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| str | dir1 = "./sampledata/scale-5.4.5/scale-rm/test/tutorial/real/experiment/run/" |
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| str | ftype = "history" |
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| str | domainlabel = "_d01" |
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| str | timelabel = "" |
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| int | PRC_NUM_X = 2 |
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| int | PRC_NUM_Y = 2 |
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| str | dir_out = "./fig/" |
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| bool | savefig = True |
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| str | key2dname = "MSLP" |
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| str | key2dunit = "(Pa)" |
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| str | var2dname = "PREC" |
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| str | var2dunit = "(kg/m2/s)" |
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| str | var3dname = "RH" |
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| str | var3dunit = "(%)" |
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| | levels_var3d1 = np.linspace(0,100,21) |
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| str | cmap_var3d1 = "BrBG" |
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| int | blct = 1 |
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| int | blcy = 30 |
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| int | blcx = 30 |
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| | fpathlist = get_fpathlist(dir1,ftype,domainlabel,timelabel,PRC_NUM_X,PRC_NUM_Y) |
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| | xrvar = get_xrvar(fpathlist) |
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| | z = xrvar.coords["z"] |
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| | time = xrvar.coords["time"] |
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| | y = xrvar.coords["y"] |
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| | x = xrvar.coords["x"] |
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| | index |
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| | key2d |
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| | weights_sum_sorted |
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| | var2d_sorted = get_var2d_sorted_bykey2d(xrvar,var2dname,blct,blcy,blcx,index) |
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| | var3d_sorted = get_var3d_sorted_bykey2d(xrvar,var3dname,blct,blcy,blcx,index) |
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| | rhow_sorted = get_var3d_sorted_bykey2d(xrvar,"RHOW",blct,blcy,blcx,index) |
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| | key2dmean = np.average(key2d,weights=weights_sum_sorted,axis=0) |
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| | key2drank = np.arange(int(key2dmean.shape[0])) |
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| | psi3dmean = np.cumsum(np.sum(rhow_sorted*weights_sum_sorted[:,:,np.newaxis],axis=0),axis=0) / np.sum(np.sum(weights_sum_sorted[:,:,np.newaxis],axis=0),axis=0) |
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| | zsize = var3d_sorted.shape[2] |
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| | var3d1mean = np.average(var3d_sorted,weights=np.tile(weights_sum_sorted[:,:,np.newaxis],(1,1,zsize)),axis=0) |
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| | var2d1mean = np.average(var2d_sorted,weights=weights_sum_sorted,axis=0) |
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| | fig |
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| | ax |
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| | levels_psi = np.linspace(psi3dmean.min(),psi3dmean.max(),11) |
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| | CS = ax.contour(key2drank,z*1e-3,psi3dmean.T,levels=levels_psi) |
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| | inline |
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| | True |
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| | fontsize |
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| | cax = ax.contourf(key2drank,z*1e-3,var3d1mean.T,levels=levels_var3d1,cmap=cmap_var3d1) |
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| | cbar = plt.colorbar(cax,ax=ax) |
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