Python pyplot.figure函数代码示例

    def show(self, rescale=True, ax=None):
        """Visualization of a design matrix

        Parameters
        ----------
        rescale: bool, optional
                 rescale columns magnitude for visualization or not
        ax: axis handle, optional
            Handle to axis onto which we will draw design matrix

        Returns
        -------
        ax: axis handle
        """
        import matplotlib.pyplot as plt

        # normalize the values per column for better visualization
        x = self.matrix.copy()
        if rescale:
            x = x / np.sqrt(np.sum(x ** 2, 0))
        if ax is None:
            plt.figure()
            ax = plt.subplot(1, 1, 1)

        ax.imshow(x, interpolation='Nearest', aspect='auto')
        ax.set_label('conditions')
        ax.set_ylabel('scan number')

        if self.names is not None:
            ax.set_xticks(range(len(self.names)))
            ax.set_xticklabels(self.names, rotation=60, ha='right')
        return ax

def draw_ranges_for_parameters(data, title='', save_path='./pictures/'):
  parameters = data.columns.values.tolist()

  # remove flight name parameter
  for idx, parameter in enumerate(parameters):
    if parameter == 'flight_name':
      del parameters[idx]

  flight_names = np.unique(data['flight_name'])

  print len(flight_names)

  for parameter in parameters:
    plt.figure()

    axis = plt.gca()

    # ax.set_xticks(numpy.arange(0,1,0.1))
    axis.set_yticks(flight_names)
    axis.tick_params(labelright=True)
    axis.set_ylim([94., 130.])
    plt.grid()

    plt.title(title)
    plt.xlabel(parameter)
    plt.ylabel('flight name')

    colors = iter(cm.rainbow(np.linspace(0, 1,len(flight_names))))

    for flight in flight_names:
      temp = data[data.flight_name == flight][parameter]

      plt.plot([np.min(temp), np.max(temp)], [flight, flight], c=next(colors), linewidth=2.0)
    plt.savefig(save_path+title+'_'+parameter+'.jpg')
    plt.close()

    def plot_predict_is(self,h=5,**kwargs):
        """ Plots forecasts with the estimated model against data
            (Simulated prediction with data)

        Parameters
        ----------
        h : int (default : 5)
            How many steps to forecast

        Returns
        ----------
        - Plot of the forecast against data 
        """     

        figsize = kwargs.get('figsize',(10,7))

        plt.figure(figsize=figsize)
        date_index = self.index[-h:]
        predictions = self.predict_is(h)
        data = self.data[-h:]

        t_params = self.transform_z()

        plt.plot(date_index,np.abs(data-t_params[-1]),label='Data')
        plt.plot(date_index,predictions,label='Predictions',c='black')
        plt.title(self.data_name)
        plt.legend(loc=2)   
        plt.show()          

def plot_scenario(strategies, names, scenario_id=1):
    probabilities = get_scenario(scenario_id)

    plt.figure(figsize=(6, 4.5))

    ax = plt.subplot(111)
    ax.spines["top"].set_visible(False)
    ax.spines["bottom"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.spines["left"].set_visible(False)

    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()

    plt.yticks(fontsize=14)
    plt.xticks(fontsize=14)
    plt.xlim((0, 1300))

    # Remove the tick marks; they are unnecessary with the tick lines we just plotted.
    plt.tick_params(axis="both", which="both", bottom="on", top="off",
                    labelbottom="on", left="off", right="off", labelleft="on")

    for rank, (strategy, name) in enumerate(zip(strategies, names)):
        plot_strategy(probabilities, strategy, name, rank)

    plt.title("Bandits: " + str(probabilities), fontweight='bold')
    plt.xlabel('Number of Trials', fontsize=14)
    plt.ylabel('Cumulative Regret', fontsize=14)
    plt.legend(names)
    plt.show()

def plotResults(datasetName, sampleSizes, foldsSet, cvScalings, sampleMethods, fileNameSuffix):
    """
    Plots the errors for a particular dataset on a bar graph. 
    """

    for k in range(len(sampleMethods)):
        outfileName = outputDir + datasetName + sampleMethods[k] + fileNameSuffix + ".npz"
        data = numpy.load(outfileName)

        errors = data["arr_0"]
        meanMeasures = numpy.mean(errors, 0)

        for i in range(sampleSizes.shape[0]):
            plt.figure(k*len(sampleMethods) + i)
            plt.title("n="+str(sampleSizes[i]) + " " + sampleMethods[k])

            for j in range(errors.shape[3]):
                plt.plot(foldsSet, meanMeasures[i, :, j])
                plt.xlabel("Folds")
                plt.ylabel('Error')

            labels = ["VFCV", "PenVF+"]
            labels.extend(["VFP s=" + str(x) for x in cvScalings])
            plt.legend(tuple(labels))
    plt.show()

  def test_get_obs(self):

    plt.figure()
    ant_sigs = antennas.antennas_signal(self.ants, self.ant_models, self.sources, self.rad.timebase)
    rad_sig_full = self.rad.sampled_signal(ant_sigs[0, :], 0)
    obs_full = self.rad.get_full_obs(ant_sigs, self.utc_date, self.config)

    ant_sigs_simp = antennas.antennas_simplified_signal(self.ants, self.ant_models, self.sources, self.rad.baseband_timebase, self.rad.int_freq)
    obs_simp = self.rad.get_simplified_obs(ant_sigs_simp, self.utc_date, self.config)


    freqs, spec_full_before_obs = spectrum.plotSpectrum(rad_sig_full, self.rad.ref_freq, label='full_before_obs_obj', c='blue')
    freqs, spec_full = spectrum.plotSpectrum(obs_full.get_antenna(1), self.rad.ref_freq, label='full', c='cyan')
    freqs, spec_simp = spectrum.plotSpectrum(obs_simp.get_antenna(1), self.rad.ref_freq, label='simp', c='red')
    plt.legend()

    self.assertTrue((spec_full_before_obs == spec_full).all(), True)


    plt.figure()
    plt.plot(freqs, (spec_simp-spec_full)/spec_full)
    plt.show()

    print len(obs_full.get_antenna(1)), obs_full.get_antenna(1).mean()
    print len(obs_simp.get_antenna(1)), obs_simp.get_antenna(1).mean()

def plotTestData(tree):
	plt.figure()
	plt.axis([0,1,0,1])
	plt.xlabel("X axis")
	plt.ylabel("Y axis")
	plt.title("Green: Class1, Red: Class2, Blue: Class3, Yellow: Class4")
	for value in class1:
		plt.plot(value[0],value[1],'go')
	plt.hold(True)
	for value in class2:
		plt.plot(value[0],value[1],'ro')
	plt.hold(True)
	for value in class3:
		plt.plot(value[0],value[1],'bo')
	plt.hold(True)
	for value in class4:
		plt.plot(value[0],value[1],'yo')
	plotRegion(tree)
	for value in classPlot1:
		plt.plot(value[0],value[1],'g.',ms=3.0)
	plt.hold(True)
	for value in classPlot2:
		plt.plot(value[0],value[1],'r.', ms=3.0)
	plt.hold(True)
	for value in classPlot3:
		plt.plot(value[0],value[1],'b.', ms=3.0)
	plt.hold(True)
	for value in classPlot4:
		plt.plot(value[0],value[1],'y.', ms=3.0)
	plt.grid(True)
	plt.show()

def regress_show4( yEv, yEv_calc, disp = True, graph = True, plt_title = None, ms_sz = None):

	# if the output is a vector and the original is a metrix, 
	# the output is translated to a matrix. 

	r_sqr, RMSE, MAE, DAE = estimate_accuracy4( yEv, yEv_calc, disp = disp)
	
	if graph:
		#plt.scatter( yEv.tolist(), yEv_calc.tolist())	
		plt.figure()	
		if ms_sz is None:
			ms_sz = max(min( 6000 / yEv.shape[0], 8), 3)
		# plt.plot( yEv.tolist(), yEv_calc.tolist(), '.', ms = ms_sz) # Change ms 
		plt.scatter( yEv.tolist(), yEv_calc.tolist(), s = ms_sz) 
		ax = plt.gca()
		lims = [
			np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes
			np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes
		]
		# now plot both limits against eachother
		#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
		ax.plot(lims, lims, '-', color = 'pink')
		plt.xlabel('Experiment')
		plt.ylabel('Prediction')
		if plt_title is None:
			plt.title( '$r^2$={0:.1e}, RMSE={1:.1e}, MAE={2:.1e}, MedAE={3:.1e}'.format( r_sqr, RMSE, MAE, DAE))
		elif plt_title != "": 
			plt.title( plt_title)
		# plt.show()
	
	return r_sqr, RMSE, MAE, DAE

def cv_show( yEv, yEv_calc, disp = True, graph = True, grid_std = None):

	# if the output is a vector and the original is a metrix, 
	# the output is translated to a matrix. 
	if len( np.shape(yEv_calc)) == 1:	
		yEv_calc = np.mat( yEv_calc).T
	if len( np.shape(yEv)) == 1:
		yEv = np.mat( yEv).T

	r_sqr, RMSE = jchem.estimate_accuracy( yEv, yEv_calc, disp = disp)
	if graph:
		#plt.scatter( yEv.tolist(), yEv_calc.tolist())	
		plt.figure()	
		ms_sz = max(min( 4000 / yEv.shape[0], 8), 1)
		plt.plot( yEv.tolist(), yEv_calc.tolist(), '.', ms = ms_sz) # Change ms 
		ax = plt.gca()
		lims = [
			np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes
			np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes
		]
		# now plot both limits against eachother
		#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
		ax.plot(lims, lims, '-', color = 'pink')
		plt.xlabel('Experiment')
		plt.ylabel('Prediction')
		if grid_std:
			plt.title( '($r^2$, std) = ({0:.2e}, {1:.2e}), RMSE = {2:.2e}'.format( r_sqr, grid_std, RMSE))
		else:
			plt.title( '$r^2$ = {0:.2e}, RMSE = {1:.2e}'.format( r_sqr, RMSE))
		plt.show()
	return r_sqr, RMSE

def make_fish(zoom=False):
    plt.close(1)
    plt.figure(1, figsize=(6, 4))
    plt.plot(plot_limits['pitch'], plot_limits['rolldev'], '-g', lw=3)
    plt.plot(plot_limits['pitch'], -plot_limits['rolldev'], '-g', lw=3)
    plt.plot(pitch.midvals, roll.midvals, '.b', ms=1, alpha=0.7)

    p, r = make_ellipse()  # pitch, off nominal roll
    plt.plot(p, r, '-c', lw=2)

    gf = -0.08  # Fudge on pitch value for illustrative purposes
    plt.plot(greta['pitch'] + gf, -greta['roll'], '.r', ms=1, alpha=0.7)
    plt.plot(greta['pitch'][-1] + gf, -greta['roll'][-1], 'xr', ms=10, mew=2)

    if zoom:
        plt.xlim(46.3, 56.1)
        plt.ylim(4.1, 7.3)
    else:
        plt.ylim(-22, 22)
        plt.xlim(40, 180)
    plt.xlabel('Sun pitch angle (deg)')
    plt.ylabel('Sun off-nominal roll angle (deg)')
    plt.title('Mission off-nominal roll vs. pitch (5 minute samples)')
    plt.grid()
    plt.tight_layout()
    plt.savefig('fish{}.png'.format('_zoom' if zoom else ''))

def make_entity_plot(filename, title, fixed_noip, fixed_ip, dynamic_noip, dynamic_ip):
    plt.figure(figsize=(12,5))

    plt.title("Settings comparison - " + title)
    
    plt.xlabel('Time (ms)', fontsize=12)
    plt.xlim([0,62000])

    x = 0
    barwidth = 0.5
    bargroupspacing = 1.5

    fixed_noip_mean,fixed_noip_conf = conf_stats(fixed_noip)
    fixed_ip_mean,fixed_ip_conf = conf_stats(fixed_ip)
    dynamic_noip_mean,dynamic_noip_conf = conf_stats(dynamic_noip)
    dynamic_ip_mean,dynamic_ip_conf = conf_stats(dynamic_ip)

    values = [fixed_noip_mean,fixed_ip_mean,dynamic_noip_mean, dynamic_ip_mean]
    errs = [fixed_noip_conf,fixed_ip_conf,dynamic_noip_conf, dynamic_ip_conf]

    y_pos = numpy.arange(len(values))
    plt.barh(y_pos, values, xerr=errs, align='center', color=['r', 'b', 'r', 'b'],  ecolor='black', alpha=0.7)
    plt.yticks(y_pos, ["Fixed | no I.P.", "Fixed | I.P.", "Dynamic | no I.P.", "Dynamic | I.P."])
    plt.savefig(output_file(filename))
    plt.clf()

def delta():     
    beta = 0.99
    N = 1000
    u = lambda c: np.sqrt(c)
    W = np.linspace(0,1,N)
    X, Y = np.meshgrid(W,W)
    Wdiff = (X-Y).T
    index = Wdiff <0
    Wdiff[index] = 0
    util_grid = u(Wdiff)
    util_grid[index] = -10**10
    
    Vprime = np.zeros((N,1))
    delta = np.ones(1)
    tol = 10**-9
    it = 0
    max_iter = 500
    
    while (delta[-1] >= tol) and (it < max_iter):
        V = Vprime
        it += 1;
        val = util_grid + beta*V.T
        Vprime = np.amax(val, axis = 1)
        Vprime = Vprime.reshape((N,1))
        delta = np.append(delta,np.dot((Vprime-V).T,Vprime-V))
        
    plt.figure()
    plt.plot(delta[1:])
    plt.ylabel(r'$\delta_k$')
    plt.xlabel('iteration')
    plt.savefig('convergence.pdf')
    plt.clf()

	def blue_sideband_thermal_tester():
		#BSB sideband = +1
		import matplotlib.pyplot as plt
		dataobj = ReadData('2014Jun19',experiment = 'RabiFlopping' )
		data = dataobj.get_data('1212_15')
		sideband = 1
		trap_freq =  2.57
		nbar_init= .3 #default 20.
		rabi = RabiFlop(data)
		rabi.setData(data)
		f_rabi = thermal_tester() #f_rabi is the same on same set of data
		print("f_rabi from thermal tester is :"+str(f_rabi))
		initial_guess = {'nbar':nbar_init, 'f_rabi':f_rabi,  #same as that of thermal tester
				     'delta':0.0, 'delta_fluctuations':0.,
				      'trap_freq':trap_freq, 'sideband':sideband, 'nmax':1000,
				      'angle': 50./360.*2*np.pi, 
				      'rabi_type':'thermal','eta': 0.04 #rabi.guess_eta()#0.05
				}
		fit_params = {}
		#Put it in the fit params format 
		for key in initial_guess.keys():
		    fit_params[key] = (False, False, initial_guess[key]) # fixed most of the parameters
		fit_params['nbar'] = (False, False, initial_guess['nbar'])
		fit_params['angle'] = (True, False, initial_guess['angle'])
		#fit_params['delta'] = (True, False, initial_guess['delta']) #we decided to fix delta
		rabi.setUserParameters(fit_params)
		x,y = rabi.fit()
		plt.figure()
		plt.plot(data[:,0], data[:,1],'o') # plotting raw data : excitation probability versus time
		plt.plot(x,y) #plotting the fit
		nbar = rabi.get_parameter_value('nbar') # rabi frequency in Hz
		angle= rabi.get_parameter_value('angle')
		print ('nbar: {}'.format(nbar))
		print ('{} radians = {} degrees'.format(str(angle), str(angle/(2*np.pi)*360)))

	def thermal_tester():
		import matplotlib.pyplot as plt
		dataobj = ReadData('2014Jun19',experiment = 'RabiFlopping' )
		car_data = dataobj.get_data('1219_57')
		sideband = 0
		trap_freq =  2.57
		nbar_init= 0.1 #default 20.
		carrier_rabi = RabiFlop(car_data)
		initial_guess = {'nbar':nbar_init, 'f_rabi':carrier_rabi.guess_f_rabi(),  #changing inital guess for rabi frequency to 1/2 max 
				     'delta':0., 'delta_fluctuations':0.,
				      'trap_freq':trap_freq, 'sideband':sideband, 'nmax':1000,
				      'angle':10./360.*2*np.pi  , 'rabi_type':'thermal'
				,'eta': 0.05 }
		fit_params = {}
		#Put it in the fit params format 
		for key in initial_guess.keys():
		    fit_params[key] = (False, False, initial_guess[key]) # fix most of the parameters
		fit_params['f_rabi'] = (True, False, initial_guess['f_rabi']) # fit for the rabi frequency
		carrier_rabi.setUserParameters(fit_params)
		x,y = carrier_rabi.fit()
		plt.figure()
		plt.plot(car_data[:,0], car_data[:,1],'o') # plotting raw data : excitation probability versus time
		plt.plot(x,y) #plotting the fit
		# Note: Only get_parameter_value returns the user_guess. Use get_parameter_info for autofit result. 
		f_rabi = carrier_rabi.get_parameter_info()['f_rabi'][2][2] #autofit result (rabi frequency in Hz)
		return f_rabi

def plot_data(kx,omega,F,F_R,F_L,K,O):
    #plt.figure(4)
    #plt.imshow(K,extent=[omega[0],omega[-1],kx[0],kx[-1]],\
    #        interpolation = "nearest", aspect = "auto")
    #plt.xlabel('KX')
    #plt.colorbar()
    
    #plt.figure(5)
    #plt.imshow(O,extent =[omega[0],omega[-1],kx[0],kx[-1]],interpolation="nearest", aspect="auto")
    #plt.xlabel('omega')
    #plt.colorbar()
    
    plt.figure(6)
    pylab.subplot(1,2,1)
    plt.imshow(abs(F_R), extent= [omega[0],omega[-1],kx[0],kx[-1]], interpolation= "nearest", aspect = "auto")
    plt.xlabel('abs FFT_R')
    plt.colorbar()
    plt.subplot(1,2,2)
    plt.imshow(abs(F_L), extent= [omega[0],omega[-1],kx[0],kx[-1]], interpolation= "nearest", aspect = "auto")
    plt.xlabel('abs FFT_L')
    plt.colorbar()
    
    
    plt.figure(7)
    plt.subplot(2,1,1)
    plt.imshow(abs(F_L+F_R),extent=[omega[0],omega[-1],kx[0],kx[-1]],interpolation= "nearest", aspect = "auto")
    plt.xlabel('abs(F_L+F_R)  reconstructed')
    plt.colorbar()
    pylab.subplot(2,1,2)
    plt.imshow(abs(F),extent=[omega[0],omega[-1],kx[0],kx[-1]],interpolation ="nearest",aspect = "auto")
    plt.xlabel('FFT of the original data')
    plt.colorbar()

    #plt.show()
    return