code: import re
  import numpy as np
  from openpnm.io.Pandas import Pandas
  from openpnm.io import GenericIO, Dict
  from openpnm.utils import logging, Workspace
  logger = logging.getLogger(__name__)
  ws = Workspace()
  
  
  class CSV(GenericIO):
      r"""
      Reads and writes CSV (comma-separated-value files) containing pore and
      throat data
  
      Notes
      -----
      There are a few rules governing how the data is be stored:
  
      1. The first row of the file (column headers) must contain the
      property names. The subsequent rows contain the data.
  
      2. The property names should be in the usual OpenPNM format, such as
      of ``pore.volume`` or ``throat.surface_area``.
  
      3. Each column represents a specific property.  For Np x 1 or Nt x 1
      data such as *pore.volume* this is straightforward.  For Np x *m* or
      Nt x *m* data, each of the *m* columns should have their own column in
      in the CSV file, with a numpy-style index indicating which axis it
      corresponds to.  For instance, the *pore.coords* values should be stored
      as three separate columns with the headings: *pore.coords[0]*,
      *pore.coords[1]*, and *pore.coords[2]*.  OpenPNM will convert that back
      into an Np x *m* array upon loading.
  
      4. The file can contain both or either pore and throat data.
  
      5. Labels can be imported by placing the characters TRUE and FALSE
      in a column corresponding to the label name (i.e. *pore.front*).  TRUE
      indicates where the label applies and FALSE otherwise.
  
      """
  
      @classmethod
      def save(cls, *args, **kwargs):
          r"""
          This method is to be deprecated. Use ``export_data`` instead.
          """
          cls.export_data(*args, **kwargs)
  
      @classmethod
      def export_data(cls, network=None, phases=[], filename='', delim=' | '):
          r"""
          Save all the pore and throat property data on the Network (and
          optionally on any Phases objects) to CSV files.
  
          Parameters
          ----------
          network : OpenPNM Network
              The Network containing the data to be stored
  
          phases : list of OpenPNM Phases (optional)
              The Phases whose data should be stored.
  
          filename : string or path object
              The name of the file to store the data
  
          Notes
          -----
          The data from all Geometry objects is added to the file automatically.
  
          """
          project, network, phases = cls._parse_args(network=network,
                                                     phases=phases)
          df = Pandas.to_dataframe(network=network, phases=phases,
                                   join=True, delim=delim)
          # Write to file
          if filename == '':
              filename = project.name
          fname = cls._parse_filename(filename=filename, ext='csv')
          df.to_csv(fname, index=False)
  
      @classmethod
      def load(cls, *args, **kwargs):
          r"""
          This method will be deprecated.  Use ``import_data`` instead.
          """
          proj = cls.import_data(*args, **kwargs)
          return proj
  
      @classmethod
      def import_data(cls, filename, project=None, delim=' | '):
          r"""
          Opens a 'csv' file, reads in the data, and adds it to the **Network**
  
          Parameters
          ----------
          filename : string (optional)
              The name of the file containing the data to import.  The formatting
              of this file is outlined below.
  
          project : OpenPNM Project object
              A GenericNetwork is created and added to the specified Project.
              If no Project object is supplied then one will be created and
              returned.
  
          Returns
          -------
          project : list
              An OpenPNM project containing the data assigned to Generic
              versions of the objects from which it was exported.
  
          """
          from pandas import read_table
  
          if project is None:
              project = ws.new_project()
  
          fname = cls._parse_filename(filename, ext='csv')
          a = read_table(filepath_or_buffer=fname,
                         sep=',',
                         skipinitialspace=True,
                         index_col=False,
                         true_values=['T', 't', 'True', 'true', 'TRUE'],
                         false_values=['F', 'f', 'False', 'false', 'FALSE'])
  
          dct = {}
          # First parse through all the items and re-merge columns
          keys = sorted(list(a.keys()))
          for item in keys:
              m = re.search(r'\[.\]', item)  # The dot '.' is a wildcard
              if m:  # m is None if pattern not found, otherwise merge cols
                  pname = re.split(r'\[.\]', item)[0]  # Get base propname
                  # Find all other keys with same base propname
                  merge_keys = [k for k in a.keys() if k.startswith(pname)]
                  # Rerieve and remove arrays with same base propname
                  merge_cols = [a.pop(k) for k in merge_keys]
                  # Merge arrays into multi-column array and store in DataFrame
                  dct[pname] = np.vstack(merge_cols).T
                  # Remove key from list of keys
                  for k in keys:
                      if k.startswith(pname):
                          keys.pop(keys.index(k))
              else:
                  dct[item] = np.array(a.pop(item))
  
          project = Dict.from_dict(dct, project=project, delim=delim)
  
          return project
  
apis: pandas.read_table numpy.vstack