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posterior_analysis_elisa.py
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posterior_analysis_elisa.py
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"""
This file is part of gempy.
gempy is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
gempy is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with gempy. If not, see <http://www.gnu.org/licenses/>.
@author: Elisa Heim, Alexander Schaaf, Miguel de la Varga
(I guess, copied some code from posterior_analysis_DEP.py)
"""
import warnings
try:
import pymc
except ImportError:
warnings.warn("pymc (v2) package is not installed. No support for stochastic simulation posterior analysis.")
import numpy as np
import pandas as pn
import gempy as gp
try:
import tqdm
except ImportError:
warnings.warn("tqdm package not installed. No support for dynamic progress bars.")
import matplotlib.pyplot as plt
from mpl_toolkits import axes_grid1
import matplotlib.colors
class Posterior():
def __init__(self, dbname, model_type='map', entropy=False,
topography=None, interpdata=None, geodata=None):
if entropy:
print('All post models are calculated. Based on the model complexity and the number of iterations, '
'this could take a while')
# if topography:
self.topography = topography
# else:
# print('no topography defined. Methods that contain the word _map_ are not available')
self.interp_data = interpdata
self.geo_data = geodata
# self.verbose = verbose
self.db = pymc.database.hdf5.load(dbname) # load database
self.n_iter = self.db.getstate()['sampler']['_iter'] - self.db.getstate()["sampler"]["_burn"]
self.trace_names = self.db.trace_names[0]
self.input_data = self.db.input_data.gettrace()
if entropy:
if topography and model_type == 'map': # better resolution
self.all_maps = self.all_post_maps()
self.map_prob = self.compute_prob(np.round(self.all_maps).astype(int))
self.map_ie = self.calculate_ie_masked(self.map_prob)
elif model_type == 'model':
self.lbs, self.fbs = self.all_post_models()
if len(self.lbs) != 0:
self.lith_prob = self.compute_prob(np.round(self.lbs).astype(int))
self.lb_ie = self.calculate_ie_masked(self.lith_prob)
if len(self.fbs) != 0:
self.fault_prob = self.compute_prob(np.round(self.fbs).astype(int))
self.fb_ie = self.calculate_ie_masked(self.fault_prob)
else:
print('if there is no topography defined, model_type must be set to model')
# self.ie_total = self.calculate_ie_total()
def _change_input_data(self, i):
i = int(i)
# replace interface data
self.interp_data.geo_data_res.interfaces[["X", "Y", "Z"]] = self.input_data[i][0]
# replace foliation data
self.interp_data.geo_data_res._orientations[["G_x", "G_y", "G_z", "X", "Y", "Z", "dip", "azimuth", "polarity"]] = \
self.input_data[i][1]
self.interp_data.update_interpolator()
# if self.verbose:
# print("interp_data parameters changed.")
return self.interp_data
def all_post_maps(self):
all_maps = []
for i in range(0, self.n_iter):
# print(i)
self._change_input_data(i)
# geomap = self.topography.calculate_geomap(interpdata = self.interp_data, plot=True)
geomap, faultmap = gp.compute_model_at(self.topography.surface_coordinates[0], self.interp_data)
all_maps.insert(i, geomap[0])
return all_maps
def all_post_models(self):
lbs = []
fbs = []
for i in range(0, self.n_iter):
# print(i)
self._change_input_data(i)
lith_block, fault_block = gp.compute_model(self.interp_data)
if lith_block.shape[0] != 0:
lbs.insert(i, lith_block[0])
if fault_block.shape[0] != 0:
n = 0
while n < fault_block.shape[0]:
# print(fault_block.shape[0])
fbs.insert(i, fault_block[n])
n += 2
return lbs, fbs
def compute_prob(self, blocks):
lith_id = np.unique(blocks)
# lith_count = np.zeros_like(lith_blocks[0:len(lith_id)])
count = np.zeros((len(np.unique(blocks)), blocks.shape[1]))
for i, l_id in enumerate(lith_id):
count[i] = np.sum(blocks == l_id, axis=0)
prob = count / len(blocks)
# print(lith_prob)
return prob
def calculate_ie_masked(self, prob):
ie = np.zeros_like(prob[0])
for l in prob:
pm = np.ma.masked_equal(l, 0) # mask where prob is 0
ie -= (pm * np.ma.log2(pm)).filled(0)
return ie
def calculate_ie_total(self, ie, absolute=False):
if absolute:
return np.sum(ie)
else:
return np.sum(ie) / np.size(ie)
##### plotting methods #####
def plot_section(self, iteration=1, block='lith', cell_number=3, **kwargs):
'''kwargs: gempy.plotting.plot_section keyword arguments'''
self._change_input_data(iteration)
lith_block, fault_block = gp.compute_model(self.interp_data)
if 'topography' not in kwargs:
if self.topography:
topography = self.topography
else:
topography = None
if block == 'lith':
gp.plot_section(self.geo_data, lith_block[0], cell_number=cell_number, topography=topography, **kwargs)
else:
gp.plot_section(self.geo_data, block, cell_number=cell_number, topography=topography, **kwargs)
else:
if block == 'lith':
gp.plot_section(self.geo_data, lith_block[0], cell_number=cell_number, **kwargs)
else:
gp.plot_section(self.geo_data, block, cell_number=cell_number, **kwargs)
def plot_map(self, iteration=1, **kwargs):
self._change_input_data(iteration)
# geomap = self.topography.calculate_geomap(interpdata = self.interp_data, plot=True)
geomap, faultmap = gp.compute_model_at(self.topography.surface_coordinates[0], self.interp_data)
# gp.plotting.plot_map(geomap)
gp.plotting.plot_map(self.geo_data, geomap=geomap[0].reshape(self.topography.dem_zval.shape), **kwargs)
def plot_map_ie(self, plot_data=False):
if plot_data:
gp.plotting.plot_data(geo_data, direction='z')
dist = 12
else:
dist = 1
im = plt.imshow(self.map_ie.reshape(self.topography.dem_zval.shape), extent=self.geo_data.extent[:4],
cmap='viridis')
self.add_colorbar(im, pad_fraction=dist)
plt.title('Cell entropy of geological map')
def plot_section_ie(self, block='lith', cell_number=10, direction='y', **kwargs):
# for lithblock
if block == 'lith':
norm = matplotlib.colors.Normalize(self.lb_ie.min(), self.lb_ie.max())
gp.plotting.plot_section(geo_data, self.lb_ie, cell_number=cell_number, direction=direction, cmap='viridis',
norm=norm, **kwargs)
# self.add_colorbar(im)
elif block == 'fault':
norm = matplotlib.colors.Normalize(self.fb_ie.min(), self.fb_ie.max())
gp.plotting.plot_section(geo_data, self.fb_ie, cell_number=cell_number, direction=direction, cmap='viridis',
norm=norm, **kwargs)
# self.add_colorbar(im)
def add_colorbar(self, im, aspect=20, pad_fraction=1, **kwargs):
"""Add a vertical color bar to an image plot. Source: stackoverflow"""
divider = axes_grid1.make_axes_locatable(im.axes)
width = axes_grid1.axes_size.AxesY(im.axes, aspect=2. / aspect)
pad = axes_grid1.axes_size.Fraction(pad_fraction, width)
current_ax = plt.gca()
cax = divider.append_axes("right", size=width, pad=pad)
plt.sca(current_ax)
return im.axes.figure.colorbar(im, cax=cax, **kwargs)