In [2]:
authors_list = []
poster_dict = {}
# making authors list
Nmax = 1000000;
i = 0
with open('AbstractsCosyne2012.csv') as f:
next(f)
for line in f:
i += 1
if i<Nmax:
split = line.split('|')
authors = split[2].split(',')
authors_clean = []
for author in authors:
authors_clean.append(author.lstrip())
# authors list
for author in authors_clean:
author= author.lstrip()
if not(author in authors_list):
authors_list.append(author.lstrip())
authors_list = sorted(authors_list)
# making dictionnary for indexing author
authors_dict = {}
i = 0
for author in authors_list:
authors_dict[author] = i
i+=1
# construction poster dictionary
with open('AbstractsCosyne2012.csv') as f:
next(f)
for line in f:
split = line.split('|')
posterid = int(split[0])
authors = split[2].split(',')
authors_id = []
for author in authors:
authors_id.append(authors_dict[author.lstrip()])
poster_dict[posterid] = authors_id
In [53]:
import numpy as np
# make adjacency matrix
N=len(authors_list)
M = np.zeros((N,N))
def make_pairs(l1,l2):
if len(l1)>0:
#print [l1,l2]
for i in range(len(l1)):
l2.append([l1[0],l1[i]])
make_pairs(l1[1:],l2)
else:
return l2
return l2
# Listing of pairwise collaboration
pair_dict = {}
for p in poster_dict:
a = poster_dict[p]
pairs = make_pairs(a,[])
#print a, pairs
for pair in pairs:
pair = tuple(pair)
M[pair[0]][pair[1]] +=1
if pair in pair_dict:
pair_dict[pair]+=1
else:
pair_dict[pair]=1
# Connex
import networkx as nx
G=nx.Graph()
G.add_nodes_from(range(len(pair_dict)))
G.add_edges_from(pair_dict.keys())
cc = nx.connected_components(G)
In [81]:
# generating json file
with open("miserables.json", "w") as outfile:
#nodes
outfile.write("{\n\"nodes\":[\n")
for i_name in range(len(authors_list)):
name = authors_list[i_name]
for i_group in range(len(cc)):
for j in range(len(cc[i_group])):
if (i_name == cc[i_group][j]):
value = i_group
outfile.write("{\"name\":\"" + name +"\",\"group\":"+str(value+1)+"}" )
if i_name < len(authors_list)-1:
outfile.write("," )
outfile.write("\n" )
outfile.write("],\n")
#links
outfile.write("\"links\":[\n")
keys = pair_dict.keys()
for i_pair in range(len(keys)):
pair = keys[i_pair]
value = pair_dict[pair]
outfile.write("{\"source\":" + str(pair[0]) +",\"target\":"+str(pair[1])+",\"value\":"+str(value)+"}")
if i_pair < len(pair_dict)-1:
outfile.write(",")
outfile.write("\n" )
outfile.write("]\n}")
In [80]:
len(cc[0])
i_name
Out[80]:
854
In [68]:
520 in cc[0]
Out[68]:
True
In [18]:
from matplotlib import pyplot, patches
fig = pyplot.figure(figsize=(15, 15)) # in inches
K = 100
pyplot.imshow(M[:K,:K],cmap="Greys",interpolation="none")
Out[18]:
<matplotlib.image.AxesImage at 0xbc2a1ac>
In [45]:
pair_dict.keys()
Out[45]:
[(583, 142),
(53, 53),
(454, 454),
(756, 756),
(76, 76),
(63, 63),
(26, 782),
(620, 145),
(302, 302),
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In [16]:
pair_dict
Out[16]:
{(1, 1): 1,
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In [19]:
poster_dict
Out[19]:
{11: [370],
12: [49],
16: [255],
17: [343],
18: [249],
21: [587],
22: [826],
23: [844],
24: [583],
30: [123],
32: [743],
34: [555],
36: [28],
38: [442],
39: [327],
40: [600],
43: [279],
44: [188],
45: [607],
46: [791],
47: [423],
48: [795],
49: [134],
53: [814],
55: [636],
56: [342],
63: [450],
66: [745],
67: [740],
69: [407],
70: [108],
71: [614],
72: [763],
73: [386],
75: [344],
76: [115],
77: [248],
79: [387],
80: [285],
81: [638],
83: [178],
84: [285],
85: [852],
88: [540],
90: [706],
91: [853],
92: [654],
93: [696],
94: [136],
95: [2],
98: [408],
100: [703],
101: [367],
103: [559],
105: [214],
106: [842],
107: [227],
109: [829],
111: [742],
112: [598],
115: [853],
116: [230],
120: [259],
121: [851],
122: [472],
123: [467],
128: [336],
131: [479],
132: [685],
134: [325],
138: [627],
139: [348],
140: [193],
141: [729],
144: [293],
147: [596],
150: [366],
154: [644],
155: [357],
158: [717],
159: [203],
162: [381],
164: [90],
165: [217],
166: [830],
167: [365],
168: [837],
171: [331],
173: [31],
174: [646],
176: [458],
178: [670],
179: [448],
180: [554],
181: [310],
184: [572],
187: [831],
188: [699],
189: [542],
190: [87],
196: [395],
197: [13],
201: [709],
202: [120],
203: [147],
205: [147],
206: [774],
208: [16],
210: [495],
212: [773],
215: [779],
216: [565],
217: [599],
219: [200],
223: [432],
226: [118],
227: [359],
229: [668],
230: [21],
231: [54],
234: [323],
235: [183],
237: [801],
240: [500],
241: [789],
242: [46],
245: [457],
246: [324],
248: [320],
249: [610],
251: [447],
252: [286],
255: [783],
256: [373],
259: [748],
260: [71],
262: [557],
263: [714],
264: [476],
266: [698],
267: [677],
269: [516],
271: [705],
272: [468],
274: [83],
276: [648],
278: [377],
279: [578],
280: [376],
281: [430],
285: [383],
286: [277],
287: [236],
288: [169],
290: [117],
294: [799],
296: [420],
297: [539],
298: [96],
300: [710],
301: [849],
302: [556],
306: [99],
307: [452],
308: [59],
309: [371],
311: [400],
312: [9],
314: [568],
316: [36],
320: [692],
322: [24],
326: [617],
327: [68],
329: [525],
330: [56],
331: [437],
333: [823],
334: [162],
335: [152],
337: [760],
339: [82],
340: [304],
341: [256],
344: [752],
348: [254],
350: [686],
351: [38],
352: [498],
353: [205],
355: [334],
356: [426],
359: [659],
360: [263],
361: [72],
362: [545],
363: [697],
364: [350],
365: [86],
366: [26],
367: [63],
369: [111],
370: [253],
372: [240],
374: [245],
375: [180],
376: [502],
379: [353],
380: [345],
381: [854],
382: [39],
384: [229],
385: [819],
387: [363],
388: [449],
389: [552],
390: [168],
392: [338],
393: [22],
396: [678],
401: [687],
402: [433],
403: [64],
404: [98],
405: [589],
407: [388],
408: [530],
409: [218],
411: [167],
412: [302],
414: [374],
415: [76],
416: [514],
418: [362],
419: [558],
420: [800],
421: [80],
422: [239],
423: [79],
425: [131],
426: [439],
427: [196],
430: [53],
431: [78],
432: [112],
433: [704],
434: [377],
435: [305],
437: [712],
440: [307],
441: [140],
442: [199],
443: [33],
444: [537],
445: [139],
448: [778],
449: [451],
450: [85],
451: [694],
452: [242],
454: [198],
456: [676],
458: [720],
459: [656],
460: [132],
461: [35],
465: [684],
466: [616],
467: [683],
468: [580],
469: [480],
470: [292],
471: [97],
473: [616],
475: [170],
476: [333],
477: [368],
479: [489],
480: [681],
481: [295],
484: [267],
485: [477],
488: [172],
489: [352],
490: [211],
491: [551],
493: [689],
496: [39],
497: [676],
499: [30],
500: [37],
501: [701],
505: [440],
506: [98],
510: [796],
512: [805],
514: [192],
517: [1],
518: [176],
519: [328],
520: [781],
522: [671],
523: [582],
524: [94],
525: [438],
526: [708],
527: [649],
528: [693],
530: [833],
531: [84],
532: [602],
533: [806],
536: [152],
537: [628],
541: [184],
542: [156],
543: [413],
544: [737],
545: [754],
546: [770],
548: [166],
550: [674],
552: [615],
555: [215],
556: [608],
557: [101],
558: [749],
559: [669]}
In [21]:
author in authors_list
Out[21]:
False
In [23]:
'Aaron Bornstein' in authors_list
Out[23]:
True
In [ ]:
Content source: vincentadam87/cosyne-visualization
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