def a_centripetal2(model=None, s=None, tau=None, r=3, centre='rho', z=None): ''' Computes centripetal acceleration from rotation around column s (s is given by the "snake" module). TODO: from 3D to 2D ''' if not model: model=_model if z != None: to2d=True else: to2d=False if tau == None: tau=1. if s == None: tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int) tau1=int(round(np.mean(tau1_level))) s=snake.snake_from_box(model.z.rho,radius=r,start=tau1) if z == 'tau': z=tau1 X, Y, Z = meshgrid(model) v2 = model.z.v1**2+model.z.v2**2+model.z.v3**2 aX = np.zeros_like(v2) aY = np.zeros_like(v2) pint = np.array(s, dtype=int) for p0 in pint: if to2d and p0[2] != z: continue if centre == 'rho': pX, pY, pZ = model.z.xc1[p0[0],0,0], model.z.xc2[0,p0[1],0], model.z.xc3[0,0,p0[2]] else: pX, pY, pZ = model.z.xc1[centre[0],0,0], model.z.xc2[0,centre[1],0], model.z.xc3[0,0,p0[2]] dX, dY = X[:,:,p0[2]]-pX, Y[:,:,p0[2]]-pY r2 = dX**2+dY**2 if centre == 'rho': r2[p0[0],p0[1]] = 1.e-10 else: r2[centre[0],centre[1]] = 1.e-10 aX[:,:,p0[2]] = -v2[:,:,p0[2]]/r2*dX aY[:,:,p0[2]] = -v2[:,:,p0[2]]/r2*dY if to2d: return aX[:,:,z], aY[:,:,z] else: return aX, aY
def advection2(vec, model=None, dq=None, tau=None, z=None): ''' Computes advection of vec. ''' if not model: model=_model if dq == None: dq=bool(_parfile) if dq: tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int) tau1=int(round(np.mean(tau1_level))) else: tau1=int(np.size(model.z.xc3)/2.) dim = len(vec) if dim == 3 and tau != None: to2d=True else: to2d=False if tau == None: tau=1. if to2d: v1,v2,v3 = model.z.v1[:,:,tau1],model.z.v2[:,:,tau1],model.z.v3[:,:,tau1] else: v1,v2,v3 = model.z.v1,model.z.v2,model.z.v3 dvXdX,dvXdY,dvXdZ = gradient(vec[0], model, dq, tau, z) dvYdX,dvYdY,dvYdZ = gradient(vec[1], model, dq, tau, z) if dim == 3: dvZdX,dvZdY,dvZdZ = gradient(vec[2], model, dq, tau, z) else: dvZdX = 0. dvZdY = 0. dvZdZ = 0. advX = v1*dvXdX+v2*dvXdY+v3*dvXdZ advY = v1*dvYdX+v2*dvYdY+v3*dvYdZ advZ = v1*dvZdX+v2*dvZdY+v3*dvZdZ return (advX, advY, advZ)
def a_breaking_ratio(model=None, s=None, dq=None, odb=None, tau=1., z=None, r=3, full_advection=False, only_boundary=True, centre='rho', advect=False): ''' Computes the "breaking ratio" of gradient p to centripetal acceleration: (a_g-a_c)/a_c. For an ideal cylindrically symetric field rotating around a given centre, this ratio is zero. If full_advection is True, replaces a_c by advection term. ''' if not model: model=_model if dq == None: dq=bool(_parfile) if dq: tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int) tau1=int(round(np.mean(tau1_level))) tau1min=int(round(np.min(tau1_level))) else: tau1=int(np.size(model.z.xc3)/2.) if s == None: s=snake.snake_from_box(model.z.rho,radius=r,start=tau1) if z == None or z == 'tau': pass if z == 'mintau': tau1 = tau1min if z == 'halftau': tau1 = int(round(.5*(tau1min+tau1))) tau1_sindex = int(list(s[:,2]).index(tau1)) xmean = int(s[tau1_sindex,0]) ymean = int(s[tau1_sindex,1]) if not odb: odb=snake.select_disk(model.z.rho[:,:,tau1],(xmean,ymean)) o, d, b = odb a_g = a_gradient((xmean,ymean), model,tau1) if not advect: a_c = a_centripetal((xmean,ymean),model,tau1) #a_c = (a_c[0][:,:,tau1], a_c[1][:,:,tau1]) a_c = np.sqrt(a_c[0]**2+a_c[1]**2) #a_g = (a_g[0][:,:,tau1], a_g[1][:,:,tau1]) else: a_c1 = advection(model.z.v1,model,tau1) a_c2 = advection(model.z.v1,model,tau1) a_c3 = advection(model.z.v1,model,tau1) a_c = np.sqrt(a_c1**2+a_c2**2+a_c3**2) #a_g = (a_g[0][:,:,tau1], a_g[1][:,:,tau1], a_g[2][:,:,tau1]) a_g = np.sqrt(a_g[0]**2+a_g[1]**2) if only_boundary: return np.sqrt(np.mean(((a_g[d]-a_c[d])/a_c[d])**2)),np.max(np.abs((a_g[d]-a_c[d])/a_c[d])) else: o_star = np.zeros_like(a_c,dtype=bool) o_star[o] = True o_star[xmean,ymean] = False o_star=np.where(o_star) return np.sqrt(np.mean(((a_g[o_star]-a_c[o_star])/np.mean(a_c[o_star]))**2)),np.max(np.abs((a_g[o_star]-a_c[o_star])/np.mean(a_c[o_star])))
def wilson_depression(model=None,s=None,odb=None,tau=1.,r=3): ''' Computes the Wilson depression. ''' if not model: model=_model tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int) tau1=int(round(np.mean(tau1_level))) if s is None: s=snake.snake_from_box(model.z.rho,radius=r,start=tau1) tau1_sindex = int(list(s[:,2]).index(tau1)) xmean = int(s[tau1_sindex,0]) ymean = int(s[tau1_sindex,1]) if not odb: o,d,b=snake.select_disk(model.z.rho[:,:,tau1],(xmean,ymean)) else: o,d,b=odb min_tau1=int(np.min(tau1_level[o])) extended_b = np.ones_like(model.z.rho[:,:,0],dtype=bool) extended_b[o] = False extended_b = np.where(extended_b) mean_tau1=int(round(np.mean(tau1_level[extended_b]))) return model.z.xc3[0,0,mean_tau1]-model.z.xc3[0,0,min_tau1]
def contrast(arr,model=None,s=None,odb=None,tau=1.,r=3):
'''
Computes local and global contrast of arr; (arr_i-<arr>)/<arr>.
<arr> is taken in the local neighbourhood and on the whole z=cst
slice, at <tau>=tau (=1 by default).
'''
if not model: model=_model
tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int)
tau1=int(round(np.mean(tau1_level)))
if s is None: s=snake.snake_from_box(model.z.rho,radius=r,start=tau1)
tau1_sindex = int(list(s[:,2]).index(tau1))
xmean = int(s[tau1_sindex,0])
ymean = int(s[tau1_sindex,1])
if not odb: o,d,b=snake.select_disk(model.z.rho[:,:,tau1],(xmean,ymean))
else: o,d,b=odb
arr_mean=np.mean(arr[:,:,tau1])
arr_bmean=np.mean(arr[:,:,tau1][b])
contrast_global = (arr[xmean,ymean,tau1]-arr_mean)/arr_mean
contrast_local = (arr[xmean,ymean,tau1]-arr_bmean)/arr_bmean
return contrast_global, contrast_local
def report(ireport=None, model=None, s=None, dq=None, odb=None, tau=1., z=None, r=3, savefig=False, show=True):
if not model: model=_model
if dq == None: dq=bool(_parfile)
if dq:
tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int)
tau1=int(round(np.mean(tau1_level)))
tau1min=int(round(np.min(tau1_level)))
else: tau1=int(np.size(model.z.xc3)/2.)
if s == None: s=snake.snake_from_box(model.z.rho,radius=r,start=tau1)
if z == None or z == 'tau': pass
if z == 'mintau': tau1 = tau1min
if z == 'halftau': tau1 = int(round(.5*(tau1min+tau1)))
tau1_sindex = int(list(s[:,2]).index(tau1))
xmean = int(s[tau1_sindex,0])
ymean = int(s[tau1_sindex,1])
print(str(xmean)+', '+str(ymean))
print(str(np.unravel_index(np.argmin(model.z.v1[46:56,39:50,tau1]**2+model.z.v2[46:56,39:50,tau1]**2),(10,11))))
if not odb: odb=snake.select_disk(model.z.rho[:,:,tau1],(xmean,ymean),threshold=.5)
rot_index = np.argmin(model.z.v1[:,:,tau1][odb[0]]**2)
xrot,yrot = odb[0][0][rot_index], odb[0][1][rot_index]
print(str(xrot)+', '+str(yrot))
#
#if not odb: odb=snake.select_disk(tau1_level,(xmean,ymean),mask_flag=False,threshold=.8)
plt.imshow(model.z.rho[:,:,tau1],origin='bottom')
oring=np.zeros_like(model.z.rho[:,:,tau1],dtype=bool)
oring[odb[0]]=True
plt.imshow(oring,origin='bottom',alpha=.7,interpolation='none')
plt.quiver(model.z.v2[:,:,tau1],model.z.v1[:,:,tau1])
#odb2=snake.select_disk(model.z.rho[:,:,tau1],(xmean,ymean),threshold=.8)
#plt.figure()
#plt.imshow(model.z.rho[:,:,tau1],origin='bottom')
#oring=np.zeros_like(model.z.rho[:,:,tau1],dtype=bool)
#oring[odb2[0]]=True
#plt.imshow(oring,origin='bottom',alpha=.7,interpolation='none')
#plt.quiver(model.z.v2[:,:,tau1],model.z.v1[:,:,tau1])
#plt.figure()
#plt.imshow(model.z.v2[:,:,tau1]**2+model.z.v1[:,:,tau1]**2,origin='bottom',interpolation='none',cmap=cm.gray)
#
fmt='{0: <20}'
if _modelfile: print(fmt.format('File:')+_modelfile)
else: print(fmt.format('File:')+'Unknown')
wd=wilson_depression(model,s,odb,tau,r)
print(fmt.format('Wilson depression:')+str(wd))
#c_rho=contrast(model.z.rho,model,s,odb,tau,r)
c_rho=contrastf(model.z.rho[:,:,tau1],odb,np.min)
print(fmt.format('Density contrast:')+str(c_rho))
#c_p=contrast(model.dq.P,model,s,odb,tau,r)
c_p=contrastf(model.dq.P[:,:,tau1],odb,np.min)
print(fmt.format('Pressure contrast:')+str(c_p))
a_ratio=a_breaking_ratio(model,s,dq,odb,tau,z,r,centre='rho')
print(fmt.format('Acceleration ratio:')+str(a_ratio))
adv_ratio=a_breaking_ratio(model,s,dq,odb,tau,z,r,centre='rho',advect=True)
print(fmt.format('Advection ratio::')+str(adv_ratio))
if _modelfile:
title = _modelfile.split('.')
title = '.'.join(title[:max(1,len(title)-1)])
if ireport and _modelfile:
id_name = title.split('nMBP')[-1]
with open(ireport) as f:
lines = f.readlines()
names = [l.split('\t')[0] for l in lines]
lines = [[f.strip() for f in l.split('\t')] for l in lines[1:]]
idx = int(names.index(id_name))+1
diam = float(lines[idx][1])
c_I = (float(lines[idx][2]),float(lines[idx][3]))
time = float(lines[idx][4])
print(fmt.format('Diametre:')+str(diam))
print(fmt.format('Intensity contrast:')+str(c_I))
print(fmt.format('Lifetime:')+str(time))
boxtext = 'Wilson depression: '+str(int(round(wd/1.e5)))+' [km]\n'
boxtext+= 'Density contrast: '+str(int(round(100*c_rho[0])))+'%, '
boxtext+= str(int(round(100*c_rho[1])))+'%\n'
boxtext+= 'Pressure contrast: '+str(int(round(100*c_p[0])))+'%, '
boxtext+= str(int(round(100*c_p[1])))+'%'
#boxtext+= 'Acceleration ratio: '+str(int(round(100*a_ratio[0])))+'%, '
#boxtext+= str(int(round(100*a_ratio[1])))+'%'
if ireport and _modelfile:
delta = model.z.xc1[1,:,:]-model.z.xc1[0,:,:]
boxtext+= '\nDiametre: '+str(int(round(diam*delta/1.e5)))+' [km]'
boxtext+= '\nIntensity contrast: '+str(int(round(100*c_I[0])))+'%, '
boxtext+= str(int(round(100*c_I[1])))+'%'
boxtext+= '\nLifetime: '+str(int(round(time)))+' [s]'
if savefig:
sY,sX=plotv_slice(model.z.rho,model,s,dq,tau,r,boxtext,show=False)
if savefig == True: plt.savefig(title+'.png')
else: plt.savefig(savefig)
if show:
plt.show()
plt.close()
else:
sY,sX=plotv_slice(model.z.rho,model,s,dq,tau,r,boxtext,show=show)
plt.close()
def plotv_slice(arr,model=None,s=None,dq=None,tau=1.,r=3,boxtext=None,show=True,rf=10,tight=True):
'''
Plots vertical slices of arr at y=cst and x=cst respectively.
Depending on optional parameters, also plots tau=tau (=1 by
default) isosurface. Marks in red the "eye" of the nMBP.
'''
if not model: model=_model
if dq == None: dq=bool(_parfile)
if dq:
tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int)
tau1=int(round(np.mean(tau1_level)))
else: tau1=int(np.size(model.z.xc3)/2.)
if s is None: s=snake.snake_from_box(model.z.rho,radius=r,start=tau1)
sx, sy, sz = np.shape(arr)
x, y, z = np.array(s, dtype=int).T
xc, yc, zc = model.z.xc1[:,0,0]/1.e5, model.z.xc2[0,:,0]/1.e5, model.z.xc3[0,0,:]/1.e5
if dq:
tau1_sindex = int(list(s[:,2]).index(tau1))
xmean = int(s[tau1_sindex,0])
ymean = int(s[tau1_sindex,1])
else:
xmean = int(round(np.mean(x)))
ymean = int(round(np.mean(y)))
tau1 = 0
sharey = False
if tight: z0 = z
else:
if dq:
xp,yp,z1p,z2p = xc, yc, zc[tau1_level[:,ymean]]-zc[tau1],zc[tau1_level[xmean,:]]-zc[tau1]
z0min = z[0]
z0max = z[-1]+1
z0min = min(z0min,np.min(tau1_level[:,ymean]))
z0max = max(z0max,1+np.max(tau1_level[:,ymean]))
z0min = min(z0min,np.min(tau1_level[xmean,:]))
z0max = max(z0max,1+np.max(tau1_level[ymean,:]))
delta = z0max - z0min
z0min = max(0, int(z0min-.1*delta))
z0max = min(sz, int(z0max+.1*delta))
z0 = np.arange(z0min, z0max)
sharey=True
else:
z0 = range(sz)
sliceY=arr[np.ix_(range(sx),[ymean],z0)][:,0,:].T
sliceX=arr[np.ix_([xmean],range(sy),z0)][0,:,:].T
vY=model.z.v2[np.ix_(range(sx),[ymean],z0)][:,0,:].T
vX=model.z.v1[np.ix_([xmean],range(sy),z0)][0,:,:].T
if rf > 1: # Refine factor
vY = refine(vY,8)
vX = refine(vX,8)
vY=(vY>=0.)
vX=(vX>=0.)
extY=np.array([xc[0],xc[sx-1],zc[z0[0]]-zc[tau1],zc[z0[-1]]-zc[tau1]])
extX=np.array([yc[0],yc[sy-1],zc[z0[0]]-zc[tau1],zc[z0[-1]]-zc[tau1]])
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,5), sharey=sharey)
ax1.set_aspect('equal')
ax2.set_aspect('equal')
plt.tight_layout(pad=4)
if _modelfile:
title = _modelfile.split('.')
title = '.'.join(title[:max(1,len(title)-1)])
plt.suptitle(title,fontsize=16)
vmin = min(np.min(sliceY), np.min(sliceX))
vmax = max(np.max(sliceY), np.max(sliceX))
im1 = ax1.imshow(sliceY, origin='bottom',alpha=1.,cmap=None, extent=extY, vmin=vmin, vmax=vmax)
im2 = ax2.imshow(sliceX, origin='bottom',alpha=1.,cmap=None, extent=extX, vmin=vmin, vmax=vmax)
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.83, 0.15, 0.02, 0.69])
cbar = f.colorbar(im2, cax=cbar_ax)
cbar.set_label('Density [g$\,$cm$^{-3}$]', rotation=270, labelpad=20)
ax1.imshow(-vY, origin='bottom',alpha=.4,cmap=cm.gray, extent=extY)
ax2.imshow(vX, origin='bottom',alpha=.4,cmap=cm.gray, extent=extX)
xp,yp,zp = xc[x],yc[y],zc[z]-zc[tau1]
if rf > 1:
xp,yp,zp = refine(xp,rf), refine(yp,rf), refine(zp,rf)
xp = savitzky_golay(xp, rf**2+1, 3)
yp = savitzky_golay(yp, rf**2+1, 3)
zp = savitzky_golay(zp, rf**2+1, 3)
ax1.plot(xp,zp,'r')
ax2.plot(yp,zp,'r')
#ax1.plot(xc[x],zc[z]-zc[tau1],'r')
#ax2.plot(yc[y],zc[z]-zc[tau1],'r')
if dq:
xp,yp,z1p,z2p = xc, yc, zc[tau1_level[:,ymean]]-zc[tau1],zc[tau1_level[xmean,:]]-zc[tau1]
if rf > 1:
xp,yp,z1p,z2p = refine(xp,rf), refine(yp,rf), refine(z1p,rf), refine(z2p,rf)
xp = savitzky_golay(xp, rf*(rf-1)+1, 3)
yp = savitzky_golay(yp, rf*(rf-1)+1, 3)
z1p = savitzky_golay(z1p, rf*(rf-1)+1, 3)
z2p = savitzky_golay(z2p, rf*(rf-1)+1, 3)
ax1.plot(xp,z1p,'b')
ax2.plot(yp,z2p,'b')
# ax1.plot(xc,zc[tau1_level[:,ymean]]-zc[tau1],'b')
# ax2.plot(yc,zc[tau1_level[xmean,:]]-zc[tau1],'b')
ax1.set_xlim((extY[0],extY[1]))
ax1.set_ylim((extY[2],extY[3]))
ax2.set_xlim((extX[0],extX[1]))
ax2.set_ylim((extX[2],extX[3]))
ax1.set_xlabel("Spatial horizontal position (X axis) [km]")
ax2.set_xlabel("Spatial horizontal position (Y axis) [km]")
ax1.set_ylabel("Height [km]")
if boxtext:
props = dict(boxstyle='round, pad=.7, rounding_size=.2', facecolor='white', edgecolor='black', alpha=.8)
ax1.text(0.05, 0.9, boxtext, size='smaller', ha='left', va='top', transform=ax1.transAxes, bbox=props)
if show: plt.show()
return sliceY, sliceX
def gradient2(arr, model=None, dq=None, tau=None, z=None): ''' Computes gradient of arr from a nMBP. ''' if not model: model=_model dim = len(np.shape(arr)) if dim == 3 and tau != None: to2d=True else: to2d=False if tau == None: tau=1. if dq == None: dq=bool(_parfile) if dq: tau1_level=np.array(pybold.level(model.dq.tau,tau),dtype=int) tau1=int(round(np.mean(tau1_level))) else: tau1=int(np.size(model.z.xc3)/2.) if z != None: tau1=z if to2d: gradX = np.empty_like(arr[:,:,tau1]) gradY = np.empty_like(arr[:,:,tau1]) if dim == 3: gradZ = np.empty_like(arr[:,:,tau1]) else: gradX = np.empty_like(arr) gradY = np.empty_like(arr) if dim == 3: gradZ = np.empty_like(arr) [[m1,m2,m3],[n1,n2,n3]] = model.z.dimension X, Y, Z = meshgrid(model) #if dim == 3: X, Y, Z = meshgrid(model) #else: X, Y = meshgrid2D(model) if to2d: gradX[1:-1,:] = (arr[2:,:,tau1]-arr[:-2,:,tau1])/(X[2:,:,tau1]-X[:-2,:,tau1]) gradY[:,1:-1] = (arr[:,2:,tau1]-arr[:,:-2,tau1])/(Y[:,2:,tau1]-Y[:,:-2,tau1]) if tau1>0 and tau1<n3-m3: gradZ[:,:] = (arr[:,:,tau1+1]-arr[:,:,tau1-1])/(Z[:,:,tau1+1]-Z[:,:,tau1-1]) elif tau1==0: gradZ[:,:] = arr[:,:,1]+(arr[:,:,1]-arr[:,:,2])/(Z[:,:,1]-Z[:,:,2])*(Z[:,:,0]-Z[:,:,1]) elif tau1==n3-m3: gradZ[:,:] = arr[:,:,n3-m3-1]+(arr[:,:,n3-m3-1]-arr[:,:,n3-m3-2])/(Z[:,:,n3-m3-1]-Z[:,:,n3-m3-2])*(Z[:,:,n3-m3]-Z[:,:,n3-m3-1]) gradX[0,:] = arr[1,:,tau1]+(arr[1,:,tau1]-arr[2,:,tau1])/(X[1,:,tau1]-X[2,:,tau1])*(X[0,:,tau1]-X[1,:,tau1]) gradX[-1,:] = arr[-2,:,tau1]+(arr[-2,:,tau1]-arr[-3,:,tau1])/(X[-2,:,tau1]-X[-3,:,tau1])*(X[-1,:,tau1]-X[-2,:,tau1]) gradY[:,0] = arr[:,1,tau1]+(arr[:,1,tau1]-arr[:,2,tau1])/(Y[:,1,tau1]-Y[:,2,tau1])*(Y[:,0,tau1]-Y[:,1,tau1]) gradY[:,-1] = arr[:,-2,tau1]+(arr[:,-2,tau1]-arr[:,-3,tau1])/(Y[:,-2,tau1]-Y[:,-3,tau1])*(Y[:,-1,tau1]-Y[:,-2,tau1]) return (gradX, gradY, gradZ) elif dim == 3: gradX[1:-1,:,:] = (arr[2:,:,:]-arr[:-2,:,:])/(X[2:,:,:]-X[:-2,:,:]) gradY[:,1:-1,:] = (arr[:,2:,:]-arr[:,:-2,:])/(Y[:,2:,:]-Y[:,:-2,:]) gradZ[:,:,1:-1] = (arr[:,:,2:]-arr[:,:,:-2])/(Z[:,:,2:]-Z[:,:,:-2]) gradX[0,:,:] = gradX[1,:,:]+(gradX[1,:,:]-gradX[2,:,:])/(X[1,:,:]-X[2,:,:])*(X[0,:,:]-X[1,:,:]) gradX[-1,:,:] = gradX[-2,:,:]+(gradX[-2,:,:]-gradX[-3,:,:])/(X[-2,:,:]-X[-3,:,:])*(X[-1,:,:]-X[-2,:,:]) gradY[:,0,:] = gradY[:,1,:]+(gradY[:,1,:]-gradY[:,2,:])/(Y[:,1,:]-Y[:,2,:])*(Y[:,0,:]-Y[:,1,:]) gradY[:,-1,:] = gradY[:,-2,:]+(gradY[:,-2,:]-gradY[:,-3,:])/(Y[:,-2,:]-Y[:,-3,:])*(Y[:,-1,:]-Y[:,-2,:]) gradZ[:,:,0] = gradZ[:,:,1]+(gradZ[:,:,1]-gradZ[:,:,2])/(Z[:,:,1]-Z[:,:,2])*(Z[:,:,0]-Z[:,:,1]) gradZ[:,:,-1] = gradZ[:,:,-2]+(gradZ[:,:,-2]-gradZ[:,:,-3])/(Z[:,:,-2]-Z[:,:,-3])*(Z[:,:,-1]-Z[:,:,-2]) return (gradX, gradY, gradZ) else: gradX[1:-1,:] = (arr[2:,:]-arr[:-2,:])/(X[2:,:]-X[:-2,:]) gradY[:,1:-1] = (arr[:,2:]-arr[:,:-2])/(Y[:,2:]-Y[:,:-2]) gradX[0,:] = gradX[1,:]+(gradX[1,:]-gradX[2,:])/(X[1,:]-X[2,:])*(X[0,:]-X[1,:]) gradX[-1,:] = gradX[-2,:]+(gradX[-2,:]-gradX[-3,:])/(X[-2,:]-X[-3,:])*(X[-1,:]-X[-2,:]) gradY[:,0] = gradY[:,1]+(gradY[:,1]-gradY[:,2])/(Y[:,1]-Y[:,2])*(Y[:,0]-Y[:,1]) gradY[:,-1] = gradY[:,-2]+(gradY[:,-2]-gradY[:,-3])/(Y[:,-2]-Y[:,-3])*(Y[:,-1]-Y[:,-2]) return (gradX, gradY)
def report():
'''
Prints a report of the current nMBP together with the corresponding
plots.
'''
global _model, _parameters, _rad
model = _model
tau = 1.
r = 3
tau1_level = np.array(pybold.level(model.dq.tau, tau), dtype=int)
tau1 = int(round(np.mean(tau1_level)))
tau1min = int(round(np.min(tau1_level)))
seed = [
_parameters[0][0] - model.z.dimension[0][0],
_parameters[0][1] - model.z.dimension[0][1], tau1
]
s = snake.snake_from_box(model.z.rho,
radius=r,
start=tau1,
periodic=False,
seed=seed)
tau1_sindex = int(list(s[:, 2]).index(tau1))
xmean = int(s[tau1_sindex, 0])
ymean = int(s[tau1_sindex, 1])
odb = snake.select_disk(model.z.rho[:, :, tau1], (xmean, ymean),
threshold=.5)
rot_index = np.argmin(model.z.v1[:, :, tau1][odb[0]]**2)
xrot, yrot = odb[0][0][rot_index], odb[0][1][rot_index]
xc1, xc2 = model.z.xc1.flatten() / 1.e5, model.z.xc2.flatten() / 1.e5
xb1, xb2 = model.z.xb1.flatten() / 1.e5, model.z.xb2.flatten() / 1.e5
ext = [xc1[0], xc1[-1], xc2[0], xc2[-1]]
#Ttau1 = pybold.varAtLevel(model.dq.T,model.dq.tau,1.)
rhotau1 = pybold.varAtLevel(model.z.rho, model.dq.tau, 1.)
#plt.imshow(model.z.rho[:,:,tau1].T,origin='bottom',cmap=plt.cm.gray,extent=ext)
plt.imshow(rhotau1.T, origin='bottom', extent=ext)
plt.xlim(xc1[0], xc1[-1])
plt.ylim(xc2[0], xc2[-1])
plt.xlabel('Spatial horizontal position (X axis) [km]')
plt.ylabel('Spatial horizontal position (Y axis) [km]')
oring = np.zeros_like(model.z.rho[:, :, tau1], dtype=bool)
oring[odb[0]] = True
plt.imshow(oring.T,
origin='bottom',
alpha=.15,
interpolation='none',
extent=ext,
cmap=plt.cm.gray_r)
X, Y = np.meshgrid(xc1, xc2)
arrfreq = 5
v1 = pybold.varAtLevel(model.z.v1, model.dq.tau, 1.)
v2 = pybold.varAtLevel(model.z.v2, model.dq.tau, 1.)
#plt.quiver(X[::arrfreq,::arrfreq], Y[::arrfreq,::arrfreq], model.z.v1[::arrfreq,::arrfreq,tau1].T,model.z.v2[::arrfreq,::arrfreq,tau1].T)
plt.quiver(X[::arrfreq, ::arrfreq], Y[::arrfreq, ::arrfreq],
v1[::arrfreq, ::arrfreq].T, v2[::arrfreq, ::arrfreq].T)
fmt = '{0: <20}'
if _modelfile: print(fmt.format('File:') + _modelfile)
else: print(fmt.format('File:') + 'Unknown')
wd = wilson_depression(model, s, odb, tau, r)
print(fmt.format('Wilson depression:') + str(wd))
c_rho = contrast(model.z.rho, model, s, odb, tau, r)
print(fmt.format('Density contrast:') + str(c_rho))
c_p = contrast(model.dq.P, model, s, odb, tau, r)
print(fmt.format('Pressure contrast:') + str(c_p))
if _modelfile:
title = _modelfile.split('.')
title = '.'.join(title[:max(1, len(title) - 1)])
boxtext = 'Wilson depression: ' + str(int(round(wd / 1.e5))) + ' [km]\n'
boxtext += 'Density contrast: ' + str(int(round(100 * c_rho[1]))) + '%, '
boxtext += str(int(round(100 * c_rho[0]))) + '%\n'
boxtext += 'Pressure contrast: ' + str(int(round(100 * c_p[1]))) + '%, '
boxtext += str(int(round(100 * c_p[0]))) + '%'
diam = _parameters[3]
delta = model.z.xc1[1, :, :] - model.z.xc1[0, :, :]
boxtext += '\nDiametre: ' + str(int(round(diam * delta / 1.e5))) + ' [km]'
c_I = (_parameters[1], _parameters[2])
boxtext += '\nIntensity contrast: ' + str(int(round(100 * c_I[0]))) + '%, '
boxtext += str(int(round(100 * c_I[1]))) + '%'
boxtext += '\nLifetime: ' + str(int(round(120.))) + ' [s]'
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
deltaX = xb1[1] - xb1[0]
deltaY = xb2[1] - xb2[0]
xlim_min = xc1[0] - model.z.dimension[0][0] * deltaX
ylim_min = xc2[0] - model.z.dimension[0][1] * deltaY
szX, szY = np.shape(_rad.T)
ext = [
xlim_min, xlim_min + szX * deltaX, ylim_min, ylim_min + szY * deltaY
]
ax.imshow(_rad.T, origin='bottom', cmap=plt.cm.gray, extent=ext)
ax.set_xlim(xlim_min, xlim_min + szX * deltaX)
ax.set_ylim(ylim_min, ylim_min + szY * deltaY)
ax.set_xlabel('Spatial horizontal position (X axis) [km]')
ax.set_ylabel('Spatial horizontal position (Y axis) [km]')
c1_idx = _parameters[0][0] - model.z.dimension[0][0]
c2_idx = _parameters[0][1] - model.z.dimension[0][1]
centre = [xc1[c1_idx], xc2[c2_idx]]
c_r = 50. * np.sqrt(deltaX**2 + deltaY**2) / np.sqrt(2.)
circ = plt.Circle(centre, radius=c_r, color='r', fill=False)
ax.add_patch(circ)
sY, sX = plotv_slice(model.z.rho,
model,
s,
dq=True,
tau=tau,
r=r,
boxtext=boxtext,
show=True,
tight=False)
plt.close()