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Commit c0727d90 authored by Malte Schokolowski's avatar Malte Schokolowski
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more bugfixes and tests

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1 merge request!12bug fixes and updates to code
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with 128 additions and 33 deletions
verarbeitung/start_script.py → start_script.py
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...@@ -9,4 +9,4 @@ doi_list.append('https://pubs.acs.org/doi/10.1021/acs.jcim.9b00249') ...@@ -9,4 +9,4 @@ doi_list.append('https://pubs.acs.org/doi/10.1021/acs.jcim.9b00249')
#doi_list.append('https://doi.org/10.1021/acs.jcim.9b00249') #doi_list.append('https://doi.org/10.1021/acs.jcim.9b00249')
doi_list.append('https://pubs.acs.org/doi/10.1021/acs.jcim.1c00203') doi_list.append('https://pubs.acs.org/doi/10.1021/acs.jcim.1c00203')
doi_list.append('https://doi.org/10.1021/acs.jmedchem.0c01332') doi_list.append('https://doi.org/10.1021/acs.jmedchem.0c01332')
Processing(doi_list, 2, 2, 'test.json') Processing(doi_list, 3, 2, 'test.json')
\ No newline at end of file \ No newline at end of file
verarbeitung/construct_new_graph/add_citations_rec.py
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...@@ -32,10 +32,12 @@ def get_cit_type_list(pub, cit_type): ...@@ -32,10 +32,12 @@ def get_cit_type_list(pub, cit_type):
function to create nodes and edges and call create_graph_structure_citations function to create nodes and edges and call create_graph_structure_citations
''' '''
if (cit_type == "Citation"): if cit_type == "Citation":
return(pub.citations) return(pub.citations)
else: elif cit_type == "Reference":
return(pub.references) return(pub.references)
else:
return(ValueError)
def create_global_lists_cit(input_nodes, input_edges, pub, search_depth, search_depth_max, cit_type, test_var): def create_global_lists_cit(input_nodes, input_edges, pub, search_depth, search_depth_max, cit_type, test_var):
''' '''
......
verarbeitung/construct_new_graph/initialize_graph.py
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...@@ -15,6 +15,7 @@ __status__ = "Production" ...@@ -15,6 +15,7 @@ __status__ = "Production"
import sys import sys
import gc
from pathlib import Path from pathlib import Path
from os import error from os import error
sys.path.append("../") sys.path.append("../")
...@@ -24,6 +25,43 @@ from verarbeitung.get_pub_from_input import get_pub ...@@ -24,6 +25,43 @@ from verarbeitung.get_pub_from_input import get_pub
from .export_to_json import output_to_json from .export_to_json import output_to_json
from .add_citations_rec import add_citations, create_global_lists_cit from .add_citations_rec import add_citations, create_global_lists_cit
def initialize_nodes_list_test(doi_input_list, search_depth_max, search_height_max, test_var):
'''
:param doi_input_list: input list of doi from UI
:type doi_input_list: List[String]
:param search_depth_max: maximum depth to search for references
:type search_depth_max: int
:param search_height_max: maximum height to search for citations
:type search_height_max: int
:param test_var: variable to differenciate between test and url call
:type test_var: boolean
for unit test purposes only
'''
global nodes, edges
nodes = []
edges = []
return(initialize_nodes_list(doi_input_list, search_depth_max, search_height_max, test_var))
def complete_inner_edges_test(test_nodes, test_edges):
'''
:param test_nodes: list of publications from unit test
:type test_nodes: List[Publication]
:param test_nodes: list of links from unit test
:type test_nodes: List[List[String,String]]
for unit test purposes only
'''
global nodes, edges
nodes = test_nodes
edges = test_edges
complete_inner_edges()
return(nodes, edges)
def initialize_nodes_list(doi_input_list, search_depth_max, search_height_max, test_var): def initialize_nodes_list(doi_input_list, search_depth_max, search_height_max, test_var):
''' '''
...@@ -54,6 +92,7 @@ def initialize_nodes_list(doi_input_list, search_depth_max, search_height_max, t ...@@ -54,6 +92,7 @@ def initialize_nodes_list(doi_input_list, search_depth_max, search_height_max, t
# checks if publication already exists in nodes # checks if publication already exists in nodes
not_in_nodes = True #boolean value to check if a node already exists in the set of nodes not_in_nodes = True #boolean value to check if a node already exists in the set of nodes
for node in nodes: #iterates over every node in the set of nodes for node in nodes: #iterates over every node in the set of nodes
if (pub.doi_url == node.doi_url): #determines that a node with this doi already is in the set if (pub.doi_url == node.doi_url): #determines that a node with this doi already is in the set
not_in_nodes = False #false --> node will not be created not_in_nodes = False #false --> node will not be created
...@@ -110,6 +149,15 @@ def init_graph_construction(doi_input_list, search_depth, search_height, test_va ...@@ -110,6 +149,15 @@ def init_graph_construction(doi_input_list, search_depth, search_height, test_va
:param test_var: variable to differenciate between test and url call :param test_var: variable to differenciate between test and url call
:type test_var: boolean :type test_var: boolean
:param test_var: variable to check if call is from update_graph with known nodes and edges or fresh construction
:type test_var: boolean
:param input_nodes: list of publications from update_graph
:type input_nodes: List[Publication]
:param input_nodes: list of links from update_graph
:type input_nodes: List[List[String,String]]
main function to start graph generation main function to start graph generation
''' '''
...@@ -145,7 +193,11 @@ def init_graph_construction(doi_input_list, search_depth, search_height, test_va ...@@ -145,7 +193,11 @@ def init_graph_construction(doi_input_list, search_depth, search_height, test_va
# adds edges between reference group and citation group of known publications # adds edges between reference group and citation group of known publications
complete_inner_edges() complete_inner_edges()
# calls a skript to save nodes and edges of graph in .json file # garbage collection to delete nodes and edges lists. Needed because python keeps lists after function end till next function call
output_to_json(nodes, edges, test_var = test_var) new_nodes = nodes.copy()
new_edges = edges.copy()
del nodes
del edges
gc.collect()
return(nodes,edges) return(new_nodes,new_edges)
verarbeitung/json_text.json
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View file @ c0727d90
{"nodes": [{"doi": "https://doi.org/10.1021/acs.jcim.9b00249", "name": "Comparing Molecular Patterns Using the Example of SMARTS: Applications and Filter Collection Analysis", "author": ["Emanuel S. R. Ehmki", "Robert Schmidt", "Farina Ohm", "Matthias Rarey"], "year": "May 24, 2019", "journal": "Journal of Chemical Information and Modeling", "group": "Input", "depth": 0, "citations": 5}, {"doi": "https://doi.org/10.1021/acs.chemrev.1c00107", "name": "Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems", "author": ["John A. Keith", "Valentin Vassilev-Galindo", "Bingqing Cheng", "Stefan Chmiela", "Michael Gastegger", "Klaus-Robert M\u00fcller", "Alexandre Tkatchenko"], "year": "July 7, 2021", "journal": "Chem. Rev.", "group": "Citedby", "depth": 1, "citations": 2}, {"doi": "https://doi.org/10.1021/acs.jcim.0c00741", "name": "Disconnected Maximum Common Substructures under Constraints", "author": ["Robert Schmidt", "Florian Krull", "Anna Lina Heinzke", "Matthias Rarey"], "year": "December 16, 2020", "journal": "Journal of Chemical Information and Modeling", "group": "Citedby", "depth": 1, "citations": 0}, {"doi": "https://doi.org/10.1021/acs.jmedchem.0c01332", "name": "Evolution of Novartis\u2019 Small Molecule Screening Deck Design", "author": ["Ansgar Schuffenhauer", "Nadine Schneider", "Samuel Hintermann", "Douglas Auld", "Jutta Blank", "Simona Cotesta", "Caroline Engeloch", "Nikolas Fechner", "Christoph Gaul", "Jerome Giovannoni", "Johanna Jansen", "John Joslin", "Philipp Krastel", "Eugen Lounkine", "John Manchester", "Lauren G. Monovich", "Anna Paola Pelliccioli", "Manuel Schwarze", "Michael D. Shultz", "Nikolaus Stiefl", "Daniel K. Baeschlin"], "year": "November 3, 2020", "journal": "Journal of Medicinal Chemistry", "group": "Citedby", "depth": 1, "citations": 8}, {"doi": "https://doi.org/10.1021/acs.jcim.9b00250", "name": "Comparing Molecular Patterns Using the Example of SMARTS: Theory and Algorithms", "author": ["Robert Schmidt", "Emanuel S. R. Ehmki", "Farina Ohm", "Hans-Christian Ehrlich", "Andriy Mashychev", "Matthias Rarey"], "year": "May 23, 2019", "journal": "Journal of Chemical Information and Modeling", "group": "Citedby", "depth": 1, "citations": 12}, {"doi": "https://doi.org/10.1021/acs.jcim.1c00203", "name": "AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings", "author": ["Jerome Eberhardt", "Diogo Santos-Martins", "Andreas F. Tillack", "Stefano Forli"], "year": "July 19, 2021", "journal": "Journal of Chemical Information and Modeling", "group": "Input", "depth": 0, "citations": 1}, {"doi": "https://doi.org/10.1021/acs.jctc.0c01006", "name": "Accelerating AutoDock4 with GPUs and Gradient-Based Local Search", "author": ["Diogo Santos-Martins", "Leonardo Solis-Vasquez", "Andreas F Tillack", "Michel F Sanner", "Andreas Koch", "Stefano Forli"], "year": "January 6, 2021", "journal": "Journal of Chemical Theory and Computation", "group": "Reference", "depth": -1, "citations": 14}, {"doi": "https://doi.org/10.1021/acs.jctc.9b00557", "name": "Docking Flexible Cyclic Peptides with AutoDock CrankPep", "author": ["Yuqi Zhang", "Michel F. Sanner"], "year": "September 11, 2019", "journal": "Journal of Chemical Theory and Computation", "group": "Reference", "depth": -1, "citations": 9}, {"doi": "https://doi.org/10.1021/ci300604z", "name": "Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise", "author": ["David Ryan Koes", "Matthew P. Baumgartner", "Carlos J. Camacho"], "year": "February 4, 2013", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -1, "citations": 100}, {"doi": "https://doi.org/10.1021/acs.jctc.5b00834", "name": "Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking", "author": ["Anita K. Nivedha", "David F. Thieker", "Spandana Makeneni", "Huimin Hu", "Robert J. Woods"], "year": "January 8, 2016", "journal": "Journal of Chemical Theory and Computation", "group": "Reference", "depth": -1, "citations": 48}, {"doi": "https://doi.org/10.1021/ci700036j", "name": "Lennard-Jones Potential and Dummy Atom Settings to Overcome the AUTODOCK Limitation in Treating Flexible Ring Systems", "author": ["Stefano Forli", "Maurizio Botta"], "year": "June 22, 2007", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -1, "citations": 32}, {"doi": "https://doi.org/10.1021/ci500209e", "name": "AutoDock4Zn: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins", "author": ["Diogo Santos-Martins", "Stefano Forli", "Maria Jo\u00e3o Ramos", "Arthur J. Olson"], "year": "June 15, 2014", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -1, "citations": 100}, {"doi": "https://doi.org/10.1021/jm2005145", "name": "A Force Field with Discrete Displaceable Waters and Desolvation Entropy for Hydrated Ligand Docking", "author": ["Stefano Forli", "Arthur J. Olson"], "year": "December 9, 2011", "journal": "Journal of Medicinal Chemistry", "group": "Reference", "depth": -1, "citations": 100}, {"doi": "https://doi.org/10.1021/ci300399w", "name": "Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context", "author": ["Douglas R. Houston", "Malcolm D. Walkinshaw"], "year": "January 27, 2013", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -1, "citations": 100}, {"doi": "https://doi.org/10.1021/jp9723574", "name": "Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory", "author": ["Themis Lazaridis"], "year": "April 14, 1998", "journal": "Journal of Physical Chemistry B", "group": "Reference", "depth": -1, "citations": 100}, {"doi": "https://doi.org/10.1021/jp972358w", "name": "Inhomogeneous Fluid Approach to Solvation Thermodynamics. 2. Applications to Simple Fluids", "author": ["Themis Lazaridis"], "year": "April 14, 1998", "journal": "Journal of Physical Chemistry B", "group": "Reference", "depth": -1, "citations": 100}, {"doi": "https://doi.org/10.1021/acs.jcim.0c00675", "name": "ZINC20\u2014A Free Ultralarge-Scale Chemical Database for Ligand Discovery", "author": ["John J. Irwin", "Khanh G. Tang", "Jennifer Young", "Chinzorig Dandarchuluun", "Benjamin R. Wong", "Munkhzul Khurelbaatar", "Yurii S. Moroz", "John Mayfield", "Roger A. Sayle"], "year": "October 29, 2020", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -1, "citations": 26}, {"doi": "https://doi.org/10.1021/acs.jmedchem.7b01243", "name": "Structural Biology-Inspired Discovery of Novel KRAS\u2013PDE\u03b4 Inhibitors", "author": ["Yan Jiang", "Chunlin Zhuang", "Long Chen", "Junjie Lu", "Guoqiang Dong", "Zhenyuan Miao", "Wannian Zhang", "Jian Li", "Chunquan Sheng"], "year": "September 20, 2017", "journal": "Journal of Medicinal Chemistry", "group": "Reference", "depth": -1, "citations": 12}, {"doi": "https://doi.org/10.1021/jm300687e", "name": "Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking", "author": ["Michael M. Mysinger", "Michael Carchia", "John. J. Irwin", "Brian K. Shoichet"], "year": "June 20, 2012", "journal": "Journal of Medicinal Chemistry", "group": "Reference", "depth": -1, "citations": 100}, {"doi": "https://doi.org/10.1021/acs.jcim.8b00312", "name": "Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark", "author": ["Thomas Gaillard"], "year": "July 10, 2018", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -1, "citations": 74}, {"doi": "https://doi.org/10.1021/acs.jcim.9b00778", "name": "Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity", "author": ["Nguyen Thanh Nguyen", "Trung Hai Nguyen", "T. Ngoc Han Pham", "Nguyen Truong Huy", "Mai Van Bay", "Minh Quan Pham", "Pham Cam Nam", "Van V. Vu", "Son Tung Ngo"], "year": "December 30, 2019", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -1, "citations": 66}, {"doi": "https://doi.org/10.1021/jm0306430", "name": "Glide:\u2009 A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy", "author": ["Richard A. Friesner", "Jay L. Banks", "Robert B. Murphy", "Thomas A. Halgren", "Jasna J. Klicic", "Daniel T. Mainz", "Matthew P. Repasky", "Eric H. Knoll", "Mee Shelley", "Jason K. Perry", "David E. Shaw", "Perry Francis", "Peter S. Shenkin"], "year": "February 27, 2004", "journal": "Journal of Medicinal Chemistry", "group": "Reference", "depth": -1, "citations": 97}, {"doi": "https://doi.org/10.1021/jm020406h", "name": "Surflex:\u2009 Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine", "author": ["Ajay N. 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Hajduk"], "year": "April 17, 2008", "journal": "Journal of Chemical Information and Modeling", "group": "Reference", "depth": -2, "citations": 100}, {"doi": "https://doi.org/10.1021/jm020155c", "name": "Do Structurally Similar Molecules Have Similar Biological Activity?", "author": ["Yvonne C. Martin", "James L. Kofron", "Linda M. Traphagen"], "year": "August 13, 2002", "journal": "Journal of Medicinal Chemistry", "group": "Reference", "depth": -2, "citations": 100}, {"doi": "https://doi.org/10.1021/jm9602928", "name": "The Properties of Known Drugs. 1. Molecular Frameworks", "author": ["Guy W. Bemis", "Mark A. Murcko"], "year": "July 19, 1996", "journal": "Journal of Medicinal Chemistry", "group": "Reference", "depth": -2, "citations": 100}, {"doi": "https://doi.org/10.1021/ci025599w", "name": "Molecular Shape Diversity of Combinatorial Libraries:\u2009 A Prerequisite for Broad Bioactivity\u2020", "author": ["Wolfgang H. B. Sauer", "Matthias K. Schwarz"], "year": "March 14, 2003", "journal": "J. Chem. Inf. Comput. Sci.", "group": "Reference", "depth": -2, "citations": 99}], "links": [{"source": "https://doi.org/10.1021/acs.chemrev.1c00107", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00741", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.jmedchem.0c01332", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.jcim.9b00250", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/acs.jctc.0c01006"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/acs.jctc.9b00557"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/ci300604z"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/acs.jctc.5b00834"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/ci700036j"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/ci500209e"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/jm2005145"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/ci300399w"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/jp9723574"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/jp972358w"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/acs.jcim.0c00675"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/acs.jmedchem.7b01243"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/jm300687e"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/acs.jcim.8b00312"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/acs.jcim.9b00778"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/jm0306430"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/jm020406h"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/ci300493w"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00203", "target": "https://doi.org/10.1021/jm049314d"}, {"source": "https://doi.org/10.1021/acsomega.1c04320", "target": "https://doi.org/10.1021/acs.jcim.1c00203"}, {"source": "https://doi.org/10.1021/acs.jpcb.1c08383", "target": "https://doi.org/10.1021/acs.chemrev.1c00107"}, {"source": "https://doi.org/10.1021/acs.jpca.1c06264", "target": "https://doi.org/10.1021/acs.chemrev.1c00107"}, {"source": "https://doi.org/10.1021/acsmedchemlett.1c00251", "target": "https://doi.org/10.1021/acs.jmedchem.0c01332"}, {"source": "https://doi.org/10.1021/acs.jmedchem.1c00416", "target": "https://doi.org/10.1021/acs.jmedchem.0c01332"}, {"source": "https://doi.org/10.1021/acs.chemrev.1c00107", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jcim.1c00226", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00741", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jmedchem.0c01332", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.chemrestox.0c00006", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.est.9b06379", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jcim.9b00249", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00675", "target": "https://doi.org/10.1021/ci049714+"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00675", "target": "https://doi.org/10.1021/ci3001277"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00675", "target": "https://doi.org/10.1021/acs.jcim.5b00559"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00675", "target": "https://doi.org/10.1021/ci7004498"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00675", "target": "https://doi.org/10.1021/jm020155c"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00675", "target": "https://doi.org/10.1021/jm9602928"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00675", "target": "https://doi.org/10.1021/ci025599w"}, {"source": "https://doi.org/10.1021/acs.jcim.5b00559", "target": "https://doi.org/10.1021/ci049714+"}, {"source": "https://doi.org/10.1021/acs.jcim.5b00559", "target": "https://doi.org/10.1021/ci3001277"}, {"source": "https://doi.org/10.1021/acs.jcim.5b00559", "target": "https://doi.org/10.1021/jm300687e"}, {"source": "https://doi.org/10.1021/acs.jcim.8b00312", "target": "https://doi.org/10.1021/ci300604z"}, {"source": "https://doi.org/10.1021/acs.jctc.0c01006", "target": "https://doi.org/10.1021/ci700036j"}, {"source": "https://doi.org/10.1021/acs.jctc.9b00557", "target": "https://doi.org/10.1021/ci700036j"}, {"source": "https://doi.org/10.1021/ci500209e", "target": "https://doi.org/10.1021/ci700036j"}, {"source": "https://doi.org/10.1021/acs.jctc.0c01006", "target": "https://doi.org/10.1021/ci500209e"}, {"source": "https://doi.org/10.1021/acs.jctc.0c01006", "target": "https://doi.org/10.1021/jm2005145"}, {"source": "https://doi.org/10.1021/acs.jcim.8b00312", "target": "https://doi.org/10.1021/jm2005145"}, {"source": "https://doi.org/10.1021/ci500209e", "target": "https://doi.org/10.1021/jm2005145"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00741", "target": "https://doi.org/10.1021/jm300687e"}, {"source": "https://doi.org/10.1021/acs.jcim.9b00778", "target": "https://doi.org/10.1021/acs.jcim.8b00312"}, {"source": "https://doi.org/10.1021/acs.jctc.0c01006", "target": "https://doi.org/10.1021/acs.jcim.9b00778"}, {"source": "https://doi.org/10.1021/acs.jctc.0c01006", "target": "https://doi.org/10.1021/ci049714+"}, {"source": "https://doi.org/10.1021/ci7004498", "target": "https://doi.org/10.1021/jm020155c"}, {"source": "https://doi.org/10.1021/acsmedchemlett.1c00251", "target": "https://doi.org/10.1021/ci025599w"}, {"source": "https://doi.org/10.1021/acs.jmedchem.0c01332", "target": "https://doi.org/10.1021/ci025599w"}]} {"nodes": [{"doi": "https://doi.org/10.1021/acs.jcim.9b00249", "name": "Comparing Molecular Patterns Using the Example of SMARTS: Applications and Filter Collection Analysis", "author": ["Emanuel S. R. Ehmki", "Robert Schmidt", "Farina Ohm", "Matthias Rarey"], "year": "May 24, 2019", "journal": "Journal of Chemical Information and Modeling", "group": "Input", "depth": 0, "citations": 5}, {"doi": "https://doi.org/10.1021/acs.chemrev.1c00107", "name": "Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems", "author": ["John A. Keith", "Valentin Vassilev-Galindo", "Bingqing Cheng", "Stefan Chmiela", "Michael Gastegger", "Klaus-Robert M\u00fcller", "Alexandre Tkatchenko"], "year": "July 7, 2021", "journal": "Chem. Rev.", "group": "Citedby", "depth": 1, "citations": 2}, {"doi": "https://doi.org/10.1021/acs.jcim.0c00741", "name": "Disconnected Maximum Common Substructures under Constraints", "author": ["Robert Schmidt", "Florian Krull", "Anna Lina Heinzke", "Matthias Rarey"], "year": "December 16, 2020", "journal": "Journal of Chemical Information and Modeling", "group": "Citedby", "depth": 1, "citations": 0}, {"doi": "https://doi.org/10.1021/acs.jmedchem.0c01332", "name": "Evolution of Novartis\u2019 Small Molecule Screening Deck Design", "author": ["Ansgar Schuffenhauer", "Nadine Schneider", "Samuel Hintermann", "Douglas Auld", "Jutta Blank", "Simona Cotesta", "Caroline Engeloch", "Nikolas Fechner", "Christoph Gaul", "Jerome Giovannoni", "Johanna Jansen", "John Joslin", "Philipp Krastel", "Eugen Lounkine", "John Manchester", "Lauren G. Monovich", "Anna Paola Pelliccioli", "Manuel Schwarze", "Michael D. Shultz", "Nikolaus Stiefl", "Daniel K. Baeschlin"], "year": "November 3, 2020", "journal": "Journal of Medicinal Chemistry", "group": "Citedby", "depth": 1, "citations": 8}, {"doi": "https://doi.org/10.1021/acs.jcim.9b00250", "name": "Comparing Molecular Patterns Using the Example of SMARTS: Theory and Algorithms", "author": ["Robert Schmidt", "Emanuel S. R. Ehmki", "Farina Ohm", "Hans-Christian Ehrlich", "Andriy Mashychev", "Matthias Rarey"], "year": "May 23, 2019", "journal": "Journal of Chemical Information and Modeling", "group": "Citedby", "depth": 1, "citations": 12}], "links": [{"source": "https://doi.org/10.1021/acs.chemrev.1c00107", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00741", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.jmedchem.0c01332", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.jcim.9b00250", "target": "https://doi.org/10.1021/acs.jcim.9b00249"}, {"source": "https://doi.org/10.1021/acs.chemrev.1c00107", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jcim.0c00741", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jmedchem.0c01332", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}, {"source": "https://doi.org/10.1021/acs.jcim.9b00249", "target": "https://doi.org/10.1021/acs.jcim.9b00250"}]}
\ No newline at end of file \ No newline at end of file
verarbeitung/test/construct_graph_unittest.py
+ 55
− 18
View file @ c0727d90
import unittest import unittest
import sys import sys
from pathlib import Path
sys.path.append("../") sys.path.append("../")
from verarbeitung.construct_new_graph.initialize_graph import init_graph_construction, initialize_nodes_list from verarbeitung.construct_new_graph.initialize_graph import init_graph_construction, initialize_nodes_list_test, complete_inner_edges_test
from verarbeitung.construct_new_graph.add_citations_rec import get_cit_type_list
from verarbeitung.test.input_test import input_test_func
class ConstructionTest(unittest.TestCase): class ConstructionTest(unittest.TestCase):
maxDiff = None maxDiff = None
def testCycle(self): def testCycle(self):
nodes, edges = init_graph_construction(['doiz1'],1,1,True) nodes, edges = init_graph_construction(['doiz1'],1,1,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes, ['doiz1', 'doiz2']) self.assertCountEqual(doi_nodes, ['doiz1', 'doiz2'])
self.assertCountEqual(edges, [['doiz1', 'doiz2'], ['doiz2', 'doiz1']]) self.assertCountEqual(edges, [['doiz1', 'doiz2'], ['doiz2', 'doiz1']])
nodes, edges = init_graph_construction(['doiz1'],2,2,True) nodes, edges = init_graph_construction(['doiz1'],2,2,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes, ['doiz1', 'doiz2']) self.assertCountEqual(doi_nodes, ['doiz1', 'doiz2'])
self.assertCountEqual(edges, [['doiz2', 'doiz1'], ['doiz1', 'doiz2']]) self.assertCountEqual(edges, [['doiz2', 'doiz1'], ['doiz1', 'doiz2']])
...@@ -29,56 +31,56 @@ class ConstructionTest(unittest.TestCase): ...@@ -29,56 +31,56 @@ class ConstructionTest(unittest.TestCase):
#def testEmptyDepth(self): #def testEmptyDepth(self):
def testEmptyDepthHeight(self): def testEmptyDepthHeight(self):
nodes, edges = init_graph_construction(['doi1'],0,0,True) nodes, edges = init_graph_construction(['doi1'],0,0,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi1']) self.assertCountEqual(doi_nodes,['doi1'])
self.assertCountEqual(edges, []) self.assertCountEqual(edges, [])
nodes, edges = init_graph_construction(['doi1', 'doi2'],0,0,True) nodes, edges = init_graph_construction(['doi1', 'doi2'],0,0,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes, ['doi1','doi2']) self.assertCountEqual(doi_nodes, ['doi1','doi2'])
self.assertCountEqual(edges, [['doi1', 'doi2']]) self.assertCountEqual(edges, [['doi1', 'doi2']])
nodes, edges = init_graph_construction(['doi1', 'doi2', 'doi3'],0,0,True) nodes, edges = init_graph_construction(['doi1', 'doi2', 'doi3'],0,0,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes, ['doi1','doi2', 'doi3']) self.assertCountEqual(doi_nodes, ['doi1','doi2', 'doi3'])
self.assertCountEqual(edges, [['doi3', 'doi1'], ['doi1', 'doi2']]) self.assertCountEqual(edges, [['doi3', 'doi1'], ['doi1', 'doi2']])
def testInnerEdges(self): def testInnerEdges(self):
nodes, edges = init_graph_construction(['doi_ie1'],1,1,True) nodes, edges = init_graph_construction(['doi_ie1'],1,1,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi_ie1','doi_ie2','doi_ie3']) self.assertCountEqual(doi_nodes,['doi_ie1','doi_ie2','doi_ie3'])
self.assertCountEqual(edges,[['doi_ie1','doi_ie2'],['doi_ie3','doi_ie1'],['doi_ie3','doi_ie2']]) self.assertCountEqual(edges,[['doi_ie1','doi_ie2'],['doi_ie3','doi_ie1'],['doi_ie3','doi_ie2']])
def testRightHeight(self): def testRightHeight(self):
nodes, edges = init_graph_construction(['doi_h01'],0,1,True) nodes, edges = init_graph_construction(['doi_h01'],0,1,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi_h01']) self.assertCountEqual(doi_nodes,['doi_h01'])
self.assertCountEqual(edges, []) self.assertCountEqual(edges, [])
nodes, edges = init_graph_construction(['doi_h02'],0,1,True) nodes, edges = init_graph_construction(['doi_h02'],0,1,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi_h02','doi_h1']) self.assertCountEqual(doi_nodes,['doi_h02','doi_h1'])
self.assertCountEqual(edges, [['doi_h1','doi_h02']]) self.assertCountEqual(edges, [['doi_h1','doi_h02']])
nodes, edges = init_graph_construction(['doi_h02'],0,2,True) nodes, edges = init_graph_construction(['doi_h02'],0,2,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi_h02','doi_h1','doi_h2']) self.assertCountEqual(doi_nodes,['doi_h02','doi_h1','doi_h2'])
self.assertCountEqual(edges, [['doi_h1','doi_h02'], ['doi_h2','doi_h1']]) self.assertCountEqual(edges, [['doi_h1','doi_h02'], ['doi_h2','doi_h1']])
def testRightDepth(self): def testRightDepth(self):
nodes, edges = init_graph_construction(['doi_d01'],1,0,True) nodes, edges = init_graph_construction(['doi_d01'],1,0,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi_d01']) self.assertCountEqual(doi_nodes,['doi_d01'])
self.assertCountEqual(edges, []) self.assertCountEqual(edges, [])
nodes, edges = init_graph_construction(['doi_d02'],1,0,True) nodes, edges = init_graph_construction(['doi_d02'],1,0,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi_d02','doi_d1']) self.assertCountEqual(doi_nodes,['doi_d02','doi_d1'])
self.assertCountEqual(edges, [['doi_d02','doi_d1']]) self.assertCountEqual(edges, [['doi_d02','doi_d1']])
nodes, edges = init_graph_construction(['doi_d02'],2,0,True) nodes, edges = init_graph_construction(['doi_d02'],2,0,True,False)
doi_nodes = keep_only_dois(nodes) doi_nodes = keep_only_dois(nodes)
self.assertCountEqual(doi_nodes,['doi_d02','doi_d1','doi_d2']) self.assertCountEqual(doi_nodes,['doi_d02','doi_d1','doi_d2'])
self.assertCountEqual(edges, [['doi_d02','doi_d1'], ['doi_d1','doi_d2']]) self.assertCountEqual(edges, [['doi_d02','doi_d1'], ['doi_d1','doi_d2']])
...@@ -89,19 +91,54 @@ class ConstructionTest(unittest.TestCase): ...@@ -89,19 +91,54 @@ class ConstructionTest(unittest.TestCase):
# initialize_graph.py: # initialize_graph.py:
def test_initialize_nodes_list(self): def test_initialize_nodes_list(self):
references_pub_obj_list, citations_pub_obj_list = initialize_nodes_list(['doi_lg_1_i','doi_lg_2_i'], 0, 0, True) references_pub_obj_list, citations_pub_obj_list = initialize_nodes_list_test(['doi_lg_1_i','doi_lg_2_i'], 0, 0, True)
doi_references = keep_only_dois(references_pub_obj_list) doi_references = keep_only_dois(references_pub_obj_list)
doi_citations = keep_only_dois(citations_pub_obj_list) doi_citations = keep_only_dois(citations_pub_obj_list)
self.assertCountEqual(doi_references, []) self.assertCountEqual(doi_references, [])
self.assertCountEqual(doi_citations, []) self.assertCountEqual(doi_citations, [])
references_pub_obj_list, citations_pub_obj_list = initialize_nodes_list(['doi_lg_1_i','doi_lg_2_i'], 1, 1, True) references_pub_obj_list, citations_pub_obj_list = initialize_nodes_list_test(['doi_lg_1_i','doi_lg_2_i'], 1, 1, True)
doi_references = keep_only_dois(references_pub_obj_list) doi_references = keep_only_dois(references_pub_obj_list)
doi_citations = keep_only_dois(citations_pub_obj_list) doi_citations = keep_only_dois(citations_pub_obj_list)
self.assertCountEqual(doi_references, ['doi_lg_1_d11','doi_lg_1_d12','doi_lg_2_d11','doi_lg_2_d12']) self.assertCountEqual(doi_references, ['doi_lg_1_d11','doi_lg_1_d12','doi_lg_2_d11','doi_lg_2_d12'])
self.assertCountEqual(doi_citations, ['doi_lg_1_h11','doi_lg_1_h12','doi_cg_i','doi_lg_2_h11','doi_lg_2_h12']) self.assertCountEqual(doi_citations, ['doi_lg_1_h11','doi_lg_1_h12','doi_cg_i','doi_lg_2_h11','doi_lg_2_h12'])
def test_complete_inner_edges(self):
pub_lg_1_i = input_test_func('doi_lg_1_i')
pub_lg_1_i.group = 0
pub_lg_1_h_12 = input_test_func('doi_lg_1_h12')
pub_lg_1_h_12.group = 1
pub_lg_1_d_12 = input_test_func('doi_lg_1_d12')
pub_lg_1_d_12.group = -1
nodes = [pub_lg_1_i, pub_lg_1_h_12, pub_lg_1_d_12]
edges = [['doi_lg_1_i','doi_lg_1_d12'],['doi_lg_1_h12','doi_lg_1_i']]
processed_nodes, processed_edges = complete_inner_edges_test(nodes, edges)
self.assertCountEqual(processed_nodes, [pub_lg_1_i, pub_lg_1_h_12, pub_lg_1_d_12])
self.assertCountEqual(processed_edges, [['doi_lg_1_i','doi_lg_1_d12'],['doi_lg_1_h12','doi_lg_1_i'],['doi_lg_1_h12','doi_lg_1_d12']])
# add_citations_rec.py:
def test_get_type_list(self):
pub_lg_1_i = input_test_func('doi_lg_1_i')
pub_lg_1_i.group = 0
self.assertEqual(get_cit_type_list(pub_lg_1_i, "Hallo"), ValueError)
pub_lg_1_h_12 = input_test_func('doi_lg_1_h12')
pub_lg_1_h_12.group = 1
pub_lg_1_h_12_refs = get_cit_type_list(pub_lg_1_h_12, "Reference")
pub_lg_1_h_12_cits = get_cit_type_list(pub_lg_1_h_12, "Citation")
self.assertCountEqual(keep_only_dois(pub_lg_1_h_12_refs), keep_only_dois(pub_lg_1_h_12.references))
self.assertCountEqual(keep_only_dois(pub_lg_1_h_12_cits), keep_only_dois(pub_lg_1_h_12.citations))
pub_lg_1_d_12 = input_test_func('doi_lg_1_d12')
pub_lg_1_d_12.group = -1
pub_lg_1_d_12_refs = get_cit_type_list(pub_lg_1_d_12, "Reference")
pub_lg_1_d_12_cits = get_cit_type_list(pub_lg_1_d_12, "Citation")
self.assertCountEqual(keep_only_dois(pub_lg_1_d_12_refs), keep_only_dois(pub_lg_1_d_12.references))
self.assertCountEqual(keep_only_dois(pub_lg_1_d_12_cits), keep_only_dois(pub_lg_1_d_12.citations))
def test_create_graph_structure_citations(self):
print("Hallo")
def keep_only_dois(nodes): def keep_only_dois(nodes):
......
verarbeitung/test_output.json
+ 1
− 1
View file @ c0727d90
{"nodes": [{"doi": "doi_lg_1_i", "name": "title_lg_1_i", "author": ["contributor_lg_1_i"], "year": "date_lg_1_i", "journal": "journal_lg_1_i", "group": "Input", "depth": 0, "citations": 2}, {"doi": "doi_lg_1_d11", "name": "title_lg_1_d11", "author": ["contributor_lg_1_d11"], "year": "date_lg_1_d11", "journal": "journal_lg_1_d11", "group": "Reference", "depth": -1, "citations": 1}, {"doi": "doi_lg_1_d12", "name": "title_lg_1_d12", "author": ["contributor_lg_1_d12"], "year": "date_lg_1_d12", "journal": "journal_lg_1_d12", "group": "Reference", "depth": -1, "citations": 2}, {"doi": "doi_lg_1_h11", "name": "title_lg_1_h11", "author": ["contributor_lg_1_h11"], "year": "date_lg_1_h11", "journal": "journal_lg_1_h11", "group": "Citedby", "depth": 1, "citations": 2}, {"doi": "doi_lg_1_h12", "name": "title_lg_1_h12", "author": ["contributor_lg_1_h12"], "year": "date_lg_1_h12", "journal": "journal_lg_1_h12", "group": "Citedby", "depth": 1, "citations": 2}, {"doi": "doi_lg_1_h21", "name": "title_lg_1_h21", "author": ["contributor_lg_1_h21"], "year": "date_lg_1_h21", "journal": "journal_lg_1_h21", "group": "Citedby", "depth": 2, "citations": 0}, {"doi": "doi_lg_1_h22", "name": "title_lg_1_h22", "author": ["contributor_lg_1_h22"], "year": "date_lg_1_h22", "journal": "journal_lg_1_h22", "group": "Citedby", "depth": 2, "citations": 0}, {"doi": "doi_lg_1_h23", "name": "title_lg_1_h23", "author": ["contributor_lg_1_h23"], "year": "date_lg_1_h23", "journal": "journal_lg_1_h23", "group": "Citedby", "depth": 2, "citations": 0}, {"doi": "doi_lg_1_d21", "name": "title_lg_1_d21", "author": ["contributor_lg_1_d21"], "year": "date_lg_1_d21", "journal": "journal_lg_1_d21", "group": "Reference", "depth": -2, "citations": 2}, {"doi": "doi_lg_1_d22", "name": "title_lg_1_d22", "author": ["contributor_lg_1_d22"], "year": "date_lg_1_d22", "journal": "journal_lg_1_d22", "group": "Reference", "depth": -2, "citations": 2}, {"doi": "doi_lg_1_d23", "name": "title_lg_1_d23", "author": ["contributor_lg_1_d23"], "year": "date_lg_1_d23", "journal": "journal_lg_1_d23", "group": "Reference", "depth": -2, "citations": 2}], "links": [{"source": "doi_lg_1_i", "target": "doi_lg_1_d11"}, {"source": "doi_lg_1_i", "target": "doi_lg_1_d12"}, {"source": "doi_lg_1_h11", "target": "doi_lg_1_i"}, {"source": "doi_lg_1_h12", "target": "doi_lg_1_i"}, {"source": "doi_lg_1_h21", "target": "doi_lg_1_h11"}, {"source": "doi_lg_1_h22", "target": "doi_lg_1_h11"}, {"source": "doi_lg_1_h22", "target": "doi_lg_1_h12"}, {"source": "doi_lg_1_h23", "target": "doi_lg_1_h12"}, {"source": "doi_lg_1_d11", "target": "doi_lg_1_d21"}, {"source": "doi_lg_1_d11", "target": "doi_lg_1_d22"}, {"source": "doi_lg_1_d21", "target": "doi_lg_1_d22"}, {"source": "doi_lg_1_d22", "target": "doi_lg_1_d21"}, {"source": "doi_lg_1_d12", "target": "doi_lg_1_d23"}, {"source": "doi_lg_1_h12", "target": "doi_lg_1_d12"}]} {"nodes": [{"doi": "doi_h02", "name": "title_h02", "author": ["contributor_h02"], "year": "date_h02", "journal": "journal_h02", "group": "Input", "depth": 0, "citations": 1}, {"doi": "doi_h1", "name": "title_h1", "author": ["contributor_h1"], "year": "date_h1", "journal": "journal_h1", "group": "Citedby", "depth": 1, "citations": 1}, {"doi": "doi_h2", "name": "title_h2", "author": ["contributor_h2"], "year": "date_h2", "journal": "journal_h2", "group": "Citedby", "depth": 2, "citations": 1}], "links": [{"source": "doi_h1", "target": "doi_h02"}, {"source": "doi_h2", "target": "doi_h1"}]}
\ No newline at end of file \ No newline at end of file
verarbeitung/update_graph/connect_new_input.py
+ 0
− 1
View file @ c0727d90
...@@ -19,7 +19,6 @@ from os import error ...@@ -19,7 +19,6 @@ from os import error
sys.path.append("../") sys.path.append("../")
from .import_from_json import input_from_json
from verarbeitung.construct_new_graph.initialize_graph import init_graph_construction from verarbeitung.construct_new_graph.initialize_graph import init_graph_construction
from verarbeitung.construct_new_graph.add_citations_rec import add_citations, get_cit_type_list, create_global_lists_cit from verarbeitung.construct_new_graph.add_citations_rec import add_citations, get_cit_type_list, create_global_lists_cit
......
verarbeitung/update_graph/update_depth.py
+ 11
− 5
View file @ c0727d90
...@@ -72,7 +72,10 @@ def get_old_max_references(old_depth): ...@@ -72,7 +72,10 @@ def get_old_max_references(old_depth):
old_max_references = [] old_max_references = []
for pub in processed_input_list: for pub in processed_input_list:
if (abs(pub.group) == old_depth): if (abs(pub.group) == old_depth):
old_max_references.append(pub.references) for reference in pub.references:
for ref_pub in processed_input_list:
if reference.doi_url == ref_pub.doi_url:
old_max_references.append(ref_pub)
return(old_max_references) return(old_max_references)
def get_old_max_citations(old_height): def get_old_max_citations(old_height):
...@@ -85,7 +88,10 @@ def get_old_max_citations(old_height): ...@@ -85,7 +88,10 @@ def get_old_max_citations(old_height):
old_max_citations = [] old_max_citations = []
for pub in processed_input_list: for pub in processed_input_list:
if (abs(pub.group) == old_height): if (abs(pub.group) == old_height):
old_max_citations.append(pub.citations) for citation in pub.citations:
for cit_pub in processed_input_list:
if citation.doi_url == cit_pub.doi_url:
old_max_citations.append(cit_pub)
return(old_max_citations) return(old_max_citations)
def update_depth(obj_input_list, input_edges, new_depth, new_height, test_var): def update_depth(obj_input_list, input_edges, new_depth, new_height, test_var):
...@@ -123,10 +129,10 @@ def update_depth(obj_input_list, input_edges, new_depth, new_height, test_var): ...@@ -123,10 +129,10 @@ def update_depth(obj_input_list, input_edges, new_depth, new_height, test_var):
# adds publications and links for new recursion levels # adds publications and links for new recursion levels
elif (old_depth < new_depth): elif (old_depth < new_depth):
old_max_references = get_old_max_references() old_max_references = get_old_max_references(old_depth)
add_citations(processed_input_list, valid_edges, old_max_references, old_depth+1, new_depth, "Reference", test_var) add_citations(processed_input_list, valid_edges, old_max_references, old_depth+1, new_depth, "Reference", test_var)
elif (old_height < new_height): elif (old_height < new_height):
old_max_citations = get_old_max_citations() old_max_citations = get_old_max_citations(old_height)
add_citations(processed_input_list, valid_edges, old_max_citations, old_height+1, new_height, "Citation", test_var) add_citations(processed_input_list, valid_edges, old_max_citations, old_height+1, new_height, "Citation", test_var)
back_to_valid_edges(valid_edges, processed_input_list) back_to_valid_edges(valid_edges, processed_input_list)
......
verarbeitung/update_graph/update_graph.py
+ 0
− 1
View file @ c0727d90
...@@ -20,7 +20,6 @@ sys.path.append("../../") ...@@ -20,7 +20,6 @@ sys.path.append("../../")
from input.publication import Publication from input.publication import Publication
from verarbeitung.get_pub_from_input import get_pub from verarbeitung.get_pub_from_input import get_pub
from verarbeitung.construct_new_graph.initialize_graph import init_graph_construction
from .Knoten_Vergleich import doi_listen_vergleichen from .Knoten_Vergleich import doi_listen_vergleichen
from .delete_nodes_edges import delete_nodes_and_edges from .delete_nodes_edges import delete_nodes_and_edges
from .connect_new_input import connect_old_and_new_input from .connect_new_input import connect_old_and_new_input
......
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