lakesuperior_legacy/lakesuperior/toolbox.py

import logging
import pickle

from collections import defaultdict
from hashlib import sha1

from flask import request, g
from rdflib.term import Literal, URIRef, Variable

from lakesuperior.dictionaries.namespaces import ns_collection as nsc


class Toolbox:
    '''
    Utility class to translate and generate strings and other objects.
    '''

    _logger = logging.getLogger(__name__)

    ROOT_NODE_URN = nsc['fcsystem'].root

    def __init__(self):
        '''
        Set the base URL for the requests. This class has to be instantiated
        within a request context.
       '''
        self.base_url = request.host_url + g.url_prefix


    def uuid_to_uri(self, uuid):
        '''Convert a UUID to a URI.

        @return URIRef
        '''
        uri = '{}/{}'.format(self.base_url, uuid) if uuid else self.base_url

        return URIRef(uri)


    def uri_to_uuid(self, uri):
        '''Convert an absolute URI (internal or external) to a UUID.

        @return string
        '''
        if uri == self.ROOT_NODE_URN:
            return None
        elif uri.startswith(nsc['fcres']):
            return str(uri).replace(nsc['fcres'], '')
        else:
            return str(uri).replace(self.base_url, '').strip('/')


    def localize_string(self, s):
        '''Convert URIs into URNs in a string using the application base URI.

        @param string s Input string.

        @return string
        '''
        if s.strip('/') == self.base_url:
            return str(self.ROOT_NODE_URN)
        else:
            return s.strip('/').replace(self.base_url+'/', str(nsc['fcres']))


    def localize_term(self, uri):
        '''
        Convert an URI into an URN.

        @param rdflib.term.URIRef urn Input URI.

        @return rdflib.term.URIRef
        '''
        return URIRef(self.localize_string(str(uri)))


    def localize_graph(self, g):
        '''
        Locbalize a graph.
        '''
        q = '''
        CONSTRUCT {{ ?s ?p ?o . }} WHERE {{
          {{
            ?s ?p ?o .
            FILTER (
              STRSTARTS(str(?s), "{0}")
              ||
              STRSTARTS(str(?o), "{0}")
              ||
              STRSTARTS(str(?s), "{0}/")
              ||
              STRSTARTS(str(?o), "{0}/")
            ) .
          }}
        }}'''.format(self.base_url)
        flt_g = g.query(q)

        for t in flt_g:
            local_s = self.localize_term(t[0])
            local_o = self.localize_term(t[2]) \
                    if isinstance(t[2], URIRef) \
                    else t[2]
            g.remove(t)
            g.add((local_s, t[1], local_o))

        return g


    def globalize_string(self, s):
        '''Convert URNs into URIs in a string using the application base URI.

        @param string s Input string.

        @return string
        '''
        return s.replace(str(nsc['fcres']), self.base_url + '/')


    def globalize_term(self, urn):
        '''
        Convert an URN into an URI using the application base URI.

        @param rdflib.term.URIRef urn Input URN.

        @return rdflib.term.URIRef
        '''
        if urn == self.ROOT_NODE_URN:
            urn = nsc['fcres']

        return URIRef(self.globalize_string(str(urn)))


    def globalize_graph(self, g):
        '''
        Globalize a graph.
        '''
        q = '''
        CONSTRUCT {{ ?s ?p ?o . }} WHERE {{
          {{
            ?s ?p ?o .
            FILTER (
              STRSTARTS(str(?s), "{0}")
              ||
              STRSTARTS(str(?o), "{0}")
              ||
              STRSTARTS(str(?s), "{1}")
              ||
              STRSTARTS(str(?o), "{1}")
            ) .
          }}
        }}'''.format(nsc['fcres'], self.ROOT_NODE_URN)
        flt_g = g.query(q)

        for t in flt_g:
            global_s = self.globalize_term(t[0])
            global_o = self.globalize_term(t[2]) \
                    if isinstance(t[2], URIRef) \
                    else t[2]
            g.remove(t)
            g.add((global_s, t[1], global_o))

        return g


    def globalize_rsrc(self, rsrc):
        '''
        Globalize a resource.
        '''
        g = rsrc.graph
        urn = rsrc.identifier

        global_g = self.globalize_graph(g)
        global_uri = self.globalize_term(urn)

        return global_g.resource(global_uri)


    def parse_rfc7240(self, h_str):
        '''
        Parse `Prefer` header as per https://tools.ietf.org/html/rfc7240

        The `cgi.parse_header` standard method does not work with all possible
        use cases for this header.

        @param h_str (string) The header(s) as a comma-separated list of Prefer
        statements, excluding the `Prefer: ` token.
        '''
        parsed_hdr = defaultdict(dict)

        # Split up headers by comma
        hdr_list = [ x.strip() for x in h_str.split(',') ]
        for hdr in hdr_list:
            parsed_pref = defaultdict(dict)
            # Split up tokens by semicolon
            token_list = [ token.strip() for token in hdr.split(';') ]
            prefer_token = token_list.pop(0).split('=')
            prefer_name = prefer_token[0]
            # If preference has a '=', it has a value, else none.
            if len(prefer_token)>1:
                parsed_pref['value'] = prefer_token[1].strip('"')

            for param_token in token_list:
                # If the token list had a ';' the preference has a parameter.
                print('Param token: {}'.format(param_token))
                param_parts = [ prm.strip().strip('"') \
                        for prm in param_token.split('=') ]
                param_value = param_parts[1] if len(param_parts) > 1 else None
                parsed_pref['parameters'][param_parts[0]] = param_value

            parsed_hdr[prefer_name] = parsed_pref

        return parsed_hdr


    def rdf_cksum(self, g):
        '''
        Generate a checksum for a graph.

        This is not straightforward because a graph is derived from an
        unordered data structure (RDF).

        What this method does is ordering the graph by subject, predicate,
        object, then creating a pickle string and a checksum of it.

        N.B. The context of the triples is ignored, so isomorphic graphs would
        have the same checksum regardless of the context(s) they are found in.

        @TODO This can be later reworked to use a custom hashing algorithm.

        @param rdflib.Graph g The graph to be hashed.

        @return string SHA1 checksum.
        '''
        # Remove the messageDigest property, which very likely reflects the
        # previous state of the resource.
        g.remove((Variable('s'), nsc['premis'].messageDigest, Variable('o')))

        ord_g = sorted(list(g), key=lambda x : (x[0], x[1], x[2]))
        hash = sha1(pickle.dumps(ord_g)).hexdigest()

        return hash