import yt
from yt_velmodel_vis import seis_model as sm
from yt_velmodel_vis import shapeplotter as sp
import numpy as np
import os
import matplotlib.pyplot as plt
fname='NWUS11-S_percent.nc'
out_dir='./output'
model=sm.netcdf(fname)
# interpolate the cartesian (or load it if it exists)
mx=50000
dvs_n=os.path.join(out_dir,fname.split('.')[0]+'_dvs_interp_'+str(mx)+'.npy')
if os.path.isfile(dvs_n):
model.coordTransform('sphere2cart')
print("Loading cartesian-interpolated data from file")
dvs = np.load(dvs_n)
else:
model.interp2cartesian(fields=['dvs'],res=[10000,10000,10000], input_units='m',max_dist=mx)
dvs=model.interp['data']['dvs']
np.save(dvs_n,dvs)
data={}
data['dvs']=dvs
# set some gaussians for the TF
bnds=[-6,10.]
tf = yt.ColorTransferFunction((bnds[0],bnds[1]))
# [center location, peak width, (red, green, blue, alpha)]
# TF_gausians=[[-2,1,(1.,0.0,0.0,.8)]]
# for gau in TF_gausians:
},
'tfs': [{
'tftype': 'step',
'values': (-3, -2, [1.0, 0., 0., 1.0])
}, {
'tftype': 'step',
'values': (-2, -1, [1.0, 0.5, 0., .8])
}],
'tf_bounds': [-10., 10.],
'sigma_clip':
.4
}
out_dir = './output/' + fname.split('.')[0] # output directory for figures
# load the model
model = sm.netcdf(fname, settings['interp'])
shortname = fname.split('.')[0]
datafld = settings['interp']['field']
data = {datafld: model.interp['data'][datafld]} # dict container for yt scene
# add the transfer functions
bnds = settings.get('tf_bounds', [-5, 5])
tf = yt.ColorTransferFunction((bnds[0], bnds[1]))
for TFtoadd in settings['tfs']:
v = TFtoadd['values']
if TFtoadd['tftype'] == 'step':
tf.add_step(v[0], v[1], v[2])
elif TFtoadd['tftype'] == 'gaussian':
tf.add_gaussian(v[0], v[1], v[2])
# plot the TF with a histogram
import numpy as np
import os
import matplotlib.pyplot as plt
import geopandas as gpd
fname = 'NWUS11-S_percent.nc'
# fname='NA07_percent.nc'
out_dir = './output'
# load model, include interpolation
interpDict = dict(field='dvs',
res=[10000, 10000, 10000],
input_units='m',
max_dist=50000,
interpChunk=int(1e6))
model = sm.netcdf(fname, interpDict)
# load the data as a uniform grid, create the 3d scene
data = {'dvs': model.interp['data']['dvs']}
sc_mult = 1.0 # scale multiplier
bbox = model.cart['bbox'] # list-like [[xmin,xmax],[ymin,ymax],[zmin,zmax]]
ds = yt.load_uniform_grid(data,
data['dvs'].shape,
sc_mult,
bbox=bbox,
nprocs=1,
periodicity=(True, True, True),
unit_system="mks")
sc = yt.create_scene(ds, 'dvs')
# add TF