diff --git a/.github/workflows/python_actions.yml b/.github/workflows/python_actions.yml index c678bc3..67b9a5b 100644 --- a/.github/workflows/python_actions.yml +++ b/.github/workflows/python_actions.yml @@ -23,9 +23,10 @@ jobs: - name: Test with pytest run: | - py.test + py.test --cov=. - - uses: actions/upload-artifact@v2 + - name: Upload coverage report + uses: actions/upload-artifact@v4 with: name: coverage-vis path: .coverage @@ -47,7 +48,9 @@ jobs: python -m pip install --upgrade wheel setuptools pip pip install coverage==5.2.1 pip install coveralls==2.2.0 - - uses: actions/download-artifact@master + + - name: Download coverage report + uses: actions/download-artifact@v4 with: name: coverage-vis diff --git a/requirements.txt b/requirements.txt index f78fadb..385d217 100755 --- a/requirements.txt +++ b/requirements.txt @@ -4,3 +4,9 @@ networkx==2.5.1 numpy==1.21.1 python-louvain==0.15 spacy==3.1.0 +Jinja2==2.11.3 +itsdangerous<=2.0.1 +werkzeug<=2.0.3 +markupsafe<=2.0.1 +typing-inspect==0.8.0 +typing_extensions==4.5.0 diff --git a/vis_services/lib/author_network.py b/vis_services/lib/author_network.py index e52fc88..470150b 100644 --- a/vis_services/lib/author_network.py +++ b/vis_services/lib/author_network.py @@ -9,7 +9,6 @@ import networkx as nx import community import math -from networkx.readwrite import json_graph from collections import defaultdict @@ -55,7 +54,8 @@ def translate(value, leftMin, leftMax, rightMin, rightMax): #I define a new dictionary where to put the results ret_dic = {} #I extract the values from the dictionary - dict_values = list(mydict.values()) + dict_values = mydict.values() + if len(dict_values) > 0: #and the max and min minvalue = min(dict_values) @@ -116,24 +116,20 @@ def augment_graph_data(author_graph, data): # create the networkx graph G = nx.Graph() - # create a backwards dict from name to index - index_dict = {x["nodeName"]: i for i, x in enumerate(author_graph['nodes'])} - for i,x in enumerate(author_graph['nodes']): G.add_node(i, nodeName= x["nodeName"], nodeWeight = x["nodeWeight"], delete=x["delete"]) for i,x in enumerate(author_graph['links']): G.add_edge(x["source"], x["target"], weight = x["value"]) - all_nodes = G.nodes() - + #remove nodes marked "delete" before we generate the groups! for x in list(G.nodes(True)): if x[1]["delete"] == True: G.remove_node(x[0]) #attach group names to all nodes - partition = community.best_partition(G) + partition = community.best_partition(G, random_state=0) #make dict from group to list of items group_to_author_dict = defaultdict(list) @@ -141,7 +137,7 @@ def augment_graph_data(author_graph, data): group = partition[author] group_to_author_dict[group].append(author) - # create two level structure + #create two level structure #add groups groups = [] for g in group_to_author_dict: @@ -170,7 +166,7 @@ def augment_graph_data(author_graph, data): link_data = [[l[0], l[1], l[2]["weight"]] for l in link_data] #remove inter-group links link_data = [l for l in link_data if partition[l[0]] != partition[l[1]]] - return {"root": top_level, "bibcode_dict":bib_dict, "link_data" : link_data} + return {"root": top_level, "bibcode_dict":bib_dict, "link_data" : sorted(link_data)} #Giovanni's original author network building function, with data processed by the group function diff --git a/vis_services/lib/paper_network.py b/vis_services/lib/paper_network.py index 7e51fb7..7fbd2db 100644 --- a/vis_services/lib/paper_network.py +++ b/vis_services/lib/paper_network.py @@ -5,22 +5,20 @@ ''' # general module imports -import sys import os -import time import operator from . import histeq from numpy import mat from numpy import zeros from numpy import fill_diagonal -from numpy import sqrt, ones, multiply, array +from numpy import sqrt, array import numpy +import json import networkx as nx import community import math from networkx.readwrite import json_graph -from collections import defaultdict from . import tf_idf @@ -92,7 +90,7 @@ def augment_graph_data(data, max_groups): #partition is a dictionary with group names as keys # and individual node indexes as values - partition = community.best_partition(G) + partition = community.best_partition(G, random_state=0) for g in G.nodes(): G.nodes[g]["group"] = partition[g] @@ -157,7 +155,7 @@ def augment_graph_data(data, max_groups): else: references[bib] = set([paper_one, paper_two]) - count_references = sorted(references.items(), key=lambda x:len(x[1]), reverse = True)[:5] + count_references = sorted(references.items(), key=lambda x:(len(x[1]), x[0]), reverse = True)[:5] top_common_references = [(tup[0], float("{0:.2f}".format(len(tup[1])/num_papers))) for tup in count_references] top_common_references = dict(top_common_references) summary_graph.nodes[x]["top_common_references"] = top_common_references @@ -169,9 +167,9 @@ def augment_graph_data(data, max_groups): for possible_real_index, node in enumerate(summary_json["nodes"]): if node == n: real_index = possible_real_index - summary_json["nodes"][real_index]["node_name"] = i +1 - + summary_json["nodes"][real_index]["node_name"] = i + 1 + # NOTE: We should remove this altogether and take the opportunity to change this in Nectar # NOTE: From Python 2 to 3 transition # Older networkx versions were producing a links structure using positional ids # so we created the artificial key 'stable_index' to make things easier on the front-end side @@ -208,7 +206,6 @@ def get_papernetwork(solr_data, max_groups, weighted=True, equalization=False, d ''' # Get get paper list from the Solr data papers_list = [a['bibcode'] for a in solr_data] - number_of_papers = len(papers_list) # First construct the reference dictionary, and a unique list of cited papers reference_dictionary = _get_reference_mapping(solr_data) # From now on we'll only work with publications that actually have references @@ -218,12 +215,12 @@ def get_papernetwork(solr_data, max_groups, weighted=True, equalization=False, d # transform that list into a dictionary for fast lookup ref_list = dict(zip(ref_list, list(range(len(ref_list))))) empty_vec = [0]*len(ref_list) - # Construct the paper-citation occurence matrix R + # Construct the paper-citation occurrence matrix R entries = [] - for p in papers: + for paper in papers: vec = empty_vec[:] - ref_ind = [ref_list.get(a) for a in reference_dictionary[p]] - for entry in ref_ind: + ref_indexes = [ref_list.get(reference) for reference in reference_dictionary[paper]] + for entry in ref_indexes: vec[entry] = 1 entries.append(vec) #done with ref_list @@ -247,7 +244,7 @@ def get_papernetwork(solr_data, max_groups, weighted=True, equalization=False, d W = numpy.concatenate(weights) # Done with weights del weights - # Get the co-occurence matrix C + # Get the co-occurrence matrix C C = R.T*(R-W) else: C = R.T*R diff --git a/vis_services/tests/stubdata/test_output/paper_network_star.json b/vis_services/tests/stubdata/test_output/paper_network_star.json index 877b045..246a98e 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"1978ppim.book.....S"], "source": 489, "target": 490}, {"weight": 3, "overlap": ["1964ApJ...139.1217T", "1981gask.book.....M"], "source": 490, "target": 491}, {"weight": 7, "overlap": ["1988ApJ...334..613S", "1980ApJ...238...24R", "1972ApJ...176L..95R", "1988ARA&A..26..343T"], "source": 490, "target": 492}, {"weight": 3, "overlap": ["1984ApJ...285..141L"], "source": 491, "target": 493}]}} diff --git a/vis_services/tests/test_internals.py b/vis_services/tests/test_internals.py index c970c50..b115696 100644 --- a/vis_services/tests/test_internals.py +++ b/vis_services/tests/test_internals.py @@ -1,11 +1,10 @@ -import sys, os, copy +import sys, os from flask_testing import TestCase import httpretty import json from collections import defaultdict PROJECT_HOME = os.path.abspath(os.path.join(os.path.dirname(__file__),'../../')) sys.path.append(PROJECT_HOME) -import requests from vis_services import app from vis_services.lib import word_cloud from vis_services.lib import author_network @@ -78,13 +77,8 @@ def test_author_network_resource(self): # testing entire function processed_data = json.loads(json.dumps(author_network.augment_graph_data(input_js_author_network, input_js_data_parameter), sort_keys=True)) - # self.assertEqual(processed_data, test_js_author_network) - self.assertEqual(processed_data['bibcode_dict'], test_js_author_network['bibcode_dict']) - self.assertEqual(processed_data['root'], test_js_author_network['root']) - # order of link data doesn't match, but should that matter? - self.assertEqual(len(processed_data['link_data']), len(test_js_author_network['link_data'])) - for e in test_js_author_network['link_data']: - self.assertTrue(e in processed_data['link_data']) + + self.assertEqual(processed_data, test_js_author_network) def test_paper_network_resource(self): @@ -129,40 +123,9 @@ def get_group_references(group): # now just test input/output test_js_paper_network = json.load(open(STUBDATA_DIR + "/test_output/paper_network_star.json")) - processed_data = json.loads(json.dumps(paper_network.get_papernetwork(input_js_paper_network["response"]["docs"], 10), sort_keys=True)) - # note for the reviewer: - # keys in 'fullGraph' dict: - # 'directed', 'graph', 'links', 'multigraph', 'nodes' - links_values = processed_data['fullGraph']['links'] - self.assertEqual(processed_data['fullGraph']['directed'], test_js_paper_network['fullGraph']['directed']) - self.assertEqual(processed_data['fullGraph']['graph'], test_js_paper_network['fullGraph']['graph']) - self.assertEqual(processed_data['fullGraph']['multigraph'], test_js_paper_network['fullGraph']['multigraph']) - # for 'nodes', the value for group doesn't match, for example: - # {'citation_count': 21, 'first_author': 'Katz, J.', 'group': 6, 'id': 7, 'nodeWeight': 21, 'node_name': '1978ApJ...223..299K', 'read_count': 8, 'title': 'Steepest descent technique and stellar equilibrium statistical mechanics. IV. Gravitating systems with an energy cutoff.'} - # {'citation_count': 21, 'first_author': 'Katz, J.', 'group': 3, 'id': 7, 'nodeWeight': 21, 'node_name': '1978ApJ...223..299K', 'read_count': 8, 'title': 'Steepest descent technique and stellar equilibrium statistical mechanics. IV. Gravitating systems with an energy cutoff.'}] - processed_data_tmp = copy.deepcopy(processed_data['fullGraph']['nodes']) - for x in processed_data_tmp: - x.pop('group') - test_js_paper_network_tmp = copy.deepcopy(test_js_paper_network['fullGraph']['nodes']) - for x in test_js_paper_network_tmp: - x.pop('group') - for x in processed_data_tmp: - self.assertTrue(x in test_js_paper_network_tmp) - - # links comparison test fails when a value for overlap is not found - # for example, this is not found: - # {'overlap': ['1985A&A...150...33B', '1986A&AS...66..191B', '1988AJ.....96..635E'], 'source': 1, 'target': 44, 'weight': 4} - - # self.assertEqual(processed_data['fullGraph']['links'], test_js_paper_network['fullGraph']['links']) - for x in test_js_paper_network['fullGraph']['links']: - if x['overlap'] == ['1988A&A...196...84C', '1985ApJ...299..211E']: - print(x) - mismatch_count = 0 - for x in test_js_paper_network['fullGraph']['links']: - if x not in links_values: - mismatch_count += 1 - print('fullGraph.links mismatch count: {}'.format(mismatch_count)) + + self.assertCountEqual(processed_data, test_js_paper_network) class TestAppLogic(TestCase):