initial upload
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338
examples/scripts_of_generate_hdf5_model/2_plot_models.py
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338
examples/scripts_of_generate_hdf5_model/2_plot_models.py
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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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import os
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output_path = "figs"
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os.makedirs(output_path, exist_ok=True)
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# %% [markdown]
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# # eg1. plot constant velocity model
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# %%
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# ---------------- read model files ----------------
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# file names
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# init_model_file = 'models/constant_velocity_N61_51_101_PyTomoATT.h5' # initial model file
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init_model_file = 'models/constant_velocity_N61_51_101_loop.h5' # initial model file
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par_file = 'input_params/input_params.yaml' # parameter file
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# read initial and final model file
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att_model = ATTModel.read(init_model_file, par_file)
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init_model = att_model.to_xarray()
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# interp vel at depth = 20 km
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depth = 20.0
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vel_init = init_model.interp_dep(depth, field='vel') # vel_init[i,:] are (lon, lat, vel)
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# ----------------- pygmt plot ------------------
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fig = pygmt.Figure()
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pygmt.makecpt(cmap="seis", series=[5, 7], background=True, reverse=False) # colorbar
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# ------------ plot horizontal profile of velocity ------------
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region = [0, 2, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa1","ya1","+tVelocity (km/s)"], projection="M10c") # base map
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depth = 20.0
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prof_init = init_model.interp_dep(depth, field='vel') # prof_init[i,:] are (lon, lat, vel)
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lon = prof_init[:,0] # longitude
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lat = prof_init[:,1] # latitude
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vel = prof_init[:,2] # velocity
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grid = pygmt.surface(x=lon, y=lat, z=vel, spacing=0.04,region=region)
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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 = ["a1","y+lVp (km/s)"], position="+e+w4c/0.3c+h")
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fig.shift_origin(xshift=0, yshift= 1.5)
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# ------------ plot horivertical profile of velocity ------------
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fig.shift_origin(xshift=11, yshift= 0)
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region = [0, 40, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa20+lDepth (km)","ya1+lLatitude","nSwE"], projection="X3c/5c") # base map
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start = [1,0]; end = [1,1]; gap = 1
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prof_init = init_model.interp_sec(start, end, field='vel', val = gap) # prof_init[i,:] are (lon, lat, dis, dep, vel)
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lat = prof_init[:,1] # lat
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dep = prof_init[:,3] # depth
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vel = prof_init[:,4] # velocity
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grid = pygmt.surface(x=dep, y=lat, z=vel, spacing="1/0.04",region=region)
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fig.grdimage(grid = grid) # plot figure
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fig.savefig("figs/constant_velocity.png") # save figure
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fig.show()
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# %% [markdown]
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# # eg2. plot linear velocity model
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# %%
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# ---------------- read model files ----------------
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# file names
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# init_model_file = 'models/linear_velocity_N61_51_101_PyTomoATT.h5' # initial model file
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init_model_file = 'models/linear_velocity_N61_51_101_loop.h5' # initial model file
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par_file = 'input_params/input_params.yaml' # parameter file
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# read initial and final model file
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att_model = ATTModel.read(init_model_file, par_file)
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init_model = att_model.to_xarray()
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# # interp vel at depth = 20 km
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# depth = 20.0
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# vel_init = init_model.interp_dep(depth, field='vel') # vel_init[i,:] are (lon, lat, vel)
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# ----------------- pygmt plot ------------------
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fig = pygmt.Figure()
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pygmt.makecpt(cmap="seis", series=[5, 8], background=True, reverse=False) # colorbar
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# ------------ plot horizontal profile of velocity ------------
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region = [0, 2, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa1","ya1","+tVelocity (km/s)"], projection="M10c") # base map
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depth = 20.0
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prof_init = init_model.interp_dep(depth, field='vel') # prof_init[i,:] are (lon, lat, vel)
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lon = prof_init[:,0] # longitude
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lat = prof_init[:,1] # latitude
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vel = prof_init[:,2] # velocity
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grid = pygmt.surface(x=lon, y=lat, z=vel, spacing=0.04,region=region)
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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 = ["a1","y+lVp (km/s)"], position="+e+w4c/0.3c+h")
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fig.shift_origin(xshift=0, yshift= 1.5)
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# ------------ plot horivertical profile of velocity ------------
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fig.shift_origin(xshift=11, yshift= 0)
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region = [0, 40, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa20+lDepth (km)","ya1+lLatitude","nSwE"], projection="X3c/5c") # base map
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start = [1,0]; end = [1,1]; gap = 1
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prof_init = init_model.interp_sec(start, end, field='vel', val = gap) # prof_init[i,:] are (lon, lat, dis, dep, vel)
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lat = prof_init[:,1] # lat
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dep = prof_init[:,3] # depth
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vel = prof_init[:,4] # velocity
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grid = pygmt.surface(x=dep, y=lat, z=vel, spacing="1/0.04",region=region)
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fig.grdimage(grid = grid) # plot figure
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fig.savefig("figs/linear_velocity.png") # save figure
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fig.show()
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# %% [markdown]
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# # eg3. plot checkerboard model
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# %%
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# ---------------- read model files ----------------
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# file names
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init_model_file = 'models/linear_velocity_N61_51_101_PyTomoATT.h5' # initial model file
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ckb_model_file = 'models/linear_velocity_ckb_N61_51_101_PyTomoATT.h5' # checkerboard model file
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# ckb_model_file = 'models/linear_velocity_ckb_N61_51_101_loop.h5' # checkerboard model file
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par_file = 'input_params/input_params.yaml' # parameter file
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# read initial and final model file
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att_model = ATTModel.read(init_model_file, par_file)
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init_model = att_model.to_xarray()
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att_model = ATTModel.read(ckb_model_file, par_file)
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ckb_model = att_model.to_xarray()
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# # interp vel at depth = 20 km
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# depth = 20.0
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# vel_init = init_model.interp_dep(depth, field='vel') # vel_init[i,:] are (lon, lat, vel)
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# vel_ckb = ckb_model.interp_dep(depth, field='vel') # vel_ckb[i,:] are (lon, lat, vel)
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# ----------------- pygmt plot ------------------
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fig = pygmt.Figure()
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pygmt.makecpt(cmap="../utils/svel13_chen.cpt", series=[-10,10], background=True, reverse=False) # colorbar
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# ------------ plot horizontal profile of velocity ------------
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region = [0, 2, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa1","ya1","+tVelocity perturbation (%)"], projection="M10c") # base map
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# velocity perturbation at depth = 15 km
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depth = 15.0
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prof_init = init_model.interp_dep(depth, field='vel') # prof_init[i,:] are (lon, lat, vel)
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prof_ckb = ckb_model.interp_dep(depth, field='vel') # prof_ckb[i,:] are (lon, lat, vel)
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lon = prof_init[:,0] # longitude
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lat = prof_init[:,1] # latitude
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vel_pert = (prof_ckb[:,2] - prof_init[:,2])/prof_init[:,2] * 100 # velocity perturbation related to initial model
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grid = pygmt.surface(x=lon, y=lat, z=vel_pert, spacing=0.01,region=region)
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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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# fast velocity directions (FVDs)
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samp_interval = 3 # control the density of anisotropic arrows
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width = 0.06 # width of the anisotropic arrow
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ani_per_1 = 0.01; ani_per_2 = 0.05; scale = 0.5; basic = 0.1 # control the length of anisotropic arrows related to the amplitude of anisotropy. length = 0.1 + (amplitude - ani_per_1) / (ani_per_2 - ani_per_1) * scale
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ani_thd = ani_per_1 # if the amplitude of anisotropy is smaller than ani_thd, no anisotropic arrow will be plotted
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phi = ckb_model.interp_dep(depth, field='phi', samp_interval=samp_interval) # phi_inv[i,:] are (lon, lat, phi)
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epsilon = ckb_model.interp_dep(depth, field='epsilon', samp_interval=samp_interval) # epsilon_inv[i,:] are (lon, lat, epsilon)
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ani_lon = phi[:,0].reshape(-1,1)
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ani_lat = phi[:,1].reshape(-1,1)
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ani_phi = phi[:,2].reshape(-1,1)
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length = ((epsilon[:,2] - ani_per_1) / (ani_per_2 - ani_per_1) * scale + basic).reshape(-1,1)
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ani_arrow = np.hstack([ani_lon, ani_lat, ani_phi, length, np.ones((ani_lon.size,1))*width]) # lon, lat, color, angle[-90,90], length, width
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# remove arrows with small amplitude of anisotropy
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idx = np.where(epsilon[:,2] > ani_thd)[0] # indices of arrows with large enough amplitude of anisotropy
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ani_arrow = ani_arrow[idx,:] # remove arrows with small amplitude of anisotropy
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# plot anisotropic arrows
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fig.plot(ani_arrow, style='j', fill='yellow1', pen='0.5p,black') # plot fast velocity direction
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# colorbar
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fig.shift_origin(xshift=0, yshift=-1.5)
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fig.colorbar(frame = ["a10","y+ldlnVp (%)"], position="+e+w4c/0.3c+h")
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fig.shift_origin(xshift=0, yshift= 1.5)
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# ------------ plot horivertical profile of velocity ------------
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fig.shift_origin(xshift=11, yshift= 0)
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region = [0, 40, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa20+lDepth (km)","ya1+lLatitude","nSwE"], projection="X3c/5c") # base map
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start = [0.875,0]; end = [0.875,1]; gap = 1
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prof_init = init_model.interp_sec(start, end, field='vel', val = gap) # prof_init[i,:] are (lon, lat, dis, dep, vel)
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prof_ckb = ckb_model.interp_sec(start, end, field='vel', val = gap) # prof_ckb[i,:] are (lon, lat, dis, dep, vel)
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lat = prof_init[:,1] # lat
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dep = prof_init[:,3] # depth
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vel = (prof_ckb[:,4] - prof_init[:,4])/prof_init[:,4] * 100 # velocity perturbation related to initial model
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grid = pygmt.surface(x=dep, y=lat, z=vel, spacing="1/0.01",region=region)
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fig.grdimage(grid = grid) # plot figure
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fig.savefig("figs/checkerboard_velocity.png") # save figure
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fig.show()
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# %% [markdown]
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# # eg4. plot flexible checkerboard model
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# %%
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# ---------------- read model files ----------------
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# file names
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init_model_file = 'models/linear_velocity_N61_51_101_PyTomoATT.h5' # initial model file
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ckb_model_file = 'models/linear_velocity_ckb_flex_N61_51_101.h5' # checkerboard model file
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par_file = 'input_params/input_params.yaml' # parameter file
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# read initial and final model file
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att_model = ATTModel.read(init_model_file, par_file)
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init_model = att_model.to_xarray()
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att_model = ATTModel.read(ckb_model_file, par_file)
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ckb_model = att_model.to_xarray()
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# # interp vel at depth = 20 km
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# depth = 20.0
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# vel_init = init_model.interp_dep(depth, field='vel') # vel_init[i,:] are (lon, lat, vel)
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# vel_ckb = ckb_model.interp_dep(depth, field='vel') # vel_ckb[i,:] are (lon, lat, vel)
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# ----------------- pygmt plot ------------------
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fig = pygmt.Figure()
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pygmt.makecpt(cmap="../utils/svel13_chen.cpt", series=[-10,10], background=True, reverse=False) # colorbar
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for depth in [4,14,28]:
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# ------------ plot horizontal profile of velocity ------------
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region = [0, 2, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa1","ya1","NsEw"], projection="M10c") # base map
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# velocity perturbation at depth = 15 km
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prof_init = init_model.interp_dep(depth, field='vel') # prof_init[i,:] are (lon, lat, vel)
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prof_ckb = ckb_model.interp_dep(depth, field='vel') # prof_ckb[i,:] are (lon, lat, vel)
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lon = prof_init[:,0] # longitude
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lat = prof_init[:,1] # latitude
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vel_pert = (prof_ckb[:,2] - prof_init[:,2])/prof_init[:,2] * 100 # velocity perturbation related to initial model
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grid = pygmt.surface(x=lon, y=lat, z=vel_pert, spacing=0.01,region=region)
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fig.grdimage(grid = grid) # plot figure
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# fast velocity directions (FVDs)
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samp_interval = 3 # control the density of anisotropic arrows
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width = 0.06 # width of the anisotropic arrow
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ani_per_1 = 0.01; ani_per_2 = 0.05; scale = 0.5; basic = 0.1 # control the length of anisotropic arrows related to the amplitude of anisotropy. length = 0.1 + (amplitude - ani_per_1) / (ani_per_2 - ani_per_1) * scale
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ani_thd = ani_per_1 # if the amplitude of anisotropy is smaller than ani_thd, no anisotropic arrow will be plotted
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phi = ckb_model.interp_dep(depth, field='phi', samp_interval=samp_interval) # phi_inv[i,:] are (lon, lat, phi)
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epsilon = ckb_model.interp_dep(depth, field='epsilon', samp_interval=samp_interval) # epsilon_inv[i,:] are (lon, lat, epsilon)
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ani_lon = phi[:,0].reshape(-1,1)
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ani_lat = phi[:,1].reshape(-1,1)
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ani_phi = phi[:,2].reshape(-1,1)
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length = ((epsilon[:,2] - ani_per_1) / (ani_per_2 - ani_per_1) * scale + basic).reshape(-1,1)
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ani_arrow = np.hstack([ani_lon, ani_lat, ani_phi, length, np.ones((ani_lon.size,1))*width]) # lon, lat, color, angle[-90,90], length, width
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# remove arrows with small amplitude of anisotropy
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idx = np.where(epsilon[:,2] > ani_thd)[0] # indices of arrows with large enough amplitude of anisotropy
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ani_arrow = ani_arrow[idx,:] # remove arrows with small amplitude of anisotropy
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# plot anisotropic arrows
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fig.plot(ani_arrow, style='j', fill='yellow1', pen='0.5p,black') # plot fast velocity direction
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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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# plot vertical profile
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fig.plot(x=[0.9, 0.9], y=[0, 1], pen="2p,black,-") # vertical line
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fig.shift_origin(xshift=0, yshift=-6)
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# colorbar
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fig.shift_origin(xshift=0, yshift= 4.5)
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fig.colorbar(frame = ["a10","y+ldlnVp (%)"], position="+e+w4c/0.3c+h")
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fig.shift_origin(xshift=0, yshift= 1.5)
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# ------------ plot horivertical profile of velocity ------------
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fig.shift_origin(xshift=11, yshift= 0)
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region = [0, 40, 0, 1] # region of interest
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fig.basemap(region=region, frame=["xa20+lDepth (km)","ya1+lLatitude","nSwE"], projection="X3c/5c") # base map
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start = [0.9,0]; end = [0.9,1]; gap = 1
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prof_init = init_model.interp_sec(start, end, field='vel', val = gap) # prof_init[i,:] are (lon, lat, dis, dep, vel)
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prof_ckb = ckb_model.interp_sec(start, end, field='vel', val = gap) # prof_ckb[i,:] are (lon, lat, dis, dep, vel)
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lat = prof_init[:,1] # lat
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dep = prof_init[:,3] # depth
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vel = (prof_ckb[:,4] - prof_init[:,4])/prof_init[:,4] * 100 # velocity perturbation related to initial model
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grid = pygmt.surface(x=dep, y=lat, z=vel, spacing="1/0.01",region=region)
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fig.grdimage(grid = grid) # plot figure
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fig.savefig("figs/flexible_checkerboard_velocity.png") # save figure
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fig.show()
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