def venn(inp, names, title="venn", folder=''):
"""
Plots a venn diagram using the pyvenn package
Args:
-----
inp: list[set()] of sets of values (e.g. [(1,2,3,4),(2,3),(1,3,4,5)])
names: list[str] of the name of each leaf
title: str the plot title
folder: str of location where to save the plot, won't save if empty
"""
labels = pyvenn.get_labels(inp, fill=['number', 'logic'])
if len(inp) == 2:
fig, ax = pyvenn.venn2(labels, names=names)
elif len(inp) == 3:
fig, ax = pyvenn.venn3(labels, names=names)
elif len(inp) == 4:
fig, ax = pyvenn.venn4(labels, names=names)
elif len(inp) == 5:
fig, ax = pyvenn.venn5(labels, names=names)
elif len(inp) == 6:
fig, ax = pyvenn.venn6(labels, names=names)
else:
raise ValueError('need to be between 2 to 6')
ax.set_title(title)
if folder:
fig.savefig(folder + title + '_venn.pdf')
fig.show()
plt.pause(0.1)
def generate_venn_diagram(nb_dim, files, names, sep, label):
"""Cette fonction fait l'appel du fichier venn.py (qui doit être présent dans le même dossier que generate_venn.py)
pour générer les diagrammes de Venn.
:param: nombre de dimensions présent dans le diagramme de Venn (1 < nb_dim <= 6)
:param: files: liste des chemins d'accès aux fichiers contenant les résultats des modèles
:param: names: liste des noms des modèles (doit être en adéquation avec la liste des modèles files)
:param: sep: séparateur présent dans filename séparant les id-instance des label
:label: label que l'on souhaite extraire
"""
if nb_dim < 2 or nb_dim > 6:
print(
'Le diagramme de Venn ne peut être généré que pour un nombre de dimensions supérieur à un et inférieur ou égal à 6.'
)
else:
i = 0
models = []
while i < nb_dim:
models.append(
get_id_instances_title(files[i], sep=sep, label=label))
i += 1
labels = venn.get_labels(models)
if nb_dim == 2:
fig, ax = venn.venn2(labels, names=names)
plt.show()
elif nb_dim == 3:
fig, ax = venn.venn3(labels, names=names)
plt.show()
elif nb_dim == 4:
fig, ax = venn.venn4(labels, names=names)
plt.show()
elif nb_dim == 5:
fig, ax = venn.venn5(labels, names=names)
plt.show()
elif nb_dim == 6:
fig, ax = venn.venn6(labels, names=names)
plt.show()
else:
print(
'Le diagramme de Venn ne peut être généré que pour un nombre de dimensions supérieur à un et inférieur ou égal à 6.'
)
def uniq_share_cal(self, infile):
for line in infile:
s = line.rstrip()
orths = list(s.split('\t'))
ids = []
for x in xrange(len(orths)):
item = orths[x].split('|')[0]
if item in self.ids:
ids.append(item)
target = set(ids)
for key in self.data:
if set(key) == target:
self.data[key] += 1
labels = {}
for key in self.data:
key2 = ''
for x in xrange(len(self.ids)):
if self.ids[x] in key:
key2 += '1'
else:
key2 += '0'
labels[key2] = self.data[key]
if len(self.ids) == 3:
fig, ax = venn.venn3(labels, names=self.ids)
fig.savefig('gene_family_3taxa.pdf')
elif len(self.ids) == 4:
fig, ax = venn.venn4(labels, names=self.ids)
fig.savefig('gene_family_4taxa.pdf')
elif len(self.ids) == 5:
fig, ax = venn.venn5(labels, names=self.ids)
fig.savefig('gene_family_5taxa.pdf')
elif len(self.ids) == 6:
fig, ax = venn.venn6(labels, names=self.ids)
fig.savefig('gene_family_6taxa.pdf')
def cohort_overlap(cohorts: dict):
"""
Helper method to intersect MRNs from different cohorts
and generate a venn diagram based on that.
Args:
cohorts: dict of label and Cohort object
Returns:
dict with overlapping MRNs and figure
"""
cohort_list = list(cohorts.values())
overlapping_mrns = cohort_list[0].mrns.copy()
for cohort in cohort_list:
overlapping_mrns &= cohort.mrns
labels = venn.get_labels([c.mrns for c in cohorts.values()])
figure, ax = venn.venn3(labels, names=cohorts.keys())
return {
'count': len(overlapping_mrns),
'mrns': overlapping_mrns,
'figure': figure
}
n34 = len(intersection(tp[ids[2]], tp[ids[3]]))
n35 = len(intersection(tp[ids[2]], tp[ids[4]]))
n45 = len(intersection(tp[ids[3]], tp[ids[4]]))
area5 = len(tp[ids[4]])
area4 = len(tp[ids[3]])
area3 = len(tp[ids[2]])
area2 = len(tp[ids[1]])
area1 = len(tp[ids[0]])
venn_diag_list = [area1, area2, area3, area4, area5, n12, n13, n14, n15,n23, n24, n25,
n34, n35, n45, n123, n124, n125, n134, n135, n145, n234, n235, n245, n345, n1234,
n1235, n1245, n1345, n2345, n12345]
print('Overlap list needed for VennDiagram in R')
print(venn_diag_list)
out1.write('\n\n## Overlap list needed for VennDiagram in R:\n\n%s' % venn_diag_list)
if plot.lower() == 't' or plot.lower() == 'true':
if n_comparing == 5:
labels = venn.get_labels([tp[ids[0]], tp[ids[1]], tp[ids[2]], tp[ids[3]], tp[ids[4]]], fill = ['number'])
fig, ax = venn.venn5(labels, names = ids)
elif n_comparing == 4:
labels = venn.get_labels([tp[ids[0]], tp[ids[1]], tp[ids[2]], tp[ids[3]]], fill = ['number'])
fig, ax = venn.venn4(labels, names = ids)
elif n_comparing == 3:
labels = venn.get_labels([tp[ids[0]], tp[ids[1]], tp[ids[2]]], fill = ['number'])
fig, ax = venn.venn3(labels, names = ids)
elif n_comparing == 2:
labels = venn.get_labels([tp[ids[0]], tp[ids[1]]], fill = ['number'])
fig, ax = venn.venn2(labels, names = ids)
filename = save+'_pred_compared.pdf'
fig.savefig(filename)
axes[1].tick_params(labelsize=14)
plt.tight_layout(w_pad=2.5)
plt.savefig("f1ab_new.png", dpi=600)
plt.close()
labels_A0101 = venn.get_labels(pred_labels['A0101'], fill=['number', 'logic'])
labels_A0201 = venn.get_labels(pred_labels['A0201'], fill=['number', 'logic'])
labels_B3501 = venn.get_labels(pred_labels['B3501'], fill=['number', 'logic'])
# Format label a bit
for key in labels_A0101:
labels_A0101[key] = labels_A0101[key][4:]
for key in labels_A0201:
labels_A0201[key] = labels_A0201[key][4:]
for key in labels_B3501:
labels_B3501[key] = labels_B3501[key][4:]
# Plot venn
#matplotlib.rcParams.update({"font.size": 40})
sns.set_context("talk")
ax3 = venn.venn3(labels_A0101, names=['NP', 'NetMHCpan', 'MS.True'])
plt.savefig("A0101.venn_new.png", dpi=300, bbox_inches='tight')
plt.close()
ax4 = venn.venn3(labels_A0201, names=['NP', 'NetMHCpan', 'MS.True'])
plt.savefig("A0201.venn_new.png", dpi=300, bbox_inches='tight')
plt.close()
ax5 = venn.venn3(labels_B3501, names=['NP', 'NetMHCpan', 'MS.True'])
plt.savefig("B3501.venn_new.png", dpi=300, bbox_inches='tight')
plt.close()
"""
@author: tctianchi
https://github.com/tctianchi/pyvenn
"""
import matplotlib.pyplot as plt
import venn
labels = venn.get_labels([range(10), range(5, 15)], fill=['number', 'logic'])
fig, ax = venn.venn2(labels, names=['list 1', 'list 2'])
fig.savefig('venn2.png', bbox_inches='tight')
plt.close()
labels = venn.get_labels(
[range(10), range(5, 15), range(3, 8)], fill=['number', 'logic'])
fig, ax = venn.venn3(labels, names=['list 1', 'list 2', 'list 3'])
fig.savefig('venn3.png', bbox_inches='tight')
plt.close()
labels = venn.get_labels(
[range(10), range(5, 15),
range(3, 8), range(8, 17)],
fill=['number', 'logic'])
fig, ax = venn.venn4(labels, names=['list 1', 'list 2', 'list 3', 'list 4'])
fig.savefig('venn4.png', bbox_inches='tight')
plt.close()
labels = venn.get_labels(
[range(10),
range(5, 15),
range(3, 8),
n25 = len(intersection(comp[ids[1]], comp[ids[4]]))
n34 = len(intersection(comp[ids[2]], comp[ids[3]]))
n35 = len(intersection(comp[ids[2]], comp[ids[4]]))
n45 = len(intersection(comp[ids[3]], comp[ids[4]]))
area5 = len(comp[ids[4]])
area4 = len(comp[ids[3]])
area3 = len(comp[ids[2]])
area2 = len(comp[ids[1]])
area1 = len(comp[ids[0]])
venn_diag_list = [area1, area2, area3, area4, area5, n12, n13, n14, n15,n23, n24, n25,
n34, n35, n45, n123, n124, n125, n134, n135, n145, n234, n235, n245, n345, n1234,
n1235, n1245, n1345, n2345, n12345]
print('Overlap list needed for VennDiagram in R')
print(venn_diag_list)
#out1.write('\n\n## Overlap list needed for VennDiagram in R:\n\n%s' % venn_diag_list)
if n_comparing == 5:
labels = venn.get_labels([comp[ids[0]], comp[ids[1]], comp[ids[2]], comp[ids[3]], comp[ids[4]]], fill = ['number'])
fig, ax = venn.venn5(labels, names = ids)
elif n_comparing == 4:
labels = venn.get_labels([comp[ids[0]], comp[ids[1]], comp[ids[2]], comp[ids[3]]], fill = ['number'])
fig, ax = venn.venn4(labels, names = ids)
elif n_comparing == 3:
labels = venn.get_labels([comp[ids[0]], comp[ids[1]], comp[ids[2]]], fill = ['number'])
fig, ax = venn.venn3(labels, names = ids)
elif n_comparing == 2:
labels = venn.get_labels([comp[ids[0]], comp[ids[1]]], fill = ['number'])
fig, ax = venn.venn2(labels, names = ids)
filename = save+'_pred_compared.pdf'
fig.savefig(filename)
# %matplotlib inline
# python console requires this
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import venn
labels = venn.get_labels([range(10), range(5, 15)], fill=['number', 'logic'])
fig, ax = venn.venn2(labels, names=['list 1', 'list 2'])
fig.savefig('venn2.png', bbox_inches='tight')
plt.close()
labels = venn.get_labels([range(10), range(5, 15), range(3, 8)], fill=['number', 'logic'])
fig, ax = venn.venn3(labels, names=['list 1', 'list 2', 'list 3'])
fig.savefig('venn3.png', bbox_inches='tight')
plt.close()
labels = venn.get_labels([range(10), range(5, 15), range(3, 8), range(8, 17)], fill=['number', 'logic'])
fig, ax = venn.venn4(labels, names=['list 1', 'list 2', 'list 3', 'list 4'])
fig.savefig('venn4.png', bbox_inches='tight')
plt.close()
labels = venn.get_labels([range(10), range(5, 15), range(3, 8), range(8, 17), range(10, 20)], fill=['number', 'logic'])
fig, ax = venn.venn5(labels, names=['list 1', 'list 2', 'list 3', 'list 4', 'list 5'])
fig.savefig('venn5.png', bbox_inches='tight')
plt.close()
labels = venn.get_labels([range(10), range(5, 15), range(3, 8), range(8, 17), range(10, 20), range(13, 25)], fill=['number', 'logic'])
fig, ax = venn.venn6(labels, names=['list 1', 'list 2', 'list 3', 'list 4', 'list 5', 'list 6'])
# Remove '.' from sets
tmp = []
for i in range(0, len(set_list)):
tmpset = {x for x in set_list[i] if x==x}
tmpset = set([x for x in tmpset if '.' not in x])
tmp.append(tmpset)
del set_list
set_list = tmp
del tmp
### Venn diagram of clonal sequences for each sample
pdf = PdfPages('outfile.pdf')
labels = venn.get_labels([set_list[0], set_list[1], set_list[2]],fill=['number','number'])
fig, ax = venn.venn3(labels, names = (argv[2], argv[3], argv[4]))
plt.tight_layout()
plt.savefig(pdf, format='pdf')
plt.close()
pdf.close()
#Now print overlapped sequences
cdr3dic = defaultdict(lambda: [])
for i in range(0, len(set_list)):
for cdr3 in set_list[i]:
cdr3dic[cdr3].append(argv[i+2])
for cdr3 in sorted(cdr3dic.keys(), key=lambda x: len(cdr3dic[x]), reverse=True):
if len(cdr3dic[cdr3]) > 1:
elif "full_model" in results_file:
all_labels.append("Full model")
for r_label in results:
# print unique TP to this result
tps = set(results[r_label]["all"]) & set(gold_pairs["all"])
for r_label2 in results:
if r_label2 == r_label:
continue
tps = tps - set(results[r_label2]["all"])
print()
print("unique to {}:".format(r_label))
print(len(tps))
print(tps)
print()
print(all_labels)
# print(results)
labels = venn.get_labels(all_pairs, fill=["number"])
if len(all_pairs) == 2:
fig, ax = venn.venn2(labels, names=all_labels)
elif len(all_pairs) == 3:
fig, ax = venn.venn3(labels, names=all_labels)
elif len(all_pairs) == 4:
fig, ax = venn.venn4(labels, names=all_labels)
elif len(all_pairs) == 5:
fig, ax = venn.venn5(labels, names=all_labels)
elif len(all_pairs) == 6:
fig, ax = venn.venn6(labels, names=all_labels)
fig.savefig("{}.png".format("_".join(all_labels)), bbox_inches="tight")
plt.close()
def create_venn3(labels, names):
fig, ax = venn.venn3(labels, names=names)
#fig.savefig('venn4.png', bbox_inches='tight')
plt.show()