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VisualizePaths.py
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VisualizePaths.py
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import pandas as pd
import networkx as nx
import re
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import io
import base64
from Neo4jSearch import GenerateNodeColors
from PubMedSearch import PubMedCoMentionsSimple
import dash_cytoscape as cyto
def tryint(s):
"""
Return an int if possible, or `s` unchanged.
"""
try:
return int(s)
except ValueError:
return s
def alphanum_key(s):
"""
Turn a string into a list of string and number chunks.
>>> alphanum_key("z23a")
["z", 23, "a"]
"""
return [ tryint(c) for c in re.split('([0-9]+)', s) ]
def human_sort(l):
"""
Sort a list in the way that humans expect.
"""
l.sort(key=alphanum_key)
def networx2cytoscape(G):
cyto.Cytoscape(
id='cytoscape-two-nodes',
layout={'name': 'preset'},
style={'width': '100%', 'height': '400px'},
elements=[
{'data': {'id': 'one', 'label': 'Node 1'}, 'position': {'x': 75, 'y': 75}},
{'data': {'id': 'two', 'label': 'Node 2'}, 'position': {'x': 200, 'y': 200}},
{'data': {'source': 'one', 'target': 'two'}}
]
)
#xy_positions = nx.nx_agraph.graphviz_layout(G)
cy = nx.readwrite.json_graph.cytoscape_data(G)
# for n in cy['elements']['nodes']:
# for v in n.items():
# v['label'] = v.pop('value')
print(cy)
#print(xy_positions)
return
def CytoscapeVisualize(df, selected_rows):
cols = df.columns
node_cols = []
edge_cols = []
searched_cols = []
count_cols = [x for x in cols if " counts" in x]
print(selected_rows)
for col in cols:
if col.count('node')==1:# and df.at[row,col]!="?":
if "protein names" not in col:
searched_cols.append(col)
node_cols.append(col)
elif col.count('edge')==1:# and df.at[row,col]!="?":
searched_cols.append(col)
edge_cols.append(col)
human_sort(node_cols)
human_sort(edge_cols)
print(searched_cols)
print(node_cols)
print(edge_cols)
print(count_cols)
node_elements = [
{'data': {'id':[], 'label':[]},
'position': {'x':[], 'y':[]},
'locked': False,
'grabbable': True,
'selectable': True,
'selected': False,
#'classes': 'blue square'
}
]
edge_elements = [
{'data': {'source':[], 'target':[], 'label':[]}}
]
return True
def VisualizeAnswerRow(df,selected_rows,elements,edge_labels=True, pubmed_comentions=True, all_rows=False):
# if not selected_rows:
# return elements
cols = df.columns
node_cols = []
edge_cols = []
searched_cols = []
count_cols = [x for x in cols if " counts" in x]
print(selected_rows)
for col in cols:
if col.count('node')==1:# and df.at[row,col]!="?":
if "protein names" not in col:
searched_cols.append(col)
node_cols.append(col)
elif col.count('edge')==1:# and df.at[row,col]!="?":
searched_cols.append(col)
edge_cols.append(col)
#human_sort(searched_cols)
human_sort(node_cols)
human_sort(edge_cols)
#df = df[searched_cols].reindex(searched_cols, axis=1)
print(searched_cols)
print(node_cols)
print(edge_cols)
print(count_cols)
nodetypecolors = GenerateNodeColors([x[7:].replace('`','').replace('biolink:','') for x in node_cols])
added_nodes = {}
node_colors = []
added_edges = []
if all_rows == True:
selected_rows = [x for x in range(len(df))]
for row in selected_rows:
for col in node_cols:
if df.at[row,col] != "?":
if df.at[row,col].replace(' ','\n') in added_nodes.keys():
continue
else:
added_nodes.update({df.at[row,col].replace(' ','\n'):col[7:].replace('`','').replace('biolink:','')})
node_colors.append(nodetypecolors[col[7:].replace('`','').replace('biolink:','')])
for i in range(len(searched_cols)):
if 'edge' in searched_cols[i]:
predicate_pos = cols.get_loc(searched_cols[i])
if df.iat[row,predicate_pos] == "?":
continue
#n_pos = node_cols.get_loc(df = df.reindex(human_sort(cols), axis=1))
subject_pos = cols.get_loc(searched_cols[i-1])
if df.iat[row,subject_pos] == "?":
n_pos = node_cols.index(searched_cols[i-1])
a = 1
while df.iat[row,subject_pos] == "?":
if (n_pos+a) <= (len(node_cols)-1) & (n_pos-a) >= 0:
if node_cols[n_pos+a][:6] == node_cols[n_pos][:6]:
subject_pos = cols.get_loc(node_cols[n_pos+a])
elif node_cols[n_pos-a][:6] == node_cols[n_pos][:6]:
subject_pos = cols.get_loc(node_cols[n_pos-a])
else:
a+=1
continue
elif (n_pos+a) <= (len(node_cols)-1) & (n_pos-a) < 0:
if node_cols[n_pos+a][:6] == node_cols[n_pos][:6]:
subject_pos = cols.get_loc(node_cols[n_pos+a])
else:
a+=1
continue
elif (n_pos+a) > (len(node_cols)-1) & (n_pos-a) >= 0:
if node_cols[n_pos-a][:6] == node_cols[n_pos][:6]:
subject_pos = cols.get_loc(node_cols[n_pos-a])
else:
a+=1
continue
else:
a+=1
object_pos = cols.get_loc(searched_cols[i+1])
if df.iat[row,object_pos] == "?":
n_pos = node_cols.index(searched_cols[i+1])
a = 1
while df.iat[row,object_pos] == "?":
if (n_pos+a) <= (len(node_cols)-1) & (n_pos-a) >= 0:
if node_cols[n_pos+a][:6] == node_cols[n_pos][:6]:
object_pos = cols.get_loc(node_cols[n_pos+a])
elif node_cols[n_pos-a][:6] == node_cols[n_pos][:6]:
object_pos = cols.get_loc(node_cols[n_pos-a])
else:
a+=1
continue
elif (n_pos+a) <= (len(node_cols)-1) & (n_pos-a) < 0:
if node_cols[n_pos+a][:6] == node_cols[n_pos][:6]:
object_pos = cols.get_loc(node_cols[n_pos+a])
else:
a+=1
continue
elif (n_pos+a) > (len(node_cols)-1) & (n_pos-a) >= 0:
if node_cols[n_pos-a][:6] == node_cols[n_pos][:6]:
object_pos = cols.get_loc(node_cols[n_pos-a])
else:
a+=1
continue
else:
a+=1
added_edges.append((df.iat[row,subject_pos].replace(' ','\n'),df.iat[row,predicate_pos].replace('_','\n'),df.iat[row,object_pos].replace(' ','\n')))
n = 0
node_elements = list()
edge_elements = list()
for node in added_nodes.keys():
element = {
'data': {'id':f"n{n}", 'label':node},
'position': {'x':n*5, 'y':0},
#'locked': False,
#'grabbable': True,
#'selectable': True,
#'selected': False,
'classes': added_nodes[node]
}
if not node_elements:
node_elements = [element]
else:
node_elements = node_elements + [element]
n+=1
e = 0
for edge in added_edges:
source = [x['data']['id'] for x in node_elements if x['data']['label']==edge[0]][0]
source_name = [x['data']['label'] for x in node_elements if x['data']['id']==source][0]
target = [x['data']['id'] for x in node_elements if x['data']['label']==edge[2]][0]
target_name = [x['data']['label'] for x in node_elements if x['data']['id']==target][0]
if edge_labels == True:
label = edge[1]
if pubmed_comentions == True:
label = label + f" ({PubMedCoMentionsSimple(source_name,target_name,expand=True)})"
element = {
'data': {'source':source, 'target':target, 'label':label}
}
else:
if pubmed_comentions == True:
label = f"({PubMedCoMentionsSimple(source_name,target_name,expand=True)})"
element = {
'data': {'source':source, 'target':target, 'label':label}
}
else:
element = {
'data': {'source':source, 'target':target}
}
if not edge_elements:
edge_elements = [element]
else:
edge_elements = edge_elements + [element]
e+=1
elements = node_elements + edge_elements
cyto_elements = []
[cyto_elements.append(x) for x in elements if x not in cyto_elements]
stylesheet=[
{'selector': 'node',
'style': {
'label': 'data(label)',
'text-wrap':'wrap'
}},
{'selector': 'edge',
'style': {
'label': 'data(label)',
'curve-style': 'bezier' #segments
}}
]
for color in nodetypecolors.keys():
stylesheet = stylesheet + [{'selector':f".{color}", 'style': {'background-color': tuple(256*x for x in nodetypecolors[color]),'line-color': tuple(256*x for x in nodetypecolors[color])}}]
#print(cyto_elements)
return [cyto_elements,stylesheet]
'''
elements=[{'data': {'id': x, 'label': x},'position': {'x': 2*len(x), 'y': 2*len(x)}} for x in added_nodes]+[{'data': {'source': e[0], 'target': e[2], 'label': e[1].replace('biolink:','')}} for e in added_edges]
print(elements)
#Elements for Cytoscape Figure
cytoscape_figure = cyto.Cytoscape(
id='cytoscape-figure',
layout={'name': 'preset'},
style={'width': '100%', 'height': '100%'},
elements=elements
# [{'data': {'id': 'Metoprolol', 'label': 'Metoprolol'}, 'position': {'x': 20, 'y': 20}},
# {'data': {'id': 'DNM1', 'label': 'DNM1'}, 'position': {'x': 8, 'y': 8}},
# {'data': {'id': 'Alzheimer\ndisease', 'label': 'Alzheimer\ndisease'}, 'position': {'x': 34, 'y': 34}},
# {'data': {'source': 'Metoprolol', 'target': 'DNM1', 'label': 'entity positively regulates entity'}},
# {'data': {'source': 'DNM1', 'target': 'Alzheimer\ndisease', 'label': 'entity negatively regulates entity'}}]
)
'''
'''
G = nx.Graph()
G.add_nodes_from(added_nodes)
for e in added_edges:
G.add_edge(e[0],e[2], type=e[1].replace('biolink:',''))
edge_labels = nx.get_edge_attributes(G,'type')
fig = plt.figure(figsize = (7,7))
plt.margins(x=0.1,y=0.1)
#plt.tight_layout()
#plt.gca().set_facecolor('blue') #Background color of the whole app
# pos={} #To set nodes evenly spaced
# x=0
# for n in added_nodes:
# n_pos={n:[x,0]}
# pos.update(n_pos)
# x+=(-1*len(added_nodes))
# pos={added_nodes[0]:[0,0]} #To set node distance based on edge label
# y=0
# for n in range(len(added_edges)):
# y+=(-1*len(added_edges[n]))
# n_pos={added_nodes[n+1]:[0,y]}
# pos.update(n_pos)
#nodecharacters=[len(x) for x in G.nodes()]
nodesize=[300*len(x) for x in G.nodes()]
#nodesize=400*max(nodecharacters)
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_size=nodesize, node_color=node_colors, font_weight='bold')
# for p in pos: # raise text positions
# t=list(pos[p])
# t[0]=t[0]+0.1
# pos[p]=tuple(t)
nx.draw_networkx_edge_labels(G, pos, font_size=10, edge_labels=edge_labels,
#connectionstyle='arc3, rad = 0.1',
rotate=False,
#horizontalalignment='left',
bbox=dict(alpha=0))
#networx2cytoscape(G)
#xy_positions = nx.nx_agraph.graphviz_layout()
fig.set_facecolor('white')
buf = io.BytesIO() # in-memory files
plt.savefig(buf, format = "png") # save to the above file object
plt.close()
data = base64.b64encode(buf.getbuffer()).decode("utf8") # encode to html elements
return "data:image/png;base64,{}".format(data)
'''
def VisualizePubmedCounts(df,selected_rows,elements):
cols = df.columns
node_cols = []
edge_cols = []
for col in cols:
if col.count('node')==1 and df.at[selected_row,col]!="?" and "protein names" not in col:
node_cols.append(col)
elif col.count('edge')==1 and df.at[selected_row,col]!="?":
edge_cols.append(col)