1 | from dynamico import unstructured as unst |
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2 | from dynamico import dyn |
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3 | from dynamico import time_step |
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4 | from dynamico import meshes |
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5 | from dynamico import xios |
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6 | from dynamico import precision as prec |
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7 | import math as math |
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8 | import matplotlib.pyplot as plt |
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9 | import numpy as np |
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10 | import time |
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11 | import argparse |
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12 | |
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13 | def thermal_bubble_3D(Lx,nx,Ly,ny,llm,ztop=1000., zc=350., |
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14 | rc=250, thetac=0.5, x0=0., y0=0.): |
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15 | Cpd, Rd, g, p0,theta0, T0 = 1004.5, 287.,9.81, 1e5, 300., 300. |
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16 | nqdyn = 1 |
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17 | |
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18 | Phi = lambda eta : g*ztop*eta |
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19 | p = lambda Phi : p0*np.exp(-Phi/(Rd*T0)) |
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20 | zz = lambda p: -(Rd*T0*np.log(p/p0))/g |
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21 | rr = lambda x,y,p: np.sqrt((x-x0)**2 + (y-y0)**2 + (zz(p)-zc)**2) |
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22 | sa = lambda x,y,p: rr(x,y,p) < rc |
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23 | deform = lambda x,y,p: (0.5*thetac*(1+np.cos(np.pi*rr(x,y,p)/rc)))*sa(x,y,p) |
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24 | temp = lambda p: theta0*(p/p0)**(Rd/Cpd) |
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25 | T = lambda x,y,p: deform(x,y,p) + temp(p) |
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26 | |
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27 | alpha_k = (np.arange(llm) +.5)/llm |
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28 | alpha_l = (np.arange(llm+1)+ 0.)/llm |
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29 | x_ik, alpha_ik = np.meshgrid(mesh.lon_i, alpha_k, indexing='ij') |
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30 | y_ik, alpha_ik = np.meshgrid(mesh.lat_i, alpha_k, indexing='ij') |
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31 | x_il, alpha_il = np.meshgrid(mesh.lon_i, alpha_l, indexing='ij') |
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32 | y_il, alpha_il = np.meshgrid(mesh.lat_i, alpha_l, indexing='ij') |
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33 | x_ek, alpha_ek = np.meshgrid(mesh.lon_e, alpha_k, indexing='ij') |
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34 | y_ek, alpha_ek = np.meshgrid(mesh.lat_e, alpha_k, indexing='ij') |
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35 | |
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36 | thermo = dyn.Ideal_perfect(Cpd, Rd, p0, T0) |
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37 | |
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38 | Phi_il = Phi(alpha_il) |
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39 | Phi_ik = Phi(alpha_ik) |
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40 | p_ik = p(Phi_ik) |
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41 | T_ik = T(x_ik, y_ik, p_ik) |
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42 | |
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43 | gas = thermo.set_pT(p_ik,T_ik) |
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44 | mass_ik = mesh.field_mass() |
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45 | for l in range(llm): |
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46 | mass_ik[:,l]=(Phi_il[:,l+1]-Phi_il[:,l])/(g*gas.v[:,l]) |
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47 | Sik, ujk, Wil = gas.s*mass_ik, mesh.field_u(), mesh.field_w() |
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48 | |
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49 | print 'ztop (m) = ', Phi_il[0,-1]/g, ztop |
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50 | ptop = p(g*ztop) |
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51 | print 'ptop (Pa) = ', gas.p[0,-1], ptop |
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52 | params=dyn.Struct() |
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53 | params.ptop=ptop |
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54 | params.dx=dx |
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55 | params.dx_g0=dx/g |
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56 | params.g = g |
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57 | |
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58 | # define parameters for lower BC |
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59 | pbot = p(alpha_il[0]) |
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60 | print 'min p, T :', pbot.min(), temp(pbot/p0) |
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61 | gas_bot = thermo.set_pT(pbot, temp(pbot/p0)) |
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62 | params.pbot = gas_bot.p |
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63 | params.rho_bot = 1e6/gas_bot.v |
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64 | |
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65 | return thermo, mesh, params, prec.asnum([mass_ik,Sik,ujk,Phi_il,Wil]), gas |
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66 | |
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67 | def diagnose(Phi,S,m,W): |
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68 | s=S/m ; s=.5*(s+abs(s)) |
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69 | for l in range(llm): |
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70 | v[:,l]=(Phi[:,l+1]-Phi[:,l])/(g*m[:,l]) |
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71 | w[:,l]=.5*params.g*(W[:,l+1]+W[:,l])/m[:,l] |
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72 | z[:,l]=.5*(Phi[:,l+1]+Phi[:,l])/params.g |
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73 | gas = thermo.set_vs(v,s) |
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74 | return gas, w, z |
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75 | |
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76 | def reshape(data): return data.reshape((nx,ny)) |
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77 | |
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78 | def plot(): |
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79 | x, y = map(reshape, (xx,yy) ) |
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80 | zz=np.zeros((nx,ny,llm)) |
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81 | ss=np.zeros((nx,ny,llm)) |
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82 | ww=np.zeros((nx,ny,llm)) |
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83 | x3d=np.zeros((nx,ny,llm)) |
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84 | |
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85 | for l in range(llm): |
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86 | zz[:,:,l],ss[:,:,l],ww[:,:,l] = map(reshape, (z[:,l],gas.s[:,l],w[:,l]) ) |
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87 | x3d[:,:,l]=x[:,:] |
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88 | |
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89 | jj=ny/2 |
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90 | xp=x3d[:,jj,:] |
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91 | zp=zz[jj,:,:]/1000. |
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92 | sp=ss[jj,:,:] |
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93 | wp=ww[jj,:,:] |
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94 | |
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95 | f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(12,4)) |
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96 | |
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97 | c=ax1.contourf(xp,zp,sp,20) |
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98 | ax1.set_ylim((0.,1.)), ax1.set_ylabel('z (km)') |
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99 | plt.colorbar(c,ax=ax1) |
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100 | ax1.set_title(title_format % (it*T,)) |
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101 | |
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102 | c=ax2.contourf(xp,zp,wp,20) |
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103 | ax2.set_ylim((0.,1.)), ax2.set_ylabel('z (km)') |
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104 | plt.colorbar(c,ax=ax2) |
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105 | ax2.set_title('Vertical velocity at t=%g s (m/s)' % (it*T,)) |
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106 | plt.savefig('fig_NH_3D_bubble/%02d.png'%it) |
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107 | |
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108 | #------------------------- main program -------------------------- |
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109 | |
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110 | parser = argparse.ArgumentParser() |
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111 | |
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112 | parser.add_argument("--mpi_ni", type=int, default=64, |
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113 | help="number of x processors") |
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114 | parser.add_argument("--mpi_nj", type=int, default=64, |
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115 | help="number of y processors") |
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116 | |
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117 | parser.add_argument("--python_stepping", type=bool, default=False, |
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118 | help="Time stepping in Python or Fortran") |
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119 | parser.add_argument("--dt", type=float, default=.25, |
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120 | help="Time step in seconds") |
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121 | parser.add_argument("--T", type=float, default=5., |
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122 | help="Length of time slice in seconds") |
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123 | parser.add_argument("--Nslice", type=int, default=10, |
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124 | help="Number of time slices") |
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125 | |
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126 | parser.add_argument("--thetac", type=float, default=30., |
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127 | help="Initial extra temperature of bubble") |
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128 | parser.add_argument("--Lx", type=float, default=2000., |
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129 | help="Size of box in meters") |
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130 | parser.add_argument("--Ly", type=float, default=2000., |
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131 | help="Size of box in meters") |
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132 | parser.add_argument("--nx", type=int, default=20, |
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133 | help="Resolution in the x direction") |
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134 | parser.add_argument("--ny", type=int, default=20, |
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135 | help="Resolution in the y direction") |
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136 | parser.add_argument("--llm", type=int, default=79, |
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137 | help="Number of vertical levels") |
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138 | |
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139 | args = parser.parse_args() |
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140 | |
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141 | with xios.Client() as client: # setup XIOS which creates the DYNAMICO communicator |
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142 | comm = client.comm |
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143 | mpi_rank, mpi_size = comm.Get_rank(), comm.Get_size() |
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144 | print '%d/%d starting'%(mpi_rank,mpi_size) |
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145 | T, Nslice, dt, thetac = args.T, args.Nslice, args.dt, args.thetac |
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146 | Lx, nx, Ly, ny, llm = args.Lx, args.nx, args.Ly, args.ny, args.llm |
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147 | |
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148 | nqdyn, dx = 1, Lx/nx |
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149 | Ly,ny,dy = Lx,nx,dx |
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150 | |
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151 | g=9.81 |
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152 | unst.setvar('g',g) |
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153 | |
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154 | filename = 'cart_%03d_%03d.nc'%(nx,ny) |
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155 | print 'Reading Cartesian mesh ...' |
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156 | def coriolis(lon,lat): return 0.*lon |
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157 | meshfile = meshes.DYNAMICO_Format(filename) |
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158 | pmesh = meshes.Unstructured_PMesh(comm,meshfile) |
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159 | pmesh.partition_curvilinear(args.mpi_ni,args.mpi_nj) |
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160 | mesh = meshes.Local_Mesh(pmesh, llm, nqdyn, None, coriolis) |
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161 | |
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162 | thermo, mesh, params, flow0, gas0 = thermal_bubble_3D(Lx,nx,Ly,ny,llm, thetac=thetac) |
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163 | |
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164 | # compute hybrid coefs from initial distribution of mass |
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165 | mass_bl,mass_dak,mass_dbk = meshes.compute_hybrid_coefs(flow0[0]) |
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166 | print 'Type of mass_bl, mass_dak, mass_dbk : ', [x.dtype for x in mass_bl, mass_dak, mass_dbk] |
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167 | unst.ker.dynamico_init_hybrid(mass_bl,mass_dak,mass_dbk) |
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168 | |
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169 | dz = flow0[3].max()/(params.g*llm) |
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170 | # courant = 2.8 |
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171 | #dt = courant*.5/np.sqrt(gas0.c2.max()*(dx**-2+dy**-2+dz**-2)) |
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172 | #dt = courant*.5/np.sqrt(gas0.c2.max()*(dx**-2+dy**-2)) |
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173 | nt = int(math.ceil(T/dt)) |
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174 | dt = T/nt |
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175 | print 'Time step : %d x %g = %g s' % (nt,dt,nt*dt) |
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176 | |
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177 | # #caldyn_thermo, caldyn_eta = unst.thermo_theta, unst.eta_mass |
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178 | caldyn_thermo, caldyn_eta = unst.thermo_entropy, unst.eta_mass |
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179 | # #caldyn_thermo, caldyn_eta = unst.thermo_entropy, unst.eta_lag |
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180 | |
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181 | if args.python_stepping: # time stepping in Python |
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182 | caldyn = unst.Caldyn_NH(caldyn_thermo,caldyn_eta, mesh,thermo,params,params.g) |
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183 | scheme = time_step.ARK2(caldyn.bwd_fast_slow, dt) |
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184 | def next_flow(m,S,u,Phi,W): |
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185 | return scheme.advance((m,S,u,Phi,W),nt) |
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186 | |
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187 | else: # time stepping in Fortran |
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188 | scheme = time_step.ARK2(None, dt) |
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189 | caldyn_step = unst.caldyn_step_NH(mesh,scheme,nt, caldyn_thermo,caldyn_eta, |
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190 | thermo,params,params.g) |
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191 | def next_flow(m,S,u,Phi,W): |
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192 | # junk,fast,slow = caldyn.bwd_fast_slow(flow, 0.) |
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193 | caldyn_step.mass[:,:], caldyn_step.theta_rhodz[:,:], caldyn_step.u[:,:] = m,S,u |
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194 | caldyn_step.geopot[:,:], caldyn_step.W[:,:] = Phi,W |
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195 | caldyn_step.next() |
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196 | return (caldyn_step.mass.copy(), caldyn_step.theta_rhodz.copy(), caldyn_step.u.copy(), |
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197 | caldyn_step.geopot.copy(), caldyn_step.W.copy()) |
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198 | |
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199 | m,S,u,Phi,W=flow0 |
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200 | if caldyn_thermo == unst.thermo_theta: |
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201 | s=S/m |
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202 | theta = thermo.T0*np.exp(s/thermo.Cpd) |
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203 | S=m*theta |
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204 | title_format = 'Potential temperature at t=%g s (K)' |
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205 | else: |
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206 | title_format = 'Specific entropy at t=%g s (J/K/kg)' |
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207 | |
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208 | w=mesh.field_mass() |
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209 | z=mesh.field_mass() |
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210 | xx,yy = mesh.lat_i, mesh.lon_i |
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211 | |
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212 | # XIOS writes to disk every 24h |
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213 | # each iteration lasts it*nt seconds but |
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214 | # we pretend that each iteration lasts 24h to make sure data is written to disk |
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215 | |
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216 | with xios.Context_Curvilinear(mesh,1, 24*3600) as context: |
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217 | # now XIOS knows about the mesh and we can write to disk |
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218 | |
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219 | v = mesh.field_mass() # specific volume (diagnosed) |
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220 | |
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221 | for it in range(Nslice): |
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222 | context.update_calendar(it+1) |
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223 | |
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224 | # Diagnose quantities of interest from prognostic variables m,S,u,Phi,W |
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225 | gas, w, z = diagnose(Phi,S,m,W) |
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226 | |
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227 | # write to disk |
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228 | context.send_field_primal('temp', gas.T) |
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229 | context.send_field_primal('p', gas.p) |
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230 | context.send_field_primal('theta', gas.s) |
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231 | context.send_field_primal('uz', w) |
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232 | |
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233 | print 'ptop, model top (m) :', unst.getvar('ptop'), Phi.max()/unst.getvar('g') |
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234 | |
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235 | if args.mpi_ni*args.mpi_nj==1: plot() |
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236 | |
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237 | time1, elapsed1 =time.time(), unst.getvar('elapsed') |
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238 | m,S,u,Phi,W = next_flow(m,S,u,Phi,W) |
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239 | time2, elapsed2 =time.time(), unst.getvar('elapsed') |
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240 | factor = 1000./nt |
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241 | print 'ms per full time step : ', factor*(time2-time1), factor*(elapsed2-elapsed1) |
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242 | factor = 1e9/(4*nt*nx*ny*llm) |
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243 | print 'nanosec per gridpoint per full time step : ', factor*(time2-time1), factor*(elapsed2-elapsed1) |
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244 | |
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245 | context.update_calendar(Nslice+1) # make sure XIOS writes last iteration |
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246 | |
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247 | print('************DONE************') |
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