190 lines
7.7 KiB
Python
190 lines
7.7 KiB
Python
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# %%
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import pygmt
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pygmt.config(FONT="16p", IO_SEGMENT_MARKER="<<<")
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from pytomoatt.model import ATTModel
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from pytomoatt.data import ATTData
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import numpy as np
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# %%
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# plot sensitivity kernel at the XX iteration
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# 1. set "output_kernel" to be "True" in the input_params.yaml file
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# ---------------- read files ----------------
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Nstep = "0007"
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kernel_list = {} # dictionary to store all the kernels
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# file names
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data_file = 'OUTPUT_FILES/out_data_sim_group_0.h5' # data file
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par_file = 'input_files/input_params.yaml' # parameter file
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grid_file = 'OUTPUT_FILES/out_data_grid.h5' # grid file
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group = 'model'
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# (Option 1) read original sensitivity kernel
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# Ks: kernel w.r.t. slowness at the 7-th iteration
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dataset = 'Ks_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# Kxi: kernel w.r.t. xi (anisotropic parameter) at the 7-th iteration
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dataset = 'Kxi_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# Keta: kernel w.r.t. eta (anisotropic parameter) at the 7-th iteration
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dataset = 'Keta_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# %%
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# (Option 2) read kernel_density
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# Ks_den: kernel density w.r.t. slowness at the 7-th iteration
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dataset = 'Ks_density_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# Kxi_den: kernel density w.r.t. xi (anisotropic parameter) at the 7-th iteration
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dataset = 'Kxi_density_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# Keta_den: kernel density w.r.t. eta (anisotropic parameter) at the 7-th iteration
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dataset = 'Keta_density_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# %%
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# kernel density normalization is performed after smoothing. So tag 'Ks_over_Kden_inv_%s'%(Nstep) is not available for version > 1.0.3
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# # (Option 3) read normalized kernel, K/(k_den)^\zeta
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# dataset = 'Ks_over_Kden_inv_%s'%(Nstep)
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# data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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# kernel_list[dataset] = data.to_xarray()
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# # Kxi_norm: normalized kernel w.r.t. xi (anisotropic parameter) at the 7-th iteration
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# dataset = 'Kxi_over_Kden_inv_%s'%(Nstep)
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# data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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# kernel_list[dataset] = data.to_xarray()
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# # Keta_norm: normalized kernel w.r.t. eta (anisotropic parameter) at the 7-th iteration
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# dataset = 'Keta_over_Kden_inv_%s'%(Nstep)
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# data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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# kernel_list[dataset] = data.to_xarray()
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# %%
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# this part works for version > 1.0.3
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# # (Option 3) read kernel density smoothed by multigrid parameterization,
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# dataset = 'Ks_density_update_inv_%s'%(Nstep)
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# data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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# kernel_list[dataset] = data.to_xarray()
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# # Kxi_norm: normalized kernel w.r.t. xi (anisotropic parameter) at the 7-th iteration
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# dataset = 'Kxi_density_update_inv_%s'%(Nstep)
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# data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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# kernel_list[dataset] = data.to_xarray()
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# # Keta_norm: normalized kernel w.r.t. eta (anisotropic parameter) at the 7-th iteration
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# dataset = 'Keta_density_update_inv_%s'%(Nstep)
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# data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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# kernel_list[dataset] = data.to_xarray()
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# %%
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# (Option 4) read normalized kernel smoothed by multigrid parameterization
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# Ks_update: smoothed normalized kernel w.r.t. slowness at the 7-th iteration
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dataset = 'Ks_update_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# Kxi_update: smoothed normalized kernel w.r.t. xi (anisotropic parameter) at the 7-th iteration
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dataset = 'Kxi_update_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# Keta_update: smoothed normalized kernel w.r.t. eta (anisotropic parameter) at the 7-th iteration
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dataset = 'Keta_update_inv_%s'%(Nstep)
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data = ATTData.read(data_file, par_file, grid_file, group, dataset)
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kernel_list[dataset] = data.to_xarray()
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# %%
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import os
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try:
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os.mkdir('img')
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except:
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pass
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# %%
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# ---------------- access 3D array ----------------
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# we can access 3D dataset:
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dep_1d_array = kernel_list['Ks_inv_0007']["dep"]
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lat_1d_array = kernel_list['Ks_inv_0007']["lat"]
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lon_1d_array = kernel_list['Ks_inv_0007']["lon"]
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print("3D array of coordinates. \n dep: ", dep_1d_array.shape, " \n lat: ", lat_1d_array.shape, " \n lon: ", lon_1d_array.shape)
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array = kernel_list['Ks_inv_0007']["Ks_inv_0007"]
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print("3D array of kernel. \n Ks: ", array.shape)
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# %%
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# ---------------- 2D depth profile of kernels ----------------
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for dataset in kernel_list:
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# interp vel at depth = 20 km
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depth = 20.0
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kernel = kernel_list[dataset].interp_dep(depth, field=dataset) # kernel[i,:] are (lon, lat, kernel)
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print("kernel at depth = ", depth, " km. kernel:", kernel.shape, ", (lon, lat, kernel)")
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# plot
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fig = pygmt.Figure()
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fig.basemap(region=[0,2,0,2], frame=["xa1","ya1","+t%s"%(dataset)], projection="M10c") # base map
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pygmt.makecpt(cmap="jet", series=[-0.5, 0.5], background=True, reverse=True) # colorbar
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x = kernel[:,0]; # longitude
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y = kernel[:,1]; # latitude
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value = kernel[:,2]/np.nanmax(np.abs(kernel[:,2])) # traveltime
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grid = pygmt.surface(x=x, y=y, z=value, spacing=0.04,region=[0,2,0,2])
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fig.grdimage(grid = grid) # plot figure
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fig.text(text="%d km"%(depth), x = 0.2 , y = 0.1, font = "14p,Helvetica-Bold,black", fill = "white")
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# colorbar
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fig.shift_origin(xshift=0, yshift=-1.5)
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fig.colorbar(frame = ["a0.5","y+l%s"%(dataset)], position="+e+w4c/0.3c+h")
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fig.savefig("img/3_dep_%s.png"%(dataset))
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# %%
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# ---------------- 2D vertical profile of kernels ----------------
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for dataset in kernel_list:
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# interp from [0,0.6] in lon-lat to [2,0.6] in lon-lat, gap = 1 km
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start = [0,0.6]; end = [2,0.6]; gap = 1
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kernel_sec = kernel_list[dataset].interp_sec(start, end, field=dataset, val = gap) # kernel_sec[i,:] are (lon, lat, dis, dep, kernel)
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print("kernel_sec:", kernel_sec.shape, ", (lon, lat, distance, depth, kernel)")
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# plot
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fig = pygmt.Figure()
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fig.basemap(region=[0,2,0,40], frame=["xa1+lLongitude","ya20+lDepth (km)","+t%s"%(dataset)], projection="X10c/-2c") # base map
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pygmt.makecpt(cmap="jet", series=[-0.5, 0.5], background=True, reverse=True) # colorbar
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x = kernel_sec[:,0]; # longitude
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y = kernel_sec[:,3]; # depth
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value = kernel_sec[:,4]/np.nanmax(np.abs(kernel_sec[:,4])) # traveltime
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grid = pygmt.surface(x=x, y=y, z=value, spacing="0.04/1",region=[0,2,0,40])
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fig.grdimage(grid = grid) # plot figure
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fig.text(text="A", x = 0.1 , y = 5, font = "14p,Helvetica-Bold,black", fill = "white")
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fig.text(text="A@+'@+", x = 1.9 , y = 5, font = "14p,Helvetica-Bold,black", fill = "white")
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# colorbar
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fig.shift_origin(xshift=0, yshift=-2)
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fig.colorbar(frame = ["a0.5","y+l%s"%(dataset)], position="+e+w4c/0.3c+h")
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fig.savefig("img/3_sec_%s.png"%(dataset))
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