'overplot':0*np.arange(ny/8-1)}) gamma_th = Frame(np.array(soln['gammamax'][1:ny/10]),meta={'dx':dky,'x0':dky,'stationary':True,'nt':gamma_exp.nt,'yscale':'linear','title':r'$\gamma_{linear}$','fontsz':28,'ylabel':r'$\frac{\gamma}{ \omega_{ci}}$','xlabel':r'$k_y \rho_s$','ticksize':28,'style':'--','linewidth':15}) #let's create single frame import matplotlib.ticker as ticker from matplotlib.ticker import FormatStrFormatter lin_formatter = ticker.ScalarFormatter() from pylab import legend lin_formatter.set_powerlimits((1, 1)) #plt.autoscale(axis='x',tight=True) #self.ax.axis('tight') #pp = PdfPages('gamma.pdf') fig = plt.figure() gamma_exp.ax = None gamma_exp.t = 100 gamma_th.ax = None gamma_exp.render(fig,111) gamma_th.render(fig,111) plt.tick_params(axis='both',direction='in',which='both',labelsize=20) print dir( gamma_th.ax.yaxis.get_offset_text()) gamma_th.ax.yaxis.get_offset_text().set_size(20) #exit() gamma_exp.ax.yaxis.set_major_formatter(lin_formatter) plt.setp(gamma_th.img, color='b', linewidth=5.0,alpha=.7) plt.setp(gamma_exp.img, color='r', linewidth=5.0,alpha=.7) gamma_exp.ax.xaxis.set_label_coords(.65, -0.05) plt.autoscale(axis='x',tight=True) print 'gamma.img: ',gamma_exp.img leg = plt.legend([gamma_exp.img,gamma_th.img],
# pmin = popt # print pmin #print pmin # popt, pcov= fit_lambda2(nave[0:xstop],pos[0][0:xstop,5], # p0=[nave[np.int(nx/2.0)],est_lam,np.int(nx/2.0)]) # #print 'min parameters: ',popt,res[0] # n_fit = popt[0]*np.exp(-pos[0][xstart:xstop,5]/popt[1]) # n_fit = Frame(n_fit,meta={'dx':dx,'x0':pos[0][xstart,5],'stationary':True}) frames= [frm_data1D,[frm_n_AC,a_contour],blobs_data1D,[frm_blob,dw_contour]] #frames= [frm_data1D,[frm_data,phi_contour],frm_log_data1D,frm_log_data] frm_n.t = 0 # frm_Ak.t = 0 # frm_Ak.reset() # frm_data.reset() # alpha_contour.reset() #FrameMovie([[frm_blob_AC,dw_contour]],fast=True,moviename=save_path+'/'+key+str(t2),fps = 10,encoder='ffmpeg') frm_n.t = 0 frm_Ak.t = 0 frm_Ak.reset() frm_n.reset() a_contour.reset() FrameMovie(frames,fast=True,moviename=save_path+'/'+key+str(t2),fps = 10,encoder='ffmpeg') #print time, n_fit.shape,popt,pcov,nave[0:40],popt frm_n.t = 0
import matplotlib.ticker as ticker from matplotlib.ticker import FormatStrFormatter lin_formatter = ticker.ScalarFormatter() from pylab import legend lin_formatter.set_powerlimits((1, 1)) #plt.autoscale(axis='x',tight=True) #self.ax.axis('tight') #let's create single frame pp = PdfPages('gamma.pdf') fg = plt.figure() gamma.ax = None gamma.t = nt-2 gamma_th.ax = None gamma.render(fig,111) gamma_th.render(fig,111) gamma.ax.yaxis.set_major_formatter(lin_formatter) plt.setp(gamma_th.img, color='b', linewidth=3.0,alpha=.7) plt.setp(gamma.img, color='r', linewidth=2.0,alpha=.7) plt.autoscale(axis='x',tight=True) print 'gamma.img: ',gamma.img leg = plt.legend([gamma.img,gamma_th.img],('BOUT++', 'analytic'), 'best', shadow=False, fancybox=True) leg.get_frame().set_alpha(0.6) fig.savefig(pp,format='pdf') plt.close(fig) pp.close()
gamma_ave = np.mean(gamma_num[-80:-20,:,:],axis=0) analytic_soln = gamma_theory(ny,dky) gamma_th = Frame(np.array(analytic_soln['gammamax'][1:ny/3]), meta={'dx':dky,'x0':dky,'stationary':True,'yscale':'linear', 'title':r'$\gamma$','fontsz':20, 'ylabel':r'$\frac{\omega}{\omega_{ci}}$', 'xlabel':r'$k_y$','ticksize':14}) gamma_num = Frame(gamma_num[:,1:ny/3,0],meta={'dx':dky,'xlabel':r'$k_y$', 'title':r'$\gamma$', 'ylabel':r'$\frac{\omega}{\omega_{ci}}$', 'x0':dky,'shareax':False,'style':'ro', 'stationary':False,'ticksize':14,'fontsz':20}) gamma_num.t = nt -.7*nt gamma_num.render(fig,223) gamma_th.render(fig,223) print data.shape namp = abs(data).max(1).max(1) namp = Frame(namp,meta={'ticksize':14}) namp.render(fig,224) fig.savefig(pp,format='pdf') plt.close(fig) pp.close() pp = PdfPages('gamma.pdf') fig = plt.figure()
#self.ax.axis('tight') #let's create single frame pp = PdfPages('lamda.pdf') fig = plt.figure() lam_history = Frame(lam,meta={'stationary':False,'title':'','fontsz':18,'ylabel':'','xlabel':r'$t$','ticksize':14}) lam_history.render(fig,111) fig.savefig(pp,format='pdf') plt.close(fig) pp.close() pp = PdfPages('gamma.pdf') fig = plt.figure() gamma.ax = None gamma.t = 1 gamma_th.ax = None gamma.render(fig,111) gamma_th.render(fig,111) gamma.ax.yaxis.set_major_formatter(lin_formatter) plt.setp(gamma_th.img, color='b', linewidth=3.0,alpha=.7) plt.setp(gamma.img, color='r', linewidth=2.0,alpha=.7) plt.autoscale(axis='x',tight=True) print 'gamma.img: ',gamma.img leg = plt.legend([gamma.img,gamma_th.img],('BOUT++', 'analytic'), 'best', shadow=False, fancybox=True) leg.get_frame().set_alpha(0.6) fig.savefig(pp,format='pdf') plt.close(fig) pp.close()