D-Net

from json import JSONEncoder

import sys

import re

from elasticsearch import Elasticsearch

from elasticsearch_dsl import *

import os

from os import path

pid_resolver = {

    "pdb": "http://www.rcsb.org/pdb/explore/explore.do?structureId=%s",

    "ncbi-n": "http://www.ncbi.nlm.nih.gov/gquery/?term=%s",

    "pmid": "http://www.ncbi.nlm.nih.gov/pubmed/%s",

    "pmcid": "http://www.ncbi.nlm.nih.gov/pmc/articles/%s",

    "pubmedid": "http://www.ncbi.nlm.nih.gov/pubmed/%s",

    "doi": "http://dx.doi.org/%s",

    "genbank": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "nuccore": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "swiss-prot": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "arrayexpress": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "biomodels": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "bmrb": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "ena": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "geo": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "ensembl": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "mgi": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "bind": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "pride": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "ddbj": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "bioproject": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "embl": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

    "sra": "http://www.ncbi.nlm.nih.gov/nucest/%s?report=genbank",

}

def resolveIdentifier(pid, pid_type):

    if pid_type != None:

        regex = r"\b(10[.][0-9]{4,}(?:[.][0-9]+)*/(?:(?![\"&\'<>])\S)+)\b"

        if re.match(regex,pid):

            print "It should be doi"

            pid_type = 'doi'

        if pid_type.lower() in pid_resolver:

            return pid_resolver[pid_type.lower()] % pid

        else:

            if pid_type.lower() == 'openaire':

                return "https://www.openaire.eu/search/publication?articleId=%s" % pid.replace('oai:dnet:', '')

            elif pid_type.lower() == 'url':

                return  pid

            else:

                return "http://identifiers.org/%s:%s" % (pid_type, pid)

    return ""

def get_property():

    f = open(path.join(os.path.dirname(os.path.realpath(__file__)), '../../api.properties'))

    p = {}

    for line in f:

        data = line.strip().split("=")

        p[data[0].strip()] = data[1].strip()

    return p

def create_typology_filter(value):

    return Q('match', typology=value)

def create_pid_type_filter(value):

    args = {'localIdentifier.type': value}

    return Q('nested', path='localIdentifier', query=Q('bool', must=[Q('match', **args)]))

def create_pid_query(value):

    args = {'localIdentifier.id': value}

    return Q('nested', path='localIdentifier', query=Q('bool', must=[Q('match', **args)]))

def create_publisher_filter(value):

    return Q('match', publisher=value)

def create_datasource_filter(value):

    args = {'datasources.datasourceName': value}

    return Q('nested', path='datasources', query=Q('bool', must=[Q('match', **args)]))

class DLIESResponseEncoder(JSONEncoder):

    def default(self, o):

        return o.__dict__

class DLIESResponse(object):

    def __init__(self, facet=None, total=0, hits=[]):

        if facet is None:

            facet = dict(pid=[], typology=[], datasource=[])

        self.facet = facet

        self.total = total

        self.hits = hits

class DLIESConnector(object):

    def __init__(self):

        props = get_property()

        self.index_host = [x.strip() for x in props['es_index'].split(',')]

        self.client = Elasticsearch(hosts=self.index_host)

        self.index_name = props['api.index']

    def get_main_page_stats(self):

        stats = dict(total =Search(using=self.client, index=self.index_name).doc_type('scholix').execute().hits.total/2)

        for item in ['dataset', 'publication']:

            s= Search(using=self.client, index=self.index_name).doc_type('object').query(Q('match', typology=item))

            stats[item] = s.execute().hits.total

        return stats

    def query_by_id(self, id):

        s = Search(using=self.client, index=self.index_name).doc_type('object')

        s = s.query(create_pid_query(id))

        s.aggs.bucket('typologies', 'terms', field='typology')

        s.aggs.bucket('all_datasources', 'nested', path='datasources').bucket('all_names', 'terms',

                                                                              field='datasources.datasourceName')

        s.aggs.bucket('all_publisher', 'terms', field='publisher')

        s.aggs.bucket('all_pids', 'nested', path='localIdentifier').bucket('all_types', 'terms',

                                                                           field='localIdentifier.type')

        response = s.execute()

        hits = []

        for index_result in response.hits:

            input_source = index_result.__dict__['_d_']

            fixed_titles = []

            for ids in input_source.get('localIdentifier', []):

                ds = resolveIdentifier(ids['id'], ids['type'])

                ids['url'] = ds

            for t in input_source.get('title', []):

                if len(t) > 0 and t[0] == '"' and t[-1] == '"':

                    fixed_titles.append(t[1:-1])

                else:

                    fixed_titles.append(t)

            input_source['title'] = fixed_titles

            hits.append(input_source)

        pid_types = []

        for tag in response.aggs.all_pids.all_types.buckets:

            pid_types.append(dict(key=tag.key, count=tag.doc_count))

        datasources = []

        for tag in response.aggs.all_datasources.all_names.buckets:

            datasources.append(dict(key=tag.key, count=tag.doc_count))

        typologies = []

        for tag in response.aggs.typologies.buckets:

            typologies.append(dict(key=tag.key, count=tag.doc_count))

        publishers = []

        for tag in response.aggs.all_publisher.buckets:

            if len(tag.key) > 0:

                publishers.append(dict(key=tag.key, count=tag.doc_count))

        return DLIESResponse(total=response.hits.total,

                             facet=dict(pid=pid_types, typology=typologies, datasource=datasources,

                                        publishers=publishers), hits=hits)

    def simple_query(self, textual_query, start=None, end=None, user_filter=None):

        s = Search(using=self.client, index=self.index_name).doc_type('object')

        q = Q('match', _all=textual_query)

        s.aggs.bucket('typologies', 'terms', field='typology')

        s.aggs.bucket('all_datasources', 'nested', path='datasources').bucket('all_names', 'terms',

                                                                              field='datasources.datasourceName')

        s.aggs.bucket('all_publisher', 'terms', field='publisher')

        filter_queries = []

        if user_filter is not None and len(user_filter) > 0:

            for f in user_filter.split('__'):

                filter_key = f.split('_')[0]

                filter_value = f.split('_')[1]

                if filter_key == 'typology':

                    filter_queries.append(create_typology_filter(filter_value))

                elif filter_key == 'datasource':

                    filter_queries.append(create_datasource_filter(filter_value))

                elif filter_key == 'pidtype':

                    filter_queries.append(create_pid_type_filter(filter_value))

                elif filter_key == 'publisher':

                    filter_queries.append(create_publisher_filter(filter_value))

        if len(filter_queries) > 0:

            s = s.query(q).filter(Q('bool', must=filter_queries))

        else:

            s = s.query(q)

        s.aggs.bucket('all_pids', 'nested', path='localIdentifier').bucket('all_types', 'terms',

                                                                           field='localIdentifier.type')

        if start is not None:

            if end is None:

                end = start + 10

            s = s[start:end]

        response = s.execute()

        hits = []

        for index_result in response.hits:

            input_source = index_result.__dict__['_d_']

            fixed_titles = []

            for ids in input_source.get('localIdentifier', []):

                ds = resolveIdentifier(ids['id'], ids['type'])

                ids['url'] = ds

            for t in input_source.get('title', []):

                if len(t) > 0 and t[0] == '"' and t[-1] == '"':

                    fixed_titles.append(t[1:-1])

                else:

                    fixed_titles.append(t)

            input_source['title'] = fixed_titles

            hits.append(input_source)

        pid_types = []

        for tag in response.aggs.all_pids.all_types.buckets:

            pid_types.append(dict(key=tag.key, count=tag.doc_count))

        datasources = []

        for tag in response.aggs.all_datasources.all_names.buckets:

            datasources.append(dict(key=tag.key, count=tag.doc_count))

        typologies = []

        for tag in response.aggs.typologies.buckets:

            typologies.append(dict(key=tag.key, count=tag.doc_count))

        publishers = []

        for tag in response.aggs.all_publisher.buckets:

            if len(tag.key) > 0:

                publishers.append(dict(key=tag.key, count=tag.doc_count))

        return DLIESResponse(total=response.hits.total,

                             facet=dict(pid=pid_types, typology=typologies, datasource=datasources,

                                        publishers=publishers), hits=hits)

    def related_type(self, object_id, object_type, start=None):

        args = {'target.objectType': object_type}

        query_type = Q('nested', path='target', query=Q('bool', must=[Q('match', **args)]))

        args_id = {'source.dnetIdentifier': object_id}

        query_for_id = Q('nested', path='source', query=Q('bool', must=[Q('match', **args_id)]))

        s = Search(using=self.client).index(self.index_name).doc_type('scholix').query(query_for_id & query_type)

        if start:

            s = s[start:start + 10]

        response = s.execute()

        hits = []

        for index_hit in response.hits:

            current_item = index_hit.__dict__['_d_']

            if 'target' in current_item:

                ids = []

                for item in current_item['target']['identifier']:

                    c_it = item

                    c_it['url'] = resolveIdentifier(item['identifier'], item['schema'])

                    ids .append(c_it)

                current_item['target']['identifier'] = ids

            hits.append(current_item)

        return hits

    def fix_collectedFrom(self, source, relation):

        if relation is None:

            return

        relSource = relation.get('source')

        collectedFrom = relSource['collectedFrom']

        for coll in collectedFrom:

            for d in source['datasources']:

                if d['datasourceName'] == coll['provider']['name']:

                    d['provisionMode'] = coll['provisionMode']

        return source

    def item_by_id(self, id, type=None, start=None):

        try:

            res = self.client.get(index=self.index_name, doc_type='object', id=id)

            hits = []

            input_source = res['_source']

            fixed_titles = []

            for t in input_source.get('title',[]):

                if len(t) > 0 and t[0] == '"' and t[-1] == '"':

                    fixed_titles.append(t[1:-1])

                else:

                    fixed_titles.append(t)

            input_source['title'] = fixed_titles

            for ids in input_source.get('localIdentifier', []):

                ds = resolveIdentifier(ids['id'], ids['type'])

                ids['url'] = ds

            related_publications = []

            related_dataset = []

            related_unknown = []

            rel_source = None

            if input_source.get('relatedPublications') > 0:

                if 'publication' == type:

                    related_publications = self.related_type(id, 'publication', start)

                else:

                    related_publications = self.related_type(id, 'publication')

                if len(related_publications) > 0:

                    rel_source = related_publications[0]

                else:

                    rel_source = {}

            if input_source.get('relatedDatasets') > 0:

                if 'dataset' == type:

                    related_dataset = self.related_type(id, 'dataset', start)

                else:

                    related_dataset = self.related_type(id, 'dataset')

                rel_source = related_dataset[0]

            if input_source.get('relatedUnknown') > 0:

                if 'unknown' == type:

                    related_unknown = self.related_type(id, 'unknown', start)

                else:

                    related_unknown = self.related_type(id, 'unknown')

                rel_source = related_unknown[0]

            input_source = self.fix_collectedFrom(input_source, rel_source)

            hits.append(input_source)

            hits.append(dict(related_publications=related_publications, related_dataset=related_dataset,

                             related_unknown=related_unknown))

            return DLIESResponse(total=1, hits=hits)

        except Exception as e:

            print "Error on getting item "

            print e

            print "on line %i" % sys.exc_traceback.tb_lineno

            return DLIESResponse()