B: Time Constant = 1')
        pl.title('Double Decay Probelm - Nuclei with Different Time Constans')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A: Time Constant = 1')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B: Time Constant = 2')
        pl.title('Double Decay Probelm - Nuclei with Different Time Constans')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b', label='Number of Nuclei A')
        pl.plot(self.t, self.n_B, 'g+', label='Number of Nuclei B')
        pl.title('Double Decay Probelm-Situation 3')
        pl.xlim(0.0, 5.0)
        pl.ylim(25.0, 75.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A')
        pl.plot(self.t, self.n_B, 'b', label='Number of Nuclei B')
        pl.plot(self.t, self.n_A_true, 'g--', label='True Number of Nuclei A')
        pl.plot(self.t, self.n_B_true, 'g', label='True Number of Nuclei B')
        pl.title('Double Decay Probelm-Approximation Compared with True')
        pl.xlim(0.0, 2.5)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)
        pl.grid(True)

def show_results(self):
        pl.plot(self.t, self.n_A1, 'b--', label='A1')
        pl.plot(self.t, self.n_B1, 'g', label='B1')
        pl.plot(self.t, self.n_A2, 'r--', label='A2')
        pl.plot(self.t, self.n_B2, 'c', label='B2')
        pl.plot(self.t, self.n_A3, 'm', label='A3')
        pl.plot(self.t, self.n_B3, 'k+', label='B3')
        pl.title('Double Decay Probelm-Three Situations')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc="best", shadow=True, fontsize='small')

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A: Time Constant = 2')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B: Time Constant = 1')
        pl.title('Double Decay Probelm - Nuclei with Different Time Constans')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A')
        pl.plot(self.t, self.n_B, 'b', label='Number of Nuclei B')
        pl.plot(self.t, self.n_A_true, 'g--', label='True Number of Nuclei A')
        pl.plot(self.t, self.n_B_true, 'g', label='True Number of Nuclei B')
        pl.title('Double Decay Probelm-Approximation Compared with True')
        pl.xlim(0.0, 2.5)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)
        pl.grid(True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A')
        pl.plot(self.t, self.n_B, 'b', label='Number of Nuclei B')
        pl.plot(self.t, self.n_A_true, 'g--', label='True Number of Nuclei A')
        pl.plot(self.t, self.n_B_true, 'g', label='True Number of Nuclei B')
        pl.title('Double Decay Probelm-Approximation Compared with True')
        pl.xlim(0.0, 2.5)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)
        pl.grid(True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B')
        pl.title('Double Decay Probelm-Situation 2')
        pl.xlim(0.0, 5.0)
        pl.ylim(10.0, 90.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show(self):
        pl.plot(self.t, self.theta, label = '$F_D =$' + str(self.F_D))
        pl.xlim(0, 100)
        pl.ylim(-4, 4)
        pl.xlabel('time ($s$)')
        pl.ylabel('$\\theta$ (radians)')
        pl.legend()
#        pl.text(32, 2, '$\\theta$ versus time $F_D =$' + str(self.F_D))

#pl.subplot(311)
#r1 = routes_to_chaos(amplitude = 1.35)
#r1.calculate()
#r1.show()
#pl.subplot(312)
#r2 = routes_to_chaos(amplitude = 1.44)
#r2.calculate()
#r2.show()
#pl.subplot(313)
#r3 = routes_to_chaos(amplitude = 1.465)
#r3.calculate()
#r3.show()
#pl.show()

#r= routes_to_chaos(amplitude = 1.465)
#r.calculate()
#r.show()

def DrawTs(pl, ts=[], lines = None, title="", high=[], low=[],mid=[], save_file=False,legends=None):
    """?????, ts: closes, save_file: ?????????"""
    pl.figure
    legend = []
    if len(ts)>0:
        pl.plot(ts)
        legend.append('ts')
    if len(high)>0:
        pl.plot(high)
        legend.append('high')
    if len(low)>0:
        pl.plot(low)
        legend.append('low')
    if len(mid)>0:
        pl.plot(mid)
        legend.append('mid')

    prop = fm.FontProperties(fname="c:/windows/fonts/simsun.ttc")
    if title != "":
        pl.title(title, fontproperties=prop)
    if lines != None:
        i = lines
        if i>=len(ts):
            i = len(ts)-1
        pl.plot([i,i], [ts[i]-ts[i]*0.1, ts[i]+ts[i]*0.1], 'g')
        legend.append('lines')
    if legends is not None:
        legend = legends
    pl.legend(legend, loc='upper left')
    if save_file:
        fname = 't3.png'
        pl.savefig(fname)
        return fname
    pl.show()
    pl.close()

def DrawClosesAndVolumes(pl, closes, volumes, zz=None, avg=None, trade_index=None,\
                         title=None, closes_dp=None, closes_bankuai=None):
    """?closes??df???
    closes_dp: ??
    closes_bankuai: ??
    """
    legend = []
    pl.figure
    pl.subplot(211)
    if title != None:
        pl.title(title, fontproperties=getFont())
    pl.plot(closes)
    legend.append('close')
    if zz != None:
        DrawZZ(pl, zz, c='r')
    if avg != None:
        pl.plot(avg)
    if not agl.IsNone(closes_dp):
        pl.plot(closes_dp)
        legend.append('dapan')
    if not agl.IsNone(closes_bankuai):
        pl.plot(closes_bankuai)
        legend.append('bankuai')
    if trade_index != None:
        pl, index, ts = pl, trade_index, closes
        _DrawVLine(pl, index, ts)   
    pl.legend(legend, loc='upper left')
    pl.subplot(212)
    pl.plot(volumes)
    pl.show()
    pl.close()

def legend(self, *args, **kwargs):
        pl.legend(*args, **kwargs)

def plot_checked(self):
        import pylab as pl
        pl.ioff()
        from statistics import expectation
        exp = []

        # The expectation plotter
        if len(self._stored_t) != 0:
            pl.figure(2)
            pl.title(" Method %s"%("OFSP"))
            pl.xlabel("Time, t")
            pl.ylabel("Expectation")

            for i in range(len(self._stored_t)):
                exp.append(expectation((self._stored_domain_states[i],self._stored_p[i])))

            EXP = np.array(exp).T

            for i in range(EXP.shape[0]):
                pl.plot(self._stored_t,EXP[i,:],'x-',label=self.model.species[i])
            pl.legend()

        # The probability plotter
        if len(self._probed_t) != 0:
            pl.figure(3)
            pl.title(" Method %s | Probing States over Time "%("OFSP"))
            pl.xlabel("Time, t")
            pl.ylabel("Probability")

            probs = np.array(self._probed_probs).T

            for i in range(probs.shape[0]):
                pl.plot(self._probed_t,probs[i,:],'x-',label=str(self._probed_states[0][:,i]))
            pl.legend()

        pl.show()

def plot_checked(self):
        """
        plot_checked plots the expectations of the data check pointed.
        """
        import pylab as pl
        pl.ioff()

        if len(self._stored_t) != 0:
            pl.figure(2)
            pl.title(" Method %s"%(self.model_name))
            pl.xlabel("Time,t")
            pl.ylabel("Expectation")

            exp = []

            for i in range(len(self._stored_t)):
                exp.append(np.sum(np.multiply(self._stored_X[i],self._stored_w[i]),axis=1))

            EXP = np.array(exp).T


            for i in range(EXP.shape[0]):
                pl.plot(self._stored_t,EXP[i,:],'x-',label=self.model.species[i])

            pl.legend()

        # The probability plotter
        if len(self._probed_t) != 0:
            pl.figure(3)
            pl.title(" Method %s | Probing States over Time "%(self.model_name))
            pl.xlabel("Time, t")
            pl.ylabel("Marginal Probability")

            probs = np.array(self._probed_probs).T

            for i in range(probs.shape[0]):
                pl.plot(self._probed_t,probs[i,:],'x-',label=str(self._probed_states[0][self.stoc_vector,i]))
            pl.legend()
        pl.show()