Source code for openpnm.io.Statoil

import numpy as np
from openpnm.topotools import trim
from openpnm.utils import logging
from openpnm.io import GenericIO
from openpnm.network import GenericNetwork
from pathlib import Path
logger = logging.getLogger(__name__)


[docs]class Statoil(GenericIO): r""" The StatOil format is used by the Maximal Ball network extraction code of the Imperial College London group This class can be used to load and work with those networks. Numerous datasets are available for download from the group's `website <http://tinyurl.com/zurko4q>`_. The so-called 'Statoil' format consists of 4 different files in a single folder. The data is stored in columns with each corresponding to a specific property. Headers are not provided in the files, so one must refer to various theses and documents to interpret their meaning. """
[docs] @classmethod def load(cls, *args, **kwargs): r""" This method is being deprecated. Use ``import_data`` instead. """ return cls.import_data(*args, **kwargs)
[docs] @classmethod def import_data(cls, path, prefix, network=None): r""" Load data from the \'dat\' files located in specified folder. Parameters ---------- path : string The full path to the folder containing the set of \'dat\' files. prefix : string The file name prefix on each file. The data files are stored as \<prefix\>_node1.dat. network : OpenPNM Network Object If given then the data will be loaded on it and returned. If not given, a Network will be created and returned. Returns ------- An OpenPNM Project containing a GenericNetwork holding all the data """ from pandas import read_table, DataFrame net = {} # Parse the link1 file path = Path(path) filename = Path(path.resolve(), prefix+'_link1.dat') with open(filename, mode='r') as f: link1 = read_table(filepath_or_buffer=f, header=None, skiprows=1, sep=' ', skipinitialspace=True, index_col=0) link1.columns = ['throat.pore1', 'throat.pore2', 'throat.radius', 'throat.shape_factor', 'throat.total_length'] # Add link1 props to net net['throat.conns'] = np.vstack((link1['throat.pore1']-1, link1['throat.pore2']-1)).T net['throat.conns'] = np.sort(net['throat.conns'], axis=1) net['throat.radius'] = np.array(link1['throat.radius']) net['throat.shape_factor'] = np.array(link1['throat.shape_factor']) net['throat.total_length'] = np.array(link1['throat.total_length']) filename = Path(path.resolve(), prefix+'_link2.dat') with open(filename, mode='r') as f: link2 = read_table(filepath_or_buffer=f, header=None, sep=' ', skipinitialspace=True, index_col=0) link2.columns = ['throat.pore1', 'throat.pore2', 'throat.pore1_length', 'throat.pore2_length', 'throat.length', 'throat.volume', 'throat.clay_volume'] # Add link2 props to net cl_t = np.array(link2['throat.length']) net['throat.length'] = cl_t net['throat.conduit_lengths.throat'] = cl_t net['throat.volume'] = np.array(link2['throat.volume']) cl_p1 = np.array(link2['throat.pore1_length']) net['throat.conduit_lengths.pore1'] = cl_p1 cl_p2 = np.array(link2['throat.pore2_length']) net['throat.conduit_lengths.pore2'] = cl_p2 net['throat.clay_volume'] = np.array(link2['throat.clay_volume']) # --------------------------------------------------------------------- # Parse the node1 file filename = Path(path.resolve(), prefix+'_node1.dat') with open(filename, mode='r') as f: row_0 = f.readline().split() num_lines = int(row_0[0]) array = np.ndarray([num_lines, 6]) for i in range(num_lines): row = f.readline()\ .replace('\t', ' ').replace('\n', ' ').split() array[i, :] = row[0:6] node1 = DataFrame(array[:, [1, 2, 3, 4]]) node1.columns = ['pore.x_coord', 'pore.y_coord', 'pore.z_coord', 'pore.coordination_number'] # Add node1 props to net net['pore.coords'] = np.vstack((node1['pore.x_coord'], node1['pore.y_coord'], node1['pore.z_coord'])).T # --------------------------------------------------------------------- # Parse the node1 file filename = Path(path.resolve(), prefix+'_node2.dat') with open(filename, mode='r') as f: node2 = read_table(filepath_or_buffer=f, header=None, sep=' ', skipinitialspace=True, index_col=0) node2.columns = ['pore.volume', 'pore.radius', 'pore.shape_factor', 'pore.clay_volume'] # Add node2 props to net net['pore.volume'] = np.array(node2['pore.volume']) net['pore.radius'] = np.array(node2['pore.radius']) net['pore.shape_factor'] = np.array(node2['pore.shape_factor']) net['pore.clay_volume'] = np.array(node2['pore.clay_volume']) net['throat.area'] = ((net['throat.radius']**2) / (4.0*net['throat.shape_factor'])) net['pore.area'] = ((net['pore.radius']**2) / (4.0*net['pore.shape_factor'])) if network is None: network = GenericNetwork() network = cls._update_network(network=network, net=net) # Use OpenPNM Tools to clean up network # Trim throats connected to 'inlet' or 'outlet' reservoirs trim1 = np.where(np.any(net['throat.conns'] == -1, axis=1))[0] # Apply 'outlet' label to these pores outlets = network['throat.conns'][trim1, 1] network['pore.outlets'] = False network['pore.outlets'][outlets] = True trim2 = np.where(np.any(net['throat.conns'] == -2, axis=1))[0] # Apply 'inlet' label to these pores inlets = network['throat.conns'][trim2, 1] network['pore.inlets'] = False network['pore.inlets'][inlets] = True # Now trim the throats to_trim = np.hstack([trim1, trim2]) trim(network=network, throats=to_trim) return network.project