'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()
