def theano_f(self):
# Importing the data from csv files and settign extent and resolution
geo_data = gempy.import_data([0, 10, 0, 10, -10, 0], [50, 50, 50],
path_f="./GeoModeller/test_a/test_a_Foliations.csv",
path_i="./GeoModeller/test_a/test_a_Points.csv")
# data_interp = gempy.set_interpolator(geo_data,
# dtype="float64",
# verbose=['solve_kriging'])
# Set all the theano shared parameters and return the symbolic variables (the input of the theano function)
#input_data_T = data_interp.interpolator.tg.input_parameters_list()
# Prepare the input data (interfaces, foliations data) to call the theano function.
# Also set a few theano shared variables with the len of formations series and so on
# input_data_P = data_interp.interpolator.data_prep(u_grade=[3])
# Compile the theano function.
# compiled_f = theano.function(input_data_T, data_interp.interpolator.tg.whole_block_model(),
# allow_input_downcast=True, profile=True)
data_interp = gempy.InterpolatorInput(geo_data, dtype='float64')
compiled_f = data_interp.compile_th_fn()
return data_interp, compiled_f
gp.plot_data(geo_data, direction="z") # In[5]: gp.plot_data(geo_data, direction="x") # ## 3.3 - Computing the 3D Model # In[ ]: interp_data = gp.InterpolatorInput(geo_data, dtype="float32") # In[ ]: lith_block, fault_block = gp.compute_model(interp_data) # ## 3.4 - Model visualization # # ### 3.4.1 - 2D Sections # In[ ]:
import theano
import numpy as np
import matplotlib.pyplot as plt
theano.config.compute_test_value = "ignore"
# This is for sphenix to find the packages
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
sys.path.append("../gempy")
sys.path.append("../")
# Importing GeMpy modules
import gempy as gp
import pandas as pn
geo_data = gp.read_pickle('../Prototype Notebook/sandstone.pickle')
interp_data = gp.InterpolatorInput(geo_data, compile_theano=False)
import sys
import gempy as gp
from gempy.GeoPhysics import GeoPhysicsPreprocessing
inter_data = gp.InterpolatorInput(geo_data, compile_theano=False)
gpp = GeoPhysicsPreprocessing(
inter_data,
580, [7.050000e+05, 747000, 6863000, 6925000, -20000, 200],
res_grav=[70, 40],
n_cells=100,
mode='n_closest')
#res_grav = [125, 85],
#n_cells =1000)
print(gpp)