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# Copyright (C) 2015 Chintalagiri Shashank 

# 

# This file is part of Tendril. 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU Affero General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program 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 Affero General Public License for more details. 

# 

# You should have received a copy of the GNU Affero General Public License 

# along with this program.  If not, see <http://www.gnu.org/licenses/>. 

""" 

EntityHub Transforms Module documentation (:mod:`entityhub.transforms`) 

======================================================================= 

""" 

 

import csv 

 

 

class TransformFile(object): 

    def __init__(self, tfpath): 

        self._transform = {} 

        self._ideal = {} 

        self._status = {} 

        with open(tfpath) as f: 

            rdr = csv.reader(f) 

            for row in rdr: 

                self._transform[row[0].strip()] = row[1].strip() 

                self._ideal[row[0].strip()] = row[2].strip() 

                try: 

                    self._status[row[0].strip()] = row[3].strip() 

                except IndexError: 

                    self._status[row[0].strip()] = '' 

 

    def get_canonical_repr(self, contextual): 

        return self._transform[contextual.strip()] 

 

    def get_ideal_repr(self, contextual): 

        return self._ideal[contextual.strip()] 

 

    def get_status(self, contextual): 

        return self._status[contextual.strip()] 

 

    def get_contextual_repr(self, canonical): 

        for (k, v) in self._transform.iteritems(): 

            if v == canonical.strip(): 

                return k 

        return None 

 

    def has_contextual_repr(self, contextual): 

        if contextual.strip() in self._transform.keys(): 

            return True 

        else: 

            return False