"""Rock component."""
import warnings
import numpy as np
import matplotlib.pyplot as plt
from .base_spatial import SpatialComponent
from .decorators import apply_to_each_input, state_check, ndim_check
from .plot_utils import show_cube_static, show_cube_interactive
from .utils import rolling_window, get_single_path
from .parse_utils import read_ecl_bin
[docs]class Rock(SpatialComponent):
"""Rock component of geological model."""
def _load_ecl_binary(self, path_to_results, attrs, basename, logger=None):
path = get_single_path(path_to_results, basename + '.INIT', logger)
if path is None:
return
sections = read_ecl_bin(path, attrs, logger=logger)
for k in ['PORO', 'PERMX', 'PERMY', 'PERMZ']:
if (k in attrs) and (k in sections):
setattr(self, k, sections[k])
@apply_to_each_input
def _to_spatial(self, attr, dimens, inplace):
"""Spatial order 'F' transformations."""
return self.reshape(attr=attr, newshape=dimens, order='F', inplace=inplace)
def _make_data_dump(self, attr, fmt=None, float_dtype=None, **kwargs):
"""Prepare data for dump."""
if fmt.upper() != 'HDF5':
return super()._make_data_dump(attr, fmt=fmt, **kwargs)
data = self.ravel(attr=attr, inplace=False)
return data if float_dtype is None else data.astype(float_dtype)
[docs] @apply_to_each_input
def pad_na(self, attr, actnum, fill_na=0., inplace=True):
"""Add dummy cells into the rock vector in the positions of non-active cells if necessary.
Parameters
----------
attr: str, array-like
Attributes to be padded with non-active cells.
actnum: array-like of type bool
Vector representing a mask of active and non-active cells.
fill_na: float
Value to be used as filler.
inplace: bool
Modify сomponent inplace.
Returns
-------
output : component if inplace else padded attribute.
"""
data = getattr(self, attr)
if np.prod(data.shape) == actnum.size:
return self if inplace else data
if data.ndim > 1:
raise ValueError('Data should be raveled before padding.')
padded_data = np.full(shape=(actnum.size,), fill_value=fill_na, dtype=float)
padded_data[actnum.ravel(order='F')] = data
if inplace:
setattr(self, attr, padded_data)
return self
return padded_data
[docs] @apply_to_each_input
def strip_na(self, attr, actnum, inplace=True):
"""Remove non-active cells from the rock vector.
Parameters
----------
attr: str, array-like
Attributes to be stripped
actnum: array-like of type bool
Vector representing mask of active and non-active cells.
inplace: bool
Modify сomponent inplace.
Returns
-------
output : component if inplace else stripped attribute.
"""
if self.state.spatial and inplace:
raise ValueError('Inplace is not allowed in spatial state.')
data = self.ravel(attr, inplace=False)
if data.size == np.sum(actnum):
return self if inplace else data
stripped_data = data[actnum.ravel(order='F')]
if inplace:
setattr(self, attr, stripped_data)
return self
return stripped_data
[docs] def show_histogram(self, attr, actnum=None, **kwargs):
"""Show properties distribution.
Parameters
----------
attr : str
Attribute to compute the histogram.
actnum : array, optional
Actnum array. If None, all cell are active.
kwargs : misc
Any additional named arguments to ``plt.hist``.
Returns
-------
plot : Histogram plot.
"""
data = getattr(self, attr)
if actnum is not None:
data = data * actnum
plt.hist(data.ravel(), **kwargs)
plt.show()
return self
[docs] @state_check(lambda state: state.spatial)
@ndim_check(3)
def show_cube(self, attr, x=None, y=None, z=None, actnum=None, figsize=None, **kwargs):
"""Visualize slices of 3D array. If no slice is specified, all 3 slices
will be shown with interactive slider widgets.
Parameters
----------
attr : str
Attribute to show.
x : int or None, optional
Slice along x-axis to show.
y : int or None, optional
Slice along y-axis to show.
z : int or None, optional
Slice along z-axis to show.
actnum : array, optional
Actnum array. If None, all cell are active.
figsize : array-like, optional
Output plot size.
kwargs : dict, optional
Additional keyword arguments for plot.
"""
data = getattr(self, attr)
if actnum is not None:
data = data * actnum
if np.all([x is None, y is None, z is None]):
show_cube_interactive(data, figsize=figsize, **kwargs)
else:
show_cube_static(data, x=x, y=y, z=z, figsize=figsize, **kwargs)
return self
[docs] @state_check(lambda state: state.spatial)
@apply_to_each_input
def upscale(self, attr, factors, volumes, weights=None):
"""Upscale properties.
Parameters
----------
attr : str, list of str
Attributes to be upscaled.
factors : tuple, int
Scale factors along each axis. If int, factors are the same for each axis.
volumes : ndarray, same shape as `attr`
Cell volumes.
weights : ndarray, same shape as `attr`, optional
Cell weights.
Returns
-------
out : ndarray
Upscaled properties.
"""
factors = np.atleast_1d(factors)
if factors.size == 1:
factors = np.repeat(factors, 3)
data = getattr(self, attr)
binned_weights = rolling_window(volumes, factors)
if weights is not None:
binned_weights *= rolling_window(weights, factors)
out = rolling_window(data, factors) * binned_weights
binned_weights = np.sum(binned_weights, axis=(-3, -2, -1))
zero_mask = np.isclose(binned_weights, 0)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
out = np.sum(out, axis=(-3, -2, -1)) / binned_weights
out[zero_mask] = 0
return out